A backdoor sample attack is an attack that causes a deep neural network to misrecognize data that include a specific trigger because the model has been trained on malicious data that insert triggers into the deep neural network. The deep neural network correctly recognizes data without triggers, but incorrectly recognizes data with triggers. These backdoor attacks have mainly been studied in the image domain; however, defense research in the text domain is insufficient. In this study, we propose a method to defend against textual backdoor samples using a detection model. The proposed method detects a textual backdoor sample by comparing the resulting value of the target model with that of the model trained on the original training data. This method can defend against attacks without access to the entire training data. For the experimental setup, we used the TensorFlow library, and the MR and IMDB datasets were used as the experimental datasets. As a result of the experiment, when 1000 partial training datasets were used to train the detection model, the proposed method could classify the MR and IMDB datasets with detection rates of 79.6% and 83.2%, respectively.

Crowd counting is a crucial task in computer vision, which poses a significant challenge yet holds vast potential for practical applications in public safety and transportation. Traditional crowd counting approaches typically rely on a single framework to predict density maps or head point distributions. However, the straightforward architectures often fall short in cases of over-counting or omission, particularly in diverse crowded scenes. To address these limitations, we introduce the Density to Point Transformer (D2PT), an innovative approach for effective crowd counting and localization. Specifically, D2PT employs a Transformer-based teacher-student framework that integrates the insights of density-based and head-point-based methods. Furthermore, we introduce feature-aligned knowledge distillation, formulating a collaborative training approach that enhances the performance of both density estimation and point map prediction. Optimized with multiple loss functions, D2PT achieves state-of-the-art performance across five crowd counting datasets, demonstrating its robustness and effectiveness for intricate crowd counting and localization challenges.

With the rapid development of Internet technology, the type and quantity of network traffic data have increased accordingly, and network traffic classification has become an important research task. In previous research, there are methods based on traditional machine learning and deep learning; compared to machine learning, deep learning can obtain good results by converting network traffic into two-dimensional images and utilizing deep learning classification models. However, all of these methods have some limitations: the trained models cannot learn sustainably, and the generalization ability of the models is limited. In order to solve this problem, we propose a network traffic classification methods based on incremental learning and Mixup, which is based on generative adversarial networks. First, the network traffic is converted into a 2D image, the original database is linearly interpolated using Mixup to reduce the overfitting tendency of the model and improve the generalization ability, and the traffic is classified using the ability of deep learning on the image. Secondly, we improve the traditional incremental learning algorithm. To effectively address the imbalance between old and new categories in incremental learning. The experimental results show that the model performs well in classification experiments, reaching 92.26% and 93.86% accuracy on the ISCXVPN2016 and USTC datasets, respectively, and we can maintain a high accuracy rate with limited storage space in the process of increasing new categories.

Rail surface anomaly detection, referring to the process of identifying and localizing abnormal patterns in rail surface images, faces the limitation of robustness because of the large diversity of scale, quantity, and morphology of surface anomalies. To address this challenge, we propose a multi-scale rail surface anomaly detection method (MRS-AD) based on a distribution model, which cooperates neighborhood information to precisely locate rail surface anomalies. Specifically, MRS-AD integrates multi-scale structures to enhance the perception of different scale information of anomalies. Furthermore, the neighborhood information is utilized to capture the correlations between adjacent regions, and thereby a weighted multivariate Gaussian distribution model is estimated to improve the recognition capability of anomalous morphologies. To validate the effectiveness of MRS-AD, we collected and built a Rail Surface Anomaly Detection dataset (RSAD), considering the scale and quantity of rail surface anomalies. Extensive experiments on RSAD, RSDD and NEU-RSDD-2 demonstrate the superiority of MRS-AD. The code and dataset are publicly available at https://github.com/lyy70/MRS-AD

In our work, we propose a lightweight two-stage convolutional recurrent network (BP-CRN) for multichannel speech enhancement (mcse), which consists of beamforming and post-filtering. Drawing inspiration from traditional methods, we design two core modules for spatial filtering and post-filtering with compensation, named BM and PF, respectively. Both core modules employ a convolutional encoding-decoding structure and utilize complex frequency-time long short-term memory (CFT-LSTM) blocks in the middle. Furthermore, the inter-module mask module is introduced to estimate and convey implicit spatial information and assist the post-filtering module in refining spatial filtering and suppressing residual noise. Experimental results demonstrate that, our proposed method contains only 1.27M parameters and outperforms three other mcse methods in terms of PESQ and STOI metrics.

Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model while adding new data points. However, a module predicted as “non-defective” can result in fewer test cases for such modules. Thus, a defective module can be overlooked during testing. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. To suppress the negative influence, we propose to apply a method that fixes the prediction as positive during the initial stage of online learning. Additionally, we improved the method to consider the probability of defect overlooking. In our experiment, we demonstrate this negative influence on prediction accuracy and the effectiveness of our approach. The results show that our approach did not negatively affect AUC but significantly improved recall.

Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. In CPDP, deciding on a particular historical project to build a training model can be difficult. To help with this decision, a Bandit Algorithm (BA) based approach has been proposed in prior research to select the most suitable learning project. However, this BA method could lead to the selection of unsuitable data during the early iteration of BA (i.e., early stage of software testing). Selecting an unsuitable model can reduce the prediction accuracy, leading to potential defect overlooking. This study aims to improve the BA method to reduce defects overlooking, especially during the early testing stages. Once all modules have been tested, modules tested in the early stage are re-predicted, and some modules are retested based on the re-prediction. To assess the impact of re-prediction and retesting, we applied five kinds of BA methods, using 8, 16, and 32 OSS projects as learning data. The results show that the newly proposed approach steadily reduced the probability of defect overlooking without degradation of prediction accuracy.

This paper develops certified defenses for deep neural network (DNN) based content-based image retrieval (CBIR) against adversarial examples (AXs). Previous works put their effort into certified defense for classification to improve certified robustness, which guarantees that no AX to cause misclassification exists around the sample. Such certified defense, however, could not be applied to CBIR directly because the goals of adversarial attack against classification and CBIR are completely different. To develop the certified defense for CBIR, we first define the new certified robustness of CBIR, which guarantees that no AX that changes the ranking results of CBIR exists around the input images. Then, we propose computationally tractable verification algorithms that verify whether a given feature extraction DNN satisfies the certified robustness of CBIR at given input images. Our proposed verification algorithms are achieved by evaluating the upper and lower bounds of distances between feature representations of perturbed and non-perturbed images in deterministic and probabilistic manners. Finally, we propose robust training methods to obtain feature extraction DNNs that increase the number of inputs that satisfy the certified robustness of CBIR by tightening the upper and lower bounds. We experimentally showthat our proposed certified defenses can guarantee robustness deterministically and probabilistically on various datasets.

The bicube is derived from the hypercube, and it provides a topology for interconnection networks of parallel systems. The bicube can interconnect the same number of nodes with the same degree as the hypercube while its diameter is almost half of that of the hypercube. In addition, the bicube preserves the property of node symmetry. Hence, the bicube attracts much attention. In this paper, we focus on the bicube with faulty nodes and propose three fault-tolerant routing methods to find a fault-free path between any pair of non-faulty nodes in it.

The study of the spread of pandemics, including COVID-19, is an emerging concern to promote self-care management through social distancing, using state-of-the-art tools and technologies. Existing technologies provide many opportunities to acquire and process large volumes of data to monitor user activities from various perspectives. However, determining disease hotspots remains an open challenge considering user activities and interactions; providing related recommendations to susceptible individuals requires attention. In this article, we propose an approach to determine disease hotspots by modeling users' activities from both cyber- and real-world spaces. Our approach uniquely connects cyber- and physical-world activities to predict hazardous regions. The availability of such an exciting data set is a non-trivial task; therefore, we produce the data set with much hard work and release it to the broader research community to facilitate further research findings. Once the data set is generated, we model it as a directed multi-attributed and weighted graph to apply classical machine learning and graph neural networks for prediction purposes. Our contribution includes mapping user events from cyber- and physical-world aspects, knowledge extraction, dataset generation, and reasoning at various levels. Within our unique graph model, numerous elements of lifestyle parameters are measured and processed to gain deep insight into a person's status. As a result, the proposed solution enables the authorities of any pandemic, such as COVID-19, to monitor and take measurable actions to prevent the spread of such a disease and keep the public informed of the probability of catching it.

