Reviewerly/exHarmony huggingface dataset:
Towards History-Aware Self-Adaptation with Explanation Capabilities

Self-adaptive systems (SAS) increasingly use techniques such as AI-based learning and evolutionary programming. In this paper, we argue that a SAS needs an infrastructure and capabilities to look at its own history to explain and reason why the system has reached its current state and exhibits its current behaviour. Achieving this is no simple feat: there are different challenges with respect to the feasibility of storing past system history, querying it and applying the information in the context of a given decision-making algorithm. We introduce 4 levels of capabilities that should be exposed by reflective, self aware and self-adaptive systems, and which will guide our future research on the topic in the longer term. We demonstrate our results for the first two levels using temporal graph-based models. Specifically, we explain how the first level covers forensic analysis of the execution results. This is followed by the description of our results in enabling historical analyses while the self-adaptive system is running, based on the capabilities provided by the second level. Required system architectures are also proposed, as well as the overheads that would be imposed by live analysis. Research opportunities provided by the set of levels are also discussed.
SLS-STQ: A Novel Scheme for Securing Spatial–Temporal Top-$k$ Queries in TWSNs-Based Edge Computing Systems

A novel network paradigm of edge computing, namely, two-tiered wireless sensor networks (TWSNs), has been proposed by researchers in recent years for its high scalability and robustness. However, in the TWSNs-based edge computing systems, the storage nodes, which are located at the upper layer of the systems, are prone to be attacked by adversaries because they play a key role in bridging sensor nodes and Sink, which may lead to the disclosure of all the data stored on them as well as some other potentially devastating results. In this article, we study the integrity-and-privacy preservation problem for spatial- temporal Top-k queries in the TWSNs-based edge computing systems and propose a sequence-encryption-based lightweight scheme named sequence-encryption-based lightweight scheme for securing spatial-temporal Top-k queries (SLS-STQ) to solve the problem. In SLS-STQ, three algorithms, namely, the report preparation algorithm, the query processing algorithm, and the integrity verification algorithm, are designed for the sensor nodes, the storage nodes, and Sink, respectively. The theoretical analysis shows that SLS-STQ is able to achieve both integrity validation and privacy preservation with low computational complexity, and the simulation results show that SLS-STQ is much more efficient than the related state-of-the-art schemes.
Privacy Preserving Recursive Least Squares Solutions

Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions; hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret. As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.
Obstacles Awareness Methods from Occupancy Map for Free Walking in VR

With Head Mounted Displays (HMD) equipped with extended tracking features, users can now walk in a room scale space while being immersed in a virtual world. However, to fully exploit this feature and enable free-walking, these devices still require a large physical space, cleared of obstacles. This is an essential requirement that not any user can meet, especially at home, thus this constraint limits the use of free-walking in Virtual Reality (VR) applications. In this poster, we propose ways of representing the physical obstacles surrounding the user. There are generated from an occupancy map and compared to the representation as a point cloud. We propose three visualisation modes: integrating an occupancy map into the virtual floor, generating lava lakes where obstacles are and building a semi-transparent wall along the obstacles boundaries. We found that although showing the obstacles on the floor only impacts lightly the navigation, the preferred visualization mode remains the point cloud.
Planning System for Integrated Autonomous Infrastructure Inspection using UAVs

Structural inspection is a critical task nowadays. The huge amount of civil infrastructures and industrial facilities makes tedious the task of continuously track their health. This task requires several resources and takes excessive time to operators. This article contributes with a complete system to facilitate the automation of infrastructure inspection using Unmanned Aerial Vehicles (UAVs). The system autonomously controls the UAV towards a set of inspection points chosen by the operators, allowing them to inspect unreachable locations in seconds. A planning algorithm computes the best location to observe the target point and calculates feasible safe trajectories for the UAV to reach each of the locations. All the system is packed into an human-machine interface which facilitates the inspection tasks.
Fake News Detection by Image Montage Recognition

Fake news have been a problem for multiple years now and in addition to this "fake images" that accompany them are becoming increasingly a problem too. The aim of such fake images is to back up the fake message itself and make it appear authentic. For this purpose, more and more images such as photo-montages are used, which have been spliced from several images. This can be used to defame people by putting them in unfavorable situations or the other way around as propaganda by making them appear more important. In addition, montages may have been altered with noise and other manipulations to make an automatic recognition more difficult. In order to take action against such montages and still detect them automated, a concept based on feature detection is developed. Furthermore, an indexing of the features is carried out by means of a nearest neighbor algorithm in order to be able to quickly compare a high number of images. Afterwards, images suspected to be a montage are reviewed by a verifier. This concept is implemented and evaluated with two feature detectors. Even montages that have been manipulated with different methods are identified as such in an average of 100 milliseconds with a probability of mostly over 90%.
LSTM-Based Language Models for Very Large Vocabulary Continuous Russian Speech Recognition System

