Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentence similarity using weighted path and similarity matrices

Sentence similarity is the task of assessing how similar the two snippets of text are. Similarity techniques are used extensively in clustering, summarization, classification, plagiarism detection etc. Due to a small set of vocabularies, sentence similarity is considered to be a difficult problem in natural language processing. There are two issues in solving this problem: (1) Which similarity techniques to be used for word pair similarity and (2) How to generalize that to sentence pairs. We have used the weighted path, a WordNet-based similarity assessment, and the paraphrase database to obtain word pair similarity values. Thereafter, we extracted maximum values from the pairwise similarity matrix and computed a similarity value for a sentence pair. We have also incorporated a vector space model technique to form a robust similarity measure. Our method outperformed state-of-the-art methods on the STSS65 test dataset in Pearson’s correlation of 87% compared to human similarity scores. Moreover, our approach performed on par with other methods on the STSS131 test data using the same test. Our approach outperforms all the other WordNet-based methods compared on both datasets.
AUTOMATED SYSTEMS FOR TESTING ANDROID APPLICATIONS TO DETECT SENSITIVE INFORMATION LEAKAGE

AUTOMATED SYSTEMS FOR TESTING ANDROID APPLICATIONS TO DETECT SENSITIVE INFORMATION LEAKAGE SARKER TANVEER AHMED RUMEE, Ph.D. The University of Texas at Arlington, 2017 Supervising Professor: Yu Lei Smart phones have become an important daily companion and often used by users to store various private data such as contacts, photos, messages, various social network accounts etc. Users can furthermore extend the functionality of their phone by downloading applications (or apps) from various developers and online application stores. However, apps may misuse the data stored on the phone or obtained from the sensors and users do not have any direct means to track that. Hence, the need for improved mechanisms to better manage the privacy of user data is very important. There has been a lot of effort to detect and thwart unauthorized access to these private data. However, there is no consensus method which can ensure protection of user sensitive information from mobile devices and at the same time easily deployable at user side. This dissertation aims at developing methods to test Android applications for privacy leakage detection. For this, it presents a new technique: if an application is run twice and all program inputs and environment conditions are kept equal, then it should produce identical outputs. So, if a sensitive input is changed in two separate executions of the target application, and a variance is observed at output, then the output contains information from that sensitive
A bibliometric analysis of topic modelling studies (2000–2017)

Topic modelling is a powerful text mining tool that has been applied in many fields such as software engineering, political and linguistic sciences. To evaluate the development of topic modelling studies, the present study reports a bibliometric analysis of SCIE, SSCI and A&HCI listed articles published from 2000 and 2017. Bibliometric indices for productive authors, countries and institutions are analysed. In addition, thematic changes concerning topic modelling are also examined. Results show that China plays a leading role in this field. Topic modelling has established itself as an important technique in not only natural and formal sciences but also social sciences. LDA, social networks and text analysis are the topics with increasing popularity, while certain models (e.g. pLSA) and applications (e.g. topic detection) are declining in popularity. The findings could help researchers optimise research topic choices, seek collaboration with appropriate partners and stay up-to-date with the development of the field.
Simulation and control of a heterogeneous fleet of vehicles

In traffic flow dynamics there exists the so-called Stop and Go effect. It is a characteristic which arises from collective human driving behaviour, and it usually appears when the density of vehicles is high enough. Instead of achieving the same speed each one of the individual vehicles, they throttle towards a speed which is above the capability of the road, so that at a point they must brake below it. When they are stopped they throttle again and the process is repeated so on. It is because of it that this project? s ultimate objectives are, by means of simulations with controlled autonomous vehicles: - improve traffic flow; in particular, the average speed of vehicles, - reduce or eliminate the magnitude of the Stop and Go effect. Any progress in either case, or in both cases, will make this project useful.
Real-time Ball Detection for Robocup Soccer Using Convolutional Neural Networks

The RoboCup Soccer is one of the largest competitions in the robotics ﬁeld of research. It considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. In this paper, an approach using Convolutional Neural Networks for object detection is described. The soccer ball is the chosen object and three state-ofart convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an average precision of 0.972 with an intersection over union of 50% and 9.64 frames per second, running on CPU.
Characterizing Bell nonlocality and steering in generalized probabilistic theories

