Reviewerly/exHarmony huggingface dataset:
Adaptive Fine Distortion Correction Method for Stereo Images of Skin Acquired with a Mobile Phone

With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images.
Point-of-Interest Recommendation based on Geographical Influence and Extended Pairwise Ranking

In recent years, recommendation based on explicit feedback data has been extensively studied. However, in the field of Point-of-Interest (POI) recommendation, check-in information is usually implicit feedback, that is, we can only observe positive data where users interact with POIs. The lack of negative samples brings difficulties to the research of POI recommendation. Although there have been recently studies converted the rating prediction into the POIs ranking by constructing pairwise preference assumption, they only consider the optimization of the ranking of one POI pair, which the value of negative data is underutilized. In addition, the geographical influence has not been fully utilized. Hence, we propose a recommendation model based on geographic influence and extended pairwise ranking (GIEPR). Extensive empirical studies on two publicly available datasets show that our method performs significantly better than state-of-the-art methods for POI recommendation.
A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method

Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.
Specialized Image Descriptors for Signboard Photographs Classification

We propose several types of advertising sign photo descriptors that are useful for images classification. We also collected a dataset of commercial building facade photographs grouped by a type of provided services in order to perform comparison between different classification methods. Finally we performed comparison between methods based on the proposed descriptor usage and combined advertising sign classifier and obtained better performance using our system.
Turkish Sentiment Analysis System via Ensemble Learning

Nowadays, sentiment analysis (SA) also known as opinion mining (OM) is widely used and has an impressive effect in many fields, such as marketing, politics, and even, company's products are now adjusted based on users' opinions. In this paper, a new efficient sentiment analysis system that supports the Turkish language has been introduced. In addition, as Turkish is an agglutinative language, which requires special processing, an efficient preprocessing model was also implemented as a part of the developed system. Several experiments using the challenging and benchmark "The Turkish movie reviews" dataset have been conducted, and it is obvious that the constructed approach can efficiently support the Turkish language and can achieve a quite good performance.
Data Clustering Using Environmental Adaptation Method

Extracting useful information from a large-scale dataset and transforming it into the required structure for further use is termed data mining. One of the most important techniques of data mining is data clustering that is responsible for grouping the data into meaningful groups (clusters). Environmental Adaptation Method (EAM) is an optimization algorithm that has already proved its efficacy in solving global optimization problems. In this paper, an approach based on a new version of EAM has been suggested for solving the data clustering problem. To validate the utility of the suggested approach, four recently developed metaheuristics have been implemented and compared for six standard benchmark datasets. Various comparative performance analysis based on experimental values have justified the competitiveness and effectiveness of the suggested approach.
Sparse Representation Based Super-Resolution of MRI Images with Non-Local Total Variation Regularization

Diffusion-weighted and Spectroscopic MR images are found to be very helpful for diagnostic purposes as they provide complementary information to that provided by conventional MRI. These images are also acquired at a faster rate, but with low signal-to-noise ratio. This limitation can be overcome by applying image super-resolution techniques. In this paper, we propose a single-image super-resolution (SISR) technique via sparse representation for diffusion-weighted (DW) and spectroscopic MR (MRS) images. It is based on non-local total variation approach to regularize an ill-posed inverse problem of SISR. Experiments are conducted for both DW and MRS test images and the results are compared with other recent regularization-based methods using sparse representation. The comparison also validates the potential of the proposed method for clinical applications.
Speckle Methods in Experimental Mechanics

Speckle techniques are a class of whole-field methods that offer sensitivities ranging between those obtainable by moire methods and those of hologram interferometry. Additional advantages accrue, and hence these techniques have found a number of applications in experimental mechanics. A diffuse object illuminated by a coherent beam of radiation produces a grainy structure in space. This grainy structure is called the speckle pattern. The grains (speckles) in the pattern undergo changes in both position and irradiance when the generating surface undergoes changes. Therefore speckle techniques can be basically grouped under two heads: (1) those based on the positional shift of the speckles, and (2) those based on the irradiance changes of the speckles [1]. Speckle photography includes all those methods where positional changes of speckles are monitored, Whereas speckle interferometry includes methods that are based on the measurement of phase changes and hence irradiance changes. If instead of phase change we measure its gradient, the technique is one of speckle shear interferometry. Both speckle interferometry and speckle shear interferometry can be performed using electronic detection, and the technique is broadly known as electronic speckle pattern interferometry (ESPI) [2].
Anonymization: Securing privacy in IoT

