Reviewerly/exHarmony huggingface dataset:
Improved GM-PHD filter based on threshold separation clusterer for space-based starry-sky background weak point target tracking

The movement of the background caused by the movement of a sensor platform has always been a major challenge in space-based infrared weak and small target tracking. In addition to clutter, false alarms, noise, and other disturbances, the most serious source of interference in space-based infrared weak target tracking comes from the stars. This kind of interference always exists and is large enough to be often mistakenly recognized as a new target by traditional algorithms. To reduce the influence of sensor platform movement and stellar interference on the target tracking of weak points in space, this paper respectively proposes a fast iterative closest point (ICP) registration algorithm for sparse target points and a threshold separation clustering algorithm. After pruning and merging using a Gaussian mixture probability hypothesis density (GM-PHD) filter-based algorithm, the threshold separation cluster separates the weak point targets by thresholds, and then clusters according to the Euclidean distance between the targets. Moreover, to prevent stars with special positions from being clustered into a single group, a dynamic weight extraction scheme is adopted to better distinguish stars in the group. We compared the proposed algorithm with the original GM-PHD, forward-backward smoothing (FBS), and N-scan approaches in starry-sky tracking scenarios simulated using the Tycho-2 catalog. Experimental results show that the proposed algorithm has better tracking accuracy and a lower rate of false detections.
Use of Information and Communication Technologies (ICTs) in Communication and Collaboration: A Comparative Study between University Students from Spain and Italy

The aim of this study is to determine the level of competence that university students from Spain and Italy have in the use of Information and Communication Technologies (ICTs) for communication and collaboration purposes. We also intend to identify possible differences and similarities between both countries, as well as factors associated with their competence that may lead to a digital gap. In order to fulfil these objectives, we performed a positivist exploratory study based on a quantitative methodology supported by a descriptive method with a survey design. The data collection is performed with a questionnaire, the data from which are analyzed through two statistical studies: a descriptive one and a causal one. The results show good competence from the students in this area, being somewhat higher in Italian students. We identified some of the factors associated with competence, such as Internet access or having a tablet. In conclusion, it is clear that politics on a global, national and local level are essential to the acquisition of digital competence, especially regarding the digital gap caused by access to resources. In this sense, governments must guarantee free and universal access to technology to all their citizens in order to diminish the digital gap between them.
Robust Optimization Model for Probabilistic Protection under Uncertain Virtual Machine Capacity in Cloud

This paper presents robust optimization models for minimizing the required backup capacity with probabilistic protection against multiple simultaneous failures of physical machines in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties, failure event and virtual machine capacity. By adopting the robust optimization technique, we formulate three mixed integer linear programming problems. There are differences of robustness between the three models. Then we provide theorems concerned with the differences and prove them. The theorems are supported by numerical results. Our three presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty.
An Education Literature Review on Digitization, Digitalization, Datafication, and Digital Transformation

Digital innovation and transformation provide opportunities in both education and workplace environments, raising the importance of digital competencies in an increasingly digitalized world.Such importance has been heightened by the recent COVID-19 outbreak.To explore the related discussions in the education literature, this article conducts a scientometric analysis of 782 articles collected from the Web of Science (WoS) database.By examining the main sources and keywords, it shows several cross-disciplinary research fronts that involves disciplines such as engineering, management, communication, etc., and topics such as collaboration, digital economy, digital platform, etc.Also, the digital transformation research has signaled a shift from technology to organization and people.The findings offer a preliminary roadmap for educators, researchers, and policymakers how more sustainable livelihood and life potentials can be achieved by harnessing digital potentials.
Digital begriffsgeschichte: Tracing semantic change using word embeddings

Recently, the use of word embedding models (WEM) has received ample attention in the natural language processing community.These models can capture semantic information in large corpora of text by learning distributional properties of words, that is how often particular words appear in specific contexts.Scholars have pointed out the potential of WEMs for historical research.In particular, their ability to capture semantic change might assist historians studying conceptual change or specific discursive formations over time.Concurrently, others voiced their criticism and pointed out that WEMs require large amounts of training data, that they are challenging to evaluate, and they lack the specificity looked for by historians.The ability to examine semantic change resonates with the goals of historians such as Reinhart Koselleck, whose research focused on the formation of concepts and the transformation of semantic fields.However, word embeddings can only be used to study particular types of semantic change, and the model's use is dependent on the size, quality, and bias in training data.In this article, we examine what is required of historical data to produce reliable WEMs, and we describe the types of questions that can be answered using WEMs.
Deep Spectral-Spatial Network for Single Image Deblurring

