Detecting key frames in videos has garnered substantial attention in recent years, it is a point-level task and has deep research value and application prospect in daily life. For instances, video surveillance system, video cover generation and highlight moment flashback all demands the technique of key frame detection. However, the task is beset by challenges such as the sparsity of key frame instances, imbalances between target frames and background frames, and the absence of post-processing method. In response to these problems, we introduce a novel and effective Temporal Interval Guided (TIG) framework to precisely localize specific frames. The framework is incorporated with a proposed Point-Level-Soft non-maximum suppression (PLS-NMS) post-processing algorithm which is suitable for point-level task, facilitated by the well-designed confidence score decay function. Furthermore, we propose a TIG-loss, exhibiting sensitivity to temporal interval from target frame, to optimize the two-stage framework. The proposed method can be broadly applied to key frame detection in video understanding, including action start detection and static video summarization. Extensive experimentation validates the efficacy of our approach on action start detection benchmark datasets: THUMOS’14 and Activitynet v1.3, and we have reached state-of-the-art performance. Competitive results are also demonstrated on SumMe and TVSum datasets for deep learning based static video summarization.

Automatic detection and recognition systems have numerous applications in smart city implementation. Despite the accuracy and widespread use of device-based and optical methods, several issues remain. These include device limitations, environmental limitations, and privacy concerns. The FMWC sensor can overcome these issues to detect and track moving people accurately in commercial environments. However, single-chip mmWave sensor solutions might struggle to recognize standing and sitting people due to the necessary static removal module. To address these issues, we propose a real-time indoor people detection and tracking fusion system using mmWave radar and cameras. The proposed fusion system approaches an overall detection accuracy of 93.8% with a median position error of 1.7 m in a commercial environment. Compared to our single-chip mmWave radar solution addressing an overall accuracy of 83.5% for walking people, it performs better in detecting individual stillness, which may feed the security needs in retail. This system visualizes customer information, including trajectories and the number of people. It helps commercial environments prevent crowds during the COVID-19 pandemic and analyze customer visiting patterns for efficient management and marketing. Powered by an IoT platform, the system can be deployed in the cloud for easy large-scale implementation.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) can provide mobile users (MU) with additional computing services and a wide range of connectivity. This paper investigates the joint optimization strategy of task offloading and resource allocation for UAV-assisted MEC systems in complex scenarios with the goal of reducing the total system cost, consisting of task execution latency and energy consumption. We adopt a game theoretic approach to model the interaction process between the MEC server and the MU Stackelberg bilayer game model. Then, the original problem with complex multi-constraints is transformed into a duality problem using the Lagrangian duality method. Furthermore, we prove that the modeled Stackelberg bilayer game has a unique Nash equilibrium solution. In order to obtain an approximate optimal solution to the proposed problem, we propose a two-stage alternating iteration (TASR) algorithm based on the subgradient method and the marginal revenue optimization method. We evaluate the effective performance of the proposed algorithm through detailed simulation experiments. The simulation results show that the proposed algorithm is superior and robust compared to other benchmark methods and can effectively reduce the task execution latency and total system cost in different scenarios.

In many real-world problems, a complex task is typically composed of a set of subtasks that follow a certain execution order. Traditional multi-agent reinforcement learning methods perform poorly in such multi-task cases, as they consider the whole problem as one task. For such multi-agent multi-task problems, heterogeneous relationships i.e., subtask-subtask, agent-agent, and subtask-agent, are important characters which should be explored to facilitate the learning performance. This paper proposes a dynamic heterogeneous graph based agent allocation-action learning framework. Specifically, a dynamic heterogeneous graph model is firstly designed to characterize the variation of heterogeneous relationships with the time going on. Then a multi-subgraph partition method is invented to extract features of heterogeneous graphs. Leveraging the extracted features, a hierarchical framework is designed to learn the dynamic allocation of agents among subtasks, as well as cooperative behaviors. Experimental results demonstrate that our framework outperforms recent representative methods on two challenging tasks, i.e., SAVETHECITY and Google Research Football full game.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation performance by pruning difficult-to-match parameters during the distillation process. Experimental results on two benchmark datasets show the superiority of the proposed method.

