Reviewerly/exHarmony huggingface dataset:
Evolutionary Machine Learning With Minions: A Case Study in Feature Selection

Many decisions in a machine learning (ML) pipeline involve nondifferentiable and discontinuous objectives and search spaces. Examples include feature selection, model selection, and hyperparameter tuning, where candidate solutions in an outer optimization loop must be evaluated via a learning subsystem. Evolutionary algorithms (EAs) are prominent gradient-free methods to handle such tasks. However, EAs are known to pose steep computational challenges, especially when dealing with large-instance datasets. As opposed to prior works that often fall back on parallel computing hardware to resolve this big data problem of EAs, in this article, we propose a novel algorithm-centric solution based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolutionary multitasking</i> . Our approach involves the creation of a band of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minions</i> , i.e., small data proxies to the main target task, that are constructed by subsampling a fraction of the large dataset. We then combine the minions with the main task in a single multitask optimization framework, boosting evolutionary search by using small data to quickly optimize for the large dataset. Our key algorithmic contribution in this setting is to allocate computational resources to each of the tasks in a principled manner. The article considers wrapper-based feature selection as an illustrative case study of the broader idea of using multitasking to speedup outer loop evolutionary configurations of any ML subsystem. The experiments reveal that multitasking can indeed speedup baseline EAs, by more than 40% on some datasets.
BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems

Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.
Bidirectional Retrospective Generation Adversarial Network for Anomaly Detection in Videos

Anomaly detection in videos is the task of identifying frames from a video sequence that depict events that do not conform to expected behavior, which is an extremely challenging task due to the ambiguous and unbounded properties of anomalies. With the development of deep learning, video anomaly detection methods based on deep neural networks have made great progress. The existing methods mainly follow two routes, namely, frame reconstruction and frame prediction. Due to the powerful generalization ability of neural networks, the application of reconstruction-based methods is limited. Recently, anomaly detection methods based on prediction have achieved advanced performance. However, their performance suffers when they cannot guarantee lower prediction errors for normal events. In this paper, we propose a novel future frame prediction model based on a bidirectional retrospective generation adversarial network (BR-GAN) for anomaly detection. To predict a future frame with higher quality for normal events, first, we propose a bidirectional prediction combined with a retrospective prediction method to fully mine the bidirectional temporal information between the predicted frame and the input frame sequence. Then, the intensity and gradient loss between the predicted frame and the actual frame together with an adversarial loss are used for appearance (spatial) constraints. In addition, we propose a sequence discriminator composed of a 3-dimensional (3D) convolutional neural network to capture the long-term temporal relationships between frame sequences composed of predicted frames and input frames; this network plays a crucial role in maintaining the motion (temporal) consistency of the predicted frames for normal events. Such appearance and motion constraints further facilitate future frame prediction for normal events, and thus, the prediction network can be highly capable of distinguishing normal and abnormal patterns. Extensive experiments on benchmark datasets demonstrate that our method outperforms most existing state-of-the-art methods, validating the effectiveness of our method for anomaly detection.
Cascaded Split-and-Aggregate Learning with Feature Recombination for Pedestrian Attribute Recognition

Multi-label pedestrian attribute recognition in surveillance is inherently a challenging task due to poor imaging quality, large pose variations, and so on. In this paper, we improve its performance from the following two aspects: (1) We propose a cascaded Split-and-Aggregate Learning (SAL) to capture both the individuality and commonality for all attributes, with one at the feature map level and the other at the feature vector level. For the former, we split the features of each attribute by using a designed attribute-specific attention module (ASAM). For the later, the split features for each attribute are learned by using constrained losses. In both modules, the split features are aggregated by using several convolutional or fully connected layers. (2) We propose a Feature Recombination (FR) that conducts a random shuffle based on the split features over a batch of samples to synthesize more training samples, which spans the potential samples’ variability. To the end, we formulate a unified framework, named CAScaded Split-and-Aggregate Learning with Feature Recombination (CAS-SAL-FR), to learn the above modules jointly and concurrently. Experiments on five popular benchmarks, including RAP, PA-100K, PETA, Market-1501 and Duke attribute datasets, show the proposed CAS-SAL-FR achieves new state-of-the-art performance.
A Cross-Chain Trusted Reputation Scheme for a Shared Charging Platform Based on Blockchain

