Reviewerly/exHarmony huggingface dataset:
Hybrid Cryptography based E2EE for Integrity &amp; Confidentiality in Multimedia Cloud Computing

Cloud Computing has become a desirable state for the mobile users by providing anytime and anywhere access to various services and resources. Likewisemultimedia data such as audio, images, video and gif files covers the maximum part of the network traffic, as most of the data shared, produced, processed and stored through numerous sources such as smart phones, computers, sensor networks, satellites, medical records etc. is of multimedia type. Such form of data requires huge computing power and storage capacities along with the major security constraints. Mobile devices with limited configuration and low computing powers are not able to match with the high level computing and manipulations required by costly multimedia softwares. That’s why demand for the Multimedia Cloud Computing came into frames. As multimedia data has distinct computing, storage and security constraints, this field has become anemerging and latest area of research. Many researches have also proposed separate architectures for Multimedia Cloud Computing. Security, integrity and confidentiality of private and secret media files is always a matter of stake for cloud users as well as for cloud providers. For multimedia cloud security concerns, cryptography is a guaranteed solution.This paper has discussed about the various needs and challenges faced by the multimedia cloud providers. This work has also proposed a hybrid cryptography algorithm based end-to-end encryption (E2EE) approach to maintain integrity and confidentiality in multimedia cloud computing environment. The second last section provides a performance analysis for the algorithm in terms of tables and graphs to justify the results.
Integrated Exertion—Understanding the Design of Human–Computer Integration in an Exertion Context

Human–computer interaction (HCI) is increasingly interested in supporting exertion experiences so more people can benefit from physical activity. So far, most systems have focused on sensing and presenting information to the user via screens to support the exertion experience. Interestingly, emerging technology can also act on the exerting user's body based on sensed information, granting researchers the potential to develop technology that not only “presents” but also “acts” on information throughout an integrated exertion experience. As a result, design opportunities surrounding computing machinery as contextually aware exertion partners are now available. However, there are currently no frameworks to guide the design of human–computer integration in an exertion context. To contribute to closing this gap, we designed three eBike systems to investigate different forms of integration with the exerting user and we studied the resulting user experiences. Based on the results of these three case studies, we present the first framework, including associated design tactics, to offer guidance on how to design human–computer integration in an exertion context.
Muskits: an End-to-end Music Processing Toolkit for Singing Voice Synthesis

This paper introduces a new open-source platform named Muskits for end-to-end music processing, which mainly focuses on end-to-end singing voice synthesis (E2E-SVS). Muskits supports state-of-the-art SVS models, including RNN SVS, transformer SVS, and XiaoiceSing. The design of Muskits follows the style of widely-used speech processing toolkits, ESPnet and Kaldi, for data prepossessing, training, and recipe pipelines. To the best of our knowledge, this toolkit is the first platform that allows a fair and highly-reproducible comparison between several published works in SVS. In addition, we also demonstrate several advanced usages based on the toolkit functionalities, including multilingual training and transfer learning. This paper describes the major framework of Muskits, its functionalities, and experimental results in single-singer, multi-singer, multilingual, and transfer learning scenarios. The toolkit is publicly available at https://github.com/SJTMusicTeam/Muskits.
The relationship between open technological innovation, intellectual property rights capabilities, network strategy, and AI technology under the Internet of Things

The problems faced by the open technological innovation of China’s new ICT (information and communications technology) industry under IoT (Internet of Things) technology are expected to be analyzed to improve the overall innovation ability and ensure the sustainable development of related industries. An evaluation model is constructed for open technological innovation in the IoT industry by analyzing the development of IPR (Intellectual Property Rights) management, network strategy, and AI (artificial intelligence) technology under the development of IoT technology. Meanwhile, IBM SPSS Statistics 20.0 and IBM SPSS Amos 19.0 are used to analyze the data information of 306 enterprises in the information technology industry. Besides, the proposed hypotheses are verified by factor analysis, multiple regression, and Back Propagation Neural Network. Finally, a new evaluation index system is constructed for open technological innovation in the new ICT industry. The development of IoT technology provides a primary guarantee for the open technological innovation of the new ICT industry, and the network strategy has the greatest influence on the internal knowledge output mode. Besides, the experimental results indicate that the IoT and artificial intelligence have a critical display value for the open technological innovation of the emerging ICT industry, with the highest weight ratio of 48.25%. This result demonstrates that artificial intelligence is positively correlated with the external input information. Intellectual property management is a crucial guarantee of open technology innovation in the ICT industry. The evaluation model of open technological innovation in the ICT industry has good performance through case analysis, with the highest accuracy of 91.25%. Therefore, the evaluation index system reported here can reflect the important factors affecting the development of innovative technology, which can provide a theoretical basis and practical value for improving the existing open technology innovation system.
For to all those who have, will more be given? Evidence from the adoption of the SELFIE tool for the digital capacity of schools in Spain

