Reviewerly/exHarmony huggingface dataset:
Evaluation of Machine Learning-based Detection against Side-Channel Attacks on Autonomous Vehicle

Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world has introduced new security risks. Recent studies have shown that using Cache-based Side-Channel Attacks (SCAs) could infer sensitive users' information (e.g., which route the user is taking) highlighting significant vulnerability posed to today's computer systems. As a result, it is crucial to propose effective detection mechanisms against emerging microarchitectural SCAs on autonomous driving systems. In response, we first identify the threat model and victim applications of autonomous driving systems in this work. Next, we explore the suitability of various machine learning-based classifiers trained by information collected from built-in hardware performance counter registers available in modern autonomous vehicle systems. To this end, various supervised machine learning models are implemented for cache-based SCAs detection and precisely compared and characterized in terms of detection accuracy, robustness, and latency of the detection. Our experiments conducted on an Intel Xeon, which Waymo autonomous driving vendor uses, demonstrate that J48 achieves 99.5% accuracy with the highest efficiency compared with other investigated models.
On the Impact of Attachment Strategies for Payment Channel Networks

Payment channel networks, such as Bitcoin's Lightning Network, promise to improve the scalability of blockchain systems by processing the majority of transactions off-chain. Due to the design, the positioning of nodes in the network topology is a highly influential factor regarding the experienced performance, costs, and fee revenue of network participants. As a consequence, today's Lightning Network is built around a small number of highly-connected hubs. Recent literature shows the centralizing tendencies to be incentive-compatible and at the same time detrimental to security and privacy. The choice of attachment strategies therefore becomes a crucial factor for the future of such systems. In this paper, we provide an empirical study on the (local and global) impact of various attachment strategies for payment channel networks. To this end, we introduce candidate strategies from the field of graph theory and analyze them with respect to their computational complexity as well as their repercussions for end users and service providers. Moreover, we evaluate their long-term impact on the network topology.
Single image HDR synthesis using a Densely Connected Dilated ConvNet

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding

The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature. Automated machine learning (AutoML) techniques have recently been introduced into KG to design task-aware scoring functions, which achieve the state-of-the-art performance in KG embedding. However, the effectiveness of searched scoring functions is still not as good as desired. In this paper, observing that existing scoring functions can exhibit distinct performance on different semantic patterns, we are motivated to explore such semantics by searching relationa-ware scoring functions. But the relation-aware search requires a much larger search space than the previous one. Hence, we propose to encode the space as a supernet and propose an efficient alternative minimization algorithm to search through the supernet in a one-shot manner. Finally, experimental results on benchmark datasets demonstrate that the proposed method can efficiently search relation-aware scoring functions, and achieve better embedding performance than state-of-the-art methods <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Encrypted cloud photo storage using Google photos

Cloud photo services are widely used for persistent, convenient, and often free photo storage, which is especially useful for mobile devices. As users store more and more photos in the cloud, significant privacy concerns arise because even a single compromise of a user's credentials give attackers unfettered access to all of the user's photos. We have created Easy Secure Photos (ESP) to enable users to protect their photos on cloud photo services such as Google Photos. ESP introduces a new client-side encryption architecture that includes a novel format-preserving image encryption algorithm, an encrypted thumbnail display mechanism, and a usable key management system. ESP encrypts image data such that the result is still a standard format image like JPEG that is compatible with cloud photo services. ESP efficiently generates and displays encrypted thumbnails for fast and easy browsing of photo galleries from trusted user devices. ESP's key management makes it simple to authorize multiple user devices to view encrypted image content via a process similar to device pairing, but using the cloud photo service as a QR code communication channel. We have implemented ESP in a popular Android photos app for use with Google Photos and demonstrate that it is easy to use and provides encryption functionality transparently to users, maintains good interactive performance and image quality while providing strong privacy guarantees, and retains the sharing and storage benefits of Google Photos without any changes to the cloud service.
EVMAS: An Energy-aware Virtual Machine Allocation Scheme in Fog Centers

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm.
A Medical Ontology Informed User Experience Taxonomy to Support Co-creative Workflows for Authoring Mixed Reality Medical Education Spaces

Curriculum size constantly challenges healthcare education and training. Technology enhanced, immersive educational content like Virtual, Augmented, or Mixed Reality (VR/AR/MR collectively XR) constantly aims to facilitate knowledge retention and skills acquisition in the healthcare sector. Core challenge in this effort is the increasing costs, in time and resources, required for designing and developing XR immersive educational content. An approach to address this challenge is participatory design methods. Co-creation approaches distribute the burden of content development amongst the educators' community and facilitate decentralized bottoms-up content creation. This approach requires data modeling approaches that facilitate digital asset discoverability, reusability and consumption through visual authoring tools. This work describes the conceptualization and implementation of a UX taxonomy for annotating immersive AR/VR/MR content at the asset level for maximum repurposing capacity. A brainstorming session between educational and technology experts was conducted and conceptual details of the terms of the taxonomy were described. The Simple Knowledge Organization System (SKOS) vocabulary was utilized to organize this devised taxonomy and link it with existing medical ontology terms, formulating an RDF endpoint of the nominated ENTICE ontology. This ontology was able to link medical terms with UX and educational properties in a data structure that can annotate and contextually encapsulate any XR digital asset. An example such term is described and presented as proof of application. The semantic modelling implemented in this work is directly applicable to a previously proposed visual data structure and subsequent authoring environment that could facilitate XR resource design and authoring from non-technical experts.
Spatiotemporal-Spectral Graph Convolutional Networks For Skeleton-Based Action Recognition

