Reviewerly/exHarmony huggingface dataset:
A Novel Method to Safeguard Patients Details in IoT Healthcare Sector Using Encryption Techniques

Internet has become a part of our daily life. Most of the communications or the transfer of the data across the globe is happening over the internet. With Internet of Things [IoT], the devices can transmit data with each other over the internet. As the internet play a crucial role, the security of the data also becomes important. Confidentiality is important in the healthcare sector where encryption plays an important role. There are many effective encryption algorithms available, but through this work we are proposing a novel method that aims at adding the randomness to the encryption algorithm. The random number generation technique used in this paper is Web Scraping, here the data is scraped from the web page where the data gets constantly updated which adds to the randomness. The random number generation module is added to the existing encryption algorithm and the encryption algorithm is tested on different file sizes to test the level of encryption.
A Graph Theoretic Approach to Construct Desired Cryptographic Boolean Functions

In this paper, we present four product operations to construct cryptographic boolean functions from smaller ones with predictable Walsh spectrum. A lot of cryptographic properties of boolean functions can be presented by their Walsh spectrum. In our method, we use the product of Cayley graphs to present new boolean functions with desired Walsh spectrum and investigate their non-linearity, algebraic and correlation immunity.
Leveraging Indexical Pragmatics (OFIP) for Search Engine: An Ontology- based Approach

The relevance of search results is an important indicator of information retrieval performance.A domain-specific Search Engine (SE), distinct from a general web SE, focuses on a specific segment of online content and may increase search results relevance.Traditional methods to improve domainspecific SE precision heavily depend on query expansion, lexical analysis of texts, and large amounts of training data.These methods suffer from limited effectiveness and efficiency because expanded query terms and coarse language features bring in uncontrollable complexity and increase dimensionality.Our design, leveraging the integrated power of computational syntax, semantics, and indexical pragmatics, proposes an ontology-driven framework that is tailored to work in a dynamic Internet environment without large amounts of manually annotated training data.This article presents our design, that is essential for building a domain-specific SE, and its instantiation in the terrorism domain.
읽기이해 예측지표에서 나타나는 난독현상 경험 아동과 일반아동의 차이점 연구

The authors investigated whether a difference exists in reading comprehension predictors between students with and without reading difficulties. A series of multiple regression analyses were conducted to determine reading comprehension predictors. The results showed that vocabulary and working memory accounted for 35% of reading comprehension in students without reading difficulties, while fluency and listening comprehension were associated with 43% of reading comprehension in students with reading difficulties. The follow-up analyses further pointed to reading fluency as the foremost predictor for students with reading difficulties. The results also suggested that as the students mature and become more successful readers, they spend more cognitive resources on higher-order cognition skills such as vocabulary and working memory while less skillful readers focus on using text-oriented skills such as reading fluency during their reading comprehension.
Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing.First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles.Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition.Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed.The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal.
Optimizing scheduling performance through time slice management based on Max Min strategy in cloud system

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Homomorphic Encryption for Finite Automata.

We describe a somewhat homomorphic GSW-like encryption scheme, natively encrypting matrices rather than just single elements. This scheme offers much better performance than existing homomorphic encryption schemes for evaluating encrypted (nondeterministic) finite automata (NFAs). Differently from GSW, we do not know how to reduce the security of this scheme from LWE, instead we reduce it from a stronger assumption, that can be thought of as an inhomogeneous variant of the NTRU assumption. This assumption (that we term iNTRU) may be useful and interesting in its own right, and we examine a few of its properties. We also examine methods to encode regular expressions as NFAs, and in particular explore a new optimization problem, motivated by our application to encrypted NFA evaluation. In this problem, we seek to minimize the number of states in an NFA for a given expression, subject to the constraint on the ambiguity of the NFA.
Analysis of Learning Material on 4-Stroke Engine Tune-Up Practice of Motorcycle Engineering

.
Definition of the ROLIE Vulnerability Extension

This document extends the Resource-Oriented Lightweight InformationExchange (ROLIE) core to add the information type categories andrelated requirements needed to support Vulnerability use cases.Additional categories, properties, and requirements based on contenttype enables a higher level of interoperability between ROLIEimplementations, and richer metadata for ROLIE consumers. Inparticular, usage of the Common Vulnerability Enumeration (CVE) [cve]format is discussed.
The Concept of Dilthey’s Hermeneutics as Reflected in Juliasih’s Potensi Perempuan Amerika (Tinjauan Feminisme)

This paper aims at the description of Dilthey’s hermeneutics concept in the book entitled “Potensi Perempuan Amerika (tinjauan feminisme)”. This research is qualitative descriptive research. The data were analyzed by Dilthey’s hermeneutic approach. The result showed that in understanding and interpreting meaning, there are 3 formulas that can be used as interpretive methods according to Dilthey, namely Erlebnis (living experience), Ausdruck (expression), and Verstehen (understanding). The experience referred to here is the life history of the author, everything that was experienced by the director during his lifetime. The expressions are works written by the author through the language used to voice their desires, opinions, and actions. Understanding is the process of bringing together all the data generated from the process of experience and expression so that it can draw conclusions. In the book of Juliasih Kusharyanto, the three main aspects of the hermeneutics have been carried out well so that at the end of the research, Juliasih was able to provide a complete meaning from the historical side as well as language expressions that exist in the storyline of each novel studied.
PLAC: Partitioning Based Lazy Classification

