Reviewerly/exHarmony huggingface dataset:
Application of recent nature‐inspired meta‐heuristic optimisation techniques to small permanent magnet DC motor parameters identification problem

In this study, an attempt is made to find an effective solution on the small direct current (DC) motor's parameters identification problem by employing two recently introduced stochastic nature-inspired techniques. The first one is called ‘salp swarm algorithm’ (SSA) while the second one is called ‘ant-lion optimiser’ (ALO). These algorithms have been inspired by the interaction strategy between the ocean salps and the ants and ant-lions, respectively, in nature. To appraise the effectiveness of these algorithms, a DC motor model has been appropriately implemented and its performance is evaluated by using speed step responses, while all model's parameters are considered unknown and therefore search variables. Integral of the square of the error (ISE), integral absolute error and integral in time of absolute error have been adopted as objective functions for the algorithms’ evaluation. In order to judge the acceptability of these algorithms, the simulation results are compared with those of another one similar technique, namely the ‘grey wolf optimiser’ (GWO). The obtained results reveal very satisfactory performance and confirm that the examined SSA, ALO and GWO algorithms can identify accurately the DC motor parameters and can be applied effectively to the specific problem. Another finding is that SSA combined with ISE criterion seems to be the most appropriate technique among the three algorithms.
An hyper elliptic curve based efficient signcryption scheme for user authentication

With ever growing popularity, wireless communication system also vulnerable to various security attacks. To provide high level security, many cryptographic solutions have been proposed. One such solution is signcryption, where authenticity and confidentiality provided by single logical step. Therefore, signcryption scheme helps to reduce computational cost, but it is not feasible for resource constraint environments. Because, most of the existing approaches were based on El-Gamal, bilinear pairing, Rivest-Shamir-Adleman (RSA), and Elliptic curve Cryptography (ECC). They consume more energy due to their increased key size. Hence, the new signcryption approach is proposed based on Hyper Elliptic Curve Cryptosystem (HECC) whose key size is much lesser than ECC. It significantly reduces the cost of computation and communication overhead by half the amount of ECC which suits well for resource constraint environments. Further, the proposed scheme attains necessary security features along with forward secrecy and public verifiability. On the other hand, the security of the approach is validated through an automated protocol validation tool – AVISPA.
VR Simulation of Novel Hands-Free Interaction Concepts for Surgical Robotic Visualization Systems

In microsurgery, visualization systems such as the traditional surgical microscope are essential, as surgeons rely on the highly magnified stereoscopic view for performing their operative tasks. For well-aligned visual perspectives onto the operating field during surgery, precise adjustments of the positioning of the system are frequently required. This, however, implies that the surgeon has to reach for the device and each time remove their hand(s) from the operating field, i.e. a disruptive event to the operative task at hand. To address this, we propose two novel hands-free interaction concepts based on head-, and gaze-tracking, that should allow surgeons to efficiently control the 6D positioning of a robotic visualization system with little interruptions to the main operative task. The new concepts were purely simulated in a virtual reality (VR) environment using a HTC Vive for a robotic visualization system. The two interaction concepts were evaluated within the virtual reality simulation in a quantitative user study with 11 neurosurgeons at the Charité hospital and compared to conventional interaction with a surgical microscope. After a brief introduction to the interaction concepts in the virtual reality simulation, neurosurgeons were 29% faster in reaching a set of virtual targets (position and orientation) in simulation as compared to reaching equivalent physical targets on a 3D-printed reference object.
Heavy Vehicle Classification Through Deep Learning

Understanding the flow of traffic on road networks is increasingly important especially with the continued urbanization of the global population. Numerous hardware and software technologies have been applied to measure traffic volumes by Government agencies and/or organizations such as Google, however they are either expensive to deploy; limited in their ability to disambiguate the kinds of vehicles on the road network, or of increasing importance, they infringe on the privacy of individuals, e.g. tracking phones. In this paper we describe work applying deep learning technologies to identify and classify different vehicles on the road network of Victoria with specific focus on heavy goods vehicles (trucks and trailers). Specifically, we present an approach to automatically detect, classify and count the unique classes of trucks and trailers that are found on the road network and the direction of travel. We apply and compare leading deep learning approaches including You Only Look Once version 3 (YOLOv3) and Single Shot Multi-Box Detector (SSD). This paper builds upon earlier work [] which focused on data (video) from a single traffic junction in Melbourne. This work is based on a wider range of data (videos) from locations reflecting the diversity of road use including multi-lane motorways, rural roads and city roads.
The Intelligent Control of Street Light System in Oman through Internet of Things Technology

