Reviewerly/exHarmony huggingface dataset:
Full-reference IPTV image quality assessment by deeply learning structural cues

Image quality assessment (IQA) attempts to quantify the quality-aware visual attributes perceived by humans. They can be divided into subjective and objective image quality assessment. Subjective IQA algorithms rely on human judgment of image quality, where the human visual perception functions as the dominant factor However, they cannot be widely applied in practice due to the heavy reliance on different individuals. Motivated by the fact that objective IQA largely depends on image structural information, we propose a structural cues-based full-reference IPTV IQA algorithm. More specifically, we first design a grid-based object detection module to extract multiple structural information from both the reference IPTV image (i.e., video frame) and the test one. Afterwards, we propose a structure-preserved deep neural networks to generate the deep representation for each IPTV image. Subsequently, a new distance metric is proposed to measure the similarity between the reference image and the evaluated image. A test IPV image with a small calculated distance is considered as a high quality one. Comprehensive comparative study with the state-of-the-art IQA algorithms have shown that our method is accurate and robust.
Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale.Its use began with processing items baskets to learn patterns and have a directed marketing strategy but it is generalized to telecommunication alarms management with several works.Our work is in line with this, as it tries to locate patterns and identify them to make predictive statements about certain patterns.It is axed around providing a way to break sequences into episodes and assigning them a value of confidence and support, more precisely in the discovery of frequent patterns of episodes within a time window.Experimental results have shown the effectiveness of our sequential pattern mining approach and its adaptability to alarm management and analytics.
Breathin

New interaction modalities in human computer interaction often explore common sensory inputs including touch, voice, gesture or motion. However, these modalities are not inclusive of the entire population type, and cannot be utilized by a group of people who suffer from any limitation of that sensory input. Here we propose BreathIn: an interface tool for enabling interaction with computer applications by using discreet exhalation patterns. The intent is that such patterns can be issued by anyone who can breathe. Our concept is based on detecting a user's forced exhalation patterns in a time duration using a MEMS microphone placed below the user's nose. We breakdown the signal into FFT components and identify peak frequencies for forced voluntary "breath events" and use that in real-time to distinguish between "exhalation events" and noise. We show two major applications of such an interaction tool: a) adaptation of computer applications using breath, b) using the breath interface as a discreet, emergency signal for prospective victims of crime.
XD-STOD: Cross-Domain Superresolution for Tiny Object Detection

Monitoring the restoration of natural habitats after human intervention is an important task in the field of remote sensing. Currently, this requires extensive field studies entailing considerable costs. Unmanned Aerial vehicles (UAVs, a.k.a. drones) have the potential to reduce these costs, but generate immense amounts of data which have to be evaluated automatically with special techniques. Especially the automated detection of tree seedlings poses a big challenge, as their size and shape vary greatly across images. In addition, there is a tradeoff between different flying altitudes. Given the same camera equipment, a lower flying altitude achieves higher resolution images and thus, achieving high detection rates is easier. However, the imagery will only cover a limited area. On the other hand, flying at larger altitudes, allows for covering larger areas, but makes seedling detection more challenging due to the coarser images. In this paper we investigate the usability of super resolution (SR) networks for the case that we can collect a large amount of coarse imagery on higher flying altitudes, but only a small amount of high resolution images from lower flying altitudes. We use a collection of high-resolution images taken by a drone at 5m altitude. After training the SR models on these data, we evaluate their applicability to low quality images taken at 30m altitude (in-domain). In addition, we investigate and compare whether approaches trained on a highly diverse large data sets can be transferred to these data (cross-domain). We also evaluate the usability of the SR results based on their influence on the detection rate of different object detectors. We found that the features acquired from training on standard SR data sets are transferable to the drone footage. Furthermore, we demonstrate that the detection rate of common object detectors can be improved by SR techniques using both settings, in-domain and cross-domain.
Automatic method for Recognition of Colombian Sign Language for vowels and numbers from zero to five by using SVM and KNN

