Reviewerly/exHarmony huggingface dataset:
Digital Image Anti-Forensic Model Using Exponential Chaotic Biogeography-Based Optimization Algorithm

Abstract The innovation in visual imagery has led to massive growth in technologies, wherein digital cameras are obtainable at affordable prices. Thus, the digital images are easily captured and processed due to the internet connectivity. On the other hand, the development of strong image editing software facilitated the forgers to manipulate the accessible images with different tampering operations. Several techniques are devised for detecting the forgeries. Accordingly, this paper devises an anti-forensic model, namely Exponentially Weighted Moving Average-Chaotic Biography Based Optimization (E-CBBO) for joint photographic experts group (JPEG) compression to mitigate the forgeries occurred on the internet while transmitting data. The proposed E-CBBO is designed by integrating the properties of the exponentially weighted moving average (EWMA) with the chaotic biography-based optimization (CBBO). The suggested JPEG anti-forensic model is used to eliminate JPEG compression artifacts through the use of unique deblocking, smoothing with dither and decalibration operations. In addition, the goal is to balance visual quality and forensic undetectability when compressing the JPEG image. The fitness function is developed using the structural similarity index (SSIM), universal image quality index (UIQI) and histogram deviation parameters. With a maximum accuracy of 93.2%, a minimal MSE of 0.110, a maximum SSIM of 0.932 and a maximum UIQI of 0.890, the suggested E-CBBO beat existing approaches.
Re-visiting the past in search for silver-lining: Moroccan students’ perceptions of technology-mediated faculty practices under COVID-19

Despite the wide-ranging research on technology-mediated learning, which has contributed immensely to a better understanding of how best it can be implemented, lockdowns during COVID-19 times have revived debates on teacher preparedness to adequately engage in this modality of instruction. Research-validated evidence has shown that a complex combination of technology, pedagogy and content is necessary for impactful teaching practices. Thus, the present paper reports on a qualitative study that investigated students’ perceptions of their teachers’ performance during the lockdown period. Online interviews and focus groups were conducted with a cohort of 30 university students. The students were selected by means of purposeful sampling informed by a set of pre-defined criteria. Overall, the students reported that most of their teachers utilised asynchronous teaching modalities. Also of interest was the finding that a small proportion of faculty varied the instructional deliverable with a clear intent to cater for a wider spectrum of student learning pace and style. Under COVID-19, additionally, distance education was hugely crippled by student psychological ill-being. One of the implications of these findings, among others, is the pressing need, now more than ever, for comprehensive and intensive teacher professional programs focused on technology-enhanced methodology.
Proposal for the Evaluation of the Teaching/Learning Process in Children with Autism Spectrum Disorder Through a Mobile Application with Augmented Reality

Scientific and technological advances have brought about changes in society, making it possible to obtain data and manage large volumes of information with the simple movement of a finger or with a few clicks on our computer. The processes for acquisition of documents, such as education, especially the integration and intellectual development of children and young people with disabilities or neurological disorders, are not exempt from them, such as Autism Spectrum Disorder (ASD). Those who, supported by technologies such as Augmented Reality (AR), greatly improve their neurocognitive situation, being able to interact and relate openly with the real and digital world. Based on this, the following proposal will cover the study of the problem, from which it is desired to provide an augmented reality environment using mobile devices for marker processing, where children with ASD can interact and consolidate a more dynamic learning of science.
A FaaS Approach for Long-Term Monitoring in Rehabilitation

The function as a service (FaaS) model, in which the costs are linked to the function time of use, is especially suitable in IoT or similar systems, where the information is collected periodically to consume less energy. A FaaS approach also allows us to fine-tune the application’s behaviour at a function level, controlling which functions should be executed at any given time. This makes it possible to apply optimisation processes in an automated way. In this work, a FaaS-based platform is proposed to develop e-Health applications that adapt their behaviour to the different stages of the patient rehabilitation process. Its operation is illustrated by a case study on patients with heart disease. The system’s operation will vary throughout the different stages of the rehabilitation, adjusting to the available sensors, the evolution of the patient’s physical condition and other parameters such as the required precision.
Modeling Interactions and Awareness Capabilities of Mobile Collaborative Systems

