Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI METODE WEBQUAL DAN CUSTOMER SATISFACTION INDEX UNTUK MENGEVALUASI WEBSITE PERGURUAN TINGGI NEGERI DI BANDAR LAMPUNG

&lt;p&gt;This study provides an overview of the quality of six state University website services in Bandar Lampung which refered to three dimensions, website usability, information quality, and interaction quality website. This study also used the Customer Satisfaction Index (CSI) method to determine the level of user satisfaction. In addition, to improve the usability of a website, website performance testing was performed using automatic tools. The result of this research find that the quality of website services is influenced by the content on the website. The result of CSI analysis on the website of state Universities in Bandar Lampung are satisfied with the website service at each state University in Bandar Lampung. Based on the results of the usability evaluation using automatic tools on the website, it is known that the website of state Universities in Bandar Lampung is good enough as evidenced by the page load time value which was less the three seconds.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Quality of Website Service, WebQual, CSI, Usability&lt;/p&gt;&lt;p&gt;Penelitian ini mengkaji kualitas layanan pada enam website universitas negeri di Bandar Lampung dengan menggunakan metode Webqual yang merujuk pada 3 hal yaitu – website usability, kualitas informasi dan kualitas interaksi website. Kepuasan pengguna diukur menggunakan metode Customer Satisfaction Index (CSI). Untuk meningkatkan website usability dilakukan website performance tes menggunakan alat otomatis. Hasil dari penelitian ini memperlihatkan kualitas layanan website dipengaruhi oleh konten website. Sedangkan hasil Analisa CSI pada website universitas negeri di Bandar Lampung adalah memuaskan untuk setiap website universitas tersebut. Berdasarkan hasil evaluasi usability, website universitas negri di Bandar Lampung menunjukkan sudah cukup baik dengan waktu load halaman kurang dari tiga detik.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Kata Kunci&lt;/strong&gt;: Kualitas Layanan Website, WebQual, CSI, Usability&lt;/p&gt;&lt;p&gt;&lt;br /&gt;&lt;br /&gt;&lt;/p&gt;
Multi-Channel Human-Computer Cooperative Interaction Algorithm in Virtual Scene

With the development of computer technology, virtual reality technology has also been widely developed, among which human-computer interaction technology based on virtual hand is a very key technology in the field of virtual reality. However, in the process of interaction with virtual reality technology, there are often virtual objects which are too far away from each other and blocked, which make users unable to operate the interactive virtual objects accurately and efficiently. Therefore, this paper proposes a multi-channel fusion of human-machine collaborative interaction algorithm, first of all, through the Intel RealSense estimate the gesture in driving virtual hand, using the iFLYTEK speech recognition SDK user's speech input, and then to identify voice syntax segmentation of user operation intention, finally get through to the virtual scene scene perception scene active transformation to assist users to complete the operation. Experiments show that the algorithm can help users to complete interactive tasks accurately and efficiently.
KWA: A New Method of Calculation and Representation Accuracy for Speech Keyword Spotting in String Results

This study proposes a new method to measure and represent accuracy for Keyword Spotting (KWS) problem in nonaligned string results.Our approach, called Keyword Spotting Accuracy (KWA), was improved from the Levenshtein Distance algorithm, that used to evaluate the accuracy of the keywords in KWS by measuring the minimum distance between two strings.The main improved algorithm is to show the status of each keyword in training phase for predicted and true labels.In which, representing which words are correct, which ones need to be inserted, substituted or deleted when comparing the prediction labels with true ones during the training phase.In addition, a new method of presenting the multiple keywords in results was proposed to indicate the accuracy of each keyword.This method can display detailed results by keywords, from which, we can obtain the accuracy, distribution, and balance of the keywords in the training dataset by actual speech variance, not by counting keywords in true labels as usual.
Feedback Systems on Extended Hilbert Space-Normality and Linearization

ThestudyofthenormalityofafeedbacksystemonanextendedHilbertspacehasbeenmade. Theresultsofapproximation&#x0D; of the solutions of such a nonlinear system by another linear are also established. This study represents an extension of the work of (Vaclav Dolezal, 1979), on a Hilbert space.
Investment in Agriculture and Extractive Industry: A Panacea for National Development

