Reviewerly/exHarmony huggingface dataset:
Coalition Game of Radar Network for Multitarget Tracking via Model-Based Multiagent Reinforcement Learning

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational and communication costs, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a multiagent decision-making and learning problem where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interacts with other agents for the sake of the best network utility. To describe the decision preferences of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the coalition game with transferable utility is developed for task assignment where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent random Fourier features reinforcement learning algorithm is proposed to solve the optimal solution to the game in the high-dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.
Infrared Image Generation From RGB Images Using CycleGAN

Thermal imaging is more robust than optical imaging against the illumination related issues. Therefore, it is preferred or utilized with RGB data in some of the essential problems such as surveillance, environmental monitoring and so on. Deep learning has been used in various fields including the problems in the scope of thermal imaging and proved its ability to solve lots of problems. However, due to the requirement of large datasets in deep learning and the lack of public thermal data because of the constrains of thermal imaging, deep learning can not be used much in thermal imaging. In this study, we mainly investigate whether using CycleGAN on paired images rather than impaired ones increases the success rate and the effect of our electromagnetic spectrum based normalization approach. Evaluations on public data sets show that our approach has potential to increase the success rate of CycleGAN, but further study is required.
Quantum Attacks on Lai-Massey Structure

Aaram Yun et al. considered that Lai-Massey structure has the same security as Feistel structure. However, Luo et al. showed that 3-round Lai-Massey structure can resist quantum attacks of Simon’s algorithm, which is different from Feistel structure. We give quantum attacks against a typical Lai-Massey structure. The result shows that there exists a quantum CPA distinguisher against 3-round Lai-Massey structure and a quantum CCA distinguisher against 4-round Lai-Massey Structure, which is the same as Feistel structure. We extend the attack on Lai-Massey structure to quasi-Feistel structure. We show that if the combiner of quasi-Feistel structure is linear, there exists a quantum CPA distinguisher against 3-round balanced quasi-Feistel structure and a quantum CCA distinguisher against 4-round balanced quasi-Feistel Structure.
Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan

Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20-31% of the descriptions in discharge summaries overlap with the content of the inpatient records. However, it remains unclear how the summaries should be generated from the unstructured source. To decompose the physician's summarization process, this study aimed to identify the optimal granularity in summarization. We first defined three types of summarization units with different granularities to compare the performance of the discharge summary generation: whole sentences, clinical segments, and clauses. We defined clinical segments in this study, aiming to express the smallest medically meaningful concepts. To obtain the clinical segments, it was necessary to automatically split the texts in the first stage of the pipeline. Accordingly, we compared rule-based methods and a machine learning method, and the latter outperformed the formers with an F1 score of 0.846 in the splitting task. Next, we experimentally measured the accuracy of extractive summarization using the three types of units, based on the ROUGE-1 metric, on a multi-institutional national archive of health records in Japan. The measured accuracies of extractive summarization using whole sentences, clinical segments, and clauses were 31.91, 36.15, and 25.18, respectively. We found that the clinical segments yielded higher accuracy than sentences and clauses. This result indicates that summarization of inpatient records demands finer granularity than sentence-oriented processing. Although we used only Japanese health records, it can be interpreted as follows: physicians extract "concepts of medical significance" from patient records and recombine them ...
Regional Financial Data Processing Based on Distributed Decoding Technology

Aiming at the problems of low storage efficiency, small storage space, and poor stability of traditional financial data storage systems, a dynamic and secure financial data storage system based on distributed decoding is designed. The system uses a cloud security storage service system to store a large number of financial data obtained from the sensing layer and achieve the reasonable scheduling of financial dynamic cloud data through the constraint software function. In addition, a distributed security computing scheme based on hierarchical coding is proposed, and the influence of different subtask sizes and different computing layers on the scheme is analyzed. The results show that the distributed security computing scheme based on hierarchical coding can significantly reduce the completion time of the overall computing task and enhance the stability of dynamic financial data storage. This system has a good storage performance of financial big data, and its efficiency is good, which provides strong support for the financial cause of our country.
A multi-objective optimization of resource management and minimum batch VM migration for prioritized task allocation in fog-edge-cloud computing

Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms.
Siamese Network Based Multiscale Self-Supervised Heterogeneous Graph Representation Learning