This paper presents language models based on Long Short-Term Memory (LSTM) neural networks for very large vocabulary continuous Russian speech recognition. We created neural networks with various numbers of units in hidden and projection layers using different optimization methods. Obtained LSTM-based language models were used for N-best list rescoring. As well we tested a linear interpolation of LSTM language model with the baseline 3-gram language model and achieved 22% relative reduction of the word error rate with respect to the baseline 3-gram model.
Efficient Kinodynamic Multi-Robot Replanning in Known Workspaces

In this work, we consider the problem of online centralized kinodynamic multi-robot replanning (from potentially non-stationary initial states) and coordination in known and cluttered workspaces. Offline state lattice reachability analysis is leveraged to decouple the planning problem into two sequential graph searches-one in the explicit geometric graph of the environment and the other in the graph of the higher-order derivatives of the robot's state-in a manner such that the intermediate vertices of a safe set of geometric paths are guaranteed to have a feasible assignment of higher-order derivatives. Without additional iterative refinement procedures, the resulting time parameterized polynomial trajectories are dynamically feasible and collision-free. Planning results with up to 20 robots in two and three dimensional workspaces suggest the suitability of the proposed approach for multi-robot replanning in known environments.
Cyber Security Skill Set Analysis for Common Curricula Development

The field of cyber security is getting diversified day by day, with new specialist responsibilities and roles at different levels of competence being required by the industry. The competencies can be mapped with required skills set in multiple cyber security certification programs. However, different certification programs use different curricula and terminology, which makes the offerings overlap in some aspect and be distinct in others. This makes it hard for new institutes and cyber ranges to decide upon their training offerings. The aim of this study is to identify commonalities in skill set requirements for multiple cyber security roles like penetration tester, security operation center analysts, digital forensic and incident responders and information security managers. The identified commonalities will be used for the development of a standard common curricula to set skill set requirements for the achievement of specific competence levels in a specific cyber security field.
Age Estimation for Low-Quality Facial Images: from Separate DCNNs to a Decision Fuser

In this paper, we contribute an age estimation method towards dealing with low quality face images. This is a practical and important problem because an image we received may have low resolution or be affected by some noise via transmission. Upon reviewing the literature on facial age estimation, we notice that few articles tackle this low quality image based facial age estimation problem. In our framework, we propose a newly designed deep convolutional neural networks architecture, consisting of five major steps. Firstly, a data augmentation step is utilized to ease the training procedure. Secondly, we propose to use a super-resolution method to enhance the input images. Thirdly, we use a deep network to conduct gender grouping. Fourthly, two recently proposed deep networks are modified with depthwise separable convolutions to perform age estimation within male and female groups. Finally, a fusion procedure is added to further boost age estimation accuracy. In the experiment, we use two benchmark datasets, IMDB-WIKI and MORPH-II, to verify our proposed method and also show a significantly performance improvement over some state-of-the-art methods.
NSGA-II with Local Search for Multi-objective Application Deployment in Multi-Cloud

Cloud computing allows software application or service providers to purchase computational resources for hosting software applications or services. There is an increasing number of cloud service vendors, each offering different types of computational resources, such as virtual machines (VMs), in distinct physical locations, meaning that the chosen computational resources directly affect the cost and performance of the deployed applications. Given the increasing number of cloud providers and types of computational resources, the process of selecting the most suitable resources to meet multiple quality of service (QoS) requirements becomes quite challenging. Thus, the aim of this paper is to propose an efficient and effective approach to the problem of application deployment in multi-cloud with the objective of minimizing overall deployment costs and response time. This is accomplished by employing a hybrid NSGA-II approach with a local search method. Experimental evaluations with benchmark datasets demonstrate that our proposed hybrid approach outperforms NSGA-II and SPEA2.
Detecting Anomalies in Image Classification by Means of Semantic Relationships

