Reviewerly/exHarmony huggingface dataset:
A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.
Automatic software vulnerability assessment by extracting vulnerability elements

Software vulnerabilities take threats to software security. When faced with multiple software vulnerabilities, the most urgent ones need to be fixed first. Therefore, it is critical to assess the severity of vulnerabilities in advance. However, increasing number of vulnerability descriptions do not use templates, which reduces the performance of the existing software vulnerability assessment approaches. In this paper, we propose an automated vulnerability assessment approach that using vulnerability elements for predicting the severity of six vulnerability metrics (i.e., Access Vector, Access Complexity, Authentication, Confidentiality Impact, Integrity Impact and Availability Impact). First, we use BERT-MRC to extract vulnerability elements from vulnerability descriptions. Second, we assess six metrics using vulnerability elements instead of full descriptions. We conducted experiments on our manually labeled dataset. The experimental results show that our approach has an improvement of 12.03%, 14.37%, and 38.65% on Accuracy over three baselines.
Exploring the Reactions of Early Users of ChatGPT to the Tool using Twitter Data: Sentiment and Topic Analyses

Recently, large language models have gained considerable interest due to their impressive performance on different tasks. OpenAl's ChatGPT is a revolutionary language model that has quickly become popular among early adopters. Many users have gone so far as to call it a disruptive technology in many different domains. Its large, pre-trained model provides powerful capabilities already being applied to various use cases. Gaining an understanding of the thoughts and feelings of those who are quick to embrace new technologies is essential, as it can offer valuable insights into the technology's potential successes, failures, and other characteristics. In this paper, a mixed-method study was developed using 463,983 tweets from early ChatGPT users. We first apply topic modeling to identify the maj or topics and then conduct an extensive qualitative sentiment analysis. We observed that most people were pleased with the tool, but some expressed apprehension about its potential ethical and legal implications. After its launch, however, the fears were largely alleviated as people began to use it and recognize its benefits for personal and business purposes. It is important to be aware of any new technology's possible drawbacks and use it responsibly.
Revisiting Multiple Ring Oscillator-Based True Random Generators to Achieve Compact Implementations on FPGAs for Cryptographic Applications

The generation of random numbers is crucial for practical implementations of cryptographic algorithms. In this sense, hardware security modules (HSMs) include true random number generators (TRNGs) implemented in hardware to achieve good random number generation. In the case of cryptographic algorithms implemented on FPGAs, the hardware implementation of RNGs is limited to the programmable cells in the device. Among the different proposals to obtain sources of entropy and process them to implement TRNGs, those based in ring oscillators (ROs), operating in parallel and combined with XOR gates, present good statistical properties at the cost of high area requirements. In this paper, these TRNGs are revisited, showing a method for area optimization independently of the FPGA technology used. Experimental results show that three ring oscillators requiring only three LUTs are enough to build a TRNG on Artix 7 devices from Xilinx with a throughput of 33.3 Kbps, which passes NIST tests. A throughput of 50 Kbps can be achieved with four ring oscillators, also requiring three LUTs in Artix 7 devices, while 100 Kbps can be achieved using an structure with four ring oscillators requiring seven LUTs.
Few-helping-card Protocols for Some Wider Class of Symmetric Boolean Functions with Arbitrary Ranges

In card-based cryptography, which uses a physical deck of cards to realize secure multiparty computations, a one-bit value is usually encoded by a pair of cards. Thus, when performing a secure computation of an n-input Boolean function, a sequence of 2n cards representing n bits is needed for input, and some helping cards are typically added to form a protocol. In 2020, Ruangwises and Itoh constructed a card-based protocol for a symmetric Boolean function with an arbitrary range using two helping cards. (Note that a symmetric Boolean function depends only on the number of 1s in its input). At the same time, they showed that the helping cards can be eliminated if the target function is limited to “doubly symmetric” Boolean functions (also known as symmetric self-anti-dual functions). A doubly symmetric Boolean function satisfies the following for all k: when inputting exactly a number k of 1s, the output is the same as the output when inputting exactly a number n − k of 1s. In this paper, we loosen the restriction on doubly symmetric Boolean functions by fixing k = 0, and construct new protocols which require less than two helping cards for that wider class of symmetric Boolean functions. Specifically, we design a one-helping-card protocol for any n > 4, and helping-card-free protocols for n = 3 and n = 4.
AI-Based Autonomous Weapons and Individual Criminal Responsibility Under the Rome Statute

