Reviewerly/exHarmony huggingface dataset:
Centralized Control Architecture for Cooperative Object Transportation using Multiple Omnidirectional AGVs

This paper addresses the problem of cooperative transportation using multiple omnidirectional Automated Guided Vehicles (AGVs). To enhance flexibility and application potentials, mecanum-wheeled platform is considered for the proposed multi-AGV system while the cooperative transportation is executed without physical link/gripper to fix the object on the AGVs. Therefore, the position and number of AGV is adjustable depending on the size and weight of the transported object. Analysis of force distribution to each AGV during cooperative transportation is presented. Furthermore, the gradient projection method is exploited to regulate internal force according to the operational capability of each AGV. Moreover, an adaptive sliding mode controller is designed for AGV to cope with dynamic uncertainty during cooperative transportation. Stability of the proposed controller is proven by using Lyapunov Theorem. Finally, numerical simulation is presented to demonstrate the performance of the proposed control system.
Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.
SGX-based Users Matching with Privacy Protection

For users who rely heavily on social networks for entertaining or making friends, sensitive information such as ages, incomes and addresses will be stored in a database without protection. While many companies try their best to protect user privacy, data breaches still happen, resulting in the loss of millions or billions of dollars and the faith of their customers. Therefore, we propose a solution that guarantees the confidentiality and integrity of information while preserving the ability to perform matching over encrypted values. Our solution is built on homomorphic encryption with secure hardware enclaves such as Intel SGX. Our solution resolves challenges such as performing user profile matching on encrypted values without revealing any information to anyone. With the help of multiple servers, user privacy can be protected as long as at least one server is honest and the guarantee of secure hardware makes the secret unlikely to be revealed. Furthermore, a prototype of our system is implemented to measure its performance. The performance analysis and security analysis show the feasibility of our proposed protocols.
Protecting Multimedia Privacy from Both Humans and AI

With the development of artificial intelligence (AI), multimedia privacy issues have become more challenging than ever. AI-assisted malicious entities can steal private information from multimedia data more easily than humans. Traditional multimedia privacy protection only considers the situation when humans are the adversaries, therefore they are ineffective against AI-assisted attackers. In this paper, we develop a new framework and new algorithms that can protect image privacy from both humans and AI. We combine the idea of adversarial image perturbation which is effective against AI and the obfuscation technique for human adversaries. Experiments show that our proposed methods work well for all types of attackers.
Hand Gesture-Based Virtual Reality Training Simulator for Collaboration Rescue of a Railway Accident

Abstract Training simulator is an efficient and innovative tool to help users learn professional skills due to its convenience and safety. However, complex human–computer interaction is one of the main disadvantages that limit its effectiveness in safety training, especially for the rescue of a railway accident that requires collaborations. Through designing a set of task-specific hand gestures, we developed a training simulator for the recovery of a railway accident that helps the rescuers learn and practice rescue skills in a life-like environment and gain the firsthand experience. To test the validity of our training simulator, a user experiment is designed to compare it with the controller-based simulator in a between-groups study with 51 participants, focusing on different aspects of effectiveness. The results demonstrate that the hand gesture-based controller can be more efficient and usable to deal with complex interactions than the traditional hand-held controller.
A Multi-task Network for Localization and Recognition of Text in Images

We present an end-to-end trainable multi-task network that addresses the problem of lexicon-free text extraction from complex documents. This network simultaneously solves the problems of text localization and text recognition and text segments are identified with no post-processing, cropping, or word grouping. A convolutional backbone and Feature Pyramid Network are combined to provide a shared representation that benefits each of three model heads: text localization, classification, and text recognition. To improve recognition accuracy, we describe a dynamic pooling mechanism that retains high-resolution information across all RoIs. For text recognition, we propose a convolutional mechanism with attention which out-performs more common recurrent architectures. Our model is evaluated against benchmark datasets and comparable methods and achieves high performance in challenging regimes of non-traditional OCR.
The potential of collaborative learning as a tool for forensic students: Application to signature examination

