Reviewerly/exHarmony huggingface dataset:
On the application of the reachability distance in the suppression of mixed Gaussian and impulsive noise in color images

Abstract In this paper, we address the problem of mixed Gaussian and impulsive noise reduction in color images. A robust filtering technique is proposed, which is utilizing a novel concept of pixels dissimilarity based on the reachability distance. The structure of the denoising method requires the estimation of the impulsiveness of each pixel in the processing block using the introduced local reachability concept. Furthermore, we determine the similarity of each pixel in the block to the central patch consisting of the processed pixel and its neighbors. Both measures are calculated as an average of modified reachability distances to the most similar pixels of the central patch and the final filtering output is a weighted average of all pixels belonging to the processing block. The proposed technique was compared with widely used filtering methods and the performed experiments proved its satisfying denoising properties. The introduced filtering design is insensitive to outliers and their clusters introduced by the impulsive noise process, preserves details and is able to efficiently suppress the Gaussian noise while enhancing the image edges. Additionally, we proposed a method which estimates the noise contamination intensity, so that the proposed filter is able to adaptively tune its parameters.
Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java

Constrained interval type-2 (CIT2) fuzzy sets are a class of type-2 fuzzy sets that has been recently proposed as a way to extend type-1 membership functions to interval type-2 (IT2) while keeping a semantic connection between the IT2 fuzzy set and the concept it models. Recent work has shown how their mathematical properties can be used to design CIT2 fuzzy logic systems that are able to provide explanations for their outputs. Although the CIT2 representation can be a valuable alternative to the IT2 one, no software library for their implementation is available for the research community. The aim of this paper is to introduce a new Java library, Juzzy Constrained, that has been developed as an extension of the popular type-1 and type-2 Java toolkit Juzzy, adding support for CIT2 sets and systems. Throughout the paper, the main classes and the structure of the new library are described, together with a working example that illustrates how to build a CIT2 fuzzy system from scratch and how it can be used to produce explanations for the output.
An Ensemble Classification Algorithm for Text Data Stream based on Feature Selection and Topic Model

How to mine valuable information that users are interested in from a continuous text data stream, text data stream classification has received widespread attention as a core technology to solve the problem. This paper proposes a text data stream ensemble classification algorithm that combines feature selection and topic model. Firstly, the mutual information feature selection method is used to remove features that are not related to classification. Secondly, the LDA topic model is used to establish the document-topic distribution. Finally, the pre-processed text data stream is classified by an ensemble classification model. The experimental results show that the proposed text data stream ensemble classification algorithm can improve the classification performance of text data stream.
Efficient Visual Classification by Fuzzy Rules

The paper proposes a method for classifying and fast retrieving images which uses boosting metalearning to search for the most salient image features. We use local image keypoints as image features. We construct by boosting a set fuzzy rules describing image feature parameters. The rules constitute a set of weak classifiers voting for the final image class. The method can use various image features, engineered and learned by deep learning methods. We checked the methods on some real-world images.
End-to-End Speech Endpoint Detection Utilizing Acoustic and Language Modeling Knowledge for Online Low-Latency Speech Recognition

Speech endpoint detection (EPD) benefits from the decoder state features (DSFs) of online automatic speech recognition (ASR) system.However, the DSFs are obtained via the ASR decoding process, which can become prohibitively expensive especially in limited-resource scenarios such as the embedded devices.To address this problem, this paper proposes a language model (LM)-based end-of-utterance (EOU) predictor, which is trained to determine the framewise probabilities of the EOU token conditioned on the previous word history obtained from the 1-best decoding hypothesis of the ASR system in an end-to-end manner without an actual decoding process in the test step.Further, a novel end-to-end EPD strategy is presented to incorporate a phonetic embedding (PE)-based acoustic modeling knowledge and the proposed EOU predictor-based language modeling knowledge into an acoustic feature embedding (AFE)-based EPD approach within the recurrent neural networks (RNN)-based EPD framework.The proposed EPD algorithm is built upon the ensemble RNNs, which are independently trained for the three parts, which are the proposed LM-based EOU predictor, AFE-based EPD, and PE-based acoustic model (AM) in accordance with each target.The ensemble RNNs are concatenated at the level of the last hidden layers and then attached into the fully-connected deep neural networks (DNN)-based EPD classifier, which is trained in accordance with the ultimate EPD target.Thereafter, they are jointly retrained at the second step of the DNN training to yield the lower endpoint error.The proposed EPD framework was evaluated in terms of the endpoint accuracy and word error rate for the CHiME-3 and large-scale ASR tasks.The experimental results turn out that the proposed EPD algorithm efficiently outperforms the conventional EPD approaches.INDEX TERMS Acoustic model (AM), end-of-turn detection, end-of-utterance (EOU) detection, feature embedding, language model (LM), online speech recognition, pause hesitation, speech endpoint detection (EPD), spoken dialogue system.
Smart Surveillance Robot using Object Detection