Objective: international law obligates states to prosecute those who have violated laws in armed conflicts, particularly when the international community now has International Criminal Court (ICC). That is why the aim of the paper is to discover the responsibility for the crimes made with the use of AI-based autonomous vehicles in accordance with the provisions of the Rome Statute of the ICC. Methods: doctrinal analysis allowed to research the positions of experts on the responsibility for the crimes made with the use of AI-based autonomous vehicles in accordance with the provisions of the Rome Statute of the ICC. Results: this paper argues that the ICC can only exercise jurisdiction over natural persons who allegedly have committed the crimes under its jurisdiction, as compared to autonomous weapons. This paper argues that the persons who facilitate the commission of the alleged crimes are highly likely to be criminally responsible for providing means for the alleged crimes to be committed by AI-based autonomous weapons under Article 25(3)(c) of the Rome Statute and concludes that the Rome Statute provides a solution even to AI-based autonomous weapons. Scientific novelty: this paper addresses to the highly relevant issues of the responsibility for the crimes made with the use of AI-based autonomous vehicles in accordance with the provisions of the Rome Statute of the ICC. Practical significance: the results achieved in the paper can be used in regulation design for AI-based autonomous weapons. It can also be used as a basis for the future research in the sphere of liability of AI-based autonomous weapons and AI in general
Orienteering with One Endomorphism

In supersingular isogeny-based cryptography, the path-finding problem reduces to the endomorphism ring problem. Can path-finding be reduced to knowing just one endomorphism? It is known that a small degree endomorphism enables polynomial-time path-finding and endomorphism ring computation (in: Love and Boneh, ANTS XIV-Proceedings of the Fourteenth Algorithmic Number Theory Symposium, volume 4 of Open Book Ser. Math. Sci. Publ., Berkeley, 2020). An endomorphism gives an explicit orientation of a supersingular elliptic curve. In this paper, we use the volcano structure of the oriented supersingular isogeny graph to take ascending/descending/horizontal steps on the graph and deduce path-finding algorithms to an initial curve. Each altitude of the volcano corresponds to a unique quadratic order, called the primitive order. We introduce a new hard problem of computing the primitive order given an arbitrary endomorphism on the curve, and we also provide a sub-exponential quantum algorithm for solving it. In concurrent work (in: Wesolowski, Advances in cryptology-EUROCRYPT 2022, volume 13277 of Lecture Notes in Computer Science. Springer, Cham, 2022), it was shown that the endomorphism ring problem in the presence of one endomorphism with known primitive order reduces to a vectorization problem, implying path-finding algorithms. Our path-finding algorithms are more general in the sense that we don't assume the knowledge of the primitive order associated with the endomorphism.
Progress in Blind Image Quality Assessment: A Brief Review

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem.
UIT-ADrone: A Novel Drone Dataset for Traffic Anomaly Detection

Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that has attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to street monitoring systems. However, there is a notable lack of adequate drone-based datasets available to detect unusual events in the urban traffic environment, especially in roundabouts, due to the density of interaction between road users and vehicles. To promote the development of anomalous event detection with drones in the complex traffic environment, we construct a novel large-scale drone dataset to detect anomalies involving realistic roundabouts in Vietnam, covering a large variety of anomalous events. Traffic at a total of three different roundabouts in Hochiminh city was recorded from a camera-equipped drone. The resulting dataset contains 51 videos with total data traffic of nearly 6.5 hours, captured across 206K frames with ten abnormal event types. Based on this dataset, we comprehensively evaluate the current state-of-the-art algorithms and what anomaly detection can do in drone-based video surveillance. This study presents a detailed description of the proposed UIT-ADrone dataset, along with information regarding data distribution, protocols for evaluation, baseline experimental results on our dataset and other benchmark datasets, discussions, and paves the way for future work. The dataset is available online for non-commercial research at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://uit-together.github.io/datasets/UIT-ADrone/</uri> .
A color-based deep-learning approach for tissue slide lung cancer classification

