Reviewerly/exHarmony huggingface dataset:
The Operation of Smart Online Teaching of College English on the Basis of the Advanced Technology of Computer and Information in Vocational Colleges

The teachers of college English and their students both have to stay at home and are separated from each other off the campus due to the emergence of the global epidemic of COVID-19 pneumonia. And there is a tremendous difficulty to organize the classroom teaching of college English. However, college English is a required and important course in vocational colleges of China and hence the teaching of college English can never afford to cease. As a result, the smart online teaching of college English is carried out and becomes popular on the basis of the Internet in vocational colleges of China, and it turns out that operation of the smart online teaching of college English has become successful as long as the good preparation is made by means of advanced technology of computer and information. And furthermore, the smart online teaching of college English also improves the teaching reform of college English course in vocational colleges of China.
Edge Intelligence-Based Object Detection System Using Neural Compute Stick for Visually Impaired People

There are around 16 million people who are blind, and this system is to support them for object detection and as well as person recognition in future. This is future of artificial intelligence. This system used Intel neural compute stick which has OpenVINO API and supports various frameworks like TensorFlow, Caffe, MXNet, ONNX, Kaldi, etc. This system can detect object from the frame correctly. For testing, implementation is done on raspberry pi 3B+ which uses Raspbian operating system, Intel’s neural compute stick, and Web camera. This can be artificial eye for blind people, and beauty of project is edge intelligence, without Internet and cloud. When data is analyzed and aggregated in a spot, where it is captured in a network is known as edge intelligence. There are many advantages of intelligence at the edge like it will minimize latency and help to reduce bandwidth, and cost reduction is also one of the important features addition to reducing threats which also minimizes duplication as well as improves reliability.
Application of Data Mining for Analysis and Prediction of Crime

Crime is a significant component of every society. Its costs and consequences touch just about everyone to a remarkable extent. About 10% of the culprits commit about 50% of the crimes (Nath in Crime Pattern Detection Using Data Mining. IEEE, 2006, []). Explorations that aid in resolving violations quicker will compensate for itself. But, due to the massive increase in the number of crimes, it becomes challenging to analyze crime manually and predict future crimes based on location, pattern, and time. Also today, criminals are becoming technologically advanced, so there is a need to use advanced technologies to keep police ahead of them. Information mining can be employed to demonstrate wrongdoing apprehension issues. Considerable research work turned out to be published earlier upon this topic. In the proposed work, we thoroughly review some of them. The main focus is on the techniques and algorithms used in those papers for examination and expectation of violation.
On syzygies of Segre embeddings of P1×P1

We construct a nonminimal graded free resolution of Segre embeddings of P1×P1, although we don’t compute all maps. We use this to prove an explicit formula for certain nonzero entries in the graded Betti table, at the end of the first row. We work over an arbitrary field k.
Indonesian Conjunction Rule Based Sentiment Analysis For Service Complaint Regional Water Utility Company Surabaya

PDAM (Regional Drinking Water Company) is a company that provides clean water. PDAM develops their services with by considering complaints, suggestions and complaints from users. Over time PDAM services users are increasing thus allowing the number of complaints to also increase and PDAM is impossible to analyze the complaint data using the manual data. In this research proposes ideas to analyze PDAM complaints data with rule based sentiment analysis and categorization methods. The rule based sentiment analysis in this research used twelve rules, where the uniqueness of this rule based is a detection conjunction. Indonesian conjunction detection is the first method available in Indonesia. Detection of conjunction is proposed to find out whether conjunction has an important influence in the meaning of a sentence. The result of sentiment analysis is a score from complaint sentence are negative, positive or neutral. And categorization is a method to provide sentence score including complaints on turbid, leaky water, leakage, meters, usage, or not getting water. An experiment sentiment analysis was conducted on 392 data containing conjunctions and have score manually sentiment. The accuracy value obtained used rule based with conjunctions detection increases 13% than rule based do not use conjunction detection. And the accuracy value of categorization on 100 complaint data are 84% true and 16% false. So for High accuracy values in Conjunction detection needs to notice the context of the sentence and word dictionary and in categorization must also notice to words and write priority categories in the program.
IWILDS'20

