Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Multi-robot Path Planning Based on Genetic Algorithm

A genetic multi-robot path planning (GMPP) algorithm is proposed to rationalize the path planning of multi-robots in multi-obstacle scenarios, especially in large and complex environments with path length constraints and path safety feasibility. On the basis of ensuring path safety, the GMPP uses the shortest path length as the objective function. Secondly, the GMPP considers the task execution problem of multi-robot path planning to avoid collisions in a shared working environment. As a result, the GMPP introduces a central planner to make collision-free driving between multiple robots through two mechanisms: collision detection and collision elimination. Comparison experiments between the GMPP and other swarm optimization algorithms are carried out. The results show that the GMPP has better overall performance in terms of anti-collision safety and efficiency for multi-robot path planning.
Enhance Face Recognition Using Time-series Face Images

In recent years, with the gradual development of computer vision-related technologies, the face recognition-related technology has gradually become one of the mainstream security system options. In this study, we use MTCNN[1] for preprocessing and LRCNs[2] network structure and time-series face image data to try to solve the problem of unrecognizable user due to face blocking or face angle, when a frame is unrecognizable, we can still compare the user's characteristics with other frames and determine their identity.
THE COVID-19 PANDEMIC AND THE CHANGES IN CONSUMER HABITS AND BEHAVIOR

In a short time, the COVID-19 pandemic transformed people’s behavior, undermining firms and businesses and changing the global economy. In this context, the study aimed to understand the impacts the COVID-19 pandemic caused on consumer behavior. The research method adopted was the literature review, investigating the Scopus and Web of Science databases, two of the main scientific databases. A total of 205 articles published in 2020 were identified, and, based on the proposed criteria, 30 studies showed high adherence to the topic and contributed to the understanding of changes in consumer habits and behavior. The main themes identified were: e-commerce growth, panic buying, repressed demand and stricken sectors, do-it-yourself, increased apps usage, machine learning methods, data internet security, and online marketing and shopping platforms.
Assessing quality and agreement of structured data in automatic versus manual abstraction of the electronic health record for a clinical epidemiology study

Objective We evaluate data agreement between an electronic health record (EHR) sample abstracted by automated characterization with a standard abstracted by manual review. Study Design and Setting We obtain data for an epidemiology cohort study using standard manual abstraction of the EHR and automated identification of the same patients using a structured algorithm to query the EHR. Summary measures of agreement (e.g., Cohen’s kappa) are reported for 12 variables commonly used in epidemiological studies. Results Best agreement between abstraction methods is observed among demographic characteristics such as age, sex, and race, and for positive history of disease. Poor agreement is found in missing data and negative history, suggesting potential impact for researchers using automated EHR characterization. EHR data quality depends upon providers, who may be influenced by both institutional and federal government documentation guidelines. Conclusion Automated EHR abstraction discrepancies may decrease power and increase bias; therefore, caution is warranted when selecting variables from EHRs for epidemiological study using an automated characterization approach. Validation of automated methods must also continue to advance in sophistication with other technologies, such as machine learning and natural language processing, to extract non-structured data from the EHR, for application to EHR characterization for clinical epidemiology.
Combo separability criteria and lower bound on concurrence

Abstract Detection and quantification of entanglement are extremely important in quantum information theory. We can extract information by using the spectrum or singular values of the density operator. The correlation matrix norm deals with the concept of quantum entanglement in a mathematically natural way. In this work, we use Ky Fan norm of the Bloch matrix to investigate the disentanglement of quantum states. Our separability criterion not only unifies some well-known criteria but also leads to a better lower bound on concurrence. We explain with an example how the entanglement of the given state is missed by existing criteria but can be detected by our criterion. The proposed lower bound on concurrence also has advantages over some investigated bounds.
The Effect of Online Learning on Students Learning Motivation

Online learning began in early March 2020. With this online learning, most parents complained when online learning began. Because many students are angry, and yell at their parents when they are told to study, even when there is a test they don't want to study at all. This study aims to determine how big the level of student learning motivation during online learning at SDN Puton, Diwek District, Jombang Regency. This study uses quantitative methods in processing the data using samples. The sample used in this study was grade 6 students, totaling 32 students. In this study using data collection techniques in the form of questionnaires and documentation. Questionnaires are aimed at students to find out about their learning motivation, and documentation aims to obtain information about student data, student learning outcomes and a brief history of the establishment of the school, the school's vision and mission, school conditions and so on. The results in this study can be concluded that there is a significant effect of online learning on the level of student motivation. Where the significant value of tcount (5% = 170.592) is greater than ttable (0.361) and at a significant level of 0.000 less than 0.05 so it can be concluded that in the alternative hypothesis research Ha is accepted and Ho is rejected. This means that there is a very large influence of online learning on learning motivation in grade 6 students at SDN Puton, seen from the calculation of the coefficient of determination of 85% of the influence. Keywords: Influence, online learning, motivation
A Crucial Decade for European Digital Sovereignty