These days, we often get updated with the realities about lethal accidents because of harmful conditions, cavern investigation, mining and military surveillance and so forth. This has definitely been a zone of worry over an extensive stretch of time, as the valuable human life has been in question under the cost of investigative purpose. So as to defeat this we assemble a savvy and productive mechanical framework, customized to give a stage to observable reason, consequently going about as a substitute for human in risky exercises like mining, investigation, watching delicate regions and for different applications that are at a more secure separation. This helps us to drive the robot with a camera feed returning to your computer. One can able to use the arrow keys to drive the robot towards any path and at anywhere and therefore the video and sound feed allows you to drive it in any direction, once the automaton is a way out. One can control it remotely from any remote area through IP address. By making use of these video feed we can perform constant object discovery so we can distinguish the object.
Applications and Evaluation of the IRIS Earthquake Browser: A Web-Based Tool That Enables Multidimensional Earthquake Visualization

Abstract The Incorporated Research Institutions for Seismology Earthquake Browser (IEB; see Data and Resources) is a web-based tool that enables anyone to query an earthquake database composed of over five million events recorded over the past 50 yr. The IEB visitor can query on earthquake magnitude, depth, timing, and location and can visually display the results in 2D map view or as an interactive pseudo 3D view. The user can toggle features such as plate tectonic boundaries, terrain or satellite mapping, and zoom to place the results in a geologic or geopolitical context and add visual appeal. To better understand who visits the IEB and why, to include information on demographics and how users perceive the IEB functionality and ability to meet their needs, we conducted a pop-up user survey on the IEB from 25 January to 21 February 2018. We received 495 useable responses from 58 countries, with 40% of the total respondents from the United States. The largest demographic consists of interested citizens who are 55 yr of age or older and have a high school education. We also find that visitors come to the IEB to learn about earthquakes for two main reasons: for their own personal knowledge or because expanding their knowledge is important to their research or work in a professional context. We also find a dramatic increase in survey respondent activity following the 16 February 2018 M 7.2 Oaxaca, Mexico, earthquake, with many respondents interested in finding more information about recent earthquake events that affect them or their family. Our observations indicate that users are successful and satisfied with the ease of use and amount of time spent on the IEB to find answers to their questions about earthquakes. The most beneficial feature of the IEB as identified by survey respondents is the spatiotemporal visual display of real earthquake data.
Movie Recommendation Systems

Recommender systems have defined our choices online whether buying a book on Amazon or choosing a movie on Netflix or reading an article on Medium. However, they are still in a nascent stage of development and far from being perfect. In this paper, we discuss movie recommender systems in particular and discuss various kinds of recommendation techniques along with the challenges they face. We also try to critically examine some work done on movie recommendation systems and discuss some research papers which have helped solve several challenges faced by these recommendation systems. However, in spite of all these advances, recommender systems still need to be to be improved to a larger extent in order to be more effective in making accurate recommendations on a wide array of applications.
Intelligent Scheduling for Stratosphere Cloud Platforms

