Reviewerly/exHarmony huggingface dataset:
Locally-iterative Distributed (Δ + 1)-coloring and Applications

We consider graph coloring and related problems in the distributed message-passing model. Locally-iterative algorithms are especially important in this setting. These are algorithms in which each vertex decides about its next color only as a function of the current colors in its 1-hop-neighborhood . In STOC’93 Szegedy and Vishwanathan showed that any locally-iterative Δ + 1-coloring algorithm requires Ω (Δ log Δ + log * n ) rounds, unless there exists “a very special type of coloring that can be very efficiently reduced” [ 44 ]. No such special coloring has been found since then. This led researchers to believe that Szegedy-Vishwanathan barrier is an inherent limitation for locally-iterative algorithms and to explore other approaches to the coloring problem [ 2 , 3 , 19 , 32 ]. The latter gave rise to faster algorithms, but their heavy machinery that is of non-locally-iterative nature made them far less suitable to various settings. In this article, we obtain the aforementioned special type of coloring. Specifically, we devise a locally-iterative Δ + 1-coloring algorithm with running time O (Δ + log * n ), i.e., below Szegedy-Vishwanathan barrier. This demonstrates that this barrier is not an inherent limitation for locally-iterative algorithms. As a result, we also achieve significant improvements for dynamic, self-stabilizing, and bandwidth-restricted settings. This includes the following results: We obtain self-stabilizing distributed algorithms for Δ + 1-vertex-coloring, (2Δ - 1)-edge-coloring, maximal independent set, and maximal matching with O (Δ + log * n ) time. This significantly improves previously known results that have O(n) or larger running times [ 23 ]. We devise a (2Δ - 1)-edge-coloring algorithm in the CONGEST model with O (Δ + log * n ) time and O (Δ)-edge-coloring in the Bit-Round model with O (Δ + log n ) time. The factors of log * n and log n are unavoidable in the CONGEST and Bit-Round models, respectively. Previously known algorithms had superlinear dependency on Δ for (2Δ - 1)-edge-coloring in these models. We obtain an arbdefective coloring algorithm with running time O (√ Δ + log * n ). Such a coloring is not necessarily proper, but has certain helpful properties. We employ it to compute a proper (1 + ε)Δ-coloring within O (√ Δ + log * n ) time and Δ + 1-coloring within O (√ Δ log Δ log * Δ + log * n ) time. This improves the recent state-of-the-art bounds of Barenboim from PODC’15 [ 2 ] and Fraigniaud et al. from FOCS’16 [ 19 ] by polylogarithmic factors. Our algorithms are applicable to the SET-LOCAL model [ 25 ] (also known as the weak LOCAL model). In this model a relatively strong lower bound of Ω (Δ 1/3 ) is known for Δ + 1-coloring. However, most of the coloring algorithms do not work in this model. (In Reference [ 25 ] only Linial’s O (Δ 2 )-time algorithm and Kuhn-Wattenhofer O (Δ log Δ)-time algorithms are shown to work in it.) We obtain the first linear-in-Δ Δ + 1-coloring algorithms that work also in this model.
BRCars: a Dataset for Fine-Grained Classification of Car Images

Fine-grained computer vision tasks refer to the ability of distinguishing objects that belong to the same parent class, differentiating themselves by subtle visual elements. Image classification in car models is considered a fine-grained classification task. In this work, we introduce BRCars, a dataset that seeks to replicate the main challenges inherent to the task of classifying car images in many practical applications. BRCars contains around 300K images collected from a Brazilian car advertising website. The images correspond to 52K car instances and are distributed among 427 different models. The images are both from the exterior and the interior of the cars and present an unbalanced distribution across the different models. In addition, they are characterized by a lack of standardization in terms of perspective. We adopted a semi-automated annotation pipeline with the help of the new CLIP neural network, which enabled distinguishing thousands of images among different perspectives using textual queries. Experiments with standard deep learning classifiers were performed to serve as baseline results for future work on this topic. BRCars dataset is available at https://github.com/danimtk/brcars-dataset.
Extreme Learning Machine Based Channel Estimator and Equalizer for Underground Mining VLC Systems

Visible light communication (VLC) systems have numerous applications in harsh environments, such as underground mining. However, the irregular tunnel features, the random orientation of the optical components, physical phenomena such as shadowing and scattering, as well as the inherent generation of non-linearities of the optical signal greatly affect the VLC link. Therefore, it is necessary to investigate novel adaptive mitigation techniques to improve the performance of underground mining VLC systems. The literature has initially presented channel estimation schemes knowing a prior the channel state information (CSI). Currently, these schemes have evolved favorably given the research done regarding the use of machine learning techniques to estimate and equalize the communication channel. In this paper, channel estimation and equalization based on the standard extreme learning machine (ELM) scheme applied to underground mining VLC systems is presented. To verify the performance of the proposed technique, typical channel estimation schemes such as least square (LS), minimum mean square error (MMSE), and spectral temporal averaging (STA) were implemented using zero forcing (ZF) as equalizer. Furthermore, the ideal VLC channel and perfect channel estimation with matched filter (MF) and ZF are considered as reference cases. The performance analysis is validated through Monte Carlo simulations in terms of the bit error rate (BER). Results demonstrate that the novel ELM scheme performs better than traditional techniques and performs similarly compared to the perfect channel estimation case.
Deep relational self-Attention networks for scene graph generation