With the development of electric vehicles, the shortage of charging piles (CPs) has gradually been exposed. In response to this situation, CP operators have taken private CPs into the shared charging system. Due to the lack of maintenance personnel for private CPs that join shared charging, users often face the problems of damaged CPs and poor service attitudes of CP owners. Reputation solutions based on third-party platforms face a problem of single-point failures and reputation solutions based on blockchain face problems of storage and query efficiency. To improve storage and query efficiency, this article proposes a multichain charging model that stores different types of information on different blockchains. However, it faces the problem of unreliable information called across chains, when calculating reputation across chains. Therefore, this article proposes a cross-chain trusted smart contract ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{2}T$ </tex-math></inline-formula> smart contract) to ensure the authenticity, real-time, and interchain write mutual exclusion of cross-chain information, making reputation calculation in the multichain charging model more convenient and more accurate. Especially, we propose a data mutual trust mechanism based on Merkle proof to ensure the authenticity of cross-chain information and prevent forged information from participating in calculating reputation. Furthermore, we present a data structure composed of multiple counting Bloom filters (MCBFs) to verify the real time of information and filter out non-real-time information, thereby ensuring the real time of the calculated reputation. In addition, we put forward an algorithm to guarantee the interchain write mutual exclusion by hash mutexes, making the reputation calculation process more accurate and complete. The security analysis and experimental results demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{2}T$ </tex-math></inline-formula> smart contract is feasible in practice.
AAGCN: Adjacency-aware Graph Convolutional Network for person re-identification

Person re-identification (ReID) is an important topic of computer vision. Existing works in this field focus primarily on learning a feature extractor that maps the pedestrian images into a feature space, in which feature vectors corresponding to the same identity are close to each other. In this paper, we propose the adjacency-aware Graph Convolutional Network (AAGCN) to smooth the intra-class features and thus reduce the intra-class variance. Specifically, our AAGCN takes the features learned by a backbone as the input nodes; it first establishes the connections or adjacency relations for the intra-class features, then the adjacent nodes (i.e., the intra-class features) would be smoothed thanks to the property of low-pass filtering of Graph Convolutional Network (GCN). In this paper, we propose two methods, i.e., the Mahalanobis Neighborhood Adjacency (MNA) and Non-Linear Mapping (NLM), to learn the adjacency relations for the intra-class features. The MNA defines the adjacency weight between two nodes as the negative exponent of the Mahalanobis distance between their corresponding features, therefore it aims to learn a small Mahalanobis distance between the intra-class features and a large Mahalanobis distance between the inter-class ones. The NLM enables the non-linear mapping from the features of the nodes to their corresponding adjacency weights. The experimental results on both visible ReID and visual–infrared ReID verify the effectiveness of our method, for instance, our model achieves 95.7% rank-1 and 93.1% mAP on Market1501, as well as 58.6% rank-1 and 60.0% mAP on SYSU.
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing

Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing number of parameters, trained with massive amounts of data, are being deployed in a wide array of computer vision tasks from self-driving cars to medical imaging. The insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including Optical Neural Networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the popular MNIST dataset, we have benchmarked this new architecture against a traditional CNN based on the seminal LeNet model. We have also compared the performance with previously reported ONNs, namely the GridNet and ComplexNet, as well as a Quantum Optical Neural Network (QONN) that we built by combining the ComplexNet with quantum based sinusoidal nonlinearities. In essence, our work extends the prior research on QONN by adding quantum convolution and pooling layers preceding it. We have evaluated all the models by determining their accuracies, confusion matrices, Receiver Operating Characteristic (ROC) curves, and Matthews Correlation Coefficients. The performance of the models were similar overall, and the ROC curves indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer. We conclude that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models, while achieving unprecedented boosts in computational performances and drastic reduction in power consumption.
Multi-cue based four-stream 3D ResNets for video-based action recognition

Action recognition is one of the important computer vision tasks, which has many applications. This paper proposes a Multi-cue based Four-stream 3D ResNets (MF3D) model for action recognition. The proposed MF3D model contains four streams: a video saliency stream, an appearance stream, a motion stream and an audio stream. Four cues (i.e. the appearance cue, the motion cue, the video saliency cue and audio cue) are captured by the four streams of our proposed MF3D model. In addition, three different connections between different streams are injected, which can transfer different cues between different streams to obtain more effective spatiotemporal features. Experiments are conducted on the Kinetics and Kinetics-Sounds datasets, and the results verify that our MF3D model is effective and outperforms current existing models.
A Novel Deep Learning Approach for Anomaly Detection of Time Series Data

Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison.
A Robust Approach for Continuous Interactive Actor-Critic Algorithms

Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the environment to learn how to perform a task. The characteristics of the environment may change over time or be affected by disturbances not controlled, avoiding the agent finding a proper policy. Some approaches attempt to address these problems, as interactive reinforcement learning, where an external entity helps the agent learn through advice. Other approaches, such as robust reinforcement learning, allow the agent to learn the task, acting in a disturbed environment. In this paper, we propose an approach that addresses interactive reinforcement learning problems in a dynamic environment, where advice provides information on the task and the dynamics of the environment. Thus, an agent learns a policy in a disturbed environment while receiving advice. We implement our approach in the dynamic version of the cart-pole balancing task and a simulated robotic arm dynamic environment to organize objects. Our results show that the proposed approach allows an agent to complete the task satisfactorily in a dynamic, continuous state-action domain. Moreover, experimental results suggest agents trained with our approach are less sensitive to changes in the characteristics of the environment than interactive reinforcement learning agents.
Forecasting the Energy and Economic Benefits of Photovoltaic Technology in China’s Rural Areas