Data centres play an important role in developing machine learning algorithms and in wireless networks. The large scale use of data centres increases the operational costs for cloud computing service providers. The operational costs can be reduced by siting data centres in locations such as the stratosphere. The siting of data centres in stratosphere reduces cooling costs and enhances the power usage effectiveness (PUE). This paper examines the PUE improvement obtainable with data centres located in the stratosphere. Performance evaluation shows that the stratosphere's cooling capacity increases with the proportion of green house gases. The PUE is enhanced by at least 27% on average when 33.3% of data centres in a cloud platform are located in the stratosphere. The PUE is enhanced by up to 40.1% on average when 50% of data centres in a cloud platform are located in the stratosphere.
Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges

Technical Debt Management (TDM) is a fast-growing field that in the last years has attracted the attention of both academia and industry. TDM is a complex process, in the sense that it relies on multiple and heterogeneous data sources (e.g., source code, feature requests, bugs, developers' activity, etc.), which cannot be straightforwardly synthesized; leading the community to using mostly qualitative empirical methods. However, empirical studies that involve expert judgement are inherently biased, compared to automated or semi-automated approaches. To overcome this limitation, the broader (not TDM) software engineering community has started to employ machine learning (ML) technologies. Our goal is to investigate the opportunity of applying ML technologies for TDM, through a Systematic Literature Review (SLR) on the application of ML to software engineering problems (since ML applications on TDM are limited). Thus, we have performed a broader scope study, i.e., on machine learning for software engineering, and then synthesize the results so as to achieve our high-level goal (i.e., possible application of ML in TDM). Therefore, we have conducted a literature review, by browsing the research corpus published in five high-quality SE journals, with the goal of cataloging: (a) the software engineering practices in which ML is used; (b) the machine learning technologies that are used for solving them; and (c) the intersection of the two: developing a problem-solution mapping. The results are useful to both academics and industry, since the former can identify possible gaps, and interesting future research directions, whereas the latter can obtain benefits by adopting ML technologies.
Gradient Mechanism to Preserve Differential Privacy and Deter Against Model Inversion Attacks in Healthcare Analytics

Advanced sensing technologies, driven by the Internet of Things, have caused a sharp increase in data availability within the healthcare system. The newfound availability of data offers an unprecedented opportunity to develop new analytical methods to improve the quality of patient care. Data availability, however, is a double-edged sword. Malicious attacks and data breaches are increasingly seen in the healthcare field, which result in costly disruptions to operations. Adversaries exploit analytic models to infer participation in a dataset or estimate sensitivity attributes about a target patient. This paper is aimed at developing a differentially private gradient-based mechanism and assessing its utility in mitigating the impact of these attack risks within the context of the intensive care units. Experimental results showed that this methodology is capable of greatly reducing the risk of model inversion while retaining model accuracy. Thus, health systems that employ this technique can be given more peace of mind that high-quality services can be delivered in such a way that privacy is preserved.
Generative Adversarial Networks for Robust Breast Cancer Prognosis Prediction with Limited Data Size

Accurate cancer patient prognosis stratification is essential for oncologists to recommend proper treatment plans. Deep learning models are capable of providing good prediction power for such stratification. The main challenge is that only a limited number of labeled patients are available for cancer prognosis. To overcome this, we proposed Wasserstein Generative Adversarial Network-based Deep Adversarial Data Augmentation (wDADA) that leverages generative adversarial networks to perform data augmentation and assist in model training. We used the proposed framework to train our model for predicting disease-specific survival (DSS) of breast cancer patients from the METABRIC dataset. We found that wDADA achieved 0.6726± 0.0278, 0.7538±0.0328, and 0.6507 ±0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy: 0.6889±0.0159; AUC: 0.7546± 0.0183; concordance index: 0.6542±0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.
Niblack Binarization on Document Images: Area Efficient, Low Cost, and Noise Tolerant Stochastic Architecture

Binarization plays a crucial role in Optical Character Recognition (OCR) ancillary domains, such as recovery of degraded document images. In Document Image Analysis (DIA), selecting threshold is not trivial since it differs from one problem (dataset) to another. Instead of trying several different thresholds for one dataset to another, we consider noise inherency of document images in our proposed binarization scheme. The proposed stochastic architecture implements the local thresholding technique: Niblack’s binarization algorithm. We introduce a stochastic comparator circuit that works on unipolar stochastic numbers. Unlike the conventional stochastic circuit, it is simple and easy to deploy. We implemented it on the Xilinx Virtex6 XC6VLX760-2FF1760 FPGA platform and received encouraging experimental results. The complete set of results are available upon request. Besides, compared to conventional designs, the proposed stochastic implementation is better in terms of time complexity as well as fault-tolerant capacity.
A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.
A Recommender System to Help Discovering Cohorts in Rare Diseases

