Reviewerly/exHarmony huggingface dataset:
Structure Preprocessing Method for the System of Unclosed Linear Algebraic Equations

The complexity of open linear algebraic equations makes it difficult to obtain analytical solutions, and preprocessing techniques can be applied to coefficient matrices, which has become an effective method to accelerate the convergence of iterative methods. Therefore, it is important to preprocess the structure of open linear algebraic equations to reduce their complexity. Open linear algebraic equations can be divided into symmetric linear equations and asymmetric linear equations. The former is based on 2 × 2. The latter is preprocessed by the improved QMRGCGS method, and the applications of the two methods are analyzed, respectively. The experimental results show that when the step is 500, the pretreatment time of quasi-minimal residual generalized conjugate gradient square 2 method is 34.23 s, that of conjugate gradient square 2 method is 35.14 s, and that of conjugate gradient square method is 45.20 s, providing a new reference method and idea for solving and preprocessing non-closed linear algebraic equations.
Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.
Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.
Who controls children’s education data? A socio-legal analysis of the UK governance regimes for schools and EdTech

A socio-legal analysis of the UK governance regime for data collected from children at school for teaching and learning contrasts the government-mandated data collection by schools to inform educational policy and planning with data processed and shared with third parties by commercial EdTech providers. We find the former is effectively governed by the government’s ‘Five Safes Framework’ with some problematic exceptions. By contrast, EdTech providers process a growing volume of personal data under the DPA 2018/UK GDPR with a looser enforcement regime. While schools have few mechanisms and insufficient expertise or resources to hold EdTech providers accountable for processing children’s data, EdTech providers have considerable latitude in interpreting the law. Consequently, and paradoxically, regulations governing (mostly) deidentified data used for public purposes are more systematically enforced than those governing personal (identifiable) data used for public and commercial purposes. We conclude with recommendations so that education data can serve children’s best interests.
Improving solar photovoltaic installation energy yield using bifacial modules and tracking systems: An analytical approach

In this paper, we present the results of a simulation of a 3 MW p photovoltaic plant in Nigeria using four case study scenarios: ground-mounted fixed inclined monofacial, and bifacial photovoltaic installation, as well as monofacial and bifacial photovoltaic installations with trackers. The bifacial gains, tracker gains, and bifacial-tracker gains were calculated for each configuration. The fixed ground-mounted inclined bifacial PV system gained 12% annual average bifacial gain over a fixed ground-mounted monofacial system, while the bifacial system with a solar tracker gained 8.9% over a monofacial system with a solar tracker. A monofacial PV system with a tracker over a fixed inclined monofacial system has a tracker gain of 16%, while a bifacial PV system with a tracker over a fixed inclined bifacial system has a tracker gain of 13.2%. The monofacial system with a solar tracker outperforms a fixed inclined bifacial system by about 4%. The sensitivity analysis performed to determine the effect of system parameters such as albedo and ground clearance height on bifacial PV systems reveals that the increase in specific energy production per kilowatt-hour per year is directly proportional to the albedo of the surface area on which the bifacial PV systems are installed, and adequate ground clearance height is required between the ground and the installed bifacial PV plants.
Dynamic Convolution-based Encoder-Decoder Framework for Image Captioning in Hindi

In sequence-to-sequence modeling tasks, such as image captioning, machine translation, and visual question answering, encoder-decoder architectures are state of the art. An encoder, convolutional neural network (CNN) encodes input images into fixed dimensional vector representation in the image captioning task, whereas a decoder, a recurrent neural network, performs language modeling and generates the target descriptions. Recent CNNs use the same operation over every pixel; however, all the image pixels are not equally important. To address this, the proposed method uses a dynamic convolution-based encoder for image encoding or feature extraction, Long-Short-Term-Memory as a decoder for language modeling, and X-Linear attention to make the system robust. Encoders, attentions, and decoders are important aspects of the image captioning task; therefore, we experiment with various encoders, decoders, and attention mechanisms. Most of the works for image captioning have been carried out for the English language in the existing literature. We propose a novel approach for caption generation from images in Hindi. Hindi, widely spoken in South Asia and India, is the fourth most-spoken language globally; it is India’s official language. The proposed method utilizes dynamic convolution operation on the encoder side to obtain a better image encoding quality. The Hindi image captioning dataset is manually created by translating the popular MSCOCO dataset from English to Hindi. In terms of BLEU scores, the performance of the proposed method is compared with other baselines, and the results obtained show that the proposed method outperforms different baselines. Manual human assessment in terms of adequacy and fluency of the captions generated further determines the efficacy of the proposed method in generating good-quality captions.
Hypergraph-Enhanced Textual-Visual Matching Network for Cross-Modal Remote Sensing Image Retrieval via Dynamic Hypergraph Learning

