Reviewerly/exHarmony huggingface dataset:
The Value of Trust as a «Glue» of a Modern University’s Corporate Culture

The article states the stratification of the university’s corporate culture as its traditional cultural base into the institutional and administrative (realizing the «third mission» of the university and bearing the tendency of its transformation into a business corporation) and the culture of the teaching staff (which has preserved the traditional university functions) and culture. There are suggested ways to eliminate this mismatch.The authors substantiate the role and significance of the ideological attitude of the university’s corporate culture to trust as a «glue» of its stratified cultural base, which maintains a balance between the traditional and modern university’s mission (the concept of «glue» is suggested by the Russian researcher L. Gudkov, who uses this term to denote a mechanism holding society together into a unity and a whole).The methodology of conceptual reasoning is based on a sociocultural approach that suggests considering changes of any social institution in the context of responding to the challenge of the globalized world of network structures and market relations.The article identifies the causes and consequences of lacking coordination in the cultural base of the university.Communicative rationality, which, as a style of scientific and philosophical thinking, initiates the construction of modern social ontologies, is proven to be a possible complementarity instrument («glue») of the two university cultures. It is argued that today trust reveals its not only psychological, but also ontological and epistemological significance, orienting both components of corporate culture towards their unity in implementing of the university’s research and educational missions together with its «third mission».The article originally defines the university’s corporate culture as focused on the formation of students’ trust as a key factor of sparing life in the modern world with its ideological, economic, and political tensions. On this basis, it is proposed to form a university management strategy and to restructure the educational process.
Risk Analysis for Worthless Crypto Asset Networks

Blockchain technology has been used as the basis of crypto assets since the proposal of Bitcoin [] by Satoshi Nakamoto, and new operation methods are being discussed every day. However, because of decentralization which is one of the characteristics of the blockchain network, such network cannot easily stop the service, and the blockchain network with high anonymity may be abused. Therefore, in this study, we investigated the Altcoin network of approximately 240 stocks was investigated and analyzed and considered the sustainability of the network, which has no asset value and is close to service outage, was analyzed. It was confirmed that there are active nodes in a worthless cryptoasset network, and that there are malicious nodes regardless of the worth of a cryptoasset network.
Invnet: A Deep Learning Approach To Invert Complex Deformation Fields

Inverting a deformation field is a crucial part for numerous image registration methods and has an important impact on the final registration results. There are methods that work well for small and relatively simple deformations. However, a problem arises when the deformation field consists of complex and large deformations, potentially including folding. For such cases, the state-of-the-art methods fail and the inversion results are unpredictable. In this article, we propose a deep network using the encoder-decoder architecture to improve the inverse calculation. The network is trained using deformations randomly generated using various transformation models and their compositions, with a symmetric inverse consistency error as the cost function. The results are validated using synthetic deformations resembling real ones, as well as deformation fields calculated during registration of real histology data. We show that the proposed method provides an approximate inverse with a lower error than the current state-of-the-art methods.
Developing a novel Text mining Model for Exploring Knowledge from an Arabic text: Al-Hadeeth Al-shareef as case study

Stemming is an attempt to reduce word to its root form. It is a preprocessing step in Text Mining Applications as well as a prevalent need thing of Natural Language Processing (NLP) filed, Information Retrieval systems and text classifiers. Many research focusing on extracting Arabic root to reduce different grammatical forms of words like its nouns, verbs, and adjectives. In this study we developed a new Text Mining model consist of two algorithms, the first is a stemming algorithm which process the word and match it with the suitable diacritics or vocalize word pattern as ل ) ع ف (f-a-l and ( ل اع ف (faeil then extract root without using root file. When we applied our Stemmer algorithm on (Sahih Al-Bukhari) textbook, which is one of seven primary books in Al-hadith AlSharif in Prophet’s Sunnah beside Muslim and others textbook, we achieved 95.8% accuracy of root extraction and 96.4% inflection accuracy. The second algorithm is concerned to mining the knowledge by detecting the entities as verb time (Past, Present, and Imperative), noun and proper noun.
“Revolution” tendencies in higher education system through actor–network theory

