Reviewerly/exHarmony huggingface dataset:
Building sensor fault detection and diagnostic system

In this paper, we developed a Building Sensor Fault Detection and Diagnostics System. It consists of a smartphone application integrated with airflow and temperature sensors, to enable Facilities' crews to collect flow, supply, and zone temperatures from each zone with a simple walk-through. This data is then uploaded to a cloud for further analysis using machine learning algorithms trained to identify zones' faulty sensors based on comparisons with building data available from the Building Management Systems (BMS). We develop two different data-driven fault classifiers and compare our system with two state-of-the-art sensing fault schemes using the Receiver Operating Characteristic (ROC) curve, showing an improvement in the Area Under the ROC Curve (AUC) of 7.1% and 26.3%, and 12.9% and 55.1% in fault detection performance improvement for a 10% false positive rate.
Generalized Concatenated Codes over Gaussian and Eisenstein Integers for Code-Based Cryptography

The code-based McEliece and Niederreiter cryptosystems are promising candidates for post-quantum public-key encryption. Recently, q-ary concatenated codes over Gaussian integers were proposed for the McEliece cryptosystem, together with the one-Mannheim error channel, where the error values are limited to the Mannheim weight one. Due to the limited error values, the codes over Gaussian integers achieve a higher error correction capability than maximum distance separable (MDS) codes with bounded minimum distance decoding. This higher error correction capability improves the work factor regarding decoding attacks based on information-set decoding. The codes also enable a low complexity decoding algorithm for decoding beyond the guaranteed error correction capability. In this work, we extend this coding scheme to codes over Eisenstein integers. These codes have advantages for the Niederreiter system. Additionally, we propose an improved code construction based on generalized concatenated codes. These codes extend to the rate region, where the work factor is beneficial compared to MDS codes. Moreover, generalized concatenated codes are more robust against structural attacks than ordinary concatenated codes.
Data clustering using bacterial colony optimization with particle swarm optimization

grouping a set of data items based on similarity is called data clustering. The bacterial colony optimization (BCO) algorithm is a distinguished method. Lately, it is used to apply a problem of data clustering. But, the convergence rate of BCO is very low. In this present research paper, the particle swarm optimization (PSO) is applied to improve the BCO convergence rate and improve the effectiveness of the problem. The investigational effects exhibit that the proposed BCO-PSO formed higher performance when compared with some benchmark data clustering algorithms.
Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recent years. Likewise, Machine Learning (ML) approaches is one of the interesting research domains that are highly helpful and are increasingly applied in several business domains. In this background, the current research paper focuses on the design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviated as DHOA-FNN model. The proposed DHOA-FNN technique involves four different stages namely, preprocessing, feature extraction, classification, and parameter tuning. In addition to the above, the proposed DHOA-FNN model has two stages of feature extraction namely, Glove and N-gram approach. Moreover, FNN model is utilized as a classification model whereas GTOA is used for the optimization of parameters. The novelty of current work is that the GTOA is designed to tune the parameters of FNN model. An extensive range of simulations was carried out on the benchmark dataset and the results were examined under diverse measures. The experimental results highlighted the promising performance of DHOA-FNN model over recent state-of-the-art techniques with a maximum accuracy of 0.9928.
Three-Stream 3D deep CNN for no-Reference stereoscopic video quality assessment