Inspired by the great success of the deep neural networks in various fields of computer vision, studies for image deblurring have begun to become more active in recent days. However, most previous approaches often fail to accurately remove the blur artifacts, e.g., ghosting effects at the object boundaries and degradation of local details, in restored results. In this paper, we propose a deep spectral-spatial network (DSSN) for resolving the problem of single image deblurring. Specifically, the proposed method is able to efficiently recover scene characteristics in a global manner by minimizing differences of the frequency magnitude between the blurred input and corresponding sharp image via the spectral restorer, and the spatial restorer fine-tunes local details of the intermediate result, which is estimated by the spectral one, based on the intensity similarity. This cascaded scheme of deblurring processes is fairly desirable for clearly restoring edge-like structures as well as the textural information in a coarse-to-fine manner. Experimental results on benchmark datasets demonstrate that the proposed DSSN outperforms state-of-the-art methods. The code and model are publicly available at: https://github.com/SeokjaeLIM/DSSN_release.
IT2-GSETSK: An evolving interval Type-II TSK fuzzy neural system for online modeling of noisy data

As a core part of a fuzzy neural system, the rule base antecedents and consequents may carry uncertainties because they are trained using noisy data. So, handling the uncertain rule base is an important need in some specific problems such as noisy non-dynamic problems which leads a better data modeling. As a solution, Interval Type-II (IT2) version of GSETSK (Generic Self-Evolving Takagi-Sugeno-Kang), namely IT2-GSETSK, is presented in this paper. This solution uses IT2 membership functions for handling uncertainties, plus having Type-I (GSETSK) capabilities. In this way IT2-GSETSK is a fully-online model able to handle data streams and cope with time-variant data. It also provides up-to-date, relevant and compact rule base that is easily interpretable by human. The IT2-GSETSK is tested over several applications including medical, environmental and financial predictions, which show satisfactory performance of IT2-GSETSK. Moreover, it is observed that while GSETSK performs well enough for dynamic problems with less noise, noisy non-dynamic problems benefit significantly from IT2-GSETSK.
An Ontological Framework for Opportunistic Composition of IoT Systems

As the number of connected devices rapidly increases, largely thanks to uptake of IoT technologies, there is significant stimulus to enable opportunistic interactions between different systems that encounter each other at run time.However, this is complicated by diversity in IoT technologies and implementation details that are not known in advance.To achieve such unplanned interactions, we use the concept of a holon to represent a system's services and requirements at a high level.A holon is a self-describing system that appears as a whole when viewed from above whilst potentially comprising multiple sub-systems when viewed from below.In order to realise this world view and facilitate opportunistic system interactions, we propose the idea of using ontologies to define and program a holon.Ontologies offer the ability to classify the concepts of a domain, and use this formalised knowledge to infer new knowledge through reasoning.In this paper, we design a holon ontology and associated code generation tools.We also explore a case study of how programming holons using this approach can aid an IoT system to self-describe and reason about other systems it encounters.As such, developers can develop system composition logic at a high-level without any preconceived notions about low-level implementation details.
ManQ: Many-objective optimization-based automatic query reduction for IR-based bug localization

An information retrieval-based bug localization (IRBL) method is proposed to localize buggy files using a bug report as a query. The performance of this method strongly depends on the quality of the query. However, these queries contain noise terms that hinder their use for IRBL. To improve the quality of a query, an automatic query reduction (AQR) technique that removes noise words from the query is needed. Our objective is to develop an AQR method for IRBL. Most existing AQR techniques are based on single objective optimization, which presents issues in terms of biased and limited performance. To solve these issues, it is necessary to find a subquery that comprehensively satisfies all of their objectives. We propose an AQR technique called ManQ, which is a many-objective optimization-based AQR method for IRBL. We design 15 objective functions to (1) maintain the query quality properties, (2) maintain the important terms, (3) maintain the initial information, and (4) minimize the query length. ManQ finds a final subquery that maximize the return values of these objective functions. The experimental results show that ManQ improves the quality of poor queries. We also show that if we select the best query among the candidates generated by ManQ, we can increase the number of improved queries by more than 53.4% of all queries. ManQ improves the performance of IRBL by improving the quality of queries through a many-objective optimization approach.
Towards Real Time Object Recognition For Context Awareness in Mixed Reality: A Machine Learning Approach

