Reviewerly/exHarmony huggingface dataset:
Forged handwriting verification: a public domain dataset for training machine learning models

Handwriting verification is an important task in the legal framework where the paternity of handwritten wills or con-tracts needs to be ascertained. Due to the lack of automated tools, the court currently relies exclusively on handwriting experts, who often show a wide degree of uncertainty in their responses. Machine learning models, and in particular artificial Neural Networks (NNs) might be a valuable aid to experts by providing an objective and automated instrument for handwriting analysis, especially when expert witnesses are undecided. However, at the state of the art there is a scarcity of datasets which are suitable for training NNs for the handwriting verification task, preventing the development of models accurate enough to be introduced into forensic practice. In this paper, a dataset of 3320 genuine and 3320 forged handwritten samples from 166 subjects is presented, considering two scenarios: copy (imitation of handwriting maintaining the same text, 3320 samples) and spontaneous production (imitation of handwriting generating a new text, 3320 samples). We provide baseline results for deep siamese convolutional neural networks, that are deep learning models widely adopted in similar tasks. In the proposed dataset, such NNs were able to reach accuracies over 75% in identifying forged samples in the copy scenario and of almost 82% in the spontaneous production scenario. Finally, a sample of 550 humans were tested in the same classification task. The experiments show that NNs perform significantly better than humans in the spontaneous production scenario, which is more complex than the copy one. We publicly release our dataset to encourage the future development of advanced Deep Learning models for the forged handwriting verification task.
Software Requirements Definition Processes in Gamification Development for Immersive Environments

The implementation of gamification in immersive environments is a complex and multidimensional process. A socio-technical approach is necessary to cover all the specifications that the system needs to satisfy the needs and the purpose of its genesis. The use of virtual reality (VR) technologies in mental healthcare associated with gamification mechanisms has been gaining popularity. Two projects were developed using VR, one that allows people to experience and better understand mental health conditions through empathy construct and the other can be used to help patients with social phobia or Arachnophobia to reduce their phobias using VR solutions and real-time biofeedback. The authors analyze the aspects that influence the development of immersive environments and gamification mechanisms and propose a socio-technical methodology based on actor-network theory for the survey and definition of requirements.
Aspect-sited hybrid graph neural networks for sentiment classification

Aspect-based sentiment classification is a fine-grained task aimed at inferring the sentiment polarities over specific aspect words in a sentence. Unlike previous approaches that mostly model directly on a given sentence, syntactic dependency trees constructed by graph neural networks have been used in recent years to describe the inner connections between aspect words and related sentiment polarities in sentences with good results. However, we found that the way to use graph neural networks is to model directly on a single kind of multilayer graph neural network, and this approach may not fully explore the inner connections between aspect words and context words in a sentence. Also, the direct modeling of the sentences does not utilize the sequence position information in the sentences and mistakenly identifies irrelevant context words as clues for judging aspect sentiment. In this paper, we propose a new model, ASH-GNNs, which integrates various kinds of graph neural network implementations of semantic dependency trees describing the intrinsic connections between aspects words and related sentiment words, and adds relative position to the input sentences to guarantee the sequence position information in the sentences. The experimental results show that ASH-GNNs outperform all the baseline models.
A Visual Analysis of Virtual Reality Research in Education Using CiteSpace

Virtual reality (VR) is considered as a promising technology for teaching and learning that is gaining popularity in both educational research and practice. However, despite the bits and pieces of research on VR, there has been a lack of synthesis efforts that help visualize and systematize current knowledge on this area. To address this research need, this study employed CiteSpace and Bicomb software to analyze 17 years of relevant VR literature (2005–2021) visually and statistically, with the purpose of revealing trending patterns of VR research in education. The results have located the main regions, institutions, and authors that contribute to the scholarship, and revealed a series of research hotspots and frontiers with their evolving trends. Additionally, the co-citation status was also examined to identify most influential researchers and representative publications. Several implications for future research were proposed based on the analysis results.
Parallel Parking and Parking Assist System for Autonomous and Semi-Autonomous Vehicles

