Reviewerly/exHarmony huggingface dataset:
Through Hawks’ Eyes: Synthetically Reconstructing the Visual Field of a Bird in Flight

Birds of prey rely on vision to execute flight manoeuvres that are key to their survival, such as intercepting fast-moving targets or navigating through clutter. A better understanding of the role played by vision during these manoeuvres is not only relevant within the field of animal behaviour, but could also have applications for autonomous drones. In this paper, we present a novel method that uses computer vision tools to analyse the role of active vision in bird flight, and demonstrate its use to answer behavioural questions. Combining motion capture data from Harris' hawks with a hybrid 3D model of the environment, we render RGB images, semantic maps, depth information and optic flow outputs that characterise the visual experience of the bird in flight. In contrast with previous approaches, our method allows us to consider different camera models and alternative gaze strategies for the purposes of hypothesis testing, allows us to consider visual input over the complete visual field of the bird, and is not limited by the technical specifications and performance of a head-mounted camera light enough to attach to a bird's head in flight. We present pilot data from three sample flights: a pursuit flight, in which a hawk intercepts a moving target, and two obstacle avoidance flights. With this approach, we provide a reproducible method that facilitates the collection of large volumes of data across many individuals, opening up new avenues for data-driven models of animal behaviour.The online version contains supplementary material available at 10.1007/s11263-022-01733-2.
Influence of Hand Tracking in Immersive Virtual Reality for Memory Assessment

Few works analyze the parameters inherent to immersive virtual reality (IVR) in applications for memory evaluation. Specifically, hand tracking adds to the immersion of the system, placing the user in the first person with full awareness of the position of their hands. Thus, this work addresses the influence of hand tracking in memory assessment with IVR systems. For this, an application based on activities of daily living was developed, where the user must remember the location of the elements. The data collected by the application are the accuracy of the answers and the response time; the participants are 20 healthy subjects who pass the MoCA test with an age range between 18 to 60 years of age; the application was evaluated with classic controllers and with the hand tracking of the Oculus Quest 2. After the experimentation, the participants carried out presence (PQ), usability (UMUX), and satisfaction (USEQ) tests. The results indicate no difference with statistical significance between both experiments; controller experiments have 7.08% higher accuracy and 0.27 ys. faster response time. Contrary to expectations, presence was 1.3% lower for hand tracking, and usability (0.18%) and satisfaction (1.43%) had similar results. The findings indicate no evidence to determine better conditions in the evaluation of memory in this case of IVR with hand tracking.
Approach to Machine Learning for Extraction of Real-World Data Variables from Electronic Health Records

ABSTRACT Background As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI’s ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. Methods We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (eg, clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (ie, not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information. Results We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates. Conclusions NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.
Declarative Secure Placement of FaaS Orchestrations in the Cloud-Edge Continuum

The decision-making related to the placement of applications made from orchestrated serverless functions onto Cloud-Edge infrastructures is a challenging problem as it must consider functional and non-functional requirements. In this article, we propose a novel declarative methodology to determine the placement of FaaS orchestration onto Cloud-Edge resources while satisfying all the requirements of the FaaS orchestrations and relying on information-flow analyses and padding techniques to prevent information leaks through side channels. A motivating example from Augmented Reality is used to showcase the open-source declarative prototype implementing our proposal. Besides, the prototype is assessed via simulation to evaluate execution times, placement success rates and energy consumption.
Accountability Issues, Online Covert Hate Speech, and the Efficacy of Counter‐Speech

Concerning individual or institutional accountability for online hate speech, research has revealed that most such speech is covert (veiled or camouflaged expressions of hate) and cannot be addressed with existing measures (e.g., deletion of messages, prosecution of the perpetrator). Therefore, in this article, we examine another way to respond to and possibly deflect hate speech: counter-speech. Counter-narratives aim to influence those who write hate speech, to encourage them to rethink their message, and to offer to all who read hate speech a critical deconstruction of it. We created a unique set of parameters to analyze the strategies used in counter-speech and their impact. Upon analysis of our database (manual annotations of 15,000 Twitter and YouTube comments), we identified the rhetoric most used in counter-speech, the general impact of the various counter-narrative strategies, and their specific impact concerning several topics. The impact was defined by noting the number of answers triggered by the comment and the tone of the answers (negative, positive, or neutral). Our data reveal an overwhelming use of argumentative strategies in counter-speech, most involving reasoning, history, statistics, and examples. However, most of these argumentative strategies are written in a hostile tone and most dialogues triggered are negative. We also found that affective strategies (based on displaying positive emotions, for instance) led to a positive outcome, although in most cases these narratives do not receive responses. We recommend that education or training—even machine learning such as empathetic bots—should focus on strategies that are positive in tone, acknowledging grievances especially.
The Circular U-Net with Attention Gate for Image Splicing Forgery Detection

