Reviewerly/exHarmony huggingface dataset:
Analisis Kinerja Teknologi Informasi Pada Universitas Jember Menggunakan Metode It Balanced Scorecard

In the face of the development of ICT (Information and Communication Technology) in tertiary institutions which is increasing, so that it requires an IT performance analysis method that can assess company performance accurately and thoroughly. This research was conducted to determine the extent of IT performance, whether it has met the needs and business strategies of tertiary institutions and IT strategies, and which still need improvements and improvements. In addition, the results of the analysis can be used as a basis as a reference for improving IT performance in Jember University. The purpose of using the IT Balanced Scorecard method is to analyze the IT performance of Jember University from four perspectives, namely: the perspective of the company's contribution, the perspective of user orientation, the perspective of operational improvement, and the perspective of future orientation. From the results of measurements that have been carried out it is known that the overall IT performance of Jember University is quite good, this is indicated by the results of the IT Balanced Scorecard size generated from each perspective.
Fast Gradient Projection Method for Text Adversary Generation and Adversarial Training

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training. Gradient-based attacks, which are very efficient for images, are hard to be implemented for synonym substitution based text attacks due to the lexical, grammatical and semantic constraints and the discrete text input space. Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. We then incorporate FGPM with adversarial training and propose a text defense method called Adversarial Training with FGPM enhanced by Logit pairing (ATFL). Experiments show that ATFL could significantly improve the model robustness and block the transferability of adversarial examples.
Efficient Way to Analysis the Texural Features of Brain Tumor MRI Image Using Glcm

Tumor spotting and elimination is one problematic issue in medical science that still remains. Untimely imaging techniques CT and MRI imaging techniques aid specialist in coming up with preferable perception, but because of high variability in tumor tissue of divergent patient process come up with laborious risks .In this paper, tumor image processing concern the three stages, namely pre-processing, segmentation and feature extraction using GLCM. After the accession of the source MRI image, it is preprocessed by converting the original image to grayscale image, then use of filters for removal of noise and using arithmetic operators for enhancement, then the stage of thresholding segmentation and watershed segmentation is done followed by a morphological operation to detection of tumor, thereby texture analysis using Gray Level Co -Occurrence Matrix. The above proposed procedures and techniques is applicable in accomplishing the reports axiomatically in less span of time.
An Analysis of the Educational Testing Service Major Field Test for Business Performance: Further Evidence

Assurance of learning is the key to students’ academic success. To this end, one of the largest historically Black colleges and universities (HBCUs) in the United States administers the Educational Testing Service Major Field Test for Business (ETS MFT-B) to assess student learning of business concepts. This study used a set of predictor variables to explain the ETS MFT-B performance at the subject HBCU. The results of the study are expected to close the gap between student performance and expected outcomes in the subject institution, as well as contribute to the broader issue of improving curriculum and teaching effectiveness in business schools in the United States. Using data from fall 2012 to fall 2018, the findings of the regressions indicated that new variables, namely, English language proficiency, hours attempted to hours earned, first-generation student status, and desire to pursue higher studies were important predictors of the ETS MFT-B total score and sub-scores. Among the traditional variables, the cumulative GPA and student’s major area were important predictors. These findings point toward several corrective actions in the assurance-of-learning process for the subject school. The subject school can add a business English class to the curriculum, improve teaching effectiveness, provide advising assistance when students retake classes, and adopt measures that will motivate students to prepare for the ETS MFT-B.
DEEP: Extending the Digital Forensics Process Model for Criminal Investigations

The importance of high quality, reliable forensic analysis -an issue that is central to the delivery of justice-has become a topic for marked debate with scientists, specialists and government bodies calling for improved standards and procedures.At the same time, Law Enforcement agencies are under pressure to cut the cost of criminal investigations.The detrimental impact that this has had on all forensic disciplines has been noted internationally, with the UK's House of Lords warning that if the trend continues, crimes could go unsolved and miscarriages of justice may increase.The pivotal role that digital forensics plays in investigating and solving modern crimes is widely acknowledged: in Britain, the police estimate it features in 90% of cases.In fact, today's law enforcement officers play a key part in the recovery, handling and automated processing of digital devices yet they are often poorly trained to do so.They are also left to interpret outputs, with the results being presented in court.This, it is argued, is a dangerous anomaly and points to a significant gap in the current, four-stage digital forensics process model (DFPM).This paper presents an extension to that model, the Digital Evidence Enhanced Process (DEEP), with the aim of fine-tuning the mechanism and ensuring that all digital evidence is scrutinised by a qualified digital forensics analyst.The consequence of adopting DEEP in actual criminal investigations will be to ensure that all digital evidence is analysed and evaluated to the highest professional and technical competency standards, resulting in the enhanced reliability of digital evidence presented in court which will serve the cause of justice in terms of reduced instances of associated unsafe convictions and/or unjustified exculpations.
The Phonological Interference in EFL Reading

