Reviewerly/exHarmony huggingface dataset:
Incentive Assignment in Hybrid Consensus Blockchain Systems in Pervasive Edge Environments

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system to enhance the security for transactions and determine the incentive for miners in edge computing environments. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem for a fair incentive to PoW miners. We formulate the problem and propose an iterative and another heuristic algorithm to determine the incentive that the miner will receive for a new block. We further prove that the iterative algorithm can obtain global optimal results. Numerical simulation results show that our proposed algorithm can give a reasonable incentive to miners under different system parameters in edge blockchain systems.
An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration

Autonomous anomaly detection is a fundamental step in visual surveillance systems, and so we have witnessed great progress in the form of various promising algorithms. Nonetheless, majority of prior algorithms assume static surveillance cameras that severely restricts the coverage of the system unless the number of cameras is exponentially increased, consequently increasing both the installation and the monitoring costs. In the current work we propose an anomaly detection system based on mobile surveillance cameras, i.e., moving robots which continuously navigate a target area. We compare the newly acquired test images with a database of normal images using geo-tags. For anomaly detection, a Siamese network is trained which analyses two input images for anomalies while ignoring the viewpoint differences. Further, our system is capable of updating the normal images database with human collaboration. Finally, we propose a new tester dataset that is captured by repeated visits of the robot over a constrained outdoor industrial target area. Our experiments demonstrate the effectiveness of the proposed system for anomaly detection using mobile surveillance robots.
SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning

Imbalanced data can always be observed in our daily life and various practical tasks. A lot of well-constructed machine learning methodologies may produce ineffective performance, when conducted on this kind of data. This originates from the produced high training biases that towards the majority class instances. Among all the solutions of this problem, data generation of the minority class is always considered the most effective approach. However, in all the previous works, data are always processed sample-wisely and the distribution of each single data attribute is never noticed. So, in this paper, to estimate the mechanism of how each attribute contributes to its label, we explore the potential connection between the two items by Conditional Generative Adversarial Networks (CGAN) separately and individually. Then, the constructed new instances are purified by a designed attribute-based minimax filter and the survivors are concatenated to form the eventual generated data. In other words, different from the CGAN based data generation way, the proposed approach improves it by additionally considering all the single attribute patterns of the data that to construct new instances. In addition, we extend the binary class imbalanced learning framework to multiple class one. In the experimental part, the improved model is compared against GAN, CGAN and some other standard multiple-class oversampling algorithms on several widely used datasets. Results, in terms of four common measurements, have shown that the proposed approach can produce comparable and always superior performance when compared with the competitors.
P2LBS: Privacy Provisioning in Location-Based Services

The acceptability of a location-based service (LBS) hinges on its ability to efficiently provide service while protecting both the privacy of the customer and the server's economic interests. These include the privacy of the user, point-of-interests (POIs) privacy of the service provider, and provision for anonymous payment. Existing schemes fulfill these requirements only partially. Most of the schemes protect the query privacy of the user and the privacy of the services provided by the server, however, at high computation and communication costs. We propose a privacy-provisioning location-based service(P2LBS) scheme that provides unconditional privacy to the user's query and protects the basket of services offered by the service provider from unnecessary revelation along with an inbuilt anonymous payment scheme. To ensure the authentication of the user and the server, the P2LBS scheme uses a ring signature and anonymous payment protocol respectively. Query privacy and the services' reply privacy are provided through an oblivious transfer (OT) protocol. Results show that the P2LBS scheme is efficient compared to other current state-of-the-art schemes in terms of communication and computation costs. It fulfills all the end-to-end requirements needed to make an LBS scheme viable.
Terrain classification-based rover traverse planner with kinematic constraints for Mars exploration

In Mars exploration mission, the rover path planning is essential to ensure the safety and efficiency of the rover traverse. At present, traverse planning deployed Mars rovers requires experienced geologists and planetary scientists to spend a lot of time on surface-topography analysis. To a large extent, this is a costly and time-consuming manual process. Meanwhile, traditional optimal search algorithms (A∗ and Dijkstra) can search for a globally optimal path, but the path found does not satisfy the nonholonomic constraint of the vehicle, thus not directly drivable. The Terrain Classification-based Rover Traverse Planner (TCRTP), introduced in this article, is an automated algorithm that uses deep learning-based terrain classification as the guide to generate the optimal traverses with kinematic constraints. TCRTP combines the terrain classification with nonholonomic constraints, and it can quickly and accurately obtain the global optimal solution under the heuristic function. We also propose modifications of the cost function for path optimization to improve the smoothness of the solution. This algorithm plans forward and reverse movement, and penalizes reverse driving and changing the direction of movement. The TCRTP algorithm is easy to be extended, and the optimization traversal under multiple missions can be realized by adding traffic constraints, such as terrain roughness, elevation changes and so on. The TCRTP-based rover allows for real-time and frequent planning, and the addition of terrain constraints ensures the safety of the planned route. Our planner, combined with computer vision, shows the potential to reduce operator workloads and explore future planetary space.
Efficient Execution of Temporal Convolutional Networks for Embedded Keyword Spotting

