Miracle-dz/UniFAR-FTUs huggingface dataset:
Attenuating Crosstalk Noise of Simultaneous-Source Least-Squares Reverse Time Migration With GPU-Based Excitation Amplitude Imaging Condition

Least-squares reverse time migration (LSRTM) can provide higher quality images than conventional reverse time migration, which is helpful to image simultaneous-source data. However, it still faces the problems of the crosstalk noise, great computation time, and storage requirement. We propose a new LSRTM approach by using the excitation amplitude (EA) imaging condition to suppress the crosstalk noise. Since only the maximum amplitude or limited local maximum amplitudes at each imaging point and the corresponding travel time step(s) need to be saved, the great storage problem can be naturally solved. Consequently, the proposed algorithm can avoid the frequent memory transfer and is suitable for the graphics processing unit (GPU) parallelization. Besides, the shared memory with high bandwidth is used to optimize the GPU-based algorithm. In order to further improve the image quality of EA imaging condition, we adopt the shaping regularization as a constraint. The single-source tests with Marmousi and salt models show the feasibility of our algorithm to image the complex and subsalt structures, among which a wrong background velocity is used to test its sensitivity to the velocity error. The noise-free and noise-included simultaneous-source examples demonstrate the ability of EA imaging condition to suppress the crosstalk noise. During the implementation of the GPU parallelization, we find that the shared memory cannot always optimize the GPU parallel algorithm and just works well for the eighth- or higher order spatial finite difference scheme.
Discriminative Graph Autoencoder

With the abundance of graph-structured data in various applications, graph representation learning has become an effective computational tool for seeking informative vector representations for graphs. Traditional graph kernel approaches are usually frequency-based. Each dimension of a learned vector representation for a graph is the frequency of a certain type of substructure. They encounter high computational cost for counting the occurrence of predefined substructures. The learned vector representations are very sparse, which prohibit the use of inner products. Moreover, the learned vector representations are not in a smooth space since the values can only be integers. The state-of-the-art approaches tackle the challenges by changing kernel functions instead of producing better vector representations. They can only produce kernel matrices for kernel-based methods and not compatible with methods requiring vector representations. Effectively learning smooth vector representations for graphs of various structures and sizes remains a challenging task. Motivated by the recent advances in deep autoencoders, in this paper, we explore the capability of autoencoder on learning representations for graphs. Unlike videos or images, the graphs are usually of various sizes and are not readily prepared for autoencoder. Therefore, a novel framework, namely discriminative graph autoencoder (DGA), is proposed to learn low-dimensional vector representations for graphs. The algorithm decomposes the large graphs into small subgraphs, from which the structural information is sampled. The DGA produces smooth and informative vector representations of graphs efficiently while preserving the discriminative information according to their labels. Extensive experiments have been conducted to evaluate DGA. The experimental results demonstrate the efficiency and effectiveness of DGA comparing with traditional and state-of-the-art approaches on various real-world datasets and applications, e.g., classification and visualization.
Effect of Class Imbalanceness in Detecting Automobile Insurance Fraud

This article demonstrates a novel fraud identification methodology in auto insurance sector with the help of an adaptive oversampling technique (ADASYN). Initially, the data imbalanceness is removed from the original claim data set by employing ADASYN on the minority class instances. Three different classifiers, namely, Support Vector Machine, Decision Tree and Multi Layer Perceptron are further used for classifying the anomalous records from the normal ones. The efficacy of the models are tested with the 10-fold cross validation methodology. The performance of the model is demonstrated by several tests carried out on an auto insurance data set. Moreover, the outcomes justify the effectiveness of the proposed algorithm on a balanced data set over an unbalanced one.
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies

There has been a rapid growth of digitally available music data, including audio recordings, digitized images of sheet music, album covers and liner notes, and video clips. This huge amount of data calls for retrieval strategies that allow users to explore large music collections in a convenient way. More precisely, there is a need for cross-modal retrieval algorithms that, given a query in one modality (e.g., a short audio excerpt), find corresponding information and entities in other modalities (e.g., the name of the piece and the sheet music). This goes beyond exact audio identification and subsequent retrieval of metainformation as performed by commercial applications like Shazam [1].
Online Inertial-Aided Monocular Camera Self-Calibration