Bell nonlocality and EPR steering are both important quantum features and quantum resources with wide applications in many quantum information processing tasks. We investigate the Bell nonlocality and steering in the framework of generalized probabilistic theories. We formulate the characterizations of Bell nonlocality and steering in this more general framework and present new insight into the essence of Bell nonlocality and steering. PACS numbers: 03.65. Ud, 03.67. -a, 03.75. Gg
Design, kinematics, and statics of a novel wave energy converter with parallel mechanism

In order to design an ocean wave energy generator robot, a novel wave energy converter with parallel mechanism is designed and analyzed. A creative thinking that parallel mechanism can be applied to the wave energy converter is presented and verified during the wave energy using process. The design principles of the wave energy converter are given according to wave motion characteristics. Based on the principles, a novel wave energy converter with 4UPS/UP parallel mechanism is designed, which includes the design of the parallel mechanism, hydraulic cylinders, oil circuit, and converter integration. Then the kinematics model and statics model of the wave energy converter with 4UPS/UP parallel mechanism are derived by MATLAB and ADAMS; with these two methods, we found that the errors of rod length, velocity, and acceleration were 1.13 mm, 0.04 mm/s, and 0.38 mm/s2, respectively. Maximum stress error and maximum constraint moment errors were 1.52 N and 0.57 N·mm. So the correctness of the models is verified. This article can not only provide a reference for other types of parallel mechanisms applied to the wave energy converter, but also provide a theoretical foundation for the experimental prototype and practical application of the wave energy converter.
Consumer Perspectives on Service Quality of Electronic Commerce Web Sites

Eighty-two information technology professionals provided their expectations of service quality for a Web-based e-commerce retailer. Using a modified version of SERVQUAL, respondents also provided perceptions of the quality of service provided by three different Web-based book dealers; Amazon.com, Barnes & Noble, and Bookstore. About.com. Perceptions of service quality were significantly lower than respondents' expectations. Specific areas where Web site improvements could have an impact included improving on-line help, making human assistance quickly and readily available, and creating a visual appearance that conveys trust and safety in on-line transactions.
Virtual Assistant in Business: A Primer

Virtual assistants refer to artificial intelligence-based systems that offer help with any of a multiple of tasks. Virtual assistants are tools controlled by human; they are not meant to replace human. They are only invented to assist human in carrying out some of the day-to-day tasks and responsibilities. They allow human to invest their power and time on more impactful things. However, the application of this technological innovation is still very new, although some are home-based applications while some are work-based applications. This paper introduces virtual assistants as technological innovation meant to relieve both the business owners and their employees from stress and wastage.
Performance Evaluation of Three Unsupervised Clustering Algorithms

Open Access/Spectrum 2019/Special Issue www.mahendrapublications. com Performance Evaluation of Three Unsupervised Clustering Algorithms Rajdeep Baruri1, Anannya Ghosh2 1Department of Computer Science and Engineering, Jadavpur University, 188, Raja S. C. Mallick Rd, Kolkata -700 032, West Bengal, India. 2Institute of Engineering & Management, Y – 12, Block – EP, Sector – V, Saltlake Electronics Complex, Kolkata -700091, West Bengal, India.
EMG Pattern Recognition in the Era of Big Data and Deep Learning

The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed.
Employing ontology to capture expert intelligence within GEOBIA: automation of the interpretation process

The importance of remote sensing image analysis is ever increasing due to its ability to supply meaningful geographic information that informs local and global problems, such as measuring urban sprawl, mapping vegetation communities, monitoring the impacts of global climate change, and managing natural resources and urban planning. In this process of geo-object extraction, geographic object-based image analysis (GEOBIA) provides a method to identify real-world geographic objects from remotely sensed imagery. GEOBIA uses teclmiques analogous to those used by humans to perceive and distinguish geo-objects in imagery, usually acquired from satellite or airborne platforms. Experts use domain knowledge and measurement data extracted from remote sensing images for object-based analysis. This signifies a need for human involvement in the form of applying expert knowledge at the time of image object identification. The need for such human intervention acts as a barrier to the automation of GEOBIA processes. In this regard, knowledge representation techniques such as the use of ontologies provide possibilitie for modeling expert knowledge in a manner that contributes to the further development of GEOBIA. In this chapter, we will discuss the importance of the human factors in GEOBIA. To this end, we will draw on literature from both GEOBIA and ontology use.
MMOG User Participation Based Decentralized Consensus Scheme and Proof of Participation Analysis on the Bryllite Blockchain System

