Reviewerly/exHarmony huggingface dataset:
Model-Driven Simulation-Based Analysis for Multi-Robot Systems

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic robustness analysis of multi-robot systems in sim-illation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission, and facilitates the specification of the team's intelligence. We evaluate ATLAS and demonstrate its effectiveness on two oceanic exploration missions performed by a team of unmanned underwater vehicles developed using the MOOS-IvP robotic simulator.
Modified Decomposition Framework and Algorithm for Many-objective Topology and Weight Evolution of Neural Networks

This paper presents a modified decomposition framework to support the Many-Objective Topology and Weight Evolution of Artificial Neural Networks (MaO-TWEANNs). Next, an algorithm, which is termed NEWS/D, is devised using the proposed framework. To validate its optimization capabilities, a numerical study is carried out. The performed numerical study includes demonstration problems ranging from three to seven objectives for which the ideal points are known. Finally, an additional numerical study is performed with respect to a possible real-life application. The latter study suggests that evolving class experts for multi-class classification problems could be enhanced using NEWS/D in a non-intuitive approach.
Adaptive Graph Convolutional Network With Adversarial Learning for Skeleton-Based Action Prediction

The purpose of action prediction is to recognize an action before it is completed to reduce recognition latency. Because action prediction has lower latency than action recognition, it can be applied to a variety of surveillance scenarios and responds faster. However, action prediction is more difficult because it cannot obtain the complete action execution. In this article, we study the action prediction which is based on skeleton data and propose a new network called adaptive graph convolutional network with adversarial learning (AGCN-AL) for it. The AGCN-AL uses adversarial learning to make the features of the partial sequences as similar as possible to the features of the full sequences to learn the potential global information in the partial sequences. Besides, partial sequences with different numbers of frames contain different amounts of information. We introduce temporal-dependent loss functions to prevent the network from paying too much attention to partial sequences whose observation ratios are small, and ignoring partial sequences whose observation ratios are large. Moreover, the AGCN-AL is combined with the local AGCN into a two-stream network to enhance the prediction, proving that the local information and the potential global information in partial sequences are complementary. We evaluate the proposed approach on two data sets and show excellent performance.
Coherent Superpositions of Photon Creation Operations and Their Application to Multimode States of Light

We present a concise review of recent experimental results concerning the conditional implementation of coherent superpositions of single-photon additions onto distinct field modes. Such a basic operation is seen to give rise to a wealth of interesting and useful effects, from the generation of a tunable degree of entanglement to the birth of peculiar correlations in the photon numbers and the quadratures of multimode, multiphoton, states of light. The experimental investigation of these properties will have an impact both on fundamental studies concerning, for example, the quantumness and entanglement of macroscopic states, and for possible applications in the realm of quantum-enhanced technologies.
Intelligent Speech Processing Technique for Suspicious Voice Call Identification Using Adaptive Machine Learning Approach

With recent advances in the field of data, there are many advantages of speedy growth of internet and mobile phones in the society, and people are taking full advantage of them. On the other hand, there are a lot of fraudulent happenings everyday by stealing the personal information/credentials through spam calls. Unknowingly, we provide such confidential information to the untrusted callers. Existing applications for detecting such calls give alert as spam to all the unsaved numbers. But all calls might not be spam. To detect and identify such spam calls and telecommunication frauds, the authors developed the application for suspicious call identification using intelligent speech processing. When an incoming call is answered, the application will dynamically analyze the contents of the call in order to identify frauds. This system alerts such suspicious calls to the user by detecting the keywords from the speech by comparing the words from the pre-defined data set provided to the software by using intelligent algorithms and natural language processing.
KGDAL

