Reviewerly/exHarmony huggingface dataset:
A Comparative Machine Learning Study to Predict Drug Addiction in Bangladesh

Drug Addiction is one of the growing threats all over the world. According to Dhaka Tribune, more than 7.5 million people are addicted to drugs in Bangladesh. There are a lot of differences between a drug-addicted and a non-addicted person on health condition, social life, personal life, and familial life behaviors. So, steps should be taken to prevent drug addiction with proper curative issues. In this paper, we dig for the influential factors behind drug addiction and possible solutions to reduce the drug addiction rate. The research is held on the people of Dhaka, Bangladesh. Most of the data of drug-addicted people are collected from ‘Drug Rehab’ and for non-addicted person data we have collected from different schools, colleges, and universities in Dhaka, Bangladesh. All are male and the age group of 17 to 45 years. Our primary data set is constructed including only 188 qualitative data. A total of 5 algorithms have been employed including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (SVM) and their results are compared. Among the algorithms Random Forest comes up with the highest accuracy of 97.3484%, XGBoost & Decision Tree Classifier delivers the accuracy of 96.2768% and 94.68%.
Engineering Adaptive Authentication

Adaptive authentication systems identify and enforce suitable methods to verify that someone (user) or something (device) is eligible to access a service or a resource. An authentication method is usually adapted in response to changes in the security risk or the user's behaviour. Previous work on adaptive authentication systems provides limited guidance about i) what and how contextual factors can affect the selection of an authentication method; ii) which requirements are relevant to an adaptive authentication system and iii) how authentication methods can affect the satisfaction of the relevant requirements. In this paper, we provide a holistic framework informed by previous research to characterize the adaptive authentication problem and support the development of an adaptive authentication system. Our framework explicitly considers the contextual factors that can trigger an adaptation, the requirements that are relevant during decision making and their trade-offs, as well as the authentication methods that can change as a result of an adaptation. From the gaps identified in the literature, we elicit a set of challenges that can be addressed in future research on adaptive authentication.
On decidability of concept satisfiability in Description Logic with product semantics

The aim of the present paper is to prove that concept validity and positive satisfiability with an empty ontology in the Fuzzy Description Logic IALE, under standard product semantics and with respect to quasi-witnessed models, are decidable. In our framework we are not considering reasoning tasks over ontologies. The proof of our result consists in reducing the problem to a finitary consequence problem in propositional product logic with Monteiro-Baaz delta operator, which is known to be decidable. Product FDL and first order logic are known not to enjoy the finite model property, so we cannot restrict to finite interpretations. Thus, in order to obtain our result, we need to codify infinite interpretations using a finite number of propositional formulas. Such result was conjectured in [10], but the proof given was subsequently found incorrect. In the present work an improved reduction algorithm is proposed and a proof of the same result is provided.
Requirements Engineering in the DevOps Era

Traditional requirements engineering does not benefit from modern software development techniques. The goal of this tutorial is to revisit requirements elicitation and to bridge the gap between traditional requirements engineering and modern software development. This tutorial will provide the attendees with concrete and immediately applicable guidance by demonstrating how to operationalize a fully-fledged toolchain going from user stories to automated acceptance testing using open-source tools.
Summary Generation Using Deep Learning

Summarization is the process of converting any given long text into a lossy compressed version while preserving the overall essence and meaning of the original given text. A problem in natural language processing is the task of summarizing a voluminous text. This research, lays emphasis on abstractive summarization of the text, a process which generates a coherent, short and meaningful text by learning the context from given input source text. This work is a preliminary attempt to generate abstractive summary. The proposed work uses the Attentional Encoder and Decoder based Sequence to Sequence Recurrent Neural Network model. The proposed research’s primary objective is to get insights on the working of the mentioned Sequence architecture and the intricacies of different subcomponents of the encoders and the decoders architecture working together so as to give a particular end result. The scope of this paper covers an experimentation of performing text summarization process using Encoder and Decoder based Sequential Learning Recurrent Neural Network design. Satisfactory summary results are obtained after the model is experimented and analyzed on the Amazon Fine Food reviews.
An Object Detection Algorithm Combining FPN Structure With DETR

