Reviewerly/exHarmony huggingface dataset:
Hybrid Dimensionality Reduction for Outlier Detection in High Dimensional Data

The infrequent data objects in a dataset containing important characteristics are called outliers.Dimensionality reduction techniques are used for reducing the memory requirement and processing time while handling high dimensional datasets.Principal Component Analysis (PCA) is one of the most commonly used techniques for reducing the dimensions in high dimensional datasets.Autoencoders (AE) are used in deep learning for dimensionality reduction.In this paper, we evaluate the performance of these dimensionality reduction techniques for outlier detection task and also propose a hybrid dimensionality reduction technique that can be used in the preprocessing step of many machine learning models.Experimental results using publicly available datasets show that the proposed method outperforms the PCA method and autoencoder based method in outlier detection tasks using isolation forest.
Beyond Questionnaires: Innovative Approaches to Evaluating Mixed Reality

Mixed Reality (MR) technologies, including augmented and virtual reality, are increasingly used in a number of sectors, thanks to their capabilities to immerse users in multisensory interaction environments.However, as indicated by some systematic reviews, questionnaire remains the main method for evaluating the interaction quality of MR.There is a lack of innovative approaches addressing unique features of MR.It can dampen the advances of MR, as evaluation feedback can inform its future development.In this workshop we aim to explore this issue by inviting participants to share their practical experiences or conceptual ideas of evaluating MR in various contexts, using different methods and tools.The ultimate aim is to produce a research agenda on this topic for the community to examine it further in the future.Mixed reality.
Recognizing Ancient Characters from Tamil Palm Leaf Manuscripts using Convolution Based Deep Learning

Palm leaf manuscripts has been one of the ancient writing methods but the palm leaf manuscripts content requires to be inscribed in a new set of leaves. This study has provided a solution to save the contents in palm leaf manuscripts by recognizing the handwritten Tamil characters in manuscripts and storing them digitally. Character recognition is one of the most essential fields of pattern recognition and image processing. Generally Optical character recognition is the method of e-translation of typewritten text or handwritten images into machine editable text. The handwritten Tamil character recognition has been one of the challenging and active areas of research in the field of pattern recognition and image processing. In this study a trial was made to identify Tamil handwritten characters without extraction of feature using convolutional neural networks. This study uses convolutional neural networks for recognizing and classifying the Tamil palm leaf manuscripts of characters from separated character images. The convolutional neural network is a deep learning approach for which it does not need to retrieve features and also a rapid approach for character recognition. In the proposed system every character is expanded to needed pixels. The expanded characters have predetermined pixels and these pixels are considered as characteristics for neural network training. The trained network is employed for recognition and classification. Convolutional Network Model development contains convolution layer, Relu layer, pooling layer, fully connected layer. The ancient Tamil character dataset of 60 varying class has been created. The outputs reveal that the proposed approach generates better rates of recognition than that of schemes based on feature extraction for handwritten character recognition. The accuracy of the proposed approach has been identified as 97% which shows that the proposed approach is effective in terms of recognition of ancient characters.
Effect of Environmental Cost on Performances of Quoted Firms in Sub-Saharan Africa, 2007-2016

The study examined the effect of environmental costs on performances of quoted firms in Sub Saharan Africa.The study adopted longitudinal/panel ex-post facto research design and random sampling technique while quantitative secondary data covering 2007 to 2016 were obtained for sixty-four extractive and industrial firms quoted in the Stock Exchanges of four Sub-Sahara African countries namely South Africa, Nigeria, Ghana and Tanzania.The models for the study were estimated using Ordinary least square regression (OLS) built on panel data analysis.In the regional level analysis as well as in South Africa and Nigeria specific country analyses, the study revealed that environmental costs represented by employee health and safety, waste management and community development costs have no significant effect on return on capital employed, earnings per share and return on equity.The study showed that in Ghana, the predictor variables demonstrated significant effect on return on capital employed and return on equity while only waste management cost has significant effect on return on capital employed and return on equity in Tanzania.The implication of the preponderance of the findings, save for the aforementioned exceptions in Ghana and Tanzania, is that quoted firms in the region are yet to adequately indulge in environmental responsibility or their environmental engagements are not adequately captured and disclosed to the extent that can cause significant swings in the measures of firm performance.The implication of the exceptions found in Ghana and Tanzania is that of comparative improvement in environmental responsibilities, compliances and disclosures by quoted firms in the two countries.The study recommended among other things that firms in Sub Saharan Africa should give greater attention to environmental responsibility, cost recognition, classification and disclosures in the annual, integrated and sustainability reports.
The impact of e-readiness on strategic flexibility

