Reviewerly/exHarmony huggingface dataset:
A Fast and Reliable Balanced Approach for Detecting and Tracking Road Vehicles

Introduction: An advanced, reliable and fast vehicle detection-and-tracking technique is proposed, implemented and tested. In this paper, an advanced-and-reliable vehicle detectionand- tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and- Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). Methods: The Real-Time Vehicle Detection-and-Tracking (RT_VDT) is proposed, and it is mainly a pipeline of reliable computer-vision and machine-learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main emphasis is the careful fusion of the employed algorithms, where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. Results: The RT_VDT is tested and its performance is evaluated using actual road images and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions. Discussion: Robust real-time vehicle detection and tracking is required for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC).
Multi-Robot Guided Policy Search for Learning Decentralized Swarm Control

Multi-robot learning has been extensively studied recently. Developing provably-correct algorithms for learning decentralized control policies remains challenging. In this letter, we propose a sample-efficient multi-robot learning method based on guided policy search to learn decentralized swarm control policies. The proposed method uses distributed trajectory optimization to provide guiding trajectory samples for policy training. In turn, the learned policy is exploited to update the trajectory optimization results so that the guiding trajectories are reproducible by the current policy. A learning algorithm is designed to alternate between distributed trajectory optimization and policy optimization, which eventually converges to a solution with good long-term performance. We demonstrate the effectiveness of our method in a multi-robot rendezvous problem. The simulation results in a robot simulator show that our method efficiently learn decentralized control policy with substantially less training samples.
Spin-momentum entanglement in a Bose–Einstein condensate

Entanglement is at the core of quantum information processing and may prove essential for quantum speed-up. Inspired by both theoretical and experimental studies of spin-momentum coupling in systems of ultra-cold atoms, we investigate the entanglement between the spin and momentum degrees of freedom of an optically trapped BEC of 87Rb atoms. We consider entanglement that arises due to the coupling of these degrees of freedom induced by Raman and radio-frequency fields and examine its dependence on the coupling parameters by evaluating von Neumann entropy as well as concurrence as measures of the entanglement attained. Our calculations reveal that under proper experimental conditions significant spin-momentum entanglement can be obtained, with von Neumann entropy of 80% of the maximum attainable value. Our analysis sheds some light on the prospects of using BECs for quantum information applications.
Adversarial Teaching Approach to Cybersecurity: A Mathematical Model Explains Why It Works Well

Teaching cybersecurity means teaching all possible ways how software can be attacked - and how to fight such attacks. From the usual pedagogical viewpoint, a natural idea seems to be to teach all these ways one by one. Surprisingly, a completely different approach works even better: when the class is divided into sparring mini-teams that try their best to attack each other and defend from each other. In spite of the lack of thoroughness, this approach generates good specialists - but why? In this paper, by analyzing a simple mathematical model of this situation, we explain why this approach work - and, moreover, we show that it is optimal in some reasonable sense.
Analysis of 13 implementations of the software engineering management and engineering basic profile guide of ISO/IEC 29110 in very small entities using different life cycles

Abstract We are living in an age of growing demand for software products. This growing demand creates opportunities for very small entities (VSEs) to not only survive but also flourish. In this context, VSEs need to produce high‐quality products to meet market needs. However, in their quest to produce high‐quality software, VSEs need to overcome the challenge of implementing international standards, which they find difficult to do because of lack of knowledge and practical experience. This paper provides an analysis performed to 13 teams of VSEs, using different life cycles, which achieved the implementation of the ISO/IEC 29110, to analyze the effort each team invests to implement the best practices provided by the standard. Besides, the paper provides an analysis of the difficulties, and the benefits of using the six‐step method are included. This analysis is of interest because software engineering knowledge developed by researchers should be transferred to the industry to reduce the gap between them. The results highlight, on the one hand, the practices representing more effort for teams by life cycle. On the other hand, the results highlight the six‐step method that allowed the 13 teams to achieve a high level of coverage of the ISO/IEC 29110 international standard.
A Weight-based k-prototypes Algorithm for Anomaly Detection in Smart Grid