Abstract This paper explores participation trends in interventions that promote self‐evaluation exercises on the effective use of digital technologies in schools. We use a unique dataset consisting of 83,185 respondents from 924 Spanish schools that used SELFIE, a tool based on self‐reflection questionnaires that capture different dimensions of school's digital capacity. We benefit from a natural experiment situation caused by the parallel use of SELFIE by two groups of schools. The first group was externally selected as part of a representative sample of Spanish schools. Conversely, the second group voluntarily decided to use SELFIE as a diagnostic tool for a subsequent self‐evaluation exercise. Moreover, a subset of schools were located in regions where authorities embedded SELFIE in broader digitalisation programmes. By comparing these groups, it is shown that schools that decide to participate in SELFIE voluntarily are those with a lower initial digitalisation level. It is also found that the promotion of the use of SELFIE as part of public interventions can increase participation but mainly attracts digitally advanced schools. In conclusion, policy interventions aiming to develop the digital capacity of schools need to plan how to reach those schools that need it more in order to be more equitable. Practitioner notes What is already known about this topic Research has shown the existence of a Matthew effect in the usage of digital technologies in education. The promotion of schools self‐evaluation exercises on digital education is a common policy intervention that is growing in importance. There is a surprising lack of attention to the inequitable effects that programmes aiming to incorporate technologies in educational institutions may generate. What this paper adds This paper investigates the self‐selection trends and (un)equity effects of SELFIE, an EU programme designed to prompt schools' self‐evaluations of digital capacity. When schools decide autonomously, schools with low digital capacity levels tend to participate in SELFIE more. Incorporation of SELFIE into broader public programmes enlarges participation in SELFIE. Incorporation of SELFIE into broader public programmes over‐attracts digitally advanced schools. Implications for practice and/or policy Public policies promoting self‐evaluation exercises on school digital capacity in schools might be a good way for upscaling these exercises. However, these policies should be carefully designed to reduce inequalities and reach these schools that need digitalisation more.
Forensic accounting tools for fraud deterrence: a qualitative approach

Purpose The purpose of this paper is to understand if forensic accounting techniques and tools could contribute to the deterrence of fraud in financial statements, considering the expertise of forensic accountant on ex post activities and that the traditional mechanisms to prevent this type of fraud have not been sufficient to stop the impact on companies, investors, auditors, employees and on society in general. Design/methodology/approach This research was carried out using a qualitative exploratory study with a phenomenological approach conducted through in-depth interviews with professional experts in the forensic field. Findings The findings confirm that the use of forensic accounting techniques and tools could contribute to the prevention of fraud in financial reporting not only when the risk of fraud has been materialized. Similar studies, about fraud prevention addressing the situation under a qualitative approach from the perspectives of its protagonists, have not been observed in the bibliographical review, so this research contributes to expanding the scientific research, the study and practice of forensic accounting. Originality/value From a business management perspective, this study contributes a paradigm shift from the traditional ex post forensic auditing activity toward an ex ante activity to improve management control systems within organizations anywhere in the world. Because this study is guided to prevent fraudulent financial statements, other fraud categories such as misappropriation or corruption could be addressed in other studies and various countries.
A Serendipity-Oriented Personalized Trip Recommendation Model

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.
Virtual fieldwork on a ship’s bridge: virtual reality-reconstructed operation scenarios as contextual substitutes for fieldwork in design education

Designing for professional, high-risk user contexts often implies limited accessibility for interaction designers to conduct field research and field testing, and the measures taken by most universities in Norway in 2020 to prevent COVID-19 spread have further contributed to the problem of achieving the contextual insight needed throughout the design process by severely restricting travel for research purposes. In this paper, we describe the use of virtual reality-reconstructed operation scenarios (VRROS) for Arctic-going vessels implemented in support of and as a substitute for the contextual aspects of fieldwork in the education of master's students studying interaction design. The virtual reality rig contains three scenarios contextualizing ships' bridges and their surroundings originally developed for research on designing navigation and operation applications using augmented reality technology. We evaluate whether aspects of the VRROS can substitute for real fieldwork by evaluating students' use of the VRROS using a student questionnaire. Finally, we discuss the value and potential of using VRROS as a supplement and support when studying how to design for hard-to-reach contexts in the future.
Sensing Eating Events in Context: A Smartphone-Only Approach

While the task of detecting eating events has been examined in prior work using a variety of wearable devices, the use of the smartphone as a standalone device to infer eating events remains as an open issue. In this paper, we propose a framework that infers eating vs. non-eating events from passive smartphone sensing, and evaluated on a dataset of 58 college students. First, we show that time of the day and features from modalities such as screen usage, accelerometer, app usage, and location are indicative of eating and non-eating. Then, we show that eating events can be inferred with an AUROC (area under the receiver operating characteristics curve) of 0.65 (F1-score of 0.75) using a subject-independent machine learning model, which can be further improved up to 0.81 (F1-score of 0.85 for subject-dependent and 0.81 for hybrid models) using personalization techniques. We also show that users have different behavioral and contextual routines around eating episodes, that require specific feature groups in training fully personalized models. These findings are of potential value for future mobile food diary apps that are context-aware by: (i) enabling scalable sensing-based eating studies using only smartphones; (ii) detecting under-reported eating events, thus increasing data quality in self report-based studies; (iii) providing functionality to track food consumption and generate reminders for on-time collection of food diaries; and (iv) supporting mobile interventions towards healthy eating practices.
Construction and Evaluation of Construction Safety Management System Based on BIM and Internet of Things