In order to solve the problem of low detection accuracy of the DETR model for small and medium objects, an object detection algorithm with improved feature extraction combined with FPN structure combined with DETR is proposed. This method first extracts features from the original image through the improved Darknet53 network. In this process, the 104*104 size feature map after the first residual error in the second stage is additionally output as a fourth-scale feature map. Combine this feature map with the feature maps output from the original 3 stages to form 4 feature map outputs of different scales. Secondly, it uses FPN to down-sample and up-sample the feature maps of 4 scales, and to merge them to output 52*52 scales. Then, the feature map and the positional encoding are combined and input into the Transformer to obtain the data, and the category and position information of the predicted object are output through FFNs. On the COCO2017 data set, the accuracy has been improved compared with other models.
Construction of Teaching Behaviours Analysis Model for Smart Classroom

Smart classroom is an intelligent and efficient classroom based on the &#x201C;Internet +&#x201D; and the new generation information technologies such as big data and cloud computing. The teaching process of smart classroom contains a large amount of teaching behaviour data with the value of research and application. Based on the review of smart classroom teaching behaviours analysis and evaluation, this paper first compared classical classroom teaching behaviours analysis methods at home and abroad, and then proposed the construction principles and process, and designed the smart classroom teaching behaviours analysis model (SCTBA) to provide research foundation for the analysis and evaluation of teaching behaviours on smart classroom.
Multi - Level Online Learning and Reasoning for Self-Integrating Systems

Self-improving and self-integrating systems (SISSY) often employ runtime models to represent their state and environment, and reason upon them to determine the required adaptation logic for reaching their goals. However, most model-based approaches rely on static modeling languages and cannot handle runtime uncertainty (e.g. dynamically integrated resources) that requires online language extensions. In previous work, we proposed an approach to extend the system's modeling language with new monitoring and action dimensions. However, the solution generates a high number of new language elements, slowing down the reasoning process for large systems. In this position paper, we propose a multi-level approach for extending the modeling language at runtime, and aim to provide online learning and reasoning at multiple levels of abstraction. Increasing the modeling abstraction decreases the number of concepts to reason about, hence improving scalability. We provide a preliminary validation of this proposal by detecting novel abstract dimensions from monitoring data from the smart home domain.
Design of verification platform for FPGA shape selection algorithm and its application in grain classification

With the rapid development of FPGA image algorithm, the verification of FPGA image algorithm is more and more important. This paper presents a fast verification platform for shape selection. Different from other methods, the verification platform can generate testbench files quickly and verify simulation results intuitively. In this paper, through the verification of FPGA shape selection algorithm in grain classification, it proves its superiority. The result shows that the verification platform can effectively verify the defects and shorten the development time of FPGA shape selection algorithm. Therefore, the verification platform can be used as a powerful tool to optimize and upgrade the FPGA shape selection algorithm.
Equivalent Modeling of Microgrids Based on Optimized Broad Learning System

The DC microgrid is an important structure of microgrids. Aiming at the problem of the grid-connected DC microgrid modeling, a grid-connected DC microgrid equivalent modeling method based on the optimized Broad Learning System (BLS) is proposed. Taking the electrical parameter data of the grid-connected DC microgrid access point as the training data set of BLS, the equivalent model of the grid-connected equivalent model is constructed. In order to further improve the accuracy and generalization performance of the model, the shark smell optimization (SSO) algorithm is used to optimize the input weights and thresholds of the BLS. Furthermore, the shark smell optimization-Broad Learning System (SSO-BLS) algorithm is proposed. SSO-BLS is compared with RBF, BLS, BFO-ELM, and other algorithms. The results show that the grid-connected DC microgrid model based on SSO-BLS has good accuracy and generalization characteristics.
Drift Detection in Text Data with Document Embeddings

