Reviewerly/exHarmony huggingface dataset:
Density-Aware and Semantic-Guided Fusion for 3-D Object Detection Using LiDAR-Camera Sensors

Three-dimensional object detection plays a key role in autonomous driving systems. The performance of light detection and ranging (LiDAR)-based models is limited because of the sparsity and inhomogeneity of point clouds. Researchers have developed numerous camera-LiDAR-based fusion methods to address the limitations of LiDAR-based models. Still, because of the major differences in representations between pixel and point cloud, these fusion methods cannot appropriately leverage the benefits of both types of sensors. In this study, we developed a 3-D object detection model based on LiDAR-camera fusion. The model first augments raw points with possibility features extracted from a camera stream and subsequently uses possibility-guided farthest point sampling (P-FPS) for high-quality feature extraction in the 3-D backbone. In addition, we designed a detection head for density-aware artificial point sampling and fusion that can aggregate geometric and semantic features by using high-resolution artificial points and shift the position of an artificial point according to the density of points in a region of interest (ROI) to obtain more representative point features. We conducted massive experiments by using the public KITTI dataset. Our method outperforms previously developed state-of-the-art 3-D object detection methods, especially in detecting small and distant objects.
Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels

The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry.The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency.Classical tools and methods provide good approximations to reach the optimal global solution.Quantum computing and quantum machine learning provide a new approach to solving combinatorial optimization of problems faster due to inherent speedups of quantum effects.Many solutions of VRP are offered across different quantum computing platforms using hybrid algorithms such as quantum approximate optimization algorithm and quadratic unconstrained binary optimization.In this work, we build a basic VRP solver for 3 and 4 cities using the variational quantum eigensolver on a fixed ansatz.The work is further extended to evaluate the robustness of the solution in several examples of noisy quantum channels.We find that the performance of the quantum algorithm depends heavily on what noise model is used.In general, noise is detrimental, but not equally so among different noise sources.
Developing an AI-Assisted Low-Resource Spoken Language Learning App for Children

Computer-assisted Language Learning (CALL) is a rapidly developing area accelerated by advancements in the field of AI. A well-designed and reliable CALL system allows students to practice language skills, like pronunciation, any time outside of the classroom. Furthermore, gamification via mobile applications has shown encouraging results on learning outcomes and motivates young users to practice more and perceive language learning as a positive experience. In this work, we adapt the latest speech recognition technology to be a part of an online pronunciation training system for small children. As part of our gamified mobile application, our models will assess the pronunciation quality of young Swedish children diagnosed with Speech Sound Disorder, and participating in speech therapy. Additionally, the models provide feedback to young non-native children learning to pronounce Swedish and Finnish words. Our experiments revealed that these new models fit into an online game as they function as speech recognizers and pronunciation evaluators simultaneously. To make our systems more trustworthy and explainable, we investigated whether the combination of modern input attribution algorithms and time-aligned transcripts can explain the decisions made by the models, give us insights into how the models work and provide a tool to develop more reliable solutions.
Cartesian Genetic Programming Parameterization in the Context of Audio Synthesis

This article presents an evaluation of the effects of elitism, recurrence probability, and prior knowledge on the fitness achieved by Cartesian Genetic Programming (CGP) in the context of DSP audio synthesis. Prior knowledge was introduced using a probabilistic learning method where the distribution of nodes in the expected solutions was used to generate and mutate new individuals. Best results were obtained with traditional elitist selection, no recurrence, and when prior knowledge was used for node initialization and mutation. These results suggest that the apparent benefits of recurrence in CGP are context-dependent, and that selecting nodes from a uniform distribution is not always optimal.
Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs

This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prior information</i> about the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">density of motifs</i> of the unknown graph to enhance the inference of classical Gaussian graphical models. Directly dealing with the density of motifs constitutes a challenging combinatorial task. However, we note that if two graphs have similar motif densities, one can show that the expected value of a polynomial applied to their empirical spectral distributions will be similar. Guided by this, we first assume that we observe a reference graph with a density of motifs similar to that of the sought graph, and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the graph learning optimization problem. The (non-)convexity of the optimization problem is discussed, and a computationally efficient alternating majorization-minimization algorithm is designed. We assess the performance of the proposed method through exhaustive numerical experiments, where different constraints are considered and compared against popular alternatives on both synthetic and real-world datasets.
Novel Formulations of M-Term Overlap-Free Karatsuba Binary Polynomial Multipliers and Their Hardware Implementations

