Reviewerly/exHarmony huggingface dataset:
Cognitively managed multi-level authentication for security using Fuzzy Logic based Quantum Key Distribution

Nuclear Command and Control Center (NCCC) security must be considered a high priority in the nation, country, and ultimately humanity’s best interest. Presently, with the expanding nuclear security risks, it is pivotal to guarantee that malevolent agent never acquires nuclear access. The purpose of this manuscript is to develop Cognitively Managed Multi-Level Authentication (CMMLA) through a collaboration among Cognitive Computing (CC), Quantum Computing (QC) and Cyber Security (CS). Thus, the proposed scheme will provide NCCC with the capability of self-learning, management, inherent security, rigorous, and reliable authentication protocols. Fuzzy Logic-based Quantum Key Distribution (FLQKD) is used to implement CMMLA and perform the experimentation for tangible results over the Simulink from MATLAB R2021a. For the reliability analysis of irregular Charles H. Bennett and Gilles Brassard 1984, (BB84) algorithm that we used and is a variant of Quantum Key Distribution (QKD), experimentation is done through an online QKD Simulator. Consequently, the results of FLQKD implementation show high throughput for the security of NCCC by combining the advantages of well-known techniques of their kind, where the authorized and unauthorized persons are successfully identified, and access granted only to the authenticated persons while considering the different scenarios of access allowance
Portal Vein and Hepatic Vein Segmentation in Multi-Phase MR Images Using Flow-Guided Change Detection

Segmenting portal vein (PV) and hepatic vein (HV) from magnetic resonance imaging (MRI) scans is important for hepatic tumor surgery. Compared with single phase-based methods, multiple phases-based methods have better scalability in distinguishing HV and PV by exploiting multi-phase information. However, these methods just coarsely extract HV and PV from different phase images. In this paper, we propose a unified framework to automatically and robustly segment 3D HV and PV from multi-phase MR images, which considers both the change and appearance caused by the vascular flow event to improve segmentation performance. Firstly, inspired by change detection, flow-guided change detection (FGCD) is designed to detect the changed voxels related to hepatic venous flow by generating hepatic venous phase map and clustering the map. The FGCD uniformly deals with HV and PV clustering by the proposed shared clustering, thus making the appearance correlated with portal venous flow robustly delineate without increasing framework complexity. Then, to refine vascular segmentation results produced by both HV and PV clustering, interclass decision making (IDM) is proposed by combining the overlapping region discrimination and neighborhood direction consistency. Finally, our framework is evaluated on multi-phase clinical MR images of the public dataset (TCGA) and local hospital dataset. The quantitative and qualitative evaluations show that our framework outperforms the existing methods.
Detecting accounting fraud in companies reporting under US GAAP through data mining

The accounting fraud detection models developed on financial data prepared under US Generally Accepted Accounting Principles (GAAP) in the current literature achieve significantly weaker performance than models based on financial data prepared under different accounting standards. This study contributes to the US GAAP accounting fraud data mining literature through the attainment of higher model performance than that reported in the prior literature. Financial data from the 10-K forms of 320 fraudulent financial statements (80 fraudulent companies) and 1,200 nonfraudulent financial statements (240 nonfraudulent companies) were collected from the US Security and Exchange Commission. The eight most commonly used data mining techniques were applied to develop prediction models. The results were cross-validated on a testing dataset and then compared according to parameters of accuracy, F-measure, and type I and II errors with existing studies from the US, China, Greece, and Taiwan. As a result, the developed predictive models for accounting fraud achieved performance comparable to those achieved by models built on data from other accounting standards. Moreover, the developed models also significantly outperformed (accuracy 10.5%, F-measure 16.1%, type I error 12.2% and type II error 15.2%) existing studies based on US GAAP financial data. Furthermore, this study provides an extensive literature review encompassing recent accounting fraud theory. It enhances the existing US fraud data mining literature with a performance comparison of studies based on other accounting standards.
LiteReconfig

An adaptive video object detection system selects different execution paths at runtime, based on video content and available resources, so as to maximize accuracy under a target latency objective (e.g., 30 frames per second). Such a system is well suited to mobile devices with limited computing resources, and often running multiple contending applications. Existing solutions suffer from two major drawbacks. First, collecting feature values to decide on an execution branch is expensive. Second, there is a switching overhead for transitioning between branches and this overhead depends on the transition pair. LiteReconfig, an efficient and adaptive video object detection framework, addresses these challenges. LiteReconfig features a cost-benefit analyzer to decide which features to use, and which execution branch to run, at inference time. Furthermore, LiteReconfig has a content-aware accuracy prediction model, to select an execution branch tailored for frames in a video stream. We demonstrate that LiteReconfig achieves significantly improved accuracy under a set of varying latency objectives than existing systems, while maintaining up to 50 fps on an NVIDIA AGX Xavier board. Our code, with DOI, is available at https://doi.org/10.5281/zenodo.6345733.
A spatially constrained skew Student’s-t mixture model for brain MR image segmentation and bias field correction

Accurate segmentation of brain magnetic resonance images is a key step in quantitative analysis of brain images. Finite mixture model is one of the most widely used methods in brain magnetic resonance image segmentation. However, due to the presence of intensity inhomogeneity artifact and noise, the image histogram distribution of brain MR images may follow a heavy tailed distribution or asymmetric distribution, which makes traditional finite mixture model, such as Gaussian mixture model, hard to achieve accurate segmentation results. To alleviate these problems, a novel spatially constrained finite skew student's-t mixture model is proposed in this paper. Firstly, we propose anisotropic two-level spatial information, which combines the prior and posterior probabilities, to reduce the impact of noise. The proposed spatial information can preserve rich details, such as edges and corners. Secondly, we couple the anisotropic spatial information into the skew student's-t distribution to fit the intensity distribution of observation data with heavy tail distribution or asymmetric distribution. Thirdly, we use a linear combination of a set of orthogonal basis functions to model the intensity inhomogeneities. Finally, the objective function integrates both tissue segmentation and the bias field estimation. In the implementation, we used an improved expectation maximization (EM) algorithm to estimate the model parameters. The experimental results of our model on synthetic data and brain magnetic resonance images are better than other state-of-the-art segmentation methods.
Two Years or More of Co-speculation: Polylogues of Philosophers, Designers, and a Tilting Bowl

This article investigates new relations with things that are expansive and inclusive of the pluralities and differences within our entanglements with technologies. We do this by extending our commitments to the methodological approaches of material speculation and co-speculation that led us to engage in multi-year conversations between ourselves as design researchers, philosophers, and a counterfactual artifact we designed, known as a Tilting Bowl. The philosophers lived with the Tilting Bowl during this period. We call these conversations, polylogues, of which the aim is to co-speculate on a range of new possible relations by which to consider living with technological things. The contributions of our article are two-fold. Firstly, through our polylogues, we offer descriptions of three relations with things. These include (1) non-anthropocentric care: care that is non-anthropocentric and existential; (2) non-presumptive relations: not-knowing in relating to and engaging with things; and (3) ideologized relations: ideologies that frame relations with technologies. Secondly, the article elaborates and critically reflects on co-speculation as a method for relational and situated knowing that can be of benefit to HCI researchers.
A Secure and Efficient Decentralized Access Control Scheme Based on Blockchain for Vehicular Social Networks

The vehicular social network (VSN) is an emerging mobile communication system combining a vehicle ad hoc network (VANET) with a social network. It provides a new means of sharing, disseminating, and delivering data for passengers, drivers, and vehicles. However, a VSN may expose users’ private information, such as identities, location information, and trajectories, and tampering with shared data may lead to security and safety problems in vehicle systems. Considering the security and privacy preservation of shared data, we propose a lightweight decentralized multiauthority access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) and blockchain, by which a decentralized multiauthorization node supports vehicle users by performing lightweight calculations with the assistance of the vehicle cloud service provider (VCSP). We use blockchain to record storage and access transactions, achieving self-verification by users and tamper-resistance of ciphertexts. An improved smart contract reduces the workload of verification by users and achieves privacy preservation by hiding the policy. It supports user revocation and outsourced decryption, enabling more flexibility and better performance. A security and performance analysis shows that our scheme has clear advantages over existing schemes.
A CNN-based multi-task framework for weather recognition with multi-scale weather cues

Automatic image-based weather recognition has a great significance in the practical use of computer visual applications. The weather recognition approaches via multi-task learning obtain the description of weather conditions by jointly tackling weather-cue segmentation task and weather classification task. However, the existing approaches neglect the multiple scales of weather cues, which may decrease the ability to deal with both large and small weather cues in weather-cue segmentation. They also fail to assign appropriate weights, which the multi-task learning for weather recognition relies on, to the two tasks. To address these problems, we propose a novel end-to-end Convolutional Neural Network (CNN)-based Multi-task Weather Recognition Network with multi-scale weather cues (CMWRN). Specifically, in the weather-cue segmentation task, we propose to segment weather cues, e.g. blue sky and black clouds, by capturing multi-scale weather-cue features. Arbitrary-scale weather-cue regions in the outdoor images can be accurately and effectively classified via resampling the weather-cue features at multiple scales. In the weather classification task, a generalized pooling method is introduced to generate more discriminative weather representations. To further promote the weather classification, the multi-scale weather-cue features are exploited to provide weather clues for weather representations. Moreover, we develop an adaptive weighting scheme to automatically balance the weather classification task and weather-cue segmentation task for more effective training. Compared with the state-of-the-arts, experimental results on two public available benchmark datasets demonstrate the superior performance of our approach.
BacklitNet: A dataset and network for backlit image enhancement

Backlit images are usually taken when the light source is opposite to the camera. The uneven exposure (e.g., underexposure on the foreground and overexposure on the background) makes the backlit images more challenging than general image enhancement tasks that only need to increase or decrease the exposure on the whole images. Compared to traditional approaches, Convolutional Neural Networks perform well in enhancing images due to the abilities of exploiting contextual features. However, the lack of large benchmark datasets and specially designed models impedes the development of backlit image enhancement. In this paper, we build the first large-scale BAcklit Image Dataset (BAID), which contains 3000 backlit images and the corresponding ground truth manually adjusted by trained photographers. It covers a broad range of categories under different backlit conditions in both indoor and outdoor scenes. Furthermore, we propose a saliency guided backlit image enhancement network, namely BacklitNet, for robust and natural restoration of backlit images. In particular, our model innovatively combines a nested U-structure with bilateral grids, which enables fully extracting multi-scale saliency information and rapidly enhancing arbitrary resolution images. Moreover, a carefully designed loss function based on prior knowledge of brightness distribution of backlit images is proposed to enforce the network to focus more on backlit regions during the training phase. We evaluate the proposed method on the BAID dataset and two public small-scale backlit image datasets. Experimental results demonstrate that our method performs favorably against the state-of-the-art approaches. • We build the first large-scale backlit image dataset (BAID) which contains 3000 backlit images and the corresponding high-quality ground truth. The constructed dataset makes end-to-end learning of robust backlit enhancers possible and promotes the application of neural networks in backlit image enhancement. • We design a novel backlit image enhancement framework, which efficiently restores ill-exposed regions of backlit images based on subject salience and brightness distribution prior knowledge without damaging the well-exposed regions. • We evaluate the proposed method on the BAID dataset and two public small-scale backlit image datasets. The results show that our model achieves state-of-the-art performance both on visual effects and commonly-used metrics.
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task.
Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates

Mobile learning has become an essential telematic tool to facilitate and compliment online teaching and learning during the pandemic. This study investigates the change of behaviour and acceptance of using mobile learning specifically for engineering undergraduates due to this shift. The data collected pre-Covid19 (n = 326) and post-pandemic (n = 349) indicated an inclination for utilizing laptops than smartphones, while Telegram prevails as a popular tool for communicating and sharing information within the learning community. Next, while video conferencing tools and online learning management systems utilization increased, educational games and reading behaviour via mobile devices declined. Concurrently, behavioural intention post-pandemic were found to reduce marginally as importance were also given towards establishing learning communities via social influence compared to perceived usefulness. The outcome of this study contributes to the limited body of literature on engineering education mobile learning acceptance, and recommendations are provided for further investigation to ensure continuous sustainable use.
Modeling the Constraints to the Utilization of the Internet of Things in Managing Supply Chains of Off-Site Construction: An Approach toward Sustainable Construction

Despite persistent calls for cleaner production and improved automation of construction processes, the adoption of the Internet of Things (IoT) in managing the supply chains of off-site construction businesses has been discouraged due to various constraints. This paper methodically identifies and prioritizes the crucial factors that impede the application of the Internet of Things (IoT) in off-site construction. Content analysis and an expert-based evaluation strategy were used to identify and evaluate the constraints affecting Internet of Things adoption in off-site construction. The ISM, MICMAC, and DEMATEL techniques were used to analyze the data. This study identifies the “lack of clear strategy for governing IoT utilization in supply chain management” as the most significant factor that impedes the application of the Internet of Things (IoT) in off-site construction businesses. The outcomes also provide a rich source of insights into off-site construction businesses to clearly recognize the implications of utilizing IoT technologies in managing the supply chains of businesses and what to expect when applying IoT technologies and solutions. While this paper advocates for improved green construction practices, cleaner production, and automation in the construction industry, it has set the stage for integrating IoT technologies in the supply chain management of off-site construction businesses.
Variational Quantum Simulation of Chemical Dynamics with Quantum Computers

Classical simulations of real-space quantum dynamics are challenging due to the exponential scaling of computational cost with system dimensions. Quantum computers offer the potential to simulate quantum dynamics with polynomial complexity; however, existing quantum algorithms based on the split-operator techniques require large-scale fault-tolerant quantum computers that remain elusive in the near future. Here, we present variational simulations of real-space quantum dynamics suitable for implementation in noisy intermediate-scale quantum (NISQ) devices. The Hamiltonian is first encoded onto qubits using a discrete variable representation and binary encoding scheme. We show that direct application of a real-time variational quantum algorithm based on the McLachlan's principle is inefficient as the measurement cost grows exponentially with the qubit number for general potential energy, and an extremely small time-step size is required to achieve accurate results. Motivated by the insights that many chemical dynamics occur in the low-energy subspace, we propose a subspace expansion method by projecting the total Hamiltonian, including the time-dependent driving field, onto the system low-energy eigenstate subspace using quantum computers, and the exact quantum dynamics within the subspace can then be solved classically. We show that the measurement cost of the subspace approach grows polynomially with dimensionality for general potential energy. Our numerical examples demonstrate the capability of our approach, even under intense laser fields. Our work opens the possibility of simulating chemical dynamics with NISQ hardware.
Can we generate shellcodes via natural language? An empirical study

Writing software exploits is an important practice for offensive security analysts to investigate and prevent attacks. In particular, shellcodes are especially time-consuming and a technical challenge, as they are written in assembly language. In this work, we address the task of automatically generating shellcodes, starting purely from descriptions in natural language, by proposing an approach based on Neural Machine Translation (NMT). We then present an empirical study using a novel dataset (Shellcode_IA32), which consists of 3,200 assembly code snippets of real Linux/x86 shellcodes from public databases, annotated using natural language. Moreover, we propose novel metrics to evaluate the accuracy of NMT at generating shellcodes. The empirical analysis shows that NMT can generate assembly code snippets from the natural language with high accuracy and that in many cases can generate entire shellcodes with no errors.
Drivers’ visual interaction performance of on-board computer under different heat conditions: Based on ELM and entropy weight

In-vehicle thermal environment is important to driving behaviours. The behaviours are always related to thermal comfort, fatigue and information reading, which are sensitive to the interior design. However, previous studies have merely analysed the impact of in-vehicle temperature and in-vehicle design on driving behaviours separately. There is still a gap to understand how the two affect driving behaviours simultaneously. Therefore, this study aims to investigate the driving behaviours (e.g., eye movement characteristics and completion time of operation tasks) subject to different combinations of temperature and interface layouts. The study was conducted based on a laboratory experiment among 53 subjects, extreme learning machine (ELM) model and entropy weight method. The subjects were inquired to conduct two tasks including navigation and music control for analysing the variation of fixation duration, fixation times and completion time. The temperature conditions consisted of six levels ranging from 22 to 32 °C and there were two types of interface layouts (checkerboard type and linear type). The results show a non-linear relationship between completion time and the factors of temperature, interface layout and operation task, and the co-impacts of these factors were more complex. Furthermore, the analysis indicates that the most suitable temperatures for navigation and music control were 22 and 24 °C. Moreover, the ELM model outperformed the back propagation neural network (BPNN) model in predicting the relationship between visual ergonomics and operation efficiency with the intervention of ambient temperature. In addition, entropy weights imply that both fixation duration and completion time were more sensitive to in-vehicle temperature than fixation times. Overall, beyond previous studies on the impact of in-vehicle design on driving behaviours, this study considers the additional heat-induced impacts on driving behaviours, which is meaningful to provide a reference for in-vehicle environment design.