Reviewerly/exHarmony huggingface dataset:
Device Onboarding in Eclipse Arrowhead Using Power of Attorney Based Authorization

Large-scale onboarding of industrial cyber physical systems requires efficiency and security. In situations with the dynamic addition of devices (e.g., from subcontractors entering a workplace), automation of the onboarding process is desired. The Eclipse Arrowhead framework, which provides a platform for industrial automation, requires reliable, flexible, and secure device onboarding to local clouds. In this paper, we propose a device onboarding method in the Arrowhead framework where decentralized authorization is provided by Power of Attorney. The model allows users to subgrant power to trusted autonomous devices to act on their behalf. We present concepts, an implementation of the proposed system, and a use case for scalable onboarding where Powers of Attorney at two levels are used to allow a subcontractor to onboard its devices to an industrial site. We also present performance evaluation results.
Resource Optimization of SFC Embedding for IoT Networks Using Quantum Computing

Embedding Service Function Chain (SFC) into the massive and resource-hungry Internet of Things (IoT) substrate graph is a critical optimization research problem. Unfortunately, the classical Integer Linear Programming (ILP) formulation for such problems is usually NP-hard. Thus, this research study presses on the need to go beyond the realms and employ Quantum Annealing (QA) to speed up the computation. To comply, we reformulate the SFC embedding problem into IoT graphs as Quadratic Unconstrained Binary Optimization (QUBO) format and propose a hybrid warm start quantum annealing (WSQA) optimization technique. Simulation results show that our proposed WSQA can improve resource utilization, accelerate computing time, and achieve a better scalability success rate at solving large-scale SFC deployment compared to standalone QA. Further along the line, this research inspires the application of quantum optimization for resource allocation in next-generation networks even with the limited availability of qubits.
Structural reinforcement-based graph convolutional networks

Graph Convolutional Network (GCN) is a tool for feature extraction, learning, and inference on graph data, widely applied in numerous scenarios. Despite the great success of GCN, it performs weakly under some application conditions, such as a multiple layers model or severely limited labeled nodes. In this paper, we propose a structural reinforcement-based graph convolutional network (SRGCN), which contains two essential techniques: structural reinforcement and semantic alignment. The model's core is to learn and reinforce structural information for nodes from both feature and graph perspectives and then expect the extracted semantic mappings to be similar by semantic alignment technique. The main advantage of SRGCN is structural reinforcement, i.e. the model improves the propagation of features in the graph by reinforcing the structural semantics. This method will alleviate the problems of over-smoothing and over-fitting. We evaluate the model on three standard datasets with node clustering tasks, and the experimental results demonstrate that SRGCN can outperform relative state-of-the-art (SOTA) baselines.
Safe reinforcement learning for dynamical systems using barrier certificates

Safety control is a fundamental problem in policy design. Basic reinforcement learning is effective at learning policy with goal-reaching property. However, it does not guarantee safety property of the learned policy. This paper integrates barrier certificates into actor-critic-based reinforcement learning methods in a feedback-driven framework to learn safe policies for dynamical systems. The safe reinforcement learning framework is composed of two interactive parts: Learner and Verifier. Learner trains the policy to satisfy goal-reaching and safety properties. Since the policy is trained on training datasets, the two properties may not be retained on the whole system. Verifier validates the learned policy on the whole system. If the validation fails, Verifier returns the counterexamples to Learner for retraining the policy in the next iteration. We implement a safe policy learning tool SRLBC and evaluate its performance on three control tasks. Experimental results show that SRLBC achieves safety with no more than 0.5× time overhead compared to the baseline reinforcement learning method, showing the feasibility and effectiveness of our framework.
Foreign object detection algorithm for high voltage transmission lines incorporating atrous convolution

Abstract There are problems of complex background, serious occlusion, and low accuracy of foreign object detection on high-voltage transmission lines. In this paper, we propose an algorithm incorporating atrous convolution for the detection of four types of foreign objects that are more likely to appear on high-voltage transmission lines: bird nests, balloons, kites, and rubbish. The algorithm is based on the original YOLOv4 model, replacing the original SPP structure with an ASPP structure. By using the atrous convolution instead of the original pooling operation, the network is able to have a larger field of perception during feature extraction, while the resolution of the feature map is not degraded too much to avoid losing too much information. The experimental results show that the accuracy of this method reaches 93.73% on the transmission line dataset used, which is a significant improvement over the original YOLOv4 model and improves the detection capability of foreign object targets that are obscured in complex environments.
Automatic Depth Image Generation for Unseen Object Amodal Instance Segmentation

Abstract This research proposes an automatic instance-wise depth image estimation method for Unseen Object Amodal Instance Segmentation (UOAIS), which predicts depth images automatically by sharing features of other prediction branches. The depth images can provide valuable information for amodal mask generation. To achieve good performance, the model estimates the depth images in two steps by adding two extra depth branches to the original UOAIS model. A new feature fusion scheme incorporating these two branches is also designed to facilitate information sharing between tasks. The new model is evaluated on OCID and UOAIS-Sim evaluation datasets. Compared with the performance of the original model with pure RGB input, the AP50 of the new model increases by 0.685% on the UOAIS-Sim evaluation dataset, and the overlap and boundary F measures increase by 2.3% and 1.6% respectively on the OCID dataset. Consequently, the model achieves similar performance to those which need RGB-D images as input. It has the potential to serve as a compromise plan for UOAIS if the collection of real scene-depth images is difficult or unavailable.
Using normative inquiry and co-design to embed inclusive design in social design education

