Reviewerly/exHarmony huggingface dataset:
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search

According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval.The PRP holds when two conditions are met:[C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported with certainty.We know however that deep neural networks (DNNs) are often not well calibrated and have several sources of uncertainty, and thus [C1] and [C2] might not be satisfied by neural rankers.Given the success of neural Learning to Rank (L2R) approaches-and here, especially BERT-based approaches-we first analyze under which circumstances deterministic neural rankers are calibrated for conversational search problems.Then, motivated by our findings we use two techniques to model the uncertainty of neural rankers leading to the proposed stochastic rankers, which output a predictive distribution of relevance as opposed to point estimates.Our experimental results on the ad-hoc retrieval task of conversation response ranking 1 reveal that (i) BERTbased rankers are not robustly calibrated and that stochastic BERT-based rankers yield better calibration; and (ii) uncertainty estimation is beneficial for both risk-aware neural ranking, i.e. taking into account the uncertainty when ranking documents, and for predicting unanswerable conversational contexts. 1The source code and
New technology in Museums: AR and VR video games are coming

Museums have gone through a modernization process which has seen the adoption of new technologies in what they offer visitors. Within the framework of the new critical museology, these organizations have been transformed into places of encounter and experience, the key tools in this change being socialization and play. Gamification are now intrinsic to collections and are a way of inviting visitors to share new museum experiences through the latest technology such as AR (Augmented Reality) and VR (Virtual Reality). In this way, the museum becomes a playground and a space for creativity (Borja-Villel et al., 2014). In this research, we focus on what we consider to be an important link between the three central aspects of museum change: sociability, gamificaction and virtualization; and the growing interest in museums for videogames. Our aim is to reach a better understanding of the AR and VR video games developed for museums and how these technologies can not only motivate visitors’ interest but also improve their learning skills. Our analysis focuses of literature published between 2015 to 2018 and follows the analytic structure established by Connolly et al. (2012) with additional features related to learning experience, platforms, and the use of technologies (VR and AR). The general aim is to map the interest of the research community in the field of museum-developed video games, more specifically those that use augmented and virtual reality.
Detecting Extraneous Content in Podcasts

Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions.We present classifiers that leverage both textual and listening patterns in order to detect such content in podcast descriptions and audio transcripts.We demonstrate that our models are effective by evaluating them on the downstream task of podcast summarization and show that we can substantively improve ROUGE scores and reduce the extraneous content generated in the summaries.
Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.
Region-by-Region Registration Combining Feature-Based and Optical Flow Methods for Remote Sensing Images

While geometric registration has been studied in remote sensing community for many decades, successful cases are rare, which register images allowing for local inconsistency deformation caused by topographic relief. Toward this end, a region-by-region registration combining the feature-based and optical flow methods is proposed. The proposed framework establishes on the calculation of pixel-wise displacement and mosaic of displacement fields. Concretely, the initial displacement fields for a pair of images are calculated by the block-weighted projective model and Brox optical flow estimation, respectively in the flat- and complex-terrain regions. The abnormal displacements resulting from the sensitivity of optical flow in the land use or land cover changes, are adaptively detected and corrected by the weighted Taylor expansion. Subsequently, the displacement fields are mosaicked seamlessly for subsequent steps. Experimental results show that the proposed method outperforms comparative algorithms, achieving the highest registration accuracy qualitatively and quantitatively.
A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.
An Efficient CNN Model for Automated Digital Handwritten Digit Classification

Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits with a higher accuracy is needed to improve the limitations from past research, which mostly used deep learning approaches.Objective: Two most noteworthy limitations are low accuracy and slow computational speed. The current study is to model a Convolutional Neural Network (CNN), which is simple yet more accurate in classifying English handwritten digits for different datasets. Novelty of this paper is to explore an efficient CNN architecture that can classify digits of different datasets accurately.Methods: The author proposed five different CNN architectures for training and validation tasks with two datasets. Dataset-1 consists of 12,000 MNIST data and Dataset-2 consists of 29,400-digit data of Kaggle. The proposed CNN models extract the features first and then performs the classification tasks. For the performance optimization, the models utilized stochastic gradient descent with momentum optimizer.Results: Among the five models, one was found to be the best performer, with 99.53% and 98.93% of validation accuracy for Dataset-1 and Dataset-2 respectively. Compared to Adam and RMSProp optimizers, stochastic gradient descent with momentum yielded the highest accuracy.Conclusion: The proposed best CNN model has the simplest architecture. It provides a higher accuracy for different datasets and takes less computational time. The validation accuracy of the proposed model is also higher than those of in past works.
Automatic detection of safety helmet wearing based on head region location

