Reviewerly/exHarmony huggingface dataset:
SMARTPHONE USE IN LEARNING AS PERCEIVED BY UNIVERSITY UNDERGRADUATES: BENEFITS AND BARRIERS

Smartphones are attractive tools for the majority of young people particularly university undergraduates and integrating them in learning is something valuable. Nevertheless, they might do harm to their achievement if the use barriers and bad effects are not identified. Motivated by this idea, the present study was undertaken. Participants were (42) university undergraduates at the departments of mathematics and English language at Najran University in the first semester of the academic year 2020/2021. Findings showed that students were agreeable to smartphone usefulness, ease of use, and barriers. However, their responses to the effects of smartphone use were not clear. The majority of them were neutral, i.e., could not decide whether smartphones affect their learning or not. In light of these results, the study concluded that smartphone technology integration in learning is very crucial but when barriers and bad effects are clear to both teachers and students.
Dynamics and redistribution of entanglement and coherence in three time-dependent coupled harmonic oscillators

We study the dynamics and redistribution of entanglement and coherence in three time-dependent coupled harmonic oscillators. We resolve the Schrödinger equation by using time-dependent Euler rotation together with a linear quench model to obtain the state of vacuum solution. Such state can be translated to the phase space picture to determine the Wigner distribution. We show that its Gaussian matrix [Formula: see text] can be used to directly cast the covariance matrix [Formula: see text]. To quantify the mixedness and entanglement of the state, one uses respectively linear and von Neumann entropies for three cases: fully symmetric, bi-symmetric and fully nonsymmetric. Then we determine the coherence, tripartite entanglement and local uncertainties and derive their dynamics. We show that the dynamics of all quantum information quantities are driven by the Ermakov modes. Finally, we use an homodyne detection to redistribute both resources of entanglement and coherence.
Neutron: an attention-based neural decompiler

Abstract Decompilation aims to analyze and transform low-level program language (PL) codes such as binary code or assembly code to obtain an equivalent high-level PL. Decompilation plays a vital role in the cyberspace security fields such as software vulnerability discovery and analysis, malicious code detection and analysis, and software engineering fields such as source code analysis, optimization, and cross-language cross-operating system migration. Unfortunately, the existing decompilers mainly rely on experts to write rules, which leads to bottlenecks such as low scalability, development difficulties, and long cycles. The generated high-level PL codes often violate the code writing specifications. Further, their readability is still relatively low. The problems mentioned above hinder the efficiency of advanced applications (e.g., vulnerability discovery) based on decompiled high-level PL codes.In this paper, we propose a decompilation approach based on the attention-based neural machine translation (NMT) mechanism, which converts low-level PL into high-level PL while acquiring legibility and keeping functionally similar. To compensate for the information asymmetry between the low-level and high-level PL, a translation method based on basic operations of low-level PL is designed. This method improves the generalization of the NMT model and captures the translation rules between PLs more accurately and efficiently. Besides, we implement a neural decompilation framework called Neutron. The evaluation of two practical applications shows that Neutron’s average program accuracy is 96.96%, which is better than the traditional NMT model.
Article 13 on social media and news media: disintermediation and reintermediation on the modern media landscape

The former Article 13 (now Article 17) of the European directive on copyright and the internet (Directive EC2019/790) has been under negotiations since 2016 and was finally approved in 2019. In Portugal, however, the issue was mostly absent from public scrutiny and debate until November 2018. In that month, the issue arose to a prominent level, both in news media and in social media, following a wave of alerts issued by various young youtubers, incentivized by YouTube management. In this paper, we engage in the discussion concerning disintermediation, studying the way in which such alerts spread both in news media and social media, and understanding the role played by the users of social media platforms in modelling the social relevance and the social discourse of the issue of copyright and the internet. To do so, we used digital methods, collecting and analysing data from Twitter, YouTube and from online news media, mapping Article 13 discussions and identifying key actors in each field, as well as the connections between them. The results show that the ease of access provided by platforms such as Twitter or YouTube converts some users to prominent influencers and that, in some cases, those influencers are able to shift and model the public discourse about relevant collective issues.
iSTART StairStepper—Using Comprehension Strategy Training to Game the Test

Literacy skills are critical for future success, yet over 60% of high school seniors lack proficient reading skills according to standardized tests. The focus on high stakes, standardized test performance may lead educators to “teach-to-the-test” rather than supporting transferable comprehension strategies that students need. StairStepper can fill this gap by blending necessary test prep and reading comprehension strategy practice in a fun, game-based environment. StairStepper is an adaptive literacy skill training game within Interactive Strategy Training for Active Reading and Thinking (iSTART) intelligent tutoring system. StairStepper is unique in that it models text passages and multiple-choice questions of high-stakes assessments, iteratively supporting skill acquisition through self-explanation prompts and scaffolded, adaptive feedback based on performance and self-explanations. This paper describes an experimental study employing a delayed-treatment control design to evaluate users’ perceptions of the StairStepper game and its influence on reading comprehension scores. Results indicate that participants enjoyed the visual aspects of the game environment, wanted to perform well, and considered the game feedback helpful. Reading comprehension scores of students in the treatment condition did not increase. However, the comprehension scores of the control group decreased. Collectively, these results indicate that the StairStepper game may fill the intended gap in instruction by providing enjoyable practice of essential reading comprehension skills and test preparation, potentially increasing students’ practice persistence while decreasing teacher workload.
Speech Emotion Analysis of Netizens Based on Bidirectional LSTM and PGCDBN

In recent years, the application of speech emotion recognition (SER) in the supervision of Internet public opinion has received increasing attention.This study proposes a new SER algorithm to analyze the public opinion information of network platforms.Firstly, we extract different spectrum features from speech signals and combine them into frame level speech features.Then, we select conditional deep confidence network (CDBN) which has the ability to learn sequential features as the final classification model.We apply particle swarm optimization (PSO) and genetic algorithm (GA) during the fine-tuning stage of the CDBN to obtain more suitable optimal weights of the whole network, and propose the PSO-GA-CDBN (PGCDBN) model.Compare with the traditional back propagation (BP) algorithm, our training method accelerates the convergence speed of the network and improves the robustness and recognition performance of the network.In our experiment, we used the Chinese Academy of Sciences' Institute of automation (CASIA) Chinese emotional corpus and self-collected Chinese speech datasets, which were collected from Sina Weibo, Tik tok and other online social media platforms.Compare with the popular emotion classifiers such as support vector machine (SVM), deep residual network (ResNet), long short-term memory (LSTM) neural network, DBN, our proposed PGCDBN achieves the best recognition results from both datasets.In addition, we use bidirectional LSTM before PGCDBN to further process the extracted speech features, and the result of bidirectional LSTM has stronger speech signal expression ability.The average recognition accuracy of this new hybrid deep learning model algorithm in two datasets is 98.67%, which can be used for the supervision of netizens' opinions.
How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation

Manual evaluation is essential to judge progress on automatic text summarization.However, we conduct a survey on recent summarization system papers that reveals little agreement on how to perform such evaluation studies.We conduct two evaluation experiments on two aspects of summaries' linguistic quality (coherence and repetitiveness) to compare Likert-type and ranking annotations and show that best choice of evaluation method can vary from one aspect to another.In our survey, we also find that study parameters such as the overall number of annotators and distribution of annotators to annotation items are often not fully reported and that subsequent statistical analysis ignores grouping factors arising from one annotator judging multiple summaries.Using our evaluation experiments, we show that the total number of annotators can have a strong impact on study power and that current statistical analysis methods can inflate type I error rates up to eight-fold.In addition, we highlight that for the purpose of system comparison the current practice of eliciting multiple judgements per summary leads to less powerful and reliable annotations given a fixed study budget.
On the Synergy between Virtual Reality and Multi-Agent Systems

Multi-agent systems integrate a great variety of artificial intelligence techniques from different fields, these systems have made it possible to create intelligent systems more efficiently. On the other hand, virtual reality applications are accepted as viable techniques in different areas such as visualization, simulation, design, and research. The combined use of these two technologies has led to the development of realistic and interactive applications. This work aims to do a Systematic Mapping Study (SMS) relying on the guidelines of Kitchenham and Petersen to analyze the state of the art of VR applications using multi-agent systems. Inclusion and exclusion criteria have been applied to identify relevant papers, 82 articles were selected and categorized according to the publication type, the research type, the asset type, and the purpose of the work. A complete review of the 82 selected articles was performed, based on the research questions that were established. This review made it possible to clarify the open lines of research that exist and to know where research in this field can be directed.
Compliance analysis of business information system under classified protection 2.0 of cybersecurity

Classified protection of cybersecurity is an important part of cybersecurity system of China’s information system. It is the basic requirement of business system compliance operation, and is also a prerequisite for security assessment and solving information system security problems. This article studies and discusses the management system, infrastructure, protection equipment and other aspects. Analyze the business information system, and sort out the compliance of the information system item by item under classified protection of cybersecurity 2.0. According to the analysis results of the compliance of the information system, the non-compliance is classified according to the difficulty of rectification. The information system is rectified and reformed according to the strategy from easy to difficult. So, in short time, the protection level of information system is improved and the security risk of information system is reduced.
Request Merging Based Cross‐User Deduplication for Cloud Storage with Resistance Against Appending Chunks Attack

Cross-user deduplication is an emerging technique to eliminate redundant uploading in cloud storage. Its deterministic response indicating the existence of data creates a side channel to attackers, which makes the privacy in the cloud at risk. Such kind of attack as well as further appending chunks attack, still cannot be well resisted in current solutions, thus is becoming a big obstacle in using this technique. We propose a secure cross-user deduplication, called Request merging based deduplication scheme (RMDS), which takes the lead to consider resistance against appending chunks attack in a lightweight way, let alone side channel attack. We utilize the proposed XOR based chunk-level server-side storage structure together with a request merging strategy to obfuscate attackers in minimized communication overhead. The experiment results show that, with security guaranteed, the proposed scheme is more efficient comparing with the state of the art.
An obstacles avoidance method for serial manipulator based on reinforcement learning and Artificial Potential Field

Abstract The obstacles avoidance of manipulator is a hot issue in the field of robot control. Artificial Potential Field Method (APFM) is a widely used obstacles avoidance path planning method, which has prominent advantages. However, APFM also has some shortcomings, which include the inefficiency of avoiding obstacles close to target or dynamic obstacles. In view of the shortcomings of APFM, Reinforcement Learning (RL) only needs an automatic learning model to continuously improve itself in the specified environment, which makes it capable of optimizing APFM theoretically. In this paper, we introduce an approach hybridizing RL and APFM to solve those problems. We define the concepts of Distance reinforcement factors (DRF) and Force reinforcement factors (FRF) to make RL and APFM integrated more effectively. We disassemble the reward function of RL into two parts through DRF and FRF, and make them activate in different situations to optimize APFM. Our method can obtain better obstacles avoidance performance through finding the optimal strategy by RL, and the effectiveness of the proposed algorithm is verified by multiple sets of simulation experiments, comparative experiments and physical experiments in different types of obstacles. Our approach is superior to traditional APFM and the other improved APFM method in avoiding collisions and approaching obstacles avoidance. At the same time, physical experiments verify the practicality of the proposed algorithm.
Analisis Kompetensi Menulis Karya Tulis Ilmiah Mahasiswa di Indonesia

This study aimed to determine the competence of writing student scientific papers in Indonesia and its factors. This research is a type of library research (library research). The data sources that are the study of this research are books, scientific articles from journals, papers, magazines, and so on, which are relevant to this study and can answer the main problem. At the same time, the approach used in this research is philosophical. While the data collection technique used is documentation. In order to direct the accuracy and accuracy of the data under study, the analysis method used is content analysis. The findings of this study indicate that the competence of Indonesian students in writing scientific papers is still very low. In writing, most of them do not heed the guidelines and rules of writing scientific papers, and the level of plagiarism is very high. Based on the research results, the most challenging problems students face in writing scientific papers are difficulties in obtaining references and not knowing writing techniques. Meanwhile, the factors that cause difficulties in writing scientific papers are, for example, low motivation of students in writing, lack of self-confidence and lack of mood, low reading interest, low student curiosity, and students' understanding of scientific writing that is not good as well as low mastery, against his writing conventions.
A Fast and Reliable Optical 3D Scanning System for Human Arm

Abstract The article discusses the design of an acquisition system for the 3D surface of human arms. The system is composed by a 3D optical scanner implementing stereoscopic depth sensors and by an acquisition software responsible for the processing of the raw data. The 3D data acquired by the scanner is used as starting point for the manufacturing of custom-made 3D printed casts. Specifically, the article discusses the choices made in the development of an improved version of an existing system presented in [1] and presents the results achieved by the devised system.
GAFAM and Hate Content Moderation: Deplatforming and Deleting the Alt-right