Economic diversification into agriculture and extractive industry in Nigeria has been a fascinating and crucial economic issue that deserves consideration especially as the country is shifting from mono-economy (caused by oil boom) to other viable economic sectors. The global economic meltdown and depression have stimulated countries to look into other sectors of the economy in order to enhance their national development. Hence, this study tries to examine the contribution of agriculture and extractive industry to the Nigeria’s real gross domestic product (RGDP). The study makes use of time series data gathered from CBN Statistical Bulletin ranging from 1981-2017 and employs Ordinary Least Squares (OLS) method as the statistical tool with the aid of e-views version 9. The findings reveal that agriculture has a robust and noteworthy positive impact on RGDP while the solid mineral equally has a substantial positive influence on RGDP. However, crude petroleum (proxy for crude petroleum &amp; natural gas) has a positive inconsequential effect on RGDP. This brings the study to a conclusion that investment in agriculture and solid minerals is highly imperative at the moment. Therefore, the study has suggested that economic diversification should be focused more on agriculture and solid mineral extraction. In addition, the government should try to manage the crude petroleum and natural gas exploration so as to prevent fund repatriation and transfer to other countries due to borrowed technology.
Israel Complementary International Airport

Israel has just one major international airport named Ben-Gurion International Airport.Ben-Gurion Airport is overloaded and currently the Air Traffic Control in the airport is a very tough and complicated task.In addition Ben-Gurion airport operates 24 hours a day for landings and in some days also 24 hours for takeoffs, which causes intolerable noise level.This paper explores the current state of affairs in Ben-Gurion Airport and suggests a possible complementary International Airport in Dothan Valley.
Research on display system for agricultural science and technology support data based on Microsoft data warehouse

Based on the introduction of Microsoft data warehouse service and related software architecture, in order to solve the problems of slow analysis queries caused by the great amount of the original data, complex classification and wide scope, the display system for agricultural science and technology support data was presented in this paper. In proposed system, agricultural science and technology support data were showed clearly and intuitively.
Performance Evaluation of Deep Autoencoder Network for Speech Emotion Recognition

The learning methods with multiple levels of representation is called deep learning methods.The composition of simple but now linear modules results in deep-learning model.Deep-learning in near future will have many more success, because it requires very little engineering in hands and it can easily take ample amount of data for computation.In this paper the deep learning network is used to recognize speech emotions.The deep Autoencoder is constructed to learn the speech emotions (Angry, Happy, Neutral, and Sad) of Normal and Autistic Children.Experimental results evident that the categorical classification accuracy of speech is 46.5% and 33.3% for Normal and Autistic children speech respectively.Whereas, Auto encoder shows a very low classification accuracy of 26.1% for only happy emotion and no classification accuracy for Angry, Neutral and Sad emotions.
Security Issues in Serverless Computing Architecture

Serverless computing allows a company to run its backend services on an as-used basis.Precisely, could providers are offering business the chance to pay for cloud computing based on their computation needs without the need to reserve a certain number of servers or secure a fixed amount of bandwidth.While offering flexibility through paying for computing power rather than fixed server space and bandwidth, serverless computing presents unique security challenges and issues not characteristic of classic cloud computing.The purpose of this paper is to bring to light these security issues and challenges in addition to developing a proposed framework to solve it.
Detecting stabbing by a deep learning method from surveillance videos

Stabbing is one of culprits threatening public safety. Once it happens, it will make immeasurable consequences in a short period of time. In order to strengthen the supervision of public safety and prevent the emergence of stabbing, the tool detection technology can play a vital role. The existing methods for tool detection are metal detector and X-ray detector, which are applicable to stations, airports and other specific areas but not feasible in public areas crowds of people. This paper proposes the use of a deep learning method with high precision and speed for tool detection and by comparison finally chooses the YOLOV3 method for tool detection in public areas. To validate the performance of YOLOV3 method, a total of 1,738 images of different tools are acquired by simulating real scenes and the web crawler technology. Meanwhile, the number of samples are amplified by image enhancement techniques, and a datasets of 21,000 images are filtered. To improve tool detection accuracy, this paper proposes a method that combines hand features and tool features into new features. Experiments have shown that the detection accuracy is improved by 2.57 % with these new features.
FCJ-227 Survey and Project: On the (Im)possibility of Scholarship in an Era of Networked Knowledge

