Reviewerly/exHarmony huggingface dataset:
VRBodyMem: A Virtual Full-Body Illusion for the Study of Episodic Memory

Cyberpsychology, Behavior, and Social NetworkingVol. 26, No. 9 CyberEuropeVRBodyMem: A Virtual Full-Body Illusion for the Study of Episodic MemoryCompiled by: Cosimo Tuena, Ling Zeng, Maria Hashmi, and Giuseppe RivaCompiled by: Cosimo TuenaAddress correspondence to: Dr. Cosimo Tuena, Applied Technology for Neuro-Psychology Laboratory, IRCCS Istituto Auxologico Italiano, Via Magnasco 2, 20149 Milan, Italy E-mail Address: [email protected]Applied Technology for Neuro-Psychology Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy.Search for more papers by this author, Ling ZengLing Zeng, Université Paris Cité, 45 Rue des Saints-Pères, 75006 Paris, France E-mail Address: [email protected]Universidade Lusofona de Humanidades e Technologias—ULHT, Lisbon, Portugal.Université Paris Cité, Paris, France.Search for more papers by this author, Maria HashmiMaria Hashmi, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain E-mail Address: [email protected]Universidade Lusofona de Humanidades e Technologias—ULHT, Lisbon, Portugal.Universitat de Barcelona, Barcelona, Spain.Search for more papers by this author, and Giuseppe RivaProf. Giuseppe Riva, Humane Technology Laboratory, Catholic University of Sacred Heart, Largo Gemelli 1, 20123 Milan, Italy E-mail Address: [email protected]Applied Technology for Neuro-Psychology Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy.Humane Technology Laboratory, Catholic University of Milan, Milan, Italy.Search for more papers by this authorPublished Online:11 Sep 2023https://doi.org/10.1089/cyber.2023.29287.ceuAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 26Issue 9Sep 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Cosimo Tuena, Ling Zeng, Maria Hashmi, and Giuseppe Riva.VRBodyMem: A Virtual Full-Body Illusion for the Study of Episodic Memory.Cyberpsychology, Behavior, and Social Networking.Sep 2023.724-726.http://doi.org/10.1089/cyber.2023.29287.ceuPublished in Volume: 26 Issue 9: September 11, 2023Online Ahead of Print:June 20, 2023PDF download
Lightweight skeleton-based action recognition research

Action recognition is a cutting-edge research direction in the field of computer vision, widely used in video surveillance, video retrieval, human-computer interaction, and other fields. However, existing action recognition algorithms improve prediction accuracy at the cost of having too many model parameters and high computational complexity, which limits their practical application. In order to address this issue and improve recognition accuracy with higher computational efficiency, we propose a dense residual 3D depth-separable convolutional network model based on channel attention mechanism. By extending the depth separable convolution to 3D, the model can maintain high accuracy while greatly reducing the number of model parameters and operational complexity. The dense residual structure is introduced to enhance the training ability of the network, improve feature extraction, and further improve network performance by learning the correlation between channels through the channel attention mechanism. Compared with other action recognition algorithms on two datasets, NTU RGB-D and Fall Dataset Lei2, the proposed action recognition algorithm has better robustness under lightweight condition, with 415.9 MFLOPs of computation, 205.4k of parameters, and 90.5% of CS, 95.5% of CV and 95.97% of accuracy, respectively. The proposed algorithm has a high recognition accuracy and greatly reduces the calculation cost, which can provide better performance for practical applications.
Pathfinding algorithm of unmanned aerial vehicle based on deep learning

Pathfinding algorithm has a decisive impact on unmanned aerial vehicle flight. In practical applications, aerial vehicle is often required to respond to emergencies quickly and plan a new path in time. Therefore, we designed a pathfinding optimization algorithm, combined with the object detection algorithm based on deep learning network, to identify objects that suddenly appear on the original path, timely avoid and re-plan the path. Experiments show that the proposed method can re-plan the new safe path in a short time which satisfies the requirements in response time and path planning.
Psychological and pedagogical problems of readiness lecturers to the organization of distance learning