Web search is one of the most ubiquitous online activities and often used for learning purposes, i.e., to extend one's knowledge or skills about certain topics or procedures. The importance of learning as an outcome of Web search has been recognized in research at the intersection of information retrieval, human-computer interaction, psychology, and educational sciences. Search as Learning (SAL) research examines relationships between querying, navigation, and reading behavior during Web search and the resulting learning outcomes, and how they can be measured, predicted, and supported. IWILDS aims to provide a platform to the interdisciplinary SAL community, with the objective to bring together interested researchers, provide room for presentation and discussion of novel research insights, and to inspire future directions of SAL research.
Design of Automatic Solar Tracking System Prototype to Maximize Solar Energy Extraction

This research presents the design of an automatic solar tracking system for optimal energy extraction. A prototype system based on two mechanisms was designed. The first subsystem is the search mechanism (PILOT) which locates the position of the sun while the second mechanism (Intelligent PANELS or optimal energy extraction mechanism) aligns itself with the PILOT only if maximum energy possible could be extracted. The unique feature of this designed system is that instead of taking the earth as its reference, it takes the sun as a guiding source with an innovative alignment procedure for accurate and precise alignment between PILOT and PANEL so that maximum energy is extracted. The two systems are driven by a microcontroller ATMEGA 328P. At maximum, the solar tracker was perpendicular to the light source by 90 degrees.
Data Segmentation via t-SNE, DBSCAN, and Random Forest

This research proposes a data segmentation algorithm which combines t-SNE, DBSCAN, and Random Forest classifier to form an end-to-end pipeline that separates data into natural clusters and produces a characteristic profile of each cluster based on the most important features. Out-of-sample cluster labels can be inferred, and the technique generalizes well on real data sets. We describe the algorithm and provide case studies using the Iris and MNIST data sets, as well as real social media site data from Instagram. This is a proof of concept and sets the stage for further in-depth theoretical analysis.
Another Look at Privacy-Preserving Automated Contact Tracing

In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have been proposed, and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this article, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability, and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing Bluetooth Low Energy based ACT solutions, including centralized and decentralized ones. In addition, we examine the existing venue-based ACT solutions and identify their privacy and security concerns. Then, we propose a generic venue-based ACT solution and a concrete instantiation based on Bluetooth Low Energy technology. Our solution monitors users’ contacting history only in virus-spreading-prone venues and offers higher-level protection for both security and privacy than its predecessors. Finally, we evaluate our solution from security, privacy, and efficiency perspectives, and also highlight how to reduce false positives in some specific indoor environments.
A Decentralized System for Fair Token Distribution and Seamless Users Onboarding

Tokens are digital, transferable and programmable assets and one of the most promising tools offered by blockchains. They could enable a wide range of applications, from down to earth to futuristic. One of the main issues in achieving wide adoption of tokens is onboarding: main platforms require users to deal with specific tools such as wallets, transaction fees, key generation and storage.The most common solution to offer a familiar experience to naive users are custodial intermediaries, which have the important drawback of centralizing the process, and keeping users away from the intrinsic advantages of blockchains.In this paper we present a process for the distribution of digital tokens to end users, exploiting "physical" objects for initial distribution. The process is aimed at making onboarding of users not yet accustomed with blockchain tools and concepts maximally easy, in a secure and decentralized way.
Deployment of Facial Recognition Models at the Edge: A Feasibility Study

Model training and inference in Artificial Intelligence (AI) applications are typically performed in the cloud. There is a paradigm shift in moving AI closer to the edge, allowing for IoT devices to perform AI function onboard without incurring network latency. With the exponential increase of edge devices and data generated, capabilities of cloud computing would eventually be limited by the bandwidth and latency of the network. To mitigate the potential risks posed by cloud computing, this paper discusses the feasibility of deploying inference onboard the device where data is being generated. A secure access management system using MobileNet facial recognition was implemented and the preliminary results showed that the deployment at the edge outperformed the cloud deployment in terms of overall response speed while maintaining the same recognition accuracy. Thus, management of the automated deployment of inference models at the edge is required.
Combining Speech Features for Aggression Detection Using Deep Neural Networks

Predicting the intensity level of aggression is a challenging problem in surveillance applications. Since there are no trivial fusion rules or classifiers, we developed a fusion framework to accomplish this complex task using Deep Neural Networks. This framework used a low level that contains the audio-visual features, an intermediate level composed of a set of concepts (meta-features) and a high level which is a final evaluation of the multimodal aggression detection. In this paper, we study the prediction of multimodal level for aggression detection and both Context and Semantics meta-features. This prediction is based on the audio modality using sensor and semantic information. Using meta-features for the semantic part of speech, we show the added value of such extra-information on the fusion process when the situations are more complicated. We also propose to use different text-based features such as linguistic and word affect features that will provide significant results for predicting the two meta-features and the multimodal aggression level using Deep Neural Networks when they are fused with the acoustic features although the nature of spontaneous speech.
Adaptive 3D Interpolation of Images

In this paper a scheme of adaptive 3D interpolator, suitable with the structure of neighborhood picture elements in local area 2x2x2 is developed. The type of the interpolation changes adaptively depending on the presence or absence of contours in the 3D area of the studied fragment, which improves the visual quality. On this base an algorithm for adaptive 3D interpolation of halftone images, worked in Matlab environment for operating system Windows 7 is developed. The given experimental results from working of developed interpolator in different directions, based on a set of real test video sequences are obtained.
An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it “All-Search”. To reduce the complexity of All-Search, we propose “Itersplit”, a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.
The Implementation of Boyer-Moore Algorithm in WEB Based Computer and Informatic Terms Dictionary

A dictionary is a tool used to translate a term or word. The use of a dictionary is highly needed to ease the user in translating a term. It needs an application that can accommodate the needs of every user of an application that is easily accessible and can be used anytime and anywhere effectively. The application is in the form of a web-based dictionary of computer and informatics terms. Boyer-Moore algorithm is one of the algorithms of searching for precise and well-known strings that are used in matching single patterns. Besides, it is considered to be very fast in their performance. Boyer-Moore algorithm uses a string matching method from right to left by scanning character patterns starting from the rightmost character. By using 1501 vocabulary words, the implementation of the Boyer-Moore algorithm in translating the terms computer and informatics has an average processing time of 29,240106 milliseconds, and average memory usage of 0,392 megabytes.
Decentralizing Privacy-Preserving Data Aggregation Scheme Using Blockchain in Smart Grid

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. In recent years, a large number of privacy-preservation data aggregation schemes have been proposed for smart grid, which relies on trusted third party (TTP) or central authority (CA). If the TTP or CA fails, these schemes become insecure. Therefore, this paper proposes a smart grid data aggregation scheme based on blockchain, which does not rely on TTP or CA and achieves decentralization. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s electricity consumption data. The confidentiality and integrity of user data are guaranteed, which is convenient for billing and power regulation. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.
On the Desired Properties of Linear Feedback Shift Register (LFSR) Based High-Speed PN-Sequence-Generator

The linear feedback shift register (LFSR) based PN-sequence generator has found wide-ranging applications in network security, cryptography, compressive sensing, watermark, and so on. Therefore, in the literature, the PN sequences with different specifications have been introduced. Among them, the sequences with interleaved structure (time-multiplexed) have been widely used. However, most of the attentions have been paid to the length and speed of the PN-code-generator while the linear complexity (LC) while the statistic properties of the proposed sequences: have not been thoroughly discussed. In this paper, we will show that one needs to employ different effective tools to evaluate the performances of such sequences so that suitable sequences for application case can be correctly selected.
Financial Investment Recommendation and Decentralized Account Management