Cross-modal remote sensing (RS) image retrieval aims to retrieve RS images using other modalities (e.g., text) and vice versa. The relationship between objects in the RS image is complex, i.e., the distribution of multiple types of objects is uneven, which makes the matching with query text inaccurate, and then restricts the performance of remote sensing image retrieval. Previous methods generally focus on the feature matching between RS image and text and rarely model the relationships between features of RS image. Hypergraph (hyperedge connecting multiple vertices) is an extended structure of a regular graph and has attracted extensive attention for its superiority in representing high-order relationships. Inspired by the advantages of the hypergraph, in this work, a hypergraph-enhanced textual-visual matching network (HyperMatch) is proposed to circumvent the inaccurate matching between the RS image and query text. Specifically, a multiscale RS image hypergraph network is designed to model the complex relationships between features of the RS image for forming the valuable and redundant features into different hyperedges. In addition, a hypergraph construction and update method for an RS image is designed. For constructing a hypergraph, the features of an RS image running as vertices and cosine similarity is the metric to measure the correlation between them. Vertex and hyperedge attention mechanisms are introduced for the dynamic update of a hypergraph to realize the alternating update of vertices and hyperedges. Quantitative and qualitative experiments on the RSICD and RSITMD datasets verify the effectiveness of the proposed method in cross-modal remote sensing image retrieval.
Consumer-source authentication with conditional anonymity in information-centric networking

Information-Centric Networking (ICN) necessitates several inherent security mechanisms, e.g., source authentication is performed by embedding content signature per data packet. However, existing ICN source authentication solutions suffer from two security threats: from the end-hosts perspective, any content consumers could retrieve any content no matter whether they are legitimate or not; from a privacy concern perspective, each consumer-source pair has been leaked because the request-respond information is exposed to the network nodes. We propose an anonymous protection mechanism that ensures secure content transmission from multiple sources to one consumer to address both issues. Furthermore, our mechanism enables each anonymously authenticated content consumer to batch verify the authenticity of different types of requested content belonging to different anonymous content producers (sources). To the best of our knowledge, for the first time, we simultaneously consider two-way authentication with consumer anonymity protection for ICN. We focus on the provable security analysis of the proposed scheme. Theoretical analysis results show that our design has significant advantages compared to the existing frameworks.
Efficient secure and verifiable KNN set similarity search over outsourced clouds

KNN set similarity search is a foundational operation in various realistic applications in cloud computing. However, for security consideration, sensitive data will always be encrypted before uploading to the cloud servers, which makes the search processing a challenging task. In this paper, we focus on the problem of KNN set similarity search over the encrypted datasets. We use Yao's garbled circuits and secret sharing as underlying tools. To achieve better querying efficiency, we construct a secure R-Tree index structure based on a novel secure grouping protocol, which enables grouping appropriate private values in an oblivious way. Along with several elaborately designed secure arithmetic subroutines, we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds. Theoretically, we analyze the complexity of our schemes in detail, and prove the security in the presence of semi-honest adversaries. Finally, we evaluate the performance and feasibility of our proposed methods by extensive experiments.
Sovereignty in the Digital and Contact Tracing Apps

Recently, the concept of sovereignty in the digital has attracted much attention. Several publications dealing with this concept assume that it can best be described as a network of different, overlapping exercises of power. Nevertheless, there is a need for further research on how exactly sovereignty in the digital can be understood. In order to contribute to a better understanding of this concept, I illustrate its complex structure using contact tracing apps as a paradigmatic example. I conduct a narrative review to show what sovereignty looks like in the context of these apps. In the context of digital contact tracing apps, sovereignty is best understood as a complex network of three actors-nations, (big tech) companies, and individuals-that exercise various forms of power against or on behalf of each other to claim sovereignty for themselves and to either weaken or strengthen the sovereignty claims of other actors. Since large parts of the results can be generalized from the particular context of contact tracing apps, they contribute to a better overall understanding of the concept of sovereignty in digital. This might, in turn, be helpful for discussions about this technology as well as about the regulation and governance of the digital in general.
Modelling doctoral dissertations in Wikidata knowledge graph: Selected issues

Academic libraries have a long tradition of collecting, preserving, and offering access to information about doctoral dissertations through the repositories of electronic theses and dissertations. In recent years libraries have turned their attention to Wikidata as a knowledge graph for publishing structured data on the Web. These initiatives include the publication of metadata about theses and dissertations. However, Wikidata is a sociotechnical infrastructure, where the responsibility for its development is in the hands of its users. This includes asynchronous collaborative work on its ontology by many non-expert users. This may cause tensions between traditional metadata control and socially developed schemas. The goal of this article is to develop a conceptual understanding of the representation patterns of doctoral dissertations in a socially constructed Wikidata knowledge graph. This study uses an interpretative approach guided by the method of the close reading of the infrastructure to develop an understanding of what a doctoral dissertation is in this socially constructed representation of reality and how doctoral dissertations are described using Wikidata ontology. The ontological status of the doctoral dissertation is revealed by interpreting the place of this concept in the taxonomic structure of the Wikidata ontology. The expressiveness of Wikidata ontology for the description of doctoral dissertations is limited to three selected properties, authorship, doctoral advisor, and the institution to which the dissertation was submitted. The results of this study show on the one hand the redundancies in modelling doctoral dissertations, and on the other inconsistencies in the descriptions. This calls for close attention from libraries to metadata curatorial practices in Wikidata sociotechnical infrastructure.
An efficient composite cloud service model using multi-criteria decision-making techniques

