Reviewerly/exHarmony huggingface dataset:
Slow is good

This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.
Implementation of Blockchain in Cross Border Money Transfer

The focus of the paper is to conduct research on the implementation of blockchain in the cross-border money transfer. The problem being faced in the current scenario in overseas money transfer is remittance that results in high transaction fees, no 24*7 availability, lack of transparency and name a few. Blockchain provides a platform which vanishes these problems providing a stable and scalable platform for cross border asset transfer. The distributed ledger is promising in every aspect for these transactions which provide a better benefit to the financial sector.
IoT-based Fall Prevention and Detection for Senior Citizens, Physically and Intellectually Disabled

Research shows that occurrence of falls among senior citizens, intellectually and physically disabled people leads to major injuries and even death in emergency rooms, home, workplace, etc. This paper proposes an IoT-based fall prevention and detection system for such individuals to promote independent living without depending on others or the need to be monitored constantly, thereby increasing their quality of lives. Our system prototype detects a fall using a threshold based accelerometer algorithm and uses BLE modules deployed at strategic locations to prevent a fall. This alerts users to be more careful in those areas by generating audio message and even if a fall does occur, it notifies parents, caretakers and medical in-charge people via. E-mail and SMS so that they can do the needful further.
Query Expansion for Myanmar Information Retrieval Used by WordNet

Myanmar information retrieval (MIR) is the system of searching for the information in a document, searching for documents themselves, and also searching for texts in databases, which is written in Myanmar language. Unfortunately, many problems remain in MIR, such as word segmentation, indexing, semantic ambiguous words and many others. This paper proposes a method of semantic query expansion by using WordNet to resolve semantic ambiguous words problem in MIR. The experimental results showed improvements in evaluating information retrieval potential for the Burmese language with 65.4% precision, 78.9%recall and 70.96% F-measure.
Review of Intellectual Video Surveillance Through Internet of Things

Surveillance is a vital utility in all places to enable the provision of a secure environment for the well-being of living folks and can be effectively achieved with the help of a video. Industries, along with researchers, are endeavoring hard to overcome the difficulties seen in the implementation of a mature Internet of Things (IoT) framework for a video surveillance application. Video surveillance and the IoT are converged for ensuring compactness for the nodes of video surveillance, ease of time, and high efficiency, and for providing video surveillance over Internet of Things (VS-IoT). This chapter highlights the analysis of VS-IoT and articulates the important stages of enactment. Added to that, it also depicts the contemporary technologies and challenges present in each stage, so that it could lead the research of successful intelligent video surveillance enactment.
Intelligent Voice-based Authentication System

This paper presents an intelligible voice based authentication system for IoT devices. As voice changes over time due to different characteristics and other environmental effects, limited features based training approaches may not provide the desire level of performance. The accuracy of the system is also dependent on several other factors like- the control over recording equipment, recording conditions, cooperation from speaker, etc. Therefore, this work analyzes the voice variation of the speech over varied conditions to address the long term speaker variability to enhance the performance of the voice based authentication system. The integration of IoT technology with voice based communication technology may help users to make their systems work based on voice commands instead of typed commands. A number of tests were also conducted to show the effectiveness of the presented model under varied conditions.
Second-order topological insulator in a coinless discrete-time quantum walk

Higher-order topological insulators not only exhibit exotic bulk-boundary correspondence principle, but also have an important application in quantum computing. However, they have never been achieved in quantum walk. In this paper, we construct a two-dimensional coinless discrete-time quantum walk to simulate second-order topological insulator with zero-dimensional corner states. We show that both of the corner and edge states can be observed through the probability distribution of the walker after multi-step discrete-time quantum walks. Furthermore, we demonstrate the robustness of the topological corner states by introducing the static disorder. Finally, we propose a possible experimental implementation to realize this discrete-time quantum walk in a three-dimensional integrated photonic circuits. Our work offers a new route to explore exotic higher-order topological matters using discrete-time quantum walks.
Short Paper: Swarm Intelligence Amplifies the IQ of Collaborating Teams