With the rapid accumulation of electronic health record (EHR) data, deep learning (DL) models have exhibited promising performance on patient risk prediction. Recent advances have also demonstrated the effectiveness of knowledge graphs (KG) in providing valuable prior knowledge for further improving DL model performance. However, it is still unclear how KG can be utilized to encode highorder relations among clinical concepts and how DL models can make full use of the encoded concept relations to solve real-world healthcare problems and to interpret the outcomes. We propose a novel knowledge graph guided double attention LSTM model named KGDAL for rolling mortality prediction for critically ill patients with acute kidney injury requiring dialysis (AKI-D). KGDAL constructs a KG-based two-dimension attention in both time and feature spaces. In the experiment with two large healthcare datasets, we compared KGDAL with a variety of rolling mortality prediction models and conducted an ablation study to test the effectiveness, efficacy, and contribution of different attention mechanisms. The results showed that KGDAL clearly outperformed all the compared models. Also, KGDAL-derived patient risk trajectories may assist healthcare providers to make timely decisions and actions. The source code, sample data, and manual of KGDAL are available at https://github.com/lucasliu0928/KGDAL.
Deep Learning Assisted Channel Estimation Refinement in Uplink OFDM Systems Under Time-Varying Channels

In various practical orthogonal frequency-division multiplexing (OFDM) systems, the estimation accuracy at the receiver is challenging, and, specifically when operate over time-varying channels. This occurs mostly due to the presence of multipath Doppler shifts. Meanwhile, deep learning has quite recently demonstrated its superiority in extracting features information from big data. To this end, in this paper, a deep learning-assisted approach for channel estimation refinement is proposed in OFDM systems, under uplink time-varying channels. By exploitingfully-connected deep neural network (FC-DNN) properly, we successfully design a channel parameter refine network (CPR-Net) which combines deep learning with existing channel estimation algorithms. Simulation results demonstrate that, compared with conventional channel estimation algorithms, the proposed CPR-Net can significantly improve the estimation accuracy of channel parameters and provide more accurate and robust signal recovery performance.
SAPS: Self-Attentive Pathway Search for weakly-supervised action localization with background-action augmentation

Weakly supervised temporal action localization is a challenging computer vision task, which aims to derive frame-level action identifier based on video-level supervision. Attention mechanism is a widely used paradigm for action recognition and localization in most recent methods. However, existing attention-based methods mostly focus on capturing the global dependency of the frame sequence regardless of the local inter-frame distances. Moreover, during background modeling, different background contents are typically classified into one category, which inevitably jeopardizes the discriminative ability of classifiers and brings about irrelevant noise. In this paper, we present a novel self-attentive pathway search framework, namely SAPS, to address the above challenges. To achieve comprehensive representation with discriminative attention weights, we design a NAS-based attentive module with a path-level searching process, and construct a competitive attention structure revealing both local and global dependency. Furthermore, we propose the action-related background modeling for robust background-action augmentation, where knowledge derived from background can provide informative clues for action recognition. An ensemble T-CAM operation is subsequently designed to incorporate background information to further refine the temporal action localization results. Extensive experiments on two benchmark datasets (i.e., THUMOS14 and ActivityNet1.2) have clearly corroborated the efficacy of our method.
“Just for the Sake of Transparency”: Exploring Voter Mental Models of Verifiability

Verifiable voting schemes allow voters to verify their individual votes and the election outcome. The voting protocol Selene offers verification of plaintext votes while preserving privacy. Misconceptions of verification mechanisms might result in voters mistrust of the system or abstaining from using it. In this paper, we interviewed 24 participants and invited them to illustrate their mental models of Selene. The drawings demonstrated different levels of sophistication and four mental models: 1) technology understanding, 2) meaning of the verification phase, 3) security concerns, and 4) unnecessary steps. We highlight the misconceptions expressed regarding Internet voting technologies and the system design. Based on our findings, we conclude with recommendations for future implementations of Selene as well as for the design of Internet voting systems in general.
Straggler-Aware Parallel Graph Processing in Hybrid Memory Systems

