Reviewerly/exHarmony huggingface dataset:
A Novel Hardware Architecture for Enhancing the Keccak Hash Function in FPGA Devices

Hash functions are an essential mechanism in today’s world of information security. It is common practice to utilize them for storing and verifying passwords, developing pseudo-random sequences, and deriving keys for various applications, including military, online commerce, banking, healthcare management, and the Internet of Things (IoT). Among the cryptographic hash algorithms, the Keccak hash function (also known as SHA-3) stands out for its excellent hardware performance and resistance to current cryptanalysis approaches compared to algorithms such as SHA-1 and SHA-2. However, there is always a need for hardware enhancements to increase the throughput rate and decrease area consumption. This study specifically focuses on enhancing the throughput rate of the Keccak hash algorithm by presenting a novel architecture that supplies efficient outcomes. This novel architecture achieved impressive throughput rates on Field-Programmable Gate Array (FPGA) devices with the Virtex-5, Virtex-6, and Virtex-7 models. The highest throughput rates obtained were 26.151 Gbps, 33.084 Gbps, and 38.043 Gbps, respectively. Additionally, the research paper includes a comparative analysis of the proposed approach with recently published methods and shows a throughput rate above 11.37% Gbps in Virtex-5, 10.49% Gbps in Virtex-6 and 11.47% Gbps in Virtex-7. This comparison allows for a comprehensive evaluation of the novel architecture’s performance and effectiveness in relation to existing methodologies.
SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection

In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption.
Design and Development of Autonomous VTOL for Medicine Deliveries in Hilly Areas

This paper discusses the configuration of an autonomous hybrid VTOL (Vertical Take-Off and Landing) aircraft is systematic for the last mile delivery of medicines to rural and remote places. The developed aircraft has a payload capacity of 4 kg, a flying period of up to 45 min, and a high stability speed of 65 km per hour. This UAV is fitted with intelligent systems that enable it to operate autonomously even in places without a Global Positioning System and away from obstructions, which is essential in disaster-prone areas. This VTOL's folding wing structure reduces the Take-off and Landing area, making it more reliable.
Online Video Anomaly Detection

With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.
Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition

Datamation is designed to animate an analysis pipeline step by step, serving as an intuitive and efficient method for interpreting data analysis outcomes and facilitating easy sharing with others. However, the creation of a datamation is a difficult task that demands expertise in diverse skills. To simplify this task, we introduce Datamator, a language-oriented authoring tool developed to support datamation generation. In this system, we develop a data query analyzer that enables users to generate an initial datamation effortlessly by inputting a data question in natural language. Then, the datamation is displayed in an interactive editor that affords users the ability to both edit the analysis progression and delve into the specifics of each step undertaken. Notably, the Datamator incorporates a novel calibration network that is able to optimize the outputs of the query decomposition network using a small amount of user feedback. To demonstrate the effectiveness of Datamator, we conduct a series of evaluations including performance validation, a controlled user study, and expert interviews.
Security Supply Chain Using UAVs: Validation and Development of a UAV-Based Model for Qatar’s Mega Sporting Events

Unmanned aircraft vehicles (UAVs) are now used to support security precautions in search and rescue operations to track and evaluate critical services, to provide cybersecurity measures by transporting security supply chain management (SCM) to sports events, and to aid efforts to safeguard the spectators from attacks. A drone may quickly fly over sports grounds, scan the area for potential dangers, and offer aerial footage and still photographs. Although UAVs provide benefits to their operators, there is a possibility that they may also pose cybersecurity threats. This guide offers recommendations for best security practices, intending to assist sports operators in protecting their networks, materials, and staff for Qatar’s mega sporting events. The literature comprises several theoretical frameworks and conceptual models for security supply chains. Unfortunately, there is no practical model for measuring the behavioral intentions of professional IT and security experts. Therefore, this study conducted research in two stages. In the first stage, an in-depth systematic literature review was conducted to identify the factors and themes of UAV-based SCM for security measures. In the second phase, a survey questionnaire (N = 712) was implemented, comprising the themes and items from the literature review among professional IT and security experts. Exploratory factor analysis (EFA) was carried out with IBM SPSS, and confirmatory factor analysis (CFA) was employed with IBM AMOS. This study proposed and developed a UAV-based SCM model to provide security for Qatar’s mega sporting events, which comprised five factors: traceability, security and privacy, trust, acceptability, and preparedness. This study also confirmed the validity and reliability of the newly developed scales, offering practical and proposed implications for the IT and security industries. The key findings of the study are: (1) a valid and reliable UAV-based cybersecurity framework for FIFA mega sporting events was developed; (2) five critical factors were identified, including traceability, security and privacy, trust, acceptability, and preparedness; (3) all factors were significantly and positively correlated, highlighting the complexity of managing security systems in mega sporting events.
Quantum entanglement and coherence in a system of four-level atom interacting with a nonlinear field