Abstract The article considers modern tendencies prevailing in the higher education system while training technical specialists nowadays. According to the authors, excluding the humanitarian courses from curriculum results in the complete dissolution of subjectivity in the impersonal world, which is deprived of “living” knowledge, that is, definite knowledge of a definite person. The application of such an approach is illustrated by the actor–network theory (ANT). While studying several works by ANT founders, it turned out to be clear that such an approach eliminates any differences between natural and humanitarian, engineering, and philosophical knowledge. As a result, the net emerges consisting of numerous actants and which requires from every participant his “building‐in” a model with the functions being delegated to him in advance. The article concludes that an “alive” component is disappearing from the higher education nowadays when the main stress is being made on the knowledge acquired a priori, not a posteriori.
Criteria for Differing the Types of Auxiliary Verbs in Korean

In Korean linguistics, the study of auxiliary verbs, beginning with Chhve Hyon Be's study of traditional grammar in the 30s of the 20th century, is still the object of continuous research. This article focuses on the auxiliary verbs in Korean linguistics and the criteria for their differentiation. It examines the subordinate verbs' attachment to the leading verb, and the auxiliary verbs add meaning to the leading verb. The properties of auxiliary verbs are semantically and syntactically dependent on the leading verb, and have aspect and modal meanings. It also clarifies the relation of auxiliary verbs with related forms and the use of similar verbs together with the main verbs.
BoxCox multi-output linear regression for 10.7 cm solar radio flux prediction

Abstract We consider the problem of predicting the mid-term daily 10.7cm solar radio flux (F10.7), a widely-used solar activity index. A novel approach is proposed for this task, in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models, and then a multi-output linear regression model is used to predict future F10.7 values. The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressiveModel, Multi-layer Perceptron, and Support Vector Regression.
A Feasibility Study of Log-Based Monitoring for Multi-cloud Storage Systems

With more cloud customers are storing their data in multiple Cloud Service Providers (CSPs), they are responsible for managing the data in the multi-cloud storage environment, including monitoring the events on the cloud. They could monitor various cloud storage services by collecting, processing, and analyzing the cloud storage log files generated by multiple CSPs. In this paper, we investigate the feasibility of log-based monitoring for multi-cloud storage systems. We evaluate the current state of cloud object storage services and their logging functionality by analyzing cloud storage log files generated by a proof-of-concept cloud storage broker system using the three largest public CSPs: Amazon Web Services, Google Cloud Platform, and Microsoft Azure. We discover the logging functionality of cloud storage services could create severe security and reliability issues for cloud customers monitoring the multi-cloud storage systems due to cloud storage log files might not record unauthenticated and unauthorized requests with unpredictable delivery time.
Effective Splicing Localization Based on Image Local Statistics

In the digital era, people freely share pictures with their loved ones and others using smartphones or social networking sites. The news industry and the court of law use the pictures as evidence for their investigation. Simultaneously, user-friendly photo editing tools make the validity of pictures on the internet are questionable to trust. Intense research work is going on in image forensics over the last two decades to bring out such a picture’s trustworthiness. In this paper, an efficient statistical method based on Block Artificial Grids in double compressed JPEG images is proposed to identify areas attacked by image manipulation. In contrast to existing approaches, the proposed approach extracts the local characteristics from individual objects of the manipulated image instead of the entire image, and pair-wise dissimilarity is obtained between those objects and exploits the manipulated region, which has the highest variance among other objects. The experimental results reveal the proposed method’s superiority over other current methods.
The Convergence of Slide-Type Reductions

In this work, we apply the dynamical systems analysis of Hanrot et al. (CRYPTO’11) to a class of lattice block reduction algorithms that includes (natural variants of) slide reduction and block-Rankin reduction. This implies sharper bounds on the polynomial running times (in the query model) for these algorithms and opens the door to faster practical variants of slide reduction. We give heuristic arguments showing that such variants can indeed speed up slide reduction significantly in practice. This is confirmed by experimental evidence, which also shows that our variants are competitive with state-of-the-art reduction algorithms.
Image segmentation for neuroscience: lymphatics

Abstract A recent discovery in neuroscience prompts the need for innovation in image analysis. Neuroscientists have discovered the existence of meningeal lymphatic vessels in the brain and have shown their importance in preventing cognitive decline in mouse models of Alzheimer’s disease. With age, lymphatic vessels narrow and poorly drain cerebrospinal fluid, leading to plaque accumulation, a marker for Alzheimer’s disease. The detection of vessel boundaries and width are performed by hand in current practice and thereby suffer from high error rates and potential observer bias. The existing vessel segmentation methods are dependent on user-defined initialization, which is time-consuming and difficult to achieve in practice due to high amounts of background clutter and noise. This work proposes a level set segmentation method featuring hierarchical matting, LyMPhi, to predetermine foreground and background regions. The level set force field is modulated by the foreground information computed by matting, while also constraining the segmentation contour to be smooth. Segmentation output from this method has a higher overall Dice coefficient and boundary F1-score compared to that of competing algorithms. The algorithms are tested on real and synthetic data generated by our novel shape deformation based approach. LyMPhi is also shown to be more stable under different initial conditions as compared to existing level set segmentation methods. Finally, statistical analysis on manual segmentation is performed to prove the variation and disagreement between three annotators.
Ontology-Based Travel Recommender System

