Reviewerly/exHarmony huggingface dataset:
Ontology-Driven Framework for Trend Analysis of Vulnerabilities and Impacts in IoT Hardware

The number of publicly known cyber-security vulnerabilities submitted to the National Vulnerability Database (NVD) has increased significantly. However, it is cumbersome to explore useful information from this large corpus of unstructured data to find meaningful trends over time without proper tools. Prior works with this purpose have mainly focused on the software vulnerabilities and fail to provide a storytelling framework that can extract useful information about the relationship and trends within the CVE and CWE databases over time. Additionally, hardware attacks on IoT devices are evolving very rapidly due to the recent proliferation of computing devices in mobile and IoT domains. Thus, herein, we focus on IoT hardware vulnerabilities and develop an Ontology-driven Storytelling Framework (OSF) which aims to identify similar patterns of vulnerabilities over time, to help mitigate the impacts of vulnerabilities or predict and prevent future vulnerabilities.
An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.
EEPSA as a core ontology for energy efficiency and thermal comfort in buildings

Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to address these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process and aids them to discover which are the relevant variables for the matter at hand, or informing about relationships among relevant data. In this article, the EEPSA (Energy Efficiency Prediction Semantic Assistant) ontology which supports such an assistant is presented. The ontology is developed on the basis that a proper axiomatization shapes the set of admitted models better, and therefore, establishes the ground for a better interoperability. On the contrary, underspecification facilitates the admission of non-isomorphic models to represent the same state which hampers interoperability. This ontology is developed on top of three ODPs (Ontology Design Patterns) which include proper axioms in order to improve precedent proposals to represent features of interest and their respective qualities, as well as observations and actuations, the sensors and actuators that generate them, and the procedures used. Moreover, the ontology introduces six domain ontology modules integrated with the ODPs in such a manner that a methodical customization is facilitated.
Analysis of User Preferences for Robot Motions in Immersive Telepresence

This paper considers how the motions of a telepresence robot moving autonomously affect a person immersed in the robot through a head-mounted display. In particular, we explore the preference, comfort, and naturalness of elements of piecewise linear paths compared to the same elements on a smooth path. In a user study, thirty-six subjects watched panoramic videos of three different paths through a simulated museum in virtual reality and responded to questionnaires regarding each path. Preference for a particular path was influenced the most by comfort, forward speed, and characteristics of the turns. Preference was also strongly associated with the users’ perceived naturalness, which was primarily determined by the ability to see salient objects, the distance to the walls and objects, as well as the turns. Participants favored the paths that had a one meter per second forward speed and rated the path with the least amount of turns as the most comfortable.
An overall statistical analysis of AI tools deployed in Cloud computing and networking systems

As the vast amount of data destined to cloud systems never stop growing in seconds, minutes, hours and daily basis, the development of dynamic, autonomous and proactive architectures for cloud resources scheduling becomes a veritable prerequisite. This dominant trend is inciting to further seek for complete and accurate forecasting and predictive models to support decision making in several cloud-scheduling levels. In this perspective, this paper is a result of a meticulous statistical analysis of about five hundred relevant research articles dealing with proactive resources scheduling in cloud, fog, edge computing and networking systems, using a complete panoply of Artificial Intelligence (AI) predictive techniques. The first aim is to highlight for the first time current trends bridging the gap between cloud services management and AI tools.
SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty

We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraints: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. On average, the Gaussian method requires ~ 5 ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 9 ms on average in scenarios with 4 agents.
Formal Modeling of Smart Contract-based Trading System

With the development of blockchain technology, the fields of use of smart contracts are diversifying. Blockchain-based smart contracts are suitable in areas where integrity and transparency must be guaranteed with distributed ledger technology as the core. However, once the system is deployed, it cannot be modified, so it is important to ensure that the system works with the requirements and principles of the smart contract at the design stage. Therefore, in this paper, we aim to show that the system is accurate without contradictions/errors through formal verification using UPPAAL, a formal verification tool for the public descending auction system (Dutch Auction).
Multi-derivative physical and geometric convolutional embedding networks for skeleton-based action recognition

Action involves rich geometric and physical properties hidden in the spatial structure and temporal dynamics. However, there is a lack of synergy in investigating these properties and their joint embedding in the existing literature. In this paper, we propose a multi-derivative physical and geometric embedding network (PGEN) for action recognition from skeleton data. We model the skeleton joint and edge information using multi-derivative physical and geometric features. Then, a physical and geometric embedding network is proposed to learn co-occurrence features from joints and edges, respectively, and construct a unified convolutional embedding space, where the physical and geometric properties can be integrated effectively. Furthermore, we adopt a multi-task learning framework to explore the inter-dependencies between the physical and geometric properties of the action, which significantly improves the discrimination of the learned features. The experiments on the NTU RGB+D, NTU RGB+D 120, and SBU datasets demonstrate the effectiveness of our proposed representation and modeling method.
Product Owner’s Journey to SAFe®—Role Changes in Scaled Agile Framework®

