Reviewerly/exHarmony huggingface dataset:
APLIKASI MODEL RANTAI MARKOV UNTUK MENGANALISIS TINGKAT KENYAMANAN DI KOTA MAJENE BERDASARKAN TEMPERATURE HUMIDITY INDEX (THI)

This study applied the Markov chain model on the daily temperature and relative humidity data that was collected from the Meteorology and Geophysics Agency station in Majene district for the period 1983 to 2011. This study aims to analyze the comfortable level category in the Majene city based on the Temperature Humidity Index by calculating the probability of steady-state, the mean residence time and the mean first passage time. Categorizing the level of comfortable which is based on the Temperature Humidity Index consists of three categories, namely the comfortable, quite comfortable and uncomfortable. The trend of comfortable levels in the Majene city from 1983 to 2011 was fluctuated in the categories of quite comfortable and uncomfortable. Uncomfortable category occurs in October and November each year. The steady-state probability values indicates that the quite comfortable category has the highest chance of appearance, which is around 70%, and the comfortable category has the smallest chance of appearance, which is only about 5%. Meanwhile, the mean residence time and the mean first passage time indicate that the quite comfortable category have the longest duration of occurrence, which is around 5 days, and has the shortest duration to recur after occurring in the previous event, which is around 1.43 days.
Familiar words but strange voices: Modelling the influence of speech variability on word recognition

We present a deep neural model of spoken word recognition which is trained to retrieve the meaning of a word (in the form of a word embedding) given its spoken form, a task which resembles that faced by a human listener.Furthermore, we investigate the influence of variability in speech signals on the model's performance.To this end, we conduct a set of controlled experiments using wordaligned read speech data in German.Our experiments show that (1) the model is more sensitive to dialectical variation than gender variation, and (2) recognition performance of word cognates from related languages reflect the degree of relatedness between languages in our study.Our work highlights the feasibility of modeling human speech perception using deep neural networks.
Accuracy and complexity as connected growers in L2 English speech at secondary school – a case study of a good, average, and poor language learner

Complex Dynamic Systems Theory (CDST), which originated in the natural sciences, has recently been applied to second language acquisition, underlining the interdisciplinary character of this humanistic discipline. According to this theory, language is a complex dynamic system consisting of subsystems which develop in a non-linear way, forming different kinds of supportive, competitive, or conditional relationships. What is more, these subsystems compete for the learner’s limited resources, which causes trade-offs within and between language subsystems, such as complexity, accuracy, and fluency, especially in speech. The present paper constitutes a part of a short series of articles which present different aspects of the same longitudinal case study on the development of L2 English speech at secondary school. The aim of this paper is to examine the relationships between language accuracy and a number of specific measures of syntactic complexity, i.e. general sentence complexity, subordination, coordination, and nominalisation; as well as lexical complexity, i.e. lexical density, sophistication, and variation, in the case of a good, average, and poor language learner at secondary school. In general, the results showed that the relationships between the selected variables fluctuated over time and often differed in the case of a good, average, and poor language learner.
Efficient System to Prevent Session Hijacking Attack on Servers using Proxy Server

HTTP is a stateless protocol thus we tend to use cookie to maintain session. For maintaining a session server creates a unique identifier to remember on going session for particular client. This distinctive identifier is nothing but a randomly generated text which is stored on client browser in the form of cookie. These cookies are generally created in the login process, after successful authentication process, server generates unique id for requested client and send it to client. Browser sends this cookie on every request where authentication of user is required. Authentication of cookie become temporary replacement of user password authentication for the entire session. Cookies are static in nature, they do not change in the session life time, because of this nature anyone can steal and use this cookie for their benefit. Use of cookies introduces a number of risks in security especially in session authentication. For secure communication use of HTTPS is not easy for those applications that are highly distributed due to performance and financial issues and HTTPS provide security at network only there are many ways were attacker can steal cookie by using different attack like cross site scripting attack, cross site tracing attack, domain related attack etc. Hence, creating a system using Concept of one-time dynamic cookie (OTDC) for authentication instead of HTTPS. OTDC will prevent various attacks on servers. A reverse proxy server with OTDC, IP Address, session ID and browser fingerprinting are used to prevent opponent from capturing session credentials. Setting up HTTPS to HTTP Reverse Proxy Server, Session time out implementation on Proxy server because "The less time you give your account to be cracked, the better for you." Generating log on attack detection and reporting to administrator.
Detect and Classify – Joint Span Detection and Classification for Health Outcomes

