Reviewerly/exHarmony huggingface dataset:
Prioritized SIPP for Multi-agent Path Finding with Kinematic Constraints

Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its importance, the problem is well-studied and multiple optimal and approximate algorithms are known. However, many of them abstract away from the kinematic constraints and assume that the agents can accelerate/decelerate instantaneously (Fig. 1). This complicates the application of the algorithms on the real robots. In this paper, we present a method that mitigates this issue to a certain extent. The suggested solver is essentially, a prioritized planner based on the well-known Safe Interval Path Planning (SIPP) algorithm. Within SIPP we explicitly reason about the speed and the acceleration thus the constructed plans directly take kinematic constraints of agents into account. We suggest a range of heuristic functions for that setting and conduct a thorough empirical evaluation of the suggested algorithm.
Behind Block Explorers: Public Blockchain Measurement and Security Implication

Blockchain data has become a popular subject in studying various aspects of blockchains including the security of underlying mechanisms. However, the main chain block data, usually available from block explorer services, does not serve as a sufficient source of transaction and block dynamics that are only visible from a large-scale event measurement. In this paper, the transaction and block arrival events of the two popular public blockchains, i.e., Bitcoin and Ethereum, are measured to investigate the hidden dynamics of blockchain networks. We share our key findings and security implications including a false universal assumption of previous mining related studies and an invalid transaction propagation problem that can be exploited to launch a Denial-of-Service attack on a network.
Drill Pipe Counting Method Based on Local Dense Optical Flow Estimation

To solve the problem of drill pipe counting in coal mines, we propose a drill pipe counting method based on local dense optical flow (LDOF) estimation. Compared to the general tracking method, we provide a new perspective for resolving the problem of drill pipe counting. Taking into account the various working conditions of the drill pipe movement and the complex lighting environment in coal mines, we leverage the local dense optical flow to obtain the movement information of the drill pipe. The model is further established through optical flow to realize the counting of drill pipe. In order to validate the proposed approach, we collect 5 videos of drill pipe movement in coal mines, which cover strong light source and occlusion situations. The experiments demonstrate that our method can realize automatic counting of drill pipe, with an average drill pipe counting accuracy of 97.92% and an average frame per second (FPS) rate of 64.2.
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions within layers – among rows of the DNN weight matrix, for simplified operations in hardware inference, while preserving accuracy. Furthermore, this paper makes a different observation from the prior work that the quantization error does not necessarily exhibit the layer-wise sensitivity, and actually can be mitigated as long as a certain portion of the weights in every layer are in higher precisions. This observation enables layer-wise uniformality in the hardware implementation towards guaranteed inference acceleration, while still enjoying row-wise flexibility of mixed schemes and multiple precisions to boost accuracy. The candidates of schemes and precisions are derived practically and effectively with a highly hardware-informative strategy to reduce the problem search space.With the offline determined ratio of different quantization schemes and precisions for all the layers, the RMSMP quantization algorithm uses Hessian and variance based method to effectively assign schemes and precisions for each row. The proposed RMSMP is tested for the image classification and natural language processing (BERT) applications, and achieves the best accuracy performance among state-of-the-arts under the same equivalent precisions. The RMSMP is implemented on FPGA devices, achieving 3.65× speedup in the end-to-end inference time for ResNet-18 on ImageNet, comparing with the 4-bit Fixed-point baseline.
AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification.Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique.Recent AMC methods are designed for a single-carrier system identifying a few modulation types.To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2pyramid) with three significant operations, namely, signal fortification, feature engineering and modulation classification.First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts.Hence, before preprocessing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency.For low quality, quality augmentation is applied.Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation.In the feature engineering step, feature extraction and clustering are presented using the gated feature response pyramid network, and a twin-functioned human mental search algorithm is used.The modulation classification is implemented using amulti-distance-based nearest centroid classifier, and improved Q-learning is used to identify signals as any of 16QAM, 32QAM, 64QAM, 128QAM, QPSK, BPSK, DPSK, ASK and FSK.The performance of the proposed AMC2-pyramid is implemented using MatlabR2017b, where accuracy, precision, recall, F-score, error rate and computational time is computed for the proposed work including previous well-known methods.The proposed work proves that this method outperforms the previous ones.
Internet of Things Cybersecurity: Blockchain as First Securitisation Layer of an IoT Network

