Reviewerly/exHarmony huggingface dataset:
Federated Learning for Privacy-Preserving Internet of Things

The rapid evolution of the Internet of Things (IoT) and relevant applications have been paving the way toward the fulfillment of smart cities. Smart cities are thought to come up with multiple crucial smart IoT applications i.e., smart manufacturing, smart transportation, autonomous driving/flight, smart buildings, smart healthcare, smart grid, and so many others (Lo et al. in ACM Comput. Surv., 2021). Effective deployment of these IoT applications and services and making them available for users usually entail a vast number of IoT devices. Conferring to some global statistics, the total of IoT devices is believed to rises to 125 billion by 2030. This in turn reflects expected large growth in the amount of IoT-generated data. some other statistics show that, by 2025, the size of IoT-generated data will grow up to reach 79.4 zettabytes (ZB).
Artificial Intelligence-Enabled Data Value Curation on AI-Data Commons

Recently, as the artificial intelligence technologies have been growing, the demands for “good” data which affects the prediction accuracy of AI also have been increased. Therefore, there has been extensive work conducted on various aspects of dealing with data such as collecting, analyzing and monetizing. However, there are issues to be considered in trading data. In this paper, we introduce the enhanced AI-Data Commons which is ameliorated to solve a couple of problems in trading data.
An Event Model for Smart Agriculture

Events are a very important aspect of building cyber-physical systems. The article discusses an event model that is integrated into a reference architecture for cyber-physical applications. In addition, an adaptation of the event model is presented for use in intelligent agriculture. Two versions of an interpreter of event models are also discussed.
Modeling combined global, beam, and diffuse clear-sky indices with Markov-chain mixture distribution models

This study uses the N-state Markov-chain mixture distribution model and the multiple-component N-state Markov-chain mixture distribution model to simulate global, beam, and diffuse horizontal clear-sky index. The models are data-driven such that when trained on single or multiple clear-sky index time-series, the models generate arbitrarily long synthetic clear-sky index time-series for the same components. The models were tested on solar irradiance datasets from two different climatic regions: Norrköping, Sweden, and Oahu, Hawaii, USA. The results show high probability distribution and temporal autocorrelation goodness-of-fit for all models and high cross correlation goodness-of-fit as well as accurate correlation between the component datasets for the multiple component model simulations. When combined with, e.g., the Hay and Davies model, the output from this model could, for example, be used to generate realistic time-series of incident solar irradiance on tilted planes.
An Evaluation of Methods on Detecting, Recognizing and Understanding Graphics Images in Technical Documents

Proper document analysis is dependent on the accurate processing of each individual modalities [1], [2], [3]. Graphics images are such document modalities that illustrate visually information about the behavior of experimental systems, the trend of the financial market and etc. The automatic processing of information graphics aims to provide summarizations for such datasets with applications to document retrieval, enabling reading for visually impaired people and etc. In this paper we review existing chart analysis methodologies that work towards these goals. More specifically, we categorize the chart analysis methodologies into detection, recognition and understanding of graphics images. Graphics detection includes the localization of areas that potentially include chart figures. The area of graphics recognition focuses on identifying the legends and axis of inputs charts as well as the structural characteristics of their curves. Graphics understanding aims to capture the underlying message of input chart images and provide their summarizations. Additionally, we provide a discussion for each area of research about the characteristics and merits of their presented methodologies. Finally, we propose a comparison scheme that is based on subjective metrics that reflect the needs of modern applications.
Machine learning assisted non-destructive beam profile monitoring

We present a non-destructive beam profile monitoring concept that utilizes numerical optimization tools, namely genetic algorithm with a gradient descent-like minimization. The signal picked up by a button BPM includes information about the transverse profile content of the beam. A genetic algorithm is used to transform an arbitrary Gaussian beam in such a way that it eventually reconstructs the transverse position and the shape of the original beam to match the signal on the BPM electrodes. A case study for the developed algorithm is proton EDM experiment where conventional beam profile measurements are not possible. This method allows visualization of fairly distorted beams with non-Gaussian distributions as well.
Digital Forensic Tools for Cloud Computing Environment

