Reviewerly/exHarmony huggingface dataset:
A Theoretical Framework of Robust <i>H</i>-Infinity Unscented Kalman Filter and Its Application to Power System Dynamic State Estimation

This paper presents a new theoretical framework that, by integrating robust statistics and robust control theory, allows us to develop a robust dynamic state estimator of a cyber-physical system. This state estimator combines the generalized maximum-likelihood-type (GM) estimator, the unscented Kalman filter (UKF), and the H-infinity filter into a robust H-infinity UKF filter in the Krein space, which is able to handle large system uncertainties as well as suppress outliers while achieving a good statistical efficiency under Gaussian and non-Gaussian process and observation noises. Specifically, we first use the statistical linearization approach to build a linearlike regression model in the Krein space. Then, we show that the H-infinity UKF is just the Krein space Kalman filter that exhibits a bounded estimation error in presence of system uncertainties while minimizing the least squares criterion; consequently, it suffers from a lack of robustness to outliers and non-Gaussian noise. Because the GM estimator is able to handle outliers, but it may yield large estimation errors in the presence of system uncertainties, we propose to combine it with the H-infinity UKF in a robust H-infinity UKF. We carry out a theoretical analysis to demonstrate the connections that our filter has with the H-infinity UKF and the GM-UKF. The good performance of the new filter is demonstrated via extensive simulation performed on the IEEE 39-bus power system.
Identifying the Components and Interrelationships of Smart Cities in Indonesia: Supporting Policymaking via Fuzzy Cognitive Systems

Multiple Indonesian cities currently aim to qualify as “smart cities.” Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a “smart city” are impacted by an intervention?) as well as what-if policy questions. We started with a systems' science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., “smart city”) in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention.
Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint

Traffic sign detection is a central part of autonomous vehicle technology. Recent advances in deep learning algorithms have motivated researchers to use neural networks to perform this task. In this paper, we look at traffic sign detection as an image segmentation problem and propose a deep convolutional neural network-based approach to solve it. To this end, we propose a new network, the SegU-Net, which we form by merging the state-of-the-art segmentation architectures-SegNet and U-Net to detect traffic signs from video sequences. For training the network, we use the Tversky loss function constrained by an L1 term instead of the intersection over union loss traditionally used to train segmentation networks. We use a separate network, inspired by the VGG-16 architecture, to classify the detected signs. The networks are trained on the challenge free sequences of the CURE-TSD dataset. Our proposed network outperforms the state-of-the-art object detection networks, such as the Faster R-CNN inception Resnet V2 and R-FCN Resnet 101, by a large margin and obtains a precision and recall of 94.60% and 80.21%, respectively, which is the current state of the art on this part of the dataset. In addition, the network is tested on the German Traffic Sign Detection Benchmark (GTSDB) dataset, where it achieves a precision and recall of 95.29% and 89.01%, respectively. This is on a par with the performance of the aforementioned object detection networks. These results prove the generalizability of the proposed architecture and its suitability for robust traffic sign detection in autonomous vehicles.
Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography

To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists' sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis. The mammograms were performed between February 7, 2008 (earliest) and January 8, 2016 (latest), and were all originally interpreted as negative in conjunction with R2 ImageChecker CAD, version 10.0. In this study, the readers analyzed the 122 studies before and after review of cmAssist™, an AI-CAD software for mammography. The statistical significance of our findings was evaluated using Student's t test and bootstrap statistical analysis. There was a substantial and significant improvement in radiologist accuracy with use of cmAssist, as demonstrated in the 7.2% increase in the area-under-the-curve (AUC) of the receiver operating characteristic (ROC) curve with two-sided p value < 0.01 for the reader group. All radiologists showed a significant improvement in their cancer detection rate (CDR) with the use of cmAssist (two-sided p value = 0.030, confidence interval = 95%). The readers detected between 25 and 71% (mean 51%) of the early cancers without assistance. With cmAssist, the overall reader CDR was 41 to 76% (mean 62%). The percentage increase in CDR for the reader panel was significant, ranging from 6 to 64% (mean 27%) with the use of cmAssist. There was less than 1% increase in the readers' false-positive recalls with use of cmAssist. With the use of cmAssist TM, there was a substantial and statistically significant improvement in radiologists' accuracy and sensitivity for detection of cancers that were originally missed. The percentage increase in CDR for the radiologists in the reader panel ranged from 6 to 64% (mean 27%) with the use of cmAssist, with negligible increase in false-positive recalls.
A New Hybrid Seagull Optimization Algorithm for Feature Selection