The study aimed at exploring the phonological interference made by Pendalungan students in EFL reading, particularly in vowel phonemes. Pendalungan is an assimilation of two majority ethnics in Eastern Java; Java and Madura. Due to the unique characteristics of the language used by this group of community, it is assumed that the errors made by EFL Pendhalungan students might show different patterns as those found in either Javanese and Madurese students. To answer the problem of the research, the descriptive qualitative design was employed. There were eight students who belonged to the Pendalungan community, from a private university in East Java were taken as respondents. The secondary instruments used was an oral test of reading an English text. The finding showed that there were 12 incorrecty pronounced vowels; /ɪ, aʊ, ɔ:, e, Ʌ, əʊ, aɪ, ɪə, ə, ɒ, eɪ, æ/. The finding was analyzed based on the types of phonological interference, namely sound addition, sound omission, and sound replacement. The first two types have the least cases of two for each, while the last type has the most. Furthermore,the phonological interference emerged in the finding is somehow related to the nature of both languages, Javanese and Madurese. Inaccuracy mostly occurs because students pronounce the words as they are written.
IMPROVING THE EFFICIENCY OF DES ALGORITHM USING NEURAL NETWORKS

Cryptography is the technique of changingdata or information into unreadable for unauthorized persons.In cryptography process information transfer from sender to receiver in a manner that prevents from unauthorized third person.There are so many cryptography methods are available which based on number theory.An Artificial Neural Network (ANN) have many characteristics such as learning, generation, less data requirement, fast computation, ease of implementation and software and hardware availability.It is very useful for many applications, in this paper we explore the implementation of DES cryptography algorithm using ANN to reduce the execution time.this paper provides comparison between normal DES and ANN DES.
A Crane Robot of Three Axes Dimensional Using Trajectory Planning Method

This study aims to design a crane robot that has good performance with good stability, good accuracy in the gripper clamping the object at the point of balance and reach the target location well. The crane controller is installed with US-100 ping sensor and proximity infrared sensor to detect position of object. The robot crane moves on the x , y and z axes or in three dimensions using motors as actuator and it can be adjusted with motor drive. The crane moves on the x and y axes using DC motor and z axis using servo motor. The crane automatically moves when it detects an object. The crane's movement uses the trajectory determination method by maintaining speed. Finally, the average accuracy of the gripper clamping exactly at the midpoint of the object is 93%. The length of the object when it is clamped has an accuracy of 95%. The performance of the crane robot is evaluated to transfer an object to the destination location takes 11 seconds with a track length of 86.055 cm
L<sub>p</sub>-Method-Noise Based Regularization Model for Image Restoration

Various regularization techniques have been sufficiently developed to improve the quality of the image restoration. By utilizing existing image smoothing operators, method noise provides a new way to formulate regularization functions. The so-called method noise refers to the difference of an image and its smoothed version, obtained by an image smoothing operator. It is concluded that the method noise of a clear image mainly contains edges and small scaled details, and should be as sparse as possible. Based on this conclusion, we introduce Lp-norm penalty on the method noise, which can accurately describe its sparse prior distribution. We formulate an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -method-noise based regularization model and analyze its advantages in terms of its solution and performance in image restoration. Specifically, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm penalty of the method noise is better than other forms of norm in removing noise and keeping the details. Moreover, a modified Bregmanized operator splitting algorithm is designed for the proposed model. Experimental results show that the proposed method can obtain better results than other method noise based regularization methods.
ONLINE EDUCATION IN HIGHER SCHOOL IN RUSSIA: MAIN ACTORS OF INSTITUTIONALIZATION AND SOCIAL CONSEQUENCES