In general, digital forensics process can be stated as a process of identifying, analyzing and presenting the digital evidence. It comprises activities like identification, preservation, examination, analysis. Complication and complexity in the execution of these activities increase with the size of data. Cloud computing technology exacerbate the situation. Various forensic tools have been developed to improve the performance and efficiency of forensic activities. This paper highlights such digital forensic tools and provides brief information about the forensic tools. This paper examines traditional digital forensic tools helpful for the cloud forensic as well as tools that are specially designed for the cloud environment. It includes commercial as well as open-source tools.
Explainable source code authorship attribution algorithm

Abstract Source Code Authorship Attribution is a problem that is lately studied more often due improvements in Deep Learning techniques. Among existing solutions, two common issues are inability to add new authors without retraining and lack of interpretability. We address both these problem. In our experiments, we were able to correctly classify 75% of authors for diferent programming languages. Additionally, we applied techniques of explainable AI (XAI) and found that our model seems to pay attention to distinctive features of source code.
Reconstructing a quantum state with a variational autoencoder

Quantum state tomography (QST) is an important and challenging task in the field of quantum information, which has attracted a lot of attentions in recent years. Machine learning models can provide a classical representation of the quantum state after trained on the measurement outcomes, which are part of effective techniques to solve QST problem. In this work, we use a variational autoencoder (VAE) to learn the measurement distribution of two quantum states generated by MPS circuits. We first consider the Greenberger–Horne–Zeilinger (GHZ) state which can be generated by a simple MPS circuit. Simulation results show that a VAE can reconstruct 3- to 8-qubit GHZ states with a high fidelity, i.e., 0.99, and is robust to depolarizing noise. The minimum number ([Formula: see text]) of training samples required to reconstruct the GHZ state up to 0.99 fidelity scales approximately linearly with the number of qubits ([Formula: see text]). However, for the quantum state generated by a complex MPS circuit, [Formula: see text] increases exponentially with [Formula: see text], especially for the quantum state with high entanglement entropy.
Stratified Cross-Validation on Multiple Columns

Stratified cross-validation is one of the standard methods of how to evaluate classifier’s generalization accuracy. However, conventional implementations of cross-validation can stratify only by a single column. In this paper, we propose to utilize Integer Linear Programming in order to enable stratification by multiple columns. Our experiments using an extensive set of multi-label data sets shows that the proposed method significantly outperforms non-stratified cross-validation.
Bit-Levels Encryption for RGB Telescope Images

Digital image security is a main concern in network transmission especially with the involvement of sensitive data. With the rapid development of telecommunications, tons of digital images in various fields are transmitted throughout the network. This paper discusses the RGB bit-levels encryption algorithm for radio telescope imageries using Random Generation Number method. Each color model in an image (Red, Green and Blue) was encrypted by two levels of scrambling algorithms, namely Level I and Level II scrambling. The results show that, by evaluating the stability and scrambling degree, Level II scrambling algorithm is more reliable in scrambling the radio telescope images used in the present study.
DNN Model Deployment on Distributed Edges

Deep learning-based visual analytic applications have drawn attention by suggesting fruitful combinations with Deep Neural Network (DNN) models and visual data sensors. Because of the high cost of DNN inference, most systems adopt offloading techniques utilizing a high-end cloud. However, tasks that require real-time streaming often suffer from the problem of an imbalanced pipeline due to the limited bandwidth and latency between camera sensors and the cloud. Several DNN slicing approaches show that effectively utilizing the edge computing paradigm effectively lowers the frame drop rate and overall latency, but recent research has primarily focused on building a general framework that only considers a few fixed settings. However, we observed that the optimal split strategy for DNN models can vary significantly based on application requirements. Hence, we focus on the characteristics and explainability of split points derived from various application goals. First, we propose a new simulation framework for flexible software-level configuration, including latency and bandwidth, using dockercompose, and we experiment on a 14-layered Convolutional Neural Network (CNN) model with diverse layer types. We report the results of the total process time and frame drop rate of 50 frames with three different configurations and further discuss recommendations for providing proper decision guidelines on split points, considering the target goals and properties of the CNN layers.
A Comparison of Saliency Methods for Deep Learning Explainability

