Reviewerly/exHarmony huggingface dataset:
A hybrid distributed batch-stream processing approach for anomaly detection

Batch and stream processing are separately and efficiently applied in many applications. However, some newer data-driven applications such as the Internet of Things and cloud computing call for hybrid processing approaches in order to handle the speed and accuracy required for processing such complex data. In this paper, we propose a Hybrid Distributed Batch-Stream (HDBS) architecture for anomaly detection in real-time data. The hybrid architecture, while benefiting from the accuracy provided by batch processing, also enjoys the speed and real-time features of stream processing. In the proposed architecture, our focus is on the algorithmic aspects of hybrid processing including the interaction models between batch and stream processing units, the characteristics of batch and stream machine learning algorithms and the principles of merging the results of different processing units. The driving idea of such combination is that the results of batch and stream processing units are complementary with each other, as one of them constructs accurate models based on previous data, and the other one is capable of processing new stream data in real-time. Furthermore, we propose a generalized version of the HDBS with respect to its algorithms and communication policy levels. In the generalized HDBS architecture, we address the various aspects of the interaction between the batch and stream processing units, and the merging operations to produce the final results. the evaluations of the proposed architecture using various criteria (accuracy, space complexity, and time complexity) demonstrate that the accuracy of the proposed method is higher than the accuracy of the batch processing methods, its time complexity is also similar to one of the stream processing methods and much less than the batch processing methods, which makes our proposed architecture an efficient and practical solution for real-time anomaly detection.
On the Potentialities of Spaces of Care: Openness, Enticement, and Variability in a Psychiatric Center

Science and technology studies (STS) scholars have turned their attention to the materiality of objects and buildings in order to examine what they make users do in practice. Taking a close look at a therapeutic community in a psychiatric day care center for teenagers, this paper joins these discussions by exploring the materiality of “spaces of care” as part of the center’s everyday practice. The analysis incorporates the concepts of scripts and dispositifs to describe the conditions of possibility in which caregivers and youths may position themselves in relation to others and to the space itself. This paper describes how spaces of care offer open, enticing, and variable conditions for fostering a dynamic of personal and relational responses as part of the care work. In this sense, the material environment entails potentialities in ways that are unpredictable but nonetheless consequential. Rather than arguing that material arrangements and things act, this paper draws attention to their impact via their potential within dispositifs of care that request participants’ attentiveness and responsiveness. Describing these potentialities brings out the subtler requirements of material environments in care practices that aim at circumventing the coercion of disciplinary spaces and their impersonal classifications.
Polarization-based method of highlight removal of high-reflectivity surface

Structured light projection is widely applied in 3D shape measurement. Affected by uneven illumination, the highlight is produced on high-reflectivity surfaces, and the fringe pattern on the surface will be covered by it completely. To solve this problem, a polarization-based method is proposed to remove the image highlight. The experiment result proved that the image highlights are removed and the SNR of polarization image is improved. Meanwhile, a normalized weighting algorithm is applied in this method to restore the surface information in the highlighted area. The experiment results show that most of the fringe pattern is restored.
SCUT-HCCDoc: A new benchmark dataset of handwritten Chinese text in unconstrained camera-captured documents

In this paper, we introduce a large-scale dataset, called SCUT-HCCDoc, to address challenging detection and recognition problems of handwritten Chinese text (HCT) in the camera-captured documents. Despite extensive studies of optical character recognition (OCR) and offline handwriting recognition for document images, text detection and recognition in the camera-captured documents remains an unsolved problem that is worth for extensive study and investigation. With recent advances in deep learning, researchers have proposed useful architectures for feature learning, detection, and recognition for the scene text. However, the performance of deep learning methods highly depends on the amount and diversity of training data. Previous OCR and offline HCT datasets were built under specific constraints, and most of the recent scene text datasets are for non-handwritten text. Hence, there is a lack of a comprehensive scene handwritten text benchmark. This study focuses on scenes with handwritten Chinese text. We introduce the SCUT-HCCDoc database for HCT detection, recognition and spotting. SCUT-HCCDoc contains 12,253 camera-captured document images with 116,629 text lines and 1,155,801 characters. The diversity of SCUT-HCCDoc can be described at three levels: (1) image-level diversity: image appearance and geometric variances caused by camera-captured settings (such as perspective, background, and resolution) and different applications (such as note-taking, test papers, and homework); (2) text-level diversity: variances of text line length, rotation, etc.; (3) character-level diversity: variances of character categories (up to 6109 classes with additional English letters, and digits), character size, individual writing style, etc. Three kinds of baseline experiments were conducted, where we used several popular text detection methods for text line detection, CTC-based/attention-based methods for text line recognition, and combine text detectors with CTC-based recognizer to achieve end-to-end text spotting. The results indicate the diversity of SCUT-HCCDoc and the challenges of HCT understanding in document images. The dataset is available at https://github.com/HCIILAB/SCUT-HCCDoc_Dataset_Release.
Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework

The aim of this article is to analyze multiple repairable systems data under the presence of dependent competing risks. It is known that the dependence effect in this scenario influences the estimates of the model parameters. Hence, under the assumption that the cause-specific intensities follow a power law process (PLP), we propose a frailty-induced dependence approach to incorporate the dependence among the cause-specific recurrent processes. Moreover, the misspecification of the frailty distribution may lead to errors when estimating the parameters of interest. Because of this, we considered a nonparametric approach to model the frailty density using a Dirichlet process mixture prior, which offers more flexibility to provide consistent estimates for the PLP model, as well as insights about heterogeneity among the systems. We proposed an orthogonal parametrization for the PLP model parameters that allowed us to specify a joint prior distribution for the parameters that returned closed-form estimators for the posterior mean. Additionally, a hybrid MCMC sampler algorithm composed by Hamiltonian Monte Carlo and Gibbs sampling was built for computing the posterior estimates with respect to the frailty distribution. A simulation study was conducted to evaluate the efficiency of our estimates. This method was used to analyze a real dataset. Algorithms, code, and data are provided in supplementary material available online.
Fractal Dimension of Color Fractal Images With Correlated Color Components

We mathematically prove that color fractal images with two and three correlated color components generated with the midpoint displacement approach obey the property of self-similarity, thus enabling the estimation of their color fractal dimension. We generate various sets of color fractal images with two and three correlated color components, controlled both by the Hurst parameter and the variance-covariance matrix, with and without a global normalization, and use them for the calibration of the embraced color fractal dimension estimator. We improve the existing fractal dimension estimator based on probabilistic box-counting by reducing the variance of the regression line estimators through the iterative elimination of most error-ed measurement points. We independently estimate the variance-covariance matrix and Hurst parameter for the sets of generated color fractal images with correlated color components. We show the experimental results and discuss both the improvements and the limitations of the proposed approach.
Generalized task allocation and route planning for robots with multiple depots in indoor building environments

Recent advancements in sensing and robotic technologies facilitate the use of on-demand building service robots in the built environment. Multi-robot based systems have arguably more advantages when compared to fixed sensor-based and single-robot based systems. These task-oriented building service robots face several challenges, such as task-allocation and route-planning. Previous studies adopted approaches from other domains, such as outdoor logistics, and made application-specific assumptions. This study proposes a new methodology to optimize the task-allocation and route-planning for multiple indoor robots with multiple starts and destination depots where each robot begins and ends at the same depot (referred to as a fixed destination multi-depot multiple traveling salesman problem-fMmTSP). The performance of the proposed algorithm was compared with two existing outdoor-based algorithms. Results show that the proposed algorithm performs better in almost all the cases for the assumed network, which supports the need to develop algorithms specifically for indoor networks.
An Online Anomaly Detection Approach For Unmanned Aerial Vehicles

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.
When does my program do this? learning circumstances of software behavior

A program fails. Under which circumstances does the failure occur? Our Alhazenapproach starts with a run that exhibits a particular behavior and automatically determines input features associated with the behavior in question: (1) We use a grammar to parse the input into individual elements. (2) We use a decision tree learner to observe and learn which input elements are associated with the behavior in question. (3) We use the grammar to generate additional inputs to further strengthen or refute hypotheses as learned associations. (4) By repeating steps 2 and 3, we obtain a theory that explains and predicts the given behavior. In our evaluation using inputs for find, grep, NetHack, and a JavaScript transpiler, the theories produced by Alhazen predict and produce failures with high accuracy and allow developers to focus on a small set of input features: "grep fails whenever the --fixed-strings option is used in conjunction with an empty search string."
Explainable AI for the Choquet Integral