A health outcome is a measurement or an observation used to capture and assess the effect of a treatment.Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making.Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a pre-defined set of categories depending on an outcome that is mentioned somewhere in that text.However, this decoupling of span detection and classification is problematic from a modelling perspective and ignores global structural correspondences between sentence-level and wordlevel information present in a given text.To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification.In addition to injecting contextual information to hidden vectors, we use label attention to appropriately weight both word and sentence level information.Experimental results on several benchmark datasets for health outcome detection show that our proposed method consistently outperforms decoupled methods, reporting competitive results.
Correction to: Gaze Control in Law Enforcement: Comparing a Tactical Police Unit to Patrol Officers

A correction to this paper has been published: https://doi.org/10.1007/s11896-021-09443-0
The Sustainability and Profitability of Bitcoin Mining: A Time-Varying Granger Causality Approach

In this paper, we investigate the Granger causal relationships between Bitcoin mining, mining profitability and sustainability (the resultant energy consumption and electronic waste), where importantly we allow such relationships to varying over time. We consider three questions relevant to energy-usage, environmental and investors' perspectives. We find that increased mining difficulty not only has a significant effect on the power consumption used for Bitcoin mining from late 2018 to the middle of 2019 but also Granger causes the electronic waste generated from mining from January/February 2018 to April 2019. Moreover, we provide an explanation of why the deployment of massive computer power in mining for Bitcoin as the hash rate Granger causes miners' revenues during the entire sample period 2013-19.
توظيف ترکيبة مکعب الأسئلة في تنمية بعض مهارات التخطيط لدى أطفال الروضة من (5-6) سنوات Using Questions Dice Structure in improving some Planning Skills of the Kindergarten Children from (5-6) years

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A description and demonstration of SAFAR framework

Several tools and resources have been developed to deal with Arabic NLP.However, a homogenous and flexible Arabic environment that gathers these components is rarely available.In this perspective, we introduce SAFAR which is a monolingual framework developed in accordance with software engineering requirements and dedicated to Arabic language, especially, the modern standard Arabic and Moroccan dialect.After one decade of integration and development, SAFAR possesses today more than 50 tools and resources that can be exploited either using its API or using its web interface.
FORMATION OF RESEARCH COMPETENCE OF FUTURE TEACHERS IN THE CONTEXT OF MODERN REQUIREMENTS

The article considers the research competence of the future teacher as a requirement of modernity and an integral part of his professional competence. The analysis of the concept of “research competence” is carried out. The definition of the term “research competence of a teacher” is offered. The main components of the structure of research competence of the future teacher are determined: cognitive, motivational-value, personal-motivational, activity-operational. It is proved that the research competence of future teachers is the result of effective research activities of students, which develops the ability to research and is carried out within the educational process by means of cognition according to the logic of scientific research. The tasks of research work of students in a higher education institution are outlined. The directions of research activity of students of institutions of higher education are characterized: research work as an integral element of the educational process (educational research activity); research work carried out outside the educational process; scientific and organizational activities. Forms of organization of research work of students serving the formation of research competence of future teachers are presented. It is proved that the effective formation of a competent, competitive, capable of self-development and self-realization of the teacher-researcher is facilitated by the creation of an active scientific environment and modernization, in accordance with the competence approach, content, forms and methods of educational process.
A Color Image Blind Digital Watermarking Algorithm Based on QR Code

With the rapid development of network technology and multimedia, the current color image digital watermarking algorithm has the problems of small capacity and poor robustness. In order to improve the capacity and anti-attack ability of digital watermarking. A color image blind digital watermarking algorithm based on QR code is proposed. The algorithm combines Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). First, the color image was converted from RGB space to YCbCr space, and the Y component was extracted and the second-level discrete wavelet transform is performed; secondly, the LL2 subband was divided into blocks and carried out discrete cosine transform; finally, used the embedding method to embed the Arnold transform watermark information into the block. The experimental results show that the PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being attacked, its PSNR is more than 30dB and NC is more than 0.95. It is proved that the algorithm has good robustness and can achieve blind watermark extraction.
Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that’s why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.
A New View of Multi-modal Language Analysis: Audio and Video Features as Text “Styles”

