Reviewerly/exHarmony huggingface dataset:
Design and Application of an AI-Based Text Content Moderation System

Cloud computing, 5G mobile network, and other new technologies have been applied in higher education in recent years. The education video resource service system with adaptive multiterminals has received widespread attention from the field. From these videos, students can get new knowledge and use the system’s built-in text comment function to communicate and interact with others. However, due to the fast increase in the number of such text comments, the traditional text content moderation methods such as the keyword method and the regular expression method can no longer meet the growing business needs. Therefore, to solve this matter, this study designed a text content moderation (TCM) system based on artificial intelligence (AI), which uses artificial intelligence and cloud-based algorithm models to analyse and recognize the text comments submitted from the web-end and app-end of the education video resource service system and completes operations such as automatic detection and manual moderation. The proposed TCM system can significantly improve the efficiency of text content moderation.
An Investigation of Teaching Aids Used by English Teachers in Teaching Vocabulary for Young Learners

This qualitative study aims at investigating the teaching aids used to teach vocabulary for young learners. The data were collected using questionnaires and interview to determine the kinds of teaching aids used to teach vocabulary, the challenges in implementing them, and how English teachers face these challenges. The data collected were analyzed qualitatively by referring to Miles &amp; Huberman (1994). The results show that the dominant type of teaching aids used by the English teachers to teach English to the young learners is audio-visual which is a combination of visual and audio aids was seldomly used by the teachers. According to the interview, the teachers felt that students were more engaged by visuals and audiovisuals because teachers can place interesting decorations to guide the students; for example, a student's favorite cartoon character or animal. The result of the interview also showed that the teachers found challenges in implementing the teaching aids, namely (a) time consuming (b) quality of teaching aids (c) technical difficulties. To face the challenges, teachers usually consider alternatives that are equivalent to the media that should have been used, allocated time, and also skills in operating electronic to implement the teaching aids.
Building Multiple Access Channels with a Single Particle

A multiple access channel describes a situation in which multiple senders are trying to forward messages to a single receiver using some physical medium. In this paper we consider scenarios in which this medium consists of just a single classical or quantum particle. In the quantum case, the particle can be prepared in a superposition state thereby allowing for a richer family of encoding strategies. To make the comparison between quantum and classical channels precise, we introduce an operational framework in which all possible encoding strategies consume no more than a single particle. We apply this framework to an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-port interferometer experiment in which each party controls a path the particle can traverse. When used for the purpose of communication, this setup embodies a multiple access channel (MAC) built with a single particle.We provide a full characterization of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-party classical MACs that can be built from a single particle, and we show that every non-classical particle can generate a MAC outside the classical set. To further distinguish the capabilities of a single classical and quantum particle, we relax the locality constraint and allow for joint encodings by subsets of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>1</mml:mn><mml:mo>&amp;#x003C;</mml:mo><mml:mi>K</mml:mi><mml:mo>&amp;#x2264;</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> parties. This generates a richer family of classical MACs whose polytope dimension we compute. We identify a "generalized fingerprinting inequality'' as a valid facet for this polytope, and we verify that a quantum particle distributed among <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> separated parties can violate this inequality even when <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>K</mml:mi><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math>. Connections are drawn between the single-particle framework and multi-level coherence theory. We show that every pure state with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math>-level coherence can be detected in a semi-device independent manner, with the only assumption being conservation of particle number.
Bitslice Masking and Improved Shuffling:

We revisit the popular adage that side-channel countermeasures must be combined to be efficient, and study its application to bitslice masking and shuffling. Our main contributions are twofold. First, we improve this combination: by shuffling the shares of a masked implementation rather than its tuples, we can amplify the impact of the shuffling exponentially in the number of shares, while this impact was independent of the masking security order in previous works. Second, we evaluate the masking and shuffling combination’s performance vs. security tradeoff under sufficient noise conditions: we show that the best approach is to mask first (i.e., fill the registers with as many shares as possible) and shuffle the independent operations that remain. We conclude that with moderate but sufficient noise, the “bitslice masking + shuffling” combination of countermeasures is practically relevant, and its interest increases when randomness is expensive and many independent operations are available for shuffling. When these conditions are not met, masking only is the best option. As additional side results, we improve the best known attack against the shuffling countermeasure from ASIACRYPT 2012. We also recall that algorithmic countermeasures like masking and shuffling, and therefore their combination, cannot be implemented securely without a minimum level of physical noise.
A Multi-task Learning Framework for Product Ranking with BERT

