Reviewerly/exHarmony huggingface dataset:
Design Interventions of an Exergame for Fall Problem in Indian Older Adults

In the field of elderly - exergame research, there is limited information available in the Indian context. There is a need to explore how the Indian elderly respond towards such interventions to do the exercise, and if it is not positive what kind changes need to make it more context specific. A study is conducted among Indian elderly (three sets of pilot studies with twenty Indian elderly (age 60 years and above)) with an existing game (DDR) and a tailored exergame as a fall preventive measure. The aim of the study is to give a few design recommendations to design exergame for Indian elderly. The new stepping exergame design process includes the Technology Acceptance Decision Tree (TADT), Indian Anthropometric data. The design recommendations include the prescribed sets of exercise need to convert into a game goal, repeated movements to complete one goal to avoid confusions, self-paced game speed, game theme, and avatar in the Indian context, etc. The findings of the study will be helpful for the researchers in the field of exergame for the elderly while designing for Indian or similar context.
A Radar Measurement Setup with a Ground Truth System for Micro-Doppler Human Movements

Human gestures and movements recognition is becoming an indispensable task in today's world for many ranges of applications. For example, radar sensors are of big interest in the automotive industry due to its many advantages such as the ability to work in various weather conditions beside performing during night as well as during daytime. A ground truth system would be advantageous in labelling and obtaining more details of the measured scenarios. In this paper, a measurement setup with a ground truth system using Kinect is going to be used. Processing of the data obtained from Kinect proves to be helpful in getting more information about the performed gestures. The synchronization between Kinect and radar is also an important issue, which is performed here on a hardware level to minimize the delays from the software level. In a world where deep learning is increasingly used in various aspects of life, having more synchronized data points from Kinect beside the ones from the radar is also needed.
A Novel Framework for Fine Grained Action Recognition in Soccer

Sports analytics have become a topic of interest in the field of Artificial intelligence. With the availability of huge volumes of high level data, significant progress has been made in the domain of action recognition in the past. Though video based action recognition has progressed well using state of the art deep learning techniques, its applications are limited to some higher level actions like throwing, jumping, running etc. There has been some work in fine-grained action recognition technique, such as, identification of type of throws in Basketball, and the type of a player’s shots in Tennis. However with larger play field and with many players on field, multi player sports such as Soccer, Rugby, Hockey and etc. pose bigger challenges and remain unexplored. These games in general are live fed through field view cameras or skycams which aren’t stationary. For these reasons, we chose to recognize player’s actions in the game of Soccer and thereby, explore the capabilities of existing architectures and deep neural networks for these kind of games. Our main contributions are the proposed framework that can automatically recognize actions of players in live football game which will be helpful for text query based video search, for extracting stats in a football game and to generate textual commentary and the Soccer-8k dataset which consists of different action clips in the soccer play.
Efficient cluster-based portfolio optimization

The sample mean and covariance matrix of historical data provide a disappointing out-of-sample performance in mean-variance portfolio rules. This poor performance is certainly due to the high estimation error incurred in the optimization model. Our purpose in this article is to find a method that enhances the out-of-sample performance of the portfolio weights. Using hierarchical clustering, we propose an alternative cluster-based portfolio to obtain a sequence of cluster assets. On the basis of Gram-Schmidt orthogonalization, the estimation risk of the data set becomes the sum of the estimations of the clusters in the sequence. The performance of our method and its competitors is compared empirically and via some simulations in high dimension.
Methods to Edit Multi-label Training Sets Using Rough Sets Theory

In multi-label classification problems, instances can be associated with several decision classes (labels) simultaneously. One of the most successful algorithms to deal with this kind of problem is the ML-kNN method, which is lazy learner adapted to the multi-label scenario. All the computational models that realize inferences from examples have the common problem of the selection of those examples that should be included into the training set to increase the algorithm's efficiency. This problem in known as training sets edition. Despite the extensive work in multi-label classification, there is a lack of methods for editing multi-label training sets. In this research, we propose three reduction techniques for editing multi-label training sets that rely on the Rough Set Theory. The simulations show that these methods reduce the number of examples in the training sets without affecting the overall performance, while in some case the performance is even improved.
Multi-mother Wavelet Neural Network Training Using Genetic Algorithm-Based Approach to Optimize and Improves the Robustness of Gradient-Descent Algorithms: 3D Mesh Deformation Application