Novel binary polynomial multipliers have been designed using M-term overlap-free Karatsuba multiplication (OFKM), where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is 5–8. The proposed designs were realized in digital hardware and implemented on field-programmable gate array (FPGA) and the best value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> was selected and presented for common National Institute of Standards and Technology (NIST) operand sizes from 64 to 571 bits. The implemented hardware designs use a hybrid approach that combines a given M-term overlap-free Karatsuba multipliers with two-term splitting to reduce the need for zero-padding in the final recurrent stages. Compared to the traditional M-term Karatsuba multipliers, the proposed overlap-free implementations offer reductions in delay and area-delay product (ADP). The proposed designs also compare favorably to previous implementations of binary polynomial multipliers. Their favorable characteristics make the proposed overlap-free Karatsuba polynomial multipliers viable options for use in cryptographic systems where speed is a significant consideration and hardware resource consumption must be limited.
AI Regulations in the Context of Natural Language Processing Research

Recent advances in development and applications of AI technologies including Natural Language Processing (NLP) models, such as the newly developed model known as GPT3 that can generate human-line text/speech on demand, has created a great deal of hype and excitement. With increasing use of NLP models, extensive and proper guardrails need to be in place to ensure safety and trustworthiness of these systems.Numerous policy and regulation initiatives, such as EU AI Act, UNESCO recommendation on AI, US AI Bill of right, OECD AI principle, China’s AI regulatory initiatives, and the US-EU AI agreement, are shaping the global AI regulation landscape. The proposed EU AI Act, which is the world's first comprehensive attempt to regulate AI, is expected to set global standards for emulation by other jurisdictions. NLP models do not escape the existing regulations, notably the rules on data protection such as the EU General Data Protection Regulation (GDPR).In this paper, we briefly introduce some AI regulation initiatives in Chapter 1, we then introduce how the GDPR applies to AI usage in Chapter 2, and we discuss specifically the proposed EU AI Act and its risk-based approach to AI in Chapter 3, and NLP as applied to the risk-based approach of EU AI Act in Chapter 4. In all these chapters, we focus on how these proposals or laws relate to NLP research.
Practical Study of Deep Learning Models for Speech Synthesis

Speech synthesis systems, also known as Text-To-Speech (TTS) systems, are increasingly frequent nowadays, with multiple applications such as voice assistants and screen readers for visually impaired or blind people. These applications require strong real-time capabilities to be usable in practice, which can be at the cost of a reduced quality in the synthesized voices. Deep Learning models, which have shown impressive results in the task of audio generation, are hardly ever used for everyday TTS because of their high demand in computational resources. Training such models also requires a large amount of good quality data, which is not available for most languages. This paper explores the benefits of cross-lingual transfer learning, both in terms of training time and amount of data that is needed to obtain good quality models. Our contributions are evaluated with respect to other TTS systems available for the French language. The main observation is that good quality single-speaker models can be trained within half a week on a single GPU, with a limited number of good quality data, by combining transfer learning with few-shot learning.
Learning Spatial–Temporal Consistency for Satellite Image Sequence Prediction

As an extremely challenging spatial-temporal sequence prediction task, satellite image sequence prediction has various and significant applications in real-world scenarios. Although lots of deep learning prediction models are developed for spatial-temporal sequence prediction, the methods still deliver unsatisfactory performance in terms of keeping spatial-temporal consistency, which leads to inaccurate and blurry satellite image sequence predictions. To maintain spatial-temporal consistency and achieve high-quality satellite image sequence prediction, we propose a novel and effective spatial-temporal consistency network (STCNet). In STCNet, a multi-level motion memory-based predictor is proposed to accurately predict motion patterns of satellite image sequences to ensure temporal consistency. Then, a time-variant frame discriminator is carefully designed and proposed, which can enhance the perception quality of predicted frames to guarantee spatial consistency and simultaneously maintain the motion coherency of predicted sequences. Moreover, a scheduled sampling strategy is proposed to reduce the optimizing difficulty and better train the proposed method. Comprehensive experiments conducted on satellite image sequences from FY-4A meteorological satellite verify the effectiveness, applicability, and adaptability of our proposed method compared to state-of-the-art approaches under challenging scenarios.
Electric Logistics Vehicle Path Planning Based on the Fusion of the Improved A-Star Algorithm and Dynamic Window Approach

