Reviewerly/exHarmony huggingface dataset:
Discriminative Region Mining for Object Detection

In generic object detection, detectors are often susceptible to foreground objects and background regions that share similar appearances. In this paper, we propose a novel discriminative region mining (DRM) module for object detection, which enables discriminative region localization and representation for accurate object identification. The DRM module is collaboratively optimized by an extra intramodule classification loss in addition to the usual detection loss, which ensures its adequate discriminative capability. Specifically, two derivatives of the DRM module, namely a local DRM module and a contextual DRM module are proposed to excavate local and contextual discriminative regions, respectively. Furthermore, we extend the local DRM module to capture multiple local discriminative regions with a diversity constraint. To explore informative local features, an image upsampling branch is introduced to generate fine-grained representation for the local DRM module. Extensive experiments on the PASCAL VOC and MS COCO datasets demonstrate the effectiveness of the proposed method. Simple baseline detectors with the built-in DRM can achieve state-of-the-art detection performance. For example, the proposed detector achieves a mean average precision of 81.0% on PASCAL VOC 2007 with an input size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{300} \times \text{300}$</tex-math></inline-formula> using a ResNet-18 backbone, which runs at 24.2 fps on an Nvidia Titan X GPU.
vertTIRP: Robust and efficient vertical frequent time interval-related pattern mining

Time-interval-related pattern (TIRP) mining algorithms find patterns such as "A starts B" or "A overlaps B". The discovery of TIRPs is computationally highly demanding. In this work, we introduce a new efficient algorithm for mining TIRPs, called vertTIRP which combines an efficient representation of these patterns, using their temporal transitivity properties to manage them, with a pairing strategy that sorts the temporal relations to be tested, in order to speed up the mining process. Moreover, this work presents a robust definition of the temporal relations that eliminates the ambiguities with other relations when taking into account the uncertainty in the start and end time of the events (epsilon-based approach), and includes two constraints that enable the user to better express the types of TIRPs to be learnt. An experimental evaluation of the method was performed with both synthetic and real datasets, and the results show that vertTIRP requires significantly less computation time than other state-of-the-art algorithms, and is an effective approach.
Illumination normalization techniques for makeup-invariant face recognition

Face recognition research in unconstrained environments has focused mainly on the three classical causes of variation, i.e., Pose, Illumination, and Expression (PIE). Another recently identified issue is facial makeup that poses a significant challenge to face recognition systems. We believe that applying makeup can cause changes similar to the ones observed due to illumination variation. Therefore, in this paper, we have investigated the effectiveness of illumination normalization techniques for decreasing the variations caused by makeup in a face recognition system. First, we apply photometric illumination normalization techniques with their parameters adjusted for face recognition. Next, we extract facial features using texture-based feature extraction methods and perform face recognition using Support Vector Machines. Experiments carried out on both constrained and unconstrained databases clearly show that illumination normalization techniques improve face recognition results.
The Application of Artificial Intelligence in Foreign Language Teaching

With the continuous development of big data and cloud computing, artificial intelligence technology plays an important role in education. Traditional foreign language teaching can't satisfy learners' individualized, innovative and cooperative learning style. The mode supported by artificial intelligence brings opportunities and challenges to foreign language teaching. This paper analyzes the deep integration of artificial intelligence and foreign language teaching, and then from the modeling of foreign language learners and teachers under the artificial intelligence environment, further analyzes the application of artificial intelligence in foreign language teaching, and forecasts the development of artificial intelligence in the future. The deep integration of artificial intelligence technology in education improves the quality and effectiveness of foreign language teaching.
CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems

CF4J 2.0 is a framework for conducting research experiments based on collaborative filtering. This framework has been designed keeping the scientific community in mind. It includes major features such as a high number of implemented algorithms from the state-of-the-art, several quality measures and parallel execution of the techniques, as well as abstract classes and interfaces to allow developers to extend and customize the library. Furthermore, this new version of the library focuses on the following key features: simple deployment of collaborative filtering experiments, reproducible science, hyper-parameter optimization, data analysis, and openness to the community as an open-source project.
Security Evaluation of Deep Neural Network Resistance Against Laser Fault Injection

Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious consequences. It is necessary to know to which extent are Deep Neural Networks (DNNs) robust against various types of adversarial conditions. In this paper, we experimentally evaluate DNNs implemented in embedded device by using laser fault injection, a physical attack technique that is mostly used in security and reliability communities to test robustness of various systems. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. Our results point out the misclassification possibilities for DNNs achieved by injecting faults into the hidden layers of the network. We evaluate DNNs by using several different attack strategies to show which are the most efficient in terms of misclassification success rates. Outcomes of this work should be taken into account when deploying devices running DNNs in environments where malicious attacker could tamper with the environmental parameters that would bring the device into unstable conditions. resulting into faults.
CATCH: Context-Based Meta Reinforcement Learning for Transferrable Architecture Search

Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They also lack efficient mechanisms to reuse knowledge when confronting multiple tasks. These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. CATCH utilizes a probabilistic encoder to encode task properties into latent context variables, which then guide CATCH's controller to quickly "catch" top-performing networks. The contexts also assist a network evaluator in filtering inferior candidates and speed up learning. Extensive experiments demonstrate CATCH's universality and search efficiency over many other widely-recognized algorithms. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified. This is the first work to our knowledge that proposes an efficient transferrable NAS solution while maintaining robustness across various settings.
Road Marking Detection Based on Mask R-CNN Instance Segmentation Model

In recent years, deep learning has made a lot of progress in the field of image segmentation. In the intelligent driving, high accuracy in road marking detection is required. Traditional detection methods need manual adjustment parameters and face many difficulties. It is still challenging to design a robust detection algorithm. Based on this, we propose a road marking model detection method. Our method based on the Mask R-CNN for road image instance segmentation to improve precision of detecting road marking. The experimental results show that the method can robustly extract road markers under various complex traffic scene.
On the nature of eye-hand coordination in natural steering behavior

Eyes and hand movements are known to be coordinated during a variety of tasks. While steering a vehicle, gaze was observed to be tightly linked with steering wheel angle changes over time, with the eyes leading the hands. In this experiment, participants were asked to drive a winding road composed of bends with systematically manipulated radii of curvature, under regular and automatic steering conditions. With automatic steering, the vehicle followed the road, but the steering wheel and participants hands did not move. Despite the absence of physical eye-hand coordination in that condition, the eye and [what the hands should have done] to produce the action on the steering wheel were found to be coordinated, as under regular steering. This result brings a convincing piece of evidence that eye movements do more than just guiding the hands. In addition, eye-hand coordination was also found to be intermittent, context and person-dependant.
IM-LDP: Incentive Mechanism for Mobile Crowd-Sensing Based on Local Differential Privacy

In recent years, the rapid development of embedded technology has given rise to mobile crowd sensing (MCS) systems to outsource sensing tasks to the public crowd equipped with various mobile devices. Sensing data often involves the workers' privacy, but overprotection of workers' data leads to the decrease of the data accuracy. Therefore, a crucial issue in such systems is how to balance workers' data privacy and data aggregation accuracy. The local differential privacy guarantees the data privacy by returning the privacy budget to workers. However, existing works only considered the workers' reputation as the weight of the aggregation result, but did not correlate with the rewards that workers deserve, which restrained workers' incentive of participation. Different from these works, by quantifying workers' reputation, we propose IM-LDP, an incentive mechanism for MCS based on local differential privacy, which includes four mechanisms of incentive, reputation, data perturbation and data aggregation. Specifically, incentive mechanisms are able to select workers who can provide more accurate data and compensate themselves for their privacy costs. The reputation mechanism quantifies the workers' reputation to improve their payments. The data perturbation mechanism ensures the tradeoff between the data privacy and aggregation accuracy, and the data aggregation mechanism generates highly accurate aggregation results. We evaluate the proposed IM-LDP through theoretical analysis and extensive experiments.
Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System

