Reviewerly/exHarmony huggingface dataset:
Integer codes correcting single asymmetric errors

This paper presents a class of integer codes capable of correcting single asymmetric errors. The presented codes are defined over the ring of integers modulo 2b– 1 and are constructed with the help of a computer. The results of an exhaustive search have shown that, for practical lengths up to 4096 bits, the proposed codes use the same number of check bits as the best systematic single asymmetric error-correcting codes (SAECCs). Besides this, it is found that for some lengths the presented codes are perfect. Finally, the paper shows that the encoding/decoding complexity of the proposed codes is notably lower than that of the best systematic SAECCs.
A Forecasting-Based DLP Approach for Data Security

Sensitive data leakage is the major growing problem being faced by enterprises in this technical era. Data leakage causes severe threats for organization of data safety which badly affects the reputation of organizations. Data leakage is the flow of sensitive data/information from any data holder to an unauthorized destination. Data leak prevention (DLP) is set of techniques that try to alleviate the threats which may hinder data security. DLP unveils guilty user responsible for data leakage and ensures that user without appropriate permission cannot access sensitive data and also provides protection to sensitive data if sensitive data is shared accidentally. In this paper, data leakage prevention (DLP) model is used to restrict/grant data access permission to user, based on the forecast of their access to data. This study provides a DLP solution using data statistical analysis to forecast the data access possibilities of any user in future based on the access to data in the past. The proposed approach makes use of renowned simple piecewise linear function for learning/training to model. The results show that the proposed DLP approach with high level of precision can correctly classify between users even in cases of extreme data access.
Design and Trajectory Planning Optimization of a Morphing Airfoil for 3-D Flight Maneuvers

This paper presents a new approach for simultaneously optimizing the design and flight trajectories of a morphing wing unmanned aerial vehicle (UAV). A new strategy for coupling a motion planning problem with a design optimization problem is presented, which leverages sampling based motion planning techniques. A set of motion primitives are also presented that can be used to generate candidate feasible trajectories for morphing wing aircraft. The new strategy will be tested on a multi-objective morphing wing UAV design and motion planning optimization problem, where the objectives considered represent the minimum time to fly between different flight states. Results are presented demonstrating that the proposed approach can produce optimal morphing wing UAV designs with different maneuverability characteristics.
Improved ECO Algorithm Based on Residual Neural Network

Abstract Video target tracking is one of hot fields of computer vision, and its application is also very extensive. However, due to the complexity and variability of tracking environment, which brings some challenges to the research of target tracking. ECO(Efficient Convolution Operators) Algorithm is proposed based on convolutional neural network in three aspects. Firstly, residual neural network ResNet50 is adopted instead of convolutional neural network to extract the appearance features of target, and deeper residual neural network is applied to obtain more abundant target semantic information, so as to improve the tracking effect of tracking algorithm.Secondly, sample space classification strategy is improved. Different weights are assigned to shallow feature and deep feature, which make the deep feature play a more important role and improve the effect of target tracking. Finally, the method of scale estimation is improved so that better bounding boxes can be estimated with the scale changing. Experimental results show that the distance accuracy and success rate of the algorithm.
CCA secure and efficient proxy re-encryption scheme without bilinear pairing

Proxy re-encryption enables the conversion of ciphertext of a message encrypted with one users public key into a ciphertext that could be decrypted by another users private key. Proxy re-encryption can be used as tool for delegation in wide variety of applications. Since proxy re-encryption is used as a primitive for delegation in varied applications, the primitive should be secure and efficient. Also, depending on application scenario, it should satisfy various properties. The proxy re-encryption scheme without bilinear pairing are preferred, as the bilinear pairing is costly operation. We propose an efficient proxy re-encryption scheme without bilinear pairing. The proposed scheme is unidirectional, key optimal, collusion safe, non-interactive, non-transitive, proxy visible, provides original access and satisfies temporary property. The scheme handles the temporary property elegantly compared to the existing schemes without affecting the signing capability of the delegator. Also, the proxy visible property allows the delegatee to prioritize his responses. The proposed scheme is proved to be secure against the chosen ciphertext attack under random oracle model. The proposed scheme is shown to be efficient than the existing schemes.
Exploring the Relationship Between Topology and Function in Evolved Neural Networks

Understanding the relationship between structure and function in neural networks is essential to explaining their operation. Greater awareness of the link between topology and application could lead to wider adoption, particularly in mission-critical systems. Here, we examine and analyze the topology of very small, minimally sized neurocontrollers that have been evolved for an extended number of generations. Previously demonstrated Lamarckian-inherited neuromodulated evolutionary neurocontrollers are synthesized to operate a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. Constraints in the number of neurons and synapses are used to control network size. Additional objectives are added to the multiobjective optimization algorithm to encourage the selection of neural networks with the fewest neurons and synapses. It is shown that patterns emerge in the neuromodulatory neurons, in the direct connections between neurocontroller inputs and outputs, and that topologies similar to those used in classical control are evolved. Additionally, a neurocontroller constructed from the most commonly occurring neurons that successfully capture the evader is demonstrated.
A Progressive Stack Face-based Network for Detecting Diabetes Mellitus and Breast Cancer

Currently, diabetes mellitus and breast cancer have become more widespread than ever before. Those suffering from these two types of diseases usually need a blood test or biopsy, where both extract fluids or tissues from the human body, which brings pain and a sense of discomfort. With the rise of medical biometrics, it is possible to perform non-invasive detection according to the biometric identifiers from the face of the patients. However, it is still difficult to simultaneously perform disease detection on both diabetes mellitus and breast cancer accurately. To resolve this issue, in this paper, we propose a progressive stack face-based network (PF-Net) to perform multi-class classification on diabetes mellitus, breast cancer, and healthy control using facial information. To perform diagnosis in a progressive way, a latent facial representation is first generated from a stacked sparse autoencoder. Later, the representation is fed into an ensemble layer containing several classifiers. Finally, only the effective classifiers are activated in the classification layer to make the final decision. The experiments showed our proposed method achieved an overall Accuracy of 92.94%, which outperforms a number of classification methods.
Designing Card Game Strategies with Genetic Programming and Monte-Carlo Tree Search: A Case Study of Hearthstone

This paper addresses an agent design problem of a digital collectible card game, Hearthstone, which is a two-player turn-based game. The agent has to play cards based on the game state, the hand cards, and the deck of cards to defeat the opponent. First, we design a rule-based agent by searching for the board evaluation criterion through genetic programming (GP). Then, we integrate the rule-based agent into the Monte-Carlo tree search (MCTS) framework to generate an advanced agent. Performance of the proposed agents are verified by playing against three participants in two recent Hearthstone competitions. Experimental results showed that the GP-agent can beat a simple MCTS agent and the mid-level agent in the competition. The MCTS-GP agent showed competitive performance against the best agents in the competition. We also examine the rule found by GP and observed that GP is able to identify key attributes of game states and to combine them into a useful rule automatically.
Adversarial training for few-shot text classification

In recent years, Deep Learning methods have become very popular in classification tasks for Natural Language Processing (NLP); this is mainly due to their ability to reach high performances by relying on very simple input representations, i.e., raw tokens. One of the drawbacks of deep architectures is the large amount of annotated data required for an effective training. Usually, in Machine Learning this problem is mitigated by the usage of semi-supervised methods or, more recently, by using Transfer Learning, in the context of deep architectures. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs) in the context of Computer Vision. In this paper, we adopt the SS-GAN framework to enable semi-supervised learning in the context of NLP. We demonstrate how an SS-GAN can boost the performances of simple architectures when operating in expressive low-dimensional embeddings; these are derived by combining the unsupervised approximation of linguistic Reproducing Kernel Hilbert Spaces and the so-called Universal Sentence Encoders. We experimentally evaluate the proposed approach over a semantic classification task, i.e., Question Classification, by considering different sizes of training material and different numbers of target classes. By applying such adversarial schema to a simple Multi-Layer Perceptron, a classifier trained over a subset derived from 1% of the original training material achieves 92% of accuracy. Moreover, when considering a complex classification schema, e.g., involving 50 classes, the proposed method outperforms state-of-the-art alternatives such as BERT.
An Experimental Activity to Develop Usability and UX Heuristics

The heuristic evaluation is one of the most used inspection methods to assess the usability of interactive systems and interfaces. It can be performed using generic heuristics, such as Nielsen's heuristics, or domain-specific heuristics, that can detect relevant domain related usability issues. In the mobile scope, more and more applications are starting to emerge with particular characteristics. This paper intends to analyse specific heuristics to evaluate mobile applications that interact with external equipment. The inexistence of such domain-specific heuristics led us to create an experimental activity in order to propose a more suitable list of heuristics, following the methodology proposed by Quiñones et al. to develop usability and UX heuristics for a specific domain. Therefore, this paper contains the output of the application of the first two phases of this methodology, giving particular emphasis to the "experimental phase" analysing and discussing data from different experiments in order to obtain additional information about the chosen domain.
An Analysis of Kinect-Based Human Fall Detection System