Intelligent Street Lights System based on LED lights is one of the most important concepts in today's life. As now a days world depends on the IoT technologies in different areas. Street lights system will be controlled through IoT technology. In the whole world, enormous energy is consumed by the street lights, which are turned ON automatically all the night. Thus, this will waste a huge energy of the country. The intelligent street light system will work in such a way to turn the LED lights ON when the vehicle is passing through it and when not the LED lights turned OFF. The primary aim of this research paper is to reduce the power energy consumption and cost of the street lights system.This paper presents the intelligent street lights system that is controlled through IOT technology, which is connecting to sensor device "Ultrasonic" and then to microcontroller device using Wi-Fi connectivity. In addition, it will use the cloud services and mobile application to present the information of the system for the system administrator.
Exploiting sparsity in robot trajectory optimization with direct collocation and geometric algorithms

This paper presents a robot trajectory optimization formulation that builds upon numerical optimal control and Lie group methods. In particular, the inherent sparsity of direct collocation is carefully analyzed to dramatically reduce the number of floating-point operations to get first-order information of the problem. We describe how sparsity exploitation is employed with both numerical and analytical differentiation. Furthermore, the use of geometric algorithms based on Lie groups and their associated Lie algebras allow to analytically evaluate the state equations and their derivatives with efficient recursive algorithms. We demonstrate the scalability of the proposed formulation with three different articulated robots, such as a finger, a mobile manipulator and a humanoid composed of five, eight and more than twenty degrees of freedom, respectively. The performance of our implementation in C++ is also validated and compared against a state-of-the-art general purpose numerical optimal control solver.
Selection of Best Software Methodology Using Entropy and TOPSIS

With the advent of disruptive methodologies to ensure higher success rates in software engineering processes, the agile software development principles have gained the interest of large organizations. Agile methodology is characterized by iterative and incremental nature, customer-oriented development, adaptability and brisk development cycles. This study analyses the criteria of gathering data and evaluation is conducted by the expert's judgement from which valuable information is extracted to deliver quality software. Critical Success Factors (CSF) is dynamic in implementing an autonomous methodology using entropy approach from the organisation perspective CSF must be known initially following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which is a multiple-criteria decision-making (MCDM) approach. The approach's aim is to in parallel minimize and maximise the critical factors. CSF which needs more attention are emphasized from this research findings and is arranged from high to low. We conclude this study by selecting the best software methodology using entropy and TOPSIS.
Software development data for architecture analysis

Recently we have worked with a dozen industrial collaborators to pinpoint and quantify architecture debts, from multi-national corporations to startup companies. Our technology leverages a wide range of project data, from source file dependencies to issue records, and we interacted with projects of various sizes and characteristics. Crossing the border between research and practice, we have observed significant gaps in terms of data availability and quality among projects of different kinds. Compared with successful open source projects, data from proprietary projects are rarely complete or well-organized. Consequently, not all projects can benefit from all the features and analyses we provide. This, in turn, made them realize they needed to improve their development processes. In this talk, we categorize the commonly observed differences between open source and proprietary project data, analyze the reasons for such differences, and propose suggestions to minimize the gaps, to facilitate advances to both software research and practice.
Smart Wristband for Gesture Recognition

This paper aim to design a smart wristband for gesture recognition. Tendon movements around the wrist were measured by FSR sensors as input variables to classify different gestures. Polydimethylsiloxane material (PDMS) was applied to encapsulate FSR sensors, so that the wristband is flexible and suitable for people with different wrist sizes. Subsequently, the sensor data was transmitted to the computer via Bluetooth low energy (BLE) technology. MATLAB was used to train a classifier with ensemble subspace discrimination algorithm. After that, the received signal was processed by this trained classifier and made prediction. The accuracy is about 99.4%. Additionally, the paper explored how predict accuracy would be impacted when twisting the wrist. The result showed that a gesture in different angles was classified as different gestures. Overall, the wristband is rechargeable, portable and can accurately recognize over 6 gestures.
Object-oriented Semantic Graph Based Natural Question Generation

Generating a natural question can enable autonomous robots to propose problems according to their surroundings. However, recent studies on question generation rarely consider semantic graph mapping, which is widely used to understand environments. In this paper, we introduce a method to generate natural questions using object-oriented semantic graphs. First, a graph convolutional network extracts features from the graph. Then, a recurrent neural network generates the natural question from the extracted features. Using graphs, we can generate natural questions for both single and sequential scenes. The proposed method outperforms conventional methods on a publicly available dataset for single scenes and can generate questions for sequential scenes.
On the effectiveness of using virtual reality games in social marketing

