Reviewerly/exHarmony huggingface dataset:
A new provably secure certificateless signature scheme for Internet of Things

With the rapid popularization of Internet of Things (IoT) in various fields, the security of the IoT has been widely concerned. Security authentication technology is the foundation of the security of the IoT. Certificateless signature, which removes the intricate certificate management and key escrow, is one of the practical methods to provide data integrity and identity authentication for the IoT. At present, many certificateless signature schemes have been put forward, but few of them are secure and suitable for the IoT. Recently, Jia et al. designed a certificateless signature scheme for the IoT deployment. The authors demonstrated that their scheme can withstand attacks of two types of super adversaries. However, we prove that Jia et al.'s scheme cannot resist attacks from a normal Type I adversary, not to mention a super Type I adversary. Then, we put forward a certificateless signature scheme on the basis of elliptic curve cryptosystem, and prove the scheme cannot be forged by two types of super adversaries. Our certificateless signature scheme performs better than the existing certificateless signature schemes, and it is the best combination of high security and efficiency so far and is more appropriate for the resource-constrained IoT environment.
Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device

This paper proposes an automatic fall detector in a wearable device that can reduce risks by detecting falls and promptly alerting caregivers. For this purpose, we propose cluster-analysis-based user-adaptive fall detection using a fusion of heart rate sensor and accelerometer. The objectives of the proposed fall detector are to have high accuracy with a low-complexity model regardless of diverse conditions. To meet the objectives, we propose the best 13-dimensional feature subset by using feature selection. In addition, we verify the performance increment of combining a heart rate sensor with an accelerometer and the effectiveness of the cluster-analysis-based anomaly detection. We also show the effectiveness of the user-adaptive method when using both heart rate and acceleration signals that were hardly covered in other papers. Finally, we prove that the performance of the proposed fall detector achieves is better than that of recent user-adaptive and user-independent approaches. This study is the first attempt to demonstrate the merits of the user-adaptive approach using a combination of heart rate and acceleration signals to detect falls. Moreover, this paper also contributes to fall detection area by providing the data we collected.
Classifying Daily and Sports Activities Invariantly to the Positioning of Wearable Motion Sensor Units

We propose techniques that achieve invariance to the positioning of wearable motion sensor units on the body for the recognition of daily and sports activities. Using two sequence sets based on the sensory data allows each unit to be placed at any position on a given rigid body part. As the unit is shifted from its ideal position with larger displacements, the activity recognition accuracy of the system that uses these sequence sets degrades slowly, whereas that of the reference system (which is not designed to achieve position invariance) drops very fast. Thus, we observe a tradeoff between the flexibility in sensor unit positioning and the classification accuracy. The reduction in the accuracy is at acceptable levels, considering the convenience and flexibility provided to the user in the placement of the units. We compare the proposed approach with an existing technique to achieve position invariance and combine the former with our earlier methodology to achieve orientation invariance. We evaluate our proposed methodology on a publicly available data set of daily and sports activities acquired by wearable motion sensor units. The proposed representations can be integrated into the preprocessing stage of existing wearable systems without significant effort.
Touch? Speech? or Touch and Speech? Investigating Multimodal Interaction for Visual Network Exploration and Analysis

Interaction plays a vital role during visual network exploration as users need to engage with both elements in the view (e.g., nodes, links) and interface controls (e.g., sliders, dropdown menus). Particularly as the size and complexity of a network grow, interactive displays supporting multimodal input (e.g., touch, speech, pen, gaze) exhibit the potential to facilitate fluid interaction during visual network exploration and analysis. While multimodal interaction with network visualization seems like a promising idea, many open questions remain. For instance, do users actually prefer multimodal input over unimodal input, and if so, why? Does it enable them to interact more naturally, or does having multiple modes of input confuse users? To answer such questions, we conducted a qualitative user study in the context of a network visualization tool, comparing speech- and touch-based unimodal interfaces to a multimodal interface combining the two. Our results confirm that participants strongly prefer multimodal input over unimodal input attributing their preference to: 1) the freedom of expression, 2) the complementary nature of speech and touch, and 3) integrated interactions afforded by the combination of the two modalities. We also describe the interaction patterns participants employed to perform common network visualization operations and highlight themes for future multimodal network visualization systems to consider.
AI-Based Abnormality Detection at the PHY-Layer of Cognitive Radio by Learning Generative Models

Introducing a data-driven Self-Awareness (SA) module in Cognitive Radio (CR) can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum in order to make the CR learn wrong behaviours and take mistaken actions. A basic SA module includes the ability to learn generative models and detect abnormalities inside the radio spectrum. In this work, we propose and implement Artificial Intelligence (AI)-based Abnormality Detection techniques at the physical (PHY)-layer in CR enabled by learning Generative Models. Specifically, two real-world practical applications related to different data dimensionality and sampling rates are presented. The first application implements the Conditional Generative Adversarial Network (C-GAN) investigated on generalized state vectors extracted from spectrum representation samples to study the dynamic behaviour of the wideband signal. While the second application is based on learning a Dynamic Bayesian Network (DBN) model from a generalized state vector which contains sub-bands information extracted from the radio spectrum. Results show that both of the proposed methods are capable of detecting abnormal signals in the spectrum and pave the road towards Self-Aware radio.
HawkesEye: Detecting Fake Retweeters Using Hawkes Process and Topic Modeling