Human fall detection system has become one of the most important things especially for indoor environment application. This system has been used in respective areas of elderly care and at child care houses. It helps to detect any human fall and will alert the caretaker about the accident. Kinect sensor can be used to perform the detection due to its capability in scanning and tracking human as well as its affordability. One of the widely used algorithm in human fall detection using Kinect is the skeleton-based method where it works by calculating the distances of every joint with the floor-plane. The joints are detected using the skeleton space coordinate system. When the floor-plane is not visible and the Y-coordinate is less than the given value, a fall is detected. Due to its widely usage, there is a need to study its performance to know the best condition that this algorithm could offer. Performance of selected parameters were observed through a few experiments conducted using Visual Studio as the interface. In this work, a mobile-based Kinect is used due to its mobility and better future implementation for indoor navigation. The best parameter can be identified quantitatively in order to choose the ideal scene that can be used to detect human fall detection using this skeleton-based method. Among the parameters are the distance of the human to the Kinect, the light intensity, the time to track human and the speed of fall. It can be concluded that the most ideal conditions would be at a distance of 3 meters to 3.5 meters with lightings of 1007 lux and of 2 persons at the scene. These conditions can be helpful for others when considering to use the algorithm for human fall detection using Kinect.
Saliency detection via cross-scale deep inference

The small, moderate, and large scale saliency patterns in images are valuable to be extracted in saliency detection. By the observation that the probability of small and large saliency patterns appearing in datasets is lower than that of moderate scale saliency patterns. As results, a deep saliency model trained on such datasets would converge to moderate scale saliency patterns, and it is hard to well infer the small and large scale saliency patterns because they are not encoded efficiently in the model for their low probability. Thus a novel but simple saliency detection method using cross-scale deep inference is presented in this paper. Moreover, a new network architecture, in which the attention mechanism is exploited by multiple layers, is proposed to improve the receptive fields of various scale saliency patterns in different scale images. The presented cross-scale deep inference could improve the representation power of small and large scale saliency patterns encoded in multiple scale images efficiently. The quantitative and qualitative evaluation demonstrates our deep model achieves a promising results across a wide of metrics.
Unmasking Communication Partners: A Low-Cost AI Solution for Digitally Removing Head-Mounted Displays in VR-Based Telepresence

Face-to-face conversation in Virtual Reality (VR) is a challenge when participants wear head-mounted displays (HMD). A significant portion of a participant's face is hidden and facial expressions are difficult to perceive. Past research has shown that high-fidelity face reconstruction with personal avatars in VR is possible under laboratory conditions with high-cost hardware. In this paper, we propose one of the first low-cost systems for this task which uses only open source, free software and affordable hardware. Our approach is to track the user's face underneath the HMD utilizing a Convolutional Neural Network (CNN) and generate corresponding expressions with Generative Adversarial Networks (GAN) for producing RGBD images of the person's face. We use commodity hardware with low-cost extensions such as 3Dprinted mounts and miniature cameras. Our approach learns end-to-end without manual intervention, runs in real time, and can be trained and executed on an ordinary gaming computer. We report evaluation results showing that our low-cost system does not achieve the same fidelity of research prototypes using high-end hardware and closed source software, but it is capable of creating individual facial avatars with personspecific characteristics in movements and expressions.
Invisible Geniuses: Could the Knowledge Frontier Advance Faster?

The advancement of the knowledge frontier is crucial for technological innovation and human progress. Using novel data from the setting of mathematics, this paper establishes two results. First, we document that individuals who demonstrate exceptional talent in their teenage years have an irreplaceable ability to create new ideas over their lifetime, suggesting that talent is a central ingredient in the production of knowledge. Second, such talented individuals born in low- or middle-income countries are systematically less likely to become knowledge producers. Our findings suggest that policies to encourage exceptionally-talented youth to pursue scientific careers–especially those from lower income countries–could accelerate the advancement of the knowledge frontier.
Between the Frames - Evaluation of Various Motion Interpolation Algorithms to Improve 360° Video Quality