Modern autonomous vehicles are designed to provide convenience for people in their daily lives. A level 5 Advanced Driver Assistance System (ADAS) does not require any human attention and they are free from Geo-fencing. There is research going on and is not yet fully implemented. So this system can only be classified as fully autonomous driving level 1 to 4. Here Level-1 cars involves the human drivers and it is lowest level of automation. And in Level-2 there is no selfdriving but instead the human sits in the car and this level of vehicles controls both steering, accelerating/decelerating. In Level-3 includes human interaction, if the system designed fails, designed on the basis of environmental detection. Level-4 vehicles are Self-driving, there are speed limits already designed within the systems. Assistive parking is the first application of autonomous vehicles that is fully autonomous when considering both risks and environments. In this research, we propose parallel parking assistance for autonomous and semi-autonomous vehicles. The plan is based on the kinematic model of the vehicle which is used as a parameter for a Model Predictive Controller (MPC) to control the vehicle autonomously and use the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is a Informed Search Algorithm or a Best-First Search Algorithm it aims to determine the shortest path possible to reach the specified goal node, and here this algorithm is used to find the path planning and parking. Using this system, the vehicle is retrieved from the parking spot and re-parked by reversing along the obtained path. We propose methods of planning independent of a vehicle's initial position or orientation. The vehicle parks by following reference trajectories generated by the system.
Learning Distinct Relationship in Package Recommendation With Graph Attention Networks

Recommendation systems have been widely developed and extensively used in various websites and platforms to promote products or services to interested users. However, in quite a few sale scenarios, the platform has the necessity to display users a series of items, which is called package recommendation. There is very little research in this area. This article develops a novel and realistic package recommendation system named package graph attention network (PGAT) based on graph neural network. PGAT integrates users, items, and packages to build a unified heterogeneous graph and treat them as a whole. PGAT incorporates an attention mechanism in the first-order neighborhood aggregation operation, which can differentiate the weight of different neighbor nodes to the center node. By performing graph attention and graph convolution operations on the tripartite graph, PGAT can learn node embeddings more expressively and address the problem of data sparse to a large extent. Extensive experiment results on two real-world datasets validate the outstanding performance of PGAT, which is superior to the state-of-the-art baselines by 0.77%–10.12%.
Analysing the Relationship Between Dependency Definition and Updating Practice When Using Third-Party Libraries

Using third-party libraries is common practice when developing software. Package managers have made it easy to add third-party libraries as dependencies and to keep dependency versions up to date. Nevertheless, research shows that developers are prone to not updating their dependencies. We study how the type of version requirements used in the package manager manifest files affect dependency updating lag time (measured in days) and how this lag affects dependencies to vulnerable library versions. We focus on the package managers commonly used in iOS development, i.e., CocoaPods, Carthage and Swift PM. We first measure how the dependency updating lag time evolves over time for each package manager. Then we analyze whether and how the chosen type of version requirement affects the dependency updating lag time. Third, we investigate how not re-running package manager version resolution affects library updates. Lastly, we analyse how many vulnerable dependencies could have been fixed by updating the dependency. We found that dependency updating lag time differs between package managers but grows over time for all of them. We also found that the preferred version requirement types differ between package managers. As expected, version requirement types that are less restrictive produce less dependency updating lag. Moreover, we found that keeping library dependency versions up to date results in less vulnerable dependencies. Interestingly, some of the vulnerable dependencies could have been fixed by simply rerunning the package manager version resolution.
Development of a Secure Cloud-based Breast Cancer Diagnosis System

Breast cancer is one of the most common cancers that cause death in women. Breast cancer was the most common cancer in 2020, with 2.26 million new cases. Cancer mortalities can be reduced by early detection and treatment. There are two components of early detection: early diagnosis and screening. Our work is related to early diagnosis. Early diagnosis helps to start treatment early, which is more effective and less expensive. The lives of cancer patients can be improved significantly by early detection and avoiding treatment delays. This paper presents the design and development of a secure cloud-based breast cancer diagnosis system. The proposed system uses using convolutional neural network for breast mass classification. Fragile zero watermarking will be implemented to secure the transmission of mammographs. The proposed system will enable women living in remote areas to get the facility for the routine initial screening near their homes without making their identity and medical data vulnerable. It will reduce the cost and time by avoiding frequent visits of the patients to specialized health care centers. On the other hand, it will help enhance the quality of service at specialized healthcare centers by reducing the number of patients in the initial screening.
Bus factor in practice

