Reviewerly/exHarmony huggingface dataset:
Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric (radio frequency) fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In this paper, we prove analytically and experimentally that these solutions are highly vulnerable to impersonation attacks. We also demonstrate that such a unique device-specific signature can be abused to track devices, thus violating privacy. We propose RF-Veil, a radiometric fingerprinting solution that is not only robust against impersonation attacks but also effective in protecting privacy by obfuscating the radiometric fingerprint of the transmitter for non-legitimate receivers. Specifically, we introduce a randomized pattern of phase errors to the transmitted signal such that only the intended receiver can extract the original fingerprint of the transmitter. In a series of experiments and analyses, we expose the vulnerability of adopting naive randomization to statistical attacks and introduce countermeasures. Finally, we show the efficacy of RF-Veil experimentally in protecting user privacy and enhancing security. More importantly, our proposed solution allows communicating with other devices, which do not employ RF-Veil.
Covid Connect: Chat-Driven Anonymous Story-Sharing for Peer Support

The mental-health impact of the Covid-19 pandemic and the related restrictions and isolation have been immense. In this paper, we present a system designed to break down loneliness and isolation, and to allow people to share their stories, complaints, emotions, and gratitude anonymously with one another. Using a chatbot interface to collect visitor stories, and a custom visualization to reveal related past comments from others, Covid Connect links people together through shared pandemic experiences. The collected data also serves to reflect the experiences of the community of participants during the third through fifth waves of the pandemic in the local region. We describe the Covid Connect system, and analyze the collected data for themes and patterns arising from stories shared with the chatbot. Finally, we reflect on the experience through an autobiographical lens, as users of our own system, and posit ideas for the application of similar approaches in other mental health domains.
A Search-Based Method For optimizing Software Architecture Reliability

Choosing the optimal software architecture in the search space by considering quality criteria is beyond human capabilities and is very challenging. It is necessary to search the design space automatically to improve the existing architectural features. To do this, we can use search-based software engineering approaches. In this study, we examine the methods of optimizing and evaluating software architecture and provide a search-based method to improve the reliability of software architecture. The proposed method is based on the use of NSGAII algorithm and genetic programming and the use of software architecture reliability tactics in it. In the proposed method, we optimize the software architecture in two steps. First, we use the genetic programming algorithm to extract how to apply the software architecture reliability tactics, and in the next step, we use the NSGA-II algorithm to search for the optimal allocation of components to the hardware servers. To evaluate the proposed method, we use a reporting system case study. The results of applying the proposed optimization steps show that the reliability of the whole system as well as most of its most frequent functionalities is improved.
Impact of Polyethylene Terephthalate in Different Temperatures and Storage Duration on Some Physicochemical Properties of Drinking Bottled Water

This study aims to evaluate the physicochemical properties of bottled waterafter it has been exposed to different storage times and temperatures due to the effect of the plastic bottles on the drinkable water and changing its initial properties. A total of 48 samples of plastic bottled water were collected from the (Slemani water company). They were filled with distilled water, placed at different temperatures and tested at different storage times for evaluating pH, alkalinity, calcium, magnesium, chloride, and total hardness. Results show that theere is highly significant change observed in the alkalinity level in the 2-week duration period of storage when they were exposed to the sunlight, freezing, and refrigerator temperatures, B2 and B3 (B2 89.67 ± 4.509, B3 246.7 ± 25.17) and significant in B1 and B3 (B1 129.7 ± 60.91, B3 246.7 ± 25.17) in 2 weeks period but in 24 h period of time all the temperatures have shown no significant results (B1 95.00 ± 5.00, B2 100.0 ± 17.32, B3 160.0 ± 103.9). Exposure to plastic bottled water to different ranges of temperature causes various effects on the quality and physicochemical properties of the drinking water. The storage of the bottled water in a 24 h condition with exposure to freezing temperature produced a significant change in the calcium level, and slight changes were noticed at the levels of pH, Electrical conductivity, total hardness, magnesium, chloride of the water in both 2 weeks and 24-h period. The highest significant change was observed from the alkalinity between temperatures B2 (Refrigerator 4 °C) and B3 (freezer −18 °C) in 2 weeks’ storage time. Finally, no significant results were observed in pH for different temperatures, B1(Sun 30 °C), B2(Refrigerator 4 °C), and B3(freezer −18 °C) in 2 weeks’ time.
Aligning requirements and testing through metamodeling and patterns: design and evaluation