The article considers psychological and pedagogical problems of teachers’ readiness to organize distance learning. The analysis of works of domestic and foreign pedagogical literature in the aspect of the researched problem shows that: the modern level of organization and carrying out of distance learning can be provided on condition: use of new network technologies; ensuring access of all participants in the educational process to computers and broadband computer networks; use of modern instrumental systems for the organization of distance learning; use of modern forms and methods of teaching based on active independent work of students, the use of interactive, multimedia electronic teaching aids, based on technology hypertext, hypermedia; organization of special training of teachers in the field of creation and use of distance learning courses with the use of instrumental software systems. It is noted that the effectiveness of the educational process of distance learning will be high quality only when the development of the information subsystem of education will be accompanied by radical changes in all subsystems: pedagogical, economic, organizational, psychological and others. It is emphasized that the role of the teacher in terms of informatization of education remains not only leading, but also further strengthened. Thus, the content of teacher training in the field of information and communication technologies (ICT) should be aimed not only at training competent users, but mainly to study issues related to the use of these technologies in educational practice. It is proved that distance learning requires special training of teachers, which will expand professional psychological and pedagogical, methodological knowledge and skills on the peculiarities of the use of distance learning technologies, features of pedagogical communication in distance learning; conditions for overcoming communication barriers when students master the content of distance learning courses.
Creative approach to teaching Ukrainian literature in institutions of general secondary education: project of the Ukrainian Cultural Foundation «Ukrainian literature in comics»

In this article examines the essence of a philologist teacher in a new Ukrainian school, his creative approach to teaching Ukrainian literature in the conditions of a mixed form of education – online and offline. In this context, the author’s project «Ukrainian literature in comics» is presented. It creates conditions for students’ self – realization, as well as for increasing their motivation to study and developing intellectual abilities, speech culture, activating the visualization of material perception and forming research skills and interests in modern Ukrainian literature. This promotes the spread of Ukrainian literature and culture abroad.
S3Mix: Same Category Same Semantics Mixing for Augmenting Fine-grained Images

Data augmentation is a common technique to improve the generalization performance of models for image classification. Although methods such as Mixup and CutMix that mix images randomly are indeed instrumental in general image classification, randomly swapping or masking regions is not friendly to fine-grained images, since the key to fine-grained image classification precisely lies in discriminative and informative regions, and it is unreasonable to generate labels solely consistent with the proportion of synthesis. Some erasing methods like Cutout even endanger fine-grained image classification because of erasing the discriminative regions by chance. In this article, we propose the Same Category Same Semantics Mixing method (S3Mix) corresponding to the characteristics of fine-grained images. Specifically, we limit the mixture to regions of the same category and semantics. The core of the method is two constraints. The exchange with the semantic region ensures the discrimination and semantics integrity of the generated image, and the exchange in the same class avoids the problem of unreasonable label generation. At the same time, we propose a homology loss to promote the semantic relationship between the generated positive image pairs. Experiments have been conducted on four fine-grained datasets, and the results show the proposed method is superior to the traditional image augmentation methods as well as some fine-grained data augmentation methods.
Research on logistics UAV path planning

This article is based on the logistics drone to deliver path planning problems in the urban environment. Combined with the flying characteristics of drones and the distribution location, considering the conditions such as the customer's collection and delivery needs, the logistics drone's delivery path planning model is established. Secondly, the model is processed. For the first time, the NSGA-II algorithm is used to solve the order of the distribution path of the logistics drone, and the second will be solved by improving the traditional algorithm. In the drone execution task, if the weight coefficient of the target functions is adjusted, different transportation sequences and paths can be obtained, and different planning results can be obtained by adjusting the attention of customer satisfaction. The analysis of the algorithm is available. Compared with the traditional A*algorithm, the algorithm transportation path adopted in this article is shorter and the delivery is faster.
Research on topology mapping strategy based on improved genetic algorithm

The progress of scientific development requires the use of high-performance computers for large-scale simulations, resulting in a significant communication overhead and thereby constraining the computer's performance. To address this issue, optimizing the topological mapping of application processes to computing nodes is essential for enhancing the communication performance of high-performance computers. However, this topic has not been extensively explored in the literature. In order to reduce the communication overhead of high-performance applications, this study formulates the optimization of topological mapping from application processes to computing nodes as a quadratic allocation problem. The proposed method collects communication features to assess the communication intimacy between processes and considers the communication relationship between application processes and network topology. To overcome the limitations of traditional genetic algorithms, this study introduces elite learning and adaptive selection into the mutation operator. In this algorithm, individuals undergoing mutation learn from fragments of the best individuals in the current population. Additionally, three functions are selected to control the probability of selecting the elite learning mutation during the mutation process, thereby enhancing the algorithm's efficiency and accuracy. The results of the experiments demonstrate that the suggested methodology yields a noteworthy enhancement in communication performance compared to the widely adopted round-robin approach in NPB test suites. Furthermore, the enhanced genetic algorithm displays superior optimization efficiency in comparison to conventional genetic algorithms and other heuristic approaches.
Reorganization and Optimization of Import and Export Inspection and Quarantine Processes under Comprehensive Quarantine