Non-Small Cell Lung Cancer is the most common type of lung cancer, accounting for more than 80% of all cases. The analysis of histopathological images is the appropriate way to detect lung cancer in its initial stages. However, due to the heterogeneous patterns of molecular structures and the high magnification factor, the classification of whole slide images is difficult. Furthermore, these large-pixel images cannot be considered directly due to the high computational requirements. Several deep-learning models have been developed for the classification of histopathological images, but only in the default RGB format. Existing models are complex and rely on pathologist annotations which increases the computational cost and time. The present paper proposes a deep-learning framework, a Color-based Dilated Convolutional Neural Network (CD-CNN) for the multi-class classification of patch-based whole slide images of lung cancer. The proposed CD-CNN framework includes data preparation, color space transformation, and the classification stages. It thoroughly examines the impact of five color spaces on classification performance. The proposed model was also compared to three pre-trained models and the Convolutional Neural Network model on three different datasets namely: The Cancer Genome Atlas dataset, the Clinical Proteomic Tumor Analysis Consortium cohorts, and the LC25000 dataset. The analysis of all the models reveals that the HSV color space outperforms the other transformations for all of the datasets tested. The results depict that the proposed CD-CNN achieved the best classification results in HSV color space for all three datasets i.e. accuracy of 0.97–0.99, precision, recall, and F1 score 0.97–0.98 and AUC value of 0.970–0.984. In addition, the highest value of the kappa score 0.986 has been achieved by the proposed model. These findings demonstrate the efficacy of the proposed method and show an improvement in diagnostic systems.
Detect, Grow, Seg: A weakly supervision method for medical image segmentation based on bounding box

Weakly supervised semantic segmentation based on bounding box has been fully developed in natural scenes, but in medical image scenes it often faces the difficulties of blurred edges, imaging noise, and mutual interference between multiple organs, which makes the methods suitable in natural scenes tend to show poor performance in medical images. In this paper, we propose a weakly supervised segmentation method for medical images based on Detection, Growth, and Segmentation (DGS). For weak labels in the form of bounding boxes, the detector YOLOV5 and GradCAM are introduced to obtain the heatmap representation, while the additional unlabeled data can be also utilized through the detector. The thresholded heatmap is used to generate seed points for adaptive regional growth so that better pseudo labels are obtained. Finally, the pseudo labels are used for supervised training on UNet to obtain end to end segmentation. Sufficient experiments show that, compared with other methods, the pseudo label generation and segmentation model formed by our method achieve the best performance, while the use of unlabeled data by the detector brings significant improvement for segmentation in low-label scenarios, which greatly reduce the time and demand of labeling.
Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

The optimal management of personal resources impacts everyone’s quality of life. An investment in graduate education is a sustainable opportunity for improved outcomes in human life, including cognition, behavior, life opportunities, salary, and career. Advanced technology dramatically reduces the risk of personal resources in graduate program admission recommendations that depend on multiple individual needs and preferences. In the digital age, a dynamic recommender system enhances the suitably effective solution for students’ university selections. This study focused on designing, developing, and testing a recommender system for graduate admission using a dynamic multi-criteria AHP and fuzzy AHP approach. The explicit multi-criteria recommender system was a platform as a service (PaaS) web application created to aid in graduate admissions management and decision-making. The design proposed that the bit representation store a dynamic explicit multi-criteria data structure. The recommendations adopting dynamic multi-criteria were validated by comparing them to the programs to which the students were actually admitted and enrolled. They individually ranked the evaluation outcomes of dynamic explicit multi-criteria and alternative preferences to provide graduate admission recommendations. Eighty graduate students in information technology evaluated the recommender system. Using top-1, top-2, and F1-score accuracy, the effective system accuracy performance on the dynamic multi-criteria recommender system was evaluated using AHP and fuzzy AHP approaches. The fuzzy AHP demonstrated marginally greater practical accuracy than the AHP method.
A new univariate feature selection algorithm based on the best–worst multi-attribute decision-making method

