Reviewerly/exHarmony huggingface dataset:
Robust Variational Autoencoders and Normalizing Flows for Unsupervised Network Anomaly Detection

In recent years, the integration of connected devices in smart homes has significantly increased, thanks to the advent of the Internet of things (IoT). However, these IoT devices introduce new security challenges, since any anomalous behavior has a serious impact on the whole network. Network anomaly detection has always been of considerable interest for every actor in the network landscape. In this paper, we propose GRAnD, an algorithm for unsupervised anomaly detection. Based on Variational Autoencorders and Normalizing Flows, GRAnD learns from network traffic metadata a normal profile representing the expected nominal behavior of the network. Then, this model is optimized to detect anomalies. Unlike existing anomaly detectors, our method is robust to the hyperparameter selection and outliers contaminating the training data. Extensive experiments and sensitivity analyses on public network traffic benchmark datasets demonstrate the effectiveness of our approach in network anomaly detection.
Swap Test with Quantum Dot Charge Qubits

We propose the implementation of the swap test using a charge qubit in a double quantum dot. The swap test is a fundamental quantum subroutine in quantum machine learning and other applications for estimating the fidelity of two unknown quantum states by measuring an auxiliary qubit. Our proposal uses a controlled three-qubit gate, which is natural to quantum dot charge qubits. It allows us to implement a swap test with a circuit depth of six layers, and an estimated gate time of less than 3 ns, that is below the coherence time of double quantum dots. This work paves the way for enhancing the toolbox of quantum machine learning developments in semiconductor qubits.
A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA. Secondly, in order to improve the diversity of the population and enhance the search of the surrounding space, the logarithmic spiral strategy is introduced to improve the sparrow search mechanism. Finally, the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration. The best chaotic map is determined by different test functions, and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems. The simulation results show that the iterative map is the best chaotic map, and CLSSA is efficient and useful for engineering problems, which is better than all comparison algorithms.
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts

Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scientific articles and magazines is prevalent in literature, one of the main challenges for digitization of newspaper lies in its complex layout (e.g. articles spanning multiple columns, text interrupted by images) analysis, which is necessary to preserve human read-order. This work provides a major breakthrough in the digitization of newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated, real-world newspaper images from 21 different U.S. states representing an extensive variety of complex layouts for document layout analysis; second, proposing layout segmentation as a precursor to existing optical character recognition (OCR) engines, where multiple state-of-the-art image segmentation models and several post-processing methods are explored for document layout segmentation; third, providing a thorough and structured evaluation protocol for isolated layout segmentation and end-to-end OCR.
Analysis of breast cancer prediction and visualisation using machine learning models

Breast cancer is one of the most commonly occurring malignancies cancer in women, and there are millions of new cases diagnosed among women's and over 400,000 deaths annually worldwide. In our dataset, we have 30 real-valued attributes as features which are computed from the fine needle aspirate (FNA) test. Our dataset values are calculated from the processed image of a first needle aspirate test of a breast mass. Our input values are extracted from the digitalised image of the FNA test. There are many algorithms used for prediction systems. We are choosing the best algorithms based on the precision result, accuracy, and error rate. We are making a comparison of an effective way of applying algorithms and classifying data. We have different machine learning algorithms, a performance comparison conducted between those algorithms on the breast cancer datasets. Data visualisation and descriptive statistics have been presented. SVM with all features achieves 95% of precision, recall, and F1-score. After tuning the SVM parameters, accuracy has improved to 97%.
An In-depth Analysis of Duplicated Linux Kernel Bug Reports

