Reviewerly/exHarmony huggingface dataset:
Turning the Finger into a Writing Tool

A novel writing platform composed of a wearable sensor on the fingernail and classification algorithms is described. Findings from using this platform to translate fingertip writing into shapes, letters, and numbers on a range of surfaces are reported. The new wearable platform leverages an architecture with miniaturized electronic circuitry to precisely measure a set of forces in the longitudinal and transverse directions using multiple strain gauges. We find that the directional pressure patterns are translated from the fingertip to the fingernail. Deformation of fingernails in the longitudinal and transverse directions are detected by the fingernail sensor which sends the data wirelessly to a portable electronic system. Fingernail pressure patterns are categorized through signal processing to recognize a range of shapes, numbers, and letters, enabling fingertip writing recognition. Use of the writing platform following a short training session, shows human fingertip writing on multiple surfaces were automatically transcribed to a computer.
On the Sample Coefficient of Nominal Variation

Categorical dispersionWeiß, Christian H. is commonly measured in terms of the index of qualitative variation (Gini index), but a transformed version of it, the coefficient of nominal variation (CNV), is recommended as being better interpretable. We consider the sample version of the CNV and derive its asymptotic distribution both for independent and time series data. The finite-sample performance of this approximation is analyzed in a simulation study. The CNV is also applied to a real-data example.
A Distributed Method Based on Mondrian Algorithm for Big Data Anonymization

There exist a multitude of techniques for privacy preservation in data publishing, but these methods are mostly designed for traditional and small databases and are incapable of handling big data. The leading method of privacy preservation in data publishing is called de-identification, and one of the best-known techniques of de-identification is k-anonymity. The Mondrian algorithm is among the best and fastest algorithms developed for implementing this technique. This algorithm has been made scalable through solutions such as data partitioning for multiple workers and then running Mondrian on individual nodes, or implementing the algorithm in parallel runs. This study examined and tested a solution involving the use of k-means clustering for data partitioning and then distributing clusters among workers. The test was performed to measure the improvement over the serial method in terms of de-identification accuracy, memory usage, and runtime. The results show that the serial method is completely incapable of handling big data, but the problem can be resolved with the proposed solution.
Optimising the learning process with immersive virtual reality and non-immersive virtual reality in an educational environment

Teachers in higher education have a growing interest in embedding virtual reality in learning and teaching. This means that students can have a virtual exploration or a simulated experience in a range of situations. They can have an immersive virtual reality (IVR) experience using their smartphones mounted with cardboard boxes. Alternatively, tablets and notebook computers can also provide a non-immersive virtual reality (nIVR) experience. Given that bandwidth can be limited in many teaching environments, educators can arrange the options for both IVR and nIVR to maximise the possibility of learning engagement. This paper reports on student experiences of two undergraduate courses, 'Pharmacology and Therapeutics' and 'Understanding Ecotourism', in which educators deployed this innovative pedagogy. Survey and focus group interviews with students were conducted after the VR classes. These showed that students appreciated the VR experience, which broadened their awareness and knowledge.
Reservoir Computing using VCSEL Polarization Dynamics

The huge amount of data generated everyday motivates the development of new approaches for information processing. Machine-learning is one of these approaches: it explores the possibility to solve problems without having its explicit formulation. In this field, reservoir computing allows to easily implement a neural network with physical components since the training of the network is performed only at the readout layer and typically from linear regression methods [1]. A readout layer is trained using a linear regression [1]. Several all-optical architectures have been developed for reservoir computing, e.g. using fiber-based optic [2] and optical integrated components [3].
Determining the Location of Shelters for Tsunami Evacuation Based on Service Area Analysis in Paseban Village, Kencong Sub-District, Jember District

Abstract In each earthquake and tsunami event, efforts to evacuate to a safe place are always the right choice. This is because after the earthquake, the damage caused is usually quite massive. There is little time to react in an evacuation effort to move to a place safe from the reach of the tsunami wave. This study aimed to identify areas affected by the tsunami and the number of people exposed. The data will be used to select the temporary evacuation points that can be used to protect oneself from tsunami waves. The evacuation point was done by the scoring method. The determining of evacuation points took into account the parameters of building functions, distance from shoreline, building area, altitude, number of building floors, distance from the road, and its capacity. The results of this study indicates that, the determining of evacuation sites greatly impacts the area and service areas for Temporary Evacuation Shelter (TES)
MedEx - Data Analytics for Medical Domain Experts in Real-Time.

