Reviewerly/exHarmony huggingface dataset:
Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment

Convolutional Neural Networks (CNNs), which are currently state-of-the-art for most image analysis tasks, are ill suited to leveraging the key benefits of ultrasound imaging - specifically, ultrasound's portability and real-time capabilities. CNNs have large memory footprints, which obstructs their implementation on mobile devices, and require numerous floating point operations, which results in slow CPU inference times. In this paper, we propose three approaches to training efficient CNNs that can operate in real-time on a CPU (catering to the clinical setting), with a low memory footprint, for minimal compromise in accuracy. We first demonstrate the power of 'thin' CNNs, with very few feature channels, for fast medical image segmentation. We then leverage separable convolutions to further speed up inference, reduce parameter count and facilitate mobile deployment. Lastly, we propose a novel knowledge distillation technique to boost the accuracy of light-weight models, while maintaining inference speed-up. For a negligible sacrifice in test set Dice performance on the challenging ultrasound analysis task of nerve segmentation, our final proposed model processes images at 30fps on a CPU, which is 9× faster than the standard U-Net, while requiring 420× less space in memory.
Blockchain Based Sensitive Attribute Storage and Access Monitoring in Banking System

In earlier banking systems, the data owners were unable to access or update their financial information. Nowadays, user financial information is stored in Cloud storage instead of traditional storage, but security risks are high in financial sectors. Sometimes, Cloud services providers and public auditors modify the user sensitive attribute values. Hence, the decentralized storage system is required for providing better security and integrity of user sensitive attribute values. Blockchain is a decentralized technology for providing tamper-proof storage for sensitive attribute values. Hence, the account holder information is segregated as sensitive and non-sensitive attributes. Before storing into off-chain mode of blocks, the sensitive attributes are grouped into ‘n' number of groups and encrypted by separate group key. The non-encrypted, non-sensitive attributes are stored in cloud storage. The access information is stored in the on-chain mode of the block for easy monitoring. Hence, the security and integrity of sensitive information are preserved.
A Deep Learning Approach for Optical Character Recognition of Handwritten Devanagari Script

Handwritten Character Recognition is one of the most challenging and demanding area of interest for researchers in domains of pattern recognition and image processing. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, we propose development of Convolutional Neural Network (CNN) based Optical Character Recognition system (OCR) for Handwritten Devanagari Script which is observed to recognize the characters accurately.
A Submodularity-based Clustering Algorithm for the Information Bottleneck and Privacy Funnel

For the relevant data S that nests in the observation X, the information bottleneck (IB) aims to encode X into X̂ in X, with order to maximize the extracted useful information I(S; X̂) with the minimum coding rate I(X; X). For the dual privacy funnel (PF) problem where S denotes the sensitive/private data, the goal is to minimize the privacy leakage I(S; X̂) while maintain a certain level of utility I(X; X̂). For both problems, we propose an efficient iterative agglomerative clustering algorithm based on the minimization of the difference of submodular functions (IAC-MDSF). It starts with the original alphabet X̂ := X and iteratively merges the elements in the current alphabet X̂ that optimizes the Lagrangian function I(S; X̂)-λI(X; X̂). We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of X̂ by the existing MDSF algorithms. By varying the value of the Lagrangian multiplier λ, we obtain the experimental results on a heart disease data set in terms of the Pareto frontier: I(S; X̂) vs. -I(X; X̂). We show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex.
VROOM: Virtual Robot Overlay for Online Meetings

Telepresence robots allow users to freely explore a remote space and provide a physical embodiment in that space. However, they lack a compelling representation of the remote user in the local space. We present VROOM (Virtual Robot Overlay for Online Meetings), a two-way system for exploring how to improve the social experience of robotic telepresence. For the local user, an augmented-reality (AR) interface shows a life-size avatar of the remote user overlaid on a telepresence robot. For the remote user, a head-mounted virtual-reality (VR) interface presents an immersive 360° view of the local space with mobile autonomy. The VR system tracks the remote user's head pose and hand movements, which are applied to an avatar. This provides the remote user with an identifiable self-embodiment and allows the local user to see the remote user's head direction and arm gestures.
Reactive Navigation Under Non-Parametric Uncertainty Through Hilbert Space Embedding of Probabilistic Velocity Obstacles