Social marketing aims to influence behaviors that would be beneficial to individuals and communities. To achieve this goal, it is based on various interventions. Such interventions need to include all four P’s of marketing mix: product, price, place, and promotion. Among all these elements, promotion is assumed to be the most important and associated with common understanding of social campaign. Although, interventions which only use the promotion are not considered to be social marketing, social advertising is very essential for the success of the campaign as it plays a key role in terms of its perception and draws attention of a wide audience to the problem. Thanks to the occurrence of advertising in the media it can reach the larger number of recipients and initiate many positive changes. Recently, next to the traditional communication channels, like TV and press, relatively new media have occurred (mobile phones, social media, etc.). Apart of that, social marketing campaigns may also use advertising in games. This type of entertainment is widely popular and provides a deep recipient’s engagement, especially when the game is presented in virtual reality. Therefore, it can significantly increase the effectiveness of social advertising. The aim of the article is to present an experiment that allows to study the efficacy of virtual reality game in social advertising. The research is designed to gather data from two different sources - survey and cognitive neuroscience tools. The study proposal is based on the simple action game that aims to raise players’ awareness of healthy eating. The application of virtual reality for this purpose is very promising since it is quite novel technology and because of its features allows for much deeper immersion into game’s world that traditional computer or video games.
Resource Scheduling on Basis of Cost-Effectiveness in Cloud Computing Environment

Cloud computing is a computing software that enables a user to manipulate, configure, and provide access to the applications over the network. It is a model that allows the user to take advantage of computing facilities over the web as per user demand and to share resources such as network, data storage, servers, and applications without the need of actually installing it on their device. Thus, it can be said that it is a computing model that helps manage cost and saves time. Cloud computing has its applications over many fields like health, education or banking due to its special features. Cloud computing is an Internet-based software, and thus, it is the responsibility of cloud service providers (CSPs) to maintain data stored by users at data centers. Scheduling in cloud plays a very important role to achieve maximum utilization, and user satisfaction resources need to be allocated effectively. In this paper, we have discussed some optimum scheduling technique to enhance the performance of cloud. We consider cost as one of the major attributes that result in enhancing the performance (Cloud computing tutorial tutorials point), (Armbrust et al. in Commun ACM 53:50–58, 2010).
Immunohistochemical analysis of oral cancer tissue images using support vector machine

This paper describes an automatic image analysis technique for p53 immunostained tissue sections of oral cancer. The tissue images are segmented using the entropy thresholding and clustered cells are resolved by selectively applying watershed transform. Each cell nuclei of tissue images is classified as positive or negative according to the staining intensity using support vector machine, and then, tissue score is determined as per J-scoring protocol. The performance of the feature and also scoring technique has been evaluated separately by an individual dataset. According to the experimental result, the feature extracted from the blue component has attained the highest classification accuracy of 98.01% with sensitivity and specificity of 98.86% & 94.74% respectively. The outcome of automatic technique based on the blue component has a strong agreement with the manual score. Therefore, automatic tissue scoring has high potential in the field of modern cancer diagnosis and specific therapy design for the patients.
Augmented Situated Visualization for Spatial and Context-Aware Decision-Making

Whenever accessing indoor spaces such as classrooms or auditoriums, people might attempt to analyze and choose an appropriate place to stay while attending an event. Several criteria may be accounted for, and most are not always self-evident or trivial. This work proposes the use of data visualization allied to an Augmented Reality (AR) user interface to help users defining the most convenient seats to take. We consider sets of arbitrary demands and project information directly atop the seats and all around the room. Users can also narrow down the search by switching and combining the attributes being displayed, e.g., temperature, wheelchair accessibility. The proposed approach was tested against a comparable 2D interactive visualization of the same data in usability assessments of seat-choosing tasks with a set of users (N = 16) to validate the solution. Qualitative and quantitative data indicated that the AR-based solution is promising, suggesting that AR may help users make more accurate decisions, even in an ordinary daily task. Regarding Augmented Situated Visualization, our results open new avenues for the exploration of context-aware data.
VisSched: An Auction-Based Scheduler for Vision Workloads on Heterogeneous Processors

