Reviewerly/exHarmony huggingface dataset:
How to cheat with metrics in single-image HDR reconstruction

Single-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improvement over existing methods by reporting higher image quality scores. This paper, however, highlights that such improvements in objective metrics do not necessarily translate to visually superior images. The first problem is the use of disparate evaluation conditions in terms of data and metric parameters, calling for a standardized protocol to make it possible to compare between papers. The second problem, which forms the main focus of this paper, is the inherent difficulty in evaluating SI-HDR reconstructions since certain aspects of the reconstruction problem dominate objective differences, thereby introducing a bias. Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics. Surprisingly, we found that methods that do not even reconstruct HDR information can compete with state-of-the-art deep learning methods. We show how such results are not representative of the perceived quality and that SI-HDR reconstruction needs better evaluation protocols.
Multi-mode twin-beam states in the mesoscopic intensity domain

Parametric down conversion is a quantum nonlinear process of paramount importance in the Quantum Optics context since it produces entangled states of light. Such states, usually called twin-beam (TWB) states, exhibit perfect correlations in many degrees of freedom, among which the number of photons. In this review paper, we focus on their generation and characterization in the rather unusual mesoscopic intensity regime. The use of photon-number-resolving detectors allows the experimental reconstruction of the statistical properties of TWB states and the demonstration of their nonclassical nature. This characterization is preliminary and necessary in view of the application of TWB states to the development of Quantum Technologies. In this work we focus on the robustness of mesoscopic TWB states against losses and noise sources for their exploitation in the field of Quantum Communication.
A Decade of Cryptocurrency ‘Hacks’: 2011 – 2021

We survey large-scale cryptocurrency thefts over the past decade, estimating the total value of stolen cryptocurrencies. We find that depending on the timing of when the cryptocurrency was converted to fiat, the stolen amounts are valued between $5.7 billion to $78 billion, depending on how long the thief held the cryptocurrency versus converting to fiat currency. We also document that as the cryptocurrency market develops, the incidence of large-scale cryptocurrency thefts has also increased in both number and stolen values in recent years. Large-scale thefts can be attributed to security breaches accounting for 15 of the 24 thefts, human errors accounting for 4, and agency problems arising from insider thefts accounting for the remaining 5.
A proof system for disjoint parallel quantum programs

In this paper, we define the operational and denotational semantics of a special class of parallel quantum programs, namely disjoint parallel quantum programs. Based on them, a proof system for reasoning about disjoint parallel quantum programs is developed, which is (relatively) complete even when entanglement between different processes appears in the preconditions and postconditions.
Who Tracks the Trackers?

Apple's Find My protocol allows lost devices, such as AirTags, to relay their location to their owners via a network of over a billion active Apple devices. This convenient feature for device owners may also be a tool for malicious actors to cheaply and effectively track unknowing targets. Apple has introduced a featured known as "item safety alerts'' to prevent AirTags from being used this way. We demonstrate that it is possible to create a custom device, with similar features to an AirTag in terms of cost, size, and battery life, which can participate in and be tracked by Apple's Find My network while not triggering any item safety alerts. This implies that Apple's protection mechanism is insufficient. We suggest natural mitigations for two of our malicious tracker techniques but note that the third would require substantially altering the Find My protocol to defend against.
Data-sharing practices in publications funded by the Canadian Institutes of Health Research: a descriptive analysis

As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada's data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers' data-sharing practices to best practices for research data management and sharing.We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse.Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse.Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable.
Asymptotically Optimal Path Planning for Ground Surveillance by a Team of UAVs

This letter addressesa path planning problem for a team of mobile robots, such as unmanned aerial vehicles, patrolling a ground region for surveillance. An effective navigation algorithm is developed and proved to be asymptotically optimal in the sense that the ratio of the revisit period of the algorithm and the minimum possible revisit period converges to one as the area of the region tends to infinity. Illustrative examples and comparisons with an existing method show the efficiency of the developed approach.
BERT-SMAP: Paying attention to Essential Terms in passage ranking beyond BERT

