Reviewerly/exHarmony huggingface dataset:
Effective Learning Strategies And Artificial Intelligence (Ai) Support For Accelerated Math Acquisition

Results in learning mathematics leads to a conclusion that students' perception have changed and traditional learning methods are not so effective as they were before.As mathematics has a great influence on future professions, the question is: "How to make developing of mathematical skills faster in the age of smart technologies?"The purpose is to identify effective learning strategies and support of AI that promotes students' motivation and improves attitude in long term.In the future it will promote development of qualitative support system for teachers of mathematics what correspond to demands of "The fourth industry".Research methodology includes research methods of students` attitude and pedagogical processes.A survey of 183 students has been conducted during 3 years and a selection of effective learning strategies created, identifying the positive impact on indexed students' preferences.Possibilities of AI support for accelerated learning of mathematical skills have been identified, converging on created strategies.The assessment provided in the research can help teachers to choose the most appropriate strategies for students' learning style to make learning more exciting and digital.AI has so many positive features that its support in the future can complement or even replace current learning strategies.Different strategies are necessary 1) to cover all the students' wishes identified in the research; 2) because students learn mathematics at a different pace and according to previous knowledge.In turn, effective methods based on research, Big Data and AI can make mathematics a more accessible and meaningful subject in the future.
Particle Filter and Entropy-Based Measure for Tracking of Video Objects

In this paper, a particle filter-based novel approach is proposed for tracking the video objects in both outdoor and indoor scenes. This scheme uses the time motion history of the scene for improved tracking. Initially, the target is modeled by the particle, and the entropy of the target frame is considered. Object detection in the time motion frame is achieved by searching for the particle having the entropy which incurs minimum error with that of the target model. Object detection is followed by object tracking which is achieved by the motion of the particles in the particle filter algorithm. The proposed algorithm is tested with three data sets, namely I_MC_02 (LASIESTA), kite-surf (DAVID-2016), and O_SU_01 (LASIESTA). The performance of the algorithm is compared with those of other two existing algorithms, and the proposed algorithm is found to exhibit improved performance.
A Novel Design of a Lightweight Magnetic Plate for a Delivery Drone

This work describes a lightweight magnetic plate for the pick and place task of objects with a ferromagnetic surface. The plate can be installed on any type of platform, namely both on ground and aerial vehicles. Its structure makes it fully customizable in size or attraction power. The main contributions of the proposed solution are a) the lightness of the device and b) the reduced power consumption required, compared to other typical solutions used in pick and place operations. This is even more relevant in long time operations where suction grippers or electromagnets would require an intensive power consumption. In this paper the design and implementation of the system along with the experimental results are presented.
Implementation of Gesture Control Robotic Arm for Automation of Industrial Application

&lt;p&gt;In today’s world, in almost all sectors, most of the work is done by robots or robotic arm having different number of degree of freedoms (DOF’s) as per the requirement. This project deals with the Design and Implementation of a “Wireless Gesture Controlled Robotic Arm with Vision”. The system design is divided into 3 parts namely: Accelerometer Part, Robotic Arm and Platform. It is fundamentally an Accelerometer based framework which controls a Robotic Arm remotely utilizing a, little and minimal effort, 3-pivot (DOF's) accelerometer by means of RF signals. The Robotic Arm is mounted over a versatile stage which is likewise controlled remotely by another accelerometer. One accelerometer is mounted/joined on the human hand, catching its conduct (motions and stances) and hence the mechanical arm moves in like manner and the other accelerometer is mounted on any of the leg of the client/administrator, catching its motions and stances and in this way the stage moves as needs be. In a nutshell, the robotic arm and platform is synchronised with the gestures and postures of the hand and leg of the user / operator, respectively. The different motions performed by robotic arm are: PICK and PLACE / DROP, RAISING and LOWERING the objects. Also, the motions performed by the platform are: FORWARD, BACKWARD, RIGHT and LEFT.&lt;/p&gt;
An Efficient Outsourced Oblivious Transfer Extension Protocol and Its Applications

