Reviewerly/exHarmony huggingface dataset:
Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model

IOTA is a new cryptocurrency system designed for the Internet of Things based on directed an acyclic graph structure. It has the advantages of supporting high concurrency, scalability, and zero transaction fees; however, due to the particularity of the directed acyclic graph structure, IOTA faces more complex security threats than the sequence blockchain, in which a parasite chain attack is a common double-spending attack. In this work, we propose a scheme that can effectively prevent parasite chain attacks to improve the security of the IOTA ledger. Our main idea is to analyze the behavior strategies of IOTA nodes based on evolutionary game theory and determine the key factors affecting the parasite chain attack and the restrictive relationship between them. Based on the above research, we provide a solution to resist the parasite chain attack and further prove the effectiveness of the scheme by numerical simulation. Finally, we propose the parasite chain attack prevention algorithms based on price splitting to effectively prevent the formation of the parasite chain.
Design automation of photonic resonator weights

Abstract Neuromorphic photonic processors based on resonator weight banks are an emerging candidate technology for enabling modern artificial intelligence (AI) in high speed analog systems. These purpose-built analog devices implement vector multiplications with the physics of resonator devices, offering efficiency, latency, and throughput advantages over equivalent electronic circuits. Along with these advantages, however, often come the difficult challenges of compensation for fabrication variations and environmental disturbances. In this paper, we review sources of variation and disturbances from our experiments, as well as mathematically define quantities that model them. Then, we introduce how the physics of resonators can be exploited to weight and sum multiwavelength signals. Finally, we outline automated design and control methodologies necessary to create practical, manufacturable, and high accuracy/precision resonator weight banks that can withstand operating conditions in the field. This represents a road map for unlocking the potential of resonator weight banks in practical deployment scenarios.
Deep Unsupervised Hashing with Latent Semantic Components

Deep unsupervised hashing has been appreciated in the regime of image retrieval. However, most prior arts failed to detect the semantic components and their relationships behind the images, which makes them lack discriminative power. To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. Based on this prior, DSCH regards the semantic components as latent variables under the Expectation-Maximization framework and designs a two-step iterative algorithm with the objective of maximum likelihood of training data. Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited. Besides, coarse-grained semantics components, are discovered by considering the homology relationships between fine-grained components, and the hierarchy organization is then constructed. Secondly, DSCH makes the images close to their semantic component centers at both fine-grained and coarse-grained levels, and also makes the images share similar semantic components close to each other. Extensive experiments on three benchmark datasets demonstrate that the proposed hierarchical semantic components indeed facilitate the hashing model to achieve superior performance.
Wasmati: An efficient static vulnerability scanner for WebAssembly

WebAssembly is a new binary instruction format that allows targeted compiled code written in high-level languages to be executed with near-native speed by the browser's JavaScript engine. However, given that WebAssembly binaries can be compiled from unsafe languages like C/C++, classical code vulnerabilities such as buffer overflows or format strings can be transferred over from the original programs down to the cross-compiled binaries. As a result, this possibility of incorporating vulnerabilities in WebAssembly modules has widened the attack surface of modern web applications. This paper presents Wasmati, a static analysis tool for finding security vulnerabilities in WebAssembly binaries. It is based on the generation of a code property graph (CPG), a program representation previously adopted for detecting vulnerabilities in various languages but hitherto unapplied to WebAssembly. We formalize the definition of CPG for WebAssembly, introduce techniques to generate CPG for complex WebAssembly, and present four different query specification languages for finding vulnerabilities by traversing a program's CPG. We implemented ten queries capturing different vulnerability types and extensively tested Wasmati on four heterogeneous datasets. We show that Wasmati can scale the generation of CPGs for large real-world applications and can efficiently find vulnerabilities for all our query types. We have also tested our tool on WebAssembly binaries collected in the wild and identified several potential vulnerabilities, some of which we have manually confirmed to exist unless the enclosing application properly sanitizes the interaction with such affected binaries.
Le temps des cerises: efficient temporal stack safety on capability machines using directed capabilities

Capability machines are a type of CPUs that support fine-grained privilege separation using capabilities , machine words that include forms of authority. Formal models of capability machines and associated calling conventions have so far focused on establishing two forms of stack safety properties, namely local state encapsulation and well-bracketed control flow. We introduce a novel kind of directed capabilities and show how to use them to make an earlier suggested calling convention more efficient. In contrast to earlier work on capability machine models we do not only consider integrity properties but also confidentiality properties; we provide a unary logical relation to reason about the former and a binary logical relation to reason about the latter, each expressive enough to reason about temporal stack safety. While the logical relations are useful for reasoning about concrete examples, they do not on their own demonstrate that stack safety holds for a large class of programs. Therefore, we also show full abstraction of a compiler from an overlay semantics that internalizes the calling convention as a single call step and explicitly keeps track of the call stack and frame lifetimes to a base capability machine. All results have been mechanized in Coq.
Immersive E-Learning Resources: Design Methods

Proper design is the basis for the success of any application development, regardless of industry and field of application. This fully applies to both software design and learning design. Designing e-learning resources is a hybrid activity that significantly increases risks due to the speed of technological change. The risks are even greater when it comes to technologies of increased attention - immersive. In this regard, it is important to develop design methods for immersive e-learning resources – educational, scientific, informational, reference materials and tools used in an immersive environment, reproduced by immersive technical tools, and necessary for effective organization of the educational process.
Effectiveness of Virtual Reality Simulations for Civilian, Ab Initio Pilot Training

Aviation training in the immersive Virtual Reality (VR) world has the power to overcome physical constraints, presenting cues and stimuli that would not be available in flight, nor in a two-dimensional (2D) environment. This gives VR powerful potential as a simulation tool for learning complex skills and maneuvers in the cockpit. This study evaluated the effectiveness of VR simulations as compared to traditional 2D desktop simulations in teaching maneuvers and skills to ab initio (inexperienced) civilian pilot trainees. This quasi-experimental project involved 17 freshman pilot students in an experimental college course at a private university campus in the fall semester of 2020. The participants were split into two sections: Section 1 completed CBT activities and simulations in 2D only, while Section 2 completed CBT activities in 2D and simulations in VR. Academic performance data was collected in the Canvas Learning Management System, broken down by understanding of a maneuver learned in a given lesson module. Descriptive statistics collected included quizzes, discussion board activity, and simulation completion scores. Paired samples t-tests compared perceived benefits of using the various course materials. Researchers also administered post-semester surveys to gather both qualitative and quantitative data, in which participants shared their perceptions of the course, preference for learning material type, and general feedback. Results indicated that students in both groups found the sims/tutorials and VR to be enjoyable and gratifying; the majority of students indicated that simulations were preferred over other learning materials. Early results indicate that although the students perceived that the simulations were beneficial, there were no significant differences in the final course scores or learning rates between those who utilized 2D sims as opposed to VR sims. The most important finding is that for ab initio pilots, VR simulations do not hinder learning mastery, as compared with traditional 2D desktop simulations.
BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration quality. Deep networks are, however, capable of inducing strong and hardly predictable hallucinations. Networks implicitly learn to be jointly faithful to the observed data while learning an image prior; and the separation of original data and hallucinated data downstream is then not possible. This limits their wide-spread adoption in image restoration. Furthermore, it is often the hallucinated part that is victim to degradation-model overfitting. We present an approach with decoupled network-prior based hallucination and data fidelity terms. We refer to our framework as the Bayesian Integration of a Generative Prior (BIGPrior). Our method is rooted in a Bayesian framework and tightly connected to classic restoration methods. In fact, it can be viewed as a generalization of a large family of classic restoration algorithms. We use network inversion to extract image prior information from a generative network. We show that, on image colorization, inpainting and denoising, our framework consistently improves the inversion results. Our method, though partly reliant on the quality of the generative network inversion, is competitive with state-of-the-art supervised and task-specific restoration methods. It also provides an additional metric that sets forth the degree of prior reliance per pixel relative to data fidelity.
Multi-View Integrative Attention-Based Deep Representation Learning for Irregular Clinical Time-Series Data

Electronic health record (EHR) data are sparse and irregular as they are recorded at irregular time intervals, and different clinical variables are measured at each observation point. In this work, to handle irregular multivariate time-series data, we consider the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human knowledge</i> of the aspects to be measured and time to measure them in different situations, known as multi-view features, which are indirectly represented in the data. We propose a scheme to realize multi-view features integration learning via a self-attention mechanism. Specifically, we devise a novel multi-integration attention module (MIAM) to extract complex information that is inherent in irregular time-series data. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations in a simultaneous manner. In addition, we build an attention-based decoder as a missing value imputer that helps empower the representation learning of the interrelations among multi-view observations for the prediction task this decoder operates only in the training phase so that the final model is implemented in an imputation-free manner. We validated the effectiveness of our method over the public MIMIC-III and PhysioNet challenge 2012 datasets by comparing with and outperforming the state-of-the-art methods in three downstream tasks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , prediction of the in-hospital mortality, prediction of the length of stay, and phenotyping. Moreover, we conduct a layer-wise relevance propagation (LRP) analysis based on case studies to highlight the explainability of the trained model.
Implementation of Cyber-Physical-Social System Based on Service Oriented Architecture in Smart Tourism

One of the biggest attractions in the tourism industry in Bandung is nature tourism. There is still such a constraint related to get information about nature tourism in Bandung because new attractions in Bandung always appear every year. This is felt particularly for foreign tourists outside of Bandung. Tourists are still confused to find new and popular tourist attractions, which are places that are worth visiting or not. By implementing Cyber-Physical-Social System (CPSS) with a new approach that is emphasized on social aspect in smart tourism based on Service Oriented Architecture (SOA) as methodology can influence other travelers to visit tourist attractions in Bandung. The main results are tourists will get information such as location, route, images, rating, captions of tourist attractions, and the most important thing is to be able to exchange information with others. Smart tourism is more flexible because it is web based and does not depend on the operating system used, does not require database storage, does not take up storage space, and is free. Tourists can access smart tourism anytime and anywhere.
Hybrid resource allocation and task scheduling scheme in cloud computing using optimal clustering techniques

In diverse parallel and distributed computing systems, resource allocation is the progression of distributing consumer tasks for processing elements during execution in which some performance intentions are optimised. This research explains about the innovative resource allocation algorithm for the computing grid environment. In the scheduling problem of independent task in cloud computing, summarise other scheduling algorithms introduce a modified fuzzy C-means (MFCM) clustering algorithm. Our algorithm abstract resource into a model to analyse these characteristics of resources with the MFCM algorithm. From that our proposed technique could decrease a execution time and memory space allocation of the system. For the optimal selection of virtual machines hybrid whale genetic (HWGA) optimisation algorithm is used. Since the virtual machines are optimally selected on the basis of feature values, our proposed method provides reduced load balancing as well as improved parallel execution of tasks.
Usage Patterns and Implementation of Random Forest Methods for Software Risk and Bugs Predictions

The software bugs predictions whereby the datasets of different types of bugs are evaluated for further predictions. In this research manuscript, the pragmatic evaluation of random forest approach is done and compared with results with traditional artificial neural networks (ANN) so that the results can be compared. From the outcome, the extracts from random forest are better on the accuracy level with the test datasets used in a specific format. The process of Random Forest (RF) Approach is adopted in this work that gives the effectual outcomes in most of the cases as compared to ANN and thereby the usage patterns of RF are performance aware. The paradigm of RF is used widely for the engineering optimization to solve the complex problems and generation of the dynamic trees. The outcomes and results obtained and presented in this work is giving the variations in favor random forest based optimization for the software risk management and predictive mining. The need of the proposed work and background of the study includes the effective and performance based software bugs detection. The current problem addressed includes the accuracy and multi-dimensional evaluations. The key methodology adopted here to solve the existing problem is the integration of Random Forest approach and the findings are quite effective and cavernous in assorted aspects
Graph-Based Object Semantic Refinement for Visual Emotion Recognition

The rich semantic information contained in images is an important clue to explore visual emotions. Therefore, exploring the correlation between visual emotion and the semantic relationship of objects, and extracting more effective semantic features through explicit or implicit modeling is very important for visual emotion analysis. In this paper, a novel Graph-based Object Semantic Refinement (GOSR) model is proposed to extract multi-level semantic features for visual emotion classification, in which graph structures is used to represent the object semantics and their position relationships of an image, and Graph Convolutional Networks (GCN) is used to refine object information by the aggregating neighbor object with their position relationships. The different convolutional layer’s features from GCN are further fused by Gated Recurrent Units (GRU) networks to achieve high-level semantic features. Then a framework with two branches to leverage visual and semantic information for visual sentiment analysis is proposed, which uses convolutional neural networks to extract visual features from images, and collaborates with semantic features from GOSR model to achieve better emotion recognition results. Besides, for alleviating the potentially unreasonable predictions and promote models collaboration, a novel tendency loss function based on the correlations among emotion labels is proposed to adjust the output activation value other than the target label. Extensive experiments on four widely used benchmark datasets show that our proposed method can achieve competitive performance and outperform most of the state-of-the-art methods on visual emotion recognition.
Pterodactyl: Development and Performance of Guidance Algorithms for a Mechanically Deployed Entry Vehicle

Pterodactyl is a NASA Space Technology Mission Directorate (STMD) project focused on developing a design capability for optimal, scalable, Guidance and Control (G&C) solutions that enable precision targeting for Deployable Entry Vehicles (DEVs). This feasibility study is unique in that it focuses on the rapid integration of targeting performance analysis with structural and packaging analysis, which is especially challenging for new vehicle and mission designs. This paper will detail the guidance development and trajectory design process for a lunar return mission, selected to stress the vehicle designs and encourage future scalability. For the different combinations of guidance approaches and control effectors considered, the Fully Numerical Predictor-Corrector Entry Guidance (FNPEG) was selected for configurations requiring bank angle guidance and FNPEG with Uncoupled Range Control (URC) was developed for configurations requiring angle of attack and sideslip angle guidance. Successful G&C configurations are defined as those that can deliver payloads to the intended descent and landing initiation point while abiding by trajectory constraints for nominal and dispersed trajectories.
Learning 3D Face Morphable Model from In-the-Wild Images

As a classic statistical model of 3D facial shape and albedoAlbedo, 3D Morphable Model (3DMM3DMM) is widely used in facial analysis, e.g., model fitting, and image synthesis. Conventional 3DMM3DMM is learned from a set of 3D face scans with associated well-controlled 2D face images, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM3DMM can be limited.
Status Forecast and Fault Classification of Smart Meters Using LightGBM Algorithm Improved by Random Forest

The objectives are to explore the effect of a random forest algorithm on the state prediction and fault classification of smart meters, so that the smart meters can run more stably. Based on the principle of the random forest algorithm and Light Gradient Boosting Machine (LightGBM) algorithm, its theoretical basis and application are deeply analyzed and improved. An improved fault classification and state prediction model of smart meters is designed based on a random forest-improved LightGBM algorithm. The built model algorithm is evaluated by utilizing public data sets. The results show that, by preprocessing the fault data set of smart meters, 8 fault feature types including fault type, working time, and fault month are obtained. When the improved LightGBM algorithm is trained based on random forest, the average accuracy of the algorithm is 67.65%, the average recall rate is 64.11%, and the average F1 value is 65.73%. Meanwhile, the difference between the algorithm and the random forest algorithm and the Correlation-based Feature Selection (CFS) algorithm is studied. Therefore, the prediction accuracy and fault classification of the constructed model algorithm for smart meters are higher than those of the other two algorithms. It indicates that the algorithm has a good application effect and high practical application value and can provide a scientific and useful reference for the follow-up research of smart meters.
Hybrid attention network for image captioning

Machine attention mechanisms are widely used in the task of image captioning. Such mechanisms dynamically focus on different regions to guide the word generation process. However, existing attention models may fail to concentrate on correct regions and mislead the word prediction without explicit supervision. In this study, we exploit the human captioning attention encoding rich information that human beings perceive during captioning, and propose a novel Hybrid Attention Network (HAN) that incorporates the prevailing machine attention mechanisms with human captioning attention. The proposed HAN addresses the problem of "object hallucination" by re-weighting bottom-up attention, and improves the diversity of the generated captioning by complementing top-down attention with human captioning attention. Extensive experiments are conducted on Flickr30K and MS COCO datasets, demonstrating that the proposed method effectively improves the performance of the current image captioning methods.