In order to solve the problem of difficult and low precision in the detection of safety helmet wearing in the complex pose of construction worker, a detection method of safety helmet wearing based on pose estimation is proposed. In the pose estimation model of OpenPose, the residual network optimized feature extraction is introduced to obtain the skeletal point information of the construction worker, and then the pose of the construction worker is estimated based on the skeletal point information, three-point localization method is proposed for the front and back pose, and skin colour detection method is proposed for the side pose, and then to determine the head region. The YOLO v4 is used to detect the safety helmet region, and then the construction worker's safety helmet wearing is judged according to whether the head region intersects the safety helmet region or not. Experimental results show that the detection accuracy of the method is higher than other methods, and the adaptability to the environment is stronger.
Quantum discord for multiqubit systems

We evaluate analytically the quantum discord for a large family of multiqubit states. It is interesting to note that the quantum discord of three-qubits and five-qubits is the same, as is the quantum discord of two-qubits and six-qubits. We discover that the quantum discord of this family states can be concluded into three categories. The level surfaces of the quantum discord in the three categories is shown through images. Furthermore, we investigated the dynamic behavior of quantum discord under decoherence. For the odd partite systems, we prove the frozen phenomenon of quantum discord doesn't exist under the phase flip channel, while it can be found in the even partite systems.
Take it to the limit

To increase utilization, datacenter schedulers often overcommit resources where the sum of resources allocated to the tasks on a machine exceeds its physical capacity. Setting the right level of overcommitment is a challenging problem: low overcommitment leads to wasted resources, while high overcommitment leads to task performance degradation. In this paper, we take a first principles approach to designing and evaluating overcommit policies by asking a basic question: assuming complete knowledge of each task's future resource usage, what is the safest overcommit policy that yields the highest utilization? We call this policy the peak oracle. We then devise practical overcommit policies that mimic this peak oracle by predicting future machine resource usage. We simulate our overcommit policies using the recently-released Google cluster trace, and show that they result in higher utilization and less overcommit errors than policies based on per-task allocations. We also deploy these policies to machines inside Google's datacenters serving its internal production workload. We show that our overcommit policies increase these machines' usable CPU capacity by 10-16% compared to no overcommitment.
Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&amp;T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&amp;T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.
Quantum Microwave Radiometry with a Superconducting Qubit

The interaction of photons and coherent quantum systems can be employed to detect electromagnetic radiation with remarkable sensitivity. We introduce a quantum radiometer based on the photon-induced dephasing process of a superconducting qubit for sensing microwave radiation at the subunit photon level. Using this radiometer, we demonstrate the radiative cooling of a 1 K microwave resonator and measure its mode temperature with an uncertainty $\ensuremath{\sim}0.01\text{ }\text{ }\mathrm{K}$. We thus develop a precise tool for studying the thermodynamics of quantum microwave circuits, which provides new solutions for calibrating hybrid quantum systems and detecting candidate particles for dark matter.
Hierarchical Clustering: A Survey

There is a need to scrutinise and retrieve information from data in today's world.Clustering is an analytical technique which involves dividing data into groups of similar objects.Every group is called a cluster, and it is formed from objects that have affinities within the cluster but are significantly different to objects in other groups.The aim of this paper is to look at and compare two different types of hierarchical clustering algorithms.Partition and hierarchical clustering are the two main types of clustering techniques.Hierarchical clustering algorithm is one of the algorithms discussed here.The aforementioned algorithms are described and analysed in terms of factors such as dataset size, data set type, number of clusters formed, consistency, accuracy, and efficiency.Hierarchical clustering is a cluster analysis technique that aims to create a hierarchy of clusters.A hierarchical clustering method is a set of simple (flat) clustering methods arranged in a tree structure.These methods create clusters by recursively partitioning the entities in a top-down or bottom-up manner.We examine and compare hierarchical clustering algorithms in this paper.The intent of discussing the various implementations of hierarchical clustering algorithms is to assist new researchers and beginners to understand how they function, so they can come up with new approaches and innovations for improvement.
Influence of Interactivity and Social Environments on User Experience and Social Acceptability in Virtual Reality

