Reviewerly/exHarmony huggingface dataset:
Visualizing the Off-Screen Evolution of Trajectories

Abstract In the context of volunteered geographic information, large sets of trajectories of humans and animals are collected. Analyzing these trajectories visually is often complicated due to limited display sizes. For instance, when a user chooses a large map scale to inspect the details of a trajectory, only a small part of the trajectory is visible in the map. Therefore, in this article, we present an approach for visualizing the off-screen evolution of trajectories, i.e., their continuation outside of the displayed map. We propose visual cues in the form of glyphs that are displayed at the map’s boundary and that consist of one or multiple disk sectors of varying size and opening angle. These glyphs indicate the direction and variability of direction of a trajectory’s continuation outside the map frame. We present an algorithm for computing the glyphs efficiently and evaluate them in a user study. The results show that the glyphs are intuitive to understand even without explanation. We further present suggestions for improving the glyph design based on the results.
Construction of the Interactive Relationship between Law Enforcement and Legislation Based on the Background of Big Data

Under the background of the current era of ever-increasing speed of information technology iteration and increasing application of big data, it is necessary to conduct in-depth research on the interaction between administrative law enforcement and legislation and try to construct its system. From the perspective of big data, building an interactive system of law enforcement and legislation is also an important exploration. Starting from clarifying the existing relationship between law enforcement and legislation, this study analyzes the positive impact of big data on the construction of the interactive relationship between law enforcement and legislation. Finally, this study attempts to construct the interaction between administrative law enforcement and legislation under the background of big data by focusing on the construction and operation mechanism of the big data platform, the interaction mechanism between law enforcement and law enforcement in the big data platform, and the guarantee mechanism of the interaction between law enforcement and legislation under the big data platform to promote the establishment and improvement of a “complete legal norm system” and an “efficient rule of law enforcement system” to achieve precise governance.
Discrete Samplers for Approximate Inference in Probabilistic Machine Learning

Probabilistic reasoning models (PMs) and probabilistic inference bring advantages when dealing with small datasets or uncertainty on the observed data, and allow to integrate expert knowledge and create interpretable models. The main challenge of using these PMs in practice is that their inference is very compute-intensive. Therefore, custom hardware architectures for the exact and approximate inference of PMs have been proposed in the SotA. The throughput, energy and area efficiency of approximate PM inference accelerators are strongly dominated by the sampler blocks required to sample arbitrary discrete distributions. This paper proposes and studies novel discrete sampler architectures towards efficient and flexible hardware implementations for PM accelerators. Both cumulative distribution table (CDT) and Knuth-Yao (KY) based sampling algorithms are assessed, based on which different sampler hardware architectures were implemented. Innovation is brought in terms of a reconfigurable CDT sampling architecture with a flexible range and a reconfigurable Knuth-Yao sampling architecture that supports both flexible range and dynamic precision. All architectures are benchmarked on real-world Bayesian Networks, demonstrating up to 13 × energy efficiency benefits and 11 × area efficiency improvement of the optimized reconfigurable Knuth-Yao sampler over the traditional linear CDT-based samplers used in the PM SotA.
Eigen-guided deep metric learning

This paper proposes a new approach to improve the optimization performance of gradient-based optimizers for training neural networks in metric learning. In metric learning, the neural networks use a linear activation in their output layer to generate real-valued data embeddings. Hence, the optimal model parameters can be deterministically calculated with respect to the output layer on the basis of the eigenvalue decomposition. These optimal model parameters that were computed by the eigenvalue decomposition were referred to eigen-guidance. The training performance of a gradient-based optimizer is boosted by replacing the current model parameters with the eigen-guidance at each pre-defined number of epochs. In addition, the optimal number of embedding dimensions is determined directly while computing the eigen-guidance without additional complexities. In our experiments, gradient-based optimizers with the eigen-guidance converged significantly faster than the gradient-based optimizers alone in metric learning tasks.
Absolute convergence and error thresholds in non-active adaptive sampling

In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.
Analysis of Wavelets on Discrete Wavelet Transform for Image Compression and Transmission in Wireless Sensor Networks