Hybrid memory systems composed of DRAM and Non-Volatile Memory (NVM) can offer very large memory capacity for data-intensive applications such as graph processing. The performance of parallel graph processing is often affected by straggler tasks due to asymmetrical graph partitioning and sub-graph processing. In hybrid memory systems, the significant difference of performance between DRAM and NVM may exacerbate the load imbalance of parallel graph processing. Traditional load balancing schemes that only aim to balance computing loads among multiple processors are no longer effective in hybrid memory systems.In this paper, we first explore how hybrid memory systems affect the efficiency of existing graph processing systems. We make two observations: 1) Traditional work-stealing schemes may be not efficient enough in hybrid memory systems. Data migration combining with work-stealing can further improve the efficiency of parallel graph processing; 2) Interleaving accesses to different graph partitions due to data dependency have a significant impact on the effectiveness of data migration. To address these problems, we propose NVMGraph, a parallel graph processing scheme to mitigate memory access imbalance among partitions in hybrid memory systems. We first recognize data dependency among all partitions according to graph algorithms and then coalesce the randomly-accessed data required by a working thread in a single partition to mitigate data dependency. To reduce migration cost while still speeding up the straggler task, we only migrate randomly-accessed data blocks in the most frequently-accessed partition from NVM to DRAM. We implement NVMGraph based on Ligra and evaluate it in a hybrid memory system using real NVM devices. Experimental results demonstrate that NVMGraph can improve the performance of graph processing by up to 40.3% compared with the state-of-the-art Ligra and Polymer.
Image Descriptor Generator Using Encoder-Decoder Model

A visual representation is an image of a situation, object, or person, whereas a description is a description of the picture action. The generation of an image description is critical in many industries, including medicine, advertising, and robotics. This paper considers the well-known deep learning problem of generating a textual description of an image. The proposed model has used InceptionV3and Beam search to run the picture descriptor generator.
Real-Time 3D Object Detection From Point Cloud Through Foreground Segmentation

This paper aims to apply real-time light-weight high-precision 3D detection for autonomous driving. We propose LIDAR-based 3D object detection based on foreground segmentation using a fully sparse convolutional network (FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 3D). We design a sparse convolutional backbone network and a sparse convolutional detection head to efficiently use the computing and memory resources and accelerate the inference. Instead of using the anchor-based method, we convert the detection problem into a foreground segmentation problem on a bird's-eye view. The sparse convolutional detection head predicts the objectness and bounding box on each active point on the sparse feature map. We design a new oriented bounding box coding method and corresponding loss functions. We predict the endpoints of two mutually perpendicular lines that pass through the foreground active points and indirectly predict the objects' oriented bounding box from these four endpoints. We use the indirectly calculated object center, size, and orientation as inputs of loss functions in the training step. Experiments on the KITTI dataset show that the sparse backbone network we designed is 2.2 times faster and 18.4 times fewer FLOPs than the dense backbone network. The average improvement of the loss functions based on the bounding box code is 1.1% and 0.8% on the BEV and 3D detection, respectively, compared to no addition of these losses. Moreover, FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 3D outperforms the state-of-the-art LIDAR-based method in speed and precision for both cars and cyclists.
Model for IaaS Security Model: MISP Framework

Cloud computing offers plenty of services having benefits of cost cutting, flexibility, availability, elasticity and pay per use for cloud users and centralized control, safety and management for Cloud Service Providers (CSP). Out of several service models, Infrastructure-as-a-Service (IaaS) preludes by offering access to computing resources like CPUs, servers, network devices through software like APIs, UIs and GUIs over the internet. Along with numerous merits of IaaS, there exist several security and privacy issues and threats to Confidentiality, Integrity and Authentication (CIA) triad. In this paper, we explore security and privacy issues related to IaaS components through rigorous study and determine counter measures for the same. Furthermore, with the help of MITRE ATT&CK knowledge base, a Model for IaaS Security and Privacy: MISP framework is proposed. The framework is exhibited as a multidimensional structure, each dimension having different parameters. This leads to addition of important features currently missing in the existing models. The proposed framework enhances security and privacy in IaaS model and guides for safer adoption of the delivery model by organizations and enterprises.
Medical Images Compression and Encryption using DCT, Arithmetic Encoding and Chaos-Based Encryption

With the need for incognito and security for the medical image transmission and to reduce the size of the file, the authors proposed chaotic sequence-based encryption combined with the arithmetic encoding and discrete cosine transform (DCT) for the compression. First, the image is compressed by using DCT. Then, the image is compressed again with the arithmetic encoding technique. After that, the result of the compression is encrypted by using two chaotic sequence functions to shuffle and scramble the file. The compression ratio of the proposed technique is 0.748 and a PSNR of 41.70 dB.
Entity Alignment Between Knowledge Graphs Using Entity Type Matching

