Reviewerly/exHarmony huggingface dataset:
Fraud usage detection in internet users based on log data

The Internet has become one of the most important daily social, financial and other activities. The number of customers who use the Internet to conduct their business and purchases is very large. This results in billions of dollars being transferred every day online. Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities. “Fraud” is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both. The proposed system in this paper includes efficient data extraction from the web file through data collection and preprocessing. and web usage mining procedure to extract features that demonstrate user behavior. And feature-extracting URL analysis to detect website phishing addresses. After that, the features from the above two parts are combined to make the number of features sixty-three. Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate. Suggested algorithms performance is determined by using a confusion matrix that shows the robustness of the proposed system.
Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants

Solar energy penetration has been on the rise worldwide during the past decade, attracting a growing interest in solar power forecasting over short time horizons. The increasing integration of these resources without accurate power forecasts hinders the grid operation and discourages the use of this renewable resource. To overcome this problem, Virtual Power Plants (VPPs) provide a solution to centralize the management of several installations to minimize the forecasting error. This paper introduces a method to efficiently produce intra-day accurate Photovoltaic (PV) power forecasts at different locations, by using free and available information. Prediction intervals, which are based on the Mean Absolute Error (MAE), account for the forecast uncertainty which provides additional information about the VPP node power generation. The performance of the forecasting strategy has been verified against the power generated by a real PV installation, and a set of ground-based meteorological stations in geographical proximity have been used to emulate a VPP. The forecasting approach is based on a Long Short-Term Memory (LSTM) network and shows similar errors to those obtained with other deep learning methods published in the literature, offering a MAE performance of 44.19 W/m2 under different lead times and launch times. By applying this technique to 8 VPP nodes, the global error is reduced by 12.37% in terms of the MAE, showing huge potential in this environment.
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of quantum devices produced over a next decade. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and effcient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.
Contextualized Attention-Based Knowledge Transfer for Spoken Conversational Question Answering

Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora. Different from traditional text question answering (QA) tasks, SCQA involves audio signal processing, passage comprehension, and contextual understanding. However, ASR systems introduce unexpected noisy signals to the transcriptions, which result in performance degradation on SCQA. To overcome the problem, we propose CADNet, a novel contextualized attention-based distillation approach, which applies both cross-attention and self-attention to obtain ASR-robust contextualized embedding representations of the passage and dialogue history for performance improvements. We also introduce the spoken conventional knowledge distillation framework to distill the ASR-robust knowledge from the estimated probabilities of the teacher model to the student. We conduct extensive experiments on the Spoken-CoQA dataset and demonstrate that our approach achieves remarkable performance in this task.
CLRNet: Component-Level Refinement Network for Deep Face Parsing

Face parsing aims to assign pixel-wise semantic labels to different facial components (e.g., hair, brows, and lips) in given face images. However, directly predicting pixel-level labels for each facial component over the whole face image would obtain limited accuracy, especially for tiny facial components. To address this problem, some recent works propose to first crop tiny patches from the whole face image and then predict masks for each facial component. However, such cropping-and-segmenting strategy consists of two independent stages, which cannot be jointly optimized. Besides, as one valuable piece of information for parsing the highly structured facial components, context cues are not elaborately explored by the existing works. To address these issues, we propose a component-level refinement network (CLRNet) for precisely segmenting out each facial component. Specifically, we introduce an attention mechanism to bridge the two independent stages together and form an end-to-end trainable pipeline for face parsing. Furthermore, we incorporate the global context information into the refining process for each cropped facial component patch, providing informative cues for accurate parsing. Extensive experiments are carried out on two benchmark datasets, LFW-PL and HELEN. The results demonstrate the superiority of the proposed CLRNet over other state-of-the-art methods, especially for tiny facial components.
Highly Parallel Autoregressive Entity Linking with Discriminative Correction

Generative approaches have been recentlyshown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation).However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data.In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder.Moreover, we augment the generative objective with an extra discriminative component, i.e. a correction term which lets us directly optimize the generator's ranking.When taken together, these techniques tackle all the above issues: our model is >70 times faster and more accurate than the previous generative method, outperforming stateof-the-art approaches on the standard English dataset AIDA-CoNLL. 1
Development of the combined method of identification of near duplicates in electronic scientific works

