Reviewerly/exHarmony huggingface dataset:
Cloud Data Storage Optimization by Using Novel De-Duplication Technique

Cloud Computing, an internet-based computing is composed of various applications and hardware deployed to the end-user. Existing system deals with block level de-duplication. But in case of block-level de-duplication maintenance of large number of blocks is highly difficult. It also requires high processing power when compared to other de-duplication techniques. This is the reason why File-level De-duplication comes into picture. File based de-duplication is considered due to faster access and easier storage and retrieval of files. This paper proposes to develop a Dynamic cloud storage system and improving the performance of cloud storage by minimizing the data replications using the De-duplication technology - De-duplication Based Cloud Storage (DBCS). In de-duplication, memory occupied by the replicates of the original file in the main server is reduced without removing them from the subsequent servers. For increasing the cloud storage efficiency, de-duplication and compression techniques are adapted. These techniques are to be implemented in the cloud environment via Amazon Web Services (AWS), which is an online web service provider. To develop a dynamic cloud storage system, the operations proposed are: 1. Inline de-duplication using MD5 (client side) 2. Dynamic data reconfiguration 3. Updating resources across servers. The main advantage of using cloud storage from the customers' point of view is that customers can reduce their expenditure in purchasing and maintaining storage infrastructure while only paying for the amount of storage requested, which can be scaled-up and down upon demand. The experiments are performed on different scenarios like 100% popular files, second combination of 30% unpopular files, 70% popular files and third combination of 70% unpopular files, 30% popular files. In all these cases, the proposed approach performs 95% better than traditional techniques in terms of space requirements.
Using the Research Results in Implementing Educational Programs for the Direction «Library and Information Activities»

Research activity is an integral part of the educational process at the university. The formation of educational programs of higher education includes not only the development of the main components of the of students training, but also the consideration of scientific directions and research in the field of culture, including library and information activities. The purpose of the article is to prove that there is a clearrelationship between the research activities of university teachers, the research work ofstudents and the formation of educational programs with the approbation of the methodology for analyzing this relationship. Within the framework of the conducted research, the results of the research activities of students and teachers – final qualification papers and publications of teachers of the Faculty of Information and Library Technologies of the Kemerovo State Institute of Culture (KEMGIK) were analyzed; the information obtained was compared with educational programs for bachelor’s and master’s degrees in library and information activities. The analysis of the topics of the final qualifying works is carried out, the leading directions of student research are identified. The correspondence of the disciplines of the curriculum to the research directions is presented. Based on the analysis of publications and an online survey of faculty teachers, scientific interests have been identified, which are reflected in the educational process. The influence of the results of students and teachers researchs on the formation and implementation of educational programs of library and information activities is considered. The analysis of research activities at the faculty has shown that in the course of the educational process, the results of students and teachers researches are used to modernize the content of disciplines, develop new courses, as well as retraining and advanced training programs for library staff. The authors prove that there is a clear relationship between the research activities of university teachers, the research work of students and the formation of educational programs.
Early Late Fusion of features for Skeleton based Human Action Recognition using Adaptive Kernel Learning

The identification of human action or behavior is very much discussed through the use of computer vision and artificial intelligence. Various action recognition strategies were proposed by many researchers to extract the action features in order to identify the action accurately. In the present article, we developed a new geometric feature fusion framework like score and features fusion on 3D skeletal data. First, geometric features like relative distance features, relative angle features are extracted from skeleton data. Next, various fusion models were adopted in early fusion combining features and late fusion after classifier to combine the output scores of both geometric features. In this work, the extracted features trained with the adaptive kernel learning classifier for recognizing actions. To test the working of our architecture, tested with other architectures on our own PACE3DAction and benchmark skeleton action datasets like PKU-MMD and NTU RGB-D.
Entity Recognition in Clinical Text Using A Hybrid Model Based on LSTM and Neural Networks

Successful analysis of clinical text is a challenge with sparse data available on the internet due to various issues. This paper proposes a model for Clinical text analysis to correctly predict and annotate the various keywords or entities present in a medical text which is mostly unstructured. The proposed solution uses deep learning models and NLP techniques. In this model, The basic training of the neural network is done using Bi-directional LSTM and Conditional Random Fields. The model uses an NLP pipeline that has the following stages: document assembler, sentence detector along with tokenization functions, and clinical Named Entity Recognition and Named Entity Recognition converter. With this model, the accuracy obtained was close to 86% on test data, of which individually the Named Entity Recognition converter has an overall accuracy of 90 percent. Improved accuracy was obtained by setting certain hyper-parameters and changing the required deep learning models even with smaller size of data.
Trust Prediction: Use of Decision Tree in Training &amp; Evaluation

The growth of Cloud Computing is the outcome of numerous benefits associated with this technology. Like: Reduce Energy consumption, waste &amp; carbon emission and affluence &amp; Comfort of its working apparatus. At the same point of time its security concern is making vibrations in the research Community and regulating organizations to find the means of making it a secure and reliable stream where consumers’ interests are ensured &amp; protected. More uncertainty arises in this sector, when data theft cases are cracked up and consumers have compromised some confidential details due to intentional or unintentional action of Cloud providers. It leads the need of Trust evaluation of the provider. This trust evaluation should be in the quantitative terms and completed before selection &amp; registration with the provider for cloud services. This paper is using predictive modeling technique to predict the trust level of the provider. Classification, a supervised Machine Learning technique that uses certification attainment status of the provider to predict its trust level.
Research on Text Recognition Method of Financial Documents

Aiming at the problems of text box overlap and missing text content in the recognition of financial documents in the Detecting Text in Natural Image with Connectionist Text Proposal Network(CTPN) model, it is proposed to improve the CTPN network structure based on the Inception network and optimize the feature extraction process. At the same time, the confidence loss function is introduced in the classification to optimize the candidates. Selection of the position of the frame anchor. A comparative experiment with the CTPN model proves that the improved network model has a certain improvement effect on text target detection. Created and implemented a text recognition model of DenseNet+Bi-directional Long Short-Term Memory(BLSTM)+CTC. The DenseNet network establishes a connection between feature maps, achieves the reuse of features, reduces the amount of parameters, and reduces the amount of calculations for each layer of the network. BLSTM can associate contextual information to assist prediction. The CTC loss function solves the text prediction and recognition when the input and output are not aligned. Experiments show that the model designed in this paper has a certain improvement in the recognition performance of traditional recognition systems and CRNN models.
Discovering Anomalous Patterns in Flight Data at Najaf Airport using Lstm Auto Encoders

Due to the continuous development of flight systems, it is quite hard to identify all abnormal cases of aviation in advance. For this reason, advanced techniques are always attempting to analyze aviation data and detect anomalies that General Aviation (GA) has not identified to ensure safety and reduce possible accidents in the future. This paper proposes to provide a model that combines AutoEncoding and neural networks to describe a Normal flight trajectory profile of NajafDubai flights (B738) data at Najaf International Airport and to detect anomalies by setting a specific threshold for the amount of error allowable for those flights (DXB). This approach involves the use of five basic features (Latitude, Longitude, Altitude, Speed, and Direction) for each ADS-B message. The procedure was also strengthened by two techniques, DBSCAN (Density-Based Clustering of Applications with Noise), and KNN (K-Nearest Neighbors) since they focus on the theory of distance and similarity for the initial handling of flightradar24 data. They are raw and untitled to obtain Normal training data and Abnormal testing data of the model. The model has an accuracy of 99.5%.
Supporting Readability by Comprehending the Hierarchical Abstraction of a Software Project

Exploring the source code of a software system is a prevailing task that is frequently done by contributors to a system. Practitioners often use call graphs to aid in understanding the source code of an inadequately documented software system. Call graphs, when visualized, show caller and callee relationships between functions. A static call graph provides an overall structure of a software system and dynamic call graphs generated from dynamic execution logs can be used to trace program behaviour for a particular scenario. Unfortunately a call graph of an entire system can be very complicated and hard to understand. Hierarchically abstracting a call graph can be used to summarize an entire system’s structure and more easily comprehending function calls. In this work, we mine concepts from source code entities (functions) to generate a concept cluster tree with improved naming of cluster nodes to complement existing studies and facilitate more effective program comprehension for developers. We apply three different information retrieval techniques (TFIDF, LDA, and LSI) on function names and function name variants to label the nodes of a concept cluster tree generated by clustering execution paths. From our experiment in comparing automatic labelling with manual labeling by participants for 12 use cases, we found that among the techniques on average, TFIDF performs better with 64% matching. LDA and LSI had 37% and 23% matching respectively. In addition, using the words in function name variants performed at least 5% better in participant ratings for all three techniques on average for the use cases.
Collaborative Attribute-Based Group Signatures from Lattices

This paper proposes a new Attribute-based Group Signature scheme that allows generating a signature collaboratively at an emergency when any group member cannot solely satisfy the given policy. In order to prevent colluding of group members and to control the emergency, we permit the involvement of semi-trusted authorities, namely, group manager and unifier. The new role unifier is responsible for generating collaborative attribute-based group signatures on behalf of the group with the support of the group manager and the group members. On the other hand, the group manager is responsible for preventing the unifier from generating signatures further after the emergency. We borrow the verifier-local revocation mechanism to support the group manager’s work. Moreover, we maintain general attribute-based group signature functionality in our proposal. Thus if a member can satisfy the given policy, he can generate a signature on behalf of the group. We construct our scheme from the post quantum primitive lattices. Thus based on the hardness of lattice based algorithms our scheme is secure against quantum attacks.
Painting from Part

This paper studies the problem of painting the whole image from part of it, namely painting from part or part-painting for short, involving both inpainting and outpainting. To address the challenge of taking full advantage of both information from local domain (part) and knowledge from global domain (dataset), we propose a novel part-painting method according to the observations of relationship between part and whole, which consists of three stages: part-noise restarting, part-feature repainting, and part-patch refining, to paint the whole image by leveraging both feature-level and patch-level part as well as powerful representation ability of generative adversarial network. Extensive ablation studies show efficacy of each stage, and our method achieves state-of-the-art performance on both inpainting and outpainting benchmarks with free-form parts, including our new mask dataset for irregular outpainting. Our code and dataset are available at https://github.com/zhenglab/partpainting.
A Self-Configurable Model for Cloud Resource Allocation

Cloud computing is a technology in which shared resources (networks, servers, storage locations, applications and services) are available through an extensive and dynamic network, and the cloud user can get access to it as quickly as possible and at the lowest cost. Cloud resource management is the process of provisioning, dispatching and monitoring resources. Extensive research has been conducted in the field of resource management, which includes resource provisioning, resource allocation, resource adapting, resource mapping, resource modeling, resource prediction, and resource brokering. Many techniques and architectures have been presented in this field, each of which has its own characteristics. Since the automating of cloud resource management procedures has always been a desirable goal, also it has always been a challenge. Amount of reliability in cloud systems is a success resource management criteria. The occurrence of errors and failures in the cloud systems, in addition to creating legal and financial penalties, is also effective in losing the trust of cloud users. The aim of this research is to achieve an acceptable level of reliability and availability by providing a self-configurable cloud model for resource allocation.
NeuralD: Detecting Indistinguishability Violations of Oblivious RAM With Neural Distinguishers

Adversaries can deduce confidential information processed by a program by analyzing its memory access patterns. Oblivious RAM (ORAM) converts a sequence of program memory accesses to an oblivious form, hence preventing adversarial inference. In recent years, a flourishing growth of sophisticated and effective ORAM protocols has occurred. Nonetheless, due to the complexity of these protocols, some of them contain defects in their implementations or even in their design, jeopardizing their obliviousness when processing certain memory access sequences. In this paper, we present <monospace>NeuralD</monospace>, a practical tool for testing ORAM protocols and detecting violations of their stated obliviousness. We train a neural distinguisher to form a <i>probabilistic testing oracle</i> capable of determining with a bounded high probability if a pair of ORAM inputs violates the obliviousness guarantee. <monospace>NeuralD</monospace> incorporates a set of techniques and optimizations to provide a highly effective and practical testing pipeline. Additionally, it features a delta debugging-like method to minimize error-triggering inputs (i.e., counterexamples) &#x2014; developers can use these counterexamples to debug their ORAM protocols and identify root problems. <monospace>NeuralD</monospace> is evaluated using well-known ORAM protocols and real-world ORAM applications (e.g., secure key-value storage). Within a few minutes, <monospace>NeuralD</monospace> can detect subtle violations of stated obliviousness.
Data Security Framework Using Block Chain Technology

Information technology plays an important role in processing the information and securing the data. Many universities and industries with small scale and large scale are using this frame work for their implementations. The block chain with the hyper ledger framework helps to build the trust between the industries and universities. This paper provides the link of block chain with cryptography algorithms. Some companies are not effective to predict the fraud cases in terms of academic certificates, credits or percentages. This application finds out the fraud candidates and fraud bio data using advanced techniques.
A Combined K-Mean Semantic Approach for the Implicit Document Clustering

In this study, an effective document clustering algorithm is introduced. The suggested algorithm comprises of existing K-mean algorithm with semantic approach. The PSO clustering algorithm is applied to assess the performance of the developed document with proposed hybrid algorithm. The present study focuses on assessing the proposed algorithm using reports on four different document dataset like 20NG, Reuters, WebKb. Finally, this newly proposed algorithm is compared with the clustering algorithm like Hierarchical to improve the factors such as efficiency, scalability and quality of clustering.
Sentimental Analysis through Speech and text for IMDB Dataset