The probabilistic velocity obstacle (PVO) extends the concept of velocity obstacle (VO) to work in uncertain dynamic environments. In this paper, we show how a robust model predictive control (MPC) with PVO constraints under non-parametric uncertainty can be made computationally tractable. At the core of our formulation is a novel yet simple interpretation of our robust MPC as a problem of matching the distribution of PVO with a certain desired distribution. To this end, we propose two methods. Our first baseline method is based on approximating the distribution of PVO with a Gaussian Mixture Model (GMM) and subsequently performing distribution matching using Kullback Leibler (KL) divergence metric. Our second formulation is based on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to interpret our robust MPC as a problem of minimizing the distance between the desired distribution and the distribution of the PVO in the RKHS. Both the RKHS and GMM based formulation can work with any uncertainty distribution and thus allowing us to relax the prevalent Gaussian assumption in the existing works. We validate our formulation by taking an example of 2D navigation of quadrotors with a realistic noise model for perception and ego-motion uncertainty. In particular, we present a systematic comparison between the GMM and the RKHS approach and show that while both approaches can produce safe trajectories, the former is highly conservative and leads to poor tracking and control costs. Furthermore, RKHS based approach gives better computational times that are up to one order of magnitude lesser than the computation time of the GMM based approach.
Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space.Data objects in real-world applications are commonly described by both numeric and categorical attributes.In this research, considering that the partitional clustering algorithms designed for this type of mixed data are prone to get trapped into local optima and the cuckoo search approach is efficient in solving global optimization problems, we propose CCS-K-Prototypes, a novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes, for clustering mixed numeric and categorical data.To deal with different types of attributes, we develop a novel representation for candidate solutions, and suggest two formulas for the cuckoo to search for the potential solution around the existing solutions or in the entire attribute space.Finally, the performance of the proposed algorithm is assessed by a series of experiments on five benchmark datasets.
Text Classification Research Based on Bert Model and Bayesian Network

The Bert model is a pre-training model based on deep learning. It has refreshed the best performance of 11 NLP missions as soon as it appears, and it also has a wide range of applications. The text data of people's livelihood governance is huge, and there is a large amount of unstructured data, which makes the traditional text analysis and mining technology increase in the space-time complexity of computing; so it is very important to choose appropriate text classification technology. Here we propose to use the Bert model in combination with the Bayesian network to achieve one new classification of text. That is, the Bayesian network is used to perform the classification of two categories first, and we can get the approximate category range of each text, and then the Bert model is used to classify the text into specific categories. The combination of these two methods can greatly reduce the errors caused by the classification defects of using only one of the methods. Thereby achieving an improvement in the accuracy of text classification.
Tone-Mapping Using Perceptual-Quantizer and Image Histogram

A new tone-mapping algorithm is presented for visualization of high dynamic range (HDR) images on low dynamic range (LDR) displays. In the first step, the real-world pixel intensities of the HDR image are transformed to a perceptual domain using the perceptual-quantizer (PQ). This is followed by construction of the histogram of the luminance channel. Tone-mapping curve is generated from the cumulative histogram. It is known that histogram-based tone-mapping approaches can lead to excessive stretching of contrast in highly populated bins, whereas the pixels in sparse bins can suffer from excessive compression of contrast. We handle these issues by restricting the pixel counts in the histogram to remain below a defined limit, determined by a uniform distribution model. The proposed method is compared with state-of-the-art algorithms, using some well-known metrics that quantify the quality of tone-mapped images, and is found to have the best performance.
Outlier Detection in Sensor Data Using Machine Learning Techniques for IoT Framework and Wireless Sensor Networks: A Brief Study

Outlier or anomaly detection in the sensed data for Internet of Things framework and Wireless Sensor Networks is a growing trend among researchers. Wireless Sensor Networks form the basis for Internet of Things framework in which the sensors sense a huge amount of data based on which certain actions or decisions or taken. So, the quality of data must be thoroughly checked as any kind of outlier may degrade the quality of the data and hence affect the final decision. Thus, it becomes imperative to maintain the quality of the data. In this work, some machine learning approaches have been discussed which have proved their mettle in outlier detection.
Weighted consensus clustering and its application to Big data

