Reviewerly/exHarmony huggingface dataset:
Multivariate statistical analysis for sedimentary characteristics in an estuarine environment of the east flood canal river, Semarang

Abstract The East Flood Canal River stretches across the eastern part of Semarang, an area that is densely populated with residential and industrial areas. There are many human and industrial activities around this watershed, including the textile industry, food factories, and fish auction sites. Therefore, this river becomes an interesting research object to examine the influence of industrial activities around the river on the characteristics of the sediment. This research was conducted by examining sediment core samples from the estuary of the East Flood Canal. The research aims to reveal the relationship between the characteristics and distribution of sediments and geochemical trace elements. A total of 31 samples of sediment core slices with an interval of 1 cm were analyzed. The granulometric analysis was conducted by Mastersizer 2000, to determine the grain size parameters that were used as the basis for the clustering. Geochemical analysis was conducted by the XRF (X-Ray Fluorescence) to determine the trace elements concentration. The study is interpreted using compositional data analysis associated with a grain size trend analysis (GSTA) and cluster analysis (CA). The GSTA value showed generally dominated by medium to very fine silt. The cluster showed that four lithofacies zones have different sedimentary characteristics. The distribution of the characteristics of the large grain size of the seafloor sediment in the East Flood Canal indicates that as the deposition is farther from the river mouth, the poorer it’s sorting. It was recorded that, at a depth of 19 to 27 cm from the surface there was an increase in fine to very fine sand accompanied by an increase in the Fe and Mn which indicated that there was a supply of sediment that occurred from land caused by human activities and flood events.
Solar Irradiance Measurements Using Smart Device

The levels of solar radiation are an important factor that should be taken into consideration when deciding the position of photovoltaic systems, measuring the safety levels for work environments, education, etc. Specific areas of smartphone applications for assessing the level of solar radiation are considered. The effectiveness of the offered software is analyzed with the help of a specially developed mechanical platform on which a smart device is attached. It provides measurement at different angles of solar radiation. Actual values of the level of solar radiation are those obtained from a professional meteorological station, under the same measurement conditions. The results of measuring the level of solar radiation with a smartphone and a precise climate station at different angles and positions of the sun are compared. They justify new areas of application of the proposed method — preliminary audit in the construction of small photovoltaic installations, learning process related to photovoltaic conversion, etc.
Intuitionistic Fuzzy Estimations of Implementation of Port Knocking on Routeros

The article presents one of the aspects affecting network security, in particular accessibility to communication devices by executing a sequence of rules for a specific time interval. Intuitionistic fuzzy estimators were used to calculate the degrees of membership non-membership and hesitation margin of the packets falling into the allowed lists. Generalized nets and indexed matrices are used to modeling the processes described port knocking.
Regularized Echo State Network with an Improved Log Penalty

Echo state networks (ESNs) are a kind of special recurrent neural networks, which have super performance on time series predictions. However, ESNs with over large reservoirs may lead to the output collinearity problem. In this paper, a regularized ESN with an improved log penalty is proposed to solve this problem. First, an improved log penalty is introduced to the quadratic loss function. Second, the coordinate descent algorithm is employed to optimize the loss function for a sparse solution. Finally, the proposed model is tested using two benchmark data sets. The experimental results show that our method improves the ability of ESNs to deal with the output collinearity problems.
Extreme Learning Machine Algorithm Based on IPSO for Prediction of Blood Glucose Concentration in Patients with Type 1 Diabetes

Aiming at the complexity and instability of blood glucose data of diabetic patients, this paper introduces an extreme learning machine algorithm (ELM) based on improved particle swarm optimization (IPSO) into the prediction of blood glucose concentration in patients with type I diabetes. First, the blood glucose concentration time series of diabetes patients collected by dynamic blood glucose monitoring is smoothed and normalized to improve the smoothness of the blood glucose data sequence and weaken the randomness of the original blood glucose data sequence. Then, the extreme learning machine is optimized, the improved particle swarm optimization algorithm is introduced to select the appropriate parameters required by the extreme learning machine, and the optimized algorithm is applied to the prediction of blood glucose concentration of diabetic patients. The experimental results show that the extreme learning machine algorithm based on improved particle swarm optimization has higher accuracy for short-term blood glucose concentration prediction of patients.
Experimental Comparison between MLP and FCCN in Convolutional Neural Networks

