Reviewerly/exHarmony huggingface dataset:
Generic constructions of PoRs from codes and instantiations

Abstract In this paper, we show how to construct – from any linear code – a Proof of Retrievability ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>𝖯𝗈𝖱</m:mi> </m:math> {\mathsf{PoR}} ) which features very low computation complexity on both the client ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>𝖵𝖾𝗋𝗂𝖿𝗂𝖾𝗋</m:mi> </m:math> {\mathsf{Verifier}} ) and the server ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>𝖯𝗋𝗈𝗏𝖾𝗋</m:mi> </m:math> {\mathsf{Prover}} ) sides, as well as small client storage (typically 512 bits). We adapt the security model initiated by Juels and Kaliski [PoRs: Proofs of retrievability for large files, Proceedings of the 2007 ACM Conference on Computer and Communications Security—CCS 2007, ACM, New York 2007, 584–597] to fit into the framework of Paterson, Stinson and Upadhyay [A coding theory foundation for the analysis of general unconditionally secure proof-of-retrievability schemes for cloud storage, J. Math. Cryptol. 7 2013, 3, 183–216], from which our construction evolves. We thus provide a rigorous treatment of the security of our generic design; more precisely, we sharply bound the extraction failure of our protocol according to this security model. Next we instantiate our formal construction with codes built from tensor-products as well as with Reed–Muller codes and lifted codes, yielding <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>𝖯𝗈𝖱</m:mi> </m:math> {\mathsf{PoR}} s with moderate communication complexity and (server) storage overhead, in addition to the aforementioned features.
Privacy Preservation Using L-Diversity

An increasing number of organizations maintain collections of data about individuals.Hospitals keep medical records of their patients, commerce companies collect information of their clients and web service companies keep track of the preferences of their users.Publication of these data can be useful for research, epidemic studies, commerce development, statistical analysis, etc.It is difficult to keep all the data open on internet, in some cases, to keep confidence and in safe manner where it contains some important or sensitive details.The removal of directly identifying information (such as Name, Disease) from the published records is not enough to guarantee individuals privacy.The personal data can be misused, for a variety of purposes.Maintaining the privacy for high dimensional database has become difficult.A potential attacker could infer a record's identity by linking public external information sources (voters registration lists, phone number catalogues, etc.) with a combination of other attributes, like age, gender and postal code, which are not generally unique per person.The main goal is to focus on Privacy and utility of the shared data.l -Diversity is one of the methods to preserve the data which is very sensitive and confidential.Keeping more sensitive data in dataset helps us to preserve the database more safe and secure manner.The advantage of using ldiversity provides a greater distribution of sensitive attributes within the group, thus increasing data protection.This method protects against attribute disclosure, which is an enhancement of k-anonymity technique.
A REALITY IN A DEVELOPING COUNTRY- BENIN: DIAGNOSIS OF CANCER WITH THE ACTIVATION OF AUTOMATED IMMUNOHISTOCHEMISTRY

Background: In the Benin-West state of Africa, cancer is one of the most frequent causes of death. For this reason from September 2015 at Saint John of God Hospital in Tanguieta – Benin – a Histopathology Laboratoy is active with the aim to make diagnosis and address towards adequate therapeutic programs patients affected from cancer and other diseases. The Laboratory has been equipped with a tele pathology system (Cross SS, Dennis T, Start RD. 2002 and Della Mea V. &amp; Beltrami CA. 2000) and from 2018 with an Automatic System for Immunochemistry.Objectives, methods and outcomes: In the last 20 years, in collaboration with the association APOF (Pathologists across the border), diagnostic laboratories have been developed in various African countries: Tanzania - Uganda - Djibouti, etc. Immunohistochemistry has been introduced in these laboratories to formulate a correct and complete diagnosis.In Benin we had a technological breakthrough by introducing an automated immunohistochemistry system. The funds were obtained from the FAI (International Assistance Foundation) of Lugano (Switzerland) and from the UTA – Onlus.The use of validated protocols, the contribution of international experts through telepathology, the training of technical personnel on site, allowed the formulation of correct diagnoses.Conclusion: The development of complete histopathological diagnostic services of automated ICHs, such as that of Benin, is of fundamental importance for the fight against cancer in developing countries. Cancer is fought with research but also with a correct diagnosis.
Web Browser and E-mail Forensics