Conceiving a mobile collaborative system involves determining, at an early stage of the development process, the interaction and awareness services that such an application will provide to end-users, software agents, and smart objects. The literature recognizes this definition as a challenging activity that requires the joint participation of stakeholders and developers in order to get an agreement about these services. This paper presents a collaborative visual modeling tool intended to help both parties to create, evaluate and evolve models that represent this service agreement through several views. The tool was evaluated and the results of this assessment study and the feedback issued by the participating researchers and developers offer us positive indications in its practical application.
A Hybrid Approach to Ephemeral PKI Credentials Validation and Auditing

IoT/M2M solutions are expected to rely on near computing infrastructures for deployment of services, frequently ephemeral, that will need adequate protection. Communication protocols in IoT services have widely adopted TLS/PKI as the de facto security standard despite PKI was not designed for issuing short lived credentials. Moreover, after several Certificate Authorities were compromised, some Certificate Pinning proposal were developed to give an additional verification to PKI certificates. Some Certificate Pinning solutions, as Certificate Transparency, provide long term auditing information for PKI certificates issued by renowned Certificate Authorities only, whereas others, as DANE, are able to verify self-issued certificates and give support for security islands that would benefit the development of IoT/M2M micro services but cannot provide long term auditing information. This article describe DANEAudits, a novel service with the objective of complementing DANE with long term auditing information without the need of new Trusted Third Parties different from the information owner.
“Matching Learning”: Profiling and Clustering Users on Tinder Based on Emotion and Sentiment Analysis

Emotion and sentiment analysis tools offer the possibility of detecting emotion in several ways. In this paper, we study IBM Watson’s Natural Language Understanding (Emotion and Sentiment), and CoreNLP’s Sentiment Analysis accuracy levels against an annotated dataset, so as to observe any difference when comparing a discrete emotion classification approach (Watson’s Emotion) versus a one-dimensional approach (Watson and CoreNLP’s Sentiment). We have found that one-dimensional approaches were more accurate (85.5% for Watson, 62.7% for CoreNLP) than the discrete approach (37.6%). Being aware of those accuracy rates, and how they classified those emotions, we use those same services to analyse Tinder biographies, and observe how users present themselves, and finally applying clustering algorithms to analyse any trends between the emotion and sentiment of their biographies and other markers, such as age, gender, location, etc. After analysing the biographies, we have observed a tendency for more Neutral presentations (45–70%), followed by Positive (15–38%) presentations, though IBM Watson’s sentiment over classified joyful presentations (73%). In terms of clustering, we observe three distinct groups according to the emotional tone of their biographies, and the information provided. With this, we aim to contribute a better understanding on how two widely used emotion and sentiment analysis tools compare against each other, and how users can be classified according to the emotional/sentiment tones of their biographies.
Analysis of Accelerometer Data for Personalised Abnormal Behaviour Detection in Activities of Daily Living

This paper proposes a novel approach to identify personalised abnormal behaviour in Activities of Daily Living (ADLs) using accelerometer sensor data. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee.Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. Monitoring ADLs for detecting abnormal behaviour is of particular importance due to the potential life changing consequences that could result from not acting timely. Prior to performing ADLs, the participants were asked six questions related to their well-being and mood. In addition to data collected from accelerometers, data was also collected from contact and thermal sensors, and radar. The work presented is a first step towards a more. personalised approach in which individual user profiles are considered as it is acknowledged that people behave differently from each other. Thus, data was collected seven times for each participant. We have evaluated our approach with accelerometer data collected from 15 participants. The experimental results show that accelerometer data is sufficient to identify the main stages of the ADLs considered, and therefore, any unusual changes in the signals and duration could mean that abnormal behaviour occurred.
ThermalYOLO: A Person Detection Neural Network in Thermal Images for Smart Environments