With the growth of edge computing, applicationspecific workloads based on computer vision are steadily migrating to edge cloudlets. Scheduling has been identified to be a major problem in these cloudlets. In this article, we propose a generic architectural solution, VisSched, that leverages the fact that most vision workloads share similar code kernels (such as library code for linear algebra), and as a result, they tend to exhibit similar phase behavior. This allows us to create an auction theory-based scheduling mechanism, where we give each thread a replenishable virtual wallet, and threads are scheduled based on the amounts that they bid for executing on a free core. We show that in 20%-40% of the cases, our scheduling algorithm is theoretically optimal, and in the remaining cases, it reaches a global optimum obtained using Monte Carlo simulations 90%-95% of the time. Our results for the MEVBench vision workloads show a 17% higher performance and a 14% lower ED <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> as compared to the nearest competing algorithm in the literature.
Variational Bayesian Parameter-Based Policy Exploration

Reinforcement learning has shown success in many tasks that cannot provide explicit training samples and can only provide rewards. However, because of a lack of robustness and the need for hard hyperparameter tuning, reinforcement learning is not easily applicable in many new situations. One reason for this problem is that the existing methods do not account for the uncertainties of rewards and policy parameters. In this paper, for parameter-based policy exploration, we use a Bayesian method to define an objective function that explicitly accounts for reward uncertainty. In addition, we provide an algorithm that uses a Bayesian method to optimize this function under the uncertainty of policy parameters in continuous state and action spaces. The results of numerical experiments show that the proposed method is more robust than comparing method against estimation errors on finite samples, because our proposal balances reward acquisition and exploration.
DSBFT: A Delegation Based Scalable Byzantine False Tolerance Consensus Mechanism

Blockchain, a trustless decentralized ledger and cryptography based technology, is a fast-growing computing paradigm, born with Bitcoin. Benefit from its core features, a number of applications have arisen in various application fields, e.g. finance, healthcare, supply chain and so on. A consensus mechanism is a foundational component of a blockchain-based system. The existing mechanisms rare achieve high efficiency and high scalability simultaneously. In this paper, we propose a hybrid consensus mechanism, a delegation based scalable byzantine false tolerant (DSBFT), which combines PoW and BFT to achieve a well-balanced between efficiency and scalability. Our mechanism has been shown to achieve linear scalability and sustain security even when more than 1/3 computing power is byzantine malicious.
Distributed Test, Track and Trace System as a Contributor to Epidemic Containment Management

The outcome of the recent novel Coronavirus pandemic is profound and particularly poignant when countries like South Korea, who have learned from previous pandemics, appear to be better able to respond to the challenge. Since lock down due to the novel Coronavirus of 2019, the Government of the United Kingdom has adopted retrospective testing and a smartphone application for contact tracing as part of its retroactive test, track and trace strategy. However, to thwart future epidemics, this paper proposes that a different approach should be adopted in preparation for the next epidemic. This consists of a distributed system solution comprising front-end testing nodes at the borders as well as test laboratories connected to a backend cloud-based data processing and data storage system. This core system can be used to test, track and trace infection hotspots at the start of an epidemic to avoid significant outbreaks in the United Kingdom. This hotspot data can be made available to local authorities so that more informed decisions can be made regarding approaches to contain any infectious viral spread. This would need to involve traditional epidemiological track and trace surveillance activities to understand the propagation effect; and therefore, understand the resultant disease coverage in the community so that mitigation measures can be successfully applied in a timelier fashion.
Recurrent Neural Architecture Search based on Randomness-Enhanced Tabu Algorithm

Deep neural networks have achieved highly competitive performance in multiple tasks in recent years. However, discovering state-of-the-art neural network architectures requires substantial effort from human experts. To speed up the process, neural architecture search (NAS) has been proposed to search promising architectures automatically. Nevertheless, the search process of NAS is computing-expensive and time-consuming, which even costs thousands of GPU days. In this paper, to solve the bottleneck, we apply the randomness-enhanced tabu algorithm as a controller to sample candidate architectures, which balances the global exploration and local exploitation for the architectural solutions. In addition, more aggressive weight-sharing strategy is introduced into our method, which significantly reduces the overhead of evaluating sampled architectures. Our approach discovers the recurrent neural architecture within 0.78 GPU hour, which is 15.3x more efficient than ENAS [1] in terms of search time, and the architecture we discovered achieves the test perplexity of 56.1 on Penn Tree Bank (PTB) dataset, which is lower than ENAS by 2.2. In addition, we further demonstrate the usefulness of the learned architecture by transferring it to wiki-text-2 (WT2) dataset well. Moreover, the extended experiments on the WT2 dataset also show promising results.