Reviewerly/exHarmony huggingface dataset:
Comparison of Flutter and React Native Platforms

Mobile application development processes have become very important with the increasing use of mobile devices.Android and IOS operating systems, which are the most preferred mobile operating systems, have different development tools and software languages.Developing and testing and debugging the same application separately for each platform with development tools can lead to time loss and high maintenance costs.Cross-platform software that provides testing and development for different operating systems at the same time offer an alternative structure to these processes.Cross-platform software accelerates software processes and provides advantages by reducing application development costs.The comparison of Flutter and React Native, which are cross-platform development software will be presented in this study.Artan mobil cihaz kullanımı ile birlikte mobil uygulamaları geliştirme süreçleri de oldukça önemli hale gelmiştir.Mobil işletim sistemlerinden en çok tercih edilen Android ve IOS işletim sistemleri farklı geliştirme araçları ve yazılım dillerine sahiptir.Geliştirme araçları ile her platform için ayrı olarak, aynı uygulamayı geliştirmek ve test ederek yayınlamak zaman kaybına ve yüksek bakım maliyetlerine
Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents?

In the context of depth- k pooling for constructing web search test collections, we compare two approaches to ordering pooled documents for relevance assessors: The prioritisation strategy (PRI) used widely at NTCIR, and the simple randomisation strategy (RND). In order to address research questions regarding PRI and RND, we have constructed and released the WWW3E8 dataset, which contains eight independent relevance labels for 32,375 topic-document pairs, i.e., a total of 259,000 labels. Four of the eight relevance labels were obtained from PRI-based pools; the other four were obtained from RND-based pools. Using WWW3E8, we compare PRI and RND in terms of inter-assessor agreement, system ranking agreement, and robustness to new systems that did not contribute to the pools. We also utilise an assessor activity log we obtained as a byproduct of WWW3E8 to compare the two strategies in terms of assessment efficiency. Our main findings are: (a) The presentation order has no substantial impact on assessment efficiency; (b) While the presentation order substantially affects which documents are judged (highly) relevant, the difference between the inter-assessor agreement under the PRI condition and that under the RND condition is of no practical significance; (c) Different system rankings under the PRI condition are substantially more similar to one another than those under the RND condition; and (d) PRI-based relevance assessment files (qrels) are substantially and statistically significantly more robust to new systems than RND-based ones. Finding (d) suggests that PRI helps the assessors identify relevant documents that affect the evaluation of many existing systems, including those that did not contribute to the pools. Hence, if researchers need to evaluate their current IR systems using legacy IR test collections, we recommend the use of those constructed using the PRI approach unless they have a good reason to believe that their systems retrieve relevant documents that are vastly different from the pooled documents. While this robustness of PRI may also mean that the PRI-based pools are biased against future systems that retrieve highly novel relevant documents, one should note that there is no evidence that RND is any better in this respect.
Review of Different Error Metrics: A Case of Solar Forecasting

Renewable energy systems (RES) are no longer confined to being used as a stand-alone entity in the modern era. These RES, especially solar panels are also used with the grid power systems to supply electricity. However, precise forecasting of solar irradiance is necessary to ensure that the grid operates in a balanced and planned manner. Various solar forecasting models (SFM) are presented in the literature to produce an accurate solar forecast. Nevertheless, each model has gone through the step of evaluation of its accuracy using some error measures. Many error measures are discussed in the literature for deterministic as well as probabilistic solar forecasting. But, each study has its own selected error measure which sometimes landed on a wrong interpretation of results if not selected appropriately. As a result, this paper offers a critical assessment of several common error metrics with the goal of discussing alternative error metrics and establishing a viable set of error metrics for deterministic and probabilistic solar forecasting. Based on highly cited research from the last three years (2019-2021), error measures for both types of forecasting are presented with their basic functionalities, advantages &amp; limitations which equipped the reader to pick the required compatible metrics
Modelling Selectional Super-Flexibility