Product ranking is a crucial component for many e-commerce services. One of the major challenges in product search is the vocabulary mismatch between query and products, which may be a larger vocabulary gap problem compared to other information retrieval domains. While there is a growing collection of neural learning to match methods aimed specifically at overcoming this issue, they do not leverage the recent advances of large language models for product search. On the other hand, product ranking often deals with multiple types of engagement signals such as clicks, add-to-cart, and purchases, while most of the existing works are focused on optimizing one single metric such as click-through rate, which may suffer from data sparsity. In this work, we propose a novel end-to-end multi-task learning framework for product ranking with BERT to address the above challenges. The proposed model utilizes domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs multi-task learning to optimize multiple objectives simultaneously, which yields a general end-to-end learning framework for product search. We conduct a set of comprehensive experiments on a real-world e-commerce dataset and demonstrate significant improvement of the proposed approach over the state-of-the-art baseline methods.
Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports

Issue tracking systems are used in the software industry for the facilitation of maintenance activities that keep the software robust and up to date with ever-changing industry requirements. Usually, users report issues that can be categorized into different labels such as bug reports, enhancement requests, and questions related to the software. Most of the issue tracking systems make the labelling of these issue reports optional for the issue submitter, which leads to a large number of unlabeled issue reports. In this paper, we present a state-of-the-art method to classify the issue reports into their respective categories i.e. bug, enhancement, and question. This is a challenging task because of the common use of informal language in the issue reports. Existing studies use traditional natural language processing approaches adopting key-word based features, which fail to incorporate the contextual relationship between words and therefore result in a high rate of false positives and false negatives. Moreover, previous works utilize a uni-label approach to classify the issue reports however, in reality, an issue-submitter can tag one issue report with more than one label at a time. This paper presents our approach to classify the issue reports in a multi-label setting. We use an off-the-shelf neural network called RoBERTa and fine-tune it to classify the issue reports. We validate our approach on issue reports belonging to numerous industrial projects from GitHub. We were able to achieve promising F-1 scores of 81 %, 74%, and 80% for bug reports, enhancements, and questions, respectively. We also develop an industry tool called Automatic Issue Classifier (AIC), which automatically assigns labels to newly reported issues on GitHub repositories with high accuracy.
Use of Social Media in Marketing Library Resources and Services

This study has investigated the use of social media in marketing library resources and services at MUHAS and SUA. A mixed research approach was used to process, collect and analyse data in the study. While some library staff and all users were conveniently selected, key informants were purposively selected. The data were mainly collected through survey methods. The qualitative data collected were subjected to content analysis while quantitative data were analysed using SPSS. The study’s findings suggest that Facebook is the effectively used platform by both the studied libraries in the marketing of library services and resources due to its popularity and convenience. The other effectively used media is WhatsApp, although this was noted only at MUHAS. However, notwithstanding this, the media’s usage to market library resources and services has been found generally ineffective. This has been attributed to different challenges such as lack of a written policy for social media usage, unreliable internet connectivity, low promotion, content obsoleteness, and inadequate computer facilities. The study recommends that social media policies should be formulated, investment in ICT infrastructure development should be made, and promotion of social media usage among information users should be done.
Words That Harm: Defending the Dignity Approach to Hate Speech Regulation

Abstract The dignity approach to racist hate speech regulation maintains that hate speech ought to be regulated because it impugns targets’ dignity and poses a threat to their equal treatment. This approach faces the significant causal challenges of showing that hate speech has the power to erode its targets’ dignity and that regulations can successfully protect that dignity. My aim is to show how a friend of the dignity approach can resolve these challenges. To do so, I borrow insights from the critical legal studies (CLS) approach to hate speech. Specifically, I argue that hate speech can erode its targets’ dignity 1) by constituting an act of discrimination, and 2) by enacting norms that call for treating targeted groups as inferior. Yet while I maintain that the CLS approach offers valuable resources for shoring up the dignity approach, I reject the CLS approach in favor of the dignity approach.
Design of Student Bedding Management System Based on Intelligent Monitoring Bed

