Reviewerly/exHarmony huggingface dataset:
A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset.
Causality extraction model based on two-stage GCN

As one of the indirect causality, cascaded causality can be used to construct the event knowledge graph, causal inference, scenario analysis, etc. The existing GCN methods lack the mining of context information and relevant entity information, resulting in the poor ability of causality inference, which inevitably affects the extraction accuracy of cascade causality. To solve this problem, this paper proposes a causality extraction model based on a two-stage GCN to improve the extraction accuracy. To obtain rich features of entities, this work combines sentiment polarity and knowledge base to get the causality candidate entity library. Firstly, the BERT model is pre-trained using context information and relevant entity information extracted from the entity library to obtain the final entity nodes. Secondly, using the semantic dependency graph, each possible edge between any two entity nodes can be obtained, which are input into the first stage GCN to get a preliminary directed graph of causality. Finally, the directed graph of causality is input into the second stage GCN to achieve deep causality multi-hop inference. Thus, the cascade causality is inferred and extracted by the two-stage GCN model. Experiments show that the extraction accuracy of cascade causality has been further improved.
UBOL: User-Behavior-aware One-Shot Learning for Safe Autonomous Driving

In autonomous and self-driving vehicles, visual perception of the driving environment plays a key role. To achieve this goal, the systems of the vehicle rely on deep neural networks which are extensively trained on manually annotated databases. This work proposes a semi-parametric approach and aims to bypass the manual annotation required for training vehicles perception systems used in autonomous and self-driving. We present a novel &#x201C;User-Behavior aware One-Shot Learning (UBOL)&#x201D; for efficient data generation, which takes user action and response into consideration in addition to the synthetic traffic data generated as Pareto optimal solutions from one-shot objects using a set of generalization functions. This adds more robustness and safety features to autonomous driving. The Pareto-optimization is applied to improve the performance of classification and ensure the distribution of target and generated data is identical. We evaluate the proposed framework on environment perception challenges encountered in autonomous vision.
Towards an Antivirus for Quantum Computers

Researchers are today exploring models for cloud-based usage of quantum computers where multi-tenancy can be used to share quantum computer hard-ware among multiple users. Multi-tenancy has a promise of allowing better utilization of the quantum computer hardware, but also opens up the quantum computer to new types of security attacks. As this and other recent research shows, it is possible to perform a fault injection attack using crosstalk on quantum computers when a victim and attacker circuits are instantiated as co-tenants on the same quantum computer. To ensure such attacks do not happen, this paper proposes that new techniques should be developed to help catch malicious circuits before they are loaded onto quantum computer hardware. Following ideas from classical computers, a compile-time technique can be designed to scan quantum computer programs for mali-cious or suspicious code patterns before they are compiled into quantum circuits that run on a quantum computer. This paper presents ongoing work which demonstrates how crosstalk can affect Grover's algorithm, and then presents suggestions of how quantum programs could be analyzed to catch circuits that generate large amounts of crosstalk with malicious intent.
On the Feasibility of Training-time Trojan Attacks through Hardware-based Faults in Memory

Training-time trojan attacks have been one of the major security threats that can tamper integrity of deep learning models. Existing trojan attacks either require poisoning of the training dataset or depend on control of the training process. In this paper, we investigate the practicality of leveraging hardware-based fault attacks to introduce trojan in deep neural networks (DNNs) at training time. Specifically, we consider a memory-based fault injection using the rowhammer attack vector. We propose a new attack framework where the adversary injects faults to the feature map of DNN models during training. We investigate the impact of bit flips in feature maps and derive a bit flip strategy that enables the victim model to associate a perturbed feature map pattern with a target label without impacting the prediction of normal inputs. We further propose an input trigger identification algorithm that obtains the trigger pattern for the trojaned model at inference time. Our evaluation shows that our attack can trojan DNN models with very high attack success rate. Our work highlights the importance of understanding the impact of hardware-based fault attacks in machine learning training.
An FPGA Implementation of A Portable DNA Sequencing Device Based on RISC-V