Imposing the style of one image onto another is called style transfer.For example, the style of a Van Gogh painting might be imposed on a photograph to yield an interesting hybrid.This paper applies the adaptive normalization used for image style transfer to language semantics, i.e., the style is the way the words are said (tone of voice and facial expressions) and these are style-transferred onto the text.The goal is to learn richer representations for multi-modal utterances using style-transferred multi-modal features.The proposed Style-Transfer Transformer (STT) grafts a stepped styled adaptive layer-normalization onto a transformer network, the output from which is used in sentiment analysis and emotion recognition problems.In addition to achieving performance on par with the state-of-the art (but using less than a third of the model parameters), we examine the relative contributions of each mode when used in the downstream applications.
ESTIMATION OF DAILY ENERGY GAIN OF SOLAR TRACKING SURFACES BASED ON GEOGRAPHIC POSITION

The performance of solar systems depends on the effective incident solar energy on the capture surface. This factor is dependent on the geographic position, the orientation of the surface, atmospheric conditions, and other geo-climatic factors. Solar trackers, typically uni- and biaxial, are used to optimize the capture of solar radiation via movement of the plane of incidence of these devices. A reliable estimation of the energy gain of these trackers with respect to a fixed surface facilitates more accurate analyses of the feasibility, operation, and maintenance of solar ventures. In this work, a method for estimating the relative energy gain of uni- and biaxial solar trackers is developed. This approach applies to locations between the Arctic Circle and the Antarctic Circle, using geographic coordinates and the altitude of the location of interest, providing daily results throughout the year. Unlike empirical results or case studies, the proposed method applies to any location and has a low error. It can be implemented using iterative algorithms with a low computational cost and can be easily utilized by researchers and technicians in the field.
Error Analysis and the Role of Morphology

We evaluate two common conjectures in error analysis of NLP models: (i) Morphology is predictive of errors; and (ii) the importance of morphology increases with the morphological complexity of a language.We show across four different tasks and up to 57 languages that of these conjectures, somewhat surprisingly, only (i) is true.Using morphological features does improve error prediction across tasks; however, this effect is less pronounced with morphologically complex languages.We speculate this is because morphology is more discriminative in morphologically simple languages.Across all four tasks, case and gender are the morphological features most predictive of error.
Construct-Inference-Process (CIP) Framework for UX Model Building and EvaluationOver Time

This paper used a literature review strategy to investigate the approaches used in building user experience (UX) models and in evaluating UX over time. The paper reports that there is no uniform and consensus approach in the strategies used in developing user experience models and in evaluating UX over time. Researchers adopt divergent ways in building UX models and evaluating UX. This makes the development of a common approach difficult. It is also shown that there are three frequently employed approaches in the development of UX models and in the evaluation of UX over time. The three approaches are the construct, inference and process (CIP) approaches. The first two are associated with UX model development while the third is associated with UX evaluation over time utilizing the developed model. Researchers seldom use the three approaches combined together due to the rare use of longitudinal research in UX domain. Consequently, this paper strongly recommends and proposes the use of the combination of the three approaches to build UX models and to employ the developed model in evaluating UX over time.
A Lightweight Blockchain Framework for IoT Integration in Smart Cities

Smart cities heavily rely on technological enablers for their success, and more specifically on the IoT. This network, which will reach billions of components, now suffers from several constraints. In recent research, the blockchain has been proposed to provide answers on the limits of the centralized model, and on security. The integration of blockchain and IoT, however, still has issues that are being resolved, which are energy consumption, computing capacity, and storage capacity, due to the low capabilities of connected objects. In this article, we present our lightweight framework that we implemented for the integration of blockchain and IoT, and simulating on machines close to the configuration of the majority of current connected objects.
Optimized Design of Dynamic and Real-Time Cash Flow Statement Interface on Mobile Terminal

With the rapid development of the information technology era, mobile apps with different functions continue to emerge.In early 2020, affected by the new crown pneumonia epidemic, the number of users who use mobile apps to work online has also increased significantly.This requires that the interface design of mobile apps should be closer to user needs and focus on user experience.This article aims to optimize the cash flow statement attached table that can reflect the cash flow trends generated in the business activities, investment activities, and financing activities of the company.The data and information can assist the management of the company to discover, analyze, and solve problems in a timely manner, and then make Scientific and reasonable business decisions that are conducive to the development of the enterprise will advance its management functions from "after the event" to "in the event."Andthrough the analysis of eye movement trajectories, hot spots and eye movement experimental data, considering that the information users of the cash flow statement are mainly business managers, there must be differences between professional and non-professionals.The results obtained after the experiment are try to browse the original report is quite different, and the dynamic real-time cash flow statement on the mobile terminal is more inclusive.
A 2D Convolutional Gating Mechanism for Mandarin Streaming Speech Recognition

Recent research shows recurrent neural network-Transducer (RNN-T) architecture has become a mainstream approach for streaming speech recognition. In this work, we investigate the VGG2 network as the input layer to the RNN-T in streaming speech recognition. Specifically, before the input feature is passed to the RNN-T, we introduce a gated-VGG2 block, which uses the first two layers of the VGG16 to extract contextual information in the time domain, and then use a SEnet-style gating mechanism to control what information in the channel domain is to be propagated to RNN-T. The results show that the RNN-T model with the proposed gated-VGG2 block brings significant performance improvement when compared to the existing RNN-T model, and it has a lower latency and character error rate than the Transformer-based model.
Query Generation for Multimodal Documents

This paper studies the problem of generating likely queries for multimodal documents with images.Our application scenario is enabling efficient "first-stage retrieval" of relevant documents, by attaching generated queries to documents before indexing.We can then index this expanded text to efficiently narrow down to candidate matches using inverted index, so that expensive reranking can follow.Our evaluation results show that our proposed multimodal representation meaningfully improves relevance ranking.More importantly, our framework can achieve the state of the art in the first-stage retrieval scenarios.
Detecting Hate Speech and Offensive Language on Twitter using Machine Learning﻿

Toxic on-line content has become a serious issue in today's world because of Associate in Nursing exponential increase within the use of net by folks of various cultures and academic background. Differentiating hate speech and offensive language could be a key challenge in automatic detection of virulent text content. During this paper, we tend to propose Associate in Nursing approach to mechanically classify tweets on Twitter into 3 classes: hateful, offensive and clean. Victimization Twitter dataset, we tend to perform experiments considering n-grams as options and spending their term frequency-inverse document frequency (TFIDF) values to multiple machine learning models. We tend to perform comparative analysis of the models considering many values of n in n-grams and TFIDF normalisation strategies. When standardization the model giving the most effective results, we tend to accomplish ninety five.6% accuracy upon evaluating it on take a look at knowledge. we tend to conjointly produce a module that is Associate in Nursing intermediate between user and Twitter.
Concept-based Recommendation System for Finding Serendipity

Current information recommendation systems obtain users’ preferences from Web browsing histories and activities such as purchase of products, and efficiently provide the users with their preferable information. In such a case, however, the same or similar information is always recommended, which is called filter bubble and it decreases the users’ satisfaction to the systems. If information recommendation systems could provide users with something surprising and useful as output information, the user’s satisfaction to the systems would drastically increase. Therefore, “serendipity” is paid attention to in this research. In this paper, a new information recommendation system using a concept-based information retrieval is proposed to provide the users with serendipitous information. In this system, concepts which describe features or roles of items are input instead of the items themselves, and information which can meet the concepts are output as candidates of serendipitous information. The serendipitous information is extracted from the output information using the criteria which are the indexes of serendipity defined in this research. Through the evaluation experiment, it is revealed that the proposed system achieves the accuracy of 70% for the serendipitous information determination and the accuracy of 100% for the information retrieval, which are satisfactory for this research purpose.