As agile software development methods are spreading through the industry, they are no longer sufficient in their original design. With the increasing adoption by various types and sizes of organizations, these methods are scaled and tailored. The most popular framework for scaling Agile is the Scaled Agile Framework®, registered trademark of Scaled Agile, Inc. Boulder, USA. Some roles originating from the agile methods, such as Product Owner, are part of the framework and are impacted by scaling and tailoring. A Product Owner role is critical to the success of projects in agile environments. This paper aims to describe and discuss the evolution of and changes in Product Owner activities since Agile started to spread in the industry until the current concept of the Product Owner role in the Scaled Agile Framework. By identifying the activities typical of Product Owners outside of the Scaled Agile Framework context and mapping these activities to the Product Owner role description in Scaled Agile Framework, the changes in Product Owner role with respect to time and role specifics in the Scaled Agile Framework are revealed. It was identified that some of the activities previously described for Product Owner are distributed between various roles in the Scaled Agile Framework. In fact, the Product Owner loses the real product ownership in Scaled Agile Framework. The loss of ownership seems connected with the fact that, in the large environments that the Scaled Agile Framework is designed for, it is impossible to cover all required activities by one role using the hierarchical structures with a top-down approach in the Scaled Agile Framework.
Attentive Representation Learning With Adversarial Training for Short Text Clustering

Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations, making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations. Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model. To further ensure robust model training for short texts, we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains.
Interfacing with Humans in Factories of the Future: Holonic Interface Services for Ambient Intelligence Environments

Human roles in manufacturing are changing along with developments in Industry 4.0. Integrating human workers into their shifting Industry 4.0 work environment is important and by no means easy. This paper proposes a method of interfacing with workers in factories of the future that facilitates their interaction with the digital management systems and machines around them. Holonic interface services are discussed as promising means to realize ambient intelligence environments, which are comprised of intelligent interfaces supported by computing and embedded networking technology. An architecture that manages communication through available interfacing services is presented as part of a digital administration shell for integrating a human worker into an Industry 4.0 environment. A manufacturing process case study is also presented to demonstrate the interfacing component of the architecture and the effectiveness of holonic interface services in an ambient intelligence environment.
A Decentralized Game Theoretic Approach for Energy-aware Resource Management in Federated Learning

The resource management in Federated Learning (FL) system has been a challenging issue since mobile users can save the energy consumption by limiting their computing resources and dataset in training their local models in which users have the energy limitation. We analyze the performance of the global model on the size of dataset and computing resources used for the local training. The performance of the final model is significantly influenced by the resource management of users. Moreover, the decisions of the users on the communication, computing resources and size of dataset can affect the time taken for one computing round. Since a large number of mobile users participate in the FL, a centralized resource management is not practical. Thus, we formulate an energy-aware resource management problem for FL in which users are interested in minimizing the time taken for one computing round with the constraints of energy consumption, communication resources and performance of the training model. Due to the coupling in the communication resource allocation, we formulate the resource management problem as a Generalized Nash Equilibrium Problem (GNEP) and propose a decentralized algorithm. In addition, we analyze the performance of the proposed algorithm on the resource management, energy and time consumption.
Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer2 and 7.41 micrometer2 for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.
An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding

Nowadays, an Internet of Things (IoT) device consists of algorithms, datasets, and models. Due to good performance of deep learning methods, many devices integrated well-trained models in them. IoT empowers users to communicate and control physical devices to achieve vital information. However, these models are vulnerable to adversarial attacks, which largely bring potential risks to the normal application of deep learning methods. For instance, very little changes even one point in the IoT time-series data could lead to unreliable or wrong decisions. Moreover, these changes could be deliberately generated by following an adversarial attack strategy. We propose a robust IoT data classification model based on an encode-decode joint training model. Furthermore, thermometer encoding is taken as a nonlinear transformation to the original training examples that are used to reconstruct original time series examples through the encode-decode model. The trained ResNet model based on reconstruction examples is more robust to the adversarial attack. Experiments show that the trained model can successfully resist to fast gradient sign method attack to some extent and improve the security of the time series data classification model.
Building a Catalogue of ISO/IEC 25010 Quality Measures Applied in an Industrial Context

