Reviewerly/exHarmony huggingface dataset:
A Deep Reinforcement Learning Algorithm Suitable for Autonomous Vehicles: Double Bootstrapped Soft-Actor–Critic-Discrete

With the rapid advancement of modern society, autonomous systems have been broadly applied in people’s daily lives. Under the guidance of this trend, autonomous vehicles have gradually become popular. However, due to some adverse factors(such as insufficient computing force and limited communication bandwidth) in edge computing scenarios and the lack of autonomous decision-making ability, the safety of autonomous vehicles is not enough. It is a good solution to use a deep reinforcement learning(DRL) algorithm, which combines deep learning(DL) and reinforcement learning(RL), to provide a fast convergence speed and an appropriate decision-making ability. In this paper, based on Soft-Actor-Critic(SAC) and Soft-Actor-Critic-Discrete(SAC-D), we propose a Double Bootstrapped Soft-Actor-Critic-Discrete(DBSAC-D) algorithm. By introducing bootstrap, the ability to explore in action space is enhanced, the value of each action is accurately judged, the convergence process is accelerated and the consumption of the computing force is reduced. In addition, we also propose a novel sampling strategy, which balances the novelty and importance of the sampled data, and improves the training value of the sampled data to the network model. The experimental results show that our proposed algorithm achieves good performances in several traffic scenes and has a fast convergence speed.
Accessibility and The Crowded Sidewalk: Micromobility's Impact on Public Space

Over the past several years, micromobility devices—small-scale, networked vehicles used to travel short distances—have begun to pervade cities, bringing promises of sustainable transportation and decreased congestion. Though proponents herald their role in offering lightweight solutions to disconnected transit, smart scooters and autonomous delivery robots increasingly occupy pedestrian pathways, reanimating tensions around the right to public space. Drawing on interviews with disabled activists, government officials, and commercial representatives, we chart how devices and policies co-evolve to fulfill municipal sustainability goals, while creating obstacles for people with disabilities whose activism has long resisted inaccessible infrastructure. We reflect on efforts to redistribute space, institute tech governance, and offer accountability to those who involuntarily encounter interventions on the ground. In studying micromobility within spatial and political context, we call for the HCI community to consider how innovation transforms as it moves out from centers of development toward peripheries of design consideration.
Image Super-Resolution With Self-Similarity Prior Guided Network and Sample-Discriminating Learning

The nonlocal self-similarity in natural image provides an effective prior for single image super-resolution (SISR), which is beneficial to contextual information capture and performance improvement, as demonstrated by conventional SISR methods. However, it is little explored to utilize this property in deep neural networks. In this paper, we propose a self-similarity prior guided (SSPG) network to incorporate self-similarity-based nonlocal operation into deep neural network for SISR. Specifically, we design a cross-scale nearest-neighbor residual (CSNNR) block via introducing cross-scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors (KNN) matching into a residual block, which can be flexibly integrated into deep networks to capture long-range correlations among multi-scale and multi-level features. Meanwhile, by stacking a CSNNR block and a sequence of wide-activated residual blocks with a local skip-connection, a multi-level residual self-similarity (MRSS) module is developed to effectively employ local and nonlocal information for detail recovery. Thus, through cascading multiple MRSS modules, the proposed SSPG network performs both self-similarity-based nonlocal operation and convolution-based local operation on multi-level features to reconstruct informative features for accurate SISR. In addition, for pursuing visually pleasing results, we apply our SSPG network to the perception-oriented SISR field by following the framework of generative adversarial networks. In particular, we explore a sample-discriminating learning mechanism based on the statistical descriptions of training samples, and include it in optimization procedure to automatically tune the contributions of different samples according to their characteristics and then focus the network on creating realistic results. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed models over the state-of-the-art methods for both distortion-oriented and perception-oriented image super-resolution tasks.
SCG: Saliency and Contour Guided Salient Instance Segmentation

Different from conventional instance segmentation, salient instance segmentation (SIS) faces two difficulties. The first is that it involves segmenting salient instances only while ignoring background, and the second is that it targets generic object instances without pre-defined object categories. In this paper, based on the state-of-the-art Mask R-CNN model, we propose to leverage complementary saliency and contour information to handle these two challenges. We first improve Mask R-CNN by introducing an interleaved execution strategy and proposing a novel mask head network to incorporate global context within each RoI. Then we add two branches to Mask R-CNN for saliency and contour detection, respectively. We fuse the Mask R-CNN features with the saliency and contour features, where the former supply pixel-wise saliency information to help with identifying salient regions and the latter provide a generic object contour prior to help detect and segment generic objects. We also propose a novel multiscale global attention model to generate attentive global features from multiscale representative features for feature fusion. Experimental results demonstrate that all our proposed model components can improve SIS performance. Finally, our overall model outperforms state-of-the-art SIS methods and Mask R-CNN by more than 6% and 3%, respectively. By using additional multitask training data, we can further improve the model performance on the ILSO dataset.
Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and dataset analysis. This study provides a systematic literature review that highlights the latest research trends in the area of SDP by providing a critical review of papers published between 2016 and 2019. Initially, 1012 papers were shortlisted from three online libraries (IEEE Xplore, ACM, and ScienceDirect); following a systematic research protocol, 22 of these papers were selected for detailed critical review. This review will serve researchers by providing the most current picture of the published work on software defect classification.
Object detection approaches in images: a survey

Computer vision is a field of artificial intelligence that trains computers to gain high-level understanding from images or videos. Among the most well-known subfields in Computer Vision are object detection, object tracking, motion estimation, etc. So, object detection is a computer vision technique for detecting instances of objects in images or videos. It can be performed in different ways, either by using delimiting frames or by using object segmentation. It is useful and widely used in the following tasks: image annotation, activity recognition, face recognition, and object tracking. There are two types of approaches to detecting objects in the image: two-stage detector-based approaches and one-stage detector-based approaches with their advantages and disadvantages. Object detection algorithms and techniques generally fall under machine learning approaches and deep learning approaches. Deep learning approaches are based on convolutional neural networks that allow us to perform many tasks, such as clustering, classification, or regression. This paper, which is a survey, aims at reviewing the different approaches and models for deep learning to detect objects in the image and then the advantages and disadvantages of each approach as well as the different fields of application. Also, many data sets that are proposed to evaluate the different methods to detect objects in the image will be presented in this article.
Tracking of Extended Object Using Random Matrix With Non-Uniformly Distributed Measurements

Extended object tracking (EOT) is gaining momentum in recent years. The random matrix method is a popular EOT method, which has a simple yet effective framework. The existing random matrix approaches usually assume that scatter centers are uniformly or symmetrically distributed on the object. However, due to weak signal strength and limited measuring angles, scatter centers are densely distributed on several (separated) parts rather than uniformly distributed on the whole object. This results in several measurement-densely-distributed parts. To address this problem, this paper proposes a new conditional Gaussian mixture model. Each component of the model characterizes a single measurement-densely-distributed part. The object extension is modeled as an ellipse by a random matrix. As a portion of the object, each measurement-densely-distributed part is modeled by scaling and shifting that ellipse and transforming that random matrix. This modeling can effectively describe the non-uniformly distributed measurements, but also inherit the simplicity of the random matrix model. Based on the new model and utilized the random matrix framework, a variational Bayesian approach is derived, which recursively and efficiently estimates kinematic and extension states. From the simulation and real experimental results, the proposed approach has a significant performance improvement over some existing random matrix approaches.
XAI-KG: Knowledge Graph to Support XAI and Decision-Making in Manufacturing

The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of those explanations and their influence on decision-making. We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions. This way, we provide means to improve forecasting models, explanations, and recommendations of decision-making options. We tailor the knowledge graph for the domain of demand forecasting and validate it on real-world data.
Universal Spectral Adversarial Attacks for Deformable Shapes

Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the existence of "universal" attacks (i.e., unique perturbations that transfer across different data points) has only been demonstrated for images to date. Part of the reason lies in the lack of a common domain, for geometric data such as graphs, meshes, and point clouds, where a universal perturbation can be defined. In this paper, we offer a change in perspective and demonstrate the existence of universal attacks for geometric data (shapes). We introduce a computational procedure that operates entirely in the spectral domain, where the attacks take the form of small perturbations to short eigenvalue sequences; the resulting geometry is then synthesized via shape-from-spectrum recovery. Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data.
Integration verification across software and hardware for a simple embedded system

The interfaces between layers of a system are susceptible to bugs if developers of adjacent layers proceed under subtly different assumptions. Formal verification of two layers against the same formal model of the interface between them can be used to shake out these bugs. Doing so for every interface in the system can, in principle, yield unparalleled assurance of the correctness and security of the system as a whole. However, there have been remarkably few efforts that carry out this exercise, and all of them have simplified the task by restricting interactivity of the application, inventing new simplified instruction sets, and using unrealistic input and output mechanisms. We report on the first verification of a realistic embedded system, with its application software, device drivers, compiler, and RISC-V processor represented inside the Coq proof assistant as one mathematical object, with a machine-checked proof of functional correctness. A key challenge is structuring the proof modularly, so that further refinement of the components or expansion of the system can proceed without revisiting the rest of the system.
Tiny-PIRATE: A Tiny model with Parallelized Intelligence for Real-time Analysis as a Traffic countEr

Due to the rapid growth in the number of vehicles over the last decade, there has been a dramatic increase in demand for highway capacity analysis. Vehicle counting, in particular, has become a key element of vision-based intelligent traffic systems deployed across metropolitan areas. Most methods solved the vehicle counting problem under the assumption of state-of-the-art computing systems. However, large-scale deployment of such systems for multi-camera processing is very inefficient. With the recent advancement of cost-efficient Internet-of-Things (IoT) devices alongside machine learning methods developed specifically for such devices, solving the vehicle counting problem for real-time traffic analysis on IoT edge devices, and thereby facilitating its large-scale deployment have become highly favorable. In this paper, we propose a framework of vehicle counting designed specifically for IoT edge computers which follows the detection-tracking-counting (DTC) model. The proposed solution aims at addressing the multimodality of contextual dynamics in traffic scenes with a small detector model, a robust tracker and a counting process that accurately estimate both a vehicle's motion of interest and its exit time from observation areas. Experimental results on AI City 2021 Track-1 Dataset showed that ours outperformed related methods with promising results regarding both accuracy and execution speed.
The use of MOOCs as additional tools for teaching NoSQL in blended and distance learning mode

Abstract Today there is a significant demand for improving the quality of the educational process in higher education institutions, in particular, through the improvement of principles and methods of teaching various sciences by enhancing the practical skills of students and graduate students. The implementation of this task is particularly difficult in the context of distance learning (for full-time and part-time) and blended learning. This study corresponds to the current direction, as it concerns the study of the possibilities and feasibility of using additional tools for teaching disciplines within NoSQL. Paper’s focus is on the justification of the use as an additional tool of a number of massive open online courses (MOOC) distance learning platforms, a comparative analysis of a number of platforms for their compliance with the established criteria. Examples of using MOOC MongoDB University within the discipline “Organization of NoSQL databases” are given. To confirm the facts of the expediency of using MOOC as an additional tool in blended learning and distance learning mode, the data of the survey of students are presented.
What Are Postdigital Humans?