The task of entity alignment between knowledge graphs (KGs) aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based entity alignment methods get extended attention. Most of them firstly embed the entities in low dimensional vectors space via relation structure, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are inadequate in utilizing entity type information. In this paper, we propose a novel entity alignment framework, which integrates entity embeddings and entity type information to achieve entity alignment. This framework uses encoding functions to extract the type features of entities for type matching, and combines the similarity of entity embeddings to improve the accuracy of entity alignment. Our experimental results on several real-world datasets shows that our proposed method achieves improvements on entity alignment compared with most methods, and is close to the state-of-the-art method on several metrics.
Attribute Revocation in ECC-based CP-ABE scheme for lightweight resource-constrained devices

Ciphertext Policy Attribute-Based Encryption (CPABE) has gained popularity in the research area among the many proposed security models for providing fine-grained access control of data. Lightweight ECC-based CP-ABE schemes can provide feasible selective sharing from resource-constrained devices. However, the existing schemes lack support for a complete revocation mechanism at the user and attribute levels. We propose a novel scheme called Ecc Proxy based Scalable Attribute Revocation (EPSAR-CP-ABE) scheme. It extends an existing ECC-based CP-ABE scheme for lightweight IoT and smart-card devices to implement scalable attribute revocation. The scheme does not require re-distribution of secret keys and re-encryption of ciphertext. It uses a proxy server to furnish a proxy component for decryption. The dependency of the proposed scheme is minimal on the proxy server compared to the other related schemes. The storage and computational overhead due to the attribute revocation feature are negligible. Hence, the proposed EPSAR-CP-ABE scheme can be deployed practically for resource-constrained devices.
Security Challenges in Industry 4.0 SCADA Systems – A Digital Forensic Prospective

Industry 4.0 is that the digital transfiguration of production and connected industries with worth creation processes called smart factory. The building blocks of Industry 4.0 are fusion of Cyber Physical systems, Artificial Intelligence, Robotics, cloud computing and the Internet of things(IoT). In Industry 4.0, the Supervisory Control and Data Acquisition (SCADA) systems performs more intelligence, automation and self-learning with the centralized technologies. With these, the SCADA controls manufacturing processes in industries by monitoring, gathering and processing of real time data. At the time of making a product through Industry 4.0 in any factory, if any faults occurred in the Supervisory systems, the entire system gets collapsed and leads to harmful to consumers. The advanced digital forensics techniques for the entire Industry 4.0 architecture are needed to identify the errors in the real time processes and new tools to be designed to ratify during live processes. In this work, some of the forensics challenges in the SCADA systems of Industry 4.0 and the available digital forensics tools for live systems are discussed with possible proposed solutions. It also discusses the limitations and difficulties faced in digital forensics of Industrial Control Systems.
An Efficient and Privacy-Preserving Multi-User Multi-Keyword Search Scheme without Key Sharing

Multi-keyword search, aiming to search the objects by a query request that consists of multiple keywords, has wide applications in personalized recommendation services. Mean-while, the fast development of cloud technology has given rise to a new trend that data are encrypted before being outsourced to a public cloud for users to enjoy pay-as-you-go services. However, most of the existing works primarily focus on the single keyword search, and consider a general scenario with a single owner and a single user. In this paper, we propose an efficient and Privacy-preserving Multi-user Multi-keyword Search (PMMS) scheme, which can support user scalability without key sharing. In particular, based on the matrix decomposition, a key derivation approach is integrated into our PMMS to generate secret keys and re-encryption keys. Furthermore, by employing threshold predicate encryption and leveraging the techniques of matrix transformation and proxy re-encryption, PMMS guarantees that only the comparison result of an inner product of two vectors and a pre-defined threshold is revealed, and enables the cloud server to perform multi-keyword search in an efficient and privacy-preserving manner. Security analysis shows that the confidentiality of owners’ data and users’ queries can be guaranteed. Extensive experiments on a real-world dataset demonstrate that PMMS is efficient in terms of multi-keyword search.
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. Each sequence can be parameterized through multiple acquisition parameters affecting MR image contrast, signal-to-noise ratio, resolution, or scan time. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. However, current generative approaches for the synthesis of MR images are only trained on images with a specific set of acquisition parameter values, limiting the clinical value of these methods as various sets of acquisition parameter settings are used in clinical practice. Therefore, we trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, image orientation). This approach enables us to synthesize MR images with adjustable image contrast. In a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.