Oblivious transfer (OT) is a cryptographic primitive originally used to transfer a collection of messages from the sender to the receiver in an oblivious manner. OT extension protocol reduces expensive asymmetric operations by running a small number of OT instances first and then cheap symmetric operations. While most earlier works discussed security model or communication and computation complexity of OT in general case, we focus on concrete application scenarios, especially where the sender in the OT protocol is a database with less computation and limited interaction capability. In this paper, we propose a generic outsourced OT extension protocol ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="double-struck">O</mi> <mi mathvariant="sans-serif">Tex</mi> </math> ) that outsources all the asymmetric operations of the sender to a semihonest server so as to adapt to specific scenarios above. We give <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="double-struck">O</mi> <mi mathvariant="sans-serif">Tex</mi> </math> a standard security definition, and the proposed protocol is proven secure in the semihonest model. In <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi mathvariant="double-struck">O</mi> <mi mathvariant="sans-serif">Tex</mi> </math> , the sender works on the fly and performs only symmetric operations locally. Whatever the number of rounds OT to be executed and the length of messages in OT to be sent, our protocol realizes optimal complexity. Besides, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi mathvariant="double-struck">O</mi> <mi mathvariant="sans-serif">Tex</mi> </math> can be used to construct high-level protocols, such as private membership test (PMT) and private set intersection (PSI). We believe our <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi mathvariant="double-struck">O</mi> <mi mathvariant="sans-serif">Tex</mi> </math> construction may be a building block in other applications as well.
Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures. We design a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input, which could be viewed as a regularization technique to enhance the model generalization ability. Additionally, we design an aggregation module (AGGM) to better integrate high-level features, low-level features and global context information for the decoder to aggregate various information. Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: F_\beta = 0.8995, E_\xi = 0.9079 and MAE = 0.0489$), with an average gain of 4.60\% for F-measure, 2.05\% for E-measure and 1.88\% for MAE over the previous best method on this task. Source code is available at http://github.com/siyueyu/SCWSSOD.
Towards Economical Live Migration in Data Centers

Live migration of virtual machines (VMs) enables maintenance, load balancing, and power management in data centers. The cost of live migration on several key metrics combined with strict service-level objectives (SLOs), however, typically limits its practical application to situations where the underlying physical host has to undergo maintenance. As a consequence, the potential benefits of live migration with respect to increased resource usage and lower power consumption remain largely untouched. In this paper, we argue that live migration-aware SLOs combined with smart live migration algorithm selection provides an economically viable model for live migration in data centers. Based on a model predicting key parameters of VM live migration, an optimization algorithm selects the live migration technique that is expected to meet client SLOs while at the same time to optimize target metrics given by the data center operator. A comparison with the state-of-the-art shows that the presented guided live migration technique selection achieves significantly fewer SLO violations while, at the same time, minimizing the effect of live migration on the infrastructure.
Medical Data Management Using Blockchain

Medical data of a person is an extremely sensitive and valuable resource. If this data gets into the wrong hands, it can be misused in unimaginable ways. Any organization handling this sort of sensitive data is taking on an extremely huge responsibility. A patient should not have to worry about the safety of his data in the hands of a third-party organization. If he is given control of his sensitive data by eliminating the middle man, then handling such data would become a lot easier. This is where the concept of blockchain is introduced. The blockchain technology proves to be an excellent technology to safeguard such data. A blockchain application which connects multiple entities like hospitals, labs, patients and doctors proves to be the perfect solution to ensure that there is no tampering of data.
Arabic Question Answering System Using Graph Ontology

The search engines are doing a great effort in getting answers for questions, especially, sophisticated answers. Question answering systems are used as modules of the search engines to enrich the search mechanism. This paper aims to present an Arabic question answering system using graph ontology, by using multiple semantic techniques. Graph ontology is used as the main source of getting answers, in addition to a web search API as an alternative path to get answers and enrich the ontology. The proposed system is tested on three datasets. The system achieved an accuracy (C@1) of 0.846 with an increase of 0.486 over similar systems and a recall of 0.958 in the second experiment, which is less than the compared systems by 0.008.
Holding the Cosmos in Your Hand: Developing 3D Modeling and Printing Pipelines for Communications and Research

