Reviewerly/exHarmony huggingface dataset:
Mobile Recharger Path Planning and Recharge Scheduling in a Multi-Robot Environment

In many multi-robot applications, mobile worker robots are often engaged in performing some tasks repetitively by following pre-computed trajectories. As these robots are battery-powered, they need to get recharged at regular intervals. We envision that, in the future, a few mobile recharger robots will be employed to supply charge to the energy-deficient worker robots recurrently to keep the overall efficiency of the system optimized. In this setup, we need to find the time instants and locations for the meeting of the worker robots and recharger robots optimally. We present a Satisfiability Modulo Theory (SMT)-based approach that captures the activities of the robots in the form of constraints in a sufficiently long finite-length time window (hypercycle) whose repetitions provide their perpetual behavior. Our SMT encoding ensures that for a chosen length of the hypercycle, the total waiting time of the worker robots due to charge constraints is minimized under certain condition, and close to optimal when the condition does not hold. Moreover, the recharger robots follow the most energy-efficient trajectories. We show the efficacy of our approach by comparing it with another variant of the SMT-based method which is not scalable but provides an optimal solution globally, and with a greedy algorithm.
Cross-Domain Security Asset Management for Healthcare

Abstract Healthcare is one of the most peculiar between all Critical Infrastructures due to its context and role in the society. The characteristics of openness and pervasive usage of IT systems and connected devices make it particularly exposed to both physical threats, such as theft and unauthorized access to restricted areas, and cyber attacks, like the notorious wannacry ransomware that abruptly disrupted the British National Health System in May 2017. Even the recent COVID-19 pandemic period has been negatively characterized by an increase of both physical and cyber incidents that specifically targeted hospitals and undermined an essential public service like healthcare. Effective security solutions are necessary in order to protect and enhance the resiliency of the Critical Infrastructures. This paper presents the work being developed in the context of the SAFECARE H2020 project, that specifically considers the requirements for security of hospitals. A particular focus is given to the asset management that consider cross-domain aspects of security, like the physical location and virtual connections that link different components of a hospital. This allows advanced knowledge that enables to infer and forewarn of possible elaborated cyber-physical kill chains . This is particularly important and useful during crisis, as allows to have a holistic overview of the status of the hospital and the potential impacts of one or more incidents to the critical assets. The description and simulation of an attack scenario is also given, together with the description of the messages exchanged by the security systems and the information made available to security operators.
Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots

The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.
QoE-Based Server Selection for Mobile Video Streaming

Mobile devices make up the bulk of clients that stream video content over the internet. Improving one of the most popular services, i.e., mobile video streaming, has the potential to make the most market impact. Video streaming giants like YouTube, Netflix, Hulu, and Amazon video aim to provide the best quality service and expand market share. The problem of selecting the best server is critical for ensuring the qualified experience for video streaming on a mobile device. Traditional server selection strategies use proximity as a server selection rule. Improved strategies select servers by considering more factors that also impact the quality of experience (QoE). Currently, reinforcement learning is being used to maximize QoE when selecting servers. This paper seeks to further develop an RL agent that performs better on mobile devices. The result is an RL agent that quickly learns to select servers that offer the best QoE.
A Variational Auto-Encoder Approach for Image Transmission in Noisy Channel

Recent advancements in information technology and the widespread use of the Internet have led to easier access to data worldwide. As a result, transmitting data through noisy channels is inevitable. Reducing the size of data and protecting it during transmission from corruption due to channel noises are two classical problems in communication and information theory. Recently, inspired by deep neural networks' success in different tasks, many works have been done to address these two problems using deep learning techniques. In this paper, we investigate the performance of variational auto-encoders and compare the results with standard auto-encoders. Our findings suggest that variational auto-encoders are more robust to channel degradation than auto-encoders. Furthermore, we have tried to excel in the human perceptual quality of reconstructed images by using perception-based error metrics as our network's loss function. To this end, we use the structural similarity index (SSIM) as a perception-based metric to optimize the proposed neural network. Our experiments demonstrate that the SSIM metric visually improves the quality of the reconstructed images at the receiver.
Face Recognition in Video Streams and its Application in Freedom Fighters Discovery - A Machine Learning Approach