The selectional flexibility of some attitude verbs (e.g. know, realize, report) between declarative and interrogative complements has been the subject of much recent work in formal semantics. However, little attention has been paid to verbs (e.g. see, remember, observe) that embed an even wider variety of complements (incl. subject-controlled gerundive small clauses and concrete object-denoting DPs). Since the familiar types of some of these complements resist an embedding in the type for questions [= sets of propositions], these verbs challenge Theiler, Roelofsen &amp; Aloni’s (2018) uniform interpretation strategy for the complements of responsive verbs. My paper answers this challenge by uniformly interpreting the different complements of selectionally super-flexible verbs like remember in a generalized type for questions, viz. as parametrized centered questions. It shows that the resulting semantics captures the intuitive entailment pattern of these verbs.
Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

&lt;p&gt;&lt;a&gt;&lt;/a&gt;&lt;a&gt;&lt;/a&gt;&lt;a&gt;For a ground moving target indication (GMTI) radar, the presence of &lt;/a&gt;&lt;a&gt;&lt;/a&gt;&lt;a&gt;&lt;/a&gt;&lt;a&gt;&lt;/a&gt;&lt;a&gt;Doppler blind zone (DBZ)&lt;/a&gt; results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.&lt;/p&gt; &lt;p&gt; &lt;/p&gt;
A heuristic approach for multiple instance learning by linear separation

Abstract We present a fast heuristic approach for solving a binary multiple instance learning (MIL) problem, which consists in discriminating between two kinds of item sets: the sets are called bags and the items inside them are called instances. Assuming that only two classes of instances are allowed, a common standard hypothesis states that a bag is positive if it contains at least a positive instance and it is negative when all its instances are negative. Our approach constructs a MIL separating hyperplane by preliminary fixing the normal and reducing the learning phase to a univariate nonsmooth optimization problem, which can be quickly solved by simply exploring the kink points. Numerical results are presented on a set of test problems drawn from the literature.
There's no business like drone business [drone light shows]

The display seen across London on the New Year&apos;s Eve used 300 drones to "paint" the sky with tributes to NHS staff and notable figures from 2020. A turtle was displayed which was accompanied with a voice message from Sir David Attenborough about saving our planet. Drone light shows are now an essential part of any high-profile outdoor event. So how do they work? Designing drones shows is a new discipline that requires new tools. So how does an artist put metaphorical pen to paper, when the canvas is several hundred spots of light that can be arbitrarily arranged in the sky? For both SkyMagic&apos;s software and Dobrovolskiy&apos;s Drone Show Software , the process begins in Blender. This is an open-source modelling package that is used across the visual effects industry to make 3D models for everything from video games to Hollywood movies. The artist can use Blender to do the creative work of designing the animation, dragging, and dropping the on-screen drones to where in the sky they need to be, in 3D space. And they can play back the animation at will, tweaking it to make sure everything is where it needs to be.
The geometry of Hermitian self-orthogonal codes

Abstract We prove that if $$n &gt;k^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>&gt;</mml:mo> <mml:msup> <mml:mi>k</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> then a k -dimensional linear code of length n over $${\mathbb F}_{q^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> has a truncation which is linearly equivalent to a Hermitian self-orthogonal linear code. In the contrary case we prove that truncations of linear codes to codes equivalent to Hermitian self-orthogonal linear codes occur when the columns of a generator matrix of the code do not impose independent conditions on the space of Hermitian forms. In the case that there are more than n common zeros to the set of Hermitian forms which are zero on the columns of a generator matrix of the code, the additional zeros give the extension of the code to a code that has a truncation which is equivalent to a Hermitian self-orthogonal code.
An Emitter Localization Method Based on Multiple Differential Doppler Measurements

The localization of radio frequency (RF) emitting sources is a significant topic in civil and military applications, including communications and radar systems. The most notorious methods used for RF emitter localization are based on angle of arrival (AOA), time difference of arrival (TDOA), received signal strength (RSS), as well as hybrid methods. Nevertheless, differential Doppler measurements can be used to locate emitters using a technique known as frequency difference of arrival (FDOA). The choice of an FDOA-based method must take into account the characteristics of the scenario of interest, including sensor limitations and system response time. This work presents fundamental concepts about FDOA and details the geometrical method based on polar coordinates. It also presents an "on-the-fly" approach to estimate an emitter location based on multiple FDOA measurements. Simulations were carried out in two scenarios: acoustic and electromagnetic. In the electromagnetic scenario, the performance of the proposed method was also compared to the maximum likelihood estimator.
An Innovative Video Quality Assessment Method and An Impairment Video Dataset

