Reviewerly/exHarmony huggingface dataset:
Experimental Demonstration of Automatic Reconfiguration and Failure Recovery of Silicon Photonic Circuits

Automatic reconfiguration with failure recovery of photonic functions has not been experimentally demonstrated as far as we know, which is important for real-time operating programmable photonics. Here we experimentally demonstrate automatic reconfiguration and failure recovery for silicon photonic circuits by bacterial foraging algorithm.
Analysis on the level of higher universities in different countries using entropy weight method and analytic hierarchy process

The higher education system is an important factor in measuring the education level and national strength of a country. A healthy and sustainable higher education system can effectively improve the country's competitiveness. The establishment of a complete and unified model for evaluating the pros and cons of the national higher education system is the basis for improving the level of the higher education system. In this article, we will build the relevant model in four steps. The first step is to search for documents and data, and use data on factors such as knowledge protection in multiple countries, higher education enrollment rates, education system quality, student Internet access, government education expenditures, the number of citations per paper, and the value of degrees. Build a model based on it. And standardize all data. In the second step, since the factors may have a certain correlation, the principal component analysis method is used to convert the equi-related variables into another set of unrelated variables and establish a model. The third step is to analyze the health and sustainability levels of higher education systems in Canada, France, Germany, Italy, Japan, Russia, the United Kingdom, the United States and other countries based on this model, and obtain rankings and comprehensive scores. The fourth step is to use the entropy method to calculate the specific weight of each factor. Select one of the countries to reform the most weighted factor. Import the reformed data into the model again, observe and analyze its changes.
DI++: A deep learning system for patient condition identification in clinical notes

Accurately recording a patient's medical conditions in an EHR system is the basis of effectively documenting patient health status, coding for billing, and supporting data-driven clinical decision making. However, patient conditions are often not fully captured in structured EHR systems, but may be documented in unstructured clinical notes. The challenge is that not all disease mentions in clinical notes actually refer to a patient's conditions. We developed a two-step workflow for identifying patient's conditions from clinical notes: disease mention extraction and disease mention classification. We implemented this workflow in a prototype system, DI++, for Disease Identification. An advanced deep learning model, CLSTM-Attention model, is developed for disease mention classification in DI++. Extensive empirical evaluation on about one million pages of de-identified clinical notes demonstrates that DI++ has significant performance advantage over existing systems on F1 Score, Area Under the Curve metrics, and efficiency. The proposed CLSTM-Attention model outperforms the existing deep learning models for disease mention classification.
Internet of Healthcare Things (IoHT) and Blockchain: An Efficient Integration for Smart Health Care Systems

Blockchain has emerged as an efficient technology for distribution and handling of useful information by cluster of computers. It offers wide number of applications such as personal identification, asset management, and smart appliances, insurance: claim processing and healthcare management etc. In this study, authors explored the role of blockchain in the field of healthcare application in integration with the new emerging field known as internet of health care things (IoHT). In this chapter, we discussed the basic foundation and applications of blockchain and internet of Technology for seeking better healthcare solutions through integration of both of them. Further limitations and challenges in implementation of IoHT are discussed, particularly in the current pandemic situation of corona virus.
Extraction of Personality Traits from Handwriting with Machine Learning

In the field of graphology, the fundamental idea is to establish a connection between the handwriting style of people and their personality traits. In this study, an analysis was made on the science of graphology using machine learning and psychology. A basic personality test was applied to the users who voluntarily participated in our study, some distinctive writing characteristics were extracted from the text samples taken from the same users, and machine learning-based prediction models were developed based on the features constructed over these font characteristics. Here, the aim is to examine how successful matching can be implemented automatically from the user's writing style with regard to personality analysis. The results show that even though not very sensitive, the personality traits can be predicted to a certain extent from the writing style by the machine learning models.
Big Data Processing and Intelligent Analysis System for Students’ Learning State Based on PaddlePaddle

This paper studies the construction idea, method and process of an intelligent learning state analysis system on the background of big data acquisition and processing.Based on the software and hardware of Baidu PaddlePaddle, the intelligent subsystem of feature extraction, face recognition and learning state recognition in the process of teaching process is built.Based on the idea of distributed scalability, big data processing architecture for large-scale data acquisition, storage and analysis is designed.The actual scene shows that this is an intelligent system used for students learning state analysis, which is supported by big data processing, can quickly carry out the teaching attendance, examination verification and class state detection, and can be widely used in learning state analysis of all kinds of schools at all levels.
RETROSPECTIVE COMPARATIVE ANALYSIS OF STUDENT PERFORMANCE BEFORE AND DURING THE PANDEMIC