Scene graph generation (SGG) aims to simultaneously detect objects in an image and predict relations for these detected objects. SGG is challenging that requires modeling the contextualized relationships among objects rather than only considering relationships between paired objects. Most existing approaches address this problem by using a CNN or RNN framework, which can not explicitly and effectively models the dense interactions among objects. In this paper, we exploit the attention mechanism and introduce a relational self-attention (RSA) module to simultaneously model the object and relation contexts. By stacking such RSA modules in depth, we obtain a deep relational self-attention network (RSAN), which is able to characterize complex interactions thus facilitating the understanding of object and relation semantics. Extensive experiments on the benchmark Visual Genome dataset demonstrate the effectiveness of RSAN.
ProficiencyRank: Automatically ranking expertise in online collaborative social networks

Users of online collaborative social networks (e.g., StackExchange, Yahoo! Answers, and Yask, among others) are distributed across a continuous range of expertise, ranging from beginners to experts. However, only a minority of users contribute to their communities by posting questions and answers, while the majority (lurkers) only contribute by voting up or down on others’ posts. Since current methods for ranking expertise are based mainly on user submitted posts, their results are limited to that minority. We introduce ProficiencyRank, a wrapping method on top of PageRank that leverages voting information to determine user expertise for both contributors and lurkers. We validated our method to measure the proficiency of English Language Learners in the Yask social network. The new data set is naturally annotated with self-assessments of proficiency given by the users themselves, providing ground truth for all users. Experimental validation showed that ProficiencyRank produces a meaningful ranking of users that overcame various baselines based on intrinsic and extrinsic information. We conclude that this technology can benefit these communities by expanding the coverage of user expertise/reputation measurement and providing a re-ranking method for the expert finding task.
Data-fusion for robust off-road perception considering data quality of uncertain sensors

Robust off-road perception for autonomous navigation is hard to achieve. Versatile environments, different hardware, and numerous disturbances limit the perceptional portability in changing applications and cross-platform. This contribution proposes sensor-fusion considering the data quality of uncertain sensors to increase the classification and mapping components’ perceptual robustness. The resulting benefits on perception are demonstrated using the autonomous off-road robot U5023.
Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally

Different graph types might differ in group comparison due to differences in underlying graph schemas. Thus, this study examined whether graph schemas are based on perceptual features (i.e., each graph has a specific schema) or common invariant structures (i.e., graphs share several common schemas), and which graphic type (bar vs. dot vs. tally) is the best to compare discrete groups. Three experiments were conducted using the mixing-costs paradigm. Participants received graphs with quantities for three groups in randomized positions and were given the task of comparing two groups. The results suggested that graph schemas are based on a common invariant structure. Tally charts mixed either with bar graphs or with dot graphs showed mixing costs. Yet, bar and dot graphs showed no mixing costs when paired together. Tally charts were the more efficient format for group comparison compared to bar graphs. Moreover, processing time increased when the position difference of compared groups was increased.
Development of Wearable Device and Clustering Based Method for Detecting Falls in the Elderly

This work presents a wearable inertial sensor-based system for detecting falls in the elderly. The system is fixed on the elderly’s head to obtain stable human body acceleration. To detect a variety of falls, a K-means model based on abundant daily activity data is constructed, and then the fall samples are detected by comparing the similarity with the other non-fall samples. The system requires no fall samples for training, and achieves an accuracy of 96.8%.
Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing

We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies' prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes' Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of "Wallstreetbet and GameStop"1 that took place in January 2021.
Simulation of Teacher Interventions in a Training Course of Mathematics Teacher Educators

This work aimed to characterize simulated teacher interventions to manage a didactic configuration in which students solve problem situations using an ICT resource. We also aimed to identify patterns of interaction between the teacher and the students that make it possible to characterize styles of interaction and assess both their interactional and their ecological suitability. To do this, we used the tools of the Onto-semiotic Approach to Mathematical Cognition and Instruction to analyze the level of reflection of 102 educators of preservice teachers of mathematics, about hypothetical problem resolutions of their students and the types of interactions simulated by them to manage the students’ learning. The educators were participants of a Teacher Training of the Teacher Specialization in Education and ICT in Argentina. The results show that there are three levels of reflection and four types of interaction (maieutic, falsificationist, dogmatic and paternalistic). We did not find a high interactional suitability in their proposals of dialogues, and participants do not consider aspects of the ecological suitability, such as the adaptation to the official curriculum. We conclude that the type of interventions that teacher educators propose is closely related to their level of reflection about the reasons why their students respond to problems in one manner or another.
Energy Blockchain for Demand Response and Distributed Energy Resource Management