When Industrial Internet of Things (IIoT) appears in our enterprise network, a new security breach arise, with new possibilities, and at least with a request of one special layer of cybersecurity (CS) which should be applied to it.And one of the methods which is growing in importance due to its simplicity and its inner strength is the use of Blockchain (BC) as a first layer to secure the sensors, the data, the actuators and also the actors, persons which access and monitor IIoT information from smart devices, existing in this special part of our enterprise network.The aim of the chapter is to set some of the bases of this mix of IIoT and Blockchain for enterprise Cybersecurity.
GBMVis: Visual Analytics for Interpreting Gradient Boosting Machine

The gradient boosting machine (GBM) composed of multiple weak learners is an efficient and widely used machine learning method. As a key factor in the prediction process of the gradient boosting machine, feature affects the performance of the gradient boosting machine when splitting nodes. The idea of GBM gives it a natural advantage to discover a variety of distinguishing features and feature combinations. Once the gradient boosting machine has the correct features, other factors play a relatively weak role. However, the GBM is a complicated and tedious process with diverse structure and attributes of decision tree, leading the model to be less interpretable, especially for high risk areas such as medical diagnosis and financial analytics that require transparent prediction. To tackle this issue, we have proposed an interactive visual analytic system, GBMVis, to help experts quickly analyze and of the gradient boosting machine. In addition to providing information about the features, we have also provided a visualization of the structure of boosting trees, which aims to display the major data flow in the gradient boosting machine. We have demonstrated the effectiveness of our system in a real dataset.
A New Robust Centered Error Entropy Cubature Kalman Filter

The heavy-tailed non-Gaussian noise often appears in the actual system and the classical cubature Kalman filter (CKF) algorithm will have reduced filtering accuracy or even filtering divergence in this condition. To make the CKF algorithm more robust, the centered error entropy cubature Kalman filter (CEECKF) algorithm is derived by combining the Spherical-Radial cubature rule and the centered error entropy (CEE) criterion. The proposed algorithm uses the cubature rule to obtain the one-step prediction state mean and covariance and then uses the CEE criterion to update the posterior state. The application in attitude determination shows the effectiveness of the algorithm.
Res-Attention Net: An Image Dehazing Network

In the image dehazing task, there are three key subtasks need to be performed. The first one is extracting the finer scale features, e.g. the detail textures of objects, covered by haze. The second one is retaining the coarser scale features, e.g. the contours of objects, as complete as possible. And third one is fusing the finer scale features and the coarser scale features together. Aiming at the three points, we propose a single image dehazing network named Res-Attention Net based on the encoding-decoding structure similar to U -Net. The encoder and decoder of Res-Attention Net are designed for the objective that extracting the detail textures and retrieving the contours at the same time. We construct the encoder of the Res-Attention Net by the residual blocks (RBs) with different depths and downsampling for performing the first two subtasks, i.e. extracting the multiscale image features from the original hazy image. The decoder of the Res-Attention is based on the attention gates (AGs) and upsampling. The decoder can retrieve the coarser scale features from the output of the encoder and can also fuse them with the multiscale features from the encoder together. That is to say, the decoder is for performing the last two subtasks. The experimental results show that the Res-Attention Net proposed performs better than several state-of-the-art methods.
A CCTV Camera-Based Target Detection and Positioning UAV System

The objective of this paper was to propose a CCTV camera-based target detection and positioning UAV system. The proposed approach is applicable in the tracking and detection of a suspect or a missing person. The need for such a system was that the conventional way of getting hold of a suspect is very time consuming and also not very accurate. It may also lead to the in- charges losing track of the suspect. Basically, the main idea was to combine the advantages and application of the CCTV surveillance system and drones. In the recent scenario, the number of missing persons per day has increased exponentially. In the year 2019, an average of about 200 children was reported to be missing each day. This means approximately 8 children per hour. Apart from this, the number of suspects per year would also lead to a greater count which makes it necessary to come up with an advanced approach to tackle this situation. As for facial recognition we will be using ‘Keras', ‘TensorFlow’ and ‘OpenCV’. This system can also be further enhanced to carry out more tasks as per requirement. To plan the missions of the drone we will be using ‘Mission Planner’ software. The proposed method can ensure in not losing track of the suspect until the officers reach and also make them aware of the challenges ahead of them by giving a real-time video surveillance.
Updatable Private Set Intersection