The aim of this study is the development of a weighted consensus clustering that assigns weights to single clustering methods using the purity utility function. In the case of Big data that does not contain labels, the utility function based on the Davies-Bouldin index is proposed in this paper. The Banknote authentication, Phishing, Diabetic, Magic04, Credit card clients, Covertype, Phone accelerometer, and NSL-KDD datasets are used to assess the efficiency of the proposed consensus approach. The proposed approach is evaluated using the Euclidean, Minkowski, squared Euclidean, cosine, and Chebychev distance metrics. It is compared with single clustering algorithms (DBSCAN, OPTICS, CLARANS, k-means, and shared nearby neighbor clustering). The experimental results show the effectiveness of the proposed approach to the Big data clustering in comparison to single clustering methods. The proposed weighted consensus clustering using the squared Euclidean distance metric achieves the highest accuracy, which is a very promising result for Big data clustering. It can be applied to expert systems to help experts make group decisions based on several alternatives. The paper also provides directions for future research on consensus clustering in this area.
Di‐phase midway convolution and deconvolution network for brain tumor segmentation in MRI images

Abstract A novel automatic image segmentation technique in magnetic resonance imaging (MRI) based on di‐phase midway convolution and deconvolution network is proposed. It consists of three convolutional and deconvolutional blocks for downsampling and upsampling layers respectively. In first block, each input slice is separately convolved using two paths with 3 × 3 and 7 × 7 kernels to produce different feature maps. Then the mean value of these feature maps is processed into upcoming blocks in downsampling and upsampling layers. This processed outcome is classified and segmented using softmax classification. Further, the volume, probability density distribution of tumor, and normal tissue regions are calculated using tissue‐type mapping technique. This method is extensively tested with BRATS 2012, BRATS 2013, and BRATS 2018 data sets. Our experimental results achieved higher dice similarity coefficient values of 24.3%, 27.5%, and 3.4%, respectively, for these three data sets when compared to the state‐of‐art brain tumor segmentation methods.
On the Capacity of Private Information Retrieval from Coded, Colluding, and Adversarial Servers

In this work, we first prove the capacity of coded, linear symmetric private information retrieval (SPIR) in the presence of colluding, adversarial, and nonresponsive servers, giving a positive closure to the conjecture stated by Tajeddine et al. It is also shown that, further restricting to strongly-linear PIR schemes with linear interference cancellation, the so-called star product scheme proposed by Freij-Hollanti et al. is optimal. This observation enables to prove the capacity of strongly-linear (non-symmetric) PIR schemes for any number of files. Further, it also provides a positive proof in this practical special case for the conjectures stated in the asymptotic regime by Freij-Hollanti et al. and Tajeddine et al.
PHOTONAI—A Python API for rapid machine learning model development

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.
Robust Dual-Color Watermarking Based on Quaternion Singular Value Decomposition

This paper proposes a robust dual-color watermarking based on quaternion singular value decomposition (QSVD), which can embed large payloads into color images with low distortion, and can obtain strong robustness to process color image in a holistic manner.First, two notes are proposed for designing the proposed watermarking scheme, one of which is about three strong correlations found in U that can be used for watermark embedding, and the other is analyzing the feasibility of V compensation for QSVD-based watermarking scheme.In addition, a fast structure-preserving algorithm is used to calculate the singular value decomposition (SVD) of a quaternion matrix, which makes the procedure computationally more flexible and efficient.Then a new watermarking scheme is proposed to protect the copyright of color images.This scheme uses quaternion to make the color image channels correlated so that the proposed watermarking scheme has strong anti-attack performance.Experimental results show that the proposed dualcolor watermarking is not only imperceptible but also robust to some common attacks, and the performance of the proposed method outperforms other methods considered in this work.
Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts

State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than other words and may be beneficial for identifying semantic relations. Other approaches using fixed text triggers capture such information but ignore the lexical diversity. To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. Firstly, we extract syntactic indicators under the guidance of syntactic knowledge. Then we construct a neural network to incorporate both syntactic indicators and the entire sentences into better relation representations. By this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms the state-of-the-art methods.
Altitude and Position Control of Parrot Mambo Minidrone with PID and Fuzzy PID Controllers

Minidrones are widely used as they are not only fast quadcopters with high manoeuvrability but also excellent research platforms for indoor usage. In this study, we provide the design and deployment of the PI-PD and Fuzzy PI-PD (FPI-PD) structures to solve position control problem of the Parrot Mambo Minidrone. In this context, we derive nonlinear mathematical model of Parrot Mambo Minidrone to obtain the control models of the minidrone. Then, via the control models, firstly PI-PD control systems are designed for the altitude and position control systems. Then, to handle the coupled nonlinearities, FPI-PD controllers are designed and employed in the position control loop of minidrone. The presented comparative real-world experiment results clearly show that the proposed control systems outperform the built-in ones. Moreover, the results show that the performance of FPI-PD is better than the built-in PD and designed PI-PD control systems.
A Survey on Chat-Bot system for Agriculture Domain

Chat-Bot system is a part of natural language processing, where it requires system to be trained as per the human language, so that it can satisfy the needs of the user. Agriculture domain is leading source of countries growth. At present farmers are not much aware about recent technologies and practices being used in agriculture field. Extraction of meaningful answer by machine learning techniques is a problem, that has been studied by many machine learning experts as well as advanced machine learning techniques are introduced. These techniques are applied to extract the accurate answer. We can call this as an Agriculture Question Answering System, where the farmer can query the system and the system understands the query and responds to a given query. In this paper, we have reviewed extracting a precise answer for a given question by mainly focusing on machine learning techniques. We have made suggestions and provided the comparative analysis.
How Do Developers Act on Static Analysis Alerts? An Empirical Study of Coverity Usage

Static analysis tools (SATs) often fall short of developer satisfaction despite their many benefits. An understanding of how developers in the real-world act on the alerts detected by SATs can help improve the utility of these tools and determine future research directions. The goal of this paper is to aid researchers and tool makers in improving the utility of static analysis tools through an empirical study of developer action on the alerts detected by Coverity, a state-of-the-art static analysis tool. In this paper, we analyze five open source projects as case studies (Linux, Firefox, Samba, Kodi, and Ovirt-engine) that have been actively using Coverity over a period of at least five years. We investigate the alert occurrences and developer triage of the alerts from the Coverity database; identify the alerts that were fixed through code changes (i.e. actionable) by mining the commit history of the projects; analyze the time an alert remain in the code base (i.e. lifespan) and the complexity of code changes (i.e. fix complexity) in fixing the alert. We find that 27.4% to 49.5% (median: 36.7%) of the alerts are actionable across projects, a rate higher than previously reported. We also find that the fixes of Coverity alerts are generally low in complexity (2 to 7 lines of code changes in the affected file, median: 4). However, developers still take from 36 to 245 days (median: 96) to fix these alerts. Finally, our data suggest that severity and fix complexity may correlate with an alert's lifespan in some of the projects.
Impact of Cyberattacks by Malicious Hackers on the Competition in Software Markets

The number of malicious hacking incidents in our increasingly IT-enabled world has been increasing over the years. Conventional wisdom focuses on negative impacts of these malicious hacker activities. We posit that malicious hacker activities also might lead to some unintended consequences, specifically related to altering of software market structure, and associated stakeholder consequences. In this study, we model the competition between two software platforms in the presence of malicious hackers who perform cyberattacks against one or both software platforms. We compare a benchmark case where malicious hackers are either absent, or if present do not target the software platforms, against a first scenario where only one software platform is targeted, and a second scenario where both software platforms are targeted. Interestingly, we find the presence of malicious hackers’ activities is not always detrimental to all software industry stakeholders. In general, the results suggest that the presence of malicious hackers is more likely to result in a competitive market, while their absence is more likely to result in a monopoly. Furthermore, we show that under certain market conditions, the unsecure software platform targeted by hackers potentially can drive its more secure competitor out of the market.