Owing to label-free modeling of complex heterogeneity, self-supervised heterogeneous graph representation learning (SS-HGRL) has been widely studied in recent years. The goal of SS-HGRL is to design an unsupervised learning framework to represent complicated heterogeneous graph structures. However, based on contrastive learning, most existing methods of SS-HGRL require a large number of negative samples, which significantly increases the computation and memory costs. Furthermore, many methods cannot fully extract knowledge from a heterogeneous graph. To learn global and local information simultaneously at low time and space costs, we propose a novel Siamese Network based Multi-scale bootstrapping contrastive learning approach for Heterogeneous graphs (SNMH). Specifically, we first obtain views under the meta-path schema and the 1-hop relation type schema through dual-schema view generation. Then, we propose cross-schema and cross-view bootstrapping contrastive objectives to maximize the similarity of node representations between different schemas and views. By integrating and optimizing the above objectives, we can extract local and global information and eventually obtain the node representations for downstream tasks. To demonstrate the effectiveness of our model, we conduct experiments on several public datasets. Experimental results show that our model is superior to the state-of-the-art methods on the premise of lower time and space complexity. The source code and datasets are publicly available at https://github.com/lorisky1214/SNMH.
Language-guided graph parsing attention network for human-object interaction recognition

This paper focuses on the task of human-object interaction (HOI) recognition, which aims to classify the interaction between human and objects. It is a challenging task partially due to the extremely imbalanced data among classes. To solve this problem, we propose a language-guided graph parsing attention network (LG-GPAN) that makes use of the word distribution in language to guide the classification in vision. We first associate each HOI class name with a word embedding vector in language and then all the vectors can construct a language space specified for HOI recognition. Simultaneously, the visual feature is extracted from the inputs via the proposed graph parsing attention network (GPAN) for better visual representation. The visual feature is then transformed into the linguistic one in language space. Finally, the output score is obtained via measuring the distance between the linguistic feature and the word embedding of classes in language space. Experimental results on the popular CAD-120 and V-COCO datasets validate our design choice and demonstrate its superior performance in comparison to the state-of-the-art.
HLTD-CSA: Cover selection algorithm based on hybrid local texture descriptor for color image steganography

Cover selection is one of the important techniques to improve the security of image steganography. However, existing methods mostly focus on cover selection of grayscale image. In this paper, we propose a novel Cover Selection Algorithm of color images based on Hybrid Local Texture Descriptor, named HLTD-CSA. A green-channel related Local Binary Pattern (LBP) is designed which utilizes local statistics of intra-channel and cross-channel correlations efficiently. Besides, Local Phase Quantization is introduced to serve as a complementary component of our improved LBP. To further enhance the performance of cover selection for color image, a hybrid local texture descriptor (HLTD) is obtained by combining these two types of local texture descriptors with a proper combination strategy. Finally, the proposed algorithm selects images which have larger values of HLTD to construct the cover image set. Extensive experiments are conducted to verify the effectiveness of our method.
Understanding COUNTER 5 metrics in context: a case study on E-book vendor usage reports

AbstractAbstractThis study examines the effectiveness of the COUNTER 5 metrics relating to e-book usage reporting. Through three simulated processes on two major e-book platforms, EBSCOhost and ProQuest Ebook Central, the authors were able to gain insights into how usage was counted at item level within each e-book platform. The study finds that both vendors followed the principles of COUNTER 5 standard in usage reporting. Nevertheless, the result of data analysis also suggests that the difference in platform design between the two vendors had a direct impact on how usage was counted, thus affected the total outcome of e-book usage reports.Keywords: COUNTER 5 Code of PracticeRelease 5usage statisticse-book reports
A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.
Diverse Interaction Recommendation for Public Users Exploring Multi-view Visualization using Deep Learning

Interaction is an important channel to offer users insights in interactive visualization systems. However, which interaction to operate and which part of data to explore are hard questions for public users facing a multi-view visualization for the first time. Making these decisions largely relies on professional experience and analytic abilities, which is a huge challenge for non-professionals. To solve the problem, we propose a method aiming to provide diverse, insightful, and real-time interaction recommendations for novice users. Building on the Long-Short Term Memory Model (LSTM) structure, our model captures users' interactions and visual states and encodes them in numerical vectors to make further recommendations. Through an illustrative example of a visualization system about Chinese poets in the museum scenario, the model is proven to be workable in systems with multi-views and multiple interaction types. A further user study demonstrates the method's capability to help public users conduct more insightful and diverse interactive explorations and gain more accurate data insights.
A Multi-Task Dual-Encoder Framework for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is a complex and important task in aspect-based sentiment analysis task, which aims to extract aspect-sentiment-opinion triplets from review sentences, to acquire comprehensive information for sentiment analysis. Most of the existing methods use pipeline approaches or end-to-end sequence tagging approaches to solve the ASTE task. However, the pipeline approaches suffer from error accumulation in practical applications. The existing sequence tagging approaches ignore the feature information of the three elements themselves, and it is impossible to model and infer the three elements effectively by placing each word in the same position as importance. Based on this, a multi-task dual-encoder framework is proposed. First, a dual-encoder is constructed to encode and fuse sentence information and semantic information, respectively. Then, the signs and constraints implied between word pairs are used to complete multi-task inference and triplet decoding. Meanwhile, we design two grid tagging methods and their corresponding inference strategies, respectively, for two tasks. The auxiliary task is used as a regularization of the main task, which improves the correct inference ability of the inference strategy for the main task and the robustness of the framework. Extensive testing on two benchmark datasets shows that the proposed framework is simple and effective, and significantly outperforms existing methods.
Unsupervised Domain Adaptation for Semantic Segmentation of Urban Street Scenes Reflected by Convex Mirrors

