Reviewerly/exHarmony huggingface dataset:
Multi-strategy ensemble binary hunger games search for feature selection

Feature selection is a crucial preprocessing step in the sphere of machine learning and data mining, devoted to reducing the data dimensionality to improve the performance of learning models. In this paper, a vigorous metaheuristic named hunger games search (HGS) is integrated with a multi-strategy (MS) framework, including chaos theory, greedy selection, and vertical crossover, to boost search equilibrium between explorative and exploitative cores. The new MS-HGS algorithm is developed for global optimization, and its binary variant MS-bHGS is applied to the feature selection problem particularly. To evaluate and validate the performance of our proposed approach, on the one hand, MS-HGS is compared with HGS alongside single strategy embedded HGS on 23 benchmark functions and compared with seven state-of-the-art algorithms on IEEE CEC 2017 test suite. On the other hand, MS-bHGS is employed for feature selection in 20 datasets from the UCI repository and compared with three groups of methods, i.e., traditional, recent, and enhanced, respectively. The relevant experimental results confirm that MS-bHGS exceeds bHGS and most existing techniques in terms of classification accuracy, the number of selected features, fitness values, and execution time. Overall, this paper’s findings suggest that MS-HGS is a superior optimizer, and MS-bHGS can be considered a valuable wrapper-mode feature selection technique.
Causal Representation Learning for Out-of-Distribution Recommendation

Modern recommender systems learn user representations from historical interactions, which suffer from the problem of user feature shifts, such as an income increase. Historical interactions will inject out-of-date information into the representation in conflict with the latest user feature, leading to improper recommendations. In this work, we consider the Out-Of-Distribution (OOD) recommendation problem in an OOD environment with user feature shifts. To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation.
FOX: a FOX-inspired optimization algorithm

This paper proposes a novel nature-inspired optimization algorithm called the Fox optimizer (FOX) which mimics the foraging behavior of foxes in nature when hunting preys. The algorithm is based on techniques for measuring the distance between the fox and its prey to execute an efficient jump. After presenting the mathematical models and the algorithm of FOX, five classical benchmark functions and CEC2019 benchmark test functions are used to evaluate it’s performance. The FOX algorithm is also compared against the Dragonfly optimization Algorithm (DA), Particle Swarm Optimization (PSO), Fitness Dependent Optimizer (FDO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Chimp Optimization Algorithm (ChOA), Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA). The results indicate that FOX outperforms the above-mentioned algorithms. Subsequently, the Wilcoxon rank-sum test is used to ensure that FOX is better than the comparative algorithms in statistically significant manner. Additionally, parameter sensitivity analysis is conducted to show different exploratory and exploitative behaviors in FOX. The paper also employs FOX to solve engineering problems, such as pressure vessel design, and it is also used to solve electrical power generation: economic load dispatch problems. The FOX has achieved better results in terms of optimizing the problems against GWO, PSO, WOA, and FDO.
Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection

Feature selection (FS) plays a crucial role as a pre-processing tool in data mining, especially for real-world applications in medical fields; it has been utilized exponentially and becomes very interesting to detect beneficial medical decisions. FS is deemed to be an NP-hard optimization problem that seeks to reduce feature size while maximizing the generalization of machine learning (ML) models. Chameleon swarm algorithm (CSA) is a recent devised metaheuristic algorithm inspired by chameleons’ intelligent behavior in nature. Because of its simplicity and ease of implementation, it has piqued the interest of numerous researchers to apply it in different fields. However, it suffers from premature convergence, the inadequate balance between exploration and exploitation, and being trapped in local optima. Accordingly, this paper develops an improved version of the CSA for FS, referred to as modified chameleon swarm algorithm (mCSA). This improvement was achieved by introducing three modifications to the original CSA to improve how well it performed. Firstly, we propose a non-linear transfer operator to achieve a better balance between exploration and exploitation. Secondly, we introduce a randomization Lévy flight control parameter to avoid stagnation and early convergence. Thirdly, we boost the global search strategy of the original CSA by the consumption operator of the Artificial ecosystem-based optimization (AEO) algorithm. The performance of the proposed mCSA method is assessed by employing the CEC2020 test suite for numerical optimization and fourteen UCI datasets, including real-world application (breast cancer diagnosis) for feature selection, and compared with several well-known algorithms and sophisticated approaches. The experimental results indicate that the proposed mCSA performs significantly better and outperforms existing comparative methods.
RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the information communication of intermediate layers of blocks is ignored. To address this issue, in this brief, we propose to introduce a regulator module as a memory mechanism to extract complementary features of the intermediate layers, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional recurrent neural networks (RNNs) [e.g., convolutional long short-term memories (LSTMs) or convolutional gated recurrent units (GRUs)], which are shown to be good at extracting spatio-temporal information. We named the new regulated network as regulated residual network (RegNet). The regulator module can be easily implemented and appended to any ResNet architecture. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, squeeze-and-excitation ResNet, and other state-of-the-art architectures.
Recent advances on loss functions in deep learning for computer vision

