Reviewerly/exHarmony huggingface dataset:
Data and analysis script for infant and adult eye movement in an adapted ocular-motor serial reaction time task assessing procedural memory

This article provides a description of eye movement data collected during an ocular-motor serial reaction time task. Raw gaze data files for 63 infants and 24 adults along with the data processing and analysis script for extracting saccade latencies, summarizing participants' performance, and testing statistical differences, are hosted on Open Science Framework (OSF). Files (in Matlab format) available for download allow for replication of the results reported in "Procedural memory in infancy: Evidence from implicit sequence learning in an eye-tracking paradigm" [1].
Virtual Reality (VR) based Sustainable Food Education Contents for Elementary School Students

VR(Virtual Reality, 가상현실)은 3차원적이며 학습자가 능동적으로 참여할 수 있는 환경을 제공하여 학생들에게 높은 교육효과를 달성할 수 있는 교육도구로서의 가능성이 제시되고 있다. 이에 본 연구에서는 VR이 교육적으로 활용된 사례들을 최근 연구문헌 등을 통해 분석해 보고, 이를 바탕으로 초등 식생활교육 현장에서 겪고 있는 문제점을 FGI를 통해 수렴하여 VR을 접목한 초등 식생활교육 콘텐츠를 제안하였다. 최근 VR을 교육에 접목한 국내 학위논문 및 학술지논문 동향을 살펴보면, VR은 안전교육, 과학 및 농업교육, 직업훈련, 의료 및 재활교육, 박물관·미술관 재현 등 체험 콘텐츠 제작 연구물들이 다수를 차지하고 있었다. 비록 소수이가는 하나 식품의 생산단계인 농장을 가상으로 구축하여 채소와 가축을 기르고 사육하는 시스템 구축, ‘Second Life’라는 가상세계를 일반교육과 병합하여 농업교육에 접목한 사례, 그리고 가상마트에서 장을 보아 건강한 식품을 선택하는 연구도 보고되었다. 최근 식생활교육은 푸드시스템에 기반을 둔 지속가능한 식생활교육과 체험중심의 식생활역량 개발로 전환되고 있기 때문에, 식품의 생산, 가공, 유통·구매, 소비·섭취 그리고 폐기라는 전체 푸드시스템에 대한 이해를 바탕으로 체험중심의 식생활교육 내용을 개발하고 실시할 필요가 있다. 그러나 조리실습실 등 체험공간 부족, 원거리 이동에 따른 시간과 비용에 대한 부담, 안전사고 발생에 대한 우려, 반복교육의 어려움 등 교사들이 제기하는 다양한 문제점을 안고 있는 상황에서 VR은 이런 한계를 극복하고 푸드시스템 전 과정을 체험해 볼 수 있는 대안으로 판단되었다. 이에 푸드시스템 단계별로 2015 개정 실과 교육과정 중 지속가능한 식생활교육에 해당하는 10개 성취기준을 도출하고, 단계별로 VR활용 아이디어를 제시하였다. 초등학생들이 전 푸드시스템에 걸쳐 체험학습을 할 수 있는 VR시스템이 개발된다면 어린이들의 식생활역량 향상에 크게 기여할 것이며, 향후 지속가능한 식생활교육 세부 주제별로 구체적인 VR 개발 연구가 이어져야 할 것이다.The potential of Virtual Reality (VR) is being suggested as a powerful educational tool that provides 3-dimensional, learner-active environment. Therefore, this study reviewed previous educational researches on VR to figure out its application trend for education, and suggested VR-based elementary sustainable food education contents based on food system that could overcome the limits of school classroom through a FGI from 6 elementary school teachers. The areas of VR application were mainly safety education, science and agriculture education, job training, medical and rehabilitation education, and museum contents. There were a few studies on food education including a virtual farm where elementary students could experience the cultivation of produce and rearing poultry, an agricultural education system by using a virtual world of second life, and a virtual supermarket designed to educate students how to select healthy food. The paradigm of food education is being shifted to experience-based competence development as well as sustainable food education based on food system. Therefore, it is necessary to develop educational contents including the stages of food production, processing, marketing, consumption and waste disposal for students to obtain competence of managing their dietary life in a sustainable manner. Because current elementary schools have various limits such as the lack of cooking or experiencing facilities, student safety issue, time and cost issues on field trips, difficulty of repetition, etc., VR could be considered as an alternative to overcome these obstacles. Various VR contents that suit the 10 achievement standards of the 2015 national curriculum of Practical Arts subject were suggested based on the stages of food system. The development of VR-based food education contents would contribute for the development of food competency of elementary students, and future studies should be followed to design and develop VR systems for each subject of food education.
An Approach for Sentiment Analysis Using Gini Index with Random Forest Classification

Person to person communication locales have turned out to be famous and basic spots for sharing wide scope of feelings through short messages. These feelings incorporate joy, pity, tension, dread, and so forth. Dissecting short messages helps in distinguishing the sentiment expressed by the group. Sentiment Analysis on movie reviews recognizes the general estimation or sentiment communicated by a commentator towards a movie. Numerous analysts are dealing with pruning the sentiment analysis model that plainly recognizes and distinguishes between a positive review and a negative review. In the proposed work, demonstrate that the utilization of features optimized by Gini index feature selection are then concatenating with Machine Learning classification gives better results both in terms of accuracy and class details parameters when tested against classifiers like Gini index with SVM, correlation with random forest and information with random forest. The proposed model unmistakably separates between a positive reviews and negative reviews.
Optimizing the Complexity of Time in the Process of Multiplying Matrices in the Hill Cipher Algorithm Using the Strassen Algorithm

Hill Cipher is one of the symmetric key cryptographic algorithms that have several advantages in data encryption. To avoid key invertible matrices, key matrices are generated using Newton's binomial coefficients. Hill Cipher is one of the symmetric key cryptographic algorithms. The Hill Cipher algorithm uses a matrix measuring m x m as a key for encryption and decryption. The basic matrix theory used in Hill Cipher includes multiplication between matrices and inverses the matrix. Hill Cipher uses mathematical calculations called Linear Algebra and specifically requires users to understand the basis of the matrix. During this time the matrix multiplication used to encrypt and decrypt this method is only done by Divide and Conquer. This is less effective in minimizing the processing time. A good algorithm must not only be correct but also efficient in its use. In addition, an algorithm must also have low complexity. Algorithms like this must be able to optimize its performance, especially in data processing which is getting more and more in number. An algorithm with low complexity is an algorithm that is able to minimize the use of time and memory space even though more and more data is used. Therefore the author applies the Strassen method in optimizing the matrix multiplication process to encrypt and decrypt it. However, this algorithm will only apply to use of keys and plaintexts of the same order size, which is nxn where n = 2a(a> 0). Then the encryption and decryption process will be done by matrix multiplication using the Strassen method. The final results show that by using the Strassen algorithm the encryption and decryption process in the Hill Cipher method can be optimized. Where every time the data to be processed increases the complexity of the time required will not be too high an increase. And this shown that Strassen more effective than the Divide and Conquer method.
Constructing a Comprehensive Clinical Database Integrating Patients' Data from Intensive Care Units and General Wards

Collection and analysis of large volumes of ICU data are invaluable to the advancement of clinical knowledge, and large-scale ICU databases have been effective resources to understand risk factors and perform predictive analysis by using machine learning. This paper introduces the construction of a comprehensive clinical database: PLAGH-ICU database, which integrated data from nine intensive care units, emergency department and general wards. Data from several sources were extracted and integrated in the database, including patient demographics, hospital administrative data, physiological data, medications, lab test, fluid balance data, notes and reports etc. Detailed information about the database, such as patient characteristics, disease distribution, category of data, data records were illustrated. As far as we know, this is the first comprehensive ICU database developed and used as a research database in datathon event in China. Such kind of database will promote the research progress in critical care medicine, as well as the development of modern hospital information system in China.
Virtual Reduction and Interaction of Chinese Traditional Furniture and Its Usage Scenarios

Chinese traditional furniture is an important part of Chinese traditional culture. Chinese traditional furniture is made of solid wood and uses mortise and tenon to combine all the parts of the furniture without any metal nails. This research is aimed at the display and interaction of the unique mortise and tenon structure of Chinese traditional furniture, drawing plane and 3D models, restoring the usage scenarios of Chinese traditional furniture. In this scenario, combination of reality and virtual, static and dynamic would be shown with the help of motion capture technology, sensing technology, AR and VR technology, interactive technology. From the interaction of the Chinese traditional furniture and the visitors in the exhibition room, people would obtain better experience of visiting, and learn more knowledge with more interest.
Visualizing an Ethics Framework: A Method to Create Interactive Knowledge Visualizations From Health Policy Documents