Security-related issues have become more significant due to the proliferation of IT. Collating security-related information in a database improves security. For example, Common Vulnerabilities and Exposures (CVE) is a security knowledge repository containing descriptions of vulnerabilities about software or source code. Although the descriptions include various entities, there is not a uniform entity structure, making security analysis difficult using individual entities. Developing a consistent entity structure will enhance the security field. Herein we propose a method to automatically label select entities from CVE descriptions by applying the Named Entity Recognition (NER) technique. We manually labeled 3287 CVE descriptions and conducted experiments using a machine learning model called BERT to compare the proposed method to labeling with regular expressions. Machine learning using the proposed method significantly improves the labeling accuracy. It has an f1 score of about 0.93, precision of about 0.91, and recall of about 0.95, demonstrating that our method has potential to automatically label select entities from CVE descriptions.

A 150 GHz fundamental oscillator employing an inter-stage matching network based on a transmission line is presented in this letter. The proposed oscillator consists of a two-stage common-emitter amplifier loop, whose inter-stage connections are optimized to meet the oscillation condition. The oscillator is designed in a 130-nm SiGe BiCMOS process that offers fT and fMAX of 350 GHz and 450 GHz. According to simulation results, an output power of 3.17 dBm is achieved at 147.6 GHz with phase noise of -115 dBc/Hz at 10 MHz offset and figure-of-merit (FoM) of -180 dBc/Hz.

With the recent advances in IoT, there is a growing interest in multi-surface computing, where a mobile app can cooperatively utilize multiple devices' surfaces. We propose a novel framework that seamlessly augments mobile apps with multi-surface computing capabilities. It enables various apps to employ multiple surfaces with acceptable performance.

Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).

Information-theoretic lower bounds of the Bayes risk have been investigated for a problem of parameter estimation in a Bayesian setting. Previous studies have proven the lower bound of the Bayes risk in a different manner and characterized the lower bound via different quantities such as mutual information, Sibson's α-mutual information, f-divergence, and Csiszár's f-informativity. In this paper, we introduce an inequality called a “meta-bound for lower bounds of the Bayes risk” and show that the previous results can be derived from this inequality.

Synthesis problems on multiplayer non-zero-sum games (MG) with multiple environment players that behave rationally are the problems to find a good strategy of the system and have been extensively studied. This paper concerns the synthesis problems on stochastic MG (SMG), where a special controller other than players, called nature, which chooses a move in its turn randomly, may exist. Two types of synthesis problems on SMG exist: cooperative rational synthesis problem (CRSP) and non-cooperative rational synthesis problem (NCRSP). The rationality of environment players is modeled by Nash equilibria, and CRSP is the problem to decide whether there exists a Nash equilibrium that gives the system a payoff not less than a given threshold. Ummels et al. studied the complexity of CRSP for various classes of objectives and strategies of players. CRSP fits the situation where the system can make a suggestion of a strategy profile (a tuple of strategies of all players) to the environment players. However, in real applications, the system may rarely have an opportunity to make suggestions to the environment, and thus CRSP is optimistic. NCRSP is the problem to decide whether there exists a strategy σ0 of the system satisfying that for every strategy profile of the environment players that forms a 0-fixed Nash equilibrium (a Nash equilibrium where the system's strategy is fixed to σ0), the system obtains a payoff not less than a given threshold. In this paper, we investigate the complexity of NCRSP for positional (i.e. pure memoryless) strategies. We consider ω-regular objectives as the model of players' objectives, and show the complexity results of the problem for several subclasses of ω-regular objectives. In particular, the problem for terminal reachability (TR) objectives is shown to be Σp2-complete.