Collaborations of universities with different stakeholders can extend students’ learning and result in capacity development among all the collaborators. The project described in this article, SIDe, involved partners from the UK and Turkey who develop a mechanism together to effectively embed inclusive design and co-design in social design education. The knowledge production through normative inquiry and the experience of a shared equal voice among all the partners resulted in the SIDe Model, enabling collaborators to understand their unique contributions. The model was tested with students from four different disciplines at the Faculty of Architecture, Mimar Sinan Fine Arts University. This article offers original insights into how normative inquiry can be realized in course design and project execution to drive inclusion in social design education; it also provides the improved design process model – the SIDe model – to facilitate potential wider adoption of the good practice.
Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.
Audio adversarial detection through classification score on speech recognition systems

Deep neural networks (DNNs) provide effective performance for many computer vision problems. However, DNNs are vulnerable to adversarial examples, generated by adding an imperceptible noise to a normal sample. To address this problem, we propose a method of defense that detects audio adversarial examples without the need for a separate module or process. When adversarial examples are generated, there will be a pattern in the classification scores between the incorrect class and the normal class, which arises because there is a point at which misclassification of the normal data is induced using the minimal amount of distortion. The proposed method uses this characteristic of the classification scores to detect audio adversarial examples. For testing, Mozilla test data and LibriSpeech test data were used as experimental test data, and a DeepSpeech model was used as the speech recognition model. In addition, as the experimental environment, a limited environment without noise in the process of voice transmission was assumed. The experimental results show that the proposed method can detect audio adversarial examples with a detection rate of 91% and 93% (Mozilla voice dataset and LibriSpeech dataset) while reducing the error rate on normal samples to 8% and 7% (Mozilla voice dataset and LibriSpeech dataset). A comparison of the proposed method with three state-of-the-art defense methods shows that the proposed method had a high detection rate on the demonstrably advanced white-box adversarial examples.
HAKA: HierArchical Knowledge Acquisition in a sign language tutor

Communication between people from different communities can sometimes be hampered by the lack of knowledge of each other's language. A large number of people needs to learn a language in order to ensure a fluid communication or want to do it just out of intellectual curiosity. To assist language learners' needs tutor tools have been developed. In this paper we present a tutor for learning the basic 42 hand configurations of the Spanish Sign Language, as well as more than one hundred of common words. This tutor registers the user image from an off-the-shelf webcam and challenges her to perform the hand configuration she chooses to practice. The system looks for the configuration, out of the 42 in its database, closest to the configuration performed by the user, and shows it to her, to help her to improve through knowledge of her errors in real time. The similarities between configurations are computed using Procrustes analysis. A table with the most frequent mistakes is also recorded and available to the user. The user may advance to choose a word and practice the hand configurations needed for that word. Sign languages have been historically neglected and deaf people still face important challenges in their daily activities. This research is a first step in the development of a Spanish Sign Language tutor and the tool is available as open source. A multidimensional scaling analysis of the clustering of the 42 hand configurations induced by Procrustes similarity is also presented.
The Viability of Continuous Experimentation in Early-Stage Software Startups

Background: Continuous experimentation (CE) has been proposed as a data-driven approach to software product development. Several challenges with this approach have been described in large organisations, but its application in smaller companies with early-stage products remains largely unexplored. Aims: The goal of this study is to understand what factors could affect the adoption of CE in early-stage software startups. Method: We present a descriptive multiple-case study of five startups in Finland which differ in their utilisation of experimentation. Results: We find that practices often mentioned as prerequisites for CE, such as iterative development and continuous integration and delivery, were used in the case companies. CE was not widely recognised or used as described in the literature. Only one company performed experiments and used experimental data systematically. Conclusions: Our study indicates that small companies may be unlikely to adopt CE unless 1) at least some company employees have prior experience with the practice, 2) the company's limited available resources are not exceeded by its adoption, and 3) the practice solves a problem currently experienced by the company, or the company perceives almost immediate benefit of adopting it. We discuss implications for advancing CE in early-stage startups and outline directions for future research on the approach.
A Meta-Heuristic Algorithm Based on the Happiness Model

Recent work has attempted to determine the appropriate global minimum for complex problems. The paper presents a population and direct-based swarm optimization algorithm called the happiness optimizer (HPO) algorithm. An HPO algorithm is designed based on personal behavior and demonstrated in 30 and 100 dimensions on benchmark functions. The model includes four questions: “what do you want?”, “what do you have?”, “what do others have?”, and “what happened?”, which guide the development of a happiness behavior model. By considering the balancing between exploration and exploitation operators in the search space problem, efficiency, robustness, and stability were demonstrated for synthetic and real cases. For comparison, our proposed algorithm and some well-known algorithms will be 30 times applied on the benchmark functions and then compared with statistical value and Wilcoxon signed-rank test. As a consequence, the performance, reliability, and stability of our work have been demonstrated better than the others.
Reading is not believing: A multimodal adversarial attacker for Chinese-NLP model

The research of adversarial examples has extended from image to text in the last few years. However, these attacks are typically limited to the English language and simple substitution strategies. To further expose the vulnerability of NLP models, we study the linguistic characteristics of Chinese, the quintessential ideogram with over 1.2 billion native speakers. Accordingly, a novel attack framework named ZH-Deceiver is proposed to generate Chinese adversarial examples from the perspective of morphology, phonetics, semantics, and basic transformation. In particular, a CNN-based Siamese Network is integrated to ameliorate the quality of adversarial examples. To elaborate the validity of ZH-Deceiver, extensive experiments are conducted on two datasets. Compared with four benchmarks such as Genetic, PWWS, TextBugger, and SememePSO, our attack achieves impressive performance on effectiveness, efficiency, imperceptibility, and human evaluation by deceiving seven AI models including CNN and BERT. Furthermore, the transferability, as well as the robustness, is further analyzed and the former is successfully applied to attack three commercial APIs: Tencent, ALi, and Baidu. ZH-Deceiver acts as a wake-up call for multilingual processing models, and tangibly extends the application and methodology of adversarial textual attack.
Simulation of Quantum Key Distribution Using Entangled Photon Pairs Over Free-Space Channels

We develop a quantum network simulation framework designed for integration into tactical simulation tools. It extends the quantum networking tool SQUANCH with realistic fiber and free-space channel models and implements subroutines for simulating entanglement-based QKD. We benchmark our framework using a published free-space QKD experiment.
QnAs with Thorne Lay

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Efficient Motion Planning Under Obstacle Uncertainty with Local Dependencies

Minimum Risk Motion Planning (MRMP) has been shown to remain NP-Hard even under the conditions that lead to the practical efficiency of sampling-based and grid-based motion planning algorithms [25]. However, the hardness proof does not eliminate the importance of finding a practical solution to this problem. In this paper we identify a parameter which directly controls the hardness of MRMP. We present experiments that suggest this parameter is small for many practical MRMPs and present an algorithm guaranteed to efficiently yield high-quality solutions whenever this parameter is small. When the parameter is large, the algorithm fails gracefully—it returns a solution with bounded suboptimality. We also explore a connection between our work and previous work on the minimum constraint removal problem (MCR).
Design Space Exploration for Sampling-Based Motion Planning Programs with Combinatory Logic Synthesis

Motion Planning is widely acknowledged as a fundamental problem of robotics. Due to the continuous efforts of the scientific community, various algorithmic families emerged that have different strengths and weaknesses. Finding a suitable motion planning program is often not trivial for real-world problems, as various domain-specific factors must be considered. An obvious example is a potential trade-off between path length, computation time, and resource constraints. We propose a technique to systematically explore the space of suitable programs, aiming to find Pareto optimal algorithm configurations. Our approach makes use of Combinatory Logic Synthesis to perform component-based software composition. Software components are injected with domain-knowledge, effectively restricting the solution space of synthesizable programs. We synthesize sample-based global planning programs that make use of the Open Motion Planning Library (OMPL) and evaluate the produced programs to yield numeric result vectors. These steps are encapsulated in a black-box function which is used with a multi-objective optimization tool (Hypermapper) to yield an automatic, learning-based search procedure for a given feature space. We validate our approach with a series of experiments that demonstrate the extensibility and transferability of our methodology regarding different robotic systems and planning instances.
Posterior consistency for the spectral density of non‐Gaussian stationary time series

Abstract Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non‐Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non‐Gaussian time series. Small sample properties of this approach are illustrated with several examples of non‐Gaussian time series.
An efficient federated learning framework for multi-channeled mobile edge network with layered gradient compression

A fundamental issue for federated learning (FL) is how to achieve efficient training performance under complex dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge server via multiple communication channels (e.g., 4G, LTE, and 5G) because multi-channel communication can increase the communication bandwidth and has lower communication costs and energy consumption than a single high-speed communication channel. However, if the communication data cannot be properly allocated to multiple channels in a complex dynamic communication network, multi-channel communication will still waste resources (e.g., bandwidth, battery life, and monetary cost). In this paper, we propose an efficient FL framework called, which consists of two parts, the layered gradient compression (LGC), and a learning-driven control algorithm. Specifically, with LGC, local gradients from a device are coded into several layers, and each layer is sent to the server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model and sends the result back to the devices. Furthermore, we prove the convergence of LGC and formally define the problem of resource-efficient with LGC. We then propose a learning-driven algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e., the compression level of different layers and the layer-to-channel mapping) in each iteration. Results from extensive experiments show that significantly reduces the training time and improves the resource utilization (energy consumption and money cost) while achieving a similar test accuracy compared with well-known FL baselines.