Know Your Customer (KYC) is performed by banks and financial institutions for customer verification. The process of KYC is tedious, redundant and time consuming since banks have to repeat this procedure for the same set of customers leading to heavy costs. A customer also has to undergo the same process multiple times. Further challenge faced by the customers is the time and fee related to interbank transactions. Not only this, but the hassle of managing all the different bank accounts is tiring and cumbersome. For an average customer, as if these challenges were not enough, comes the added issue of approaching a costly financial planner for investment recommendation. These escalating expenses demand a system which provides a way to complete the one-time KYC process along with investment recommendation and account management using a single interface. In this paper, we leverage properties of blockchain such as its tamper proof nature and non-repudiation for developing a decentralized network that ensures one-time KYC along with ease in inter-bank transactions. Further, the long-term retention capability of the RNN-LSTM model is used to analyze the time series data and predict the most suitable option for investment after calculating the risk-taking capacity of the user.
Research on Maritime Target Tracking for High Frequency Over-the-horizon Radar

High frequency over-the-horizon radar(HFOTHR) is an effective method to detect targets below the horizon without the limitation of curvature of the earth. Thus, it is of great strategic significance for all-weather, wide-range detection and surveillance of sea-surface, near-earth and low-altitude targets to maintain the maritime and air safety aross the Exclusive Economic Zone. With the increasing application of HFOTHR, the detection environment is more complicated and the requirement for real-time becomes more urgent. In order to solve the real time tracking problem of ships or flight targets at sea in multi-target clutter environment, we proposed a new scheme with combining the Extend Kalman Filtering(EKF) and the Joint Probabilistic Data Association(JPDA) which is implemented by a fast algorithm. It will be exhibited that the presented method can give promising results for different detection environment.
Network Fingerprinting: Routers under Attack

Nowadays, simple tools such as traceroute can be used by attackers to acquire topology knowledge remotely. Worse still, attackers can use a lightweight fingerprinting technique, based on traceroute and ping, to retrieve the routers brand, and use that knowledge to launch targeted attacks. In this paper, we show that the hardware ecosystem of network operators can greatly vary from one to another, with all potential security implications it brings. Indeed, depending on the autonomous system (AS), not all brands play the same role in terms of network connectivity. An attacker could find an interest in targeting a specific hardware vendor in a particular AS, if known defects are present in this hardware, and if the AS relies heavily on it for forwarding its traffic.
Yapay Zeka Algoritmalarindan Word2vec ile Müşteri Bildirimlerinin Kategorilendirilmesi : Categorizing Customer Notifications with an Artificial Intelligence Method Word2vec

Very important to resolve requests or complaints by directing the demands and complaints submitted by the customers to the correct business units in corporate companies. Automatically interpreting and transmitting such incoming information and texts according to the relevant business unit category eliminates a huge human resource burden, saving time and resources. In this study, it is aimed to make sense of the information in the forms that come to a corporate firm as a customer complaint or request and to convey it to the business units divided into certain categories. A system was tried to be developed to determine in which categories the files may be based on the words or the content of the texts by scanning the file. An automatic classification system was developed by evaluating the incoming information on a pre-trained data set belonging to certain categories. In this classification process, using the TensorFlow, Keras, and Torch libraries, which are among the deep learning libraries, with the help of the BERT model, the machine learning algorithms were tried to be automated with Word2vec. By using data mining pretreatment algorithms on the document or form in the study, the data is transformed into a workable structure. Keywords are weighted with this generated study data. Finally, with the help of machine learning algorithms, the text is classified according to the specified categories. The success of the obtained classification results is shown in detail.
An Ontological Approach to Structural Health Monitoring

In the current work, an ontological framework for structural health monitoring (SHM) is discussed. Ontologies are used in disciplines like knowledge engineering and natural language processing, but their structure and goals also fit the purposes of SHM. In SHM projects – as in all projects – many problems arise during knowledge sharing and application. Ontologies can deal with these problems and at the same time have more benefits for SHM processes, as their modularity may assist in extending and transferring knowledge. An SHM-specific ontology is constructed here and described; It contains many objects that can be used in the procedure of monitoring structures. The ontology can also be used as a database to store data acquired, but also serves as a knowledge-base for the current discipline's algorithms and methods. Further, having close connections to object-oriented programming, ontologies straightforwardly facilitate software development and reusability of their components. Certainly, the ontology can be used to save time during the application of SHM, but also can be applied to improve performance of existing methods, by finding within the ontology the best algorithm to fit the purpose of each method.