Reviewerly/exHarmony huggingface dataset:
On the Asymptotic Capacity of Information-Theoretic Privacy-Preserving Epidemiological Data Collection

The paradigm-shifting developments of cryptography and information theory have focused on the privacy of data-sharing systems, such as epidemiological studies, where agencies are collecting far more personal data than they need, causing intrusions on patients' privacy. To study the capability of the data collection while protecting privacy from an information theory perspective, we formulate a new distributed multiparty computation problem called privacy-preserving epidemiological data collection. In our setting, a data collector requires a linear combination of K users' data through a storage system consisting of N servers. Privacy needs to be protected when the users, servers, and data collector do not trust each other. For the users, any data are required to be protected from up to E colluding servers; for the servers, any more information than the desired linear combination cannot be leaked to the data collector; and for the data collector, any single server can not know anything about the coefficients of the linear combination. Our goal is to find the optimal collection rate, which is defined as the ratio of the size of the user's message to the total size of downloads from N servers to the data collector. For achievability, we propose an asymptotic capacity-achieving scheme when E<N-1, by applying the cross-subspace alignment method to our construction; for the converse, we proved an upper bound of the asymptotic rate for all achievable schemes when E<N-1. Additionally, we show that a positive asymptotic capacity is not possible when E≥N-1. The results of the achievability and converse meet when the number of users goes to infinity, yielding the asymptotic capacity. Our work broadens current researches on data privacy in information theory and gives the best achievable asymptotic performance that any epidemiological data collector can obtain.
Improving Entanglement Generation Using the Coherence of a Nonthermal Reservoir

Abstract The effect of environmental coherence on bipartite and tripartite quantum entanglements using quantum collision models is studied. In the collision model, an open quantum system of two qubits indirectly interacts with the reservoir via auxiliary qubits. The reservoir is modeled as a collection of initially uncorrelated qubits prepared in an identically nonthermal state. It is shown that the coherence of the nonthermal reservoir contributes to the generation and protection of entanglement. It is found that adding coherence to the initial state of the reservoir qubit can significantly enhance the steady‐state entanglement of the system, and the presence of coherence in the reservoir allows steady‐state entanglement to be obtained in a higher temperature region.
A Synthetic Indicator of the Quality of Support for Businesses in Burkina-Faso, Cameroon, and Ghana

Abstract This paper proposes a synthetic indicator of the quality of support for companies and identifies the factors that can contribute towards improving the quality of such support in three countries (i.e., Burkina-Faso, Cameroon, and Ghana). The study uses static mechanics and applies techniques of factor analysis. A principal component analysis is performed on the data collected from 80 business support structures in the sampled countries. After constructing the indicators, correlates are provided on how the constructed indicators are linked to the objectives of sustainable development. Our results are robust after controlling for variables relating to the general characteristics of the support structure. The findings are consistent with the position that taking sustainable development objectives into account in business support practices would significantly improve business performance in sampled countries and, by extension, in sub-Saharan Africa. The originality of the study stems from the fact that it considers specific sustainable development goals and assesses their contribution to improving the quality of support for companies, a research area that has not been investigated hitherto by the extant literature. Implications for all stakeholders in the entrepreneurial ecosystem and future research directions are discussed.
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where participating learners and the communication graph between them may vary during the learning process. In particular, whenever the communication graph changes, the Metropolis-Hastings method [ 69 ] is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir’s secret sharing (SSS) scheme [ 61 ] is integrated to facilitate privacy in reaching consensus of the global model. The article establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-world dataset.
Analysis of ‘Graduation Qualification Work’ Defense at the Faculty of Economics of Lomonosov Moscow State University