This letter presents a solution for large classroom interactions using cloud computing and mobile devices. A lecturer can collect student photos or texts and give real-time feedback. Students confirmed in anonymous surveys that this solution enabled them to actively participate in classes and enhanced their learning even in large classrooms.

The relevant model based on convolutional neural networks (CNNs) has been proven to be an effective solution in speech enhancement algorithms. However, there needs to be more research on CNNs based on microphone arrays, especially in exploring the correlation between networks associated with different microphones. In this paper, we proposed a CNN-based feature integration network for speech enhancement in microphone arrays. The input of CNN is composed of short-time Fourier transform (STFT) from different microphones. CNN includes the encoding layer, decoding layer, and skip structure. In addition, the designed feature integration layer enables information exchange between different microphones, and the designed feature fusion layer integrates additional information. The experiment proved the superiority of the designed structure.

Visible-infrared person re-identification (VI-ReID) aims to achieve cross-modality matching between the visible and infrared modalities, thus enabling usage in all-day monitoring scenarios. Existing VI-ReID methods have indeed achieved promising performance by considering the global information for identity-related discriminative learning. However, they often overlook the importance of local information, which can contribute significantly to learning identity-specific discriminative cues. Moreover, the substantial modality gap typically poses challenges during the model training process. In response to the aforementioned issues, we propose a VI-ReID method called partial enhancement and channel aggregation (PECA) and make efforts in the following three aspects. Firstly, to capture local information, we introduce the global-local similarity learning (GSL) module, which compels the encoder to focus on fine-grained details by increasing the similarity between global and local features within various feature spaces. Secondly, to address the modality gap, we propose an inter-modality channel aggregation learning (ICAL) approach, which progressively guides the learning of modality-invariant features. ICAL not only progressively alleviates modality gap but also augments the training data. Additionally, we introduce a novel instance-modality contrastive loss, which facilitates the learning of modality-invariant and identity-related features at both the instance and modality levels. Extensive experiments on the SYSU-MM01 and RegDB datasets have shown that PECA outperforms state-of-the-art methods.

With the rise of cyber threats, identifying APT groups becomes increasingly crucial for enterprise security experts. This paper introduces a comprehensive framework for profiling APT groups, focusing on Lazarus and APT29. It underscores the critical role of malware hash unit profiling in contemporary cyber security efforts, aiming to fortify organizational defenses against evolving APT threats.

Recently, we introduced and investigated a colored variant of finite automata, so-called “colored finite automata.” Its accepting states are able to be differently colored each and therefore a single automaton can classify and distinguish multiple languages at once. In this paper, we further extend the concept of colored accepting states and propose a new automaton, called bilaterally colored finite automaton (biCFA) which can possess as many differently colored initial states as possible, rather than a specified single initial state.

We next introduce its regular expression counterpart, called bilaterally colored regular expression (biCRE), which exactly expresses the same tuple of languages as accepted by the corresponding biCFA. Notably, the mono-colored version of colored regular expression is a succinct and intuitive alternative of the existing ordinary regular expression.

We also demonstrate the usefulness and feasibility of biCRE by applying the concept to extended (i.e., regular right part) context-free grammar and exhibit a super-short pure Python program which parses basic arithmetic expressions with addition and multiplication operators.

Game theory on graphs is a basic tool in computer science. In this paper, we propose a new game-theoretic framework for studying the privacy protection of a user who interactively uses a software service. Our framework is based on the idea that an objective of a user using software services should not be known to an adversary because the objective is often closely related to personal information of the user. We propose two new notions, O-indistinguishable strategy (O-IS) and objective-indistinguishability equilibrium (OIE). For a given game and a subset O of winning objectives (or objectives in short), a strategy of a player is O-indistinguishable if an adversary cannot shrink O by excluding any objective from O as an impossible objective. A strategy profile, which is a tuple of strategies of all players, is an OIE if the profile is locally optimal in the sense that no player can expand her set of objectives indistinguishable from her real objective from the viewpoint of an adversary. We analyze the complexities of deciding the existence of O-IS and prove the decidability of the existence of OIE under a weaker assumption on rationality.

