Reviewerly/exHarmony huggingface dataset:
Tabu-Driven Quantum Neighborhood Samplers

Combinatorial optimization is an important application targeted by quantum computing. However, near-term hardware constraints make quantum algorithms unlikely to be competitive when compared to high-performing classical heuristics on large practical problems. One option to achieve advantages with near-term devices is to use them in combination with classical heuristics. In particular, we propose using quantum methods to sample from classically intractable distributions -- which is the most probable approach to attain a true provable quantum separation in the near-term -- which are used to solve optimization problems faster. We numerically study this enhancement by an adaptation of Tabu Search using the Quantum Approximate Optimization Algorithm (QAOA) as a neighborhood sampler. We show that QAOA provides a flexible tool for exploration-exploitation in such hybrid settings and can provide evidence that it can help in solving problems faster by saving many tabu iterations and achieving better solutions.
Performance Comparison of Tree-Based Machine Learning Classifiers for Web Usage Mining

Web usage mining plays a very important role in finding a new patterns and recognition from the web server log file. It is the subcategory of web mining that is used to mine data over the web. In this paper, the authors classified the server log file, identified new patterns of data, and analyzed the data over the tree-based classification algorithms. Authors accessed the tree algorithms (treeJ48, RandomTree, RandomForest, and REPTree) to classify the weblog data and analyzed the results. This paper summarizes the results and compared the tree-based algorithms classification data and check which algorithm gives a better result.
Reverse AD at Higher Types: Pure, Principled and Denotationally Correct

Abstract We show how to define forward- and reverse-mode automatic differentiation source-code transformations or on a standard higher-order functional language. The transformations generate purely functional code, and they are principled in the sense that their definition arises from a categorical universal property. We give a semantic proof of correctness of the transformations. In their most elegant formulation, the transformations generate code with linear types. However, we demonstrate how the transformations can be implemented in a standard functional language without sacrificing correctness. To do so, we make use of abstract data types to represent the required linear types, e.g. through the use of a basic module system.
Judging Mental Health Disorders Using Decision Tree Models

This research presents a categorization replica to have the discernment of the result of distinct psychological health hazard which got improved with the implementation of the replica of decision tree. Among 3000 contestants approximately for different medical analysis, we get the instruction data regarding decision tree information from the answers of the queries. It is displayed by the exploratory outcomes that the suggested replica of the decision tree can find the significant framing of conclusion which influences Clinical discernment Precision. Such conclusions framing comprising in result such as recurrence or non-recurrence for clinical physical sickness, maturity, sex, duration of psychologically physical sickness, span for having drugs as well as suggested drugs that will be able to be applied as an instance of the assessment of the comprehensive precision of medical professionals.
CriPI: an efficient critical pixels identification algorithm for fast one-pixel attacks

Deep neural networks (DNN) have been studied intensively in recent years, leading to many practical applications. However, there are also concerns about the security problems and vulnerabilities of DNN. Studies on adversarial network development have shown that relatively more minor perturbations can impact the DNN performance and manipulate its outcome. The impacts of adversarial perturbations have led to the development of advanced techniques for generating image-level perturbations. Once embedded in a clean image, these perturbations are not perceptible to human eyes and fool a well-trained deep learning (DL) convolutional neural network (CNN) classifier. This work introduces a new Critical-Pixel Iterative (CriPI) algorithm after a thorough study on critical pixels’ characteristics. The proposed CriPI algorithm can identify the critical pixels and generate one-pixel attack perturbations with a much higher efficiency. Compared to a one-pixel attack benchmark algorithm, the CriPI algorithm significantly reduces the time delay of the attack from seven minutes to one minute with similar success rates.
Digital Transformation of Russian Universities: Analytics of Best Practices

The relevance of the study is due to the fact that the orientation of the transformation processes of Russian universities is aimed at forming a new management model that takes into account globalization processes, both in foreign economic activity and in the technological field in the context of society digitalization. The purpose of this study is to analyze the best practices of university management in the context of digital transformation. The experience and result of the implementation of neurolearning, artificial intelligence, chat bots and other digital and resource-saving technologies in the educational and business processes of the university are presented. This study proves that digital technologies can give impetus to the development of a university management system.
Fast MILP-based Task and Motion Planning for Pick-and-Place with Hard/Soft Constraints of Collision-Free Route

We present new models of optimization-based task and motion planning (TAMP) for robotic pick-and-place (P&P), which plan action sequences and motion trajectory with low computational costs. We improved an existing state-of-the-art TAMP model integrated with the collision avoidance, which is formulated as a mixed-integer linear programing (MILP) problem. To enable the MILP solver to search for solutions efficiently, we introduced two approaches leveraging features of collision avoidance in robotic P&P. The first approach reduces number of binary variables, which are related to the collision avoidance of delivery objects, by reformulating them as continuous variables with additional hard constraints. These hard constraints maintain consistency by conditionally propagating binary values, which are related to the carry action state and collision avoidance of robots, to the reformulated continuous variables. The second approach is more aware of the branch-and-bound method, which is the fundamental algorithm of modern MILP solvers. This approach guides the MILP solver to find integer solutions with shallower branching by adding a soft constraint, which softly restricts a robot's routes around delivery objects. We demonstrate the effectiveness of the proposed approaches with a modern MILP solver.
Blockchain-Based Trust Management Approach for IoV

Internet of Vehicles (IoV) brings new specific vehicular use cases further from Vehicular Ad-hoc Networks (VANETs) and offers sophisticated applications for more efficient Intelligent Transport Systems (ITS) in which autonomous driving technology is the quantum leap in smart connected vehicles. However, this innovation brings a security concern and remains cautious and suspicious as vehicular networks become more vulnerable. Traditional security techniques may not tackle all the security issues and provide the comprehensive protection in the current IoV system scenarios. It is therefore of paramount importance to organize trust in such environment and distinguish dishonest entity from trustworthy. Accordingly, trust management-based approaches are further needed to assist for effective vehicular networks deployment. In this paper, a trust management-based model is proposed to secure the vehicular communication system. The introduced trust approach executes a serie of security activities to ensure the trustworthiness of the exchanged data. In our scheme, vehicles also validate the trustworthiness of received data relying on location-trust metric. In this way, our approach will be efficient in calculating and maintaining trust scores in vehicular networks while accommodating related imperatives as Quality of Service (QoS) satisfaction.
Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

Video shreds of evidence are usually admissible in the court of law all over the world.However, individuals manipulate these videos to either defame or incriminate innocent people.Others indulge in video tampering to falsely escape the wrath of the law against misconducts.One way impostors can forge these videos is through inter-frame video forgery.Thus, the integrity of such videos is under threat.This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events.This leads to an increasing concern about the trustworthiness of video contents.Hence, it continues to affect the social and legal system, forensic investigations, intelligence services, and security and surveillance systems as the case may be.The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge.These video editing tools can easily manipulate videos without leaving obvious traces and these tampered videos become viral.Alarmingly, even the beginner users of these editing tools can alter the contents of digital videos in a manner that renders them practically indistinguishable from the original content by mere observations.This paper, however, leveraged on the concept of correlation coefficients to produce a more elaborate and reliable inter-frame video detection to aid forensic investigations, especially in Nigeria.The model employed the use of the idea of a threshold to efficiently distinguish forged videos from authentic videos.A benchmark and locally manipulated video datasets were used to evaluate the proposed model.Experimentally, our approach performed better than the existing methods.The overall accuracy for all the evaluation metrics such as accuracy, recall, precision and F1-score was 100%.The proposed method implemented in the MATLAB programming language has proven to effectively detect inter-frame forgeries.
The information-seeking behaviour of Egyptian parents of children with Autism Spectrum Disorder (ASD): a descriptive study

Purpose The purpose of this study is to investigate the information-seeking behavior of Egyptian parents of autistic children. Design/methodology/approach The study sample consists of 61 parents across Egypt, divided into six focus groups, as equal as possible, to represent Northern, Middle and Southern Egypt. Findings Dmographically, of the 61 parents, 32 (52.5%) were fathers, and 29 (47.5%) were mothers. A large number of parents' ages ranged from 31 to 45 years old. The highest percentage of them was holding a BA, followed by a high school/diploma, and the highest percentage was found to be married. The parents of autistic children followed many methods and channels to obtain supportive information related to their children's disease. They used many types of information, which varied between formal and informal sources. While just over a quarter of Egyptian parents prefer to use formal sources (books, newspapers, magazines, health publications, pamphlets, as well as specialized libraries), more than three-quarters of them used informal sources, such as mobile/smartphones, the Web, social media and social networking sites. The information related to the search for institutions supporting autistic people came in the first place, then that information related to searching for specialized centers in treating autistic children, then information related to rehabilitation, integration and psychological support centers for these children and then information related to the search for financial aid provided by charitable or even government agencies. Some parents were found to have limited awareness of their children's treatment mechanism, as some of them do not consider the need to treat their children in a systematic, continuous, and systematic manner. Similarly, the lack of basic services and shortage of government centers specializing in the treatment of these children. Parents also emphasized the lack of government support, as well as the lack of curative research centers. Some also pointed out that there was a shortage of workshops for the rehabilitation of their children and themselves as well. Originality/value This research is considered to be the first research of its kind at the local and Arab levels, which is also one of the few studies at the regional level that is interested in this community of information seekers. The findings of this research can raise awareness about the information behavior of Egyptian parents of autistic children among those who are interested in the role of the information and its use by specific groups of the information society, as well as decision makers. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0494
Genre as digital social action: the case of archiving, tagging and searching in digital media culture

Purpose The purpose of this article is to develop a contemporary understanding of genre as digital social action. Particular emphasis will be on archiving, tagging, and searching as social actions afforded by digital media as a function of their materiality. Design/methodology/approach The approach is critical analysis and discussion. Findings It is shown through an examination and a concrete example of how the genre is understood as digital social action, how the materiality of digital media affords particular communicative actions. Originality/value The article contributes with an understanding of the genre as digital social action consisting of two communicating parts: users’ actions and materiality.
Exploration of Online Education Mode for Postgraduate Education under the Background of COVID-19

Objective: This paper explores the application of online education mode for a postgraduate teaching education and its influence on postgraduate development under the background of COVID-19.Methods: This paper discusses the innovation modes of various online education platforms in postgraduate teaching, and analyzes the influence of online education in postgraduate curriculum education and academic ability education.Results: After the outbreak of COVID-19, online teaching methods are more abundant and innovative content is more, which stimulated the learning enthusiasm of postgraduates.Online platforms provide more opportunities for academic exchanges among postgraduates, which helps to improve the teaching quality of postgraduates.Conclusion: Online education has gradually developed into a more effective and potential education method.Combined with the application status of online teaching mode in postgraduate education, this paper provides specific development suggestions for universities and colleges to further construct and develop high-quality online teaching mode for postgraduates.
Scholarly Text Classification with Sentence BERT and Entity Embeddings

This paper summarizes our participated solution for the shared task of the text classification (scope detection) of peer review articles at the SDPRA (Scope Detection of the Peer Review Articles) workshop at PAKDD 2021. By participating this challenge, we are particularly interested in how well those pre-trained word embeddings from different neural models, specifically transformer models, such as BERT, perform on this text classification task. Additionally, we are also interested in whether utilizing entity embeddings can further improve the classification performance. Our main finding is that using SciBERT for obtaining sentence embeddings for this task provides the best performance as an individual model compared to other approaches. In addition, using sentence embeddings with entity embeddings for those entities mentioned in each text can further improve a classifier’s performance. Finally, a hard-voting ensemble approach with seven classifiers achieves over 92% accuracy on our local test set as well as the final one released by the organizers of the task. The source code is publicly available at https://github.com/parklize/pakdd2021-SDPRA-sharedtask.
A literature review of crowd-counting system on convolutional neural network

Abstract With the proliferation usage of video surveillance for safety, traffic control, and privacy purposes and with the constant growth of population, it is important to keep monitoring using Closed-Circuit Television (CCTV). With new upcoming developed technologies, new systems and algorithms are introduced and implemented to the crowd counting system today retrieving live video surveillance from the CCTV. However, recent studies show that there are some challenges still faced regarding the crowd counting system which uses the density estimation. The problems that occurred have resulted from the inaccuracy of the system that is caused by several factors. Factors such as the perspective distortion which is caused by the lack of data training and the method such as face detection is an ineffective method to determine the population density. Studies proposed have projected the idea of developing a more robust crowd counting methodology by implementing crowd counting by detection, clustering, and regression. Implementing these methods using the Convolutional Neural Network (CNN) will better the result of the detection since in CNN the image can be inputted and it will undergo several layers which will result in the system being able to differentiate one image from the other. With CNN the process of crowd counting will be able to be more advanced.
Fifty-six years of surface solar radiation and sunshine duration over São Paulo, Brazil: 1961–2016

Abstract. A total of 56 years (1961–2016) of daily surface downward solar irradiation, sunshine duration, diurnal temperature range and the fraction of the sky covered by clouds in the city of São Paulo, Brazil, were analysed. The main purpose was to contribute to the characterization and understanding of the dimming and brightening effects on solar global radiation in this part of South America. As observed in most of the previous studies worldwide, in this study, during the period between 1961 and the early 1980s, a negative trend in surface solar irradiation was detected in São Paulo, characterizing the occurrence of a dimming effect. Sunshine duration and the diurnal temperature range also presented negative trends, in opposition to the positive trend observed in the cloud cover fraction. However, a brightening effect, as observed in western industrialized countries in more recent years, was not observed. Instead, for surface downward irradiation, the negative trend persisted, with a trend of −0.13 MJ m−2 per decade, with a p value of 0.006, for the 56 years of data and in consonance with the cloud cover fraction increasing trend, but not statistically significant, of 0.3 % per decade (p value = 0.198). The trends for sunshine duration and the diurnal temperature range, by contrast, changed signal, as confirmed by a piecewise linear regression model. Some possible causes for the discrepancy are discussed, such as the frequency of fog occurrence, urban heat island effects, horizontal visibility (as a proxy for aerosol loading variability) and greenhouse gas concentration increase. Future studies on the aerosol effect are planned, particularly with higher temporal resolution, as well as modelling studies, to better analyse the contribution of each possible cause.
Imbalanced Cardiotocography Data Classification Using Re-sampling Techniques

To create a good classification model, a well-balanced dataset is extremely important. Medical datasets are not always balanced in their target class. In case the dataset is extremely imbalanced, most existing classification methods are not very effective in examples of minority classes. They aim to improve overall accuracy without taking into account the relative distribution of each class. This paper explores the efficiency of 7 re-sampling techniques for balancing the data on Cardiotocography (CTG) of UCI (University of California, Irvine) repository and 2 ensemble model techniques for the better performance of the classification models. Six states of the art classifiers: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and the Gaussian Naïve Bayes (GNB) are used to classify Fetal health state. From experimental results, an analytical attempt has been made to compare the performance of various re-sampling and ensemble approaches for the classification of CTG data.
Multi-Feature 360 Video Quality Estimation

We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quality features on viewports extracted from 360-degree videos. A new model is learnt to properly combine these features into a metric that closely matches subjective quality scores. The main motivations for the proposed approach are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) the use of viewports easily supports different projection methods being used in current 360-degree video systems; and 3) no individual objective image quality metric always performs the best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions. Experimental results, based on both the largest available 360-degree videos quality dataset and a cross-dataset validation, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.
An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.