Convolutional Neural Networks (CNNs) have achieved great success in learning computer vision tasks, particularly 3D CNNs, for extracting spatio-temporal features from the given videos. However, 3D CNNs have not been well-examined for the Stereoscopic Video Quality Assessment (SVQA). To our best knowledge, most of the state-of-the-art methods used the traditional hand-crafted feature extraction methods for the SVQA. Very few methods used the power of deep learning for SVQA, and they just considered the spatial information, ignoring the disparity and motion information. In this paper, we propose a No-Reference (NR) deep 3D CNN architecture that jointly focuses on spatial, disparity, and temporal information between consecutive frames. A 3-Stream 3D CNN, shortly 3S-3DCNN, by performing 3D CNNs, extracts features from spatial, motion, and depth channels to estimate the stereo video’s quality. It captures the degradations in the quality of the stereoscopic video in multiple dimensions. Firstly, the scene flow, which is the joint prediction of the optical flow and stereo disparity, is calculated. Then, the spatial information, optical flow, and disparity map of a given video are used as input to the 3S-3DCNN model. The extracted features are concatenated and utilized as inputs to the fully connected layers for doing the regression. We split the input videos into cube patches for data augmentation and remove the cubes that confuse our model from the training and testing sets. Two standard stereoscopic video quality assessment benchmarks of LFOVIAS3DPh2 and NAMA3DS1-COSPAD1 were used to evaluate our method. Experimental results show that our 3S-3DCNN method’s objective score significantly correlates with the subjective SVQ scores in multiple video datasets. The RMSE for NAMA3DS1-COSPAD1 dataset is 0.2757, which outperforms other methods by a large margin. The SROCC value for the blur distortion of the LFOVIAS3DPh2 dataset is more than 98%, indicating that the 3S-3DCNN is consistent with human visual perception.
Improving Medical Image Decision‐Making by Leveraging Metacognitive Processes and Representational Similarity

Improving the accuracy of medical image interpretation can improve the diagnosis of numerous diseases. We compared different approaches to aggregating repeated decisions about medical images to improve the accuracy of a single decision maker. We tested our algorithms on data from both novices (undergraduates) and experts (medical professionals). Participants viewed images of white blood cells and made decisions about whether the cells were cancerous or not. Each image was shown twice to the participants and their corresponding confidence judgments were collected. The maximum confidence slating (MCS) algorithm leverages metacognitive abilities to consider the more confident response in the pair of responses as the more accurate "final response" (Koriat, 2012), and it has previously been shown to improve accuracy on our task for both novices and experts (Hasan et al., 2021). We compared MCS to similarity-based aggregation (SBA) algorithms where the responses made by the same participant on similar images are pooled together to generate the "final response." We determined similarity by using two different neural networks where one of the networks had been trained on white blood cells and the other had not. We show that SBA improves performance for novices even when the neural network had no specific training on white blood cell images. Using an informative representation (i.e., network trained on white blood cells) allowed one to aggregate over more neighbors and further boosted the performance of novices. However, SBA failed to improve the performance for experts even with the informative representation. This difference in efficacy of the SBA suggests different decision mechanisms for novices and experts.
Evaluation of Pseudo-Random Number Generation on GPU Cards

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.
Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

Abstract The increase of available seismic data prompts the need for automatic processing procedures to fully exploit them. A good example is aftershock sequences recorded by temporary seismic networks, whose thorough analysis is challenging because of the high seismicity rate and station density. Here, we test the performance of two recent Deep Learning algorithms, the Generalized Phase Detection and Earthquake Transformer, for automatic seismic phases identification. We use data from the December 2019 Mugello basin (Northern Apennines, Italy) swarm, recorded on 13 permanent and nine temporary stations, applying these automatic procedures under different network configurations. As a benchmark, we use a catalog of 279 manually repicked earthquakes reported by the Italian National Seismic Network. Due to the ability of deep learning techniques to identify earthquakes under poor signal‐to‐noise‐ratio (SNR) conditions, we obtain: (a) a factor 3 increase in the number of locations with respect to INGV bulletin and (b) a factor 4 increase when stations from the temporary network are added. Comparison between deep learning and manually picked arrival times shows a mean difference of 0.02–0.04 s and a variance in the range 0.02–0.07 s. The improvement in magnitude completeness is ∼0.5 units. The deep learning algorithms were originally trained using data sets from different regions of the world: our results indicate that these can be successfully applied in our case, without any significant modification. Deep learning algorithms are efficient and accurate tools for data reprocessing in order to better understand the space‐time evolution of earthquake sequences.
A Brief Survey of text driven image generation and maniulation