The fully connected cascade network (FCCN) is an emerging neural network architecture which is known to be compact, having only one structural parameter which makes it convenient to use. FCCN, just like MLP, can be used in convolutional neural networks (CNN) as the final fully connected component. However, there has not been report of the use of FCCN in CNNs. In this paper we describe two CNNs with identical structure except for the fully connected component, one of which using MLP which is called CNN-MLP and the other using FCCN which is called CNN-FCCN. These two CNNs are applied to the famous MNIST digit recognition problem and their performances are compared. We used various sizes and two loss functions (MSE and cross-entropy). The performance data we obtained suggest that the two machines offer comparable performances, whereas for MSE loss, FCCN is slightly better, and for cross-entropy loss, MLP is slightly better.
Robust Structure-Aware Low-Light Enhancement via a Latent Low-Rank Embedding Retinex Model

Robustness to noise is a significant issue in the field of low-light image enhancement (LLIE). Most existing LLIE methods, which ignore the intensive noise contained in images or use a pre/postprocessing step to remove noise, fail to achieve pleasant visual effects in practical tasks. To address this problem, in this paper, based on Retinex theory, we propose jointly integrating low-light enhancement and denoising into a unified framework with latent low-rank embedding. Specifically, an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm term is first introduced to measure the reconstruction error and enhance the robustness of decomposed components to various types of noise. Then, a smooth prior is applied to the illumination component, and a structure-aware gradient fidelity term and latent low-rank embedding are deployed to resist noise and retain the texture of the reflectance component. Considering the expensive time cost of low-rank processing, we design an accelerated low-rank approximation strategy without losing structural information. Afterward, the decomposed components are sequentially estimated, avoiding noise from the other component. Finally, the enhanced images are generated by combining the estimated reflectance with the illumination adjusted based on gamma correction. Extensive experiments demonstrate that our method performs significantly better than several state-of-the-art methods in terms of enhancing and denoising on three public datasets.
Cardio Vec: Searching Heart Health Information Using ECG Signals

Health Social Networks (HSN s) provides a scalable, sustainable, and rich medical knowledge base. However, retrieving the right information from HSN can be time-consuming and challenging as users are often required to use the right keywords to search and filter relevant information. IoT provides a non-invasive, easy, low-cost way to collect patient data. However, the current IoT approaches cannot provide interpretable clinical information. IoT also cannot be directly interfaced with HSN s for searching health conditions. To overcome the disadvantages of both approaches, we develop an ECG-IoT search engine, called Cardio Vect. Cardio Vect converts ECG signals into human-readable clinical descriptions to interface ECG-IoT with HSN. This allows doctors and patients directly search relevant articles on the Internet and HSN s using ECG signals collected through IoT devices or portable ECG recorders. The search results achieved precision of 79.14% in top-one search results. Our proposed Cardio Vec improves the effectiveness and usefulness of loT and HSN for patients to find right information on cardiovascular diseases and learn about their potential health risks more easily and conveniently.
Multi-scaled Topic Embedding for Text Classification

Topic modeling and word embedding are commonly used techniques in natural language processing. However, word embedding lacks discriminating information of homonymy and polysemy as they typically assign a single vector for each word even if they have different meanings in different contexts. Many models were proposed to solve this issue by jointly learning topic modeling and word embedding together so that words can have different embeddings under various latent topics. However, the number of latent topics is set manually by human experiences before learning the topic modeling, which makes the final performance of the topical model largely rely on the subjective human judgment. If the number of topics is set too many, the topics learned may suffer from overfitting issues. On the other hand, if the number of topics is too small, the topics may contain little useful information. Experiments from other researchers show that the scale we used to model the latent topics is crucial to the performance of the topical word embeddings. We proposed Multi-scaled Topic Embedding (MTE) to learn the document representation with multiple latent topics for topic modeling based on this idea. With multi-scaled topics, MTE learns topical information on a large scale and captures key information from dense topic distributions. Most importantly, MTE reduces the influence of the number of topics on the model's performance. In this paper, we apply MTE in three commonly used datasets and test the performances of our model for text classification tasks on these datasets. The experimental results show that our model outperforms deep learning baseline models and typical topical word embeddings.
Echo State Networks with Special Memory Module

Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs), which have attracted extensive attention due to their simple training processes and special reservoir structures. However, if the nonlinearity of the network is improved, the memory capability is decreased. Therefore, this paper proposes a novel ESN model (MM-ESN) to solve this problem. We introduce the linear memory network (LMN) into the reservoir based on the idea of the relative separation for memory and nonlinearity. Therefore, the memory capability of the network is improved while maintaining the nonlinearity. Experimental results on the Lorenz chaotic time series datasets and randomly generated datasets have demonstrated the effectiveness of the proposed MM-ESN.
An Improved GaborNet Visual Encoding Model with Nonlinear Mapping and Data Augmentation