Collections of text documents such as product reviews and microblogs often evolve over time. In practice, however, classifiers trained on them are updated infrequently, leading to performance degradation over time. While approaches for automatic drift detection have been proposed, they were often designed for low-dimensional sensor data, and it is unclear how well they perform for state-of-the-art text classifiers based on high-dimensional document embeddings. In this paper, we empirically compare drift detectors on document embeddings on two benchmarking datasets with varying amounts of drift. Our results show that multivariate drift detectors based on the Kernel Two-Sample Test and Least-Squares Density Difference outperform univariate drift detectors based on the Kolmogorov-Smirnov Test. Moreover, our experiments show that current drift detectors perform better on smaller embedding dimensions.
Bayesian Modelling of Alluvial Diagram Complexity

Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.
The Application of Multimedia and Network Technology in Practical Writing Teaching of Higher Vocational Education

Abstract With the rapid development of global multimedia technology and Internet technology, the world has entered a new information age. The great development of the Internet industry, information equipment and intelligent mobile equipment has brought new impacts and opportunities to people’s lives and has a great impact on people’s daily life, work and learning. These also deeply affect and urge the educational progress and development of higher vocational colleges in China. The practical writing in higher vocational colleges is the basic course of higher vocational colleges, and the development of its teaching mode has been in the bottleneck for a long time. This paper aims to analyze the advantages and feasible measures of the combination of practical writing teaching and the advantages of multimedia and network technology in the information age, to express my own opinions on the development of the practical writing teaching mode in higher vocational education.
Leveraging Network Delay Variability to Improve QoE of Latency Critical Services

Even as cloud providers offer strict guarantees on the intra-cloud delay of requests for Latency-Critical (LC) Services, a high external network delay can result in a large end-to-end delay, causing a low user Quality of Experience (QoE). Furthermore, due to the variability in the external network delay, there is a disconnect between the user&#x2019;s QoE and the cloud guaranteed service level objective (SLO). Specifically, a request that meets the SLO, can have a high or low QoE depending on the external network delay. In this work we propose a usercentric End-to-end Service Level Objective (ESLO), an extension of the traditional cloud-centric SLO, that guarantees stricter bounds on end-to-end delay and thereby achieving a higher QoE. We show how the variability in the external network delay can be both addressed and leveraged to meet the ESLO and improve server utilization. We propose ESLO-aware extensions to the Kubernetes infrastructure, that uses information about the external network delay and its distribution - (a) to reduce the number of QoE-violating responses by using deadline-based scheduling at the service instances, and (b) to appropriately scale service instances with load. We implement the ESLO-aware framework on the NSF Chameleon cloud testbed and present experimental results demonstrating the benefit of the proposed paradigm.
Research on the Construction of College English Online education Resources Based on Cluster Analysis Algorithm

With the use and popularization of digital learning system, a large amount of original data is generated during the interaction between students and the system. Therefore, data mining technology can be used to extract useful information from these data, so as to improve the management, teaching and research efficiency of higher education institutions. This paper focuses on exploring the feasibility and construction methods of sharing English professors' network resources among universities, and puts forward that the establishment of college English network teaching resources based on cluster analysis algorithm needs the strong support and guidance of relevant education authorities, and the construction resources of network education are very important, so the guarantee mechanism should be improved. It is necessary to introduce market mechanism into the construction and maintenance of network teacher resources in colleges and universities, fully mobilize the enthusiasm of the construction of network teacher resources in colleges and universities, and promote the construction and long-term development of network teacher resources in colleges and universities.
Integrated Teaching Content Design of Programming Courses Based On Ability of Algorithm Thinking and Program Application

Combined with the characteristics of close connection between program design and data structure courses, this paper divides and combines the teaching contents again, designs the integrated teaching contents, forms a unified and coherent course system, enables students to study systematically, and improves the ability of algorithm thinking and program application.
The Study of Bending and Twisting Input Modalities in Deformable Interfaces

