Reviewerly/exHarmony huggingface dataset:
The Reliability of IoT Networks With Characteristics of Abnormal Induced Signals

The abnormal nodes of the Internet of Things (IoT) are closely related to the reliability of the network, but it is always a difficulty to predict the characteristics of the induced signals that threaten reliability. In this article, a feature analysis method for the abnormal signal inducing the unreliable anomaly in IoT based on chaos attractor is proposed to make phase space reconstruction of the abnormal signal that the nodes of IoT can induce unreliability. The phase space structure is constructed by the sequence of abnormal signals. The chaotic attractor is used to analyze the phase space characteristics and reflect the phase space characteristics of unreliable signals induced by the network from the geometric point of view. The mutual information method is used to solve the time delay of the unreliable abnormal signal of IoT, calculate the embedding dimension, find out the chaotic attractors under different characteristics, and judge the reliability change characteristics by analyzing the chaotic attractors. The experimental results show that the method has a better time slot consumption, throughput, and idle energy indexes. Furthermore, our experiments show that the chaos attractor method can effectively predict the reliability trend of IoT, and better predict the reliability anomalies of IoT.
Learning to Play Cup-and-Ball with Noisy Camera Observations

Playing the cup-and-ball game is an intriguing task for robotics research since it abstracts important problem characteristics including system nonlinearity, contact forces and precise positioning as terminal goal. In this paper, we present a learning model based control strategy for the cup-and-ball game, where a Universal Robots UR5e manipulator arm learns to catch a ball in one of the cups on a Kendama. Our control problem is divided into two sub-tasks, namely (i) swinging the ball up in a constrained motion, and (ii) catching the free-falling ball. The swing-up trajectory is computed offline, and applied in open-loop to the arm. Subsequently, a convex optimization problem is solved online during the ball's free-fall to control the manipulator and catch the ball. The controller utilizes noisy position feedback of the ball from an Intel RealSense D435 depth camera. We propose a novel iterative framework, where data is used to learn the support of the camera noise distribution iteratively in order to update the control policy. The probability of a catch with a fixed policy is computed empirically with a user specified number of roll-outs. Our design guarantees that probability of the catch increases in the limit, as the learned support nears the true support of the camera noise distribution. High-fidelity Mujoco simulations and preliminary experimental results support our theoretical analysis (video link- GitHub link).
Applications of AI in cybersecurity

Issues related to digital security are, there is no doubt for this, of utmost importance in the development of methods and support measures for organisations to successfully prepare for as well as realise their digital transformation. While big organisations and businesses may afford to buy services or develop their own in-house know-how and tools, small and medium-sized businesses are not having the means for this, be them financial resources, human resources or technology itself. This dystopic situation may on the other hand offer an unexpected and - as of today - yet unprecedented chance for innovations in terms of bridging the gap and addressing the need with use of AI technologies and services. In the paper we elaborate on a scenario that we have been developing as part of a European project that is part of the European Horizons 2020 project CS-AWARE.
Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability

Recent advances in methods focused on the grounding problem have resulted in techniques that can be used to construct a symbolic language associated with a specific domain. Inspired by how humans communicate complex ideas through language, we developed a generalized Symbolic Semantic (S2) framework for interpretable segmentation. Unlike adversarial models (e.g., GANs), we explicitly model cooperation between two agents, a Sender and a Receiver, that must cooperate to achieve a common goal. The Sender receives information from a high layer of a segmentation network and generates a symbolic sentence derived from a categorical distribution. The Receiver obtains the symbolic sentences and cogenerates the segmentation mask. In order for the model to converge, the Sender and Receiver must learn to communicate using a private language. We apply our architecture to segment tumors in the TCGA dataset. A UNet-like architecture is used to generate input to the Sender network which produces a symbolic sentence, and a Receiver network cogenerates the segmentation mask based on the sentence. Our Sementation framework achieved similar or better performance compared with state-of-the-art segmentation methods. In addition, our results suggest direct interpretation of the symbolic sentences to discriminate between normal and tumor tissue, tumor morphology, and other image characteristics.
Inclusive human development in sub-Saharan Africa