Anomaly detection is a typical method to find abnormal behaviors in smart grid, where the data may contain both categorical and numerical attributes with distinct significance. The k-prototypes algorithm is one of the most common algorithms for clustering mixed categorical and numerical data, however, it does not consider the significance of different attributes towards the clustering process. In this paper, we propose a weight based k-prototypes algorithm for anomaly detection in smart grid. Specifically, we first introduce an improved cost function to measure the categorical and numerical attributes uniformly and assign the weight to each attribute. We also propose two entropy metrics to calculate weight values and embed them into the k-prototypes algorithm for mixed data clustering in smart grid. Finally, we compare our proposed algorithm with existing clustering algorithms and the experimental results show that our algorithm is effective for anomaly detection in smart grid.
Automatic generation of dense non-rigid optical flow

There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today. The reason lies mainly in the required setup to derive ground truth optical flows: a series of images with known camera poses along its trajectory, and an accurate 3D model from a textured scene. Human annotation is not only too tedious for large databases, it can simply hardly contribute to accurate optical flow. To circumvent the need for manual annotation, we propose a framework to automatically generate optical flow from real-world videos. The method extracts and matches objects from video frames to compute initial constraints, and applies a deformation over the objects of interest to obtain dense optical flow fields. We propose several ways to augment the optical flow variations. Extensive experimental results show that training on our automatically generated optical flow outperforms methods that are trained on rigid synthetic data using FlowNet-S, LiteFlowNet, PWC-Net, and RAFT. Datasets and implementation of our optical flow generation framework are released at https://github.com/lhoangan/arap_flow
Enhancing the privacy of negative surveys using negative combined categories

In recent years, the negative survey, which can preserve the privacy of individuals when used for collecting sensitive information, has attracted significant attention. However, the privacy of the typical negative survey is limited by the number of categories. When the number of categories is small, the typical negative survey exhibits weak privacy preservation. In particular, when only two categories exist, the typical negative survey cannot preserve the privacy of individuals. Moreover, at times, the privacy requirements of participants are strict. In such a situation, the typical negative survey fails to provide satisfactory privacy preservation. In this paper, two novel negative survey models that use negative combined categories (NCCs), NCC-I and NCC-II, are proposed. They can provide improved individual privacy preservation, in particular for sensitive information with only two categories. The experimental results demonstrate that the proposed methods can achieve superior privacy preservation and provide accurate reconstructed results.
Smooth Actor-Critic Algorithm for End-to-End Autonomous Driving

For the intelligent sequential decision-making tasks like autonomous driving, decisions or actions made by the agent in a short period of time should be smooth enough or not too choppy. In order to help the agent learn smooth actions (steering, accelerating, braking) for autonomous driving, this paper proposes the smooth actor-critic algorithm for both deterministic policy and stochastic policy systems. Specifically, a regularization term is added to the objective function of actorcritic methods to constrain the difference between neighbouring actions in a small region without affecting the convergence performance of the whole system. Then, the theoretical analysis and proof for the modified methods are conducted so that it can be theoretically guaranteed in terms of iterative improvements. Moreover, experiments in different simulation systems also prove that the methods can generate much smoother actions and obtain more robust performance for reinforcement learning-based End-to-End autonomous driving.
Additive and linear conjucyclic codes over $ {\mathbb{F}}_4 $

&lt;p style='text-indent:20px;'&gt;Conjucyclic codes were first introduced by Calderbank, Rains, Shor and Sloane in [&lt;xref ref-type="bibr" rid="b1"&gt;1&lt;/xref&gt;] because of their applications in quantum error-correction. In this paper, we study linear and additive conjucyclic codes over the finite field &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ {\mathbb{F}}_{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and produce a duality for which the orthogonal, with respect to that duality, of conjucyclic codes is conjucyclic. Moreover, we show that this is not the case for other standard dualities. We show that additive conjucyclic codes are the only non-trivial conjucyclic codes over &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {\mathbb{F}}_{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and we use a linear algebraic approach to classify these codes. We will also show that additive conjucyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ {\mathbb{F}}_{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are isomorphic to binary cyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ 2n. $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;&lt;/p&gt;
Achieving Efficient and Privacy-Preserving Max Aggregation Query for Time-Series Data