Data have become an essential factor in driving health research and are key to the development of personalized and precision medicine. Primary and secondary use of personal data holds significant potential for research; however, it also introduces a new set of challenges around consent processes, privacy, and data sharing. Research institutions have issued ethical guidelines to address challenges and ensure responsible data processing and data sharing. However, ethical guidelines directed at researchers and medical professionals are often complex; require readers who are familiar with specific terminology; and can be hard to understand for people without sufficient background knowledge in legislation, research, and data processing practices.This study aimed to visually represent an ethics framework to make its content more accessible to its stakeholders. More generally, we wanted to explore the potential of visualizing policy documents to combat and prevent research misconduct by improving the capacity of actors in health research to handle data responsibly.We used a mixed methods approach based on knowledge visualization with 3 sequential steps: qualitative content analysis (open and axial coding, among others); visualizing the knowledge structure, which resulted from the previous step; and adding interactive functionality to access information using rapid prototyping.Through our iterative methodology, we developed a tool that allows users to explore an ethics framework for data sharing through an interactive visualization. Our results represent an approach that can make policy documents easier to understand and, therefore, more applicable in practice.Meaningful communication and understanding each other remain a challenge in various areas of health care and medicine. We contribute to advancing communication practices through the introduction of knowledge visualization to bioethics to offer a novel way to tackle this relevant issue.
The call-by-value Lambda-Calculus with generalized applications

The lambda-calculus with generalized applications is the Curry-Howard counterpart to the system of natural deduction with generalized elimination rules for intuitionistic implicational logic. In this paper we identify a call-by-value variant of the system and prove confluence, strong normalization, and standardization. In the end, we show that the cbn and cbv variants of the system simulate each other via mappings based on extensions of the protecting-by-a-lambda compilation technique.
Lost in translation

There are gaps in understanding in and between those who design systems of AI/ ML, those who critique them, and those positioned between these discourses. This gap can be defined in multiple ways - e.g. methodological, epistemological, linguistic, or cultural. To bridge this gap requires a set of translations: the generation of a collaborative space and a new set of shared sensibilities that traverse disciplinary boundaries. This workshop aims to explore translations across multiple fields, and translations between theory and practice, as well as how interdisciplinary work could generate new operationalizable approaches.
ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment

Recent studies show that the elderly population has increased considerably in European society in recent years. This fact has led the European Union and many countries to propose new policies for caring services directed to this group. The current trend is to promote the care of the elderly in their own homes, thus avoiding inverting resources on residences. With this in mind, there are now new solutions in this direction, which try to make use of the continuous advances in computer science. This paper tries to advance in this area by proposing the use of a personal assistant to help older people at home while carrying out their daily activities. The proposed personal assistant is called ME3CA, and can be described as a cognitive assistant that offers users a personalised exercise plan for their rehabilitation. The system consists of a sensorisation platform along with decision-making algorithms paired with emotion detection models. ME3CA detects the users’ emotions, which are used in the decision-making process allowing for more precise suggestions and an accurate (and unbiased) knowledge about the users’ opinion towards each exercise.
Noise Removal in the Developing Process of Digital Negatives

Most modern color digital cameras are equipped with a single image sensor with a color filter array (CFA). One of the most important stages of preprocessing is noise reduction. Most research related to this topic ignores the problem associated with the actual color image acquisition process and assumes that we are processing the image in the sRGB space. In the presented paper, the real process of developing raw images obtained from the CFA sensor was analyzed. As part of the work, a diverse database of test images in the form of a digital negative and its reference version was prepared. The main problem posed in the work was the location of the denoising and demosaicing algorithms in the entire raw image processing pipeline. For this purpose, all stages of processing the digital negative are reproduced. The process of noise generation in the image sensors was also simulated, parameterizing it with ISO sensitivity for a specific CMOS sensor. In this work, we tested commonly used algorithms based on the idea of non-local means, such as NLM or BM3D, in combination with various techniques of interpolation of CFA sensor data. Our experiments have shown that the use of noise reduction methods directly on the raw sensor data, improves the final result only in the case of highly disturbed images, which corresponds to the process of image acquisition in difficult lighting conditions.
Accurate Camera Syncronization Using Deep-Shallow Mixed Models

To synchronize multiple cameras has been attracting more and more attentions since the surge of 360-degree view videos and VR/AR applications. Usually only the professional cameras with dedicated external-trigger interface can be synchronized easily and accurately. However, those cameras suffer from the high price and the complex operating procedure. For network cameras, some researchers have proposed synchronization method by using the NTP protocol. To our best knowledge, there is no effective method for synchronizing consuming cameras. In this work, we propose a method for synchronizing nearly all kinds of cameras given that they are connected into on PC. The experiment show that for average consuming webcams, our method can achieve the mean absolute difference (MSD) below 12 ms, which is totally meet the requirement of stereo vision or other multiple vision tasks.
Prácticas educativas mediadas por TIC. Un análisis de la metodología del profesorado a través del Programa Escuela 2.0