In the natural world, Swarm Intelligence (SI) is a well-known phenomenon that enables groups of organisms to make collective decisions with significantly greater accuracy than the individuals could do on their own. In recent years, a new AI technology called Artificial Swarm Intelligence (ASI) has been developed that enables similar benefits for human teams. It works by connecting networked teams into real-time systems modeled on natural swarms. Referred to commonly as "human swarms" or "hive minds," these closed-loop systems have been shown to amplify group performance across a wide range of tasks, from financial forecasting to strategic decision-making. The current study explores the ability of ASI technology to amplify the IQ of small teams. Five small teams answered a series of questions from a commonly used intelligence test known as the Raven's Standard Progressive Matrices (RSPM) test. Participants took the test first as individuals, and then as groups moderated by swarming algorithms (i.e. "swarms"). The average individual achieved 53.7% correct, while the average swarm achieved 76.7% correct, corresponding to an estimated IQ increase of 14 points. When the individual responses were aggregated by majority vote, the groups scored 56.7% correct, still 12 IQ points less than the real-time swarming method.
A Browsing Approach to Explore Web Image Search Results

Image objects are frequently used online available multimedia content due to their extensive information delivering power. The rapid development of image search engines fulfills the user's image information needs. Traditionally the unsatisfactory linear presentations of image results often lead issues mainly related to browsing, exploration, and visualization. The exploration of image results allows us to navigate, organize, and compare the results. Navigation mainly involves browsing, which will enable users to find the desired image results satisfactorily. Due to the linear presentation and browsing approaches, the current image results exploration is inefficient. In this research, an approach is proposed to browse and explore image results while reducing the associated reachability issues. The approach utilizes the multimodal graph for non-linear exploration of web image results. The similarity relationships in the graph are further exploited to partition the groups into multiple clusters, whereas multimodal similarity relationship deals with textual and visual information associated with image results. Moreover, a search tool is also built to provide an interactive search user interface design to explore the image results in a usable way.
Evolution of Machine Learning Algorithms on Autonomous Robots

In this paper, an overview of the use of machine learning algorithms on autonomous robotic systems is presented. The concept of hardware and software integration and development is explained through a small robot such as Turtlebot used in the examples taken. This paper presents a survey of how machine learning can be utilized on a hardware platform and is the initial stage of the research work. This is a work in progress and the next stage is using this literature survey towards developing research towards a comparison of machine learning algorithms on the Turtlebot.
Quantum Codes from the Cyclic Codes Over $$\mathbb {F}_{p}[v,w]/\langle v^{2}-1,w^{2}-1,vw-wv\rangle $$

In this article, for any odd prime p, we study the cyclic codes over the finite ring $$R=\mathbb {F}_{p}[v,w]/\langle v^{2}-1,w^{2}-1,vw-wv\rangle $$ to obtain the quantum codes over $$\mathbb {F}_{p}$$. We obtain the necessary and sufficient condition for cyclic codes which contain their duals and as an application, some new quantum codes are presented at the end of the article.
Incorporating Supervised Learning Algorithms with NLP Techniques to Classify Bengali Language Forms

Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Pair Extraction of Aspect and Implicit Opinion Word based on its Co-occurrence in Corpus of Bahasa Indonesia

In implicit opinion sentences containing implicit opinions, the use of traditional dictionary-based method is no longer effective, since there is no any opinion word serving as a clue to identify the sentiment value. This study proposes an approach to obtain pairs of aspect and opinion word from sentences containing implicit opinion. Two methods are developed this approach. The first method serves to separate clauses from a compound implicit opinion sentence and refine its corresponding parse-tree from clauses as a result of the separation. The second method determines the opinion word from the implicit opinion clause. This method uses co-occurrence of aspect and opinion word based on a corpus of explicit opinion sentences. In this study, an initial experiment was conducted using a data set containing 30 implicit opinion sentences with 76 clauses. The proposed approach is capable of detecting pairs of aspect and opinion from the given corpus of Bahasa Indonesia.
Reinforcement Learning for Decentralized Stochastic Control

