Reviewerly/exHarmony huggingface dataset:
Cognitive and Time Predictable Task Scheduling in Edge-cloud Federation

In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.
Machine Learning Selection of Candidate Ontologies for Automatic Extraction of Context Words and Axioms from Ontology Corpus

Ontology selection for reuse is a challenging task an ontology/knowledge engineer can face due to the diversity and abundance of ontologies on the internet today. This has become an active research area in ontology engineering. In this study, candidate ontologies are selected from a corpus using Skip-gram, a neural networks algorithm. The word embeddings of the vocabulary of 12 ontologies in the corpus were used to train the Skip-gram models. Thereafter, concepts from a text corpus were utilized to test the models and select the candidate ontologies for context words and axioms extraction. The results showed that the proposed method can automatically extract context words, and the URIs and labels of different types of axioms from the candidate ontologies. This gives the knowledge/ontology engineers detailed information about the contents of candidate ontologies so that they can make an informed decision on the parts of the ontologies they would like to reuse.
MultiROS: ROS-Based Robot Simulation Environment for Concurrent Deep Reinforcement Learning

On the journey of true autonomous robotics, applying deep reinforcement learning (DRL) techniques to solve complex robotics tasks has been a growing interest in academics and the industry. Currently, numerous simulation frameworks exist for evaluating DRL algorithms with robots, and they usually come with prebuilt tasks or provide tools to create custom environments. Among these, one of the highly sought approaches is using Robot Operating System (ROS) based DRL frameworks for simulation and deployment in the real world. The current ROS-based DRL simulation frameworks like openai_ros or Gym-gazebo provide a framework for creating environments; however, they do not support training with vectorised environments for speeding up the training process and parallel simulations for testing and evaluating meta-learning, multi-task learning and transfer learning approaches. Therefore, we present MultiROS, a 3D robotic simulation framework with a collection of prebuilt environments for deep reinforcement learning (DRL) research to overcome these limitations. This package interfaces with the Gazebo robotic simulator using ROS and provides a modular structure to create ROS-based RL environments. Unlike the others, MultiROS provides support for training with multiple environments in parallel and simultaneously accessing data from each simulation. Furthermore, since MultiROS uses the popular OpenAI Gym interface, it is compatible with most OpenAI Gym based reinforcement learning algorithms that use third-party python frameworks.
RF Fingerprinting Identification Based on Spiking Neural Network for LEO–MIMO Systems

Low earth orbit (LEO) satellites promise to be a source of innovation in wireless communication and their physical layer security deserves attention. However, many physical layer security techniques are hampered by excessive power consumption and high latency when used for LEO satellites. This letter proposes an energy-efficiency radio frequency fingerprinting identification (RFFI) method for LEO satellite communication systems based on spiking neural networks (SNN). In addition, we investigate a channel-independent RFF feature transformation and data augmentation to enhance system robustness. Numerical results show that our proposed model can yield identification accuracy up to 95.26% at a signal-to-noise ratio (SNR) of 25dB on orthogonal frequency division multiplexing (OFDM) signals, and reduces power consumption by 63.3% compared to existing models of comparable accuracy on FPGA.
Single Identity System for Identification papers based on Blockchain

The fast rise of the internet has both advantages and problems. Almost everything has flaws, but they will be addressed or repaired in the long term. As a result, our personally identifiable information (PII) should be kept highly secure. Our identities are the most important things that offer us with an identity as well as certain rewards. We have been asked to give proof of our identification. Any of these is acceptable, including buying a sim card. Will occasionally receive unsolicited calls from people with advertisements or credit card offers. Have you ever considered how these folks obtained their information? We disclose every piece of information about ourselves. There are certain tried-and-true strategies for protecting our identity. However, present solutions are not completely reliable in terms of preventing data tampering. Here comes blockchain, which will play a significant role in data security and guaranteeing that data is not tampered with. In this proposal, a blockchain-based identity management system with a validator that ensures our identity is confirmed without revealing any of our personally identifiable information.
Multi-modal Recommendation System with Auxiliary Information

Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user’s behaviour. Research into context-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available in auxiliary knowledge related to items. This study extends the existing research by evaluating a multi-modal recommendation system that exploits the inclusion of comprehensive auxiliary knowledge related to an item. The empirical results explore extracting vector representations (embeddings) from unstructured and structured data using data2vec. The fused embeddings are then used to train several state-of-the-art transformer architectures for sequential user-item representations. The analysis of the experimental results shows a statistically significant improvement in prediction accuracy, which confirms the effectiveness of including auxiliary information in a context-aware recommendation system. We report a 4% and 11% increase in the NDCG score for long and short user sequence datasets, respectively.
Efficient Sampling-Based Planning for Subterranean Exploration

The paper proposes a path planning solution for autonomous robotic exploration of complex subterranean environments. The work contributes to the family of graph-based planners by bringing the following improvements. Firstly, an occupancy grid-based sample-and-project solution to terrain-assessment is proposed instead of building an explicit elevation map of the environment. Secondly, the solution-search method is formulated as a constraint-satisfaction problem to obtain a good-enough solution instead of optimizing for a single objective function parametrized by penalty gains. This method is shown to significantly improve the computational efficiency of the planner. Thirdly, a coordination solution is proposed that relies on the position histories of the robots instead of merged-maps or merged-graphs, therefore, making the planning solution resilient to inter-robot map misalignments, while also reducing the communication bandwidth required to carry out coordination. Finally, the planner also takes into account changes in the environment such as blocked passages that are initially open. The proposed planning solution is demonstrated at the DARPA Subterranean Challenge final event by team MARBLE, the third place finisher of the challenge.
Manufacturing a Smart Locker Security System for Public Spaces using E-KTP as a Primary Access

Everyone requires protection of belongings, especially security in public spaces such as locker rooms. Several locations in public areas keep hiring conventional security systems based on keys and padlocks, while others provide merely keyless lockers guarded by personnel. As a result, the user must ensure that the goods deposited with the locker storage officer are adequately secured. Numerous technologies have been used in conventional security systems, ranging from the difficult-to-imitate shape of keys and padlocks to the use of a padlock combination of numbers. The security system for storage places is also a purpose of information technology. This research aims to provide information on security systems in public spaces, especially shared access lockers. This project employs an experimental approach to create a prototype in the form of a smart locker that incorporates information technology. The lockers were constructed utilising plywood with an HPL finish on the exterior and PVC sheets on the interior. The dimensions of the locker were determined using the standard sizes of small and large luggage. Additionally, the approach to materials is addressed to minimise production waste. This research employs RFID technology that runs on the Arduino Nano to supplement the locker security system. Numerous RFID cards, including E-Toll cards and E-KTP, have been tested as the principal access method for opening and closing the smart locker's door.
Feature Expansion with FastText on Topic Classification Using the Gradient Boosted Decision Tree on Twitter

Twitter is a microblogging website that allows users to post and read short messages (tweets) with a character limit of 280 characters. It often leads to very short tweets, does not always utilize proper grammar, and frequently includes slang. Due to these characteristics, topic classification experiments in tweets frequently have low accuracy. As a result, feature expansion is used in this study to decrease word mismatches. Some researchers have previously conducted research with feature expansion using Word2Vec, WordNet, etc. However, as far as researchers are looking, no one has used fastText for feature expansion, therefore this study aims to identify the effect of feature expansion with fastText when paired with the Gradient Boosted Decision Tree algorithm. The results showed that the topic classification system using the Gradient Boosted Decision Tree method and with feature expansion using fastText resulted in the highest accuracy of 91.39%.
Hyperparameter Optimization Using Iterative Decision Tree (IDT)

Machine learning and deep learning have gained a lot of attention from researchers because of their promising predictive performance and the availability of extensive high-dimensional data and high-performance computational hardware. However, the performance of these algorithms is susceptible to the choice of hyperparameters, while optimizing these hyperparameters is usually computationally expensive. For this reason, this study proposed a novel sequential search algorithm using decision tree regression as the surrogate function. In each iteration of this algorithm, several new combinations of hyperparameters were selected from a few best-performed leaves of the decision tree, called Iterative Decision Tree (IDT). Our approach could reduce the computational time from repetitive training the surrogate function compared to conventional sequential search algorithms and enable parallel computing. To confirm the effectiveness of the proposed algorithm, it was compared with six popular benchmark optimization algorithms, including Grid Search, Random Search, Bayesian Optimization, Random Forest, Tree-Structured Parzen Estimation, and Genetic Algorithm. The comparison was examined by optimizing three benchmark nonconvex functions, and hyperparameter tuning of two machine learning algorithms (Support Vector Machine and Random Forest) and two deep learning models (Autoencoder and Convolutional Neural Networks). As a result, the proposed algorithm achieved competitive performance with high stability in addition to the feature importance metrics.
A Pragmatic Study of Racial Hate Speech

