Reviewerly/exHarmony huggingface dataset:
Factors of creation and functioning of AI: anthropogenic vs. social

The purpose of the work is to determine, systematise, and rank the factors of creation and functioning of AI and to determine the perspective measures of state management of these factors for quick development and reduction of risks of using AI in the modern digital economy. The authors use the methods of systematisation and classification within the systemic approach. Also, the authors use formalisation and sociological survey for determining the significance of the factors of creation and functioning of AI. The respondents are 323 developers of AI in Russia in 2019. The results of the research showed that the processes of creation and functioning of AI are influenced by the social and anthropogenic factors. The social factors are primary - they are more significant and determine the anthropogenic factors. This shows that digital modernisation of economy based on AI is to serve the interests of acceleration of economic growth and increase of competitiveness of the economic system and (what is more important) the interests of increase of population's quality of life.
Anomaly Detection

Detecting anomalous situations early can be a lifesaver. Imagine the catastrophes saved being able to spot a manufacturing defect in a car engine before being shipped in a million cars. The manufacturer can save huge on the potential damage control for recalling all the cars, let alone the embarrassment caused.
Development of Innovative Technologies in Education

The problems of interaction between international organizations and universities correlate with a set of key problems in the theory of international relations because they are associated with the phenomena of globalization, regionalism, and multiculturalism. They also relate to the prospects for the influence of universal humanitarian and technological development ideas on the policies of individual countries and consolidated groups of states. The authors claim that the successful promotion of the European Union in harmonizing the common space of higher education and the demand for the European university model in other regions of the world, including in the post-Soviet space, is ruled by the combination of its universalism and tolerance with the concept of the diversity value in higher education.
A Critical Survey on Cloud Security and privacy issues and its associated solutions

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.
A robust multi-level sparse classifier with multi-modal feature extraction for face recognition

In the past few years, face recognition based on sparse representation is providing satisfactory classification accuracy. Face images involved in real life applications usually exhibit considerable pose, lighting, and expression variations, resulting in significant performance degradation of traditional sparse-based algorithms. In this paper, a novel face recognition method is developed as multi-level sparse (MLS) classifier with multi-modal feature extraction, which integrates benefits of sparse representation manifolds. In MLS classifier, sparse representation-based classification is performed at multiple levels to extract the hierarchical relationship information between training and testing images, which not only improves classification accuracy but also makes the system scalable. Also, the use of multi-modal feature in MLS classifier makes it discriminative to face changes while robust to intra-personal variations. To highlight the competency of proposed method, results are compared with sparse representation and other existing state-of-art methods in terms of mean classification error. An investigation on classification accuracy is performed to showcase the reliability of proposed method.
Wearable Technology: Signal Recovery of Electrocardiogram from Short Spaced Leads in the Far-field Using Discrete Wavelet Transform Based Techniques

Bipolar ECG leads recorded from closely spaced electrodes are challenging in any context.When they are positioned distally with respect to the source field (far-field), the recovery of clinically useful signal content represents an even greater challenge.Due to the increased interest in ambulatory wellness devices, particularly wrist-worn devices, there is a renewed interest in recovering ECG signals from distally located bipolar leads.In this study 10 bipolar leads were simultaneously recorded at various locations along the left arm.At the same time, a conventional proximal reading on the chest using Lead I was also recorded and stored.This process was repeated for 11 healthy subjects.ECGs were recorded for a period of approximately 6 minutes for each subject and sampled at a frequency of 2048 Hz.Wavelet-based filtering using Daubechies 4 wavelet decomposition and soft threshold was applied to each lead.QRS detection performance was assessed against Lead I for each subject.This investigation found that a lead positioned transversally (using BIS gelled electrodes) on the upper arm provided the best accuracy against the benchmark QRS detection (SEN = 0.998, PPV = 0.984).The most distally positioned bipolar lead using dry electrodes faired least favourable (SEN = 0.272, PPV = 0.202).
Interferometric quantum random number generation on chip

We demonstrate an on-chip, high-speed quantum random number generator based on the interference of two gain-switched pulsed lasers. FPGA-based electronics allows for real-time processing, providing 8 Gbps random numbers passing all the NIST tests.
A Description Profound Fusion Recommender Scheme based on Self-Chipper with Neural Communal Filtering

The Web makes fabulous open entryways for associations to give tweaked online organizations to their customers. Recommender systems intend to normally make altered suggestions of things/organizations to customers (business or individual). Regardless of the way that recommender systems have been particularly inspected, there are up 'til now two challenges in the enhancement of a recommenderstructure,particularlyinauthenticworldB2Be- services. In Proposed a recommendation framework utilizing the speedy scattering and information sharing limit of an extensive customer orchestrate. This framework actualized a GRS dependent on conclusion elements that considers these connections utilizing a brilliant loads lattice to drive the procedure.InGRSs,asuggestionistypicallyfiguredbyabasic collection strategy for individual data the proposed technique [described as the client driven recommender framework (CRS)] pursues the community oriented sifting (CF)rule howeverperformsdispersedandnearbylooksforcomparative neighbors over a client arrange so as to produce a suggestion list.
The Voice

Voice and singing are fundamental to music. Scales and content reflect our personal culture. Something beautiful and inspiring to one person may be a boring cacophony to another. Viewing musical evolution from the perspective of culture is therefore varied and individual. Input from science is generally less obvious, except for changes generated from acoustics of buildings, broadcasting, and electronic sound equipment. Medical studies reveal how we form sounds and tone quality, and modern electronic signal processing shows the complexity of the harmonic content of singing. The changes between sweetness, harshness, carrying power, and so on, all depend on not just volume, but the fundamental note and its harmonics, plus all the other frequencies generated in our vocalization. One fundamental may have 50 or more other frequencies. This signal processing tool is invaluable for understanding voice production.
Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

Cloud computing supports the technological need of the industry supporting many other technologies. Also, the demand for computing power and storage by recent technologies is reasonably growing in a drastic way. Cloud computing, serving for these technologies are to be developed with advancements that lead to performance improvement both in support to the technologies like block-chain and big data. The allocation of cloud resources is an important strategy to be followed in a wiser manner to incorporate the needs of extra ordinary computing power. In this paper, an efficient resource allocation strategy (FTVMA) is introduced that involves the creation of effective virtual machines (VMs) and performs VM allocation in an efficient manner by considering the failure rates, previous history of failure of VM, execution efficiency as a part of effective scheduling. There exist many reasons for cloudlet failure in VMs. Some of them are overloading of VMs and non-availability of VMs. The introduced FTVMA algorithm considers the failure rate of the physical machine, load of virtual machines and the cost priority of the tasks in order to achieve Quality of Service (QoS) and Quality of Experience (QoE) of the user. The FTVMA methodology proposed in this paper works better for computation intensive VMs and is tested using CloudSim environment. The QoS metrics used to measure the performance of the proposed algorithm are Makespan and VM Utilization. The metric to measure QoE are Priority Miss Rate and Failure Rate. The proposed algorithm shows its improvement in terms of the QoS and QoE metrics. The results obtained are compared with the existing resource scheduling algorithms and it is inferred that the proposed algorithm performs better in terms of QoS and QoE.
EMOTION ANALYSIS USING SELF-TRAINING ON MALAYSIAN CODE-MIXED TWITTER DATA

Microblogs such as Twitter has made available a vast resource of User Generated Content (UGC) on which emotion analysis may be performed.Organizations increasingly value the opinions obtained from emotion analysis.These insights help to drive decision-making activities and provide constructive inputs to engage their customers and services.In a multiracial country such as Malaysia, it is common to find that tweets are written in mixed languages of Malay, Malaysian slang and English.These tweets increase the complexity of the emotion analysis task, especially considering that there is a serious lack of labeled data available in order to make use of supervised learning techniques.This paper explores the use of self-training, a semi-supervised technique that only requires a small initial labeled dataset to conduct emotion analysis of Malaysian code-mixed Twitter data.The results are promising as the accuracy achieved is higher compared to the baseline models.
Heuristic Search Based Feature Selection and Discretive Self-Organized Map Clustering for Spatio-Temporal Pattern Discovery

Spatio-temporal pattern discovery is an essential one in data mining for predictive analytics. Since it manages both space and time information depending on their characteristics and the preferred applications performances. The predictive analytics uses the Spatio-temporal features to discover future outcomes. The several works have been done in the Spatio-temporal pattern discovery. But the accurate pattern discovery is the major challenges. In order to improve the accurate pattern discovery, Heuristic Best-First Search based Discretized Self-Organizing Feature Map (HBFS-DSOFM) Model is introduced. The HBFS-DSOFM model comprises two processes namely, Spatio-temporal feature selection and clustering. Initially, the Heuristic Best-First Search Algorithm is used for selecting the relevant Spatio-temporal features from the large dataset for pattern discovery. Best-first search explores a decision tree for selecting the relevant Spatio-temporal features through the maximum information gain value. After that, the Spatio-temporal data are clustered with the selected features by using Discretized Self-Organizing Feature Mapping Algorithm for Spatio-temporal pattern discovery. In Discretized Self-Organizing Feature Mapping, input spatio-temporal data is connected to the prototype neurons through the synaptic weight. For the clustering process, weights of the neurons (i.e. cluster) are initialized with random values. After that, the Manhattan distance is used to compute the distance between the input vector and cluster weight value. The gradient descent is applied to discover closest distance. The cluster whose weight is closest to the input data is grouped into the particular cluster. Then the weight of the cluster is updated with the previous weight value for grouping the entire data. This clustering process gets iterated until it satisfies termination condition. Finally, the outputs of Spatio-temporal data are combined to form a spatio-temporal pattern for efficient predictive analytics. Experimental evaluation is carried out for El Nino Dataset and taxi trajectory dataset using the factors such as time complexity, clustering accuracy, and false positive rate. The results confirm that the proposed HBFS-DSOFM model increases the Spatio-temporal pattern discovery in terms of high clustering accuracy with a less false positive rate as well as minimum time complexity. Based on the clarification, HBFS-DSOFM model is more efficient than the state-of-the-art methods.
Securing Information for Commercial File Sharing by Combining Raster Graphic a nd Vector Graphic Stseganographies

Commercial applications cannot declare use of steganography as its security model so as not to spoil the norm of “hidden” secret. Proposed paper suggests a model that can withhold security even after declaration of steganography’s presence allowing applicability in commercial scope. File to be secured is compressed and converted to byte stream and hidden in both raster graphic and vector graphic cover images and encapsulated in a PDF and shared. To extract the secured file from the cover images a key is required by the party intending to receive the secured file. This key is shared by Elliptic Curve Diffie Hellman Key Agreement Protocol. The intended receiver extracts the file by applying steganalysis. To implement this model an android application is developed that shares files by securing it in aforesaid fashion and transferring them via Wi-Fi Direct. Most of the digital image steganography researched are based on raster graphic cover images. The proposed model is designed so that limitations present in raster and vector steganographies each can be counterfeited to produce a secure solution that can be offered in digital image steganography. This solution is suggested as security model in commercial scope.
A PageRank-Based WeChat User Impact Assessment Algorithm

This article has been withdrawn at the request of the editor and publisher. The Tech Science Press Policy on Article Withdrawal can be found at https://www.techscience.com/JNM/info/auth_instru. The publisher regrets that a technical error occurred, which led to the duplicate publication. We apologize to the authors and the readers for this unfortunate error and any inconvenience this may have caused.
Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning

Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.
Perception of youth on Digital India

Digital India is seen as the engine for transition of India into an empowered nation. In this direction, several pro-people initiatives like MyGov, Digilocker, e-basta, and e-hospital are being implemented. Through a self-structured survey questionnaire, the study evaluates awareness about, implementation and utilisation of various e-services offered under the Digital India Program among Delhi University students. It examines the extent to which the services are being utilised and main barriers/challenges restricting its utilisation. The data has been analysed using ANOVA. The study reveals a high level of awareness among students. They believe it will improve the quality of services leading to good governance. Major factors restricting its implementation include lack of computer knowledge, fear of frauds and resistance to change. The study stressed on educating people about the benefits and usage of Digital India. The nation as a whole can move towards green and responsible governance by improving its acceptability.
Data modelling for large-scale social media analytics: design challenges and lessons learned

We live in a world of big data; organisations collect, store, and analyse large volumes of data for various purposes. The five V's of big data introduce new challenges for developers to handle when performing data processing and analysis. Indeed, data modelling is one of the most challenging and critical aspects of big data because it determines how data will be structured and stored; these decisions then impact how that data can be processed and analysed. In this paper, we report on designing a data model for storing and analysing Twitter data in support of crisis informatics. In this work, we leverage the data model provided by columnar NoSQL data stores to design column families that can efficiently index, sort, store and analyse large Twitter datasets. In particular, our column families are designed to achieve efficient batch data processing. We evaluate these claims and discuss our future work.
Humanoids

The C# programming language was designed for developing programs for Microsoft’s .NET Framework.
“Logic and critical thinking”: the campus course setting

The article discusses the software setting implemented in the education system of the campus course “Logic and Critical Thinking”. It shows the unsatisfactory nature of the traditional information educational model, focused on the transfer of knowledge of a reproductive nature. Innovative research education is impossible without critical thinking. The attention is focused on the fact that critical thinking is not formed by itself, it is necessary to develop a strategy for its development. The key setting of the campus course is analyzed, whose goal is to form effective, rational thinking. The art of thinking can be critically taught and therefore the campus course studies language as a means of cognition, concept, statement, deductive and believable conclusions, logical and epistemic aspects of argumentation. Special attention is paid to the study of creative thinking - the theory of solving inventive problems. It is concluded that the campus course “Logic and Critical Thinking” contributes to the development of creative abilities of students, it serves the idea of preparing an independently and critically thinking specialist.
Coupled Retinex

Retinex is a colour vision model introduced by Land more than 40 years ago. Since then, it has also been widely and successfully used for image enhancement. However, Retinex often introduces colour and halo artefacts. Artefacts are a necessary consequence of the per channel color processing and the lack of any strong control for controlling the locality of the processing (halos are very local errors). In this paper we relate an input to the corresponding output processed retinex image by using a single shading term which is both spatially varying and smooth and a global colour shift. This coupling dramatically reduces common Retinex artefacts. Coupled Retinex is strongly preferred in preference tests.
Swami Vivekananda’s Introspection on Education: A Study based on 24 Parganas(s), West Bengal, India

Education is part and parcel of our life we cannot live without education so education is called the backbone-mirror of our society. Human-being is superior to other animals, animals can be trained but human-beings can be educated. Education is a dynamic process that starts from birth and continues until death. Education has two major parts first one is social education and the second one is formal education both are the dependent variable with each other. This project mainly deals with formal education, the study analysis of different aspects which will affect our education system in SOUTH 24 PARGANAS DISTRICT. The research design undertaken for the study was descriptive research and the convenience sampling method is used and the sample size consists of 200 respondents. Only a simple percentage analysis method used to analyze collected data and results of the study shows that education system has both positive-negative impacts on teachers, students and guardians' satisfaction.DOI: http://doi.org/10.31098/ijeiece.v1i2.35
Area Efficient High Speed Vedic Multiplier

Very large scale integration is a process of integrating hundreds of thousands of transistors or devices into a single chip. VLSI can be categorized into two fields Frontend and Backend. Digital VLSI design falls under the Frontend design. Multiplication is an arithmetic operation important for the Digital Signal Processing (DSP) and for processors. Multiplier is the main hardware block for the digital circuit. More than 70% of the applications in a digital circuit are either addition or multiplication. As these operations dominates most of the execution time so we need fast multipliers. The overall objective of a good multiplier is to have high speed, low power consumption unit, less area. Vedic multipliers are the fast multipliers and occupy less area. They are based on the Vedic mathematics sutra "Urdhava-Triyakbhyam" . The paper contain a high speed multipliers and use of different adder structures.