In this paper, we present a novel manner to perform an online camera calibration, without any prior knowledge of the environment, based in the readings of an IMU and in the track of a single 3D point projected into the camera image frames while the camera moves around. Our method is intended to be used alongside with a visual odometry or visual-inertial navigation system, since this type of algorithm strongly depends on the precision of the intrinsic camera parameters but they normally rely in offline calibration methods and take their results as constants. But it is known that there are several factors that can change the intrinsic camera parameters over time. Our approach can be used to correct these values as they are changed since it is an online method. An additional contribution of the method proposed in this work, is that it estimates, alongside with the camera intrinsic parameters, the motion performed by the camera while the algorithm is running. Our approach utilizes an EKF to perform its estimations. We demonstrate the efficiency of the method in a real world experiment, where we could perform the camera calibration and estimate the camera motion with our approach.
VRIMP: Virtual Reality System for Impulsivity Assessment

This work presents the integration of a low-cost virtual reality video game system, called VRIMP, specifically designed in order to improve experimental procedures for impulsivity assessment in psychology, by creating an immersive experience for participants in such evaluation. Through the implementation from scratch of a client-server system, using free software tools and inexpensive commercial hardware, a platform was constructed allowing the participants to interact with special virtual reality games that implement specific measurable aspects of impulsive behavior. At the same time, VRIMP collects information on the actions performed by the user during the game session, for later analysis, and allows the connection of real-time supervisory clients. The VRIMP system has been submitted to an initial experts validation at the Psychology Laboratory of the University of San Buenaventura, in B020bl'l Colombia.
Automating Developers' Responses to App Reviews

App reviews are important as they contain valuable information for improving the quality of such software systems. To this end, most users providing app reviews expect a response; to the extent that prior research has shown that when developers respond to app reviews these responses improve app ratings and users' satisfaction. However, unfortunately, user reviews largely go unanswered for most apps due to the high prevalence of reviews. This challenge may be addressed by creating a system that automatically generates responses having learned from the responses already posted by developers for a given app. These generated responses may then be modified by developers if required. This work presents a system that recommends socially-acceptable responses based on principles adopted from three domains: information retrieval, social norms and userinterface design. We then evaluate the newly developed system against Google Play's de facto review response system, which requires developers to write responses manually. The evaluation of the two systems involved measuring participants' feedback on three aspects – usability, cognitive load and performance. The goal of the evaluation was to investigate whether users prefer the new system over the existing system. Our outcomes show that there were statistically significant differences between the two systems on all three aspects evaluated, with users preferring the newly proposed system over Google Play's response system. In particular, the proposed system has the potential to reduce the overall workload of developers considerably.
Generic Embedded Semantic Dictionary for Robust Multi-Label Classification

Multi-label classification has attracted great attention in various applications and generated significant interest in data mining and learning fields. For the incompleteness of multi-label data, numerous approaches were developed to address partially missing labels in multi-label data, and traditional multi-label algorithms mainly adopt low-rank embedding and graph regularizer to recover the missing labels. However, how to simultaneously approach missing labels and discriminant multi-label embedding within the low-rank regime is still unclear. In this work, we propose a Generic Embedded Semantic Dictionary (GESD) learning framework for robust multi-label classification, where we both consider the partially and totally missing labels for the visual data. Specifically, we explore a low-rank coding strategy to encode visual features with recovered label matrix by constructing an effective semantic dictionary. In this way, the low-rankness will be appropriately propagated to recover multi-labels and improve label correlation, given missing labels in the training stage. Extensive experiments on six real-world benchmarks verify that our method can correctly capture label correlation and achieve better label recovery & prediction results than the state-of-the-art algorithms.
The k-Means Forest Classifier for High Dimensional Data

The priority search k-means tree algorithm is the most effective k-nearest neighbor algorithm for high dimensional data as far as we know. However, this algorithm is sensitive to attribute noise which is common in high dimensional spaces. Therefore, this paper presents a new method named k-means forest that combines the priority search k-means tree algorithm with random forest. The main idea is to create multiple priority search k-means trees by randomly selecting a fixed number of attributes to make decisions and get the final result by voting. We also design a parallel version for the algorithm. The experimental results on artificial and public benchmark data sets demonstrate the effectiveness of the proposed method.
Named Entity Filters for Robust Machine Reading Comprehension

The machine reading comprehension problem aims to extract crucial information from the given document to answer the relevant questions. Although many methods regarding the problem have been proposed, the similarity distraction problem inside remains unsolved. The similarity distraction problem addresses the error caused by some sentences being very similar to the question but not containing the answer. Named entities have the uniqueness which can be utilized to distinguish similar sentences to prevent models from being distracted. In this paper, named entity filters (NE filters) are proposed. NE filters can utilize the information of named entities to alleviate the similarity distraction problem. Experiment results in this paper show that the NE filter can enhance the robustness of the used model. The baseline model increases 5% to 10% F1 score on two adversarial SQuAD datasets without decreasing the F1 score on the original SQuAD dataset. Besides, by adding the NE filter, other existing models increases 5% F1 score on the adversarial datasets with less than 1% loss on the original one.
Phase Annotated Learning for Apache Spark: Workload Recognition and Characterization

