Reviewerly/exHarmony huggingface dataset:
Existing mechanisms for data deduplication

The success of any business in the present scenario depends on the utilization of data effectively. Access of same data again for acquiring correct data for business analytics and each organization uses different sources to collect data hence most of the data are duplicated. This duplicated data increases the desirable data extraction time as well as the probability of errors. Data duplication is the major challenge in modern business. Data duplication may occur when data is collected from different sources. Data deduplication is generally called single-instance storage. This is the procedure that focuses to identify the duplicate copies and eliminates those duplicate copies and reduces the storage overhead. Many techniques are used in data deduplication for determining a single instance of stored data. If deduplication technology is used in the appropriate situation, it can help in a great impact and provide a better solution. This chapter provides a comprehensive review of different techniques and existing mechanisms that are used for data deduplication.
Variable Rate Syndrome-Trellis Codes for Steganography on Bursty Channels

This paper presents a type of variable rate syndrome-trellis codes (VR-STC) for bursty channels. It can embed message bits with two different embedding rates. In the embedding, a cover vector is sliced into segments, and the embedding rates for each segment depend on the local channel distribution. The parities of stego segments are exploited to indicate the selected embedding rates according to a mapping function between parities and embedding rates. The core of the VR-STC is the parity-aware encoder, which can simultaneously output two candidate stego segments with different embedding rates and opposite parities, either of which can be used to constitute the final stego vector. A Viterbi algorithm is also suggested to find the closed stego segments. Besides, the mapping function between parities and embedding rates is designed by minimizing the embedding costs on a down sampled version of the cover vector. It can further improve the undetectability of the VR-STC. Experimental results on artificial signals and binary images suggest that the proposed VR-STC can provide high success rate of embedding and reduce the embedding cost on bursty channels.
AN ESCAPE FROM VARDANYAN’S THEOREM

Abstract Vardanyan’s Theorems [36, 37] state that $\mathsf {QPL}(\mathsf {PA})$ —the quantified provability logic of Peano Arithmetic—is $\Pi ^0_2$ complete, and in particular that this already holds when the language is restricted to a single unary predicate. Moreover, Visser and de Jonge [38] generalized this result to conclude that it is impossible to computably axiomatize the quantified provability logic of a wide class of theories. However, the proof of this fact cannot be performed in a strictly positive signature. The system $\mathsf {QRC_1}$ was previously introduced by the authors [1] as a candidate first-order provability logic. Here we generalize the previously available Kripke soundness and completeness proofs, obtaining constant domain completeness. Then we show that $\mathsf {QRC_1}$ is indeed complete with respect to arithmetical semantics. This is achieved via a Solovay-type construction applied to constant domain Kripke models. As corollaries, we see that $\mathsf {QRC_1}$ is the strictly positive fragment of $\mathsf {QGL}$ and a fragment of $\mathsf {QPL}(\mathsf {PA})$ .
Forensic Analytics Using Cluster Analysis: Detecting Anomalies in Data

Cluster analysis is a data analytics technique that can help forensic accountants effectively detect anomalies in complex financial datasets. This article provides a description of clustering analysis, discusses how it can be implemented to detect anomalies in data, and illustrates its use through a worked example using the Tableau software.
The application of ground high-precision magnetic survey to the geology and mineral investigation in Huanghe Town area

Abstract Three projects have been done to investigate the geology and mineral distribution in Huanghe town area A’nyemaqen-Bayanhar orogen, during the year 2013~2017. Ground high-precision magnetic method on a scale of 1:50000 has been used to study the magnetic character of the geological body and rocks. Correction has been taken in order to normalize magnetic anomaly calculated by different zero points. With the understanding of magnetic character, the distribution of stratum and geological structure can be known. After four years’ job, 16 faults have been recognized and an acid-intermediate intrusive lithosome has been found.
Research on Intercultural Communication Teaching Mode Based on Skype Computer Network Technology

Abstract In today’s information age, network technology has become an important carrier of information dissemination, which has revolutionized the communication between people. Cross-cultural communication teaching is a process of information dissemination, and a large amount of knowledge needs to be transferred between teachers and students in the classroom. Therefore, software based on computer network technology, such as Skype, injects new vitality into the teaching process, helps students integrate what they have learned in a virtual environment, and expands the application of knowledge.
Fast Bug Detection Algorithm for Identifying Potential Vulnerabilities in Juliet Test Cases