Organisations inherently seek to realise their optimal potential by developing their strategic flexibility, to reach the highest levels in order to achieve their objectives and maintain survival, continuity, competition and excellence. Part of this, particularly in the modern globalised, networked economy, is seeking to raise electronic readiness to the highest possible level to keep abreast of technological development. Based on this orientation, this paper aims to identify the impact of electronic readiness dimensions: information and communication technology infrastructure, human resources and management on the strategic flexibility dimensions: resources flexibility, information flexibility, and coordination flexibility. The study population consisted of all employees in control and supervisory positions in Greater Amman Municipality (upper and middle management levels), comprising a total of 479 people. The study showed a statistically significant effect of electronic readiness on the strategic flexibility and suggested some practical recommendations.
The Impact of Blockchain on the Accounting Profession

The implementation of the blockchain technology in accounting highlights the automation of traditional accounting functions and the transparency, significantly mitigates the risk of internal fraud and increases the importance of the financial information provided and the trust in that information.Professional accountants need to prepare to accept digitalisation as a challenge to become more experienced and to consolidate their status as a reliable third party.In this article we present the blockchain technology and its probable impact over the accounting profession.The aim of this study is to analyse the research on information technologies in accounting and the way these technologies could modify the industries and professions in time.
Abnormality Detection Using LBP Features and K-Means Labelling based Feed-Forward Neural Network in Video Sequence

Video surveillance is widely used in various domains like military, commercial and consumer areas. One of the objectives in video surveillance is the detection of normal and abnormal behavior.It has always been a challenge to accurately identify such events in any real time video sequence. In this paper, abnormality detection method using Local Binary Pattern and k-means labeling basedfeed-forward neural network has been proposed. The performance of the proposed method has also been compared with four other techniques in literature to show its worthiness. It can be seen in the experimental results that an accuracy of up to 98% has been achieved for the proposed technique.
Blockchain-Based Decentralized Digital Self-Sovereign Identity Wallet for Secure Transaction

Blockchain (BC) as the widespread innovations in the 21st century has recognized itself to be immutable, tamper-resistant, decentralize and secure.This emerging technology is used as a functional technology for refining present technology and forming new applications for its robustness and disintermediation.Decentralized Digital Self-Sovereign Identity (DDSSI) is an identity mapped with individual identity information along with the user's reputation in the transaction.User's information will be preserved in the decentralized cloud server which will be controlled and maintained by the user.In this research work, we suggest a Blockchain-centered DDSSI wallet to modernizes the existing identity management system that will be used to identify as well as access control to provide validation and endorsement of entities in a digital system.BC technology in this innovation ensures credible and safe information in a transaction besides.Here, we use Bitcoin cryptocurrencies to generate secure and unique DDSSI public key addresses by integrating the private key with the random number for transferring and accepting information and a token-based system to identify customer reputation.
Amalgam Of Hamming Weight-Based RSA And Multi Party Computations To Enhance Security In Multi Cloud Ambience

Cloud Computing, apparent technology has both advantages and disadvantages. In spite of multitudinous benefits Cloud is offering, broad acceptance of Cloud has taken a toll on the basic security in cloud environments. It becomes more pronounced when thinking of multi cloud environments. This paper focuses on literature work associated with different variants of RSA in fusion with other methods and a proposed methodology which integrates dual techniques Improved RSA (based on principle of Hamming weight) and SSS (Secret Sharing Scheme) methods to improvise the security of data in cloud environments. The proposed method was compared with existing method taking in one parameter and was been proved that it excels the performance drastically.
An Intellectual Individual Performance Abnormality Discovery System i n Civic Surroundings

Each year the computerized visual investigation of behavior gives a few key building pieces towards an mental vision framework. The capacity to see individuals and their activities by vision may be a key for a machine to interface vigorously and effectively with a human-computer association world. Due to various conceivable basic applications, "watching people" is at show a standout among the foremost energetic application spaces in vision. One of the foremost applications of visual investigation is peculiarity location in human exercises. An irregularity can show up in different shapes they speak to the different levels of human security issues. The location and following of bizarre exercises in observation have propelled an expanding level of concentration in computer vision.A novel approach to screen an variation from the norm within the open environment, here a generalized system is created for following the deviation by extricating the neighborhood highlights utilizing traits as neighborhood thickness and movement vector. In this moderate highlight movement is affected by typical behavior of people and quick include movement affected by the unusual behavior of people, for way better discovery it includes in computing the movement outline for flow of movement vectors within the scene by coordination the movement and appearances. The test investigation illustrates the viability of this approach in comparison with classifiers which is proficient to run and accomplishes 96% execution, in any case, for compelling approval of the framework is tried with standard UMN datasets and claim datasets.
Psychological and pedagogical aspects of consolidation of social capital of higher education institutions

The aim of the study was to identify psychological and pedagogical factors that ensure the consolidation of social capital in higher education institutions and to assess their effectiveness through expert critical evaluations. The study used the exploratory design and was based on the use of qualitative methods to evaluate the selected studies. The research methodology relied on the methods recommended for feasibility studies to determine the possibility of implementation of the selected best practices. The systematic review of scientific sources and best practices found the psychological and pedagogical aspects that ensure the consolidation of social capital in higher education institutions, namely: individual psychological factors of economically motivated behaviour, social network technology, second-order psychological factors, teachers’ assistance of students’ professional self-determination, education as a means, and psychological factors based on the attributes of the institution. The results of expert evaluation of the above factors suggest that the last three ones mentioned have the potential for duplication, dissemination and transferability in higher education institutions in Ukraine.
Robust Student’s T Distribution Based PHD/CPHD Filter for Multiple Targets Tracking Using Variational Bayesian Approach

Measurement-outliers caused by non-linear observation model or random disturbance will lead to the accuracy decline of a target tracking filter. This paper proposes a robust probability hypothesis density (PHD) filter to handle the measurement-outlier problem based on Student's T Kalman (TK) filtering technique and Varia-tional Bayesian (VB) method.First, the non-standard measurement noise is considered to follow the Student's T distribution.Second, the TK filtering technique is employed to update the target states.Third, the posterior likelihood is updated by the VB approach.Simulation results show that the proposed method can reduce the optimal subpattern assignment (OSPA) error in the non-standard observation scenarios with measurement-outliers, compared with other typical multiple target tracking filters.
Video watermarking using neural networks

Copyright protection for videos is important to prevent revenue loss for video generation companies by using video watermarking methods. Though many methods exists, still certain scope is noticed in robust video watermarking methods. Achieving features like trade-off between robustness and imperceptibility, speed, blind watermarking simultaneously is very challenging. The proposed work achieves the above said features using log-polar, DWT, and SVD techniques to embed watermark in a video and extract it when necessary. The objective is to protect the copyright and make the watermarking system blind, robust against frame drop attacks as well as achieving above features. This work also leverages scrambling, deep learning-based approach to generate secret sharing image from watermark to improve the speed compared to conventional tabular-based approach. We evaluated the method on our own dataset and proved that this method is outperforming compared to state-of-the-art methods in DWT and SVD domain.
A Secure and Verifiable Color Visual Cryptography Scheme with LSB Based Image Steganography

A secure and verifiable color visual cryptography has becoming an important field of study in recent development of visual cryptography.In recent era there is a need of security and verifiability of shares.This research paper introduce the new secure and verifiable color secrete sharing scheme which identifies and analyses cheating while presenting the share.Integrity and reliability of share is identified before superimposition of share to revel the original secrete image.Secrete and verification image of size n x n are used for creating two shares then created shares are embedded into two cover images using LSB (Least Significant Bit) based stenography to create the meaningful and stenographic shares.Color channel decomposition and Jarvis half toning method is applied on each color channel of RGB secrete and verification image for creation of shares.Method proposed eliminates the problem such as explicit requirement of codebook design, pixel expansion, and poor visual quality of reconstructed images.Structural Similarity(SS) and Mean Square Error(MSE) measures of original verification image with revealed verification image verifies the soundness and originality of the secrete.The method also compares the result with previously proposed method; experimental results and statistical analysis are used to prove the efficiency of the method proposed.
UT-ATD: Universal Transformer for Anomalous Trajectory Detection by Embedding Trajectory Information