In this paper, we introduce and evaluate a novel resource modeling technique for workload profiling, detection and resource usage prediction for Spark workloads. Specifically, we profile and annotate resource usage data in Spark with the application contexts where the resources were used. We then model the resource usage, per context, based on a Mixture of Gaussians (MOG) probabilistic distribution technique. When we recognize a similar workload, we can thus predict its resource usage for the contexts modeled a priori. In order to experimentally test the functionality of our Spark stage annotator and workload modeling tool we performed workload profiling for eight Apache Spark workloads. Our results show that, whenever a previously modeled workload is detected, our MOG models can be used to predict resource consumption with high accuracy.
Design of CLLC Resonant Converters for the Hybrid AC/DC Microgrid Applications

the CLLC resonant converter is widely used in the hybrid AC/DC microgrid as a DC transformer to interlink the AC and DC bus, because of its advantages of high power density and the capacity of bidirectional power transfer. In the hybrid AC/DC microgrid applications, as the voltages of AC and DC bus are controlled by the energy management system, the CLLC resonant converter operates under open-loop condition as a DC transformer. This paper mainly focuses on the parameters design of the CLLC resonant converters, with fully consideration of the parameter variations caused by the real working conditions, such as the operation power and ambient temperature. A circuit design method for the CLLC resonant converter to counteract the side effect caused by the working conditions is proposed. With the proposed method, the open-loop CLLC resonant converter can enjoy favourable power transmission and voltage regulation ability although the real values of the resonant inductances and capacitances deviate from the designed values. The proposed method is validated by the experiments.
Prediction of Negative Symptoms of Schizophrenia from Objective Linguistic, Acoustic and Non-verbal Conversational Cues

Speech disorders are among the salient characteristics of negative symptoms of schizophrenia. Such impairments are often exhibited through disorganized speech, inappropriate affective prosody, and poverty of speech. The current method of detecting such symptoms requires the expertise of a trained clinician, which may be prohibitive due to cost, stigma or high patient-to-clinician ratio. An objective method to extract non-verbal and verbal speech-related cues can help to automate and simplify the assessment method of severity of speech-related symptoms of schizophrenia. In this paper, a novel automated method is presented which uses speech content from schizophrenic patients to predict the clinician-assigned subjective ratings of their negative symptoms. Specifically, the interviews of 50 schizophrenia patients were recorded and features related to acoustics, linguistics and non-verbal conversation were extracted. The subjective ratings can be accurately predicted from the objective features with an accuracy of 64-82% using machine learning algorithms with leave-one-out cross-validation. Our findings support the utility of automated speech analysis to aid clinician diagnosis, monitoring and understanding of schizophrenia.
Toward Adaptive Manufacturing: Scheduling Problems in the Context of Industry 4.0

Adaptive manufacturing and Cyber-Physical Systems (CPS) have recently emerged as main topics in academia and the industry since recent projects even on a governmental level has been launched to investigate and propose innovations under the title "Smart Industry". This movement has been even expressed as the trigger toward the fourth industrial revolution. The concepts of smart industry represent the future form of industrial network, in which the physical elements of manufacturing environments are coupled with IT-services to achieve a cyber representation of the real manufacturing environments. Industrial environments exhibit naturally high structural complexity since an enormous number of factors related to market changes, heterogeneity of customer demands and the internal operating procedures are highly dynamic. Although achieving a cyber-physical system promises a manufacturing environment with plenty of competitive advantages from a monitoring point of view, extracting knowledge from the massive amount of collected data is an extremely difficult task. In this paper, a general overview on the optimization potential of different industry 4.0 concepts will be presented. Thereafter, we will focus on the optimization potential of scheduling policies for the management of the shop floor and answer the following question: What is the impact of industry 4.0 concepts on the traditional optimization methods for solving scheduling problems?
CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters

Distributed dataflow systems have been developed to help users analyze and process large datasets. While they make it easier for users to develop massively-parallel programs, users still have to choose the amount of resources for the execution of their jobs. Yet, users do not necessarily understand workload and system dynamics, while they often have constraints like runtime targets and budgets. Addressing this problem, systems have been developed that automatically select the required amount of resources to fulfill the users' constraints. However, interference with co-located workloads can introduce a significant variance into the runtimes of jobs and make accurate runtime prediction harder. This paper presents CoBell, a resource allocation system that incorporates information about co-located workloads to improve the runtime prediction for jobs in shared clusters. CoBell receives jobs from users with runtime and scale-out constraints and then reserves resources based on predicted runtimes. We implemented CoBell as a job submission tool for YARN. As such, it works with existing YARN cluster setups. The paper evaluates CoBell using five different distributed dataflow jobs, showing that using CoBell results in runtimes that do not violate the runtime constraints by more than 7.2%.
Design and Simulation of Solar Grid-Connected Charger for Electric Vehicles