Poorly executed requirements engineering activities profoundly affect the deliverables' quality and project's budget and schedule. High-quality requirements reuse through requirement patterns has been widely discussed to mitigate these adverse outcomes. Requirement patterns aggregate similar applications' behaviors and services into well-defined templates that can be reused in later specifications. The abstraction capabilities of metamodeling have shown promising results concerning the improvement of the requirement specifications' quality and professionals' productivity. However, there is a lack of research on requirement patterns beyond requirements engineering, even using metamodels as the underlying structure. Besides, most companies often struggle with the cost, rework, and delay effects resulting from a weak alignment between requirements and testing. In this paper, we present a novel metamodeling approach, called Software Pattern MetaModel (SoPaMM), which aligns requirements and testing through requirement patterns and test patterns. Influenced by well-established agile practices, SoPaMM describes functional requirement patterns and acceptance test patterns as user stories integrated with executable behaviors. Another novelty is the evaluation of SoPaMM's quality properties against a metamodel quality evaluation framework. We detail the evaluation planning, discuss evaluation results, and present our study's threats to validity. Our experience with the design and evaluation of SoPaMM is summarized as lessons learned.
Ultra-fast single-photon counting with waveguide-integrated detectors for quantum technologies

With recent advances in quantum technologies for applications such as communication, cryptography, computing, metrology and sensing, the performance and scalability of single-photon detection as a vital key component is becoming increasingly important. At the same time, ongoing efforts in the development of high-performance photonic integrated circuits (PIC) benefit the miniaturization and scalability of these quantum technologies. Waveguide-integrated superconducting nanowire single-photon detectors (WI-SNSPDs) allow to combine excellent performance metrics, such as high detection efficiency, low dark-count rates and low timing jitter below 20 ps with the scalability and functionality that PIC platforms such as Si<sub>3</sub>N<sub>4</sub> provide. We have previously demonstrated broadband efficient single-photon detection with a single device over a range from visible to mid-infrared wavelengths and ultra-fast detector recovery times allowing for up to GHz count rates. Here, we present the utilization of WI-SNSPDs for discrete-variable quantum cryptography receivers with the complete photonic circuitry embedded together with the single-photon detectors on a single silicon chip, where the secret-key rates greatly benefit from the short recovery times of the detectors especially for metropolitan distances. We further realize a fully packaged 64 channel WI-SNSPD matrix for use in a wavelengthdivision multiplexed QKD setup.
Isolated Arabic Characters Recognition System Based On Combined Features And DNN

this paper presents a system for recognizing the isolated printed Arabic characters. In this type of systems, the feature extraction step is considered as the most important stage to better recognize the characters. For this purpose, we have proposed in this work to use a combined feature methods in order to improve the character recognition rate. The proposed system is based on the combined features extraction, which are: the Discrete Cosine Transform (DCT), Zernike moments and the Weighted Gradient Local Binary Pattern (WGLBP) and the Deep Neural Network (DNN) classifier. Experiments are carried out on the Printed Arabic Text set PAT-A01.The result confirm that the use of the features combination improves the recognition accuracy rate.
Towards a Multi-Modular Decentralized System for Dealing with EHR Data

With the rise of “big data”, finding computationally efficient and privacy-preserving solutions for large-scale machine learning problems has gained paramount importance, especially in the case of medical data which is collected in huge volumes by modern healthcare systems. Since a large amount of data resides in different locations and owned by different entities, accessing sufficient data while keeping ethical, legal, economic, and technical challenges related to privacy in mind, precludes the medical data from being fully exploited by ML. Thus, to counter these challenges, we propose a novel blockchain-based Federated Learning architecture for healthcare consortia, which provides a solution to the current problems while highlighting the challenges and considerations that need to be addressed. The authors suggest a multi-modular system that can be broken down into three main modules - decentralized medical history module, differentially private institutional analytics module, and Federated Learning based patient prognosis. We conduct extensive experimentation using Logistic Regression and TabNet and receive an accuracy of 83.82 under IID settings with a client fraction of 10%. Further, we show that TabNet outperforms Logistic Regression under conditions of showing less data.
A Method of Sample Consensus Based on Data Geometric Topological Distribution and MCMC