The high impact of demand reduction on the energy grid management and the importance of reducing loss of distributed energy resources (DERs), in addition to the necessity of a secure distributed data storing system motivate us to propose an energy blockchain solution. This paper presents a demand response (DR) solution utilizing energy blockchain to reduce demand, save the extra DERs, and efficiently incorporate customers block mining ability. In this work, a real dataset of customer demand profiles and PV generation in the Ottawa region is used to deploy a DR Stackelberg game between a control agent (CA) and local customers to negotiate demand reduction by integrating the block mining method as DERs saving. This article presents a novel and well-suited consensus algorithm, Proof of Energy Saving (PoES), that is used to incentivize the customers to reduce their demand, discharge their electric vehicle (EV) and maximize their chance for block mining to earn monetary rewards and DER savings. This results in lower peak demand, customer bill reduction, and transforms energy savings into monetary resources. Furthermore, the results show that our proposed consensus algorithm is robust and secure against malicious actions of users.
HR optical and SAR image registration using uniform optimized feature and extend phase congruency

With increasing spatial resolution, the differences in nonlinear radiation and geometric properties between optical and synthetic aperture radar (SAR) images will be magnified, which will lead to lower registration accuracy. This paper addresses this problem and proposes an automatic registration method for HR optical and SAR images using the uniform optimized feature and the extended phase congruency (EPC) model. First, we combine two different gradient operators and a quadtree algorithm to improve the detection performance of the multi-scale Harris detector, especially for the repeatability and uniformity of feature points in optical and SAR images with large nonlinear radiation and severe speckle noise. Next, we use the EPC model with illumination and contrast invariance to generate the multi-scale orientation index map (MS-OIM) and the multi-orientation phase congruency amplitude map (MO-PCAM) and then build a novel GLOH-like local descriptor to obtain corresponding features based on the two feature maps above. Finally, the fast sample consensus (FSC) method is utilized to remove false matches and calculate the transformation parameters of sensed image based on the piecewise linear model. The detection results on synthetic data show that the proposed detector has better repeatability and uniformity than the other three classical detectors. The registration results obtained on real data indicate that our method is effective for optical and SAR images with speckle noise interference and nonlinear radiometric differences and achieves high registration accuracy.
A multi-domain ontology on healthy ageing for the characterization of older adults status and behaviour

Abstract Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users’ profile.
A commentary of Data trusts in MIT Technology Review 2021

Data trust is an emerging concept in trust typology studies and contemporary legislation on trusts. Trust systems originated in the UK and were developed in the US. In the legal sense, trust refers to the act of trusting a trustee, where the trustor, in accordance with his/her own interests, hands over assets to the trustee to manage. The act of a trustee who manages the data or data rights of a group of individuals is known as data trust. The data trustee manages the client's data or data rights as well as interests, similar to how a doctor acts in the best interest of a patient. Theoretically, data trusts allow users to exercise their rights as data producers.①
Distributed Sampling-based Planning for Non-Myopic Active Information Gathering

This paper addresses the problem of active information gathering for multi-robot systems. Specifically, we consider scenarios where robots are tasked with reducing uncertainty of dynamical hidden states evolving in complex environments. The majority of existing information gathering approaches are centralized and, therefore, they cannot be applied to distributed robot teams where communication to a central user is not available. To address this challenge, we propose a novel distributed sampling-based planning algorithm that can significantly increase robot and target scalability while decreasing computational cost. In our non-myopic approach, all robots build in parallel local trees exploring the information space and their corresponding motion space. As the robots construct their respective local trees, they communicate with their neighbors to exchange and aggregate their local beliefs about the hidden state through a distributed Kalman filter. We show that the proposed algorithm is probabilistically complete and asymptotically optimal. We provide extensive simulation results that demonstrate the scalability of the proposed algorithm and that it can address large-scale, multi-robot information gathering tasks, that are computationally challenging for centralized methods.
Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’ requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.
Watermarking of Deep Recurrent Neural Network Using Adversarial Examples to Protect Intellectual Property

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UbiEi-Edge: Human Big Data Decoding Using Deep Learning on Mobile Edge

Human big data decoding is of great potential to reveal the complex patterns of human dynamics like physiological and biomechanical signals. In this study, we take special interest in brain visual dynamics, e.g., eye movement signals, and investigate how to leverage eye signal decoding to provide a voice-free communication possibility for ALS patients who lose ability to control their muscles. Due to substantial complexity of visual dynamics, we propose a deep learning framework to decode the visual dynamics when the user performs eye-writing tasks. Further, to enable real-time inference of the eye signals, we design and develop a mobile edge computing platform, called UbiEi-Edge, which can wirelessly receive the eye signals via low-energy Bluetooth, execute the deep learning algorithm, and visualize decoding results. This real word implementation, developed on an Android Phone, aims to provide real-time data streaming and automatic, real-time decoding of brain visual dynamics, thereby enabling a new paradigm for ALS patients to communicate with the external world. Our experiment has demonstrated the feasibility and effectiveness of the proposed novel mobile edge computing prototype. The study, by innovatively bridging AI, edge computing, and mobile health, will greatly advance the brain dynamics decoding-empowered human-centered computing and smart health big data applications.