Yajisan-Kazusan and Stained Glass are Nikoli's pencil puzzles. We study the computational complexity of Yajisan-Kazusan and Stained Glass puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

Accurate water level prediction systems improve safety and quality of life. This study introduces a method that uses clustering and deep learning of multisite data to enhance the water level prediction of the Three Gorges Dam. The results show that Cluster-GRU-based can provide accurate forecasts for up to seven days.

Sign constraints are a handy representation of domain-specific prior knowledge that can be incorporated to machine learning. This paper presents new stochastic dual coordinate ascent (SDCA) algorithms that find the minimizer of the empirical risk under the sign constraints. Generic surrogate loss functions can be plugged into the proposed algorithm with the strong convergence guarantee inherited from the vanilla SDCA. The prediction performance is demonstrated on the classification task for microbiological water quality analysis.

Recent studies on facial expression recognition mainly employs discrete category labels to represent emotion states. However, current intelligent emotion interaction systems require more diverse and precise emotion representation metrics, which has been proposed as Valence, Arousal, Dominance (VAD) multi-dimensional continuous emotion parameters. But there are still very less datasets and methods for VAD analysis, making it difficult to meet the needs of large-scale and high-precision emotion cognition. In this letter, we build multi-dimensional facial expression recognition method by using multi-task learning to improve recognition performance through exploiting the consistency between dimensional and categorial emotions. The evaluation results show that the multi-task learning approach improves the prediction accuracy for VAD multi-dimensional emotion. Furthermore, it applies the method to academic outcomes prediction which verifies that introducing the VAD multi-dimensional and multi-task facial expression recognition is effective in predicting academic outcomes. The VAD recognition code is publicly available on github.com/YeeHoran/Multi-task-Emotion-Recognition .

Drug discovery, characterized by its time-consuming and costly nature, demands approximately 9 to 17 years and around two billion dollars for development. Despite the extensive investment, about 90% of drugs entering clinical trials face withdrawal, with compound toxicity accounting for 30% of these instances. Ethical concerns and the discrepancy in mechanisms between humans and animals have prompted regulatory restrictions on traditional animal-based toxicity prediction methods. In response, human pluripotent stem cell-based approaches have emerged as an alternative. This paper investigates the scalability challenges inherent in utilizing pluripotent stem cells due to the costly nature of RNAseq and the lack of standardized protocols. To address these challenges, we propose applying Mixup data augmentation, a successful technique in deep learning, to kernel SVM, facilitated by Stochastic Dual Coordinate Ascent (SDCA). Our novel approach, Exact SDCA, leverages intermediate class labels generated through Mixup, offering advancements in both efficiency and effectiveness over conventional methods. Numerical experiments reveal that Exact SDCA outperforms Approximate SDCA and SGD in attaining optimal solutions with significantly fewer epochs. Real data experiments further demonstrate the efficacy of multiplexing gene networks and applying Mixup in toxicity prediction using pluripotent stem cells.

Constructing a suitable schedule for sports competitions is a crucial issue in sports scheduling. The round-robin tournament is a competition adopted in many professional sports. For most round-robin tournaments, it is considered undesirable that a team plays consecutive away or home matches; such an occurrence is called a break. Accordingly, it is preferable to reduce the number of breaks in a tournament. A common approach is to first construct a schedule and then determine a home-away assignment based on the given schedule to minimize the number of breaks (first-schedule-then-break).

In this study, we concentrate on the problem that arises at the second stage of the first-schedule-then-break approach, namely, the break minimization problem (BMP). We propose a novel integer linear programming formulation called the “bigram based formulation.” The computational experiments show its effectiveness over the well-known integer linear programming formulation. We also investigate its valid inequalities, which further enhances the computational performance.