Transferring theoretical knowledge to practical skills remains a big challenge in forensic science, especially in questioned documents. The examination of handwriting and signatures requires years of practice to develop the necessary skills. While students (and to some extent the general population) often have the impression that it is easy to differentiate handwriting from different persons, in practice, particularly when dealing with simulated signatures, there is a high risk of reaching a wrong conclusion when questioned document experts do not use a systematic approach and/or are not sufficiently experienced (see for example the famous French Dreyfus case). Thus, a novel teaching approach, based on collaborative learning, has been introduced in a theoretical handwriting class to improve the students' theoretical knowledge, and additionally make them aware of the limitations of their practical skills and give them tools to improve them in their future practice. Through five activities, the students took the roles of victims, forgers, teachers and experts and created their own learning materials (i.e. signatures and mock casework). During those interactive activities, they learned to describe their signature's characteristics, intra-variability and complexity, and thus evaluate their own signature's vulnerability (as potential victims). They learned techniques to simulate signatures and detect the resulting forgeries' characteristics (in the role of forgers). In the role of teachers, they prepared mock casework scenarios and gave feedback to their colleague's examination of the produced material. As experts, they carried out signature examination as they would in a proficiency test and were exposed to the difficulties an actual expert may encounter in practice. The evaluation of this novel teaching scenario was very positive, as students learned more extensively the possibilities and limitations of signature comparison. They were more active and motivated in their learning experiences. The teaching team also had an improved experience. Some students complained of an increased workload and imprecise instructions. Improvements were tested and are discussed in this paper.
An Integration of OpenDaylight and OpenNebula for Cloud Management Improvement using SDN

Software-Defined Networking (SDN) and Cloud Computing are now two of the most adopted technologies, on which many organizations are working to enhance everyday. For instance, SDN is particularly emerging to solve networking complexity in cloud data centers, so we see many attempts to integrate Network and Cloud Managers. In this paper we address an integration of these two technologies, particularly a yet undiscussed combination of two popular frameworks: OpenNebula and OpenDaylight. These open source solutions are widely used for cloud management and network management, yet there are no developed modules for communication between the two. Therefore we propose a simple way for OpenDaylight to manage OpenNebula's compute nodes, using a common component they both support: OpenvSwitch. We compared OpenNebula with the popular OpenStack cloud manager, as it is attracting more attention in both academia and industry, as well as not having been compared in recent years as numerous features have been added to both. We evaluated some relevant time metrics and discussed the advantages and differences of the proposed technologies. Our results show that OpenNebula's deployment time as well as clean-up time is significantly lower than OpenStack, but OpenStack takes less time to the running state.
Computing a Minimal Set of t-Spanning Motion Primitives for Lattice Planners

In this paper we consider the problem of computing an optimal set of motion primitives for a lattice planner. The objective we consider is to compute a minimal set of motion primitives that t-span a configuration space lattice. A set of motion primitives t-span a lattice if, given a real number t greater or equal to one, any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than t times the cost of the optimal path to that configuration. Determining the smallest set of t-spanning motion primitives allows for quick traversal of a state lattice in the context of robotic motion planning, while maintaining a t-factor adherence to the theoretically optimal path. While several heuristics exist to determine a t-spanning set of motion primitives, these are presented without guarantees on the size of the set relative to optimal. This paper provides a proof that the minimal t-spanning control set problem for a lattice defined over an arbitrary robot configuration space is NP-complete, and presents a compact mixed integer linear programming formulation to compute an optimal t-spanner. We show that solutions obtained by the mixed integer linear program have significantly fewer motion primitives than state of the art heuristic algorithms, and out perform a set of standard primitives used in robotic path planning.
Mask-RCNN based object segmentation and distance measurement for Robot grasping

This paper proposes a new application that the object can be determined by the optimal model. To extract the target from the clutter background accurately, Mask-RCNN (Mask-Region Convolutional Neural Network) model is utilized for the segmentation process. Meanwhile, target object in front of the camera can be localized with the help of the Mask-RCNN segmentation and the geometric stereo matching method. Experiments show that distance values are calculated efficiently. And then the robot manipulator is performed to grasp the target object effectively.
A Comprehensive Survey on Solving Clustering and Classification Problems Using Gravitational Search Algorithm