In recent years, with the rapid development of China’s economy, China’s energy demand has also been growing rapidly. Promoting the use of renewable energy in China has become an urgent need. This study evaluates the potential of solar photovoltaic (PV) power generation on the roofs of residential buildings in rural areas of mainland China and calculates the area that can used for generating energy, the installed capacity, and the power generation, and conducts a comprehensive analysis of the economic benefits of investing in the construction of distributed PV systems in various provinces. The findings unveiled in this study indicate that China still has more than 6.4 billion m2 of rural construction area available for the installation of PV modules. If this is all used for solar power generation, the annual power generation can reach up to 1.55 times the electricity consumption of urban and rural residents for the whole society. Through a comprehensive evaluation of energy efficiency and economic benefits, the Chinese mainland can be divided into three types of resource areas. The three types of resource areas have their own advantages and disadvantages. According to their own characteristics and advantages, we can reasonably formulate relevant policies to accelerate the development of PV system application in rural areas.
Robust Reflection Removal with Reflection-free Flash-only Cues

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. We observe that this flash-only image is visually reflection-free, and thus it can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR, 0.04 in SSIM, and 0.068 in LPIPS. Our source code and dataset are publicly available at github.com/ChenyangLEI/flash-reflection-removal.
Fine-Grained Analysis of the Use of Neutral and Controversial Terms for COVID-19 on Social Media

During the COVID-19 pandemic, 'Chinese Virus' emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments have justifications of their own, quantitative analysis of how these terms are being used in real life is lacking. In this paper, we attempt to fill this gap with fine-grained analysis. We find that tweets with a controversial term can be easily distinguished from those with neutral terms using state-of-the-art classifiers, that they cover different substantive topics, and that they possess different linguistic features reflecting sentiment and psychological states. All evidence suggests that the real life use of the controversial terms is distinctively different from that of the neutral terms.
VRComm

Tools and platforms that enable remote communication and collaboration provide a strong contribution to societal challenges. Virtual meetings and conferencing, in particular, can help to reduce commutes and lower our ecological footprint, and can alleviate physical distancing measures in case of global pandemics. In this paper, we outline how to bridge the gap between common video conferencing systems and emerging social VR platforms to allow immersive communication in Virtual Reality (VR). We present a novel VR communication framework that enables remote communication in virtual environments with real-time photorealistic user representation based on colour-and-depth (RGBD) cameras and web browser clients, deployed on common off-the-shelf hardware devices. The paper's main contribution is threefold: (a) a new VR communication framework, (b) a novel approach for real-time depth data transmitting as a 2D grayscale for 3D user representation, including a central MCU-based approach for this new format and (c) a technical evaluation of the system with respect to processing delay, CPU and GPU usage.
Uniformity in Heterogeneity: Diving Deep into Count Interval Partition for Crowd Counting

Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the indices of pre-defined interval bins of counts instead of the count values themselves. However, an inappropriate interval setting might make the count error contributions from different intervals extremely imbalanced, leading to inferior counting performance. Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk. Then to mitigate the inevitably introduced discretization errors in the count quantization process, we propose another criterion called Mean Count Proxies (MCP). The MCP criterion selects the best count proxy for each interval to represent its count value during inference, making the overall expected discretization error of an image nearly negligible. As far as we are aware, this work is the first to delve into such a classification task and ends up with a promising solution for count interval partition. Following the above two theoretically demonstrated criterions, we propose a simple yet effective model termed Uniform Error Partition Network (UEPNet), which achieves state-of-the-art performance on several challenging datasets. The codes will be available at: TencentYoutuResearch/CrowdCounting-UEPNet.
How Robust are Randomized Smoothing based Defenses to Data Poisoning?

Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality in achieving high certified adversarial robustness. Specifically, we propose a novel bilevel optimization based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Unlike other poisoning attacks that reduce the accuracy of the poisoned models on a small set of target points, our attack reduces the average certified radius (ACR) of an entire target class in the dataset. Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation[8], MACER[36], and SmoothAdv[29] that achieve high certified adversarial robustness. To make the attack harder to detect, we use clean-label poisoning points with imperceptible distortions. The effectiveness of the proposed method is evaluated by poisoning MNIST and CIFAR10 datasets and training deep neural networks using previously mentioned training methods and certifying the robustness with randomized smoothing. The ACR of the target class, for models trained on generated poison data, can be reduced by more than 30%. Moreover, the poisoned data is transferable to models trained with different training methods and models with different architectures.