In Russian universities the graduation qualification work (GQW) is the final step for acquiring a bachelor’s degree. Doing the research work a student may face a few problems, starting from insufficient communication with the supervisor and ending with the lack of relevant data for the study. The quality of the research work and further performance and defense of it can be affected by both the internal psychological characteristics of a student and academic abilities. This article includes quantitative analysis of GQW defense at the Faculty of Economics (bachelor program – Economics) of Lomonosov Moscow State University. Authors mention a list of contradictions arising during the GQW defense procedure. The main factors for determining the student’s score are identified and a proxy variable of abilities explaining the largest share of the spread of points is compiled to collect information.
Classifications of benign and malignant mammogram images using <scp>Gabor‐modified CNN</scp> architecture

Abstract Breast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.
A New Irrigation System Without Any External Sources

India is the second most populous country in the world and relies heavily on agriculture as its main source of income. The combination of agriculture and technology is needed to accelerate the development of agriculture, especially in rural areas, to increase productivity, efficiency and profitability. The application of technology in agriculture will enhance the farmer’s involvement in their occupation. Agriculture mainly consists of field grounding, transplantation, and harvesting. In each step of agriculture, irrigation is the main requirement. Farmers are spending more to irrigate their farms for agriculture. So, the application of technology to irrigation is the most important requirement to sustain the farmers. In this work, a pendulum-operated irrigation model is presented. The designed model is required to attach to the hand pump. The developed system will work without any external source of power, which will provide maximum profit to farmers.
Research and pedagogical personnel formation principles in the 1920s (by the example of Saratov institutes of higher education)

The article discusses the trends in the research and pedagogical personnel formation for the Soviet higher education in the 1920s. The main factors that influenced on the the quantitative and qualitative composition of the research and pedagogical community of Saratov institutes in the decade under review were identified. On the basis of archival material, the composition of the scientific corporation by party affiliation is analyzed. The activities of the authorities of a new category of scientists creation from among the workers and peasants are considered. Postgraduate study and promoted students institution are characterized.
Fra logg til blogg: Yrkesfaglæreres digitale kompetanseutvikling gjennom designbasert praktikerforskning

Professional digital competences (PDC) have a significant influence on Norwegian and international innovation in education and have been extended to include several aspects of the teacher’s role. There is a need for more research on vocational teachers/education in general, and specifically on digital competence. This article is an intervention study in Norwegian vocational education and training, exploring the development of vocational teacher’s professional digital competences through design-based practitioner research. The aim has been to study how the implementation of a new artefact, using blog as an ePortfolio and a boundary object, influence the practitioner researcher’s and vocational teacher colleagues’ development of PDC. The study was carried out in subjects of vocational specialisation with youth enterprise as the method. The results and analysis contribute to identifying participatory knowledge about vocational teachers’ development opportunities and competence profile related to the use of technology as boundary objects and the further establishment of new boundary-crossing practices. This study contributes to an expanded understanding of vocational teachers’ competence profile and development opportunities related to PDC, and the case contributes with increased knowledge about how practitioner research provides opportunities for development.
Data Sensitivity and Domain Specificity in Reuse of Machine Learning Applications

Abstract Data sensitivity and domain specificity challenges arise in reuse of machine learning applications. We identify four types of machine learning applications based on different reuse strategies: generic, distinctive, selective, and exclusive. We conclude with lessons for developing and deploying machine learning applications.
A Face Detector with Adaptive Feature Fusion in Classroom Environment

Face detection in the classroom environment is the basis for student face recognition, sensorless attendance, and concentration analysis. Due to equipment, lighting, and the uncontrollability of students in an unconstrained environment, images include many moving faces, occluded faces, and extremely small faces in a classroom environment. Since the image sent to the detector will be resized to a smaller size, the face information extracted by the detector is very limited. This seriously affects the accuracy of face detection. Therefore, this paper proposes an adaptive fusion-based YOLOv5 method for face detection in classroom environments. First, a very small face detection layer in YOLOv5 is added to enhance the YOLOv5 baseline, and an adaptive fusion backbone network based on multi-scale features is proposed, which has the ability to feature fusion and rich feature information. Second, the adaptive spatial feature fusion strategy is applied to the network, considering the face location information and semantic information. Finally, a face dataset Classroom-Face in the classroom environment is creatively proposed, and it is verified with our method. The experimental results show that, compared with YOLOv5 or other traditional algorithms, our algorithm portrays better performance in WIDER-FACE Dataset and Classroom-Face dataset.
Library Service Innovation as Learning Media at The Universitas Terbuka Distance Education Library

