Reviewerly/exHarmony huggingface dataset:
Quantum interferometric metrology with entangled photons

Quantum interferences of entangled photons have engendered tremendous intriguing phenomena that lack any counterpart in classical physics. Hitherto, owing to the salient properties of quantum optics, quantum interference has been widely studied and provides useful tools that ultimately broaden the path towards ultra-sensitive quantum metrology, ranging from sub-shot-noise quantum sensing to high-resolution optical spectroscopy. In particular, quantum interferometric metrology is an essential requisite for extracting information about the structure and dynamics of photon-sensitive biological and chemical molecules. This article reviews the theoretical and experimental progress of this quantum interferometric metrology technology along with their advanced applications. The scope of this review includes Hong–Ou–Mandel interferometry with ultrahigh timing resolution, entanglement-assisted absorption spectroscopy based on a Fourier transform, and virtual-state spectroscopy using tunable energy-time entangled photons.
Solving the multicollinearity problem to improve the stability of machine learning algorithms applied to a fully annotated breast cancer dataset

Among the different types of cancer, breast cancer is the most common cancer affecting females in Jordan. Recurrent breast cancer after treatment is a significant concern for patients and oncologists. Developing countries like Jordan suffer from a lack of quality data on computational medicine (CM). This paper discusses the design, construction, and evaluation of an extensive, fully annotated breast cancer dataset extracted from the King Hussein Cancer Center's registry database (KHCC) in Amman, Jordan. The Jordan Breast Cancer dataset (JBRCA) has 20 attributes and 7562 instances of breast cancer patients. It can be considered a valuable resource to motivate future research in CM in the country. By illustration, the study describes the problems facing the compilation of the dataset. A thorough analysis of the dataset brought up many issues that required remedies before the dataset could be used in machine learning (ML) applications. These issues included missing values and outliers, unnormalized and imbalanced data, and the multicollinearity problem between the attributes. Multicollinearity occurs when two or more independent variables are highly correlated in a regression model, which might affect its stability. This is mainly a problem because we might not differentiate between the effects of the independent variables on the dependent variable. To handle these issues, we deleted missing values and outliers, applied the min-max normalization to control the attributes' different scales, and used SMOTE to solve the highly imbalanced problem. We also used the variance inflation factor (VIF) to solve the multicollinearity problem. Domain experts from KHCC help to identify the best subset of attributes to be removed from the dataset to enhance the stability and performance of the ML algorithms. We used classification models such as logistic regression, decision tree, k-nearest neighbors, Gaussian Naive Bayes, multilayer perceptron, and a stacking classifier combining all five classifiers to evaluate the compiled dataset. The stacking classifier outperformed the other base learners based on accuracy, sensitivity, and F1-score rates.
An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research, correlation filter (CF) based trackers become popular because of their high speed and considerable accuracy. Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter. However, the related studies are insufficient. By exploring the potential of trackers in these two aspects, a novel adaptive padding correlation filter (APCF) with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework. In the tracker, three feature groups are fused by use of the weighted sum of the normalized response maps, to alleviate the risk of drift caused by the extreme change of single feature. Moreover, to improve the adaptive ability of padding for the filter training of different object shapes, the best padding is selected from the preset pool according to tracking precision over the whole video, where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames. The sequence features include three traditional features and eight newly constructed features. Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.
Economics of Open-Source Solar Photovoltaic Powered Cryptocurrency Mining

Solar photovoltaic (PV) technology offers a promising means to alleviate environmental and electricity costs challenges for cryptocurrency miners. To analyze this promise, this study investigated the feasibility of using electricity from individually optimized PV systems to power: 1) an individual Bitcoin miner, 2) a DIY intermodal shipping container holding 50 miners, and 3) a commercial mining farm container holding 408 miners. In a controlled lab environment, miners were monitored for electricity use and numerical simulations of both the PV system yield and sensitivity ranges based on the Bitcoin price, Bitcoin halving events, and miner hardware were investigated for informed financial planning. In addition, a sensitivity for geographic locations in North America, utility electric rates and PV capital costs were analyzed. The profitability and return on investment (ROI) varied by location primarily because of the geographic distribution of solar flux and utility rates. The ROI for using PV with Bitcoin mining was found to be negative for Toronto and Montreal because of low-cost electricity, while it was 8% for Calgary. In the U.S. cities evaluated, the ROIs were substantial and ranged from 34% in New York and 64% (Boulder), up to 104% (Los Angeles). Although the study is based in North America regarding energy rates, climate, and energy laws; the analysis methodology is generalizable globally and grants the average cryptocurrency business the knowledge to make an informed decision on whether to pursue this venture from a financial and environmental perspective. This study contributes to the body of knowledge in cryptocurrency mining by providing an economic means of environmental preservation by powering cryptocurrency miners with renewable solar energy.
Academic IS Risk Management using OCTAVE Allegro in Educational Institution