Although the current sampling inspection and quarantine efficiency, there are still prevention and control of security risks.Therefore, the existing process is reorganized and optimized, and a comprehensive quarantine strategy is proposed from a theoretical point of view.The results show safety and efficiency problems in the sampling inspection process.The full inspection before reorganization and optimization is also safer than the sampling inspection, but the time consumption will increase correspondingly.Besides, the process is difficult to judge the epidemic products accurately.Compared with the full inspection and sampling inspection after reorganization and optimization, the full inspection takes more time.However, compared with the full inspection and sampling inspection before optimization, it takes less time.It can be seen that the reorganized and optimized process can be safe and efficient in the quarantine part.
Research on Smart Elderly Care Health Monitoring System Based on Data Analysis Algorithm

With the development of society, people have higher and higher requirements for living standards, and at the same time, the problem of aging in our country is becoming more and more serious.In such an era with a large population base, good economic conditions and a variety of pension options, our country's pension problem is particularly prominent.The physical and mental health of the elderly often brings a huge burden to the society.Therefore, how to effectively utilize these resources has become an important issue that needs to be considered and solved.Smart city is a new type of community construction model and management concept.It integrates traditional public medical service system and advanced information technology to provide more humanized, convenient and safe services for the elderly while meeting the needs of the elderly.This paper takes the smart elderly care health monitoring system as the research object, and introduces it in detail in combination with the current development status of smart community construction.Firstly, a data analysis algorithm based on the decision tree algorithm is proposed to build a complete, efficient, safe and reliable smart city integrated management system, and the data analysis based on the decision tree algorithm is analyzed and studied.Secondly, the related technologies implemented by this system are described.Finally, the design and implementation of the smart old-age health monitoring system are studied.
Students' anxiety in learning English writing in higher education

Research studies on students' writing anxiety have been done in many EFL teaching contexts. However, there are still very limited scientific articles published about university students' foreign language writing anxiety and the causes in the Indonesian university context. Therefore, this study's main purpose was to analyze students' anxiety in EFL writing and its causes. This study employed a descriptive quantitative method. The study subjects were chosen from the first-year students of the Accounting Study Program of Muhammadiyah University of Palopo academic year 2021-2022, which consists of three classes. Two types of questionnaires were used for collecting the data. The first questionnaire was based on Second Language Writing Anxiety Inventory (SLWAI) developed by Cheng (2014). The questionnaire consisted of 21 items designed to assess students' English writing anxiety. The second questionnaire, the Second Language Writing Fear Inventory (CSLWFI), derived from Rezaei and Jafari's (2014), which was adopted to find out the causes of a feeling of anxiety in writing, consisted of ten questions. SPSS ver. 20 was used to analyze the gathered data. The findings revealed that the student's English writing anxiety was significant, and the key causes were poor understanding of writing concepts, linguistic obstacles, insufficient writing skill practice, and time restrictions.
Metaheuristic Optimization Review: Algorithms and Applications

Metaheuristic optimisation algorithms have become more well liked in recent years due to their success in solving challenging optimisation problems. Only a few of the metaheuristic optimisation techniques covered in this work include genetic algorithms, particle swarm optimisation, simulated annealing, ant colony optimisation, and many others. This paper discusses the history, operation, and applications of each method, including applications in engineering, finance, and bioinformatics.
Is There a Campaign to Silence Dissent at the Federal Circuit?

This article examines a Complaint against Federal Circuit Judge Pauline Newman, alleging that a “disability” caused her productivity loss and delays in authoring her opinions, thereby rendering her performance unacceptable compared to other active judges on the court. The article shows that the productivity standard used in the Complaint counts only majority and per curiam opinions but not dissenting opinions, thereby prejudicing Judge Newman, who dissents in the majority of her cases. A detailed statistical analysis of a comprehensive dataset of Federal Circuit Decisions was used to analyze the number of authored opinions attributed to Judge Newman and to other active judges on the court as “controls.” The analysis of the most recent period shows that when including dissenting opinions in the count, the productivity attributed to Judge Newman exceeded that attributed to one judge on the court, and the average pendency for issuing majority opinions attributed to her was shorter than that attributed to each of three of her colleagues on the court. In addition, a temporal trend analysis was conducted comparing the number of authored panel opinions over two consecutive periods, the latter being the period during which Judge Newman allegedly suffered a “disability.” The analysis shows that the relative change in the number of authored opinions attributed to Judge Newman between those two consecutive periods was statistically indistinguishable from the corresponding relative change in the number of authored opinions attributed to all other active judges over these consecutive periods. The inference is that any change in authored opinion productivity attributed to Judge Newman essentially tracked similar changes in the productivity attributed to her colleagues and must therefore be presumed unrelated to, or not caused by, specific attributes unique to Judge Newman’s performance. The article concludes that the available evidence does not support the Complaint’s contentions on Judge Newman’s productivity loss. It is shown that the Complaint’s application of the prejudicial productivity standard creates at least the appearance of a conflict of interest. It raises the question whether the Complaint seeks to remove Judge Newman from office to silence dissent at the Federal Circuit.
The CIPM MRA—success and performance

