Reviewerly/exHarmony huggingface dataset:
Extended SESIM: A Tool to Support the Generation of Synthetic Datasets for Human Activity Recognition

The accessibility of datasets that capture the performance of Activities of Daily Living is limited by the difficulties in setting up test beds. The Covid-19 pandemic recently compounded such challenges. Smart Environments employed as test-beds consist of sensors and applications formulated to develop a comfortable and safe environment for their inhabitants. Despite the increase in quantities of Smart Environments, accessibility of these spaces for researchers has become even more challenging amidst a pandemic. Computing power has enabled researchers to generate virtual Smart Environments with fewer overheads and less complexity. This article proposes an Extended Smart Environment Simulator (ESESIM), with multiple inhabitants possibly utilised for dataset generation. The proposed simulation tool has a virtual space with multiple script-regulated inhabitants. While the various inhabitants probe the Smart Environment, sensor readings are recorded and stored in a dataset. The virtual space developed in this study generated synthetic datasets that can be employed for Human Activity recognition in machine learning. This study also evaluated two deep machine learning models and performance mechanisms in recognising four activities of daily living, namely personal hygiene, dressing, cooking and sleeping, on the SESIM dataset. Findings from this study indicate that simulation can be used as a tool for generating human activity datasets.
The “Other” Agent: Interaction with AI and Its Implications on Social Presence Perceptions of Online Customer Experience

Advancements in Artificial Intelligence (AI) such as digital assistants and conversational agents are being adopted fast and wide across consumer industries such as e-commerce, where they act as frontline service agents and interact with customers in service encounters. It is suggested that technology is no longer only the mediator of communication between customers and a company but has potential to become the "other'' with whom customers interact. Based on this idea, this research adopts Social Response Theory to measure the effects of anthropomorphism of AI, para-social interaction with AI and personalization on perceived social presence of the customer experience, customer loyalty and intentions to engage in eWOM. An online survey with a sample of online consumers, who have previously engaged with a form of AI-technology, is conducted. Quantitative analysis of the data through CFA and SEM shows that perceived social presence has a strong effect on both customer intentions to engage in eWOM and customer loyalty. Further, social presence serves as a mediator for the relationship between an anthropomorphism of AI and para-social interaction with AI on eWOM intentions and customer loyalty. A discussion of these findings and implications concludes this paper.
Toward a GDPR Compliant Blockchain Governance Framework

Recent research has highlighted multiple incompatibilities between blockchain technology and the General Data Protection Regulation (GDPR) regarding data controller and data deletion. Such incompatibilities impede the adoption of blockchain technology on a larger scale. This paper aims to resolve these incompatibilities, exploring the issues that need to be considered while developing a GDPR compliant blockchain governance framework. We collected data using 20 semi-structured interviews and discussions from 18 different IT companies involved in blockchain-based service development. We analyzed the data using the Gioia approach. We identified three major governance dimensions that must be considered for GDPR compliant blockchain services, namely community, blockchain protocol, and compliance; each of which has several sub-dimensions. Our study extends prior governance frameworks, suggesting the guidelines to comply with GDPR requirements. This guidelines might help organizations to build a GDPR compliant blockchain business model. Based on our findings, we also put forward directions for future inquiry.
Pedagogical Implications of Post COVID Restrictions on the Korean University EFL Classroom from the Teachers Perspective －A Case Study

This study investigated how EFL teachers at a Korean university perceived the introduction of post COVID restrictions and protocols during their classes during the transition from online classes to in-person classes in the spring of 2022. Data were collected from 17 teachers employed at a private university in the greater Seoul area. A survey questionnaire consisting of multiple choice, Likert scale and open-ended questions was used as the initial data collection method. In addition, survey participants who consented were contacted by e-mail to discuss responses in greater detail. Data was analyzed using general qualitative analysis and discussed using descriptive statistics. The findings show that despite the restrictions, teachers were generally able to continue their teaching duties but there were high levels of frustration with the limited number of pedagogical options available to them. In addition, they view masks and socially distancing as a significant hindrance to communication, classroom engagement, and performance. Administrational and educational implications of these findings as well as limitations are also discussed.
Interfacing Intelligent Personal Assistant to SDI/O with one click