Face Recognition is an automated process implemented by using various tools and techniques, which is capable of detecting and identifying human faces from any image or video stream. Machine learning is an approach, which involves in training a machine for prediction and decision making. It simulates the function of a brain through proper training. In this paper, a machine learning approach is used to predict the faces of Indian freedom fighters from any video stream given as an input. To accomplish this task, the system must be trained before with an appropriate and adequate numbers of facial dataset. The training dataset is also created from a video stream, which is named as training video. Provided, that particular video stream must contain only one specific freedom fighter's images of different stages and different styles. From the training video stream, image frames consist of their face were identified, extracted and stored for each person. Then these frames of faces were used to train the machine. After proper training, a model was created and this model was used for prediction. In this work, Indian freedom fighters' images were used for training and prediction. The same approach could be utilized for the prediction of any person or list of persons from a video stream after procuring proper training.
Partial Label Learning via Self-Paced Curriculum Strategy

Partial-Label Learning (PLL) aims to learn from the training data, where each example is associated with a set of candidate labels, among which only one is correct. Existing PLL methods to deal with such problem usually treat each training example equally and few works take the complexities of training examples into consideration. In this paper, inspired by the human learning mode that gradually learns from "easy" to "hard", we propose a novel Self-Paced Curriculum strategy based Partial-Label Learning (SPC-PLL) algorithm, where curriculum strategy can predetermine prior knowledge to adjust the learning priorities of training examples, while self-paced strategy can dynamically select "easy" training examples for model induction according to its current learning progress. The combination of such two strategies is analogous to "instructor-student-collaborative" learning mode, which not only utilizes prior knowledge flexibly but also effectively avoids the inconsistency between the predetermined curriculum and the dynamically learned models. Extensive experimental comparisons and comprehensive ablation study demonstrate the effectiveness of such strategy on solving PLL problem.
Public Dataset of Parking Lot Videos for Computational Vision Applied to Surveillance

In this work, we propose a new dataset, the UFPArk, which consists of a selection of video clips based on a surveillance camera’s footage throughout a day. The aforementioned dataset is relevant to areas such as computer vision-based object detection, human behavior detection and Internet of Things (IoT) applications, which gained momentum with recent Deep Learning (DL) breakthroughs. To validate our dataset, we conduct some experiments that illustrate the relevance of this effort and its applicability in academic and commercial environments.
An Industrial Distributed Network of Intelligent Robotic Security Guards Based on the Internet of Robotic Things Paradigm

The purpose of this paper is to present an advanced robotic security solution based on the Internet of Robotic Things (IoRT) paradigm under design and prototyping by Vitrociset - a Leonardo Company. An internet of intelligent security robotic guards based on a Vitrociset AI framework called VIKI, with an integrated biometric module able to provide ePassport instant photo images, downloading them by RFID and correlating them with enhanced and cutting-edge deep learning-based face recognition techniques, using commercial-off-the-shelf (COTS) electro-optical sensors. The distributed robots will be equipped also with enhanced functions like real-time thermal image analysis for illness prediction alarms and license plate recognition for vehicles access into the controlled area. The solution is under design for a fixed internet of multi-robot configuration, and successively will be switched easily to a mobile one. The internet of robotic guards will have a human interaction module with speech synthesis functions and natural language processing for knowledge-based automatic reasoning useful for question-answering sessions during access control interactive interviews. In this way, during the biometric reading, people will be able to ask questions to the robotic guards, which will interact and collaborate with humans, adding security questions and clarifications during access control to restricted and protected areas.
Data Augmentation for Retrieval- and Generation-Based Dialog Systems