With the increasing availability of 360° video content, it becomes important to provide smoothly playing videos of high quality for end users. For this reason, we compare the influence of different Motion Interpolation (MI) algorithms on 360° video quality. After conducting a pre-test with 12 video experts in [3], we found that MI is a useful tool to increase the QoE (Quality of Experience) of omnidirectional videos. As a result of the pretest, we selected three suitable MI algorithms, namely ffmpeg Motion Compensated Interpolation (MCI), Butterflow and Super-SloMo. Subsequently, we interpolated 15 entertaining and realworld omnidirectional videos with a duration of 20 seconds from 30 fps (original framerate) to 90 fps, which is the native refresh rate of the HMD used, the HTC Vive Pro. To assess QoE, we conducted two subjective tests with 24 and 27 participants. In the first test we used a Modified Paired Comparison (M-PC) method, and in the second test the Absolute Category Rating (ACR) approach. In the M-PC test, 45 stimuli were used and in the ACR test 60. Results show that for most of the 360° videos, the interpolated versions obtained significantly higher quality scores than the lower-framerate source videos, validating our hypothesis that motion interpolation can improve the overall video quality for 360° video. As expected, it was observed that the relative comparisons in the M-PC test result in larger differences in terms of quality. Generally, the ACR method lead to similar results, while reflecting a more realistic viewing situation. In addition, we compared the different MI algorithms and can conclude that with sufficient available computing power Super-SloMo should be preferred for interpolation of omnidirectional videos, while MCI also shows a good performance.
Functional gaze prediction in egocentric video

Streaming 360° videos to a head-mounted display (HMD) client is challenging due to their high network resource consumption and computational load. This is due to the use of gaze point prediction or image saliency features from the field of view (FoV) since, in real-time scenarios, FoV extraction is computationally demanding. We propose a functional gaze prediction system that addresses these issues by relying on a tiling scheme for gaze prediction. We condition gaze point prediction on virtual reality (VR) content and long short-term memory (LSTM)-encoded eye movement history. Further, we encode image flow and saliency maps of RGB images via VGG16, using a convolutional neural network (CNN). Future gaze points are then predicted using a novel sinusoidal encoding technique. In experiments, our tile-based approach outperforms state-of-the-art FoV-based schemes in terms of computational load and predicted gaze position.
Statistical Boosting: A Preprocessing Technique to Enhance Performance of Machine Learning and Deep Learning Models on Partially Occluded Traffic Signs

Computer vision based traffic sign recognition is an active field of research with numerous applications such as (1) autonomous driving (2) driver assistance and (3) automatic inventory management. In some cases the traffic signs are degraded due to aging or some portion is suppressed by another nearby object like a tree or a pole etc. In order to facilitate recognition of such signs using feature extraction or convolutional neural networks based techniques, we propose a preprocessing statistical boosting layer. This scheme helps enhancing visible portions and suppresses the occluded parts which consequently makes extracting/learning invariant features more probable. Since there is no benchmark available for occluded and degraded traffic signs, we develop two datasets containing samples from German Traffic Sign Recognition Benchmark (GTSRB) and self collected/generated images. Experimental results show that with the addition of our proposed statistical boosting layer the performance of famous recognition algorithms can be significantly enhanced.
Deep Positional Attention-based Bidirectional RNN with 3D Convolutional Video Descriptors for Human Action Recognition

Abstract This article presents the Joints and Trajectory-pooled 3D-Deep Positional Attention-based Bidirectional Recurrent convolutional Descriptors (JTPADBRD) for recognizing the human activities from video sequences. At first, the video is partitioned into clips and these clips are given as input of a two-stream Convolutional 3D (C3D) network in which the attention stream is used for extracting the body joints locations and the feature stream is used for extracting the trajectory points including spatiotemporal features. Then, the extracted features of each clip is needed to aggregate for creating the video descriptor. Therefore, the pooled feature vectors in all the clips within the video sequence are aggregated to a video descriptor. This aggregation is performed by using the PABRNN that concatenates all the pooled feature vectors related to the body joints and trajectory points in a single frame. Thus, the convolutional feature vector representations of all the clips belonging to one video sequence are aggregated to be a descriptor of the video using Recurrent Neural Network (RNN)-based pooling. Besides, these two streams are multiplied with the bilinear product and end-to-end trainable via class labels. Further, the activations of fully connected layers and their spatiotemporal variances are aggregated to create the final video descriptor. Then, these video descriptors are given to the Support Vector Machine (SVM) for recognizing the human behaviors in videos. At last, the experimental outcomes exhibit the considerable improvement in Recognition Accuracy (RA) of the JTDPABRD is approximately 99.4% achieved on the Penn Action dataset as compared to the existing methods.
Incremental Data-Driven Topology Learning for Time-Varying Graph Signals

In this paper the topology learning for time-varying graph signals with incremental data is studied. In order to learn the topology which is slowly time-varying during data collection, we separate the data of observation into multiple groups by time, and for each group we model the topology learning as a sparse optimization problem, in which the penalty function is designed to consider both the incremental data and previous topology information for graph learning. Moreover, a correction function for dynamic topology is developed by considering a priori information of topology changes. Based on that, by solving the optimization problem we then propose a dynamic topology learning and tracking algorithm to learn as well as track the varying graph topology. Simulations on synthetic and real-world dataset are performed to reveal the performance gain of the proposed algorithm.