There has been an increasing interest in hate speech due to its impact on both individuals and societies. The term 'hate speech' covers expressions in any form deemed humiliating to any race, religion, ethnic or national group. Hate speech is connected to the freedom of expression , advocacy of hatred and incitement of violence. Thus, it has to be understood in the different contexts in which it occurs. The present study deals with racial hate speech. The main aim of this study is to identify the speech acts employed in articles published in a Rwandan newspaper called Kangura. In order to carry out a pragmatic analysis of racial hate speech, seven extracts have been chosen. The main conclusions arrived at is that directives are the most common ones used in the dissemination of racial hate speech and indirect speech acts outnumber the direct ones.
Multidimensional Framework for Characterizing Verification and Validation of Automated Systems

Verification and Validation (V&V) of automated systems is becoming more costly and time-consuming because of the increasing size and complexity of these systems. Moreover, V&V of these systems can be hindered if the methods and processes are not properly described, analysed, and selected. It is essential that practitioners use suitable V&V methods and enact adequate V&V processes to confirm that these systems work as intended and in a cost-effective manner. Previous works have created different taxonomies and models considering different aspects of V&V that can be used to classify V&V methods and tools. The aim of this work is to provide a broad, comprehensive and a easy to use framework that addresses characterisation needs, rather than focusing on individual aspects of V&V methods and processes. To this end, in this paper, we present a multi-domain and multi-dimensional framework to characterize and classify V&V methods and tools in a structured way. The framework considers a comprehensive characterization of different relevant aspects of V&V. A web-based repository has been implemented on the basis of the framework, as an example of use, in order to collect information about the application of V&V methods and tools. This way, practitioners and researchers can easily learn about and identify suitable V&V processes.
Force Sensing Based on Nail Deformation for Measurement of Fingertip Force in Detailed Work

In this paper, we describe a method of measuring the force applied to a finger pad by attaching two strain gauges to the nail. We target sewing work at production sites. To measure the dexterous fingertip force work, we determine the position where the strain gauge is attached based on how the nails are distorted upon force application. Additionally, we devise a procedure for proper installation and removal of the proposed sensor. Then, we verify via experiments that the force in the shear direction can be measured and work needing fine fingertip force can be classified. In conclusion, the proposed sensor offers more detailed sensing than conventional sensors.
GradAuto: Energy-Oriented Attack on Dynamic Neural Networks

Dynamic neural networks could adapt their structures or parameters based on different inputs. By reducing the computation redundancy for certain samples, it can greatly improve the computational efficiency without compromising the accuracy. In this paper, we investigate the robustness of dynamic neural networks against energy-oriented attacks. We present a novel algorithm, named GradAuto, to attack both dynamic depth and dynamic width models, where dynamic depth networks reduce redundant computation by skipping some intermediate layers while dynamic width networks adaptively activate a subset of neurons in each layer. Our GradAuto carefully adjusts the direction and the magnitude of the gradients to efficiently find an almost imperceptible perturbation for each input, which will activate more computation units during inference. In this way, GradAuto effectively boosts the computational cost of models with dynamic architectures. Compared to previous energy-oriented attack techniques, GradAuto obtains the state-of-the-art result and recovers 100% dynamic network reduced FLOPs on average for both dynamic depth and dynamic width models. Furthermore, we demonstrate that GradAuto offers us great control over the attacking process and could serve as one of the keys to unlock the potential of the energy-oriented attack. Please visit https://github.com/JianhongPan/GradAuto for code.
Review of Wearable Computing Technology: Core Technologies, Typical Systems and Research Trends

Wearable computing is a new type of mobile computing system, which has positive applications in sports, health, military, pension and other fields, and has a profound impact on the future development. In order to better understand the development and research status of wearable computing, this paper combs all aspects of wearable system. Firstly, the basic information of wearable computing is introduced, then the core technologies and typical systems of wearable computing are summarized, and finally the future development of this field is prospected. The research of this paper has reference value for grasping the relevant knowledge and future development trend of wearable computing.
A Systematic Review on Augmented Reality Applications in Informal Learning Environments

