Reviewerly/exHarmony huggingface dataset:
Application Research on the Evaluation of College English Teaching Effect Based on Big Data Platform Architecture

In the development of social economy and technological innovation, enterprises in various fields have increasingly high demand for English talents. College English has put forward new requirements and technical standards for English teaching. In order to adapt to the new social environment and teaching objectives more quickly, college teachers should improve the effectiveness of English classes on the basis of changing the traditional teaching methods and teaching models, and construct a scientific and reasonable teaching effect evaluation system. Only in this way can classroom teaching efficiency and students' learning level be further improved. After understanding the influence of the construction of big data platform on the composition of college English teaching effect, this paper puts forward a college English teaching effect evaluation system with the structure of big data platform as the core. The final experimental results show that the whole system contains multiple functions, which can organically combine the whole process of teaching evaluation and teaching activities, further improve the level of education and teaching, and accelerate the pace of teaching model reform.
Task-Specific Adaptive Differential Privacy Method for Structured Data

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy–utility trade-off problem.
Intrinsic motivation learning for real robot applications

OPINION article Front. Robot. AI, 10 February 2023Sec. Humanoid Robotics Volume 10 - 2023 | https://doi.org/10.3389/frobt.2023.1102438
Digitally embodied lifespan neurocognitive development and Tactile Internet: Transdisciplinary challenges and opportunities

Mechanisms underlying perceptual processing and inference undergo substantial changes across the lifespan. If utilized properly, technologies could support and buffer the relatively more limited neurocognitive functions in the still developing or aging brains. Over the past decade, a new type of digital communication infrastructure, known as the “Tactile Internet (TI),” is emerging in the fields of telecommunication, sensor and actuator technologies and machine learning. A key aim of the TI is to enable humans to experience and interact with remote and virtual environments through digitalized multimodal sensory signals that also include the haptic (tactile and kinesthetic) sense. Besides their applied focus, such technologies may offer new opportunities for the research tapping into mechanisms of digitally embodied perception and cognition as well as how they may differ across age cohorts. However, there are challenges in translating empirical findings and theories about neurocognitive mechanisms of perception and lifespan development into the day-to-day practices of engineering research and technological development. On the one hand, the capacity and efficiency of digital communication are affected by signal transmission noise according to Shannon’s (1949) Information Theory. On the other hand, neurotransmitters, which have been postulated as means that regulate the signal-to-noise ratio of neural information processing (e.g., Servan-Schreiber et al., 1990 ), decline substantially during aging. Thus, here we highlight neuronal gain control of perceptual processing and perceptual inference to illustrate potential interfaces for developing age-adjusted technologies to enable plausible multisensory digital embodiments for perceptual and cognitive interactions in remote or virtual environments.
Empowering Authenticated and Efficient Queries for STK Transaction-Based Blockchains

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.
THE IMPACT OF MARKETING STRATEGY ON THE MARKETING INNOVATION AND THE MARKETING COMPETITIVE ADVANTAGE IN THE JORDANIAN FURNITURE INDUSTRY

This study is designed to explore the influence of marketing strategy on marketing innovation and the marketing competitive advantage. Additionally, in this study, elements of marketing strategy (product strategy, pricing strategy, promotion strategy and distribution strategy) were adopted, and marketing innovation was used as a mediator. Quantitative approach was applied, and a questionnaire was distributed by hand-to-hand method to 267 employees working in the furniture industry on the basis of a convenience sample. SPSS and AMOS were applied as statistical techniques to examine and analyze the primary data. Results of analysis accepted the all-proposed hypothesis. The research has empirically confirmed that marketing strategies have a positive impact on marketing innovation and the marketing competitive advantage; and that marketing innovation had a positive impact as mediator on the marketing competitive advantage. Moreover, it found that the elements of marketing strategy: product, price, promotion, and distribution, have a positive impact on both marketing innovation and marketing competitive advantage. In addition to the above results, it found that elements of marketing strategy have a more positive impact on the marketing competitive advantage than marketing innovation. The study recommended that the companies should determine the right marketing strategy that encourages innovation to distinguish themselves and to gain a competitive advantage in the market by obtaining customer satisfaction considering the changes in the business environment.
Presuppositions of Social Activism in Marcus Rashford’s Open Letter

This study analyzed the presuppositions in Marcus Rashford's open letter to the UK government regarding free school meals for children all over UK during the outbreak of Covid-19. His open letter is an essential part of social activism in a changing society. It was written with enthusiasm, optimism, and positivity for a new future. The study is conducted to fill the gap between the previous studies since no research has analyzed an open letter before. This study used Yule's theory (1996) of presuppositions and Cutting (2002) to analyze the data found in the letter. The results of the study found that the open letter used four kinds of presuppositions, which is existential, factive, lexical, and counterfactual presuppositions. Rashford was able to provide the audience with new data as an addition to the material they already knew by utilizing those presuppositions. Additionally, it refutes a common misconception. In this case, the UK government's stance is successfully influenced.
Massive Multiplayer Online Role-Playing Games to Enrich Vocabulary to Foreign Language Learners: The Implementation

