Reviewerly/exHarmony huggingface dataset:
Perspective Chapter: On Entanglement Measures – Discrete Phase Space and Inverter-Chain Link Viewpoint

In contrast to abstract statistical analyses in the literature, we present a concrete physical diagrammatic model of entanglement characterization and measure with its underlying discrete phase-space physics. This paper serves as a pedagogical treatment of this complex subject of entanglement measures. We review the important inherent concurrence property of entangled qubits, as well as underscore its emergent qubit behavior. From the discrete phase space point of view, concurrence translates to translation symmetry of entangled binary systems in some quantitative measure of entanglement. Although the focus is on bipartite system, the technique is readily extendable to multi-partite system of qubits, as can easily be deduced from the physical inverter-chain link model. A diagrammatic analysis of the entanglement of formation for any multi-partite qubit system is given. We show that quantum mechanical joint distribution are entanglements.
Mathematics Enrichment through Accelerated Learning to mitigate learning loss due to COVID-19 pandemic and distance learning

The COVID-19 pandemic caused a social and economic disruption around the globe. This affected 90% of the world’s school children and caused the shutdown of schools in more than 190 countries; in the US, the school closure of K – 12 schools affected 55.1 million students in 124,000 public and private schools during the 2019–2020 academic year. This caused a transition to virtual/remote teaching and learning with little or no preparation for teachers and students. K-12 school enrollment dropped by 2.9 million from 2019 to 2020, widening pre-existing disparities in access and technological opportunities. Students in high-poverty schools were hit hardest in areas of math and reading. Black and Latino students lost 6 months in math compared to their counterparts. This has resulted in a lot of “unfinished learning” from the past years. The latest NAEP results show that on average, nationwide math and reading scores for thirteen-year-olds are the lowest in decades. Due to the pandemic, today’s students may earn $49,000 to $61,000 less over their lifetime when they enter the workforce, and this could cost the US economy $128 billion to $188 billion every year especially because they lack the skills, behaviors, and mindset to succeed in their workplaces.
Depth Learning Methods For Bridges Inspection Using UAV

This paper is investigating learning methods using depth as a cue measurement that can be used for bridge inspection. We investigate learning methods based on mono, stereo, and multiview image input and discuss the constraints that allow some methods to perform better than others in various scenarios. We go over the state-of-the-art deep learning methods, including supervised and unsupervised methods. These methods will be compared and evaluated, based on constraints, performance, and accuracy, and how top methods should be selected for each scenario. The same database should be used for fair comparison between all methods ensuring that evaluations are unbiased, replicable, and meaningful.
IMPLEMENTATION OF THE PROJECT METHOD IN THE FOREIGN LANGUAGE LEARNING PROCESS OF UKRAINIAN AND INTERNATIONAL STUDENTS

International scientific conference «Pedagogy and psychology in the modern world: interaction vectors» : conference proceedings (August 2–3, 2023 Wloclawek, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 88 pages.
A sparse-point guided photometric stereo method for the metal complex surfaces measurement and high-fidelity geometry recovery

Accurate recovery of complex surfaces with dense points and normals often requires informative measurement data, posing a great challenge for measurement. In this paper, a contact sensor and a photometric stereo (PS) vision device are integrated together to recover the geometry with high-fidelity details. A two-branch deep PS neural network is developed and the Gaussian process regression (GPR) is used to reconstruct a global geometric shape from some sparse points captured by the contact probe. This prior geometry is sent to the proposed cross-convolution attention residual block (CCARB) based network to guide the PS for better normal recovery. The resulting normals are further fused with the sparse points by a normal integral model to obtain dense and high-accuracy points. Synthetic datasets are constructed to train the network. Experiments are conducted on both synthetic and real data, and show the superiority in the high-fidelity reconstruction for the measurement of complex surfaces.
Measuring the transmitted wavefront of large-aperture flat optics