Retweets are essential to boost the popularity of a tweet, and a large number of fake retweeters can contribute heavily to this aspect. We define a fake retweeter as a Twitter account that retweets spammy tweets, retweets an abnormally large amount of tweets in a short period, or misuses a trending hashtag to promote events irrelevant to the topic of discussion. We introduce an up-to-date, temporally diverse, trend-oriented labeled dataset to address the problem of fake retweeter detection. We develop a novel classifier, called HawkesEye which makes predictions based on a temporal window, in contrast to existing approaches which require a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph-like</i> relationship between tweet entities, or the presence of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entire retweeting timeline</i> of a retweeter. HawkesEye utilizes both temporal and textual information using a class-specific topic model and Hawkes processes. Experiments on our curated dataset show significant improvement over four state-of-the-art methods, with precision and recall scores of 0.964 and 0.960 on a balanced dataset, respectively – HawkesEye beats the best baseline by 6.16% and 25.98% relative improvement in terms of precision and recall, respectively. We also diagnose our model to understand the advantages and pitfalls of the underlying mechanism. We believe that the extent of this study is not restricted to Twitter, but generalizable to other social media systems such as Facebook and Instagram with similar reposting capabilities.
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low- cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult- to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.
Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints

This paper describes a novel trajectory planning algorithm for an unmanned aerial vehicle (UAV) under the constraints of system positioning accuracy. Due to the limitation of the system structure, a UAV cannot accurately locate itself. Once the positioning error accumulates to a certain degree, the mission may fail. This method focuses on correcting the error during the flight process of a UAV. The improved genetic algorithm (GA) and A* algorithm are used in trajectory planning to ensure the UAV has the shortest trajectory length from the starting point to the ending point under multiple constraints and the least number of error corrections.
Foxels

Introducing interactive components into furniture has proven difficult due to the different lifespans of furniture and digital devices. We present Foxels, a modular, smart furniture concept that allows users to create their own interactive furniture on demand by simply snapping together individual building blocks. The modular design makes the system flexible to accommodate a variety of interactive furniture setups, making it particularly well-suited for re-configurable spaces. Considering the trade-off between ease-of-use and high versatility, we explored a number of interaction methods that can be applied to modular interactive furniture, thereby extending the well-known tangible programming paradigm. After explaining our implementation, we demonstrate the validity of the proposed concepts by presenting how Foxels can be used in an ideation workshop along with many additional real-world examples.
Deep Deterministic Policy Gradient for Navigation of Mobile Robots in Simulated Environments

This paper presents a study of a deep reinforcement learning technique that uses a Deep Deterministic Policy Gradient network for application in navigation of mobile robots. In order for the robot to arrive to a target on a map, the network has 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target as inputs. As outputs, the network has the linear and angular velocity. From the results analysis, it is possible to conclude that the deep reinforcement learning's algorithms, with continuous actions, are effective for decision-make of a robotic vehicle. However, it is necessary to create a good reward system for the intelligent agent to accomplish your objectives. This research uses different virtual simulation environments provided by ROBOTIS in the robot simulation software Gazebo in order to test the performance of the algorithm. A supplementary video can be accessed at the following link: https://youtu.be/NhGxEC3g7sU. That shows the performance of the proposed system.
Lightweight V-Net for Liver Segmentation

The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To address these issues, we design a lightweight V-Net (LV-Net) for liver segmentation in this paper. The proposed network makes two contributions. The first is that we design an inverted residual bottleneck block (IRB block) and a 3D average pooling block and apply them to the proposed LV-Net. Compared with vanilla convolution, depth-wise convolution and point-wise convolution employed by the IRB block can not only reduce the number of parameters significantly, but also extract features sufficiently well by decoupling cross-channel corrections and spatial correlations. The second is that the LV-Net employs 3D deep supervision to improve the final loss function in training phase, which makes the proposed LV-Net acquire a more powerful discrimination capability between liver areas and non-liver areas. The proposed LV-Net is evaluated on public LiTS dataset, and experiments demonstrate that the proposed LV-Net is superior to popular 2D and 3D networks in terms of segmentation performance, parameter quantity and computational cost.
Rethinking urban sustainability using fuzzy cognitive mapping and system dynamics

The extant literature reveals a growing need to rethink urban sustainability. Sustainable urban development is becoming more important to city strategic planning since sustainability is a critical aspect of environmental protection, social cohesion, and economic growth. However, decisions are currently not always taking into account the need to maintain sustainability because either decision makers do not fully understand the decision problems at hand or they do not focus on finding realistic, contextualized solutions. In addition, most existing models of urban sustainability assessment are static. Therefore, new urban sustainability assessment systems based on landsenses ecology are needed, which should combine natural elements, physical senses, and psychological perceptions, and assist decision makers develop successful management policies. Using fuzzy cognitive mapping and system dynamics, this study sought to develop a fresh, holistic perspective on urban sustainability. Based on the knowledge and experience of a panel of experts in urban development, some of the most significant determinants of urban sustainability were identified, namely: sustainable construction; urban planning and/or design; health; economy; culture, citizenship, and education; environmental quality; public policies and governance; and mobility and/or accessibility. The results obtained were validated both by the panel members and the director of the Department of Urban Planning of the Lisbon City Council, Portugal. The advantages and limitations of our approach are also discussed, as well as recommendations for future research.
Guided CNN for generalized zero-shot and open-set recognition using visual and semantic prototypes

In the process of exploring the world, the curiosity constantly drives humans to cognize new things. Supposing you are a zoologist, for a presented animal image, you can recognize it immediately if you know its class. Otherwise, you would more likely attempt to cognize it by exploiting the side-information (e.g., semantic information, etc.) you have accumulated. Inspired by this, this paper decomposes the generalized zero-shot learning (G-ZSL) task into an open set recognition (OSR) task and a zero-shot learning (ZSL) task, where OSR recognizes seen classes (if we have seen (or known) them) and rejects unseen classes (if we have never seen (or known) them before), while ZSL identifies the unseen classes rejected by the former. Simultaneously, without violating OSR’s assumptions (only known class knowledge is available in training), we also first attempt to explore a new generalized open set recognition (G-OSR) by introducing the accumulated side-information from known classes to OSR. For G-ZSL, such a decomposition effectively solves the class overfitting problem with easily misclassifying unseen classes as seen classes. The problem is ubiquitous in most existing G-ZSL methods. On the other hand, for G-OSR, introducing such semantic information of known classes not only improves the recognition performance but also endows OSR with the cognitive ability of unknown classes. Specifically, a visual and semantic prototypes-jointly guided convolutional neural network (VSG-CNN) is proposed to fulfill these two tasks (G-ZSL and G-OSR) in a unified end-to-end learning framework. Extensive experiments on benchmark datasets demonstrate the advantages of our learning framework.
Moving vehicle detection in aerial infrared image sequences via fast image registration and improved YOLOv3 network

Owing to its powerful adaptability and robustness to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more applied for aerial photography. Nowadays, how to make full use of the aerial infrared image sequences for the moving vehicle detection has attracted widespread attention. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is extremely difficult to detect the moving vehicles. In our work, an effective and efficient moving vehicle detection approach in the aerial infrared image sequences is proposed via fast image registration and You Only Look Once Version 3 (YOLOv3) network. First, to compensate for the motion of the aerial infrared camera, a fast infrared image registration method is put forward. To improve the accuracy and efficiency of image registration, we construct a multi-screening based mechanism for screening out the incorrect and redundant feature points. For feature description, the low-level and high-level descriptors are combined to further improve the registration accuracy. Then, the 2-frame difference and image masking are introduced to acquire the frame mask images, where only the region-of-interest is reserved, and the remaining regions are masked. Next, we construct a new structure of an improved YOLOv3 network with only 23 layers. Due to the insufficiency of the infrared vehicle samples, transfer learning is introduced to train the improved YOLOv3 network. Finally, the proposed approach is evaluated on the Defence Advanced Research Projects Agency (DARPA) Video verification of Identify (VIVID) and Northwest Polytechnical University (NPU) data sets. Experiments and comprehensive analyses show that the proposed approach can achieve satisfactory and competitive moving vehicle detection results.
Scheduling of space to ground quantum key distribution

Abstract Satellite-based platforms are currently the only feasible way of achieving intercontinental range for quantum communication, enabling thus the future global quantum internet. Recent demonstrations by the Chinese spacecraft Micius have spurred an international space race and enormous interest in the development of both scientific and commercial systems. Research efforts so far have concentrated upon in-orbit demonstrations involving a single satellite and one or two ground stations. Ultimately satellite quantum key distribution should enable secure network communication between multiple nodes, which requires efficient scheduling of communication with the set of ground stations. Here we present a study of how satellite quantum key distribution can service many ground stations taking into account realistic constraints such as geography, operational hours, and most importantly, weather conditions. The objective is to maximise the number of keys a set of ground stations located in the United Kingdom could share while simultaneously reflecting the communication needs of each node and its relevance in the network. The problem is formulated as a mixed-integer linear optimisation program and solved to a desired optimality gap using a state of the art solver. The approach is presented using a simulation run throughout six years to investigate the total number of keys that can be sent to ground stations.
A Survey on Applications of Quantified Boolean Formulas

The decision problem of quantified Boolean formulas (QBFs) is the archetypical problem for the complexity class PSPACE. Beside such theoretical aspects QBF also provides an attractive framework for encoding and solving various application problems ranging from symbolic reasoning in artificial intelligence to the formal verification and synthesis of computing systems. In this paper, we survey the different application areas that exploit QBF technology for solving their specific problems.
Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections at 1x1x1000 micron resolution with multiple varying stains. We pose registration as penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting there is no assumption of symmetry between atlas and target. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to ``background'' or ``artifact''. The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.