Today, the use of technology is a common thing that is used to support everyday life. However, this technology also carries risks that can compromise the security of information in organizations. Kalbis Institute is a private campus in the East Jakarta area that has been established since 2012. The academic information system used there includes all actors in the campus environment. This risk analysis is carried out to see and understand what risks exist in the current information system. This risk analysis will assess how likely there are threats and vulnerabilities to information systems. This study uses the OCTAVE Allegro method with the help of the OCTAVE Allegro Worksheet. The purpose of this study is to conduct a risk analysis of the academic information system at Kalbis Institute. The result of this study is to look at risk assessments and recommendations for strategies to protect information systems within organization.
An efficient approach for textual data classification using deep learning

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.
Screen Content Video Quality Assessment Model Using Hybrid Spatiotemporal Features

In this paper, a full-reference video quality assessment (VQA) model is designed for the perceptual quality assessment of the screen content videos (SCVs), called the hybrid spatiotemporal feature-based model (HSFM). The SCVs are of hybrid structure including screen and natural scenes, which are perceived by the human visual system (HVS) with different visual effects. With this consideration, the three dimensional Laplacian of Gaussian (3D-LOG) filter and three dimensional Natural Scene Statistics (3D-NSS) are exploited to extract the screen and natural spatiotemporal features, based on the reference and distorted SCV sequences separately. The similarities of these extracted features are then computed independently, followed by generating the distorted screen and natural quality scores for screen and natural scenes. After that, an adaptive screen and natural quality fusion scheme through the local video activity is developed to combine them for arriving at the final VQA score of the distorted SCV under evaluation. The experimental results on the Screen Content Video Database (SCVD) and Compressed Screen Content Video Quality (CSCVQ) databases have shown that the proposed HSFM is more in line with the perceptual quality assessment of the SCVs perceived by the HVS, compared with a variety of classic and latest IQA/VQA models.
Toward blockchain-based fog and edge computing for privacy-preserving smart cities

The rapid development of Smart Cities is aided by the convergence of information and communication technologies (ICT). Data is a key component of Smart City applications as well as a serious worry. Data is the critical factor that drives the whole development life-cycle in most Smart City use-cases, according to an exhaustive examination of several Smart City use-cases. Mishandling data, on the other hand, can have severe repercussions for programs that get incorrect data and users whose privacy may be compromised. As a result, we believe that an integrated ICT solution in Smart Cities is key to achieve the highest levels of scalability, data integrity, and secrecy within and across Smart Cities. As a result, this paper discusses a variety of modern technologies for Smart Cities and proposes our integrated architecture, which connects Blockchain technologies with modern data analytic techniques (e.g., Federated Learning) and Edge/Fog computing to address the current data privacy issues in Smart Cities. Finally, we discuss and present our proposed architectural framework in detail, taking into account an online marketing campaign and an e-Health application use-cases.
TAAL: Test-Time Augmentation for Active Learning in Medical Image Segmentation

Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing which samples are best to annotate in order to maximize the performance of the task model. While frameworks for active learning have been widely explored in the context of classification of natural images, they have been only sparsely used in medical image segmentation. The challenge resides in obtaining an uncertainty measure that reveals the best candidate data for annotation. This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation that exploits the uncertainty information offered by data transformations. Our method applies cross-augmentation consistency during training and inference to both improve model learning in a semi-supervised fashion and identify the most relevant unlabeled samples to annotate next. In addition, our consistency loss uses a modified version of the JSD to further improve model performance. By relying on data transformations rather than on external modules or simple heuristics typically used in uncertainty-based strategies, TAAL emerges as a simple, yet powerful task-agnostic semi-supervised active learning approach applicable to the medical domain. Our results on a publicly-available dataset of cardiac images show that TAAL outperforms existing baseline methods in both fully-supervised and semi-supervised settings. Our implementation is publicly available on https://github.com/melinphd/TAAL .
Design and Construction of University Book Layout Based on Text Image Preprocessing Algorithm in Education Metaverse Environment

Books have a significant role in the diffusion of cultural information since they not only carry the weight of cultural inheritance but also serve to preserve historical culture. The major goals of contemporary book layout design are to establish harmony and coherence; maintain overall coordination between the internal and external; and have distinctive, inventive, and modern aesthetic impacts in the context of the educational metacosmos. It is important to emphasize the artistic value and beauty of contemporary books using proper creative language design strategies. The discussion of contemporary book layout design therefore helps to increase understanding of contemporary book layout design. This essay briefly addresses book layout design and examines how books are constructed in colleges and universities. Based on the educational metauniverse environment, it outlines a number of issues that should be taken into account when designing the book's layout. Layout analysis and text image preparation methods are used in modelling. The experimental findings indicate that this algorithm can recognise text regions with an accuracy of 94.8% and a recognition rate of 94.5%, respectively. It is concluded that the method suggested in this work has some reference value for the advancement of university book layout design and can be applied effectively in layout analysis and text typesetting.
Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa

Computer Coded Verbal Autopsy (CCVA) algorithms are commonly used to determine the cause of death (CoD) from questionnaire responses extracted from verbal autopsies (VAs). However, they can only operate on structured data and cannot effectively harness information from unstructured VA narratives. Machine Learning (ML) algorithms have also been applied successfully in determining the CoD from VA narratives, allowing the use of auxiliary information that CCVA algorithms cannot directly utilize. However, most ML-based studies only use responses from the structured questionnaire, and the results lack generalisability and comparability across studies. We present a comparative performance evaluation of ML methods and CCVA algorithms on South African VA narratives data, using data from Agincourt Health and Demographic Surveillance Site (HDSS) with physicians' classifications as the gold standard. The data were collected from 1993 to 2015 and have 16,338 cases. The random forest and extreme gradient boosting classifiers outperformed the other classifiers on the combined dataset, attaining accuracy of 96% respectively, with significant statistical differences in algorithmic performance (p < 0.0001). All our models attained Area Under Receiver Operating Characteristics (AUROC) of greater than 0.884. The InterVA CCVA attained 83% Cause Specific Mortality Fraction accuracy and an Overall Chance-Corrected Concordance of 0.36. We demonstrate that ML models could accurately determine the cause of death from VA narratives. Additionally, through mortality trends and pattern analysis, we discovered that in the first decade of the civil registration system in South Africa, the average life expectancy was approximately 50 years. However, in the second decade, life expectancy significantly dropped, and the population was dying at a much younger average age of 40 years, mostly from the leading HIV related causes. Interestingly, in the third decade, we see a gradual improvement in life expectancy, possibly attributed to effective health intervention programmes. Through a structure and semantic analysis of narratives where experts disagree, we also demonstrate the most frequent terms of traditional healer consultations and visits. The comparative approach also makes this study a baseline that can be used for future research enforcing generalization and comparability. Future study will entail exploring deep learning models for CoD classification.
Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation

Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher’s knowledge, useful for the depth completion, we introduce local similarity-preserving knowledge distillation (LSPKD), which allows similarities between local neighbors to be transferred during the distillation. With our LSPKD, a lightweight student network is precisely guided by a heavy teacher network, regardless of the density of the ground-truth data. Experimental results demonstrate that our method is effective to reduce computational costs during both training and inference stages while achieving superior performance over other lightweight networks.
Open-VICO: An Open-Source Gazebo Toolkit for Vision-based Skeleton Tracking in Human-Robot Collaboration

Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.
STE

The task of spotting events in videos has gained high attention recently, as it helps in better video understanding, and it is a key step in automating the highlights generation. In this work, we tackle the problem of action spotting in soccer videos by introducing a simple, light-weight Spatio-Temporal Encoder (STE) based on 1D convolutions and fully connected layers. The model consists of 3 blocks: a spatial encoder that captures the spatial semantics for each frame, then the temporal encoder that fetches features across frames. The prediction block maps the learnt spatial and temporal features to the predicted action class in the window. On the SoccerNet-v2 test dataset, the STE model scored 74.09% on the loose average-mAP evaluation metric. The SoccerNet 2022 challenge for action spotting restricted the upper bound for temporal tolerance on the predicted actions. On the former loose metric, the tolerance was allowed up to 60 seconds, which was dropped to 5 seconds on the new tight metric. The STE model obtained 40.1% on the tight a-mAP for the test data. To improve the temporal precision, a modification was applied on the prediction block to predict two outputs; the class and the frame index for the event. Thus, we introduced a modified version of the model: STE-v2 that improved the tight a-mAP to reach 58.71% on the challenge split and 58.48% on the test split. The code is publicly available for reproducibility at https://github.com/amdarwish/SoccerEventSpotting/tree/main/STE-v2
A Multi-Task Learning Approach for Delayed Feedback Modeling

Conversion rate (CVR) prediction is one of the most essential tasks for digital display advertising. In industrial recommender systems, online learning is particularly favored for its capability to capture the dynamic change of data distribution, which often leads to significantly improvement of conversion rates. However, the gap between a click behavior and the corresponding conversion ranges from a few minutes to days; therefore, fresh data may not have accurate label information when they are ingested by the training algorithm, which is called the delayed feedback problem of CVR prediction. To solve this problem, previous works label the delayed positive samples as negative and correct them at their conversion time, then they optimize the expectation of actual conversion distribution via important sampling under the observed distribution. However, these methods approximate the actual feature distribution as the observed feature distribution, which may introduce additional bias to the delayed feedback modeling. In this paper, we prove the observed conversion rate is the product of the actual conversion rate and the observed non-delayed positive rate. Then we propose Multi-Task Delayed Feedback Model (MTDFM), which consists of two sub-networks: actual CVR network and NDPR (non-delayed positive rate) network. We train the actual CVR network by simultaneously optimizing the observed conversion rate and non-delayed positive rate. The proposed method does not require the observed feature distribution to remain the same as the actual distribution. Finally, experimental results on both public and industrial datasets demonstrate that the proposed method outperforms the previous state-of-the-art methods consistently.