Recently, the use of keyword spotting (KWS) has become prevalent in mobile devices. State-of-the-art deep learning algorithms such as temporal convolutional networks (TCNs) have been applied to this task achieving superior accuracy results. These models can, however, be mapped in multiple ways onto embedded devices, ranging from real-time streaming inference with or without computational sprinting to delayed batched inference. Although functionally equivalent, these deployment settings, however, strongly impacts average power consumption and latency of this real time task, hence requiring a thorough optimization. This work analyzes the challenges, benefits, and drawbacks of the different execution modes available for TCN-based KWS inference on dedicated hardware. With this objective, this research contributes to: 1) presenting a complete deep learning accelerator optimized for TCN inference; 2) evaluating the impact on performance and power of the different deployment options for TCN inference applied to KWS obtaining up to 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> for real-time operation; and 3) optimizing real-time power consumption for KWS inference by exploiting the use of cascaded neural networks (NNs), achieving up to 35% additional power savings.
SNPR

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited. As a result, users become bored with obvious recommendations. To address this issue, we propose Serendipity-oriented Next POI Recommendation model (SNPR), a supervised multi-task learning problem, with objective to recommend unexpected and relevant POIs only. To this end, we define the quantitativeserendipity as a trade-off ofrelevance andunexpectedness in the context of next POI recommendation, and design a dedicated neural network with Transformer to capture complex interdependencies between POIs in user's check-in sequence. Extensive experimental results show that our model can improverelevance significantly while theunexpectedness outperforms the state-of-the-art serendipity-oriented recommendation methods.
Unsupervised Learning of Visual Representations via Rotation and Future Frame Prediction for Video Retrieval

Due to rapid technological advancements, the growth of videos uploaded to the internet has increased exponentially. Most of these videos are free of semantic tags, which makes indexing and retrieval a challenging task, and requires much-needed effective content-based analysis techniques to deal with. On the other hand, supervised representation learning from large-scale labeled dataset demonstrated great success in the image domain. However, creating such a large scale labeled database for videos is expensive and time consuming. To this end, we propose an unsupervised visual representation learning framework, which aims to learn spatiotemporal features by exploiting two pretext tasks i.e. rotation prediction and future frame prediction. The performance of the learned features is analyzed by the nearest neighbor task (video retrieval). For this, we choose the UCF-101 dataset to experiment with. The experimental results shows the competitive performance achieve by our method.
A consortium blockchain based energy trading scheme for Electric Vehicles in smart cities

The real time Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) energy trading have ameliorated the smart city environment. Electric Vehicles (EVs) in smart cities have made it possible to balance the energy demand and supply without burdening the power grids. They also help in minimizing both the heap amounts of pollution and the greenhouse gas emissions. EVs not only charge their batteries from Charging Stations (CSs), but when the smart city faces energy deficiency, they supply their surplus energy to the power grids to fulfill energy demand as well. In addition, EVs also balance the energy demand and supply through energy trading at the local level during peak hours, which strengthens V2V energy trading. However, some issues, such as security threats and privacy leakage prevent EVs from participating in the energy trading process. In addition, the lack of incentives and knowledge about the cost of reaching the energy trading place within minimum time are also big challenges. Therefore, to solve the aforementioned challenges, a secure and efficient scheme for V2V and V2G energy trading is proposed in this paper. The proposed scheme also helps in promoting environmental friendliness and making the smart cities sustainable and reliable. In the proposed scheme, energy trading transactions are secured using consortium blockchain, wherein the Local Aggregators (LAGs) are selected as authorized nodes. LAGs perform their role as energy brokers and are responsible for validating the energy trading requests using Proof of Authority (PoA) consensus mechanism. Moreover, a solution to find accurate distance with required expenses and time to reach the charging destination is also proposed, which effectively guides EVs to reach the relevant CSs and encourages energy trading. Besides, we propose a fair payment mechanism using a smart contract to avoid financial irregularities. An incentive provisioning mechanism is also given in the proposed work to prevent EVs from acting selfishly. The efficient power flow having minimized losses in the vehicular network is of much importance. Therefore, the energy losses incurred in both V2G and V2V are discussed in this work. Furthermore, Oyente is used for smart contract’s security analysis and for testing Ethereum’s resilience against different security flaws. Two attacker models are proposed and the security analyses of the models are also provided. The analyses show that the proposed system is robust against the double spending and Sybil attacks. Finally, the efficient performance of our proposed scheme is validated and analyzed. The simulation results proved that our proposed work outperforms existing work in terms of providing a secure and efficient energy trading platform for both V2G and V2V environments.
Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies, has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency, auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.
Bayesian Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays

This paper presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralisation and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximise the agreement between the modelled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis; using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an apriori spatial structure which embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector

Due to the success of Bidirectional Encoder Representations from Transformers (BERT) in natural language process (NLP), the multi-head attention transformer has been more and more prevalent in computer-vision researches (CV). However, it still remains a challenge for researchers to put forward complex tasks such as vision detection and semantic segmentation. Although multiple Transformer-Based architectures like DETR and ViT-FRCNN have been proposed to complete object detection task, they inevitably decreases discrimination accuracy and brings down computational efficiency caused by the enormous learning parameters and heavy computational complexity incurred by the traditional self-attention operation. In order to alleviate these issues, we present a novel object detection architecture, named Convolutional vision Transformer-Based Attentive Single Shot MultiBox Detector (CvT-ASSD), that built on the top of Convolutional vision Transormer (CvT) with the efficient Attentive Single Shot MultiBox Detector (ASSD). We provide comprehensive empirical evidence showing that our model CvT-ASSD can leads to good system efficiency and performance while being pretrained on large-scale detection datasets such as PASCAL VOC and MS COCO. Code has been released on public github repository at https://github.com/albert-jin/CvT-ASSD.
Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives

National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi-disciplinary rationale for explainability auditing. Specifically, we propose that explainability auditing can ensure the quality of explainability of systems in applied contexts and can be the basis for certification as a means to communicate whether systems meet certain explainability standards and requirements. Moreover, we emphasize that explainability auditing needs to take a multi-disciplinary perspective, and we provide an overview of four perspectives (technical, psychological, ethical, legal) and their respective benefits with respect to explainability auditing.
An Efficient Centralized Planner for Multiple Automated Guided Vehicles at the Crossroad of Polynomial Curves

In this letter, we introduce acentralized planner with low computational cost to schedule the motions of multiple Automated Guided Vehicles (AGVs) at the intersection of pre-defined polynomial curves. In particular, we find that the collision conditions between two AGVs along polynomial paths can be formulated as a set of polynomial inequalities. Furthermore, by solving these inequalities, the continuous boundaries of potential collision areas for vehicles can be determined offline and stored in a table. During the online phase, by also taking into account the priority setup among AGVs, the planner will use efficient table lookup to determine a list of intermediate goals for each AGV to move toward based on their real-time position feedback. In this way, the multiple robots are able to navigate on polynomial guide paths safely and efficiently at the crossroad, and the performance of our approach is demonstrated in a number of simulated scenarios. Videos of the experiments are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/zqzhang/agvsplanner</uri> .
Synthesis of 3D jigsaw puzzles over freeform 2-manifolds

We present a simple algorithm for synthesizing 3D jigsaw puzzles from arbitrary 3D freeform 2-manifold geometric models represented with trimmed NURBS surfaces. The construction algorithm is based on a few conventional geometric operations on freeform curves and surfaces. In particular, we need to compute the offset of freeform NURBS surfaces (for thickening the 2-manifold surfaces) and the functional composition of a univariate curve representation to a bivariate rational surface (for breaking up a 3D model into curved jigsaw tiles). It is thus almost straightforward to convert the proposed algorithm to a practical system using standard tools available in B-rep based geometric modeling systems, that employ trimmed NURBS surfaces. We demonstrate the effectiveness of the proposed approach by fabricating several test sets of 3D jigsaw puzzles for freeform solids consisting of trimmed NURBS surface models.
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression

Existing deep convolutional neural networks (CNNs) generate massive interlayer feature data during network inference. To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps. In this paper, we propose an efficient hardware accelerator with an interlayer feature compression technique to significantly reduce the required on-chip memory size and off-chip memory access bandwidth. The accelerator compresses interlayer feature maps through transforming the stored data into frequency domain using hardware-implemented <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> discrete cosine transform (DCT). The high-frequency components are removed after the DCT through quantization. Sparse matrix compression is utilized to further compress the interlayer feature maps. The on-chip memory allocation scheme is designed to support dynamic configuration of the feature map buffer size and scratch pad size according to different network-layer requirements. The hardware accelerator combines compression, decompression, and CNN acceleration into one computing stream, achieving minimal compressing and processing delay. A prototype accelerator is implemented on an FPGA platform and also synthesized in TSMC 28-nm COMS technology. It achieves 403GOPS peak throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times \sim 3.3\times $ </tex-math></inline-formula> interlayer feature map reduction by adding light hardware area overhead, making it a promising hardware accelerator for intelligent IoT devices.