The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, compared to the A* algorithm, the upgraded A* algorithm not only avoids the obstruction border but also removes unnecessary nodes and minimizes turning angles. Then, the DWA algorithm is fused with the enhanced A* algorithm to achieve dynamic obstacle avoidance. In addition to RVIZ of ROS, MATLAB simulates and verifies the upgraded A* algorithm and the A* fused DWA. The MATLAB simulation results demonstrate that the approach based on the enhanced A* algorithm combined with DWA not only shortens the path by 4.56% when compared to the A* algorithm but also smooths the path and has dynamic obstacle-avoidance capabilities. The path length is cut by 8.99% and the search time is cut by 16.26% when compared to the DWA. The findings demonstrate that the enhanced method in this study successfully addresses the issues that the A* algorithm’s path is not smooth, dynamic obstacle avoidance cannot be performed, and DWA cannot be both globally optimal.
An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.
Estimation of GPA at Undergraduate Level using MLR and ANN at Arab International University During the Syrian Crisis: A Case Study

Abstract The aim of the current study is to estimate the graduation grade point average (GPA) at the Arab International University (AIU). The importance of this study is that it is the first of its kind in Syria, particularly under the conditions of the bitter war, which has not yet come to its end. The sample of the study consists of data collected from AIU registry system, which covers students of six faculties from both sexes over a period of 5 years, i.e., between 2014 and 2018. The variables of the study include the student’s high school GPA, the source of the student’s certificate, and the gender of the student (GR). MLR and ANN approaches were employed in the analytical study. The correlation coefficients and several statistical criteria were calculated to evaluate the developed models. Based on the results, it can be concluded that the male students were among the key factors affecting the output, i.e., GPA. In addition, ANN tool is more accurate when the estimation of GPA value is concerned. Further, re-evaluation of this study can be done in future particularly when the male student’ trepidation disappears as stability and safety return to the country.
Digital fingerprinting for identifying malicious collusive groups on Twitter

Abstract Propagation of malicious code on online social networks (OSNs) is often a coordinated effort by collusive groups of malicious actors hiding behind multiple online identities (or digital personas). Increased interaction in OSN has made them reliable for the efficient orchestration of cyberattacks such as phishing click bait and drive-by downloads. URL shortening enables obfuscation of such links to malicious websites and massive interaction with such embedded malicious links in OSN guarantees maximum reach. These malicious links lure users to malicious endpoints where attackers can exploit system vulnerabilities. Identifying the organized groups colluding to spread malware is non-trivial owing to the fluidity and anonymity of criminal digital personas on OSN. This paper proposes a methodology for identifying such organized groups of criminal actors working together to spread malicious links on OSN. Our approach focuses on understanding malicious users as ‘digital criminal personas’ and characteristics of their online existence. We first identify those users engaged in propagating malicious links on OSN platforms, and further develop a methodology to create a digital fingerprint for each malicious OSN account/digital persona. We create similarity clusters of malicious actors based on these unique digital fingerprints to establish ‘collusive’ behaviour. We evaluate the ability of a cluster-based approach on OSN digital fingerprinting to identify collusive behaviour in OSN by estimating within-cluster similarity measures and testing it on a ground-truth dataset of five known colluding groups on Twitter. Our results show that our digital fingerprints can identify 90% of cyber personas engaged in collusive behaviour and 75% of collusion in a given sample set.
The semi-T3-separation axiom of Khalimsky topological spaces

The paper initially studies both the s-T3-separation and the semi-T3-separation axiom of Khalimsky (K-for brevity) topological spaces. To do this work, first we investigate some properties of semi-open and semi-closed sets with respect to the operations of union or intersection and further, a homeomorphism, and a semi-homeomorphism. Next, we study various properties of semi-topological properties of K-topological spaces such as simple K-paths. Finally, after introducing the notion of a semi-T3-separation axiom which is broader than the s-T3-separation axiom, we find a sufficient and necessary condition for a Khalimsky topological space to satisfy the semi-T3-separation axiom.
Sensor Management with Dynamic Clustering for Bearings-Only Multi-Target Tracking via Swarm Intelligence Optimization

Sensor management is a crucial research subject for multi-sensor multi-target tracking in wireless sensor networks (WSNs) with limited resources. Bearings-only tracking produces further challenges related to high nonlinearity and poor observability. Moreover, energy efficiency and energy balancing should be considered for sensor management in WSNs, which involves networking and transmission. This paper formulates the sensor management problem in the partially observable Markov decision process (POMDP) framework and uses the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter for tracking. A threshold control method is presented to reduce the impact on tracking accuracy when using bearings-only measurements for sequential update. Moreover, a Cauchy–Schwarz divergence center is defined to construct a new objective function for efficiently finding the optimal sensor subset via swarm intelligence optimization. This is also conducive to dynamic clustering for the energy efficiency and energy balancing of the network. The simulation results illustrate that the proposed solution can achieve good tracking performance with less energy, and especially that it can effectively balance network energy consumption and prolong network lifetime.
Multi-actor mechanism for actor-critic reinforcement learning

Value estimation is a critical problem in Value-Based reinforcement learning. Most related studies focus on using multi-critic to reduce estimation bias and seldom consider the multi-actor impact on value estimation. This paper proposes a multi-actor mechanism (MAM) for Actor-Critic reinforcement learning that can provide multiple behavior choices in the same state, resulting in diverse Q-values that provide richer information and enhance exploration capability. MAM contains two technologies. One is obsolescence technology, which quickly generates high-quality experience to help the agent find the optimal policy. The other is Q-value weighting technology, which leverages multiple Q-values to achieve a more accurate value estimation. The proposed mechanism, MAM, is general and can be applied to any Actor-Critic reinforcement learning algorithm. Specifically, we embed MAM into DDPG and TD3 and demonstrate that MAM can mitigate estimation bias, enhance exploration, and yield state-of-the-art results in various MuJoCo tasks, including the challenging Humanoid-v2 and Walker2d-v2 benchmarks.
High School Students’ Use of Information, Media, and Technology Skills and Multidimensional 21st-Century Skills: An Investigation within the Context of Students, Teachers, and Curricula

The aim of this study is to determine high school students’ information, media, and technology skills and their level of use of multidimensional 21st-century skills. A mixed quantitative and qualitative method was preferred in the study conducted in Northern Cyprus. Quantitative data were obtained from 612 senior high school students, and qualitative data were obtained from teachers and three different curricula. Face-to-face interviews were conducted with 10 teachers in order to determine the in-class activities that teachers carried out to provide information, media, and technology skills. The degree to which the Turkish language and literature, English, and mathematics curricula, which are among the basic courses offered in the senior year of high school, have outcomes for gaining information, media, and technology skills was determined through document analysis. The results obtained from the study revealed that the students’ use of information, media, and technology skills was generally moderate. The students’ use of programming skills, which are among the sub-dimensions of information, media, and technology skills as well as their problem-solving skills, were found to be very inadequate. Students’ use of information and data literacy, communication, and collaboration skills, which are among the sub-dimensions of information, media, and technology skills, were found to be adequate. Similarly, it was concluded that the students’ use of multidimensional 21st-century skills was also adequate. The results revealed that the classroom activities implemented by the teachers were insufficient in developing students’ problem-solving skills in particular. Moreover, the results obtained with respect to curricula revealed that the mathematics curriculum had the highest number of outcomes related to information, media, and technology skills. The Turkish language and literature curriculum had the least number of outcomes related to information, media, and technology skills. The results of the research also revealed that the learning outcomes in the curricula were mostly related to communication and collaboration skills. The skill with the lowest number of related outcomes was found to be problem-solving.