This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PSO) PID, SSO PID, and the combination of the fuzzy theory with PSO PID and SSO PID, respectively. The main contribution of this study is the pioneering implementation of SSO in a fuzzy PI/PID controller. Moreover, by adding the original gain parameters Kp, Ki, and Kd in the PID controller with delta values, which are calculated by fuzzy logic designer, we can tune the parameters of PID controllers in real time. This makes our control system more accurate, adaptive, and robust.
RGB-T Crowd Counting from Drone: A Benchmark and MMCCN Network

Crowd counting aims to identify the number of objects and plays an important role in intelligent transportation, city management and security monitoring. The task of crowd counting is much challenging because of scale variations, illumination changes, occlusions and poor imaging conditions, especially in the nighttime and haze conditions. In this paper, we present a drone based RGB-Thermal crowd counting dataset (DroneRGBT) that consists of 3600 pairs of images and covers different attributes, including height, illumination and density. To exploit the complementary information in both visible and thermal infrared modalities, we propose a multi-modal crowd counting network (MMCCN) with a multi-scale feature learning module, a modal alignment module and an adaptive fusion module. Experiments on DroneRGBT demonstrate the effectiveness of the proposed approach.
Blind Quality Assessment of Screen Content Images Via Macro-Micro Modeling of Tensor Domain Dictionary

Screen content images (SCIs) have been rapidly and widely applied in interactive multimedia applications. The problem of quality assessment for SCIs is an interesting research topic. Most of the existing methods use subjective and independent features in gray domain to predict the image quality, which cannot comprehensively characterize the image properties or lack unified mathematical explanation for SCIs. To address these problems, we propose a novel blind quality assessment method based on macro-micro modeling of tensor domain dictionary for SCIs in this article. In the proposed method, the tensor decomposition is explored first to avoid the loss of color information, and then a target dictionary is learned more effectively with the principal components. Furthermore, a macro-micro model is established to characterize the micro and macro features in the target dictionary space, which can provide a systematic mathematical interpretation for feature extraction. For the micro features, a log-normal pooling scheme is designed to enhance the effectiveness of feature aggregation by analyzing the particularity of the statistical distribution of sparse codes. Additionally, the statistical properties are mainly discussed and studied based on the Bernoulli law of large numbers, and then a reliable macro feature is generated to describe the relationship between the statistical distribution and quality degradation of SCIs. Experimental results determined by using three public SCI databases show that the proposed method can perform better than relevant existing methods in the prediction of the visual quality of SCIs, especially in terms of the generalization for distortion type and interpretability for feature generation.
SPO: A Secure and Performance-aware Optimization for MapReduce Scheduling

MapReduce is a common framework that effectively processes multi-petabyte data in a distributed manner. Therefore, MapReduce is widely used in heterogeneous environments, such as cloud, to provide performance adequate for system needs. Despite the MapReduce benefits, tweaking the system configuration to achieve the maximum performance is still challenging and needs deep expertise. Besides, some new MapReduce security issues, which has not been well-addressed yet, are recently raised. In this paper, we present a performance-aware and secure framework, named SPO, to minimize the makespan of the tasks while considering task security constraints. Inspired by the HEFT algorithm, first, we introduce SPO, which proposes a two-stage static scheduler in Map and Reduce phases, respectively, to minimize makespan while considering network traffic. Plus, SPO∗ introduces a mathematical optimization model of the proposed scheduler aiming to estimate the system performance while considering security constraints with an error of less than 2%. The experimental results demonstrate that SPO outperforms Hadoop-stock in terms of makespan and network traffic by 29% and 31%, respectively, for the tasks running in heterogeneous environments.
Novel User Preference Recommender System Based on Twitter Profile Analysis

Recommender systems can help provide preference-based personalized services to consumers and help them make informed decisions. However, a key shortcoming of the recommender systems is the lack of interactive methods to dynamically change the weights of recommendation algorithms. Our proposed system uses the Twitter profile and tweets to identify the interests of a user and then recommends the relevant products and services to that user. Our recommendation system is built to predict and personalize products and services based on the result of mining and analyzing the user’s Twitter timeline. The proposed recommender system is built upon an artificial intelligence platform called IBM Watson. The experimental result from the platform displayed the category of goods and services the user is most likely to consume. Our recommender system also showed a strong correlation between the category of products and services a user consumes and his/her tweets.
An Intertwining of Curvelet and Linear Canonical Transforms