Gravitational search algorithm is a physics-based optimization algorithm inspired by Newton's law of gravitation and laws of motion. Both clustering and classification are two important steps in machine learning and getting expertise in them is the need of today's artificial intelligence era. Inventing new methods for mastering clustering and classification for reducing the complexity of the data is always welcome. This paper presents a review of applications of gravitational search algorithm and its variants for clustering and classification problems. In clustering, the GSA is used with various traditional clustering algorithms to find the interesting patterns in data and to divide the data set into different clusters. For solving classification problems, the GSA is hybridized with other swarm optimization algorithms to increase the classification accuracy and finding optimal classification rules.
Learning Local and Global Multi-context Representations for Document Classification

Context-based attention models achieve state-of-the-art results on constructing document representations. Current methods build document representations based on a trainable global context vector for all documents, while local contexts capturing document-specific information are ignored. Representing document context as a vector also restricts the model's capacity to capture complete semantic components of documents. In this work, we address these limitations by proposing novel attention-based approaches. We first propose a local and global context attention (LGCA) model, which constructs the context representations of a document as a combination of global and local contexts. The local context is used to capture local document-specific information. Furthermore, a multi-context attention (MCA) model is proposed to capture multiple local contexts within a document, thereby fusing the overall semantic information of documents. We conduct experiments on document-level sentiment classification tasks where extra user and product information is shown to be important. Experimental results indicate that our methods significantly outperform previous models with and without user and product information. It is also demonstrated that our model has great potential of handling extra information to improve document classification performance.
Texture synthesis quality assessment using perceptual texture similarity

Texture synthesis plays an important role in computer game and movie industries. Although it has been widely studied, the assessment of the quality of the synthesised textures has received little attention. Inspired by the research progress in perceptual texture similarity estimation, we propose a Texture Synthesis Quality Assessment (TSQA) approach. To our knowledge, this is the first attempt to exploit perceptual texture similarity for the TSQA task. In particular, we introduce two perceptual similarity principles for synthesis quality assessment. Correspondingly, we train two Random Forest (RF) regressors. Given a pair of sample and synthesised textures, the two regressors can be used to predict the global and local quality scores of the synthesised texture respectively. An overall score is generated from the two scores. Our results show that the deep Bag-of-Words (BoW) descriptors, extracted by a pre-trained Convolutional Neural Network (CNN), perform better than, or comparably to, the other nine types of hand-crafted or CNN descriptors and an image quality assessment measure, together with the proposed TSQA approach.
Autonomous Vehicle Driving Using the Stream-Based Real-Time Hardware Line Detector

To achieve the level 5 autonomous driving, which enables a totally driver-less vehicle, image recognition ability that is close to the human level is essential, since most information required for safe driving is currently provided as visual information, such as traffic lanes and signs. Though the image recognition includes various technologies, we focus on line detection in this paper, which can be used especially for lane keeping. To achieve real-time line detection with lower latency and power consumption, we prefer stream-based hardware implementation using an FPGA. A line segment detector (LSD) is an algorithm for line detection based on intensity gradient, and is better than the well-known Hough transform in terms of processing speed and accuracy. However, to implement the LSD on FPGAs in a stream manner is difficult due to its iterative approach. Therefore, we propose a simple and stream-friendly line detection algorithm based on the LSD. Evaluation results reveal that the implemented system is compact while maintaining 60 fps throughput for VGA moving images. We also introduce other components to be used to build an autonomous driving system in this paper.
Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data