Skeleton-based action recognition has been a hot topic with the increasing development of Graph Convolutional Networks(GCNs). Previous work constructed the skeleton sequences into graphs and focused on extracting spatial-temporal information from various actions. However, they ignored the hidden information in the spectral domain of the whole graphs. In this paper, a novel graph network based on both spatio-temporal information and spectral-domain information is proposed(SS-GCN), adopting a two-stream graph topology and can be trained in an end-to-end manner. Besides, along with other GCN methods that optimize only the spatial-temporal graph, our spectral stream helps in further performance improvements. Our method(SS-GCN) is evaluated on two large skeleton-based datasets, NTU-RGBD and Kinetics-Skeleton. The experiment results demonstrate the effectiveness of SS-GCN.
Face Mask and Social Distancing Detection for Surveillance Systems

There is a huge panic among the people in recent times due to the spread of communicable diseases. People are in close vicinity to one another when in closed spaces like shops, restaurants, classrooms, etc. There is also a cause for worry in workplaces regarding the safety of the workplace. This paper discusses about two models which can be used to detect the distance between people to ensure social distancing and to detect if people are wearing a mask which can be implemented to follow safety measures. To implement these models deep learning techniques are used. For the social distancing model object detection is done to detect humans and this is done through the YOLOv3. For the mask detection model, the MobileNetV2 is the algorithm which is used for classification. This is used to detect if the people are wearing a mask. These two models can be used for the purpose of prevention against widely spreading diseases. For example, if the people of an organization have to request their customers to stay 6 feet apart or wear a mask in cases where the customers are not following the standard safety protocols, the people of the organization should go directly up to them and request for it. This increases the contact between people and at the same time increases the risk factor for the people working in that organization. When these models are implemented, it reduces unnecessary human contact while also ensuring to alert the customers if they break these protocols.
Diversifying Semantic Image Synthesis and Editing via Class‐ and Layer‐wise VAEs

Abstract Semantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class‐ and layer‐wise extension to the variational autoencoder (VAE) framework that allows flexible control over each object class at the local to global levels by learning multiple latent spaces. Furthermore, we demonstrate that our method generates images that are both plausible and more diverse compared to state‐of‐the‐art methods via extensive experiments with real and synthetic datasets in three different domains. We also show that our method enables a wide range of applications in image synthesis and editing tasks.
Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

Complex mission specifications can be often specified through temporal logics, such as Linear Temporal Logic and its syntactically co-safe fragment, scLTL.Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment, where neither locations of regions or objects of interest in the environment nor the obstacle space are known a priori.We propose an algorithm that, while exploring the environment, learns important semantic dependencies in the form of a semantic abstraction, and uses it to bias the growth of an Rapidly-exploring random graph towards faster mission completion.Our approach leads to finding trajectories that are much shorter than those found by the sequential approach, which first explores and then plans.Simulations comparing our solution to the sequential approach, carried out in 100 randomized office-like environments, show more than 50% reduction in the trajectory length.
Self-referential quality diversity through differential MAP-Elites

Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.
The Application of Text Entailment Techniques in COLIEE 2020

We develop a method to identify entailment relationships in the texts of case law documents, in the context of two tasks of the Competition on Legal Information Extraction and Entailment (COLIEE 2020). The first task consists in, given a 1) base case, 2) a text fragment from that base case, and 3) the list of paragraphs from a noticed case, identify which of those paragraphs entail the given text fragment. For that problem, we apply a combination of transformer-based and textual similarity techniques. The second task consists in identifying entailment relationships between yes/no questions extracted from the Japanese law bar exam and paragraphs extracted from the Japanese statute legislation. Our approach is to first define a set of classes of textual entailment that cover our analysis of the statute law data, and then implement a pre-processing step to construct the statute law-specific entailment types before exploiting them with deep learning.
Transtexture Lamp: Composing a Deformable Device as a Computational Whole