An algorithm of sample consensus based on data geometric topological distribution and MCMC is proposed for the robust matched model estimation in the data with high outlier ratio. In this algorithm the outliers filtered out according to their distributions, and then, the sample probability of each matched pair is adjusted based on the feedback of every sampling with the help of steady-state probability obtained by the Markov Chain and transition matrix. The process is repeated until all matched points’ sampling probability has no significant change. Finally, a series of tests indicate the proposed algorithm has a lower number of iterations and performance period than other algorithms, and can process heavily contaminated data, even though the outlier ratio is higher than 90%.
GPU Accelerated QoSE based Web Service Selection Framework

Web Services are considered as the predominant aspect of the world wide web which renders online services to its clients based on the user requested functionality. Due to its popularity for more than two decades, the count of functionally similar web services are growing exponentially which makes the process of selecting the web services more complex. The research community have proposed multiple solutions for solving this issue using varied techniques like Bio Inspired Algorithms, Multi Criteria Decision Making, Optimization techniques and others. This work focuses on Multi Criteria Decision Making methods to solve the real world Web Service Selection issue. The current research trend in the area of applying Multi Criteria Decision Making for Web Service Selection is found to be in discordance with the real world data. Hence, this work aims analyzing the prevalent real world issues related to Web Service Selection and proposes a feasible solution to solve it. An original framework that incorporates the power of QoS and QoE values with GPU acceleration for selection of web services is proposed and it is found that the proposed framework is highly time efficient when compared with the literature works. Also, the correlation of the ranking scheme given by the proposed framework with the original ranking is higher which is a clear indication that the proposed framework is more suitable for real time Web Service Selection process.
A Sound Design and Electronic Music Production STEAM Course for Secondary Education

In this chapter, a music-centered STEAM course implemented in the European School (Schola Europaea) Brussels III is presented. This course, driven by constructivist conversation pedagogy, aims at students in secondary grade and is independent of their prior involvement in music. In the Sound Design module of the course, which is presented in detail, students explore the world of electronic, software-based instruments through the use of software synthesizers and subtractive synthesis. Visual programming with Max/MSP is applied for the design and implementation of basic synthesizers although dedicated software synthesizers are also used. In this chapter, a brief overview on the composition, arrangement, production, mastering, and development modules of the course is also provided.
Adoption of Wireless Network and Artificial Intelligence Algorithm in Chinese-English Tense Translation

In order to solve the problem of tense consistency in Chinese-English neural machine translation (NMT) system, a Chinese verb tense annotation model is proposed. Firstly, a neural network is used to build a Chinese tense annotation model. During the translation process, the source tense is passed to the target side through the alignment matrix of the traditional Attention mechanism. The probability of the candidate words inconsistent with the corresponding tense of source words in the candidate translation word set is also reduced. Then, the Chinese-English temporal annotation algorithm is integrated into the MT model, so as to build a Chinese-English translation system with temporal processing function. The essence of the system is that, in the process of translation, Chinese-English temporal annotation algorithm is used to obtain temporal information from Chinese sentences and transfer it to the corresponding English sentences, so as to realize the temporal processing of English sentences and obtain the English sentences corresponding to the tenses of the original Chinese sentences. The experimental results show that the Chinese tense annotation model of bidirectional long short-term memory (LSTM) is more accurate for the prediction of Chinese verb tense, so the improvement effect of NMT model is also the most obvious, especially on the NIST06 test set, where the BLEU value is increased by 1.07%. As the mainstream translation model, the transformer model contains multihead Attention mechanism, which can pay attention to some temporal information and has a certain processing ability for temporal translation. It solves the tense problems encountered in the process of MT and improves the credibility of Chinese-English machine translation (MT).
Fault Detection in Wind Turbines using Deep Learning

Institutions have been redirecting investments away from fossil fuels, creating a path for clean energy generation. The wind industry has seen an exponential increase in recent years. Early fault detection creates an alternative for operation and maintenance (OM), allowing costs to be avoided before they reach a catastrophic stage, and improving turbine reliability. Predictive maintenance was the solution that presented itself for this problem, in which faults are detected before they occur and fixed accordingly. LSTM-Autoencoder and time-series data collected from SCADA sensors installed in wind turbines are used to detect anomalies in several components of the wind turbines that insinuate a major fault might occur. The dataset is collected from a wind farm in the West African Gulf of Guinea in 2016. Results have shown how PCA can be productive in identifying the features with the most influence on the prediction process, with the ability to predict faults 17.5 days prior on average.
Smart System: Joint Utility and Frequency for Pattern Classification

Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFC gen and UFC fast . Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFC fast algorithm outperforms the levelwise-based UFC gen algorithm in terms of both execution time and memory consumption.
Measuring representative volume elements from high‐resolution grain‐scale strain fields

Abstract Most crystalline materials present a highly heterogeneous response at the microscale, which can be affected by both internal factors (such as microstructural parameters) and external factors (such as loading). Relating microscale inhomogeneities to the macroscale response of a material requires the use of homogenisation techniques, usually based on the concept of a representative volume element (RVE)—the smallest volume of material that represents the global average response. In this work, we present a new and robust experimental method of measuring the size of a strain‐based RVE from high‐resolution grain‐scale strain fields obtained using digital image correlation (DIC). The proposed method is based on the statistical (stereological) nature of the RVE, which has been widely adopted in numerical studies, and involves dividing a strain field into randomly selected regions of varying sizes and statistically analysing the distributions of average strains within them. To validate the new method, we generate a large number of synthetic strain fields from a fractional Gaussian noise algorithm. The proposed stereological method is shown to be capable of producing reliable RVE measurements from a very large range of possible microscale strain fields while at the same time being robust in that it can produce RVE measurement results even in cases where other existing methods may be unable to do so. The proposed method has a low field‐of‐view requirement, only needing a field‐of‐view about 1.2 times as large as the RVE to produce reliable measurements. In addition, the stereological method offers significant flexibility since its statistical nature allows for control over how strict the RVE measurement should be in each case.
Multi-Criteria Parameter Factor in the Assessment of Public Cloud Services Providers’ Associated Risks

The cloud computing technology is indisputably assisting individuals, businesses and institutions in increasing their capability, productivity and efficiency. It has eliminated the border restriction of obtaining specialized computing resources and expertise that are not available locally to the organizations and individuals without the need to invest in new infrastructure. However, in spite of the immeasurable benefits the technology promises, it has also raised major challenges, particularly as regards its information security and service availability, stressing the need for an elaborate technique of assessing the associated risk of selecting a particular public cloud services provider among available alternatives using the right risk criteria. This paper proposes an expanded multi-criteria risk parameters for the evaluation of public cloud service providers’ associated risk to produce a more accurate results that meets the dynamic nature of the cloud technology.
Pseudodeterminism: promises and lowerbounds

A probabilistic algorithm A is pseudodeterministic if, on every input, there exists a canonical value that is output with high probability. If the algorithm outputs one of k canonical values with high probability, then it is called a k-pseudodeterministic algorithm. In the study of pseudodeterminism, the Acceptance Probability Estimation Problem (APEP), which is to additively approximate the acceptance probability of a Boolean circuit, is emerging as a central computational problem. This problem admits a 2-pseudodeterministic algorithm. Recently, it was shown that a pseudodeterministic algorithm for this problem would imply that any multi-valued function that admits a k-pseudodeterministic algorithm for a constant k (including approximation algorithms) also admits a pseudodeterministic algorithm (Dixon, Pavan, Vinodchandran; ITCS 2021).
Sustainable Machine-to-Everything Infrastructure Management: Blockchain-Based Impact Indexing for Distributed Collaboration Systems

Digitization expansion enables business transactions operating in distributed systems encompassing Internet- and Machine-to-Everything (M2X) economies. Distributed collaboration systems growth comes at a cost of rapidly rising numbers of machines, infrastructure, machine-infrastructure traffic, and consequently a significant augmentation of associated carbon emissions. In order to investigate M2X’s carbon footprint, we design an impact index application layer using blockchain technology of smart contracts to empower a sustainable management of distributed collaboration systems. The impact measurement methodology based on transparent liquid data secures trusted inter-organizational collaborations and supports traceable standardization of sustainability regulation.
WITHDRAWN: Cancer prediction using simplex method based social spider optimization technique

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The Lifecycle of Developing Overall Evaluation Criterion in AB Testing for Netflix Messaging

Many organizations use multiple key metrics to measure success for online controlled experimentation. By combining multiple metrics, we introduce Overall Evaluation Criterion (OEC) which aligns with organizational long-term success. We will cover the definition of OEC, its critical properties, a case study on Netflix Messaging and four critical key steps in designing your own organizational OEC.