In the study of stochastic dynamic team problems, analytical methods for finding optimal policies are often inapplicable due to lack of prior knowledge of the cost function or the state dynamics. Reinforcement learning offers a possible solution to such coordination problems. Existing learning methods for coordinating play either rely on control sharing among controllers or otherwise, in general, do not guarantee convergence to optimal policies. In a recent paper, we provided a decentralized algorithm for finding equilibrium policies in weakly acyclic stochastic dynamic games, which contain stochastic teams as an important special case. However, stochastic dynamic teams can in general possess suboptimal equilibrium policies whose cost can be arbitrarily higher than a team optimal policy's cost. In this paper, we present a reinforcement learning algorithm and its refinements, and provide probabilistic guarantees for convergence to globally optimal policies. The algorithms presented here are strictly decentralized in that they require only access to local information such as cost realizations, previous local actions, and state transitions.
IFML-Based Web Application Modeling

In order to solve the design problem of the increasingly complex web application front-end interface, this paper proposes a modeling method based on IFML to generate user interface. Firstly, it introduces the extension of IFML to web application, the advantages of the model conversion method and the development environment. Secondly, the specific method from IFML mapping to the design environment is given. Finally, the conclusions and prospects for the future are given.
Self-timed LUT Layout Simulation

The paper proposes Self-timed LUT for FPGAs. The following options are considered: Self-timed LUT with dual channel, spacer circuit and indicator; DC- Self-timed LUT; DNF - Self-timed LUT. Simulation LUT in the system of automated design of specialized (custom) integrated circuits Microwind.
Automatically Annotate TV Series Subtitles for Dialogue Corpus Construction

In recent years, the scarcity of dialogue corpus is becoming the bottleneck of Chinese dialogue generation systems. Although subtitles provide favorable material to construct dialogue corpus because of their abundance and diversity, lacking speaker information makes it hard to extract dialogues from subtitles directly. To utilize these resources, we proposed an improved method to automatically annotate bilingual TV subtitles with speaker and scene tags using their corresponding scripts. First, tags of speakers and scene boundaries in the scripts are mapped to the subtitles through an information retrieval method. Then, the mapping errors are detected with a convolutional network and corrected by heuristic strategies to improve the annotation quality. We applied this method on 779 bilingual subtitle files of 4 TV series and obtained a Chinese dialogue corpus Tv4Dialog <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> It is publicly available at https://github.com/zll17/TV4Dialog containing 260674 utterances. Experiment result shows that our method can achieve an accuracy of 94.62% on speaker tag annotation, improving nearly 12% on the previous state-of-the-art result.
Prefiltering Approach for Web Service Selection Based on QoS

The web service has widely spread in recent years which is showed by the large number of published web services. However, users may not always find their needs in one web service which has consequently spawned a composition of web services. This composition lead to many task difficulties including the selection of web services, which also consists of choosing the best QoS-based services among other similar services provided. From this perspective, there are several approaches to the selection of web services, however, this remains deficient at the level of execution time due to the rage number of competing web services. In this paper, we propose a new selection approach based on prefiltering and Skyline, this approach is tested and improved using a real dataset. We used two steps to solve this problem, prefiltering and Skyline. The first step is the use of K-Means clustering to bundle web services with similar Quality of Service (QoS) properties under one umbrella is explored. The second one is defining the most dominant web service using the Branch-and-Bound Skyline algorithm.
A Survey on Digital Forensics Phases, Tools and Challenges

The digital technologies are grown in such way that they are also leading to growth in digital crimes. The aim of digital forensics is to collect, analyze and present evidence related to digital crime and in front of court of law. There are several methods and tools in evidence collection and analysis. This paper gives a survey on digital forensic evidence collection and analysis. Recently, the cloud forensics has become very interesting area of research, as cloud computing is a collection of computer resources and services that can be easily implemented and managed, generally over the Internet. It also discusses about the challenges to be faced in evidence collection and its analysis.
Application of Blockchain Technology in Tax Collection and Management