Entanglement and atomic coherence are investigated for a system that consists of a four-level atom (FLA) coupled with a nonlinear field. We explore the influence of field deformation and photon transition on the dynamical behavior of the quantum measures when the quantized field is initially in a coherent state or in a Schrödinger cat state with and without energy dissipation. The results indicate that the entanglement and coherence in the FLA–field system can be controlled and manipulated through the nature and initial state of the quantized field.
Tracking by segmentation with future motion estimation applied to person-following robots

Person-following is a crucial capability for service robots, and the employment of vision technology is a leading trend in building environmental understanding. While most existing methodologies rely on a tracking-by-detection strategy, which necessitates extensive datasets for training and yet remains susceptible to environmental noise, we propose a novel approach: real-time tracking-by-segmentation with a future motion estimation framework. This framework facilitates pixel-level tracking of a target individual and predicts their future motion. Our strategy leverages a single-shot segmentation tracking neural network for precise foreground segmentation to track the target, overcoming the limitations of using a rectangular region of interest (ROI). Here we clarify that, while the ROI provides a broad context, the segmentation within this bounding box offers a detailed and more accurate position of the human subject. To further improve our approach, a classification-lock pre-trained layer is utilized to form a constraint that curbs feature outliers originating from the person being tracked. A discriminative correlation filter estimates the potential target region in the scene to prevent foreground misrecognition, while a motion estimation neural network anticipates the target's future motion for use in the control module. We validated our proposed methodology using the VOT, LaSot, YouTube-VOS, and Davis tracking datasets, demonstrating its effectiveness. Notably, our framework supports long-term person-following tasks in indoor environments, showing promise for practical implementation in service robots.
Variational quantum algorithm for generalized eigenvalue problems and its application to the finite-element method

Generalized eigenvalue problems (GEPs) play an important role in the variety of fields including engineering, machine learning, and quantum chemistry. Especially, many problems in these fields can be reduced to finding the minimum or maximum eigenvalue of GEPs. One of the key problems to handle GEPs is that the memory usage and computational complexity explode as the size of the system of interest grows. This paper aims at extending sequential quantum optimizers for GEPs. Sequential quantum optimizers are a family of algorithms that iteratively solve the analytical optimization of single-qubit gates in a coordinate descent manner. The contribution of this paper is as follows. First, we formulate the GEP as the minimization or maximization problem of the fractional form of the expectations of two Hermitians. We then show that the fractional objective function can be analytically minimized or maximized with respect to a single-qubit gate by solving a GEP of a $4\ifmmode\times\else\texttimes\fi{}4$ matrix. Second, we show that a system of linear equations characterized by a positive-definite Hermitian can be formulated as a GEP and thus be attacked using the proposed method. Finally, we demonstrate two applications to important engineering problems formulated with the finite-element method. Through the demonstration, we have the following bonus finding; a problem having a real-valued solution can be solved more effectively using quantum gates generating a complex-valued state vector, which demonstrates the effectiveness of the proposed method.
Digital Scarlet Letter on Journalists: Weaponized Harassment Against Journalists in South Korea