Due to the development of the transportation industry, a large amount of trajectory data is pouring into the Internet all the time.Based on these trajectory data, anomalous trajectory detection technology provides great support for traffic safety assurance and traffic risk prediction.Most existing anomalous trajectory detection methods are based on trajectory's physical characteristics or representation learning, and they achieve good performance in a few scenarios.But they still face the following problems.(1) The imperfect utilization of trajectory points.(2) The sparsity of trajectory data, which leads to generalization issues.(3) Longer model training time consumed, which can't adapt to the large amount of trajectory data generated every day.To solve the above problems, we propose a novel anomalous trajectory detection model based on Universal Transformer, called UT-ATD.UT-ATD captures the information of trajectory positions by learning trajectory embedding for classification.UT-ATD has a faster training speed, relatively few model parameters, and sufficient portability, which are ideal for the realistic scene requirements.Our model achieves state-of-the-art performance in most aspects, and its effectiveness is verified by a series of experiments on the real-world taxi trajectory dataset.
Phase shifting profilometry based on Hilbert transform: An efficient phase unwrapping algorithm

Digital fringe projection profilometry based on phase-shifting technology is a reliable method for complex shape measurement, and the phase is one of the most important factors affecting measurement accuracy. The calculation of the absolute phase depends on the calculation of the wrapped phase and encoding technology. In this paper, a technique of obtaining the absolute phase of multi-frequency heterodyne fringe images using the Hilbert transform is presented. Since the wrapped phase can be calculated from only one fringe image of each frequency, the method does not need phase-shifting. The absolute phase can be obtained from the wrapped phase by applying the heterodyne method. The measurement time and computational complexity are dramatically reduced, the measurement efficiency is greatly improved, and this benefit from the number of images is greatly reduced. The experimental results show that the method presented in this paper performs well in the application, and the accuracy is no different from that of the phase-shifting method while the efficiency is greatly improved.
IoT Based Voting Machine with Fingerprint Verification

Abstract: In a democratic country, like India voting is an important way where the citizen can cast their vote. Usually voting is done by casting their vote in polling booth. As the technology increases, nowadays electronic voting machine is used for casting vote. This paper is about an IoT based voting machine with fingerprint verification. The main aim of this project is to make voting secure using fingerprint verification and also to reduce malpractices. The details of the voter along with their fingerprint in stored in database. If the fingerprint matches with the stored fingerprint, the system checks the aadhar number of the user and if authenticated, it checks if multiple votes have been cast. If the fingerprint matching is not correct “Matching failed” message will be displayed and if aadhar number is not correct, then “Aadhar not match” message will be displayed. Voter can enter his/her native place and vote for the corresponding candidate using thingspeak and the result can be obtained using the same. The Arduino Uno is the controller used in this project. Fingerprint is used to authenticate the user. There is at least a slight difference between the fingerprints of each person. When a malpractice occurs, “Already voted”message will be displayed. The arduino IDE is used for programming the board and cloud is used to display ballot card and to store the result. System provides an alert on malpractice and only an authorized voter can cast the vote. This project safeguards the citizen’s right to vote and guarantee fair election.
Should attention be all we need? The epistemic and ethical implications of unification in machine learning

"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many researchers are excited that similar model architectures can be successfully deployed across diverse applications in vision, language and beyond. We consider the benefits and risks of these waves of unification on both epistemic and ethical fronts. On the epistemic side, we argue that many of the arguments in favor of unification in the natural sciences fail to transfer over to the machine learning case, or transfer over only under assumptions that might not hold. Unification also introduces epistemic risks related to portability, path dependency, methodological diversity, and increased black-boxing. On the ethical side, we discuss risks emerging from epistemic concerns, further marginalizing underrepresented perspectives, the centralization of power, and having fewer models across more domains of application