Reviewerly/exHarmony huggingface dataset:
Power and Solar Energy Predictions Based on Neural Networks and Principal Component Analysis with Meteorological Parameters of Two Different Cities: Case of Diass and Taïba Ndiaye

The overuse of conventional resources based on fossil fuels increases the vulnerability of our environment. Faced with this effect, increasing the penetration rate of intermittent (non-polluting) energies in the electrical networks has become of paramount importance. However, this increase in the penetration rate allows on the one hand to improve the satisfaction of the producers and reduces the consumption of fossil fuels, on the other hand it is a major point of suffering for the non-smart electrical networks. In a dynamic of promoting intermittent energies while ensuring a permanent balance between consumption and production, the forecasting of these energies is an important lever. Hence, this paper studies artificial neural networks to predict the power and energy output of the Diass solar power plant in the short and medium term. Thus, the proposed approach consists in using not only the meteorological data of the city where the power plant is located, but also the data of a nearby city with a data acquisition station. The selection of the variables is done by principal component analysis (PCA). Moreover, our results were compared to the literature using only the meteorological data of the plant of implementation. The results obtained are more satisfactory with mean absolute and root mean square errors of 0.0223 KWh and 0.003 KWh respectively and a prediction accuracy of 94.57% in terms of energy and power. In terms of resource, it consumes more with simulation times varying between 1788 seconds and 2201 seconds.
LOAD BALANCING IN CLOUD COMPUTING

Cloud computing has proposed a new perspective for provisioning the large-scale computing resources by using virtualization technology and a pay-per-use cost model. Load balancing is taken into account as a vital part for parallel and distributed systems. It helps cloud computing systems by improving the general performance, better computing resources utilization, energy consumption management, enhancing the cloud services’ QoS, avoiding SLA violation and maintaining system stability through distribution, controlling and managing the system workloads. In this paper we study the necessary equirements and considerations for designing and implementing a suitable load balancer for cloud environments. In addition we represent a complete survey of current proposed cloud load balancing solutions which according to our classification, They can be classified into three categories: General Algorithm-based, Architectural-based and Artificial Intelligence-based load balancing mechanisms. Finally, we propose our evaluation of these solutions based on Suitable metrics and discuss their pros and cons. Index Terms—Cloud Computing, Load Balancing, Distributed Systems, Virtual Machine.
Toward High Capacity and Robust JPEG Steganography Based on Adversarial Training

JPEG steganography has become a research hotspot in the field of information hiding. However, the capacity of conventional JPEG steganography methods is hard to meet the requirements in high-capacity application scenarios and also can not extract secret messages accurately after JPEG compression. To mitigate these problems, we propose a high-capacity and robust JPEG steganography based on adversarial training called HRJS, which implements an end-to-end framework in the JPEG domain for the first time. The encoder is responsible for embedding the secret message while the decoder can reconstruct the original secret message. To enhance robustness, an attack module forces the neural network to automatically learn how to correctly recover the secret message after an attack. Experimental results show that our method achieves near 100 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>%</mo> </math> decoding accuracy against JPEG_50 compression at 1/3 bits per channel (bpc) payload while preserving the imperceptibility of the stego image. Compared with conventional JPEG steganography methods, the proposed method is feasible with high capacity (e.g., 1 bpc) and has an obvious advantage in terms of robustness against JPEG compression at the same time.
A Deep Neural Network-Based Approach to Media Hotspot Discovery

In recent years, with the rapid development of social network media, it has become a valuable research direction to quickly analyze these texts and find out the current hotspots from them in real time. To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. Then, the K-means algorithm is optimized for the initial point selection problem, and a clustering algorithm based on the maximum density selection method in the moving range is proposed. Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. The accuracy of the proposed media hotspot discovery method and the comprehensive evaluation of the computing time have been verified experimentally.
Integrating Functional Status Information into Knowledge Graphs to Support Self-Health Management