With the advent and rapid development of image tampering technology, it has become harmful to many aspects of our society. Thus, image tampering detection has been increasingly important. Although current forgery detection methods have achieved some success, the scale of the tampered areas in each forgery image are different, and previous methods do not take this into account. In this paper, we believe that the inability of the network to accommodate tampered regions of various sizes is the main reason for the low precision. To address the mentioned problem, we propose a neural network architecture called CAU-Net, which adds residual propagation and feedback, attention gate and Atrous Spatial Pyramid Pooling with CBAM to the U-Net. The Atrous Spatial Pyramid Pooling with CBAM can capture information from multiple scales and adapt to differently sized target areas. In addition, CAU-Net can solve the vanishing gradient issue and suppress the weight of untampered regions, and CAU-Net is an end-to-end network without redundant image processing; thus, it is fast to detect suspicious images. In the end, we optimize the proposed network structure by ablation study, and the experimental results and visualization results demonstrate that our network has a better performance on CASIA and NIST16 compared with state of the art methods.
X-Ray Baggage Object Detection Using Neural Networks Approach for Safety Purpose

Airport security a matter of urgent attention and this calls for measures to ensure that all baggage that move within the airport contain non-harmful objects that may people at the airport in danger. Over the last decade, X-ray machines have evolved to scan baggage, while an airport officer verifies that the content of the baggage bag contains benign items; if anomaly items are identified, the owner of the baggage is called aside to have further enquiries. However, this process is time consuming and moreover lacks accuracy at times due to fatigue on the side of the officer checking the scanned X-ray images. This paper is prepared to develop a convolutional neural network (CNN) to aid in the process of X-ray baggage object detection. The inference speed of the proposed model is discussed in the paper and compared with other convolutional neural networks. The efficiency of the proposed model is evaluated by means of quantitative metrics. The proposed model leverages the YOLOv5 algorithm, which achieved an accuracy of 90%, precision of 90.4%, recall of 84.6%, and an F1-score of 87.40%. The designed and proposed model is capable of real-time anomaly object detection with a very fast inference speed in baggage to help increase the security at airports.
Mobilizing the Resource Potential of the Scientific and Pedagogical Community in Russian Universities: From Problem to Concept

The article is devoted to the problem of mobilizing the resource potential of the scientific and pedagogical community (SPC) in Russian universities, as well as the possibilities for its conceptualization. The authors substantiate the special role of this community as a driver in the development of specific universities and higher education as a whole. It is shown that the development of the SPC resource capacity and fine-tuning of its management is becoming one of the main factors in increasing the competitiveness of Russian higher education in difficult modern conditions. The development of ways to mobilize the resources of the community becomes the most important management problem in higher education. The aim of the article is to outline the scientific concept of resource mobilization of the SPC as the basis of university management practices. The objectives of the article are to consider such key issues as the sociological interpretation of the SPC in the context of its resources and the possibilities for mobilizing them, the interpretation of the concept and structure of SPC resources, as well as the essence and features of the mobilization approach towards managing SPC resource potential. The proposition is to reveal the essence of this approach through the concept of a management strategy aimed at finding, converting and focusing scarce community resources in the interests of national and university level programs for developing higher education. Being an object of the concept, the SPC is considered as a socio-professional, research and educational community. The subject would be the community’s traditional and current resource potential and managing its mobilization. As a condition for the effectiveness of such management, a new approach towards assessing the resource potential of the SPC is shown, which involves a comprehensive diagnosis of its objective and subjective aspects, as well as a deep differentiation of the structure of the resources of the SPC — socio-demographic, educational-pedagogical, scientific-qualification, publication, moral-ideological, symbolic, communicative, temporal and others.
Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We thirdly deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.
Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Abstract Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result.
An enumeration of 1-perfect ternary codes

We study codes with parameters of the ternary Hamming (n=(3m−1)/2,3n−m,3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n−m+1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4; we find that there are 93241327 equivalence classes of such codes.
Minimizing Setup in Broadcast-Optimal Two Round MPC