Internet applications already installed on Windows can give important information about user actions performed previously on his/her computer. For instance, a web browser is the only way to access the Internet, and criminals are using it to commit crimes related to the Internet or to target other users online. Internet users use web browsers to socialize, purchase online items, or to send e-mails and browse the web contents, among other things. This fact makes web browsers the preferred target for malicious actors to steal confidential information like account credentials.
Normally approximated Bayesian credible interval of binomial proportion

The success probability in a binomial distribution has been of much interest in many fields. Most articles about its interval estimation have given focus on traditional approaches, while there were seldom on Bayesian approaches. In this article we give focus on the interval estimation based on normal approximation to posterior distribution, and compare its performance with major interval estimations in terms of coverage probability related measures. The approximate Bayesian credible interval, based on similar ideas to that of the Wald or Wilson interval, is easily derived and simple to memorize and shows comparable results to popular classical interval methods.
Tumble Avoidance System for Rescue Robot by Estimating the Contact Points using a 3D Depth Sensor

A C TRescue robots are expected to perform works in hazardous areas.However, when the robot runs on a rough terrain, fatal rollover falling sometimes occurs.Therefore, we propose a tumble-risk-assessment system, which calculates a normalized energy stability margin by using a 3D depth sensor.Moreover, a control system to avoid the tumble situation by moving the sub-crawlers automatically is proposed.We implemented the proposed systems to a real robot, carried out experiments, and confirmed the effectiveness of the proposed method.
Interactive Light Map and Irradiance Volume Preview in Frostbite

This chapter presents the real-time global illumination (GI) preview system available in the Frostbite engine. Our approach is based on Monte Carlo path tracing running on the GPU, built using the DirectX Raytracing (DXR) API. We present an approach to updating light maps and irradiance volumes in real time according to elements constituting a scene. Methods to accelerate these updates, such as view prioritization and irradiance caching, are also described. A light map denoiser is used to always present a pleasing image on screen. This solution allows artists to visualize the result of their edits, progressively refined on screen, rather than waiting minutes to hours for the final result using the previous CPUbased GI solver. Even if the GI solution being refined in real time on screen has not converged after a few seconds, it is enough for artists to get an idea of the final look and assess the scene quality. It enables them to iterate faster and so achieve a higher-quality scene lighting setup.
A Comparative Study of Blood Culture, Widal Test and Immunochromatographic Assay for Rapid Diagnosis of Typhoid Fever in a Tertiary Care Centre

Typhoid fever continues to be a major public health problem and the emergence of antimicrobial resistance by Salmonellae typhi adds to the complexity in treating the patients.Salmonella enterica subspecies enterica serovar typhi, the human specific, causative agent of typhoid fever, is one of the most common infectious diseases in developing countries like India 1. Widal test is associated with numerous limitations, but is still considered and extensively used as the diagnostic tool in our area.The bacteriological identification by blood culture is the best confirmative test of Typhoid fever.The aim of the study was to determine the reliability of Immunochromatographic test for the early diagnosis of typhoid fever when compared to the Widal test.The present study was carried out in Tirunelveli Medical College and Hospital, Tirunelveli for a period of one year from June 2017 to July 2018.A total number of 100 clinically suspected Typhoid fever patient's blood samples were taken and blood culture, widal test and Immunochromatographic tests were done.A total of 14 Salmonella typhi were isolated from 100 clinically suspected typhoid cases.A total of 47 samples were tested positive in Widal test.Out of this only one sample was positive for blood culture (True positivity rate -2.1% and True negativity rate -75.4%).Out of 26 samples positive for IgM in ICT, 10 samples were positive for blood culture (True positivity rate -38.5%).Out of 74 samples that were negative for IgM, only 4 samples were found to be positive for blood culture (True negativity rate -94.6%).In conclusion, the study implies that rapid ICT tests offers increased sensitivity, rapidity and simplicity over blood culture and Widal test, and can be used as a reliable, alternate early diagnostic tool to the most commonly used serological tests.
The Role of the iPad as Instructional Tool in Optimizing Young Learners' Achievement in EFL Classes in the Saudi Context