The vision of future intelligent information society will be globally data driven, enabled by Internet of Things (IoT) techniques. In any IoT-enabled applications, huge volumes of time-series data are continuously generated by IoT devices, which will be fed for high-level functions. Among these functions, the max aggregation query over a specific time interval is one of the frequently used operations. However, due to the limited resources in IoT devices, a common way to deal with the max aggregation query is to involve powerful cloud servers. Nevertheless, as the sensed data from IoT devices and its pattern (e.g., local ranking sequences) are usually private and the cloud servers are not fully trusted, the data should be encrypted before being outsourced to cloud servers. Obviously, the data encryption will incur some efficiency issues. In this paper, to mitigate the privacy and efficiency issues, we propose an efficient and privacy-preserving max aggregation query scheme for time-series data in IoT scenarios. Specifically, we first employ a segment tree based data structure to represent the data collected by IoT devices. Then, to protect the privacy, we leverage two encryption techniques to encrypt the data structure. With the encrypted data structure, our proposed scheme can handle a ranged max aggregation query with O(log L) time complexity, where L is the range length of the query. Detailed security analysis and performance evaluation show that our scheme can not only preserve the privacy of data and its local ranking sequences, but also achieve efficient ranged max aggregation query.
Robust Machine Learning against Adversarial Samples at Test Time

Though the performance of deep learning is remarkable, recent works have shown that deep learning models are vulnerable to adversarial samples that are close to their original samples to human eyes but misclassified by Deep Neural Network (DNN). This is a serious problem as many deep learning models are used in physical infrastructures and critical application domains such as medical diagnosis, self-driving cars, malware detection, as well as digital assistants like Google Assistant, Alexa, and Siri. Many researchers have attempted to secure neural networks through techniques such as defensive distillation and adversarial retraining. Nevertheless, many of these techniques are ineffective to new or slightly strong adversarial attacks such as the Carlini and Wagner (C&W)'s attack. In this paper, we propose a robust adversarial retraining method to iteratively retrain a given model so that it can not only detect the adversarial examples but also maintain the prediction accuracy for the normal dataset. Our experimental results show that the prediction accuracy on the MNIST test set is maintained while the accuracies under FGSM, C&W, and DeepFool attacks increase from 29% to 91%, 7% to 70%, and 29% to 91%, respectively.
Data Analysis with R

The goal of data analysis is to use statistical tools to describe, infer, and predict values. The number of different concepts and techniques is very vast and it is essential to structure the way we learn them. Some descriptive statistics concepts are needed to understand the data we are working with. Then we can apply inference techniques to generalize some conclusions that we have found in our data. Finally, multivariate statistics allows us to construct models used to predict values of the variables we are studying. The program R is focused in these techniques and some advanced statistics can be performed easily with it.
Mobile Platform Type Lower Limb Rehabilitation Electromechanical System

This design mainly studies the structure and control design of mobile platform type lower limb rehabilitation robot. Lower limb rehabilitation robot is a mechanical design and microcomputer controlled electromechanical system. The main reason for the design is to provide rehabilitation training and treatment for patients with lower limb movement inconvenience and lower limb movement disorder. This design has carried on the original design and the detailed clear explanation in the structure aspect. Some domestic and foreign research situations of this research are enumerated to illustrate the achievements and problems to be solved. The degrees of freedom of each joint of the mobile platform type lower limb rehabilitation robot are determined. The positions of each mechanism are reasonably allocated and the parameters of each part are specifically determined. The rationality of each part and mechanism design is checked through kinematics analysis and strength check.
Oriented Pedestrian Social Interaction Modeling and Inference

In order to drive and operate safely around humans, future autonomous vehicles will be expected to perceive visual scenes and predict human behaviors beyond explicit visual features. Inferring human interactions, for example, plays an indispensable role in predicting pedestrian trajectories, because social actions such as walking together, gathering, holding hands, and talking, influence where and how people move relative to each other and their environment. Existing methods for semantic action recognition and labeling provide inputs that, while useful to human operators, cannot be used to improve predictions made by autonomous vehicles. This paper presents a graphical model approach for jointly inferring pedestrian interactions from short video clips over time. New Markov random field algorithms are presented for modeling social interactions probabilistically using spatial and temporal observations obtained over short video clips, at a time scale useful for making real-time decisions such as collision avoidance. Experiments conducted using real-world pedestrian streaming videos show that the average interaction-inference accuracy of the proposed approach is approximately 94.6%.
A New Navigation Method for VR-based Telerobotic System via Supervisor's Real Walking in a Limited Physical Space

