Reviewerly/exHarmony huggingface dataset:
Design of a Protein with Improved Thermal Stability by an Evolution‐Based Generative Model

Efficient design of functional proteins with higher thermal stability remains challenging especially for highly diverse sequence variants. Considering the evolutionary pressure on protein folds, sequence design optimizing evolutionary fitness could help designing folds with higher stability. Using a generative evolution fitness model trained to capture variation patterns in natural sequences, we designed artificial sequences of a proteinaceous inhibitor of pectin methylesterase enzymes. These inhibitors have considerable industrial interest to avoid phase separation in fruit juice manufacturing or reduce methanol in distillates, averting chromatographic passages triggering unwanted aroma loss. Six out of seven designs with up to 30 % divergence to other inhibitor sequences are functional and two have improved thermal stability. This method can improve protein stability expanding functional protein sequence space, with traits valuable for industrial applications and scientific research.
WPAxFuzz: Sniffing Out Vulnerabilities in Wi-Fi Implementations

This work attempts to provide a way of scrutinizing the security robustness of Wi-Fi implementations in an automated fashion. To this end, to our knowledge, we contribute the first full-featured and extensible Wi-Fi fuzzer. At the time of writing, the tool, made publicly available as open source, covers the IEEE 802.11 management and control frame types and provides a separate module for the pair of messages of the Simultaneous Authentication of Equals (SAE) authentication and key exchange method. It can be primarily used to detect vulnerabilities potentially existing in wireless Access Points (AP) under the newest Wi-Fi Protected Access 3 (WPA3) certification, but its functionalities can also be exploited against WPA2-compatible APs. Moreover, the fuzzer incorporates: (a) a dual-mode network monitoring module that monitors, in real time, the behavior of the connected AP stations and logs possible service or connection disruptions and (b) an attack tool used to verify any glitches found and automatically craft the corresponding exploit. We present results after testing the fuzzer against an assortment of off-the-shelf APs by different renowned vendors. Adhering to a coordinated disclosure process, we have reported the discovered issues to the affected vendors, already receiving positive feedback from some of them.
Automatic multi-tissue segmentation in pancreatic pathological images with selected multi-scale attention network

The morphology of tissues in pathological images has been used routinely by pathologists to assess the degree of malignancy of pancreatic ductal adenocarcinoma (PDAC). Automatic and accurate segmentation of tumor cells and their surrounding tissues is often a crucial step to obtain reliable morphological statistics. Nonetheless, it is still a challenge due to the great variation of appearance and morphology. In this paper, a selected multi-scale attention network (SMANet) is proposed to segment tumor cells, blood vessels, nerves, islets and ducts in pancreatic pathological images. The selected multi-scale attention module is proposed to enhance effective information, supplement useful information and suppress redundant information at different scales from the encoder and decoder. It includes selection unit (SU) module and multi-scale attention (MA) module. The selection unit module can effectively filter features. The multi-scale attention module enhances effective information through spatial attention and channel attention, and combines different level features to supplement useful information. This helps learn the information of different receptive fields to improve the segmentation of tumor cells, blood vessels and nerves. An original-feature fusion unit is also proposed to supplement the original image information to reduce the under-segmentation of small tissues such as islets and ducts. The proposed method outperforms state-of-the-arts deep learning algorithms on our PDAC pathological images and achieves competitive results on the GlaS challenge dataset. The mDice and mIoU have reached 0.769 and 0.665 in our PDAC dataset.
Online Extra Trees Regressor

Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viable solution to deal with the dynamic nature of streaming data. Besides coping with the inherent challenges of streaming data, online ML solutions must be accurate, fast, and bear a reduced memory footprint. We propose a new decision tree-based ensemble algorithm for online ML regression named online extra trees (OXT). Our proposal takes inspiration from the batch learning extra trees (XT) algorithm, a popular and faster alternative to random forest (RF). While speed and memory costs might not be a central concern in most batch applications, they become crucial in data stream data learning. Our proposal combines subbagging (sampling without replacement), random tree split points, and model trees to deliver competitive prediction errors and reduced computational costs. Throughout an extensive experimental evaluation comprising 22 real-world and synthetic datasets, we compare OXT against the state-of-the-art adaptive RF (ARF) and other incremental regressors. OXT is generally more accurate than its competitors while running significantly faster than ARF and expending significantly less memory.
The Role of Organizational Support in Work Engagement Among Nurses Working in Intensive Care Units