In recent years more and more people use their mobile phones daily for work or entertainment. The increasing use of mobile devices has led researchers to seek new ways of learning with their support, beyond the confines of formal education. The increasing computing power of mobile devices has contributed to the emergence of new, rapidly evolving technologies, with augmented reality (AR) applications being at the forefront of these developments. This article provides a literature review of AR applications for mobile devices related to informal education. It analyses their structural elements to examine if they exploit powerful features such as collaboration and content modification, as well as storytelling potentials. The findings show that most of these applications use those learning affordances only to a very limited extent, as they are mainly designed for individual usage, do not allow user-originated contribution to the digital material, and do not incorporate elements of any storytelling model.
Permission and Usage Control for Virtual Tourism using Blockchain-based Smart Contracts

Virtual Tourism (VT) is a booming business with potential perspectives in the entertainment and financial industry.Due to travel restrictions, safety concerns, and expensive travelling the younger generation is showing interest in virtual tourism instead of traditional tourism.However, virtual tourism does not financially benefit the service providers as compared to traditional tourism stakeholders.An online system is essential to provide a central point of access to various tourism sites along with usage, permission, and payment control.In this paper, a secure blockchain-based broker service for users and content providers is proposed, which allows tourism sites to announce their virtual tours and provide accessibility and accountability.Meanwhile, it enables users to register, subscribe, access, and be billed according to their usage.The permission control module ensures authentication and authorization, while the usage control provides accountability to the predefined service level agreement.The transactions are stored on the blockchain to ensure the integrity of data and smart contracts are used to ensure automatic usage and permission control.An implementation on Hyperledger Fabric is provided as a proof of concept with performance measurements as a case study.
Estimating the Deployment Time for Cloud Applications using Novel Google Kubernetes Cloud Service over Microsoft Kubernetes Cloud Service

Aim:The aim of the study is to estimate the time to deploy cloud applications using novel google kubernetes cloud service over microsoft kubernetes cloud service.Materials and Methods: Sample groups that are considered in the project can be classified into two each has 20 samples, one for Novel google kubernetes cloud service and other for Microsoft kubernetes cloud service, which are tested using 0.80 for G-power to determine the sample size and for t-test analysis.20 cloud applications have been used in each group for estimating deployment time.Results: The novel google kubernetes cloud service with efficient accuracy of 83.40%, which by far seems to be better than the Microsoft kubernetes cloud service which gives around 61.60%.The significance is around 0.222 (p>0.05) and therefore there is a statistical insignificant difference among the study group.Conclusion: Novel Google kubernetes cloud service seems to be better in deploying cloud applications over the Microsoft kubernetes cloud service.
UniCoRN: A Unified Conditional Image Repainting Network

Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly. Existing methods based on two-phase architecture design assume dependency between phases and cause color-image incongruity. To solve these problems, we propose a novel Unified Conditional image Repainting Network (UniCoRN). We break the two-phase assumption in the CIR task by constructing the interaction and dependency relationship between background and other conditions. We further introduce the hierarchical structure into cross-modality similarity model to capture feature patterns at different levels and bridge the gap between visual content and color condition. A new Landscape-CIR dataset is collected and annotated to expand the application scenarios of the CIR task. Experiments show that UniCoRN achieves higher synthetic quality, better condition consistency, and more realistic compositing effect.
Decoupling the Ascending and Descending Phases in Abstract Interpretation

Abstract Interpretation approximates the semantics of a program by mimicking its concrete fixpoint computation on an abstract domain $$\mathbb {A}$$ . The abstract (post-) fixpoint computation is classically divided into two phases: the ascending phase, using widenings as extrapolation operators to enforce termination, is followed by a descending phase, using narrowings as interpolation operators, so as to mitigate the effect of the precision losses introduced by widenings. In this paper we propose a simple variation of this classical approach where, to more effectively recover precision, we decouple the two phases: in particular, before starting the descending phase, we replace the domain $$\mathbb {A}$$ with a more precise abstract domain $$\mathbb {D}$$ . The correctness of the approach is justified by casting it as an instance of the A $$^2$$ I framework. After demonstrating the new technique on a simple example, we summarize the results of a preliminary experimental evaluation, showing that it is able to obtain significant precision improvements for several choices of the domains $$\mathbb {A}$$ and $$\mathbb {D}$$ .