Intelligent Personal Assistants (IPA) are becoming an essential part of our life. The trend aiming at adopting IPA devices is driven by the uncountable applications enabled by the advent of the Internet of Things (IoT) and its role in home automation. Actually, people are able to use, for example, voice-controlled IPA devices (e.g., Amazon Alexa) to interact and control things in their surrounding environment. Yet, to enable such interactions in an environment with multiple devices, the user has to configure each device. Besides, it is not possible to apply the same operation in a specific environment used for a short period, such as a hotel room, i.e., for security reasons: the owner of a hotel cannot permit access to the devices from their customer as it may be risky. To deal with such a limitation, the adoption of a Software-Defined approach, such as Software-Defined I/O (SDI/O), is a relevant approach to decouple the access to IoT resources from their management duties. In the case of accommodation facilities, such as a hotel room, to connect an IPA to IoT devices, a set of repeated configurations has to be performed. This paper proposes “Everywhere IPA” (EIPA), an approach relying on the Software-Defined I/O approach to automatize the configuration of IoT devices.
DLT-based Data Mules for Smart Territories

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas yet, due to the lack of or too costly wide area network connectivity. With the aim to offer a practical and secure way to transport data and allow for communications in such constrained scenarios, we focus on the problem of incentivizing to data mules, i.e. devices dedicated to enable the data transfer even in the absence of the Internet. Our solution combines the use of several distributed technologies for verifying the correct behavior of all the partici-pants and incentivize them. We focus on the use of state channels to support the flow of smart-contract-based tokens as a form of payment, in a condition where participants communicate only with others in physical proximity. Furthermore, we validate the viability of the application through the simulation of peer-to-peer interactions between the participants. In this work we achieve positive results in terms of communication latency and percentage of client nodes which are able to benefit from the system.
LUSketch: A Fast and Precise Sketch for top-k Finding in Data Streams

Finding top-k flows in data streams is a fundamental task in network management. As the line rates continue to increase in a network, it becomes increasingly challenging to find the top-k flows precisely and quickly in real time. Existing algorithms that can achieve high precision suffer from a slow speed. In this paper, we propose a novel sketch, LUSketch, which is much faster than existing algorithms. LUSketch adopts a new strategy called limited-and-imperative-update to significantly improve the insertion speed. The key idea is to significantly reduce the number of update operations by establishing a connection between the sketch and heap part when tracking the top-k flows. Our experiment results show that, while maintaining a high precision, LUSketch achieves an insertion speed approximately 2 to 5 times higher than that of the state-of-the-art.
PPAS: Privacy-preserving Resource Discovery for Multi-domain SFC orchestration

Service function chaining of network-functions across multiple domains enables next-generation services to be offered efficiently. Multi-domain orchestrator decouples the complexities of setting up such chains across domains on behalf of the users of the service. Privacy preservation is essential in this scenario since domains do not want to expose their topology, available resources, cost, and other operational aspects. Similarly, users try to minimize the exposure of their requirements to the domains and the orchestrator. Prior work relied on abstraction and/or aggregation of information in advertisements to preserve privacy. This paper presents a novel cryptographic mechanism based on bilinear mapping that allows the domains and users to privately advertise their information without revealing anything to the orchestrator and other domains. A private set intersection is used to compute the common elements between domains and users. Security proof for the proposed flows is provided using the universally composable framework. Usage of cryptographic techniques reduces the need for abstraction and provides strong privacy guarantees quantified in the privacy metrics of the paper. The simulation results demonstrate that the performance of the proposed system is at par with other existing work while providing strong cryptographic privacy, which none of the other systems provide.
Engage: Session Guarantees for the Edge

Edge computing offers support for latencyconstrained applications, by replicating data in the edge. Edge storage systems need to adopt both partial replication, as only data of interest needs to be replicated, and weak consistency models, to avoid the overhead and latency induced by the coordination mechanisms of strong consistency models. In this context, session guarantees are a powerful tool that can be used to simplify the design of edge applications. This paper presents Engage, a storage system that offers efficient support for session guarantees in a partially replicated edge setting. To achieve this, Engage combines the use of vector clocks and distributed metadata propagation services with a payload propagation scheme tailored for the edge. We have implemented Engage and evaluated its performance experimentally. The results show that, when compared with previous proposals, the combination of techniques employed by Engage reduce both the number of false dependencies, that can slow down the system, and the signaling overhead, while improving the freshness of data exposed to clients.
On the Applicability of Payment Channel Networks for Allocation of Transport Ticket Revenues