Long before Covid 19 hit the world, the Universitas Terbuka library already had online services as a learning media With rapid technological advances, the world is now entering the era of the Industrial Revolution 4.0, where synchronous learning can be done not only face-to-face in traditional classes, but also through distance education with the help of technology. Universitas Terbuka that use distance learning for their students are spread throughout the archipelago and in 52 countries. In lectures at the Universitas Terbuka (UT), Modules (teaching materials) function as representatives of lecturers for students, but since the digital era, UT has conducted online tutorials (Tuton) to connect students with lecturers (Tutors). Similarly, UT has provided electronic library services. Virtual Reading Room (RBV) and Tuweb and Streaming services. Tuweb and streaming services have a positive impact on students.
Student’s Rating System for Teachers: A Tool for Teacher Scheduling Consideration

The end-of-term ratings are one of the most crucial components of improving educational institutions' ability to assess teachers' performance. The paper-based evaluation system for teachers at Cebu Technological University's Tuburan Campus is time-consuming and laborious. During the flexible learning, the school uses Google Forms to create surveys to rate the teachers which also has shortcomings since Google Forms has limited functions. To solve these issues, the proponents created a web-based evaluation system that makes it simple for students to rate their professors and generates evaluation reports at ease. Additionally, the system gives the system administrator the ability to control colleges, users, and evaluations. It will save time and manpower because the results are created automatically. The system was developed using HTML, CSS, Bootstrap, PHP, and MySQL while adhering to the Agile Model of the Software Development Life Cycle. The proponents allowed the SAST Coordinator and students to test the system and evaluate each module during the usability testing. Throughout unit testing, integration testing, and system/alpha testing, the system was successful and performed as planned. Enhancements to the system's features is highly recommended in order to augment the system’s functionality including further study on the system's technology acceptance.
Price Prediction of Cryptocurrency based on LSTM Model: Evidence from Ethereum

Contemporarily, under the impacts of COVID-19 and regional conflicts with radicalness fiscal policy, the prices of cryptocurrency have been fluctuated dramatically. Among various types of cryptocurrency, Ethereum is one of the most volatility assets. In order to avoid risks as well as gain extra return in the crypto market, it is necessary to construct accurate prediction approach. In this paper, the Long Short-Term Memory algorithm will be used to predict the future price of Ethereum by learning Ethereum's past price direction data. price trend by learning Ethereum's past price trend data. Based on the analysis, the predicted values of the trained model fit well with the actual data, with the regression evaluation index R2 of 97.08% and MAPE of 6.89%. According to the results, it is feasible to predict the future price trend through the past price trend data. Nevertheless, it should be noted that the stochastic process in data training might lead to the instability of model performances. Hence, it is necessary to train the data several time to select the best models. Overall, these results shed light on guiding further exploration of cryptocurrency price forecasting in terms of the state-of-art neural networks.
Pathological Images Classification based on the Pretrained Convolutional Neural Network

Convolutional neural network is more and more used to solve the problem of images classification, but the scale of the dataset in this filed is too small compared with that of natural image recognition, which leads to the slow convergence speed of the neural network, and the instability of the model, which is difficult to be used to predict the results. In this paper, the residual network is applied to the PCam dataset to realize the recognition of tumour cells, and pre-trained through the ImageNet dataset. In the same training epochs, the performance of the pre-train network is much higher than that of the randomly initialized network, which significantly improves the convergence speed of the neural network. In addition, the performance of the pre-train network can converge faster when the amount of data is sufficient, and in the case oof medical image recognition with less data, the performance of the pre-train network is much higher than that of the random initialization network.
Research of the Path Finding Algorithm A* in Video Games