Nowadays, low-resolution thermal cameras are gaining relevance in smart environments due to keeping user privacy by recording images and videos in domestic environments. Many neural networks obtain outstanding results from visible spectrum devices for human activity and event detection, such as fall detection, object detection or pose estimation. However, these state-of-the-art neural networks are trained in datasets that do not contain thermal images, so their performance on them is not good. The main objective of this work is human body recognition and segmentation from thermal cameras. For this purpose, we propose ThermalYOLO, a neural network based on the YOLO neural network and fine-tuned with thermal images. For the generation and auto-labelling of the thermal dataset, an IoT device with two cameras, a visible camera and a thermal camera, is used. Therefore, the user does not have to manually annotate the dataset. As a result, ThermalYOLO outperforms YOLO in thermal images from two different smart environments.
Cloud vs Serverless Computing: A Security Point of View

In the last few years, there has been an evolution of the traditional cloud architectures which offers the possibility the provider assumes a big percentage of the security to the level of infrastructure, leaving the responsibility for the security of the applications to the developers. The new model of serverless computation, represents an evolution of the cloud architecture, improving also some appearances related with the security of the applications that use this new model. In this paper, we analyze which are the advantages and problems of the serverless architectures from the point of view of the security, comparing the main risks and attack vectors in both architectures. From this comparative, we can conclude that in serverless architectures new risks appear in the applications and improve others that are found in methodologies of safe development like Open Web Application Security Project (OWASP). Given the event-driven nature of serverless architectures, this type of applications add an additional complexity and arise new risks, among which can stand out those related with the data injection of events in functions and the creation of flows between serverless functions that could increase the attack surface of an application and do it vulnerable to attacks already known. To the best of our knowledge, this is the first paper to compare cloud and serverless computing from a security point of view.
Phonendo: A Platform for Publishing Wearable Data on DLT

Modern intelligent health-based software nowadays is based on data-driven systems that collect data streams from wearable devices that perform continuous remote monitoring of patient parameters. The importance of such wearable devices and data streams is key because of their low cost, but also because they facilitate the building of a large number of solutions to multiple health-based problems. Despite of the easy manipulation of both, devices and their data streams; the necessary processes from collecting data to final analyses passing by data transformation and variable creation present an important flaw such as, it is the security of such data in applications that is mandatory. Distributed Ledger Technologies (DLT) enable systems to be endowed with characteristics such as resistance to information manipulation or resilience to the appearance of single points of failure. DLT have the potential to build wearable-based solutions offering characteristics that would allow building a wider range of possible solutions. This contribution introduces Phonendo, a platform for collecting data streams from wearable devices and publishing them on a DLT Infrastructure (DLTI). Phonendo is under development and here we focus on the presentation and justification of its interface layers, i.e., data collection and publication on a DLTI.
Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures

Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.
Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Abstract This study deals with the performance of PV2-state model in intra-hour forecasting of photovoltaic (PV) power. The PV2-state model links an empirical model for estimating the PV power delivered by a PV system under clear-sky with a model for forecasting the relative position of the Sun and clouds. Sunshine number (SSN), a binary quantifier showing if the Sun shines or not, is used as a measure for the Sun position with respect to clouds. A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is applied to predict SSN. The quality of SSN prediction conditions the overall quality of PV2-state forecasts. The PV2-state performance was evaluated against a challenging database (high variability in the state-of-the-sky, thin cloud cover, broken cloud field, isolated passing clouds) comprising radiometric data and sky-images collected on the Solar Platform of the West University of Timisoara, Romania. The investigation was performed from two perspectives: general model accuracy and, as a novelty, identification of characteristic elements in the state-of-the-sky which fault the SSN prediction. The outcome of such analysis represents the basis of further research aiming to increase the performance in PV power forecasting.
Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human Teachers