In many public transport networks, multiple providers cooperate to offer integrated services and, conse-quently, provide integrated fare collection. Thus, ticket revenues need to be redistributed so that each provider receives its respective share. Typically, the customers' travel behavior is surveyed and the fares paid are aggregated over certain periods of time, and the revenue is periodically allocated based on this information. To avoid a centralized trusted third party or the exchange of sensitive information between providers, we present an approach that integrates revenue allocation directly into the payment process: The proposed approach is based on payment channel networks and utilizes multi-hop payments to perform revenue allocation. We thereby show how to make use of payment channel networks in this setting as well as the corresponding benefits.
A Defense-Attack Game under Multiple Preferences and Budget Constraints with Equilibrium

Cyber-security research often focuses on attack-defense games where a strategic attacker seeks to destroy the defender's targets or kill him. In such a game there is a defender who just try to protect himself. In the real world, players can choose to protect themselves as well as kill their opponents to maximize their overall gain. In this case, the player allocates their budget for both defending and attacking actions and decides how well to attack and how well to defend against others. Players should allocate their budgets appropriately for each action throughout multiple rounds when playing such a game. The probabilities of surviving and killing in each round are determined by what happened in the previous rounds and the amount of the remaining budget. Players can continue playing until they die or run out of money. It is possible for the player not to be aware of everything his opponent does. Despite knowing that his opponent is playing according to one of the possible types, he cannot see which action exactly he is taking. Likewise, it may be the case that the player only sees the opponent's action, but does not know what its objective is. To meet this challenge, this paper develops a game where players decide how to allocate resources when they have partial information. For a model with complete information, equilibrium can be found and, as an extension, models with incomplete and imperfect information are also discussed. Our simulation examines how utility changes based on prior beliefs, total budgets, costs, and uncertainty.
Tracing Tor Hidden Service Through Protocol Characteristics

Tor is a relay-based communication network that provides users with anonymous access to the Internet. Tor hidden services protect the identities of the content publishers to achieve anonymity and anti-censorship. However, hidden services are abused to conduct illegal activities, including hosting botnets and trading drugs. This paper proposes a tracing approach to locate illegal hidden services based on the Tor protocol characteristics, which allows a Tor client to embed a signal into a Tor circuit connecting with the illegal hidden service. Once the Tor node nearest to the the hidden service detects the signal, the identity of the hidden service can be revealed. Our approach is simple, powerful, and stable compared with previous methods. We implemented the approach and performed experiments over the Tor network, whose results show that our approach is feasible and effective, with 100% accuracy, 99.25% true positive rate, and zero false positive rate.
X-Ray, MRI and CT scan Medical Image Compression using Huffman Coding

In hospitals, modern medical imaging generates a large amount of medical pictures. The necessity of medical image compression in telehealth cannot be overstated because it provides for excellent image quality for the medical images containing substantial diagnostic information while requiring less storage space, allowing them to be conveniently transmitted across a network and accessible in a short period of time. This research assessed the performance analysis of several image compression methods for the medical images such as X-rays, MRIs, and CT scans. Image quality parameters such as Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), and Mean Structural Similarity Index (MSSI) have been used to assess the quality of medical pictures following compression using Huffman coding.
A Robust Object Tracking and Visual Servo Method for Mobile Robot

The general Siamese network based object tracking methods tend to generate the final score map from high-level features and treat features from each position equally, which may lead to the problems of large search region and low efficiency. In order to solve these, this paper proposes a fully-connected Siamese network tracking method based on the calculation of histogram of gradient feature similarity and on feedback of the fading-memory Kalman filter. This strategy enables real-time correction and compensation, which means it could re-track the target although it is occluded or temporarily lost. The target’s bounding box obtained by object tracking method is used to produce the control command and achieve the image-based visual servo. Comparative experiments with other methods are conducted on several public datasets to prove its effectiveness. In addition, we design a mobile robot tracking system to test the algorithmic performance in real-world scenarios. Experimental results show that the robot is able to track the target accurately, and continue to track the target despite occlusion or temporary disappearance.
Two-stage Self-supervised MVS Network using Adaptive Depth Sampling

