Reviewerly/exHarmony huggingface dataset:
Whatsapp Voice Note in Speaking Class

This study deals with the use of WhatsApp voice note in speaking class. This study aimed at figuring out the teacher’s way in implementing WhatsApp voice note in teaching speaking on narrative text and investigating the perspective on WhatsApp voice note to improve speaking skill on narrative text. In conducting this study, the researcher used qualitative approach. Case study was applied as a research design in this study. This study involved 35 students of the tenth grade (X MIPA 1) and one English teacher. Instruments used in this study were classroom observation, interview, and questionnaire. The results of this study showed that nine steps were used by the teacher in the teaching and learning process through WhatsApp voice note in speaking class on narrative text. Meanwhile, most of the students gave their positive response toward the teaching and learning process. The teacher argued that WhatsApp voice note is a useful tool to provide the students opportunities to speak especially on narrative text and the students can share information easily. It also allows the students asking for help using English. Based on the findings, it can be concluded that learning to speak English using WhatsApp voice note is an attractive learning activity, positive activity, and WhatsApp voice note is easy to use. Finally, this study suggests other researchers to involve the objective data, to enrich the instrumentation, to add more sample of the study and to employ other methods and design in order to enrich the data.
Graph Kernel Prediction of Drug Prescription

Predictive models for drug prescription exist; we propose an additional such model that uses a graphical representation of an electronic health record with the prescription process formulated as a kernelized binary (success or failure) classification problem. Our approach improves accuracy while maintaining a high level of interpretability. Results using the Taiwanese National Health Insurance Research Database (NHIRD) favorably compare our approach to other models.
Quality Measurement of Images on Mobile Streaming Interfaces Deployed at Scale

With the growing use of smart cellular devices for entertainment purposes, audio and video streaming services now offer an increasingly wide variety of popular mobile applications that offer portable and accessible ways to consume content. The user interfaces of these applications have become increasingly visual in nature, and are commonly loaded with dense multimedia content such as thumbnail images, animated GIFs, and short videos. To efficiently render these and to aid rapid download to the client display, it is necessary to compress, scale and color subsample them. These operations introduce distortions, reducing the appeal of the application. It is desirable to be able to automatically monitor and govern the visual qualities of these small images, which are usually small images. However, while there exists a variety of high-performing image quality assessment (IQA) algorithms, none have been designed for this particular use case. This kind of content often has unique characteristics, such as overlaid graphics, intentional brightness, gradients, text, and warping. We describe a study we conducted on the subjective and objective quality of images embedded in the displayed user interfaces of mobile streaming applications. We created a database of typical “billboard” and “thumbnail” images viewed on such services. Using the collected data, we studied the effects of compression, scaling and chroma-subsampling on perceived quality by conducting a subjective study. We also evaluated the performance of leading picture quality prediction models on the new database. We report some surprising results regarding algorithm performance, and find that there remains ample scope for future model development.
Experimental determination of strain distribution on Printed Circuit Boards using Digital image correlation

The paper investigates the use of digital image correlation (DIC) for strain measurement on Printed Board Circuits (PCBs). Digital Image Correlation (DIC) is a full-field contactless optical method for measuring displacements and strain in experimental testing, based on the correlation of the digital images taken during test. This method is used to measure strain of the surfaces of materials and structures subjected to various stresses (such as mechanical stresses or thermal stresses). So, this method is used according to the documentation for a variety of applications such as: large deformation measurements, crack analysis, stress strain curves (σ-ε) for new materials, constructions - to evaluate certain displacements and cracks, measurement of strain caused by temperature variations. Circuit boards (PCBs) are designed to mechanically support and electrically connect an electronic component assembly. Due to screw assemblies, the surface level differences on which the PCB is placed, the process of assembling the electronic components induces a certain state of stress and deformation in the PCB. The main components affected are microprocessors due to the way they are glued to PCBs with BGA - Ball grid arrays (BGA). To determine the strain on PCBs, the actual measurement method uses strain gages. This method allows determining the strain only at certain points. The Digital Image Correlation method provides full field strain field. The experimental setup is realized with Dantec Q-400 system used for image capture and Istra 4D software for image correlations and data analyses. The maximum level of the obtained strain is compared with the allowable limit.
Representing Aboutness: Automatically Indexing 19th- Century Encyclopedia Britannica Entries

Representing aboutness is a challenge for humanities documents, given the linguistic indeterminacy of the text. The challenge is even greater when applying automatic indexing to historical documents for a multidisciplinary collection, such as encyclopedias. The research presented in this paper explores this challenge with an automatic indexing comparative study examining topic relevance. The setting is the NEH-funded 19th-Century Knowledge Project, where researchers in the Digital Scholarship Center, Temple University, and the Metadata Research Center, Drexel University, are investigating the best way to index entries across four historical editions of the Encyclopedia Britannica (3rd, 7th, 9th, and 11th editions). Individual encyclopedia entry entries were processed using the Helping Interdisciplinary Vocabulary Engineering (HIVE) system, a linked-data, automatic indexing terminology application that uses controlled vocabularies. Comparative topic relevance evaluation was performed for three separate keyword extraction algorithms: RAKE, Maui, and Kea++. Results show that RAKE performed the best, with an average of 67% precision for RAKE, and 28% precision for both Maui and Kea++. Additionally, the highest-ranked HIVE results with both RAKE and Kea++ demonstrated relevance across all sample entries, while Maui’s highest-ranked results returned zero relevant terms. This paper reports on background information, research objectives and methods, results, and future research prospects for further optimization of RAKE’s algorithm parameters to accommodate for encyclopedia entries of different lengths, and evaluating the indexing impact of correcting the historical Long S.
Cluster-Wise Jaccard Accuracy of KPower Means on Multipath Datasets

This paper presents the accuracy performance of the KPower Means (KPM) algorithm in clustering wireless multipaths using the generated datasets from COST2100 channel model (C2CM).KPM is one of the popular techniques used to cluster wireless multipath components (MPCs) and has been a basis of other complex multipath clustering approaches.KPM is implemented in Matlab using eight different channel scenarios obtained from C2CM representing various indoor and semi-urban environments at 2.85 MHz and 5.3 GHz bands, respectively.Results show that KPM performs well in an indoor environment than in a semi-urban due to the presence of numerous scatterers in a semi-urban environment yielding more multipaths.Jaccard similarity index is used to validate the accuracy performance of the KPM.
Web Service Testing Techniques: A Systematic Literature Review

These days continual demands on loosely coupled systems have web service gives basic necessities to deliver resolution that are adaptable and sufficient to be work at runtime for maintaining the high quality of the system.One of the basic techniques to evaluate the quality of such systems is through testing.Due to the rapid popularization of web service, which is progressing and continuously increasing, testing of web service has become a basic necessity to maintain high quality of web service.The testing of the performance of Web service based applications is attracting extensive attention.In order to evaluate the performance of web services, it is essential to evaluate the QoS (Quality of Service) attributes such as interoperability, reusability, auditability, maintainability, accuracy and performance to improve the quality of service.The purpose of this study is to introduce the systematic literature review of web services testing techniques to evaluate the QoS attributes to make the testing technique better.With the intention of better testing quality in web services, this systematic literature review intends to evaluate what QoS parameters are necessary to provide better quality assurance.The focus of systematic literature is also to make sure that quality of testing can be encouraged for the present and future.Consequently, the main attention and motivation of the study is to provide an overview of recent research efforts of web service testing techniques from the research community.Each testing technique in web services has identified apparent standards, benefits, and restrictions.This systemic literature review provides a different testing resolution to industry to decide which testing technique is the most efficient and effective with the testing assignment agenda with available resources.As for the significance, it can be said that web service testing technique are still broadly open for improvements.
A Trusted System Framework for Electronic Records Management Based on Blockchain

The credibility and authenticity of electronic records determine whether electronic documents have legal effect, preservation value, use effectiveness and reproduction history. Firstly, the existing research was analyzed and the consensus reached is that the reliable technical system is the key to ensure the dual requirements of evidence retention and information utilization during the activities of electronic records management. Secondly, based on the life cycle theory of electronic records and the methodology of whole process continuous management, an application framework of electronic records trusted management based on critical business process management is constructed. And then, according to the technical characteristics and application principle of block chain, the evidence chain model of trustworthy managing electronic records is proposed and the construction of evidence chain model based on type or time limitation with the help of electronic records classification management is studied. Finally, the related key technologies and their application scenarios are analyzed.
A Taxonomy for Social Engineering Attacks via Personal Devices

Social engineering attacks are a major threat to organizations and individuals as digitization and connectivity through the internet increase.This study aims to review scholarly research analyzing the topic of social engineering and further chart the evolution of the threat.The review identifies methods of such attacks on various platforms and devices and discusses motivations behind social engineering attacks.Finally, the paper analyzes the nature and impact of social engineering attacks and presents a taxonomy on socially engineered attacks by analyzing their anatomy.
Context Vectors are Reflections of Word Vectors in Half the Dimensions

This paper takes a step towards theoretical analysis of the relationship between word embeddings and context embeddings in models such as word2vec. We start from basic probabilistic assumptions on the nature of word vectors, context vectors, and text generation. These assumptions are supported either empirically or theoretically by the existing literature. Next, we show that under these assumptions the widely-used word-word PMI matrix is approximately a random symmetric Gaussian ensemble. This, in turn, implies that context vectors are reflections of word vectors in approximately half the dimensions. As a direct application of our result, we suggest a theoretically grounded way of tying weights in the SGNS model.
QUANTITATIVE MORPHOMETRIC AND HYPSOMETRIC ANALYSIS USING REMOTE SENSING AND GIS TECHNIQUES

Water and land are limited natural resources to be distributed among all equitably.Rainfall is the main source of runoff.The accurate estimate of available water resource is required to meet the raising demand for water from different sectors.Morphometric parameters and runoff estimation help in appropriate planning for, optimum utilization of water, conservation, recharging of the ground water zones, reducing the soil erosion and management of floods.The study area chosen is Kabini Command area spreads in Mysore and Chamarajnagara districts.It is located between 76 0 12 ' 0 '' E and 77 0 12 ' 0 '' E longitude and 11 0 58 ' 0 '' N and 12 0 16 ' 0 '' N with an area of 5985.48 km 2 and is covered in Survey of India (SOI) Top sheet number 57H04, 57H03, 57D16, 58E01, 58A13, 57D12, 57D08, 58A05 and 58A01 on 1:50000 scale.Remote sensing and GIS techniques are used to delineate the entire catchment into 7 watersheds.Morphometric analysis is carried out at the watershed level to understand the hydrological process and to derive the inferences from the watershed parameters.The bifurcation ratio ranges between 4 to 5 which shows the geological structure do not distort drainage pattern.The drainage density varies from 1.49 to 2.65 km/km 2 indicating the area is coarse and moderate in texture.Watershed is more elongated as the elongation ratio ranges from 0.34 to 0.42.Hypsometric analysis is done for the catchment to reveal the stages of geomorphic development.Hypsometric integral quantifies the geological stages of development and erosion proness of Kabini command area.Hypsometric integral has estimated by graphical plot of measured contour elevation and encompassed area by using empirical formulae.
Incorporating Background Checks with Sentiment Analysis to Identify Violence Risky Chinese Microblogs

Based on Web 2.0 technology, more and more people tend to express their attitude or opinions on the Internet. Radical ideas, rumors, terrorism, or violent contents are also propagated on the Internet, causing several incidents of social panic every year in China. In fact, most of this content comprises joking or emotional catharsis. To detect this with conventional techniques usually incurs a large false alarm rate. To address this problem, this paper introduces a technique that combines sentiment analysis with background checks. State-of-the-art sentiment analysis usually depends on training datasets in a specific topic area. Unfortunately, for some domains, such as violence risk speech detection, there is no definitive training data. In particular, topic-independent sentiment analysis of short Chinese text has been rarely reported in the literature. In this paper, the violence risk of the Chinese microblogs is calculated from multiple perspectives. First, a lexicon-based method is used to retrieve violence-related microblogs, and then a similarity-based method is used to extract sentiment words. Semantic rules and emoticons are employed to obtain the sentiment polarity and sentiment strength of short texts. Second, the activity risk is calculated based on the characteristics of part of speech (PoS) sequence and by semantic rules, and then a threshold is set to capture the key users. Finally, the risk is confirmed by historical speeches and the opinions of the friend-circle of the key users. The experimental results show that the proposed approach outperforms the support vector machine (SVM) method on a topic-independent corpus and can effectively reduce the false alarm rate.
Playing the Sprint Retrospective

In this paper, we report on a replication of the study by Przybylek & Kotecka [2017]. The aim of our study was to revise the work practices related to Sprint Retrospectives in Bluebay Polska Sp. z.o.o. by adopting collaborative games. The feedback received from two Scrum teams confirms the findings from the original study and indicates that collaborative games improve participants’ creativity, involvement, and communication as well as produce better results than the standard retrospective.
Forecasting the Grant Duration of a Patent using Predictive Analytics

