Reviewerly/exHarmony huggingface dataset:
MUSEUM AS A HEALTH AND WELLBEING FACILITATOR IN PANDEMIC ERA: A PERSPECTIVE FROM MUSEUM COMMUNICATION

During the pandemic, museums in the world began to move to expand their role in social interaction. Referring to the proposed new museum definition from ICOM which is still being discussed, museums are expected to contribute to the health and well-being sector. Currently, all museums in the world are changing the way they work by using technology and carrying out all activities remotely by utilizing various applications to stay connected with their visitors. What about the condition of museums in Indonesia? how museums in Indonesia carry out their role as direct actors of social development beyond their educational role? Are museums in Indonesia more proactive in improving society lives with special consideration for marginalized groups, like a senior adult, disabled person, special need, people with dementia, schizophrenia, or people with mental health? How museums in Indonesia use technology to communicate with their users? Researchers use qualitative studies and virtual ethnographic methods to explore museums that use social media. The results of the study show that most of the museums in Indonesia are not aware of their role to contribute to the quality of urban life, especially during the pandemic. And museum communication skills in the pandemic era must be expanded through the use of the internet while still paying attention to the ethics and limitations that apply to each type of collection owned.
Contour Primitive of Interest Extraction Network Based on Dual-Metric One-Shot Learning for Vision Measurement

Although many existing vision measurement systems have achieved high performances, they are object-specific and have limitations in flexibility. Toward intelligent vision measurement that can be conveniently reused for novel objects, this article focuses on the image geometric feature extraction with one-shot learning ability. We propose a contour primitive of interest (CPI) extraction network with dual metric (CPieNet-DM), which can obtain a designated CPI in a query image of a novel object under the guidance of only one annotated support image. First, the dual-metric learning mechanism is proposed, which not only utilizes inter-image similarity as guidance but also leverages the intra-image coherency of CPI pixels to facilitate the inference. Second, a neural network is designed to infer the CPI map based on the dual metric, which also predicts the CPI's geometric parameters. Moreover, the dual context aggregator is plugged in to provide the awareness of both images’ contexts. Third, the network training is jointly supervised by the multiple tasks of dual-metric learning, geometric parameters regression, and CPI extraction. The online hard example mining is utilized to improve the training outcome. The effectiveness of the proposed methods is validated with a series of experiments.
Image Segmentation for Self-Driving Car

Machine learning plays a vital role in making a car autonomous. The machine learning algorithms interpret the surrounding data collected from the car's sensors and camera and perform desired actions. Semantic segmentation is one of the various techniques which can be used in self-driving cars. High computational costs will be needed to obtain higher accuracy in semantic segmentation, which is not suited for embedded systems. So, there is a need to get high accuracy models with low computational requirements. This paper used a U-Net model for the semantic segmentation task, which shows good accuracy. We will be using the cityscape image pair dataset to train and test our model. We will evaluate our model based on the Receiver Operating Characteristic (ROC) curve and Intersection over Union (IOU).
Design of Physician Recommendation System for Patients

Patients' view of their consideration & medical care experts' impression of patient-focused perspectives like regard for the patient's qualities & requirements, coordination & incorporation of care, patient data, correspondence, & schooling, actual solace & basic reassurance are the essential focal point of this review. Patient & medical care experts' impression of the main parts of patient-focused care, for example, regard for the patient's qualities & necessities, coordination & combination of care, association of loved ones, data, correspondence & training gave to patients, actual solace & basic encouragement at tertiary medical care habitats are the essential targets of this review. There are calculations that compromise luck & inclusion under return for further developed precision, for instance, through suggesting things that have more information. Also, under light of the fact that each of the three of these actions are vital to clients under numerous applications, planning calculations thusly might be satisfactory now & again, yet less valuable under others, like clinical circumstances. For this situation, a Dill approach, which fuses the specialist into the calculation, could be helpful. We will require more far reaching proportions of value; including exactness, good fortune, & inclusion, to permit calculation fashioners to further develop compromises custom fitted to clinical situations under Dill settings. We can join decisions that are innately human (e.g., what is intriguing?) into the suggestion interaction.
Transfer-Learning Based User-Personalization of Indian Sign Language Recognition System