Sensor nodes in Wireless Sensor Networks have physical constraints in terms of energy, processing power, memory, and so on. As a result, solutions must be devised to make the most of restricted resource availability during the transmission of content-rich multimedia data. In this proposed model an analysis is done on Discrete Wavelet Transform, for pre-processing of images for compression and its subsequent transmission using different types of wavelets. After Discrete Wavelet Transform process, the output is further processed with Run Length Encoding procedure and the output is evaluated in terms of number of bits that are transmitted through the network, before and after Discrete Wavelet Transform. The findings of the simulation reveal that there is a significant reduction in the amount of transmitted data which in turn save the network resources and prolongs the network lifetime.
Collection of End User Requirements and Use Cases during a Pandemic—Towards a Framework for Applied Research Projects

Research projects in the security domain often aim to develop innovative technology-based solutions for end users (e.g., situational awareness tools, crisis management tools). The pandemic crisis hit hard and without warning, not only influencing our everyday life but also the scientific community. To continue applied research projects during a pandemic, work structures needed to be adapted (e.g., user requirements collection, use case development), as face-to-face events were impossible but crucial to collect high quality requirements with a variety of different stakeholders. To ensure continued multi-stakeholder engagement we developed an overarching framework for collecting user requirements and use cases in an online setting and applied the framework within two research projects. The framework consists of four steps with the aim to assure high quality user requirements and use case collection (first analysis, stakeholder consultation, evaluation and prioritization, technical evaluation). The two projects presented in this paper provide insight on the potential of the framework. The framework offers a structured approach that fits for many different security research projects in terms of the easy application and its transferability. The main advantages (e.g., easily adaptable, reduced workshop time, no need to travel, suitability for different contexts and project types, etc.) and drawbacks (e.g., organization of online events, feedback collection time, etc.) of the framework are presented and discussed in this paper to offer increased stakeholder engagement. Empirical testing of the framework is proposed.
Model Establishment of Cross-Disease Course Prediction Using Transfer Learning

In recent years, the development and application of artificial intelligence have both been topics of concern. In the medical field, an important direction of medical technology development is the extraction and use of applicable information from existing medical records to provide more accurate and helpful diagnosis suggestions. Therefore, this paper proposes using the development of diseases with easily discernible symptoms to predict the development of other medically related but distinct diseases that lack similar data. The aim of this study is to improve the ease of assessing the development of diseases in which symptoms are difficult to detect, and to improve the utilization of medical data. First, a time series model was used to capture the continuous manifestations of diseases with symptoms that could be easily found at different time intervals. Then, through transfer learning and attention mechanism, the general features captured were applied to the predictive model of the development of diseases with insufficient data and symptoms that are difficult to detect. Finally, we conducted a comprehensive experimental study based on a dataset collected from the National Health Insurance Research Database in Taiwan. The results demonstrate that the effectiveness of our transfer learning approach outperforms state-of-the-art deep learning prediction models for disease course prediction.
Task scheduling of the collaborative aerial–ground system for the search and capture of multiple targets

Small unmanned rotorcraft (SUR) have been used widely in various civil applications since the opening of the low-altitude airspace. They enable public security maintenance missions via surveilling, searching, and tracking specific areas and targets. SUR functions can be further extended by complementing the ground vehicle and human. In this study, a new collaborative aerial–ground system is introduced to perform a search and capture (SAC) of multiple ground targets. The system comprises several SUR, cars, and humans. The targets primarily refer to criminals or terrorists who tend to escape from the aerial–ground system with holistic and scattered running modes, depending on different situations. A heuristic enumeration optimization (HEO) algorithm is proposed to maximize the probability of detecting the targets in the search space by determining the waypoints for the SUR and cars that can cover additional areas where the targets may occur. Moreover, a dynamic grouping-based task scheduling (DGBTS) algorithm is designed to allocate targets and calculate waypoints for SUR, cars, and humans. In the task allocation problem, the targets are assigned to the SUR by considering the balance of resources, and the quadrant each human is heading for is specified so as to surround the target from different directions. Accordingly, the waypoints are calculated in a distributed approach to highlight the different significance levels of SUR, cars, and humans in the capture phase. Simulation results reveal that the HEO algorithm can detect targets in fewer steps compared to the simulated annealing (SA) algorithm and the asynchronous planning strategy. Moreover, the DGBTS algorithm can deal with different situations in real applications. Additionally, the collaborative aerial–ground system exhibits a better search ability and comprehensive functionality compared to the car–human system and SUR–car systems.
<scp>MedLatinEpi</scp> and <scp>MedLatinLit</scp> : Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts

We present and make available MedLatinEpi and MedLatinLit , two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively, labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets, we provide experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a text of unknown authorship was written by a candidate author. We also make available the source code of the authorship verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other researchers. We also describe the application of the above authorship verification system, using these datasets as training data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars.
A Whale Optimization Algorithm with Convergence and Exploitability Enhancement and Its Application

The whale optimization algorithm (WOA) is inspired by humpback whale social behavior and WOA is a popular swarm intelligence algorithm. Yet, the WOA does have certain flaws, such as a restricted global search capability and an inconsistent convergence speed, and when dealing with complex optimization problems, WOA is easy to fall into local optimum. To address the WOA’s shortcomings, a modified whale optimization algorithm with convergence and exploitability enhancement called MWOA-CEE is proposed. Three operators, opposition-based learning, exponentially decreasing function, and elite-guided Cauchy mutation, are integrated into the WOA to enhance the exploration performance and avoid local optimum. First, the opposition-based learning used the opposition-based concept to generate the opposite position of the candidate solution, which can enhance the convergence ability and quality of the solution. Then, the exponentially decreasing function balances the exploitation and exploration phases and boosts the exploitation capability. In the end, the elite-guided Cauchy mutation generated a wide disturbance near the current candidate solution, which improves solutions diversity and increases the algorithm’s global search capability. MWOA-CEE is validated on 19 optimization benchmark functions and 14 CEC2014 benchmark functions and used to identify IIR systems. It is compared to seven of the commonly used algorithms. The results prove that MWOA-CEE outperforms the other algorithms in terms of final solution quality and convergence rate.
An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

It is proposed to improve the study of particle optimization and its application in order to solve the problem of inefficiency and lack of local optimization skills in the use of particle herd optimization. Firstly, the basic principle, mathematical description, algorithm parameters, and flow of the original (Particle Swarm Optimization, PSO) algorithm are introduced, and then the standard PSO algorithm is introduced; thirdly, over the last 10 years, four types of improvements have been proposed through the study of improved particle algorithms. The improved algorithm is applied to the extreme value optimization problem of multivariable function. The simulation results show that the basic (Cloud Particle Swarm Optimization, CPSO) algorithm within 500 generations has not reached convergence for 8 times, 6 times, 4 times, and 5 times, respectively. In the case of convergence, the average number of steps is much higher than ICPSO, and the improved algorithm converges completely. In terms of time performance, the convergence time of ICPSO is much better than that of CPSO algorithm. Therefore, the improved particle optimization algorithm ensures the effectiveness of the improvement measures, such as small optimization algebra, fast merging speed, high efficiency, and good population diversity.
Academic Support for Deaf and Hard of Hearing Students in Inclusive Engineering Education Programs: Key Decisions

This article discusses key decisions of academic support of hearing impaired students within inclusive programs of engineering education at Technical University of a general type. The key challenge of such students is the limited content accessibility of STEM disciplines. The set of effective accommodations provided for these students is divided into two constituents. The first one includes services and technologies on digital transformation of classrooms to support for both hearing impaired students and their teachers both in face-to-face and in distance learning. In specially organized digital classrooms cognitive technologies of education that ensure mastering educational information, producing knowledge and forming competencies according to requirements of both Federal State Education Standard and Self-Established Education Standard of the University are being used. The most effective ones are cognitive technological supportive disciplines (CTSDs), visualizing educational information and tutoring. These technologies include elements of digital transformation of education materials and teaching ways. When used in digital classrooms, they form an accessible environment, without which it is impossible or difficult to master inclusive programs for this contingent.
SACC: Split and Combine Approach to Reduce the Off-chip Memory Accesses of LSTM Accelerators