This paper presents a semantic anomaly detection method (SAD) to detect anomalies in the predictions of any pixelwise semantic segmentation algorithm. This semantic information (e.g., relative positions and sizes of all the object pairs in an image), learned from the training set and stored in a knowledge base as configuration rules, allows the detection of potential misclassifications in the baseline model predictions. Our approach highlights the objects which are not consistent with the contextual information in the knowledge base. It also provides an interpretable motivation for the detected anomaly, based on the semantic information provided by the configuration rules.
Dynamic Policy Attribute Based Encryption and its Application in Generic Construction of Multi-Keyword Search

Attribute based encryption (ABE) is an encryption technique which provides a good solution to the security issues in the cloud environment. Through ABE, a data owner can achieve the fine-grained sharing of data encrypted under attributes or an access policy which they possess. The relation among these attributes is represented by the access policy which is expressed as an access tree. In this article, the authors first present an ABE scheme which supports frequent changes in the access tree and hence, it is named a dynamic policy ABE. Also, the proposed scheme generates secret keys of constant size which can save bandwidth. The proposed scheme is based on key-policy design and supports monotonic access structure that consists of AND, OR and Threshold gates. Inspired by the proposed dynamic policy ABE scheme the authors then present a multi-keyword search scheme which inherits all the features of the proposed ABE scheme. Therefore, it provides a constant size trapdoor and support for fast search. The construction of a multi-keyword search scheme is generic in nature and any ABE scheme can be converted to the multi-keyword search scheme using the transformation method given in the paper. Finally, the proposed schemes are proven to be secure under Decisional Bilinear Diffie-Hellman (DBDH) assumption.
Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples

Detection andRay, Kaushik identificationShil, Sukhen of parasiteSaharia, Sarat eggsSarma, Nityananda is oneKarabasanavar, Nagappa S. of the important tasks in diagnosis of many diseases. Manual detection and identification process of parasite egg is time consuming and prone to error. Automation of this detection and identification process of various parasite eggs can save time and reduce the error in the diagnosis process. In this paper, we proposed a system that automatically detect the parasite eggs present in the microscopic fecal images of pig and identify Ascaris lumbricoides from the detected eggs. In our study, first, different objects are segmented including parasite egg and other non-egg artifacts present in the microscopic images. In the feature extraction stage, we extracted five different types of features from the segmented images. For classification, Artificial Neural Network (ANN) and Multi-class Support Vector Machine (MC-SVM) are used. The experimental result shows about 95% and 93% accuracy rate in identifying Ascaris eggs using MC-SVM and ANN, respectively.
Goal-Driven Navigation for Non-holonomic Multi-Robot System by Learning Collision

In this paper, we propose the reinforcement learning based multi-robot collision avoidance approach by learning collision. Dynamical path re-planning, which is massively used in classical collision avoidance methods, needs overall information of the environment. Also, training agent robots to avoid the collision and pursue a goal point simultaneously is inefficient since the agent should learn two tasks. As the number of tasks that the agent should learn increases, it is difficult to make the performance of an algorithm consistent, which is known as reproducibility issue. To overcome these limitations, Collision Avoidance by Learning Collision (CALC), which learns collision instead of avoiding an obstacle robot is suggested. To solve the collision avoidance problem efficiently, the proposed method divides the problem into training and planning. In the training algorithm, an agent robot learns how to collide with a single obstacle robot and then generates a trained policy. With the trained policy, the agent can pursue a goal point since the policy leads the agent to `collide' with the goal. Furthermore, by taking action in a reverse way from the trained policy, the agent can avoid multiple obstacle robots in the planning algorithm at once. The proposed method is validated both in the robot simulation and real robot experiment, and compared with the existing collision avoidance method.
A Memetic Fuzzy Whale Optimization Algorithm for Data Clustering

Data clustering is a well-known data analysis methodology in the optimization community that can be formulated as a Np-hard problem. The main concept of the clustering procedure is to identify and assign data objects into sensible disjointed groups based on similarity measures, such as the Euclidean distance. In this case, data objects within a group are similar to each other with regard to the similarity measure, and each group is dissimilar to each other. Fuzzy clustering is also a popular and powerful research method with many applications, such as medical imaging processing with real words. In the fuzzy clustering research field, the fuzzy c-means (FCM) algorithm is one of the most important fuzzy clustering approaches for efficient and easy implementation; however, the FCM algorithm has a weakness in which it can easily become trapped in local optima. Thus, some metaheuristic algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), can be combined with FCM to solve the clustering problem. Recently, a novel metaheuristic algorithm inspired by the social behavior of humpback whales, called the whale optimization algorithm (WOA), has been proposed. In addition, the WOA has obtained more efficient solutions than some popular metaheuristic algorithms, such as the gene GA and PSO. In this paper, a fuzzy clustering algorithm that called the memetic fuzzy whale optimization (MFWO) algorithm, is proposed to solve the data clustering problem. The performance of the proposed algorithm is evaluated using eight real-world UCI benchmarks, and the FCM and fuzzy PSO algorithms are compared. The experimental results show that the proposed MFWO algorithm is more efficient than the compared algorithms.
A Real-Time Projection System for Golf Training using Virtual Shadow