The convergence of object automation with the association of internet system has become very communal in our routine life. At home, health centres, industries etc. each field has internet-related control systems installed to support organization’s proper execution, thereby, increasing the efficiency and saving time. Beside multi-fold benefits, IoT has brought a plethora of privacy risks and security issues. To maintain the privacy of both, the user and the objects many counter measures are carried out and techniques have been found to address illegitimate access and minimize their effect on private data, communication, location etc. The paper focuses on the IoT threats related to individual’s identity and the various techniques like anonymization to preserve and protect individual’s identity.
A new high-precision sphere-fitting method with small segment angles

Abstract Measuring a spherical surface with small segment angle (e.g. 1°) precisely is a big challenge in scientific research and engineering applications. To fit sphere surfaces with small segment angles with high precision, a five-parameter constrained nonlinear least square fitting (CNLSF) method is presented in this work, where an Adam optimization algorithm with a prominent anti-interference ability is employed. We discuss various factors which influence the final uncertainty evaluation of the sphere’s radius. A Monte Carlo simulation is conducted by superimposing Gaussian noise on the ideal spherical data to simulate practical instrument errors, then the relationship between the fitting bias of the sphere’s radius and noise the level, the number of sampling points and the segment angle is investigated by comparing the performance of the linear least square fitting algorithm, the nonlinear least square fitting algorithm and the CNLSF algorithm. Finally, the simulated and experimental results show that the CNLSF method performs more precisely and robustly than the other algorithms, even when the corresponding segment angle approaches 1°, which illustrates that it is a promising and practical method in the real measurement world.
Security of practical quantum key distribution with weak-randomness basis selection

The security of the perfect quantum key distribution protocol can be guaranteed by applying the fundamental laws of quantum mechanics, but the practical quantum key distribution system may be attacked by utilizing imperfect state preparation and measurement devices. In this work, we analyze the security of the practical quantum key distribution system with weak-randomness basis selection, where the basis selection may be partly controlled by the eavesdropper due to the imperfect quantum devices. We propose the parameter $\ensuremath{\epsilon}$ to quantify the deviation value between the practical basis selection probability and the perfect basis selection probability, and the secret key rate of the practical quantum key distribution system is obtained under different $\ensuremath{\epsilon}$ values. By applying the practical experimental parameters, the analysis result demonstrates that the maximal transmission distance will be reduced from 142 to 139 km if $\ensuremath{\epsilon}\ensuremath{\le}0.1$, but no secret key can be generated in the case of $\ensuremath{\epsilon}\ensuremath{\ge}0.34$.
Effective Metaheuristic Algorithms for Bag-of-Tasks Scheduling Problems Under Budget Constraints on Hybrid Clouds

Cloud computing is an ideal platform for executing bag-of-task (BoT) applications due to its capability of delivering high-quality and pay-per-use computing services. This paper presents a family of genetic algorithm (GA)-based metaheuristics for scheduling the tasks of data-intensive BoT applications on hybrid clouds. The scheduling objective is to minimize the flowtime of BoT applications under a specified budget constraint. We take into account the impact of communication time and communication cost to formulate the optimization model for the data-intensive BoT scheduling problem. By using a task sequence to represent the scheduling solution, the proposed algorithms start with using a low-complexity strategy to generate an initial solution. The generated initial solution is identified as the best chromosome in the initial population of GA framework. We improve the standard crossover operator in GA’s evolutionary procedure by incorporating a probabilistic model. In addition, we design an efficient task dispatching method to evaluate the scheduling quality of each chromosome. Built upon the improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the BoT scheduling problem considered in this work. Extensive experiments are performed to verify the performance of the proposed algorithms in scheduling data-intensive BoT applications.
Deep K-Means: A Simple and Effective Method for Data Clustering

