Reviewerly/exHarmony huggingface dataset:
Multilevel Image Segmentation Using Modified Red Deer Algorithm

In this paper, an evolution strategy based, Modified Red Deer Algorithm (MRDA) has been proposed to find optimum thresholds of publicly available gray scale images. In the recent year, the activity of red deers has been thoroughly observed in the course of their breading season by a group of researchers. Later, this inspired them to introduce a renowned meta-heuristic, popularly known as Red Deer Algorithm (RDA). The RDA is capable to handle several combinatorial optimization problems of various real-world applications. In this paper, a number of adaptive strategy has been suggested to alter the intrinsic operators and parameters used in RDA to improve its performance. The efficiency of MRDA has been judged with RDA and classical Particle Swarm Optimization (PSO) using two publicly accessible real world benchmark images. The performance of each of the competitive method has been analysed with regards to a variety of measure quantitatively and visually. Finally, as a statistical comparison, Kruskal-Wallis test has been carried out between the proposed method and others. The obtained results prove that MRDA is the best performing method than others in all aspects and provides immensely competitive results.
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation

Current scene segmentation methods suffer from cumbersome model structures and high computational complexity, impeding their applications to real-world scenarios that require real-time processing. This paper proposes a novel Efficient Pyramid Representation Network (EPRNet), which strikes an innovative record on segmentation accuracy, model lightness and inference efficiency. Unlike existing methods delivering transfer learning based on pixel features of limited receptive fields encoded by shallow image classification backbones, EPRNet distributes multi-scale representations throughout the feature encoding flow to quickly enlarge and enrich receptive fields. Specifically, we introduce an extremely lightweight and efficient Multi-scale Processing Unit (MPU) that encodes multi-scale features through parallel convolutions of different kernels. By combining MPU and residual learning, we propose a core Pyramid Representation Module (PRM) to correctly acquire and aggregate region-based contexts in both shallow and deep layers. In this way, EPRNet can encode discriminative and comprehensive representations of multi-scale objects with a compact structure. We conduct extensive experiments on Cityscapes and CamVid datasets, demonstrating the superiority. Without any extra and coarse labeled data, EPRNet obtains mIoU 73.9% on the Cityscapes test set with only 0.9 million parameters at a speed of 42 FPS.
Ant colony optimization–evolutionary hybrid optimization with translation of problem representation

Abstract Different hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm—a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (namely, the Ant Colony Optimization and Evolutionary Algorithm) is presented. However, because of the intrinsic differences between the two algorithms (a vector of labels and a pheromone table when solving the traveling salesman problem, for example), several dedicated algorithms for translating the solutions between these two representations of the problem are proposed. The hybrid algorithm constructed with the application of the translation methods turns out to be significantly better in solving the TSP compared to non‐hybrid versions (relevant experimental results are presented and discussed). This paves the way for new possibilities of constructing hybrid metaheuristics by putting together completely different ones (using different representations); the impact of the presented research is aimed far beyond the hybridization of only ant colony optimization and evolutionary algorithm.
Interaction Techniques for Chord Diagrams

Chord diagrams are a popular high-dimensional method for showing connections between nodes, however they have scalability limitations and lack advanced methods for interaction and multivariate links. In this paper we introduce a novel interaction and layout method for improving the scalability of chord diagrams, explore sketch-based methods for showing multiple links and direction, and introduce a sketch-based brushing technique for filtering. We demonstrate the interaction techniques on a real-world call-center dataset and report feedback from domain experts in the call center industry.
Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.
A flexible framework for anomaly Detection via dimensionality reduction

Anomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.
Enabling Secure and Space-Efficient Metadata Management in Encrypted Deduplication

Encrypted deduplication combines encryption and deduplication in a seamless way to provide confidentiality guarantees for the physical data in deduplicated storage, yet it incurs substantial metadata storage overhead due to the additional storage of keys. We present a new encrypted deduplication storage system called <inline-formula><tex-math notation="LaTeX">${\sf Metadedup}$</tex-math></inline-formula> , which suppresses metadata storage by also applying deduplication to metadata. Its idea builds on indirection, which adds another level of metadata chunks that record metadata information. We find that metadata chunks are highly redundant in real-world workloads and hence can be effectively deduplicated. We further extend <inline-formula><tex-math notation="LaTeX">${\sf Metadedup}$</tex-math></inline-formula> to incorporate multiple servers via a distributed key management approach, so as to provide both fault-tolerant storage and security guarantees. We extensively evaluate <inline-formula><tex-math notation="LaTeX">${\sf Metadedup}$</tex-math></inline-formula> from performance and storage efficiency perspectives. We show that <inline-formula><tex-math notation="LaTeX">${\sf Metadedup}$</tex-math></inline-formula> achieves high throughput in writing and restoring files, and saves the metadata storage by up to 93.94 percent for real-world backup workloads.
Common-covariance based person re-identification model