In this study we improve the model performance by data augmentation for dialog system. Contrary to previous work which proposes novel models, we enlarge the training data to achieve the same effect. We assume that there exists a many-to-many relationship between queries and replies in dialog system and we try to find such query-reply pairs. Specifically, we use existing conversational corpus (which consists of one to one query-reply pairs) to build a state-of-the-practice retrieval system, then the agent is asked to select the k most likely replies for queries inside the corpus, adding them to original conversation dataset. By doing this, we acquire many-to-many dialogue corpus called augmented corpus. Finally, we use the original corpus and the augmented corpus to conduct experiments on both generative and neural retrieval dialogue system by utilizing dual retrieval model and sequence to sequence generative model, obtaining strong performance improvement only by replacing the training corpus.
Partitioned Private User Storages in End-to-End Encrypted Online Social Networks

In secure Online Social Networks (OSN), often end-to-end encryption is used to ensure the privacy of the communication. To manage, store, or transfer cryptographic keys from one device to another, encrypted private storages can be used. To gain access to such storages, login credentials, only known to the user, are needed. Losing these credentials results in a permanent loss of cryptographic keys and messages because the storage is encrypted. We present a scheme to split encrypted user storages into multiple storages. Each one can be reconstructed with the help of other participants of the OSN. The more of the storages can be reconstructed, the higher the chance of successfully reconstructing the complete private storage is. Therefore, regaining possession of the cryptographic keys used for communication is increased. We achieve high rates of successful reconstructions, even if a large fraction of the distributed shares is not accessible anymore because the shareholders are inactive or malicious.
Adaptive Resolution-Based Tradeoffs for Energy-Efficient Visual Computing Systems

The real world presents interpretable visual detail at different scales in different situations. While empowering face recognition, augmented reality, and other computer vision tasks, mobile systems should be able to dynamically adapt the spatiotemporal resolution of the visual sensing pipeline to capture image frames at high resolutions for task precision and low resolutions for energy savings. Facilitating real-time decisions to reconfigure resolutions will let systems dynamically adapt to the needs of the vision algorithms, as well as the environmental situation of the visual scene. This article will review system challenges and opportunities of image-resolution-based tradeoffs toward energy-efficient visual computing through device driver and media framework optimization.
Towards Blockchain-Driven Network Log Management System

Log data is crucial to detect mischievous activities conducted in a computing environment. Traditional log management systems that depend on cloud and centralized storage servers are vulnerable to a single point of failure and lack transparency and trust since the adversaries tamper log records. In literature, blockchain is used to design log storage to resolve these issues. Although some solutions have been introduced, the existing works still cannot guarantee log data confidentiality, lack of efficient query mechanism, real-time implementation, and performance analysis. In this paper, we propose a blockchain-based network log data storage, query, and audit system. We have implemented and deployed the scheme in physical networking environment to collect log records. Our system ensures transparency, data accountability, and data confidentiality and incurs low overhead for performance analysis. Our work demonstrates the feasibility of blockchain to build a time-efficient log management system while ensuring the privacy of log data.
Progressive Guidance Edge Perception Network for Semantic Segmentation of Remote-Sensing Images

Remarkable improvements have been seen in the semantic segmentation of remote-sensing images. As an effective structure to aggregate shallow information and deep information, encoder–decoder structure has been widely used in many state-of-the-art models, but it possesses two drawbacks that have not been fully addressed. On the one hand, encoder–decoder structure fuses the features obtained from shallow and deep layers directly; despite harvesting some detailed information, it also brings in noisy features owing to the poor discriminant ability of the shallow layers. On the other hand, existing encoder–decoder structure merely fuses the high-level information generated by the last layer of encoder once, which neglects its guidance ability to the feature aggregation process in the decoder. In this letter, we first propose an edge perception module (EPM) to eliminate the noisy features in the shallow information, as well as enhance features’ structural information. And then, we generate the most suitable guidance information adaptively for different stages in the decoder through high-level information module (HIM). Finally, we apply the guidance information to achieve feature aggregation in the feature aggregation module (FAM). Combined with EPM, HIM, and FAM, our proposed model achieves 89.5% overall accuracy (OA) on the challenging ISPRS Vaihingen test set, which is the new state-of-the-art in the semantic segmentation of remote-sensing images.
An interpretable mechanism for personalized recommendation based on cross feature

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.
A Patient-Centric Key Management Protocol for Healthcare Information System based on Blockchain