Clustering is one of the most fundamental techniques in statistic and machine learning. Due to the simplicity and efficiency, the most frequently used clustering method is the k-means algorithm. In the past decades, k-means and its various extensions have been proposed and successfully applied in data mining practical problems. However, previous clustering methods are typically designed in a single layer formulation. Thus the mapping between the low-dimensional representation obtained by these methods and the original data may contain rather complex hierarchical information. In this paper, a novel deep k-means model is proposed to learn such hidden representations with respect to different implicit lower-level characteristics. By utilizing the deep structure to conduct k-means hierarchically, the hierarchical semantics of data is learned in a layerwise way. The data points from same class are gathered closer layer by layer, which is beneficial for the subsequent learning task. Experiments on benchmark data sets are performed to illustrate the effectiveness of our method.
Development of Auto-parking and Collision Avoidance Algorithms on Car type Autonomous Mobile Robots

This paper discusses the development and implementation of a motion planning algorithm for a Nonholonomic Car type Wheeled Mobile Robot (WMR) through a specified via point. This technique will prove useful specifically; when the WMR needs to avoid obstacles which are present on the predefined trajectory and when the WMR is to be parked autonomously in the parking slot. A via-point is specified by the user and a trajectory passing through this via-point is generated using the property of differential flatness. Local optimization is employed for optimal trajectory planning. This generated trajectory is always smooth with feasible minimum length satisfying the nonholonomic and holonomic constraints on the car type vehicle and passes through the via-point specified by the user. The contribution of the work lies in reducing complexity of the problem by introducing via-point and local optimization, which results into easy implementation of the algorithm on the hardware setup. The experimentation is carried out on 1:10 reduced model of Cheverolet Camero model to show efficacy of the proposed scheme.
Removing human players from the loop: AI-assisted assessment of Gaming QoE

Quality of Experience (QoE) assessment for video games is known for being a heavy-weight process, typically requiring the active involvement of several human players and bringing limited transferability across games. Clearly, to some extent, QoE is correlated with the achieved in-game score, as player frustration will arise whenever realized performance is far from what is expected due to conditions beyond player control such as network congestion in the increasingly prevalent case of networked games. To disrupt the status quo, we propose to remove human players from the loop and instead exploit Deep Reinforcement Learning (DRL) agents to play games under varying network conditions. We apply our framework to a set of Atari games with different types of interaction, showing that the score degradation observed with DRL agents can be exploited in networking devices (e.g., by prioritizing scheduling decisions), reinforcing fairness across games, and thus enhancing the overall quality of gaming experience.
Rule-based anomaly detection for railway signalling networks

We propose a rule-based anomaly detection system for railway signalling that mitigates attacks by a Dolev-Yao attacker who is able to inject control commands to perform semantic attacks by issuing licit but mistimed control messages. The system as well mitigates the effects of a signal box compromised by an attacker with the same effect. We consider an attacker that could cause train derailments and collisions, if our countermeasure is not employed. We apply safety principles of railway operation to create a distributed anomaly detection system that inspects incoming commands on the signals and points. The proposed anomaly detection system detects mistimed control messages against light signals, points and train detection systems that lead to derailments and collisions without producing false positives, while it requires only a small amount of overhead in terms of network communication and latency compared to normal train operation.
CRLEDD: Regularized Causalities Learning for Early Detection of Diseases Using Electronic Health Record (EHR) Data

The availability of Electronic Health Records (EHR) in health care settings has provided tremendous opportunities for early disease detection. While many supervised learning models have been adopted for EHR-based disease early detection, the ill-posed inverse problem in the parameter learning has imposed a significant challenge on improving the accuracy of these algorithms. In this paper, we propose CRLEDD - Causality-Regularized Learning for Early Detection of Disease, an algorithm to improve the performance of Linear Discriminant Analysis (LDA) on top of diagnosis-frequency vector data representation. While most existing regularization methods exploit sparsity regularization to improve detection performance, CRLEDD provides a unique perspective by ensuring positive semi-definiteness of the sparsified precision matrix used in LDA which is different from the regular regularization method (e.g., L2 regularization). To achieve this goal, CRLEDD employs Graphical Lasso to estimate the precision matrix in the ill-posed settings for enhanced accuracy of LDA classifiers. We perform extensive evaluation of CRLEDD using a large-scale real-world EHR dataset to predict mental health disorders (e.g., depression and anxiety) of college students from 10 universities in the U.S. We compare CRLEDD with other regularized LDA and downstream classifiers. The result shows that CRLEDD outperforms all baselines in terms of accuracy and F1 scores.
Kalman Filter Based on Multiple Scaled Multivariate Skew Normal Variance Mean Mixture Distributions With Application to Target Tracking