Differently abled people who cannot speak or hear often face difficulties when communicating with others, due to unavailability of translators, and sign languages not being common knowledge. Several research works report Sign Language Recognition system leveraging deep learning algorithms. However, till date, a well-performing, highly generalizable, and cost-effective system is not available for commercial use. In this work, classification of 50 signs from the Indian Sign Language (ISL) is carried out using convolutional neural network on time-series data. Data for SLR is captured using wireless multi-modality wearable sensors. A novel deep transfer learning algorithm is proposed for personalization of the sign language recognition (SLR) system for a new user. The performance of the proposed approach is tested on 5 new users. The proposed model yields the best average accuracy of 95.6&#x0025; after applying transfer-learning based personalization of the network with six samples of each sign from the new user. This is significantly better as compared to the 3&#x0025; accuracy obtained when the model is tested without applying transfer learning, proving the effectiveness of proposed approach in handling subject variability.
HEI-BCT: A Framework to Implement Blockchain-Based Self-Sovereign Identity Solution in Higher Education Institutions

Blockchain Technology, a decentralized and distributed ledger, has received extensive attention of industries to revamp operations and functionality of organizations. Implementing smart technologies such as Blockchain is an attractive solution for organizations, including the Higher Education Institutions (HEIs). Self-Sovereign Identity is one of Blockchain applications that promises to provide a more efficient management systems for organizations. This paper explains how a Self-Sovereign Identity using Blockchain Technology can be used to manage students' credentials in HEIs. It also proposes a framework to successfully implement the solution. This paper answers two questions: “How to use Self-Sovereign Identity as credentials management system” and “What are the major steps to be followed when implementing a Blockchain-based solution?”
Speedup Training Artificial Intelligence for Mahjong via Reward Variance Reduction

Despite significant breakthroughs in developing gaming artificial intelligence (AI), Mahjong remains quite challenging as a popular multi-player imperfect information game. Compared with games such as Go and Texas Hold’em, Mahjong has much more invisible information, unfixed game order, and a complicated scoring system, resulting in high randomness and variance of the rewarding signals during the reinforcement learning process. This paper presents a Mahjong AI by introducing Reward Variance Reduction (RVR) into a new self-play deep reinforcement learning algorithm. RVR handles the invisibility via a relative value network which leverages the global information to guide the model to converge to the optimal strategy under an oracle with perfect information. Moreover, RVR improves the training stability using an expected reward network to adapt to the complex, dynamic, and highly stochastic reward environment. Extensive experimental results show that RVR significantly reduces the variance in Mahjong AI training and improves the model performance. After only three days of self-play training on a single server with 8 GPUs, RVR defeats 62.5% opponents on the Botzone platform.
Detecting the relationships among multivariate time series using reduced auto-regressive modeling

An information theoretic reduction of auto-regressive modeling called the Reduced Auto-Regressive (RAR) modeling is applied to several multivariate time series as a method to detect the relationships among the components in the time series. The results are compared with the results of the transfer entropy, one of the common techniques for detecting causal relationships. These common techniques are pairwise by definition and could be inappropriate in detecting the relationships in highly complicated dynamical systems. When the relationships between the dynamics of the components are linear and the time scales in the fluctuations of each component are in the same order of magnitude, the results of the RAR model and the transfer entropy are consistent. When the time series contain components that have large differences in the amplitude and the time scales of fluctuation, however, the transfer entropy fails to detect the correct relationships between the components, while the results of the RAR modeling are still correct. For a highly complicated dynamics such as human brain activity observed by electroencephalography measurements, the results of the transfer entropy are drastically different from those of the RAR modeling.
«I-CONCEPT» AS A FACTOR OF PROFESSIONAL SELF-DETERMINATION OF THE PERSONALITY FROM THE POSITIONS OF R. BURNS

The article, based on the research of Robert Burns, reveals the essence of the concept of «self-concept», which characterizes it as a relatively stable, conscious system of ideas of an individual about himself, on the basis of which he builds his interaction with other people and treats himself. The author theoretically substantiates the mentioned phenomenon as a determining factor of the professional self-determination of an individual who is aware of himself as a subject of a specific professional activity, including self-assessment of his own individual and psychological qualities and comparing his capabilities with the requirements for this field of training. Professional self-determination of the future specialist is presented as one of the important stages of the process of personality development, which is quite complex in its content. The author characterizes it as a dynamic, integral process of entry into the profession, the result of which is the selection and design of future pedagogical activities, which takes place in the educational environment of a higher education institution, through a conscious and active attitude of the student to his own life perspective, to mastering the future profession, and provides a high the level of professional and pedagogical communication, developed empathy, respect for the individual, psychological insight and the ability to work in a team. He defines the self-concept as an important factor in the professional self-determination of the future specialist, which significantly affects his success, and also accumulates at its core a set of ideas: what an individual is, what he thinks about himself, his understanding of the basics of his own activity, comparison of opportunities and abilities, outline of development prospects in the future. In order to ensure the success of professional formation and self-determination, the student must comprehensively know and evaluate himself, his capabilities, develop pedagogical reflection in himself, which contributes to the formation of a conscious attitude to the problem of self-determination and purposefulness of this process, the dynamics of which are significantly influenced by pedagogical creativity. The student's self-concept is gradually formed through his awareness of himself as a subject of action, through his perception of himself as a subject in the system of relationships in the new student environment, which orients him to self-affirmation in educational and social activities. In the future, this directs him to self-determination and conscious self-regulation of his actions based on the internal formation of a professional position and to the feeling of himself as the author of his own activity, the creator of his actions.
Secure multi-channel scheme for optical image encryption (Conference Presentation)