This article covers two main aspects. Firstly, it describes the motives of the main actors in the system of higher professional education for promoting or opposing plans to introduce online education to higher education in Russia. Secondly, there are suggestions about the social consequences of the institutionalization of online education in the curricula of Russian universities. The authors substantiate the hypothesis that online education will lead to the complete disappearance of the social environment, where high quality educational services are provided. This will result in a significant decrease in the intellectual, creative and spiritual development of students.
Textual Mining- Evaluation of Mann Ki Baat Repository

Background and Objective: As computers and the Internet are broadly utilized in nearly every region, numerous computerized text data is produced each day.It becomes a fundamental task to explore and effectively search such massive data.The main aim of the present study is to emphasize the recurrence of topics and identifying main ideas from a popular monthly addressing radio program Mann Ki Baat by using topic modeling technique.Data and Method: The present study utilizes the unstructured data of Mann ki Baat from January 2020 to March 2020, collected from the PMINDIA website.This program was initiated by the Honorable Prime Minister of India, Mr. Narendra Modi.This examination uses a popular technique Topic modeling based on LDA (Latent Dirichlet Allocation).Findings: The results show that the method automatically extracts the main ideas and issues discussed.Besides it provides information about the most likely topics and themes discussed in each month that left an impact on people and helped in raising awareness.Novelty: This is a first study of the application of popular technique topic modelling on Mann ki Baat.Further, this is the first attempt to extract the ideas discussed in a social campaign using a statistical model.
Application of Project Management in the Management of Teaching and Research Projects in University

The project of teaching and research in university is an important link to deepen the reform of education and teaching, strengthen the teaching level of schools and improve the competitiveness of schools.To strengthen the process management of teaching and research projects and apply the knowledge system and technical tools in project management to the process of project practice is helpful for the project to effectively improve the project efficiency and ensure the results and quality of the project research in the process of topic selection, project approval, implementation and conclusion.This paper analyzes the problems and shortcomings in the process of teaching and research project management, makes use of project combination thinking, improves the management process, strengthens the supervision and management of the project process, and promotes the transformation of project results, so as to improve the success rate of project establishment and ensure the smooth implementation and quality of the project, and improve the output and application of the project results.
Is the Binary Search Faster when Two Variables are Added in the Middle of the Data?

Modern architecture has developed new algorithms for large and distributed data, In this digital era, algorithms which intended to quickly process, search, and sort data are very much needed by IT developers.This research will discuss the modification of Binary Search Algorithm, which is previously known for its existing low, mid, and high variables.This research will discuss how we can modify the binary search algorithm by adding two variables-the variable mid-low and mid-high.It will show how by adding two variables between the mid variable could improve the performance of the Binary Search Algorithm.
THE ROLE OF CONTEXT IN THE INTERPRETATION OF PRAGMATIC MEANING

This article entitled "The Role of Context in Interpreting Pragmatic Meanings" aims to find the role of contexts in interpreting pragmatic meanings. The study is a literature review that is interpretive in nature. Data collection techniques are done by reviewing the work of the experts to map the author’s perspective on the context. The steps of the analysis are identifying the theories being reviewed, classifying the results of the identification, interpreting the results of the review, and writing a journal article. The findings of the study include 11 aspects of context to determine pragmatic meanings. Based on the findings, it can be discussed that the semantic meaning is different from the pragmatic meaning because the pragmatic meaning always depends on the context. On the basis of these findings, it can be concluded that the pragmatic meaning is determined by the extralingual contexts.
Development of Local History Learning with the Heroism Theme of K. Gholib Based on E-Book for Private Vocational School Students in Pringsewu Regency

The purpose of this research is to discuss the development of material that supports the history of the struggle of K.H Gholib, who then compiles it into an e-book using the software "Flip PDF Professional 2.4.6.8". It was developed separately to analyze the material and increase student interest in Private Vocational High Schools in Pringsewu District by instilling the values of nationalism, heroism, and patriotism of a religious figure and fighter who participated in the struggle for independence in Lampung from the attack of the Dutch Military Aggression II in 1949. The research method used was the research and development of "R&amp;D". The focus of e-book development uses the ADDIE development model through five intoxices, namely: analysis, design, development, implementation, and evaluation. The final results of this study show how the preparation and implementation of e-books in the classroom, so that the product developed can foster student interest in learning.
EFFICIENT TEXT SUMMARIZER USING POINT TO GENERATOR TECHNIQUE