Passage ranking has attracted considerable attention due to its importance in information retrieval (IR) and question answering (QA). Prior works have shown that pre-trained language models (e.g. BERT) can improve ranking performance. However, these simple BERT-based methods tend to focus on passage terms that exactly match the question, which makes them easily fooled by the overlapping but irrelevant (distracting) passages. To solve this problem, we propose a self-matching attention-pooling mechanism (SMAP) to highlight the Essential Terms in the question-passage pairs. Further, we propose a hybrid passage ranking architecture, called BERT-SMAP, which combines SMAP with BERT to more effectively identify distracting passages and downplay their influence. BERT-SMAP uses the representations obtained through SMAP to enhance BERT's classification mechanism as an interaction-focused neural ranker, and as the inputs of a matching function. Experimental results on three evaluation datasets show that our model outperforms the previous best BERTbase-based approaches, and is comparable to the state-of-the-art method that utilizes a much stronger pre-trained language model.
Does Digitalization Lead to Better Transparency: Bibliometric Approach

In recent years, digitalization has been an important factor in developing political, economic, social, and cultural relations. The rapid growth of digital technologies in global and national contexts leads to significant quantitative and qualitative changes in the economic development of countries. Digitalization leads to better transparency and is also related to new damages to the national economy. Thus, this research deals with the identification of interconnectedness between digitalization and transparency. Fulfillment of this task is realized with the VOSviewer bibliometric tool based on the analysis of 320 Scopus articles on “transparency” and “digitalization” in 1999-2021. Analysis of the dynamic of publications on transparency and digitalization allows concluding that there were only several publications a year in 1999-2009. In 2010-2018 the number of relevant publications demonstrated considerable growth, while in 2019-2021, there was a boost of scientific interest to the cohesion between transparency and digitalization. The bibliometric analysis allows pointing out that there is a strong relationship between transparency and digitalization. Basically, under network analysis, six clusters of research were identified, which focused on the implementation of digital technologies in business, logistics, public governance, the financial sphere, information management, and personal privacy. Moreover, considering visualization results, it might be concluded that more recent publications on transparency and digitalization are focused on the research of blockchain technologies, supply technologies, and artificial intelligence. The density visualization showed that the most relevant keywords in publications on transparency and digitalization are digitalization, blockchain, and industry 4.0. These bibliometric results might create a theoretical background on clarification of scale up to which development of digital sphere does not damage national security.
K-means – Laplacian clustering revisited

Nowadays, clustering the real-life data using attribute information alone is not enough because the data come from various resources and may have data object relations. Thus, it enforces to include the relation information of the data while clustering. Knowing its importance, many researchers have used it along with attribute information. The Integrated K-means Laplacian (IKL) algorithm is one such type that integrates the attribute and pair-wise relations to cluster the data. It is well known for its way of clustering the data. However, it has issues in the creation of the normalized Laplacian matrix. The current study proposes three different ways of creating the normalized Laplacian matrix to rectify those issues. Based on these modifications, three new variants of the IKL algorithm are produced. Besides, the pair-wise similarity matrix (W) is another crucial element in the IKL algorithm. Earlier, the Gaussian function was used to create W in IKL, whereas this study proposes 12 different kernel functions to form W instead. Their influences on the existing and proposed algorithms’ performance are studied. Nine benchmark datasets are used to demonstrate the same. Further, the performances of proposed algorithms are compared with existing algorithms in recent literature by using the seven clustering evaluation metrics and running time of algorithms. The comparison studies reveal that the proposed modifications to the IKL algorithm are significant, and the statistical tests prove the same. Besides, an analysis is carried out by replacing the XX’ matrix with kernel functions, and the improvements in the performances are studied.
A hybrid model for efficient cervical cell classification

Abnormal cell recognition from cervical pap cytology is crucial for early cervical cancer screening and treatment. However, the current manual analysis requires the undivided attention of the pathologists and is prone to diagnostic errors. To tackle this issue, several techniques have been proposed in the last few decades. The success of an automation-based cervical screening and diagnosis system relies on accurate identification of cell types. This paper proposes an efficient automated hybrid framework for enhancing the cell classification accuracy of cervical cytology images. In this work, a Pyramid Scene Parsing Model is used for the segmentation of cell components. The integration of the feature pyramid with local and global context prior makes the model suitable for the segmentation of small cell components. Nuclear as well as cell features of segmented regions are extracted and categorized using an ensemble-based stack classifier. The performance of five different machine learning classifiers is also investigated and it was found that the CNN-based segmentation followed by ensemble-based stack classifier achieved the best performance. The model was evaluated on a publicly available data set and achieved an average accuracy and AUC of 99.7% and 0.996 for 2-class classification and 75.55% and 0.909 for 4-class classification respectively. The experiments demonstrate that the proposed framework achieves promising performance on pap stained cytology images.
Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE (10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.
Bi-Rads-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images