In this article, we introduce a novel curvelet transform by combining the merits of the well-known curvelet and linear canonical transforms. The motivation towards the endeavour spurts from the fundamental question of whether it is possible to increase the flexibility of the curvelet transform to optimize the concentration of the curvelet spectrum. By invoking the fundamental relationship between the Fourier and linear canonical transforms, we formulate a novel family of curvelets, which is comparatively flexible and enjoys certain extra degrees of freedom. The preliminary analysis encompasses the study of fundamental properties including the formulation of reconstruction formula and Rayleigh’s energy theorem. Subsequently, we develop the Heisenberg-type uncertainty principle for the novel curvelet transform. Nevertheless, to extend the scope of the present study, we introduce the semidiscrete and discrete analogues of the novel curvelet transform. Finally, we present an example demonstrating the construction of novel curvelet waveforms in a lucid manner.
Duality Diagram Similarity: A Generic Framework for Initialization Selection in Task Transfer Learning

In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models. We propose a new highly efficient and accurate approach based on duality diagram similarity (DDS) between deep neural networks (DNNs). DDS is a generic framework to represent and compare data of different feature dimensions. We validate our approach on the Taskonomy dataset by measuring the correspondence between actual transfer learning performance rankings on 17 taskonomy tasks and predicted rankings. Computing DDS based ranking for $$17\times 17$$ transfers requires less than 2 min and shows a high correlation (0.86) with actual transfer learning rankings, outperforming state-of-the-art methods by a large margin ( $$10\%$$ ) on the Taskonomy benchmark. We also demonstrate the robustness of our model selection approach to a new task, namely Pascal VOC semantic segmentation. Additionally, we show that our method can be applied to select the best layer locations within a DNN for transfer learning on 2D, 3D and semantic tasks on NYUv2 and Pascal VOC datasets.
A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model

Large-scale relationships like user-item preferences in a recommender system are mostly described by a high-dimensional and sparse (HiDS) matrix. A latent factor analysis (LFA) model extracts useful knowledge from an HiDS matrix efficiently, where stochastic gradient descent (SGD) is frequently adopted as the learning algorithm. However, a standard SGD algorithm updates a decision parameter with the stochastic gradient on the instant loss only, without considering information described by prior updates. Hence, an SGD-based LFA model commonly consumes many iterations to converge, which greatly affects its practicability. On the other hand, a proportional-integral-derivative (PID) controller makes a learning model converge fast with the consideration of its historical errors from the initial state till the current moment. Motivated by this discovery, this paper proposes a PID-incorporated SGD-based LFA (PSL) model. Its main idea is to rebuild the instant error on a single instance following the principle of PID, and then substitute this rebuilt error into an SGD algorithm for accelerating model convergence. Empirical studies on six widely-accepted HiDS matrices indicate that compared with state-of-the-art LFA models, a PSL model achieves significantly higher computational efficiency as well as highly competitive prediction accuracy for missing data of an HiDS matrix.
Non-maximum suppression for object detection based on the chaotic whale optimization algorithm

Non-maximum suppression (NMS) as a post-processing step for object detection is mainly used to remove redundant bounding boxes in the object and plays a vital role in many detectors. Its positioning accuracy mainly depends on the bounding box with the highest score, and this strategy is difficult to eliminate the false positive. In order to solve the problem, this paper regards the post-processing step as a combinatorial optimization problem and combines the chaotic whale optimization algorithm and non-maximum suppression. The chaotic search method is used to generate an initial combinatorial solution, and the whale optimization algorithm is discretized to create an updated combinatorial strategy. Under the guidance of the fitness function, the optimal combination is searched. In addition, the method of difference set area (DSA) is proposed to optimize the final detection result. The experiment uses the current mainstream framework Faster R-CNN as the detector on PASCAL VOC2012, COCO2017 and the Warships datasets. The experimental results show that the proposed method can significantly improve the average precision (AP) of detectors compared with the most advanced methods.