Robot Learning from Demonstration (RLfD) allows non-expert users to teach a robot new skills or tasks directly through demonstrations. Although modeled after human–human learning and teaching, existing RLfD methods make robots act as passive observers without the feedback of their learning statuses in the demonstration gathering stage. To facilitate a more transparent teaching process, we propose two mechanisms of Learning Engagement , Z2O-Mode and D2O-Mode, to dynamically adapt robots’ attentional and behavioral engagement expressions to their actual learning status. Through an online user experiment with 48 participants, we find that, compared with two baselines, the two kinds of Learning Engagement can lead to users’ more accurate mental models of the robot’s learning progress, more positive perceptions of the robot, and better teaching experience. Finally, we provide implications for leveraging engagement expression to facilitate transparent human-AI (robot) communication based on our key findings.
Unimodular rows over affine algebras over algebraic closure of a finite field

In this paper, we prove that if [Formula: see text] is an affine algebra of dimension [Formula: see text] over [Formula: see text] and [Formula: see text] then any unimodular row over [Formula: see text] of length [Formula: see text] can be mapped to a factorial row by elementary transformations.
LSUnetMix: Fuse channel feature information with long–short term memory

Abstract Medical image segmentation based on deep learning is becoming popular. To improve the segmentation accuracy of medical images such as cells and vessels, we propose the LSUnetMix model, which can effectively enhance the ability to extract channel information and capture image details better. It surpasses both traditional and latest models on medical datasets. This model is mainly based on Unet, and three modules have been added. The first is the Channel Information Transmission module, which uses long–short term memory to get the sequential features of layers and unidirectionally transfer the channel information of the upper layer to the lower layer, which combines multiple layers of information while avoiding information redundancy. The second is the Prospect Enhancement module, which activates the channel information of the image and enhances the ability to recognise the segmentation targets. The third is the Multiscale Dilated Convolution module, which uses multi‐scale dilated convolution to strengthen the ability and to extract the image details. After experiments, their model performs best on GlaS and MoNuseg datasets, which can segment image details better and avoid redundant information, and the Dice coefficient reaches 91.92% on GlaS and 81.8% on MoNuSeg.
Conditional differential analysis on the KATAN ciphers based on deep learning

Abstract KATAN ciphers are block ciphers using non‐linear feedback shift registers. In this study, the authors improve the results of conditional differential analysis on KATAN by using deep learning. Multi‐differential neural distinguishers are built to improve the accuracy of the neural distinguishers and increase the number of its rounds. Moreover, a conditional differential analysis framework is proposed based on deep learning with the multi‐differential neural distinguishers, resulting in a significant improvement than the previous. We present a practical key recovery attack on the 97‐round KATAN32 with 2 15.5 data complexity and 2 20.5 time complexity. The attack of the 82‐round KATAN48 and 70‐round KATAN64 are also presented as the best known practical results.
A survey of cyber attacks on blockchain based IoT systems for industry 4.0

Abstract The industry 4.0 revolution leads by technologies such as distributed digital ledgers, big data processing techniques, automation, and the internet of things (IoT). The interest in the industrial internet of things (IIoT) has progressed rapidly with huge internet areas such as research, academics, and industries. Through a decentralised and distributed approach blockchain and IIoT ensure maximum security. In the distributed approach, every block is chained together with each other. This paper presented a survey on industry 4.0 and blockchain‐based systems. The integration of blockchain, IoT devices, and industry 4.0 provides security, transparency, and scalability to the system. The paper also addresses many types of cyber attacks and possible solutions for blockchain based industrial internet of things (BIIoT) systems.
The monitoring of bridge under complex illumination based on digital image technology

In the actual bridge monitoring field, the change of illumination is an urgent problem to be solved in digital image technology. At present, when using markers to measure the displacement of bridges, problems such as the change of image gray value, uneven distribution of light, and surface defects of material cause identification difficulties. In this paper, a new recognition algorithm called the dual-channel method is proposed. Based on the dark channel principle and OSTU theory, the dual-channel method can automatically select the image gray threshold, which is used to solve the recognition problem to achieve long-term monitoring. Experimental results show this method can accurately identify the markers under complex lighting conditions and greatly expand the range of gray thresholds. Compared with the traditional displacement meter, measurement errors of the dual-channel method are less than 3%. The new method can achieve real-term effective bridge monitoring, which provides a theoretical basis for practical engineering.