Reviewerly/exHarmony huggingface dataset:
Multi-Media Geochemical Exploration in the Critical Zone: A Case Study over the Prairie and Wolf Zn–Pb Deposits, Capricorn Orogen, Western Australia

In this study, we compared traditional lithochemical sample media (soil) with hydrochemical (groundwater), biogeochemical (plant matter of mulga and spinifex), and other near-surface sample media (ferro-manganese crust), in a case study applied to mineral exploration in weathered terrain, through the critical zone at the fault-hosted Prairie and Wolf Zn–Pb (Ag) deposits in Western Australia. We used multi-element geochemistry analyses to spatially identify geochemical anomalies in samples over known mineralization, and investigated metal dispersion processes. In all near-surface sample media, high concentrations of the metals of interest (Zn, Pb, Ag) coincided with samples proximal to the mineralization at depth. However, the lateral dispersion of these elements differed from regional (several km; groundwater) to local (several 100′s of meters; solid sample media) scales. Zinc in spinifex leaves over the Prairie and Wolf deposits exceeded the total concentrations in all other sample media, while the metal concentrations in mulga phyllodes were not as pronounced, except for Ag, which exceeded the concentrations in all other sample media. These observations indicate potential preferential metal-specific uptake by different media. Pathfinder elements in vegetation and groundwater samples also indicated the Prairie Downs fault zone at the regional (groundwater) and local (vegetation) scale, and are, therefore, potentially useful tools to trace fault systems that host structurally controlled, hydrothermal Zn–Pb mineralization.
Discovering the Ethereum2 P2P Network

Achieving the equilibrium between scalability, sustainability, and security while keeping decentralization has prevailed as the target solution for decentralized blockchain applications over the last years.Several approaches have been proposed by multiple blockchain teams to achieve it, Ethereum being among them.Ethereum is on the path of a major protocol improvement called Ethereum 2.0 (Eth2), implementing Sharding and introducing the Proof-of-Stake (PoS).As the change of consensus mechanism is a delicate matter, this improvement will be achieved through different phases, the first of which is the implementation of the Beacon Chain.As Ethereum1, Eth2 relies on a decentralized peer-to-peer (p2p) network for the message distribution.Up to date, we estimate that there are around 5.000 nodes in the Eth2 main net geographically distributed.However, the topology of this one still prevails unknown.In this paper, we present the results obtained from the analysis we performed on the Eth2 p2p network.Describing the topology of the network, as possible hazards that this one implies.
A Privacy Preserving Framework for Health Records using Blockchain

Electronic Health Records (EHR) is the electronic form of storing a patient's medical history. EHR contains patient’s data such as progress notes, medications, prescriptions, vital signs, scan reports and laboratory data. Transferring EHR over the internet improves the quality of health care and reduces medical costs. However, in the traditional system, the EHR are stored across different decentralised hospitals, making data sharing difficult and increasing the risk of patient privacy. A privacy-preserving framework for electronic health records using blockchain technology is implemented to address these issues. The patient has complete control over the EHR, and the patient can share their health records with doctors of various medical institutions. The privacy and security of the patient’s EHR are guaranteed by the verifiability and immutability property of the blockchain technology. The doctor upload the EHR, and it is encrypted using the SHA256 hashing algorithm and stored as a separate block. The patient shares the EHR with the doctor of any medical institution through the unique key shared via the doctor’s email. The doctor can access and update the EHR using the shared key. The block validation is done using Delegated Proof of Stake (DPoS) consensus algorithm, which guarantees the privacy of the patient’s data. The proposed system based on the DPoS algorithm has considerabe reduction in resource utilisation, computational capacity, time, and cost for EHR transactions.
No-reference stereoscopic image quality evaluator based on human visual characteristics and relative gradient orientation

Stereoscopic image quality assessment (SIQA) is of great significance to the development of modern three-dimensional (3D) display technology. In this work, by further mining the relationship between visual features and stereoscopic image quality perception, we build a new no-reference SIQA model, which combines the monocular and binocular features. Statistical quality-aware structural features from relative gradient orientation (RGO) map and texture features from the histogram of the weighted local binary pattern (LBP) in the texture image (TLBP) are not only extracted from both monocular view, but also extracted from binocular views to predict binocular quality perception. Meanwhile, the color statistical features ignored by most models and the binocularity feature is extracted to complement the monocular features and the above binocular features, respectively. Finally, all the extracted features and subjective scores are used to predict the objective quality score through the support vector regression (SVR) model. Experiments on four popular stereoscopic image databases show that the proposed model achieves high consistency with subjective assessment, and the performance of the model is very competitive with the latest models.
Application of Deep Learning Model Based on Big Data in Semantic Sentiment Analysis

In the era of big data, in the research of sentiment classification, due to the difficulty and complexity of text feature extraction, traditional learning methods have been gradually replaced by deep learning. Deep learning is an improvement of traditional neural networks, with the continuous enhancement of computer hardware resources and the expansion of data scale. Deep learning has a powerful automatic feature extraction function, and has been widely used in various fields such as speech recognition, machine translation, image recognition, and human-computer interaction. The purpose of this article is to study the application of deep learning models based on big data in semantic sentiment analysis. This article mainly solves the problem of comprehension of natural language text. Natural language is prone to ambiguity due to its complex form, and it is constantly developing and changing, which brings a lot of difficulties to the use of computer programs to solve this type of problem. At the same time, due to the particularity of Chinese, there are many difficulties such as the establishment of text vectors and Chinese word segmentation. This article first introduces the recognition of related emotion words, mainly grammar construction and recognition methods, then studies the semantic emotion analysis technology, and finally realizes the preprocessing operation of the semantic emotion analysis. Experimental research results show that: it is obvious that the best performance is W2V-Att-CNN, which is around 0.87, followed by W2V-CNN, which is about 0.85, and then W2V-SVW, which is around 0.81, and finally CBOW-SVM, it is about 0.77. Through the results of four sets of comparative experiments, it can be obtained that the W2V-Att-CNN model has better accuracy than other models.
Experimental Investigation of Sufficient Criteria for Relations to Have Kernels

We investigate four well-known criteria for the existence of kernels in directed graphs/relations which can be tested efficiently, viz. to be irreflexive and symmetric, to be progressively finite, to be bipartite and to satisfy Richardson's criterion. The numerical data, obtained by the evaluation of relation-algebraic problem specifications using RelView show that even the most general of them is very far away from a characterisation of the class of directed graphs/relations having kernels.
Towards Norm Classification: An Initial Analysis of HIPAA Breaches

Regulatory policies, like the US Health Insurance Portability and Accountability Act (HIPAA), impose the social norms mediated by software-intensive systems. Breaches, modeled as norm violations, can help elicit security and privacy requirements to prevent future system failures. This paper reports our initial analysis of 38 HIPAA breaches with the objective of classifying them into the different norm types: commitments, authorizations, or prohibitions. The results show only limited distinguishing power of textual features, and reveal the fundamental interchangeability of commitments and prohibitions.
Development of Hybrid Algorithm Using Moth-Flame and Particle Swarm Optimization

This paper focuses on the development of a hybrid algorithm using moth-flame and particle swarm optimization. The main aim of the proposed algorithm is to improve the conventional moth-flame algorithm's performance for obtaining the faster convergence speed and best global optimal solution. Also, the proposed algorithm is to enhance the performance of particle swarm on getting global searchability. The proposed hybrid optimization algorithm's performance is evaluated on solving the well-known benchmark functions. The results show that the proposed hybrid optimization performed better than conventional moth-flame and particle swarm algorithms to obtain the best optimal solution with faster convergence.
The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance Data

Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization (i.e., graphical representation of quantitative information) in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed (excluding geographical maps and flowcharts). The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar ( n = 54; 36.0%) and line charts ( n = 42; 28.1%) were preferred visualization types. Visualization problems comprised color scheme mismatches, double y -axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve color use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards. Conclusion: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship toward more effective visualizations that improve data insights, knowledge, and clinical decision-making.
Reading Pointer Meter Through One Stage End-to-End Deep Regression

The recognition of analog pointer meters under nature environment is commonly a challenge task due to many influences like types of meters, shooting angle, lighting condition, etc. Most existing recognition algorithms consist of multiple steps including the detection and extraction of dial, scale marks and pointers followed by reading calculation, which is complex and sensitive to image quality. To address this issue, a one-stage, end-to-end recognition method for pointer meter based on deep regression is proposed in this paper. The proposed method simultaneously locates the position of the end point of a pointer, obtains a meter reading and determines whether the pointer exceeds the normal range through a fully convolutional neural network. Without complicated image pre-processing and post-processing, the algorithm can read multiple meters in one image just through a simple one-round forward inference. Experimental results show that the recognition accuracy achieves 92.59% under ±5% reading error, and the processing speed reaches approximately 25 FPS on a NVIDIA GTX 1080 GPU.
Steganography Based Approach to Image Authentication