Pathfinding is an essential component of many video games. This review provides a simple overview of the classical pathfinding algorithm A*, including a brief summary of the A* algorithm, as well as a few optimizations and applications of A* in video games. In the gaming industry, A* is a widely used pathfinding algorithm. Based on breadth first search and heuristic search, A* uses a heuristic function to quickly find the shortest path in a game map. A common optimization of A* is to use a binary heap to save the open queue, a variety of heuristic functions are also commonly used to increase the accuracy and speed of A*. This paper summarizes work done on the A* algorithm. The goal of this paper is to give researchers and developers a simple overview of the A* algorithm and its applications in video games.
Comparison based on Multiple Machine Learning Algorithms in the Case of Image Classification

With the upsurge of usage of the internet, the society has turned into a Big Data Era. Living in the world overwhelmed with data, the image is among the most common forms. Utilizing these images smartly is the big topic. Image Classification refers to the process of extracting information classes from a raster image that consists of multiple bands by analyzing the raster image. It is the basis for lots of applications, like radar and autopilot. Among tremendous channels for dealing with image, deep learning, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), is always the first thing flashing in peoples’ mind. In this paper, studies will be implemented and discuss the way to perform image classification utilizing machine learning algorithms. Four algorithms will be implemented, namely Random Forest, KNN, Decision Tree, and Naive Bayes. And at the end of the paper, this result shows that Random Forest Classifier has the best performance compared with the rest of the three algorisms. But it is still far away from the requirement for daily application use like radar. Poor performance and the long operation time make machine learning algorisms out of date when doing image classification. By this comparison, deep learning replaced machine learning and becomes the first choice do this task.
Root Mirror Sites Identification and Service Area Analysis

The operation of today’s Internet can only be achieved with the domain name system (DNS), and the essential part of the DNS is the root servers. Adding anycast mirrors has been used to maintain the security of root servers, but many problems accompany this technique. In this paper, we used 36198 probe points deployed worldwide to probe 1160 root mirror sites and analyzed the data with root mirrors’ identification and localization (RMIL). RMIL is a method to identify and locate root mirrors. It contains probing and analyzing the network services ID (NSID) and traceroute data to identify and locate root mirror sites. Using this method, 821 (70.78% of the total) sites were accurately identified and located, and city-level localization was achieved for 281 other sites. Finally, the identification results were used in the service area analysis. The analysis contained multiple dimensions: locations, autonomous system numbers (ASN), internet service providers (ISP), and IPV4 prefixes. As such, we helped identify and locate root mirror sites more precisely and discover which ones have a greater service area in different dimensions.
Facial expression classification

At present, emotion classification has become a hot topic in artificial intelligence pattern recognition. The facial expression recognition (FER) is indispensable for computers to understand the emotional information conveyed by expressions. In the past, using traditional features to extract and classify facial expressions has not achieved satisfactory accuracy, so the classification of facial emotions is still a challenge. The model used in the paper is an existing one - MINI_XCEPTION, the dominant framework for CNNs that extracts features from images to identify and classify seven facial emotions. The model was trained on a dataset of people’s facial expressions (Kaggle). This model has a significant improvement over the previous model.
Developments of Sensing Technology and its Applications in Virtual Reality

Virtual Reality is one of the hot research directions recent years, as a 5G rea oriented technology and a new way of displays, virtual reality has shown us its huge potential in industries, especially in entertainment, medical science and education. Sensing technology is widely used on VR in many aspects such as the HMD displaying system, position and motion tracking module and the input devices like VR controllers, while most overview papers pay attention to the concepts and applications, though some of them illustrated all the technologies roughly. This overview article has focused on sensing technology in VR, introducing the techniques in some commercially successful VR devices, comparing and summarized some of the latest research based on them, with which it may help users and researchers understand VR devices better and choose the appropriate devices while using. In the end, the paper summarizes the whole paper and prospects the future development direction.