This paper presents the implementation of genetic algorithm which aims at searching for an optimal or near optimal solution to the deformation 3D objects problem based on multi-mother wavelet neural network training. First, we introduce the problem of 3D high mesh deformation using Multi-Mother Wavelet Neural Network architecture (MMWNN). Furthermore, gradient training limits of wavelet networks are characterized by their inability to evade local optima. The idea is to integrate genetic algorithms into the wavelet network to avoid both insufficiency and local minima in the 3D mesh deformation technique. Simulation results validate the generalization ability and efficiency of the proposed network based on genetic algorithms (MMWNN-GA). Thus the significant improvement of the performances in terms of quality of 3D meshes deformation.
A dynamic data classification techniques and tools for big data

Abstract Big Data is an immense term for working with the large volume and complex data sets. While data set is larger in volume and the conventional processing applications are insufficient subsequently distributed databases are needed. Big data came into reality because the earlier technologies are not able to carry such large amount of data from self-governing sources. To find the important and accurate data from large unstructured data is going to be a difficult task for any user. This is the reason why the classification technique came into picture for big data. With the help of these classification methods unstructured data can be turned into organized form as a result that any user can access the required data easily. These classification techniques can be applied over the big transactional databases to provide data services to users from huge volume of data sets. Classification is one of the aspects of machine learning and there are basically two broad categories one is supervised and other one is unsupervised classification. In this project we worked to study the variants of supervised classification methods upon we discuss some of the tools or frame works to perform classification. For each algorithm we discuss about their advantages and limitations.
Edge-Based Demosaicking Method Using Uncorrelatedness With Sensors for CFA

This paper presents a multiple edge detection by computing uncorrelatedness of the neighboring pixels in four locations: up, down, left, and right. To recover missing G in R and missing G in B, we use the derived weightings and the pixel contributions from the neighboring pixel information. When demosaicking the R and B arrays, an interpolation is used for the recovery of the missing R in B and missing B in R. The experimental results show that the proposed overall system outperforms the existing works. The proposed work is especially better than the state-of-the-art method by 1.23 dB in composite peak signal-to-noise ratio (CPSNR) and by -0.15 in a Spatial Extension to the CIE L*a*b* DeltaE Color Difference Metric (S-CIELAB), the lower the better, averaged over a standard image dataset.
State Speech as a Response to Hate Speech: Assessing ‘Transformative Liberalism’

‘Transformative liberals’ believe that the state should use its non-coercive capacities to counter hateful speech and practices, by seeking to transform the views of those who hold hateful and discriminatory beliefs. This paper critically assesses transformative liberalism, with a particular focus on the theory developed by Corey Brettschneider. For Brettschneider, the state should engage in ‘democratic persuasion’ by speaking out against views that are incompatible with the ideal of free and equal citizenship, and refusing to fund or subsidise civil society groups that hold such views. My critique has five parts. I first rebut two central justifications for transformative liberalism, regarding complicity and the undermining of equal citizenship. Second, I show that some of the central policies that Brettschneider advocates are in fact coercive. Third, I raise concerns about the nature of the complex and contestable judgments that transformative liberalism requires the state to make. Fourth, I argue that Brettschneider’s view has various troubling implications. Finally, I argue that many of these problems derive from his adoption of a thick conception of free and equal citizenship, resulting in an overly broad definition of hateful viewpoints and of hate speech. A defensible version of transformative liberalism would use a significantly narrower conception.
A Branch-and-Bound-Based Crossover Operator for the Traveling Salesman Problem

In this article, the new crossover operator BBX for Evolutionary Algorithms (EAs) for traveling salesman problems (TSPs) is introduced. It uses branch-and-bound to find the optimal combination of the (directed) edges present in the parent solutions. The offspring solutions created are at least as good as their parents and are only composed of parental building blocks. The operator is closer to the ideal concept of crossover in EAs than existing operators. This article provides the most extensive study on crossover operators on the TSP, comparing BBX to ten other operators on the 110 instances of the TSPLib benchmark set in EAs with four different population sizes. BBX, with its better ability to reuse and combine building blocks, surprisingly does not generally outperform the other operators. However, it performs well in certain scenarios. Besides presenting a novel approach to crossover on the TSP, the study significantly extends and refines the body of knowledge on the field with new conclusions and comparison results.
Hey Emotion Companion, Can You Be My Friend?

