Reviewerly/exHarmony huggingface dataset:
Performance of Classification Techniques along with Support Vector Machine

Statistical learning is one of the most notable fields studied by the researchers to understand the data in the present scenario. Recent advances in the field of machine learning and artificial intelligence have been keen to develop more powerful automated techniques for predictive modeling, specifically in regression and classification models. These approaches fall under supervised statistical learning techniques, many conventional techniques are very complex to the data when it has larger volumes, i.e., if the data deviates from the model assumption, then the conventional procedure’s results does not have the trustworthy. This paper explores and compares the classical methods with the alternatives in the context of classification, like logistic regression and support vector machine. The efficiency of these procedures has been evaluated through various measures such as confusion matrix and misclassification rate under real environment.
A Process to Develop Sign Language Corpus using Crowdsourcing

Humans use language in order to communicate between one another. There exist a number of languages which are either spoken or written. Among these languages, there exists a special type of language called Sign Language (SL). Sign language is a general term which includes any kind of gestural language that makes use of signs and gestures to convey message. Although the deaf community feels comfortable while using Sign Language as their mode of communication, but they face a lot of problems as well. Therefore, in order to help and assist the deaf community a repository of different sign languages are essential for each sign language. This work presents a process to develop a repository by collecting and validating sign language gestures of any language by involving the deaf community and language experts. A small data collection based on a proof-of-concept application has also been presented in this work. Lastly, it highlights the benefits of such corpus by discussing possible applications that can be built to serve the deaf community of the world at large.
Understanding the Emotional and Informational Influence on Customer Knowledge Contribution through Quantitative Content Analysis

Customer knowledge contribution is a vital source of business value.Existing studies paid limited attention to emotional influence on knowledge contribution.Drawing upon social support theory, this study attempts to elaborate the influence of emotional support and informational support on knowledge contribution of customers in a firm-hosted online community.Through quantitative content analysis including product feature extraction and sentiment analysis, we analyzed content data from 2318 users.A set of research hypotheses were tested via regression analysis of panel data.We found that informational support (information diagnosticity and source credibility) and emotional support (emotional consistency and emotional difference) significantly affect customer knowledge contribution.This study contributes to knowledge contribution literature by showing the emotional and informational influence, and provides insights for community managers.
Learning Towards Failure Prediction of High Performance Computing Clusters by Employing LSTM

This Failure prediction of high-performance computing clusters (HPCC) is a crucial issue and a hot problem for many years. Previous works have failed to provide a robust method for real-time failure prediction of HPCC. The available techniques are old, unrealistic and provide low accuracy. This paper presents an efficient technique which provides robust failure prediction with good accuracy and state of the art models. We have employed the concept of long short-term memory (LSTM) with reinforcement learning to correct the prediction accuracy in real-time and provide a solution to the industry with reliable results.
Heretic: Modeling Anthony Braxton's Language Music

This article presents a new system for real-time machine listening within human-machine free improvisation. Heretic uses Anthony Braxton's Language Music system as a grammatical model for contextualizing real-time audio feature data within free improvisation. Heretic hears, recognizes, and organizes unseen musical material from a human improviser into a fluid, coherent, and expressive musical language. Systems similar to Heretic often prioritize agnostic approaches to machine listening by avoiding prior musical knowledge in the system's training stage. However, prominent improvisers such as Cecil Taylor, Ornette Coleman, Joe Morris, and Anthony Braxton detail their approaches to improvisation as languages or grammatical systems. These improvisers contextualize the real-time musical materials of their band-mates by applying their formulated grammatical systems to their decision-making processes. Taylor, Coleman, Morris, and Braxton's autonomy and musical creativity are not compromised by using grammatical systems. In regards to human-machine improvisation, Heretic demonstrates that a grammatical approach to machine listening can yield idiosyncratic interactions, full machine autonomy, and novel musical output. This article details a re-imagining of Anthony Braxton's Language Music within the context of machine listening, and an implementation of Language Music within Heretic via SuperCollider's audio feature extraction functionality and Wekinator's multi-layer perceptron neural networks.
Element order is always important in XML, except when it isn't

"Which came first," begins an old joke. But the more interesting question might be, "does it even matter?" There are many obvious and several not-so-obvious ways in which the order of items (be they XML elements or attributes, or JSON maps or arrays) can be understood to be significant or insignificant. These are not new questions and how they’re answered plays out across vocabulary design, schema design, and individual documents. They are important questions when it comes deciding if two documents are “the same” or “different” and to what extent. This paper challenges the one-size-fits-all decree in XML that order needs to be preserved and reviews the implications of 'order'. When ordered elements can be moved then we have something that has some common ground with orderless. This paper establishes a continuum between ordered information and orderless information and proposes that these are not as far apart as they might at first appear.
A Mining Frame Work of CO-PO Attainment using Deep Learning Techniques

student performance measured in CO-PO (Course Outcome and Program Outcome) attainment for OMR based answer sheet automation playing very curtail role in pupil concert analysis in this approach. In the proposed work, marks evaluation sheet is consider as input image, then apply frame cropping technique to extract the marks filled table by subdividing into cells as individual images by frame cropping technique. In order to recognition of hand written digit in each frame, various machine learning models are adopted, trained. Experimental results from proposed work show that convolutional neural network excels higher in identification digits from frames. The outputs are then converted to CSV version, which is used to evaluate CO-PO attainment for each learner. The experiments have been conducted and tested in proposed work on various machine learning techniques and compared the results to pick the optimal model
Design of a Cloud Based Information System for Education Institution using TOGAF ADM

Cloud computing technology allows a service to be accessed from anywhere and anytime through the internet network, this technology can be utilized for organisations to encourage standardisation of business processes for all business units. Muhammadyah as one of the largest education institution in Indonesia currently operates more than 5000 schools from elementary to high school. In general, all schools have similar business processes that are run manually and have almost identical information system requirements. This condition causes no data structure standardisation, no data consolidation between one school and another and the inconsistency of information system development. These problems become the rationale for designing an information technology architecture model using the concept of cloud computing. Based on the results of the analysis on current conditions and using the chosen TOGAF framework, a cloud computing-based information technology architecture model is produced. The information technology architecture consists of vision architecture, business architecture, application architecture, data architecture and technology architecture. Research is expected to contribute to educational institution in Indonesia, especially as a reference in designing information technology architecture.
Identification of Malicious Accounts in Facebook

In this modern world of internet age social networking is becoming popular day by day for sharing of information and day to day communication. The social media has changed the way people pursue their life and made it a part of their life to get connected with friends, family, colleagues and with the society as a whole. Social networking can occur for social purposes, business purposes by the use of Facebook, Twitter, LinkedIn etc. Hence it has become the favorite spot for the cyber criminals. Facebook is a free social networking website that allows users to upload photos and video, send messages and keep in touch with friends, family and rest of the world. Unfortunately, spammers are also using this Facebook for their personal gain by creating fake profiles or comprising the other famous accounts. These accounts together are referred as malicious accounts. Hence it became important task to identify this spam for free communication over Facebook. However, some former researchers have proposed their work to identify these accounts but suffering from many limitations. In this paper a method is proposed called An unsupervised method to detect spam in Facebook using DBSCAN algorithm. This research work used Jaccard similarity and DBSCAN algorithm to detect spam messages. This research work is implemented using real time messages and evaluated. The experimental results show the methodology used is very efficient, having better accuracy and low false positive rate in detecting the spam messages in Facebook. This research work will help the common man in detecting malicious Facebook accounts in a great way.
A Reliable Agent System for Cloud Service Discovery using MFA Technique

Reliable Agent system for discovering suitable cloud services are very interesting and challenging process. In this research work, the proposed reliable agents’ authentication is enforced to provide security for cloud data’s. In this proposed method the agent client requires service with specific functional and technical requirements. For privacy concern, we provide authentication for each agent in cloud by using Multifactor authentication process. By using authentication id agents discover the cloud service based on service engine. In our system, we enhance the privacy of the cloud search engine by introducing new authentication technique. The authentication process is done by the use of multi factors such the secret key which is generated based on AES algorithm, biometric of user and the user’s password credentials. Focused Selection Contract Net Protocol is performed to find the relevant cloud service and it plays multiple roles in cloud service discovery. The similarity between the agent’s request and cloud service is computed based on service specifications and consumer service using Cloud ontology. Each legitimate agent provides reliable cloud services for agent clients (users).Service capability Table records the list of cloud service and their corresponding ID and services for reliable cloud discovery
Object Detection and Classification for Autonomous Drones

significantly. This paper aims to implement the state of the art custom algorithm for detection and classification of objects in a single frame with the goal of attaining high accuracy with a real time performance. The proposed system utilizes SSD architecture coupled with MobileNet to achieve maximum accuracy. The system will be fast enough to detect and recognize multiple objects even at 30 FPS.
Sentimental Analysis on Text data by using Unsupervised Methods

On the internet we can see how efficiently display the reviews by the user who brought the product so that it covers all the important points instead of just displaying few top comments or threads. The main agenda of the tool is to build and analyse all the reviews given by each customer and display the best product reviews for any app or product. As we all read reviews before we buy any product from any e-commerce or while installing any app but the major problem we face is there are huge number of reviews and most of the reviews we get is the top most review or a combination of bad and good review based on rating which sometimes may or may not tell the perks or cons of using the product so we tried to build a tool that analyse all the reviews and picks the best reviews which totally describe the product flaws defects or advantages. so for that purpose we are implementing the k-means clustering algorithm and in previous papers they have used RASP (robustical and accurate statistical parser)grammatical tagger to identify all kinds of nouns, adjectives, pronouns and etc. together. Here in our paper we are using k-means clustering algorithm which divides all kinds of identifiers based on the comments so that it gives a clear idea about the product and is given in graphical representation
Virtual Prehistory portable art collection of Siberian Mal’ta-Buret’ culture: ways of documenting, classification and representation

Modern research of museum archaeological collections, their attribution and digitizing are relevant questions of science, culture, and education in the world. The paper presents archaeological materials of the Siberian Upper Paleolithic sites Mal’ta and Buret’ and the ways of their systematization and documentation in a form of an information system on the internet. The system has a full-fledged item attribution, scientific description and extensive multimedia support including 3D models and macro photographs. The collections were obtained during the excavations under the direction of M. M. Gerasimov and A. P. Okladnikov, carried out in the early to mid-20th century ; the unique artifacts from the collections are kept in the funds of various museums in different cities of Russia (Moscow, St. Petersburg, Irkutsk) in a scattered and unsystematic form. Our project is aimed to the developing of the documentation of unique items from the collection of the Upper Paleolithic Mal’ta-Buret’ archaeological culture and presenting the results in a systematic way with visual materials. The collection includes a number of anthropomorphic, zoomorphic, ornithomorphic figurines, ornamented discs and plates, as well as items of personal ornamentation, and other unique artefacts. As part of the project, we took photographs and macro photographs of the objects, their 3D models using the photogrammetry method, made a detailed description and collected all available publications on them, both modern and the earliest. The art objects of the fragmented collection are systematized and presented in the form of a single virtual collection on the Internet – Information System of Mal’ta and Buret’ Mobile Art. Descriptions, photographic images and three-dimensional models of objects, the results of microscopic studies and trace analysis have been published.
Transfer Learning

In the previous chapter, you developed a binary image classifier. With 60,000 images, it took a while to train the model. The accuracy we achieved was about 80 to 90%. If you want a higher accuracy, more images would be required for training. As a matter of fact, a deep learning network learns better with a higher number of data points. The ImageNet ( ), the first of its kind in terms of scale, consists of 14,197,122 images organized into 21,841 subcategories which are further classified into 27 subtrees. To classify the images in ImageNet, many machine learning models were developed; these were developed mainly as a way of research and competitions. In 2017, one such model achieved an error rate of as low as 2.3%. The underlying network was very complex. Considering the number of trainable points for such a complex network, imagine the number of resources and time it would have taken to train the model.
Thematic Context Derivator Algorithm for Enhanced Context Vector Machine: eCVM

Natural Language Processing uses word embeddings to map words into vectors. Context vector is one of the techniques to map words into vectors. The context vector gives importance of terms in the document corpus. The derivation of context vector is done using various methods such as neural networks, latent semantic analysis, knowledge base methods etc. This paper proposes a novel system to devise an enhanced context vector machine called eCVM. eCVM is able to determine the context phrases and its importance. eCVM uses latent semantic analysis, existing context vector machine, dependency parsing, named entities, topics from latent dirichlet allocation and various forms of words like nouns, adjectives and verbs for building the context. eCVM uses context vector and Pagerank algorithm to find the importance of the term in document and is tested on BBC news dataset. Results of eCVM are compared with compared with the state of the art for context detrivation. The proposed system shows improved performance over existing systems for standard evaluation parameters.
Middleware for the Internet of Things

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An edge–cloud integrated framework for flexible and dynamic stream analytics

With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular type of stream analytics is the recurrent neural network (RNN) deep learning model based time series or sequence data prediction and forecasting. Different from traditional analytics that assumes data are available ahead of time and will not change, stream analytics deals with data that are being generated continuously and data trend/distribution could change (a.k.a. concept drift), which will cause prediction/forecasting accuracy to drop over time. One other challenge is to find the best resource provisioning for stream analytics to achieve good overall latency. In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM). We propose a novel edge-cloud integrated framework for hybrid stream analytics that supports low latency inference on the edge and high capacity training on the cloud. To achieve flexible deployment, we study different approaches of deploying our hybrid learning framework including edge-centric, cloud-centric and edge-cloud integrated. Further, our hybrid learning framework can dynamically combine inference results from an LSTM model pre-trained based on historical data and another LSTM model re-trained periodically based on the most recent data. Using real-world and simulated stream datasets, our experiments show the proposed edge-cloud deployment is the best among all three deployment types in terms of latency. For accuracy, the experiments show our dynamic learning approach performs the best among all learning approaches for all three concept drift scenarios.
Human and contextual factors influencing cyber-security in organizations, and implications for higher education institutions: a systematic review

Purpose A research line has emerged that is concerned with investigating human factors in information systems and cyber-security in organizations using various behavioural and socio-cognitive theories. This study aims to explore human and contextual factors influencing cyber security behaviour in organizations while drawing implications for cyber-security in higher education institutions. Design/methodology/approach A systematic literature review has been implemented. The reviewed studies have revealed various human and contextual factors that influence cyber-security behaviour in organizations, notably higher education institutions. Research limitations/implications This review study offers practical implications for constructing and keeping a robust cyber-security organizational culture in higher education institutions for the sustainable development goals of cyber-security training and education. Originality/value The value of the current review arises in that it presents a comprehensive account of human factors affecting cyber-security in organizations, a topic that is rarely investigated in previous related literature. Furthermore, the current review sheds light on cyber-security in higher education from the weakest link perspective. Simultaneously, the study contributes to relevant literature by gaining insight into human factors and socio-technological controls related to cyber-security in higher education institutions.
Dual Axis Solar Panel Weather Monitoring System

Abstract: This analysis presents associate degree analysis of a dual-axis solar chase system with weather updates mistreatment Arduino By 2035 the alternative energy can become the foremost used supply of energy for the generation of electricity or we are able to say the production of electricity within the world. this may conjointly result in greater use of inexpensive digital technology. Most of the star panels area unit mounted kind, thus so as to maximize the facility generation, we tend to area unit mistreatment the star huntsman which can follow the rotation of the sun throughout the day. If we tend to compare the total output of twin also as single-axis star huntsman system, we found that it's higher performance in terms of current, voltage, and power output. there's a rise in performance of around twenty fifth of the facility. we tend to area unit mistreatment the lightdependent resistors (LDRs) as a detector as a chase mechanism for the sun radiations and 2 servomotors connected vertically and horizontally to direct the position of the panel. The planned automatic huntsman is that it controls the elevation and orientation angles of electrical device specified the panel can maintain perpendicular to the daylight for the maximum absorption of sunshine rays. And computer code half is completed with the assistance of code written in Arduino Uno controller. Keywords: Solar panel, Arduino UNO, Light detecting resistors (LDRs), servo motors, temperature sensors, humidity sensors, rain drop sensors.