Reviewerly/exHarmony huggingface dataset:
A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

This paper intends to present main technique for feature extraction on multimeda getting well versed and a challenging task to handle big data. Analyzing and feature extracting valuable data from high dimensional dataset challenges the bounds of measurable methods and strategies. Conventional techniques in general have less performance while managing high dimensional datasets. Lower test size has consistently been an issue in measurable tests, which get bothered in high dimensional information due to more equivalent or higher component size than the quantity of tests. The intensity of any measurable test is legitimately relative to its capacity to lesser an invalid theory, and test size is a significant central factor in producing probabilities of errors for making substantial ends. Thus one of the effective methods for taking care of high dimensional datasets is by lessening its measurement through feature selection and extraction with the goal that substantial accurate data can be practically performed. Clustering is the act of finding hidden or comparable data in information. It is one of the most widely recognized techniques for realizing useful features where a weight is given to each feature without predefining the various classes. In any feature selection and extraction procedures, the three main considerations of concern are measurable exactness, model interpretability and computational multifaceted nature. For any classification model, it is important to ensure that the productivity of any of these three components isn't undermined. In this manuscript, a Weight Based Feature Extraction Model on Multifaceted Multimedia Big Data (WbFEM-MMB) is proposed which extracts useful features from videos. The feature extraction strategies utilize features from the discrete cosine methods and the features are extracted using a pre-prepared Convolutional Neural Network (CNN). The proposed method is compared with traditional methods and the results show that the proposed method exhibits better performance and accuracy in extracting features from multifaceted multimedia data.
Classification of Social Media Text Spam Using VAE-CNN and LSTM Model

Presently a day's human relations are kept up by online life systems. Customary connections now days are outdated. To keep up in affiliation, sharing thoughts, trade information between we utilize web-based social networking organizing locales. Web based life organizing locales like Twitter, Facebook, LinkedIn and so forth are accessible in the correspondence condition. Through Twitter media clients share their sentiments, interests, information to others by messages. Simultaneously a portion of the client's mislead the certifiable clients. These certified clients are additionally called requested clients and the clients what misguidance's identity is called spammers. These spammers present undesirable data on the non-spam clients. The non-spammers may retweet them to other people and they follow the spammers. Generally most of the spam messages are in the form of text, images and different multimedia formats. Considering all different formats in one process may not give the best classification results. In this paper address the process and classification of text spam messages. Classification of text messages is a complex task in order to achieve this deep learning based hybrid VAE-CNN and LSTM model is proposed and evaluated the model using the performance metrics of precision, recall and F measure metrics.
Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

In recommendation algorithms, data sparsity and cold start problems are inevitable.To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance.In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed.This model starts by building the required knowledge graph.Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities.In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability.Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information.In the ST_RippleNet model, the music data set is added to the movie and book data set; additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer.Finally, the tanh function is added to predict the click probability to improve recommendation performance.Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations.
Homo Digitalis, Digital culture and Digital Education: Explorations of Philosophical Anthropology and of Philosophy of Education

The article presents an analysis of reflections of the Western philosophical anthropology and anthropological oriented philosophy of education upon the digital culture as a new stage of the mass culture development inherited from industrial society. It was done with the aim to show the heuristic potential of the conceptualizations of the digital culture in philosophical anthropology and philosophy of education. The orientation function of the concept “homo digitalis” is unveiled, which is wide-spread using in the West-European, especially in Germany, and in the provocative philosophy of education. The ideal-typically construct of the human as the creation and the creator of the digital culture explains the distinctiveness of the communications in the space of this culture which is represented generally as a visual culture making provocation on purpose making close of the traditional writing culture. There is settling a comparative analyze of human figures created of the different generations of the German philosophical anthropology with accentuating on the semantics of homo digitalis, homo faber and homo creator which are partly complementary. The spatially and temporally characteristics of the digital culture are described, its forms of the communication are explicating in their singularity where the phatic communication predominates and risks of the digital alienation exists. The digital culture changes the self-recognition of the post-industrial societies which need world-view and moral orientation including expertly moral evaluation that is necessary for the prevention of the modernization risks. The pathologies of the digital culture, especially the digital dementia can be observed in the practices of the digital education else, but the blended Learning, if it is rationally organized and adequately administrated available resources, can minimizes those pathologies.
A Chaos-Based Encryption Application for Wrist Vein Images

In this study, the images of the wrist vein taken from the individuals were subjected to various preprocessings and then encrypted with random numbers obtained from the chaotic system. Before encryption, random numbers were generated using a chaotic system. The random numbers produced have successfully passed the NIST 800-22 tests. Images encrypted with random numbers were subjected to security analysis such as correlation, NPCR, UACI and histogram analysis. With the study carried out, it has been shown that wrist vein patterns that can be used in authentication systems can be safely stored in the database.
Current State of Hinglish Text Sentiment Analysis

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Spin-orbit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi></mml:math> states

We propose an all-optical setup to produce a wide class of $X$ states with an intense laser beam using spin-orbit modes. In order to characterize such simulated states, we reconstructed the density matrix by simulating spin-orbit tomography and studied the discord and concurrence for different states. The results show very good agreement with theoretical predictions. We also propose two different optical circuits to prepare the single-photon spin-orbit $X$ state.
Model-based cloud resource management with TOSCA and OCCI

