Reviewerly/exHarmony huggingface dataset:
Deep learning assisted visual tracking of evader-UAV

In this work the visual tracking of an evading UAV using a pursuer-UAV is examined. The developed method combines principles of deep learning, optical flow, intra-frame homography and correlation based tracking. A Yolo tracker for short term tracking is employed, complimented by optical flow and homography techniques. In case there is no detected evader-UAV, the MOSSE tracking algorithm re-initializes the search and the PTZ-camera zooms-out to cover a wider Filed of View. The camera's controller adjusts the pan and tilt angles so that the evader-UAV is as close to the center of view as possible, while its zoom is commanded in order to for the captured evader-UAV bounding box cover as much as possible the captured-frame. Experimental studies are offered to highlight the algorithm's principle and evaluate its performance.
Practical wildcard searchable encryption with tree‐based index

Wildcard searchable encryption is an advanced variant of searchable encryption that can simultaneously maintain the searchability and confidentiality of the encrypted data. The wildcard searchable encryption outperforms the standard one for the fact that the users can use it to search the desired data even with the inexact keywords. Considering the millisecond level response time in the era of 5G, there are higher demands on the efficiency and accuracy that may be a pair of contradictions in wildcard searchable encryption. To improve the efficiency without sacrificing the accuracy, we put forward a novel scheme, tree-based index scheme (TBIS), through filtering the search results step by step instead of enumeration in the prior works and in the instantiation of TBIS, the search time drops sharply to the millisecond level. By using more kinds of characters, the accuracy of search result is improved visibly. TBIS achieves nonadaptive security that is indistinguishable against chosen character set attacks proposed in this paper. The security criteria can capture the relationship among characters, keywords and documents. At last, we put forward a frame structure in machine learning as an application of the proposed scheme.
Research on false review detection Methods: A state-of-the-art review

Fake reviews are popular today where product reviewers write the reviews without experiencing or purchasing the product on e-commerce and restaurant portals. Currently, the false review recognition method uses the systematic review process to extract, summarise, and classify the meaningful content of the research, compare and analyse the representation power of various false attributes, and the recognition method's performance. Feature design and recognition method design are the key steps for false review text recognition. The procurement of a large-scale labelled review dataset is difficult in recent research. They were only identifying fake review texts used as the core of the discussion. The article presents an assessment of fake reviews detection in different domains (hotels and e-commerce). In this article, we have also identified the relation between fake reviewers and groups of fake reviewers. We have analysed and pointed out the existing research problems in data acquisition, false feature design, and recognition method design to suggest future research on false review detection.
A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.
Design of an Aerial Manipulator System Applied to Capture Missions

This paper proposes the design of an aerial manipulator system for capture missions. A novel mechanical layout is designed. A 5-degree-of freedom manipulator and the battery are placed at the front and back part of a quadrotor unmanned aerial vehicle (UAV) respectively. This layout can expand the task space and minimize the shift of the center of mass. To improve the success rate of capture, inspired by the predation maneuver of birds, online trajectory generation law for capture mission is proposed. The aerial manipulator points towards its target during the flight. Experimental results show that the proposed aerial manipulator system can capture the target on a moving platform successfully.
Blockchain in Healthcare

The fourth industrial revolution, which will alter the globe, is commonly referred to as Blockchain technology.Blockchain technology provides a decentralized, distributed, and central authority-free environment.Since Bitcoin launched Blockchain, research has been continuing on non-financial use cases to extend their applicability.Healthcare is an industry with a significant influence on the Blockchain.Healthcare has penetrated the enthusiasm for the changing nature of Blockchain technology.Blockchain is frequently viewed as the most necessary and optimal healthcare technology to handle sophisticated and complex security and interoperability concerns.More significantly, the "value" and trust-based system's smart contract mechanism can offer automatic action and reaction.Healthcare, on the other hand, is a complex system.In this paper, we introduce the blockchain and its properties, as well as the significance of the blockchain in healthcare.It also provides blockchain administration, adjudication of claims, interoperability, and application.While in several situations, we observed blockchain technology, the use of blockchain in health care was highly addressed in this paper and the reason why blockchain should be utilized.We introduce the advantages of blockchain as well.Furthermore, we examined the difficulties and prospects for the future and how they may be implemented in more healthcare industries.The paper also discusses the current level of Blockchain application development for healthcare and its limits and topics for further research.This paper aims to demonstrate how Blockchain technologies may be utilized in healthcare and what problems this technology may face in the future and what the Blockchain's prospects are.
Pixel-level Intra-domain Adaptation for Semantic Segmentation

Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. In this work, we propose a pixel-level intra-domain adaptation approach to reduce the intra-domain gaps within the target data. Compared with image-level methods, ours treats each pixel as an instance, which adapts the segmentation model at a more fine-grained level. Specifically, we first conduct the inter-domain adaptation between the source and target domain; Then, we separate the pixels in target images into the easy and hard subdomains; Finally, we propose a pixel-level adversarial training strategy to adapt a segmentation network from the easy to the hard subdomain. Moreover, we show that the segmentation accuracy can be further improved by incorporating a continuous indexing technique in the adversarial training. Experimental results show the effectiveness of our method against existing state-of-the-art approaches.
Registration strategy of point clouds based on region-specific projections and virtual structures for robot-based inspection systems