This paper develops a new covariance-matrix based person re-identification model. It takes each person as a Gaussian distribution with a common covariance matrix. The learning process is to find an optimal covariance matrix among all the distributions. This paper has two major contributions: (1) It proves the statistical meaning of Mahalanobis matrix as a common covariance matrix among all related classes; (2) The intra-class distance is measured using the determinant of the common covariance matrix. The proposed model largely reduces the computational cost. Moreover, it automatically avoids imbalanced computations between the distances for inter-class images and the distances for intra-class images. Experimental results demonstrate that the proposed model is more efficient than many state-of-the-art methods in both accuracy and time cost.
Demographic profile building for cold start in recommender system: A social media fusion approach

New user recommendation is a big challenge for social networks site like reddit, where user profile is not so much rich. The aim of this paper is to enhance recommendation process that reddit can used for its users. We know that twitter and Facebook generate rich user information for social media behavior analysis and research. reddit recommendation system usually implement generic recommendation for its users in order to recommend subreddit, other side of coin, Like LinkedIn, amazon have very specialized user and item recommendation system based on user detail and create effective user profile for future reference. In this work fusion twitter and Facebook dataset user profile building by using tweets and post posted on Facebook by authentic unique user, and after that we extract user profile feature and parallelly perform subreddit listing for specific user feature generated by profile building. As per the reddit rank it is the 5th most visited site of USA and in world it holds 13th rank of most visited site. Twitter has 330 million active users with rich user profile like user demography. In this paper we analyze user timeline and identify user interest using machine learning and extract keywords using TF-IDF. Given the output with this machine learning used as input for a web based reddit subreddit listing application.
Rainfall Prediction Due to El Nino Factors Using Recurrent Neural Networks

Abstract El Nino is one of the natural phenomena that have a significant influence on the weather, causing a longer dry season in several regions of Indonesia, one of which is the city of Lampung. One way to anticipate a long drought is to predict rainfall, by looking at the intensity of the rain. This paper proposes rainfall prediction using a recurrent neural network. Weather variables used to predict rainfall include air humidity, wind speed obtained from BMKG stations, and SOI index obtained from the NCDC website in the past 10 years. Weather data will be interpolated and extracted to find the maximum weather value per 4 weeks, the next step is overlapping, after which the data segmentation and normalization become 0-1 to make the data values not far adrift. The results showed the prediction of rainfall with a vulnerable 4 weeks using the Recurrent Neural Networks method produces an accuracy of 89.53%.
Single image super-resolution via hybrid resolution NSST prediction

Convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SR). However, most previous methods predict high-resolution (HR) images in the spatial domain, producing over-smoothed outputs while losing texture details. To address this problem, in this paper we propose to predict nonsubsampled shearlet transform (NSST) coefficients, which better represent the global topology information and local texture details of HR images. On the other hand, we propose a deep hybrid resolution network by a residual-in-residual style, which aggregates features of multiple resolutions so as to gather rich context information in compact representations. When evaluated on a newly released RealSR dataset and traditional simulated datasets, our method, namely hybrid resolution NSST prediction (HRNP), achieves more appealing results, w.r.t. PSNR and SSIM, than the state-of-the-art methods. Moreover, we find our HRNP is more capable of preserving complex edges and curves than other methods.
Using Fuzzy Probabilistic Implication in Z-set Based Inference

In our daily life the reasoning is characterized by imperfect information. This information is expressing by using fuzzy and probabilistic information. The main advantage of fuzzy logic is possibility of application in solving complex, nonlinear problems which described by words based on human perception. According to these in scientific literature there are fuzzy reasoning approach and probabilistic approximate reasoning approach, which can not operate imperfect information. This paper deals Z-set based inference using probabilistic implication. Numerical example with Z-set-based inference for approximate reasoning is considered.
Inexact Attributed Subgraph Matching

We present an approach for inexact subgraph matching on attributed graphs optimizing the graph edit distance. By combining lower bounds on the cost of individual assignments, we obtain a heuristic for a backtracking tree search to identify optimal solutions. We evaluate our algorithm on a knowledge graph dataset derived from real-world data, and analyze the space of optimal solutions.
Anomaly Detection in Edge Nodes using Sparsity Profile

Edge devices with attentive sensors enable various intelligent services by exploring streams of sensor data. However, anomalies, which are inevitable due to faults or failures in the sensor and network, can result in incorrect or unwanted operational decisions. While promptly ensuring the accuracy of IoT data is critical, lack of labels for live sensor data and limited storage resources necessitates efficient and reliable detection of anomalies at edge nodes. Motivated by the existence of unique sparsity profiles that express original signals as a combination of a few coefficients between normal and abnormal sensing periods, we propose a novel anomaly detection approach, called ADSP (Anomaly Detection with Sparsity Profile). The key idea is to apply a transformation on the raw data, identify top-K dominant components that represent normal data behaviors, and detect data anomalies based on the disparity from K values approximating the periods of normal data in an unsupervised manner. Our evaluation using a set of synthetic datasets demonstrates that ADSP can achieve 92%-100% of detection accuracy. To validate our anomaly detection approach on real-world cases, we label potential anomalies using a range of error boundary conditions using sensors exhibiting a straight line in Q-Q plot and strong Pearson correlation and conduct a controlled comparison of the detection accuracy. Our experimental evaluation using real-world datasets demonstrates that ADSP can detect 83%- 92% of anomalies using only 1.7% of the original data, which is comparable to the accuracy achieved by using the entire datasets.
Content Moderation Remedies