With the continuous development and maturity of technology, the Internet of Things has gradually been applied to various fields of work and life, for example, the use of the Internet of Things and road traffic, the use of the Internet of Things smart city for vehicle identification and GPS positioning, the use of the Internet of Things smart medical smart heart pulse monitoring device, and so on. And, the construction industry bears important responsibilities for the development of national infrastructure and people’s livelihood. This article aims to study the application of BIM and Internet of Things technology in the safety management of construction sites, to drive technological development with industry needs, help expand the application fields of the Internet of Things, and promote the improvement of the national construction safety management system. This article uses BIM and Internet of Things technology to build a construction safety management system, focusing on the protection of people, through technical means, such as personnel activity control, hazard warning, major hazard monitoring, and multiple institutional measures, such as education, training, supervision, and protection, that collaborate to complete the protection and risk avoidance of operators in the process of building construction. The evaluation index of the safety management system is proposed through the analytic hierarchy process, and the dimension of the safety management system to prevent accidents is evaluated by the analytic hierarchy process. Taking the actual project as an example, the application effect of the remote monitoring system is qualitatively analyzed. On the basis of the analysis, from the three levels of policy, enterprise, and practitioners, several suggested measures that will help new technology to be applied to construction safety management are put forward. In the experiment part, the construction of the safety management system is first explained, then the appropriate system evaluation index is selected, and then the comprehensive ranking is conducted according to the relative importance weight of the highest-level elements in the same level, and the MATLAB software is used to test the consistency. The experimental results show that the construction safety management system constructed in this paper has been well perfected. The calculated weights of people and dangerous areas are as high as 0.578, so that people and dangerous areas can be controlled effectively. Fall accidents from high places can be prevented, full-time monitoring of foundation pit projects can also avoid fall and collapse accidents from high places, improve the comprehensiveness and advancement of construction safety management, and improve the level of construction safety management.
Application of multi-angle millimeter-wave radar detection in human motion behavior and micro-action recognition

Abstract Millimeter-wave radar is widely used in family safety, rehabilitation, and assisted living due to its ability to operate in all weathers and all day. To address the problem whereby the radar detection angle significantly impacts human behavior recognition, a recognition method based on multi-angle radar observation is adopted. We proposed a novel radar selection method called the energy domain ratio method to choose a radar with more sensitive features. Then, local tangent space alignment and an adaptive extreme learning machine (ELM) are presented to enhance the recognition rate of the model in a high-noise environment. A multi-angle entropy feature and an improved ELM are developed to identify human micro-motion in a low-noise indoor environment. The effect of observation distance on the recognition effect was also explored. The experimental results show that the proposed model has a more than 86% recognition rate for human behavior in outdoor scenes and a recognition accuracy of more than 98% for indoor micro-action.
Reproducibility in learning

We introduce the notion of a reproducible algorithm in the context of learning. A reproducible learning algorithm is resilient to variations in its samples — with high probability, it returns the exact same output when run on two samples from the same underlying distribution. We begin by unpacking the definition, clarifying how randomness is instrumental in balancing accuracy and reproducibility. We initiate a theory of reproducible algorithms, showing how reproducibility implies desirable properties such as data reuse and efficient testability. Despite the exceedingly strong demand of reproducibility, there are efficient reproducible algorithms for several fundamental problems in statistics and learning. First, we show that any statistical query algorithm can be made reproducible with a modest increase in sample complexity, and we use this to construct reproducible algorithms for finding approximate heavy-hitters and medians. Using these ideas, we give the first reproducible algorithm for learning halfspaces via a reproducible weak learner and a reproducible boosting algorithm. Interestingly, we utilize a connection to foams as a higher-dimension randomized rounding scheme. Finally, we initiate the study of lower bounds and inherent tradeoffs for reproducible algorithms, giving nearly tight sample complexity upper and lower bounds for reproducible versus nonreproducible SQ algorithms.
Student e-learning service quality, satisfaction, commitment and behaviour towards finance courses in COVID-19 pandemic

Purpose This research investigates the relationship between e-learning service quality dimensions, student e-learning satisfaction, commitment and behaviour towards finance courses in higher education institutes of Pakistan. Design/methodology/approach Due to specific study objectives, the authors gathered sample data of 359 university students who were enrolled in the traditional learning system and shifted to the e-learning environment. The study employed partial least squares-structural equation modelling (PLS-SEM) based approach using Smart PLS version 3.0. Findings The results indicated that out of four e-learning service quality dimensions, three dimensions (system quality, course material and instructor quality, information technology (IT) and support service quality) positively impacted student e-learning satisfaction. The other dimension of e-learning service quality (course website quality) showed a positive but insignificant effect on e-learning satisfaction. Additionally, e-learning satisfaction was positively related to e-learning commitment, which, in turn, has a positive and significant influence on student e-learning behaviour towards finance courses. Originality/value Overall, the study’s findings provide useful policy implications for higher education institutes, particularly in the coronavirus disease 2019 (COVID-19) pandemic.