Reviewerly/exHarmony huggingface dataset:
Synchronous Maneuver Searching and Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments

In the real-time decision-making and local planning process of autonomous vehicles in dynamic environments, the autonomous driving system may fail to find a reasonable policy or even gets trapped in some situation due to the complexity of global tasks and the incompatibility between upper level maneuver decisions with the lower level trajectory planning. To solve this problem, this paper presents a synchronous maneuver searching and trajectory planning (SMSTP) algorithm based on the topological concept of homotopy. Firstly, a set of alternative maneuvers with boundary limits are enumerated on a multi-lane road. Instead of sampling numerous paths in the whole spatio-temporal space, we, for the first time, propose using Trajectory Profiles (TPs) to quickly construct the topological maneuvers represented by different routes, and put forward a corridor generation algorithm based on graph-search. The bounded corridor further constrains the maneuver’s space in the spatial space. A step-wise heuristic optimization algorithm is then proposed to synchronously generate a feasible trajectory for each maneuver. To achieve real-time performance, we initialize the states to be optimized with the boundary constraints of maneuvers, and we set some heuristic states as terminal targets in the quadratic cost function. The solution of a feasible trajectory is always guaranteed only if a specific maneuver is given. The simulation and realistic driving-test experiments verified that the proposed SMSTP algorithm has a short computation time which is less than 37 ms, and the experimental results showed the validity and effectiveness of the SMSTP algorithm.
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users and are better conversation starters.In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives. Based on comment texts, we train a model to distinguish comments that have either a high or low chance of receiving many upvotes and replies. Our evaluation on user comments from TheGuardian.com compares recurrent and convolutional neural network models, and a traditional feature-based classifier. Further, we investigate what makes some comments more engaging than others. To this end, we identify engagement triggers and arrange them in a taxonomy. Explanation methods for neural networks reveal which input words have the strongest influence on our model's predictions. In addition, we evaluate on a dataset of product reviews, which exhibit similar properties as user comments, such as featuring upvotes for helpfulness.
Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.
Exploring ubiquitous relations for boosting classification and localization

Although the weakly supervised learning can effectively avoid the tedious data annotating process of deep learning approaches, the performance is still in urgent need of enhancement. In this paper, we endeavor to mine a ubiquitous and fundamental knowledge—Relation, to boost several existing classification and localization models without changing the original structure. We first propose a universal relation exploring scheme to mine the relations among entities. This scheme can be specialized into different instantiations including object, superpixel and pixel relations to stimulating different learning models. We adopt the object relations on a few-shot classification model to concentrate on the dominant object, and to boost its discriminative capacity. The superpixel relation is utilized to improve the performance of the saliency object detection models. The sensitivity of the pixel relations to the uncertain regions makes it suitable for distinguishing the disputed area in saliency detection results. Our experiments demonstrate that all the three relation instantiations can significantly boost the performance of the state-of-the-art learning models and optimize the visual result.
SAGA: Efficient and Large-Scale Detection of Near-Miss Clones with GPU Acceleration

Clone detection on large code repository is necessary for many big code analysis tasks. The goal is to provide rich information on identical and similar code across projects. Detecting near-miss code clones on big code is challenging since it requires intensive computing and memory resources as the scale of the source code increases. In this work, we propose SAGA, an efficient suffix-array based code clone detection tool designed with sophisticated GPU optimization. SAGA not only detects Type-l and Type-2 clones but also does so for cross-project large repositories and for the most computationally expensive Type-3 clones. Meanwhile, it also works at segment granularity, which is even more challenging. It detects code clones in 100 million lines of code within 11 minutes (with recall and precision comparable to state-of-the-art approaches), which is more than 10 times faster than state-of-the-art tools. It is the only tool that efficiently detects Type-3 near-miss clones at segment granularity in large code repository (e.g., within 11 hours on 1 billion lines of code). We conduct a preliminary case study on 85,202 GitHub Java projects with 1 billion lines of code and exhibit the distribution of clones across projects. We find about 1.23 million Type-3 clone groups, containing 28 million lines of code at arbitrary segment granularity, which are only detectable with SAGA. We believe SAGA is useful in many software engineering applications such as code provenance analysis, code completion, change impact analysis, and many more.
Quantum entanglement dynamics based on composite quantum collision model*