With the extensive applicability of machine learning classification algorithms to a wide spectrum of domains, feature selection (FS) becomes a relevant data preprocessing technique due to the high dimensionality of data used in these domains. While efforts have been made to study various filters for ranking features, scholars have paid little attention to developing a unified framework that can be used as an interface for any filter. The development of such a framework would formalize the understanding of filter-based FS. This helps put scholars in the same perspective when analyzing new FS algorithms. This study proposes a new filter-based FS framework based on the best–worst multi-attribute decision-making method. The proposed algorithm is compared to two control groups: (a) no FS and (b) randomized algorithm. Furthermore, two blocking variables are considered: (i) classifier and (ii) training dataset. The performance of the classifiers was measured using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. A three-way analysis of variance (ANOVA) is used to compare the proposed approach to the control groups considering the blocking variables. This paper offers several contributions to the literature. For one thing, it is one of the few works that put forward a framework for performing filter-based FS. To the best of the authors’ knowledge, the study is the first to provide empirical evidence about the interaction between the factors considered in the literature for evaluating FS algorithms.
Navigating the legal landscape of AI copyright: a comparative analysis of EU, US, and Chinese approaches

Abstract This paper compares AI copyright approaches in the EU, US, and China, evaluating their effectiveness and challenges. It examines the recognition of AI-generated works as copyrightable and the exclusive rights of copyright owners to reproduce, distribute, publicly display, and perform such works. Differences in approaches, such as recognizing AI as a sui generis right holder in the EU and the broad fair use doctrine in the US, are highlighted. This paper evaluates strengths and weaknesses of each approach, including enforcement and ownership of copyright in AI-generated works, and clarifies issues related to AI and copyright. While the EU and US have more developed legal frameworks for AI copyright than China, all three approaches face challenges that need addressing. This paper concludes by providing insight into the legal landscape of AI copyright and steps necessary for effective protection and use of AI-generated works.
Feedback coupling induced synchronization of neural networks

Synchronization emerges ubiquitously in natural and engineering systems and at different scales. For real-world systems with invisible governing equations, recurrent neural networks provide effective approach to embed their dynamics from observations and facilitate intensive study, including the synchronization and its mechanisms. Synchronization at a scale of neural networks’ dynamics instead of the component neurons’ has seldom been studied. Here, we define the synchronization of reservoir computers at a macroscopic level, named by hyper-synchronization, from a viewpoint of dynamical systems theory. HyperSync is realized, with a merged attractor emerging, in reservoir computers trained by different chaotic systems through a proposed feedback coupling mechanism. Numerical experiments demonstrate its effectiveness, and we further provide guidance for realizing synchronization among multiple reservoir computers coupled with different topologies. This work articulates an appealing framework to realize synchronization of neural networks and anticipates potential applications in fields such as communications and biological systems.
Heterogeneous and clustering-enhanced personalized preference transfer for cross-domain recommendation

As a promising solution to alleviate the critical cold-start problem in recommendation systems, Cross-Domain Recommendation (CDR) aims to transfer users’ preferences from the source domain to the target domain. However, the negligence of semantic differences of heterogeneous relations when modeling user preferences and the non-consideration of a user’s common characteristics inferred from similar users when transferring the user’s preferences make recent CDR works cannot well handle the two core issues of CDR—‘what to transfer’ and ‘how to transfer’. To this end, we propose a novel heterogeneous and clustering-enhanced user preference transfer model for CDR (named HCCDR). To well address the first issue, the heterogeneous latent factor modeling component is firstly built to compute high-quality representations of users and items to be transferred based on the heterogeneous relations among users and items. Note that the heterogeneous relations with different semantics are processed with different models. Then, the clustering-enhanced preference transfer component well addresses the second issue by learning an effective personalized preference transfer function between two domains for a user, where the individual and common characteristics of the user are concurrently considered. Finally, the personalized recommendation component achieves the personalized recommendation in the target domain based on the transferred user embeddings calculated via the learned personalized preference transfer functions. Experimental results performed on large public datasets demonstrate that the proposed HCCDR markedly outperforms all baselines. In particular, HCCDR gains a maximum performance improvement of 12.69% (or 8.99%) for RMSE (or MAE) compared with the best baseline.