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms.In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO).In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating.Another method is to join the TEO algorithm for optimization after SOA algorithm iteration.The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm.The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature.The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.
Spherical evolution for solving continuous optimization problems

In these years, more and more nature-inspired meta-heuristic algorithms have been proposed; search operators have been their core problem. The common characteristics or mechanism of search operators in different algorithms have not been represented by a standard format. In this paper, we first propose the concept of a search pattern and a search style represented by a mathematical model. Second, we propose a new search style, namely a spherical search style, inspired by the traditional hypercube search style. Furthermore, a spherical evolution algorithm is proposed based on the search pattern and spherical search style. At the end, 30 benchmark functions of CEC2017 and a real-world optimization problem are tested. Experimental results and analysis demonstrate that the proposed method consistently outperforms other state-of-the-art algorithms.
Moving from a ‘human-as-problem” to a ‘human-as-solution” cybersecurity mindset

Cybersecurity has gained prominence, with a number of widely publicised security incidents, hacking attacks and data breaches reaching the news over the last few years. The escalation in the numbers of cyber incidents shows no sign of abating, and it seems appropriate to take a look at the way cybersecurity is conceptualised and to consider whether there is a need for a mindset change. To consider this question, we applied a “problematization” approach to assess current conceptualisations of the cybersecurity problem by government, industry and hackers. Our analysis revealed that individual human actors, in a variety of roles, are generally considered to be “a problem”. We also discovered that deployed solutions primarily focus on preventing adverse events by building resistance: i.e. implementing new security layers and policies that control humans and constrain their problematic behaviours. In essence, this treats all humans in the system as if they might well be malicious actors, and the solutions are designed to prevent their ill-advised behaviours. Given the continuing incidences of data breaches and successful hacks, it seems wise to rethink the status quo approach, which we refer to as “Cybersecurity, Currently”. In particular, we suggest that there is a need to reconsider the core assumptions and characterisations of the well-intentioned human’s role in the cybersecurity socio-technical system. Treating everyone as a problem does not seem to work, given the current cyber security landscape. Benefiting from research in other fields, we propose a new mindset i.e. “Cybersecurity, Differently”. This approach rests on recognition of the fact that the problem is actually the high complexity, interconnectedness and emergent qualities of socio-technical systems. The “differently” mindset acknowledges the well-intentioned human’s ability to be an important contributor to organisational cybersecurity, as well as their potential to be “part of the solution” rather than “the problem”. In essence, this new approach initially treats all humans in the system as if they are well-intentioned. The focus is on enhancing factors that contribute to positive outcomes and resilience. We conclude by proposing a set of key principles and, with the help of a prototypical fictional organisation, consider how this mindset could enhance and improve cybersecurity across the socio-technical system.
FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance

Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve an appropriate Question-Answer (QA) pair from a database based on a user's query. We propose a FAQ retrieval system that considers the similarity between a user's query and a question as well as the relevance between the query and an answer. Although a common approach to FAQ retrieval is to construct labeled data for training, it takes annotation costs. Therefore, we use a traditional unsupervised information retrieval system to calculate the similarity between the query and question. On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database. The recently-proposed BERT model is used for the relevance calculation. Since the number of QA pairs in FAQ page is not enough to train a model, we cope with this issue by leveraging FAQ sets that are similar to the one in question. We evaluate our approach on two datasets. The first one is localgovFAQ, a dataset we construct in a Japanese administrative municipality domain. The second is StackExchange dataset, which is the public dataset in English. We demonstrate that our proposed method outperforms baseline methods on these datasets.
SmartWall: Novel RFID-Enabled Ambient Human Activity Recognition Using Machine Learning for Unobtrusive Health Monitoring

Human activity recognition (HAR) from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including the system complexity and computational cost. In this paper, a simple, the novel ambient HAR framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on the multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. The twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled, and carers.
FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with other machines and humans. It is of prime importance to leverage this information in order to minimize downtime of machines, or even avoid downtime completely by constant monitoring. Since each device generates a different type of streaming data, it is normally the case that a specific kind of anomaly detection technique performs better than the others depending on the data type. For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. The obtained results show an increase in area under the curve (AUC) as compared to state-of-the-art anomaly detection methods when FuseAD is tested on a publicly available dataset (Yahoo Webscope benchmark). The obtained results advocate that this fusion-based technique can obtain the best of both worlds by combining their strengths and complementing their weaknesses. We also perform an ablation study to quantify the contribution of the individual components in FuseAD, i.e., the statistical ARIMA model as well as the deep-learning-based convolutional neural network (CNN) model.
IoT Passport