Traditional classification methods cannot well capture the characteristics of complex problems, thus leading to poor performance.In this paper, we propose a new framework named Partition based LAzy Classification (PLAC) to better characterize complex problems by dividing the training data space into smaller and easier-to-learn partitions.In PLAC, only the nearest partition of a new instance is used to train a local classifier that is finally used to classify the new instance.As the partitioning is performed based on information gain before receiving a new instance, the resulting partitions are groups of similar instances and the chance of the nearest instances of the new instance coming from different regions by accident is reduced.Moreover, our method uses only one partition to conduct a prediction and employs the caching mechanism to avoid work replication during classification, thus efficiency is improved.An extensive experimental evaluation on 40 real world data sets shows that PLAC effectively improves the performance of base classifiers and outperforms existing mainstream ensemble methods.
Application of CAI Computer Technology in Teaching Western Art History

The purpose of the computer aided instruction (CAI) is to improve the teachers’ teaching efficiency and stimulate the students’ interests in their learning. By using the modern means of the computer aided instruction (CAI), we can do a better job in our teaching. The application of the computer-aided instruction (CAI) in the art teaching should be guided by the new ideas and concepts in the best environment for the students to learn in the most efficient and intuitive way to promote the development of the educational modernization. The current situation of the CAI courseware assistant teaching is good as a whole, but there are still some shortcomings in three aspects: the teachers’ CAI skill proficiency, the integration and sharing of the CAI teaching resources, and the frequency of the CAI courseware teaching. The teaching of the art history needs to be improved.
A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies.

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user. In this work, we characterize and discuss different user types and show that existing approaches do not achieve optimal efficiency for all of them. As a remedy, we suggest a new type of expert question which aims at fitting the answering behavior of all analyzed experts. Moreover, we present an algorithm to optimize this new query type which is fully compatible with the (tried and tested) heuristics used in the field. Experiments on faulty real-world ontologies show the potential of the new querying method for minimizing the expert consultation time, independent of the expert type. Besides, the gained insights can inform the design of interactive debugging tools towards better meeting their users' needs.
Speech Recognition System for Isolated Words Using Ensemble-based Classification

Automatic Speech Recognition (ASR) can be defined as an independent, computer-driven transcription of spoken language into readable text in real time. In this paper an ASR system was developed to recognize ten English isolated spoken digits. The work flow of the developed system starts with the speech signal being acquired from a microphone and brought into a Matlab development environment for analysis. Next a word-detection algorithm was used to separate each uttered digit from ambient noise and silence. Then a 13 Mel Frequency Cepstral Coefficients (MFCCs) were computed to represent the feature of each frame in the speech signal. Finally, an ensemble of 4 classifiers were combined in order to improve the overall performance of the classification. The ensemble consisted of Nearest Neighbor (1NN), five Nearest Neighbor (5NN), Dynamic Time Warping (DTW) and Minimum Euclidean Distance (MED). The outputs of these classifiers were combined by a majority voting method, to produce the final predicted class label. In the case of a tie .i.e. no majority, then a sensitivity measure (Se%) is calculated for each classifier. So if an unknown uttered digit was presented to the system, it would be assigned to the class predicted by the classifier which has the highest sensitivity value. The developed ASR system was trained and tested with a dataset of size 250 samples each. The ensemble based classification proved to be superior in terms of accuracy than any of the standalone classifiers.
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.
Types and Functions of Interjections in Wole Soyinka�s Ala?pata? A?pa?ta and Yoruba Speech Community

Interjections are universal means of communication among human beings which pass across different emotions and information in different cultures and languages. However, while some are found in nearly all languages but with varied meanings and functions according to context, some are peculiar to specific languages and cultures. This work, therefore, investigates the functions and meanings of both universal interjections (specifically Oh and Ah) and the Yoruba language specific interjections found in Wole Soyinkas Ala?pata? A?pa?ta. The work also discusses two other significant Yoruba language specific interjections although they are not found in the analyzed text. The authors chose the text because it is replete with interjections Oh and Ah which carry various meanings and perform various functions (few of which have not been discussed in the literature) and also because it contains the Yoruba language specific interjections which probably have not been investigated in the literature, thereby contributing to knowledge.Keywords: universal interjections, Yoruba language specific interjections, Ala?pata?
평생학습자 쓰기 교육의 방향 모색을 위한 기초 연구 - 대학의 평생학습자 쓰기 능력 인식과 쓰기 능력 평가 분석을 중심으로 -