Three-dimensional (3D) visualization has opened up a universe of possible scientific data representations. 3D printing has the potential to make seemingly abstract and esoteric data sets accessible, particularly through the lens of translating data into forms that can be explored in the tactile modality for people who are blind or visually impaired. This article will briefly review 3D modeling in astrophysics, astronomy, and planetary science, before discussing 3D printed astrophysical and planetary geophysical data sets and their current and potential applications with non-expert audiences. The article will also explore the prospective pipeline and benefits of other 3D data outputs in accessible scientific research and communications, including extended reality and data sonification.
Evaluating the CCSDS 123 Compressor Running on RISC-V and ARM Architectures

Hyperspectral images are widely used in spatial applications and are three-dimensional data structures in which the $x$ and $y$ axes contain spatial information, and the $z$ -axis contains the spectral bands, which that can reach the order of hundreds of layers. Such images generate a large amount of data, and applying compression algorithms, such as the CCSDS 123 compressor, is highly necessary to deal with the communication and storage constraints of the platforms used to capture hyperspectral images (e.g., a spacecraft). Given that the CCSDS 123 algorithm has a high computational cost, it is necessary to evaluate which processor architectures can deal with it onboard. Given the context above, this work presents an evaluation of performance and power consumption of the CCSDS 123 algorithm running on RISC- V and ARM processors and an evaluation of using two real-time operating systems (FreeRTOS and Zephyr). The experimental results show that, for the development kits used, the RISC- V processor dissipates less power than the ARM processor, which, in turn, offers much higher performance and lower energy consumption than RISC-V. Results also show that FreeRTOS adds a lower overhead to the algorithm execution in comparison to Zephyr when running over the RISC- V processor.
Performance Analysis of Different Loss Function in Face Detection Architectures

Masked face detection is a challenging task due to the occlusions created by the masks. Recent studies show that deep learning models can achieve effective performance for not only occluded faces but also for unconstrained environments, illuminations or various poses. In this study, we have addressed the problem of occlusion due to wearing masks in masked face detection technique in deep transfer learning method. We have also reviewed the recent deep learning models for face detection and considered VGG16, VGG19, MobileNet and DenseNet as our underlying masked face detection models. Moreover, we have prepared a dataset containing masked face and without mask from 120 individuals and enhanced the dataset using augmentation. After training the deep learning models with our own dataset, we have analysed the performance of the deep learning models for several types of loss functions. From the experiment, it is clear that all the deep learning models perform well in terms of classification losses like categorical cross entropy loss and KL divergence loss.
Using Domain Ontologies for Text Classification. A Use Case to Classify Computer Science Papers

The web facilitates the creation, publication, and exchange of a wide variety of information. Particularly, the dramatic growth of unstructured web content makes text classification in a basic task to automate. Although well-known classification methods come from natural language processing and the machine learning fields, in this paper, the authors address the classification task as the ability to recognize topics or concepts in a text. To achieve this goal, we use a domain ontology as a driver of this process. The main motivation behind this work is to take advantage of the existing domain ontologies to classify and analyze the scientific production of a certain area of knowledge. Preliminary findings obtained by classifying a subset of Computer Science papers encourage us to remain researching in this area. Also, we will continue to discover a better way to improve results by combining it with other well-known approaches for text classification.
Immersivité : Une approche interdisciplinaire aux espaces d’immersion

This article proposes to shift the scholarly focus from the conceptually, terminologically, and methodologically fuzzy notion of “immersion” to the concept of “immersivity,” and thus from a discussion of experiences to an analysis of the productive forces that enable such experiences (aesthetic and otherwise). Using case studies from theme parks, film, and immersive theatre to video games and immersive educational spaces, we argue that immersivity is a distinct term that denotes an inherent quality of objects in general and of mediated, delineated, real and virtual spaces in particular. We assume that in all of these examples, spatial qualities and modes interact in particular ways to facilitate immersion. Elucidating these interactions requires discussing examples from different disciplinary contexts. It is precisely through this interdisciplinary approach that we take a first step towards understanding immersivity, as we pinpoint cross-disciplinary congruences wherever possible. The article thus offers the starting point for a transdisciplinary, qualitative, experimental, and analytical model of immersivity. Following a theoretical introduction to the topic of immersion, we will first conceptualize our interdisciplinary understanding of immersion and immersivity and then launch into a transdisciplinary dialogue about examples of immersive spaces.
Towards localisation of keywords in speech using weak supervision