Nowadays, Virtual Reality (VR) technology can be potentially used everywhere through wearable head-mounted displays. Nevertheless, it is still uncommon to see VR devices used in public settings. In these contexts, unaware bystanders in the surroundings might influence the User Experience (UX) and create concerns about the social acceptability of this technology. The user acts in a Social Environment (SE), characterized by surrounding people's number, proximity, and behavior. Simultaneously, VR applications often require a different degree of interactivity concerning body movements and controllers interaction. In this paper, the influence of Social Environments, and degree of interactivity on User Experience and social acceptability is investigated. Four Social Environments were simulated employing 360° Videos, and two VR games developed with two levels of interactivity. Results showed a statistically significant influence of Social Environments on Overall UX as well as Public VR, Interaction, Isolation, Privacy and Safety acceptability, and of the degree of interactivity on Presence, Valence, Arousal, Overall UX, UX Hedonic quality, and Safety acceptability. Findings indicate that Social Environments and degree of interactivity should be taken into account while designing VR applications.
Machine Learning Methods with Noisy, Incomplete or Small Datasets

In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
STO vs. ICO: A Theory of Token Issues under Moral Hazard and Demand Uncertainty

This paper considers a financing problem for an innovative firm that is launching a web-based platform. The entrepreneur, on one hand, faces a large degree of demand uncertainty on his product and on the other hand has to deal with incentive problems of professional blockchain participants who contribute to the development and sales of the product. We argue that hybrid tokens can be a better option for the firm compared to straight utility tokens or security tokens because they help the firm better deal with both the moral hazard problems (via profit sharing incentives) and demand uncertainty (they help the firm learn the market demand for the product). This finding is consistent with some recent evidence. The paper also generates new predictions regarding the effect of different variables on the choice of financing method that have not yet been tested.
Development of an 360-degree virtual reality video-based immersive cycle training system for physical enhancement in older adults: a feasibility study

Abstract Background Recently, there is an increased number of studies that use 360° virtual reality (VR) video for medical and rehabilitative purposes. However, the 360° VR video experience for older adults has not yet been investigated. This study aimed to examine the validity of an 360° VR video-based immersive cycling training system (360° VRCTS) for older adults and to provide preliminary evidence of efficacy. Methods We developed a new virtual reality training system using an immersive environment 360° VRCTS. Five healthy older adults (2 males and 3 females) participated in this study. The system was tested in a single training session (biking for 20 min while viewing a 360° VR video scene through a large curved screen) to identify its strengths and weakness. The usability and acceptability of our system were measured using the system usability scale (SUS) and the simulator sickness questionnaire (SSQ). Results All participants successfully completed the session without any discomfort. The average score for the SUS was 94.60 (range, 90–100), indicating high usability of the technology. The average score for the SSQ was 2.24 (standard deviation = 2.05), indicating that the system is well tolerated and has few side effects. Conclusions The 360° VRCTS may be a useful indoor training system for older adults due to its easy manipulation, high usability, and limited cybersickness. Trial registration number Clinical Research Information Services (CRiS), KCT0003555 , Registered February 25, 2019, https://cris.nih.go.kr/cris/index/index.do .
Data science for oceanography: from small data to big data

The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven. Based on the types and distribution of oceanographic data, this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science. The ocean science has not fully entered the era of big data. There are two ways to expand the amount of oceanographic data to better understanding and management of the ocean. On the data level, fully exploit the potential value of big and small ocean data, and transform the limited, small data into rich, big data, will help to achieve this. On the application level, oceanographic data are of great value if realize the federation of the core data owners and the consumers. The oceanographic data will provide not only a reliable scientific basis for climate, ecological, disaster and other scientific research, but also provide an unprecedented rich source of information that can be used to make predictions of the future.
Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application

The content posted by users on Social Networks represents an important source of information for a myriad of applications in the wide field known as 'social sensing'.The Twitter platform in particular hosts the thoughts, opinions and comments of its users, expressed in the form of tweets: as a consequence, tweets are often analyzed with text mining and natural language processing techniques for relevant tasks, ranging from brand reputation and sentiment analysis to stance detection.In most cases the intelligent systems designed to accomplish these tasks are based on a classification model that, once trained, is deployed into the data flow for online monitoring.In this work we show how this approach turns out to be inadequate for the task of stance detection from tweets.In fact, the sequence of tweets that are collected everyday represents a data stream.As it is well known in the literature on data stream mining, classification models may suffer from concept drift, i.e. a change in the data distribution can potentially degrade the performance.We present a broad experimental campaign for the case study of the online monitoring of the stance expressed on Twitter about the vaccination topic in Italy.We compare different learning schemes and propose yet a novel one, aimed at addressing the event-driven concept drift.