The year 2020 was a challenge for educational institutions around the world. From March 2020, the entire educational process had to be switched to a completely distance learning mode as soon as possible. Stress was felt by all - heads of educational institutions, teachers and students alike. Surveys showed concern among students about possible misunderstanding of the material studied due to lack of communication with teachers, and concern among teachers about insufficient mastery of digital learning technologies. However, the urgency of transferring the entire educational process to the digital environment has not cancelled the need to implement final control of students' progress. The summer session of the academic year 2019/2020 and the winter session of the academic year 2020/2021 were organised in a completely distance format. The aim of the study is to assess the impact of the emergency transfer to a fully distance learning format on students' academic performance. The authors conducted a retrospective comparative analysis of the academic performance of students of different forms of study at the Institute of Economics and Management of Ural Federal University before and during the pandemic. A conclusion is made about the reasons for the changes in the studied indicators.
An End-to-End Framework for Dynamic Crime Profiling of Places

Much effort is being made to ensure the safety of people. One of the main requirements of travelers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime location, it needs the past crime history at that location. Such data is not easily available in the public domain, but it floats around on the Internet in the form of newspaper and social media posts, albeit in an unstructured manner. Consequently, many works are reported on extracting crime information from news articles, providing piecemeal solutions to the problem. This chapter complements these works by building an end-to-end framework for crime profiling of any given location/area. It customizes individual components of the framework and provides a spatio-temporal integration of crime information. It develops an automated framework that crawls online news articles, analyzes them, and extracts relevant information to create a crime knowledge base that gets dynamically updated in real-time. The crime density can be easily visualized in the form of a heatmap which is generated by the knowledge base. As a case study, it investigates 345,448 news articles published by six daily English newspapers collected for approximately two years. Experimental results show that the crime profiling matches with the ratings calculated manually by various organizations.
Text, intertext and meaning

In the 1920s and 1930s the Prague Linguistic Circle tackled problems such as how to explain the nature of literary language, and how to explain relations between author, text and reader. Unsolved aspects of these problems can now be tackled using methods and concepts which were not available at the time. For example, corpus-assisted methods can help to identify intertextuality and its significance for the meaning of texts. An essential empirical question is: can automatic corpus-assisted methods reliably identify cases where one text refers to other texts? Essential conceptual questions include: what is the logical relation between intertextuality and meaning?; how far does the meaning of a text depend not on what the author intended, but on how readers interpret relations between texts?; how does the concept of intertextuality relate to a network of central linguistic concepts, including reference, semantic unit, paraphrase, and evaluative language? Conan Doyle’s stories about Sherlock Holmes (published 1887 to 1927) refer to identifiable texts (e.g. earlier detective stories) and allude to contemporary ideas, both scientific (e.g. the value of observational data) and pseudo-scientific (e.g. so-called ‘criminal anthropology’). The intellectual world of the late 1800s was created by many fiction and non-fiction texts. In turn, knowledge of these texts influences how readers understand Conan Doyle’s stories. The corpus provides an ideal testing ground for assessing the possibilities and limitations of corpus-assisted analysis of intertextuality, and for contributing to the Prague School project of studying how texts relate to the social world.
Liebovitch map enabled Sine Cosine algorithm

Being derived from the nonlinear dynamics system, the chaos was claimed and confirmed to be a better replacement of the pseudo random numbers in swarm-based nature-inspired algorithms. In this paper, we proposed a new kind of improvement for the Sine Cosine (SC) algorithm. Unlike other chaotic improvements, the chaotic map enabled SC algorithm with Liebovitch map would only add additional chaos to the controlled parameter balancing the exploration and exploitation ratio. Simulation experiments verified the better performance of the chaotic enabled SC algorithm than the original version.
Optimalisasi Digital Native di Era Industri 4.0