This paper presents a technique for the classification of images and recognition of vowels and numbers from 0 to 5 of Colombian Sign Language (CSL). This work contains six stages: Data set construction, pre-processing, feature extraction, sampling, classification and reporting result. The classification stage is done by using Support Vector Machines (SVM) with Kernel RBF and K-Nearest Neighbor (KNN), after applying cross-validation of 5-folds and the data is divided with different percentages of training set and test set. With the dataset and the sampling of 80%-20% the best results were for SVM with precision performance measures, Recall and F1-Score was obtained 70%, 69%, 69% respectively.
Filterbank Analysis of MFCC Feature Extraction in Robust Children Speech Recognition

This paper focused on the issue of robustness in children speech recognition system. The shape of filterbank analysis is presented in this study to suppress the additive background noise in acoustic features of children speakers. In addition, the Linear Discriminant Analysis (LDA), the Maximum Likelihood Linear Transform (MLLT) and feature space Maximum Likelihood Linear Regression (fMLLR) features are applied to build the speaker adaptive acoustic model with the help of Kaldi speech recognition toolkit. The performance of Gammatone filterbank and Bark-scale filterbank based Cepstral features were evaluated under contaminated situations using five different types of noise at a range of signal to noise ratio (SNR) 10dB to −10dB. As the detailed analysis shown, the performance of Gammatone frequency integration is superior to Mel Frequency Cepstral Coefficient (MFCC) in different types of additive background noise and various SNR situations.
An Image Compression Processing Method Based On Deep Learning

In recent years, the combination of neural network and deep learning has made outstanding achievements in image compression, recognition and other fields. However, the study found that when using a given generative adversarial network (GAN) compression model, the color image compression performance is better than the gray image. In order to improve the compression effect of gray image, a post-processing method is proposed in this paper. The main idea is to process each component of the image generated by the compression model, so as to reduce the influence of different weights of training parameters on the generated image. The method is divided into two cases. One is equal weight processing of component coefficients, which means that the generated component coefficients are all 1/3. The peak signal to noise ratio (PSNR) of images can be improved within the range of 0.10-0.41dB, and the average can be improved by 0.21dB. The other is the unequal weight processing of component coefficients. After data fitting, it is concluded that when the component coefficients are 0.08, 0.49 and 0.35 respectively, the PSNR can be increased within the range of 0.15-1.1dB, and the average can be increased by 0.55dB. Therefore, this method can effectively improve the compression quality of gray image.
A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user’s computing demands. Customers of such services would like to adjust the utilization policy promptly by considering the charge of the service, but an instantaneous response is not possible as it takes several hours or even a couple of days for cloud service providers to inform the billing information. In this article, we present an instant cost estimation model for estimating the cost of public cloud resources. Specifically, our model estimates the cost of IaaS by monitoring the usage of resources on behalf of virtual machine instances. As this is performed by generating a user-side metering daemon, it is very precise and thus similar to the resource usage evaluated by the cloud service provider. To validate our model, we run PC laboratory services for 50 students in two classes by making use of a public cloud during a semester. Experimental results show that the accuracy of our model is over 99.3% in comparison with the actual charge of the public cloud.
Detection and Removal of RainDrop from Images Using DeepLearning

Dynamic climatic conditions like rain, affects the performance of vision algorithms which are used for surveillance and analysis tasks. Removal of rain-drops is challenging for single image as the rain drops affect the entire image and makes it difficult to identify the background affected by the rain. Thus, removal of rain from still pictures is a complex and challenging task. The rain drops affect the visibility and clarity of the image which makes it difficult to read and analyze the information present in the image. In this paper, we identified and restored the rain drop affected regions using a deep learning architecture.
qCUDA-ARM: Virtualization for Embedded GPU Architectures

The emergence of Internet of Things (IOT) is changing the ways of computing resources acquisition, from centralized cloud data centers to distributed pervasive edge nodes. To cope the small amount of diversity problem for IOT devices and applications, two research trends are investigated for the system design of edge nodes: heterogeneity and virtualization. In this paper, we consider the integration of those two important trends and present a virtualization system for embedded GPU architectures, called qCUDA-ARM. The design of qCUDA-ARM is based on the framework of qCUDA, a virtualization system for x86 servers. Because of the architectural differences between x86 servers and ARM based embedded systems, many subsystems of qCUDA-ARM, such as memory management, need to be redesigned. We evaluated the performance of qCUDA-ARM with three CUDA benchmarks and two real world applications. For computational intensive jobs, qCUDA-ARM can reach similar performance of the native system; and for memory bound programs, qCUDA-ARM can also have up to 90% performance of that of the native one.
On the Bounded Distance Decoding Problem for Lattices Constructed and Their Cryptographic Applications

In this paper, we propose new classes of trapdoor functions to solve the bounded distance decoding problem in lattices. Specifically, we construct lattices based on properties of polynomials for which the bounded distance decoding problem is hard to solve unless some trapdoor information is revealed. We thoroughly analyze the security of our proposed functions using state-of-the-art attacks and results on lattice reductions. Finally, we describe how our functions can be used to design quantum-safe encryption schemes with reasonable public key sizes. Our encryption schemes are efficient with respect to key generation, encryption and decryption.
Speaker Verification in Emotional Talking Environments based on Third-Order Circular Suprasegmental Hidden Markov Model

Speaker verification accuracy in emotional talking environments is not high as it is in neutral ones. This work aims at accepting or rejecting the claimed speaker using his/her voice in emotional environments based on the “Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3)” as a classifier. An Emirati-accented (Arabic) speech database with “Mel-Frequency Cepstral Coefficients” as the extracted features has been used to evaluate our work. Our results demonstrate that speaker verification accuracy based on CSPHMM3 is greater than that based on the “state-of-the-art classifiers and models such as Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ)".
Explaining Single Predictions: A Faster Method

Machine learning has proven increasingly essential in many fields. Yet, a lot obstacles still hinder its use by non-experts. The lack of trust in the results obtained is foremost among them, and has inspired several explanatory approaches in the literature. In this paper, we are investigating the domain of single prediction explanation. This is performed by providing the user a detailed explanation of the attribute’s influence on each single predicted instance, related to a particular machine learning model. A lot of possible explanation methods have been developed recently. Although, these approaches often require an important computation time in order to be efficient. That is why we are investigating about new proposals of explanation methods, aiming to increase time performances, for a small loss in accuracy.
A Low-Cost Drift-Free Optical-Inertial Hybrid Motion Capture System for High-Precision Human Pose Detection

This paper proposes a feasible and low-cost solution for designing optical-inertial hybrid motion capture systems by integrating two subsystems: the inertial subsystem and the optical subsystem. We calibrate the relative pose of the optical sensor module and the human joint to establish the transformation between the joint parameters and the optical sensor data, so that the data fusion can be performed under the same reference frame. To construct the above motion capture system, an optical-inertial data fusion scheme is proposed, which leverages the optical data of key nodes of the human body to correct the inertial data with accumulated errors. The proposed system can support long-term drift-free operation with a low-cost hardware setup, avoiding the drift problem of the inertial motion capture system. Simulation experiments have shown that optical data can reduce the drift error to the centimeter level. The practical experiment shows that the virtual character can complete the loop detection after the inertial position is corrected by the optical data. Our motion capture system outputs standard motion capture data, which can be directly imported into engines such as Unity3D and Unreal.
Meta-Learning for Avatar Kinematics Reconstruction in Virtual Reality Rehabilitation

Virtual Reality (VR) sensorimotor rehabilitation is still in infancy but will soon require avatars, digital alter-egos of patients' physical selves. Such embodied interfaces could stimulate patients' perception in a rich and highly customized environment, where sensorimotor deficits, such as in Chemotherapy-Induced Peripheral Neuropathy, could be corrected. In such scenarios, motion prediction is a key ingredient for realistic immersion. Yet, such a task lives under hard processing latency constraints and the inherent variability of human motion. We propose a neural network meta-learning system exploiting the underlying correlations in body kinematics with potential to provide, within latency guarantees, personalized VR rehabilitation. The unsupervised meta-learner is able to extract underlying statistics of the motion data by exploiting data regularities in order to describe the underlying manifold, or structure, of motion under sensorimotor deficits. We demonstrate, through preliminary experiments the potential of such a learning system for adaptive kinematics estimation in personalized rehabilitation VR avatars.
Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.
Appraisal on User’s Comprehension in Security Warning Dialogs: Browsers Usability Perspective

End-user encounters security warnings on a daily basis in different web browsers. Effective security warning is critical to provide a secure environment to end-users to against attack. However, users always encounter problems and challenges when they encounter security warnings due to the poor warning dialogue interface. Element used in the warning interface is important to support users to make an effective decision. A poor warning design will lead a user to become a fraud victim. Hence, there is a need to design an effective warning dialogue by providing useful security features. Although the efficacy of security warning is depending on the interface of security feature, but also highly dependent on the user’s perception and understanding. This paper determine to investigates further from the end-user’s experience whilst encountering security warnings (i.e. Chrome browser context). An exploratory interview study with 65 participants was conducted to pursue in-depth information about the perceptiveness of users towards current security warnings in three different scenarios. The results show that elements such as icon, colour, wording used in the warning can impact the efficacy of the warning. All the user feedback indicated that there is still room for improvement on the current security warning.
On Unitary t-Designs from Relaxed Seeds

The capacity to randomly pick a unitary across the whole unitary group is a powerful tool across physics and quantum information. A unitary t-design is designed to tackle this challenge in an efficient way, yet constructions to date rely on heavy constraints. In particular, they are composed of ensembles of unitaries which, for technical reasons, must contain inverses and whose entries are algebraic. In this work, we reduce the requirements for generating an ε -approximate unitary t-design. To do so, we first construct a specific n-qubit random quantum circuit composed of a sequence of randomly chosen 2-qubit gates, chosen from a set of unitaries which is approximately universal on U ( 4 ) , yet need not contain unitaries and their inverses nor are in general composed of unitaries whose entries are algebraic; dubbed r e l a x e d seed. We then show that this relaxed seed, when used as a basis for our construction, gives rise to an ε -approximate unitary t-design efficiently, where the depth of our random circuit scales as p o l y ( n , t , l o g ( 1 / ε ) ) , thereby overcoming the two requirements which limited previous constructions. We suspect the result found here is not optimal and can be improved; particularly because the number of gates in the relaxed seeds introduced here grows with n and t. We conjecture that constant sized seeds such as those which are usually present in the literature are sufficient.
Jily: Cost-Aware AutoScaling of Heterogeneous GPU for DNN Inference in Public Cloud

Recently, a large number of DNN inference services have emerged in public clouds, making the low-cost deployment of DNN inference services a hot research topic. Previous studies have failed to take into account GPU heterogeneity and batch processing, both of which will seriously affect the financial cost as well as the latency. In this paper, we study the problem of DNN inference service deployment in public cloud, considering both GPU heterogeneity and batch processing. The goal is to minimize the financial costs under the constraint of latency. We propose Jily, an autoscaling scheduler for DNN inference services to minimize the cost while satisfying the given latency SLO. Jily finds the optimal heterogeneous GPU instance provisioning through a DNN inference model profiler, a latency estimator, a workload predictor and a cost-aware scaler. Simulation results demonstrate that Jily can reduce average cost by up to 28% compared to a state-of-the-art autoscaling approach. Further, Jily has been proved to have good versatility and robustness under different batching mechanisms and latency SLO constrains.
AVADHAN

This paper presents the Question Answering (QA) system for a low resource language like 'Telugu' named 'AVADHAN'. This work started with preparing a pre-tagged data set for Telugu Question Classification (QC). We also explained the ambiguities and complexities involved in the data set. AVADHAN exhibits the comparisons between Support Vector Machine (SVM), Logistic Regression (LR) and Multi-Layer Perceptron (MLP) classifiers for achieving the plausible answers. After performing various experiments the overall accuracies obtained, for both 'exact match' and 'partial match' based approaches, were for SVM (31.6%, 68.5%), LR (31%, 66.6%) and for MLP (30%, 67%) respectively.
Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.