Despite Siamese trackers attracting much attention due to their scalability and efficiency in recent years, researchers have ignored the background appearance, which leads to their inapplicability in recognizing arbitrary target objects with various variations, especially in complex scenarios with background clutter and distractors. In this paper, we present a simple yet effective Siamese tracker, where the shifted windows multi-head self-attention is produced to learn the characteristics of a specific given target object for visual tracking. To validate the effectiveness of our proposed tracker, we use the Swin Transformer as the backbone network and introduced an auxiliary feature enhancement network. Extensive experimental results on two evaluation datasets demonstrate that the proposed tracker outperforms other baselines.

Action spotting is a key component in high-level video understanding. The large number of similar frames poses a challenge for recognizing actions in videos. In this paper we use frame saliency to represent the importance of frames for guiding the model to focus on keyframes. We propose the frame saliency weighting module to improve frame saliency and video representation at the same time. Our proposed model contains two encoders, for pre-action and post-action time windows, to encode video context. We validate our design choices and the generality of proposed method in extensive experiments. On the public SoccerNet-v2 dataset, the method achieves an average mAP of 57.3%, improving over the state of the art. Using embedding features obtained from multiple feature extractors, the average mAP further increases to 75%. We show that reducing the model size by over 90% does not significantly impact performance. Additionally, we use ablation studies to prove the effective of saliency weighting module. Further, we show that our frame saliency weighting strategy is applicable to existing methods on more general action datasets, such as SoccerNet-v1, ActivityNet v1.3, and UCF101.

In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.

In this paper, we investigate a wireless-powered relays assisted batteryless IoT network based on the non-linear energy harvesting model, where there exists an energy service provider constituted by the hybrid access point (HAP) and an IoT service provider constituted by multiple clusters. The HAP provides energy signals to the batteryless devices for information backscattering and the wireless-powered relays for energy harvesting. The relays are deployed to assist the batteryless devices with the information transmission to the HAP by using the harvested energy. To model the energy interactions between the energy service provider and IoT service provider, we propose a Stackelberg game based framework. We aim to maximize the respective utility values of the two providers. Since the utility maximization problem of the IoT service provider is non-convex, we employ the fractional programming theory and propose a block coordinate descent (BCD) based algorithm with successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to solve it. Numerical simulation results confirm that compared to the benchmark schemes, our proposed scheme can achieve larger utility values for both the energy service provider and IoT service provider.

Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.

Universities collect and process a massive amount of Personal Identifiable Information (PII) at registration and throughout interactions with individuals. However, student PII can be exposed to the public by uploading documents along with university notice without consent and awareness, which could put individuals at risk of a variety of different scams, such as identity theft, fraud, or phishing. In this paper, we perform an in-depth analysis of student PII leakage at Vietnamese universities. To the best of our knowledge, we are the first to conduct a comprehensive study on student PII leakage in higher educational institutions. We find that 52.8% of Vietnamese universities leak student PII, including one or more types of personal data, in documents on their websites. It is important to note that the compromised PII includes sensitive types of data, student medical record and religion. Also, student PII leakage is not a new phenomenon and it has happened year after year since 2005. Finally, we present a study with 23 Vietnamese university employees who have worked on student PII to get a deeper understanding of this situation and envisage concrete solutions. The results are entirely surprising: the employees are highly aware of the concept of student PII. However, student PII leakage still happens due to their working habits or the lack of a management system and regulation. Therefore, the Vietnamese university should take a more active stand to protect student data in this situation.

We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].

The Variational Quantum Eigensolver (VQE) algorithm is gaining interest for its potential use in near-term quantum devices. In the VQE algorithm, parameterized quantum circuits (PQCs) are employed to prepare quantum states, which are then utilized to compute the expectation value of a given Hamiltonian. Designing efficient PQCs is crucial for improving convergence speed. In this study, we introduce problem-specific PQCs tailored for optimization problems by dynamically generating PQCs that incorporate problem constraints. This approach reduces a search space by focusing on unitary transformations that benefit the VQE algorithm, and accelerate convergence. Our experimental results demonstrate that the convergence speed of our proposed PQCs outperforms state-of-the-art PQCs, highlighting the potential of problem-specific PQCs in optimization problems.