Deep networks are undergoing rapid development. However, as the depth of networks increases, the issue of how to fuse features from different layers becomes increasingly prominent. To address this challenge, we creatively propose a cross-layer feature fusion module based on neural dendrites, termed dendritic learning-based feature fusion (DFF). Compared to other fusion methods, DFF demonstrates superior biological interpretability due to the nonlinear capabilities of dendritic neurons. By integrating the classic ResNet architecture with DFF, we devise the ResNeFt. Benefiting from the unique structure and nonlinear processing capabilities of dendritic neurons, the fused features of ResNeFt exhibit enhanced representational power. Its effectiveness and superiority have been validated on multiple medical datasets.

SLM (Scalable Logic Module) is a fine-grained reconfigurable logic developed by Kumamoto University. Its small configuration information size characterizes it, resulting in a smaller area for logic cells. We have been developing an SoC-type FPGA called SLMLET to take advantage of SLM. It keeps multiple sets of configuration data in the memory module inside the chip in a compressed form and exchanges them quickly. This paper proposes a simple run-length compression technique called TLC (Tag Less Compression). It achieved a 1.01-3.06 compression ratio, is embedded in the prototype of the SLMLET, and is available now. Then, we propose DMC (Duplication Module Compression), which uses repeatedly appearing patterns in the SLM configuration data. The DMC achieves a better compression ratio for complicated designs that are hard to compress with TLC.

Big data processing is a set of techniques or programming models, which can be deployed on both the cloud servers or edge nodes, to access large-scale data and extract useful information for supporting and providing decisions. Meanwhile, several typical domains of human activity in smart society, such as social networks, medical diagnosis, recommendation systems, transportation, and Internet of Things (IoT), often manage a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. As one of the convincing solutions to carry out analytics for big data, graph processing is especially applicable for these application domains. However, either the intra-device or the inter-device data processing in the edge-cloud architecture is truly prone to be attacked by the malicious Trojans covertly embedded in the counterfeit processing systems developed by some third-party vendors in numerous practical scenarios, leading to identity theft, misjudgment, privacy disclosure, and so on. In this paper, for the first time to our knowledge, we specially build a novel attack model for ubiquitous graph processing in detail, which also has easy scalability for other applications in big data processing, and dis-cuss some common existing mitigations accordingly. Multiple activation mechanisms of Trojans designed in our attack model effectively make the attacks imperceptible to users. Evaluations indicate that the proposed Trojans are highly competitive in stealthiness with trivial extra latency.

In this study, we devise several seat selection screens for a movie theater ticket reservation system that applies nudges to achieve spatial crowd smoothing without relying on economic incentives. We design three types of nudges that achieve the following: (i) render seats in less-crowded areas noticeable; (ii) present social norms; and (iii) suggest seats in less-crowded areas to people who have selected seats in crowded areas. Results of verification experiment show that (ii) the presentation of social norms is generally effective in avoiding congestion regardless of the ticket sales and (ii) the text of the presented social norms is more effective in avoiding congestion when it contains motivational sentences than when it is verbally expressed. Furthermore, the results indicate that (i) rendering seats in less-crowded areas more conspicuous and (iii) suggesting seats in less-crowded areas to those who select seats in more crowded areas may be effective in avoiding congestion, depending on the ticket sales. Consequently, the feasibility of spatial crowd smoothing without relying on economic incentives for the seat selection screen of a ticket reservation system that applies nudges is demonstrated.

We present a flip sequence of length ⌈ (15/14)n + 2 ⌉ for sorting the Heydari and Sudborough's stack of n pancakes, which was introduced to prove the best-known lower bound of (15/14)n for the pancake number of n pancakes.

A polygon obtained by cutting the surface of a polyhedron is called an unfolding. An unfolding obtained by cutting along only edges is called an edge unfolding. An unfolding may have overlapping, which are self-intersections on its boundary. It is a well-known open question in computational origami whether or not every convex polyhedron has a non-overlapping edge unfolding. On the other hand, Sharir and Schorr showed that any convex polyhedron could unfold without overlapping when allowed to cut its faces. Therefore, there is a gap between edge unfoldings and general unfoldings. Bridging this gap is necessary as a foothold on this open question of edge unfolding. Instead of cutting faces arbitrarily, there are studies considering whether specific cutting lines on the faces can result in unfoldings without overlaps. Lattice unfoldings of a cuboid made by unit cubes are one such example. A lattice unfolding of a cuboid is a polygon obtained by cutting the faces along the edges of unit squares. An unfolding may have overlapping, even in the case of small cuboids. In particular, Uno showed that a 1 × 1 × 3-cuboid has an overlapping lattice unfolding, while Mitani and Uehara showed the same for three faces of a 1 × 2 × 3-cuboid. In contrast, it is known that some cuboids have no overlapping lattice unfolding. Hearn showed it for a 1 × 1 × 2-cuboid, and Sugihara showed the same for a 2 × 2 × 2-cuboid. In this study, we completely clarify the existence of overlapping lattice unfoldings, which also contains the case where the sizes are non-integers.