Affective technology is increasingly becoming common in everyday life. To better understand how teachers perceive this technology in classrooms, and the role it can have in mediating the emotions of their students, we interviewed elementary (PreK-4) teachers. Our aim was to gain insights into how to design a child-emotion companion for the classroom that is mediated by teachers. From our initial interviews, we identified four interaction themes for child-emotion companions: perceived emotions, self-report contexts, playfulness, emotional wellbeing. Our early findings show how different interactions and modalities in a classroom space may change the way children perceive and communicate their emotions, and how to improve their emotional wellbeing in a classroom. We also propose design considerations that are illustrated through a low-fidelity prototype for future child-emotion companion that are mediated by teachers.
Exploring Globally Inclusive Online Collaboration for Indian and Finnish Schoolchildren

We present our exploratory work on globally inclusive online collaboration for schoolchildren in India and Finland, using CityCompass, an online virtual navigational application for conversational language learning. User studies with Indian children, who collaborated with a Finnish researcher, showed several barriers towards communication and collaboration, including issues from limited access to computers and gaming to socio-cultural effects of a large power distance and face-saving. By adding a dramatized scenario, the Bollywood Method, these barriers were reduced. Next, we replicated the study with Finnish children, who collaborated with an Indian researcher. Here, previous computer and gaming experience reduced the motivation towards communication. In this paper, we present studies conducted in Finland, compare it with the Indian ones, and discuss opportunities for inclusive collaboration between Indian and Finnish children. Overall, our findings indicate that online collaboration is affected by differences in computer skills, video gaming experience, and socio-cultural communication norms.
A Deep Learning Approach to Anomaly Detection in the Gaia Space Mission Data

The data reduction system of the Gaia space mission generates a large amount of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation. We investigate the feasibility of adoption of deep learning tools for automatic detection of data anomalies, focusing on convolutional neural networks and comparing with a multilayer perceptron. The results evidence very good accuracy ( $$\sim $$ 99.7%) in the classification of the selected anomalies.
The Use of Technologies of Full Mastery in the Organization of Classes in the Disciplines of the Mathematical Cycle

The article discusses the technology of full learning, based on a model developed by American psychologists J. Carroll and V.S. Bloom. The features of its use in the construction of the educational process in institutions of higher education are described. The experience of its use in conducting classes in the disciplines of the mathematical cycle with students in undergraduate economic specialties is described. To implement this technology, the training material is divided into separate modules with an introductory theoretical part, practical tasks and final verification work. Practical tasks are individual variants of typical tasks, which are compiled in such a way that some of them can be performed during classroom work, for example, working at the blackboard, and some at homework. The effectiveness of this technology is confirmed by comparing the results of students' examinations in the control and experimental groups.
Three–dimensional visualization of ternary prisms (T–prism): Development of a spreadsheet–based tool for Earth and material sciences

T–prism is a Microsoft–Excel–based tool that visualizes ternary space diagrams (i.e., ternary prisms) in three–dimensional (3D) space. This tool allows us to examine the overall features of a dataset in both three– and two–dimensional spaces by altering the viewing angles. T–prism involves two algorithms that coordinate the transformation and rotation of diagrams in a virtual 3D space. This paper describes a new and simple coordinate transformation from a ternary space diagram (i.e., ternary prisms) to an XYZ orthogonal system. Coordinates of a point of interest in the ternary space diagram, expressed using the proportions of three components of a basal triangle (r, l, and t, where r + l + t = 100) and an additional variable (h), are converted into coordinates in the orthogonal system as follows: x = (r + 100 − l)/2, y = √3/2t, and z = fh. Here, f represents the correction factor used to appropriately express the length of an axis perpendicular to the basal ternary diagram. T–prism is particularly suitable for visualizing phase relations in multicomponent systems, the physical properties of materials, and compositional variations in solid solutions. Hence, the tool is applicable to a broad variety of research and teaching fields, including Earth science, material science, and physical chemistry.
Flipping the Message Bits to Increase Imperceptibility in the Least Significant Bit Image Steganography

Abstract Imperceptibility is one of the issues that continue to be studied in the science of steganography in images. Imperceptibility means that the message embedded in the cover image cannot be perceived by the human senses, especially the sense of sight. Flipping the message bits is the method proposed in this study, which is inspired by the inverted LSB method, but this method is simpler because it only flips the message bit when the change in the cover image pixel exceeds 50%. Although the proposed method is simpler than the inverted LSB method, based on the test it is found that the imperceptibility quality can increase significantly when compared to the traditional LSB method. This is evidenced by the PSNR value increasing around 9dB, from 51dB to 60dB and the MSE value dropping from around 0.44 to 0.05.
Two-step phase shifting interferometry based on orientation selective monogenic filtering

We propose a phase step estimation algorithm in a two-step phase shifting interferometry setup based on orientation selective adaptive monogenic filtering (AMF). Initially, the AMF algorithm is used for the fringe pattern normalization. A minor modification in the same algorithm performs an orientation selective monogenic filtering which is capable of providing a reliable estimation of phase step. Due to the use of the same algorithm for normalization and phase step estimation, the proposed method offers computational efficiency and simplicity. Simulation and experimental results are provided to demonstrate the suitability of the proposed method in phase demodulation.
My Name Is My Password