Constructing visual encoding model is an important way to understand the human visual system. Data-driven encoding models learn features by directly fitting neural data, providing novel insights for visual encoding. Prediction accuracy is an important performance goal of visual encoding. The GaborNet visual encoding (GaborNet-VE) model has both good expressiveness and interpretability, while its prediction performance still needs to be improved. In this work, we proposed an improved GaborNet-VE model by adding data augmentation and nonlinear mapping module. The experimental results show that the improved GaborNet-VE model achieves higher prediction accuracy in V2, V3, V4 and LO. Our research provides a new framework for the data-driven encoding model.
Joint Detection Model Based on YOLOv5 to Detect Lithium Battery Defects with Noisy Label

Data-driven intelligent detection methods have been widely used in the detection of defects in lithium batteries, with outstanding results. However, there are situations of inaccurate labeling due to category similarity in the labeling process, resulting in noisy labels that subsequently influence the model's prediction. To solve this problem, we propose a joint detection model based on YOLOv5, in which the whole is decoupled into two main parts, i.e., a fully supervised detection model and a semi-supervised classification model which uses clustering to divide the data. These two parts can effectively focus on key domains so as to attenuate the impact of noise labels under the relevant domains, and achieve overall noise immunity by connecting detection with classification in the validation phase. The approach outperforms the native YOLOv5 (mAP@0.5 as metric) by 5.7 percent, 10.2 percent, and 10.7 percent at 10%, 20%, and 30% noise fractions, respectively.
A Study on Optimum Tilt Angle of PV Panels for Konya, Turkey

In order to reduce foreign dependency on Turkey, domestic and renewable energy resources and investments are of great importance. In recent years, technological innovations and research and development activities have increased the interest in renewable energy sources, especially solar and wind, in meeting the increasing energy demand. In this respect, when the potential of Turkey's wind and solar energy resources is evaluated, Konya stands out compared to other provinces. The performance of a photovoltaic panel depends on the tilt angle and the azimuth angle. In this study, wind and solar potential among Turkey's renewable energy resources are briefly discussed and compared to other countries. The optimum panel tilt angle is calculated monthly, seasonally, and yearly for Konya province. A model was proposed based on the declination angle for finding the optimum tilt angle.
Unified graph-based method for instance separation from foreground-background segmentation

Instance segmentation problem is important for many computer vision applications. Recent state-of-the-art approaches use neural networks to predict per pixel foreground/background segmentation as well as other auxiliary data with subsequent methods for instance separation. All of these instance separation algorithms are very specific and require specific auxiliary data. Thus, we propose a unified graph-based method that can work with only foreground/background segmentation as well as with other auxiliary data structures.
Identification of Characteristic Descriptors and Creation of a Model for Predicting the Stage of Cancer

The paper presents an analysis of data for patients with registered oncological diseases and application of correlation analysis for detection of the most significant characteristic descriptors that have the greatest influence on determining the stage of malignancy. Based on the conducted theoretical studies herein are presented four groups of regression models created with three methods of machine learning - Decision Tree, Support Vector Machines and Ensemble algorithms - for three independent sets of data. A generalized model is also presented.
A Comparison of Machine Learning Classification Algorithms and Methods for English Author's Works and their Translations into Bulgarian

The aim of the publication is to compare the accuracy, precision, sensitivity and F-measure of machine algorithms trained in the classification of authors of works by English authors and the classification of authors of the same works translated into Bulgarian. The algorithms examined are Multinomial Naive Bayes classifier, Bernoulli Naive Bayes classifier, Support Vector Machines, Random Forest, AdaBoost, Decision Tree and K-Nearest Neighbosr. The research results show that in the English author's classification with an equal number of works in English, Support Vector Machines and Multinomial Naive Bayes classifier receive the highest values of the studied indicators. In Bulgarian texts, the best results are obtained depending on specific authors.
Secure Collaborative Camera Attribution✱

Collaboration between law enforcement agencies (LEAs) is fundamental to successfully deal with the increasing amount of multimedia content in cross-border investigations. However, the fruitful exchange of sensitive data is highly limited due to privacy law restrictions. We propose a secure federated framework for camera attribution which enables LEAs to jointly exploit their forensic data.
Kleptography in Authentication Protocols: Why is it Still Possible?