Automated static analysis tools (ASATs) are one of the most widely used and effective ways of detecting bugs in Java code. ASATs helps to improve the security of software by detecting potential violations without executing the application. We have explored the existing automated static analysis techniques detection capabilities and noticed that, they are deficient in terms of processing time and generation of false warnings. Thus, the study proposed a Fast Bug Detection Algorithm (FBDA) to address the aforementioned deficiencies. Furthermore, we compared our results based on the FBDA to the existing automated static analysis tools. The main idea is to reduce the size of the code area to be investigated without compromising on quality and improve the processing time. Additionally, we tested the effectiveness of our framework using a designated subset of the Juliet Test Suite and the results show that our approach achieved a performance gain of 66% and can successfully detect bug patterns than existing static analysis tools. Our experimental analysis further shows that, the percentage of false positive obtained by our framework is 18.5%, which is much less than the percentage of false positive reported by ASATs.
An Automated Framework for Swift Lecture Evaluation Using Speech Recognition and NLP

Feedback produced by students can give useful insights on lecturer’s performance and ability of teaching. Many institutions use the feedbacks from students efficiently to improve the education quality. In this study. A novel framework for collection and swift processing of students’ feedbacks about lecture is proposed in this paper, which addresses the shortcomings of traditional scale-rated surveys and open-end comments. The automated framework uses speech recognition and NLP tools to produce frequency graph of mostly used words, which can help to identify the topics need to be revised. An experiment was successfully conducted to test the framework among 3rd year undergraduate students.
WITHDRAWN: Image Art Design Based on Image Compression Sensing System and Embedded Device

The image Compressed Sensing (CS), two main challenges is to develop the design and reconstruction of the sample matrix. In one aspect, a random sample matrix is generally used and signal characteristics of the signal independent negligible. On the other hand, the most advanced art design image CS method made a very good performance. High computational complexity to meet two challenges recommend using convolution neuroimaging CS architecture. Network includes sampling network (called Convolutional Neural Network (CNN)) Joint Optimization of network reconstruction. Adaptation sampling learning image samples from a training matrix, which can accommodate a plurality of images of the configuration information, better reconstruction CS measurements. Reconstruction network, the network including linear and nonlinear initial reconstructed depth reconstruction networks, learning measuring terminal between CS and the reconstructed image to the end of the map. While achieving high speed of operation, the experimental results show that the most advanced reconstruction quality provided by the state. In addition, the results also show that the trained sample matrix can significantly improve the traditional CS image reconstruction method.
Analysis focused image formation in double exposure speckle photography

Abstract This paper presents the theoretical and experimental results of the analysis of the formation of the focused image in a double-exposure speckle photography. Theoretical analysis of the formation of the focused image in double-exposure speckle photography was carried out using the Vander Lugt method. It is shown that as a result of the summation of speckle structures displaced in the space of the photographic plates, moiré fringes are formed in double-exposure speckle photography. Experimental results confirming theoretical studies are presented.
Advanced Neural Networks

In this chapter, we explain the structure of the two types of neural networks that have been the mainstays of deep learning in recent years, following the words of physics in the previous chapter. A convolutional neural network has a structure that emphasizes the spatial proximity in input data. Also, recurrent neural networks have a structure to learn input data in time series. You will learn how to provide a network structure that respects the characteristics of data.
Development of Campus Hotspot Network Infrastructure using Peer Connection Queue Method based on Voucher System

Abstract The use of wireless technology in today’s infrastructure has taken a very important role in the development of mobile communications. The use of wireless technology through the Wireless Distribution System (WDS) technique has also been widely applied to the hotspot network infrastructure in the campus area. WDS is a method of developing wireless network infrastructure that enables the development of wireless networks to become more extensive. However, distance and placement between access points become very important factors to consider that cause problem when sending data and when receiving data. Implementation of the PCQ method, which is one of the features of the proxy router, is aimed at ensuring the quality and performance of the network in good and optimal conditions. The quality of data services provided is regulated using a system voucher, the user is divided into several segments depending on the chosen service plan. This research begins with the planning stage which consists of design and topology selection, then continues with the design and implementation phase, and ends with the preparation phase. The test result data will then be compared with the standard determined by TIPHON which is 0% loss, 150 ms delay, and jitter under 75 ms.
WITHDRAWN: Home Sports Intelligent Data Simulation Based on RDMA Read Mechanism of High Performance Computing System