There are at least two dimensions to the transformation in the character of scholarly publishing and the correlative shift in the networked conditions of production of scholarly work.The scope and scale of material available has radically increased and the mechanisms of judging scholarly value have been increasingly refined.Yet, we have not done enough to critically reflect on what these transformations have done to the experience of producing scholarly work.I am referring to the simple way that everyday activities of scholarship have been transformed.An obvious example is the practice of carrying out a literature review.This brief essay presents some notes on literature review preparation and abstract writing from the perspective of a mid-career academic who is committed to assisting their research students.It then argues there are two new meta-professional skills required of scholars to function in academia and in which our research students need to develop expertise.What if we could problematise the 'project' (or 'projectify' the problem)?Rather than the coordinates of the project being determined by the administrative burden of measurement and correlative productivity according to maximum gradients of anxiety (the neoliberal academic model), what if the 'project' was configured as an instrument for suspending practices of discovery according to the maximum gradients of curiosity (the post-neoliberal academic model)?This is the difference between passive and active affections that befall scholars as they are socialised as academics.
RGBD Saliency Object Detection via Regional Feature Clustering

According to the problem in the RGBD saliency detection, this paper proposes a RGBD saliency object detection algorithm based on regional feature clustering. First, the image segmentation is performed using a superpixel algorithm that uses depth information. Then the feature vector of each region is extracted. The region is clustered using ten different bandwidth MeanShift algorithms, and then obtain ten clustered maps. Next, the ten clustered maps are calculated to generate ten saliency maps. Ten saliency maps are merged into one saliency map with a XGBoost model. Then the saliency map is added as a new feature to the feature vector mentioned above. Continue to calculate the saliency map until the loop is up to ten times to output the final saliency map. Experimental results on three public RGBD datasets show that the algorithm has better performance.
Prototype of a Compact Assistant Surveillance Robot for Search and Rescue Operations

This paper presents a prototype of a compact, affordable mobile rescue robot that can be used to gather real-time information on difficult to reach locations during rescue operations.The prototype uses Arduino Mega 2560 as the main microcontroller, FPV camera for real-time video streaming mounted on a tilt assembly.Continuous track differential wheels were utilized for all-terrain navigation.The robot can be used in simple rescue operations such as for detecting, locating injured and characterizing obstacles in collapsed structures.
LIGHTWEIGHT GROUP KEY MANAGEMENT FOR DATA DISSIMILATION FORDYNAMIC SDN MANET ENVIRONMENTS

The Internet of Things has grown the next big step in the development of that corresponding current Internet as well as communicates security protection as one of the most significant problems. In specific, use situations and deployment environments that adopt group communication systems need to be appropriately secured to protect the transfer of messages among group members from several safety assaults. A distinct way to secure group communication is based on the implementation of a symmetric group key distributed between all group members. This, in turn, needs focusing on a group key management system that is accountable for revoking and renewing the group key when nodes join or leave the group. The adopted group key management system should be effective and extremely scalable with the group size due to the resource-constrained nature of typical SDN systems.
Some Progress on Quantum Error Correction for Discrete and Continuous Error Models

Quantum computing has increasingly gained attention for decades since it can surpass classical computing in various aspects.A crucial issue of quantum computing is how to protect information from noise interference such that the error rate during quantum information processing can be limited within an acceptable bound.The technique to address this issue is quantum error correction (QEC).Developing QEC technologies needs to define noise as formal error models and design QEC approaches for these error models.Discrete error models and continuous error models are two kinds of definitions of noise.The former assumes noise occurs independently and can be discretized into a set of basic errors.The former also has a tool, named quantum operations, to describe a specific error model.The latter describes that noise is continuous in time by using differential equations.In this paper, we categorize QEC approaches into three types according to the different error models: discrete error models, specific error models, and continuous error models.We also analyze the state-of-the-art QEC approaches and discuss some future directions.Furthermore, we propose the perturbed error models and their possible definitions, aiming to find the effect of the perturbation during quantum information processing.
Interview mit Theresa Hannig

The article analyzes Theresa Hannig’s novels, The Optimizers and The Imperfect, as a creative intervention into discourses of the posthuman. Hannig’s work exposes the different arguments regarding the development of sentient robots and artificial intelligences and negotiates posthuman positions of subjectivity, consciousness transfer, and the rights of personhood for artificial beings. The article presents the evolving concepts of an artificially created posthumanity in the novels and reveals Hannig’s perspective on how to deal with a becoming-machine. Publisher's note: This article was originally published as part of issue 8.1. This has been updated to note that this article is part of issue 7.2.
Implementation of the Neural Network Algorithm in Advanced Databases

The progress of humanity is closely related to the progress of technology. The highlight of the development of technology will occur when the machines are able to do what they learn and know; think and make decisions on their own without human help. In this paper we will try to analyze how neural networks work and how they will further develop in terms of application in advanced databases. We will also explore how one of the major IT companies is developing and continuing to use neural networks.
Pengaruh Metode Round Robin terhadap Hasil Belajar IPS Siswa Kelas VII

This study aims to determine the effect of Round Robin on student learning outcomes in social studies subject matter VII Class SMP Negeri 2 Situjoah District Limo Nagari. The quantitative method used in this study is an experimental approach, in which the research design is to use a control class as a comparison of the research class. Based on the research results from the analysis it was concluded that the cooperative learning technique of Round Robin d can improve students' social learning outcomes well. Class VII student learning outcomes in SMP N 02 Kec. Situjuah Limo Nagari was divided into two groups: the first was the experimental class where the mean score was 72.9 and the second was the control class where the average value was 60.3. Means the value of the experimental class is higher than the control value because the experimental class uses the Round Robin s method while the control class uses conventional. Conclusions, that by using the Round Robin method in social studies subjects can be said to have a significant effect on learning outcomes.&#x0D; Keywords: Round Robin Method, Learning Outcomes, Social Studies
Correction to: Abiotic resource depletion potentials (ADPs) for elements revisited—updating ultimate reserve estimates and introducing time series for production data

The original version of this article unfortunately contained a mistake which was missed during typesetting. The caption to Fig. 5 was incorrect. The correct version is given below.
The internal structure of a differentially marked DP in Romanian

This paper starts from the observation that clitic doubling and DOM-pe may alternate as differential marking mechanisms for direct objects.Our hypothesis is that the clitic and pe are able to substitute to each other for the object marking purpose.We propose that the clitic and pe compete for merging in the same functional head at the left periphery of nominal phrases in Old Romanian, and eventually co-occur, in different functional heads in the same domain in Modern Romanian.This analysis clarifies the locus of the DOM particle insertion (i.e. in the nominal versus the verbal extended projection), its combinatorial possibilities (e.g. with clitic doubling), and how these syntactic properties correlate with changes in interpretation.
Map Reduce based REmoving Dependency on K and Initial Centroid Selection MR-REDIC Algorithm for clustering of Mixed Data

In machine learning, clustering is recognized as widely used task to find hidden structure of data.While handling the massive amount of data, the traditional clustering algorithm degrades in performance due to size and mixed type of attributes.The Removal Dependency on K and Initial Centroid Selection (REDIC) algorithm is designed to handle mixed data with frequency based dissimilarity measurement for categorical attributes.The selection of initial centroids and prior decision for number of cluster improves the efficiency of REDIC algorithm.To deal with the large scale data, the REDIC algorithm is migrated to Map Reduce paradigm,and Map Reduce based REDIC( MR-REDIC) algorithm is proposed.The large amount of data is divided into small chunks and parallel approach is used to reduce the execution time of algorithm.The proposed algorithm inherits the feature of REDIC algorithm to cluster the data.The algorithm is implemented in Hadoop environment with three different configuration and evaluated using five bench mark data sets.Experimental results show that the Speed up value of data is gradually shifting towards linear by increasing number of data nodes from one to four.The algorithm also achieves the near to closer value for Scale up parameter, while maintaining the accuracy of algorithm.
Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology

Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets.Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, finegrained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration.This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS).The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation.We experimented on the well-known Market1501 dataset.The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.
SYMBIOTIC RELATIONSHIP BETWEEN A HEALTHY POPULATION AND A SUSTAINABLE ECONOMY: FINANCIAL PERFORMANCE OF JULPHAR GULF PHARMACEUTICAL INDUSTRIES

Redefining the variables of macroeconomics requires the evaluation of the vital role the healthcare industry plays as an economic agent.The pharmaceutical industry constitutes a vital part of the healthcare industry.In this study, the evaluation of the performance of Julphar examined viability of such an agent.A 5-year financial ratio analysis was conducted based on the company's 5-year annual reports.The analyzed ratios were assets utilization, net profit margin, debt utilization, and the profitability ratios ROA and ROE.The overall findings showed that Julphar assets were underutilized meaning that their productivity is not optimal.ROA and ROE dropped-down consecutively, which means that the company's profitability was in question given its high debt-financing and a continuous increase in total operating expenses.This performance can affect the national and regional economic stability.It is recommended that Julphar scales down financing, debt acquisitions, and reduces its operating expenses while increasing its productivity by way of optimal assets utilization and redefined market segmentation.Market-driven projects are seen as the solution to most of its problems.Due to its position, Julphar plays important role in the national and regional economy.Future research should include other microeconomic agents such as micro-foundations in order to determine the far-reaching effects of financial performance on the economy.