Children continue to use technology at an increasing rate, more and more of which require authentication via usernames and passwords. We seek to understand how children ages 5-11 years old create and use their credentials. We investigate children's username and password understanding and practices from the perspective of both children and adults within the context of three security categories: credential composition (e.g. length of password), performance (e.g. time to enter), and credential mechanisms (e.g; a pattern or characters). We conducted a semi-structured interview with 22 children and an online survey with 33 adult participants (parents and teachers) to determine their practices and involvement in facilitating authentication for their children. Our study illustrates how children have a limited understanding of authentication, and that there are differences between children's and adult's understanding of good authentication and security practices, and what they actually do.
Performance of Classifiers on Noisy-Labeled Training Data: An Empirical Study on Handwritten Digit Classification Task

Machine learning is an important area of Artificial Intelligence. It has applications in almost all the fields of science. Supervised machine learning, for classification problems, involves training the classifiers with labeled data. There are many classifiers, each having its own strengths and weaknesses in terms of classification accuracy and the ability of dealing with noisy class labels in the training data. There is limited work reported in the literature on investigating the performance of classifiers under different levels of class noise in the training data. The current work aims to presents a thorough investigation on the effects of class mislabeling on the performance of different classifiers. Five commonly used classifiers; SVM, random forest, ANN, naïve Bayes, and KNN were investigated on a benchmark database of handwritten digit images. Classifiers were trained with different levels of labeling noise, ranging from low, to medium, to very high, and their recognition performances were evaluated and compared. The study led to some interesting observations which are presented in this paper.
Immersive Media Design and Children

This one-day workshop will bring together a community of researchers, designers, practitioners, and other experts who are interested in the responsible design of immersive media---or augmented, virtual, mixed, and cross reality---for children, while taking into account children's developmental needs, equity, and inclusivity. At the workshop, participants will discuss and make connections across their work, participate in hands-on activities, and begin producing a set of design guidelines for immersive media for children, grounded in their collective experiences, research, and knowledge of the field. After the workshop, we will publish these guidelines as a white paper to distribute to interested industries, developers, designers, and more to positively influence the thoughtful design of immersive media for children.
Virtual fire drill system supporting co-located collaboration

Background Due to the restriction of display mode, in most of the virtual reality systems with multiple people in the same physical space, the program renders the scene based on the position and perspective of the one user, so that other users just see the same scene, resulting in vision disorder. Methods To improve experience of multi-user co-located collaboration, in this study, we propose a fire drill system supporting co-located collaboration, in which three co-located users can collaborate to complete the virtual firefighting mission. Firstly, with multi-view stereoscopic projective display technology and ultra wideband (UWB) technology, co-located users can roam independently and watch virtual scenes through the correct perspective view based on their own position by wearing dedicated shutter glasses, thus carrying out different virtual tasks, which improves the flexibility of co-located collaboration. Secondly, we design simulated firefighting water-gun using the micro-electromechanical system sensor, through which users can interact with virtual environment, and thus provide a better interactive experience. Finally, we develop a workbench including a holographic display module and multi-touch operation module for virtual scene assembly and virtual environment control. Results The controller can use the workbench to adjust the virtual layout in real time, and control the virtual task process to increase the flexibility and playability of system. Conclusions Our work can be employed in a wide range of related virtual reality applications.
Determination of Trustworthiness of Cloud Service Provider and Cloud Customer

In service-oriented computing environment (e.g. cloud computing), Cloud Customers (CCs) and Cloud Service Providers (CSPs) require to calculate the trust ranks of impending partner prior to appealing in communications. Determining trustworthiness dynamically is a demanding dilemma in an open and dynamic environment (such as cloud computing) because of many CSPs providing same types of services. Presently, there are very less number of dynamic trust evaluation scheme that permits CCs to evaluate CSPs trustworthiness from multi-dimensional perspectives. Similarly, there is no scheme that permits CSPs to evaluate trustworthiness of CCs. This paper proposes a Multidimensional Dynamic Trust Evaluation Scheme (MDTES) that facilitates CCs to evaluate the trustworthiness of CSPs from various viewpoints. Similar approach can be employed by CSPs to evaluate the trustworthiness of CCs. The proposed MDTES helps CCs to choose trustworthy CSP and to have desired QoS requirements and CSPs to choose desired and legal CCs. The simulation results illustrate the MDTES is dynamic and steady in distinguishing trustworthy and untrustworthy CSPs and CCs.