Reviewerly/exHarmony huggingface dataset:
Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis

Identification of cyberbullying and cyberstalking for real-time use cases is a multi domain task that involves the design of social media data extraction, sentiment analysis, sentiment pattern evaluation, and regression models. To perform this task, researchers have proposed the use of high-density feature representation models that can extract social media sentiments, and combine them with user specific parameters like age, gender, time of post, etc. But existing models are either non-comprehensive or capable of achieving limited accuracy when used for real-time scenarios. Moreover, these models are not flexible to multimodal inputs, which further limits their scalability levels. To address these concerns, this paper proposes the development of a deep learning model for cyberbullying and cyberstalking attack mitigation via social media analysis. The proposed model initially collects tweets posted by users, extracts meta data, and analyzes language features for training a Long-Short-Term Memory (LSTM) based Convolutional Neural Network (CNN), which assists in the pre-filtering of tweets. The filtered tweets are passed through a Natural Language Processing (NLP) engine that assists in sentiment identification for these texts. Sentiment data and Word Embedding capabilities are used to anticipate cyberbullying and cyberstalking attacks. This is done via CNN based pattern analysis, which assists in the efficient identification and mitigation of these attacks. Due to the integration of these models, the proposed method is able to improve attack detection accuracy by 3.5 %, while reducing the identification delay by 8.3 % in real-time scenarios.
Extracting Time Information from Korean Documents

Most of the documents or writings we see in our daily lives contain information about time, and it has been important to develop a model for extracting the time information from unstructued texts. As the time information appears differently in different languages, there have been studies that proposed language-specific models. This study aims to extract timex3 tags of time expresisons (e.g., May 30) and event tags of eventual expressions (e.g., go, expect) from Korean documents. The Korean dataset used in this study is manually annotated, and used to finetune a pre-trained language model for the extraction task of timex3 and event tags. By experiments, we found that the model gave better performance when it is finetued for extracting the two tags simultaneously. The model achieved 0.5563 of F1 score.
RSII: A Recommendation Algorithm That Simulates the Generation of Target Review Semantics and Fuses ID Information

The target review has been proven to be able to predict the target user’s rating of the target item. However, in practice, it is difficult to obtain the target review promptly. In addition, the target review and the rating may sometimes be inconsistent (such as preference reviews and low ratings). There is currently a lack of research on the above issues. Therefore, this paper proposed a Recommendation algorithm that Simulates the generation of target review semantics and fuses the ID Information (RSII). Specifically, based on the characteristics of the target review available during the model training, this paper designed a teacher module and a review semantics learning module. The teacher module learned the semantics of the target review and guided the review semantics learning model to learn these semantics. Then, this study used the fusion module to dynamically fuse the target review semantics and the ID information, enriching the representation of predictive features, thereby, alleviating the problem of inconsistency between the target review and the rating. Finally, the RSII model was extensively tested on three public datasets. The results showed that compared with seven of the latest and most advanced models, the RSII model improved the MSE metric by 8.81% and the MAE metric by 10.29%.
User Experience of Multi-Mode and Multitasked Extended Reality on Different Mobile Interaction Platforms

“Extended Reality (XR)” refers to a unified platform or content that supports all forms of “reality”—e.g., 2D, 3D virtual, augmented, and augmented virtual. We explore how the mobile device can support such a concept of XR. We evaluate the XR user experiences of multi-mode and multitasking among three mobile platforms—(1) bare smartphone (PhoneXR), (2) standalone mobile headset unit (ClosedXR), and (3) smartphone with clip-on lenses (LensXR). Two use cases were considered through: (a) Experiment 1: using and switching among different modes within a single XR application while multitasking with a smartphone app, and (b) Experiment 2: general multitasking among different “reality” applications (e.g., 2D app, AR, VR). Results showed users generally valued the immersive experience over usability—ClosedXR was clearly preferred over the others. Despite potentially offering a balanced level of immersion and usability with its touch-based interaction, LensXR was not generally received well. PhoneXR was not rated particularly advantageous over ClosedXR even if it needed the controller. The usability suffered for ClosedXR only when the long text had to be entered. Thus, improving the 1D/2D operations in ClosedXR for operating and multitasking would be one way to weave XR into our lives with smartphones.
Improving Knowledge Tracing via Considering Two Types of Actual Differences From Exercises and Prior Knowledge