Abstract The current decade will be critical for Europe’s aspiration to attain and maintain digital sovereignty so as to effectively protect and promote its humanistic values in the evolving digital ecosystem. Digital sovereignty in the current geopolitical context remains a fluid concept as it must rely on a balanced strategic interdependence with the USA, China, and other global actors. The developing strategy for achieving this relies on the coordinated use of three basic instruments, investment, regulation, and completion of the digital internal market. Investment, in addition to the multiannual financial framework (2021–2027) instruments, will draw upon the 20% of the 750 billion recovery fund. Regulation, in addition to the Digital Governance Act and the Digital Market Act, will include the Data Act, the new AI regulation, and more that is in the pipeline, leveraging the so-called Brussels effect. Of key importance for the success of this effort remains the timing and “dovetailing” of the particular actions taken.
Improved Security Bound of (E/D)WCDM

In CRYPTO’16, Cogliati and Seurin proposed a block cipher based nonce based MAC, called Encrypted Wegman-Carter with Davies-Meyer (EWCDM), that gives 2n/3 bit MAC security in the nonce respecting setting and n/2 bit security in the nonce misuse setting, where n is the block size of the underlying block cipher. However, this construction requires two independent block cipher keys. In CRYPTO’18, Datta et al. came up with a single-keyed block cipher based nonce based MAC, called Decrypted Wegman-Carter with Davies-Meyer (DWCDM), that also provides 2n/3 bit MAC security in the nonce respecting setting and n/2 bit security in the nonce misuse setting. However, the drawback of DWCDM is that it takes only 2n/3 bit nonce. In fact, authors have shown that DWCDM cannot achieve beyond the birthday bound security with n bit nonces. In this paper, we prove that DWCDM with 3n/4 bit nonces provides MAC security up to O(23n/4) MAC queries against all nonce respecting adversaries. We also improve the MAC bound of EWCDM from 2n/3 bit to 3n/4 bit. The backbone of these two results is a refined treatment of extended mirror theory that systematically estimates the number of solutions to a system of bivariate affine equations and non-equations, which we apply on the security proofs of the constructions to achieve 3n/4 bit security.
Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

Radiologists' diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists' diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.
Virtual machines pre-copy live migration cost modeling and prediction: a survey

Abstract Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.
“Meet the Deer King”: “Splash-Ink” Interaction in the Innovative VR Game Based on Dunhuang Art and Culture

VR technologies enable 3D visualization and 3D interaction modalities, allowing the general public to get user-centered experience and explore an immersive realistic cultural environment. The art and culture of Dunhuang grotto murals is the world's art treasure. According to the traditional story of “Deer King” and the knowledges of preservation and restoration of the murals, we designed “Meet the Deer King”, a VR game. In this game, we explored and designed "splash-ink" interaction, which derives from traditional Chinese painting style, and we applied this interaction in different scenes. The information about the original game, including its creation background, research focus, game content and implementation effects are mainly described in the supplementary materials.
Sports Action Recognition Based on Image Processing Technology and Analysis of the Development of Sports Industry Pattern

The current era is an information age, and society is turning to the information age. The image processing technology is also widely used in various fields, and the technology of sports action recognition based on image processing technology can also be said to be appropriate. This article uses a spatial visual feature analysis algorithm to implement it. To implement this algorithm, a series of work such as image collection, feature extraction, and action recognition must be completed first and then implemented through texture functions and other related functions. This algorithm can be used to complete the image-based sports action recognition technology at the minimum time cost. This algorithm can help sportsmen better complete training and standardize movements to a certain extent. As for the development of China’s current sports industry structure, it is also steadily improving. The people’s love for sports is getting stronger and stronger, which also makes the development of China’s sports industry still benefit a lot.
Spin qudit tomography and state reconstruction error

We consider the task of performing quantum state tomography on a $d$-level spin qudit, using only measurements of spin projection onto different quantization axes. After introducing a basis of operators closely related to the spherical harmonics, which obey the rotational symmetries of spin qudits, we map our quantum tomography task onto the classical problem of signal recovery on the sphere. We then provide algorithms with $O(rd^3)$ serial runtime, parallelizable down to $O(rd^2)$, for (i) computing a priori upper bounds on the expected error with which spin projection measurements along $r$ given axes can reconstruct an unknown qudit state, and (ii) estimating a posteriori the statistical error in a reconstructed state. Our algorithms motivate a simple randomized tomography protocol, for which we find that using more measurement axes can yield substantial benefits that plateau after $r\approx3d$.
Cyber-animism: the art of being alive in hybrid society

The boundaries of social acceptance and models of convergence of human and non-human (for example, subjects of artificial intelligence) actors of digital reality are defined. The constructive creative possibilities of convergent processes in distributed neural networks are analyzed from the point of view of possible scenarios for building “friendly” human-dimensional symbioses of natural and artificial intelligence. A comprehensive analysis of new management challenges related to the development of cyber-physical and cybersocial systems is carried out. A model of social organizations and organizational behavior in the conditions of cyberphysical reality is developed. The possibilities of reconciling human moral principles and “machine ethics” in the processes of modeling and managing digital reality are studied. The significance of various concepts of digital, machine and cyber-anymism for the socio-cultural understanding of the development of modern cyber-physical technologies, the anthropological dimension of a smart city is revealed. The article introduces the concept of hybrid society and shows the development of its models as self-organizing collective systems that consist of co-evolving biohybrid and socio-technical spheres. The importance of modern anthropogenic research for sustainable development is analyzed. The process of marking ontological boundaries between heterogeneous modalities in the digital world is investigated. Examples of acute social contexts that are able to set the vector of practical philosophy in the modern digital era are considered.
Acquiring Word Order in Slovak as a Foreign Language: Comparison of Slavic and Non-Slavic Learners Utilizing Corpus Data

