Reviewerly/exHarmony huggingface dataset:
3D Object Detection Based on Proposal Generation Network Utilizing Monocular Images

Monocular 3-D object detection is a low-cost and challenging task for autonomous vehicles and robotics. Utilizing a monocular image for 3-D object detection is served as an auxiliary module for autonomous vehicles and is a growing concern recently. Currently, the expensive lidar and stereo cameras have a predominant performance on accurate 3-D object detection, whereas monocular-based methods are considerably lower in performance. This performance gap is minimized by reforming the monocular-based method as a single internal network. We exploit the correlation between 2-D and 3-D detection spaces, enabling 3-D boxes to leverage feature maps generated in image space. The 2-D and 3-D proposals are extracted through a proposal generation network that is enhanced and utilized for estimating accurate 3-D detection and localization. Experimental results on the KITTI dataset demonstrate that in comparison to other monocular object detection methods the proposed method considerably improved the accuracy of 3-D object detection. The mean average precision of 3-D object detection in front view is improved to 25% and the bird's eye view to 32% for the car class on a moderate difficulty level.
Gesture Recognition With Ultrasounds and Edge Computing

The aim of this work is to prove that it is possible to develop a system able to detect gestures based only on ultrasonic signals and Edge devices.A set of 7 gestures plus idle has been defined, being possible to combine them to increase the recognized gestures.In order to recognize them, Ultrasound transceivers will be used to detect the 2 dimensional gestures.The Edge device approach implies that the whole data is processed in the device at the network edge rather than depending on external devices or services such as Cloud Computing.The system presented in this paper has been proven to be able to measure Time of Flight (ToF) signals that can be used to recognize multiple gestures by the integration of two transceivers, with an accuracy between 84.18% and 98.4%.Due to the optimization of the preprocessing correlation technique to extract the ToF from the echo signals and our specific firmware design to enable the parallelization of concurrent processes, the system can be implemented as an Edge Device.
Practical security of a chip-based continuous-variable quantum-key-distribution system

A chip-based continous-variable quantum-key-distribution (CVQKD) system with a high practical confidentiality performance is crucial for constructing quantum metropolitan communication networks, but imperfections in the chip-based modulation will threaten the practical security of the chip-based CVQKD system. In this paper, we combine the plasma dispersion effect of free carriers to model the carrier fluctuations and reveal the essential mechanism of carrier fluctuations' influence on the system. The simulations show that the chip-based CVQKD system may face potential loophole threats or its performance will dramatically decrease under different carrier fluctuations. Moreover, two preliminary defense strategies are proposed to completely solve the practical security problems commonly induced by modulators in general chip-based CVQKD systems. This work proposes a set of modeling and analysis methods for general chip-based CVQKD systems' modulators, which provides constructive methods to build the chip-based CVQKD system with more rigorous practical security.
An improved ant colony optimization for path planning with multiple UAVs

As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions

We consider the problem of local planning in fixed-horizon and discounted Markov Decision Processes (MDPs) with linear function approximation and a generative model under the assumption that the optimal action-value function lies in the span of a feature map that is available to the planner. Previous work has left open the question of whether there exist sound planners that need only poly(H,d) queries regardless of the MDP, where H is the horizon and d is the dimensionality of the features. We answer this question in the negative: we show that any sound planner must query at least $\min(\exp({\Omega}(d)), {\Omega}(2^H))$ samples in the fized-horizon setting and $\exp({\Omega}(d))$ samples in the discounted setting. We also show that for any ${\delta}>0$, the least-squares value iteration algorithm with $O(H^5d^{H+1}/{\delta}^2)$ queries can compute a ${\delta}$-optimal policy in the fixed-horizon setting. We discuss implications and remaining open questions.
Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

This paper proposes a new filter, Different Adaptive Modified Riesz Mean Filter (DAMRmF), for high-density salt-and-pepper noise (SPN) removal.DAMRmF operationalizes a pixel weight function and adaptivity condition of Adaptive Median Filter (AMF).In the simulation, the proposed filter is compared with Adaptive Frequency Median Filter (AFMF), Three-Values-Weighted Method (TVWM), Unbiased Weighted Mean Filter (UWMF), Different Applied Median Filter (DAMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesáro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF) for 20 traditional test images with noise levels from 60% to 90%.The results show that DAMRmF outperforms the state-of-the-art filters in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values.Moreover, DAMRmF also performs better than the state-of-the-art filters concerning mean PSNR and SSIM results.We finally discuss DAMRmF for further research.
Copresence With Virtual Humans in Mixed Reality: The Impact of Contextual Responsiveness on Social Perceptions