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Adversarial Reconstruction Loss for Domain Generalization

The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model's dependency on the training data by leveraging many related but different data sources. We present a new method for DG that is both efficient and fast, unlike the most current state of art methods, which add a substantial computational burden making it hard to fine-tune. Our model trains an autoencoder setting on top of the classifier, but the encoder is trained on the adversarial reconstruction loss forcing it to forget style information while extracting features useful for classification. Our approach aims to force the encoder to generate domain-invariant representations that are still category informative by pushing it in both directions. Our method has proven universal and was validated on four different benchmarks for domain generalization, outperforming state of the art on RMNIST, VLCS and IXMAS with a 0.70% increase in accuracy and providing comparable results on PACS with a 0.02% difference. Our method was also evaluated for unsupervised domain adaptation and has shown to be quite an effective method against over-fitting.
Augment reality chatbot using cloud

A chat is a program that simulates the conversation with humans through text or voice commands. Now days, every company or organization is trying to reduce the manpower in many ways to gain some more profits and to increase the efficiency of the output. Chabot is one of the way to reduce the manpower and to reduce the human intervention. In early days websites and organizations use people to guide their clients and users to their respective outcome. But now days to reduce the manpower organizations are using the chatbots to interact their clients. In our paper, we want to create a chatbot using Augmented Reality and using cloud technologies. Our theme is to create a chatbot which interact with humans like there is another human talking to him and to increase interaction. This makes the organization to produce more accurate results for their clients and uses less human power.
Private cloud security architecture

Cyber security is a current topic of the cyberspace, which includes Cloud Computing (CC) systems. Current use of CC and its expected development require strong security measures. The reason is the fact that data with sensitive information from several users enters the CC system and the disruption of the system or the leakage of such information is unacceptable. As how in building architecture, it is necessary to dimension the foundations, retaining walls and other parts of the building so that as a whole they form a fixed structure, in the architecture of ICT systems we must look at all components of each partial system too. Security architecture is one viewpoint of development in CC systems. Each deployment model and each CC service model in a particular implementation environment requires its own security framework. ICT standardization organizations have issued several recommendations and standards that provide principles for followed in the specific implementation of CC. The authors of the article used them in processing the Architecture description of the OpenStack private cloud in a university environment.
A 64.1mW Accurate Real-Time Visual Object Tracking Processor With Spatial Early Stopping on Siamese Network

A low power real-time visual object tracking (VOT) processor using the siamese network (SiamNet) is proposed for mobile devices. Two key features enable a real-time VOT with low power consumption on mobile devices. First, correlation-based spatial early stopping (CSES) is proposed to reduce the computational workload. CSES reduces ~56.8% of the overall computation of the SiamNet by gradually eliminating the background. Second, the dual mode reuse core (DMRC) is proposed for supporting both the convolution layer and the cross-correlation layer with high core utilization. Finally, the proposed VOT processor is implemented in 28 nm CMOS technology and occupies 0.42 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The proposed processor achieves 0.587 for the success rate and 0.778 for the precision in the OTB-100 dataset with SiamRPN++-AlexNet. Compared to previous VOT processors, the proposed processor shows state-of-the-art performance while showing lower power consumption. The proposed processor achieves 64.1 mW peak power and 58.2 mW tracking power consumption at 32.1 frame-per-second (fps) real-time VOT on mobile devices.
Feature Ranking and Aggregation for Bug Triaging in Open-Source Issue Tracking Systems

The increasing complexity and team-based projects have lead to the rise of various project management tools and techniques. One of the important components of open- source project management is the usage of bug tracking systems. In the last few decades, software projects have experienced an inescapable appearance of bug reports. One of the main challenges in handling these incoming bugs is triaging of bug reports. Bug triaging can be considered as a mechanism for the election of a suitable software developer for a reported bug who will work towards resolving bug in a timely fashion. There exist several semi and fully automated bug triaging techniques in the existing literature. These techniques often consider varied bug parameters for prominent developer selection. Past researchers have concluded different parameters to be possessing prime importance in the optimal developer selection task. However, a common ranking scale depicting the importance among different bug parameters for bug triaging is not available. This paper presents a methodology to rank the non-textual bug parameters using feature ranking and aggregation techniques. The presented methodology has been evaluated on four open-source systems, namely, Mozilla Firefox, Eclipse, GNome and Open Office. From the experimental evaluation, it has been observed that the ranking of bug parameters is consistent among the different open-source projects of Bugzilla repository.