ABSTRACTThis article seeks to contribute to the existing research on journalist harassment by examining the experiences of Korean journalists who have faced online harassment. While extensive research has been conducted on this issue, there is a need for comparative studies to understand the unique patterns and underlying causes in different nations. The objectives of this article are to explore the experiences of Korean journalists subjected to online harassment, identify the factors they perceive as triggers for such harassment, understand the impacts it has on them, and examine their coping strategies with such incidents. One of the key findings of this research is the significant stress caused by specific shaming websites that aim to insult and discredit targeted journalists. Additionally, this study delves into the impact of online harassment on journalists’ self-censorship practice, where they may avoid covering controversial issues or skew their coverage towards less sensitive perspectives.KEYWORDS: Journalist harassmentdigital harassmentshaming websitesgender-based harassmentnewsroom cultureself-censorship Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 These interviews were initially conducted as a part of a study for the Korea Press Foundation and were subsequently supplemented.Additional informationFundingThis research was supported by the School of Media & Communication at Korea University.
Is QUIC Quicker with HTTP/3? An Empirical Analysis of Quality of Experience with DASH Video Streaming

Video streaming contributes to significant traffic on the internet. MPEG-DASH is a standard method to deliver video using short segments served by a content delivery network over HTTP. The Adaptive Bit Rate (ABR) algorithms used in MPEGDASH play a vital role in delivering better Quality of Experience (QoE) to the users. The ABR algorithms are known to perform well for HTTP/2 over TCP. To address the need for faster data delivery and lower connection establishment, a transport layer protocol QUIC and an application layer protocol HTTP/3 are being widely deployed. With this transition, it is highly desirable to revisit the ABR algorithms in terms of QoE they deliver. In this paper, the QoE/performance of the ABR algorithms are examined and compared between HTTP/3 over QUIC and HTTP/2 over TCP. Our results show that among the existing ABR algorithms, low latency streaming algorithms work in favor of HTTP/3 over QUIC and outperforms HTTP/2 over TCP in many scenarios. In lossy network conditions, HTTP/3 over QUIC achieves higher QoE by taking advantage of higher throughput of QUIC and downloading media segments faster than HTTP/2 over TCP.
Material handling machine activity recognition by context ensemble with gated recurrent units

Research on machine activity recognition (MAR) is drawing more attention because MAR can provide productivity monitoring for efficiency optimization, better maintenance scheduling, product design improvement, and potential material savings. A particular challenge of MAR for human-operated machines is the overlap when transiting from one activity to another: during transitions, operators often perform two activities simultaneously, e.g., lifting the fork already while approaching a rack, so the exact time when one activity ends and another begins is uncertain. Machine learning models are often uncertain during such activity transitions, and we propose a novel ensemble-based method adapted to fuzzy transitions in a forklift MAR problem. Unlike traditional ensembles, where models in the ensemble are trained on different subsets of data, or with costs that force them to be diverse in their responses, our approach is to train a single model that predicts several activity labels, each under a different context. These individual predictions are not made by independent networks but are made using a structure that allows for sharing important features, i.e., a context ensemble. The results show that the gated recurrent unit network can provide medium or strong confident context ensembles for 95% of the cases in the test set, and the final forklift MAR result achieves accuracies of 97% for driving and 90% for load-handling activities. This study is the first to highlight the overlapping activity issue in MAR problems and to demonstrate that the recognition results can be significantly improved by designing a machine learning framework that addresses this issue.
Impact of BB84 QKD Transmitter’s Wavelength Mismatch on Secure Key Generation Rate

In principle BB84 protocol supports secure quantum communication by assuming transmitter and receiver devices to be ideal. However, practically it is very challenging to build an ideal single photon source and detector. These imperfections in practical devices (i.e., wavelength mismatch, full width at half maximum (FWHM) pulse width mismatch and different arrival times of photon) leads to side channel attacks. In this work, we have analyzed the wavelength mismatch issue associated with four laser diode based practical BB84 transmitter. An asymptotically achievable rate of extraction of secure key (also known as key generation rate) is estimated from side-channel leakage calculation between transmitter (Alice) and adversary (Eve).
Focus-Style Proofs for the Two-Way Alternation-Free $$\mu $$-Calculus