Developments in weakly supervised and self-supervised models could enable speech technology in low-resource settings where full transcriptions are not available. We consider whether keyword localisation is possible using two forms of weak supervision where location information is not provided explicitly. In the first, only the presence or absence of a word is indicated, i.e. a bag-of-words (BoW) labelling. In the second, visual context is provided in the form of an image paired with an unlabelled utterance; a model then needs to be trained in a self-supervised fashion using the paired data. For keyword localisation, we adapt a saliency-based method typically used in the vision domain. We compare this to an existing technique that performs localisation as a part of the network architecture. While the saliency-based method is more flexible (it can be applied without architectural restrictions), we identify a critical limitation when using it for keyword localisation. Of the two forms of supervision, the visually trained model performs worse than the BoW-trained model. We show qualitatively that the visually trained model sometimes locate semantically related words, but this is not consistent. While our results show that there is some signal allowing for localisation, it also calls for other localisation methods better matched to these forms of weak supervision.
Uncertainty-Aware Deep Calibrated Salient Object Detection

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. Thus, state-of-the-art SOD networks are prone to be overconfident. In other words, the predicted confidence of the networks does not reflect the real probability of correctness of salient object detection, which significantly hinder their real-world applicability. In this paper, we introduce an uncertaintyaware deep SOD network, and propose two strategies from different perspectives to prevent deep SOD networks from being overconfident. The first strategy, namely Boundary Distribution Smoothing (BDS), generates continuous labels by smoothing the original binary ground-truth with respect to pixel-wise uncertainty. The second strategy, namely Uncertainty-Aware Temperature Scaling (UATS), exploits a relaxed Sigmoid function during both training and testing with spatially-variant temperature scaling to produce softened output. Both strategies can be incorporated into existing deep SOD networks with minimal efforts. Moreover, we propose a new saliency evaluation metric, namely dense calibration measure C, to measure how the model is calibrated on a given dataset. Extensive experimental results on seven benchmark datasets demonstrate that our solutions can not only better calibrate SOD models, but also improve the network accuracy.
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and anatomical considerations. However, matching lesions manually is labor-intensive and time-consuming. In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals. To incorporate anatomical constraints, we propose an anatomical signal encoder, which prevents lesions being matched with visually similar but spurious regions. In addition, we present a new formulation for Siamese networks that avoids the heavy computational loads of 3D cross-correlation. To present our network with greater varieties of images, we also propose a self-supervised learning (SSL) strategy to train trackers with unpaired images, overcoming barriers to data collection. To train and evaluate our tracker, we introduce and release the first lesion tracking benchmark, consisting of 3891 lesion pairs from the public DeepLesion database. The proposed method, DLT, locates lesion centers with a mean error distance of 7 mm. This is 5% better than a leading registration algorithm while running 14 times faster on whole CT volumes. We demonstrate even greater improvements over detector or similarity-learning alternatives. DLT also generalizes well on an external clinical test set of 100 longitudinal studies, achieving 88% accuracy. Finally, we plug DLT into an automatic tumor monitoring workflow where it leads to an accuracy of 85% in assessing lesion treatment responses, which is only 0.46% lower than the accuracy of manual inputs.
PRShare

We consider the task of interorganizational data sharing, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. One real-world scenario in which this problem arises is law-enforcement use of phone-call metadata: The data owner is a phone company, the data clients are law-enforcement agencies, and the data subjects are individuals who make phone calls. A key challenge in this type of scenario is that each organization uses its own set of proprietary intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic, efficient, and privacy-preserving interorganizational data sharing, while allowing each party to use its own set of proprietary attributes. We introduce the novel technique of Attribute-Based Encryption With Oblivious Attribute Translation (OTABE), which plays a crucial role in our solution and may be of independent interest.