Abstract Private set intersection (PSI) allows two mutually distrusting parties each with a set as input, to learn the intersection of both their sets without revealing anything more about their respective input sets. Traditionally, PSI studies the static setting where the computation is performed only once on both parties’ input sets. We initiate the study of updatable private set intersection (UPSI), which allows parties to compute the intersection of their private sets on a regular basis with sets that also constantly get updated. We consider two specific settings. In the first setting called UPSI with addition , parties can add new elements to their old sets. We construct two protocols in this setting, one allowing both parties to learn the output and the other only allowing one party to learn the output. In the second setting called UPSI with weak deletion , parties can additionally delete their old elements every t days. We present a protocol for this setting allowing both parties to learn the output. All our protocols are secure against semi-honest adversaries and have the guarantee that both the computational and communication complexity only grow with the set updates instead of the entire sets. Finally, we implement our UPSI with addition protocols and compare with the state-of-the-art PSI protocols. Our protocols compare favorably when the total set size is sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth.
Hafnia Hands: A Multi-Skin Hand Texture Resource for Virtual Reality Research

We created a hand texture resource (with different skin tone versions as well as non-human hands) for use in virtual reality studies. This makes it easier to run lab and remote studies where the hand representation is matched to the participants’ own skin tone. We validate that the virtual hands with our textures align with participants’ view of their own real hands and allow to create VR applications where participants have an increased sense of body ownership. These properties are critical for a range of VR studies, such as of immersion.
The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features

Modern smartphones offer advanced sensing, connectivity, and processing capabilities for data acquisition, processing, and generation: but it can be difficult and costly to develop mobile research apps that leverage these features. Nevertheless, in life sciences and other scientific domains, there often exists a need to develop advanced mobile apps that go beyond simple questionnaires: ranging from sensor data collection and processing to self-management tools for chronic patients in healthcare. We present Punya, an open source, web-based platform based on MIT App Inventor that simplifies building Linked Data-enabled, advanced mobile apps that exploit smartphone capabilities. We posit that its integration with Linked Data facilitates the development of complex application and business rules, communication with heterogeneous online services, and interaction with the Internet of Things (IoT) data sources using the smartphone hardware. To that end, Punya includes an embedded semantic rule engine, integration with GraphQL and SPARQL to access remote graph data, and support for IoT devices using Bluetooth Low Energy and Linked Data Platform Constrained Application Protocol (LDP-CoAP). Moreover, Punya supports generating Linked Data descriptions of collected data. The platform includes built-in tutorials to quickly build apps using these different technologies. In this paper, we present a short discussion of the Punya platform, its current adoption that includes over 500 active users as well as the larger app-building MIT App Inventor community of which it is a part, and future development directions that would greatly benefit Semantic Web and Linked Data application developers as well as researchers who leverage Linked Open Data resources for their research.
Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications.In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects.In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images.Multiscale feature extraction layers have four Feature Extraction (FE) blocks.Each FE block consists of five convolution layers and one CA block for weighted skip connection.In order to optimize the proposed network architectures, a variety of verification tests were conducted using validation dataset.We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration.The proposed MSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNN and DCSC, respectively.
Privacy-Preserving Authenticated Key Exchange: Stronger Privacy and Generic Constructions

