Reviewerly/exHarmony huggingface dataset:
Context-based information generation for managing UAV-acquired data using image captioning

Unmanned aerial vehicles (UAVs) can efficiently collect image data representing various situations at construction sites, however, it requires a lot of time and cost to analyze it manually to retrieve useful information for on-site management. The paper proposes a methodology to generate time-spatial and visual context-based information from UAV-acquired data. This methodology generates textual information on the position, status, movement, color, and quantity of construction resources from site images using image captioning. Then, construction site images, text generated from them, and UAV flight data containing the latitude, longitude, date, and time of day, are systemized into a database. For evaluating the proposed methodology, data obtained by UAV at actual construction sites was used. Our methodology could predict textual information with a mean average precision of 43.52%, which is superior to those of existing methods.
AGV Path Planning based on Improved A-star Algorithm

With the wide application of Automated Guided Vehicle (Hereinafter referred to as AGV) in modern manufacturing, logistics and transportation industries, improving the work efficiency of AGV has become a major focus of the industry research. In order to reduce the limitation of two factors - AGV optimal running path search and path search speed on the efficiency of AGV, this paper based on the A-star algorithm uses the characteristics of jump point search to improve the node search mode and the search speed, and adds the angle evaluation cost function to the cost function of A-star algorithm to find the path with the least inflection point, so as to quickly find the optimal path. And the simulation experiments in this paper show that the optimized A-star algorithm can search for the optimal path, of which path search speed is faster than that of the A-star algorithm.
A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

We propose a distributed approach to solve the multi-robot task allocation problem.This problem consists of two distinct sets: robots and tasks.The objective is to assign tasks to robots while optimizing a given criterion.This problem is known to be N P-hard even with small numbers of robots and tasks.The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors.We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances.The approach is composed of two phases: inclusion and consensus.During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System.During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism.The approach is implemented using Java programming language and JADE multi-agent Framework.The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions.Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.
What is Stablecoin?: A Survey on Price Stabilization Mechanisms for Decentralized Payment Systems

Since the first theoretical concept of blockchains was proposed, over 100 digital currencies have been issued by online platformers as cryptocurrencies and traded by online consumers mainly in emerging countries. From the perspective of online payment systems, several studies have regarded blockchains as decentralized payment systems (DPSs), enabling international payment with lower cost and higher traceability with sophisticated peer-to-peer protocols in contrast to other centralized systems. Despite the advantages, DPSs are not chosen by the owners of online shops due to the high volatility of cryptocurrency prices. Stablecoins are cryptocurrencies with price stabilization mechanisms to match the price of another currency with lower volatility. Our motivation is to gather various price stabilization mechanisms for the purpose of comparing them from the perspective of implementation and enterprise usage. After dividing the methods into four collateral types (fiat, crypto, commodity, and non-collateralized) and two layers (protocol and application), we show that non-collateralized stablecoin on the application layer is the simplest approach for implementation. Moreover, we discuss their connection with traditional economic studies on Hayek money, Seigniorage Share, and Tobin tax. Some current stablecoin projects are also discussed and compared. This is the first survey of stablecoins to the best of our knowledge.
Educational technology conditions to support the development of digital age skills

• Socio-economic status was not found to be a significant factor in predicting digital age learning opportunities. • School type was not found to be a significant factor in predicting digital age learning opportunities. • The most important factor in predicting digital age learning opportunities was how the technology is used by the students.
A Sentiment Analysis Method of Capsule Network Based on BiLSTM

Nowadays, capsule network model is widely used in image processing, whose feature engineering is not suitable for sentiment analysis based on texts obviously. In this paper, we propose a capsule network model with BiLSTM named caps-BiLSTM for sentiment analysis to solve the problem, and introduce the experimental results on different datasets. At the beginning of caps-BiLSTM, a convolution layer is used to transform the instance to hide vector. Then the capsule module constructs the capsule representation to the n-gram model. The state probability of a certain capsule is calculated by the capsule model. If the state probability of a given instance is the largest among all capsules, a higher coupling coefficient is assigned. Finally, in order to fusion the data features, the output of the capsule enters into a BiLSTM structure, which is used as a decoder to get the probability representation. Experimental results based on MR, IMDB and SST datasets show that the proposed method can achieve better performances than the traditional machine learning methods and the compared deeping learning models.
Unveiling the cloak of deviance: Linguistic cues for psychological processes in fake online reviews