Purpose This study aims to assess the role of income levels (low and middle) in modulating governance (political and economic) to influence inclusive human development. Design/methodology/approach The empirical evidence is based on interactive quantile regressions and 49 countries in sub-Saharan Africa for the period 2000-2002. Findings The following main findings are established. Firstly, low income modulates governance (economic and political) to positively affect inclusive human development exclusively in countries with above-median levels of inclusive human development. It follows that countries with averagely higher levels of inclusive human development are more likely to benefit from the relevance of income levels in influencing governance for inclusive development. Secondly, the importance of middle income in modulating political governance to positively affect inclusive human development is apparent exclusively in the median while the relevance of middle income in moderating economic governance to positively influence inclusive human development is significantly apparent in the 10th and 75th quantiles. Thirdly, regardless of panels, income levels modulate economic governance to affect inclusive human development at a higher magnitude, compared to political governance. Policy implications are discussed in light of the post-2015 agenda of sustainable development goals and contemporary development paradigms. Originality/value This study complements the extant sparse literature on inclusive human development in Africa.
Human-Centric Marketing in the Digital Era

The purpose of the article is to test assumptions about the appropriateness of the human-centric marketing concept in the digital era in terms of consumer perception and risks of social nature. Authors address the problem of distrust towards CSR programs in the digital business from both audience and business partners and identify related social opportunities and risks. The research reveals that the directions of social responsibility implementation in the digital environment are different, programs are inconsistent and implemented within the concepts of the societal marketing, the value marketing and traditional marketing concept. For the most part, the management of Russian IT companies do not perceive social responsibility as a significant part of business activity in the digital environment and do not make haste to assume obligations, including corporate digital responsibility. Success in human-centric marketing in the digital era depends on the awareness of the strategic value and threats of the digital transformation for business and society, systematization of CSR programs, and revision and a new interpretation of the marketing mix.
Bubble Tower

Bubble Tower explores the design and development of mapping breathing techniques into gameplay mechanics in a virtual reality action game using sensors-based technology. It's potentials in the application in game includes first-person exercise games, role-playing games and rehabilitation games. The resulting work is demonstrated through a set of mini-games where Bubble Tower was designed to train player on the fundamentals of breathing techniques, with skills to learned and to applied in the virtual reality environment. The breath input sensor device and the virtual reality action game were tested for its functionality and overall gameplay experience.
Strengthening Post-Quantum Security for Automotive Systems

The long lifecycle of automotive products demands that not only current but also future threats are considered during the design of automotive security. Therefore, the foreseeable breakthrough of quantum computers represents a risk for the automotive industry and the integration of Post-Quantum Cryptography (PQC) gets necessary. Lattice-based PQC is an attractive alternative for securing automotive systems. It usually employs Error-Correcting Codes (ECC) to increase the security level and to decrease the failure rate. However, ECCs are vulnerable to timing attacks. To this end, we present in this work three contributions. First, we present an implementation of PQC tailor-made for a microcontroller used in automotive systems. Second, we integrate a more powerful ECC into ThreeBears, which is an efficient Post-Quantum scheme, in order to improve its security level and to decrease the failure rate. Finally, we implement a protected ECC implementation able to resist timing attacks. Results show that the integration of PQC in automotive environments is feasible and that optimization techniques can lead to a 55.98% performance improvement. Moreover, our ECC exploration achieves a failure rate decrease from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-135</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-153</sup> . Alternatively, an increase of the security level from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">141</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">144</sup> can be achieved. Furthermore, the timing-protected ECC presents in total only a minor performance overhead.
Single Image Super-Resolution for Medical Image Applications

In medical imaging, high-resolution images are expected to have the ability to deliver a more precise diagnosis with the practical application of high-resolution displays. This research proposes a deep learning method for single image super-resolution that learns an end-to-end mapping between the low and high-resolution images. It redesigns the SRGAN, using VGG19 network for feature extraction, setting discriminator network's working space as feature space, and adding the loss function based on the mean square error of pixel space, gaining more details by incorporating SRCNN layers to increase the PSNR in the reconstruction at the same time. To thoroughly investigate the system, we compared the performance with other architectures on MNIST and CIFAR-10 dataset with a further evaluation conducted on Chest x-ray.
A Novel Model Driven Framework for Image Enhancement and Object Recognition