Massive Multiplayer Online Role-Playing Games (MMORPGs) are increasingly used to improve language learning. Many games have been created as products to help players improve their learning abilities. This research explores the execution of the MMORPGs to improve their vocabulary. The information was gathered through observation, documentation, and interview. There are seven chosen experienced players of MMORPG in worldwide servers who meet the requirements. Results demonstrated that implementing MMORPG can assist the EFL learners with acquiring new vocabulary from the aspect of the game like socializing, guild, mission, storyline, trading market, and things depiction assist the EFL learners with learning new vocabulary. The results of the study were expected to help the EFL learner gamer learn English, especially vocabulary, since the game has many aspects that help the EFL learners who play this game to enrich their vocabulary.
Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes

A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.
The Effectiveness of Reading Aloud to Improve Students Reading Ability in Secondary School

In teaching reading, especially for senior high school students, they have to master high reading skills such as inferential, evaluative and critical reading. To reach this purpose, many teachers ask the students to read silently through skimming or scanning. However, some researches have proven that using reading aloud in teaching reading can improve students vocabulary and make students idea sharpener in finding detail information of the text. in addition, reading aloud is related a lot with reading comprehension. That is why it is important to know whether or not reading aloud is needed for teaching reading for senior high school. The aim of this research is to answer a research question, it is about whether or not reading aloud can improve students’ reading ability. The method used in this research is quantitative. The data used in this research is the result of pre-test and post-test of experimental and control class which are taken from the twelve grade students of MAN 2 Nganjuk, Academic year 2022/2023. The result of this research shows that reading aloud can improve the students’ reading ability, or on the other word, there is a significance different of the students’ score after being taught by using reading aloud strategy. The next research about this strategy for higher students of EFL with different type of text is needed so it can be used as the references for teachers to improve their strategy for teaching reading.
MARS: A Multi-task Ranking Model for Recommending Micro-videos

Micro-videos have become very popular recently. While using a micro-video app, the user experiences are strongly affected by the ranking of micro-videos. Moreover, the micro-video recommendation is often required to satisfy multiple business indicators. The existing models mainly utilize multi-modal features whose acquisition cost is too high for start-up companies. In the paper, we propose a multi-task ranking model MARS for recommending micro-videos. MARS aims at two tasks: finishing playing prediction and playback time prediction. For providing high accuracy in performing these two tasks, MARS adopts the multi-expert structure and mines historical statistical information besides interactions between users and micro-videos. Results of offline experiments and online A/B tests show that MARS can achieve good performances on two tasks. Further, MARS has been deployed in a real-world production environment, serving thousands of users.
An Analysis the Factors of Students’ Difficulties in Speaking English Faced by non-English Department Students

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Abstract: The purpose of this research is to find out The Factors of Students’ Difficulties in Speaking English Faced by non-English Department Students. This research was conducted with qualitative research, with using survey method. This research used google form as questionnaire to gain the data. The respondents of this research are the 100 students of non-English department at Faculty of Education UIN SMH Banten. The result of this research showed that there were many factors that students have in English speaking difficulties. They are factor anxiety does not master the vocabulary has value 59% agreed, then factor shy of being an intention has value 57% agreed, then factor lack of confident has value 54% agreed, factor lack of self motivation has value 54% agreed, factor of environmental effect has value 54% agreed, factor there is no preparation has value 53% agreed, then factor fear of wrong pronouncing has value 52% agreed, factor of fear of use the wrong words has value 49% agreed, factor affective of students has value 48% agreed, and the last factor anxiety of being bullied has value 44% agreed. It means students have some factors in speaking problem that should be take more attention.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Design and research of English teaching platform based on BP neural network algorithm

In the development of modern education innovation, with the continuous innovation of social economy and science and technology, education information technology has undergone significant changes. The intelligent algorithm represented by BP neural network has been applied to the design of professional teaching platform, which not only provides technical support for students' network education learning, but also provides a new idea for English education innovation. According to the situation of English teaching at various stages in recent years, traditional English teaching pays more attention to the transmission of theoretical knowledge, and ignores the educational discussion of students' individual differences. Therefore, researchers propose to use intelligent algorithms to build a personalized education and learning platform to help students quickly understand and master professional English knowledge. Based on the understanding of BP neural network algorithm and the current research status of modern English teaching, this paper deeply discusses the English teaching platform based on BP neural network algorithm as the core, and carries on the development and test of the system operating function and performance. The final results show that the system platform design meets the needs of English teaching in the new era.
APLIKASI MOBILE LEARNING UNTUK PEMBELAJARAN BAHASA INGGRIS DI JURUSAN BIMBINGAN DAN KONSELING UNIVERSITAS NEGERI MEDAN