In the race for survival in an age of technological advancement and overspilling data, organizations are clamoring to use the easily available data for better decision making.The arrival of the next generation of innovative and disruptive technologies has also led to patenting race.Companies are reorienting their business goals and strategies to maintain their competitive edge in the market.Patent data has been an obvious choice for analysis, leading to strategic technology intelligence.The advancement of Machine Learning and access to large amounts of patent data has led to a paradigm shift from traditional patent data analysis, methodologies and approaches to novel procedures.This work aims to weigh the benefits & constraints of these approaches in patent analysis.In doing so, some of the important factors-the so called patent characteristics , cited in literature were identified as impacting the decision on grant duration of patent applications.A comprehensive comparative study of the prediction algorithms was also performed.Finally, a quantitative study of the results is presented.This research is exploratory in nature and to the best of our knowledge, first of its kind in terms of research design and the context i.e analysis of dataset from a developing country (India) and the techniques used (ML/DL) in patent grant duration prediction.
The Importance of Artificial Intelligence to Economic Growth

The rapid development of artificial intelligence technology has exerted a great influence on all fields of the world, which of course also affects the world economy.This has also aroused a large number of economists' interest in this proposition.Since the definition of artificial intelligence is not unified yet, the results from previous researches are not reliable enough.At present, most scholars use the neoclassical growth model or task-based model to explore the path of artificial intelligence on economic variables.There into, most of them use the degree of automation to represent the artificial intelligence.They find that the degree of automation can change the proportion of industries.This only verifies that artificial intelligence can affect the economic variables.But the magnitude of artificial intelligence on economic variables can not be correctly estimated.Therefore, in order to have a better understanding on the impact of artificial intelligence on economic growth, this paper systematically reviews and collates previous literature on this topic.The results of this paper indicate that both in theoretical and empirical studies, artificial intelligence has a positive effect on economic growth.Then, some suggestions and limitations have also been put forward accordingly.
Using Blockchain to Access Cloud Services: A Case of Financial Service Application

Most cloud providers use centralized servers to manage data.However, centralized servers still suffer the risks of single point of failure and data theft.We add a blockchain to the cloud service and propose a new architecture to manage data.Using blockchain as a connector to utilize the tamperproof, traceable, and data-sharing features of the blockchain to ensure that the transaction data are properly stored in each node.We use the stock simulation trading service to extend and divide the research design into two levels, namely, system and application services.First, we directly write the data into the blockchain.Second, we alternatively store the data in the cloud and then write it into the blockchain.Finally, the two versions are compared and analyzed to investigate their feasibility and performance.At the application service, we implement the smart contract for the existing stock transaction process to achieve real-time settlement.
Ground-state cooling of a single atom inside a high-bandwidth cavity

We report on vibrational ground-state cooling of a single neutral atom coupled to a high-bandwidth Fabry-P\'erot cavity. The cooling process relies on degenerate Raman sideband transitions driven by dipole trap beams, which confine the atoms in three dimensions. We infer a one-dimensional motional ground-state population close to $90%$ by means of Raman spectroscopy. Moreover, lifetime measurements of a cavity-coupled atom exceeding 40 s imply three-dimensional cooling of the atomic motion, which makes this resource-efficient technique particularly interesting for cavity experiments with limited optical access.
An Effective Scheme for Generating An Overview Report over A Very Large Corpus of Documents

How to efficiently generate an accurate, well-structured overview report (ORPT) over thousands of documents is challenging. A well-structured ORPT is divided into sections of multiple levels (e.g., a two-level structure consists of sections and subsections). None of the existing multi-document summarization (MDS) algorithms is suitable for accomplishing this task. To overcome this obstacle, we devise NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT. We then present a multi-criteria evaluation method using techniques of text mining and multi-attribute decision making on a combination of human judgments, running time, information coverage, and topic diversity. We evaluate ORPTs generated by NDORGS on two large corpora of documents, where one is classified and the other unclassified. We show that, using Saaty's pairwise comparison 9-point scale and TOPSIS, the ORPTs generated on SDS's with the length of 20% of the original documents are the best overall on both datasets.
Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction

We summarize the sixth data mining competition organized at the Knowledge Pit platform in association with the Federated Conference on Computer Science and Information Systems series, titled Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction.We outline the scope of this challenge and briefly present its results.We also discuss the problem of acquiring knowledge about new notions from video games through an active learning cycle.We explain how this task is related to the problem considered in the challenge and share results of experiments that we conducted to demonstrate usefulness of the active learning approach in practice.
Spline-Wavelet Bent Robust Codes

This paper presents an application of spline-wavelet transformation and bent-functions for the construction of robust codes.To improve the 1 non-linear properties of presented robust codes, bent-functions were used.Bentfunctions ensure maximum non-linearity of functions, increasing the probability of detecting an error in the data channel.In the work different designs of codes based on wavelet transform and bent-functions are developed.The difference of constructions consists of using different grids for wavelet transformation and using different bent-functions.The developed robust codes have higher characteristics compared to existing.These codes can be used for ensuring the security of transmitted information.