With the development of deep learning, multi-view stereo has achieved significant progress recently. Due to the expensive three-dimension supervision, self-supervised methods have more potential. In this work, a novel two-stage self-supervised learning framework for multi-view stereo is proposed to overcome photometric dependency and the effect of foreshortening. On considering that accurate depth hypothesis always plays an important role in estimating depth information. Therefore, this work concentrates on designing an adaptive depth sampling module based on neighboring spatial patches propagation, which is determined by the normal maps. From this point of view, a two-stage process is carried out in this work. In detail, the coarse initial depth maps and normal maps are obtained in the first stage, and then the network in the second stage refines the depth sampling module by taking the influence of foreshortening into account. Furthermore, the loss functions are developed including feature-metric consistency to overcome the photometric inconsistency caused by lighting variation. Moreover, the consistency between depth maps and normal maps is also employed in the loss functions. To evaluate the effectiveness of our proposed two-stage framework, the experiments are carried out on the DTU datasets. The experimental results demonstrate that our self-supervised learning framework has excellent performance compared to the baseline methods.
Trajectory Optimization on Safety, Length and Smoothness in Complex Environments with A Locally Trained and Globally Working Agent

Focused on the balance among safety, length, and smoothness, this paper proposes a novel model to train an agent with deep reinforcement learning to optimize trajectory in complex environments. Inspired by the human habit that first finds the shortest trajectory and then slightly optimizes safety and smoothness, State is initialized as a radical trajectory combined with local obstacle distribution. Action adjusts dangerous waypoints jointly. Reward penalizes length increase based on local smoothness change. Episode is early terminated to divide the whole problem into smaller ones, while reward assembles them back with a large amount of training data. This allows the agent to be trained locally and work globally to accelerate convergence. Performances in various scenarios demonstrate our method’s ability to balance safety, length, and smoothness. With the Markov property of the problem and our newly discovered mathematical property of B-spline, it adjusts waypoints under sub-grid map and can be generalized stably in various maps with dense obstacles.
Improved RRT<sup>*</sup>-A<sup>*</sup>-based Three-Dimensional Path Planning Algorithm for the Robotic Dolphin

The dolphins are fairly well endowed with high turn maneuverability in vertical and horizontal planes. This paper proposes a modified path planning algorithm fusions of rapidly-exploring random tree (RRT) and graph-based methods for a developed robotic dolphin. Considering simultaneously the both minimum yaw radius and minimum pitch radius constraints, a method of calculating a three-dimensional (3D) Dubins curve from two 2D Dubins curves by interpolation is proposed. The 3D Dubins curves and the length of the curves are utilized as the paths and costs of path planning to satisfy the motion constraints of the robotic dolphin. Furthermore, in order to meet the speed and optimization of planned path, a variant RRT algorithm combined with A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is employed to generate feasible path for the robotic dolphin. The path cost and calculation time of this method is lower. Finally, a tendon-driven continuum robotic dolphin is presented to provide the simulation platform for verifying the effectiveness of the proposed methods.
Celebrating Robustness in Efficient Off-Policy Meta-Reinforcement Learning

Deep reinforcement learning algorithms can enable agents to learn policies for complex tasks without expert knowledge. However, the learned policies are typically specialized to one specific task and can not generalize to new tasks. While meta-reinforcement learning (meta-RL) algorithms can enable agents to solve new tasks based on prior experience, most of them build on on-policy reinforcement learning algorithms which require large amounts of samples during meta-training and do not consider task-specific features across different tasks and thus make it very difficult to train an agent with high performance. To address these challenges, in this paper, we propose an off-policy meta-RL algorithm abbreviated as CRL (Celebrating Robustness Learning) that disentangles task-specific policy parameters by an adapter network to shared low-level parameters, learns a probabilistic latent space to extract universal information across different tasks and perform temporal-extended exploration. Our approach outperforms baseline methods both in sample efficiency and asymptotic performance on several meta-RL benchmarks.
F-PCNet: A New Fast Object Detection Method Based on Point Cloud Only

Although deep learning methods have greatly improved the accuracy of the object detection tasks, it is still challenging to balance the efficiency and accuracy of the algorithms under circumstances of point clouds only. In this paper, an anchor-free one-stage deep neural network, F-PCNet, is proposed to realize real-time detection based on point clouds on an autonomous driving platform while maintaining high accuracy. The proposed network takes the bird’s eye view of point clouds collected by LiDAR as input, and outputs the category and 2D bounding box of each detected object. The backbone of F-PCNet is composed of residual network modules of different sizes which effectively reduce the impact of learning degradation. The anchor-free detection head enables F-PCNet to achieve high levels of accuracy and efficiency. Experimental results show that F-PCNet achieves high detection accuracy in a short time consumption and is suitable for real-time detecting scenarios.