Reflective convex mirrors are often used on street corners or as passenger-side mirrors on cars to obtain scene information by reflecting blind spots in the field of view, which can provide safety for pedestrians and drivers on roads, driveways, and alleys that lack of visibility. In recent years, deep learning based scene understanding methods (e.g., semantic segmentation) have been rapidly developed. However, due to gaps in the geometric domain, models trained on normal images are not directly applicable to scenes with convex mirror reflections. In this paper, we propose a novel framework to reduce the domain gap between normal images and convex mirror reflection images. In particular, we geometrically model convex mirrors to obtain a differentiable convex mirror simulation layer, CMSL. With the help of CMSL, we perform adversarial domain adaptation on edges in the input space and semantic boundaries in the output space to reduce the geometric appearance gap between the synthetic and real images. To verify the effectiveness of our algorithm, we construct the first convex mirror reflection scene dataset CMR1K, which contains 268 images with fine annotations. Extensive experimental results show that our algorithm can significantly outperform the baseline and previous methods. For example, our method surpasses the baseline and AdvEnt by 10% and 3% in mIoU, respectively.
Fast CNN-Based Single-Person 2D Human Pose Estimation for Autonomous Systems

This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human pose estimation from RGB images that balances between high 2D human pose/skeleton estimation accuracy and rapid inference. Thus, it is suitable for safety-critical embedded AI scenarios in autonomous systems, where computational resources are typically limited and fast execution is often required, but accuracy cannot be sacrificed. The architecture is composed of a shared feature extraction backbone and two parallel heads attached on top of it: one for 2D human body joint regression and one for global human body structure modelling through Image-to-Image Translation (I2I). A corresponding multitask loss function allows training of the unified network for both tasks, through combining a typical 2D body joint regression with a novel I2I term. Along with enhanced information flow between the parallel neural heads via skip synapses, this strategy is able to extract both ample semantic and rich spatial information, while using a less complex CNN; thus it permits fast execution. The proposed architecture is evaluated on public 2D human pose estimation datasets, achieving the best accuracy-speed ratio compared to the state-of-the-art. Additionally, it is evaluated on a pedestrian intention recognition task for self-driving cars, leading to increased accuracy and speed in comparison to competing approaches.
A Deep Reinforcement Learning Recommender System With Multiple Policies for Recommendations

Deep reinforcement learning (DRL) based recommender systems are suitable for user cold-start problems as they can capture user preferences progressively. However, most existing DRL-based recommender systems are suboptimal, since they use the same policy to suit the dynamics of different users. We reformulate recommendation as a multitask Markov Decision Process, where each task represents a set of similar users. Since similar users have closer dynamics, a task-specific policy is more effective than a single universal policy for all users. To make recommendations for cold-start users, we use a default policy to collect some initial interactions to identify the user task, after which a task-specific policy is employed. We use Q-learning to optimize our framework and consider the task uncertainty by the mutual information regarding tasks. Experiments are conducted on three real-world datasets to verify the effectiveness of our proposed framework.
SASIMI: Evaluation Board for EM Information Leakage from Large Scale Cryptographic Circuits

In this paper, we propose a common evaluation board(Side-channel Attack Standard IMplementation and eval-uatIon board: SASIMI) for the threat of acquiring information leaked from electromagnetic(EM) noise generated by devices. To prevent this threat, it is necessary to implement circuits that do not leak secret information, like a secret key, via EM side-channel, and conduct actual measurement and evaluation environment, which makes it difficult for a third party to reproduce the results. However, since captured EM activity is affected by the surrounding EM noise, the evaluation results may vary depending on the evaluation environment. The proposed evaluation board can implement various cryp-tographic circuits. The IC must be capable of reconfiguring logic and implementing large-scale cryptographic blocks such as post quantum cryptography. To reduce the influence of environmental EM noise, an independent power supply network and measurement port are provided for the IC to be evaluated thus improving the measurement reproducibility. In order to evaluate the performance of the SASIMI board, this paper proposes an index to evaluate the strength of the information of the secret key contained in the power supply noise. This index is to find the value of the resistance to be inserted into the power supply network of the prototype board. Measurement results show that the simple amplitude value of EM noise and the intensity of information leakage do not necessarily coincide.