Reviewerly/exHarmony huggingface dataset:
Deep learning-based methods for anomaly detection in video surveillance: a review

Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.
Health Care Internet of Things (IOT) During Pandemic –A Review

With so many health problems prevalent in today's world, maintaining personal health should be of great importance. While the number of patients is large, the number of doctors is relatively small. As a result, diagnosis is delayed and some patients are neglected. This increases the patient's confidence in the doctor for regular checkups. With all these issues in mind, healthcare systems are starting to connect to the IoT to maintain a digital identity for each patient. Many health problems are undiagnosed in the healthcare sector due to a shortage of doctors/caregivers and lack of access to healthcare. On the other hand, these IoT-based medical systems allow patients and doctors to continuously monitor and easily analyze patient data. This article provides an overview of the role of the Internet of Things in healthcare related to pandemic disease COVID-19.
External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence

With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings.To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population.In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammography Challenge, was used to predict the cancer status of an individual using a standard set of screening mammography images. This study was conducted using retrospective patient data collected between 2010 and 2020 from women aged 40 years and older who underwent a routine breast screening examination and participated in the Athena Breast Health Network at the University of California, Los Angeles (UCLA).Performance of the challenge ensemble method (CEM) and the CEM combined with radiologist assessment (CEM+R) were compared with diagnosed ductal carcinoma in situ and invasive cancers within a year of the screening examination using performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).Evaluated on 37 317 examinations from 26 817 women (mean [SD] age, 58.4 [11.5] years), individual model AUROC estimates ranged from 0.77 (95% CI, 0.75-0.79) to 0.83 (95% CI, 0.81-0.85). The CEM model achieved an AUROC of 0.85 (95% CI, 0.84-0.87) in the UCLA cohort, lower than the performance achieved in the Kaiser Permanente Washington (AUROC, 0.90) and Karolinska Institute (AUROC, 0.92) cohorts. The CEM+R model achieved a sensitivity (0.813 [95% CI, 0.781-0.843] vs 0.826 [95% CI, 0.795-0.856]; P = .20) and specificity (0.925 [95% CI, 0.916-0.934] vs 0.930 [95% CI, 0.929-0.932]; P = .18) similar to the radiologist performance. The CEM+R model had significantly lower sensitivity (0.596 [95% CI, 0.466-0.717] vs 0.850 [95% CI, 0.766-0.923]; P < .001) and specificity (0.803 [95% CI, 0.734-0.861] vs 0.945 [95% CI, 0.936-0.954]; P < .001) than the radiologist in women with a prior history of breast cancer and Hispanic women (0.894 [95% CI, 0.873-0.910] vs 0.926 [95% CI, 0.919-0.933]; P = .004).This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.
Targeted Universal Adversarial Examples for Remote Sensing

Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specific goals, i.e., targeted attacks. To this end, we propose two variants of universal adversarial examples called targeted universal adversarial examples and source-targeted universal adversarial examples. Extensive experiments on three popular datasets showed strong attackability of the two targeted adversarial variants. We hope such strong attacks can inspire and motivate research on the defenses against adversarial examples in remote sensing.
Two Approaches to the Traffic Quality Intuitionistic Fuzzy Estimation of Service Compositions

Recently, intuitionistic fuzzy pairs have been used as uncertainty estimations of the request services in service systems. In the present paper, three intuitionistic fuzzy characterizations of virtual service devices are specified: intuitionistic fuzzy traffic estimation, intuitionistic fuzzy flow estimation and intuitionistic fuzzy estimation about probability. Discussed herein are two approaches to the intuitionistic fuzzy estimation of the uncertainty of compositions of services. One of the approaches is based on the definitions of the intuitionistic fuzzy pairs for one service device. The other approach is based on the aggregation operators over intuitionistic fuzzy pairs. A total of six intuitionistic fuzzy estimations of the uncertainty of comprise service device are proposed. The proposed uncertainty estimations allow for the definition of new Quality of Service (QoS) indicators and can be used to determine the quality of service compositions across a wide range of service systems.
Universal qudit gate synthesis for transmons