Modern technological trends like Internet of Things (IoT’s) essentially require prompt development of software systems. To manage this, Model Driven Architecture (MDA) is frequently applied for development of different systems like industry automation, medical, surveillance, tracking and security etc. Image processing is an integral part of such systems. Particularly, image enhancement and classification operations are mandatory in order to effectively recognize objects for different purposes. Currently, such critical image processing operations are not managed through MDA and low level implementations are performed distinctly during system development. This severely delays the system development due to integration issues. Furthermore, system testing becomes problematic as few components of systems are developed through MDA and image processing operations are implemented in isolation. This article introduces a novel framework i.e. MIEORF – Model-driven Image Enhancement and Object Recognition Framework. Particularly, a meta-model is proposed, that allows modeling and visualization of complex image processing and object recognition tasks. Subsequently, an open source customized tree editor (developed using Eclipse Modeling Framework (EMF)) and graphical modeling tool/workbench (developed using Sirius) have been developed (both distributable via eclipse plugin). Consequently, the proposed framework allows modeling and graphical visualization of major image processing operations. Moreover, it provides strong grounds for model transformation operations e.g. Model to Text Transformations (M2T) using Acceleo for generating executable Matlab code. Furthermore, it systematically combines MDA and image processing concepts which are detailed enough to be easily integrated into wide variety of systems such as industrial automation, medical, surveillance, security and biometrics etc. The feasibility of proposed framework is demonstrated via real world medical imagery case study. The results prove that the proposed framework provides a complete solution for modeling and visualization of image processing tasks and highly effective for MDA based systems development.
Cloud Storage Optimization for Video Surveillance Applications

The need for automated security devices are increasing at an unexpected rate. Operator controlled video surveillance is tiresome and hence automated video surveillance systems are on-demand. Automatic detection of moving objects can be greatly used in places like forest borders and other isolated places, where this algorithm can be used to save tremendous amounts of data that will subsequently reduce the costs. The method proposed in this paper estimates the motion using KNN algorithm. When motion is detected, the qualitative speed is determined by comparing the Euclidean distance between two successive frames following which an optimal threshold value is set to determine and store only the key frames in order to optimize the storage. The recorded videos with reduce the size are continually stored in the local storage (PC), and are uploaded to the cloud server at midnight and are deleted from the PC. The video recorded by the webcam is simultaneously live streamed to an IP address based web page. When the activity is detected, it triggers an alarm by generating automatic emails to the specified mail ID. The system has been testedin an indoor setting and the size is also reduced.
EPD-Net: A GAN-based Architecture for Face De-identification from Images

Nowadays huge amount of crowd data captured by surveillance cameras gets shared publicly in the form of images or videos through television or the internet. Although many of these videos are meant to provide public security, they also lead to a widespread concern towards privacy protection, since a lot of personal information about subjects gets revealed through this video/image data. Hence, "de-identifying" people (i.e., obscuring identity information) captured by the surveillance cameras is of utmost importance for providing privacy along with security. Traditional identity obfuscation techniques such as blurring, warping, and filtering lead to the loss of vital non-biometric information. More recent k-same-based as well as generative model-based de-identification techniques eliminate the above problem to a certain extent. Still, the visual quality of the generated images produced by these methods is not realistic. Also, these approaches are unable to maintain structural integrity and cannot preserve the required non-biometric information at a high resolution. As an improvement, in this paper, we propose a new network termed as EPD-Net and train it with suitable loss functions to maximize the emotion similarity and minimize the identity similarity. Experimental results verify the effectiveness of our approach and its superiority over other popular face de-identification techniques.
Interactive clustering: a scoping review

We present in this paper a scoping review conducted in the interactive clustering area. Interactive clustering has been applied to leverage the strengths of both unsupervised and supervised learning. In interactive clustering, supervised learning is represented by inserting the knowledge of human experts in an originally unsupervised data analysis process. This scoping review aimed to organize the knowledge on (i) the applicability of interactive clustering methods, (ii) clustering algorithms being used to support interactive clustering, (iii) how to model the expert supervision and (iv) the effects brought by the expert supervision in the results produced. A systematic search for related literature was conducted in the Scopus database, resulting in the selection of 50 primary studies published by 2018. The analysis of these studies allowed us to identify trends such as: the application in text/image; use of partitioning and hierarchical algorithms; application of strategies based on split/merge, pairwise constraints, similarity metrics learning and data reassignment; and concern with visualization. In addition, some relevant issues not yet adequately addressed were identified, such as: the evaluation of expert supervision; the evaluation of the expert's effort; and the conduction of studies effectively involving human experts, instead of computer simulations.
Sub-pixel dimensional measurement algorithm based on intensity integration threshold

