Reviewerly/exHarmony huggingface dataset:
The Application of Virtual Reality Technology in Rehabilitation

The usage of virtual environments in medicine has seen a significant increase, lately. The primary purpose of virtual reality is to provide human senses with a simulated reality scene, which is identical to its natural correspondence. Today, technology based on virtual reality has evolved, costs have declined, and intervention devices have become increasingly diverse in function and performance. The goal of the project is to build a small system that can be used in rehabilitation, based on a Raspberry Pi platform, reducing energy consumption and cost. The concept of design consists in a video projector or a large screen, a software which reproduces a series of interactive exercises, and via a camera which analyzes the patient's movements and interacts appropriately. Patient activity may also be recorded for later review. The core components of the system are the Raspberry Pi control unit that performs video processing by means of a video camera attached to the development platform. The Linux operating system is in association with Python and the OpenCv libraries for implementing the specific video camera function.
A Convex Lifting Approach to Image Phase Unwrapping

The nonlinear inverse problem of 2-D phase unwrapping consists in estimating an image, while its pixel values are observed modulo 2π. A variational formulation is considered, which consists in minimizing an energy, convex or not, under the nonconvex data fidelity constraints. We propose a new convex relaxation of this combinatorial problem. It shows similar or better performances than the state of the art.
A New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems

This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the number of noise parameters that need to be tuned in the extended Kalman filter (EKF). Furthermore, the AEKF updates the noise covariances to enhance filter stability. Most importantly, in the worst case, the AEKF converges to the conventional EKF. The AEKF performance is determined based on the mean square error (MSE) performance measure and the stability is proven. The results illustrate that the proposed AEKF has a dramatic improved performance over the conventional EKF, the estimates are more stable with less noise.
IMPROVING THE ACCURACY OF THE SELF-EVALUATION DURING ON-SCREEN SELF-REGULATED LEARNING THROUGH CALIBRATION FEEDBACK

IMPROVING THE ACCURACY OF THE SELF-EVALUATION DURING ON-SCREEN SELF-REGULATED LEARNING THROUGH CALIBRATION FEEDBACK
Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming

In Geometric Semantic Genetic Programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian Genetic Programming (SCGP) – a method capable of reducing the number of nodes in the trees generated by GSGP. SCGP executes a common Cartesian Genetic Programming (CGP) on all elementary subtrees created by GSGP and on various compositions of these optimized subtrees in order to create one compact representation of the original program. SCGP does not guarantee the (exact) semantic equivalence between the CGP individuals and the GSGP subtrees, but the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.
Reduced Complexity Image Clustering Based on Camera Fingerprints

This work presents a reduced complexity image clustering (RCIC) algorithm that blindly groups images based on their camera fingerprint. The algorithm does not need any prior information and can be implemented without and with attraction, to refine clusters. After a camera fingerprint is estimated for each image in the data set, a fingerprint is randomly selected as reference fingerprint and a cluster is constructed using this fingerprint as centroid. The clustered fingerprints are removed from the data set and the remaining fingerprints are clustered repeating the same process. A further attraction stage can be included, in which a similar algorithm is performed using the centroids of the clusters found after the first stage. Despite its simplicity, results show that RCIC algorithm has lower computational cost than existing algorithms, while maintaining similar or even better performance. Moreover, the performance of the proposed algorithm is not affected significantly when the number of cameras in the data set is much larger than the average number of images from each camera.
DeepCaps: Going Deeper with Capsule Networks

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.
Multidimensional key RC6 algorithm

Data confidentiality grows equally important along with technological advancement. Encryption technique is an essential aspect of information security. The security of encryption technique relies on its key size used. Hence, a longer key in an encryption algorithm will be harder to break compared to an algorithm using a smaller key. This paper modified the RC6 Algorithm using a multidimensional key size to increase its security. A key size was selected using 1024, 1280, 1792, 2048, and 2816bits to vary the level of security of data that is being encrypted which caters an additional layer of security to brute force attack such as exhaustive search.
The Evolution of Self-taught Neural Networks in a Multi-agent Environment

Evolution and learning are two different forms of adaptation by which the organism can change their behaviour to cope with problems posed by the environment. The second form of adaptation occurs when individuals exhibit plasticity in response to environmental conditions that may strengthen their survival. Learning has been shown to be beneficial to the evolutionary process through the Baldwin Effect. This line of thought has also been employed in evolving adaptive neural networks, in which learning algorithms, such as Backpropagation, can be used to enhance the adaptivity of the population. Most work focuses on evolving learning agents in separate environments, this means each agent experiences its own environment (mostly similar), and has no interactive effect on others (e.g., the more one gains, the more another loses). The competition for survival in such settings is not that strong, if being compared to that of a multi-agent (or shared) environment. This paper investigates an evolving population of self-taught neural networks – networks that can teach themselves – in a shared environment. Experimental results show that learning presents an effect in increasing the performance of the evolving multi-agent system. Indications for future work on evolving neural networks are also presented.
Enhancing of Particle Swarm Optimization Based Method for Multiple Motifs Detection in DNA Sequences Collections

Genome sequence data consists of DNA sequences or input sequences. Each one includes nucleotides with chemical structures presented as characters: `A', `C', 'G', and `T', and groups of motif sequences, called Transcription Factor Binding Sites (TFBSs), which are subsequences of DNA that lead to protein-synthesis. The detection of TFBSs is an important problem for bioinformatics research. With the similar patterns of motif sequences in TFBSs, computational algorithms for TFBSs detection have been improved to reduce resources used in laboratory setting. The metaheuristic algorithm is the important issue that has been continually improved to detect TFBSs with greater precision and recall. This paper proposes PSO_HD by applying Particle Swarm Optimization (PSO) as a pre-process and using Hamming distance to improve the efficiency of detecting TFBSs with more precision and recall. In order to measure its efficiency, the paper compares the TFBSs detection using PSO_HD algorithm with relevant algorithms in eight datasets. F-score is used as a measurement unit and compared to the related algorithms. The experimental results show that PSO_HD algorithm gives the highest average F-score, which can be indicated that the PSO_HD algorithm can improve the efficiency of detecting TFBSs with more precision and recall.
Finding the Gaps in America’s Magnetic Maps

A 2017 executive order mandated a plan to evaluate U.S. access to critical mineral resources, but the airborne magnetic survey maps that support this effort are sadly out of date.
Cloud-ElGamal and Fast Cloud-RSA Homomorphic Schemes for Protecting Data Confidentiality in Cloud Computing

Homomorphic encryption (HE) is an encryption form that offers a third-party with the ability to carry out computations on encrypted data. This property can be considered as a great solution to get over some obstacles limiting the wide-spread adoption of cloud computing (CC) services. Since CC environments are threatened by insider/outsider security attacks and since CC consumers often access to CC services using resource-limited devices, the HE schemes need to be promoted at security level and at running time to work effectively. For this reason, at EMENA-TSSL'16 and at WINCOM'16, the authors respectively boosted the RSA and ElGamal cryptosystems at security level, Cloud-RSA and Cloud-ElGamal. At SCAMS'17 and at EUSPN'17, the authors then suggested two fast variants of the Cloud-RSA scheme. All proposed schemes support the multiplicative homomorphism (MH) over the integers. The aim of this article is to compare the Cloud-ElGamal scheme with the Cloud-RSA schemes. This article first briefly presents the HE schemes and analyzes their security. This article then implements the schemes, compare and discuss their efficiency.
Improving Blockchain Security Validation and Transaction Processing Through Heterogeneous Computing

We are presenting a new architectural model for enhancing blockchain processing of transactions and security validation through heterogeneous computing, by focusing and improving the consensus validation process, specifically the Merkle tree implementation and validation, both in terms of processing and storage efficiency. By using a heterogeneous architecture, coupled with an efficient storage model for instant transfers of data between memory and video memory, the validation process efficiency can be improved and offloaded from the CPU, with a direct result in increased transactional speeds and enhanced security.
Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures

In this paper we investigate the incorporation of graph-based features into LOD path-based recommender systems, an approach that so far has received little attention. More specifically, we propose two normalisation procedures that adjust user-item path counts by the degree centrality of the nodes connecting them. Evaluation on the MovieLens 1M dataset shows that the linear normalisation approach yields a significant increase in recommendation accuracy as compared to the default case, especially in settings where the most popular movies are omitted. These results serve as a fruitful base for further incorporation of graph measures into recommender systems, and might help in establishing the recommendation diversity that has recently gained much attention.
Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network.

We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize to unseen rows/columns or to new matrices. To make matrix completion inductive, most previous works use content (side information), such as user's age or movie's genre, to make predictions. However, high-quality content is not always available, and can be hard to extract. Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model? In this paper, we propose an Inductive Graph-based Matrix Completion (IGMC) model to address this problem. IGMC trains a graph neural network (GNN) based purely on 1-hop subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings. It achieves highly competitive performance with state-of-the-art transductive baselines. In addition, IGMC is inductive -- it can generalize to users/items unseen during the training (given that their interactions exist), and can even transfer to new tasks. Our transfer learning experiments show that a model trained out of the MovieLens dataset can be directly used to predict Douban movie ratings with surprisingly good performance. Our work demonstrates that: 1) it is possible to train inductive matrix completion models without using side information while achieving similar or better performances than state-of-the-art transductive methods; 2) local graph patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and 3) Long-range dependencies might not be necessary for modeling recommender systems.
Share-n-Learn

Wearable sensors utilize machine learning algorithms to infer important events such as the behavioral routine and health status of their end users from time-series sensor data. A major obstacle in large-scale utilization of these systems is that the machine learning algorithms cannot be shared among users or reused in contexts different from the setting in which the training data are collected. As a result, the algorithms need to be retrained from scratch in new sensor contexts, such as when the on-body location of the wearable sensor changes or when the system is utilized by a new user. The retraining process places significant burden on end users and system designers to collect and label large amounts of training sensor data. In this article, we challenge the current algorithm training paradigm and introduce Share-n-Learn to automatically detect and learn physical sensor contexts from a repository of shared expert models without collecting any new labeled training data. Share-n-Learn enables system designers and end users to seamlessly share and reuse machine learning algorithms that are trained under different contexts and data collection settings. We develop algorithms to autonomously identify sensor contexts and propose a gating function to automatically activate the most accurate machine learning model among the set of shared expert models. We assess the performance of Share-n-Learn for activity recognition when a dynamic sensor constantly migrates from one body location to another. Our analysis based on real data collected with human subjects on three datasets demonstrates that Share-n-Learn achieves, on average, 68.4% accuracy in detecting physical activities with context-varying wearables. This accuracy performance is about 19% more than ‘majority voting,’ 10% more than the state-of-the-art transfer learning, and only 8% less than the experimental upper bound.
COMPARATIVE ANALYSIS OF RANKING FUNCTIONS FOR RETRIEVING INFORMATION FROM MEDICAL REPOSITORY

Current and forthcoming Information Retrieval algorithms demand high mean average precision with contemporary high recall rates in the technical literature. Nevertheless, the existing state-of-the-art is still not optimized for speed, average query latency, and performance. The previous researchers presented various information retrieval models in the literature but the user search led to a ranking of documents that were hopeful to be relevant. In this paper, an evaluation of various information retrieval models is presented with a range of algorithms. The aim is to elaborate and review the current information retrieval function in the context of enterprise domain- specific search. Experiments were conducted on the OHSUMED benchmark data set from MEDLINE, a medical information database. The experimental results demonstrate that BM25F ranking function outperforms other extensively used ranking functions such as BM25, TFIDF, and Cosine on precision and recall measures.
THE USE OF AUTHENTIC MATERIAL TO IMPROVE STUDENTS’ READING COMPREHENSION

Teachers often have difficulties in finding the appropriate learning materials in teaching reading. Without appropriate reading texts that suit them, students spend long hours in the classroom with poor achievement in reading comprehension. This research aims to find out whether the use of authentic material can improve students' reading comprehension toward Vocational High School Students. This research also conducted to describe and explain students' responses toward the implementation of the authentic material. Based on the aims of this research, the descriptive qualitative method chosen since it is suitable to give a clear description of this study, where twenty-nine of the students observed. The data obtained from pre-test and post-test, classroom observation and questionnaire. The result of the study reveals that the use of authentic material in teaching reading significantly improves students' reading comprehension. It recommended for the teachers to use authentic reading materials for a change. Besides, when bringing authentic materials into the classroom, it should be used following students' abilities, as well as with suitable tasks given.
A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning

In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel. Different from most of the methods in the literature that exploit the properties of the data or attempt to minimize the accuracy of a validation dataset over the first quadrant of (C, gamma), the Ortho-MADS provides convergence proof. We present the MADS, followed by the Ortho-MADS, the dynamic stopping criterion defined by the MADS mesh size and two different search strategies (Nelder-Mead and Variable Neighborhood Search) that contribute to a competitive convergence rate as well as a mechanism to escape from undesired local minima. We have investigated the practical selection of hyperparameters for the Support Vector Machine with a Gaussian kernel, i.e., properly choose the hyperparameters gamma (bandwidth) and C (trade-off) on several benchmark datasets. The experimental results have shown that the proposed approach for hyperparameter tuning consistently finds comparable or better solutions, when using a common configuration, than other methods. We have also evaluated the accuracy and the number of function evaluations of the Ortho-MADS with the Nelder-Mead search strategy and the Variable Neighborhood Search strategy using the mesh size as a stopping criterion, and we have achieved accuracy that no other method for hyperparameters optimization could reach.
Comparison of torsional amplitudes between emmetropes and myopes using after-image slides

To describe the influence of corrected refractive error on measured torsional fusional amplitudes (TA) by comparing the TA between emmetropes and spectacle corrected myopes, using the after-image slides of the synoptophore, as targets.Fifty emmetropes (Group I) and 50 myopes (Group II) with best-corrected acuity of 6/6 in each eye were included in the study. Near point of convergence (NPC), near point of accommodation (NPA), and horizontal fusional amplitudes (HFA) were assessed in all the subjects. After-image slides, both horizontally aligned, were used as targets (without the bright flashes). One of the slides was rotated inwards, till cyclo-diplopia was reported by the subject; the procedure was repeated with the slide rotated outwards. The sum of the two readings was taken as TA. NPC, NPA, HFA, and TA were analyzed.There was no significant difference in the NPC, NPA, and HFA between the two groups. The emmetropic subjects had significantly better torsional amplitude (8.4 ± 1.4 degrees) compared to myopes (7.7 ± 1.5 degrees, P = 0.03). We postulate that this difference may be due to perceived image minification, which brings the edges of retinal image of the targets closer to the fovea, thus rendering the myopes lesser tolerant to cyclodiplopia than emmetropes.Refractive error, corrected with spectacles, influences the measured TA. Myopic subjects have lesser torsional fusional amplitude than emmetropes.
Target-Based Temporal Difference Learning

The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this work, we introduce a new family of target-based temporal difference (TD) learning algorithms and provide theoretical analysis on their convergences. In contrast to the standard TD-learning, target-based TD algorithms maintain two separate learning parameters-the target variable and online variable. Particularly, we introduce three members in the family, called the averaging TD, double TD, and periodic TD, where the target variable is updated through an averaging, symmetric, or periodic fashion, mirroring those techniques used in deep Q-learning practice. We establish asymptotic convergence analyses for both averaging TD and double TD and a finite sample analysis for periodic TD. In addition, we also provide some simulation results showing potentially superior convergence of these target-based TD algorithms compared to the standard TD-learning. While this work focuses on linear function approximation and policy evaluation setting, we consider this as a meaningful step towards the theoretical understanding of deep Q-learning variants with target networks.