Translational research in the medical sector is dependent on clear communication between all participants. Visualization helps to represent data from different sources in a comprehensible way across disciplines. Existing tools for clinical data management are usually monolithic and technically challenging to set up, others require a transformation into specific data models while providing mostly non-interactive visualizations or being specialized to very particular use cases. Statistical programming languages (R, Julia) on the other hand offer great flexibility in data analytics, but are harder to access for clinicians with little to no programming expertise. Our software, the Medical Data Explorer (MedEx), aims to fill this gap as light-weight, intuitive, web-based solution with simple data import routes. We couple a modern dynamic web interface with an in-memory database solution for near real-time responsiveness. MedEx provides multiple visualization options (Scatterplot, correlation heatmap, bar chart, grouped boxplot, grouped histogram, coplot) to get an easy overview on the loaded data as well as to perform pattern discovery and elementary statistics. We demonstrate the utility of MedEx, by example, on data from the cardiology research warehouse of Heidelberg University Hospital. In summary, our tool empowers clinicians to conduct their own interactive exploratory data analysis.
Building Domain-Specific Lexicons: An Application to Financial News

Natural Language Processing (NLP) has gained attention in the recent years. Previous research (such as WordNet and Cyc) has focused on developing an all purpose (generalised) polarised lexicons. However, these lexicons do not provide much information in different domains such as Finance and Medical Sciences. Using these lexicons for text classification could affect the prediction accuracy. Therefore, there is a need for building domain-and context-specific lexicons. To achieve this, in this work, a label based propagation based word embedding algorithm has been proposed to obtain positive and negative lexicons. The proposed algorithm works on the principle of graph theory and word embedding. The proposed algorithm is tested on Dow Jones news wires text feed to classify the Financial news as hot and non-hot. Three classifiers, namely, Logistic Regression, Random Forest and XGBoost, employing polarised lexicons, seed words and random words were used. The performance of classifiers in all cases was evaluated using accuracy. Lexicons generated using the proposed approach were effective in classifying the Financial news articles as hot and non-hot compared to classifiers using seed words and random words. Proposed label propagation with word embedding algorithm generates context-specific lexicons, which aids in helps in better representation of text in natural processing tasks and avoids the problem of dimensionality.
PARENTAL INVOLVEMENT TO INCREASE CHILDREN’S READING INTEREST FOR PRESCHOOL CHILDREN’S CHARACTER DEVELOPMENT

Parental involvement plays a significant role in early childhood education. Parents are the most important persons that influence the children at their homes until the children enter preschool. Character development is an essential for children and it starts at an early age. Children reading interest enhances their character development through the exposures of values in the stories. Moral story books provide role models for children’s reading interest and thus, by reading the stories and selecting high quality reading materials, parents help the preschool children character development and change their attitudes toward people in their surroundings. The purpose of this study was to provide an overview of parent involvement in increasing children reading interest in preschool to support their character development. The study was conducted to observe the level of parental involvement in reading the stories to the children at home. This research utilized quantitative analysis of responses gathered from 109 parents. Data was collected using questionnaire with descriptive statistics analysis. The results showed that the respondents’ (parents) level of involvement is high toward the reading interest of preschool children as depicted by the overall mean score (M=48.97; SD=.36). The research finding shows that there are differences between the parents’ that are frequently involved in reading the story than the parents’ that are not. Therefore, it can be concluded that as the frequency of parental involvement in reading stories for children is high, the level of reading interest follows to increase, and thus influence positively toward the children’s character development. Keywords: Parental Involvement, Children Reading Interest, Preschool Children, Character Development
Beyond raw-RGB and sRGB: Advocating Access to a Colorimetric Image State

Most modern cameras allow captured images to be saved in two color spaces: (1) raw-RGB and (2) standard RGB (sRGB). The raw-RGB image represents a scene-referred sensor image whose RGB values are specific to the color sensitivities of the sensor's color filter array. The sRGB image represents a display-referred image that has been rendered through the camera's image signal processor (ISP). The rendering process involves several camera-specific photo-finishing manipulations intended to make the sRGB image visually pleasing. For applications that want to use a camera for purposes beyond photography, both the raw-RGB and sRGB color spaces are undesirable. For example, because the raw-RGB color space is dependent on the camera's sensor, it is challenging to develop applications that work across multiple cameras. Similarly, the camera-specific photo-finishing operations used to render sRGB images also hinder applications intended to run on different cameras. Interestingly, the ISP camera pipeline includes a colorimetric conversion stage where the raw-RGB images are converted to a device-independent color space. However, this image state is not accessible. In this paper, we advocate for the ability to access the colorimetric image state and recommend that cameras output a third image format that is based on this device-independent colorimetric space. To this end, we perform experiments to demonstrate that image pixel values in a colorimetric space are more similar across different makes and models than sRGB and raw-RGB.
Do Video Games Have Artificial Intelligence?