In this brief, we first propose a multiple scaled multivariate skew normal variance-mean mixture (MSMSNVMM) distribution to model heavy-tailed and/or skew measurement noises (HTSMN) whose each dimension has different tail and skewness behaviors. The MSMSNVMM distribution has more flexible tail behaviors and richer skewness features than Gaussian scale mixture (GScM) distribution, generalized Gaussian scale mixture (GGScM) distribution and scale mixtures of skew normal (SMSN) distribution. Furthermore, we derive a robust Kalman filter based on variational Bayesian (VB) method. The superiority of the new filter is demonstrated in a maneuvering target tracking example.
Operational and Economic Feasibility Area Estimation for Peer-to-Peer Consortium of Storage Systems in a Blockchain Framework

Peer-to-peer (P2P) energy trading in a blockchain framework has revealed an imminent potential to complement the conventional trading mechanisms. To that end, this article proposes a new framework for the economic consortium of energy storage systems. A new formulation is presented that would create operational and economic feasibility areas for choosing feasible bids traded in a blockchain framework. The former indicates the area in which the request for the energy trade is within the operational/physical limitations of the energy trader, while the latter is related to the area where the operation of the trader is economically profitable. Although the formulation provided for the feasibility areas is generic and can be utilized as a tool in various P2P applications, it is tailored in this article for energy storage scheduling. Using the proposed formulation for feasibility areas, the concept of feasibility area estimator is presented. The estimator is, then, integrated with the optimal scheduling model in order to capture operationally and economically viable bids traded in a blockchain system. Through numerical results, the efficacy and validity of the proposed model in this article are evaluated. It is demonstrated that different characteristics can be created for filtering the feasible bids to be utilized for storage scheduling. It is also shown that storage can effectively respond to and operate based on such feasible bids. Storage can exploit the arbitrage opportunities and trade energy with other peers to maximize its revenue.
CooLSM: Distributed and Cooperative Indexing Across Edge and Cloud Machines

We tackle one of the fundamental data management challenges in edge-cloud computing, the problem of data indexing. We propose Cooperative LSM (CooLSM), a distributed Log-Structured Merge Tree that is designed to overcome the unique challenges of edge-cloud indexing such as machine and workload heterogeneity and the communication latency asymmetry between the edge and the cloud. To tackle these challenges, CooLSM deconstructs the LSM tree [23] into its basic parts. This deconstruction allows a better distribution and placement of resources across edge and cloud devices. For example, append-specific functionality is managed at the edge to ensure appending and serving data in real-time, whereas resource-intensive operations such as compaction and querying is managed at the cloud where more compute resources are available.
Declarative and Mathematical Programming approaches to Decision Support Systems for food recycling

Every year about one third of the food production intended for humans gets lost or wasted.This wastefulness of resources leads to the emission of unnecessary greenhouse gas, contributing to global warming and climate change.The solution proposed by the SORT project is to "recycle" the surplus of food by reconditioning it into animal feed or fuel for biogas/biomass power plants.In order to maximize the earnings and minimize the costs, several choices must be made during the reconditioning process.Given the extremely complex nature of the process, Decision Support Systems (DSSs) could be helpful to reduce the human effort in decision making.In this paper, we present a DSS for food recycling developed using two approaches for finding the optimal solution: one based on Binary Linear Programming (BLP) and the other based on Answer Set Programming (ASP), which outperform our previous approach based on Constraint Logic Programming (CLP) on Finite Domains (CLP(FD)).In particular, the BLP and the CLP(FD) approaches are developed in ECL i PS e , a Prolog system that interfaces with various state-of-the-art Mathematical and Constraint Programming solvers.The ASP approach, instead, is developed in clingo.The three approaches are compared on several synthetic datasets that simulate the operative conditions of the DSS.