In order to solve the problems related to the management of students’ bedtime in vocational colleges and improve the efficiency of students’ bedtime management, an intelligent bed for students’ bedtime management is designed, which is composed of bed and sensor unit, signal processing unit, and ZigBee transmission unit. The algorithm of the collected pressure sensor data is improved, and an improved amplitude limiting lag average filtering algorithm is proposed. The management system uses the methods of composite collection of information, intelligent processing, big data analysis, etc. to upload the data to the cloud server, through the mobile phone app and other terminals to timely grasp the sleeping situation of students at night. The experiment shows that the system improves the efficiency of students’ bedtime management by 10%.
Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its superiority over other typical approaches on real data sets. A controlled study is performed according to computerized randomization, with 38 patients in each of the two groups. The study group has a higher resuscitation success rate and patient satisfaction than the conventional group (P < 0.05), and the time from the first consultation to the completion of the first ECG, the time from the completion of the ECG to the activation of the path lab, and the time from the emergency admission to the balloon dilation were significantly shorter in the study group than in the conventional group (P < 0.05). The emergency care process reengineering intervention helps patients with acute myocardial infarction to be treated quickly and effectively, thus improving their resuscitation success rate and satisfaction rate, and is worthy to be caused in the clinic and widely applied.
Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network

Face recognition can be used in several applications such as in surveillance, identification in login system and personalized technology.The challenge of the face detection system is the non-frontal face position and the use of accessories that cover the face area; even conventional detection systems that rely on facial features are difficult to get high accuracy.The proposed system can overcome these problems and it can detect human face with mask also.The deep learning system can recognize facial features with complex backgrounds.The VGG16 architecture based on convolutional neural network with shallow layers to produce light computing then the system can work real-time.Multiple layer detection on the last feature map is used to detect varied face sizes.The system result shows sequential images of face localization with 93% accuracy.
Effective Prediction of Bitcoin Price using Wolf Search Algorithm and Bidirectional LSTM on Internet of Things Data

Internet of things is the concept of establishing relationships and interactions with other connected devices through a network to reach a specific objective. The collected data from devices could be transformed into valuable insights by applying some intelligent learning algorithms. A distributed, permission less ledger called IOTA (MIOTA) manages micro transactions between multiple devices with the help of IoT. It provides the information about the transactions made with the specific type of crypto currency in the market. In this paper, an effective crypto currency price prediction model is proposed to identify the fluctuations in currency value from the past one-year data. Wolf search optimisation algorithm selects the best performing feature subset. Bidirectional long short-term memory (BiLSTM) model is employed to train and validate the data captured from the feature selection process. The proposed model attained 93% accuracy, significantly higher than the existing methods, portraysits significance and efficacy.
Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets

Spectrograms are fundamental tools in time-frequency analysis, being the squared magnitude of the so-called short time Fourier transform (STFT). Signal analysis via spectrograms has traditionally explored their peaks, i.e. their maxima. This is complemented by a recent interest in their zeros or minima, following seminal work by Flandrin and others, which exploits connections with Gaussian analytic functions (GAFs). However, the zero sets (or extrema) of GAFs have a complicated stochastic structure, complicating any direct theoretical analysis. Standard techniques largely rely on statistical observables from the analysis of spatial data, whose distributional properties for spectrograms are mostly understood only at an empirical level. In this work, we investigate spectrogram analysis via an examination of the stochastic geometric properties of their level sets. We obtain rigorous theorems demonstrating the efficacy of a spectrogram level sets based approach to the detection and estimation of signals, framed in a concrete inferential set-up. Exploiting these ideas as theoretical underpinnings, we propose a level sets based algorithm for signal analysis that is intrinsic to given spectrogram data, and substantiate its effectiveness via extensive empirical studies. Our results also have theoretical implications for spectrogram zero based approaches to signal analysis. To our knowledge, these results are arguably among the first to provide a rigorous statistical understanding of signal detection and reconstruction in this set up, complemented with provable guarantees on detection thresholds and rates of convergence.
Analysis of Encryption Algorithms Proposed for Data Security in 4G and 5G Generations