Many, perhaps most, video games feature so-called nonplayer characters (NPCs). These could be adversaries, allies, bystanders, or something else. The point is that they are controlled not by the player (you) but by the computer. Usually people refer to the way these NPCs behave as the “AI” of the game.
Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation

Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection. In state-of-the-art top-down methods, multi-scale information is a crucial factor for the accurate pose estimation because it contains both of local information around the keypoints and global information of the entire person. Although multi-scale information allows these methods to achieve the state-of-the-art performance, the top-down methods still require a huge amount of computation because they need to use an additional human detector to feed the cropped human image to their pose estimation model. To effectively utilize multi-scale information with the smaller computation, we propose a multi-scale aggregation R-CNN (MSA R-CNN). It consists of multi-scale RoIAlign block (MS-RoIAlign) and multi-scale keypoint head network (MS-KpsNet) which are designed to effectively utilize multi-scale information. Also, in contrast to previous top-down methods, the MSA R-CNN performs human detection and keypoint localization in a single model, which results in reduced computation. The proposed model achieved the best performance among single model-based methods and its results are comparable to those of separated model-based methods with a smaller amount of computation on the publicly available 2D multi-person keypoint localization dataset.
Context-Aware Authentication Using Co-Located Devices

This paper proposes a context-aware authentication mechanism that verifies the identity of a user via the data generated by detecting short-range radio signals from nearby devices. Many projects on user authentication have aimed to improve security and usability. Although these projects have been successful in solving individual problems, authentication using passwords remains the most dominant authentication mechanism. So far, however, only a few attempts have been made at pursuing authentication using information about co-located devices, even though an increasingly large number of wearable and Internet-of-Things (IoT) devices is appearing in our computing environments. We explore the feasibility of the proposed mechanism by conducting in-house experiments. Our experimental results suggest that the information on co-location with nearby devices has good potential for implicitly and continuously authenticating a user while modulating the authentication accuracy.
Query complexity and query refinement

At the dawn of the new millennium, Prensky (2001a; 2001b) popularized a view of the younger generations as “digital natives”. While this myth has not gone unchallenged (Prensky, 2009; Thomas, 2011), students have since then been acritically assumed to be almost naturally fluent ICT users. However, recent studies have revealed that this is not always the case. As far as Web search in particular is concerned, a tendency has been shown towards a simplistic “get in, get the answer, get out” approach (Thompson, 2013, pp. 20–21) which prevents them from taking full advantage of the Web’s potential for autonomous learning. In this context, this chapter advocates the importance of familiarizing students with advanced Web search skills as an opportunity for a rewarding accessible DDL experience while also contributing to their development of critical reasoning.
Mobile Cloud Learning System Using Laravel Framework and Android Studio Web View

In this paper the author used the Laravel and Android Studio framework to build mobile cloud learning as a new learning paradigm for college students in the digital world. Learning using mobile cloud learning is designed to overcome the problems of learning with elearning, namely the need for large, safe data storage places without high investment in the provision of computer infrastructure. Learning with mobile learning helps students learn every lecture given whenever and wherever they are, They use smartphones to interact with lecturers in online learning through mobile learning technology that is integrated in cloud computing servers. With mobile cloud learning it will create a new online learning process as a new alternative to learning.
A Relational Static Semantics for Call Graph Construction

The problem of resolving virtual method and interface calls in object-oriented languages has been a long standing challenge to the program analysis community. The complexities are due to various reasons, such as increased levels of class inheritance and polymorphism in large programs. In this paper, we propose a new approach called type flow analysis that represent propagation of type information between program variables by a group of relations without the help of a heap abstraction. We prove that regarding the precision on reachability of class information to a variable, our method produces results equivalent to that one can derive from a points-to analysis. Moreover, in practice, our method consumes lower time and space usage, as supported by the experiment result.
Exponential Moving Averaged Q-Network for DDPG