Network authentication frequently relies on nonces, and even widely deployed protocols still rely on random nonces, although they might enable kleptography attacks. Notably, for TLS a kleptography-based covert channel has been published, and despite a proposal to cure this weakness via controlled randomness including backward compatibility, the protocol description is not updated. We investigate if lack of bandwidth, i.e., lack of applicability, could be a reason not to care for such an update. Moreover, we give examples of other authentication protocols that might suffer from a similar weakness, and that possibly might profit from a similar cure, thus indicating necessity of further research.
Design and Implementation of a Field Level of an Industrial Communication Network for the Needs of the Industry

The study demonstrates the development of a Field level of an industrial communication network, operating with a wide range of machines. The TIA Portal software is used for the creation of the network, and a new generation software known as Factory I/O is used for the visualization.
The effect of different consequent setting on the effectiveness of Kóczy-Hirota fuzzy rule interpolation in colonoscopy image classification

The effect of the refinement of the consequent categories in K�czy-Hirota fuzzy rule interpolation is studied in the model system of colonoscopy image classification. The colonoscopy image segments are classified based on 99 antecedents, which are statistical parameters of the image segments. The number of consequents is increased from 2 - contains polyp, or does not contain polyp - to a more refined set distribution based on the polyp content of the image segment.
SecureScale: Exploring Synergies between Security and Scalability in Software Development and Operation

Security and scalability are core software qualities, which as non-functional aspects share certain characteristics and challenges in how they are approached during software development and operation. Based on expert interviews, this paper explores interactions and dependencies between security and scalability, as well as similarities and differences in their challenges. It concludes that the current understanding of the relationship between security and scalability is not yet mature. Further, it points to future research needs to better understand the relationship between these two quality aspects and better support practitioners in addressing security and scalability in a more integrated fashion.
Analysis of Neural Network Accuracy Degradation due to Uniform Weight Quantization of One or More Layers

In this paper, our goal is to examine which layer of the neural network (NN) is the most sensitive to the two-bit uniform weight quantization and results in the highest accuracy degradation of our trained NN model. We perform four NN accuracy analyses to qualify the efficiency of uniform two-bit quantization for each NN layer. Layer quantization importance ranking provided conclusion that the most important layer is the first layer, with the largest number of weights, while the last layer, although containing the smallest number of weights, is the second important one. This conclusion could be especially useful for preliminary anticipation in larger NN architectures with mixed-precision quantization.
Capsule routing based on Dirichlet process mixture model

Capsule networks have a new network structure that combines unsupervised learning method with the convolutional neural networks. Compared with convolutional neural networks, its advantage lies in its generalization ability to a novel viewpoint. In this paper, we combined the Dirichlet process mixture model with the capsule network, and use the coordinate ascending algorithm to realize the information transfer between capsule layers. We use a shallow network to verify the model’s generalization ability for a different viewpoint on the SmallNORB and BUAA-SID datasets. The comparison shows that our method has a lower test error than the Gaussian mixture model that is directly inferred by approximation.
Cybersecurity Threats Analysis and Management for Peer-to-Peer Energy Trading

The distributed energy resources (DERs) have significantly stimulated the development of decentralized energy system and changed the way how the energy system works. In recent years, peer-to-peer (P2P) trading has drawn attention as a promising alternative for prosumers to engage with the energy market more actively, particular by using the emerging blockchain technology. Blockchain can securely hold critical information and store data in blocks linking with chain, providing a desired platform for the P2P energy trading. This paper provides a detailed description of blockchain-enabled P2P energy trading, its essential components, and how it can be implemented within the local energy market An analysis of potential threats during blockchain-enabled P2P energy trading is also performed, which subsequently results in a list of operation and privacy requirements suggested to be implemented in the local energy market.
State-of-the-art Activity Recognition and Prediction Techniques Applicable to the Home Energy Management System

Owing to the tendency to convert ordinary homes to smart ones, there are novel opportunities for home energy management systems to exploit the binary and analog sensors collected information to enhance their performance. Having accurate forecasting about the behavior of house inhabitants, and their preferences could help the system be more efficient and reliable. In this paper, some state-of-the-art activity recognition and prediction techniques are reviewed and we introduce a conceptual platform for interactive collaboration between smart homes and energy management systems.
The State of Cloud-Based High Performance Computing

HPC applications have been gaining lot of attention in the cloud computing world. Most of these applications are scientific applications that require large CPU capabilities and are also data intensive requiring large data storage. Traditionally they have always required large number of computers inter-connected in a network such as clusters or supercomputers. These clusters are difficult to setup and maintain both technically and financially. With the advent of cloud computing and benefits of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), scientists and researchers are able to deploy their HPC applications in the cloud without worrying about the costs associated with the infrastructure and other costs involved. They also give guarantees on the quality of service (QoS). This paper focuses on documenting some of the research already done in the field of HPC applications and their current state in cloud computing.