Reviewerly/exHarmony huggingface dataset:
End-to-End Compression Towards Machine Vision: Network Architecture Design and Optimization

The visual signal compression is a long-standing problem. Fueled by the recent advances of deep learning, exciting progress has been made. Despite better compression performance, existing end-to-end compression algorithms are still designed towards better signal quality in terms of rate-distortion optimization. In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision. We propose an inverted bottleneck structure for the encoder of the end-to-end compression towards machine vision, which specifically accounts for efficient representation of the semantic information. Moreover, we quest the capability of optimization by incorporating the analytics accuracy into the optimization process, and the optimality is further explored with generalized rate-accuracy optimization in an iterative manner. We use object detection as a showcase for end-to-end compression towards machine vision, and extensive experiments show that the proposed scheme achieves significant BD-rate savings in terms of analysis performance. Moreover, the promise of the scheme is also demonstrated with strong generalization capability towards other machine vision tasks, due to the enabling of signal-level reconstruction.
Semi-Supervised Kernel Matching for Domain Adaptation

In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification.
The Students’ Needs in Developing EFL Materials ICT Based

In developing English communication skills, the student's needs, wants, and lacks are crucial. The materials given should be suitable for the students' wants. These issues are vital in developing any English materials on ICT-based. This study explores the students' needs, wants, and lacks in learning English by developing suitable materials with the ICT-based. The mixed approach was used in this study. The researchers distributed the survey to 350 students from Indonesia and other countries (Kosovo, Kuwait, Ghana, Pakistan, Philippines, India, and United Kingdom) involved in the Share to Care Community in collecting the data. They were assigned to fill the Google form about their needs in learning English. The result showed that the students need materials that fulfill their needs. It should be related to today's facts, easy enough to share with, and easy to access by the students' technologies devices, then using both target languages L2 and L1 to deliver the materials. While teaching, the lecturers used any method; role-playing is the most common, followed by oral reports, games, outdoor learning, and blended learning. Most of the students from several countries intended to have ICT in their learning process.
Inquest of Current Situation in Afghanistan Under Taliban Rule Using Sentiment Analysis and Volume Analysis

Microblogging websites and social media platforms serve as a potential source for mining public opinions and sentiments on a variety of subjects including the prevailing situations in war-afflicted countries.In particular, Twitter has a large number of geotagged tweets that make the analysis of sentiments across time and space possible.This study performs volume analysis and sentiment analysis using LDA (Latent Dirichlet Allocation) and text mining over two datasets collected for different periods.To increase the adequacy and efficacy of the sentiment analysis, a hybrid feature engineering approach is proposed that elevates the performance of machine learning models.Geotagged tweets are used for volume analysis indicating that the highest number of tweets is originated from India, the US, the UK, Pakistan, and Afghanistan.Analysis of positive and negative tweets reveals that negative tweets are mostly originated from India and the US.On the contrary, positive tweets belong to Pakistan and Afghanistan.LDA is used for topic modeling on two datasets containing tweets about the current situation after the Taliban take control of Afghanistan.Topics extracted through LDA suggest that majority of the Afghanistan people seem satisfied with the Taliban's takeover while the topics from negative tweets reveal that issues discussed in negative tweets are related to the US concerns in Afghanistan.Sentiment analysis over two different datasets indicates that the trend of the sentiments has been shifted positively over three weeks.
English Machine Reading Comprehension Datasets: A Survey

This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem.We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human performance level, and first question word.Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets.
Usability Evaluation of TV Interfaces: Subjective Evaluation Vs. Objective Evaluation

This study aims to design a usability test enhanced with subjective usability evaluation methods (concurrent think-aloud, post-task and posttest questionnaires) and objective usability evaluation methods (eye-tracking, emotion recognition and logging technologies). Furthermore, the relationships between the subjective metrics such as task difficulty level, PSSUQ (Post-Study System Usability Questionnaire) scores and objective metrics such as task completion time, eye tracking metrics (fixation count, fixation duration, average fixation duration, saccade count, saccade duration, scanpath length, blink count), logging metrics (keying and back keying count) and emotion recognition metrics (negative emotions count) are aimed to be analyzed. Therefore, a user testing study with 38 participants was conducted to evaluate the usability level of the TV interface of Digiturk, which is one of the digital TV broadcasting platforms in Turkey. The participants completed ten tasks related to the features of the TV interface, such as VOD (video-on-demand) watching, channel locking, and recording for future watches. Whether there is a significant difference between tasks in terms of subjective and objective metrics is investigated by using one-way ANOVA. The results show that while task completion time and task difficulty increase, values of all objective metrics increase. The relationships between the subjective and objective metrics are measured with Pearson correlation and the results show that there is a significant relationship between every subjective and objective metric except PSSUQ scores, which shows the perceived satisfaction level of the participants. Furthermore, exploratory factor analysis is performed to explore the latent structure of the usability measures. As a result of the factor analysis, a two-factor structure in which objective and subjective metrics load on two separate factors is obtained.
Blockchain-Based Solution for Product Recall Management in the Automotive Supply Chain