Mobile Learning For English Subject In The Department Of Guidance And Counseling Universitas Negeri Medan. The purpose of this study was to determine the shape of the android based Mobile Learning needed by students majoring in guidance and counseling Unimed on learning English. This research has been conducted using ADDIE development model. Media experts give excellent ratings for throughout the all item of Media assessment. This application is tested on 30 students of the 1st half of Guidance and Counseling Department of the Faculty of Education Unimed. The results of calculations throughout the student questionnaire responses obtained 87.15%% percentage of aspects of good feelings, 86.57% of the student's interest aspects of the use of the application, 85.85% of aspects of attention, and 88.24% of aspects of student involvement.
Tracking Visual Landmarks of Opportunity as Rally Points for Unmanned Ground Vehicles

In addition to the remote-control for piloting an Unmanned Ground Vehicle (UGV), we aim at adding a new high level command mode: reaching a visual rally point selected by the operator from the camera feed. We modify the recent TransT-M single object tracker to track the rally point as the UGV moves and we couple it with our visual odometry. We design a visibility test to discard false positives, when the rally point disappears from the field of view. We test the tracking on image sequences taken from our platform and the KITTI Vision Benchmark to demonstrate the efficiency and the robustness of the proposed tracking architecture. The visibility test improves the chance of recovery after the landmark disappears and removes false positives. We show that the operator does not need to designate the landmark with high precision as the tracker is tolerant to imprecision on the position and the size of the landmark.
APBFT: An Adaptive PBFT Consensus for Private Blockchains

As smart cities become more decentralized, the need for reliable and secure cyber-physical systems (CPS) that guarantee safe interactions and secure data storage without loss of privacy is continuously increasing. Blockchain is a rapidly emerging technology in this domain. It demonstrated effectiveness thanks to the cryptographic mechanisms it utilizes and to its immutability. Private blockchains are the most suited to applications that require privacy and confidentiality when data is very sensitive. In this case, the most commonly used consensus protocol is Practical Byzantine Fault Tolerance (PBFT). However, PBFT requires the participation of all nodes in the consensus process, which increases bandwidth consumption and consensus delay significantly. In this paper, we propose an adaptive PBFT protocol called APBFT that optimizes the number of nodes participating in the consensus based on their response time and credibility. Therefore, we reduce the amount of communication and the response delays. We maintain the asynchrony of the algorithm so that it remains resilient to DoS attacks. The simulation results show that our algorithm outperforms the original PBFT in terms of delays and message traffic.
Optimal FSM's State Encoding for Low power using Dynamic Boundary Difference Mutation Strategy in Evolutionary Programming

In this paper, a new computation intelligent approach based on evolutionary programming is applied to minimize the weighted hamming distance among states to reduce the switching frequency for low-power design in Finite State Machines. A mutation strategy is proposed to carry better exploration of solution space by deploying a dynamic differential approach between the current solution position and solution domain boundary limits. The proposed method is compared against the performances of the standard form of mutation strategies based on the self-adaptive version of Gaussian and Cauchy mutation and an advanced version of particle swarm optimization. The different combinations of Gaussian and Cauchy mutations are also examined. The performances of the proposed solution were superior and computationally efficient in comparision to all others. The robustness against variability is excellent over a large number of runs.
Self-Attention Gated Cognitive Diagnosis For Faster Adaptive Educational Assessments

Cognitive diagnosis models map observations onto psychological internal traits, which have been widely used to construct personalized educational assessments. However, the challenges of the latent ability representation, accurate performance with explanatory and cold-start problems caused by limited handcraft skills still exist. In this paper, we introduce a selfadaptive Attention Gate Cognitive Diagnosis Model (AGCDM) based on a multi-layer hierarchical structure. Specifically, in the first two layers, our model embeds the item response logs into a higher expressive latent space, and the specific self-attention mechanism captures the rich information from the inner-dependencies correlation among the examinee, quiz and knowledge. In the next layer, the gate mechanism addresses the challenge of noisy information (slip and guess) over psychological reasons. We verify the empirical performance of our model on different educational tasks and all comparisons study conducted on the same training recipe. On the item response prediction task, the experiment results demonstrate our hierarchical model achieves state-of-the-art performance. Through the estimation of adaptive learning, we also validate the model’s effectiveness in addressing the limited skill problem in real scenarios. The learning tracks show that our model empowered by the meta-learning algorithm can be adaptive faster than the base model to the new different skill distribution set. Furthermore, we present the ablation study on the hierarchical architecture and detailed hyperparameters selections. The comparison results demonstrate the attention blocks yield the scores improvement, and the estimation of the noisy errors shows the gate mechanism boosts the detection accuracy on the slip and guess. Code is available at https://github.com/TerryPei/AGCDM.