ABSTRACT Functional Status Information (FSI) describes physical and mental wellness at the whole-person level. It includes information on activity performance, social role participation, and environmental and personal factors that affect the well-being and quality of life. Collecting and analyzing this information is critical to address the needs for caring for an aging global population, and to provide effective care for individuals with chronic conditions, multi-morbidity, and disability. Personal knowledge graphs (PKGs) represent a suitable way for meaning in a complete and structured way all information related to people's FSI and reasoning over them to build tailored coaching solutions supporting them in daily life for conducting a healthy living. In this paper, we present the development process related to the creation of a PKG by starting from the HeLiS ontology in order to enable the design of an AI-enabled system with the aim of increasing, within people, the self-awareness of their own functional status. In particular, we focus on the three modules extending the HeLiS ontology aiming to represent (i) enablers and (ii) barriers playing potential roles in improving (or deteriorating) own functional status and (iii) arguments driving the FSI collection process. Finally, we show how these modules have been instantiated into real-world scenarios.
A Comprehensive Review of Lightweight Authenticated Encryption for IoT Devices

Internet of Things (IoT) is a promising technology for creating smart environments, smart systems, and smart services. Since security is a fundamental requirement of IoT platforms, solutions that can provide both encryption and authenticity simultaneously have recently attracted much attention from academia and industry. This article analyses in detail state-of-the-art lightweight authenticated encryption (LAE) targeted to IoT systems. This work provides a thorough description of the algorithms, and the study systematically classifies them to facilitate understanding of relevant intricacies of the schemes. Among reviewed algorithms, there is a trade-off to retain design security, resources cost, and efficient performance. ACORN is the effective scheme on various platforms in terms of utilization of resources and power consumption, while MORUS and AES-CLOC are the fastest in hardware platforms. However, they are susceptible to misuse despite their resistance to side channel attacks. In contrast, JOLTICK, PRIMATESs, COLM, DeoxysII, OCB, and AES-JAMBU are provably resistant to nonce misuse. The challenges for possible future research are summarized. Overall, the article provides researchers and developers with practical guidance on various design aspects and limitations as well as open research challenges in the current lightweight authenticated encryption for IoT.
Datafication, Platformization, Algorithmic Governance, and Digital Sovereignty

research-article Share on Datafication, Platformization, Algorithmic Governance, and Digital Sovereignty: Four Concepts You Should Teach Author: Randy Connolly Mount Royal University Mount Royal UniversityView Profile Authors Info & Claims ACM InroadsVolume 14Issue 1March 2023pp 40–48https://doi.org/10.1145/3583087Published:21 February 2023Publication History 1citation123DownloadsMetricsTotal Citations1Total Downloads123Last 12 Months123Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Pseudorandom: generative animation as performance in <i>Emergent</i> (2020–2022)

The body is the database of lived experience. Emergent was created during COVID-19 from the desire to explore the extended possibilities of digital performance beyond lens-based media. The work includes generative animations and sound compositions using data collected from a consumer fitness tracker worn since the start of the COVID-19 pandemic in 2020. As a portrait of experience through the data body (as both body of data and body producing data), Emergent engages with the memories of the flesh, becoming the impetus for aesthetic encounters through digital performance. In this article about the work, Putnam describes how it was produced, provides a detailed overview of the work and its theoretical context, and discusses how it functions as a digital performance between the artist and computer. The result is a work where data visualisation and sonification generates ambiguity, rather than clarity, introducing difference in how biometric sensing devices are used and understood.
Low complexity VLSI architecture for improved primal–dual support vector machine learning core

Support Vector Machine (SVM) is an efficient classification tool providing good accuracy and reliability. The primal–dual method is an interior-point method for SVM training with considerable scalability and accuracy. In this paper, an improved primal–dual method for SVM learning is proposed. The proposed primal–dual method offers faster convergence of the SVM learning core by up to 25%, which is made possible by reducing the number of iterations required for obtaining the optimal solution while maintaining its accuracy. We also propose a low-complexity pipelined very large scale integration (VLSI) architecture for implementing the improved primal–dual method both on field-programmable gate array (FPGA) and 65 nm application specific integrated circuit (ASIC) platforms. The computational complexity of the proposed VLSI architecture is independent of the size of the training data and the feature vector.
A frame and first-order logic solution for the Wumpus World: Implemented in Flora-2