Product recall management in the automotive industry is a challenging problem that affects human lives and the safe operation of automobiles.Product recalls can assist in removing potentially unsafe products from the marketplace and minimizing a company's responsibility for corporate negligence.Today's systems and technologies leveraged for product recall management in the automotive supply chain fall short in providing transparency, traceability, reliability, audit, security, and trust features.In this paper, we propose a blockchain-based approach to overcome the aforementioned problems related to product recall management.We employ the public Ethereum blockchain and integrate it with the decentralized storage of the InterPlanetary File System (IPFS) to deal with the large-sized data problem.We present the system design and six algorithms explaining the working principles, information exchange flow, and stakeholders' detail and their sequential interactions.We discuss the implementation details, generalization aspects, and cost and security analyses to evaluate the performance of the proposed approach.The proposed solution is cost-effective, secure, and enables automakers to have end-to-end visibility of information during product recalls.We make the smart contracts' code publicly available on GitHub.
BCST-APTS: Blockchain and CP-ABE Empowered Data Supervision, Sharing, and Privacy Protection Scheme for Secure and Trusted Agricultural Product Traceability System

Blockchain provides new technologies and ideas for the construction of agricultural product traceability system (APTS). However, if data is stored, supervised, and distributed on a multiparty equal blockchain, it will face major security risks, such as data privacy leakage, unauthorized access, and trust issues. How to protect the privacy of shared data has become a key factor restricting the implementation of this technology. We propose a secure and trusted agricultural product traceability system (BCST-APTS), which is supported by blockchain and CP-ABE encryption technology. It can set access control policies through data attributes and encrypt data on the blockchain. This can not only ensure the confidentiality of the data stored in the blockchain, but also set flexible access control policies for the data. In addition, a whole-chain attribute management infrastructure has been constructed, which can provide personalized attribute encryption services. Furthermore, a reencryption scheme based on ciphertext-policy attribute encryption (RE-CP-ABE) is proposed, which can meet the needs of efficient supervision and sharing of ciphertext data. Finally, the system architecture of the BCST-APTS is designed to successfully solve the problems of mutual trust, privacy protection, fine-grained, and personalized access control between all parties.
Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.
The Influence of Artificial Intelligence Technology on Teaching under the Threshold of “Internet+”: Based on the Application Example of an English Education Platform

Today’s era can be treated as the Internet era as the world revolves around the Internet for unlimited access to the resources available in a remote area. The Internet has made a deeper penetration in the education sector; the traditional offline classes are being replaced with online and offline classes to make the quick and effective teaching and learning process. Some researchers are exploring various technologies to incorporate digitization and visualization of courses to improve the independent learning among the students and for effective modeling of course contents. Artificial intelligence, deep learning, machine learning, and edge computing are some technologies implemented to increase the student’s and teachers’ interaction for improved performance of the learning process. A genetic algorithm with artificial intelligence is proposed in this research work to choose teaching and learning management with English courses in colleges and universities. For making analysis, the student performance dataset is taken from the UCI repository. The teaching and learning management of the English course is analyzed along with the student’s behavior towards the online English course. The results show that the student’s response to the classes is increased with the involvement of the artificial intelligence technology with the Internet.
Real-Time 3D Single Object Tracking With Transformer