Generation transformation be inline with the evolution and technological revolution that also contributes to a change in various fields. Digital generation, with their characteristic of be connected 24 hours per day, is comfortable with technology and prefers to communicate using technology services, rather than through conventional ways. Based on that, this generation can easily make an adjustments in the Industrial Age 4.0 where information availability is accessible fast and precise with the support of Information Technology. Located in one of Bali's well-established universities located in the center of tourism destinations of Jimbaran Bali, this study is applied to their newly accepted student candidate. Practically, the university itself has a proven E-Learning system in place. Given the opportunity to expand their English, before being officially registered on that system, these new learner, have no media to engage with their lecturers or classmates. Therefore, there is a need for educational technology that offers communication platforms, collaboration, and training. Applying descriptive analysis method, this research data is done with pre, post and evaluation scheme which is combination of conventional class learning, E-Learning, including obtaining a survey at the end of course, to get feedback from learners.Keywords-Blended learning, digital learning, native digital, industrial revolution, generation characteristic.
An Efficient Black Widow Optimization with Signcryption based Image Encryption Technique

In last decades, Internet plays a vital medium to transfer sensitive data and the security of the communicated data needs to be high. To accomplish security, image steganography has become a major technique of data hiding which assures the security of the transmitted data. Besides, the images possess maximum capacity and the occurrence of images on the Internet is abundant. With this motivation, this paper presents an efficient black widow optimization (BWO) with signcryption technique called BWO-ST for image steganography. The proposed model involves a multi-level discrete wavelet transform (DWT) transformation is employed. Besides, the optimum pixel selection process takes place using the BWO algorithm and the encryption process is performed using the signcryption technique. In addition, the encrypted image was embedding into the selected pixel point of the cover image. A detailed series of simulation analyses are performed and the results are evaluated under different dimensions. The obtained results showcased the supremacy of the BWO-ST approach compared to other techniques.
Detection of fraud in banking transactions using big data clustering technique customer behavior indicators

Human life today is intertwined with abundant trade and economic exchanges, and life would not be possible without trade and commerce. One of the main pillars of financial exchanges are banks and financial and credit institutions, which, as the vital arteries of the economy, are responsible for transferring funds and keeping the economy alive. In the world of economic competition between organizations, profitability and proper performance for stakeholders are the basic principles of the organization's survival. To increase profitability, banks must take measures that, in addition to reducing costs, increase the level of service and customer satisfaction. The best way to do this is to use new technologies and orient the bank's policies to provide services in person and independent of time and place. The use of new technologies in the banking system sometimes leads to customers' distrust and distrust of the bank. Therefore, solutions to detect fraud in banking transactions should be provided. This article aims to discover a model for face-to-face transactions and to establish a system to block fraudulently issued transactions. Therefore, a big data clustering method is designed to timely identify bribery in banking transactions. The results show that using the big data clustering method in the fastest time can detect and stop possible fraud in customers' banking transactions.
Migrating in-lab testing to at-home testing: Setup and procedures

To reduce the risk of spreading COVID-19, we developed an experimental setup that allows for testing of participants at their home with rigor comparable to the laboratory setting. Testing equipment included a touchscreen laptop, high-quality portable sound card, professional headphones, a hotspot USB for internet connection, and a microphone. All equipment was protected in a custom-molded case. The equipment was dropped off and picked up at the participant’s home before and after the experiment. The experimental programs were coded in Matlab and compiled into Matlab executables. During the experiment, the researcher conducted experiments on the laptop via remote control software. Simultaneous to testing, the researcher gave instructions and monitored the participant through video call. Participants were screened for normal hearing thresholds using a modified audiogram, between 0.25 and 8 kHz, with one-up, one-down procedure. Participants were next tested on dichotic vowel perception. Ambient sound level of the participant’s home was monitored during the experiment. Overall, participants reported that the procedure was easy to understand and follow. Results from remote testing will be compared with those from standard in-person booth testing to validate accuracy of remote testing. [This research was funded by NIH-NIDCD-R01-DC013307.]
Factors Affecting the Intention to Use Exoskeleton for exercise and the Moderating Effect of Frailty