In the advanced innovative present reality, most of the public are depending on different surveys for different utilizations and different products. Thus, to examine these audits and comprehend whether they are supporting or refuting about, opinion investigation can be utilized. The days for the remark surveys are currently disappearing and everybody is keen on hearing the audits so as they need not stop their functions. Thus, a slant investigation that does both through brief snippet and text can be used to complete work in sound. Along these lines, in this exploration paper, different Machine Learning and Deep Learning Models like Naive Bayes, Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron are used. Additionally, as of to change over voice to a message, a google API and a deep learning technique are used and best of two is exhibited and afterward sentiment analysis on those texts is performed and finally the sentiment (i.e., positive, or negative) of the speech is obtained. All these processes are carried out in real-time.
Using code models to instantiate data models

This chapter will introduce a particular graph model which we call “syntagmatic” graphs, applicable to both natural-language parse-structures and to modeling computer code. We explore some properties of queries over these graphs and briefly consider features of a virtual machine whose capabilities would include operations forming the foundation of syntagmatic-graph query-evaluation. We argue that syntagmatic-graph queries are structurally related to other software-engineering constructions, such as aspect-oriented programming, and discuss implementation strategies for static code analysis and runtime-reflection, in the sense of exposing procedures to scripting and remote-procedure interfaces. We then introduce a schematic figure we call the “semiotic saltire,” which informally classifies programming concerns into four engineering “aspects” supplemental to core type-implementations, namely GUI design, serialization, (database) persistence, and “procedural models,” the latter of which includes details such as runtime reflection and remote-procedure call capabilities, as well as annotative procedural pre- and post-conditions. We conclude by introducing certain benefits of hypergraph-based representations for data persistence.
A SIMPLE GIBBS SAMPLER FOR THE STATE ESTIMATION IN WIRELESS COMMUNICATIONS

In the recent years, wireless communications are extremely useful in many disciplines including health monitoring, environment monitoring, signal processing etc. State estimation and prediction are quite challenging tasks in wireless communications. Traditionally, in the literature, dynamic state-space models have been used for the state estimation and predic- tion purpose. The estimation method is based on Kalman-Filter which is computationally demanding. In this work, we consider computationally simpler Gibbs sampler algorithm for the state estimation. We consider three different cases, (i) continuous state values, (ii) binary (0/1) state values, and (iii) categorical state values with more than two categories. We consider a simple linear model for the prediction purpose, and the underlying regression coefcients are estimated by Gibbs sampler. We compute the misclassication proportions for assessing the practical usefulness of our estimation approach. Areal dataset where 200 wireless sensor nodes are used for measuring the temperature of a chamber is analysed in this work.
Power Generation Efficiency Prediction Methods of Photovoltaic Panel under Dust Based on Convolutional Neural Network

Dust on the surface of photovoltaic panels can cause the reduction of power generation efficiency and therefore impact efficiency of photovoltaic power plants. A prediction model based on convolutional neural network by taking image of dust status as inputs and power generation efficiency as outputs is established. The data sets of “Dust status image-Power generation efficiency” used for prediction models are collected from actual data acquisition experiments. Prediction models including four classical convolutional neural network models, such as AlexNet, VGG16, ResNet34, ResNet50, and V1, V2, V3 based on VGG16 were trained by acquired data set. The result demonstrates that the proposed V2 model with relatively simple structure and suitable learning step can accurately and quickly predict power generation efficiency of dust status image of photovoltaic panel. In addition, the proposed model provides technical support for photovoltaic power plants to improve power generation efficiency and economic benefits.
Anti-Drug Buddy: A Chatbot for Advocating the Awareness of Anti-Drug

In recent years, the spread and abuse of drugs have always been the focus of social attention because it can cause serious problems in our society. Although there have been many efforts in the domain of anti-drug, the number of guilty persons in drug cases is still rising. Therefore, there is a need to find a way to promote anti-drug awareness. At present, chatbots on the market cannot be completely applied to the anti-drug domain, so we design a dialog architecture to improve the anti-drug-related QA and to answer more detailed knowledge about drugs. Then, we propose Anti-Drug Buddy that utilizes a chatbot to advocate general knowledge about drugs and deepen anti-drug awareness for people. Currently, our Anti-Drug Buddy is already serving on LINE. For an anti-drug domain, our chatbot can answer users: Information about drugs, How to deal with drug problems, Pushing the latest news and information about drugs.