Although fake online reviews have been gaining more attention from both academics and practitioners, little effort has been made to explore the linguistic characteristics of the psychological processes related to fake reviews. This research proposes four linguistic cues (i.e., affective, cognitive, social, and perceptual) related to a reviewer’s psychological processes and explores their relationships with fake reviews while examining the influence of time distance and reviewer location on these reviews. The results of logistic regression analysis of 43,496 reviews from Yelp.com suggest that affective, social, and perceptual cues are significantly related to fake reviews with the presence of the significant effects of time distance and reviewer location. Further, the results of post-hoc analysis confirm that the effect of photos on fake reviews is limited. This study contributes to the body of knowledge on online reviews and interpersonal deception theory, providing valuable implications for practitioners in the hospitality and tourism industry.
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds

Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.
Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion

Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. It helps intelligent devices to understand and interact with the surrounding scenes. Due to the high-memory requirement, current methods only produce low-resolution completion predictions, and generally lose the object details. Furthermore, they also ignore the multi-scale spatial contexts, which play a vital role for the 3D inference. To address these issues, in this work we propose a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image. The proposed CCPNet improves the labeling coherence with a cascaded context pyramid. Meanwhile, based on the low-level features, it progressively restores the fine-structures of objects with Guided Residual Refinement (GRR) modules. Our proposed framework has three outstanding advantages: (1) it explicitly models the 3D spatial context for performance improvement; (2) full-resolution 3D volumes are produced with structure-preserving details; (3) light-weight models with low-memory requirements are captured with a good extensibility. Extensive experiments demonstrate that in spite of taking a single-view depth map, our proposed framework can generate high-quality SSC results, and outperforms state-of-the-art approaches on both the synthetic SUNCG and real NYU datasets.
An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints

This article presents a framework that extends a rapidly exploring random tree (RRT) algorithm to plan the motion for a wheeled robot under kinodynamic constraints. Unlike previous RRT-based path planning algorithms that apply complex steer functions during a path sampling phase, this framework uses a straight line to connect a pair of sampled waypoints such that an obstacle-free path can be quickly found. This path is further pruned by the short-cutting algorithm. Under the kinodynamic constraints, we propose a motion-control law that is guided by a pose-based steer function for the robot to reach its destination in a short time. A path deformation strategy is presented that shifts the waypoint away from the collision point such that the trajectory can be generated without any collision. Simulation results demonstrate that the proposed framework needs less computation to generate a smoother trajectory with shorter length than its peers, and experimental results show that simulated trajectories of our controller are very close to real ones and the performance is better than that of a prior pose-based controller.
Reliability-based fuzzy clustering ensemble

In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of consensus functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy consensuses function. The graph based consensus function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness.
Challenges and Prospects in Vision and Language Research

Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of capabilities that integrate computer vision, reasoning, and natural language understanding. However, the datasets and evaluation procedures used in these tasks are replete with flaws which allows the vision and language (V&L) algorithms to achieve a good performance without a robust understanding of vision and language. We argue for this position based on several recent studies in V&L literature and our own observations of dataset bias, robustness and spurious correlations. Finally, we propose that several of these challenges can be mitigated by creation of carefully designed benchmarks.
Towards a Trust-Enhanced Blockchain P2P Topology for Enabling Fast and Reliable Broadcast

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP, a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra- and inter- clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.
Dilated Causal Convolutional Model For RF Fingerprinting

We design a network to classify individual wireless devices based on their radio frequency (RF) fingerprints imparted on transmitted signals. The network combines a stack of dilated causal convolution layers with traditional convolutional layers which we call an augmented dilated causal convolution (ADCC) network. It is designed to work on real-world Wi-Fi and ADS-B transmissions, but we expect it to generalize to any classes of signals. We explore various aspects of the ADCC for RF fingerprinting including: classification of up to 10,000 devices, sensitivity to training set size, varying signal-to-noise ratios, and channel propagation effects.
Online reliable semi-supervised learning on evolving data streams

In todays digital era, a massive amount of streaming data is automatically and continuously generated. To learn such data streams, many algorithms have been proposed during the last decade. Due to the dynamic nature of streaming data, the learning algorithms must be adaptive to handle concept drift and work under limited memory and time. Currently, most existing works assume that the true class labels of all incoming instances are immediately available. In real-world applications, labeling every data item in data streams is time and resource consuming. A more realistic situation is that only a few instances in data streams are labeled. Thereby, how to design a new efficient and effective learning algorithm that can handle concept drift, label scarcity, and work under limited resources is of significant importance. In this paper, we propose a new online semi-supervised learning algorithm by modeling concept drifts with a set of micro-clusters. These micro-clusters are dynamically maintained to capture the evolving concepts with error-based representative learning. In this way, local concept drifts are captured more quickly and finally support effective data stream learning. Extensive experiments on several data sets demonstrate that our learning model allows yielding high classification performance compared to many state-of-the-art algorithms.
Visual appearance of the virtual hand affects embodiment in the virtual hand illusion