In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice. The employed features include the BI-RADS descriptors of shape, orientation, margin, echo pattern, and posterior features. Additionally, our approach predicts the likelihood of malignancy of the findings, which relates to the BI-RADS assessment category reported by clinicians. Experimental validation on a dataset consisting of 1,192 images indicates improved model accuracy, supported by explanations in clinical terms using the BI-RADS lexicon.
LwTE-Live

Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increases the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the bandwidth utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches, and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.
A Measurement Study on Serverless Workflow Services

Major cloud providers increasingly roll out their serverless workflow services to orchestrate serverless functions, making it possible to construct complex applications effectively. A comprehensive study is necessary to help developers understand the pros and cons, and make better choices among these serverless workflow services. However, the characteristics of these serverless workflow services have not been systematically analyzed. To fill the knowledge gap, we conduct a comprehensive measurement study on four mainstream serverless workflow services, focusing on both features and the performance. First, we review their official documentation and extract their features from six dimensions, including programming model, state management, etc. Then, we compare their performance (i.e., the execution time of functions, execution time of workflows, orchestration overhead time of workflows) under various settings considering activity complexity and data-flow complexity of workflows, as well as function complexity of serverless functions. Our findings and implications could help developers and cloud providers improve their development efficiency and user experience.
A Research Agenda for Implementing Augmented Reality in Ab Initio Pilot Training

Abstract. Augmented reality applications have the potential to change the learning experience and outcomes for the next generation of pilots. This study aimed at identifying learning issues related to ab initio flight training and exploring the potential of augmented reality in tackling them. A survey was conducted with 43 pilots (31 men, 12 women) and seven flight instructors (six men, one woman). The results show the contents of the ab initio flight course that were most difficult to learn according to the pilots’ and instructors’ experience. In addition, the participants assessed the training conditions they experienced. After familiarization with typical use cases of augmented reality, the pilots and flight instructors indicated flight training elements that could benefit from augmented reality. The results are used to specify a research agenda for developing augmented reality applications for future ab initio pilot training.
Investigation of the Impact of Augmented Reality Technology on Interactive Teaching Learning Process

Augmented Reality (AR) is growing technology that superimpose 3D images onto real world. This enhances the user’s real-world experience. This potential of AR can be utilized effectively in teaching learning for engineering graphics course.There is visualization limitation for engineering students entering in first year, and this leads them to face difficulty in understanding and developing orthographic, isometric and section view of models. AR can empower the students to visualize the actual virtual object in 3D view to match their imagination with augmented object. In this regard initially a framework of AR is conceptualized for the course of engineering graphics &amp; an AR application is developed. This paper mainly focuses on investigation &amp; impact of AR technology on interactive teaching learning process in engineering graphics. Impact of this technology is measured by student’s performance in AR interactive test. The result shows increase in student’s performance in written test by 18.52% in engineering graphics and in mental rotation test by 28.97%.
CoVShorts: News Summarization Application Based on Deep NLP Transformers for SARS-CoV-2

Amidst the grueling SARS-CoV-2 pandemic which has affected the lives of people across the world, the accelerating growth in COVID-19 related news articles is making it difficult for the general public to stay up-to-date with all the information. News articles are a crucial medium to convey coronavirus-related information across the world to the public. Short summaries of news articles can assist the public in grasping a gist of an entire article without having to read it fully. With the evolution of Deep Learning in Natural Language Processing (NLP), we exploited the power of recent advances in pre-trained and transformer-based NLP models to perform text summarization over the COVID-19 Public Media Dataset. For this, we analyzed and compared the results of BERT, GPT-2, XLNet, BART, and T5. The first three models are among the most popular extractive summarization models and the last two are abstractive summarization models. We evaluated the results of our experiments using ROUGE scores (ROUGE-2 and ROUGE-L) and found that BERT, a transformer autoencoder, outperforms the other models under consideration in SARS-CoV-2 news summarization. Thus, we leveraged BERT in our web application “CoVShorts” to summarize COVID-19 articles. Further, we visually analyzed the dataset to depict the most used words in COVID-19 news articles using Word Cloud to validate the accuracy of the summarization task. CoVShorts will serve the public by helping them in gaining brief, concise, and to-the-point summaries quickly.