Mixed Reality (MR) technologies provide users with an environment that incorporates virtual objects and metadata into their physical surroundings. This opens up exciting new possibilities for applications in various domains including: education, training, healthcare, and Computer Supported Cooperative Work (CSCW). Recognizing physical objects in the environment provides contextual clues that can improve user task performance and influence effective cognitive load for complex tasks. However, this facet of MR is still relatively nascent because of hardware limitations. Presently, MR headsets can only detect physical surfaces; they can not distinguish between physical artifacts that a user might need in their work environment. Machine Learning (ML) based object recognition solutions can help overcome this limitation. However, current commercially available MR devices have insufficient computational capabilities and are unable to support the state-of-the-art ML based object recognition solutions. To address these challenges, we describe a novel approach and the corresponding framework by introducing a hardware level separation between the two tasks of object recognition and rendering virtual components. Our approach leverages ML based object recognition, MR, and a portable IoT edge computing platform to support contextual awareness. We describe the implementation of this approach and provide a case study. The prototype implementation uses an edge device to process and recognize objects to get information about the user’s physical environment and provides the user with situational awareness through the MR device. Our case study uses a Christmas tree decoration activity to demonstrate the ability of the proposed framework to support assembly-type tasks. The case study establishes the applicability of the proposed approach to a variety of problem domains. Object recognition and context aware capabilities, combined with virtual indicators and instructions, provide a solution that increases usability, helps reduce human error, and improves the overall task performance.
Adversarial Defense based on Structure-to-Signal Autoencoders

Adversarial attacks have exposed the intricacies of the complex loss surfaces approximated by neural networks. In this paper, we present a defense strategy against gradient-based attacks, on the premise that input gradients need to expose information about the semantic manifold for attacks to be successful. We propose an architecture based on compressive autoencoders (AEs) with a two-stage training scheme, creating not only an architectural bottleneck but also a representational bottleneck. We show that the proposed mechanism yields robust results against a collection of gradient-based attacks under challenging white-box conditions. This defense is attack-agnostic and can, therefore, be used for arbitrary pre-trained models, while not compromising the original performance. These claims are supported by experiments conducted with state-of-the-art image classifiers (ResNet50 and Inception v3), on the full ImageNet validation set. Experiments, including counterfactual analysis, empirically show that the robustness stems from a shift in the distribution of input gradients, which mitigates the effect of tested adversarial attack methods. Gradients propagated through the proposed AEs represent less semantic information and instead point to low-level structural features.
A general framework for partial reversible data hiding using hamming code

A general framework for partial reversible data hiding is proposed in this paper. The original image is adjusted to form a cover image in image transferring phase. The minimal square error criterion between Hamming codeword and the bits from the original image is adopted to generate the cover image. After image transformation, some bits in the cover image are actually a Hamming codeword. In data embedding phase, the secret bits are concealed into the cover image to generate a stego image. The secret messages can be extracted from the stego image, and the stego image can be recovered back to cover image in data extraction and image restoration phase. The so-called partial reversible property indicates an almost distortion-free original image (e.g., cover image) can be obtained. Theoretical analysis on the embedding capacity and visual performance by the proposed method is given. Further, experimental and theoretical results are demonstrated to show the feasibility and advantages of the proposed method.
Robust TSK Fuzzy System Based on Semisupervised Learning for Label Noise Data

As an important branch in the field of soft computing, TSK fuzzy systems have been diversely applied to supervised learning in recent years. However, real-world data may contain label noise, which has a negative impact on supervised learning. Label noise samples change the distribution of samples in each class, mislead learning algorithms and make classification problems more complicated. There are various sources of label noise, such as wrong assignment of labels during the data collection, contamination during the data storage, and so on. Thus, it is usually costly and time-consuming to obtain data with no label noise. When dealing with label noise data, existing TSK fuzzy system algorithms still have room for improvement. This article proposes a robust TSK fuzzy system based on semisupervised learning for label noise data (RTSK-FS-SS). By introducing an intuitionistic fuzzy set method, the proposed algorithm can detect label noise samples. An improved learning vector quantization is further adopted to overcome the challenge that traditional unsupervised learning-based antecedent part generation processes unable to make full use of the label information of training samples. Finally, we discard the label of suspicious samples and a consequent parameter learning method based on semisupervised learning is proposed. The proposed algorithm is validated using extensive experiments.
Markov blanket-based universal feature selection for classification and regression of mixed-type data

