Reviewerly/exHarmony huggingface dataset:
IMPACT OF SOCIAL MEDIA ENGAGEMENT AND ARTIFICIAL INTELLIGENCE ON ONLINE BUYING BEHAVIOUR

It has been observed that the Artificial intelligence (AI) has significantly transformed the business strategies as well as consumer behaviour very rapidly. The present study proposes a framework for understanding the impact of AI involving intelligence and deliberate social media engagements on consumers' online purchase behaviour. The analysed primary data have been collected randomly from 577 respondents through a self-administered questionnaire. Both online and interview methods have been adopted for the collection of these data. The findings of the study provide an empirical evidence on how the use of artificial intelligence helps to improve the consumer purchase frequency and spending patterns. More surprisingly, the study has revealed that consumers who spend more time on shopping apps are less likely to spend more money as their purchase frequency is low. On the other hand, social media engagements and the average time spent on social media apps are observed to have a significant and positive impact on online purchase behaviour.
On Photometric Stereo in the Presence of a Refractive Interface

We conduct a discussion on the problem of 3D-reconstruction by calibrated photometric stereo, when the surface of interest is embedded in a refractive medium. We explore the changes refraction induces on the problem geometry (surface and normal parameterization), and we put forward a complete image formation model accounting for refracted lighting directions, change of light density and Fresnel coefficients. We further show that as long as the camera is orthographic, lighting is directional and the interface is planar, it is easy to adapt classic methods to take into account the geometric and photometric changes induced by refraction. Moreover, we show on both simulated and real-world experiments that incorporating these modifications of PS methods drastically improves the accuracy of the 3D-reconstruction.
The Marrakesh Treaty: Using the tools of intellectual property law to advance human rights

This chapter considers the relationship between human rights and intellectual property. It argues that constructive engagement between the two legal regimes can occur by redirecting incentives for innovation and creativity and channeling market forces toward socially desirable outcomes. The chapter explores this claim using the example of the Marrakesh Treaty to Facilitate Access to Published Works for Persons who are Blind, Visually Impaired or otherwise Print Disabled. This recently-adopted multilateral agreement provides a conceptual and practical illustration of how to use the tools and doctrines of intellectual property law to advance human rights ends. The article argues that the Marrakesh Treaty’s core obligation - mandatory exceptions to copyright to promote the rights of persons with visual disabilities - may offer a model for harmonizing human rights and intellectual property in other contexts.
Equational Theorem Proving for Clauses over Strings

Although reasoning about equations over strings has been extensively studied for several decades, little research has been done for equational reasoning on general clauses over strings.This paper introduces a new superposition calculus with strings and present an equational theorem proving framework for clauses over strings.It provides a saturation procedure for clauses over strings and show that the proposed superposition calculus with contraction rules is refutationally complete.This paper also presents a new decision procedure for word problems over strings w.r.t. a set of conditional equations R over strings if R can be finitely saturated under the proposed inference system.
Full-Period Quantum Phase Estimation

Quantum sensing can provide the superior sensitivity for sensing a physical quantity beyond the shot-noise limit. In practice, however, this technique has been limited to the issues of phase ambiguity and low sensitivity for small-scale probe states. Here, we propose and demonstrate a full-period quantum phase estimation approach by adopting the Kitaev's phase estimation algorithm to eliminate the phase ambiguity and using the GHZ states to obtain phase value, simultaneously. For an $N$-party entangled state, our approach can achieve an upper bound of sensitivity of $\ensuremath{\delta}\ensuremath{\theta}=\sqrt{3/({N}^{2}+2N)}$, which beats the limit of adaptive Bayesian estimation. By performing an eight-photon experiment, we demonstrate the estimation of unknown phases in a full period, and observe the phase superresolution and sensitivity beyond the shot-noise limit. Our Letter provides a new way for quantum sensing and represents a solid step towards its general applications.
BiCNN-CML: Hybrid Deep Learning Approach for Chronic Myeloid Leukemia

chronic myeloid leukemia (CML) is an uncommon kind marked by genetic changes in early myeloid cell progenitors. The only efficient approaches for identifying leukemia are blood smear analysis, bone marrow aspiration, and biopsy. Because the tests mentioned above are time-consuming and expensive, leukemia diagnosis needs automation. Image processing is a low-cost and efficient way of identifying leukemia using stained blood microscope pictures. The major goal of this Research is to improve the accuracy of CML diagnosis. Recently, deep learning technologies have been employed in the medical business for early cancer detection and therapy prescription. Existing algorithms are exclusively concerned with image segmentation and feature extraction, and output may be provided as a consequence. In this paper proposed BiCNN-CML framework for predicting the CML. The datasets are collected and normalized using gray scale and morphological operation. The segmentation has done with FCM and PSO. The features are selected using GLCM and finally classification has done with Bi-LSTM with CNN algorithm. the experimental results has shown the classification metrics..
Recommendation model based on generative adversarial network and social reconstruction

Social relations can effectively alleviate the data sparsity problem in recommendation, but how to make effective use of social relations is a difficulty. However, the existing social recommendation models have two deficiencies. First, these models assume that social relations are applicable to various interaction scenarios, which does not match the reality. Second, it is believed that close friends in social space also have similar interests in interactive space and then indiscriminately adopt friends' opinions. To solve the above problems, this paper proposes a recommendation model based on generative adversarial network and social reconstruction (SRGAN). We propose a new adversarial framework to learn interactive data distribution. On the one hand, the generator selects friends who are similar to the user's personal preferences and considers the influence of friends on users from multiple angles to get their opinions. On the other hand, friends' opinions and users' personal preferences are distinguished by the discriminator. Then, the social reconstruction module is introduced to reconstruct the social network and constantly optimize the social relations of users, so that the social neighborhood can assist the recommendation effectively. Finally, the validity of our model is verified by experimental comparison with multiple social recommendation models on four datasets.
Enhancement of classification and prediction accuracy for breast cancer detection using fast convolution neural network with ensemble algorithm

Breast cancer is a leading cancer found mostly in women across the world and is more in number in the developing countries where they are not diagnosed in the early stages. The recent works compare machine learning algorithms using various techniques such as ensemble methods and classification. Hence, the requirement of time is to develop the technique which gives minimum error to increase accuracy. So, this paper proposes the neural network where classifying and predicting breast cancer is enhanced with maximum accuracy. Here, the novel technique of fast convolution neural network (FCNN) has been used for enhancing the classification and for improving the prediction accuracy ensemble algorithm of gradient boosting and adaptive boosting. By this proposed technique with ensemble algorithm, the huge data has been taken and predicted for detecting the cancer and this combined boosting algorithm will reduce the misclassification and will improve the binary classification. The training and testing of the dataset has been done with FCNN where the numerous datasets can be classified for earlier detection of cancer. The simulation result shows the improved accuracy, prediction class, precision and F-1 score.
Comparison of different convolutional neural network techniques for car models classification

Car model classification is crucial as part of recognizing suspicious cars on the road that irresponsible owners have modified to obscure the views of police authorities for criminal purposes. This study presents car model classification using a deep learning state-of-the-art Neural Network architecture called Convolutional Neural Networks (CNN). Rather than being taught the concepts of a car, computers could study images, learn the concepts on their own using CNN, and classify them into their respected models using image processing methods. Different pre-trained CNNs were used in this study, including AlexNet, ResNet, and GoogleNet. The main idea of using different pre-trained CNNs is to compare the performance of the classification task by using various networks. The experimental results show that ResNet performed better than AlexNet and GoogleNet in classifying the modified cars into their actual class.
Blood cell detection and counting in microscopy images using overlapping object recognizing model

Computer vision is one of the most striking areas in data science. Like other fields of data science, the application of this field has also become part of our personal lives. Classifying an overlapping object in cell detection is one of the main challenges faced by researchers who work in object detection and recognition. The white and red blood cells have a significant part in controlling the human body's framework. One of the essential tests for assessing human medical issue is checking the amount of white blood cells and red blood cells, as well as platelets number in the blood. In this project, we used one of the Convolutional Neural Network (CNN) techniques which is Faster Region CNN to recognize, classify, and count the blood cell elements. Our objective is to detect blood cell images from dataset, separate between overlapping cells and other objects, and then count the number of each blood cell. The researchers used 720 images containing 1603 labelled blood cells from 10 categories to train the model. Those images were marked by clinical the Hospital Clinic of Barcelona ‘s pathologists and were obtained from the dissemination of blood drawn from patients during 2015-2019. After training steps were performed on 60 images, and blood cell detection and segmentation were performed in the next steps. The average accuracy was 97%, and the verification loss was 18.4%. By using this our Faster R-CNN framework object detection method can enhance the accuracy and speed of cell detection.
Degree sequence optimization in bounded treewidth

We consider the problem of finding a subgraph of a given graph which minimizes the sum of given functions at vertices evaluated at their subgraph degrees. While the problem is NP-hard already when all functions are the same, we show that it can be solved for arbitrary functions in polynomial time over graphs of bounded treewidth. Its complexity remains widely open, in particular over complete graphs and complete bipartite graphs.
Machine learning based multipurpose medical image watermarking

Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.
Tactics of lifting up an object using the foldable robotic hand

This paper describes the methods of lifting up an object using a robotic hand which has six-degree-of-freedom (DOF). The robotic hand was developed by our research group, and it has dual plates which serve as fingers, and the fingers of the hand are able to fold and changes the figure of it in order to handle various objects such as putting on an object, pinching, grasping, taking out a file from bookshelf and so on. In this paper, the mechanism of the robotic hand and the system configurations are presented, and we proposed the three methods, rolling over an object using one of the fingers and putting on the other one, pinching for small object and grasping an object that is somewhat large to lift up an object. The experiments to verify the handling methods are conducted, and the results were shown the effectiveness of proposed method.
The Blockchain Technology Applications in Higher Education

Blockchain technology is increasingly revolutionizing, not only the way we perform financial transactions, as well as increasingly gaining space in areas that also need transparency and safety. In this paper it will be identified the potential uses of Blockchain in High Education Systems, outlining its advantages and disadvantages, as well as challenges arising from the adoption of this innovative technology. To understand the Blockchain applications in education, an exploratory study was conducted, based on a quantitative methodology, supported by a questionnaire addressed to teachers, workers of academic services, administration staff, students, data protection officers, and IT technicians. This study was based on information associated with the project TRUE—Transparency of Learning Outcomes through Blockchain Technology, the survey respondents were selected from the contacts of the partners of this project. Analyzing the questionnaire results it was verified that the idea and convenience of Blockchain is not known to most respondents, specifically on High Education professionals. Raising awareness to the topic is thus of primary importance, the TRUE Project intends to help that endeavor by giving targeted information about Blockchain in education, and how it can facilitate relations between High Education institutions around Europe and in Erasmus + programs.
Verifying and Improving Neural Networks Using Testing-Based Formal Verification

Neural networks have been widely used in safety-critical systems, but those safety-critical systems containing neural networks still have security risks due to the existence of adversarial examples. The security of neural networks can be ensured to some extent by verifying them. However, since the verification of neural networks is a NP-hard problem, it is still impossible to apply the verification algorithm to large-scale neural networks. For this reason, we propose TBFV-INN, a new framework for verification and improving neural networks. First, we propose a testing-based neural network pruning algorithm, which obtains the execution path of each test case in the neural network by executing them. Secondly, test-based neural network pruning divides the original neural network into several sub neural networks. Finally, for divided sub neural network, a verification algorithm is used to verify and construct the data set to retrain the neural network, thus ensuring that each sub neural network is reliable in a particular input-output interval. We show a case study to demonstrate the feasibility of the framework.
Movie Tag Prediction System Using Machine Learning

Movies can be tagged with various details such as genre, plot structure, soundtracks, and visual and emotional experiences. This information can be used to build automatic systems to extract similar movies, enhance user experience, and improve the recommendations. In this paper, a machine learning-based approach is proposed to predict the tags which can be associated with a given movie. The problem is posed as a multi-label classification problem. To solve this, firstly, we created a fine-tuned set of various tags that exposed the varied characteristics of movie plots (a textual summary of a movie). Then, using different textual representation techniques such as TF-IDF, AVGW2V (averaged word vector), and several machine learning classifiers are used to predict the tags associated with a movie. It is believed that the proposed machine learning-based tag prediction can be useful in other tasks related to narrative analysis.
Implicit neural visual representation compression of 3D scenes