In this work, we propose a real-time golf learning system using visual feedback. The system projects the visual feedback which is imitating shadow on the ground in front of the learner. It makes it possible for the learner to receive the feedback without the posture collapse. The learner can receive the feedback with the fixed face orientation as the actual golf swing, which could not be solved in existing systems projecting feedbacks on the wall. Furthermore, a posture difference is provided by projecting the expert's outline on the learner's virtual shadow. In addition, because shadow was also used in golf practice conventionally, the cost of adaptation to this learning system which uses virtual shadow as visual feedback may be low.
Blockchain-enabled Transactive Method in Distributed Systems Considering Security Constraints

With the power system reform and the increase of distributed energy penetration, effective dispatch of distributed energy faces opportunities and challenges. Traditionally, the centralized power transaction mode has high maintenance cost, low efficiency and untimely settlement. Therefore, it is hard to adapt to the high-frequency and small-scale distributed energy trading scenarios. To this end, this paper mainly proposes a distributed power trading method based on blockchain considering security constraints. The paper first reviews the development history of blockchain technology, and explores the theory of blockchain technology. At the same time, it combines the analysis of distributed energy features and summarizes some requirements for building a distributed energy trading market. Then the mechanism and model of distributed power trading considering security constraints are constructed. Finally, a distributed power trading method based on blockchain is proposed to ensure the transparency of transactions, and to provide smart contracts for distributed power multilateral transactions. Through the example of the Ethereum blockchain, the distributed power trading method based on blockchain proposed in this paper can realize the over-limit correction of power flow and the multilateral trading of power, and realize the digital management of electric energy.
Navigating Dynamically Unknown Environments Leveraging Past Experience

To enable autonomous robot navigation among unknown dynamic obstacles, a real-time adaptive motion planner (RAMP) plans the robot motion online based on sensing the environment as the robot moves with sensors mounted on the robot. However, the sensed environmental data from the robot's local view is usually incomplete due to occlusions from obstacles and limited sensing range.This paper incorporates learning about the environment into the RAMP framework by leveraging the Hilbert Maps framework to generate a probabilistic model of occupancy of the unknown dynamic environment based on past observations. Utilizing this probabilistic model enables RAMP to reason about trajectory fitness when sensing information is partial and incomplete. This allows the RAMP robot to take advantage of what it has experienced from being in the dynamic environment before to inform its subsequent executions even though the dynamic environment changes in unknown ways. The effectiveness of incorporating such learned probabilistic data into RAMP is shown in both simulation and real experiments.
Factors that Influence Farmers’ Information Seeking Behaviour: A Study of Sri Lankan Vegetable Farmers

Information and communication technology (ICT) plays a vital role in disseminating agricultural information. In this backdrop, to make use of ICT for a prolific agriculture information dissemination, it is vitally important to gain a comprehensive understanding of the farmers’ information seeking behaviour. Despite the vitality, very few informational studies have been carried in Sri Lankan agriculture, particularly with respect to vegetable farmers in the country. To bridge this existing knowledge gap, this study sets out to investigate the information seeking behaviour (ISB) of vegetable farmers in Sri Lanka. Wilson’s general model of information seeking behaviour 1996 was selected to guide the present study. Primary data were collected through a questionnaire survey of 289 vegetable farmers and were analysed by performing a regression analysis. The regression analysis revealed that vegetable farmers’ ISB is greatly influenced by their information need and the channel characteristics. The regression results further revealed that the demographic characteristics such as age, education, farming experience and training, operational factors such as land extent, land ownership and types of farming system, and personal and role-related factors such as perception of agri-information and information sharing behaviour also influence vegetable farmers’ ISB significantly. The study findings will inform the policymakers, information systems and applications designers, and researchers about the factors that affecting on ISB of farmers. Furthermore, this study will help stimulate similar studies in developing countries.