The significant capability of synoptic coverage of satellite remote sensing data at the time of advent providing accurate and immediate valuable information on various aspects. The progressive development of remote sensing increases its capability to identify and map the precious and valuable materials and minerals. Spectral Angle Mapper (SAM) is classifiers of supervised classification for mapping and classification. The characteristic of SAM is based on similarity between image spectra and reference spectra on the behalf of tolerance level of specified maximum angle of threshold. In this research work SAM algorithms applied for mapping of Chhabadiya talc mineral. In this research work SAM algorithms defines the applicability of similarity of angle and value of threshold parameters of spectral angle which show capability to interpret or map the maximum and minimum abundance of talc minerals. For this research work Hyperion hyperspectral remote sensing data used for study of applicability and efficiency of SAM algorithms for mineral mapping. The quality and abundance of minerals completely depend on the minimum degree of SAM threshold parameter. Maximum spectral angle shows maximum mapping area with minimum similarity, but low spectral angle shows small and more abundant mapping area with maximum similarity in the hyperspectral image. Conclusion of this research work verify the identification of minerals are depend on the spectral and spectral characteristics of hyperspectral remote sensing data and mapping with qualitative abundance of minerals depend on the lower value of spectral angle and threshold of SAM algorithms or maximum similarity of spectra.
Control Design for Robotic Human-Following and Obstacle Avoidance Using an RGB-D Camera

The task of robotic human-following requires a mobile robot to detect and follow the selected target person and maintain an appropriate distance to the person. In practical applications, the robot must continuously estimate the target location considering unexpected obstacles to keep stable human-following control. This paper proposes to combine human-following control with obstacle avoidance, so that the robot can avoid obstacles while simultaneously follow the target person. In this design, obstacle as well as human features are obtained by using a RGB-D camera. A deep neural network(DNN) model works to detect and identify the user in the environment. An improved artificial potential field (APF) is applied to robot motion planning by integrating the target human position and obstacle information. Practical experiments on a mobile robot verified the proposed method. The robot can follow the user stably while avoiding obstacles in an indoor environment.
Coverless Text Steganography Based on Half Frequency Crossover Rule

In the development process of the information hiding technology, text steganography is a challenging task for researchers because of the small redundancy of information and the difficulty in hiding information. In this paper, a coverless text steganography method is proposed based on the Markov model and the half frequency crossover rule, which accords with the statistical characteristics of natural language. In order to prove the effectiveness of the algorithm, we select two large scale datasets for modeling and testing. The experimental results show that, compared with similar methods, the proposed model shows better concealment and hidden capacity in steganography.
HRM Smart Contracts on the Blockchain

Transparency is a problem in the hiring process of many institutions. For applicants, this process is often difficult to follow since applicants never know the state of their application and can't be sure that they are being evaluated in a fair manner. This is especially important in the public sector. Based on these premises, we propose a transparent and decentral model for the application processes by using Blockchain and smart contracts. In this system, vacancies are published to the blockchain as smart contracts, and candidates, as well as application reviewers and institutions, are rewarded for their honest participation in the process. This paper evaluates the proposed system and offers an example application to prove the viability of the model.
A Data-Centric Approach to Distributed Tracing

Modern applications are often implemented as distributed systems consisting of multiple application layers and spread across many machines. Monitoring and diagnosing are fundamental challenges of such systems, and many solutions have been proposed. They all aim to abstract the distributed nature and offer a concise overview of the system. Distributed logging, monitoring and tracing solutions help with root cause analysis or assessing the performance of the system. In data processing situations, data is the main driver and it should be treated accordingly. Microservices are often used to implement highly distributed data processing systems, so troubleshooting from a data point of view is also difficult. Data should be monitored and traced across machines and applications in order to get reliable insights into how the data is stored and processed. However, existing approaches to distributed tracing are based on tracing requests that are propagated throughout the system instead on their content. In this work we take a data-centric perspective to tracing and data processing to investigate how content can be traced in a distributed system. We evaluate existing tracing approaches to see if they can be extended to the content itself, then we take into consideration new ones. We implement three different approaches to data-centric distributed tracing in a highly distributed data processing system built using microservices and discuss their advantages and disadvantages.
Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation

Deep reinforcement learning trains neural networks using experiences sampled from the replay buffer, which is commonly updated at each time step. In this paper, we propose a method to update the replay buffer adaptively and selectively to train a robot arm to accomplish a suction task in simulation. The response time of the agent is thoroughly taken into account. The state transitions that remain stuck at the boundary of constraint are not stored. The policy trained with our method works better than the one with the common replay buffer update method. The result is demonstrated both by simulation and by experiment with a real robot arm.