This paper provides insight into the 3D scene compression represented by a neural implicit function. The goal of this paper is to introduce the aspects of implicit neural representation for 3D scenes such as NeRF (Neural Radiance Field) and propose a novel compression method for Neural Implicit representation for 3D scenes. We also provide the analysis of compression performance of 3D scene representation by using Neural implicit function.
A prior-guided face image super-resolution network based on attention mechanism

In this paper, we propose a face image super-resolution method, aiming to reconstruct high quality face images from a low resolution input. The proposed method introduces an attentional multi-scale feature fusion block, which aims to improve the representation power of the neural network by emphasizing the important feature maps and suppressing the unimportant ones. In addition, the facial prior information is utilized by adding a separate prior branch, an hourglass structure is used. Experiments show that the face images reconstructed by the proposed method exhibit noticeable quality improvement compared to the low-resolution images and other SR approaches.
Interactive SNS Reader That Promotes Encounters With Information From a New Perspective

Personalization in social media allows a user to consume content relevant to their interests. However, this comes at the cost of potentially trapping a user in their own filter bubble. Conventional methods involve giving user recommendation on content that a user is not aware of but might be interested in. We believe that a discovery will have a much higher sentimental value on if a user discovered it by themselves so we proposed and developed an interactive interface for social media content consumption. Our method allows more engagement from the user side and aims to provide new perspectives in order to promote serendipitous findings by the user.
Location-Aware Cloud Service Brokering in Multi-cloud Environment

More and more enterprises are increasingly gaining technical and economic benefits from the global cloud marketplace.
Domotics for the Independence and Participation in Daily Life of People with Severe Disabilities

Domotics improves quality of life of people with disabilities. Maintaining independence and participation in daily life is the main goal in inclusive projects, starting with rehabilitative interventions. Complex technological systems tailored to conditions and life style of individuals with disabilities can be implemented through open source technology. This case report discusses the design features and results of domotic projects aimed at promoting independence and participation in daily life of people with severe disabilities. Crucial factors for the success of the projects were: customization of the system according to the needs of end users and its adaptation during the progression of the disease; relevance of social connections with respect to the use of technology by the individual; potential interference of unpredictable events that could compromise the user’s technological experience. This field of technological application is full of opportunities but still needs proofs of effectiveness. Individualized projects require a lot of technical attention and human care, even in the subsequent stages of implementation. The numerous criticalities that could undermine the success of systems of this type must be addressed by appropriately considering individual cases, the use of resources and the technological intensity of the projects.
Publication of False Information under the Public Official Election Act and the Freedom of Expression

Ensuring freedom of election and securing fairness in elections are important values that cannot be neglected. However, the two sides are bound to conflict, and if the fairness of the election is emphasized, the freedom of election can be retreated, and if the freedom of election is prioritized, the fairness of the election can be undermined. Under the Public Official Election Act, the crime of public declaration of false information has the legislative purpose of securing fairness in elections, but there is also a problem that may limit freedom of election. Therefore, the interpretation criteria for acknowledging this must be detailed. The Supreme Court's unanimous decision interpreted the scope of the 'public announcement' limitedly based on the awareness of the problem to expand election freedom. However, although I agree with the awareness of the problem to reduce the scope of punishment for the crime of disclosing false information, the dictionary meaning of the publication is clear, and adding the requirement of activism is a matter of caution. This is considered a factor to be considered in the sentencing standards. Therefore, it is considered reasonable to judge whether false facts are applicable and to consider requirements such as 'active', 'unilateral', 'intention', and 'inevitability' as sentencing standards in expressive actions. In addition, it is necessary to consider the 'impact on elections' as a sentencing standard. In order to invalidate the election, it is necessary to determine whether it had a decisive influence on the election. In other words, it is necessary to introduce the impact on elections and the activism of expression as a sentencing standard. In accordance with these sentencing standards, criminal punishment is imposed if necessary, but it is reasonable to interpret strictly the extent to which the election is invalidated. In the long run, it is necessary to revise the Public Official Election Act to raise the criteria for invalidating the election of false information disclosure.