Feature selection has been successfully applied to improve the quality of data analysis in various expert and intelligent systems. However, because most real-world data nowadays come with mixed features, traditional feature selection approaches that are mainly designed to handle single-type data are not suitable for this situation. In addition, most of existing methods are only applicable to a specific problem, either classification or regression. Therefore, it is an urgent need to develop a universal feature selection method that can be applied to classification and regression with mixed-type data. In response to this, our paper presents a new feature selection method based on a Markov blanket (MB) called Mixed-MB. The key idea behind this is to embed a likelihood ratio-based generalized conditional independence test into an efficient MB search algorithm to find the minimal set of features to fully explain the target variable on mixed-type data. This new MB feature selection method eliminates the weakness of existing MB feature selection method that it only can handle single-type data, while maintaining its strengths such as theoretical soundness, simplicity, speed, and versatility. Experimental results on real-world data sets with mixed features demonstrate that the proposed method is effective for improving the accuracy of prediction models in both classification and regression. It is also shown to be able to yield more accurate results with fewer features than other methods. We believe that Mixed-MB will be widely used in expert and intelligent systems that utilize various data to create value since it can be applied to any type of data and problem.
Fast brain tumour segmentation using optimized U-Net and adaptive thresholding

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Weighted bilinear coding over salient body parts for person re-identification

Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re-identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP) to aggregate intermediate convolutional features for Re-ID. However, this strategy only considers the first-order statistics of local features and treats local features at different locations equally important, leading to sub-optimal feature representation. To deal with these issues, we propose a novel weighted bilinear coding (WBC) framework for local feature aggregation in CNN networks to pursue more representative and discriminative feature representations, which can adapt to other advanced methods and improve their performance. In specific, bilinear coding is used to encode the channel-wise feature correlations to capture richer feature interactions. Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation. To handle the spatial misalignment issue, we use a salient part net to derive salient body parts, and apply the WBC model on each part. The final representation, formed by concatenating the WBC encoded features of each part, is both discriminative and resistant to spatial misalignment. Experiments on three benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the favorable performance of our method against other outstanding methods.
A Formative Evaluation Methodology for VR Training Simulations

Virtual reality (VR) has a long history of being used to simulate real-world tasks, often for training and educational purposes. Yet, developing a VR system that is usable and effective is not trivial and is still a challenging task. In this paper, we present a methodology for formatively evaluating the usability of VR simulations based on capturing a real-world task, automating data logging, running a rigorous user study, analyzing subtask user performances, and inspecting those subtasks for which users performed worse in terms of usability. Using this methodology, we present the formative evaluation of a VR simulation designed for training troubleshooting skills during robotic surgeries. The results of our formative evaluation clearly demonstrated usability issues for some of our targeted training subtasks and we discuss how those issues will be addressed.
Breast cancer identification and prognosis with machine learning techniques - An elucidative review

Abstract Abstract Cancer is the principle wellspring of death around the globe with 2.09 million cases so far in 2018 [1]. Around 627000 deaths accounting to 6.6% are caused because of female breast cancer and it ranks five amongst the list of top causes for deaths, the prime reason being prognosis being favorable in developed countries. The timely empathy of breast cancer further makes the process of prognosis better hence improving the rates of survival, because this will indorse on time treatment which is given clinically to patients. When the classification is done in an accurate way for malignant and benign tumours, it stops the suffering of patients with excessive ailments. The best possible recognizable proof of breast cancer disease and the process of characterizing into benign and malignant groups is that the main concern of a ton of investigation and research. When thrown light on its particular advantages in significant alternatives recognition from the datasets of entangled breast cancer, the generally perceived option is Machine Learning, because of the philosophy of determination in breast cancer to arrange pattern and forecast modelling. This paper will in general, survey machine learning and assessment of this particular paper, WBCD: Wisconsin Breast Cancer Database has been used as the benchmark dataset. Subject Classification: 68T30Keywords: Breast cancerMachine learningANNDTSVMk-NNWBCD