For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict learners' performance instead of exercises. This choice ignores the actual difference among exercises, which leads to the KT model's inability to explore the rich information at the exercise level, thus, weakening the KT model's performance. Second, the actual difference in prior knowledge is neglected. Therefore, in-depth research of these two types of the actual differences offers the possibility to improve the prediction performance of KT further. To this end, we propose a fine-grained knowledge tracing model (FGKT) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to capture these two types of actual differences. We first obtain the exercise representations by considering the corresponding knowledge concepts and actual difference among exercises. Then, an effective attention mechanism is designed to capture the relevance between assessment exercises and historical interactions to acquire individual prior knowledge. Finally, we evaluate the proposed FGKT model on several available benchmark datasets. The experiment results show that our model surpasses benchmark KT models by up to 7% in prediction performance on the latest ASSISTments dataset.
Personality Trait Detection Using Handwriting Analysis by Machine Learning

Handwriting analysis with a computer using artificial intelligence (AI) is better than conventional methods of handwriting analysis. This can be termed based on different aspects such as speed, accuracy, and better pattern identification than visual inspection. Moreover, we can say machine learning-assisted analysis on the computer is much more efficient and devoid of human errors. A system is designed here to automate the basic personality trait detection tasks using handwriting analysis of graphology by using convolutional neural networks (CNNs).
Research and Development of Dual-Core Browser-Based Compatibility and Security

Aiming at the current troubles encountered by enterprise employees in their daily work when operating business systems due to web compatibility issues, a dual-core secure browser is designed and developed in the paper based on summarizing the current development status of multi-core browsers, key difficulties and challenges in the field. Based on the Chromium open-source project, the design of a dual-core browser auto-adaptation method is carried out. Firstly, dual-core encapsulation technology is implemented, followed by a study of the core auto-adaptation algorithm, and then a core cookie sharing function is developed based on Hook technology. In addition, the security of the browser is reinforced by designing a cookie manager, adding behavior monitoring functions, and unified platform control to enhance confidentiality and security, providing a safe and secure interface for employees' work and ubiquitous IoT access. While taking security into account, the browser realizes the need for a single browser compatible with all business system web pages of the enterprise, enhancing the operating experience of the client. Finally, the possible future research directions in this field are summarized and prospected.
On the Performance of Deep Learning Methods for Identifying Abnormal Encrypted Proxy Traffic

Encrypted proxies, such as Shadowsocks and v2ray, are increasingly used to protect user privacy and circumvent censorship. However, the encryption proxies used by some projects may be subject to configuration error or adversary attack makes the encryption ineffective. The resulted abnormal proxy traffic may expose the user’s real network behavior, resulting in user privacy or confidential information leakage. Meanwhile, it is important for network security regulators to identify specific user behaviors from normal encrypted proxy traffics. However, little effort has been put on fingerprinting the encryption validity of proxy traffic. To this end, we employ several typical deep learning methods including Long Short-Term Memory(LSTM), Convolutional Neural Network (CNN) and CNN-LSTM to identify proxy traffic, and investigate the performance and the impact of the sample sizes to these methods. A dataset including normal and abnormal proxy traffic from real network environments is generated to evaluate the performance. Extensive experimental results demonstrate that the mentioned deep learning methods can identify abnormal encrypted proxy traffic with the accuracy up to 99.77%. Moreover, LSTM outperform other DL methods on indentifying the specific user behaviors of normal encrypted proxy traffic.
Raspberry Pi-Based IP Public Address, CCTV, and Access Control System for Educational Purposes

This paper presents a cooperative project developed by two technical vocational schools. The project consists of the design and implementation of a prototype of an IP Public Address, closed circuit TV (CCTV) and access control system that uses the “Raspberry Pi” single-board computer as control system and non-electronic parts made with a 3D printer. The aim of the project is training students in maintenance of Voice over IP, audio and video. The project has been funded by the regional government Xunta de Galicia.
Performance loss rate comparison between satellite and ground-based sensors for normalization

Understanding and quantifying the performance loss rate (PLR) of PV plants is crucial for predicting energy production, improving future designs and construction practices and optimizing maintenance activities. One difficulty in PLR analysis is ensuring irradiance sensor accuracy, since unaccounted for sensor drift can cause a perceived shift in performance similar in magnitude to typical PLR values. This study compares the calculated PLR values for a set of 24 large-scale plants (calculated for each of the 1132 inverters) using ground-based, satellite-based, and clear sky irradiance to examine the impact.
Information technology basic course design based on Moodle platform

Information technology plays a very important role in improving teaching effect and promoting education informatization. Cultivating talents with computer literacy through information technology education is gradually becoming a hot spot of educational development. Exploring a curriculum design platform with high efficiency, low technical threshold and simple operation has undoubtedly become the most urgent need for the development of education informatization. Therefore, this paper uses the Moodle platform to design the basic course of information technology, and meets the students’ learning needs by integrating teaching resources and giving full play to the platform’s teaching advantages. Through the platform teaching feedback experiment and teaching effect experiment, this paper analyzes whether the use of Moodle platform for information technology course teaching can improve learning interest and learning ability. The self-learning ability was improved, 76.39% of the students improved their collaborative learning ability, and 68.06% of the students improved their information processing ability.
Secret Key Distillation with Speech Input and Deep Neural Network-Controlled Privacy Amplification

We propose a new high-speed secret key distillation system via public discussion based on the common randomness contained in the speech signal of the protocol participants. The proposed system consists of subsystems for quantization, advantage distillation, information reconciliation, an estimator for predicting conditional Renyi entropy, and universal hashing. The parameters of the system are optimized in order to achieve the maximum key distillation rate. By introducing a deep neural block for the prediction of conditional Renyi entropy, the lengths of the distilled secret keys are adaptively determined. The optimized system gives a key rate of over 11% and negligible information leakage to the eavesdropper, while NIST tests show the high cryptographic quality of produced secret keys. For a sampling rate of 16 kHz and quantization of input speech signals with 16 bits per sample, the system provides secret keys at a rate of 28 kb/s. This speed opens the possibility of wider application of this technology in the field of contemporary information security.
Intelligent meteorological information acquisition service platform based on Internet of Things

Although China’s meteorological departments have established a considerable number of national meteorological information monitoring networks, the information gap between meteorological departments, agricultural experts and ordinary farmers makes it impossible for them to accurately determine the objectives of agricultural production and improve the quality and efficiency of standard weather forecasts. This document addresses these issues. Therefore, this paper discusses the meteorological data collection method, collection equipment, crop fertility knowledge base and service forecast model based on the internet of things (IoT), real-time sensor data and the national integrated meteorological information sharing platform (CIMISS), develops meteorological data collection equipment suitable for small-scale agricultural environment, and develops an intelligent IoT service platform for integrated meteorology. The system has been demonstrated in a city meteorological bureau, and the results show that it can provide technical support for efficient planting, accurate management and customized services of agricultural production.
Priority-First Search and Mining Popular Packages

By the package design problem we are given a set of queries (referred to as a query log) with each being a bit string indicating the favourite activities or items of customers and required to design a package of activities (or items) to satisfy as many customers as possible. It is a typical problem of data mining. In this paper, we address this issue and propose an efficient algorithm for solving the problem based on a new tree search strategy, the so-called priority-first search, by which the tree search is controlled by using a priority queue, instead of a stack or a queue data structure. Extensive experiments have been conducted, which show that our method for this problem is promising.
Improve Maintenance by Reusing and Refining Previous Maintenance Cases

A three-step decision process using case-based reasoning to support maintenance preparation is proposed. By reviewing historical maintenance records based on case similarity, the best previous maintenance solutions can be reused for current and future equipment failures. A decision process is proposed to reuse solutions, including an improvement step to increase the maintenance accuracy of the solutions. The proposed process mitigates the difficulty of reusing previous successful maintenance solutions. The study follows a prescriptive approach. A case company was used to assess and evaluate the proposed process regarding its feasibility in an industrial context. Contrary to the current known approaches presented in research, the proposed decision process enables the reuse of complete work packages rather than single activities, the evaluation of previous solutions based on accuracy, and accounting for low-quality data otherwise seen as a barrier to the reuse of activities. The application at the case company shows a faster decision process with greater employee confidence in the applicability and accuracy of the prepared maintenance work. The proposed method also supports new employee training and ensures continuous improvement in maintenance, based on the evaluation of the success of previously prepared maintenance.
Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution; and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain.
Evaluation of equipment storage reliability based on fusing of weak parts life data

The reliability or predicted storage life of equipment can be evaluated by testing the whole equipment. But the test cost is high and the test condition is harsh. To circumvent these problems, this paper proposes an evaluation method of equipment storage reliability based on data fusion technology of weak parts life. First weak parts life data can be obtained by weighted multi-source data based on analytic hierarchy process. Then the weak parts can be divided into two kinds of performance states: available and unavailable by using fuzzy performance state judgment method, the membership function is used to calculate the state membership degree of the weak parts, and the life data is converted to the probability value representing the performance state. On this basis, the reliability information fusion database of equipment typical weak parts need to be established. The overall reliability of equipment can be evaluated by Bayesian network combined with the Fault tree. Finally, the method is used to evaluate the storage reliability of an equipment and the effectiveness of the method is verified.
Recommendation Method of College English Online Mobile Teaching Resources Based on Big Data Mining Algorithm

The current teaching resource recommendation methods mainly recommend resources according to the relationship between users’ historical data and resources, do not consider the timeliness of data, and ignore the attenuation of users’ interest in learning resources, resulting in low accuracy and poor recommendation performance. To solve the above problems, in order to improve the accuracy of recommendation of College English online mobile teaching resources, this paper studies the recommendation method of College English online mobile teaching resources based on big data mining algorithm. In the big data teaching environment of College English online mobile teaching, data mining technology is used to mine the characteristics of users’ preference for resources. Time information is introduced into the collaborative filtering model integrating neural network to realize the recommendation of teaching resources. In the method test, the recommendation accuracy of the resource recommendation method is 97.26%, and the recommendation performance is significantly improved.
Design and Development of Modular Customised Ration Storage System Silo for Service Specific Applications during Peak Winters at High Altitude Area

Conceptualisation of design and development of modular and customised ration storage system for Army rations sustainable up to -40°C was carried out. The Indian Armed Forces operate under various climatic conditions, which are inhospitable for cooking, storing fresh/perishable food products and carrying rations for their survival during an emergency. Hence, there was an ever-existent demand for a customised, user-friendly ration storage system. Considering a number of user-friendly features, a system has been designed comprising modular unit structures with insulated wall panels containing Polyurethane foam (PUF) sandwiched between metallic and composite material claddings. An insulated structure is required to maintain the temperature at high altitudes during extreme low-temperature conditions. The temperature and humidity are regulated and maintained using solar panels and humidly modulators. The modular structure with individual temperature and humidity control is suitable for the safe storage and preservation of fresh produce, fruits and vegetables, and milk and animal products. In case of perishable commodities of Defense supplies inclusive of leafy and tender vegetables, juicy fresh fruits, egg, chocolates, meat carcasses stored in silo are safe as its insulated structure ensures no freezing even upto -25° Celsius hence protects them from quality damage due to subzero temperature exposure and abuse. Therefore, the normal convention of the expected shelf life for each such commodity was extended approximately double the normal shelf life expected. An extensive design process involving CAD modelling followed by mechanical load simulation and analysis resulted in a robust and capable system. Each compartmentalised segment caters for an average one-ton material depending on the nature of the material. The system is suitable to store 3 tons of ration at a time to address the requirement of 100 individuals for 60 days in monsoon cut off and peak winter cut-off posts. Total energy consumption under fully load condition is approx. 2KW to maintain peak temperature gradient of 45-50°C.The outer surface of the structure can be camouflaged. The well-insulated structure ensures zero leakage and is sturdy to withstand a wind velocity of 60 km per hour and a standing snow load of 2 meters above it.