The deformable input provides users with the ability of physical operation equipment to interact with the system. In order to facilitate further development in flexible display interactive technology, we devised FlexSheet, an input device that can simulate the deformation environment. This paper presents two forms of deformation input, bending and twisting, with regard to three selection techniques. We conduct a controlled experiment to select discrete targets by combining two input forms and three selection strategies, taking into account the influence of visual feedback. Further, we use the deformation angle to reflect the degree of deformation and map it to the experimental variables. In accordance with the experimental results, we analyze the experimental performance under three evaluation indexes and prove the viability of our selection technology in bending and twisting input modes. Finally, we provide suggestions on the control level in bending and twisting input modes, respectively.
Statistical modelling of COVID-19 pandemic development applying branching processes

In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated.
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces

Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a large-scale, multi-track visual kinship recognition evaluation. For the fifth edition of RFIW, we continue to attract scholars, bring together professionals, publish new work, and discuss prospects. In this paper, we summarize submissions for the three tasks of this year's RFIW: specifically, we review the results for kinship verification, tri-subject verification, and family member search and retrieval. We look at the RFIW problem, share current efforts, and make recommendations for promising future directions.
ML-based classification of eye movement patterns during reading using eye tracking data from an Apple iPad device: Perspective machine learning algorithm needed for reading quality analytics app on an iPad with built-in eye tracking

Eye movements in reading can provide important information about readers' perception of texts and, with appropriate algorithms, about the level of their understanding of what is written. Both digital learning and professional training require processing of large volumes of information, mostly in the text format. Management and control of students' or trainees' levels of perception in reading can be accomplished with the use of technology, assessing attention, engagement, understanding, cognitive load and tiredness of readers. These metrics can potentially be inferred from eye tracking data during reading a text. For this task it is important to utilize features of mass-market consumer devices. For example, the latest version of iPad Pro with a standard iOS operating system has an embedded eye tracker, and thus it provides opportunities for mass adoption in the educational and training settings. Authors of this work built a stable algorithm for detecting saccades and fixations in noisy eye tracking data recorded by iPad Pro 11″ and achieved certain progress in applying a machine-learning algorithm for classifying eye movement patterns in reading. The results could be used for creating an interactive reader's assistant in the format of an iOS application.
Semi-Supervised Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a rapidly growing domain in natural language processing which is a fine-grained study. Within this broad field, most existing studies use large amounts of labeled data by deep learning methods. However, obtaining massive quantities of labeled data to train a deep neural network model is frequently time-consuming and laborious. In this paper, we focus on semi-supervised learning based on ACSA with few labeled data in restaurant reviews and scholarly paper reviews. In order to leverage information from unlabeled data, the semi-supervised learning method-Ladder network is proposed to fix the problem. Furthermore, the pre-trained language models BERT, ALBERT and Longformer are used for text pre-processing and feature extraction. Extensive experiments on both datasets demonstrate the superiority of the Longformer based Ladder Network compared with supervised learning methods and other semi-supervised learning methods including <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Gamma$</tex> -Model and VAT.
The Intelligent Agent NLP-based Customer Service System

In Nowadays, communication has been more and more important in our lives. It is difficult for us to complete many things without it. With the support of the developing technology, communication is able to become faster and easier. The human-to-machine communication is a creative application used in field of research and industry. To improve the interaction between human and machine, a communication system is specially designed. The technology of natural language processing is implemented in the system to handle the understanding and generation of the chatting language. For higher efficiency, the system is enhanced by designing in the multi-agent system. Which let agents deal with the detailed tasks in the process of NLP by interacting and integrating them together. The system is based on sending and receiving so that it is able to communicate asking question and responding answer. Provide a textbook to the Chatbot, it is capable to understanding the content of book. And then serve as a teaching assistant to help student solve their problems by answering their questions. In this thesis, we will reveal the design and implementation of different parts in the communication system. Which consists of natural language processing system, multi-agent system, user interface (Model-View-Controller Frame), and knowledge base. System is implemented by Java programme language.