In the past three years, the continuous fuzzing projects Syzkaller and Syzbot have achieved great success in detecting kernel vulnerabilities, finding more kernel bugs than those found in the past 20 years.However, a side effect of continuous fuzzing is that it generates an excessive number of crash reports, many of which are "duplicated" reports caused by the same bug.While Syzbot uses a simple heuristic to group (deduplicate) reports, we find that it is often inaccurate.In this paper, we empirically analyze the duplicated kernel bug reports to understand: (1) the prevalence of duplication; (2) the potential costs introduced by duplication; and (3) the key causes behind the duplication problem.We collected all of the fixed kernel bugs from September 2017 to November 2020, including 3.24 million crash reports grouped by Syzbot under 2,526 bug reports (identified by unique bug titles).We found the bug reports indeed had duplication: 47.1% of the 2,526 bug reports are duplicated with one or more other reports.By analyzing the metadata of these reports, we found undetected duplication introduced extra costs in terms of time and developer efforts.Then we organized Linux kernel experts to analyze a sample of duplicated bugs (375 bug reports, unique 120 bugs) and identified 6 key contributing factors to the duplication.Based on these empirical findings, we proposed and prototyped actionable strategies for bug deduplication.After confirming their effectiveness using a ground-truth dataset, we further applied our methods and identified previously unknown duplication cases among open bugs.
Meta Adversarial Weight for Unsupervised Domain Adaptation

Despite great progress in supervised image recognition, a large performance drop is usually observed when deploying the model in the wild. Unsupervised domain adaptation (UDA) methods tackle the issue by aligning the source domain and the target domain. However, most existing adversarial based methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. As a result, the representations are distorted by adversarial alignment, leading to a negative transfer. Motivated by this issue, we first claim that this issue can be solved if there exists ‘optimal’ per-sample weights for adversarial alignment, and then devise a meta-learning framework to adaptively learn such adversarial weights. Specifically, we construct a meta-dataset with targetlike distribution as meta knowledge, and use it to guide the learning of the optimal adversarial weights via a meta-learner. By this means, our framework can adaptively adjust the weights of all training samples in adversarial training based on the feedback from meta dataset and thus achieve the categorical-wise domain alignment. We conduct sufficient ablation studies and experiments to show the effectiveness of our approach. Our method is generic to existing domain alignment based methods and could achieve consistently improvements over three UDA classification benchmarks.MSC codesDomain AdaptationAdversarial LearningTransfer LearningSemi-supervised Learning
A prediction method based on fractional order displacement for dynamic multiobjective optimization

Prediction-based methods have become more popular for solving dynamic multiobjective optimization problems. However, most of these proposed methods only use the optimal solutions in the previous two or three environments to predict the new optimal solutions after a change. Doing so neglects information from earlier in the history, which may reduce the accuracy of the prediction method. Conversely, if all history information is utilized for prediction, it will incur exorbitant computing costs, and it is not necessary because it is not all old optimal solutions correlate strongly with predicted solutions. Therefore, a novel prediction method based on fractional displacement (FDPM) is proposed. In this method, the previous optimal solutions that have a certain degree of correlation with the new solutions after a change are identified using the proposed prediction model, the parameters of which are obtained by training a certain length of previous optimal solution series. Then, these identified solutions are used to predict the optimal solutions in the new environment. This can balance the accuracy and computation cost of the prediction method. The performance of the proposed method is compared with five chosen state-of-the-art algorithms over fourteen benchmark problems covering diverse properties, and the results demonstrate that the proposed method is superior to the other selected algorithms.
CLOUD SERVICES PROVIDERS EVALUATION MODEL FOR DESIGNING HIGH PERFORMANCE, REAL-TIME IOT BIG DATA SOLUTIONS

Cloud technology is the backbone of the delivery pipeline of just about every digital service today, from social media and streaming entertainment to connected cars and autonomous internet of things (IoT) infrastructure. Studies show the expectations are more than 15 billion IoT devices to be connected to Cloud services by 2029. Upcoming or new ultra-fast networks such as 5G and Wi-Fi 6E mean that more IoT data will be streamed to and from applications hosted in the cloud. Cloud services and infrastructure essentially help any other technology to be much lighter, faster, and more accessible, and this fact will be an important key driver in the migration of more services to the cloud. The use-cases and scenarios become increasingly complex. This is challenging for decision-makers such as chief information officers, solution architects, scientists, and others as they are overwhelmed by various choices available by the increasingly growing number of cloud providers. The main requirements that must be met in a pure cloud application are operational excellence, security, reliability, performance efficiency and cost optimization. These requirements are met by several cloud providers and their evaluation is important. In this article, an evaluation model is proposed that helps to identify the most appropriate Cloud Services Provider (CSP) during the design process of high performance, real-time IoT Big Data solutions.
Idiomatic Expression Paraphrasing without Strong Supervision

