Reviewerly/exHarmony huggingface dataset:
A Methodology Based on Deep Reinforcement Learning to Autonomous Driving with Double Q-Learning

Deep reinforcement learning (DRL) is a typical artificial intelligence algorithm utilized to solve problems in decision making, which has been proved efficient in go chess, video games and physical control. This paper introduced deep reinforcement learning to motor vehicle driving in order to achieve autonomous driving. Different from the traditional system, prediction algorithm and control algorithm were combined in this paper, generating an end-to-end autonomous driving methodology, the performance of which was discussed through experiments under CARLA simulator. Researchers adopted experience replay and deep neural network to make up the main part of the methodology, with RMS-prop algorithm and double Q-learning introduced to improve the performance of training part of autonomous driving. The methodology was utilized in an established route and was compared with simple deep learning method so that the superiority of double Q-learning methodology showed, which was of faster convergence rate and better predictive performance.
Emotions Mining Research Framework: Higher Education in the Pandemic Context

The pandemic situation in 2020 was a challenge for the organization of the educational process in higher education. The crisis has exacerbated inequalities between universities, which funding, digital sustainability and emergency training are weaker than their national and international competitors. The poor provision of the learning process in an electronic environment has led to a number of problems. Some of them are related to the acquisition of learning material and practical skills, lack of communication between students and academic staff. The increased use of the Internet during periods of social distance has also led to an increase in participating in social media activities, which have become forums for sharing opinions and expressing emotions through text and multimedia content. In this regard, the aim of the article is to propose a research framework for evaluation of emotional attitudes in social media. The author tested the practical applicability of the proposed framework by retrieving data from the social network Twitter and applying data mining techniques for analyzing large volumes of textual content.
The role of ICT for school leadership and management activities: an international comparison

Purpose Information and communication technologies (ICT) has an increasing impact on schools. School leaders play a key role in this context as drivers of innovation including those related to ICT. Against this background, the study presented in this article focuses on school leadership and management activities with ICT and related challenges. It sought to analyze how frequently German school principals use ICT compared to principals in other countries, what distinct clusters of German principals could be identified in terms of ICT usage and how principals viewed ICT in schools and related challenges. Design/methodology/approach A mixed-methods approach was chosen, using quantitative data from both the international comparative large-scale assessment study ICILS 2018 and the explorative qualitative data from Germany. For the international comparison, the school principal data sets of the 12 international participants of the International Computer and Information Literacy Study (ICILS) 2018 were taken into account: Chile, Denmark, Finland, France, Germany, Italy, Kazakhstan, Republic of Korea, Luxembourg, Portugal, Uruguay and the United States. To look beyond averaged frequencies, a latent class analysis (LCA) was conducted to identify possible clusters of school leaders with distinct usage patterns of ICT for leadership and management activities. Findings The results indicate that, in general, German principals use ICT for leadership and management activities on a similar level as their international colleagues. However, they seem to communicate with education authorities significantly more often than their international colleagues, whereas representative activities (presentations, home page) are rather infrequent. The qualitative data point to significant barriers to fully harnessing the potential of using ICT for leadership, management and school improvement such as lack of competencies and lack of adequate support. Originality/value To the best of the authors’ knowledge, this is the first study that focuses on school leadership and management activities using ICT with such a data set. The results provide insights into how German principals use ICT to lead and manage their schools compared to their international counterparts. The qualitative data offers additional insights into possible reasons hindering a more effective use of ICT.
Why Do Developers Remove Lambda Expressions in Java?