The ability of metamaterials to manipulate optical waves in the spatial and spectral domain has provided opportunities for image encoding. This combined with recent advances in hyperspectral imaging suggest exciting new opportunities for secure encryption. In this work, we propose a multi-channel scheme for secure image transmission across multiple wavelength channels. In contrast to conventional encryption schemes that perform a 1-to-1 transformation on a given plain image, we propose a 1-to-n transformation. We show that our scheme provides security against attacks of varying complexity provided a reasonable number of spectral channels is used.
A Method for Mining API Call Pattern Using Sample Codes

In the process of software development, developers usually use API to improve development efficiency and code reuse rate. API provides developers with a set of accessible services, and developers do not need to care about the internal source code of API, which greatly reduces the workload of developers. Due to the increasing scale and complexity of modern software, more and more APIs are used by developers in software. In order to solve the problem of insufficient recall rate in up miner, this paper proposes an API call pattern mining method based on examples, be miner, which collects data from some Q&A websites, such as stack Overflow, which extracts the API call sequences from some code examples, uses these API call sequences to mine the API call patterns that appear less frequently in the project, and does not produce a large number of redundant results. The experimental results show that the API call pattern mining method based on example can effectively mine the call patterns that appear less frequently in the project from the source program, and has higher recall rate and F-measure value than up-miner.
Switch Time Activation Function and Stopit Regression - Some Examples

In neural networks, single neuron can be considered as a generalization of a regression model and the activation function is the function which is applied to the output of a neuron to obtain some specific, generally non-linear type of transformation of the input signals. Here we consider Switch Time activation function and stopit regression based on using Switch Time distribution.
Laplace-Based 3D Human Mesh Sequence Compression

Three-dimensional (3D) human mesh sequences obtained by 3D scanning equipment are often used in film and television, games, the internet, and other industries. However, due to the dense point cloud data obtained by 3D scanning equipment, the data of a single frame of a 3D human model is always large. Considering the different topologies of models between different frames, and even the interaction between the human body and other objects, the content of 3D models between different frames is also complex. Therefore, the traditional 3D model compression method always cannot handle the compression of the 3D human mesh sequence. To address this problem, we propose a sequence compression method of 3D human mesh sequence based on the Laplace operator, and test it on the complex interactive behavior of a soccer player bouncing the ball. This method first detects the mesh separation degree of the interactive object and human body, and then divides the sequence into a series of fragments based on the consistency of separation degrees. In each fragment, we employ a deformation algorithm to map keyframe topology to other frames, to improve the compression ratio of the sequence. Our work can be used for the storage of mesh sequences and mobile applications by providing an approach for data compression.
Secret Sharing

In this chapter, we introduce the problem of secret-sharing. We present the generalized secret-sharing scheme of Ito et al. [98] and the threshold secret-sharing scheme of Shamir [135]. While secret-sharing schemes find extensive application in MPC, we showcase here another important application, namely perfectly-secure message transmission. We conclude with demonstrating what can go wrong in a secret-sharing scheme in the face of malicious adversaries. Consider the following application. There is a bank with three managers. There is a locker in the bank, which needs to be opened every day using the help of the managers. Each of the managers has got a password to operate the locker. However, no one trusts any single manager. So we want to design a system where the locker can be opened only if at least two of the managers enter their respective passwords, but the locker should remain inaccessible if only a single manager tries to open it. Similarly, consider a scenario where the passcode of a country’s nuclear missile is shared among the top three entities of the country, say the president, vice-president and the prime-minister in such a way that the missile can be launched only when at least two of these three entities agree to do the same.
Students' Speaking Anxiety during Online Learning: Causal Factors and Strategies to Overcome Them (A Narrative Inquiry Study at Islamic Senior Secondary School (MAN) Insan Cendekia Gowa)