Recommender systems are designed to suggest information to users according to their preferences. The items could be movies, books, or various kinds of products. Most of the existing recommender systems are based on a database with limited advantages. However, in this chapter, the authors propose a knowledge-driven travel recommender system to integrate semantic data built using web ontology language (OWL) ontology to allow users to find suitable destinations that fulfil users' travel preferences. This work aims to develop a travel recommendation tool and to examine the reliability, the usability of the system, and satisfaction rate of users. They are also able to demonstrate that users can obtain desired results through queries on the ontology-based system. The overall evaluation of the system shows that users are happy and satisfied with the recommendation results.
Quadratic almost bent functions—Their partial characterization and design in the spectral domain

Quadratic almost bent (AB) functions are characterized by the property that the duals of their component functions are bent functions. We prove that these duals are also quadratic and illustrate that these bent duals may give rise to vectorial bent functions (in certain cases having a maximal output dimension). A necessary and sufficient condition for ensuring bentness of the linear combinations of quadratic bent duals is provided. Moreover, we provide a rather detailed analysis related to the structure of quadratic AB functions in the spectral domain, more precisely with respect to their Walsh supports, their intersection and restrictions of these bent duals to suitable subspaces. In particular, we completely determine the intersection of Walsh supports of the coordinate (semi-bent) functions for Gold AB mappings. We also provide the design of quadratic AB functions in the spectral domain by identifying (using computer simulations) suitable sets of bent dual functions. For instance, when n=7, this approach provides several AB functions which are not CCZ-equivalent to Gold functions.
Modern Approach of Sentiment Analysis Using Naïve Bayes Machine Classifier

The most popular communication platforms like social media and digital media has become combat ground to review public opinions, comments and sentiments. Information about places, product, research, food and daily life content are reviewed based on size, direction and purpose of information on regularly basis on online platform by sentiments and opinions. The overall benefit of reviewing is it save times of numbers of user by providing information with feedback on user-related content. Natural language can solve multiple problems in today’s world of computing. The text is an unstructured data model which is not following the traditional database structure, and numerical data always follows the traditional method of database. Classify sentiments in text has always been challenge as till now we cannot convert written data in emotion and feeling classification. Already work on how to distinguish the sentiments posted by reviewers is submitted to categorize them in good or bad class which is a new challenge of text analysis and still in a way to identity the common platform for identifying and reviewing the content. Implemented in Python already, one of the hottest commodity in different research domain with as natural language toolkit (NLTK) is that we ensure that the prerequisites of a library needed for sentiment are fulfilled. The dataset that we have is in a prior manner. The study mainly focused on the NLP, DTM, Text Mining, Corpus, and preparing its text analysis after preparing the unstructured data into structured data. This basically deals with the any committed news article for text mining, using Python natural language basic set of programs libraries that will be describing how nature language libraries works on text to finally categorizing it in good and bad category of review. Based on online sentiment reviews analysing process, it predicts an evolutionary score of sentiments.
Implementation of Blockchain Technology in Supply Chain Management

Supply chain management is important in every industry in the current scenario because it starts with the procurement of raw materials and ends with the final product being delivered to the customer. The aim of this paper is to study how blockchain technology, which is a novel and upcoming technology, can be applied to supply chain management and improve upon its shortcomings, specially the intermediaries. This paper explains what blockchain technology is in essence and how certain aspects of it can be applied to supply chain management. Furthermore, a case study has been done in ONGC Petro additives Limited (OPaL), where the vehicle turnaround time was studied and the various factors affecting it were listed out. Then, the factors responsible for any delay in turnaround time and lack of efficiency in the current supply chain inside the industrial complex were identified, quantified and studied, and finally, the paper reflects how blockchain can be used to tackle these problems, which range from the presence of intermediaries to excessive paperwork being done. Not only can blockchain make the process more efficient, but it makes supply chain management more secure for all parties involved in the process. Furthermore, the paper explains how a culmination of supply chain and blockchain can be used in various industries and leads to a more efficient and secure way of doing business in the future.
From JSON to JSEN through Virtual Languages