Introduction: The challenges and difficulties of working in intensive care units put pressure on healthcare professionals, especially nurses. To support nurses working in intensive care units (ICUs), developing supportive climates in organizations may drive nurses towards increasing work engagement levels and other related factors. Objective: This study aimed to determine the relationship between organizational support and the work engagement of nurses working in ICUs. Materials and Methods: This correlational cross-sectional study was conducted on 140 nurses working in two governmental hospitals in Amman City, Jordan, from January to August 2021. The participants were selected with a simple random sampling method, and study data were collected using a 3-part questionnaire: demographics, the survey of perceived organizational support scale with 8 items, and the Utrecht work engagement scale with 9 items. Descriptive statistics, the independent t test, the Spearman correlation test, and hierarchal multiple linear regression analysis were performed to determine the contribution of perceived organizational support on nurses' work engagement. The significance level for all tests was set to be less than 0.05. Results: About 53.6% of participants were male, 55% worked in medical-surgical intensive care units, and 45% were in cardiac care units. The results showed that the mean ± SD scores of perceived organizational support and work engagement were 29.53 ± 6.71 and 3.71 ± 0.88, respectively. Also, results showed no significant differences in perceived organizational support and work engagement based on nurses' socio-demographics or work-related factors. Finally, there was a significant positive weak correlation between perceived organizational support and work engagement (r= 0.23, P=0.002). The linear regression model showed that perceived organizational support could predict work engagement (R2 = 0.039). Conclusions: Perceived organizational support among nurses working in ICUs is one of the important factors in increasing work engagement. Findings have several implications for nurses in different settings. Jordanian health policymakers must take action to improve nurses' perception of organizational support and consequently increase nurses' work engagement.
Differential Evolution With Duplication Analysis for Feature Selection in Classification

By selecting a small subset of relevant features, feature selection can reduce the dimensionality of the problem while maintaining or increasing the discriminating ability of the data. However, many existing feature selection approaches ignore the fact that there are multiple optimal solutions to a feature selection problem. Multiple feature subsets with different features selected can achieve very similar or the same classification accuracy. To search for multiple optimal feature subsets, a niching-based differential evolution (DE) method with duplication analysis is proposed. In the proposed method, the duplicated feature subsets in the population are modified by the proposed subset repairing scheme which can produce unique feature subsets. Second, the mutation operator in DE is improved, which uses both the niche and global information to produce promising feature subsets. Third, a new selection method considering the diversity among feature subsets is adopted to form a new population for the next-generation. In the experiments, the proposed method is compared with seven evolutionary feature selection algorithms and two typical feature selection methods on 18 datasets. The results show that the proposed algorithm achieves higher classification accuracy than the compared methods on most of the used datasets. Furthermore, the proposed method can find different feature subsets with very similar or the same classification accuracy.
Enhanced convolutional neural network for non-small cell lung cancer classification

&lt;p&gt;Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.&lt;/p&gt;
An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators

Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image’s pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.
Evaluating digital maturity of education

Introduction. The article examines the problem of digital transformation of modern society and education in the context of the general logic of present and future technological revolutions, called Industries 4.0 and 5.0. The purpose of the article is to identify the characteristic features of the modern understanding of digital maturity in education and the specifics of its evaluation methods (metrics). Materials and Methods. The methodology for solving the problem is based on the general research methods (synthesis and generalization of international and Russian publications focused on the stated problem). Results. The authors have analyzed and clarified the modern understanding of the concept of digital maturity in general and digital maturity of education in particular. It is noted that the currently emerging theoretical concept of digital maturity plays an essential role in determining key guidelines in the process of searching for appropriate strategies for digital transformation of education. The paper presents a comprehensive analysis of international and Russian evaluation inventories (metrics) of digital maturity, including education, aimed exclusively at the processes of collection and processing of quantitative indicators of evaluated industries at the micro-level. The existing methods of assessing digital maturity of education are supplemented by the approach developed by the authors which works at the macro level and expands the heuristic potential of existing assessment methods. Conclusions. The authors summarize the peculiarities of the modern understanding of digital maturity in education as an integral characteristic feature of introduction and implementation of end-to-end technologies within main educational processes.
Demystifying Face-Recognition with Locally Interpretable Boosted Features (LIBF)

Understanding and exposing decisions and wrong classifications of face-recognition systems is very important for social (and even legal) acceptance of high person-specific, biometric recognition techniques. In this paper we present first insights gained during the development of Locally Interpretable Boosted Features (LIBFs) – a set of well-located and easily interpretable image features by humans – for explaining the decisions of a state of the art face-recognition pipeline. In particular, we show initial results on applying an unlabelled, self-supervised embedding to specific regions of the face image for face verification tasks following the IARPA Janus Benchmark-B (IJB-B) and Benchmark-C (IJB-C) verification protocols. In addition we discuss some issues raised when applying explainable boosting machine (EBM), a recently developed, high performance explanation technique, to the features obtained.
EarGest: Hand Gesture Recognition with Earables