Along with the development of network and virtual reality (VR), VR-based telerobotic systems have been widely applied in many fields, especially in hazardous or uncertain environments. But the existing navigation methods of remote robot are implemented by operators using keyboard, mouse joystick, or walking-in-place devices in the VR environments that is telepresence of remote environments. In this paper, we propose a new navigation method for VR-based telerobotic system via operators' real walking in a limited physical space, wherein the motion of a remote robot is synchronized with that of an operator who is actually walking in a limited physical space while naturally interacting (head and body interaction) with the robot and experiencing the remote environment with the help of VR video real-timely captured by the robot. This makes the operators have better sense of presence at the remote sites than the existing methods. We design and implement a system based on this technology, and the test shows that it can make the robot flexible to be navigated in remote complex and dynamic environments.
Safe and Coordinated Hierarchical Receding Horizon Control for Mobile Manipulators

Mobile manipulators, constructed by mobile platforms and manipulators, have become a promising solution to future factories for introducing flexibility to manufacturing. This paper presents a method, hierarchical receding horizon control algorithm (HRHC), to assure safety and achieve higher time and space efficiency in robots surrounded by time-varying environments. HRHC contains an optimization based motion planning module that takes account of both the mobile platform and the manipulator to utilize the kinematic redundancy, and a low-level safety controller to deal with fast changes in the environment. With this method, we verify the performance through experiments. The result shows that space efficiency is increased and the HRHC can guarantee local safety in dynamic environments.
Segmentation of Characters from Degraded Brahmi Script Images

Segmentation of symbols or characters in the OCR process is a very critical and important phase, as it directly affects the recognition system. If objects in the image are not accurately segmented, then the recognition will also be false and this ultimately affects the performance of the system. Brahmi script contains certain alphabets which comprise some isolated symbols. In this paper, the authors have proposed an innovative approach to segment the lines from the digital Brahmi estampage document, and further segment the alphabets from the line, which are regular characters as well as special characters comprising isolated symbols. The authors have developed an algorithm that effectively segments the lines, regular characters and special characters. Advancements in the algorithm can be made to improve the results for accuracy.
Road-Condition-Aware Dynamic Path Planning for Intelligent Vehicles

Intelligent vehicles face a more complex driving environment, which requires their path planning algorithms to have the ability to respond quickly to complex environmental changes. Therefore, it is very necessary to plan the optimal path for smart vehicles in real-time. Recently, the convergence of graph dynamics and its equivalence with the shortest path solution have been proved, and the biased minimum consensus algorithm has been successfully applied to the shortest path planning problem. In this paper, considering the road conditions (degree of congestion, whether an accident occurred, etc.) and the Euclidean distance of the route, we improve biased minimum consensus algorithm to achieve optimal path planning in the complex situation of real-time updating of road conditions. In the simulation, we used the algorithm for path planning in the road topological maps of Beijing City, and compared with the RRT (Rapidly-exploring Random Trees Algorithm) algorithm and the BRRT (Bidirectional Rapidly-exploring Random Trees Algorithm) algorithm. The improved biased minimum consensus algorithm has better performance in real-time path planning problems. The research results shed new light on the real-time dynamic path planning in real traffic, which has practical significance.
The re-mediating effects of bio-sensing in the context of parental touch practices

This article investigates the remediating effect of bio-sensing technology on touch practices in the context of parent-infant interaction. We examine how the entry of a biosensing technology into the social, sensory and technological ecology of family homes interacts with the ways in which parents and babies know each other and communicate through touch. The paper centres on an exploratory case study of the Owlet Smart Sock (OSS), a bio-sensing baby monitoring device. We bring the social critical and experiential lenses of multimodality and sensory ethnography to studying the OSS as a socio-technological probe across a range of research encounters, including focus groups, home visits and video re-enactments with parents. In doing so, we provide an account of the ways in which the technology affects how babies and parents’ bodies are (re)imagined, assessed, controlled, interrelated, experienced, and cared for and move beyond generic social debate around the quantified-objectified baby and fears of touch deprivation in contemporary digital culture.