Proof of Work (PoW) based blockchains have limitations in throughput, time consumption, and energy efficiency. In these systems, a miner will consume significant time and resources to obtain a reward for contributing to the blockchain. To overcome these limitations, recent research on blockchains are focused on accelerating the speed, scalability, and enhancing the security level. By enhancing specific procedures of blockchain system, the level of data integrity supported by the blockchain can become more robust, and efficient. In this paper, a new blockchain consensus model based on the Bryllite Consensus Protocol (BCP) is proposed to support a hyper-connected massively multiplayer online game (MMOG) ecosystem. The BCP scheme enables users to participate directly in new consensus processes through a Proof of Participation (PoP) algorithm. In this model, the consensus algorithm has a simpler form while maintaining high security level. In addition, because the BCP scheme gives users an equal chance to make a contribution to the blockchain, rewards are distributed in an equal fashion, which motivates user participation. The analysis of the proposed scheme is applied to the Bryllite consortium blockchain system (homed in Hong Kong), which is a new blockchain network developed for international game industries, gamers, and game events.
PCCT: A POINT CLOUD CLASSIFICATION TOOL TO CREATE 3D TRAINING DATA TO ADJUST AND DEVELOP 3D CONVNET

Abstract. Point clouds give a very detailed and sometimes very accurate representation of the geometry of captured objects. In surveying, point clouds captured with laser scanners or camera systems are an intermediate result that must be processed further. Often the point cloud has to be divided into regions of similar types (object classes) for the next process steps. These classifications are very time-consuming and cost-intensive compared to acquisition. In order to automate this process step, conventional neural networks (ConvNet), which take over the classification task, are investigated in detail. In addition to the network architecture, the classification performance of a ConvNet depends on the training data with which the task is learned. This paper presents and evaluates the point clould classification tool (PCCT) developed at HCU Hamburg. With the PCCT, large point cloud collections can be semi-automatically classified. Furthermore, the influence of erroneous points in three-dimensional point clouds is investigated. The network architecture PointNet is used for this investigation.
Continuous-Time SLAM—Improving Google’s Cartographer 3D Mapping

: This paper shows how to use the result of Google’s simultaneous localization and mapping (SLAM) solution, called Cartographer, to bootstrap a continuous-time SLAM algorithm that was developed by the authors and presented in previous publications. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google’s results. Algorithms and data from Google are used as input for the continuous-time SLAM software. In preceding work, the continuous-time SLAM was successfully applied to a similar backpack system which delivers consistent 3D point clouds even in the absence of an IMU. Continuous-time SLAM means that the trajectory of a mobile mapping system is treated in a semi-rigid fashion, i.e., the trajectory is deformed to yield a consistent 3D point cloud of the measured environment.
Sentiment Extraction and Analysis using Machine Learning Tools-Survey

Sentiment search is clearly abstract cornerstone and essential administer in identifying user’s importance preferences. To get the quality of the product, position in evaluations is precondition. Normally, if item’s studys express constructive idea, the produce perhaps with bigger rating to some populous qualification. By analyzing the user considerations, their sentiments suggest unique experts to some target user in agreement the user culture. LDA is truly a Bayesian approach represented particularly to create the unification of studies, topics and discussions. In this paper, we have discussed about various machine learning tools and techniques for the better understanding of the concepts and efficient processing of sentiments from the huge data sets.
Dual-Tap Computational Photography Image Sensor With Per-Pixel Pipelined Digital Memory for Intra-Frame Coded Multi-Exposure