This paper intended to investigate and analyze encryption algorithms that can be proposed to perform data security in 4G and 5G networks. The research explores different standards, services, and features presented via 4G and 5G networks. Also, the basic components of encryption algorithms (e.g., ZUC, SNOW 3G, and AES) are investigated. For instance, initialization keys have been identified and analyzed due to their vital roles in determining the security of the encryption algorithms. Moreover, the researchers analyzed the effective elements of these algorithms (i.e., LFSR registers, substitution boxes, NLF functions like finite state machines, Math transformations, secret encryption keys, and non-secret IV keys). Cryptanalysis methods play important roles in determining the security of these algorithms. Thus, some cryptanalysis methods have been explored and investigated. Various weak points have been identified in initialization process of these algorithms. Therefore, different recommendations are presented that enhance the security of these ciphers, and can be reflected in data security in 4G and 5G networks.
A Linear Algebraic Threshold Essential Secret Image Sharing Scheme

A secret sharing scheme allocates to each participant a share of a secret in such a way that authorized subsets of participants can reconstruct the secret, while shares of unauthorized subsets of participants provide no useful information about the secret. For positive integers r,s,t,n with r⩽s⩽t⩽n, an (r,s,t,n)–threshold essential secret sharing scheme is an algorithm that decomposes a secret S into n shares, s of which are essential, in a way that authorized subsets are precisely those with at least t members, at least r of whom are essential. This work proposes a lossless linear algebraic (r,s,t,n)–threshold essential secret image sharing scheme that decomposes the secret, S, into equally-sized shares, each of size 1/t the size of S. For each block, B, of S, the scheme assigns to the n participants distinct signature vectors v1,v2,…,vn in the vector space F2αt, where α is a suitable positive integer, typically between 2 and 5, inclusive. These signature vectors must adhere to some admissibility conditions in order to satisfy the secret sharing threshold properties. The decomposition of B into n shares is obtained by partitioning B into t vectors, then computing the share yj of the jth participant (1≤j≤n), as a linear combination of these parts with coefficients from the signature vj. The presented simulations showcase the effectiveness and robustness of the proposed scheme against standard statistical and security attacks. They further demonstrate its superiority with respect to existing schemes.
Prediction of Metastatic Relapse in Breast Cancer using Machine Learning Classifiers

The volume and amount of data in cancerology is continuously increasing, yet the vast majority of this data is not being used to uncover useful and hidden insights.As a result, one of the key goals of physicians for therapeutic decision-making during multidisciplinary consultation meetings is to combine prediction tools based on data and best practices (MCM).The current study looked into using CRISP-DM machine learning algorithms to predict metastatic recurrence in patients with early-stage (non-metastatic) breast cancer so that treatmentappropriate medicine may be given to lower the likelihood of metastatic relapse.From 2014 to 2021, data from patients with localized breast cancer were collected at the Regional Oncology Center in Meknes, Morocco.There were 449 records in the dataset, 13 predictor variables and one outcome variable.To create predictive models, we used machine learning techniques such as Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbors (KNN) and Logistic Regression (LR).The main objective of this article is to compare the performance of these four algorithms on our data in terms of sensitivity, specificity and precision.According to our results, the accuracies of SVM, kNN, LR and NB are 0.906, 0.861, 0.806 and 0.517 respectively.With the fewest errors and maximum accuracy, the SVM classification model predicts metastatic breast cancer relapse.The unbiased prediction accuracy of each model is assessed using a 10-fold cross-validation method.
N-gram Based Croatian Language Network: Application in a Smart Environment

In the field of natural language processing, language networks represent a method for observing linguistic units and their interactions in different linguistic contexts. This paper uses the previously presented Croatian language network for building a solution capable of generating spoken notifications in Croatian language. The novelty of this paper is that it proposes an approach for generating spoken notifications in smart environments by combining specialized services that enable interaction with the environment and human users. The process employed for generating spoken notifications is described in detail. Also, a novel contribution of this paper is the case-study evaluation of the proposed approach in a smart home environment.
On the Reform of PE Teaching in Colleges and Universities and the Cultivation of Students' PE Consciousness

Sports in the process of teaching reform in colleges and universities, should realize the professional construction to have stronger forward thinking and innovative thinking, in meeting the demand of current social conditions, should also be possible to dig deeper into the future social sports teaching the demand point of the industry, thus cultivate can adapt to the future development of talents for the society.In addition, in the course of education and teaching reform of physical education specialty in colleges and universities, we should add the thought of serving local economy, social construction and national economic construction, as well as serving national employment strategy and local employment policy.Only by establishing sufficient service consciousness and service thought can we be invincible in the fierce competition in the education industry and obtain better professional development.