Research into shape-changing and deformable artifacts that explore novel interactions has been growing in design and HCI. Yet, there is little discussion on the design processes behind these approaches and in particular, a theoretical understanding of materiality that is central to deformation. Informed by Wiberg's compositional interaction design, we contribute an investigation into supporting long-term relations with deformation: the transTexture lamp. Specifically, we crafted a dynamic physical form with materials at hand. We instantiated the materiality of interaction being designed as a computational whole. Reflecting on our theory-informed RtD process enriched our understanding of compositional interaction design. These reflections on our design approach may benefit further explorations into the intersections between materiality, longevity, and actuality in the context of design-oriented HCI.
Physically Inspired Dense Fusion Networks for Relighting

Image relighting has emerged as a problem of significant research interest inspired by augmented reality applications. Physics-based traditional methods, as well as black box deep learning models, have been developed. The existing deep networks have exploited training to achieve a new state of the art; however, they may perform poorly when training is limited or does not represent problem phenomenology, such as the addition or removal of dense shadows. We propose a model which enriches neural networks with physical insight. More precisely, our method generates the relighted image with new illumination settings via two different strategies and subsequently fuses them using a weight map (w). In the first strategy, our model predicts the material reflectance parameters (albedo) and illumination/geometry parameters of the scene (shading) for the relit image (we refer to this strategy as intrinsic image de- composition (IID)). The second strategy is solely based on the black box approach, where the model optimizes its weights based on the ground-truth images and the loss terms in the training stage and generates the relit output directly (we refer to this strategy as direct). While our proposed method applies to both one-to-one and any-to-any relighting problems, for each case we introduce problem- specific components that enrich the model performance: 1) For one-to-one relighting we incorporate normal vectors of the surfaces in the scene to adjust gloss and shadows accordingly in the image. 2) For any-to-any relighting, we propose an additional multiscale block to the architecture to enhance feature extraction. Experimental results on the VIDIT 2020 and the VIDIT 2021 dataset (used in the NTIRE 2021 relighting challenge) reveals that our proposal can outperform many state-of-the-art methods in terms of well- known fidelity metrics and perceptual loss.
A Runtime Reconfigurable Design of Compute-in-Memory–Based Hardware Accelerator for Deep Learning Inference

Compute-in-memory (CIM) is an attractive solution to address the “memory wall” challenges for the extensive computation in deep learning hardware accelerators. For custom ASIC design, a specific chip instance is restricted to a specific network during runtime. However, the development cycle of the hardware is normally far behind the emergence of new algorithms. Although some of the reported CIM-based architectures can adapt to different deep neural network (DNN) models, few details about the dataflow or control were disclosed to enable such an assumption. Instruction set architecture (ISA) could support high flexibility, but its complexity would be an obstacle to efficiency. In this article, a runtime reconfigurable design methodology of CIM-based accelerators is proposed to support a class of convolutional neural networks running on one prefabricated chip instance with ASIC-like efficiency. First, several design aspects are investigated: (1) the reconfigurable weight mapping method; (2) the input side of data transmission, mainly about the weight reloading; and (3) the output side of data processing, mainly about the reconfigurable accumulation. Then, a system-level performance benchmark is performed for the inference of different DNN models, such as VGG-8 on a CIFAR-10 dataset and AlexNet GoogLeNet, ResNet-18, and DenseNet-121 on an ImageNet dataset to measure the trade-offs between runtime reconfigurability, chip area, memory utilization, throughput, and energy efficiency.
A Comprehensive Study on Automatic Speaker Recognition by using Deep Learning Techniques

In Speaker, identifying or recognizing human voices is a challenging task. Recently, the automatic speaker recognition technique has been developed by using deep learning techniques. However, there is a lot of wide-ranging reviews on the implementation of deep learning techniques. The authors of this work examine numerous main subtasks of automatic speaker recognition, such as speaker verification and identification, focusing on deep learning-based solutions. This article offers the groundwork for establishing effective voice recognition, automatic speaker recognition planning, and accurate quantitative voice recognition evaluation using deep learning classifiers. Finally, the authors have analyzed robust speaker recognition with two primary techniques like domain mismatch and noise difficulties, for speech improvement and domain adaption.
An Offline Delegatable Cryptocurrency System

Blockchain-based cryptocurrencies, facilitating the convenience of payment by providing a decentralized online solution, have not been widely adopted so far due to slow confirmation of transactions. Offline delegation offers an efficient way to exchange coins. However, in such an approach, the coins that have been delegated confront the risk of being spent twice since the delegator's behaviour cannot be restricted easily on account of the absence of effective supervision. Even if a third party can be regarded as a judge between the delegator and delegatee to secure transactions, she still faces the threat of being compromised or providing misleading assure. Moreover, the approach equipped with a third party contradicts the real intention of decentralized cryptocurrency systems. In this paper, we propose DelegaCoin, an offline delegatable cryptocurrency system to mitigate such an issue. We exploit trusted execution environments (TEEs) as decentralized "virtual agents" to prevent malicious delegation. In DelegaCoin, an owner can delegate his coins through offline-transactions without interacting with the blockchain network. A formal model and analysis, prototype implementation and further evaluation demonstrate that our scheme is provably secure and practically feasible.