Reviewerly/exHarmony huggingface dataset:
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems

Deep neural networks (DNNs), regardless of their impressive performance, are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the decision of the model. We expose the existence of an intriguing class of spatially bounded, physically realizable, adversarial examples— Universal NaTuralistic adversarial paTches—we call TnTs, by exploring the super set of the spatially bounded adversarial example space and the natural input space within generative adversarial networks. Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective—achieving high attack success rates, and universal. A TnT is universal because any input image captured with a TnT in the scene will: i) misguide a network (untargeted attack); or ii) force the network to make a malicious decision (targeted attack). Interestingly, now, an adversarial patch attacker has the potential to exert a greater level of control—the ability to choose a location independent, natural-looking patch as a trigger in contrast to being constrained to noisy perturbations—an ability is thus far shown to be only possible with Trojan attack methods needing to interfere with the model building processes to embed a backdoor at the risk discovery; but, still realize a patch deployable in the physical world. Through extensive experiments on the large-scale visual classification task, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ImageNet</monospace> with evaluations across its entire validation set of 50,000 images, we demonstrate the realistic threat from TnTs and the robustness of the attack. We show a generalization of the attack to create patches achieving higher attack success rates than existing state-of-the-art methods. Our results show the generalizability of the attack to different visual classification tasks ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIFAR-10</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GTSRB</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PubFig</monospace> ) and multiple state-of-the-art deep neural networks such as WideResnet50, Inception-V3 and VGG-16.
Consumers’ perception on artificial intelligence applications in marketing communication

Purpose This study aims to examine consumers’ perception of artificial intelligence (AI) and AI marketing communication. Design/methodology/approach Twenty in-depth interviews were conducted to collect data and phenomenological reduction was used to analyze data. Findings Findings suggest that consumers’ interpretation of AI is multidimensional and relational with a focus on functionality and emotion, as well as comparison and contrast between AI and human beings; consumers’ perception of voice-assisted AI centers on the aspects of function, communication, adaptation, relationship and privacy; consumers consider AI marketing communication to be unavoidable and generally acceptable; and consumers believe that AI marketing communication to be limited in its effect on influencing their evaluation of products/brands or shaping their consumptive behaviors. Originality/value According to the authors' knowledge, this study is the first research project to gauge consumers' perception on AI and AI marketing communication.
An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets.
CENTRE OF ISOTOPIC AND GEOCHEMICAL RESEARCH (IGC SB RAS): CURRENT STATE OF MICRO- AND MACROANALYSIS

Centre of Isotopic and Geochemical Research based on the Analytical Department of Vinogradov Institute of Geochemistry SB RAS (Irkutsk) performs a wide range of analytical studies to solve mineralogical and petrological, geochemical, prospecting, ecological, paleoclimatic and applied problems. The studies are supported by the modern equipment for electron microprobe, X-ray diffraction, X-ray fluorescence, atomic emission and mass spectrometric (including isotope) analyses, as well as the necessary international certified reference materials (SRM) and a collection of SRM of the natural and technogenic composition of our production.
A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day.Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices.The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market.When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial.A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s).In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global.The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals.The news articles divided into the mentioned classes were imbalanced.Imbalance data is a big difficulty with most classifier learning algorithms.However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries.A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach.We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges.The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques.From the result, it's clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy.From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE.Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier.
Quantum Private Set Intersection Cardinality Protocol With Application to Privacy-Preserving Condition Query

Private Set Intersection Cardinality (PSI-CA) is one of the most concerned issues with the protection of privacy, in which two parties jointly compute the intersection cardinality without revealing their respective private sets. There are important applications of PSI-CA in real society, e.g., strongly privacy-preserving data statistics in contact tracing for health authorities to fight the outbreaks of highly contagious diseases. In this paper, we present a novel quantum PSI-CA protocol, in which we adopt oblivious quantum key distribution, secure quantum summation and quantum counting algorithm. The proposed PSI-CA protocol not only ensures the approximatively perfect security but also achieves the linear communication complexity, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O(N)}$ </tex-math></inline-formula> . Furthermore, we define a new privacy protection problem, i.e., Privacy-preserving Condition Query (PCQ), and provide an efficient solution to the PCQ problem based on the proposed quantum PSI-CA protocol. Finally, we verify the correctness and the feasibility of the proposed quantum PSI-CA protocol by circuit simulations in IBM Qiskit.
Predictive models for clinical decision making: Deep dives in practical machine learning

Abstract The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. Continual interdisciplinary developments in data science, biostatistics and epidemiology also motivated us to further discuss emerging topics in predictive and data‐driven (hypothesis‐less) analytics with machine learning. Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision‐making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine.
Interactive Robotic Plastering: Augmented Interactive Design and Fabrication for On-site Robotic Plastering

This paper presents Interactive Robotic Plastering (IRoP), a system enabling designers and skilled workers to engage intuitively with an in-situ robotic plastering process. The research combines three elements: interactive design tools, an augmented reality interface, and a robotic spraying system. Plastering is a complex process relying on tacit knowledge and craftsmanship, making it difficult to simulate and automate. However, our system utilizes a controller-based interaction system to enable diverse users to interactively create articulated plasterwork in-situ. A customizable computational toolset converts human intentions into robotic motions while respecting robotic and material constraints. To accomplish this, we developed both an interactive computational model to translate the data from a motion-tracking system into robotic trajectories using design and editing tools as well as an audio-visual guidance system for in-situ projection. We then conducted two user-studies of designers and skilled workers who used IRoP to design and fabricate a full-scale demonstrator.
Learning Optical Flow with Adaptive Graph Reasoning

Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph. The proposed AGFlow can effectively exploit the context information and incorporate it within the matching procedure, producing more robust and accurate results. On both Sintel clean and final passes, our AGFlow achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming state-of-the-art approaches by 11.2% and 13.6%, respectively. Code is publicly available at https://github.com/megvii-research/AGFlow.
Augmented Reality (AR) Application in Manufacturing Encompassing Quality Control and Maintenance

Augmented Reality (AR) systems present a technological platform that allows a harmonious integration of the real and virtual worlds, structured with endless capabilities in its application. With the rise of information technology, AR has made a significant contribution to industrial applications, namely: medicine, aviation, manufacturing, etc. This paper aims to provide dipper insightful on AR technology by investigating the current state-of-the-art of AR technology usage in the manufacturing industry encompassing quality control and maintenance. It describes how AR technology has been integrated into quality control and maintenance, current and available AR devices in use, and future research direction.
Image Classification using a Hybrid LSTM-CNN Deep Neural Network

This work elaborates on the integration of the rudimentary Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM), resulting in a new paradigm in the well-explored field of image classification. LSTM is one kind of Recurrent Neural Network (RNN) which has the potential to memorize long-term dependencies. It was observed that LSTMs are able to complement the feature extraction ability of CNN when used in a layered order. LSTMs have the capacity to selectively remember patterns for a long duration of time and CNNs are able to extract the important features out of it. This LSTM-CNN layered structure, when used for image classification, has an edge over conventional CNN classifier. The model which has been proposed is based on the sets of Artificial Neural Network like Recurrent and Convolutional neural network; hence this model is robust and suitable to a wide spectrum of classification tasks. To validate these results, we have tested our model on two standard datasets. The results have been compared with other classifiers to establish the significance of our proposed model.
Group-split attention network for crowd counting

Crowd counting is a considerable yet challenging task in intelligent video surveillance and urban security systems. The performance has been significantly boosted along with the springing up of the convolutional neural networks (CNNs). However, accurate and efficient crowd counting in congested scenes remains under-explored due to scale variation and cluttered background. To address these problems, we propose a biologically inspired crowd counting method named group-split attention network (GSANet). The GSANet consists of three principal modules, namely GS module, dual-aware attention module, and aggregation module. The GS module processes the subfeatures of each group in parallel, and groups the input feature map to reduce the computational cost. The dual-aware attention module synergies the spatial and channel dimensional information to alleviate the estimation error in background regions. The aggregation module adopts a learning-based cross-group strategy to aggregate and facilitate the fusion of feature maps along different channel dimensions. Extensive experimental results on five benchmark crowd datasets demonstrate that the GSANet achieves superior performances in terms of accuracy and efficiency.
Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model

Since Professor L.A. Zadeh published “Fuzzy Set Theory” in the 1960s, the theory of fuzzy mathematics has been formally established and developed and has been gradually introduced into work in all walks of life. At the same time, fuzzy mathematics theory has also been widely used in VR industry selection. In the stock strategy, the advantages of improving unit classification accuracy, screening high-quality stocks, and constructing near-perfect investment portfolios continue to emerge. On the other hand, with the increasing maturity and continuous development of China’s computer and Internet technologies, the VR industry has gained a new round of development space, and its own investment value and the investable space between related industries have been gradually tapped. Different from the analysis of quantitative stock selection by constructing a logistics multifactor stock selection model in the existing research, the research mainly adopts the random forest algorithm based on fuzzy mathematics to construct the initial investment strategy portfolio. Secondly, different from the single effective frontier algorithm, the research is based on the random forest algorithm, calculates the average AUC of the index, and continuously checks and tests the results to obtain the optimal investment portfolio. Finally, select appropriate risk indicators and performance indicators to evaluate the performance of the strategy portfolio. The study concludes that the portfolios selected by the random forest model are highly investable and have good stability.
Toward just and equitable micro-credentials: an Australian perspective

Abstract The current historic COVID-19 Pandemic moment has thrown into sharp relief the need for flexible and rigorous higher education that meets upskilling and reskilling needs of global workforces. Discussions of micro-credentialing predate the Pandemic but have received increased focus as potentially assisting in addressing perceived skills gaps. However, not all commentators have been complimentary about the possibilities inherent in micro-credentialing. In this paper we discuss Ralston (Postdigital Science and Education 3:83–101, 2021) criticism of the “microcredentialing craze” as provocation to consider how equitable, thoughtful and just educative aims may be met. We address Ralston’s argument that micro-credentials present an educative “moral hazard” by arguing that micro-credentialing will allow universities to respond quickly to changing worker educational needs rather than only offering full degrees that may not be economically viable or personally desirable for individuals. Rather, we suggest, the potential of micro-credentials lies in their pathways and potential to enhance lifelong learning and suggest that micro-credentials do not stand outside of the pedagogical ethical imperative that learning experiences should be positive and inclusive.
Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.
Improving Automatic Speech Recognition Performance for Low-Resource Languages With Self-Supervised Models

Speech self-supervised learning has attracted much attention due to its promising performance in multiple downstream tasks, and has become a new growth engine for speech recognition in low-resource languages. In this paper, we exploit and analyze a series of wav2vec pre-trained models for speech recognition in 15 low-resource languages in the OpenASR21 Challenge. The investigation covers two important variables during pre-training, three fine-tuning methods, as well as applications in End-to-End and hybrid systems. First, pre-trained models with different pre-training audio data and architectures (wav2vec2.0, HuBERT and WavLM) are explored for their speech recognition performance in low-resource languages. Second, we investigate data utilization, multilingual learning, and the use of a phoneme-level recognition task in fine-tuning. Furthermore, we explore what effect fine-tuning has on the similarity of representations extracted from different transformer layers. The similarity analyses cover different pre-trained architectures and fine-tuning languages. We apply pre-trained representations to End-to-End and hybrid systems to confirm our representation analyses, which have obtained better performances as well.