Abstract Measuring quality is extremely important while developing a software product, but there is a lack of knowledge of which are the ISO/IEC 25010 quality measures that are currently being used in the industry. Through a literature analysis of 27 studies, the present article introduces a catalogue of 269 quality measures applied in an industrial setting, which is formed by a subset of 81 metrics defined by GQM approach, a subset of 81 specific metrics for quality characteristics, and a subset of 86 metrics defined through ISO/IEC 25023 and 21 through IT-CISQ. In conclusion, it can be said that GQM is the most widely used method, regardless of standards. Likewise, Maintainability, Performance Efficiency and Usability are the quality characteristics that have shown the highest degree of interest because they are more receptive to the end user.
The Wearable Radar: Sensing Gestures Through Fabrics

Recently, millimeter-wave radar-on-chip sensors such as Google Soli have become readily available in the mobile ecosystem. We envision such radar technology to be integrated into wearables to enable gesture-based interaction possibilities for users ‘on the go’, e.g. to control various devices such as phone, car infotainment system, etc. even when the sensor is occluded by some material such as fabrics. Towards achieving this vision, we developed a hybrid CNN+LSTM deep learning model, and conducted a systematic study investigating mid-air gesture recognition performance when the radar sensor was covered by three different fabrics (leather, wool, and cotton). We show that, when trained on no occluding material, the model performed worse than if trained with each of the three fabrics; however, this is only valid in the small data regime (N=20). When trained with large samples (N=200) on no occluding material, the model achieved remarkable performance also when the sensor was covered by each of the fabrics (95% avg. accuracy, 99% AUC). Our results show that sensing mid-air gestures through fabrics is both feasible and ready for practical applications, since it is not necessary to train a dedicated model for each type of fabric available in the market. We also contribute a repeatable procedure to systematically test mid-air gestures with radar technology, enabled by an experimental platform that we release with this paper.
Chaotic approach for improving global optimization in Yellow Saddle Goatfish

Abstract Yellow Saddle Goatfish Algorithm (YSGA) is an optimization model inspired by the hunting behavior of yellow saddle goatfish which emulates their collaborative behaviors with chaser fish and blocker fish. To improve the global convergence, chaotic maps have been combined with YSGA in this paper. Chaotic is a nonlinear deterministic system that displays complex, noisy‐like, and unpredictable behavior. Due to its non‐repetitive nature, an overall search can be carried out at a higher speed. The proposed algorithm is based on the excellence of the chaotic searching using a multi‐chaotic approach and the YSGA optimization, which has been applied to 68 benchmark functions. The results of the proposed Multi‐Chaotic Yellow Saddle Goatfish algorithm are compared with YSGA and also with nine other states of art meta‐heuristic algorithms. The results show that the proposed algorithm improves the performance of the YSGA algorithm.
WITHDRAWN: Improving feature engineering by fine tuning the parameters of Skip gram model

Word Embedding is the primary task in text mining, a subfield of Natural Language Processing. Feature Engineering also referred as feature extraction deals with building features out of existing data. This process involves constructing right features from the dataset and feeding relevant and best features into the Machine learning model for training. Feature engineering leads to greater improvements on many NLP tasks. The various aspects of feature engineering includes feature selection, feature addition, feature filtering and feature scaling. The Skip-gram architecture works better than other feature extraction models like CBOW, TD-IDF etc. This unsupervised learning technique used to find context word for a given target word. The skip-gram word2vec model outclasses the shortcomings with word-embedding matrix like loss of information from former cell, noisy information and over-fitting problems. The Skip gram algorithm trains the neural network and each time the output will be a softmax vector and it computes the cross entropy loss between output vector and true one-hot encoder. Skip-gram word2vec model shows good representation over finding out rare words or phrases. To improve the computational speed and accuracy, the parameters of skip gram model are fine tuned. Experimental results proves the efficiency of the model.
Preliminary study of phase-shifting strobo-stereoscopy for cutting tool monitoring

Monitoring cutting tool quality in the machining process is essential to the success of part manufacturing. To directly monitor the information of cutting tools under the drilling or milling operations, this paper presents a novel 3D imaging technique to enable direct cutting tool geometry measurement. This method combines stroboscopy and stereoscopy, this strobo-stereoscopy, which synchronizes the stroboscopic illumination system with the spindle control system and enables in-situ reconstructing 3D images of cutting tools. For the phase-locked mode, this technique can lock the image of the cutting tool while in motion. On the other hand, for the phase-shifting mode, all facets around the cutting tool will be scanned by shifting the phase of the light signals while collecting dimensional target information. Experimental studies of phase-locked and phase-shifting modes for monitoring an end mill tool and a wood rolling pin were presented. The directly measured maps and whole 3D images on the target tools verify the capability of the geometry identification of the proposed strobo-stereoscopic imaging technology. Lastly, the potential applications of the proposed technology were discussed.