In this paper, we proposed a sub-pixel measurement algorithm based on intensity integration threshold (IIT). The proposed method can localize the sub-pixel edges in an inexpensive way by calculating the integration of the intensity across the edge and finding the point where the integration reaches the threshold. Comparative tests show our method realized better efficiency and robustness in practical applications than other state-of-the-art algorithms.
Warehouse Automation in a Day: From Model to Implementation with Provable Guarantees

We present an end-to-end system for automatically deploying an Autonomous Mobile Robot (AMR) in a warehouse for point-to-point transportation tasks. Our approach includes creating a motion library that provides guarantees on the motion of the AMR, automatically creating a graph representing safe motions in the warehouse, and using Linear Temporal Logic (LTL) specifications and synthesis to compose appropriate motion primitives to accomplish a task, even in the presence of other people and robots in the warehouse. We demonstrate our approach in simulation and with a physical robot.
Rigorous Machine Learning for Secure and Autonomous Cyber Physical Systems

Machine learning (ML) based secure and autonomous cyber physical systems are often not reliable and interpretable mainly because the employed ML techniques suffer from false alarms that may result in physical and financial loss. We assert that reliability and interpret-ability of the ML methods depends on underlying statistical models that infer results. Therefore, we introduce a rigorous method for the model selection. Current selection methods choose a model using statistical criteria (e.g., AIC, BIC). These criteria may lead to selection of an inappropriate model (e.g. over/under-fitting) because they only consider relative-quality (statistical) of the model without considering absolute-quality (formal) of the model based on the model/data specification. To this end, we argue the suitability of recently developed-decidability procedures/solvers. Such solvers infer if a selected model can(not) classify a given data and produce a formal proof that can be used to assure reliability and security of modelled system. We demonstrate feasibility of the method through a simple example of an autonomous insulin pump.
Two-Phase Switching Optimization Strategy in Deep Neural Networks

Optimization in a deep neural network is always challenging due to the vanishing gradient problem and intensive fine-tuning of network hyperparameters. Inspired by multistage decision control systems, the stochastic diagonal approximate greatest descent (SDAGD) algorithm is proposed in this article to seek for optimal learning weights using a two-phase switching optimization strategy. The proposed optimizer controls the relative step length derived based on the long-term optimal trajectory and adopts the diagonal approximated Hessian for efficient weight update. In Phase-I, it computes the greatest step length at the boundary of each local spherical search region and, subsequently, descends rapidly toward the direction of an optimal solution. In Phase-II, it switches to an approximate Newton method automatically once it is closer to the optimal solution to achieve fast convergence. The experiments show that SDAGD produces steeper learning curves and achieves lower misclassification rates compared with other optimization techniques. Implementation of the proposed optimizer to deeper networks is also investigated in this article to study the vanishing gradient problem.
Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

In this paper, we extract quotations from Al Jazeera's news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to see how coronavirus impacts human beings. By combining noun phrases as inputs before the training and Cv measure for coherence values, we obtain an average coherence value of 0.66 with a least average number of topics of 24.8. The result covers some of the top issues that our world has been facing against the COVID-19 pandemic.
A navigational logic for reasoning about graph properties

Graphs play an important role in many areas of Computer Science. In particular, our work is motivated by model-driven software development and by graph databases. For this reason, it is very important to have the means to express and to reason about the properties that a given graph may satisfy. With this aim, in this paper we present a visual logic that allows us to describe graph properties, including navigational properties, i.e., properties about the paths in a graph. The logic is equipped with a deductive tableau method that we have proved to be sound and complete.
Informed Sampling-Based Motion Planning for Manipulating Multiple Micro Agents using Global External Fields

In this paper, we propose novel motion planning algorithms, Bi-iSST and Ref-iSST, to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion-planning algorithm. The Bi-iSST uses the bidirectional approach to speed up the searching process. A novel connection process is proposed to connect two trees efficiently by applying an optimization procedure. The Ref-iSST uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the global-routing reference trajectory. Compared to the state-of-the-art algorithms, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields.