Reviewerly/exHarmony huggingface dataset:
Establishing an integrated workflow identifying and linking surface and subsurface lineaments for mineral exploration under cover: example from the Gawler Craton, South Australia

Abstract. Mineral exploration in areas comprising thick and complex cover represents an intrinsic challenge. Cost- and time-efficient methods that help to narrow down exploration areas are therefore of particular interest to the Australian mining industry and for mineral exploration worldwide. Based on a case study around the Tarcoola gold mine in the regolith-dominated South Australian central Gawler Craton, we suggest an exploration targeting workflow based on the joint analysis of surface and subsurface lineaments. The datasets utilised in this study are a digital elevation model and radiometric data that represent surface signals and total magnetic intensity and gravity attributed to subsurface signals. We compare automatically and manually mapped lineament sets derived from remotely sensed data. In order to establish an integrated concept for exploration through cover based on the best-suited lineament data, we will point out the most striking differences between the automatically and manually detected lineaments and compare the datasets that represent surficial in contrast to subsurface structures. We further show how lineaments derived from surface and subsurface datasets can be combined to obtain targeting maps that help to narrow down areas for mineral exploration. We propose that target areas are represented by high lineament densities which are adjacent to regions comprising high density of lineament intersections.
A Quantum-Inspired Classifier for Early Web Bot Detection

This paper introduces a novel approach, inspired by the principles of Quantum Computing, to address web bot detection in terms of real-time classification of an incoming data stream of HTTP request headers, in order to ensure the shortest decision time with the highest accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each HTTP request on the preceding ones. Starting from the a-posteriori probability of each request to belong to a particular class, it is possible to assign a Qubit state representing a combination of the aforementioned probabilities for all available observations of the time series. By leveraging the underlying mathematical details of superposition and entanglement on specific subsequences, it is possible to devise a measure of membership to each class, thus enabling the system to take a reliable decision when a sufficient level of confidence is met or to continue with additional observations. The results reported in this paper objectively show the effectiveness of our quantum-inspired algorithm which outperforms other state-of-the-art approaches, including our own one based on the Sequential Probability Ratio Test.
RT-LIFE: Portable RISC-V Interface for Real-Time Lightweight Security Enforcement

With the ever-expanding attack surface of low-cost processors in IoT applications, the interest in lightweight hardware support for improving their security is growing. While industry has already adopted mostly static low-overhead mitigation approaches against code-injection attacks, the race against code-reuse attacks is not yet over. One commonly proposed measure against code-reuse attacks aims to enforce run-time-dynamic integrity. In contrast to runtime-dynamic remote attestation, which is limited by its periodic attestation interval (possibly hours or weeks), runtime-dynamic integrity enforcement performs runtime-integrity checking in parallel to the actual execution. This allows very short attack response times, ideally stopping all evil instructions in flight from actually taking effect. To guarantee a prevention-in-time, one requirement is a low latency trace of uncommitted instructions. This typically would require a deep and core-specific integration. As an abstraction layer, we present our highly portable Real-Time Lightweight Integrity enForcement intErface (RT-LIFE), which is optimized to provide the core’s state (uncommitted instructions) to an arbitrary runtime-dynamic low-latency Security Enforcement Unit (SecEU) as early as possible, while minimizing the interface’s area and clock frequency penalties. We demonstrate RT-LIFE for six very different RISC-V cores together with our initial control-flow-integrity-enforcing SecEU DExIE, discuss the hardware architecture and its timing in detail, and finally provide an open-source release of RT-LIFE.
Dog Skin Disease Recognition Using Image Segmentation and GPU Enhanced Convolutional Neural Network

Some, if not all, veterinary clinics do not have a record of skin diseases of dogs when they diagnosed them; this is due to lack of manpower over the number of different kinds of animal patient that they cater per day. This also causes some delays in diagnosing other non-visually diseases that other patients might have. Having a system that can be used in pre-examination for visually available infections such as dog’s skin disease and automatically records this diagnosis, can give an advantage to veterinary clinics. This helps the clinics prepare for the kind of patients that they will tend in certain time of the year. Developing a system that can be used in identifying common dog skin diseases for the pre-examination purpose and creating a dashboard that generates a numerical result can be used as an advantage for the veterinary clinics. These can be achieved by using image processing techniques for the prediction model and Convolutional Neural Network (CNN). However, using a common CNN approach, where the main core uses the Central Processing Unit (CPU), tends to train the model longer. To overcome this problem, the use Graphics Processing Unit (GPU) is implemented to enhance the speed of training the model for the system. Having this kind of system really helps the veterinary clinic for their daily work, but this can still be improved by using other approaches to the trained model without ignoring the efficiency and accuracy of the algorithm that is being used.
A Machine Learning based Reversible Data Hiding Scheme in Encrypted Images using Fibonacci Transform

Technological advancements and digitalization have made the life of humankind simple but at the same time imposing many challenges. As information started bursting across the internet, information management and security became major concerns. Recently, researchers have been focusing on a hot topic called reversible data hiding (RDH). RHD secures the data by covering it within another medium. It allows the recovery of the medium and hidden information on the receiver side without any loss. This work discloses a high capacity RDH scheme in the encrypted image with a Fibonacci transform image scrambling algorithm for data hiding and a convolutional neural network (CNN) based recovery. It follows a block-wise embedding process, embedding (n + 1) bits within a block of size 2<sup>n</sup> while n &#x003E; 1. The proposed scheme is tested on the USC-SIPI image data set from the University of Southern California and has resulted in an improved embedding rate compared to the existing Arnold transform-based RDH and many other well-acknowledged RDH schemes.
RectiNet-v2: A stacked network architecture for document image dewarping

With the advent of mobile and hand-held cameras, document images have found their way into almost every domain. Dewarping of these images for the removal of perspective distortions and folds is essential so that they can be understood by document recognition algorithms. For this, we propose an end-to-end CNN architecture that can produce distortion-free document images from warped documents it takes as input. We train this model on warped document images simulated synthetically to compensate for the lack of enough natural data. Our method is novel in the use of a bifurcated decoder with shared weights to prevent intermingling of grid coordinates, in the use of residual networks in the U-Net skip connections to allow the flow of data from different receptive fields in the model, and in the use of a gated network to help the model focus on structure and line-level detail of the document image. We evaluate our method on the DocUNet dataset, a benchmark in this domain, and obtain results comparable to state-of-the-art methods.
Application of Artificial Intelligence and Virtual Reality Technology in the Construction of University Physical Education

The progress of artificial intelligence and virtual reality technology has provided convenience to all sectors of the society. With the support of the core algorithm of artificial intelligence, technicians can develop, design and optimize artificial intelligence systems so that they can have intelligence similar to the human brain and simulate human intelligent behavior. Artificial intelligence can perform model independent data processing through feature learning, or use feature engineering for human data processing. Virtual reality technology uses a computer to generate a simulation environment, which is an interactive three-dimensional dynamic visual scene and entity behavior system simulation that integrates multiple information, immersing users in it. Based on this, the paper conducts research and discussion from the following aspects: firstly, the relevant concepts of artificial intelligence and virtual reality are reviewed; secondly, the application points of artificial intelligence and virtual reality in universities are discussed, including the application of various technologies, the content and the equipment selection in the application of artificial intelligence and virtual reality.
A paper quality and comment consistency detection model based on feature dimensionality reduction

As a reflection of the scholar's mastery of basic theories and professional knowledge, the dissertation is an important yardstick for measuring the level of scientific research. At present, the problem of academic misconduct is becoming increasingly prominent, which is not only related to personal academic ethics, but also related to the overall development of the national academic and scientific research field. In the traditional method of evaluating the quality of papers, it is mainly based on the evaluation experts' comments and scores. However, there are cases that the evaluation experts' comments and scores are inconsistent in practice. To address this problem, we proposed a paper quality consistency detection model based on the nearest neighbor analysis dimensionality reduction algorithm. Compared with other traditional models, the experimental results show that the detection accuracy of XGBoost model after dimensionality reduction using nearest neighbor analysis algorithm reaches 85.81%.
Visual Attention Analysis Of Pathologists Examining Whole Slide Images Of Prostate Cancer