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers, while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyze the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both Cloud Infrastructure and Cloud application layers. We propose an automated extensive mapping between the concepts of the two standards, and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully transforming and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier.
Combined Explanations of $(g-2)_\mu$, $(g-2)_e$ and Implications for a Large Muon EDM∗

We consider possible beyond-the-Standard-Model (BSM) effects that can accommodate both the long-standing tension in the anomalous magnetic moment of the muon, $a_\mu=(g-2)_\mu/2$, as well as the emerging $2.5\sigma$ deviation in its electron counterpart, $a_e=(g-2)_e/2$. After performing an EFT analysis, we consider BSM physics realized above the electroweak scale and find that a simultaneous explanation becomes possible in models with chiral enhancement. However, this requires a decoupling of the muon and electron BSM sectors to avoid the strong constraints from $\mu\to e\gamma$. In particular, this decoupling implies that there is no reason to expect the muon electric dipole moment (EDM) $d_\mu$ to be correlated with the electron EDM $d_e$, avoiding the very stringent limits for the latter. While some of the parameter space for $d_\mu$ favored by $a_\mu$ could be tested at the $(g-2)_\mu$ experiments at Fermilab and J-PARC, a dedicated muon EDM experiment at PSI would be able to probe most of this region.
NEDetector: Automatically extracting cybersecurity neologisms from hacker forums

Underground hacker forums serve as an online social platform for hackers to communicate and spread hacking techniques and tools. In these forums, a lot of latest information indirectly or directly affects cyberspace security, thereby threatening the assets of enterprises or individuals. Therefore, social media such as hacker forums and twitter have a great impact on the cybersecurity area. In recent years, analyzing hacker forum data to explore hacking activities and cybersecurity situational awareness have aroused widespread interest among researchers. Automatically identifying cybersecurity words and extracting neologisms from open source social platforms are less successful and still require further research. In order to provide early warning of cyber attack incidents, we proposed NEDetector, a novel method to automatically identify cybersecurity words and extract neologisms from unstructured content, mainly focus on attack groups and hacking tools. NEDetector firstly analyzes the cybersecurity words and proposes four group features to build cybersecurity words identification model based on Bidirectional LSTM algorithm. Secondly, NEDetector introduces 4 sets of features to identify cybersecurity neologisms based on RandomForest algorithm. The experiment result shows that the whole system of NEDetector achieves an identification precision of 89.11%. Furthermore, the proposed extracting neologisms system is often earlier than having enough data in Google Trends when performing predictions on Twitter data, which prove the validity and timeliness of presented system.
Peculiarities of The Economic Crimes Committed with the Use of Information Technologies

The article looks into peculiarities of the economic crimes committed using information technologies. Different approaches to the concept of the economic crime are analyzed to reveal types of economic crimes, which can be committed with the use of information technologies. It is stated that mostly two types of economic crimes could be committed using information technologies – online fraud and forgery. Subjects and objects of a forgery committed with the use of information technologies are analyzed. Close attention is paid to the online fraud and its specific features. The classification of types of the Internet fraud is made. Some specific characteristics of certain types of the Internet fraud are given in the article. Such types of the Internet fraud as spam, carding, phishing, fraud in online games are described. The main method used in the investigation is the method of comparative analysis, which was used to reveal differences between traditional economic crimes and economic crimes committed with the use if information technologies. The general conclusion was made that economic crimes committed with help of information technologies have a number of differences compared to traditional ones. These differences determine the features of the investigation of such crimes. In particular, such features are manifested in the implementation of operational and investigative measures and investigative actions.
Black-Box Face Recovery from Identity Features

In this work, we present a novel algorithm based on an iterative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems. We attack the state-of-the-art face recognition system (ArcFace) to test our algorithm. Another network with different architecture (FaceNet) is used as an independent critic showing that the target person can be identified with the reconstructed image even with no access to the attacked model. Furthermore, our algorithm requires a significantly less number of queries compared to the state-of-the-art solution.
Stanza Type Identification using Systematization of Versification System of Hindi Poetry

Poetry covers a vast part of the literature of any language.Similarly, Hindi poetry is also having a massive portion in Hindi literature.In Hindi poetry construction, it is necessary to take care of various verse writing rules.This paper focuses on the automatic metadata generation from such poems by computational linguistics integrated advance and systematic, prosody rule-based modeling and detection procedures specially designed for Hindi poetry.The paper covers various challenges and the best possible solutions for those challenges, describing the methodology to generate automatic metadata for "Chhand" based on the poems' stanzas.It also provides some advanced information and techniques for metadata generation for "Muktak Chhands".Rules of the "Chhands" incorporated in this research were identified, verified, and modeled as per the computational linguistics perspective the very first time, which required a lot of effort and time.In this research work, 111 different "Chhand" rules were found.This paper presents rulebased modeling of all of the "Chhands".Out of the all modeled "Chhands" the research work covers 53 "Chhands" for which at least 20 to 277 examples were found and used for automatic processing of the data for metadata generation.For this research work, the automatic metadata generator processed 3120 UTF-8 based inputs of 53 Hindi "Chhand" types, achieved 95.02% overall accuracy, and the overall failure rate was 4.98%.The minimum time taken for the processing of "Chhand" for metadata generation was 1.12 seconds, and the maximum was 91.79 seconds.
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge

Differentially private synthetic data generation offers a recent solution to release analytically useful data while preserving the privacy of individuals in the data. In order to utilize these algorithms for public policy decisions, policymakers need an accurate understanding of these algorithms' comparative performance. Correspondingly, data practitioners also require standard metrics for evaluating the analytic qualities of the synthetic data. In this paper, we present an in-depth evaluation of several differentially private synthetic data algorithms using actual differentially private synthetic data sets created by contestants in the recent National Institute of Standards and Technology Public Safety Communications Research (NIST PSCR) Division's ``"Differential Privacy Synthetic Data Challenge." We offer analyses of these algorithms based on both the accuracy of the data they create and their usability by potential data providers. We frame the methods used in the NIST PSCR data challenge within the broader differentially private synthetic data literature. We implement additional utility metrics, including two of our own, on the differentially private synthetic data and compare mechanism utility on three categories. Our comparative assessment of the differentially private data synthesis methods and the quality metrics shows the relative usefulness, general strengths and weaknesses, preferred choices of algorithms and metrics. Finally we describe the implications of our evaluation for policymakers seeking to implement differentially private synthetic data algorithms on future data products.
Exploiting ultrasound tongue imaging for the automatic detection of speech articulation errors

Speech sound disorders are a common communication impairment in childhood. Because speech disorders can negatively affect the lives and the development of children, clinical intervention is often recommended. To help with diagnosis and treatment, clinicians use instrumented methods such as spectrograms or ultrasound tongue imaging to analyse speech articulations. Analysis with these methods can be laborious for clinicians, therefore there is growing interest in its automation. In this paper, we investigate the contribution of ultrasound tongue imaging for the automatic detection of speech articulation errors. Our systems are trained on typically developing child speech and augmented with a database of adult speech using audio and ultrasound. Evaluation on typically developing speech indicates that pre-training on adult speech and jointly using ultrasound and audio gives the best results with an accuracy of 86.9%. To evaluate on disordered speech, we collect pronunciation scores from experienced speech and language therapists, focusing on cases of velar fronting and gliding of /r/. The scores show good inter-annotator agreement for velar fronting, but not for gliding errors. For automatic velar fronting error detection, the best results are obtained when jointly using ultrasound and audio. The best system correctly detects 86.6% of the errors identified by experienced clinicians. Out of all the segments identified as errors by the best system, 73.2% match errors identified by clinicians. Results on automatic gliding detection are harder to interpret due to poor inter-annotator agreement, but appear promising. Overall findings suggest that automatic detection of speech articulation errors has potential to be integrated into ultrasound intervention software for automatically quantifying progress during speech therapy.
Analysis and Comparison of Table-based Arithmetic to Boolean Masking

Masking is a popular technique to protect cryptographic implementations against side-channel attacks and comes in several variants including Boolean and arithmetic masking. Some masked implementations require conversion between these two variants, which is increasingly the case for masking of post-quantum encryption and signature schemes. One way to perform Arithmetic to Boolean (A2B) mask conversion is a table-based approach first introduced by Coron and Tchulkine, and later corrected and adapted by Debraize in CHES 2012. In this work, we show both analytically and experimentally that the table-based A2B conversion algorithm proposed by Debraize does not achieve the claimed resistance against differential power analysis due to a non-uniform masking of an intermediate variable. This non-uniformity is hard to find analytically but leads to clear leakage in experimental validation. To address the non-uniform masking issue, we propose two new A2B conversions: one that maintains efficiency at the cost of additional memory and one that trades efficiency for a reduced memory footprint. We give analytical and experimental evidence for their security, and will make their implementations, which are shown to be free from side-channel leakage in 100.000 power traces collected on the ARM Cortex-M4, available online. We conclude that when designing side-channel protection mechanisms, it is of paramount importance to perform both a theoretical analysis and an experimental validation of the method.
Artificial Intelligent Based Video Analysis on the Teaching Interaction Patterns in Classroom Environment

Recently, the development of technology has enriched the form of classroom interaction. Exploring the characteristics of current classroom teaching interaction forms can clarify the deficiencies of teaching interactions, thereby improving teaching. Based on the existing classroom teaching interactive coding system, this paper adopted ITIAS coding system, and took classroom with interactive whiteboard, interactive television or mobile terminals as research scene, selected 20 classroom videos of teaching cases in this environment as research objects. Computer vision, one of the artificial intelligent technologies was applied for video analysis from four aspects: the classroom teaching atmosphere, the teacher-student interaction, the student-student interaction, the interaction between human and technology. Through cluster analysis, three clusters of sample’s behavioral sequences were found. According to the analysis on the behavioral sequences and the behavioral transition diagram of each cluster, three classroom teaching interaction patterns were identified, including immediate interaction pattern, waiting interaction pattern and shallow interaction pattern.