Anxiety has been considered as one of the obstacles in learning English and a common issue in the EFL teaching and learning. In speaking, it also debilitates the opportunity of English Foreign Language (EFL) students to speak English. Several studies have been conducted to discover the causes and straegies to overcome them in traditional classroom. To date, not many studies have focused on students’ anxiety in online learning. Therefore, this study was designed to reveal the causal factors of the students’ speaking anxiety and strategies to overcome them during online learning at Islamic senior secondary school setting. The study employed narrative inquiry approach and used interview as the main instrument of data collection. Four students of Islamic Senior Secondary School (MAN) Insan Cendekia Gowa whom selected to be the participants of the study using purposive sampling technique. The result of this study establishes two main findings. Firstly, the causal factors of students' speaking anxiety during online learning were divided into internal and external factors. The internal factors consisted of lack of confidence, shyness, fear of making mistake, and feeling insecure. The external factors consisted of lack of preparation, limited vocabulary, friends or classmates, embarrassment, teachers’ teaching style. The study found that the external factors are indicated as the most dominant causes of students' speaking anxiety. Furthermore, the study highlights several strategies used by the students to overcome their speaking anxiety, namely: good preparation (lecture note), peer seeking, keeping silent or calm, practice speaking at home, turning the camera off, using ‘google translator’, and limiting speaking duration.
Analysis of Video Steganography in Military Applications on Cloud

The analysis of secure video file transfer with military application for video steganography on cloud computing is essential role. Video steganography is the process of hiding the secret data which is presented in the video and it is based on the reversible and irreversible schemes. The reversible scheme has the capability to insert the secret data into a video and then recover the video without any failure of information when the secret data is extracted. Irreversible methods on video steganography often deal with sensitive information, making embedded payload an important concern in the design of these data hiding systems. In video steganography, irreversible contrast mapping is considered for extracting the secret data during the process of hiding the data. During this extraction process, high quality data hiding is carried in video steganography. The analysis consequences of the proposed method Video Steganography Cloud Security (VSCS) shows that the structure for secure communication and augments the confidentiality and security in cloud. This result of the proposed method shows the better security level.
PEDAGOGICAL MANAGEMENT SYSTEM FOR THE DEVELOPMENT OF A PRIVATE INNOVATIVE UNIVERSITY IN THE CONTEXT OF THE DIGITAL TRANSFORMATION OF SOCIETY

The article defines the term – pedagogical system of a private innovative university – and reveals the specifics of managing such a university in the context of digital transformation. The activity of the head of such educational organization is realized due to the principles of flexibility, consolidation, permanent reflection and corporatism of management. The specifics of educational institution management in a culturally diverse environment is presented.
Making the “digital leap” in Finnish schools

In 2015, the Finnish Ministry of Education and Culture launched a digitalisation project called the “digital leap”. The objective of this project was to help schools quickly modernise their information and communication technology (ICT) infrastructure and pedagogy. Finnish schools have had many digitalisation projects, but the use of ICT in teaching and learning has been relatively small scale compared with other Nordic countries. Finnish teachers have autonomy for pedagogical decisions and have even abstained from top-down governing and “dumping” digital technology to schools. This article examines the challenges of school digitalisation in Finland, obstacles the teachers face in the use of ICT in education, and some perspectives on the teachers’ possibility to participate in the digitalisation process. This article is based on research done in Educating for Future Literacies Research Group (EduLit) at Tampere University. The research indicates that teachers’ professional capital plays an important role in the digitalisation process.
Occlusion-perturbed Deep Learning for Probabilistic Solar Forecasting via Sky Images

Solar forecasting is shifting to the probabilistic paradigm due to the inherent uncertainty within the solar resource. Input uncertainty quantification is one of the widely-used and best-performing ways to model solar uncertainty. However, compared to other sources of inputs, such as numerical weather prediction models, pure sky image-based probabilistic solar forecasting lags behind. In this research, an occlusion-perturbed convolutional neural network, named the PSolarNet, is developed. The PSolarNet provides very short-term deterministic forecasts, forecast scenarios, and probabilistic forecasts of the global horizontal irradiance from sky image sequences. Case studies based on 6 years of open-source data show that the developed PSolarNet is able to generate accurate to-minute ahead deterministic forecasts with a 5.62% normalized root mean square error, realistic and diverse forecast scenarios with a 0.966 average correlation with the actual time series, and reliable and sharp probabilistic forecasts with a 2.77% normalized continuous ranked probability score.