Text-to-image synthesis refers to the translation of textual description to images with similar semantic meaning. This process mainly relies on the correlation of text and image to find the best alignment between them. So, the recent development of generative models, especially GANs, using natural language as a condition paves a new path for research. In this paper, we review the most notable developments in the Field of text-to-image synthesis. In contrast to others, were viewed new methods of GAN-based text to image synthesis comparatively, considering two meaningful divisions, generation and manipulation. Additionally, there is a summary table for the characteristic of all these methods
3D saliency guided deep quality predictor for no-reference stereoscopic images

The use of 3D technologies is growing rapidly, and stereoscopic imaging is usually used to display the 3D contents. However, compression, transmission and other necessary treatments may reduce the quality of these images. Stereo Image Quality Assessment (SIQA) has attracted more attention to ensure good viewing experience for the users and thus several methods have been proposed in the literature with a clear improvement for deep learning-based methods. This paper introduces a new deep learning-based no-reference SIQA using cyclopean view hypothesis and human visual attention. First, the cyclopean image is constructed considering the presence of binocular rivalry that covers the asymmetric distortion case. Second, the saliency map is computed considering the depth information. The latter aims to extract patches on the most perceptual relevant regions. Finally, a modified version of the pre-trained Convolutional Neural Network (CNN) is fine-tuned and used to predict the quality score through the selected patches. Five distinct pre-trained models were analyzed and compared in term of results. The performance of the proposed metric has been evaluated on four commonly used datasets (3D LIVE phase I and phase II databases as well as Waterloo IVC 3D Phase 1 and Phase 2). Compared with the state-of-the-art metrics, the proposed method gives better outcomes. The implementation code will be made accessible to the public at: https://github.com/o-messai/3D-NR-SIQA
Refactoring for reuse: an empirical study

Refactoring is the de-facto practice to optimize software health. While several studies propose refactoring strategies to optimize software design through applying design patterns and removing design defects, little is known about how developers actually refactor their code to improve its reuse. Therefore, we extract, from 1,828 open source projects, a set of refactorings that were intended to improve the software reusability. We analyze the impact of reusability refactorings on the state-of-the-art reusability metrics, and we compare the distribution of reusability refactoring types, with the distribution of the remaining mainstream refactorings. Overall, we found that the distribution of refactoring types, applied in the context of reusability, is different from the distribution of refactoring types in mainstream development. In the refactorings performed to improve reusability, source files are subject to more design level types of refactorings. Reusability refactorings significantly impact, high-level code elements, such as packages, classes, and methods, while typical refactorings, impact all code elements, including identifiers, and parameters. These findings provide practical insights into the current practice of refactoring in the context of code reuse involving the act of refactoring.
SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids’ Representations

Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models.In this work, we extend the probing studies to two other models in the family, namely ELECTRA and XLNet, showing that variations in the pre-training objectives or architectural choices can result in different behaviors in encoding linguistic information in the representations.Most notably, we observe that ELECTRA tends to encode linguistic knowledge in the deeper layers, whereas XLNet instead concentrates that in the earlier layers.Also, the former model undergoes a slight change during fine-tuning, whereas the latter experiences significant adjustments.Moreover, we show that drawing conclusions based on the weight mixing evaluation strategy-which is widely used in the context of layer-wise probing-can be misleading given the norm disparity of the representations across different layers.Instead, we adopt an alternative information-theoretic probing with minimum description length, which has recently been proven to provide more reliable and informative results.
Blockchain Consensus Protocols: State-of-the-art and Future Directions

Being the major disruptive technology in current era, Blockchain is becoming the prime choice for researchers and businesses. Consensus layer acts a backbone of any blockchain application; therefore to increase the performance of a blockchain ecosystem one should need to optimize the underlying consensus method. As almost every consensus method implemented in past poses limitations in terms of low scalability, high resource requirements, low decentralization and public-private tradeoff, there is a need to have an effective consensus protocol. Therefore, we presented a literature review of recent consensus protocols to find their applicability in IoT integration in this paper. We also described some future directions based on the literature being analyzed that would intend to serve as an effective guideline for to perform research in order to improve performance of a blockchain system.
ISLAND: An Interlinked Semantically-Enriched Blockchain Data Framework