Impairments in video adversely affect the performance of computer vision applications. This paper presents a comprehensive impairment video dataset and proposes an innovative video quality assessment (VQA) method to evaluate the video impairment levels. First, a dataset of 300 short videos (see www.cdvl.org) is developed with representative impairments: 19 types of individual impairments, and 35 types of combined impairments (see www.iaitusa.com for the specific types of impairments). Second, an innovative no-reference (iNR) metric vector is presented to assess the video impairment levels. In particular, the iNR metric vector is defined by five impairment scores: the blur impairment score, the small noise patches impairment score, the whole frame illumination impairment score, the partial frame illumination impairment score, and the temporal impairment score. The iNR metric vector thus is not only able to define a novel failure rate (FR) for each video to characterize the nature of the leading impairment, but it can also quantify the impact caused by other impairments in the same video.
On the acceptance of “fake” histopathology: A study on frozen sections optimized with deep learning

The fast acquisition process of frozen sections allows surgeons to wait for histological findings during the interventions to base intrasurgical decisions on the outcome of the histology. Compared with paraffin sections, however, the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep neural networks are capable of modifying specific characteristics of digital histological images. Particularly, generative adversarial networks proved to be effective tools to learn about translation between two modalities, based on two unconnected data sets only. The positive effects of such deep learning-based image optimization on computer-aided diagnosis have already been shown. However, since fully automated diagnosis is controversial, the application of enhanced images for visual clinical assessment is currently probably of even higher relevance.Three different deep learning-based generative adversarial networks were investigated. The methods were used to translate frozen sections into virtual paraffin sections. Overall, 40 frozen sections were processed. For training, 40 further paraffin sections were available. We investigated how pathologists assess the quality of the different image translation approaches and whether experts are able to distinguish between virtual and real digital pathology.Pathologists' detection accuracy of virtual paraffin sections (from pairs consisting of a frozen and a paraffin section) was between 0.62 and 0.97. Overall, in 59% of images, the virtual section was assessed as more appropriate for a diagnosis. In 53% of images, the deep learning approach was preferred to conventional stain normalization (SN).Overall, expert assessment indicated slightly improved visual properties of converted images and a high similarity to real paraffin sections. The observed high variability showed clear differences in personal preferences.
SPECTRA: Sparse Structured Text Rationalization

Selective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences).Commonly, rationales are modeled as stochastic binary masks, requiring samplingbased gradient estimators, which complicates training and requires careful hyperparameter tuning.Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize the rationale extraction (e.g., to control the sparsity of a text highlight or the number of alignments).In this paper, we present a unified framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer.Our approach greatly eases training and rationale regularization, generally outperforming previous work on what comes to performance and plausibility of the extracted rationales.We further provide a comparative study of stochastic and deterministic methods for rationale extraction for classification and natural language inference tasks, jointly assessing their predictive power, quality of the explanations, and model variability.
Machine Learning Based Fall Detector With a Sensorized Tip

Fall detection has become an area of interest in recent years, as quick response to these events is critical to reduce the morbidity and mortality rate.In order to ensure proper fall detection, several technologies have been developed, including vision system, environmental detection systems, and wearable sensor based systems.However, in elderly or impaired people, it has been shown that the implementation of sensors in Assistive Devices for Walking, such as crutches or canes, can also be a promising alternative.In this work, a Support Vector Machine (SVM) based Fall Detection system is proposed, which uses the data provided by a Sensorized Tip which can be attached to different Assistive Devices for Walking (ADW).Unlike other approaches, the developed one is able to differentiate the fall of the ADW from the fall of the user.For that purpose, the developed Fall Detector uses two modules connected in series.The first one detects all falls, while the second differentiates between user and ADW falls.The proposed approach is validated in a set of experimental tests carried out by healthy volunteers that have simulated different falls.In addition, a comparative analysis is carried out by comparing the performance of the Sensorized Tip based Fall Detector and a state-of-the-art commercial accelerometer system.Results demonstrate that the proposed approach provides high Fall Detection Ratios (over 90%), similar or higher to wearable-sensor based approaches.
Ray Tracing-Guided Design of Plenoptic Cameras