The loss function, also known as cost function, is used for training a neural network or other machine learning models. Over the past decade, researchers have designed many loss functions for machine learning, such as mean squared error and mean absolute error. However, in deep learning, neurons of the last layer are usually activated by a sigmoid or softmax function. Thus, training with traditional losses would cause lower efficiency and accuracy. Recently, designing loss functions for deep learning methods has become one of the most challenging problems. This paper provides a comprehensive review of the recent progress and frontiers about loss functions in deep learning, especially for computer vision tasks. Specifically, we discuss the loss functions in three main computer vision tasks, i.e., object detection, face recognition, and image segmentation. Scholars have proposed several novel loss functions to cope with the specific problems such as imbalanced data, uncertain distribution of the predicted bounding boxes, varied overlapped mode between two bounding boxes and over-fitting. The survey details the source, derivation, and properties of each loss function. Furthermore, we also provide some advanced challenges about robust losses, generative adversarial networks, noise-tolerant losses, and semantic data augmentation. Finally, we deliver a summary and some promising future research directions.
Investigating Pretrained Language Models for Graph-to-Text Generation

Graph-to-text generation aims to generate fluent texts from graph-based data.In this paper, we investigate two recent pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation.We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs.We show that approaches based on PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.We report new state-of-the-art BLEU scores of 49.72 on AMR-LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets -a relative improvement of 31.8%,4.5%, and 42.4%, respectively, with our models generating significantly more fluent texts than human references.In an extensive analysis, we identify possible reasons for the PLMs' success on graph-totext tasks.Our findings suggest that the PLMs benefit from similar facts seen during pretraining or fine-tuning, such that they perform well even when the input graph is reduced to a simple bag of node and edge labels. 1
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency

Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation remains a critical challenge for cross-domain deployment. In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world. To address this challenge, we study a more realistic setting, unsupervised 3D domain adaptive detection, which only utilizes source domain annotations. 1) We first comprehensively investigate the major underlying factors of the domain gap in 3D detection. Our key insight is that geometric mismatch is the key factor of domain shift. 2) Then, we propose a novel and unified framework, Multi-Level Consistency Network (MLC-Net), which employs a teacher-student paradigm to generate adaptive and reliable pseudo-targets. MLC-Net exploits point-, instance- and neural statistics-level consistency to facilitate cross-domain transfer. Extensive experiments demonstrate that MLC-Net out-performs existing state-of-the-art methods (including those using additional target domain information) on standard benchmarks. Notably, our approach is detector-agnostic, which achieves consistent gains on both single- and two-stage 3D detectors. Code will be released.
Application-Aware Big Data Deduplication in Cloud Environment

Deduplication has become a widely deployed technology in cloud data centers to improve IT resources efficiency. However, traditional techniques face a great challenge in big data deduplication to strike a sensible tradeoff between the conflicting goals of scalable deduplication throughput and high duplicate elimination ratio. We propose AppDedupe, an application-aware scalable inline distributed deduplication framework in cloud environment, to meet this challenge by exploiting application awareness, data similarity and locality to optimize distributed deduplication with inter-node two-tiered data routing and intra-node application-aware deduplication. It first dispenses application data at file level with an application-aware routing to keep application locality, then assigns similar application data to the same storage node at the super-chunk granularity using a handprinting-based stateful data routing scheme to maintain high global deduplication efficiency, meanwhile balances the workload across nodes. AppDedupe builds application-aware similarity indices with super-chunk handprints to speedup the intra-node deduplication process with high efficiency. Our experimental evaluation of AppDedupe against state-of-the-art, driven by real-world datasets, demonstrates that AppDedupe achieves the highest global deduplication efficiency with a higher global deduplication effectiveness than the high-overhead and poorly scalable traditional scheme, but at an overhead only slightly higher than that of the scalable but low duplicate-elimination-ratio approaches.
KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.
A Framework for Expansion Planning of Data Centers in Electricity and Data Networks Under Uncertainty

This paper presents the expansion planning for data centers and data routes in the data and electricity networks considering the uncertainties in the planning horizon to ensure an acceptable rate of service to the requests received from the end-users in the data network. The objective is to determine the location and capacity of the data centers as well as the required data routes while considering the imposed constraints in the electricity and data networks. The installation cost of data centers and data routes, as well as the expected operation cost of the data centers, are minimized. The proposed problem addressed the uncertainties in the expansion planning of the electricity networks including the availability of renewable generation resources, the variations in electricity demand, the availability of generation and transmission components in the electricity network, and the uncertainties in the number of requests received by the user groups in the data network. The problem is formulated as a mixed integer linear programming problem and Bender decomposition and electricity price signals are used to capture the interaction among the data and electricity networks. The presented case study shows the effectiveness of the proposed approach.
Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection

A deep convolutional neural network (CNN) becomes a widely used tool for object detection. Many previous works have achieved excellent performance on object detection benchmarks. However, these works present generic detectors whose performance will drop rapidly when they are applied to a surveillance scene. In this paper, we propose an efficient method to construct a scene-specific regression model based on a generic CNN-based classifier. Our regression model is an adaptive deep CNN (ADCNN), which can predict object locations in the surveillance scene. First, we transfer the generic CNN-based classifier to the surveillance scene by selecting useful kernels. Second, we learn the context information of the surveillance scene in our regression model for accurate location prediction. Our main contributions are: 1) a transfer learning method that selects useful kernels in the generic CNN-based classifier; 2) a special architecture that can effectively learn the local and global context information in the surveillance scene; and 3) a new objective function to effectively train parameters in ADCNN. Compared with some state-of-the-art models, ADCNN achieves the best performance on three surveillance data sets for pedestrian detection and one surveillance data set for vehicle detection.
Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition

Face image variations such as expression and pose radically increase the intra-class variations which affect the performance of feature extraction methods. It is desirable to extract more robust local discriminative features to effectively represent such face variations. This paper proposes a novel facial feature extraction method which utilizes interpolation-based directional wavelet transform (DIWT) and local binary patterns (LBP). An efficient direction assessment method based on quadtree partitioning is implemented to facilitate adaptive direction selection in the local regions from the face images to obtain DIWT sub-bands. LBP histogram features are extracted from selected top-level DIWT sub-bands to obtain local descriptive feature set. Experimental results are simulated on ORL database, GT database, and FEI database. A comparison with various contemporary methods which include holistic, local descriptors and LBP-based non-adaptive multiresolution analysis methods illustrate the efficacy of the proposed method.
From social networks to emergency operation centers: A semantic visualization approach

Social networks are commonly used by citizens as a communication channel for sharing their messages about a crisis situation and by emergency operation centers as a source of information for improving their situation awareness. However, to utilize this source of information, emergency operators and decision makers have to deal with large and unstructured data, the content, reliability, quality, and relevance of which may vary greatly. In this paper, to address this challenge, we propose a visual analytics solution that filters and visualizes relevant information extracted from Twitter. The tool offers multiple visualizations to provide emergency operators with different points of view for exploring the data in order to gain a better understanding of the situation and take informed courses of action. We analyzed the scope of the problem through an exploratory study in which 20 practitioners answered questions about the integration of social networks in the emergency management process. This study inspired the design of a visualization tool, which was evaluated in a controlled experiment to assess its effectiveness for exploring spatial and temporal data. During the experiment, we asked 12 participants to perform 5 tasks related to data exploration and fill a questionnaire about their experience using the tool. One of the most interesting results obtained from the evaluation concerns the effectiveness of combining several visualization techniques to support different strategies for solving a problem and making decisions.
Gaussian Sum Shifted Rayleigh Filter for Underwater Bearings-Only Target Tracking Problems

In this paper, a new Gaussian sum (GS) filter with shifted Rayleigh filter (SRF) as proposal has been developed for the bearings-only tracking (BOT) problem. The proposed filter estimates the prior and posterior probability density functions as weighted sum of several Gaussian densities, where the individual Gaussian densities are generated from the SRF. Performance of the proposed filter on the BOT problem is compared with the SRF, the GS sparse-grid Gauss-Hermite filter, and other available GS and quadrature-based filters. Performance of filters is compared in terms of a root-mean-square error (RMSE), track divergence, and computational time. The effect of initial uncertainty, measurement noise covariance, and sampling time on filtering accuracy are also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer-Rao lower bound. From simulation results, it is observed that the performance of the proposed GS-SRF is superior to all other nonlinear filters considered.
Performance Measures for Binary Classification

This article is an introduction to some of the most commonly used performance measures for the evaluation of binary classifiers. These measures are categorized into three broad families: measures based on a single classification threshold, measures based on a probabilistic interpretation of error, and ranking measures. Graphical methods, such as ROC curves, precision-recall curves, TPR-FPR plots, gain charts, and lift charts, are also discussed. Using a simple example, we illustrate how to calculate the various performance measures and show how they are related. The article also explains how to assess the statistical significance of an obtained performance value, how to calculate approximate and exact parametric confidence intervals, and how to derive percentile bootstrap confidence intervals for a performance measure.
TaintHLS: High-Level Synthesis for Dynamic Information Flow Tracking

Dynamic Information Flow Tracking (DIFT) is a technique to track potential security vulnerabilities in software and hardware systems at run time.Untrusted data are marked with tags (tainted), which are propagated through the system and their potential for unsafe use is analyzed to prevent them.DIFT is not supported in heterogeneous systems especially hardware accelerators.Currently, DIFT is manually generated and integrated into the accelerators.This process is error-prone, potentially hurting the process of identifying security violations in heterogeneous systems.We present TAINTHLS, to automatically generate a microarchitecture to support baseline operations and a shadow microarchitecture for intrinsic DIFT support in hardware accelerators while providing variable granularity of taint tags.TaintHLS offers a companion high-level synthesis (HLS) methodology to automatically generate such DIFT-enabled accelerators from a high-level specification.We extended a state-of-the-art HLS tool to generate DIFT-enhanced accelerators and demonstrated the approach on numerous benchmarks.The DIFT-enabled accelerators have negligible performance and no more than 30% hardware overhead.
Momentum and contrarian effects on the cryptocurrency market

We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) Bitcoin price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.