A coded-exposure-pixel image sensor for computational imaging applications is presented. Each frame exposure time is divided into <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> subframes. Within each subframe, each pixel sorts photo-generated charge into two charge taps depending on that pixel’s 1-bit binary code. <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> global updates of arbitrary pixel-wise codes are implemented in each frame to enable <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> short global pixel-specific subexposures within one frame. To make these subexposures global, two latches per pixel are utilized in a pipelined fashion. The code for the next subframe is loaded into latch 1 in a row parallel fashion, while the code for the current subframe is being applied by latch 2 globally for photo-generated charge sorting during the current subexposure. A <inline-formula> <tex-math notation="LaTeX">$280^{H}\times 176^{V}$ </tex-math></inline-formula> image sensor prototype with <inline-formula> <tex-math notation="LaTeX">$11.2{\text{-}}\mu \text{m}$ </tex-math></inline-formula> pixel pitch has been fabricated in a <inline-formula> <tex-math notation="LaTeX">$0.11{\text{-}}\mu \text{m}$ </tex-math></inline-formula> CMOS image sensor (CIS) technology. The image sensor has been demonstrated in two computational photography applications, each using only a single frame of a video: 1) computing both albedo (a measure of reflectivity) and 3-D depth maps by means of structured-light imaging and 2) computing surface normals (3-D orientations) map by means of photometric stereo imaging. These demonstrations experimentally validate some of the unique capabilities of this computational image sensor, such as accurate 3-D visual scene reconstruction using only one camera, while maintaining its native specifications: the full spatial resolution and the maximum frame rate.
Method of Text Content Development in Creation of Professionally Oriented Online Courses for Oil and Gas Specialists

The article analyzes the experience of text content development for an online course created for oil and gas professionals. The article features a critical review of the current situation in online systems of distance learning, establishes principles of creating professionally oriented language curricula, as well as requirements to the functionality of online platforms that enable creation of such courses and their delivery to the audience. The work provides a detailed analysis of text content development principles and criteria. It presents three stages of developing text content for the first module of a professionally oriented online course.
Data-Driven Crowd Simulation using Neural Networks

In this thesis we apply the power of neural networks to data-driven crowd simulation in order to train a model that can learn the rules that govern the movement of agents in a crowd, leading to realistic looking crowd behaviour. To do so we give a thorough overview of a number of existing crowd simulation techniques, classifying them according to granularity with which they model crowds. We then go on to describe how neural networks work and how they are trained in order to use them towards crowd simulation. The focus here being on supervised learning methods, as we are creating data-driven models. We propose two neural networks that can generate new instantaneous velocities for agents given the current state of their surroundings and inner motivations. The first model being a feedforward neural network that given a description of the agents environment generates a number of possible actions at each timestep. The second proposed model is the adaptation of the feedforward model into a recurrent neural network, in order to better leverage the temporal structure of the data. Both models are trained on data gathered from areal images of crowds, with the hyperparameters being optimized by the Tree-structured Parzen Estimator algorithm. Furthermore a crowd simulation framework is described in which these two models tie in. To validate our work we defined a number of metrics that indicate realistic crowd behaviour and compared different version of our models trained with different parameters regarding the representation of the agents environment, with the already established Social Forces model. These metrics were computed for a number of different scenarios. There is a lot of variety between the performances of different models on different scenarios for different metrics, but generally we can conclude that our models manage to perform on par with the Social Forces model. With some models even outperforming the Social Forces model.
Supervised Descent Method Viola-Jones and Skin Color Based Face Detection and Tracking

: Real-time face detection and tracking is a demanding problem in many application circumstances such as faces and gesture recognition in image processing and robotics etc. The ongoing work implements a robust method using Supervised Descent Method (SDM) based Viola-Jones and Skin color based segmentation, which can locate various faces at the same time under the varying environment of illumination and complex background in online (real time) by using face detection and tracking in conjunction with depth data. To achieve robustness, the Kanade-Lucas-Tomasi point tracker is used and dedicated to work on facial features by inserting knowledge about the configuration and visual features of the face. The resulting detection and tracking method is designed to get better from the loss of points due to tracking drift or temporary occlusion. Performance calculation experiments will be carried out on a set of video sequences of several facial expressions. It has been shown that using the base methodologies, some of the points are lost, whereas new method has been proposed to overcome the drawback.
GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams

GOOWE-ML: A NOVEL ONLINE STACKED ENSEMBLE FOR MULTI-LABEL CLASSIFICATION IN DATA STREAMS Alican Büyükçakır M.S. in Computer Engineering Advisor: Fazlı Can July 2019 As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemblebased methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multilabel classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.