We study the attention of pathologists as they examine whole-slide images (WSIs) of prostate cancer tissue using a digital microscope. To the best of our knowledge, our study is the first to report in detail how pathologists navigate WSIs of prostate cancer as they accumulate information for their diagnoses. We collected slide navigation data (i.e., viewport location, magnification level, and time) from 13 pathologists in 2 groups (5 genitourinary (GU) specialists and 8 general pathologists) and generated visual attention heatmaps and scanpaths. Each pathologist examined five WSIs from the TCGA PRAD dataset, which were selected by a GU pathology specialist. We examined and analyzed the distributions of visual attention for each group of pathologists after each WSI was examined. To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist. We used these annotations to compute the overlap between the distribution of visual attention and annotated tumor region to identify strong correlations. Motivated by this analysis, we trained a deep learning model to predict visual attention on unseen WSIs. We find that the attention heatmaps predicted by our model correlate quite well with the ground truth attention heatmap and tumor annotations on a test set of 17 WSIs by using various spatial and temporal evaluation metrics.
A comparative study on IoT-aided smart grids using blockchain platform

Nowadays, blockchain, which is a revolutionary technology, is very much popular in business and enterprises such as energy trading, monitoring, maintenance, and organizational context. Most of them use this technology for its secure, cost-effective, and transparent features such as tamper-proof data structure, abolition of a third party, decentralized data management, data tracking, data integrity, authentication, authorization, transparent data exchange, etc. IoT-aided smart grids suffer from many vulnerabilities such as hacking and malicious attacks on grids, attacks on authenticity and authorization, insecure payment schemes, and no compliance monitoring. Blockchain technology solves some vulnerabilities, i.e., authentication can be done by public-private encryption with key access, which restricts the anonymous entry of intruders. All the transactions are recorded in an immutable format and decentralization makes the data exchanges transparent for all parties who are involved in this platform. This entire platform itself acts as a validator, which performs the transaction compliance monitoring and thus abolishes the third-party intervention and makes the system cost-efficient. Blockchain technology solves insecure payment vulnerabilities by creating wallets. In the case of energy trading, this technology performs a vital role to deploy the smart contracts, a negotiable software package code module to the underlying architecture, which controls the user access, validation for impure contracts, and thus provides the automation, transactional as well as contractual security. The main contribution of this work lies in a comparative study of IoT-aided smart grid management, control, and security, energy trading, payment schemes. This study shall help an end user to learn about how blockchain technology is the best fit for different use cases of IoT-aided smart grids.
Augmented Dual-Shuffle-based Moving Target Defense to Ensure CIA-triad in Federated Learning

In today&#x0027;s &#x201C;Internet of Everything (IoE)&#x201D; era, the collaboration from massive participants significantly boosts the performance and efficiency of model training. This trend also un-avoidably stirs up considerable concerns about multi-dimensional security problems. Under the circumstances, federated learning (FL) is enthusiastically adopted, as it protects privacy to a certain extent by only processing personal data locally. Nevertheless, FL&#x0027;s characteristics of concealment also pave the way for sev-eral emerging attacks during the training process, i.e., model inversion, poisoning, and backdoor. Currently, although partially mitigating attack effects, existing countermeasures against those threats are studied separately and orthogonal. This separation makes those defense methods mutually exclusive and restrictive in real-world application scenarios, far from satisfying. In this paper, we extensively model different attack paradigms into three types based on CIA-triad, the well-known information security primitive, and propose a novel dual-shuffle method to thwart aforementioned threats jointly. Concretely speaking, our primary model shuffling mechanism provides the confidentiality guarantee based on the information-theoretic notion of identifiability; then, an augmented client shuffling mechanism purges the user group of adversaries proactively without any compromise of anonymous constraints. By conducting a series of experiments on bench-mark datasets, we demonstrate that our method could achieve significant security and convergence performance against three state-of-the-art attacks.
Feature Selection Using PSO Optimized-Framework with Machine Learning Classification System via Breast Cancer Survival Data

Breast cancer is a well-known malignant tumor in women and a major contributor to rising female mortality rates. Premature efforts to assess breast cancer syndromes aided in the decision-making process about the movements that occur in critically ill patients who have achieved risk factor reduction. The breast cancer information rate from the UCI machine learning repository was used in this investigation. Machine learning classification and performance parameters such as sensitivity, accuracy, and specificity coefficient are used to improve the diagnostic model’s performance. Machine learning is a fantastic platform for addressing breast cancer issues such as early diagnosis and prognosis. The information science boom has exploded in recent years, including a wide range of industries. ML models have already received considerably immoderate accuracy in medical difficulties associated to the disease, according to the findings of various studies. This study focuses on a predictive model for determining whether or not someone have an illness. The major goal of this research is to assess the accuracy of various machine learning classifiers, such as logistic regression (LR), Naive Bayes (N-Bayes), decision tree learning (DTL), and K-nearest neighbor (KNN). As feature selection, these techniques are built into PSO optimization (FS). The study’s principal goal is to establish a benchmark models based on statistical results and performance metrics on the dataset.
RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.
FPGA-Type Configurable Coprocessor Implementation Scheme of Recurrent Neural Network for Solving Time-Varying QP Problems