Abstract The paper deals with the acquisition of Slovak word order in written texts of students of Slovak as a foreign language. Its attention is focused on identifying the correct and incorrect placement of enclitic components, and their erroneous usage is analysed with respect to different investigated variables (types of enclitic components, types of syntactic construction, distance from lexical/syntactic anchor, and realization in pre- or post-verbal position). The paper also pays attention to the error rate regarding individual proficiency levels of students, and error distribution in two language groups, Slavic and Non-Slavic learners, is compared.
Blockchain Applications in Logistics

Despite the recent hype around blockchain technology, there are clear business cases where blockchain can create significant value to logistics and supply chains. This article serves as a primer for those who want to understand more about blockchain and its potential disruptive effect. It starts with a brief discussion of what is blockchain and how it works. Then why blockchain matters to logistics and supply chains is explored based on blockchain's core and unique attributes. Popular use cases are then discussed to showcase how blockchain may create value in logistics. The article ends by providing a summary of challenges facing blockchain's further diffusion and a few design principles guiding the setup of blockchain initiatives in practice.
Blockchain Design for Logistics &amp; Supply Chain Management in Developing Regions

Blockchain led technology is unlocking value to business by boosting supply chain authenticity and expediting the entire operational process for a large rally of projects that are underway in selective industries. The current paper attempt to observe a reality check on the different useful application of blockchain technology over various sectors to reshape the economy in general and over the supply chain and logistics system in particular. The study also validates the enthusiasm of blockchain’s potential i.e., claim for revolutionising business and society over the virtual effect it is creating in the challenging environment of developing regions. Supply chain and entire logistic domain have evolved to be an active participant to took up and adapt to blockchain innovation. The current study adds to the existing literature by driving the subject matter from different studies results concerning blockchain technology along with author own outlook. An observation approach to reflect critics, supporters towards the adoption of technology led by blockchain also developed the foundation of the work.
A Little More Conversation, A Little Less Factionalisation: Thoughts for the Postdigital Future of Online Postgraduate Education

In this concluding chapter, we depart from many of the typical conventions of a conclusion (synthesizing the content of chapters; tying up loose ends) and, instead, use the ideas and conclusions of the various authors within the book as points of departure for further, and always incomplete, dialogue. Rather than wrapping things up, we leave them open. How could we do anything else, when many of the consequences of the Covid-19 pandemic for postgraduate education have yet to play out? In any case, for us, online postgraduate taught education is not something to be solved, streamlined or standardised—it is, and should remain, a rich tapestry of methods, purposes, contexts and practices. For these reasons, this chapter takes the form of a conversation between the editors, looking back over the chapters and forwards to the challenges of online postgraduate education in a postdigital context.
Cross-Modal Based Person Re-identification via Channel Exchange and Adversarial Learning

The extensive Re-ID progress in the RGB modality has obtained encouraging performance. However, in practice, the usual surveillance system is automatically switched from visible modality to infrared modality at night. The task of infrared-visible modality-based cross-modal person Re-ID (IV-Re-ID) is required. However, the existing substantial semantic gap between the visible images and the infrared images results in the IV-Re-ID still challenging. In this paper, a Cross-modal Channel Exchange Network (CmCEN) is proposed. In the CmCEN, a channel exchange network is first designed. The magnitude of Batch-Normalization (BN) is calculated to adaptively and dynamically exchange discriminative information between two different modalities sub-networks. Then, a discriminator with adversarial loss is designed to guide the network to learn the similarity distribution in the latent domain space. The evaluation results on two popular benchmark datasets demonstrated the effectiveness of our proposed CmCEN, and it obtained higher performance than state-of-the-art methods on the task of IV-Re-ID. The source code of the proposed CmCEN is available at: https://github.com/SWU-CS-MediaLab/CmCEN.
A Recent Invasion Wave Of Deep Learning In Solar Power Forecasting Techniques Using Ann

Since 2000, solar power has grown rapidly to meet the electricity demand for daily life, industry, agriculture, service… In the development of solar energy, forecasting the generation capacity of solar power plants plays an important role in stable operation, planning, and balancing their electricity production. Many models and techniques have been developed for shorterm, medium, and long term solar generation forecasting. Research literature from 2000 to the present shows that Artificial Neural Network (ANN)-based predictive models perform better than other models due to their ability to solve complex nonlinear problems Their accuracy can be further improved by combining the two models in forecasting or processing input data. This paper compares the accuracy, popularity, and forecasting scope of solar power generation forecasting methods using the ANN model in combination with other forecasting models. Currently, the combined forecasting models of Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) with other forecasting methods are being strongly developed, playing an important role in predicting the generation capacity of solar power plants.