Authenticated key-exchange (AKE) protocols are an important class of protocols that allow two parties to establish a common session key over an insecure channel such as the Internet to then protect their communication. They are widely deployed in security protocols such as TLS, IPsec and SSH. Besides the confidentiality of the communicated data, an orthogonal but increasingly important goal is the protection of the confidentiality of the identities of the involved parties (aka privacy). For instance, the Encrypted Client Hello (ECH) mechanism for TLS 1.3 has been designed for exactly this reason. Recently, a series of works (Zhao CCS’16, Arfaoui et al. PoPETS’19, Schäge et al. PKC’20) studied privacy guarantees of (existing) AKE protocols by integrating privacy into AKE models. We observe that these so called privacy-preserving AKE (PPAKE) models are typically strongly tailored to the specific setting, i.e., concrete protocols they investigate. Moreover, the privacy guarantees in these models might be too weak (or even are non-existent) when facing active adversaries.In this work we set the goal to provide a single PPAKE model that captures privacy guarantees against different types of attacks, thereby covering previously proposed notions as well as so far not achieved privacy guarantees. In doing so, we obtain different “degrees” of privacy within a single model, which, in its strongest forms also capture privacy guarantees against powerful active adversaries. We then proceed to investigate (generic) constructions of AKE protocols that provide strong privacy guarantees in our PPAKE model. This includes classical Diffie-Hellman type protocols as well as protocols based on generic building blocks, thus covering post-quantum instantiations.
Gradient-Aligned convolution neural network

Although Convolution Neural Networks (CNN) have achieved great success in many applications of computer vision in recent years, rotation invariance is still a difficult problem for CNN. Especially for some images, the content can appear in the image at any angle of rotation, such as medical images, microscopic images, remote sensing images and astronomical images. In this paper, we propose a novel convolution operation, called Gradient-Aligned Convolution (GAConv), which can help CNN achieve rotation invariance by replacing vanilla convolutions in CNN. GAConv is implemented with a prior pixel-level gradient alignment operation before regular convolution. With GAConv, Gradient-Aligned CNN (GACNN) can achieve rotation invariance without any data augmentation, feature-map augmentation, and filter enrichment. In GACNN, rotation invariance does not learn from the training set, but bases on the network model. Different from the vanilla CNN, GACNN will output invariant results for all rotated versions of an object, no matter whether the network is trained or not. This means that we only need to train the network with one canonical version of the object and all other rotated versions of this object should be recognized with the same accuracy. Classification experiments have been conducted to evaluate GACNN compared with some rotation invariant approaches. GACNN achieved the best results on the 360∘ rotated test set of MNIST-rotation, Plankton-sub-rotation, and Galaxy Zoo 2.
Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks

Classification in a possibilistic scenario is a kind of multiple class assignments for data. One of the most prominent and interpretable classifier is the learning vector quantization (LVQ) realizing a nearest prototype classifier model. Figuring out the problem of classifying based on possibilistic or probabilistic class labels (assignments) leads to the use of likelihood ratio to organize a sustainable approach. To this end, we start with a special kind of probabilistic LVQ, known as Robust Soft LVQ, and propose a possibilistic extension to pave the way to our new method. Particularly, the proposed possibilistic variant takes positive and negative reasoning known from RSLVQ into account to secure a contrastive learning model in the end. In the paper we will explain the model and give the mathematical justification.
LSDDL: Layer-Wise Sparsification for Distributed Deep Learning

With an escalating arms race to adopt machine learning (ML) into diverse application domains, there is an urgent need to efficiently support distributed machine learning (ML) algorithms. As Stochastic Gradient Descent (SGD) is widely adopted in training ML models, the performance bottleneck of distributed ML would be the communication cost to transmit gradients through the network. While a lot of existing studies aim at compressing the gradient so as to reduce the overhead of network communication, they ignore the model structure in the process of compression. As a result, while they could reduce the communication time, they would result in serious computation discontinuity for deep neural networks, which will lower the prediction accuracy. In this paper, we propose LSDDL, a scalable and light-weighted method to boost the training process of deep learning models in shared-nothing environment. The cornerstone of LSDDL lies on the observation that different layers in a neural network have different importance in the process of decompression. To exploit this insight, we devise a sparsification strategy to compress the gradient of deep neural networks which can preserve the structural information of the model. In addition, we provide a series of compression techniques to further reduce the communication overhead and optimize the overall performance. We implement our LSDDL framework in the PyTorch system and encapsulate it as a user friendly API. We validate our proposed techniques by training several real models on a large cluster. Experimental results show that the communication time of LSDDL is up to 5.43 times less than the original SGD without losing much accuracy.