Bus factor is a metric that identifies how resilient is the project to the sudden engineer turnover. It states the minimal number of engineers that have to be hit by a bus for a project to be stalled. Even though the metric is often discussed in the community, few studies consider its general relevance. Moreover, the existing tools for bus factor estimation focus solely on the data from version control systems, even though there exists other channels for knowledge generation and distribution. With a survey of 269 engineers, we find that the bus factor is perceived as an important problem in collective development, and determine the highest impact channels of knowledge generation and distribution in software development teams. We also propose a multimodal bus factor estimation algorithm that uses data on code reviews and meetings together with the VCS data. We test the algorithm on 13 projects developed at JetBrains and compared its results to the results of the state-of-the-art tool by Avelino et al. against the ground truth collected in a survey of the engineers working on these projects. Our algorithm is slightly better in terms of both predicting the bus factor as well as key developers compared to the results of Avelino et al. Finally, we use the interviews and the surveys to derive a set of best practices to address the bus factor issue and proposals for the possible bus factor assessment tool.
Roadmap of Blockchain Standardization

Economic globalization has further strengthened the strategic position of standards. Mastering the formulation of standards means mastering the discourse power of industrial development. As an emerging field, blockchain has been developing rapidly in recent years. From a worldwide perspective, blockchain standardization is still in its infancy. This chapter analyzes the requirements and value of blockchain standardization, introduces the development process of international and national standardization in China in the field of blockchain and distributed ledger technology, the latest trends of ISO, ITU and other international standardization organizations, the key direction of blockchain standardization work in China, as well as the implementation suggestions of blockchain standardization.
Improvement of Serial Approach to Anomalous Sound Detection by Incorporating Two Binary Cross-Entropies for Outlier Exposure

Anomalous sound detection systems must detect unknown, atypical sounds using only normal audio data. Conventional methods use the serial method, a combination of outlier exposure (OE), which classifies normal and pseudo-anomalous data and obtains embedding, and inlier modeling (IM), which models the probability distribution of the embedding. Although the serial method shows high performance due to the powerful feature extraction of OE and the robustness of IM, OE still has a problem that doesn't work well when the normal and pseudo-anomalous data are too similar or too different. To explicitly distinguish these data, the proposed method uses multi-task learning of two binary cross-entropies when training OE. The first is a loss that classifies the sound of the target machine to which product it is emitted from, which deals with the case where the normal data and the pseudo-anomalous data are too similar. The second is a loss that identifies whether the sound is emitted from the target machine or not, which deals with the case where the normal data and the pseudo-anomalous data are too different. We perform our experiments with DCASE 2021 Task 2 dataset. Our proposed single-model method outperforms the top-ranked method, which combines multiple models, by 2.1 % in AUC.
Automatic Segmentation of Handwritten Devanagari Word Documents Enabling Accurate Recognition

In this paper, we propose different approaches for the segmentation of handwritten Devanagari word documents into constituent characters (or pseudo-characters). For accurate identification and segmentation of shiroreakha we exploited ShiroreakhaNet which is encoder-decoder based convolutional neural network. After, segmenting the shiroreakha structural patterns/properties are exploited for the segmentation of upper and lower modifiers. For the corroboration of the efficacy of the results, we collected dataset from different domains. Comparison is also performed with the state-of-the-art methods, and it was revealed that proposed approaches significantly perform better.
Ecological risk assessment of heavy metals in soils of the Trans-Urals zone of the Republic of Bashkortostan (Russian Federation)

The development of deposits of polymetallic ores in the Republic of Bashkortostan has led to environmental pollution. The objective of the study is to assess the ecological risk of soil contamination of this territory with heavy metals. For the determination of the total concentration of soil metals, an inductively coupled plasma mass spectrometry (ICP-MS, Contr AA, Analytik Jena) was used. The geo-accumulation index (Igeo) and potential ecological risk (Er,RI) were computed. It is shown that the soils with mining production are contaminated with Cu, Zn and Cd (3.15, 1.9 and 14.5 times higher than the regional background, respectively). The mean values of Igeo increased in the following order: Pb (-1.29) < Fe (-0.77) < Ni (-0.69) < Co (-0.19) < Zn (0.05) < Mn (0.55) < Cu (0.66) < Cd (3.08). Potential ecological risks in the six counties of the Trans-Ural zone are presented in descending order: Baimak (522.31) > Sibay (493.96) > Uchaly (449.38) > Khaibullinsky district (300.90) > Baymaksky district (285.58) > Uchalinsky district (273.55). The multiple analysis indices were showed the low risk of soils in Trans-Ural zone from Pb, Cu, Ni, Co. However, it should be noted that Cd poses most of the potential ecological risks.
Prediction of the Power of the Solar Panel