Blockchain is currently one of the most popular technologies, providing privacy, transparency and trust. However, until now, it does not take into consideration the large amount of existing data and standards for decentralized data distribution and processing on the Web, that would leash new opportunities and business innovations for this emerging technology. Moreover, according to the vision of the Semantic Web, a key concept lies on data (semantic) annotations, querying and interlinking. Nevertheless, exporting knowledge resulting from different and possible interlinked blockchain networks, is still a major challenge. To address the aforementioned challenges, we propose ISLAND, a modular framework that is set to expose a unified abstraction layer to any data consumer that aims to infer meaningful knowledge from blockchain generated data, while at the same time enabling the semantic interoperability of them. In addition, a smart manufacturing use case scenario is presented as well as the potential business impacts are discussed.
p-im2col: Simple Yet Efficient Convolution Algorithm With Flexibly Controlled Memory Overhead

Convolution is the most time-consuming operation in modern deep artificial neural networks, so its performance is crucial for fast inference.One of the standard approaches to fast convolution computation is to use GeMM-based convolution algorithms relying on efficient general matrix multiplication (GeMM) from optimized BLAS libraries.However, commonly used GeMM-based algorithms may cause significant memory overhead or avoid it only at the cost of worse performance.In this paper, we propose a novel convolution algorithm, p-im2col, based on a well-known im2col algorithm that avoids memory overhead by splitting a single multiplication of a large matrix into several multiplications of smaller matrices.We theoretically and experimentally compare our algorithm with two other GeMMbased algorithms: im2col, which is widely used as a baseline, and the memory-efficient kn2row-aa.We measure the inference time of these algorithms on central processing units of x86, x86_64, ARM, and MIPS architectures for a large set of convolutional parameters.The proposed algorithm demonstrates a speedup over im2col and kn2row-aa in a number of cases and a significant reduction in additional memory requirements compared to im2col.Based on our experiments, we present a new convolution algorithm selection scheme that considers memory restrictions, CPU architecture, and convolutional parameters and provides a noticeable advantage over each particular algorithm.
Attitudes Towards COVID-19 Contact Tracing Apps: A Cross-National Survey

To help prevent the spread of COVID-19, countries around the world have implemented a range of measures and virus containment strategies, including digital contact-tracing (DCT) in the form of smartphone apps. While early studies showed a high level of acceptability of such technologies, the adoption rates varied greatly between countries after contact-tracing apps became available to download. This cross-national user survey ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}=871$ </tex-math></inline-formula> ) aims to explore public attitudes and factors that affect user acceptability and adoption of contact-tracing apps in the USA, UK, and the Republic of Ireland, which employ similar underlying technology, but have uneven adoption rates. The results indicate interactions between installation decisions and public trust in actors and institutions communicating COVID-related information, and releasing such technologies. Beyond the immediate case of contact tracing, our findings hold implications for the deployment and communicative framing of technology for public health and the public good, and inform the design of crisis response public health information systems.
Emergent Deep Learning for Anomaly Detection in Internet of Everything

This research presents a new generic deep learning (DL) framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environments. The data set is first decomposed into clusters, while similar observations in the same cluster are grouped. Five clustering algorithms were used for this purpose. The generated clusters are then trained using DL architectures. In this context, we propose a new recurrent neural network for training time-series data. Two evolutionary computational algorithms are also proposed: 1) the genetic and 2) the bee swarm, to fine-tune the training step. These algorithms consider the hyperparameters of the trained models and try to find the optimal values. The proposed solutions have been experimentally evaluated for two use cases: 1) road traffic outlier detection and 2) network intrusion detection. The results show the advantages of the proposed solutions and a clear superiority compared to state-of-the-art approaches.