Reviewerly/exHarmony huggingface dataset:
Holistic thermal-aware workload management and infrastructure control for heterogeneous data centers using machine learning

Two key contributors to the energy expenditure in data centers are information technology (IT) equipment and cooling infrastructures. The standard practice of data centers lacks a tight correlation between these two entities, resulting in considerable power wastage. Considering the cooling cost of different locations inside a data center (cooling heterogeneity) and various cooling capabilities of servers (server heterogeneity) has significant potential for saving power, yet has not been studied thoroughly in the literature. There is a necessity for state-of-the-art approaches to integrate the control of IT and cooling units. Moreover, the literature still lacks an accurate and fast thermal model for temperature prediction inside a data center. In this paper, innovative approaches to quantify data center thermal heterogeneities are presented. Using data center thermal models the cost of providing cold air at the front of servers can be (indirectly) calculated, and the capability of servers to be cooled is formulated. Our approach assigns jobs to locations that are efficient to cool (from the perspectives of both servers and cooling units) and tunes cooling unit parameters. The method, called holistic data center infrastructure control (HDIC), has the potential to save a considerable amount of power by exploiting synergies between the workload scheduler and operational parameters of cooling units.
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval

Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems. In this paper, we focus on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. First, we obtain the text instances from images by employing a text reading system. Then, we combine textual features with salient image regions to exploit the complementary information carried by the two sources. Specifically, we employ a Graph Convolutional Network to perform multi-modal reasoning and obtain relationship-enhanced features by learning a common semantic space between salient objects and text found in an image. By obtaining an enhanced set of visual and textual features, the proposed model greatly outperforms previous state-of-the-art in two different tasks, fine-grained classification and image retrieval in the Con-Text[23] and Drink Bottle[4] datasets.
Combining Multiple Classifiers for Domain Adaptation of Remote Sensing Image Classification

This article investigates the effectiveness of multiclassifier fusion technique on domain adaptation for remote sensing image classification. Since it is impossible to find a domain adaptation method that is optimal for different datasets, and it is also difficult to select the best base classifier for domain-invariant features, multiple domain adaptation fusion (MDAF) method and the multiple base classifier fusion (MBCF) method are proposed to achieve a more stable and superior classification performance. The most crucial step of the weighted fusion approach is to assign weights for classifiers. It is known that different classifiers have varied performances on different subsets of data, and therefore a samplewise adaptive weight is more desirable than a fixed one. For each sample, a desired weight should be able to characterize the reliability of a classifier, so that the advantages of different classifiers can be exploited. We propose a neighborhood consistency based adaptive weighting method, which assigns a large weight to a classifier on a sample if the prediction of the sample is consistent to the predictions of its local neighbors. Experiments with three remote sensing images demonstrate the efficiency of the proposed weighting strategy in the proposed MDAF and MBCF methods.
Action-Centric Relation Transformer Network for Video Question Answering

Video question answering (VideoQA) has emerged as a popular research topic in recent years. Enormous efforts have been devoted to developing more effective fusion strategies and better intra-modal feature preparation. To explore these issues further, we identify two key problems. (1) Current works take almost no account of introducing action of interest in video representation. Additionally, there exists insufficient labeling data on where the action of interest is in many datasets. However, questions in VideoQA are usually action-centric. (2) Frame-to-frame relations, which can provide useful temporal attributes (e.g., state transition, action counting), lack relevant research. Based on these observations, we propose an action-centric relation transformer network (ACRTransformer) for VideoQA and make two significant improvements. (1) We explicitly consider the action recognition problem and present a visual feature encoding technique, action-based encoding (ABE), to emphasize the frames with high actionness probabilities (the probability that the frame has actions). (2) We better exploit the interplays between temporal frames using a relation transformer network (RTransformer). Experiments on popular benchmark datasets in VideoQA clearly establish our superiority over previous state-of-the-art models. Code could be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/op-multimodal/ACRTransformer</uri> .
Towards Federated Learning using FaaS Fabric

Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a shared model for heterogeneous devices such as resource-constrained edge and cloud makes the efficient management of FL-clients challenging. Furthermore, with the rapid growth of FL-clients, the scaling of FL training process is also difficult.
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts

Data hiding technique using steganography prospered widely due to its high un-detectability, flexibility to the size of hidden data, and robustness against compression and other image processes. Accordingly, this paper aims to use a steganographic technique based on DICOM medical images, where one medical image is used as a cover image, and the other one is used as the secret message image. This method entails three main parts: preprocessing, data embedding based on the discrete cosine transform (DCT), and an extraction process. The performances of the algorithm were evaluated using Magnetic resonance imaging (MRI) dataset, the metrics of the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), the Structural Similarity Index (SSIM), the Universal Quality Index (UQI), and the correlation coefficient (R) values. The experimental results scored a high PSNR after the embedding process and high capacity of the hidden data by embedding a DICOM image into another DICOM image of the same size.
Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problem of an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the influence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classification and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The verification of theoretical foundations and evaluation of the effectiveness of the proposed data processing methods for real image bases is performed using the OpenCV library. The applied significance of the work is substantiated according to the criterion of data processing time without reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.
PNPDet: Efficient Few-shot Detection without Forgetting via Plug-and-Play Sub-networks

The human visual system can detect objects of unseen categories from merely a few examples. However, such capability remains absent in state-of-the-art detectors. To bridge this gap, several attempts have been proposed to perform few-shot detection by incorporating meta-learning techniques. Such methods can improve detection performance on unseen categories, but also add huge computational burden, and usually degrade detection performance on seen categories. In this paper, we present PNPDet, a novel Plug-and-Play Detector, for efficient few-shot detection without forgetting. It introduces a simple but effective architecture with separate sub-networks that disentangles the recognition of base and novel categories and prevents hurting performance on known categories while learning new concepts. Distance metric learning is further incorporated into sub-networks, consistently boosting detection performance for both base and novel categories. Experiments show that the proposed PNPDet can achieve comparable few-shot detection performance on unseen categories while not losing accuracy on seen categories, and also remain efficient and flexible at the same time.
Optimal function approximation with ReLU neural networks

In this paper, we consider the optimal approximations of univariate functions with feed-forward ReLU neural networks. We attempt to answer the following questions. For given function and network, what is the minimal possible approximation error? How fast does the optimal approximation error decrease with network size? Is optimal approximation attainable by current network training techniques? Theoretically, we introduce necessary and sufficient conditions for optimal approximations of convex functions. We give lower and upper bounds of optimal approximation errors, and approximation rate that measures how fast approximation error decreases with network size. ReLU network architectures are presented to generate optimal approximations. We then propose an algorithm to compute optimal approximations and prove its convergence. We conduct experiments to validate its effectiveness and compare with other approaches. We also demonstrate that the theoretical limit of approximation errors is not attained by ReLU networks trained with stochastic gradient descent optimization, which indicates that the expressive power of ReLU networks has not been exploited to its full potential.
3D Vehicle Detection With RSU LiDAR for Autonomous Mine

With the development of intelligent and connected vehicles, RSU (roadside unit) sensors are playing an increasingly important role for environment perception. For vehicle detection in autonomous mine, lack of diversity data on RSU LiDAR limits the application of deep learning based methods. To solve this issue, a voxel-based background filtering module is introduced into 3D object detectors for vehicle detection with RSU LiDAR in mine environments. The proposed background filtering method models average height and the number of points for each voxel as Gaussian distribution to generate a background table. To address the impact of the false negative points of the background filtering module, we also propose a multivariate Gaussian loss to model bounding box uncertainty. The predicted covariances between variates help to learn the relationship between the missed parts and the visible ones. Besides, a background filtering based data augmentation method for vehicle detection is also proposed in this paper. Three RSU LiDAR datasets with different terrains in the BaoLi mine area are used for comprehensive experiment evaluations. Experiments show that the proposed background filtering module and multivariate Gaussian loss can significantly improve the generalization ability and performance of several state-of-the-art 3D detectors on different terrain data. Moreover, most background voxels are filtered out, the inference time of the 3D detectors is about 2× faster. Besides, the effectiveness of the proposed data augmentation method is also demonstrated.
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situations. We conduct comprehensive experiments to evaluate our proposed model on three common benchmarks: (DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that MEDIRL outperforms existing models for predicting attention and achieves state-of-the-art performance. We present extensive ablation studies to provide more insights into different features of our proposed model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show experimentally that a library of pre-trained feature extractors combined with a simple feed-forward network learned with an L2-regularizer can be an excellent option for solving cross-domain few-shot image classification. Our experimental results suggest that this simple approach far outperforms several well-established meta-learning algorithms.
A Successive Linearization in Feasible Set Algorithm for Vehicle Motion Planning in Unstructured and Low-Speed Scenarios

Motion planning in unstructured and low-speed environments is a fundamental and difficult task for all mobile robotics. If we view motion planning as an optimization problem, the non-convex collision avoidance constraints and the nonlinear vehicle dynamic constraints make motion planning challenging and time-consuming. In this paper, we propose a Successive Linearization in Feasible Set (SLiFS) algorithm to address these two difficulties. SLiFS consists of two steps. The first step is to iteratively construct convex feasible sets around the current trajectory to approximate non-convex collision avoidance constraints. The second step is to successively linearize the nonlinear dynamic constraints along the current trajectory and further penalize them into the objective function to avoid infeasible linearized constraints, so that the current trajectory can be reshaped within the obtained convex feasible sets by iteratively solving the linearized optimization problem. The main innovation of SLiFS algorithm is that we consider the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm type penalty function in the second step. We find that the sparsity of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm might help to satisfy the robotic dynamic constraints by numerical experiments. Numerical testing results show that our proposed SLiFS algorithm has a high success rate to find feasible trajectories and costs much less time than the classical interior-point method.
Where to Look?: Mining Complementary Image Regions for Weakly Supervised Object Localization

Humans possess an innate capability of recognizing objects and their corresponding parts and confine their attention to that location in a visual scene where the object is spatially present. Recently, efforts to train machines to mimic this ability of humans in the form of weakly supervised object localization, using training labels only at the image-level, have garnered a lot of attention. Nonetheless, one of the well-known problems that most of the existing methods suffer from is localizing only the most discriminative part of an object. Such methods provide very little or no focus on other pertinent parts of the object. In this paper, we propose a novel way of scrupulously localizing objects using training with labels as for the entire image by mining information from complementary regions in an image. Primarily, we adapt to regional dropout at complementary spatial locations to create two intermediate images. With the help of a novel Channel-wise Assisted Attention Module (CAAM) coupled with a Spatial Self-Attention Module (SSAM), we parallely train our model to leverage the information from complementary image regions for excellent localization. Finally, we fuse the attention maps generated by the two classifiers using our Attention-based Fusion Loss. Several experimental studies manifest the superior performance of our proposed approach. Our method demonstrates a significant increase in localization performance over the existing state-of-the-art methods on CUB-200-2011 and ILSVRC 2016 datasets.
Comparison detector for cervical cell/clumps detection in the limited data scenario

Automated detection of cervical cancer cells/clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train Convolutional Neural Networks (CNN) to classify cell patches or to detect cells from the whole image. But the former is computationally expensive, while the latter often requires a large-scale dataset with expensive annotations. In this paper we propose an efficient cervical cancer cells/clumps detection method, called Comparison detector, to deal with the limited data problem. Specifically, we utilize the state-of-the-art proposal-based object detection method, Faster R-CNN with Feature Pyramid Network (FPN) as the baseline and replace the classification of each proposal by comparing it with the prototype representation of each category. In addition, we propose to learn the prototype representation of the background category from data instead of manually choosing them by some heuristic rules. Experimental results show that the proposed Comparison detector yields significant improvement on the small dataset, achieving a mean Average Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improved by about 20% comparing to the baseline. Moreover, when training on the medium-sized dataset, our Comparison detector gains a mAP of 48.8% and an AR of 64.0%, improving the AR by 5.1% and the mAP by 3.6% respectively. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code and datasets are available at https://github.com/kuku-sichuan/ComparisonDetector.
Quantum coherence measures based on Fisher information with applications

Recently various ways to quantify coherence of a quantum state have been proposed. In this paper, several reliable quantum coherence measures based on quantum Fisher information (QFI) are presented. For pure states, this coherence measure is equivalent to that based on skew information, while for mixed states, we first define two natural coherence measures via QFI and find that the two definitions are exactly identical. By virtual of the special structure of qubit, we show that the mutual unbiased bases' (MUBs) qubit coherence is constrained only by the length of the Bloch vector. Then we provide upper bounds of the coherence measure with respect to any given basis and lower bounds in the case of a set of MUBs for both pure states and mixed states of arbitrary dimension. Finally, we investigate the connection between our coherence measures and quantum metrology, thereby providing an operational meaning for these coherence measures.
DORi: Discovering Object Relationships for Moment Localization of a Natural Language Query in a Video

This paper studies the task of temporal moment localization in long untrimmed videos using natural language queries. Given a query sentence, the goal is to determine the start and end of the relevant segment within the video. Our key innovation is to learn a video feature embedding through a language-conditioned message-passing algorithm suitable for temporal moment localization which captures the relationships between humans, objects and activities in the video. These relationships are obtained by a spatial sub-graph that contextualizes the scene representation using detected objects and human features conditioned in the language query. Moreover, a temporal sub-graph captures the activities within the video through time. Our method is evaluated on three standard benchmark datasets, and we also introduce YouCookII as a new benchmark for this task. Experiments show our method outperforms state-of-the-art methods on these datasets, confirming the effectiveness of our approach.
Understanding cybersecurity behavioral habits: Insights from situational support

While the Internet has become an indispensable aspect of personal and professional lives, it has also served to render many individuals vulnerable to cybersecurity threats. Thus, the promotion of cybersecurity behaviors can effectively protect individuals from these threats. However, cybersecurity behaviors do not necessarily come naturally, and people need support and encouragement to develop and adopt them. A habit is an important factor that may motivate cybersecurity behaviors, but it has often been overlooked in past studies. To address this limitation, this study examined the formation of cybersecurity behavioral habits. Hierarchical regression analysis was used to analyze cybersecurity behavior survey data obtained from 393 college student participants. The results revealed the following: (1) efficacy and behavioral comprehensiveness predict cybersecurity behavioral habits; (2) efficacy has a positively impact on behavioral comprehensiveness; (3) situational support has a positive influence on efficacy. These findings suggest that cybersecurity behavioral habits can be formed by promoting the diversity of cybersecurity measures practiced (behavioral comprehensiveness) and efficacy.