Saliency methods are widely used to visually explain “black-box” deep learning model outputs to humans. These methods produce meaningful maps which aim to identify the salient part of an image responsible for, and so best explain, a Convolutional Neural Network (CNN) decision. In this paper, we consider the case of a classifier and the role of the two main categories of saliency methods: backpropagation and attribution. The first method is based on the gradient of the output with respect to the network parameters, while the second tests how local image perturbations affect the output. In this paper, we compare the Gradient method, Grad-CAM, Extremal perturbation, and DEEPCOVER, and highlight the complexity in determining which method provides the best explanation of a CNN's decision.
Unsupervised Learning of Affinity for Image Segmentation: An Inpainting based Approach

Unsupervised image segmentation is a fundamental problem that aims at clustering pixels into regions satisfying homogeneity (w.r.t color or texture), with no pixel annotations specified as ground truth. This paper proposes a novel unsupervised graph based segmentation framework based on a novel affinity matrix. The main hypothesis is that a convolutional neural network (CNN) trained to generate the contents of arbitrary missing image regions can provide powerful grouping cues among pixels in a given image. We encode this intuition with the generation of activation maps from state-of-the-art inpainting network, where the missing region corresponds to a superpixel of the input image. The activation weights are obtained for each pixel of every superpixel region and are utilised in graph construction over these superpixels. In contrast to the traditional graph based methods, that are primarily dependent on integrating local grouping cues, we propose to learn affinities based on high feature descriptiveness of CNNs, thus enabling reliable graph building for segmentation. Experimental results on the Berkeley segmentation database demonstrates the effectiveness of the proposed framework.
A Construction Method for the Knowledge Graph of Power Grid Supervision Business

In the centralized supervision mode of the power grid, there are a large amount of alarm information and equipment maintenance, test and defect records. Its efficient storage and accurate retrieval play an important guiding role in the operation evaluation and supervision management of power grid equipment. This paper takes the power grid equipment maintenance, defects, alarms and oil chromatography, etc. as the research object, and proposes a top-down and bottom-up method to build a knowledge graph of power grid supervision business, which assists supervision management personnel to grasp the status of equipment in a timely manner and improve the power grid's emergency handling capabilities. Firstly, the schema layer is defined, including knowledge entities and relations between entities from top-down; then, the data layer from bottom-up is constructed through the rule-based template extraction and intelligent model-based approach; besides, the trinomial tree model is used to achieve multi-source data fusion, with a matching rate of 95.33% and an error rate of 2.3%; finally, the Neo4j graph database is applied to store and visualize the power grid supervision business knowledge graph. Also, the application of the knowledge graph is analyzed, including intelligent information retrieval and equipment condition evaluation and prediction. Through the case study of the oil chromatography data evaluation, the effectiveness of the above-mentioned knowledge graph is verified.
Quick Response Code Acceptance on Digital Wallet Mobile Applications in Indonesia

Quick Response (QR) Code Payment Method is a payment method used through platforms such as digital wallets by scanning codes containing payment information. The QR Code Payment Method has been used in various digital wallet applications and its use has increased during the COVID-19 pandemic. There are problems in using QR Codes such as security, infrastructure, education, and transparency problems faced by users. This research aims to determine the factors that influence the use of the QR Code payment method on digital wallet applications in Indonesia. This research uses a Mobile Technology Acceptance Model (M-TAM) based research model with quantitative methods. The data collected in this research were 1.271 responses which were analyzed using the Covariance Based Structural Equation Modeling (CB-SEM) method with the AMOS 24 application. The results of this research indicate that the perception of transaction convenience, perception of transaction speed, mobile usability, ease of use of mobile, subjective norms, personal innovativeness, perceived security, and behavioral intentions affect the use (actual usage) of QR Code on digital wallet applications in Indonesia. This research is expected to give insights for QR Code payment service providers such as financial technology companies and regulators in improving payment services.
Modelling and Verification of Context-Aware Intelligent Assistive Formalism

Recent years have witnessed the expeditious evolution of intelligent smart devices and autonomous software technologies with the expanded domains of computing from workplaces to smart computing in everyday routine life activities. This trend has been rapidly advancing towards the new generation of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquire contextual information from the surrounding environment autonomously, perform reasoning on it, and then adapt their behaviors accordingly. With the proliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However, it is often challenging because the imperfect nature of context can cause the inconsistent behavior of the system. In this paper, we propose a context-aware intelligent decision support formalism to assist cognitively impaired people in managing their routine life activities. For this, we present a semantic knowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language (SWRL) rules. The set of contextualized information and the set of rules acquired from the ontology can be used to model Context-aware Multi-Agent Systems (CMAS) in order to autonomously plan all activities of the users and notify users to act accordingly. To illustrate the use of the proposed formalism, we model a case study of Mild Cognitive Impaired (MCI) patients using Colored Petri Nets (CPN) to show the reasoning process on how the context-aware agents collaboratively plan activities on the user's behalf and validate the correctness properties of the system.
Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.
Performance Comparison on SQL Injection and XSS Detection using Open Source Vulnerability Scanners

Web technologies are typically built with time constraints and security vulnerabilities. Automatic software vulnerability scanners are common tools for detecting such vulnerabilities among software developers. It helps to illustrate the program for the attacker by creating a great deal of engagement within the program. SQL Injection and Cross-Site Scripting (XSS) are two of the most commonly spread and dangerous vulnerabilities in web apps that cause to the user. It is very important to trust the findings of the site vulnerability scanning software. Without a clear idea of the accuracy and the coverage of the open-source tools, it is difficult to analyze the result from the automatic vulnerability scanner that provides. The important to do a comparison on the key figure on the automated vulnerability scanners because there are many kinds of a scanner on the market and this comparison can be useful to decide which scanner has better performance in term of SQL Injection and Cross-Site Scripting (XSS) vulnerabilities. In this paper, a method by Jose Fonseca et al, is used to compare open-source automated vulnerability scanners based on detection coverage and a method by Yuki Makino and Vitaly Klyuev for precision rate. The criteria vulnerabilities will be injected into the web applications which then be scanned by the scanners. The results then are compared by analyzing the precision rate and detection coverage of vulnerability detection. Two leading open source automated vulnerability scanners will be evaluated. In this paper, the scanner that being utilizes is OW ASP ZAP and Skipfish for comparison. The results show that from precision rate and detection rate scope, OW ASP ZAP has better performance than Skipfish by two times for precision rate and have almost the same result for detection coverage where OW ASP ZAP has a higher number in high vulnerabilities.
Sentiment Analysis of Arabic COVID-19 Tweets

With the COVID-19 outbreak in 2020, information about the pandemic has been exponentially increasing and spreading across various social media platforms. People across the globe have been affected in a way or another because of different aspects such as the increase in infected cases, death rate increase, financial difficulties, social distancing, being under lockdown, quarantine measures, and working remotely. With people heavily relying on social media platforms to share information more than ever, it is important to analyze their conversations to understand people's sentiments and feelings during this time of crisis to find possible ways to cope with the pandemic. This paper presents a sentiment analysis study to analyze sentiments from Arabic tweets related to COVID-19 using multiple models. After data acquisition, text preprocessing steps are performed and Term Frequency Inverse Document Frequency (TF-IDF) is used to generate feature vectors. Experiments are then done comparing multiple classifiers: Naïve Bayes, Support Vector Machine, Logic Regression, Random Forest, and K-Nearest Neighbor. A comparison of the models' performance was carried out using multiple evaluation metrics including Precision, Accuracy, Recall and F1 Score. The best performing model achieved an accuracy of around 84%.
Assessing the level of maturity of the automated management system of a higher education institution

The article contains the results of a study of the processes of automation of educational and management processes of higher education. The experience of the authors of the development and implementation of learning management systems and support for management of higher education, analytical studies of the need for automation of educational and support management processes revealed the need to measure the level of maturity of learning management systems and support for management processes (LMS&#x0026;SMP). The variety of activities in higher education institutions is due to the need for research, development and implementation of teaching methods, organization of various projects, etc. On the other hand, the management of a higher education institution involves ensuring the level of quality of education, proper qualification of teachers and satisfaction of applicants for higher education. The latter are the target audience for the provision of educational services and key customers of such services. From the point of view of e-learning and blended learning systems, four categories of users have needs for automation: students; teachers; management; support staff. In accordance with their functional processes, it is necessary to create an electronic information educational environment with a high level of maturity of the learning management system and support management processes. In many cases, automated management systems of the educational process and management of the institution are almost unconnected, which, requires duplication of information. A comprehensive system of learning management and management support for higher education allows to form an effective information ecosystem from the level of an individual teacher, department to management. The proposed method of assessing the maturity of LMS&#x0026;SMP allows to form a catalog of processes, the level of their automation, identify &#x201C;white spots&#x201D; of non-automated processes and design plans to improve the learning management system.