The iPad tablets have been introduced into various educational sectors to facilitate learning and engage students in the classroom.However, little is known about the empirical evidence with regard to iPad usage in enhancing language achievement.This study set out to examine the effects of integrating iPad tablets as an instructional tool into the second language (L2) curricula to enhance young learner's academic achievement in the English as a Foreign Language (EFL) classroom.It further endeavored to uncover the limitations that affect the utilization of the iPad in the classroom.To this end, forty Arabic first language (L1) young EFL learners enrolled in a middle school, were assigned as either an experimental group where they were exposed to study via iPad or a control group where they were taught in a traditional printed text across five weeks period.Language achievements were assessed through reading and vocabulary tests which were administered before the intervention and immediately after the intervention.To gauge the students' engagements during the intended task, their behaviors were assessed in both scenarios.Findings from quantitative and qualitative data revealed that the students who were exposed to iPad were found much engaged and significantly outscored their counterparts in language achievements in the post-tests.Furthermore, technical problems were found to be predominant impediments to the integration of the iPad usage followed by unfamiliarity with the application, and student distraction.These findings provide EFL teachers and policy makers with insight on how to better integrate the iPad into the EFL environment.Further pedagogical recommendations and research directions are also highlighted at the end of the article.
ASSESSMENT RESIKO TEKNOLOGI PADA IMPLEMENTASI SISTEM INFORMASI AKADEMIK E-UNIVERSITY

Information technology has become an essential part of human life so as to facilitate a business activity. However, the use of information technology is not separated from the risks that can affect the process of the activity. As for the purpose of this study was to conduct an assessment of risk against potential vulnerabilities and threats that can attack the academic information system E-University all at once mempersiapan action anticipation towards things that can interfere with the the system. To do the assessment, this study uses the framework NIST SP 800-30r-1 consisting of nine stages to risk assessment i.e. in the characteristics of the system are used, identify the threats that attack system, identification of vulnerability, control systems, determine the likelihood of occurring (likelihood), determine the impact (impact), the determination of risks, control recommendations and documentation of results. The results of the risk assessment against the academic information system E-University is there are three risks disrupting existing activities in the system. Then from the results of the assessment of risks in the form of recommendations are used to minimize the risks that occur on the system
An Optimal Feature Selection Method for Automatic Face Retrieval Using Enhanced Grasshopper Optimization Algorithm

Background: Retrieval of facial images based on its contents is one of the main areas of research. However, images contain high dimensional feature vectors and it is a challenging task to select the relevant features due to the variations available in the images of similar objects. Therefore, the selection of relevant features is an important step to make the facial retrieval system computationally efficient and more accurate. Objective: The main aim of this paper is to design and develop an efficient feature selection method for obtaining relevant and non-redundant features from the face images so that the accuracy and computational cost of a face retrieval system can be improved. Methods: The proposed feature selection method uses a new enhanced grasshopper optimization algorithm to obtain the significant features from the high dimensional features vector of face images. The proposed algorithm modifies the target vector by considering more than one best solution which maintain the elitism property and save the search from local optimum. Furthermore, it has been utilized to select the prominent features from the high dimensional facial features vector. Results: The performance of the proposed feature selection method has been tested on Oracle Research Laboratory face database. The proposed method eliminates 89% features which are minimum among the other methods and increases the accuracy of face retrieval system to 96.5%. Conclusion: The enhanced grasshopper optimization algorithm-based feature selection method for face retrieval system outperforms the existing methods in terms of accuracy and computational cost.
Hyperscale data center applications of optoelectronics

From subsea fiber cables to short-reach switch interconnects, optoelectronics is a key technology for hyperscale data center networks. As performance requirements increase, photonics moves deeper into the network replacing copper for shorter distances. The next move for photonics is to distances of less than 3m for in-rack applications. This talk will describe how the scale of data-bandwidth growth has challenged what is possible with traditional networks and where the next opportunities for innovation lie.
Accuracy of FNAB in Diagnosis of Breast Lump

breast cancer is the 1st one among Iraqi females. Most of them present later for diagnosis. No screening program was applied in the country. Early detection center in tertiary hospital practice FNAB for early diagnosis. Publications on accuracy are scarce. This study aimed to test the accuracy of FNAB in breast lump diagnosis. diagnostic test accuracy study, on 204 women with breast lump, attending the oncology department in 2017. Fine-needle aspiration biopsy diagnosis of histologically malignant cases were, malignant in 89 (87.3%), suspicious of malignancy in 5 (4.9%), and benign in 4 (3.9%). Complete sensitivity was 87.3%, and specificity was 100%, with 12.7% false negative results and no false positive cases. The accuracy was 94%. The study confirmed the high figures of accuracy of FNAB.
Principles of the Development of Pedagogical Competence of Teacher of Pharmaceutical Profile in the System of Scientific and Methodical Work in College

The article deals with the determination of the principles of the pharmaceutical profile teacher’s pedagogical competence in the system of scientific and methodical work of the college. It has been found that the principles of pedagogical competence development of the given teacher’s category are motivation, continuity, purposefulness, complex approach, individualization, activity, priority of self- scientific and methodological support, innovation, reflexive evaluation of your own activities, humanization. Also, the article gives the overall characteristic of the principles’ content part.
Energy Consumption Modelling of Coherent Transmission in Data Centers

We analyze energy consumption of coherent transmitters and receivers for intra- and inter-data center links and model trends in energy consumption of global-scale data center networks as the volume of traffic increases.
Programmability and end-to-end automation for Telecommunication Operators (TELCOs)

Networking equipment with OpenAPIs admit automation of end-to-end services that span optical and packet domains. Adoption will be considered and proportionate to match business logic that lubricates and couples several architectural domains in a TELCO; and constrained by scarcity of programmers adept at effecting the necessary automation.
Design and Development of the Sentence-based Collocation Recommender with Error Detection for Academic Writing

Appropriate collocations in writing of research papers in English can make the context smoother and expression of ideas more precise. In consequence, it is easier for the reader to understand and the purpose of sharing the outcome of research is accomplished. However, for non-native English speakers, the choice and use of collocations is very difficult. For this reason, this study is intended to refer to a large amount of high-quality academic literature to establish a collocation corpus and adopt natural language processing techniques and statistical methods to develop a collocation recommendation system. The system will allow users to enter sentences, automatically detect the locations and types of collocations and recommend synonymous collocations in accordance with the semantics and frequency of use. The fitness of collocations in the system for beginning sentences achieves 73.1%. Writers of academic papers can use the system to select appropriate collocations, reduce erroneous use of collocations and improve the quality of their papers.
NILAI PENDIDIKAN BERKARAKTER DALAM NOVEL HAFALAN SHOLAT DELISA KARYA TERE LIYE: KAJIAN SOSIOLOGI SASTRA

This research type is descriptive research by using approach of sociology of literature. The object of this research is the values ​​of character education in this novel. The data in this study are words, sentences, and discourses contained in this novel. The source of data in this novel. Data collection is done by reading and writing techniques. This study aims to explain the values ​​of education contained in this novel.. The results obtained in this study are 13 points, namely: (1) religious values, (2) honest values, (3) discipline value, (4) hard work value, (5) creative value, (6) independent value, (7) ) the value of curiosity, (8) the value of appreciation, (9) friendly and communicative value, (10) the value of peace love, (11) the value of reading, (12) the value of social care, (13) the value of responsibility.
A Selective Mirrored Task Based Fault Tolerance Mechanism for Big Data Application Using Cloud

With the wide deployment of cloud computing in big data processing and the growing scale of big data application, managing reliability of resources becomes a critical issue. Unfortunately, due to the highly intricate directed-acyclic-graph (DAG) based application and the flexible usage of processors (virtual machines) in cloud platform, the existing fault tolerant approaches are inefficient to strike a balance between the parallelism and the topology of the DAG-based application while using the processors, which causes a longer makespan for an application and consumes more processor time (computation cost). To address these issues, this paper presents a novel fault tolerant framework named Fault Tolerance Algorithm using Selective Mirrored Tasks Method (FAUSIT) for the fault tolerance of running a big data application on cloud. First, we provide comprehensive theoretical analyses on how to improve the performance of fault tolerance for running a single task on a processor. Second, considering the balance between the parallelism and the topology of an application, we present a selective mirrored task method. Finally, by employing the selective mirrored task method, the FAUSIT is designed to improve the fault tolerance for DAG based application and incorporates two important objects: minimizing the makespan and the computation cost. Our solution approach is evaluated through rigorous performance evaluation study using real-word workflows, and the results show that the proposed FAUSIT approach outperforms existing algorithms in terms of makespan and computation cost.
A-RAFF: A Ranked Frequent Pattern-growth Subgraph Pattern Discovery Approach

Graph mining is one of the arms of Data Mining in which voluminous complex data are represented in the form of graphs and mining is done to infer useful knowledge from them. Frequent subgraph mining (FSM) is an active research field and is considered as the essence of graph mining. FSM is defined as finding all the subgraph patterns that occur frequently over the entire set of graphs. FSM is extensively used in graph clustering, classification and building indices in the databases. In literature, different FSM algorithms have been proposed such as AGM, FSG, SPIN, SUBDUE, gSpan, FFSM, CloseGraph, FSG, GREW. Most of these FSM techniques perform very well for small to medium size graph datasets, but the computational cost of FSM becomes very critical when the graph size is increased. In accession to this, the number of frequent subgraphs patterns grows exponentially with the increasing size of graph datasets. Consequently, in this research work, a novel FSM approach A RAnked Frequent pattern-growth Framework (A-RAFF) is proposed. This work is a preliminary work to study on how to make A-RAFF both computational effective and avoid the generation of the huge number of useless frequent subgraph patterns. A-RAFF has achieved efficiency by embedding the ranking of discovering FSGs during the mining process. The experiments on the three different real benchmark graph datasets demonstrated that the mining results of A-RAFF are very promising as compared to the existing FSM techniques.
Visual Analysis Scenarios for Understanding Evolutionary Computational Techniques’ Behavior

Machine learning algorithms are used in many applications nowadays. Sometimes, we need to describe how the decision models created output, and this may not be an easy task. Information visualization (InfoVis) techniques (e.g., TreeMap, parallel coordinates, etc.) can be used for creating scenarios that visually describe the behavior of those models. Thus, InfoVis scenarios were used to analyze the evolutionary process of a tool named AutoClustering, which generates density-based clustering algorithms automatically for a given dataset using the EDA (estimation-of-distribution algorithm) evolutionary technique. Some scenarios were about fitness and population evolution (clustering algorithms) over time, algorithm parameters, the occurrence of the individual, and others. The analysis of those scenarios could lead to the development of better parameters for the AutoClustering tool and algorithms and thus have a direct impact on the processing time and quality of the generated algorithms.