Virtual humans (VHs)—automated, three-dimensional agents—can serve as realistic embodiments for social interactions with human users. Extant literature suggests that a user’s cognitive and affective responses toward a VH depend on the extent to which the interaction elicits a sense of copresence, or the subjective “sense of being together.” Furthermore, prior research has linked copresence to important social outcomes (e.g., likeability and trust), emphasizing the need to understand which factors contribute to this psychological state. Although there is some understanding of the determinants of copresence in virtual reality (VR) (cf. Oh et al., 2018 ), it is less known what determines copresence in mixed reality (MR), a modality wherein VHs have unique access to social cues in a “real-world” setting. In the current study, we examined the extent to which a VH’s responsiveness to events occurring in the user’s physical environment increased a sense of copresence and heightened affective connections to the VH. Participants ( N = 65) engaged in two collaborative tasks with a (nonspeaking) VH using an MR headset. In the first task, no event in the participant’s physical environment would occur, which served as the control condition. In the second task, an event in the participants’ physical environment occurred, to which the VH either responded or ignored depending on the experimental condition. Copresence and interpersonal evaluations of the VHs were measured after each collaborative task via self-reported measures. Results show that when the VH responded to the physical event, participants experienced a significant stronger sense of copresence than when the VH did not respond. However, responsiveness did not elicit more positive evaluations toward the VH (likeability and emotional connectedness). This study is an integral first step in establishing how and when affective and cognitive components of evaluations during social interactions diverge. Importantly, the findings suggest that feeling copresence with VH in MR is partially determined by the VHs’ response to events in the actual physical environment shared by both interactants.
A Heterogeneous Hardware Accelerator for Image Classification in Embedded Systems

Convolutional neural networks (CNN) have been extensively employed for image classification due to their high accuracy. However, inference is a computationally-intensive process that often requires hardware acceleration to operate in real time. For mobile devices, the power consumption of graphics processors (GPUs) is frequently prohibitive, and field-programmable gate arrays (FPGA) become a solution to perform inference at high speed. Although previous works have implemented CNN inference on FPGAs, their high utilization of on-chip memory and arithmetic resources complicate their application on resource-constrained edge devices. In this paper, we present a scalable, low power, low resource-utilization accelerator architecture for inference on the MobileNet V2 CNN. The architecture uses a heterogeneous system with an embedded processor as the main controller, external memory to store network data, and dedicated hardware implemented on reconfigurable logic with a scalable number of processing elements (PE). Implemented on a XCZU7EV FPGA running at 200 MHz and using four PEs, the accelerator infers with 87% top-5 accuracy and processes an image of 224×224 pixels in 220 ms. It consumes 7.35 W of power and uses less than 30% of the logic and arithmetic resources used by other MobileNet FPGA accelerators.
ADePT: Auto-encoder based Differentially Private Text Transformation

Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used to solve several privacy concerns. Multiple solutions have been proposed for the differentially-private transformation of datasets containing sensitive information. However, such transformation algorithms offer poor utility in Natural Language Processing (NLP) tasks due to noise added in the process. This paper addresses this issue by providing a utility-preserving differentially private text transformation algorithm using auto-encoders. Our algorithm transforms text to offer robustness against attacks and produces transformations with high semantic quality that perform well on downstream NLP tasks. We prove our algorithm’s theoretical privacy guarantee and assess its privacy leakage under Membership Inference Attacks (MIA) on models trained with transformed data. Our results show that the proposed model performs better against MIA attacks while offering lower to no degradation in the utility of the underlying transformation process compared to existing baselines.
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO

By supporting multi-modal retrieval training and evaluation, image captioning datasets have spurred remarkable progress on representation learning.Unfortunately, datasets have limited cross-modal associations: images are not paired with other images, captions are only paired with other captions of the same image, there are no negative associations and there are missing positive cross-modal associations.This undermines research into how inter-modality learning impacts intra-modality tasks.We address this gap with Crisscrossed Captions (CxC), an extension of the MS-COCO dataset with human semantic similarity judgments for 267,095 intra-and intermodality pairs.We report baseline results on CxC for strong existing unimodal and multimodal models.We also evaluate a multitask dual encoder trained on both image-caption and caption-caption pairs that crucially demonstrates CxC's value for measuring the influence of intra-and inter-modality learning.
Competitive advantage of the sustainable digital economy

This research focuses on factors that influence the competitive advantage of the sustainable digital economy. Digital economy helped to speed up the exchange of information flows which, in turn, impacted on the creation the new forms of business and socio-economic interactions. Such sectors as banking and finance or hospitality and services are all influenced by the advancements of the digital economy that include sharing economy or Blockchain technology and its related applications. In this article, we show that digital economy is more sustainable due to the fact that it requires less energy sources and can itself contribute to the formation of the green economy and the acceptance of the renewable energy. All these features constitute its competitive advantage that needs to be further supported and fostered. In addition, we discuss key strategies and the development of skills to adapt to a digital economy and provide an overview of high-quality training for high-tech workers. It was assumed that in future, people would be able to easily acquire new skills through vocational training and vocational training. However, the spread of digital technologies has changed the way we work and has increased the need for high-quality training for workers in a wide range of professions. To use such technologies effectively, workers in an increasing number of sectors and professions need both general and advanced ICT skills.
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT

We investigate how Multilingual BERT (mBERT) encodes grammar by examining how the high-order grammatical feature of morphosyntactic alignment (how different languages define what counts as a "subject") is manifested across the embedding spaces of different languages.To understand if and how morphosyntactic alignment affects contextual embedding spaces, we train classifiers to recover the subjecthood of mBERT embeddings in transitive sentences (which do not contain overt information about morphosyntactic alignment) and then evaluate them zero-shot on intransitive sentences (where subjecthood classification depends on alignment), within and across languages.We find that the resulting classifier distributions reflect the morphosyntactic alignment of their training languages.Our results demonstrate that mBERT representations are influenced by high-level grammatical features that are not manifested in any one input sentence, and that this is robust across languages.Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature.Together, these results provide insight into how grammatical features manifest in contextual embedding spaces, at a level of abstraction not covered by previous work. 1
Latin American and Caribbean teachers’ transition to online teaching during the pandemic: Challenges, Changes and Lessons Learned

The forced transition to online teaching during the Covid-19 pandemic has fast-tracked the integration of ICT use by teachers across Latin America and the Caribbean (LAC) . Given the slow adoption of ICT by LAC teachers before the pandemic it is useful to investigate how, why and in what ways, LAC teachers overcame their prior reluctance and resistance to learn and practice digital literacy. Using qualitative interview data from a global report on teachers’ experiences during Covid-19, this paper analyses how the pandemic changed the ways in which 53 teachers from 15 LAC countries think about and use ICT. It suggests their concern for student loss of learning due to school closures empowered them to overcome barriers that normally impede ICT use by teachers. At the same time, initial acceptance of ICT use has wavered due to ongoing widespread challenges with internet connectivity and access. This has implications for what we know about supporting professional development in the use of ICT after Covid-19 and beyond.
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence

We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing the Polyak step-size requires knowledge of the optimal function values, this information is readily available for typical modern machine learning applications. Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD). We provide theoretical convergence guarantees for SGD equipped with SPS in different settings, including strongly convex, convex and non-convex functions. Furthermore, our analysis results in novel convergence guarantees for SGD with a constant step-size. We show that SPS is particularly effective when training over-parameterized models capable of interpolating the training data. In this setting, we prove that SPS enables SGD to converge to the true solution at a fast rate without requiring the knowledge of any problem-dependent constants or additional computational overhead. We experimentally validate our theoretical results via extensive experiments on synthetic and real datasets. We demonstrate the strong performance of SGD with SPS compared to state-of-the-art optimization methods when training over-parameterized models.
E-Learning during the Covid-19 Pandemic in Spain: A Case Study with High School Mathematics Students

The covid-19 pandemic has significantly altered the organization and scheduling of high schools in Spain. Due to pandemic restrictions, most students from grades 9 to 11 come to class only on alternate days. In this situation, e-learning and distance learning have gained prominence as necessary methods of instruction. In this study 68 students from grades 9 and 10, attending on alternate days, participated in an online teaching experience for the first time in their lives. This took place during the first trimester of the 2020-2021 academic school year. On days when they did not attend class in person, students were expected to watch the mathematics classes which were broadcasted live via YouTube. Students could either watch the classes live and ask the teacher questions via an online chat, or alternatively, they could watch the pre-recorded classes at a later time. The two options were analyzed using quantitative and qualitative methods. Results showed a continuous decrease in the number of students who preferred the first option, switching progressively from online live classes to pre-recorded classes as the weeks passed. The qualitative results explained this progressive decrease as students preferred to watch the teacher’s explanations several times. Although e-learning is absolutely necessary in the 21st century, and especially during the current pandemic, controlling students’ level of application to distance work and online learning is still a challenge.
SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.
Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things.So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques.However, non-linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput.Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns.Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering.In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS).We subsequently develop c-means using the proposed JS (J-c-means).The various properties of the JS and J-c-means are discussed.All the analyses were carried out on a few real-life and synthetic databases.The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically.
Transitioning to Remote User-Centered Design Activities in the Emergency Medical Field During a Pandemic

During the COVID-19 pandemic, we had to transition our user-centered research and design activities in the emergency medical domain of trauma resuscitation from in-person settings to online environments. This transition required that we replicate the in-person interactions remotely while maintaining the critical social connection and the exchange of ideas with medical providers. In this paper, we describe how we designed and conducted four user-centered design activities from our homes: participatory design workshops, near-live simulation sessions, usability evaluation sessions, and interviews and design walkthroughs. We discuss the differences we observed in our interactions with participants in remote sessions, as well as the differences in the interactions among the research team members. From this experience, we draw several lessons and outline the best practices for remotely conducting user-centered design activities that have been traditionally held in person.