Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Based on Deep Learning Techniques

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.
Sentiment Analysis of Amazon Book Review Data Using Lexicon Based Analysis

In this digital era, people are much more interested to buy and sell things in E-Commerce websites. Book is one of the mostly sold products on online by different online stores. Amazon is one of the leading online stores, which has million dollar transactions every year with its rich variety of products. In particular, book is one of the best-selling products of Amazon. Sentiment is therefore holding a very strong position in market analysis and future business development. Based on sentiment now it is possible to predict everything about business based on the user and other NLP related fields where opinion comes from someone. This research focuses on determining the quality of books as well as authors through the analysis of review comments and rating provided by the user. To determine that, lexicon based analysis is used here and using bag of word, the positivity and negativity of the review was determined. So that people can find the best book they need within a short time and without facing difficulty.
Implications of AI-based Grammar Checker in EFL Learning and Testing: Korean High School Students’ Writing

As research interests of the influence of Artificial Intelligence (AI) on language learning have been recently increasing, the validation and effectiveness of AI-based computer programs, especially grammar checkers, were investigated as learning and testing aids for both English learners and teachers. This study explored the validation of the AI-based grammar checker in terms of detecting grammatical errors and corrections across error types combining linguistic and cognitive strategies. In addition, the current study was intended to identify the difference between three human raters and the AI-based grammar checker in evaluating the grammaticality of EFL high school students’ writing. Also, this research attempted to investigate the effectiveness of using the AI-based grammar checker to reduce grammatical errors made by EFL learners in their writing revision process. Overall, the statistical and quantitative analysis revealed that an AI-based grammar checker is not yet valid for all error types for use. Moreover, the AI program is not a perfect detector of all error types compared to the human raters. However, it served a facilitative role to improve grammatical accuracy of English learners’ writing. Those findings shed light on how AI-based grammar checkers can be more developed and sophisticated, maximizing the benefit of AI technology for EFL learners and teachers in English writing classroom.
STUDENTS’ STRATEGIES IN LEARNING SPEAKING SKILLS AT SMP NASRANI 3 MEDAN

Speaking is one of the speaking skills possessed by students. Speaking skills have been trained from an early age, but the results of speaking learning are still far applied. Many students still experience difficulties in learning to speak. such as shame, fear and anxiety and lack of confidence inhibiting learning factors in speaking to students lacking motivation, poor student learning habits, mastery of students' vocabulary is still low, as for student learning strategies are one aspect that determines the success of students in speaking, students can have student learning strategies by memorizing vocabulary, believing themselves and improving vocabulary pronunciation. This research was conducted to identify the difficulty of speaking in English faced by students and find out the strategies they used to overcome these difficulties. This researcher used a qualitative descriptive method involving as many as 17 students in grade 9 at 3 Medan Christian Middle School, research results this shows that students have difficulty speaking in English namely lack of grammatical understanding, lack of vocabulary, incorrect pronunciation of words fearing the response of others feeling nervous in making mistakes, lack of self-confidence and shame in the strategy that students do is asking help to others trying to compose sentences in different ways by using gestures, taking the rules in positive thinking, can be concluded that students have a sense of difficulty in speaking English.
MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application

To address the problem of a reduced efficiency due to an increase of the search space, it has been proposed that priors could be added as constraints to the OS+ algorithm, which are Parent and children (PC) sets of each node obtained using the Max-min parent and children (MMPC) algorithm. Experimental results indicate that compared to other competitive methods, the proposed algorithm yields better solutions while maintaining high efficiency. Bayesian network (BN) sensitivity analysis is also proposed, which allows the network structure to be determined via a proposed ordering search method. We performed sensitivity analysis to determine the accuracy of the airborne avionics system, for which a simulation model is constructed to generate data samples, and the main effect of each error index is obtained using different sensitivity analysis methods. Experimental results indicate that the proposed BN method produces more accurate results when there is insufficient sample data, and this method can elucidate causal relationships that are present in the data.
Who Pays When Things Go Wrong? Online Financial Fraud and Consumer Protection in Scandinavia and Europe

Online financial fraud targeted at consumers through phishing attacks and identity theft, for example, is a growing problem. Because it can be difficult to recover losses from the person who committed the fraud, the loss will often remain with either the financial institution or the consumer. This paper’s research question relates to how losses following online financial fraud are and should be allocated between these two parties according to relevant Scandinavian and European law. For payment-transaction fraud, questions of loss allocation are regulated by national rules implementing the liability regime for unauthorised payment transactions under the payment services directive. For other financial services, these questions are resolved according to general rules on contract and tort. The analysis shows that consumers are often left to deal with the losses caused by online financial fraud. It is argued that the digitalisation of the financial services industry has in practice led to a shift in who bears the risk for attacks against financial institutions. This conflicts with the EU’s stated policy goals to provide strong consumer protection in the field of cybercrime. The paper concludes that a larger portion of the losses incurred from online financial fraud should be allocated to financial institutions. financial services, digitalisation, fraud, consumer protection, payment services, liability, unauthorised payment transactions, electronic signatures, cyber security
An Estimation Method of Photovoltaics Power Output using Satellite Images and Power Flow

There is a growing need to use photovoltaic (PV) technology to mitigate global warming and the depletion of fossil fuels. However, the high network penetration of PVs potentially lessen the stability and the reliability of electrical power systems in various ways. Under its high network penetration, monitoring of the unmeasurable power outputs of PVs is needed for the proper operations of an electrical power system. To suffice this need, a novel method was proposed to estimate the PV power output of the system using the measured power flow and solar radiation intensity estimated from satellite image in our previous work. However, it occasionally causes large estimation errors of the PV power output based on the wrong estimation of parameters induced by a large data sampling interval. To address this issue, in this paper, we improved our proposed method by introducing an parameter estimation procedure to exclude outliers of estimated values and increase the number of data used for estimation. We confirmed its enhanced accuracy using data observed from The Kansai Electric Power Co., Inc.
A Novel Approach of QMF Bank using Modified PSO Technique for Biomedical Image Applications

Introduction Work on efficient design of two-channel quadrature mirror filter (QMF) banks has gained significant attention over the past two decades, because they are used in wide range of applications like speech signals [1, 2], image processing [3] and communication systems [4, 5]. In the past,
PROGRAM MODULE FOR COLLECTION AND ANALYSIS OF SCIENTOMETRIC DATA OF FACULTY: POSSIBILITIES FOR INTEGRATION AND FURTHER DEVELOPMENT

The current stage of development of university libraries requires active participation in the processes of formation and development of an effective system of publication activity of university researchers. the constant collection and monitoring of data on the indicators of authors in Russian and international scientometric systems is an important component of this system. Collecting information and keeping it up-to-date in order to quickly respond to incoming requests along with the ability to output data in various structured formats, are inefficient and time-consuming in case of constant access to platforms and “manual” generation of information. The article presents the stages of creation and development of a software module created and functioning at the Herzen State Pedagogical University of Russia and allowing real-time output of structured current and comparative data on scientometric indicators of university teachers and researchers. Particular attention is paid to the prospects for the development of the created complex and the possibilities of data integration, including the earlier implemented synchronization of information with the profiles of teachers on the university website. The program module was created on the basis of database interaction with the API of scientometric resources such as Russian Science Citation Index, Scopus and Web of Science with the goal of further processing and systematization of the data obtained. The program module presented by the authors and created by the employees of the fundamental library, made it possible to create a single access point to the data which can be used to fill out any kind of documents, to monitor the implementati
Culture, education and philosophy: towards an effective integration into Nigerian school curriculum

Culture and education are inseparable. Effective education then should respond importantly to cultural needs. On the contrary, education in Nigeria appears not to serve most of our cultural needs as it is in most cases incapable of offering effective responses to social challenges. Using the analytical and prescriptive methods of philosophy, this research articulated some elements of culture based education to expose the conditions of Nigerian education. Some basic principles for an effective culture based education drawn from the characteristics of culture in our Nigerian context were outlined attempting to provide answers to related questions raised. The paper concluded that some aspects of our education do not reflect Nigerian culture as the case should be. Some relevant suggestions were made.Keywords: Culture, education, Philosophy, integration and effectiveness
Collection, Transportation, and Preservation of Biological Evidences for DNA Analysis

Application of DNA technology has revolutionized the field of forensic science. To obtain an appropriate result from any biological evidence, proper collection, transportation, and preservation/storage strategies should be employed for respective samples without manipulating the chain of custody (Fig. 3.1). Many factors contribute to the generation of a good DNA profile from a biological sample, i.e., the sensitivity of a PCR setup, rate and extent of degradation, purity, and DNA content of the sample. No biological evidence is resistant to degradation. Thus, careful collection and proper storage of the biological samples can provide a useful information when subjected to DNA typing analysis. Microbial or environmental degradation does not alter the genetic profile of a subject, but can impact adversely by obtaining a partial DNA profile from the sample. Evidences suitable for DNA examination are limited to biological samples; hence, a proper precaution for collection, transportation, and storage is required for these samples which has been discussed in details in this chapter.
Hand Signs Translator using Fuzzy Logic Method

Data on people with disabilities in the world reaches around 15% or more than one billion people in the world. People with mute and deaf disabilities have limited communication, they often communicate using hand sign language. But these hand signals are not widely understood but are only understood by people who study hand signals. In this research will apply the fuzzy logic method in the device to translate hand signals into A-Z alphabet letters. also added data search methods to speed up the process of translating hand signals to letters. The presentation value of success is 84% and the percentage error is 16% because 4 letters fail, the letters J, L, R and V.
Public-key quantum signature based on phase shift operation

A public-key quantum signature (QS) scheme is proposed, in which the phase shift is the private key and the quantum state after the phase shift operation is the public key. The signatory uses the private key to encode the quantum state, and uses the Bell measurement to generate the signature. The receiver performs the unitary operation according to the signature, and then compares the quantum state with the public key to verify the signature. Our scheme does not need a trusted arbitrator, and the signature can be verified by the receiver publicly. Compared to the existing arbitrated QS scheme, our scheme will be more practical.
Efficient Apriori Algorithm using Enhanced Transaction Reduction Approach

Apriori algorithm has fundamentally identified algorithm in association rule mining. The principle key concept of this algorithm is to discover interesting recurrent patterns between different groups of data. It is a straight forward, and an original algorithm, which employs a high iterative approach viewed as level-wise search. Nevertheless, this kind of algorithm has loads of downsides. Source on this algorithm, this research denotes the constraint of the classical Apriori algorithm in terms of computational cost in mapping the entire database for discovering frequent itemsets and signifies an enhancement of Apriori by minimizing the algorithm generation cost through enhancing transaction reduction approach. The enhanced Apriori when compared with the Apriori algorithm, the length of database scanning time reduces to 58 percent, and the generation time reduces approximately 89 percent of the original Apriori.
Upper Bounds for Cyclotomic Numbers

Let q be a power of a prime p, let k be a nontrivial divisor of q -1 and write e = (q -1)/k.We study upper bounds for cyclotomic numbers (a, b) of order e over the finite field Fq.A general result of our study is that (a, b) p) .More conclusive results will be obtained through separate investigation of the five types of cyclotomic numbers: (0, 0), (0, a), (a, 0), (a, a) and (a, b), where a = b and a, b ∈ {1, . . ., e -1}.The main idea we use is to transform equations over Fq into equations over the field of complex numbers on which we have more information.A major tool for the improvements we obtain over known results is new upper bounds on the norm of cyclotomic integers.
New Generation Artificial Intelligent Vending Machine System based on LoRaWan IOT Network

The Internet of Things and artificial intelligence are one of the mainstream of technological development in this era. Machine Learning now is often used for image recognition, and it is necessary to develop different models according to requirements in the past. This paper combines Artificial Intelligence and Internet of Things into the vending system, and system is presented in the form of ticket vending machines and veterans. The proposed system can identify the customer's age, gender data, and then sends the data to cloud server through the LoRaWan IOT Network to analyze the customers and merchandise sales. It will monitor the current status of the vending machine at the same time.
Video Anomaly Detection in Post-Procedural Use of Laparoscopic Videos

Endoscopic surgery leads to large amounts of recordings that have to either be stored completely or postprocessed to extract relevant frames. These recordings regularly contain long out-of-body scenes. This paper proposes to apply anomaly detection methods to detect these irrelevant scenes. A conditional generative adversarial networks (GAN) architecture is used to predict future video frames and classify these predictions with an anomaly score. To avoid the successful prediction of anomalous frames due to the good generalization capability of convolutional neural networks (CNNs) we enhance the optimization process with a negative training phase. The experimental results demonstrate promising results for out-of-body sequence detection with the proposed approach. The enhanced GAN training framework can improve the results of the prediction framework by a large margin. The negative training phase reduces the number of false negative (FN) predictions and is shown to counteract a common problem in anomaly detection methods based on convolutional neural networks (CNNs). The good performance in standard metrics also shows the suitability for clinical use.
Printed Media Versus Digital Media: Which One is More Effective?

Media plays an important role in the teaching and learning process.It is a tool that contains materials that students learn.This paper aimed at describing the result of experimental research which investigate the effect of two different media, printed and digital, used to teach literacy particularly reading competence to elementary school students in Buleleng Regency.The samples of 214 students were determined through intact group random sampling and divided by 108 for experimental group and 106 for control group.The data were analyzed descriptively and inferentially.Descriptive statistics analysis was conducted by measuring the central tendency, while inferential statistics analysis was carried out by using t-test and one-way ANOVA.Normality and homogeneity pre-requisite tests were done before it.The result proved that there was a significant difference between two groups treated differently.The digital media was more effective than the printed media used in teaching with dialogic reading strategy.
Data Visualization of University Laboratory Digital Asset Management System

At present, data visualization has become a supporting technology for analyzing data in various fields. This paper mainly visualizes the digital assets retained in the laboratory, mainly on the papers, awards, and team profiles. Etc. In this paper, ECharts is used as the main tool for mapping data to charts. Python scripts crawl the paper keywords to generate word clouds, and finally display the laboratory results with H5. A deeper understanding of the lab is achieved by visualizing the digital asset system.
A novel exponential loss function for pathological lymph node image classification

Recent progress in deep learning, especially deep convolutional neural networks (DCNNs), has led to significant improvement in natural image classification. However, research is still ongoing in the domain of medical image analysis in part due to the shortage of annotated data sets for training DCNNs, the imbalanced number of positive and negative samples, and the difference between medical images and natural images. In this paper, two strategies are proposed to train a DCNN for pathological lymph node image classification. Firstly, the transfer learning strategy is used to deal with the shortage of training samples. Second, a novel exponential loss function is presented for the imbalance in training samples. Four state-of-the-art DCNNs (GoogleNet, ResNet101, Xception, and MobileNetv2) are tested. The experiments demonstrate that the two strategies are effective to improve the performance of pathological lymph node image classification in terms of accuracy and sensitivity with a mean of 0.13% and 1.50%, respectively, for the four DCNNs. In particular, the proposed exponential loss function improved the sensitivity by 3.9% and 4.0% for Xception and ResNet101, respectively.
Classification of Medical Sensitive Data based on Text Classification

With the medical informatization, the privacy data of patients in medical big data is gradually increasing, and the sensitivity of data is increasing. Different from traditional medical sensitive data processing methods, text classification technology is proposed for medical information privacy protection, and sensitive data is classified. Experiments have shown the feasibility of using text classification techniques to classify medically sensitive data.
Vehicle Following in Intelligent Multi-Vehicle Systems Based on SSD-MobileNet

In this paper, we propose a vehicle following control algorithm based on object detection for intelligent multi-vehicle systems. We use the SSD-MobileNet as the object detection model, and collect more than 3,000 pictures of the target signs as the training dataset. By detecting the target signs from the camera on-board, we obtain the relative position between the front and follower vehicles. Based on the relative position, the following control of the intelligent vehicle is carried out. In particular, we use the discrete incremental PID control method for the longitudinal control of the vehicle, and apply the pure pursuit algorithm to calculate the vehicle steering angles. Experimental results show the effectiveness of the control algorithms.