The article presents the results of predicting the power at the output of the solar panel by polynomials of different degrees. The article indicates the need for solar power forecast. The article describes what factors affect the forecast of solar power at the output of the solar panel. Forecasting the amount of electricity generated by a solar power plant is primarily a prediction of the amount of solar radiation received by the solar panel, which in turn depends on environmental conditions and parameters. Data were taken from 04.05.2019 - 05.05.2019 with a discreteness of 1 minute. In order to calculate the forecast, the values of solar insolation were converted to power. The hourly curve of change of solar power with a discreteness in 1 minute is presented. A two-hour curve of the change in solar power with a resolution of 10 minutes is presented. The daily curve of change of solar power with a discreteness in 1 hour is presented. The horizon at 1 hour and 1 day was chosen for forecasting. Approximation of data by means of polynomials of various degrees is checked. The article shows graphs of changes in real and predicted values of solar power at the output of the solar panel. The graphs clearly show which method of forecasting is more accurate. The accuracy of the predicted values was assessed using the average relative error. Of all the considered methods of calculating the predicted value of the power of the solar panel, the smallest error is obtained when the data are selected for 2 hours, differ by no more than 2 times and have a discreteness of 10 minutes. The benefit of using the correction of the predicted data by the Hoyne method is checked. To predict the power of the solar panel by approximation, it is advisable to adjust the predicted data. To correct the data, it is advisable to use the method of predictor-corrector. Predictor - is the predicted value, and the corrector - is the adjusted value After calculating the power forecast at the output of solar power, an algorithm was developed with which you can calculate the predicted value of power. The developed algorithm for calculating the forecast uses the following parameters: data discreteness, the period for which the data are taken for analysis, the degree of the polynomial. First, the algorithm selects data for the selected period, selects discreteness. If you want to increase the discreteness, it averages the value. But on the basis of the selected values calculates the polynomial of the selected degree. Then, based on the calculated equation, the forecast is calculated and the predicted values are displayed in the form of a graph.
Ensemble Approach for Credit Card Fraud Detection Using Champion-Challenger Analysis

Credit cards have evolved as a way of transferring money through E-payment systems, as digital payments develop to smoothly help corporate transactions around the world. Credit card fraud, on the other side, has continued to be a huge global danger to financial firms, with various reports and figures revealing the scope of the problem. In this study, we used three distinct ensemble models to implement a champion-challenger framework and evaluated the best model amongst them. With Accuracy, Precision, Recall, and F1-Score of 99.86, 99.73, 99.99, 99.86, Challenger 1, which is an aggregation of Random Forest, Adaboost, and LSTM, was crowned the winner.
Cascade Scale-Aware Distillation Network for Lightweight Remote Sensing Image Super-Resolution

Recently, convolution neural network based methods have dominated the remote sensing image super-resolution (RSISR). However, most of them own complex network structures and a large number of network parameters, which is not friendly to computational resources limited scenarios. Besides, scale variations of objects in the remote sensing image are still challenging for most methods to generate high-quality super-resolution results. To this end, we propose a scale-aware group convolution (SGC) for RSISR. Specifically, each SGC firstly uses group convolutions with different dilation rates for extracting multi-scale features. Then, a scale-aware feature guidance approach and enhancement approach are leveraged to enhance the representation ability of different scale features. Based on SGC, a cascaded scale-aware distillation network (CSDN) is designed, which is composed of multiple SGC based cascade scale-aware distillation blocks (CSDBs). The output of each CSDB will be fused via the backward feature fusion module for final image super-resolution reconstruction. Extensive experiments are performed on the commonly-used UC Merced dataset. Quantitative and qualitative experiment results demonstrate the effectiveness of our method.
A Deep Learning Approach to Classify Drones and Birds

Owing to the rising threat of employing drones in unlawful acts, detection of drones has recently gained tremendous importance in the field of security and surveillance. Artificial neural networks do not support real time object detection as it requires multiple GPUs for training the models. Deep learning architectures try to resolve this issue by designing convolutional neural networks (CNN) that can operate in real time requiring only one traditional GPU for training. In this paper, suitable deep learning architectures are utilized for detection of drones and birds. You only look once (YOLO) based algorithms are considered for this application which are one-stage approaches that looks at an image only once and has a single neural network which is trained end-to-end, to directly provide the bounding box along with the class label and probability of detection. The models are trained on a custom dataset consisting of 664 drone images and 236 bird images. Simulation results of these models have shown that YOLOv4 and YOLOv5 achieved a F1-score of 98% and 94% with a detection speed of 54fps and 77fps respectively providing a mean average precision (mAP) of 97.4% and 95%. The finding is that YOLOv4 outperformed YOLOv5 in terms of mAP, while YOLOv5 outperformed YOLOv4 in terms of detection speed.