Blockchain is one of the most significant technological innovations in information technology in recent years. It not only brings updates and iterations of information technology, but also may change the way in which big data is used. Due to the unique advantages and application prospects of blockchain, research on the application of blockchain in various fields is being actively carried out, and tax collection and management is no exception. This article mainly studies the application of blockchain technology in tax collection and management. Based on a review of the current research on the application of blockchain technology in the field of taxation, this paper analyzes the possible limitations of the application of blockchain technology in tax collection and management based on the technical characteristics of the blockchain and the current application situation and its own attributes It also puts forward the directions and possible ways to promote the application of blockchain in tax collection and management in China. The research in this paper found that the average tax evasion rate of VAT in 22 developing countries reached 30.3%. Even in developed countries with a strong sense of tax payment, the proportion of tax paid according to law is only 83.9%. The research results of this paper are of great significance to the application of blockchain technology in tax collection and management.
Automation of Attendance System Using Facial Recognition

The importance of a properly maintained attendance system is very high. Although many systems are already existing, there are many loopholes present. This paper presents an attendance system using face recognition, which is the best foot forward to reduce the loopholes encountered by these systems. The Attendance system using face recognition consists of two phases, face detection and face recognition. The performance of detection algorithms such as Viola Jones and deep learning-based detection was compared and deep learning-based detection was preferred. On the recognition front, deep learning-based face recognition was used and optimum results were obtained. It was found that dlib using Convolutional Neural Networks (CNN) yielded better results than dlib using Histogram Oriented Gradients (HOG) because HOG only detects a frontal image, while CNN detects faces from all angles and ensures no discrepancies during face detection and recognition. Also, it was observed that the speed of training datasets was higher in CNN as it uses GPU as compared to HOG, which uses CPU for training the dataset as well as recognition.
Distilling Knowledge for Non-Neural Networks

Deep neural networks (NNs) have shown high inference performance in the field of machine learning, but at the same time, researchers require their speeding-up and miniaturization methods due to the computational complexity. Distillation is drawing attention as one of the ways to overcome this problem. NNs usually have better expression power than its learning ability. Distillation bridges the gap between expressive power and learnability by training a small NN with additional information obtained from a larger already trained NN. This gap does not exist only in neural networks but also in other machine learning methods such as support vector machine, random forest, and gradient boosting decision tree. In this research, we propose a distillation method using information extracted from NNs for non-NN models. Experimental results show that distillation can improve the accuracies of other machine learning methods, and especially, the accuracy of SVM increases by 2.80%, 90.15% to 92.95%.
Performance Analysis of NB Tree, REP Tree and Random Tree Classifiers for Credit Card Fraud Data

Credit card fraud has been growing tremendously mainly in recent years. Due to this inconceivable occurrences, along with financial losses of banks, companies, NGOs and personal accounts, the reputation of the organizations is at stake. In this respect to detect fraud, accuracy has become very important to avoid harassing innocent customers. In this paper, we elicit three tree-based classifiers (NB Tree, REP Tree, and Random Tree) regarding the German credit card data set. We have combined this tree classifier with ensemble techniques to get better accuracy. Using the Resampling, Random Committee and Logit Boost, we were able to increase the performance of those classifiers.
Minimum Time Optimal Control of Second Order System With Quadratic Drag and State Constraints

The problem of mixed discrete-continuous task planning for mechanical systems, such as aerial drones or other autonomous units, can often be treated as a sequence of point-to-point trajectories. The minimum time optimal solution between points in the plan is critical not only for the calculation of the trajectory in cases where the goal has to be achieved quickly but also for the feasibility checking of the plan and the planning process itself, especially in the presence of deadlines and temporal constraint. In this work, we address the minimum time problem for a second-order system with quadratic drag, under state (velocity) and control (acceleration) constraints. Closed-form expressions for the trajectory are derived and the optimality is proven using the Pontryagin Maximum Principle. Simulations supporting the results are provided and compared with those of an open source academic optimal control solver.