Purpose – This chapter demonstrates the power that Google, Apple, Facebook, Amazon and Microsoft (or the “GAFAM”) exercise over platforms within society, highlights the alt-right’s use of GAFAM sites and services as a platform for hate, and examines GAFAM’s establishment and use of hate content moderation apparatuses to de-platform alt-right users and delete hate content. Approach – Drawing upon a political economy of communications approach, this chapter demonstrates GAFAM’s power in society. It also undertakes a reading of GAFAM “terms of service agreements” and “community guidelines” documents to identify GAFAM hate content moderation apparatuses. Findings – GAFAM are among the most powerful platforms in the world, and their content moderation apparatuses are empowered by the US government’s cyber-libertarian approach to Internet law and regulation. GAFAM are defining hate speech, deciding what’s to be done about it, and censoring it. Value – This chapter probes GAFAM’s hate content moderation apparatuses for Internet platforms, and shows how GAFAM enable and constrain the alt-right’s hate speech on their platforms. It also reflexively assesses the politics of empowering GAFAM to de-platform the alt-right.
Local and scalable detection of genuine multipartite single-photon path entanglement

How can a multipartite single-photon path-entangled state be certified efficiently by means of local measurements? We address this question by constructing an entanglement witness based on local photon detections preceded by displacement operations to reveal genuine multipartite entanglement. Our witness is defined as a sum of three observables that can be measured locally and assessed with two measurement settings for any number of parties <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>. For any bipartition, the maximum mean value of the witness observable over biseparable states is bounded by the maximum eigenvalue of an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi><mml:mo>&amp;#x00D7;</mml:mo><mml:mi>N</mml:mi></mml:math> matrix, which can be computed efficiently. We demonstrate the applicability of our scheme by experimentally testing the witness for heralded 4- and 8-partite single-photon path-entangled states. Our implementation shows the scalability of our witness and opens the door for distributing photonic multipartite entanglement in quantum networks at high rates.
Supervised quantum machine learning models are kernel methods

With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit. While such "quantum models" are sometimes called "quantum neural networks", it has been repeatedly noted that their mathematical structure is actually much more closely related to kernel methods: they analyse data in high-dimensional Hilbert spaces to which we only have access through inner products revealed by measurements. This technical manuscript summarises and extends the idea of systematically rephrasing supervised quantum models as a kernel method. With this, a lot of near-term and fault-tolerant quantum models can be replaced by a general support vector machine whose kernel computes distances between data-encoding quantum states. Kernel-based training is then guaranteed to find better or equally good quantum models than variational circuit training. Overall, the kernel perspective of quantum machine learning tells us that the way that data is encoded into quantum states is the main ingredient that can potentially set quantum models apart from classical machine learning models.
Efficient Methods to Search for Best Differential Characteristics on SKINNY

Evaluating resistance of ciphers against differential cryptanalysis is essential to define the number of rounds of new designs and to mount attacks derived from differential cryptanalysis.
Distant Supervision based Machine Reading Comprehension for Extractive Summarization in Customer Service

Given a long text, the summarization system aims to obtain a shorter highlight while keeping important information on the original text. For customer service, the summaries of most dialogues between an agent and a user focus on several fixed key points, such as user's question, user's purpose, the agent's solution, and so on. Traditional extractive methods are difficult to extract all predefined key points exactly. Furthermore, there is a lack of large-scale and high-quality extractive summarization datasets containing key points. In order to solve the above challenges, we propose a Distant Supervision based Machine Reading Comprehension model for extractive Summarization (DSMRC-S). DSMRC-S transforms the summarization task into the machine reading comprehension problem, to fetch key points from the original text exactly according to the predefined questions. In addition, a distant supervision method is proposed to alleviate the lack of eligible extractive summarization datasets. We conduct experiments on a large-scale summarization dataset collected in customer service scenarios, and the results show that the proposed DSMRC-S outperforms the strong baseline methods by 4 points on ROUGE-L.
Marketing and Financial Services in the Age of Artificial Intelligence

Marketing is changing rapidly with the advancements in AI systems. AI presents several opportunities as gaining insight, hyper-personalization, enhancing the customer experience, providing customers with better service, reducing operational costs, increase efficiency, etc. Artificial intelligence has become a game changer for marketers as it is for financial services providers. Therefore, it is paramount to build a good understanding of AI in the marketing context and mention the fundamentals of AI usage in financial services as AI is introduced as a significant competitive edge factor in financial marketing in recent years. This study aims to explore AI and marketing in a theoretical base and aims to explain the issue with a holistic approach.