By means of composite quantum collision models, we study the entanglement dynamics of a bipartite system, i.e., two qubits S 1 and S 2 interacting directly with an intermediate auxiliary qubit S A , while S A is in turn coupled to a thermal reservoir. We are concerned with how the intracollisions of the reservoir qubits influence the entanglement dynamics. We show that even if the system is initially in the separated state, their entanglement can be generated due to the interaction between the qubits. In the long-time limit, the steady-state entanglement can be generated depending on the initial state of S 1 and S 2 and the environment temperature. We also study the dynamics of tripartite entanglement of the three qubits S 1 , S 2 , and S A when they are initially prepared in the GHZ state and separated state, respectively. For the GHZ initial state, the tripartite entanglement can be maintained for a long time when the collision strength between the environment qubits is sufficiently large.
Vision measuring method for the involute profile of a gear shaft

Since the involute profile error of a gear shaft influences its transmission performance, it is desirable to accurately and quickly measure its involute profiles. With the advantages of low cost, universal applicability and in situ detection, a vision measuring method for multiple involute profiles of a gear shaft is developed only using one image in this study. The base circle radius and initial position angle of the involute are obtained by geometrically fitting the involute profile. The correctness and reliability of the method are tested by measuring the pitch deviation of the gear shaft; a specialized experiment is carried out; and the factors affecting the measurement accuracy also are analyzed.
Visual analytical thinking and mind maps for ontology engineering

The article is devoted to the practical application of the principles of visual analytical thinking for the problems of knowledge structuring for the ontology design and development. Visual analytical thinking refers to implementation of methodologies that use different types of diagrams to represent ideas, concepts, relationships and processes. From the well-known practically used types of diagrams, mind maps were selected as the most common, convenient and simple method for the proper formation and design of ontologies of complex domains. Mind maps reflect hierarchical relationships among concepts and allow the analyst to sufficiently deeply reflect the features and patterns of the domain with their specific relationships. Buzan formulated the idea of mind maps in the 1970s as a compact means of organizing abstracts, he later deepened and enhanced this idea, which was brought to software implementation and was widely used in various fields of education, research and business. The paper discusses the basic principles of the formation of such maps and analyzes the typical mistakes of analysts. For the first time, a classification of main errors and mistakes is proposed taking into account syntactic, semantic and pragmatic aspects. The analysis of the most common errors associated with the violation of the rules of ―good generalization‖ and reasonable minimalism is given. The article may be of interest to both intelligent systems’ developers and knowledge management systems’ creators; it summarizes ten years of experience in teaching and training visual analytical thinking skills in executive MBA programs and corporate trainings.
Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.
Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).
Stepwise calibration of plenoptic cameras based on corner features of raw images

Plenoptic cameras are increasingly gaining attention in various fields due to their ability to capture both spatial and angular information of light rays. Accurate geometric calibration can lay a solid foundation for the applications that use the plenoptic camera. In this paper, to the best of our knowledge, we first introduce an accurate corner detection method based on a novel selection and refinement strategy. The detected-corner candidates on raw images are selected by a random sample consensus (RANSAC)-based algorithm and optimized by the photometric similarity, as well as the sub-pixel refinement. In addition, a robust and accurate stepwise calibration method is proposed based on separated intrinsic parameters, including parameters related to the pinhole model and those unique to the plenoptic camera. Experiments on both simulated and real data demonstrate that our method outperforms the state-of-the-art methods and is able to support a more accurate calibration of plenoptic cameras.
Slicing and Dicing Soccer: Automatic Detection of Complex Events from Spatio-Temporal Data

The automatic detection of events in sport videos has important applications for data analytics, as well as for broadcasting and media companies. This paper presents a comprehensive approach for detecting a wide range of complex events in soccer videos starting from positional data. The event detector is designed as a two-tier system that detects atomic and complex events. Atomic events are detected based on temporal and logical combinations of the detected objects, their relative distances, as well as spatio-temporal features such as velocity and acceleration. Complex events are defined as temporal and logical combinations of atomic and complex events, and are expressed by means of a declarative Interval Temporal Logic (ITL). The effectiveness of the proposed approach is demonstrated over 16 different events, including complex situations such as tackles and filtering passes. By formalizing events based on a principled ITL, it is possible to easily perform reasoning tasks, such as understanding which passes or crosses result in a goal being scored. To counterbalance the lack of suitable, annotated public datasets, we built on an open source soccer simulation engine to release the synthetic SoccER (Soccer Event Recognition) dataset, which includes complete positional data and annotations for more than 1.6 million atomic events and 9,000 complex events. The dataset and code are available at https://gitlab.com/grains2/slicing-and-dicing-soccer .
Political stability and innovation in Africa