This chapter suggests that challenge of the postdigital human is to recognize the ways in which balance must be brought to bear, to realize the ways in which technologies such as algorithms, machine learning, and facial recognition and facial verification can foster injustice. It begins by exploring conceptions of the postdigital, and what it means to be human, exploring posthumanism, and the relationship between humans and machines and artificial intelligence. The second section of the chapter examines the idea of postdigital humans in the context of whole brain emulation and digital afterlife. Society is just becoming aware of the impact of digital memorialization but as yet few are grappling with the complexities of digital afterlife. In the final section, the ethical issues relating to postdigital humans are discussed in relation to human labour, machine learning, intimacy ethics, and surveillance.
Naives Bayes Algorithm for Twitter Sentiment Analysis

Abstract On 2 March 2020, the Indonesian government, through President Joko ‘Jokowi’ Widodo, announced the first two cases of COVID-19 in Indonesia. This is the first case of COVID-19 officially confirmed in that country. Several cases have continued to increase since then. President Jokowi began issuing policies on the spread of this virus. This is different from other countries, such as Malaysia and Singapore, which responded from the previous month when the Indonesian government still stated that coronavirus does not exist in Indonesia. Our case study is to find a public opinion through social network analysis of Indonesian public policy during the beginning of the Indonesian COVID-19 pandemic in March 2020. This research implements text mining and document-based sentiments on Twitter data that is reprocessed through machine learning techniques using the Naïve Bayes method. We found negative opinions in the period more dominant by 46%, while that was 35% positive sentiment and 20% neutral. This research shows that anticipation, sadness, and anger are very dominant in the emotional analysis.
Arabic Handwritten Characters Recognition Using Convolutional Neural Network

Automatic handwritten characters' recognition is one of Artificial intelligence applications which is considered an interesting research area and important in various fields. Many studies have been conducted for the recognition of English handwritten characters and fewer works are available for the Arabic language because of the diversity in characters' shapes according to their positions in the words. Convolutional Neural Networks are efficient for handwritten characters' recognition. In this paper, a Convolutional Neural Network has been proposed for handwritten characters' recognition. The model has been trained on a dataset of 16,800 images of handwritten Arabic characters with different shapes to perform classification. The proposed model achieved high recognition accuracy of 97.2%, outperforming other state-of-art models. When applying data augmentation, the model achieved better results and accuracy of 97.7%.
Ontology learning from relational databases

An ontology is a formal, explicit specification of a shared conceptualization. Ontologies are used in many fields, such as software engineering, information extraction, semantic search, knowledge management, recommender systems, etcetera. Since manual ontology building is a very costly, time-consuming, and error-prone task, automating the process of ontology building, or in other words, learning ontology from existing resources, is a good option. Nowadays, a large amount of data on the web is stored in relational databases, but databases cannot be used directly in the semantic web. Hence, in this paper, we have proposed a new approach to automatically creating an OWL ontology from a relational database. We have defined a set of rules to analyze all database components and convert them to corresponding ontology components. The core contribution of our work is the set of rules which can analyze and extract ontology elements from stored procedures, user-defined functions, views, multiple inheritance, the specific representation of single inheritance, common attributes, and the constraints on tables and their columns. The proposed approach has been compared with existing approaches using three frameworks.
Toward High‐Speed and Energy‐Efficient Computing: A WDM‐Based Scalable On‐Chip Silicon Integrated Optical Comparator

Abstract Integrated photonics has shown extraordinary potential in optical computing. Various optical computing modules have been investigated, among which the digital comparator is a significant element for any arithmetic logic unit. In this study, an architecture of a wavelength‐division‐multiplexing‐based (WDM‐based) electronic–photonic digital comparator is proposed, which can perform high‐bit comparisons for fixed‐point or floating‐point binary inputs. A silicon photonics‐based 2‐bit comparator operating at 20 Gb s −1 at a 1.55 µm wavelength is experimentally demonstrated. A detailed performance analysis reveals that this architecture outperforms the current transistor‐based electronic comparators in terms of both computational speed and power efficiency.