Many scientific and engineering applications can be formulated as a time-varying quadratic programming (TVQP) problem, and effectively solving it is an attractive issue. In order to solve the TVQP problem with multiple constraints effectively, a penalty-strategy varying-gain recurrent neural network (PSVG-RNN) combined is proposed, and is implemented with a field-programmable gate array (FPGA) and packaged into a configurable coprocessor. Comparative experiments verify that the coprocessor has at least an order of magnitude better performance than traditional Euler iterative method and Ode45 method embedded in Matlab implemented in digital computer. Experimental results show that the proposed PSVG-RNN only needs 382 lookup table random-access memories (LUTRAMs), 25583 lookup tables (LUTs) and 9549 flip-flops (FFs) of the Xilinx ZCU102 evaluation board.
A geometric view of closure phases in interferometry

Closure phase is the phase of a closed-loop product of correlations in a $\ge 3$-element interferometer array. Its invariance to element-based phase corruption makes it invaluable for interferometric applications that otherwise require high-accuracy phase calibration. However, its understanding has remained mainly mathematical and limited to the aperture plane (Fourier dual of image plane). Here, we lay the foundations for a geometrical insight. we show that closure phase and its invariance to element-based corruption and to translation are intricately related to the conserved properties (shape, orientation, and size, or SOS) of the principal triangle enclosed by the three fringes formed by a closed triad of array elements, which is referred herein as the "SOS conservation principle". When element-based amplitude calibration is not needed, as is typical in optical interferometry, the 3-element interference image formed from phase-uncalibrated correlations is a true and uncorrupted representation of the source object's morphology, except for a possible shift. Based on this SOS conservation principle, we present two geometric methods to measure the closure phase directly from a 3-element interference image (without requiring an aperture-plane view): (i) the closure phase is directly measurable from any one of the triangle's heights, and (ii) the squared closure phase is proportional to the product of the areas enclosed by the triad of array elements and the principal triangle in the aperture and image planes, respectively. We validate this geometric understanding across a wide range range of interferometric conditions using data from the Very Large Array and the Event Horizon Telescope. This geometric insight can be potentially valuable to other interferometric applications such as optical interferometry. These geometric relationships are generalised for an $N$-element interferometer.
Zero-Shot Normalization Driven Multi-Speaker Text to Speech Synthesis

Text-to-speech (TTS) systems are designed to synthesize natural and expressive speech, adapt to an unseen voice, and capture the speaking style of an unseen speaker by converting text into speech. The introduction of an unseen speaker’s speaking style into a TTS system offers a wide range of application scenarios, including personal assistant, news broadcast, and audio navigation, among others. The style of the speech varies from person to person and every person exhibits his or her style of speaking that is determined by the language, demography, culture and other factors. Style is best captured by the prosody of a signal. It is an ongoing research area with numerous real-world applications that produces high-quality multi-speaker voice synthesis while taking into account prosody and in a zero-shot manner. Despite the fact that several efforts have been made in this area, it continues to be an interesting and difficult topic to solve. In this paper, we present a novel zero-shot multi-speaker speech synthesis approach (ZSM-SS) that leverages the normalization architecture and speaker encoder with non-autoregressive multi-head attention driven encoder-decoder architecture. Given an input text and a reference speech sample of an unseen person, ZSM-SS can generate speech in that person’s style in a zero-shot manner. Additionally, we demonstrate how the affine parameters of normalization help in capturing the prosodic features such as energy and fundamental frequency in a disentangled fashion and can be used to generate morphed speech output. We generate the 256 dimensional speaker embedding using a speaker encoder based on wav2vec2.0 based architecture. We demonstrate the efficacy of our proposed architecture on multi-speaker VCTK [1] and LibriTTS [2] datasets, using visualization of hessian of proposed model, multiple quantitative metrics that measure generated speech distortion and MOS, along with speaker embedding analysis of the proposed speaker encoder model.