The Wumpus World is a classical AI problem for a knowledge based agent (a.k.a. logic agent). The existing solutions, mostly implemented in Prolog, are described in propositional and predicate logics. Flora-2 is a vast extension of Prolog that supports many features that simplify logic programming. It complies with first-order logic, frame-logic (a.k.a. F-logic), and HiLog (higher-order-logic) reasoning. Moreover, it allows the full use of existential quantifiers that eases the writing of first-order logic solutions. In this article, a new and concise first-order logic solution, implemented in Flora-2, is demonstrated, tested, and analyzed.
Dynamic Pre-trained Models Layer Selection Using Filter-Weights Cosine Similarity

Selecting fine-tuning layers in a pre-trained model is vital to adapting the target data in transfer learning. The selection process is mainly manual and or based on the last k layers of the network. So far, these methods have not yielded the best adaptation for various datasets. Therefore, it is a trial and error selection of the suitable layers for the ever-increasing pre-trained models. This paper looks at the effect of the weights based on the cosine similarity at the various layers leading to the omission or selection in the fine-tuning process. We experiment on four primary publicly available datasets utilizing four pre-trained models, and the results show better performance than the traditional approaches. Furthermore, a chest cancer imaging dataset is an application extension of the proposed approach. The results show improvements from the new methods between 0.65% in VGG16 to 39.01% for the InceptionV3 models compared to the standard fine-tuning baselines.
Efficient Feature Selection Approach for Detection of Phishing URL of COVID-19 Era

Cybercrime is a growing concern, particularly in this COVID-19 era. The COVID-19 outbreak has shown the significant impact potential of such crises on our daily lives worldwide. Phishing is a social engineering crime that can cause financial and reputational damages such as data loss, personal identity theft, money loss, financial account credential theft, etc., to people and organizations. In the recent outbreak of the COVID-19 pandemic, many companies and organizations have changed their working conditions, moved to an online environment workspace, and implemented the Work From Home (WFH) business model that increases the phishing attacks vectors and risk of breaching internal data. In this paper, we have extracted nine efficient features from the URLs and applied seven different Machine Learning algorithms to recognize phishing URLs. Machine learning algorithms are often used to detect phishing attacks more accurately before affecting users. The obtained result concludes that the Random Forest model provides the best and highest accuracy of 95.2%.
Automating Sickle Cell Counting Using Object Detection Techniques

Blood cell counting is an important part of the medico-clinical diagnostic process. However, counting sickle cells by manual visual inspection under the microscope is time intensive and subject to human error. In order to efficiently automate blood cell counting, we propose in this paper a detection method based on deep learning in this case YOLO, one of the most powerful object detection models in recent years. To perform our study, we collect and label a dataset of 223 images including about 6,012 cells (normal and sickle cell). We explored five YOLOV5 models version optimized by a pre-trained model on biological images, and the YOLOV5x one gives us better accuracy. We got a detection accuracy of 95% on the validation data. Deployed as a web application via Flask, this identification methodology can help healthcare professionals make faster and more efficient diagnoses.
Explainable Artificial Intelligence on Smart Human Mobility: A Comparative Study Approach

Explainable artificial intelligence has been used in several scientific fields to understand how and why a machine learning model makes its predictions. Its characteristics have allowed for greater transparency and outcomes in AI-powered decision-making. This building trust and confidence can be useful in human mobility research. This work provides a comparative study in terms of the explainability of artificial intelligence on smart human mobility in the context of a regression problem. Decision Tree, LIME, SHAP, and Seldon Alibi are explainable approaches to describe human mobility using a dataset generated from New York Services. Based on our results, all of these approaches present relevant indicators for our problem.
Threat Detection in URLs by Applying Machine Learning Algorithms*

Different cyber threat groups develop an infrastructure to be able to distribute malware to victims. The entry vector of these threats is usually the download of malicious files via a web link that initiates the system infection. It is possible to detect these threats at an early stage to anticipate a possible compromise by applying a malicious URL detector. This work contributes to detect cyber threats (Emotet and Qakbot, mainly) in advance given an input URL.
Online Collaborative Learning: Use and Efficiency Evaluation