Gate-based quantum computers typically encode and process information in two-dimensional units called qubits. Using $d$-dimensional qudits instead may offer intrinsic advantages, including more efficient circuit synthesis, problem-tailored encodings and embedded error correction. In this work, we design a superconducting qudit-based quantum processor wherein the logical space of transmon qubits is extended to higher-excited levels. We propose a universal gate set featuring a two-qudit cross-resonance entangling gate, for which we predict fidelities beyond $99\%$ in the $d=4$ case of ququarts with realistic experimental parameters. Furthermore, we present a decomposition routine that compiles general qudit unitaries into these elementary gates, requiring fewer entangling gates than qubit alternatives. As proof-of-concept applications, we numerically demonstrate the synthesis of ${\rm SU}(16)$ gates for noisy quantum hardware and an embedded error correction sequence that encodes a qubit memory in a transmon ququart to protect against pure dephasing noise. We conclude that universal qudit control -- a valuable extension to the operational toolbox of superconducting quantum information processing -- is within reach of current transmon-based architectures and has applications to near-term and long-term hardware.
High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.
A qualitative exploration of student perspectives of chatbot use during emergency remote teaching

In this study, a chatbot was developed using Dialogflow Messenger to support learning during face-to-face class suspension due to COVID-19. The pandemic has changed how students and teachers engage with course content and use technologies. By working with the chatbot, language for specific purposes students received guidance and support to complete homework and assignments during the face-to-face class suspension. Twenty-two (22) students participated in this interpretive qualitative study, and data were collected using focus group interviews and analysed thematically. The findings indicate that students found the pedagogical chatbot supportive by providing human-like interactions, which enhanced their sense of engagement. Participants also indicated that their interactions with the chatbot eased their sense of isolation, which had a positive impact on their learning. The findings provide insight and enrich existing knowledge for integrating chatbots into teaching and they discuss the advantages of learning future chatbot design for language acquisition.
The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games

Game tree search in games with large branching factors is a notoriously hard problem. In this paper, we address this problem with a new sampling strategy for Monte Carlo Tree Search (MCTS) algorithms, called "Naive Sampling", based on a variant of the Multi-armed Bandit problem called the "Combinatorial Multi-armed Bandit" (CMAB) problem. We present a new MCTS algorithm based on Naive Sampling called NaiveMCTS, and evaluate it in the context of real-time strategy (RTS) games. Our results show that as the branching factor grows, NaiveMCTS performs significantly better than other algorithms.
Deep Generative Inpainting with Comparative Sample Augmentation

Recent advances in deep learning techniques such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels. While more effective than conventional approaches, deep learning models require large datasets and computational resources for training, and inpainting quality varies considerably when training data differs in size and diversity. To address these problems, we present an inpainting strategy called Comparative Sample Augmentation, which enhances the quality of the training set by filtering irrelevant images and constructing additional images using information about the surrounding regions of the target image. Experiments on multiple datasets demonstrate that our method extends the applicability of deep inpainting models to training sets with varying levels of diversity, while enhancing the inpainting quality as measured by qualitative and quantitative metrics for a large class of deep models, with little need for model-specific consideration.
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

Less than 35% of recyclable waste is being actually recycled in the US [2], which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/
Requirements Engineering for Machine Learning: A Review and Reflection

Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low within-slice resolution. Improving the inter-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the inter-slice resolution. Considering that the groundtruth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing between-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$CT$</tex> dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins.
Multiview Robust Adversarial Stickers for Arbitrary Objects in the Physical World

Among different adversarial attacks on deep learning models for image classification, physical attacks have been considered easier to implement without assuming access to victims’ devices. In this paper, we propose a practical new pipeline for launching multiview robust physical-world attacks, by creating printable adversarial stickers for arbitrary objects. In particular, a 3D model is used to estimate the camera pose in the photo. Then, by perturbing a part of the 3D model’s texture, rendering it, and overlaying the perturbation onto the physical images, realistic training images can be obtained for training a robust adversarial sticker. Experiments with our pipeline show that highly effective adversarial stickers can be generated for many different objects of different sizes and shapes while also achieving a higher attack success rate than attacks that do not utilize camera pose estimation and 3D models. In addition, by using different backgrounds in training and adding randomness to training images, the created stickers continue to function in varied environments. Attacks also remain robust in black-box tests.
Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting

Accurate photovoltaic power forecasting can provide a basis for low-carbon economic dispatch of power systems with a high proportion of renewable energy. Regression networks with many times training based on multi-group multi-configuration still cannot resist the randomness of training processes, resulting in the accuracy of photovoltaic power prediction needs to be improved. This work proposes a weighted fully-connected regression network, including a feature input layer, deep fully-connected layers, particle swarm optimization, and a regression output layer. The proposed model automatically selects two networks from multi-group multi-configuration well-trained regression networks to effectively reduce photovoltaic power prediction errors without additional sensors and data sources. The errors of these two chosen well-trained networks exactly neutralize each other by fixed and simple weights. The results under the one-day-ahead hourly photovoltaic power forecasting of Natal of Brazil show that the proposed method can reduce photovoltaic power prediction errors with at least 75.9954% smaller mean absolute error than the state-of-art methods and 68.2937% than other 18 famous convolutional neural networks methods.
Physical ZKP for Makaro Using a Standard Deck of Cards

Makaro is a logic puzzle with an objective to fill numbers into a rectangular grid to satisfy certain conditions. In 2018, Bultel et al. developed a physical zero-knowledge proof (ZKP) protocol for Makaro using a deck of cards, which allows a prover to physically convince a verifier that he/she knows a solution of the puzzle without revealing it. However, their protocol requires several identical copies of some cards, making it impractical as a deck of playing cards found in everyday life typically consists of all different cards. In this paper, we propose a new ZKP protocol for Makaro that can be implemented using a standard deck (a deck consisting of all different cards). Our protocol also uses asymptotically less cards than the protocol of Bultel et al. Most importantly, we develop a general method to encode a number with a sequence of all different cards. This allows us to securely compute several numerical functions using a standard deck, such as verifying that two given numbers are different and verifying that a number is the largest one among the given numbers.
Trends in emergency higher education digital transformation during the COVID-19 pandemic

The article reveals trends in emergency higher education digital transformation during the COVID-19 pandemic. Due to the enormous amount of literature discussing global higher education responses to the Coronavirus, the preference is given to publications with a systematic literature review published in 2021. The period indicates the end of the 2-nd wave of the pandemic and the lessons learnt. However, case studies were also the focus of attention, primarily used for a convincing illustration of the trends in a particular country. Moreover, descriptive survey research allows empirical evidence of 468 students and 179 lecturers from four Ukrainian universities and highlights the theoretical insights. The analysis of cross-cultural investigations, systematic literature reviews, case studies, and empirical data from four Ukrainian HEIs makes it possible to receive experience from 30 countries about their responses to the COVID-19 pandemic in 2020-2021. This massive amount of evidence reveals universal consequences for higher education caused by the COVID-19 pandemic and shapes trends in emergency higher education transition to digital format. The findings show that the emergency higher education transition to digitally-based education during 2020-2021 worldwide is characterised as diverse and unprepared. In the case of Ukraine, the empirical data collected from four national HEIs situated in different regions of the country gives grounds for the following insights: 1) in response to the challenges of the COVID-19 pandemic, the national HEIs show trends similar to global higher education; 2) university teachers have considerable responsibility in transitioning traditional teaching methods into a virtual educational environment and have successfully coped with this challenge; 3) universal trends in higher education are observed on the national level, while there is diversity on the institutional level among national HEIs; 4) there is an opposite attitude of lecturers and generation Z students to the preference for the educational mode in the future.
Improved quantum algorithms for linear and nonlinear differential equations

We present substantially generalized and improved quantum algorithms over prior work for inhomogeneous linear and nonlinear ordinary differential equations (ODE). Specifically, we show how the norm of the matrix exponential characterizes the run time of quantum algorithms for linear ODEs opening the door to an application to a wider class of linear and nonlinear ODEs. In \cite{BCOW17}, a quantum algorithm for a certain class of linear ODEs is given, where the matrix involved needs to be diagonalizable. The quantum algorithm for linear ODEs presented here extends to many classes of non-diagonalizable matrices including singular matrices. The algorithm here is also exponentially faster than the bounds derived in \cite{BCOW17} for certain classes of diagonalizable matrices. Our linear ODE algorithm is then applied to nonlinear differential equations using Carleman linearization (an approach taken recently by us in \cite{Liue2026805118}). The improvement over that result is two-fold. First, we obtain an exponentially better dependence on error. This kind of logarithmic dependence on error has also been achieved by \cite{Xue_2021}, but only for homogeneous nonlinear equations. Second, the present algorithm can handle any sparse matrix (that models dissipation) if it has a negative log-norm (including non-diagonalizable matrices), whereas \cite{Liue2026805118} and \cite{Xue_2021} additionally require normality.