Java 8 has introduced lambda expressions, a core feature of functional programming. Since its introduction, there is an increasing trend of lambda adoptions in Java projects. Developers often adopt lambda expressions to simplify code, avoid code duplication or simulate other functional features. However, we observe that lambda expressions can also incur different types of side effects (i.e., performance issues and memory leakages) or even severe bugs, and developers also frequently remove lambda expressions in their implementations. Consequently, the advantages of utilizing lambda expressions can be significantly compromised by the collateral side effects. In this study, we present the first large-scale, quantitative and qualitative empirical study to characterize and understand inappropriate usages of lambda expressions. Particularly, we summarized seven main reasons for the removal of lambdas as well as seven common migration patterns. For instance, we observe that lambdas using customized functional interfaces are more likely to be removed by developers. Moreover, from a complementary perspective, we performed a user study over 30 developers to seek the underlying reasons why they remove lambda expressions in practice. Finally, based on our empirical results, we made suggestions on scenarios to avoid lambda usages for Java developers and also pointed out future directions for researchers.
KSL-Guide: A Large-scale Korean Sign Language Dataset Including Interrogative Sentences for Guiding the Deaf and Hard-of-Hearing

Many advancements in computer vision and machine learning have shown potential for significantly improving the lives of people with disabilities. In particular, recent research has demonstrated that deep neural network models could be used to bridge the gap between the deaf who use sign language and hearing people. The major impediment to advancing such models is the lack of high-quality and large-scale training data. Moreover, previously released sign language datasets include few or no interrogative sentences compared to declarative sentences. In this paper, we introduce a new publicly available large-scale Korean Sign Language (KSL) dataset-KSL-Guide-that includes both declarative sentences and comparable interrogative sentences, which are required for a model to achieve high performance in real-world interactive tasks deployed on service applications. Our dataset contains a total of 121K sign language video samples featuring sentences and words spoken by native KSL speakers with extensive annotations (e.g., gloss, translation, keypoints, and timestamps). We exploit a multi-camera system to produce 3D human pose keypoints as well as 2D keypoints from multi-view RGB. Our experiments quantitatively demonstrate that the inclusion of interrogative sentences in training for sign language recognition and translation tasks greatly improves their performance. Furthermore, we empirically show the qualitative results by developing a prototype application using our dataset, providing an interactive guide service that helps to lower the communication barrier between sign language speakers and hearing people.
PACA: A Pattern Pruning Algorithm and Channel-Fused High PE Utilization Accelerator for CNNs

In recent years, convolutional neural networks (CNNs) have achieved significant advancements in various fields. However, the computation and storage overheads of CNNs are overwhelming for Internet-of-Things devices. Both network pruning algorithms and hardware accelerators have been introduced to empower CNN inference at the edge. Network pruning algorithms reduce the size and computational cost of CNNs by regularizing unimportant weights to zeros. However, existing works lack intrakernel structured types to tradeoff between sparsity and hardware efficiency, and the index storage for irregularly pruned networks is significant. Hardware accelerators leverage the sparsity of pruned CNNs to improve energy efficiency. However, their process element (PE) utilization rate is low because of uneven sparsity among input convolutional kernels. To overcome these problems, we propose PACA: a Pattern pruning Algorithm and Channel-fused high PE utilization Accelerator for CNNs. It includes three parts: a pattern pruning algorithm to explore the intrakernel sparsity type and reduce the index storage, a channel-fused hardware architecture to reduce the PEs’ idle rate and improve the performance, and a heuristic and taboo search-based smart fusion scheduler to analyze the idle PE problem and schedule the channel fusion in hardware. To demonstrate the effectiveness of PACA, we have implemented the software parts by Python and the hardware architecture by RTL codes. Experimental results on various datasets show that compared with an existing work, PACA can reduce the index storage overhead by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.47\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.63\times $ </tex-math></inline-formula> with 3.85–9.12 average patterns, and it can improve the hardware performance by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.01\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.53\times $ </tex-math></inline-formula> because of PEs’ idle rate reduction.
Evaluation of Academic Performance of Students Using Fuzzy Logic

The entire education system has undergone numerous changes to stand unhindered during the current COVID-19 pandemic. All over the world, the educational system has changed its teaching and learning methods. One of its important aspects, evaluating the students’ overall performance has become a complex task with these changing patterns. The traditional approach of evaluation may not be a best fit anymore since multiple factors are required to make an all-inclusive, multifaceted decision to keep up with the upgrades in evaluation schemes and patterns. Also, Universities and educational institutes understood the importance of skill based learning and major changes are being made in the curriculum, which in turn need cognitive approach to evaluate the students’ performance. Hence, we have proposed, designed and implemented a solution, a fuzzy logic-based model. This model, while showing the difference between the traditional approach and the inference system, will enable the educational institutes not only to evaluate a students’ performance but also to understand the students in a comprehensive manner.
A Card Game for Collecting Human-Perceived Similarity Data of Artwork Images

In this paper, we propose a card game system named ''Lottery'' for collecting similarity data of artwork images.It is built based on the concept of game design called ''Audience Participation Game With a Purpose (APGWAP),'' which is to outsource computational steps in a given task to humans via a game that allows audiences to participate in a meaningful way.The game system is streamed on Twitch.In this game system, two Artificial Intelligence (AI) players match two cards that are most similar from a set of cards in their turn and discard them.The audiences can choose their roles between Assistant and Jury.Assistant helps the AI player of a given turn choose such a pair of cards, while Jury gives a score telling how similar a chosen pair is.In the experiments, different methods for providing rewards to audiences are investigated: equal reward, random reward, and performance-based reward.Experimental results show that the performance-based reward significantly promotes personal gratification and provides a better gaming experience on several other factors.In addition, it was found in our experiment that different groups of humans provided similar similarity scores for all the pairs of images, indicating that collecting promises reliable human data that are consistent through several trials is possible using our game system.
Blockchain Application in Halal Supply Chain: Literature Review and Future Research

Blockchain application in supply chain and halal has risen in recent years. This can be seen by increasing the number of papers published. The purpose of this paper was to review and analyze the previous papers on blockchain application in supply chain and halal supply chain using a systematic literature review based on the Scopus database from 2017–2021. The findings indicate that the literature review methods the most of research method used, manufacturing is the most blockchain application of industry type, trust is the most benefits achieved of blockchain application, traceability system is the most function of blockchain application. The main contribution of this paper is the systematic classification of the relevant literature on blockchain application on supply chain and halal. Therefore, academics might find the helpful review and discussion for further research.
Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks

This work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems.The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes.The PMBM based trackers handle existing tracks, undetected targets, and new births separately at each computation step by using these two processes together.In practice, the PMBM tracker aims to initiate the track as soon as possible and maintain the track continuity.Initiating track and maintaining track continuity is hard in challenging underwater environments without adapting the algorithm to changing environmental conditions.This paper uses the adaptive measurement-driven birth process and multistatic acoustic modeldependent probability of detection specifications.The adaptive measurement-driven birth process improves the robustness of the track initiation, and the multistatic acoustic model-dependent probability of detection advances the track continuity through the transition regions.These contributions to the PMBM tracker make it robust in terms of tracker performance in challenging underwater environments and acoustic transition regions where it is hard to get an accurate measurement.
Removing Impulsive Noise from Color Images via a Residual Deep Neural Network Enhanced by Post-Processing

Impulsive noise is a common type of noise that affects gray-scale/color images and videos. In this paper, we present a residual fully Convolutional Neural Network (CNN) to remove impulsive noise from color images in an end-to-end fashion. We train our residual CNN model on a customized dataset which contains noisy images with different impulsive noise density. The proposed dataset omits the need for multiple models for different noise densities. Moreover, we employ a multi-term loss function to train our model. One of the terms of the proposed loss function imposes the sparsity of the impulsive noise in the observation domain. To the best of our knowledge, this term has not been employed as a loss function for training a denoising CNN. Finally, we employ an iterative post-processing stage to further improve the performance of our method. Simulation results demonstrate that the proposed approach outperforms other notable algorithms in the literature. Furthermore, our method is quite fast, especially when implemented on a GPU-equipped system.
Effective triplet mining improves training of multi-scale pooled CNN for image retrieval

In this paper, we address the problem of content-based image retrieval (CBIR) by learning images representations based on the activations of a Convolutional Neural Network. We propose an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on the trainable aggregation layer NetVLAD (Arandjelovic et al in Proceedings of the IEEE conference on computer vision and pattern recognition CVPR, NetVLAD, 2016) and bags of local features obtained by splitting the activations, allowing to reduce the dimensionality of the descriptor and to increase the performance of retrieval. Training is performed using an improved triplet mining procedure that selects samples based on their difficulty to obtain an effective image representation, reducing the risk of overfitting and loss of generalization. Extensive experiments show that our approach, that can be effectively used with different CNN architectures, obtains state-of-the-art results on standard and challenging CBIR datasets.
Spatiotemporal Ground Risk Mapping for Uncrewed Aerial Systems operations

In this paper we propose the use of spatiotemporal population density data in the analysis of ground risk posed by UAS (Uncrewed Aerial System) operations. The spatiotemporal population density maps are generated through the combination of authoritative data sources, open source geospatial databases, and past works to dynamically classify proportions of a population to their expected daily activities based upon a given time. This adds a further dimension to analysis allowing evaluation and optimisation of ground risk, both spatially and temporally. This approach is used to analyse the ground risk posed under ballistic and gliding descents of a parameterized UAS along a case study path. An open source tool is implemented as part of this work to aid the decision making of operators and promote safer UAS operations.
Providing a Desired Quality of BPG Compressed Images for FSIM Metric

It is a common task in image lossy compression to provide a reasonable trade-off between a desired compression ratio (as large as possible) and an appropriate (preset according to a chosen metric) quality. Recently, a Better Portable Graphics (BPG) coder has been proposed that might become a new standard. A quality parameter Q is used in it to vary image quality and compression ratio. However, a given Q for particular images to be compressed produces quite different compression ratios and quality measured by a given quality metric. To provide a given quality of compressed images characterized by a visual quality metric FSIMc, we propose and analyze a two-step algorithm based on preliminary obtained average rate/distortion curve and its linear approximation. For this algorithm, the second step that calculates the improved estimate of Q and employs it for final compression allows decreasing variance of the produced FSIMc by about one order of magnitude compared to the first compression step where Q is set according to the average rate-distortion curve. The algorithm is applied for a wide set of desired values of FSIMc. It is also tested for a set of images not used in obtaining the average rate/distortion curve.
Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Abstract Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs.
ML-Based Device-Agnostic Human Activity Detection with WiFi Sniffer Traffic

Human Activity Detection plays a pivotal role in smoothly managing the health care for the elderly and those with chronic health conditions in smart home environments. Even though there are several technological advancements made in this area, solutions like smartwatches are costly to afford and the solutions that rely on sensors and cameras suffer from privacy concerns. While wireless channel state information-based approaches can address some of these limitations, these approaches either require special hardware to be deployed or modifications at the WiFi access point. In this paper, we propose to detect human activities in a device-agnostic manner by leveraging passively captured passively captured WiFi MAC-layer traffic with the help of a sniffer. In that way, elderly people and those who suffer from chronic health conditions do not need to put any sensors on their body. This approach is not only cost-effective, but it is also easy to deploy without requiring any changes at the WiFi access point or installing special sensors in the environment. We train and test six off-the-shelf machine learning models on 15+ hours worth of WiFi MAC-layer traffic collected in a home environment. We present a proof-of-concept system prototype that employs this approach. We are able to detect six activities - (a) Walking vs Sitting, (b) Sleeping vs Awake, and (c) Using Phone vs Not Using Phone in three different real-world scenarios. Our evaluation reveals that WiFi MAC-layer traffic has special signatures to detect human activities and we are able to achieve 92.49 % detection accuracy in the best case.