Traditional healthcare systems store and process personal healthcare record (PHR) in the centralized client-server architecture. PHR stored in a healthcare institution remain in depository which is not easily shared with other institutions due to technical and infrastructure related restrictions. In such a way, if a patient has to visit distinct institutions/hospitals or physicians, there is no effective and privacy-preserving data sharing mechanism. Furthermore, even if patients' privacy is protected by Health Insurance Portability and Accountability Act (HIPAA), it is still doubtful owing to the lack of the consideration of if the patient is directly involved. With the recent bloom of interest around blockchain, a technology with well-defined decentralized framework, privacy-preserving in healthcare information system (HIS) should be revisited to examine the new possibility. Actually, in the literature, the blockchain-based researches about the privacy and security in healthcare are prevalent in decentralized platform. However, they have drawn attention on the personal healthcare record management rather than focus on how to distribute the encryption/decryption key used to guarantee the confidentiality of PHR. Blockchain provides a shared, immutable and transparent history of all the transactions to build systems with trusty and decentralized environment. This provides an opportunity to develop a secure and trusty PHR data management system by blockchain technology. This paper presents the solution aiming at the patient's control by holding the knowledge of the encryption/decryption key which can be deduced from the previous transaction in blockchains. In such a way, a patient can control the personal healthcare record by controlling key usage.
Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Cycle-Consistent Adversarial Networks

Semantic segmentation is an important computer vision task for the analysis of aerial imagery in many remote sensing applications. Due to the large availability of data it is possible to design efficient convolutional neural network based deep learning models for this purpose. But these methods usually show a weak performance when they are applied without any modifications to data from another domain with different characteristics relating to aspects concerning the sensor or environmental influences. To improve the performance of these methods domain adaptation approaches can be employed. In the following work, we want to present a method for unsupervised domain adaptation for semantic segmentation. We trained an encoder-decoder model on the source domain dataset as task application and adjusted the network to the target domain. The adaptation process is based on a style transfer component, which is realized using a cycle-consistent adversarial network. Through a continuous adaptation of the task model we achieved a higher generalization of the network and increased the task method performance on the target domain.
Delay time and persistent oscillations for a shifted quantum shutter

Abstract We derive an exact analytical solution to the time-dependent Schrödinger equation based on a resonant state expansion, to explore the time-evolution of cutoff plane waves scattered by finite range potentials, within a shifted quantum shutter model. The latter allows to control both, the position of the shutter and an hypothetical detector, which are initially separated by a distance Δ X . The dynamical advance-time (negative delay-time), Δ T , is explored for the particular case of a delta potential well, by measuring the difference of the first maxima of the time-diffraction pattern of the probability density, corresponding to the free and delta potential cases. We show that in general, Δ T exhibits a monotonic behaviour as a function of Δ X , and derive a simple formula for the timescale by using the symmetry and rescaling properties of the dynamical solution. We demonstrate that below a critical value of the shutter-detector separation, Δ X c , the monotonic behaviour of Δ T is hindered due to an oscillatory phenomena of the probability density, known as persistent oscillations , that drastically distorts the maxima used in the measurement process. These persistent oscillations are periodic Rabi-type oscillations that arise from an interplay between the incidence energy of the initial quantum wave and the bound state of the system, and their frequency play an important role in the dynamics of the delay time.
Inclusion by Design: A 75-Minute Crash Course on Accessible Design

The community of researchers supporting instruction on design thinking has a significant body of materials to help students understand and master the process of creative problem solving in design. Missing, we argue are materials and processes which directly support the design of inclusive technologies for persons with disabilities. We present ‘Inclusion by Design’, an interactive and participative crash course designed to introduce students to techniques that may be useful in an inclusive design process. In a single 75-minute session, students explore the inclusive design of a transportation technology for a visually impaired persona. We report on our findings from a single pilot of the crash course involving six diverse students within a graduate course on Inclusive Design. Our findings suggest that the course may be effective in introducing techniques like storyboarding, scenario creation, and low fidelity prototyping to students using an approach that may be effective for various learning styles.