The characteristics of instability and high-variance of Q-network result in the overestimation bias in the Deep Q-network (DQN). In contrast, Double DQN and averaged DQN have mitigated this problem from different approaches. By improving on the Averaged-DQN, we theoretically prove that the effective variance reduction of the Exponential Moving Average (EMA) in DQN, further illustrating its efficiency in the target network of Deep Deterministic Policy Gradient. We further propose the A3QDDPG algorithm by introducing the EMA Q-Network which is independent of the target Q-network when updating the policy. Experiments on ten continuous control environments of MuJoCo show that A3QDDPG achieves better performance than DDPG in terms of the average return, and the overestimation phenomenon of DDPG can also be observed under some environment in terms of average Q value.
Independent work of students in professional educational institutions

The reform of education in Russia has led to emergence of new learning pattern. Today, among numerous changes that have occurred in competence-based approach context, we note tendencies towards changes in teacher’s role in a student’s life, reduction in classroom hours and increase in the share of students’ independent work. In order to preserve and increase effectiveness of student learning, educational institutions should aim at finding new ways to use classroom hours to organize students' independent work. Insufficient knowledge of this topic actualizes the process of its further development. The purpose of our research was to verify the structure of independent work developed by the authors at a technical school in order to increase student training level. For this, the authors conducted an experiment in which two groups of second-year students of 25 people each took part. The results of the differentiated classification of two groups in the course Engineering Graphics were compared. The first group of students studied according to the structure available in the college, the second - with the help of the pattern developed by the authors. As a result, it was found that the second group has a higher level of training. The results of the experiment allowed us to conclude that the structure of independent work developed by the authors is very effective and its implementation will allow achieving positive results in students' competences development and in future graduates training.
Detect Video Forgery by Performing Transfer Learning on Deep Neural Network

Nowadays, the authenticity of digital image and videos becomes hard while the forgery techniques are more advanced. Given the recent progress on Generative Neural Network (GNN) development that may generate realistic images and videos, it becomes more difficult to detect the authenticity. In this paper, we expose a popular open source video forgery library called “DeepFaceLab” by making use of deep learning. We retrain the existing state-of-the-art image classification neural networks to capture the features from manipulated video frames. After passing various sets of forgery video frames through a well-trained neural network, a bottleneck layer is created for each image, this layer contains compact information for all images, and exposes the artifacts in forgery videos. We obtained above 99% accuracy when testing on DeepFake videos. In addition, we tested our method on FaceForensics dataset and achieved good detection accuracy.
Data Sharing and Privacy–Preserving of Medical Records Using Blockchain

The present way to deal with patient health records disappoints clinicians and fails to fulfill the requirements of a diverse patient population. Many of these issues are raised due to absentiseem of direct control by doctors and patients over the innovation that serves them. As these innovations empower secure credentialing of authorized professionals and audit of clinical exchanges without the cooperation of a doctor’s facility or state middle person, we have actualized health data innovation that is bought and controlled by the people in the clinical relationship. A Blockchain is an open Distributed Ledger that can record transactions between two parties efficiently, verifiable and permanent way. This empowers every patient and each authorized specialist to claim and totally control their open source associated health records inside a protected situation. The aim is to study concepts and techniques of blockchain and to understand how blockchain technology works for different applications in different scenarios. Our arrangement is a patient-focused way to deal with privacy assurance in cloud computing and data dependent on advanced assent models, blockchain identity, and blockchain audit. Digitally signed and encrypted transactions are verified by the peers. Public blockchains, for example, Bitcoin and Ethereum have been appeared to expel institutional control and decentralize innovation with advantages to development and restriction opposition.
Estimation of vocal fold geometry and stiffness from voice acoustics

Many speech applications require estimating vocal fold properties from the produced acoustics. While there have been many previous research on solving the inverse problem, they are often based on lumped-element models of phonation, whose model parameters are difficult to relate to realistic vocal fold properties. This study explores the feasibility of inferring physiologically realistic vocal fold properties, including vocal fold length, thickness, depth, anisotropic stiffness moduli, and the subglottal pressure, from the produced acoustics, using a three-dimensional phonation model and a Bayesian inference approach. To reduce the computational cost associated with the use of a three-dimensional model and the large number of inputs to be estimated, we explore the possibility of improving computational efficiency and estimation accuracy using the large amount of three-dimensional simulation data available from our previous research. Preliminary results show that joint likelihood probabilities can be reasonably estimated based on the available simulation data, significantly reducing computational costs of Bayesian inference. The approach is able to estimate the control parameters of voice production from the produced acoustics with reasonable accuracy. It is observed that certain control parameters, particularly vocal fold stiffness, consistently have large estimation errors than others. [Work supported by NIH.]