The modern era of machine learning is focused on data-driven solutions. While this has resulted in astonishing leaps in numerous applications, explainability has not witnessed the same growth. The reality is, most machine learning solutions are black boxes. Herein, we focus on data/information fusion in machine learning. Specifically, we explore four eXplainable Artificial Intelligence (XAI) questions relative to Choquet integral; (i) what is the quality of our inputs and their interactions, (ii) how is the information being combined, (iii) what is the quality of our training data (and thus our learned models), and (iv) what trust do we place in an output? Previously, we derived an initial set of indices for (i)-(iv) on the premise of perfect knowledge. Herein, we make XAI more accurate by taking into consideration what the machine learned. A combination of synthetic data and real-world experiments from remote sensing for fusing deep learners in the context of classification are explored. Our approach leads to performance gain, insights into what was learned, and it helps us realize better future solutions.
Knowledge graph exploration

Knowledge graphs (KGs) represent facts in the form of subject-predicate-object triples and are widely used to represent and share knowledge on the Web. Their ability to represent data in complex domains augmented with semantic annotations has attracted the attention of both research and industry. Yet, their widespread adoption in various domains and their generation processes have made the contents of these resources complicated. We speak of knowledge graph exploration as of the gradual discovery and understanding of the contents of a large and unfamiliar KG. In this paper, we present an overview of the state-of-the-art approaches for KG exploration. We divide them into three areas: profiling, search, and analysis and we argue that, while KG profiling and KG exploratory search received considerable attention, exploratory KG analytics is still in its infancy. We conclude with an overview of promising future research directions towards the design of more advanced KG exploration techniques.
Industrial Federated Learning – Requirements and System Design

Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.
Soft Semantic Representation for Cross-Domain Face Recognition

The problem of cross-domain face recognition aims to identify facial images obtained across different domains, which attracts increasing attentions because of its wide applications on law-enforcement identification and camera surveillance. The problem is challenging due to the huge domain discrepancy. Despite great progress achieved in recent years, existing algorithms usually fail to fully exploit the semantic information for identifying cross-domain faces, which could be a strong clue for recognition. In this article, we propose an effective algorithm for cross-domain face recognition by exploiting semantic information integrated with deep convolutional neural networks (CNN). We first introduce a soft face parsing algorithm where the boundaries of facial components are measured as probabilistic values. By taking the original face image as the guidance to improve face parsing result, each pixel may belong partially to the facial component to avoid inaccurate segmentation around component boundaries. We then propose a hierarchical soft semantic representation framework for cross-domain face recognition. Both the soft semantic level and contour level deep features obtained via CNN are computed and combined together, which could fully exploit the identical semantic clue among cross-domain faces. We provide extensive experiments to demonstrate that the proposed soft semantic representation algorithm performs superior against state-of-the-art methods.
Improved Manta Ray Foraging Optimization Using Opposition-based Learning for Optimization Problems

Manta ray foraging optimization (MRFO) algorithm is a bio-inspired meta-heuristic algorithm. It has been proposed as an alternative optimization approach for real-world engineering problems. However, MRFO is not good at fine-tuning of solutions around optima and suffers from slow convergence speed because of its stochastic nature. It needs to be improved due to latter issues. Therefore, in this study, opposition-based learning (OBL) technique was used together with MRFO in order to obtain an effective structure for optimization problems. The proposed structure has been named as opposition-based Manta ray foraging optimization (OBL-MRFO). In the proposed algorithm, the advantage of OBL in terms of considering the opposite solutions was used to have an algorithm with better performance. The proposed algorithm has been tested on four different benchmark functions such as Sphere, Rosenbrock, Schwefel and Ackley. Statistical analyses were performed through comparing the performance of OBL-MRFO with the other algorithms such as salp swarm algorithm, atom search optimization and original MRFO. The results showed that the proposed algorithm is more effective and has better performance than other algorithms.
FPGA implementations of Grain v1, Mickey 2.0, Trivium, Lizard and Plantlet