Long Short-Term Memory (LSTM) networks are widely used in speech recognition and natural language processing. Recently, a large number of LSTM accelerators have been proposed for the efficient processing of LSTM networks. The high energy consumption of these accelerators limits their usage in energy-constrained systems. LSTM accelerators repeatedly access large weight matrices from off-chip memory, significantly contributing to energy consumption. Reducing off-chip memory access is the key to improving the energy efficiency of these accelerators. We propose a data reuse approach that splits and combines the LSTM cell computations in a way that reduces the off-chip memory accesses of LSTM hidden state matrices by 50%. In addition, the data reuse efficiency of our approach is independent of on-chip memory size, making it more suitable for small on-chip memory LSTM accelerators. Experimental results show that our approach reduces off-chip memory access by 28% and 32%, and energy consumption by 13% and 16%, respectively, compared to conventional approaches for character level Language Modelling and Speech Recognition LSTM models.
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters

Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. Both traditional software-based methods and statistical systems have been successful in protecting against cheating, but recent advances in the automatic generation of content, such as images or speech, threaten the video game industry; they could be used to generate artificial gameplay indistinguishable from that of legitimate human players. To better understand this threat, we begin by reviewing the current state of multiplayer video game cheating, and then proceed to build a proof-of-concept method, GAN-Aimbot. By gathering data from various players in a first-person shooter game we show that the method improves players' performance while remaining hidden from automatic and manual protection mechanisms. By sharing this work we hope to raise awareness on this issue and encourage further research into protecting the gaming communities.
Interactive Mixed-Dimensional Media for Cross-Dimensional Collaboration in Mixed Reality Environments

Collaboration and guidance are key aspects of many software tasks. In traditional desktop software, such aspects are well supported through built-in collaboration functions or general-purpose techniques such as screen and video sharing. In Mixed Reality environments, where users carry out actions in a three-dimensional space, collaboration and guidance may also be required. However, other users may or may not be using the same Mixed Reality interface. Users may not have access to the same information, the same visual representation, or the same interaction affordances. These asymmetries make communication and collaboration between users harder. To address asymmetries in Mixed Reality environments, we introduce Interactive Mixed-Dimensional Media. In these media, the visual representation of information streams can be changed between 2D and 3D. Different representations can be chosen automatically, based on context, or through associated interaction techniques that give users control over exploring spatial, temporal, and dimensional levels of detail. This ensures that any information or interaction makes sense across different dimensions, interfaces and spaces. We have deployed these techniques in three different contexts: mixed-reality telepresence for physical task instruction, video-based instruction for VR tasks, and live interaction between a VR user and a non-VR user. From these works, we show that Mixed Reality environments that provide users with interactive mixed-dimensional media interfaces improve performance and user experience in collaboration and guidance tasks.
Blockchain-based E-tendering Evaluation Framework

Tendering &amp; Bidding generally involve many construction units participating in a project bidding. The purchaser selects the best bidder with a short construction period, low cost, high quality, and good reputation. There are huge economic benefits and points of concern behind it. Tendering suffers an increased risk of data tampering due to the centralized database architecture, resulting in huge financial losses and unnecessary consumption of human resources. Blockchain technology has gained increasing attention in the construction industry because of its high transparency, information traceability, non-repudiation, and non-tampering characteristics. Initial explorations have been carried out to discuss blockchain benefits in the e-tendering process. However, existing studies are limited to conducting qualitative analysis or proposing conceptual frameworks. A workable blockchain-based that supports basic functions in the e-tendering process still lacks. Therefore, as an initial exploration, this paper presents a secure blockchain-based framework for tender evaluation. Besides, technical components such as tendering smart contracts are developed to support framework functionalities. The framework feasibility is validated in a tendering evaluation example. Results show that the proposed framework guarantees secure tender evaluation by keeping data traceability and immutability.