Earables have been increasingly gaining attention from consumers and manufacturers alike due to their small footprint, ease of use, and the added accessibility they bring. However, the limited interface of these devices, usually being a single button or force-sensor, inhibits their potential. Earables can provide a much richer experience by extending the ways in which users can interact with them. In this paper, we present EarGest, a novel earable-based hand gesture recognition system that does not require calibration or training, and works with commercially available BLE-enabled earphones and devices. Our proposed system is unique in its ability to leverage Bluetooth to detect hand motion near the ear to recognize gestures. By harnessing information from BLE connections between the wireless earphones and a host device, we accurately detect and classify different hand gestures performed by users, while also determining discrete levels of hand speed. EarGest operates without interfering with the regular functionality of the earphones and introduces minimal energy overhead on the host device. We implement a prototype of the system using eSense, a multi-sensory earable platform, and evaluate it in different scenarios and settings. Results show that our system can detect and classify seven near-ear hand gestures with an accuracy up to 98.5%, as well as identify hand motion speed with 96% accuracy.
Automation of Network Traffic Monitoring using Docker images of Snort3, Grafana and a custom API

In this paper we present a mechanism to automate network traffic monitoring by using Docker images. The application has three parts: a Snort3 application for capturing network traffic, a Grafana application to display statistics on the dashboard and a custom API that makes the connection between Snort3 and Grafana. We pushed these images to DockerHub. An automation script, that executes all needed steps, was created and published to a Github repository.
Lightning Whistler Wave Speech Recognition Based on Grey Wolf Optimization Algorithm

The recognition algorithm of the lightning whistler wave, based on intelligent speech, is the key technology to break the bottleneck of massive data and study the temporal and spatial variation rules of the lightning whistler wave. However, its recognition effect depends on the hyperparameters determined by manual experiments repeatedly, which takes a great deal of time and cannot guarantee the best recognition effect of the model. Therefore, we proposed the lightning whistler wave recognition algorithm based on grey wolf optimization (GWO). In this paper, the GWO algorithm is used to automatically find the best value of hyperparameters of Long Short-Term Memory (LSTM) in their limited searching space. Here we consider the number of hidden units (hu) and learning rate (lr) as the hyperparameters to be optimized, and the spatial coordinate (hu, lr) as the grey wolf position. By the end of the GWO process, we obtain the position of the wolf king α with the optimal hu and lr searched by the GWO algorithm. Then we use the optimal hu and lr to configure LSTM and perform supervised learning on the train set to obtain the final lightning whistler wave speech recognition model. Through experimental verification, the recognition model based on the GWO not only overcomes the uncertainty of the traditional model relying on manual finetuning of parameters and realizes the mechanism of automatic search and acquisition of hyperparameters, but also its recognition effect improves by about 2% in accuracy, F1score, and other metrics compared with the model trained by manually setting hyperparameters.
Blockchain-Based Formal Model for Food Supply Chain Management System Using VDM-SL

In modern society, the food supply chain management system has become an important research area realized nationally and internationally, which has established a collaborative relationship between producers, manufacturers, processors and retailers. Now, consumers are more interested in food quality, safety and the authentication of the products. Food safety has become an important issue in public health in the food market because people of all types and races around the world are affected due its poor quality. The traditional supply chains are centralized and face different issues such as lack of transparency, accountability and audit ability. The general issues in supply chain management include lack of communication, trust among the stakeholders, and interference of entities and wastage of food. A lot of work has been completed by researchers to address the above issues, but still, there is a need for effective mechanisms for the modeling of supply chain management systems. In this paper, a trusted, self-organized, traceable formal system based on blockchain and Internet of Things (IoT) is developed by using wireless sensors networks and finite automata. In the proposed model, smart contracts are designed to assure the automated payment procedures. The proposed model reduced the need for centralized authority. Unified Modeling Language (UML) is used to realize the requirements, and automata is used to capture behavior of the system. A blockchain-based model is used to provides the privacy and security mechanism for the transitions record. Wireless sensors are used to sense the information, and actors are used for decision making in case of any violation in the contact. A lot of work has been completed by researchers on smart contracts. Different smart contracts are designed to achieve the better traceability among producers, transporter/logistics and consumers. Our system provides the smart contract algorithm to show the interaction of entities in the food supply chain management system. Vienna Development Method-Specification Language (VDM-SL) is used to describe the formal system and the VDM-SL toolbox is used for the validation and analysis of the system.
Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling

Accurate photovoltaic (PV) performance modelling is crucial for increasing the penetration of PV energy into the grid, analysing returns on investment, and optimising system design prior to investment and construction. Performance models usually correct an output value known at reference conditions for the effects of environmental and system variables at arbitrary conditions. Traditional approaches to correct for the effect of the solar spectrum on performance are based on proxy variables that represent spectral influences, such as absolute air mass (AMa) and clearness index (Kt). A new methodology to account for the spectral influence on PV performance is proposed in this study. The proposed methodology is used to derive a novel spectral correction function based on the average energy of photons contained within the measured solar spectral distribution. The Average Photon Energy (APE) parameter contains information on the combined effects of multiple proxy variables and is not limited by climatic conditions such as cloud cover, as is the case with most traditional models. The APE parameter is shown to be capable of explaining almost 90% of the variability in PV spectral efficiency, compared to around 65% for AMa. The derived APE function is validated and shown to offer an increase of 30% in predictive accuracy for the spectral efficiency compared with the traditional AMa function, and a 17% improvement relative to the AMa-Kt function.