This paper examines the role of institutional factors, primarily, political stability, in shaping the dynamics of innovation activity in today's modern society.Using panel data consisting of 26 African countries from the period between 1996 and 2016 as well as employing the negative binomial estimate, we model the impact of those factors on the number of applications for patents per resident.GDP per capita, education and also other institutional factors including the rule of law and financial freedom.The results reveal a strong positive effect of the rule of law on patenting activity.
Multistate Encoding with End-To-End Speech RNN Transducer Network

Recurrent Neural Network Transducer (RNN-T) models [1] for automatic speech recognition (ASR) provide high accuracy speech recognition. Such end-to-end (E2E) models combine acoustic, pronunciation and language models (AM, PM, LM) of a conventional ASR system into a single neural network, dramatically reducing complexity and model size.In this paper, we propose a technique for incorporating contextual signals, such as intelligent assistant device state or dialog state, directly into RNN-T models. We explore different encoding methods and demonstrate that RNN-T models can effectively utilize such context. Our technique results in reduction in Word Error Rate (WER) of up to 10.4% relative on a variety of contextual recognition tasks. We also demonstrate that proper regularization can be used to model context independently for improved overall quality.
On the identifiability of Bayesian factor analytic models

Abstract A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. We introduce a post-processing scheme in order to deal with rotation, sign and permutation invariance of the MCMC sample. The exact version of the contributed algorithm requires to solve $$2^q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mi>q</mml:mi></mml:msup></mml:math> assignment problems per (retained) MCMC iteration, where q denotes the number of factors of the fitted model. For large numbers of factors two approximate schemes based on simulated annealing are also discussed. We demonstrate that the proposed method leads to interpretable posterior distributions using synthetic and publicly available data from typical factor analytic models as well as mixtures of factor analyzers. An R package is available online at CRAN web-page.
Blockchain-based Decentralized Distribution Management in E-Journals

The application of blockchain in the context of E-Journal distribution to journalists is aimed at making the management paper adequately distributed and not misused. The security system in the distribution or management paper process of an open journal system is currently considered to be very lacking because one can duplicate the journal in an open journal system easily. Furthermore, it can be transferred to anyone who is not responsible. The security system in the distribution of an open journal system and the management of the management paper process is currently considered to be very lacking because one can duplicate the journal in an open journal system easily. Furthermore, it can be transferred to anyone who is not responsible. With the implementation of this blockchain technology, there are 3 (three) benefits, namely (1) The distribution of E-Journal in the Open Journal System is more targeted, and there are no errors. (2) The reputation of the Open Journal System becomes better with a sense of trust. This research will be implemented in an E-Journal in an Open Journal System using blockchain technology. (3) The management paper processing in the open journal system runs according to the procedure so that in the management process the distribution of soft copies and hard copies of the journal is protected from hacker threats, and this blockchain is used to guarantee its security.
Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Complex problems require considerable work, extensive computation, and the development of effective solution methods. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. However, problem solving often involves human expertise and guidance. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. In previous studies, only binary numbers have been used for this purpose. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods.
Data Security in Mobile Cloud Computing: A State of the Art Review

Due to tremendous use of smartphones the concern of cloud computing in mobile devices emerges, which is known as Mobile Cloud Computing (MCC).It involves the usage of mobile devices and cloud computing to perform resource intensive tasks using the internet with minimum impact on cellular resources.Nowadays, people are relying on mobile devices due to their small size and user friendly interface but due to its limited storage capacity, people can no more rely on internal RAM.Therefore, this promotes a drastic need for technology to make it possible for anyone to access their data anywhere anytime.As a result, Mobile Cloud Computing facilitates mobile users with its enticing technology by providing its on-demand and scalable services.But privacy and security are the main concern for a mobile user in the modern era.Thus, issues regarding security can be divided into cloud security and mobile network user's security, respectively.However, the primary focus of this study is to analyze how to secure the user's data in a mobile cloud.Leading to objectives, the current study presents a comprehensive analysis of existing techniques that can be considered for securing data in MCC efficiently.Moreover, this work will contribute a state-of-the-art roadmap to research and development communities for the right selection of proposed approach.