Home games are one of the most essential to improve and ensure another quality. To do this, fix the mistake utilizing an innovation called RDMA and make it. Distant memory is utilized to move data, starting with one spot and then utilizing the control catches to improve quality and encourage downloads. Consequently, this method is generally used to improve data in the field of sports. Subsequently, this innovation makes it simple to work the controller's memory pivot with organizational innovation's assistance. Processing innovation can, likewise be extremely useful in improving data innovation. This is because distant innovation upholds works related to PC innovation to improve correspondence execution. It is anything but difficult to improve the aptitudes of competitors and investigate their presentation abilities. This is the reason this method is so broadly utilized. Cricket and numerous different games use it to improve and find the cycle of the game. Correspondence and information stockpiling when utilizing this innovation show the nature of far off innovation.
Special-Purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries

Model extraction (ME) attacks have been shown to cause financial losses for Machine-Learning-as-a-Service (MLaaS) providers. Attackers steal ML models on MLaaS platforms by building substitute models using queries to and responses from MLaaS platforms. The ML models targeted by attackers are called targeted models. In previous studies, researchers have assumed that attackers build substitute models that classify the same number of classes as targeted ones, which classify thousands or millions of classes to meet users' diverse expectations. We call such models general-purpose models. In fact, attackers can monetize stolen models if they accurately distinguish some classes from others. We call such models special-purpose models. For instance, a model that detects vehicles is useful for collision avoidance systems, and a model that detects wild animals is useful to drive them away from agricultural land. In this work, we investigate a threat of special-purpose ME attacks that steal special-purpose models. Our experimental results show that attackers can build an accurate special-purpose model, which achieves an 80% f-measure, with as few as 100 queries in the worst case. We discuss the difficulty in preventing the attacks with previously proposed defense methods and point out the necessity of a new defense method.
Holographic Health Calculation Method of Video and Image Based on Fuzzy Comprehensive Evaluation

Abstract The intelligent system of video and image information has the characteristics based on large numbers of cameras, exposed and uninterrupted operating environment, rich data types, diverse related systems and complex cascading architecture. How to implement the entire network operation situation by means of automation and intelligent monitoring has become a critical issue. The holographic health of video and images refers to the overall health value of intelligent system of video and image during the operation process, which can objectively reflect the operation situation of the video image, the perceived weaknesses during the operation process of the system, and performs analysis and warning during the process. Based on the comprehensive fuzzy model, this paper constructs a holographic health degree calculation method, designs an operation situation index evaluation system, constructs the factor set and automatically obtains the indicator monitoring value, calculates the influence factor evaluation matrix, and calculates the holographic health degree value through the Eigen-vector. Finally, this article gives the calculation examples and critical numbers extracted from the video image information intelligent system, the description of the health level and the actual situation of the system’s operating state, and verifies the validity of the holographic health degree calculation.
Implementation of Examles Non Examples in the Development Site Material in of Megalithic Lore Lindu Central Sulawesi for Student of Tadulako University, Indonesia

Abstract This research was conducted student learning outcomes through critical thinking by developing megalithic site material in Lore Lindu, Central Sulawesi. A series of researches are implemented through classroom action research, each of which goes through the stages of planning, action, observation, reflection. Data were analyzed using qualitative and quantitative descriptive methods. This research was conducted on students at Tadulako University class A, in semester 1 (one) there were 31 students. The results show that through the development of local culture-based material namely, megalithic sites through the application of examples and non examples, picture media can improve learning outcomes and students think critically in the learning process.
Regional aspects of humanitarization of university education

Modern university education in Russia requires multidimensional changes. One of the main directions is the optimization of the educational environment, in particular the management system. First of all, changes should take place at the level of education managers, who combine the qualities of a teacher, manager and psychologist. This necessity explains the increased requirements for the educational management. On the one hand, an important aspect is the competence of educational managers, on the other hand, we should deal with the system of interaction between managers and other participants of the university educational environment. Only a harmonious combination of these factors allows the university to function at the proper level. The problem of communication management in educational institutions is one of the most significant in the field of higher education. Communication is seen as one of the main resources for the development and growth of any organization. Communication as the process of sharing knowledge and information significantly affects the efficiency of an organization. The challenge of communication management is primarily in the fact that there are no clear criteria for monitoring the communication environment. This fact actualizes the problems of empirical research of communication management in the university education system. The most important dimension of optimization is the improvement of the communication environment, associated with the competence of employees and the system of their interaction. Thus, optimization of the communication environment makes it possible not only to improve management and communication processes, but also has a beneficial effect on the functioning of the university as a whole.
Descriptive Answer Script Grading System using CNN BiLSTM Network

Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
Security Prototype to Determine Critical Information and Improve the Management of a Public Organization

The increase in cyber-attacks forced public institutions to design computer security mechanisms to prevent damage to equipment or loss of information, for private use. The objective is to present a security prototype that allows determining critical information and improves the management of a public organization. The deductive method and exploratory research were used. The result was a mixed information control model, a mixed security architecture with mechanisms that comprehensively protect the information, verifying the identity and correlating the risks produced with the application of Blockchain technology, the Magerit and Octave methodology, it was also It is possible to correlate the risks of the links and the data, applying an analysis to give an immediate response. It was concluded that the hybrid architecture allowed to verify the identity and correlate the risks, Blockchain provided control against malicious attacks, together with Magerit that made use of safeguards and Octave analyzed the threats to the security of the assets, used in public management.
Hybrid Approaches for Augmentation of Translation Tables for Indian Languages

We discuss approaches for improving bilingual lexicon coverage by automatically suggesting translations for Out-Of-Vocabulary (OOV) terms, employing existing validated bilingual lexicon entries. Resource poor languages such as Hindi, Konkani and Sanskrit characterized by highly inflectional morphology were employed in our experiments. Known surface translations are mined for morphological similarities and bilingual morphemes thus learnt are used in suggesting word-word and phrase translations. Also, word-word translations are generated for the language pair Hindi-Sanskrit by pivoting bilingual stems and suffixes, with Konkani and English as bridge language, former a morphologically rich language while latter morphologically poor.
A secret sharing scheme based on game theory and BP neural network

In order to solve the problem of low efficiency of traditional secret sharing communication, we design a new rational secret sharing scheme based on BP neural network under the framework of game theory. We first propose an extended system game model for rational secret sharing, and define the set of participants, information set, feasible strategies, and utility functions of the secret sharing game. Secondly, using the characteristics of pattern recognition and classification of BP neural network, the secret input and output training patterns are correlated to realize nonlinear mapping in the neural network, thereby identifying the sub-secrets owned by honest and rational participants and recovering the correct secret. We design a communication channel by using the random function of bilinear pair on elliptic curve to ensure interactive communication among participants and reduce the communication complexity of the solution. Finally, the analysis proves the effectiveness and security of this scheme. By comparing with the existing scheme, this scheme not only has higher communication efficiency, but also meets its security requirements.
Block Halftoning for Size-Invariant Visual Cryptography Based on Two-Dimensional Lattices

Visual cryptography (VC) encodes a secret image into multiple transparencies. The secret image can be revealed by stacking a subset of these transparencies. However, the pixel expansion problem is a critical issue for VCs as a pixel in the secret image is encoded into multiple pixels. To solve this problem, many size-invariant VCs have been proposed. But there are some drawbacks in the existing size-invariant VCs. i) The input image is a binary image rather than a grayscale image. ii) Some schemes are not a ( k, n) - VC. iii) The quality of recovered image is poor. Based on two-dimensional lattices, a block halftone technology for a size-invariant VC is proposed. In particular, the proposed block error diffusion takes a grayscale image as the input. It is adaptable to any pixel expansion. The experiments show that the proposed halftone technique has a better visual quality for the stacking results than prior common schemes, such as probabilistic schemes.