The paper is focused on the proposed model of image authentication. The model is based on the steganography principle using a neural network, symmetric encryption and cryptographic hash functions. A key element of the software module is the Optimized Pulse-Coupled Neural Network Model (OM-PCNN). The neural network generates position matrices for embedding authentication data into the cover images with an emphasis on image entropy. In order to increase the security of the proposed solution, the neural network weights are initialized using a steganographic key and, in addition, the authentication data is encrypted by the AES-256 algorithm. Image integrity is tested using SHA-2 hash function with 512-bit hash.
Development of Anatomy Learning System based on Augmented Reality

Computer vision is a multidisciplinary field that allows the digital world to interact with the real world by performing a detailed image analysis so that it can predict the visual input like human brain. It has massively impacted every single domain from retail to agriculture, sports to education and military to medical field. It is the foundation for many evolving technologies. One of such promising technology is Augmented Reality (AR). AR has capability to merge digitally generated images and texts with actual environment. AR technologies have helped us in various sectors and can be used to enhance learning by facilitating one to completely comprehend a theoretical concept by representing a three-dimensional visualization through an intuitive model. In contrast to this human anatomical structures are usually difficult to learn because of constraint to envision those structures from 2D image into 3D. So, this paper presents a methodology which is involved in developing an anatomy learning framework using AR. The system generates a 3D model of human skeletal system on the 2D image using image processing, pattern recognition and feature detection algorithms. The accuracy of the framework in processing the image and rendering of model comes out to be 76.8%. The system aims to help by visualizing the complex anatomical structures more easily and improving our perception of feeling natural environments in new and enhanced forms.
Synthetic Vision in Virtual Reality Documentaries

Based on a nuanced understanding of immersion and sense of presence (SoP) as two key aesthetic effects that the application of virtual reality (VR) to cinema is believed to innovate, this paper develops the concept of synthetic vision as fundamental to understanding the visual experience of VR media, particularly VR documentaries. The concept contends that viewers’ experience in VR is based on two visions that seemingly contradict each other: first, a disembodied vision that transports them to a simulated world, and second, an embodied vision guaranteed by the freedom to control kinesthetic movement and direction of gaze. This serves to advance the idea that immersion and SoP are not unified but rather multifaceted concepts premised on a nuanced understanding of the varying relationships between the technological system of VR, its media content, and its user. For the concept of synthetic vision points to the paradoxical coexistence of viewers’ presence in the virtual world and their structural absence from the world that lays the groundwork for their immersive experience. By classifying three generic categories of contemporary VR documentaries (humanitarian and journalism documentaries, documentaries about nature, travel, and museum visits, and documentaries based on the reenactment of conscious or mnemonic realities), and by examining the aesthetic and ethical underpinnings VR brings to each of them, I argue that it hinges upon what kind of cinematic conventions and genres are remediated to determine the effective synthesis of the two visions. The varying effects of synthetic vision in the three subgenres of VR documentary stress that immersion and SoP have different political and ethical consequences of media witnessing. In the conclusion, I recapitulate multiple implications that the concept of synthetic vision has in regards to both the studies on VR and the recently flourishing investigation into cinematic VR artifacts.
Majority Logic Decoding for Certain Schubert Codes Using Lines in Schubert Varieties

In this article, we consider Schubert codes, linear codes associated to Schubert varieties, and discuss minimum weight codewords for dual Schubert codes. The notion of lines in Schubert varieties is looked closely at, and it has been proved that the supports of the minimum weight codewords of the dual Schubert codes lie on lines and any three points on a line in the Schubert variety correspond to the support of some minimum weight parity check for the Schubert code. We use these lines in Schubert varieties to construct orthogonal parity checks for certain Schubert codes and use them for majority logic decoding. In some special cases, we can correct approximately up to <inline-formula> <tex-math notation="LaTeX">$\lfloor (d-1)/2\rfloor $ </tex-math></inline-formula> many errors where <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is the minimum distance of the code.
Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks

In this paper, we present hardware accelerators created with high-level synthesis techniques for sparse and dense matrix multiplication operations. The cores can operate with different precisions and are designed to be integrated in a heterogeneous CPU-FPGA system for Edge AI applications. The methodology involves quantization-sparsity aware training and it is applied to a case study consisting of human activity classification. We initially investigate the effects of quantization and sparsity on the accuracy of neural networks with convolution, dense and recurrent layers observing better tolerance to pruning when recurrent layers are present. Then, we propose the hardware accelerators that can switch precision at run-time and work with any matrix size up to a maximum configured at compile time. We compare the performance of these accelerators at different levels of precision and sparsity levels and create a performance model to enable workload balancing. The results show that the proposed sparse matrix multipliers can outperform dense multipliers when sparsity levels are higher than 70% and the improvements are more evident when higher precision arithmetic or structural pruning is used. Additionally, sparsity levels as high as 99% can maintain the level of accuracy required in the network especially when recurrent layers are deployed. Overall, the balance between sparse and dense performance depends on matrix shape, precision, structural pruning and sparsity levels and performance modelling can be used to balance concurrent execution in a heterogeneous configuration.
Incorporating Unstructured Text in Multi-Layer Perceptron (MLP) Network: Factors Affecting Partner Selection in Pair Programming

The revealed analysis studies on pair programming so far indicate that pair programming has produced affirmative effects on some aspects of students” performance. In the academic field, the usual practice of pair programming would be pairing the students in line with the programming skills of the students by the respective lecturers. This means, compatibility of the students in terms of their programming skills is the main focus when the pairing was done by the lecturers. Yet, research on elements that the students are looking into when they are given the liberty to decide on their partner in pair programming is lacking. In this study, a multi-layer perceptron (MLP) is developed to predict the preference of opting pair programming over solo programming. The Bayesian Information Criterion was used to select the best features in the prediction. The potential of unstructured text entered by the participants as comments in the questionnaire is incorporated in the MLP model to verify its capabilities towards prediction accuracy, i.e., to verify whether their comments are connected to their preference for pair programming versus solo programming. It was found that, when the students are given the freedom to choose their partner in pair programming, in the context of Malaysia, the students would pay attention to the ethnic criterion. This also suggests that the unstructured texts in the form of comments submitted by the participants in the questionnaire did not contribute to their choices on whether to undertake solo or pair programming.
Multispectral Images Compression using PSO-based De-correlation Matrix and DWT Transform

This paper proposes a new approach of multi-spectral image compression based on the combination of the particle swarm optimization (PSO) and the discrete wavelet transforms (DWT). In the first stage, the PSO is used to reduce the redundancies in the spectral domain. In fact, the PSO transforms a given multispectral image to optimize the energy in the first band. Despite to the complexity of this kind of approach, the transformed multispectral image is easily computed by multiplying a de-correlation matrix and the input multispectral image. The de-correlation matrix is estimated via a PSO evolution derived by a defined fitness function. In the second stage, the compressed data, related to the input multispectral image, is computed from the transformed multispectral image using an efficient 2D-DWT based algorithm. In addition to this compression approach, the original multispectral image can be recovered using a decompression algorithm. Experimental results show the validity of our proposed approach. These significant results are evaluated according to Peak signal-to-noise ratio (PSNR), compression ratio (CR) and bits per pixel (bpp) metrics.
Informative and Communicative Environment for the Development of Student Creativity and Flexible Skills

This article explores the problems of developing creativity and flexible teaching skills in educational organisations. Creativity in the modern world is becoming an integral part of many processes, including education. Student creative associations linked by common interests are one of the forms of additional education. The interaction of students from different training areas mutually enriches and stimulates the development of creativity in educational organisations. On the other hand, student teamwork indirectly and directly affects the development of flexible skills, which modern employers highly value. In this regard, a study of the labour market was carried out to identify the flexible skills required for a particular profession. A model of the flexible skills of the modern specialist was developed. Also, an approach is proposed for the development of students’ flexible skills through student creative associations. As a result of the study, the communication and interaction of such structures were revealed, and a model of the information and communication environment (ICS) was proposed to solve it. This article provides a structural diagram of the ICS and also proposes criteria for its effectiveness. In addition, criticism of the proposed approach and weak points that require additional study are presented and the prospects for the development of the study.