In this paper we describe a data format suitable for storing and manipulating executable language statements that can be used for exchanging/storing programs, executing them concurrently and extending homoiconicity of the hosting language. We call it JSEN, JavaScript Executable Notation, which represents the counterpart of JSON, JavaScript Object Notation. JSON and JSEN complement each other. The former is a data format for storing and representing objects and data, while the latter has been created for exchanging/storing/executing and manipulating statements of programs. The two formats, JSON and JSEN, share some common properties, reviewed in this paper with a more extensive analysis on what the JSEN data format can provide. JSEN extends homoiconicity of the hosting language (in our case JavaScript), giving the possibility to manipulate programs in a finer grain manner than what is currently possible. This property makes definition of virtual languages (or DSL) simple and straightforward. Moreover, JSEN provides a base for implementing a type of concurrent multitasking for a single-threaded language like JavaScript.
Detection Of Replication Forks In Em Images Using Faster R-Cnn

Currently, one of the most effective techniques to study the mechanisms of DNA replication is using the electron microscopy. Typical imaging experiments result in terabytes of image data, where finding and classifying the replication forks is done manually by experts and is extremely time consuming.Here, we present a fully automated deep learning based approach for detection and classification of DNA replication forks. It is based on the Faster R-CNN architecture, following with additional classification layers. The experimental results using real data indicate that the proposed method achieves detection accuracy which approaches the performance of expert annotators, even with limited amounts of training data.
LacminCC: lightweight anonymous communication model in cloud computing

Abstract With increasing application of cloud computing and big data technologies, a large amount of personal information is stored on the Internet, which raises the issue of privacy leakage. To protect people’s data privacy, this paper firstly presents a new anonymous Identify-Based Encryption (IBE) scheme and gives the proof of its security under the Bilinear Diffie–Hellman Security Assumption. Then, by introducing the anonymous IBE scheme into anonymous communication fields, this paper introduces a new lightweight anonymous communication model for cloud computing, which guarantees the anonymity of system users and the security of messages in small groups. Our analysis shows that, the proposed communication model cannot only reduce memory consumption and improve message transmission efficiency, but also effectively resist traffic-analysis attacks, node eavesdropping, and finally achieve secure anonymous communication in cloud computing.
Designing the balinese script-to-speech synthesis system using noto serif balinese font

Abstract The existence of regional languages shows an identity of the existence of a tribe. Indonesia has many regional languages, one of which is Balinese with Balinese script as input for the script which comes from Sanskrit. Reading Balinese script requires knowledge of Balinese script and Balinese culture in order to be able to correctly pronounce the written Balinese script. Since learning Balinese script took a long learning process, it certainly causes a slowdown in knowledge about how to read the Balinese script. Therefore, it is necessary to have a system that can translate Balinese script into a speech so that it can be used in learning the Balinese script without any mistakes in pronouncing vowels. This research designed a system synthesis of Balinese script into a speech with the system synthesis method of Noto Serif Balinese Font which consists of three processes namely text pre-processing, prosody generation, and concatenation. Concatenation or combining speech will use the Pitch Synchronous Overlap Add Method (PSOLA). The result to be achieved in this research is to be able to decompose the Unicode from the Noto Serif Balinese font into a sentence which then this sentence will become a message. In the future, this research can use input from an image to directly enter into speech. Making it easier for users to translate the Balinese script.
Video Face Detection Using Bayesian Technique

Now a days, security based applications are developed widely and these systems are adopted in various real-time applications. Visual surveillance is considered as a most promising technique where certain objects can be detected, tracked and recognized using computer vision based approaches. In this field, face detection and recognition is considered as the important part of surveillance system. Several approaches have been developed for face recognition but existing approaches are applied on the face data. Recently, video face detection techniques are also introduced which provides more information to improve the security system. In this work, we emphasize on the detection of face, along with tracking and recognition using computer vision approach. In order to achieve this objective, first of all we utilized face detection and tracking approach using Kalman filtering. After face detection, we extract the combined features of the input image and stored the trained data. The learning process is developed using Bayesian learning approach. The proposed approach is implemented on benchmark datasets such as IARPA Janus Benchmark A (IJB-A), the YouTube Face repository and the Celebrity-1000 repository. A comparative performance evaluation is carried out which shows the robust performance of proposed approach.