Internet-of-Things (IoT) is a rapidly-growing transformative expansion of the Internet with increasing influence on our daily life. Since the number of "things" is expected to soon surpass human population, control and automation of IoT devices has received considerable attention from academia and industry. Cross-platform collaboration is highly desirable for better user experience due to fragmentation of user needs and vendor products with time. Centralized approaches have been used to build federated trust among platforms and devices, but limit diversity and scalability. We propose a decentralized trust framework, called IoT Passport, for cross-platform collaborations using blockchain technology. IoT Passport is motivated by the familiar use of passports for international travel but with greater dynamism. It enables platforms to establish arbitrary trust relations with each other containing specific rules for intended collaborations, enforced by a combination of smart contracts. Each interaction among devices is signed by the participants and recorded on the blockchain. The records are utilized as attributes for authorization and as proofs of incentive plans. This approach incorporates the preferences of participating platforms and end users, and opens new avenues for collaborative edge computing as well as research on blockchain-based access control mechanism for IoT environments.
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation – A Synthetic Dataset and Baselines

We introduce SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research. Humans can effortlessly recognize partially occluded objects and reliably estimate their spatial extent beyond the visible. However, few modern computer vision techniques are capable of reasoning about occluded parts of an object. This is partly due to the fact that very few image datasets and no video dataset exist which permit development of those methods. To address this issue, we present a synthetic dataset extracted from the photo-realistic game GTA-V. Each frame is accompanied with densely annotated, pixel-accurate visible and amodal segmentation masks with semantic labels. More than 1.8M objects are annotated resulting in 100 times more annotations than existing datasets. We demonstrate the challenges of the dataset by quantifying the performance of several baselines. Data and additional material is available at http://sailvos.web.illinois.edu.
Kerr-Free Three-Wave Mixing in Superconducting Quantum Circuits

Quantum-limited Josephson parametric amplifiers are crucial components in readout chains for circuit quantum electrodynamics. The power handling of state-of-the-art parametric amplifiers is limited by signal-induced Stark shifts. The authors use an innovative circuit element with a Stark-shift-free sweet spot in parameter space to boost the power handling of such an amplifier by an order of magnitude, which is quite promising for the implementation of bilinear Hamiltonians with high dynamic range in quantum information processing.
K-Multiple-Means

In this paper, we make an extension of K-means for the clustering of multiple means. The popular K-means clustering uses only one center to model each class of data. However, the assumption on the shape of the clusters prohibits it to capture the non-convex patterns. Moreover, many categories consist of multiple subclasses which obviously cannot be represented by a single prototype. We propose a K-Multiple-Means (KMM) method to group the data points with multiple sub-cluster means into specified k clusters. Unlike the methods which use the agglomerative strategies, the proposed method formalizes the multiple-means clustering problem as an optimization problem and updates the partitions of m sub-cluster means and k clusters by an alternating optimization strategy. Notably, the partition of the original data with multiple-means representation is modeled as a bipartite graph partitioning problem with the constrained Laplacian rank. We also show the theoretical analysis of the connection between our method and the K-means clustering. Meanwhile, KMM is linear scaled with respect to n. Experimental results on several synthetic and well-known real-world data sets are conducted to show the effectiveness of the proposed algorithm.
Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images

Breast cancer is one of the common type of cancer in females across the world. An early detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To diagnose breast cancer, different types of imaging modalities are used to collect samples like mammography, Computerized Tomography, Magnetic Resonance Imaging, Ultrasound and Biopsy. Histopathological images obtained from biopsy may influence how and at which stage the cancer is being diagnosed. The Computer Assisted Diagnosis (CAD) system helps the pathologists in early diagnosis of breast cancer. In this survey, the recently reported techniques for breast cancer diagnosis using histopathological images have been summarized. This study could be beneficial for: (i) Clinicians to receive second opinion from the CAD system for early diagnosis, and (ii) Researchers to analyze and enhance the existing state-of-art techniques used in CAD system, which may further reduce the gap of variability between intra and inter observer.
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is the user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this article, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved sufficient security level ( <inline-formula><tex-math notation="LaTeX">$&gt; 80$</tex-math></inline-formula> bit) and reasonable classification accuracy (99) and (77.55 percent) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images ( <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 8,000) without extra overhead.