Collaborative learning, especially online, occupies the increasingly important place in the contemporary higher education. The article undertakes an analysis of key both theoretical and practical dimensions of collaborative learning. Approaches to the definition of collaborative learning are indicated, its key elements and principles of organization and implementation are highlighted. The authors consider collaborative learning not just as a set of new teaching and learning methods but primarily as a formation of life skills in the complex interconnected society. Collaborative thinking is inextricably linked with the creative and critical thinking process within the framework of community of inquiry. It is this framework that ensures theoretical coherence of understanding and use of collaborative thinking and learning in a socially situated and technologically interconnected learning environment. The authors describe collaborative learning as the special didactic system and consider some aspects of its implementation into any form of the university educational process – high-residence, low-residence, blended, or distance learning. As a conclusion, some criteria for evaluating the effectiveness of collaborative learning are formulated.
Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis

One of the main goals of companies is to create value. In defining specific values as part of their unique value proposition, companies use comparison analysis and market research. These value identification methods are very resource-intensive and time-consuming. Automation of this process would substantially reduce costs, as well as significantly expand the number of identifiable values. The aim of this research is to introduce a novel approach to Information Extraction in the form of a new Entity Recognition task: Value Entity Recognition (VER) and to develop a methodology for the automated value identification from e-texts in air transportation domain finalizing a series of studies on automation of value identification. The result of automation of value identification is presented as a neural network model trained on a unique database of values derived from start-ups’ online promotional texts of air transportation domain. To train a model, a dataset from annotation of Twitter profiles of start-ups was utilized. The air transportation industry was taken to narrow down the domain and to facilitate an experiment. The experiment is conducted in English language as English is the international language of civil aviation and English unites air transportation industries. A trained neural network can be useful to aviation managers in their decision-making process. The methodology offered is a baseline for value identification automation and is applicable to different industries, elaborated subject to the tasks set.
Predicting Co-occurring Emotions in MetaTutor when Combining Eye-Tracking and Interaction Data from Separate User Studies

Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.
Informing Expert Feature Engineering through Automated Approaches: Implications for Coding Qualitative Classroom Video Data

While classroom video data are detailed sources for mining student learning insights, their complex and unstructured nature makes them less than straightforward for researchers to analyze. In this paper, we compared the differences between the processes of expert-informed manual feature engineering and automated feature engineering using positional data for predicting student group interaction in four middle school and high school mathematics classroom videos. Our results highlighted notable differences, including improved model accuracy for the combined (manual features + automated features) models compared to the only-manual-features models (mean AUC = .778 vs. .706) at the cost of feature interpretability, increased number of features for automated feature engineering (1523 vs. 178), and engineering approach (domain-agnostic in automated vs. domain-knowledge-informed in manual). We carried out feature importance analyses and discuss the implications of the results for potentially augmenting human perspectives about qualitatively coding classroom video data by confirming and expanding views on which body areas and characteristics may be relevant to the target interaction behavior. Lastly, we discuss our study's limitations and future work.
Multimodal Predictive Student Modeling with Multi-Task Transfer Learning

Game-based learning environments have the distinctive capacity to promote learning experiences that are both engaging and effective. Recent advances in sensor-based technologies (e.g., facial expression analysis and eye gaze tracking) and natural language processing have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics and student modeling informed by multimodal data captured during students' interactions with game-based learning environments hold significant promise for designing effective learning environments that detect unproductive student behaviors and provide adaptive support for students during learning. Learning analytics frameworks that can accurately predict student learning outcomes early in students' interactions hold considerable promise for enabling environments to dynamically adapt to individual student needs. In this paper, we investigate a multimodal, multi-task predictive student modeling framework for game-based learning environments. The framework is evaluated on two datasets of game-based learning interactions from two student populations (n=61 and n=118) who interacted with two versions of a game-based learning environment for microbiology education. The framework leverages available multimodal data channels from the datasets to simultaneously predict student post-test performance and interest. In addition to inducing models for each dataset individually, this work investigates the ability to use information learned from one source dataset to improve models based on another target dataset (i.e., transfer learning using pre-trained models). Results from a series of ablation experiments indicate the differences in predictive capacity among a combination of modalities including gameplay, eye gaze, facial expressions, and reflection text for predicting the two target variables. In addition, multi-task models were able to improve predictive performance compared to single-task baselines for one target variable, but not both. Lastly, transfer learning showed promise in improving predictive capacity in both datasets.