In this paper, three eSTREAM Portfolio 2 ciphers: Grain v1, Mickey 2.0 and Trivium, and two Grain-like stream ciphers: Lizard and Plantlet are implemented in three versions aimed at different hardware application purposes. The hardware platform adopts Xilinx's Spartan7 serial, and the simulations, syntheses and implementations are conducted in Vivado using the Verilog hardware design language. These implementations are compared with each other and those presented in existing literature in terms of performance metrics including: throughput, area consumption and throughput-area ratio. The basic version of Trivium achieves the highest frequency reaching a maximum of 416 Mbps and the serial version consumes the smallest area of 13 slices. In the parallel version, the maximum throughput-area ratio is 165.5 Mbps/Slice achieved by Trivium. At the same time, the basic version of the Mickey 2.0 algorithm achieved the second highest frequency of 384 Mbps, and the serial version of the Grain v1 algorithm achieved the second smallest area with 26 slices.
A Simple Discriminative Dual Semantic Auto-encoder for Zero-shot Classification

Most existing ZSL models focus on searching the mapping between visual space and semantic space directly. However, few models study whether the human-designed semantic information is discriminative enough to recognize different categories. On the other hand, one-way mapping typically suffers from the project domain shift problem. Inspired by the encoder-decoder paradigm, we propose a novel solution to ZSL based on learning a Discriminative Dual Semantic Auto-encoder (DDSA). DDSA aims to build an aligned space to bridge the visual space and the semantic space by learning two bidirectional mappings, which provides us the required discriminative information about the visual and semantic features in the aligned space. The key to the proposed model is that we implicitly exact the principal information from visual and semantic space to construct aligned features, which is not only semantic-preserving but also discriminative. Extensive experiments on five benchmark data sets demonstrate the effectiveness of the proposed approach.
Fourth VR: Indigenous virtual reality practice

Indigenous creators are currently using virtual reality (VR) tools, techniques and workflows in wide-ranging geographical locations and across multiple VR formats. Their radical adaptation of this new technology folds together cultural traditions and VR’s unique audiovisual configurations to resist dominant, particularly colonial, frameworks. Within this context, we ask how VR is being used to create space and capacity for Indigenous creatives to tell their stories and how do Indigenous creatives negotiate Eurocentric modes of production and distribution? To answer these questions, our Fourth VR database provides a snapshot of Indigenous VR works. By drawing on three case studies drawn from the database – The Hunt (2018), Future Dreaming (2019) and Crow: The Legend (2018) – as well as the wider patterns emerging across the database, it is possible to see an Indigenous-centred VR production framework. This framework is diverse but also contains repeated trends such as the ability to use VR to express and realize Indigenous Futurism; foreground native languages in virtual worlds; provide new articulations of Indigenous activism; embody connections between the past, present and future and demonstrate the interconnectivity of all living things. In turn, this growing body of work, engaging with the full spectrum of VR formats and tools, provides a rich contribution to the wider arena of VR practice.
An ontological artifact for classifying social media: Text mining analysis for financial data

In this paper we utilize a structured natural language processing implementation of the Financial Industry Business Ontology (FIBO) to extract financial information from the unstructured textual data of the social media platform Twitter regarding financial and budget information in the public sector, namely the two public-private agencies of the Port Authority of NY and NJ (PANYNJ), and the NY Metropolitan Transportation Agency (MTA). This research initiative uses the Design Science Research (DSR) perspective to develop an artifact to classify tweets as being either relevant to financial bonds or not. We apply a frame and slot approach from the artificial intelligence and natural language processing literature to operationalize this artifact. FIBO provides standards for defining the facts, terms, and relationships associated with financial concepts. We show that FIBO grammar can be used to mine semantic meaning from unstructured textual data and that it provides a nuanced representation of structured financial data. With this artifact, social media such as Twitter may be accessed for the knowledge that its text contains about financial concepts using the FIBO ontology. This process is anticipated to be of interest to bond issuers, regulators, analysts, investors, and academics. It may also be extended towards other financial domains such as securities, derivatives, commodities, and banking that relate to FIBO ontologies, as well as more generally to develop a structured knowledge representation of unstructured data through the application of an ontology.