Reviewerly/exHarmony huggingface dataset:
Active learning for regression using greedy sampling

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 10 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.
Joint Neural Collaborative Filtering for Recommender Systems

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization that takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-world datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24%, and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.
Random forest solar power forecast based on classification optimization

With the rapid development of the photovoltaic industry, the share of photovoltaic power generation in the power trading market is growing. The intermittent and uncontrollable characteristics of photovoltaic power generation have a huge impact on the stability of the power system. To reduce the occurrence of such conditions, it is necessary to improve the prediction accuracy of photovoltaic power generation. However, in the traditional modeling process, the accuracy of the model is often poor due to excessive noise in the original data or improper parameter adjustment. In this paper, Principal Component Analysis and K-means clustering algorithm combined with random forest algorithm optimized by Differential Evolution Grey Wolf Optimizer are used to model the photovoltaic power generation in three regions. Principal Component Analysis and K-means clustering are used to obtain the hourly point features similar to the predicted time points, and then the input data is filtered to reduce the noise data interference. At the same time, the popular optimization algorithm quickly selects the Random Forest parameters, which greatly avoids the artificial filtering factors and causes the error to be generated. Through the establishment of comparative experiments, it is found that the recommended model has higher prediction accuracy and robustness.
A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data

To apply deep learning technique for estimating hourly global solar radiation (GSR) from geostationary satellite observations, a hybrid deep network is proposed, relying on convolutional neural network (CNN) to extract spatial pattern from satellite imagery, multi-layer perceptron (MLP) to link the abstract patterns and additional time/location information to target hourly GSR. Its representative advantage lies in the ability to characterize changeable cloud morphology and simulate complex non-linear relationships. The deep network is trained using ground measured GSR values at 90 Chinese radiation stations in 2008 as well as the radiative transfer model simulation at the top of Mt. Everest which serves as constraints of extrapolation for high elevation regions. The extensibility of trained network is validated at 5 independent stations in 2008, yielding an overall coefficient of determination (R2) of 0.82, and at all stations in 2007 along with an R2 of 0.88. Comparative experiments confirm that the combination of spatial pattern and point information can lead to more accurate estimation of hourly GSR, achieving a minimum root mean square error (RMSE) of 84.18 W/m2 (0.30 MJ/m2), 1.92 MJ/m2 and 1.08 MJ/m2 in hourly, daily total and monthly total scales, respectively. Moreover, the deep network is capable of mapping spatially continuous hourly GSR which reflects the regional differences and reproduce the diurnal cycles of solar radiation properly.
Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings

We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions. We build a separate multi-modal embedding space for each PoS tag. The outputs of multiple PoS embeddings are then used as input to an integrated multi-modal space, where we perform action retrieval. All embeddings are trained jointly through a combination of PoS-aware and PoS-agnostic losses. Our proposal enables learning specialised embedding spaces that offer multiple views of the same embedded entities. We report the first retrieval results on fine-grained actions for the large-scale EPIC dataset, in a generalised zero-shot setting. Results show the advantage of our approach for both video-to-text and text-to-video action retrieval. We also demonstrate the benefit of disentangling the PoS for the generic task of cross-modal video retrieval on the MSR-VTTdataset.
Exploiting Correcting Codes: On the Effectiveness of ECC Memory Against Rowhammer Attacks

Given the increasing impact of Rowhammer, and the dearth of adequate other hardware defenses, many in the security community have pinned their hopes on error-correcting code (ECC) memory as one of the few practical defenses against Rowhammer attacks. Specifically, the expectation is that the ECC algorithm will correct or detect any bits they manage to flip in memory in real-world settings. However, the extent to which ECC really protects against Rowhammer is an open research question, due to two key challenges. First, the details of the ECC implementations in commodity systems are not known. Second, existing Rowhammer exploitation techniques cannot yield reliable attacks in presence of ECC memory. In this paper, we address both challenges and provide concrete evidence of the susceptibility of ECC memory to Rowhammer attacks. To address the first challenge, we describe a novel approach that combines a custom-made hardware probe, Rowhammer bit flips, and a cold boot attack to reverse engineer ECC functions on commodity AMD and Intel processors. To address the second challenge, we present ECCploit, a new Rowhammer attack based on composable, data-controlled bit flips and a novel side channel in the ECC memory controller. We show that, while ECC memory does reduce the attack surface for Rowhammer, ECCploit still allows an attacker to mount reliable Rowhammer attacks against vulnerable ECC memory on a variety of systems and configurations. In addition, we show that, despite the non-trivial constraints imposed by ECC, ECCploit can still be powerful in practice and mimic the behavior of prior Rowhammer exploits.
Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations

As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth's surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy.
Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks

Weakly-supervised temporal action localization (WS-TAL) is a promising but challenging task with only video-level action categorical labels available during training. Without requiring temporal action boundary annotations in training data, WS-TAL could possibly exploit automatically retrieved video tags as video-level labels. However, such coarse video-level supervision inevitably incurs confusions, especially in untrimmed videos containing multiple action instances. To address this challenge, we propose the Contrast-based Localization EvaluAtioN Network (CleanNet) with our new action proposal evaluator, which provides pseudo-supervision by leveraging the temporal contrast in snippet-level action classification predictions. Essentially, the new action proposal evaluator enforces an additional temporal contrast constraint so that high-evaluation-score action proposals are more likely to coincide with true action instances. Moreover, the new action localization module is an integral part of CleanNet which enables end-to-end training. This is in contrast to many existing WS-TAL methods where action localization is merely a post-processing step. Experiments on THUMOS14 and ActivityNet datasets validate the efficacy of CleanNet against existing state-ofthe- art WS-TAL algorithms.
Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a semantic-visual correspondence problem, by learning mappings from one feature space to the other. Despite being reasonable, previous approaches essentially discard the highly precious discriminative power of visual features in an implicit way, and thus produce undesirable results. We instead reformulate ZSL as a conditioned visual classification problem, i.e., classifying visual features based on the classifiers learned from the semantic descriptions. With this reformulation, we develop algorithms targeting various ZSL settings: For the conventional setting, we propose to train a deep neural network that directly generates visual feature classifiers from the semantic attributes with an episode-based training scheme; For the generalized setting, we concatenate the learned highly discriminative classifiers for seen classes and the generated classifiers for unseen classes to classify visual features of all classes; For the transductive setting, we exploit unlabeled data to effectively calibrate the classifier generator using a novel learning-without-forgetting self-training mechanism and guide the process by a robust generalized cross-entropy loss. Extensive experiments show that our proposed algorithms significantly outperform state-of-the-art methods by large margins on most benchmark datasets in all the ZSL settings.
Human action recognition with a large-scale brain-inspired photonic computer

The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in brain–computer interfaces and surveillance, for example. Recently, deep learning has produced remarkable results, but it can be hard to use in practice, as its training requires large datasets and special-purpose and energy-consuming hardware. In this work, we propose a photonic hardware approach. Our experimental set-up comprises off-the-shelf components and implements an easy-to-train recurrent neural network with 16,384 nodes, scalable to hundreds of thousands of nodes. The system, based on the reservoir computing paradigm, is trained to recognize six human actions from the KTH video database using either raw frames as inputs or a set of features extracted with the histograms of an oriented gradients algorithm. We report a classification accuracy of 91.3%, comparable to state-of-the-art digital implementations, while promising a higher processing speed in comparison to the existing hardware approaches. Because of the massively parallel processing capabilities offered by photonic architectures, we anticipate that this work will pave the way towards simply reconfigurable and energy-efficient solutions for real-time video processing. Photonic computing devices have been proposed as a high-speed and energy-efficient approach to implementing neural networks. Using off-the-shelf components, Antonik et al. demonstrate a reservoir computer that recognizes different forms of human action from video streams using photonic neural networks.
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at https://github.com/luzhang16/AR-CNN.
Dense Depth Estimation in Monocular Endoscopy With Self-Supervised Learning Methods

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter mean residual error. In a comparison study to recent self-supervised depth estimation methods designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous methods by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.
An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection

A novel image copy-move forgery detection scheme using a Dense-InceptionNet is proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense-feature connection, Deep Neural Network (DNN). It is the first DNN model to autonomously learn the feature correlations and search the possible forgery snippets through the matching clues. The proposed Dense-InceptionNet consists of Pyramid Feature Extractor (PFE), Feature Correlation Matching (FCM), and Hierarchical Post-Processing (HPP) modules. The PFE module is proposed to extract multi-dimensional and multi-scale dense-features. The features of each layer in this extractor module are directly connected to the preceding layers. The FCM module is proposed to learn the high correlations of deep features and obtain three candidate matching maps. Finally, the HPP module which makes use of three matching maps to obtain a combination of cross-entropies is amenable to better training via backpropagation. Experiments demonstrate that the efficiency of the proposed Dense-InceptionNet is much better than the other state-of-the-art methods while achieving the relative best performance against most known attacks.
Remote Sensing Image Mosaicking: Achievements and Challenges

Image mosaicking is often a necessary process to cover a large and full region of interest (ROI) for many remote sensing applications (e.g., geographical mapping, resource and environmental monitoring, and disaster monitoring).
Designing a 2D infinite collapse map for image encryption

Due to the unpredictability and complexity properties, chaotic maps are widely applied in security, communication, and system control. Existing one-dimensional (1D) chaotic maps can be easily predicted and high-dimensional (HD) ones have more complex structures and higher computation costs. In order to enhance the chaotic performance, this paper proposes a new two-dimensional infinite collapse map (2D-ICM). Compared with existing 2D chaotic maps, 2D-ICM has better ergodicity, hyperchaotic property, unpredictability, and a wider chaotic region. To investigate its application, we further propose an image encryption algorithm using 2D-ICM. Simulation demonstrates that the proposed image encryption algorithm has excellent performance for protecting various kinds of images.
Experimental Transmission of Quantum Information Using a Superposition of Causal Orders

Communication in a network generally takes place through a sequence of intermediate nodes connected by communication channels. In the standard theory of communication, it is assumed that the communication network is embedded in a classical spacetime, where the relative order of different nodes is well defined. In principle, a quantum theory of spacetime could allow the order of the intermediate points between sender and receiver to be in a coherent superposition. Here we experimentally realize a tabletop simulation of this exotic possibility on a photonic system, demonstrating high-fidelity transmission of quantum information over two noisy channels arranged in a superposition of two alternative causal orders.
A Secure and Verifiable Data Sharing Scheme Based on Blockchain in Vehicular Social Networks

The vehicular social networks (VSNs) supports diverse kinds of services such as traffic management, road safety, and sharing data (videos, audios, roads photos, air quality, and so on). However, its complex, large-scale and dynamic network structure poses new security challenges. Among these challenges, secure data transmission has turned to be a spotlight. Ciphertext-policy attribute-based encryption (CP-ABE) may be adopted to realize one-to-many data sharing in VSNs. In traditional CP-ABE schemes, access policy is stored and granted by the could, which lacks credibility due to centralization. In this article, we propose a secure and verifiable one-to-many data sharing scheme to solve the above problem. We use blockchain to record the access policy, realizing user self-certification and cloud non-repudiation. Considering the computing capabilities of the vehicular user, we propose an effective scheme for certificating. Meanwhile, considering the sensitive information included in the access policy, we propose a policy hiding scheme. Our scheme also supports data revocation when a vehicular user no longer wants to share the data in VSNs. Finally, security analysis and simulation show our scheme is both secure and efficient.
Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.