The exact matching problem is a constrained variant of the maximum matching problem: given a graph with each edge having a weight 0 or 1 and an integer k, the goal is to find a perfect matching of weight exactly k. Mulmuley, Vazirani, and Vazirani (1987) proposed a randomized polynomial-time algorithm for this problem, and it is still open whether it can be derandomized. Very recently, El Maalouly, Steiner, and Wulf (2023) showed that for bipartite graphs there exists a deterministic FPT algorithm parameterized by the (bipartite) independence number. In this paper, by extending a part of their work, we propose a deterministic FPT algorithm in general parameterized by the minimum size of an odd cycle transversal in addition to the (bipartite) independence number. We also consider a relaxed problem called the correct parity matching problem, and show that a slight generalization of an equivalent problem is NP-hard.

As one of electrical components in transmission lines, vibration damper plays a role in preventing the power lines dancing, and its recognition is an important task for intelligent inspection. However, due to the complex background interference in aerial images, current deep learning algorithms for vibration damper detection often lack accuracy and robustness. To achieve vibration damper detection more accurately, in this study, improved You Only Look Once (YOLO) model is proposed for performing damper detection. Firstly, a damper dataset containing 1900 samples with different scenarios was created. Secondly, the backbone network of YOLOv4 was improved by combining the Res2Net module and Dense blocks, reducing computational consumption and improving training speed. Then, an improved path aggregation network (PANet) structure was introduced in YOLOv4, combined with top-down and bottom-up feature fusion strategies to achieve feature enhancement. Finally, the proposed YOLO model and comparative model were trained and tested on the damper dataset. The experimental results and analysis indicate that the proposed model is more effective and robust than the comparative models. More importantly, the average precision (AP) of this model can reach 98.8%, which is 6.2% higher than that of original YOLOv4 model; and the prediction speed of this model is 62 frames per second (FPS), which is 5 FPS faster than that of YOLOv4 model.

Recent deep-learning-based video compression models have demonstrated superior performance over traditional codecs. However, few studies have focused on deep learning rate control. In this paper, end-to-end rate control is proposed for deep contextual video compression (DCVC). With the designed two-branch residual-based network, the optimal bit rate ratio is predicted according to the feature correlation of the adjacent frames. Then, the bit rate can be reasonably allocated for every frame by satisfying the temporal feature. To minimize the rate distortion (RD) cost, the optimal λ of the current frame can be obtained from a two-branch regression-based network using the temporal encoded information. The experimental results show that the achievable BD -rate (PSNR) and BD-rate (SSIM) of the proposed algorithm are -0.84% and -0.35%, respectively, with 2.25% rate control accuracy.

Register automaton (RA) is an extension of finite automaton for dealing with data values in an infinite domain. In the previous work, we proposed disjunctive μ^↓-calculus (μ^↓_d-calculus), which is a subclass of modal μ-calculus with the freeze quantifier, and showed that it has the same expressive power as RA. However, μ^↓_d-calculus is defined as a logic on finite words, whereas temporal specifications in model checking are usually given in terms of infinite words. In this paper, we re-define the syntax and semantics of μ^↓_d-calculus to be suitable for infinite words and prove that the obtained temporal logic, called μ^↓_dω-calculus, has the same expressive power as Büchi RA.

Image classification is a typical computer vision task widely used in practical applications. The images used for training image classification networks are often clean, i.e., without any image degradation. However, Convolutional neural networks trained on clean images perform poorly on degraded or corrupted images in the real world. In this study, we effectively utilize robust data augmentation (DA) with knowledge distillation to improve the classification performance of degraded images. We first categorize robust data augmentations into geometric-and-color and cut-and-delete DAs. Next, we evaluate the effectual positioning of cut-and-delete DA when we apply knowledge distillation. Moreover, we also experimentally demonstrate that combining the RandAugment and Random Erasing approach for geometric-and-color and cut-and-delete DA improves the generalization of the student network during the knowledge transfer for the classification of degraded images.

1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer's aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-lowlatency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.

Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.

Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.

This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.

We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.

To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.

White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named clear & return, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.

Escalators are an indispensable facility in public places. While they can provide convenience to people, abnormal accidents can lead to serious consequences. Yolo is a function that detects human behavior in real time. However, the model exhibits low accuracy and a high miss rate for small targets. To this end, this paper proposes the Small Target High Performance YOLO (SH-YOLO) model to detect abnormal behavior in escalators. The SH-YOLO model first enhances the backbone network through attention mechanisms. Subsequently, a small target detection layer is incorporated in order to enhance detection of key points for small objects. Finally, the conv and the SPPF are replaced with a Region Dynamic Perception Depth Separable Conv (DR-DP-Conv) and Atrous Spatial Pyramid Pooling (ASPP), respectively. The experimental results demonstrate that the proposed model is capable of accurately and robustly detecting anomalies in the real-world escalator scene.

We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.

Modern memory devices such as DRAM are prone to errors that occur because of unintended bit flips during their operation. Since memory errors severely impact in-memory key-value stores (KVSes), software mechanisms for hardening them against memory errors are being explored. However, it is hard to efficiently test the memory error handling code due to its characteristics: the code is event-driven, the handlers depend on the memory object, and in-memory KVSes manage various objects in huge memory space. This paper presents MemFI that supports runtime tests for the memory error handlers of in-memory KVSes. Our approach performs the software fault injection of memory errors at the memory object level to trigger the target handler while smoothly carrying out tests on the same running state. To show the effectiveness of MemFI, we integrate error handling mechanisms into a real-world in-memory KVS, memcached 1.6.9 and Redis 6.2.7, and check their behavior using the MemFI prototypes. The results show that the MemFI-based runtime test allows us to check the behavior of the error handling mechanisms. We also show its efficiency by comparing it to other fault injection approaches based on a trial model.

With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.

Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.

We address the problem of calculating the least core value of the routing game (the traveling salesman game with a fixed route) without the assumption of triangle inequalities. We propose a polynomial size LP formulation for finding a payoff vector in the least core.

A floorplan is a partition of an axis-aligned rectangle into a set of smaller rectangles. Given an axis-aligned rectangle R and a set P of points in R we wish to partition R into a set S of rectangles so that each point in P is on a boundary of a rectangle in S. We call such a partition of R a floorplan covering P. Intuitively P is the locations of columns and a floorplan covering P is a floorplan in which no column is in the proper inside of a room and each column is on a wall.

In this paper we design an algorithm to generate all floorplans covering P when P is the set of given grid points. The algorithm generates each floorplan in O(|P|) time.

A floorplan is a partition of an axis-aligned rectangle into a set of smaller rectangles. Given an integer e we regard a floorplan is desirable for its safety if each room has an escape route to a room facing the outside, where the route passes through at most e walls, and simple, which is, directs to only either (1) south or east, (2) east or north, (3) north or west or (4) west or south. In this paper we design an algorithm to generate all floorplans with exactly n rooms and satisfying the property above. Our algorithm generates all such floorplans in O(n² ) time for each.

The degeneracy of a graph G is defined as the smallest value k such that every subgraph of G has a vertex with a degree of at most k. Given a graph G, its degeneracy can be easily calculated provided sufficient memory is available. In this paper, we focus on scenarios where only o(n) bits of additional read-write memory are available, apart from the input stored in read-only memory. Within this context, we introduce two FPT algorithms for degeneracy, parameterized by neighborhood diversity and the cluster vertex deletion number.

Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same class. We propose a new metarounding algorithm under a natural assumption that a relax-based approximation algorithm exists for the combinatorial class. Our algorithm is much more efficient in both theoretical and practical aspects.