Abstract Body ownership (the feeling that one’s body belongs to oneself) is commonly studied with Rubber hand illusion (RHI) paradigm that allows inducing a temporary illusory feeling of ownership of a life-sized rubber hand. However, it remains unclear whether illusory ownership of the fake hand relies on the same mechanisms as ownership of one’s own real hand. Here, we directly compared ownership of the own hand (OH) and fake hand (FH) in the same set of conditions within immersive virtual reality. We obtained behavioral (proprioceptive drift) and subjective (questionnaire) measures of ownership and disownership for virtual OH, FH and object (Obj) that were located congruently or incongruently with the participant’s real hand and were stimulated synchronously or asynchronously with the real hand. Both OH and FH (but not Obj) were embodied after synchronous stimulation in both locations. Crucially, subjective ownership of the OH was stronger than of the FH in congruent location after synchronous stimulation. It was also present after asynchronous stimulation, being stronger when the virtual OH was subjectively more similar to the real hand. The results suggest that the detailed appearance of the body might act as an additional component in the construction of body ownership.
Enabling Rack-scale Confidential Computing using Heterogeneous Trusted Execution Environment

With its huge real-world demands, large-scale confidential computing still cannot be supported by today's Trusted Execution Environment (TEE), due to the lack of scalable and effective protection of high-throughput accelerators like GPUs, FPGAs, and TPUs etc. Although attempts have been made recently to extend the CPU-like enclave to GPUs, these solutions require change to the CPU or GPU chips, may introduce new security risks due to the side-channel leaks in CPU-GPU communication and are still under the resource constraint of today's CPU TEE.To address these problems, we present the first Heterogeneous TEE design that can truly support large-scale compute or data intensive (CDI) computing, without any chip-level change. Our approach, called HETEE, is a device for centralized management of all computing units (e.g., GPUs and other accelerators) of a server rack. It is uniquely designed to work with today's data centres and clouds, leveraging modern resource pooling technologies to dynamically compartmentalize computing tasks, and enforce strong isolation and reduce TCB through hardware support. More specifically, HETEE utilizes the PCIe ExpressFabric to allocate its accelerators to the server node on the same rack for a non-sensitive CDI task, and move them back into a secure enclave in response to the demand for confidential computing. Our design runs a thin TCB stack for security management on a security controller (SC), while leaving a large set of software (e.g., AI runtime, GPU driver, etc.) to the integrated microservers that operate enclaves. An enclaves is physically isolated from others through hardware and verified by the SC at its inception. Its microserver and computing units are restored to a secure state upon termination.We implemented HETEE on a real hardware system, and evaluated it with popular neural network inference and training tasks. Our evaluations show that HETEE can easily support the CDI tasks on the real-world scale and incurred a maximal throughput overhead of 2.17% for inference and 0.95% for training on ResNet152.
Private FL-GAN: Differential Privacy Synthetic Data Generation Based on Federated Learning

Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic "fake" data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN’s training is difficult. To address this issue, we propose private FL-GAN, a differential privacy generative adversarial network model based on federated learning. By strategically combining the Lipschitz limit with the differential privacy sensitivity, the model can generate high-quality synthetic data without sacrificing the privacy of the training data. We theoretically prove that private FL-GAN can provide strict privacy guarantee with differential privacy, and experimentally demonstrate our model can generate satisfactory data.
IRBA: An Identity-Based Cross-Domain Authentication Scheme for the Internet of Things

The incredible development of Internet of things technology promotes the integration of application systems, which enable people to enjoy the convenience of multiple application services through a single intelligent device or terminal. In order to implement value exchange and information sharing between different applications, cross-domain access is inevitable. In order to prevent illegal access, identity authentication is necessary before the terminal accesses the service. Because of the need to introduce a trusted third party, the traditional centralized authentication model not only destroys the autonomy and flexibility of the application system, but also causes issues such as single point of failure and hidden dangers of unilateral control. This paper proposes an identity-based cross-domain authentication scheme for the Internet of Things. This scheme uses the Blockchain as a decentralized trust anchor instead of the traditional certificate of authority, and uses the identity-based self-authentication algorithm to replace the traditional PKI authentication algorithm. The scheme proposed in this paper implements a decentralized authentication model, which can guarantee the autonomy and initiative of the security domain.