Electric Vehicles (EV) are playing major role in decreasing carbon emissions. The major problem so far with the Electric Vehicles are overloading the Distribution Grids and availability of enough charging stations. The main objective of this research is to design and install a solar powered charging station for EVs in the UAE environment. This research aims to focus on the need for the shifting from the traditional gas and petrol vehicles to Electric vehicles in the UAE. Additionally, the project intends to ease the problem of the additional load that these EVs impose on the grid by powering the charging station from solar energy. This will help evolve the existing transport system of the UAE into a cleaner and greener system. The project is divided mainly into three important parts. First of all, the system components are designed to match with the ratings of available most common EVs. Then the system has been modelled in DIgSILENT Power factory for the simulation and validation of design. Finally, the results from calculations and simulations are described and compared.
Semi-Automatic Self-Calibrating Indoor Localization Using BLE Beacon Multilateration

The indoor localization within public environments remains a complex and challenging task due to a number of issues related to the sensor infrastructure, space geometry and mobile device restrictions. This paper describes a hybrid indoor localization method based on received signal strength multilateration and pedestrian dead reckoning using internal smartphone sensors and relies on Bluetooth Low Energy beacons. Taking into account the beacon’s zone of proximity and internal sensor data, the proposed method includes semi-automatic online calibration procedure of log-distance path loss propagation model. The proposed procedure takes into account smartphone heading angle and beacon signal obstructions due to user’s body and moving people bodies.
Fast Iterative and Fast Iterative Square Methods for Path Planning in Mobile Robots

Autonomous mobile robots face many challenges, and path planning is one of the first to be solved. To solve path planning, many solutions have been proposed, being the deterministic methods the ones that produced the best results. One of the these methods inspired by the concept of Potential Fields is the Fast Marching Method. This method belongs to a family of methods that solve the Eikonal equation, called Fast Methods. However, the only method applied to solve path planning is Fast Marching Method, then we propose to use the Fast Iterative Method instead due its scalability feature. Also we proposed an improvement to Fast Iterative Method called Fast Iterative Method Square, this comes from the need to have methods that generates safe paths. This proposal seeks to demostrate that the Fast Iterative Method and Fast Iterative Method Square are applicable to solve path planning and that they also present a better performance than the Fast Marching Method in all the comparisons made. In addition, Fast Iterative Method allows us to handle maps of large dimensions due to its highly parallel feature.
Micromechanical Bistable Flow Sensors

Bistable microstructures demonstrate enhanced responsiveness to external stimuli in the vicinity of the critical configurations that separate stable and unstable states. This makes these devices attractive candidates for implementation in sensors. Here, after a short review of existing bistability-based sensing approaches, we explore the applicability of electrostatically and electro thermally actuated micro beams as flow sensors. Double clamped curved ≈ 1000 µ m long, ≈ 2. 8µ m wide and ≈50 µ m thick beams, deep reactive ion etched (DRIE) from a silicon on insulator (SOI) wafer were subject to an air flow in the direction perpendicular to the beam. We show theoretically and experimentally that both the flow-induced cooling of the Joule's heated structure and the drag force reduce the critical snap-through (ST) voltages. The absolute and relative measured sensitivities of the ST voltage of ≈ 1.9 Vsm−1 and ≈ 2.6 % sm−1, respectively, are consistent with the Reduced Order (RO) Galerkin model predictions.
Application of Deep Reinforcement Learning in Werewolf Game Agents

Werewolf, also known as Mafia, is a kind of game with imperfect information. Werewolf game agents must cope with two kinds of problems, "decision on who to trust or to kill", and "decision on information exchange". In this paper, we focus on the first problem. We apply techniques in Deep Q Network in building werewolf agents. We also improve representation of states and actions based on existing agents trained by Q learning method. Our proposed agents were compared with existing agents trained by Q learning method and with existing agents submitted to the AIWolf Contest, the most famous werewolf game agents contest in Japan. For every role, we prepared four agents with proposed method and investigated average win ratio of four agents in our experiments. Experimental results showed that when agents learned and played with the same group of players, our proposed agents have better player performances than existing agents trained by Q learning method and a part of agents submitted to the AIWolf Contest. We obtained promising results by using reinforcement learning method to solve "decision on who to trust or to kill" problem without using heuristic methods.