This paper proposes a scanning distance measuring interferometer (SDMI) method to measure the transmitted wavefront of large-aperture flat optics accurately. The biaxial positioning mechanism moves the test component through the interferometric path of a differential distance-measuring interferometer; both the optical path difference and distribution are obtained; thus, the wavefront can be reconstructed. A Ø360 mm flat optical substrate is measured, and the result is compared with a phase measuring interferometer (PMI). The SDMI result is 68 nm in PV and 13 nm in RMS. The PMI result is 90.8 nm in PV and 16.4 nm in RMS. The forms of the two results show a good correlation, which gives confidence in the viability of the SDMI method for large-aperture optical transmitted wavefront measurements out of range of the commercial PMI.
LBP feature and hash function based dual watermarking algorithm for database

In this paper, we propose a local binary pattern (LBP) feature and hash function based dual watermarking algorithm for database. Attribute feature columns are selected to generate zero watermarks using the Pearson correlation method. The zero watermarks are generated by the LBP. The attribute values of the selected feature columns are divided into two parts for embedding and extracting the watermark. Zero watermark feature code is embedded into the lowest significant bit of the selected attribute column by using the database watermarking algorithm with hash function for copyright authentication. The proposed method uses two-layer hashing method to improve the robustness. In the watermark extraction, a method of combining majority voting and Hamming error correction code is used to control the watermark error to ensure the correct extraction rate of the watermark. Experimental results show that the algorithm not only provides good availability of the database after embedding the watermark, but also ensures the correct extraction of the watermark after various attacks.
Deep learning with class-level abstract syntax tree and code histories for detecting code modification requirements

Improving code quality is one of the most significant issues in the software industry. Deep learning is an emerging area of research for detecting code smells and addressing refactoring requirements. The aim of this study is to develop a deep learning-based system for code modification analysis to predict the locations and types of code modifications, while significantly reducing the need for manual labeling. We created an experimental dataset by collecting historical code data from open-source project repositories on the Internet. We introduce a novel class-level abstract syntax tree-based code embedding method for code analysis. A recurrent neural network was employed to effectively identify code modification requirements. Our system achieves an average accuracy of approximately 83% across different repositories and 86% for the entire dataset. These findings indicate that our system provides higher performance than the method-based and text-based code embedding approaches. In addition, we performed a comparative analysis with a static code analysis tool to justify the readiness of the proposed model for deployment. The correlation coefficient between the outputs demonstrates a significant correlation of 67%. Consequently, this research highlights that the deep learning-based analysis of code histories empowers software teams in identifying potential code modification requirements.
Design and implementation of IMU-based locomotion mode recognition system on Zynq SoC

Active prostheses are becoming an increasingly viable option for lower limb amputees, as they can significantly improve their quality of life and mobility. However, to ensure a robust and effective control of these prostheses, the terrain environment must be considered. In this study, we have proposed a hardware implementation of a real-time low-latency human Locomotion Mode Recognition (LMR) system on a system on chip (SoC) platform from Xilinx, which can be customized and optimized based on the user's needs without requiring external computing resources or human intervention. The system uses a single IMU sensor placed on the shank, and includes all the stages in a LMR process, i.e. signal pre-processing, feature extraction, and terrains estimation using a MLP neural network classifier to classify five terrains (level walking, stair ascent/descent, ramp ascent/descent). To achieve a flexible and efficient hardware design, the proposed system architecture was optimized using parallelism and quantization optimization techniques. This approach has resulted in a significant performance improvement, with a processing speed 15 times faster than the PL non-optimized approach, and 4.3 times faster than the PS(-O3) optimized implementation on the same Zynq FPGA device. The proposed architecture was also validated in real time via the analog discovery device.
Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8

Computer simulations using agent-based approach aimed at modeling human behavior require a robust dataset derived from actual observation to serve as ground truth. This paper details an approach for developing a movement behavior dataset generator from CCTV footages with respect to two health-related behaviors: face mask wearing and physical distancing, while addressing the privacy concerns of confidential CCTV data. A two-stage YOLOv8-based cascaded approach was implemented for object tracking and detection. The first stage involves tracking of individuals in the video feed to determine physical distancing behavior using the pre-trained YOLOv8 xLarge model paired with Bot-SORT multi-object tracker and OpenCV Perspective-n-Point pose estimation. The second stage involves determining the mask wearing behavior of the tracked individuals using the best-performing model among the five YOLOv8 models (nano, small, medium, large, and xLarge), each trained for 50 epochs on a custom CCTV dataset. Results show that the custom-trained xLarge model performed the best on the mask detection task with the following metric scores: mAP50 = 0.94; mAP50-95 = 0.63; and F1 = 0.872. The faces of all the tracked individuals are blurred-out in the resulting video frames to preserve the privacy of the CCTV data. Finally, the developed system is able to generate the corresponding mask-distancing behavior dataset and annotated output videos from the input CCTV raw footages.
Pandemic Personas: Analyzing Identity Signals in COVID-19 Discourse on Twitter

The first year of the COVID-19 pandemic coincided with significant social and political changes. This article presents an exploratory analysis of Twitter users' self-representations in the context of COVID-19. While some identities remained stable throughout the year, others appear to have been influenced by external events such as the Black Lives Matter protests and the U.S. presidential election. We also examine how users' political identities are expressed in tweets, finding that right-leaning users are more likely to mention left-leaning identities, although both conservative and liberal-leaning users showed increased out-group focus in the period leading up to and after the election. Our study provides a comprehensive overview of user personas in a critical discourse topic, shedding light on identity signaling on social media. We end by identifying several clear opportunities for future research on social identity self-presentation on social media.
Energy Efficient DNN Compaction for Edge Deployment

Deep Neural Networks (DNNs) are popular deep learning models due to their numerous learnable parameters, which are required for both the training and inference phases. However, deploying these models on mobile and edge devices with limited hardware resources and power budgets is a significant challenge. To meet real-time requirements and energy efficiency, it is essential to compact DNN models. This paper proposes a fixed partition compaction technique exploiting consecutive zeros and non-zero weights/parameters in sparse DNN models. This approach reduces memory storage requirements, memory transactions and computations for DNNs. We implemented convolution and fully connected layers with the compact weights on Virtex-7 FPGA VC707. Our experiments demonstrate that compact layers have better performance and energy efficiency than layers without compaction. Results show that the compact convolution layers achieved an average performance improvement of 32.51% and 29.43% compared to state-of-the-art SMM and direct convolution respectively performed on several convolution configurations. Moreover, an energy consumption reduction of 34.14% over SMM and 29.58% over direct convolution. Experiments on the compact fully connected layers achieved an average performance improvement of 26.61% and energy consumption reduction of 30.85% over layers without compaction.
Implementation of a Perception System for Autonomous Vehicles Using a Detection-Segmentation Network in SoC FPGA

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by both high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 Vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 W, compared to the minimum of 55 W for weaker CPUs, and the small size (119mm $$\times $$ 140 mm $$\times $$ 36 mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than $$97\%$$ of the mAP (mean average precision) for object detection and above $$90\%$$ of the mIoU (mean intersection over union) score for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city.
Inductive Linear Probing for Few-Shot Node Classification

Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting the widely studied inductive setting in the broader few-shot learning community. This oversight limits our comprehensive understanding of the performance of meta-learning based methods on graph data. In this work, we conduct an empirical study to highlight the limitations of current frameworks in the inductive few-shot node classification setting. Additionally, we propose applying a competitive baseline approach specifically tailored for inductive few-shot node classification tasks. We hope our work can provide a new path forward to better understand how the meta-learning paradigm works in the graph domain.
Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions

Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model ( https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal ). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.
Modeling Human Actions in the Cart-Pole Game Using Cognitive and Deep Reinforcement Learning Approach

Designing optimal controllers still poses a challenge for modern Artificial Intelligence systems. Prior research has explored reinforcement learning (RL) algorithms for benchmarking the cart-pole control problem. However, there is still a lack of investigation of cognitive decision-making models and their ensemble with the RL techniques in the context of such dynamical control tasks. The primary objective of this paper is to implement a Deep Q-Network (DQN), Instance-based Learning (IBL), and an ensemble model of DQN and IBL for the cart-pole environment and compare these models' ability to match human choices. Forty-two human participants were recruited to play the cart-pole game for ten training trials followed by a test trial, and the human experience information containing the situations, decisions taken, and the corresponding reward earned was recorded. The human experiences collected from the game-play were used to initialize the memory (buffer) for both the algorithms, DQN and IBL, rather than following the approach of learning from scratch through environmental interaction. The results indicated that the IBL algorithm initialized with human experience could be proposed as an alternative to the Q-learning initialized with human experience. It was also observed that the ensemble model could account for the human choices more accurately compared to the Q-learning and IBL models.
Misogyny, Women in Power, and Patterns of Social Media Harassment

Online harassment is a well-documented and studied problem on social media. Who does this harassing, how, and to what degree are important questions that can inform platform policies and automated controls as well as helping understand harassers more broadly. This study investigates users who were discovered because they created a post that harassed a women in power using misogynistic slurs. Do these tend to be isolated incidents, or do such users engage in higher rates of harassment more generally? Findings from both Twitter and Parler suggest that this population uses offensive slurs at over 3 times the rate of control groups. We break down these findings and discuss the implications for moderation, automation, user well-being, and platform success.
Integrating Human Factors into Agent-Based Simulation for Dynamic Phishing Susceptibility

Many researchers focus on developing virtual testbeds to assess the magnitude of cyberattack damage and evaluate the effectiveness of cyber defense strategies in different cyber attack scenarios. These testbeds provide a controlled and cost-effective environment for simulating attacks and studying their impact on organizational security. One of the major challenges in developing such testbeds is accurately capturing the human factors in cybersecurity. Phishing attacks, in particular, exploit human vulnerabilities and can lead to significant security breaches. However, modeling and simulating human susceptibility to phishing in virtual environments is complex due to the dynamic nature of human behavior and the interplay of various factors. This paper addresses this challenge by proposing an agent-based modeling framework that incorporates human factors to simulate dynamic phishing susceptibility. The framework allows modeler to specify and assign weights to various human factors, such as personality traits and training history, which influence individuals' susceptibility to phishing attacks. By leveraging this framework, modelers can run virtual cyber attack simulations with dynamically changing phishing susceptibility among the simulated end user agents. This enables the testing and evaluation of different cyber defense strategies in the context of realistic human behavior.
Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes

Social media has emerged as a popular platform for sports fans to express their opinions regarding athletes' performance. The National Basketball Association (NBA) is widely recognized as one of the most popular sports leagues globally. However, an unfortunate aspect that has emerged in recent years is the presence of abusive fans within the league. Consequently, the focus of this research is to identify which NBA athletes experience abuse on Twitter and delve deeper into the underlying reasons behind such mistreatment. To address the research questions at hand, the study employs a curated set of keywords to query the Twitter API, gathering a comprehensive collection of tweets that potentially contain hate speech directed toward NBA players. A deep learning classification model is implemented, effectively identifying tweets that genuinely exhibit hate speech. We further use keyword search methods to detect the specific groups that are targeted by hate speech the most and identify topics of hate speech tweets. The findings of our research indicate that certain groups of athletes are particularly vulnerable to hate speech from fans. Notably, high-performing athletes, Black athletes, overweight athletes, short athletes, and athletes associated with the LGBTQ community are found to be highly susceptible to abusive remarks. Racism, physique shaming, play style, and anti-LGBTQ remarks are the major themes. These findings contribute to a broader understanding of the challenges faced by NBA athletes in the digital space and provide a foundation for developing strategies to combat hate speech and foster a more inclusive environment for all individuals involved in the NBA community.