The methods for identification of near-duplicates in electronic scientific papers, which include the content of the same type, for example, text data, mathematical formulas, numerical data, etc. were described. For text data, the method of locally sensitive hashing with the finding of Hamming distance between the elements of indices of electronic scientific papers was formalized. If Hamming distance exceeds a fixed numerical threshold, a scientific paper contains a near-duplicate. For numerical data, sub-sequences for each scientific work are formed and the proximity between the papers is determined as the Euclidian distance between the vectors consisting of the numbers of these sub-sequences. To compare mathematical formulas, the method for comparing the sample of formulas is used and the names of variables are compared. To identify near-duplicates in graphic information, two directions are separated: finding key points in the image and applying locally sensitive hashing for individual pixels of the image. Since scientific papers often include such objects as schemes and diagrams, subscriptions to them are examined separately using the methods for comparing text information. The combined method for identification of near-duplicates in electronic scientific papers, which combines the methods for identification of near-duplicates of various types of data, was proposed. To implement the combined method for the identification of near-duplicates in electronic scientific papers, an information-analytical system that processes scientific materials depending on the content type was devised. This makes it possible to qualitatively identify near-duplicates and as widely as possible identify possible abuses and plagiarism in electronic scientific papers: scientific articles, dissertations, monographs, conference materials, etc.
Two classes of new optimal ternary cyclic codes

Due to their wide applications in consumer electronics, data storage systems and communication systems, cyclic codes have been an interesting subject of study in recent years. The construction of optimal cyclic codes over finite fields is important as they have maximal minimum distance once the length and dimension are given. In this paper, we present two classes of new optimal ternary cyclic codes $ \mathcal{C}_{(2,v)} $ by using monomials $ x^2 $ and $ x^v $ for some suitable $ v $ and explain the novelty of the codes. Furthermore, the weight distribution of $ \mathcal{C}_{(2,v)}^{\perp} $ for $ v = \frac{3^{m}-1}{2}+2(3^{k}+1) $ is determined.
Hazelcast jet

Jet is an open source, high performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and provide exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents the main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.
Inversion of Soil Heavy Metal Content Based on Spectral Characteristics of Peach Trees

There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.
Online Black-Markets: An Investigation of a Digital Infrastructure in the Dark

Abstract This paper investigates the functioning of Online Black-Markets (OBMs), i.e. a digital infrastructure operating in the Dark Net that enables the exchange of illegal goods such as drugs, weapons and fake digital identities. OBMs exist notwithstanding adverse conditions such as police interventions, scams and market breakdowns. Relying on a longitudinal case study, we focus on the dynamics of interactions among actors and marketplace technologies and we identify three mechanisms explaining OBMs operations. In particular, we show that OBMs infrastructure is the result of commoditization, platformization and resilience processes. Our contribution relies on the identification of community-based mechanisms that generate the OBMs infrastructure, extending the current understanding of e-commerce and social commerce.
The “War” Against Covid-19: State of Exception, State of Siege, or (Constitutional) Emergency Powers?: The Italian Case in Comparative Perspective

Abstract Is the Covid-19 pandemic changing the constitutional-power structures of our democracies? Is this centennial public health emergency irreversibly constraining our liberties? The paper examines recent state-measures of containment during the initial phase of spread of the Covid-19 crisis. It compares primarily the Italian scenario with the Chinese and the American one. It asks whether the measures adopted particularly in the Italian case (known as DPCMs) amount to a state of exception or to a use of emergency powers. Cognizant of the authoritarian risks in severed enjoyments of constitutional rights, the authors conclude that this is not what occurred in the case of solid democracies. At the level of governmental analysis, the “decree” strategy of the Italian DPCMs allude to paternalistic forms of power-exercise that empty the self-determining prerogative of the parliament.
Interrogating the promises and perils of climate cryptogovernance: Blockchain discourses in international climate politics

This article interrogates the assumed promises and perils of climate cryptogovernance or deployment of cryptographic technology (i.e., blockchain) within climate governance. We distill how climate cryptogovernance is being discussed by influential climate policy actors, and the implications for reinforcing or challenging how climate governance currently occurs. Specifically, through discourse analysis, we explore how blockchain technology is presented in the communications of international organisations and multistakeholder initiatives in the climate policy space. We identify a dominant storyline being advanced that views blockchain as an enabler of ambitious climate action, through its potential to enhance the reliability, transparency, accountability, and democratic quality of climate governance. We critically interrogate each of these component elements of the dominant storyline, arguing that, taken as a whole, they tend to privilege a technocratic, market-oriented approach to climate governance. We conclude by reflecting on whether this risks reinforcing a problematic ‘post-political’ turn in environmental governance in the future.
Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning

Purpose: In conventional diagnosis, the visual inspection of the malaria parasite Plasmodium falciparum in infected red blood cells under a microscope, is done manually by pathologists, which is both laborious and error-prone. Recent studies on automating this process have been conducted using artificial intelligence and feature selection of positional and morphological features from blood smear cell images using convolutional neural network (CNN). However, most deep CNN models do not perform well as per the expectation on small datasets. Approach: In this context, we propose a comprehensive computer-aided diagnosis scheme for automating the detection of malaria parasites in thin blood smear images using deep CNN, where transfer learning is used for optimizing the feature selection process. As an extra layer of security, layer embeddings are extracted from the intermediate convolutional layers using the feature matrix to cross-check the selection of features in the intermediate layers. The proposal includes the utilization of the ResNet 152 model integrated with the Deep Greedy Network for training, which produces an enhanced quality of prediction. Results: The performance of the proposed hybrid model has been evaluated concerning the evaluation metrics such as accuracy, precision, recall, specificity, and F1-score, which has been further compared with the pre-existing deep learning algorithms. Conclusions: The comparative analysis of the results reported based on the accuracy metrics demonstrates promising outcomes concerning the other models. Lastly, the embedding extraction from the intermediate hidden layers and their visual analysis also provides an opportunity for manual verification of the performance of the trained model.
Solving the Problem of Class Imbalance in the Prediction of Hotel Cancelations: A Hybridized Machine Learning Approach

The cancelation of bookings puts a considerable strain on management decisions in the case of the hospitability industry. Booking cancelations restrict precise predictions and are thus a critical tool for revenue management performance. However, in recent times, thanks to the availability of considerable computing power through machine learning (ML) approaches, it has become possible to create more accurate models to predict the cancelation of bookings compared to more traditional methods. Previous studies have used several ML approaches, such as support vector machine (SVM), neural network (NN), and decision tree (DT) models for predicting hotel cancelations. However, they are yet to address the class imbalance problem that exists in the prediction of hotel cancelations. In this study, we have shortened this gap by introducing an oversampling technique to address class imbalance problems, in conjunction with machine learning algorithms to better predict hotel booking cancelations. A combination of the synthetic minority oversampling technique and the edited nearest neighbors (SMOTE-ENN) algorithm is proposed to address the problem of class imbalance. Class imbalance is a general problem that occurs when classifying which class has more examples compared to others. Our research has shown that, after addressing the class imbalance problem, the performance of a machine learning classifier improves significantly.
Comparative Study of Neural Network and Tree-Based Models in Solar Irradiance Prediction

Solar Photovoltaic Systems have become the most promising technology for clean energy generation in recent years. Solar irradiation is one of the critical factors that affect Photovoltaic output. Since the Photovoltaic output varies significantly during the day, accurate solar irradiation forecasting is essential for predicting Photovoltaic output. Many machine-learning models have been used to forecast solar irradiation in the last few decades. This paper evaluates the performance of two basic machine-learning models: Artificial Neural Networks and Tree-based methods for solar irradiance prediction. Two neural networks such as Multilayer Perceptron, Radial Basis Function Neural Network, and three tree-based models such as Classification and Regression Tree, Alternating Model Tree, and Random Forest are applied to predict the solar irradiance using the NASA Solar Irradiation dataset. The performance of those methods is assessed using various metrics like the Correlation Coefficient, Mean Absolute Error, and Root Mean Squared Error. The experimental results show that the tree-based ensemble methods perform significantly better than neural network models for solar irradiance prediction.
Research and Practice Recommendations for Mixed Reality Design – Different Perspectives from the Community

Over the last decades, different kinds of design guides have been created to maintain consistency and usability in interactive system development. However, in the case of spatial applications, practitioners from research and industry either have difficulty finding them or perceive such guides as lacking relevance, practicability, and applicability. This paper presents the current state of scientific research and industry practice by investigating currently used design recommendations for mixed reality (MR) system development. We analyzed and compared 875 design recommendations for MR applications elicited from 89 scientific papers and documentation from six industry practitioners in a literature review. In doing so, we identified differences regarding four key topics: Focus on unique MR design challenges, abstraction regarding devices and ecosystems, level of detail and abstraction of content, and covered topics. Based on that, we contribute to the MR design research by providing three factors for perceived irrelevance and six main implications for design recommendations that are applicable in scientific and industry practice.
Investigation of YOLOv5 Efficiency in iPhone Supported Systems

Object detection gaining popularity and is more used on mobile devices for real-time video automated analysis.In this paper, the efficiency of the newly released YOLOv5 object detection model has been investigated.Experimental research has been performed to find out the efficiency of YOLOv5 using a mobile device with real-time object detection tasks.For this reason, four YOLOv5 model sizes have been used: small, medium, large, and extra-large.The experiments have been performed with a well-known COCO dataset.The original dataset consists of a huge number of images, so the dataset has been reduced to fit the mobile device requirements.The experimental investigation results have shown, that reducing the COCO dataset has no significant influence on the model accuracy, but the model performance is highly influenced by the hardware architecture and system where the model is used.Apple Network Engine usage might significantly increase the YOLOv5 model performance in comparison to CPU usage.
Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm

In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.