Abstract The CIPM Mutual Recognition Arrangement (CIPM MRA) provides a technical framework to the measurement community for comparability of measurement results and international recognition of metrological capabilities declared by the national metrology institutes throughout the globe. Since its founding in 1999, the participating institutes have now published more than 25 700 peer-reviewed calibration and measurement capabilities (CMCs) in the CIPM MRA database (Key Comparison Database (KCDB)). It is these capabilities and the technical evidence behind them that underpin the international acceptance of measurements around the world. The success and wide adoption of the CIPM MRA indicate the maturity of the arrangement, however, the accompanying increased workload for the participants motivated a review of the practices with the aim to increase the efficiency while maintaining the technical rigor. This review identified a number of key factors that formed the basis of the revision of the modus operandi , including the procedures and the database. The review resulted in recommendations for the CIPM Consultative Committees (CCs), regional metrology organizations (RMOs), participating institutes, as well as the BIPM. The revamped KCDB incorporated the whole lifecycle of CMCs, familiarizing with the new system being supported by the Capacity Building and Knowledge Transfer Programme of the BIPM. The result was an improvement in not only efficiency of the CIPM MRA, but also its effectiveness. For example, the time required for the Joint Committee of the RMOs and the BIPM (JCRB) review of CMCs has dropped by more than 50% to 59 d (median) in 2022, and the number of uncompleted key comparisons (KCs) have been reduced by a factor of three to a total of 38 in March 2023, representing now less than 3% of the total KCs. In this paper we look at the key factors through the various metrological areas addressing practices by each CCs.
Design and Implementation of Motion Imitation Robot

As technology develops, robots are increasingly becoming a part of our daily life. Applications include those for rehabilitation, assisted living, education, domestic help, and warfare. All these applications require specific control strategies and controllers. To combat this, recently developed depth sensors resolve the issue relating to input response and latency issues with immediate response time. Our project presents a robotic system that can imitate human motions and work accordingly. while compared to other motion sensors, the Raspberry Pi camera makes the device more comfortable to use while collecting human movements. The gadget extracts the robot's skeletal data, which is then processed to create the robot's joint angles. The proposed design of the robot imitates the human in real-time with very little or no lag. The Raspberry Pi camera that we employ can monitor joint locations in order to create software application gesture detection and mapping into control instructions that the robotic system can display.
Training the Machine Learning Model for Clinical IoT Data and Device Interoperability

Data exploration, wrangling, and interactive analysis and visualization were made in an integrated way. How to plot feature importance in Python calculated by the XGBoost model was considered. Features engineering in a dataset has been improved with Haar Transform. The area under the receiver operating characteristic curve was increased from 0.44 for the baseline model to 0.82 for Haar Transform Model.
Lung Cancer Detection in CT Scans Employing Image Processing Techniques and Classification by Decision Tree(DT) and K-Nearest Neighbor(KNN)

Lung cancer is a hazardous, and also challenging cancer to diagnose. Since it frequently results in mortality so quickly, proper nodule analysis is particularly crucial for treatment. Initial noticing and prognosis help raise individual life expectancies. Low-dose CT is the only screening procedure that is advised for lung cancer. Screening is done to discover diseases early (LDCT). Finding aberrant lung tissue that may be cancerous with the aid of LDCT scans is possible. Images from CT scans, also known as computed tomography, are used to detect cancer. Although CT is recommended, visual interpretation of these images has the potential to be inaccurate and can delay the discovery of tumor. In order to detect lung cancer in the initial phases, Image Processing approaches are commonly used within the medical industry. An algorithmic approach for spotting melanoma in CT scan pictures is presented in this study. By employing techniques like median filter in Image Pre-processing and Morphological system for partition the lung ROI, Classification stage to categorize CT scan pictures into Normal or Cancer by computing shape based parameters such as Area, Compactness, Euler, Circularity, and Perimeter. Finally, the accuracy rates of Decision Tree(DT), K- Nearest Neighbor(KNN) are acquired as 93.88% and 94.44%, respectively.