Text summarization is the system of extracting essential facts from the given facts and writing it within the form of precis.Many people locate it tough to read the whole passage of records so that it will gather the essential key factors, so we use textual content summarization to reduce the burden of analyzing big passages.Text summarization is been used in lots of application like business evaluation and marketplace overview.On the entire, by using textual content summarization we get the statistics wished simply by using studying the summary.
Exploring Artificial Neural Networks in Cryptography – A Deep Insight

In today's digital world, cryptography became indispensable in almost all trending technologies.For ensuring a completely secure system with limited computational complexity involved in the generation of randomness, secret key and other aspects, researchers employed various techniques to the cryptosystems.Despite the considerable number of strategies, applications of Artificial neural networks (ANN) for cryptographic problems appeared to be more remarkable.Owing to this fact, this work has been devoted to reveal the applications of different kinds of artificial neural networks for different categories in cryptosystems.Artificial Neural Networks are stepping stones in search of artificial intelligence.ANNs are chosen because they are good at generalization, adaptive decision making and unknown pattern recognition.This plethora of information regarding how each neural network was used in different types of cryptosystems is mainly contributed to future researchers to aid their novel works in this discipline.Also, application of Neural networks for cryptography related problems yielded positive results in almost all the applied fields.Therefore, this work is done with the intention of highlighting the novel combination of neural networks and cryptography.
Blockchain-based federated identity and auditing

A federated identity is a single identity that enables users to access multiple services across a network of business parties. Such identities are subject to various threats and attacks and face diverse challenges including identity leaks, centralised management, auditing limitations, and long breach investigation processes. This paper proposes a framework aimed at automating and decentralising the generation and auditing of a robust and secured blockchain-based federated identity in a marketplace. Business parties participating in the marketplace form the nodes of a distributed blockchain network and participate in the creation of federated identities. Users of this network can access services provided by any one of the participating parties using a single federated identity. All transactions are fully audited in the blockchain, meaning that participating parties can monitor access to their service and users can trace the use of their identities. The proposed framework has been evaluated using two blockchain technologies (Ethereum and Hyperledger Fabric) to measure its performance in public and permissioned blockchain environments.
Cyclic Convolution Based on Fermat Number Transform (FNT) with Low Power and High Speed for Parallel Architecture

The low power high speed parallel archtechture for cyclic convolution based on fermet number transform (FNT) operated at ultra low voltages are presented.thepower consumption, Delay and Area of this new novel 4-2 Compressor Architecture is compared with Existing architecture.Therefore the proposed 4-2 compressor architectures shows better performance In the proposed architecture the outputs are efficiently used to improve Low power, high speed, performance,and less delay.FNT is exact with no round off errors and Truncation errors.The Binary Arithmetic in FNT performs the Exact Computation.To perform the cyclic convolution in FNT some techniques are implemented.To perform the (FNT) and (IFNT) the techniques used are code Conversion method without Addition (CCWA) and Butterfly Operation without Addition (BOWA).Here the Point wise Multiplication in the Convolution is accomplished by Modulo 2^n+1 Partial Products Multipliers (MPPM) and Output partial products which are Inputs to the IFNT.Thus Modulo 2^n+1 Carry save Propagation Additions are avoided in the FNT and the IFNT except their final stages and the Modulo 2^n+1 multiplier.Thus the Power and Execution delay of the entire FNT will be reduced which is only because of usage of above techniques in the Design.This will be done by using Very Large Scale Integration (VLSI) technology.The synthesis results using 180nm SOC Technology is been used.Therefore the proposed one has less Power better Throughput Performance and involves less hardware complexity.
A Solution to the High-Dimensional Classification Problem Using an Improved Hybrid Feature Selection Algorithm Guided by Interaction Information

This paper addresses the high-dimensional classification problem, which is very important in machine learning. When the number of features of the data is very high, the classification performance of a given classifier can degrade because there are not enough samples for training. One of the solutions to cope with this problem is to perform feature selection to reduce the number of features. We propose a new hybrid feature selection algorithm based on interaction information that improves upon the previous one. Our improved method employs interaction information to select candidate features to be added to the current feature subset. Cohen's d is used as the significance testing to decide whether a new feature is permanently added to the subset. We adopt new stopping criteria to allow intensive search. Our search method is efficient and is able to find excellent solutions. Experiments results on eleven high-dimensional data sets show that compared to other hybrid feature selection algorithms, our proposed algorithm provides high classification accuracy and requires a small number of features for classification.