The purpose of this research is to obtain basic data to support the improvement of writing education for lifelong learners. For this purpose, first, evaluation of writing ability of lifelong learners, second, awareness of writing ability of lifelong learners, analysis of lifelong learners'' needs for writing education, and in - depth interviews of teachers. According to the results of the survey, lifelong learners were burdened with the task of lecture, and the minimum level of writing ability required to carry out the study at university was around 40-60%. However, when compared with the minimum level required for the detailed ability of writing, his ability was relatively low. In addition, lifelong learners were highly demanded for deepening writing education, but the teachers felt that the need for deepening writing education and writing education for strengthening basic ability were more urgent. According to the results of the diagnostic evaluation, the final achievement of the lifelong learners was 49.9% of the general learners, and the writing ability was half of the general learners. Therefore, it is considered that lifelong learners'' writing education should focus on improving basic skills.
A Categorisation of Post-hoc Explanations for Predictive Models

The ubiquity of machine learning based predictive models in modern society naturally leads people to ask how trustworthy those models are? In predictive modeling, it is quite common to induce a trade-off between accuracy and interpretability. For instance, doctors would like to know how effective some treatment will be for a patient or why the model suggested a particular medication for a patient exhibiting those symptoms? We acknowledge that the necessity for interpretability is a consequence of an incomplete formalisation of the problem, or more precisely of multiple meanings adhered to a particular concept. For certain problems, it is not enough to get the answer (what), the model also has to provide an explanation of how it came to that conclusion (why), because a correct prediction, only partially solves the original problem. In this article we extend existing categorisation of techniques to aid model interpretability and test this categorisation.
Biadversarial Variational Autoencoder

In the original version of the Variational Autoencoder, Kingma et al. assume Gaussian distributions for the approximate posterior during the inference and for the output during the generative process. This assumptions are good for computational reasons, e.g. we can easily optimize the parameters of a neural network using the reparametrization trick and the KL divergence between two Gaussians can be computed in closed form. However it results in blurry images due to its difficulty to represent multimodal distributions. We show that using two adversarial networks, we can optimize the parameters without any Gaussian assumptions.
Natural language to SQL

In this research, an intelligent system is designed for the users to access the database using natural language. It accepts natural language input and then converts it into an SQL query. Using query language for dealing with databases has always been a professional and complex problem. The system currently handles single sentence natural language inputs and concentrates on MySQL database system. The system accommodates aggregate functions, multiple conditions in WHERE clause, join operations, advanced clauses like ORDER BY, GROUP BY and HAVING. The natural language statement goes through various stages of Natural Language Processing like morphological, lexical, syntactic and semantic analysis resulting in SQL query formation. Intelligent Interface is the need of database applications to enhance efﬁcient interaction between user and DBMS. The research focuses on making the system more dynamic.Improvements have been introduced to the system by incorporating preprocessing of text, named entity recognition, building hierarchical relations, semantic similarity and negation handling using dependency graphs.
The Research of Curriculum Management Information System in Colleges and Universities

With the development of information technology, the educational administration in Colleges and universities is more and more inclined to paperless and automated. How to carry out curriculum management is an indispensable part of educational administration management. On the basis of studying various curriculum management systems at home and abroad, this paper puts forward an curriculum arrangement based on priority strategy, which can select the optimal curriculum arrangement among various schemes and deal with conflicts optimally, thus improving the efficiency of curriculum arrangement and students' curriculum selection by school educational administration departments. Finally, the prototype system of the course management system is implemented based on JAVA, which facilitates the retrieval and management of courses by managers, teachers and students.
The changing face of enterprise virtualization (keynote)

As Enterprise IT infrastructure broadens beyond the traditional core data center and as new workloads move into the virtual realm, new opportunities and challenges emerge. In this talk we will focus on the changes being wrought in the virtual world by the growth of the edge, the emergence of multicloud, and the amplifying affects of the incorporation of Machine Learning and High Performance Computing into these evolving environments.
Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application

In this paper we address the problem of discovering a small set of frequent serial episodes from sequential data so as to adequately characterize or summarize the data. We discuss an algorithm based on the Minimum Description Length (MDL) principle and the algorithm is a slight modification of an earlier method, called CSC-2. We present a novel generative model for sequence data containing prominent pairs of serial episodes and, using this, provide some statistical justification for the algorithm. We believe this is the first instance of such a statistical justification for an MDL based algorithm for summarizing event sequence data. We then present a novel application of this data mining algorithm in text classification. By considering text documents as temporal sequences of words, the data mining algorithm can find a set of characteristic episodes for all the training data as a whole. The words that are part of these characteristic episodes could then be considered the only relevant words for the dictionary thus resulting in a considerably reduced feature vector dimension. We show, through simulation experiments using benchmark data sets, that the discovered frequent episodes can be used to achieve more than four-fold reduction in dictionary size without losing any classification accuracy.
Examples of Construction of Non-continuous Syntactic n-grams

In this section, we provide examples of the continuous and non-continuous syntactic n-gram construction for Spanish. We analyze the sample sentence provided in the previous chapter.