We introduce a cyclic proof system for the two-way alternation-free modal $$\mu $$ -calculus. The system manipulates one-sided Gentzen sequents and locally deals with the backwards modalities by allowing analytic applications of the cut rule. The global effect of backwards modalities on traces is handled by making the semantics relative to a specific strategy of the opponent in the evaluation game. This allows us to augment sequents by so-called trace atoms, describing traces that the proponent can construct against the opponent’s strategy. The idea for trace atoms comes from Vardi’s reduction of alternating two-way automata to deterministic one-way automata. Using the multi-focus annotations introduced earlier by Marti and Venema, we turn this trace-based system into a path-based system. We prove that our system is sound for all sequents and complete for sequents not containing trace atoms.
Low-light images enhancement and denoising network based on unsupervised learning multi-stream feature modeling

Inspired by the generative adversarial networks EnlightenGAN, this paper proposes a novel low-light images Enhancement and Denoising model based on unsupervised learning Multi-Stream feature modeling (MSED). The model has two stages: generator network and discriminator network. Generator network includes global and local feature modeling network. First, Swin Transformer Block is innovatively introduced in the global feature modeling of generator network stage. It makes the interaction between the image and the convolutional kernel related to the image content. Its shift window mechanism can model the global feature dependency of the input image with less memory consumption, and extract the color, texture, and shape features of the image, thereby effectively suppressing noise and artifacts. Second, in the local feature modeling, a multi-scale image and feature fusion branch is added. It not only extracts reduced features from large-scale low-light images, but also extracts features from multiple downsampled low-light images, and then combines the two through attention and DSFF. By utilizing the complementary information of the reduced features and downsampled images, various underexposure/ overexposure phenomena caused by low-light images can be effectively avoided. In the discriminator network stage, the deep/shallow feature aggregation module is added to enhance the discrimination ability, and the inconsistencies are suppressed by learning the contradictory information of spatial filter, so that the shallow representation of information and the deep semantic information can guide each other. Thanks to the synergy of the above three innovative work, compared with many existing advanced low-light images enhancement models, MSED can achieve SOTA level performance on several public datasets. However, when dealing with low-light images with blurry content caused by rapid motion, MSED still cannot effectively restore their detailed information.
Spatio-Temporal Two-stage Fusion for video question answering

Video question answering (VideoQA) has attracted much interest from of scholars as one of the most representative multimodal tasks in recent years. The task requires the model to interact and reason between the video and the question. Most known approaches use pre-trained networks to extract complex embeddings of videos and questions independently before performing multimodal fusion. However, they overlook two factors: (1) These feature extractors are pre-trained for the image or video classification task without taking the question into consideration, therefore may not be suitable for VideoQA task. (2) Using multiple feature extractors to extract features at different levels introduce more irrelevant information to some extent, thus making the task more difficult. For the above reasons, we propose a new model named Spatio-Temporal Two-Stage Fusion, which ties together multiple levels of feature extraction processes and divides them into two distinct stages: spatial fusion and temporal fusion. Specifically, in the spatial fusion stage, we use Vision Transformer to integrate the intra-frame information to generate frame-level features. At the same time, we design a multimodal temporal fusion module that enables the video to fuse textual information and assign different levels of attention to each frame. Then the obtained frame-level features are used to generate global video features by another Vision Transformer. In order to efficiently generate modal interaction information, we design a video–text symmetric fusion module to retain the most relevant information by mutual guidance between the two modalities. Our method is evaluated on three benchmark datasets: MSVD-QA, MSRVTT-QA and TGIF-QA, and achieves competitive results.
Validity in Choice Logics

Qualitative Choice Logic (QCL) is a framework for jointly dealing with truth and preferences. We develop the concept of degree-based validity by lifting a Hintikka-style semantic game [10] to a provability game. Strategies in the provability game are translated into proofs in a novel labeled sequent calculus where proofs come in degrees. Furthermore, we show that preferred models can be extracted from proofs.