Recent advancements in information technology have made cloud computing one of the most prominent technologies. It is most favorable for the bundle of services that it provides to its users. Since there is a wide range of cloud service providers (CSPs) with various services, it is challenging for the user to select a CSP that can meet all of its requirements. In this paper, we propose a composite cloud service model, which is handled by a cloud agent, to identify the best cloud services/criteria from a set of CSPs by considering the objective and subjective opinions collected from the cloud users' feedback and reviews. Note that the cloud agent is an intermediary between the users and CSPs. Then the agent recommends the CSPs to assemble the identified services into a unified group of services to fulfil the user requirements. Our model calculates the integrated objective and subjective scores of alternatives for a set of criteria and determines the best alternative for each criterion. For this, the application of two multi-criteria decision-making techniques, namely method based on the removal effect of criteria and extended step-wise weight assessment ratio analysis (extended SWARA), is used to calculate the objective and subjective scores, respectively. The proposed model is compared with the analytic hierarchy process-technique for order of preference by similarity to ideal solution, TOPSIS-VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR), and SWARA-VIKOR to show its effectiveness.
Gesture Recognition for American Sign Language Using Pytorch and Convolutional Neural Network

Human–computer interaction (HCI) is the most prevalent topic of active research due to the demand for machine learning and computer vision. American Sign Language (ASL) is one of the most popular languages used by deaf and dumb people in the world. The deaf and dumb people use hand gestures to communicate. Hand gestures vary from person to person in shape, size, scale, and image quality. Hence, nonlinearity exists in this problem. In the area of image processing, there has been tremendous progress made recently, and it's proven that neural networks have numerous applications in interpreting sign language. The recognition of ASL in real-time motion is employed using an efficient artificial intelligence tool, and Convolutional Neural Network (CNN) has been proposed in this work. The dataset of 27,455 images of 25 English alphabets has been used to train and validate our model. The model is tested on 7172 images which were divided into many classes. The maximum validation accuracy of the model with enhanced data was found to be 99.8% which is better than many existing methods in real-time motion.
Siam-Sort: Multi-Target Tracking in Video SAR Based on Tracking by Detection and Siamese Network

Shadows are widely used in the tracking of moving targets by video synthetic aperture radar (video SAR). However, they always appear in groups in video SAR images. In such cases, track effects produced by existing single-target tracking methods are no longer satisfactory. To this end, an effective way to obtain the capability of multiple target tracking (MTT) is in urgent demand. Note that tracking by detection (TBD) for MTT in optical images has achieved great success. However, TBD cannot be utilized in video SAR MTT directly. The reasons for this is that shadows of moving target are quite different from in video SAR image than optical images as they are time-varying and their pixel sizes are small. The aforementioned characteristics make shadows in video SAR images hard to detect in the process of TBD and lead to numerous matching errors in the data association process, which greatly affects the final tracking performance. Aiming at the above two problems, in this paper, we propose a multiple target tracking method based on TBD and the Siamese network. Specifically, to improve the detection accuracy, the multi-scale Faster-RCNN is first proposed to detect the shadows of moving targets. Meanwhile, dimension clusters are used to accelerate the convergence speed of the model in the training process as well as to obtain better network weights. Then, SiamNet is proposed for data association to reduce matching errors. Finally, we apply a Kalman filter to update the tracking results. The experimental results on two real video SAR datasets demonstrate that the proposed method outperforms other state-of-art methods, and the ablation experiment verifies the effectiveness of multi-scale Faster-RCNN and SimaNet.
Russian Science and Russian book publishing in digits and bibliometric estimates

The purpose of the study was to investigate into the flow of Russian scientific publications on the basis of various information systems and statistical data and to determine if they can be used for forecasting Russian science devеlopment, and to specify its place in the global space of scientific knowledge. The definitions for several discussable concepts are suggested. Sources and methods . The data of information systems used for bibliometrical assessment of publication flows are namely: Russian Science Citation Index (RSCI), Web of Science Core Collection (WoS CC), Scopus, and statistical data acquired by the Russian Book Chamber (RBC). The quantitative data on the number of researchers and science funding in the Russian Federation are based on the official statistics. Results . The data on the status of the publication flow in Russia were analyzed based on RBC statistics; RSCI, WoS CC and Scopus indicators are provided. The comparison enables to conclude that each system has its own tradition of integrating journals in their information flow, its own retrospection and classification system which leads to diverging estimates of Russia’s rank in different systems.
Analyzing of Political Tweets in Hindi Language Using Machine Learning and Deep Learning

Sentiment Analysis is a natural language processing task where we identify and categorize opinions based on piece of text. It is the mostly used when determining the sentiment of a text including product reviews, movies and so on. In this paper, we will first identify the text with positive, negative and neutral sentiment polarities from the dataset which obtained using tweepy. It is a library for accessing the twitter Api. The text pieces are in Hindi language and we will pre-process the data and train the dataset and apply various machine learning classifiers K-Nearest Neighbors(KNN), Decision Tree, Support Vector Machine(SVM) and also apply deep learning classifier Long short term memory model(LSTM) and get the performance of the model for the dataset.