Abstract This study aims to find relevant factors influencing the intention to use exoskeletons for exercise (IEE) among older adults and to analyze the moderating effect of frailty. The sample of this study is 310 people (65 or older) without cognitive impairment who completed an online survey. The intention to use exoskeletons was measured with three questions from the Senior Technology Acceptance Model (STAM). Potential relevant factors comprise sociodemographic characteristics, physical and psychological health, exercise, attitude towards aging, and social relationship. Linear regression analyses showed that depressive symptoms, regular exercise, attitude towards aging, and social participation were significantly related to IEE. People with more depressive symptoms and a negative attitude towards aging are more likely to have a higher level of IEE. People who exercise regularly and actively participate in social activities showed a higher level of IEE. Subgroup analyses were performed based on the frailty status measured with Korean Groningen Frailty Indicator (K-GFI). Among people without frailty (N=177), regular exercise, and social participation were positively related to IEE. The number of chronic diseases and social participation was positively related to IEE among people with frailty (N=133). The results of this study implied that poor health conditions lead to an increased need for exoskeletons. The results of this study also suggested that exercise and social participation work as facilitating factors in the context of gerontechnology acceptance. Results of subgroup analyses suggested that influencing factors on IEE can vary depending on the physical functional status.
Change Summarization of Diachronic Scholarly Paper Collections by Semantic Evolution Analysis

The amount of scholarly data has been increasing dramatically over the last years. For newcomers to a particular science domain (e.g., IR, physics, NLP) it is often difficult to spot larger trends and to position the latest research in the context of prior scientific achievements and breakthroughs. Similarly, researchers in the history of science are interested in tools that allow them to analyze and visualize changes in particular scientific domains. Temporal summarization and related methods should be then useful for making sense of large volumes of scientific discourse data aggregated over time. We demonstrate a novel approach to analyze the collections of research papers published over longer time periods to provide a high level overview of important semantic changes that occurred over the progress of time. Our approach is based on comparing word semantic representations over time and aims to support users in better understanding of large domain-focused archives of scholarly publications. As an example dataset we use the ACL Anthology Reference Corpus that spans from 1979 to 2015 and contains 22,878 scholarly articles.
Artifacts Removal in Electrodermal Signals

The removal of noise in signal processing allows optimizing and improving the operation and performance of various computational algorithms. Therefore, this document proposes a procedure for removing artifacts in electrodermal signals to highlight the changes in the signal due to external stimuli. In addition, a proposal is presented for the separation between the phasic and tonic responses. Test using signals with and without artifacts is done, showing significant differences in the output signals. Although the performance of the response separation algorithm alone presents favorable results, the identification of the phasic response stimuli is enhanced by the removal of artifacts. The artifact removal technique obtained a detection and elimination of 89.85% of the total counted artifacts.
Security and Privacy for Edge-Assisted Internet of Things Security Proof for the SKKE Protocol

As an Internet of things (IoT) technology, the ZigBee has a wide range of applications in home automation, smart energy, commercial building automation, personal, home and hospital care, telecom, and wireless sensor. The ZigBee standard has the advantage of high reliability, which is based on the security of authentication key agreement protocol, namely, the SKKE protocol. In the ZigBee standard, this protocol based on shared symmetric-key is applied on the security protocol level. It is a full symmetric-key key agreement with key confirmation scheme, while the key confirmation mechanism is provided by a message authentication coding mechanism. In this paper, we consider the security of the SKKE protocol. In the random Oracle model, we reduce the security of the SKKE protocol to the collision of the hash function and the HMAC function and the indistinguishability between the output of the random Oracle and a random number. We also give a theoretical proof with the game-based method. To our knowledge, there is no research on the provable security of the ZigBee protocol at this stage, so it is helpful to promote further research of the ZigBee protocol security.
Visual domain knowledge-based multimodal zoning for textual region localization in noisy historical document images

Document layout analysis, or zoning, is important for textual content analysis such as optical character recognition. Zoning document images such as digitized historical newspaper pages are challenging due to noise and quality of the document images. Recently, effective data-driven approaches, such as leveraging deep learning, have been proposed, albeit with the concern of requiring larger training data and thus incurring additional cost of ground truthing. We propose a zoning solution by incorporating a knowledge-driven document representation, gravity map, into a multimodal deep learning framework to reduce the amount of time and data required for training. We first generate a gravity map for each image, considering the centroid distance and area between a cell in a Voronoi tessellation and its content to encode visual domain knowledge of a zoning task. Second, we inject the gravity maps into a deep convolution neural network (DCNN) during training, as an additional modality to boost performance. We report on two investigations using two state-of-the-art DCNN architectures and three datasets: two sets of historical newspapers and a set of born-digital contemporary documents. Evaluations show that our solution achieved comparable segmentation accuracy using fewer training epochs and less training data compared to a naïve training scheme.