In this paper we consider two-round secure computation protocols which use different communication channels in different rounds: namely, protocols where broadcast is available in neither round, both rounds, only the first round, or only the second round. The prior works of Cohen, Garay and Zikas (Eurocrypt 2020) and Damgård, Magri, Ravi, Siniscalchi and Yakoubov (Crypto 2021) give tight characterizations of which security guarantees are achievable for various thresholds in each communication structure . In this work, we introduce a new security notion, namely, selective identifiable abort, which guarantees that every honest party either obtains the output, or aborts identifying one corrupt party (where honest parties may potentially identify different corrupted parties). We investigate what broadcast patterns in two-round MPC allow achieving this guarantee across various settings (such as with or without PKI, with or without an honest majority). Further, we determine what is possible in the honest majority setting without a PKI, closing a question left open by Damgård et al. We show that without a PKI, having an honest majority does not make it possible to achieve stronger security guarantees compared to the dishonest majority setting. However, if two-thirds of the parties are guaranteed to be honest, identifiable abort is additionally achievable using broadcast only in the second round. We use fundamentally different techniques from the previous works to avoid relying on private communication in the first round when a PKI is not available, since assuming such private channels without the availability of public encryption keys is unrealistic. We also show that, somewhat surprisingly, the availability of private channels in the first round does not enable stronger security guarantees unless the corruption threshold is one.
Detector–Tracker Integration Framework for Autonomous Vehicles Pedestrian Tracking

Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable appearance characteristics of pedestrians and the frequent occlusion interaction, which leads to the insufficient accuracy and stability of tracking. Therefore, this paper proposes a detector–tracker integration framework for autonomous vehicle pedestrian tracking. Firstly, a pedestrian objects detector based on the improved YOLOv7 network was established. Space-to-Depth convolution layer was adopted to improve the backbone network of YOLOv7. Then, a novel appearance feature extraction network is proposed, which integrates the convolutional structural re-parameterization idea to construct a full-scale feature extraction block, which is the optimized DeepSORT tracker. Finally, experiments were carried out on MOT17 and MOT20 public datasets and driving video sequences, and the tracking performance of the proposed framework was evaluated by comparing it with the most advanced multi-object tracking algorithms. Quantitative analysis results show that the framework has high tracking accuracy. Compared with DeepSORT, MOTA improves by 2.3% in the MOT17 dataset and MOTA improves by 4.2% in the MOT20 dataset. Through qualitative evaluation on real driving video sequences, the framework proposed in this paper is robust in a variety of climate environments, and can be effectively applied to the pedestrian tracking of autonomous vehicles.
The computation of the average speckle size from the point spread function for triangular apertures

Abstract We suggest triangular aperture modeling for the computation of speckle size using the point spread function (PSF) concept. The fast Fourier transform (FFT) algorithm indicates three models of triangular apertures in the point spread function (PSF) computation. We calculated for the first time the average speckle size from the full width at half maximum (FWHM) for the triangular apertures in the PSF. The results are compared with the speckle sizes results obtained from the autocorrelation of speckle images corresponding to the considered models showed an agreement. In the analysis, I consider the 1st model with four equilateral triangular apertures, each of equal sides (b), along the Cartesian coordinates at equal distances from the center ( x d ). In the 2nd model, I assume 16 triangular apertures in a circular contour. In contrast, in the 3rd model, a sequence of six equilateral triangular black and white (B/W) zones is shown where the central triangle is black. I consider central obstruction to attenuate the low spatial frequency in the PSF. I take a transparent triangular aperture for comparison. Also, the PSF corresponding to the circular aperture is given for comparison. In addition, reconstructed images of apertures from the speckle images are obtained. We considered MATLAB codes for all the computations and plots.
Feasibility of teaching experimental physics during Covid-19 pandemic

Abstract At the beginning of 2020, the COVID-19 pandemic surprised the world, affecting activities that promote attendance, including abruptly impacting the education area. Educational institutions, from preschool to university, were required to adopt alternative teaching methods to continue their activities. Distance learning has been adopted by educators to address this unprecedented challenge. Our University, more specifically the Department of Physics, reacted to the problem in an ambitious and creative way, taking advantage of it as an opportunity to put into practice methodologies related to the teaching of disciplines that were only practiced in person, such as the discipline of Experimental Physics. This study aims to describe the implementation of the discipline of Experimental Physics I at our University, promoted remotely by professors of the Department of Physics. We verified: (i) that the choice of the best technologies as a tool for teaching is fundamental for the better dissemination of knowledge, (ii) that inclusive programs for each course are very important, (iii) the importance of prior presentation of a schedule, (iv) the importance of defining clear assessment rules for students on remote teaching. We also evaluated the number of accesses as a measure of student participation and analysed the statistics of the discipline in several engineering courses.