Idiomatic expressions (IEs) play an essential role in natural language. In this paper, we study the task of idiomatic sentence paraphrasing (ISP), which aims to paraphrase a sentence with an IE by replacing the IE with its literal paraphrase. The lack of large-scale corpora with idiomatic-literal parallel sentences is a primary challenge for this task, for which we consider two separate solutions. First, we propose an unsupervised approach to ISP, which leverages an IE's contextual information and definition and does not require a parallel sentence training set. Second, we propose a weakly supervised approach using back-translation to jointly perform paraphrasing and generation of sentences with IEs to enlarge the small-scale parallel sentence training dataset. Other significant derivatives of the study include a model that replaces a literal phrase in a sentence with an IE to generate an idiomatic expression and a large scale parallel dataset with idiomatic/literal sentence pairs. The effectiveness of the proposed solutions compared to competitive baselines is seen in the relative gains of over 5.16 points in BLEU, over 8.75 points in METEOR, and over 19.57 points in SARI when the generated sentences are empirically validated on a parallel dataset using automatic and manual evaluations. We demonstrate the practical utility of ISP as a preprocessing step in En-De machine translation.
InQSS: a speech intelligibility and quality assessment model using a multi-task learning network

Speech intelligibility and quality assessment models are essential tools for researchers to evaluate and improve speech processing models. However, only a few studies have investigated multi-task models for intelligibility and quality assessment due to the limitations of available data. In this study, we released TMHINT-QI, the first Chinese speech dataset that records the quality and intelligibility scores of clean, noisy, and enhanced utterances. Then, we propose InQSS, a non-intrusive multi-task learning framework for intelligibility and quality assessment. We evaluated the InQSS on both the training-from-scratch and the pretrained models. The experimental results confirm the effectiveness of the InQSS framework. In addition, the resulting model can predict not only the intelligibility scores but also the quality scores of a speech signal.
Wide-angle giant photonic spin Hall effect

The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift $\ensuremath{\delta}$ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e., $\ensuremath{\delta}/\ensuremath{\lambda}<{10}^{\ensuremath{-}1}$, where $\ensuremath{\lambda}$ is the wavelength), which impedes its precise measurement. To date, proposals to generate a giant spin Hall effect (namely, with $\ensuremath{\delta}/\ensuremath{\lambda}>{10}^{2}$) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of $\mathrm{\ensuremath{\Delta}}\ensuremath{\theta}<{1}^{\ensuremath{\circ}}$). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with $\mathrm{\ensuremath{\Delta}}\ensuremath{\theta}>{70}^{\ensuremath{\circ}}$ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between $s$ and $p$ polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.
Model-Based Self-Advising for Multi-Agent Learning

In multiagent learning, one of the main ways to improve learning performance is to ask for advice from another agent. Contemporary advising methods share a common limitation that a teacher agent can only advise a student agent if the teacher has experience with an identical state. However, in highly complex learning scenarios, such as autonomous driving, it is rare for two agents to experience exactly the same state, which makes the advice less of a learning aid and more of a one-time instruction. In these scenarios, with contemporary methods, agents do not really help each other learn, and the main outcome of their back and forth requests for advice is an exorbitant communications’ overhead. In human interactions, teachers are often asked for advice on what to do in situations that students are personally unfamiliar with. In these, we generally draw from similar experiences to formulate advice. This inspired us to provide agents with the same ability when asked for advice on an unfamiliar state. Hence, we propose a model-based self-advising method that allows agents to train a model based on states similar to the state in question to inform its response. As a result, the advice given can not only be used to resolve the current dilemma but also many other similar situations that the student may come across in the future via self-advising. Compared with contemporary methods, our method brings a significant improvement in learning performance with much lower communication overheads.
Modelling human threats in security ceremonies1