Current requirements resulting from high quality standards, individualized mass production, and cost pressure call for new concepts in manufacturing metrology. Robot-based optical inspection systems have shown high potential to cope with these challenges. Since the kinematics of robots usually exhibit significant inaccuracies, an alternative approach becomes necessary to register multiple views of a sensor. With the advancement of optical 3D sensors in recent years, registration methods based on measured data (e.g. point clouds with image data) have shown tremendous progress and potential. Therefore, we present a new registration strategy which uses region-specific projections (image data) and virtual structures (point data) to support the alignment process of multiple point clouds. The fine registration is done in a pairwise manner and by means of an Iterative Closest Point (ICP) algorithm. We first introduce a procedure to obtain a set of virtual structures. Subsequently, influencing factors, such as the parametrization of the structures, noise, detection methods for the projected shapes, and the underlying surface, are examined. Based on these results, we identify virtual structures and employ them to evaluate the registration strategy in a real-world use case in the context of geometric quality assurance in the automotive industry. Acquired sphere-to-sphere distances are compared to reference measurements obtained from a coordinate measuring machine (CMM). The virtual structure comprising three point-based “columns” with an increasing point spacing in a triangular arrangement showed the best overall performance. The mean error (34 μm for one registration; 70 μm and 54 μm for two registrations respectively) in comparison to the robot kinematics was reduced by a factor of about 14 to 23 for the different reference distances. We demonstrated that our registration strategy enables a highly accurate and robust alignment of point clouds, which benefits manufacturing metrology for the inspection of sheet metal parts.
Vision-Guided Autonomous Robotic Electrical Bio-Impedance Scanning System for Abnormal Tissue Detection

In cancer surgery, the correct localization of cancerous tissue is critical for the success of the surgery. Specifically, the surgical goal is to remove the malignant tissue completely while preserving the maximum amount of healthy tissue. Unfortunately, limited technologies are available for intra-operative assessment of tumor margin in Robotic Minimally Invasive Surgery (RMIS). This work presents a vision-based autonomous robotic scanning system for abnormal tissue detection. A stereo vision system, including dense 3D surface reconstruction and tissue tracking, is used to control the scanning motion to compensate for tissue motion and deformation. A high level control strategy allows to control a robot arm to scan autonomously the tissue by means of a monopolar forceps measuring the Electric Bio-Impedance (EBI) of the tissue. The acquired information is used for discriminating cancerous from healthy tissue in real-time, and this information is presented as an impedance map overlaid on the endoscopic video using Augmented Reality (AR). The proposed system performance is evaluated on ex-vivo animal tissues, through experiments simulating the identification and localization of hepatic cancer for resection surgery. Quantitative results show that the system is able to autonomously scan the area of interest and provide a tissue characterization with an accuracy over 82%.
Counting People by Estimating People Flows

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it allows us to exploit the correlation between people flow and optical flow to further improve the results. We also show that leveraging people conservation constraints in both a spatial and temporal manner makes it possible to train a deep crowd counting model in an active learning setting with much fewer annotations. This significantly reduces the annotation cost while still leading to similar performance to the full supervision case.
Deep Reservoir Computing

This chapter surveys the recent advancements on the extension of Reservoir Computing toward deep architectures, which is gaining increasing research attention in the neural networks community. Within this context, we focus on describing the major features of Deep Echo State Networks based on the hierarchical composition of multiple reservoirs. The intent is to provide a useful reference to guide applications and further developments of this efficient and effective class of approaches to deal with times-series and more complex data within a unified description and analysis.
BLOCKCHAIN: A NON-TECHNICAL PRIMER FOR MARKETING ACADEMICS

In the ongoing march of industrial evolution there comes along technologies that have the power to transform businesses as we know it. The Internet revolutionized business/marketing in the nineties and today and bockchain has the potential to do the same for commercial transactions. Blockchain is a peer-to-peer model that can speed up processes resulting in robust tracking and reducing costs of transactions. Given the implications of this far-reaching technology, it is essential for marketing students to understand its significance in the conduct of marketing activities. Consequently, it is imperative that faculty comprehend aspects of the technology and its applications in marketing to integrate them in marketing courses. The purpose of the paper is to enlighten marketing faculty on the concept of blockchain in a non-technical manner along with highlighting its applications in marketing that can be imparted through their courses. To do so, the paper is structured around three research questions relating to (a) understanding the idea of blockchain technology; (b) expounding on the importance of the technology in marketing applications; and (c) illustrating how marketing faculty can integrate the concepts and application in marketing courses.
Sentiment Analysis for Thai Language in Hotel Domain Using Machine Learning Algorithms

Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level.Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency.However, the users' reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine.To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class.In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language.The dataset was collected manually from two online agencies (Agoda.comand Booking.com)utilizing a special Thai language.We employed 11 preprocessing steps to clean and handle the large amount of noise data.Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons.All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy.The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%.The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.
Genetic Algorithm with Multiple Fitness Functions for Generating Adversarial Examples

Studies have shown that deep neural networks (DNNs) are susceptible to adversarial attacks, which can cause misclassification. The adversarial attack problem can be regarded as an optimization problem, then the genetic algorithm (GA) that is problem-independent can naturally be designed to solve the optimization problem to generate effective adversarial examples. Considering the dimensionality curse in the image processing field, traditional genetic algorithms in high-dimensional problems often fall into local optima. Therefore, we propose a GA with multiple fitness functions (MF-GA). Specifically, we divide the evolution process into three stages, i.e., exploration stage, exploitation stage, and stable stage. Besides, different fitness functions are used for different stages, which could help the GA to jump away from the local optimum.Experiments are conducted on three datasets, and four classic algorithms as well as the basic GA are adopted for comparisons. Experimental results demonstrate that MF-GA is an effective black-box attack method. Furthermore, although MF-GA is a black-box attack method, experimental results demonstrate the performance of MF-GA under the black-box environments is competitive when comparing to four classic algorithms under the white-box attack environments. This shows that evolutionary algorithms have great potential in adversarial attacks.
Effective implementation of machine learning algorithms using <scp>3D</scp> colour texture feature for traffic sign detection for smart cities

Abstract In recent past, emerging technology, such as AI has revolutionized progress and advancement of traffic management solutions in context of smart cities. This paper describes a novel approach of using 3D colour‐texture based feature for image detection on public datasets of Chinese traffic sign research database (TSRD) and Mapillary image database. The implementation of 3D colour texture feature for traffic sign detection is evaluated using artificial neural network and multiple other machine learning algorithms as classifier for image detection purposes. Both datasets used in our experiments are considered among most diverse traffic signage datasets globally with annotations of almost all classes. Image datasets have been publicly made available to researchers for academic purposes. For classification of traffic sign images on Chinese TSRD and Mapillary datasets, the result outcome from back‐propagation neural network (NN) classifier outperforms all other and produced best results and under multiple ML algorithms, support vector machines (SVM) cubic has best results. For classification of traffic sign images on Mapillary dataset, the result outcome from rational quadratic Gaussian process regression has best results. Also, the comparisons of results with other similar novel works have also been discussed. The proposed approach outperformed the previous experiments as observed through comparative analysis performed on both datasets. The conclusion of the research work highlights that the image detection performance is noticeably improved by using the combined 3D colour‐texture feature based proposed approach.
A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

Firefly Algorithm (FA) is a recent swarm intelligence first introduced by X.S. Yang in 2008. It has been widely used to solve several optimization problems. Since then, many research works were elaborated presenting modified versions intending to improve performances of the standard one. Consequently, this article aims to present an accelerated variant compared to the original Algorithm. Through the resolving of some benchmark functions to reach optimal solution, obtained results demonstrate the superiority of the suggested alternative, so-called Fast Firefly Algorithm (FFA), when faced with those of the standard FA in term of convergence fastness to the global solution according to an almost similar precision. Additionally, a successful application for the control of a brushless direct current electric motor (BLDC) motor by optimization of the Proportional Integral (PI) regulator parameters is given. These parameters are optimized by the FFA, FA, GA, PSO and ABC algorithms using the IAE, ISE, ITAE and ISTE performance criteria.
An Interleaved Approach to Trait-Based Task Allocation and Scheduling

To realize effective heterogeneous multi-robot teams, researchers must leverage individual robots’ relative strengths and coordinate their individual behaviors. Specifically, heterogeneous multi-robot systems must answer three important questions: who (task allocation), when (scheduling), and how (motion planning). While specific variants of each of these problems are known to be NP-Hard, their interdependence only exacerbates the challenges involved in solving them together. In this paper, we present a novel framework that interleaves task allocation, scheduling, and motion planning. We introduce a search-based approach for trait-based time-extended task allocation named Incremental Task Allocation Graph Search (ITAGS). In contrast to approaches that solve the three problems in sequence, ITAGS’s interleaved approach enables efficient search for allocations while simultaneously satisfying scheduling constraints and accounting for the time taken to execute motion plans. To enable effective interleaving, we develop a convex combination of two search heuristics that optimizes the satisfaction of task requirements as well as the makespan of the associated schedule. We demonstrate the efficacy of ITAGS using detailed ablation studies and comparisons against two state-of-the-art algorithms in a simulated emergency response domain.
Sharing biosignals: An analysis of the experiential and communication properties of interpersonal psychophysiology

What is it like to hear another person’s heartbeat during communication? How would direct access to a person’s skin conductance influence our sense of interpersonal intimacy or trust? Can biosignal...
THE IMPACT OF DATA ON THE ROLE OF DESIGNERS AND THEIR PROCESS

Abstract With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.