Cohort studies have been playing a key role in helping our understanding of diseases, health conditions, and treatments. These cohorts are often composed of a small number of subjects, especially in rare diseases studies, which reduces the statistical power of the results. One solution that can strengthen the scientific findings is to combine distinct studies and perform then multi-cohort analysis. However, even studies conducted for the same purpose in distinct research groups can have different scopes and medical observations, which preclude across-cohort exploration. In this paper, we propose a recommendation system to automatically discover cohorts of interest. This methodology uses context-based retrieval techniques combined with collaborative filtering to find relevant cohorts and scientific literature about a specific clinical investigation. The system was validated in a community focused on the study of Alzheimer's diseases, which includes 62 cohorts.
Improving the Efficiency of Re-randomizable and Replayable CCA Secure Public Key Encryption

Public key encryption schemes that are simultaneously re-randomizable and replayable CCA (Rand-RCCA) secure offer a unique combination of malleability and non-malleability properties: ciphertexts can be re-randomized (and thus made unlinkable) while still retaining the important security guarantee that the message inside stays intact. In this paper we show a new public-key encryption scheme that is Rand-RCCA secure in the random oracle model. Our scheme is more efficient than the state-of-art Rand-RCCA PKE scheme of Faonio et al. (ASIACRYPT'19) but it achieves a weaker re-randomization property. On the other hand, our scheme achieves a strictly stronger re-randomization property than the PKE scheme of Phan and Pointcheval (ASIACRYPT'04).
Distributed Graph Diameter Approximation

We present an algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. In order to be efficient in terms of both time and space, our algorithm is based on a decomposition strategy which partitions the graph into disjoint clusters of bounded radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; most importantly, for a large family of graphs, it features a round complexity asymptotically smaller than the one exhibited by a natural approximation algorithm based on the state-of-the-art Δ-stepping SSSP algorithm, which is its only practical, linear-space competitor in the distributed setting. We complement our theoretical findings with a proof-of-concept experimental analysis on large benchmark graphs, which suggests that our algorithm may attain substantial improvements in terms of running time compared to the aforementioned competitor, while featuring, in practice, a similar approximation ratio.
Modularity in Biological Evolution and Evolutionary Computation

The basic principles of selectionism were transferred in a simplified form from population genetics to the field of evolutionary computation in order to solve applied problems of optimization and adaptation. Considerable practical experience has been gained for the almost half a century of development in this field of computer science, and interesting theoretical results have been obtained. One of the main properties of biological systems is modularity, which manifests itself at all levels of their organization, starting with molecular genetics and ending with entire organisms and their communities. In this survey, the phenomena and patterns associated with modularity in genetics and evolutionary computation are compared. The similarities and differences in the results from these areas of research are analyzed from the point of view of modularity, and the possibilities for sharing knowledge between them are discussed.
Revisiting paraphrase question generator using pairwise discriminator

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of obtaining word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder. This discriminator is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validate our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and provide competitive results on the paraphrase generation and sentiment analysis task on standard dataset. These results are also shown to be statistically significant.
Experimental Study on Generating Multi-modal Explanations of Black-box Classifiers in terms of Gray-box Classifiers

Artificial Intelligence (AI) is a first class citizen in the cities of the 21st century. In addition, trust, fairness, accountability, transparency and ethical issues are considered as hot topics regarding AI-based systems under the umbrella of Explainable AI (XAI). In this paper we have conducted an experimental study with 15 datasets to validate the feasibility of using a pool of gray-box classifiers (i.e., decision trees and fuzzy rule-based classifiers) to automatically explain a black-box classifier (i.e., Random Forest). Reported results validate our approach. They confirm the complementarity and diversity among the gray-box classifiers under study, which are able to provide users with plausible multi-modal explanations of the considered black-box classifier for all given datasets.