LiDAR-based 3D single object tracking is a challenging issue in robotics and autonomous driving. Currently, existing approaches usually suffer from the problem that objects at long distance often have very sparse or partially-occluded point clouds, which makes the features extracted by the model ambiguous. Ambiguous features will make it hard to locate the target object and finally lead to bad tracking results. To solve this problem, we utilize the powerful Transformer architecture and propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Point-Track-Transformer (PTT)</b> module for point cloud-based 3D single object tracking task. Specifically, PTT module generates fine-tuned attention features by computing attention weights, which guides the tracker focusing on the important features of the target and improves the tracking ability in complex scenarios. To evaluate our PTT module, we embed PTT into the dominant method and construct a novel 3D SOT tracker named PTT-Net. In PTT-Net, we embed PTT into the voting stage and proposal generation stage, respectively. PTT module in the voting stage could model the interactions among point patches, which learns context-dependent features. Meanwhile, PTT module in the proposal generation stage could capture the contextual information between object and background. We evaluate our PTT-Net on KITTI and NuScenes datasets. Experimental results demonstrate the effectiveness of PTT module and the superiority of PTT-Net, which surpasses the baseline by a noticeable margin, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 10% in the Car category. Meanwhile, our method also has a significant performance improvement in sparse scenarios. In general, the combination of transformer and tracking pipeline enables our PTT-Net to achieve state-of-the-art performance on both two datasets. Additionally, PTT-Net could run in real-time at 40FPS on NVIDIA 1080Ti GPU. Our code is open-sourced for the research community at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shanjiayao/PTT</uri> .
A Comparative Evaluation of Artificial Neural Network and Sunshine Based models in prediction of Daily Global Solar Radiation of Lalibela, Ethiopia

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Three Combinatorial Perspectives on Minimal Codes

We develop three approaches of combinatorial flavor to study the structure of minimal codes and cutting blocking sets in finite geometry, each of which has a particular application.The first approach uses techniques from algebraic combinatorics, describing the supports in a linear code via the Alon--F\" uredi theorem and the combinatorial Nullstellensatz.The second approach combines methods from coding theory and statistics to compare the mean and variance of the nonzero weights in a minimal code.Finally, the third approach regards minimal codes as cutting blocking sets and studies these using the theory of spreads in finite geometry.By applying and combining these approaches with each other, we derive several new bounds and constraints on the parameters of minimal codes.Moreover, we obtain two new constructions of cutting blocking sets of small cardinality in finite projective spaces.In turn, these allow us to give explicit constructions of minimal codes having short length for the given field and dimension.
A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.
Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines

The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task. Predicting the price of cryptocurrency is based on the previous price inflations in research. Various machine learning algorithms have been applied to predict the digital coins' exchange rate, but in this study, we present the exchange rate of cryptocurrency based on applying the machine learning XGBoost algorithm and blockchain framework for the security and transparency of the proposed system. In this system, data mining techniques are applied for qualified data analysis. The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance. The prediction process is designed by using various filters and coefficient weights. The cross-validation method was applied for the phase of training to improve the performance of the system.
A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter

Social networks have become an integral part of our daily lives. With their rapid growth, our communication using these networks has only increased as well. Twitter is one of the most popular networks in the Middle East. Similar to other social media platforms, Twitter is vulnerable to spam accounts spreading malicious content. Arab countries are among the most targeted, possibly due to the lack of effective technologies that support the Arabic language. In addition, as a complex language, Arabic has extensive grammar rules and many dialects that present challenges when extracting text data. Innovative methods to combat spam on Twitter have been the subject of many current studies. This paper addressed the issue of detecting spam accounts in Arabic on Twitter by collecting an Arabic dataset that would be suitable for spam detection. The dataset contained data from premium features by using Twitter premium API. Data labeling was conducted by flagging suspended accounts. A combined framework was proposed based on deep-learning methods with several advantages, including more accurate, faster results while demanding less computational resources. Two types of data were used, text-based data with a convolution neural networks (CNN) model and metadata with a simple neural networks model. The output of the two models combined identified accounts as spam or not spam. The results showed that the proposed framework achieved an accuracy of 94.27% with our combined model using premium feature data, and it outperformed the best models tested thus far in the literature.
Quantum Communication with Ultrafast Time-Bin Qubits

The photonic temporal degree of freedom is one of the most promising platforms for quantum communication over fiber networks and free-space channels. In particular, time-bin states of photons are robust to environmental disturbances, support high-rate communication, and can be used in high-dimensional schemes. However, the detection of photonic time-bin states remains a challenging task, particularly for the case of photons that are in a superposition of different time bins. Here, we experimentally demonstrate the feasibility of picosecond time-bin states of light, known as ultrafast time bins, for applications in quantum communications. With the ability to measure time-bin superpositions with excellent phase stability, we enable the use of temporal states in efficient quantum key distribution protocols such as the BB84 protocol.Received 17 June 2021Revised 6 January 2022Accepted 3 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010332Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum information processingQuantum Information