The methodological strategies carried out by the teacher in the classroom have been the object of study during the last decades.The inclusion of ICT in the ordinary classroom has revolutionized in one way or another the methodology used by teachers.An example of this was the Escuela 2.0 Program, which was born with the objective of digitizing the Spanish education system through the provision of resources: mini-laptops, interactive whiteboards and internet connection.In this article, and with the invaluable collaboration of four public primary education centers, we analyze the arrival of the aforementioned program, through the case study methodology, focusing on the methodological strategies carried out by teachers.For this, twelve interviews with the management team and teachers of the centers have been carried out, classroom practices have been observed, as well as an analysis of said data to offer a more current panorama.The results indicate that the very arrival of these resources in the classroom has led teachers to rethink their class dynamics, as before they focused solely and exclusively on the textbook.
Efektivitas Bahan Ajar Berbasis Android terhadap Hasil Belajar

Learning material is one of the crucial elements that must be considered in learning. However, learning materials used are still conventional in the form of hardcopy and softcopy, so students are reluctant to read. Specifically, in the English for office course, simple and practical learning material is needed so that they can support both theory and practice. Related to this problem, the purpose of this study was to analyze the effect of android-based learning material on learning achomplisment and its effectiveness of the implementation of android-based learning materials in improving learning outcomes. The study was conducted at Universitas Islam Riau on students who took English for office courses. Through random sampling techniques determined the sample of the experimental class and the control class. Data was collected through pre-test and post-test distribution in the two sample classes. Based on several stages of data analysis, the data is normally distributed and the data variants are homogeneous. Furthermore, paired sample t-test, showing a significance value of 0.039 &lt;0.05, it can be concluded that there are differences in the average learning outcomes for pre-test and post-test in the classes taught by applying android-based learningg materials. While the N-Gain score test revealed that the implementation of android-based learning material was quite effective to improve learning outcomes with a value of 63.00%. So it can be concluded that, there is an influence and quite effective android-based learning materials on learning achomplisment.
Design and Implementation of a Statistical Testing Framework for a Lightweight Stream Cipher

The Shrinking Generator (SG) is a popular synchronous, lightweight stream cipher that uses minimal computing power. However, its strengths and weaknesses have not been studied in detail. This paper proposes a statistical testing framework to assess attacks on the SG. The framework consists of a d-monomial test that is adapted to SG by applying the algebraic normal form (ANF) representation of Boolean functions, a test that uses the maximal degree monomial test to determine whether the ANF follows the proper mixing of bit values, and a proposed unique window size (UWS) scheme to test the randomness properties of the keystream. The proposed framework shows significant weaknesses in the SG output in terms of dependence between the controlling linear-feedback shift register (LFSR) and non-linearity of the resulting keystream. The maximal degree monomial test provides a better understanding of the optimal points of SG, demonstrating when it is at its best and worst according to the first couple of results. This paper uses UWS to illustrate the effect of the LFSR choice on possibly distinguishing attacks on the SG. The results confirm that the proposed UWS scheme is a viable measure of the cryptographic strength of a stream cipher. Due to the importance of predictability and effective tools, we used neural network models to simulate the input data for the pseudo-random binary sequences. Through the calculation of UWS, we obtained solid results for the predictions.
Descriptive Statistics and Visualizing Data

This chapter provides a background framework with the purpose and brief history of descriptive statistics. It also goes into the mechanics (including the calculation) of descriptive statistics. These operations include central tendency and dispersion. The chapter then talks about various types of data visualization—a data matrix, frequency table, cross tabulation, and informative graphs. The chapter then provides a step-by-step guide on how to run descriptive statistics in SPSS and R. The chapter concludes with two planning research examples.
Adaptive Tuning Curve Widths Improve Sample Efficient Learning

Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question by proposing and analyzing a learning algorithm that uses a simple reinforce-type plasticity mechanism and does not require any gradients to learn low dimensional mappings. It harnesses three bio-plausible mechanisms, namely, population codes with bell shaped tuning curves, continuous attractor mechanisms and probabilistic synapses, to achieve sample efficient learning. We show both theoretically and by simulations that population codes with broadly tuned neurons lead to high sample efficiency, whereas codes with sharply tuned neurons account for high final precision. Moreover, a dynamic adaptation of the tuning width during learning gives rise to both, high sample efficiency and high final precision. We prove a sample efficiency guarantee for our algorithm that lies within a logarithmic factor from the information theoretical optimum. Our simulations show that for low dimensional mappings, our learning algorithm achieves comparable sample efficiency to multi-layer perceptrons trained by gradient descent, although it does not use any gradients. Furthermore, it achieves competitive sample efficiency in low dimensional reinforcement learning tasks. From a machine learning perspective, these findings may inspire novel approaches to improve sample efficiency. From a neuroscience perspective, these findings suggest sample efficiency as a yet unstudied functional role of adaptive tuning curve width.
A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition

We present a photonic neuromorphic network using DFB-LDs for spatiotemporal pattern recognition. Complete input patterns are investigated theoretically and experimentally. The output peak powers decrease with the difference between the target pattern and other patterns.