Miniature and mobile DNA sequencers are steadily growing in popularity as effective tools for genetics research. As basecalling algorithms continue to evolve, basecalling poses a serious challenge for small computing devices despite its increasing accuracy. Although general-purpose computing chips such as CPUs and GPUs can achieve fast results, they are not energy efficient enough for mobile applications. This paper presents an innovative solution, a basecalling hardware architecture based on RISC-V ISA, and after validation with our custom FPGA verification platform, it demonstrates a 1.95x energy efficiency ratio compared to x86. There is also a 38% improvement in energy efficiency ratio compared to ARM. In addition, this study also completes the verification work for subsequent ASIC designs.
A Federated Learning Approach for Enhancing Autonomous Vehicles Image Recognition

Traffic sign recognition is vital for the success of the autonomous vehicles paradigm in mobile edge computing. We present a Federated Learning implementation based on a Lenet-5 CNN architecture which can enhance edge computing systems on unsupervised feature classification problems. Experiments demonstrated that our model can deliver satisfactory performance on traffic sign image recognition. Also our research has shown that less resources can be utilized on client devices to run the local training models. However, heavy data processing must be shifted to intermediate edge computing layers requiring more computing resources and the optimization of parameters such as latency, bandwidth, and privacy.
Systolic Acceleration of Polynomial Multiplication for KEM Saber and Binary Ring-LWE Post-Quantum Cryptography

Following the rapid progress in the post-quantum cryptography (PQC) field that many efforts have been gradually switched to the hardware implementation side, this paper presents a novel systolic accelerator for polynomial multiplication within two lattice-based PQC algorithms, key encapsulation mechanism (KEM) Saber and binary Ring-Learning-with-Errors (BRLWE)-based encryption scheme. Based on the observation that polynomial multiplication over ring is the key arithmetic operation for the two PQC schemes, we have proposed a novel systolic accelerator for the targeted polynomial multiplications (applicable to two PQC schemes). Mathematical formulation is given to illustrate the proposed algorithmic operation for both schemes. Then, the proposed systolic accelerator is presented. Finally, field-programmable gate array (FPGA) implementation results have been provided to confirm the efficiency of the proposed systolic accelerator under two schemes. The proposed accelerator is highly efficient, and the following work may focus on cryptoprocessor design and side-channel attacks.
A rapidly-exploring random trees approach to combined task and motion planning

Task and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks.
An accuracy-maximization learning framework for supervised and semi-supervised imbalanced data

While we attempt to develop the balanced error rate (BER) minimization learning framework for randomized learning of feedforward neural networks to deal with imbalanced datasets, it remains unclear whether the BER minimization learning framework can be effectively extended into its semi-supervised version. This paper proposes a new concept of accuracy maximization for randomized learning methods on imbalanced datasets for the first time, and theoretically proves that it is equivalent to the minimization of the generalized BER for the use of the selected neural networks. In particular, accuracy maximization can be easily extended to semi-supervised scenarios as its semi-supervised version is proved to be linearly dependent on its original. In this paper, based on the proposed accuracy maximization concept, we propose an accuracy-maximization learning framework, and further develop a new accuracy-maximization extreme learning machine AMELM by taking Extreme Learning Machine (ELM) as a typical randomized learning method for feedforward neural networks so as to handle challenging data issues such as the class imbalance and label scarcity. It is worth noting that the proposed accuracy maximization based framework is not only suitable for ELM, but can be extended to different randomized learning methods, such as Random Vector Functional Link Network (RVFL), and Schmidt Neural Network (SNN) for supervised and semi-supervised imbalanced data. The efficacy of AMELM is tested on extensive benchmark datasets. Experimental results show that AMELM can achieve satisfactory performances on labeled or partially labeled imbalanced data. Also, AMELM obtains at least comparable classification performance to other baseline methods yet has fewer hyperparameters to tune, showing its potential for practical applications.
Data-driven based multi-objective combustion optimization covering static and dynamic states

• KELM+MOEA/D strategy is introduced into the data-driven hybrid strategy. • ARMAX-PSO method is presented to optimize the parameters of PI controller. • ARMAX+TG-MOEA/D method is proposed to optimize the setpoint. • The optimization scheme covering multi-objective and different states is proposed. In order to reduce pollutant emissions and improve energy efficiency under the working conditions of static and dynamic states in the coal-fired power plants, data-driven based multi-objective combustion optimization (MOCO), which is around the implementation of air flow adjustment, is proposed to consider NO x emissions and boiler efficiency simultaneously. The strategy of combining kernel extreme learning machine (KELM) model with multi-objective evolutionary algorithm based on decomposition (MOEA/D) is presented to regulate the air distribution mode under static state. The double optimization is proposed to control the air flow under dynamic state. The first optimization is to employ auto-regressive moving-average with exogenous inputs (ARMAX) model and particle swarm optimization algorithm to optimize the parameters of PI controller. The second optimization is to use ARMAX models and tendentious guidance MOEA/D (TG-MOEA/D) to optimize the setpoint of air flow. The experiments showed that the KELM + MOEA/D strategy could further improve the effect of MOCO under static state, and the double optimization had decreased NO x emissions by an average of 12.18% with keeping the boiler efficiency at a desired level under dynamic state. Moreover, data-driven based MOCO owns a high real-time performance, so that it is suitable for online application.
PTrustE: A high-accuracy knowledge graph noise detection method based on path trustworthiness and triple embedding

Many knowledge graphs (KGs) constructed automatically or manually contain noises inevitably when adding heterogeneous data nowadays. The existing methods either have difficulty in detecting noisy triples with conflicted relations or ignore to represent triples after noise elimination, which creates obstacles for the downstream tasks to manipulate KGs. To detect various types of noises in KGs and constitute noise-free triples representation, this paper proposes a high-accuracy KG noise detection method based on path trustworthiness and triple embedding (PTrustE). First, PTrustE constructs a correlation-based path trustworthiness network to learn the global and local features in the path from the head entity to the tail entity of the triple. Next, PTrustE integrates all features of the path into the Bi-directional Gated Recurrent Unit to learn the path score matrix and path trustworthiness, to keep the sequential nature of paths. Finally, PTrustE uses the path score matrix for triple representation learning and the path trustworthiness for judging whether the triple is correct or not. Extensive experiments validate the superior performance of PTrustE. Compared with the second-placed INDIGO, PTrustE offers a 7.1% increase in fMRR on FB15K.
Kriper: A blockchain network with permissioned storage

Blockchain has been a revolution in the past few years. Beyond the new currencies that were created around different incarnations of the blockchain concept, there are many other contributions that provide interesting services as a data linked structure using a decentralized network that provide a high level of security. Companies have developed many projects to incorporate blockchain into their business logic pursuing to incorporate other related services as persistence of large volumes of data, privacy or anonymity of transactions, distributed data processing, security (confidentiality, integrity, and availability), document management or micro messages in real time. Nevertheless, as it will be discussed in this article, current blockchains do not meet the needs of companies in many aspects, leading to a scarce or superficial adoption. This article introduces Kriper, a blockchain that aims at meeting corporate world needs by responding with a community-based, open blockchain that may also be segregated and private for certain uses whereas it provides a permissioned distributed storage and micro message lightweight services.
Eliminating white noise in spectra: A generalized maximum-entropy approach

In linear filtering, high-frequency (white) noise is reduced by apodization, which is the attenuation or elimination of high-order Fourier coefficients followed by an inverse transformation. Unfortunately, apodization requires compromises to be made among noise leakage, information loss, and Gibbs oscillations. These shortcomings are avoided with the corrected maximum-entropy (CME) procedure, but this procedure applies only to Lorentzian or approximately Lorentzian features. We develop a generalized maximum-entropy method based on partial Hilbert transforms that allows CME to be applied to any spectrum, thereby eliminating white-noise completely with no deleterious side effects. As Hilbert transforms are exact Kramers–Kronig replicas of the original endpoint-discontinuity-corrected segment, new spectral processing opportunities are also realized.
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insights into how the model arrives at its predictions. Genetic programming (GP) is often cited as being uniquely well-suited to contribute to XAI because of its capacity to learn (small) symbolic models that have the potential to be interpreted. Nevertheless, like many ML algorithms, GP typically results in a single best model. However, in practice, the best model in terms of training error may well not be the most suitable one as judged by a domain expert for various reasons, including overfitting, multiple different models existing that have similar accuracy and unwanted errors on particular data points due to typical accuracy measures like mean squared error. Hence, to increase chances that domain experts deem a resulting model plausible, it becomes important to be able to explicitly search for multiple, diverse, high-quality models that trade-off different meanings of accuracy. In this paper, we achieve exactly this with a novel multi-modal multi-tree multi-objective GP approach that extends a modern model-based GP algorithm known as GP-GOMEA that is already effective at searching for small expressions.
Representation of the fermionic boundary operator

The boundary operator is a linear operator that acts on a collection of high-dimensional binary points (simplices) and maps them to their boundaries. This boundary map is one of the key components in numerous applications, including differential equations, machine learning, computational geometry, machine vision and control systems. We consider the problem of representing the full boundary operator on a quantum computer. We first prove that the boundary operator has a special structure in the form of a complete sum of fermionic creation and annihilation operators. We then use the fact that these operators pairwise anticommute to produce an $O(n)$-depth circuit that exactly implements the boundary operator without any Trotterization or Taylor series approximation errors. Having fewer errors reduces the number of shots required to obtain desired accuracies.
GON: End-to-end optimization framework for constraint graph optimization problems

Real-world computational applications often require solving combinatorial optimization problems on graphs, i.e., graph optimization problems (GOPs). An emerging trend is using graph neural networks (GNNs) to tackle GOPs. However, for GOPs with constraints, a great challenge faced by GNNs-based methods is to produce optimal solutions that satisfy the constraints. Existing methods relying on supervised learning require a large amount of labeled data, while unsupervised learning methods often require designing elaborate network architectures. To address these limitations, we propose an end-to-end optimization framework for constraint GOPs, coined Graph Optimization Network (GON). GON is a simple and effective method aiming to address general GOPs without designing specific network architectures for different problems. The proposed framework is evaluated on two typical constraint GOPs, one with only soft constraints (the balanced graph partitioning problem) and the other with hard constraints (the maximum independent set problem). We model the two problems as node assignment problems and design the corresponding constraint-aware loss function for each so that the model can be directly optimized to generate optimal solutions that satisfy the constraints. Experiments on various benchmarks show that the proposed GON equipped with a vanilla GNN can achieve excellent performance on both GOPs, superior to state-of-the-art methods. The results suggest that GON is a promising solver to address NP-hard GOPs.
Motion measurement and quality variation driven video quality assessment

Many video quality assessment (VQA) methods generally use frame difference to represent motion information which may cause foreground apertures. Besides, quality variations of video contents significantly affect visual quality predictions. To remedy these, we develop a novel video quality assessment model that considers the impact of motion estimation and quality variations on distortion perception. The model firstly decomposes a video into a reflectance component and an illumination component via the intrinsic decomposition, and extracts scene features from these two components. To alleviate the foreground apertures, a simple structure difference is calculated as the motion measurement between adjacent frames. Motion difference features are extracted from the structure difference. We additionally consider analyzing quality variations to boost video quality predictions. Finally, a support vector regressor (SVR) is used to map generated features to predicted quality scores. We evaluated our proposed model on the LIVE, CSIQ, CVD2014 and LIVE-VQC video quality databases. The results show that the proposed model achieved competitive performance in comparison with state-of-the-art methods. Source code is freely available at: https://github.com/Aca4peop/QVDVQA.
Using Saturated Count Models for User-Friendly Synthesis of Large Confidential Administrative Databases

Abstract Over the past three decades, synthetic data methods for statistical disclosure control have continually evolved, but mainly within the domain of survey data sets. There are certain characteristics of administrative databases, such as their size, which present challenges from a synthesis perspective and require special attention. This paper, through the fitting of saturated count models, presents a synthesis method that is suitable for administrative databases. It is tuned by two parameters, σ and α. The method allows large categorical data sets to be synthesized quickly and allows risk and utility metrics to be satisfied a priori, that is, prior to synthetic data generation. The paper explores how the flexibility afforded by two-parameter count models (the negative binomial and Poisson-inverse Gaussian) can be utilised to protect respondents'—especially uniques'—privacy in synthetic data. Finally, an empirical example is carried out through the synthesis of a database which can be viewed as a good substitute to the English School Census.