The design of a plenoptic camera requires the combination of two dissimilar optical systems, namely a main lens and an array of microlenses. And while the construction process of a conventional camera is mainly concerned with focusing the image onto a single plane, in the case of plenoptic cameras there can be additional requirements such as a predefined depth of field or a desired range of disparities in neighboring microlens images. Due to this complexity, the manual creation of multiple plenoptic camera setups is often a time-consuming task. In this work we assume a simulation framework as well as the main lens data given and present a method to calculate the remaining aperture, sensor and microlens array parameters under different sets of constraints. Our ray tracing-based approach is shown to result in models outperforming their pendants generated with the commonly used paraxial approximations in terms of image quality, while still meeting the desired constraints. Both the implementation and evaluation setup including 30 plenoptic camera designs are made publicly available.
On the Stance of Macroprudential Policy

In this report we outline how a formulating normative measure of macroprudential policy stance requires a framework containing objectives, tools and transmission mechanisms. To complement the currently prevailing narrative approach, we apply lessons from the monetary policy to macroprudential policy. We begin with by proposing that the ultimate objective of macroprudential policy is to minimise the frequency and severity of economic losses arising from severe financial distress and then integrate the concept of growth-at-risk into the framework. Implementation of our framework for the evaluation of the macroprudential policy stance faces a series of challenges, including availability of the appropriate data, that policymakers generally have multiple objectives and tools, and the uncertain responses of economic agents to macroprudential policy actions.
FSOCO: The Formula Student Objects in Context Dataset

This paper presents the FSOCO dataset, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions.It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks.The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access ensuring continuous growth.By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality.The effectiveness of the approach is shown by comparing prediction results of a network trained on FSOCO and its unregulated predecessor.The FSOCO dataset can be found at fsoco-dataset.com.
A DAG-based Secure Cooperative Task Offloading Scheme in Vehicular Networks

Cooperative task offloading is a promising solution to improve computation efficiency and reduce task delay in vehicular networks. However, trust risk among vehicles leads to security and privacy issues in the cooperative offloading process. In this article, we propose a Direct Acyclic Graph (DAG)-based secure cooperative task offloading framework, which provides secure and efficient computation services for the vehicular networks. The DAG-based blockchain is extended with a priority and reliability-biased random walk (PR-BRW) consensus algorithm to ensure the latency requirements of tasks in different emergency degrees. Moreover, to prevent the potential malicious behaviors of vehicles involved in offloading, we develop a vehicle credit assessment mechanism that can adaptively adjust the consensus opportunities according to vehicle credit values. Next, we formulate the cooperative task offloading problems to minimize the overall offloading delay by jointly optimizing offloading decision, data segment size, and DAG confirmation threshold. Furthermore, we analyze the PR-BRW consensus process to obtain the DAG confirmation time, and develop a Particle Swarm Optimization (PSO)-based delay-optimal cooperative offloading (PDCO) scheme to achieve the optimal solution. Simulation results demonstrate the feasibility of the proposed secure cooperative task offloading scheme.
A Prototype of Doctor Recommendation System using Classification Algorithms

Doctor recommender system is a very useful and emerging area in data science and health informatics. In recent time many approaches and applications have been proposed in this direction. This paper presents a simple prototype of recommending a doctor using classification based models. In the proposed approach patients’ information and preferences are selected as input to train the model and using classification algorithms a suitable doctor is recommended. In this work probability-based classifier and decision tree-based classifiers are used for recommending a doctor and a comparative study is also presented between them. It is found that ensemble machine learning based classifier Random Forest, performs better than C4.5 and Naïve Bayes classifier.