Socio-Technical Systems (STSs) combine the operations of technical systems with the choices and intervention of humans, namely the users of the technical systems. Designing such systems is far from trivial due to the interaction of heterogeneous components, including hardware components and software applications, physical elements such as tickets, user interfaces, such as touchscreens and displays, and notably, humans. While the possible security issues about the technical components are well known yet continuously investigated, the focus of this article is on the various levels of threat that human actors may pose, namely, the focus is on security ceremonies. The approach is to formally model human threats systematically and to formally verify whether they can break the security properties of a few running examples: two currently deployed Deposit-Return Systems (DRSs) and a variant that we designed to strengthen them. The two real-world DRSs are found to support security properties differently, and some relevant properties fail, yet our variant is verified to meet all the properties. Our human threat model is distributed and interacting: it formalises all humans as potential threatening users because they can execute rules that encode specific threats in addition to being honest, that is, to follow the prescribed rules of interaction with the technical system; additionally, humans may exchange information or objects directly, hence practically favour each other although no specific form of collusion is prescribed. We start by introducing four different human threat models, and some security properties are found to succumb against the strongest model, the addition of the four. The question then arises on what meaningful combinations of the four would not break the properties. This leads to the definition of a lattice of human threat models and to a general methodology to traverse it by verifying each node against the properties. The methodology is executed on our running example for the sake of demonstration. Our approach thus is modular and extensible to include additional threats, potentially even borrowed from existing works, and, consequently, to the growth of the corresponding lattice. STSs can easily become very complex, hence we deem modularity and extensibility of the human threat model as key factors. The current computer-assisted tool support is put to test but proves to be sufficient.
A Framework for Duplicate Detection from Online Job Postings

Online job boards have greatly improved the efficiency of job searching and have also provided valuable data for labour market research. However, there are a high proportion of duplicate job postings in most (if not all) job boards, because recruiters and job boards seek to improve their coverage of the market by integrating job postings from many different sources. These duplicate postings undermine the usability of job boards and the quality of labour market analytics derived from them. In this paper, we tackle the challenging problem of duplicate detection from online job postings. Specifically, we design a framework for duplicate detection from online job postings and, under the framework, implement and test 24 methods built with four different tokenisers, three vectorisers and six similarity measures. We conduct a comparative study and experimental evaluation of the 24 methods and compare their performance with a baseline approach. All methods are tested with a real-world dataset from a job boarding platform and are evaluated with six performance metrics. The experiment reveals that the top two methods are Overlap with skip-gram (OS) and Overlap with n-gram (OG), followed by TFIDF-cosine with n-gram (TCG) and TFIDF-cosine with skip-gram (TCS), and that all above four methods outperform the baseline approach in detecting duplicates.
Input-Specific Robustness Certification for Randomized Smoothing

Although randomized smoothing has demonstrated high certified robustness and superior scalability to other certified defenses, the high computational overhead of the robustness certification bottlenecks the practical applicability, as it depends heavily on the large sample approximation for estimating the confidence interval. In existing works, the sample size for the confidence interval is universally set and agnostic to the input for prediction. This Input-Agnostic Sampling (IAS) scheme may yield a poor Average Certified Radius (ACR)-runtime trade-off which calls for improvement. In this paper, we propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification, in an adaptive way of reducing the sampling size based on the input characteristic. Furthermore, our method universally controls the certified radius decline from the ISS sample size reduction. The empirical results on CIFAR-10 and ImageNet show that ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. Specifically, ISS achieves ACR=0.958 on ImageNet in 250 minutes, compared to ACR=0.917 by IAS under the same condition. We release our code in https://github.com/roy-ch/Input-Specific-Certification.
Feature Construction and Selection for PV Solar Power Modeling

Using solar power in the process industry can reduce greenhouse gas emissions and make the production process more sustainable. However, the intermittent nature of solar power renders its usage challenging. Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages and further design proper operations. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data. Our method extends the input dataset into higher dimensional Chebyshev polynomial space. Then, a feature selection scheme is developed with constrained linear regression to construct the predictor for different weather types. Several tests show that the proposed approach yields lower mean squared error than classical machine learning methods, such as support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT).