Reviewerly/exHarmony huggingface dataset:
Intra-Domain Consistency Enhancement for Unsupervised Person Re-Identification

Recently, unsupervised domain adaptation in person re-identification (ReID) has been widely studied to improve the generalization ability of the ReID model. Some existing methods focus on handling the intra-domain image variations caused by different camera configurations, pose, illumination, and background in target domain. However, they fail to fully mine the underlying consistency constraints contained in unlabeled target dataset. To comprehensively investigate the underlying constraints for unsupervised representation learning, we introduce two consistency constraints to deal with the intra-domain variations, namely instance-ensembling consistency and cross-granularity consistency. Specifically, the instance-ensembling consistency constraint aims to encourage similar features for a given instance and its positive samples. The cross-granularity consistency constraint is designed to enhance the collaboration of global clues and local clues in multi-granularity feature learning, which can overcome the negative effects caused by the noisy pseudo labels. By combining the advantages of the two constraints, we propose an iterative Intra-domain Consistency Enhancement (ICE) approach based on the Mean Teacher framework to fully mine the two underlying consistency constraints on multi-granularity features. The proposed ICE approach achieves significant improvement compared with the state-of-the-art, which demonstrates the superiority of the two consistency constraints.
Bithreshold Neural Network Classifier

The fully connected 2-layer feedforward network architecture with the hidden layer consisting of bithreshold neurons is considered in the paper. We design the neural network multicategory classifier based on such an architecture. We also give the upper bound on the size of the bithreshold network, which is capable to recognize an arbitrary partition of the given finite set of patterns into a predefined number of classes. The learning algorithm is proposed for such neural networks. We give simulation results of the classifier performance on the real data set and discuss the influence of algorithm parameters on the quality of learning and the representational capacity of classifier.
Multi-level attentive deep user-item representation learning for recommendation system

With the development of e-commerce platforms, user reviews have become a vital source of information to address the sparsity problems for enhancing the predictive performance of the recommendation systems (RSs). However, the traditional methods of the RSs used to model user/item latent features based on static vectors in an independent manner without considering the dynamic nature of the user-item interactions which potentially affect the accuracy of the recommendation process. Thus, this paper proposes a RS model that exploits neural attention techniques to learn user/item representations by jointly considering the fine-grained semantic information for the user-item pairs. The proposed model utilizes both review-based and interaction-specific features for the user/item reviews to learn heterogeneous user/item representations. First, a BiLSTM sequence encoder is used to learn the contextual information of words, and a Co-attention network is then designed to jointly capture the most relevant semantic information of reviews for the user-item pair. To better capture user/item latent factors comprehensively, interaction-specific features based on the rating scores are further integrated with the review-specific latent features via a shared hidden layer. Finally, an attentive factorization machine (FM) is then applied on the shared hidden layer of the integrated user/item features for the final prediction. We carry out a series of experiments using real-world datasets and the results demonstrate that our proposed method is better than the baseline approaches in terms of both rating prediction and ranking performance.
AUV 3D Path Planning Based on A* Algorithm

Aiming at the problem of global static path planning in the submarine terrain environment of autonomous underwater vehicle(AUV),A three-dimensional environment model was built based on gridmethod, and Dijsktra algorithm and A* algorithm were used to realize three-dimensional path search. The simulation results show that the search time of the two algorithms is shorter, but the A* algorithm is better than Dijsktra algorithm in terms of the number of nodes and path length.A multi-direction A* algorithm is designed to reduce the number of search nodes and further shorten the search path. AUV 3d path planning has high efficiency and real-time performance.
3D mineral prospectivity modeling based on machine learning: A case study of the Zhuxi tungsten deposit in northeastern Jiangxi Province, South China

The Zhuxi tungsten deposit is the largest tungsten deposit in the world. A mineral prospectivity analysis of the areas surrounding this deposit has the potential to identify further resources, and thus, it is important to conduct such an analysis. Therefore, a mineral prospectivity was performed on the results of completed 3D geological modeling of the area and 3D inversion of gravity, magnetic, magnetotelluric and broadband seismic data. The anomalous density, susceptibility, resistivity, P-wave velocity and lithology derived from geophysical and geological modeling were constructed on consistent grids. Then, based on the locations of known deposits, the features of the ore-bearing and ore-free zones were extracted. Then, three kinds of machine learning algorithms, namely K-nearest neighbour, back-propagation neural network and support vector machine, were trained using the extracted features. Then, prediction was applied to the 3D data for the whole area, and multiple groups of 3D mineral prospectivity modeling were obtained. By a comparison of stage percentage and the analysis of the capture efficiency diagram, the support vector machine model without lithology captures the largest number of known ore-bearing samples (75.69%) with the least voxels (2.07%), which was highly consistent with the actual geological conditions selected for the classification of prospective areas. The results show that this type of machine learning-based mineral prospectivity modeling, which uses Earth models obtained from the 3D inversion of multiple geophysical data sets and 3D geological modeling, can advance 3D mineral prediction, and thus will greatly improve the efficiency in discovering further ore deposits.
Beyond modality alignment: Learning part-level representation for visible-infrared person re-identification

Visible-Infrared person re-IDentification (VI-reID) aims to automatically retrieve the pedestrian of interest exposed to sensors in different modalities, such as visible camera v.s. infrared sensor. It struggles to learn both modality-invariant and discriminant representations. Unfortunately, existing VI-reID work mainly focuses on tackling the modality difference, which fine-grained level discriminant information has not been well investigated. This causes inferior identification performance. To address the problem, we propose a Dual-Alignment Part-aware Representation (DAPR) framework to simultaneously alleviate the modality bias and mine different level of discriminant representations. Particularly, our DAPR reduces modality discrepancy of high-level features hierarchically by back-propagating reversal gradients from a modality classifier, in order to learn a modality-invariant feature space. And meanwhile, multiple heads of classifiers with the improved part-aware BNNeck are integrated to supervise the network producing identity-discriminant representations w.r.t. both local details and global structures in the learned modality-invariant space. By training in an end-to-end manner, the proposed DAPR produces camera-modality-invariant yet discriminant features1 for the purpose of person matching across modalities. Extensive experiments are conducted on two benchmarks, i.e., SYSU MM01 and RegDB, and the results demonstrate the effectiveness of our proposed method.
Anomaly3D: Video anomaly detection based on 3D-normality clusters

Abnormal behavior detection in surveillance videos is necessary for public monitoring and safety. In human-based surveillance systems, it requires continuous human attention and observation, which is a difficult task. The autonomous detection of such events is of essential significance. However, due to the scarcity of labeled data and the low occurrence probability of these events, abnormal event detection is a challenging vision problem. In this paper, we introduce a novel two-stage architecture for detecting anomalous behavior in videos. In the first stage, we propose a 3D Convolutional Autoencoder (3D-CAE) architecture to extract spatio-temporal features from normal event training videos. In 3D-CAE, the encoder and decoder architectures are based on 3D convolutions, which can learn both appearance and the motion features effectively in an unsupervised manner. In the second stage, we group the 3D spatio-temporal features into different normality clusters, and then remove the sparse clusters to represent a stronger pattern of normality. From these clusters, one-class SVM classifier is used to distinguish between normal and abnormal events based on the normality scores. Experimental results on four different benchmark datasets show significant performance improvement compared to state-of-the-art approaches while providing results in real-time.
Vehicle logo detection based on deep convolutional networks

For intelligent transportation systems, vehicle logo is an important part in the research of vehicle information recognition. Deep convolutional neural networks (CNNs) have been more reasonable and stronger than artificial selection in feature extraction and expression for targets. In our previous work, we found that the network of feature extraction and the training policy of detection have great effects on the accuracy of vehicle logo. For improving small-scale object detecting precision, we change the training policy. According to our previous work, we propose a lightweight network structure with separable convolution to improve the real-time character for vehicle logo while implement the method in embedded devices. The experiment proves that our model can effectively improve the detection accuracy of vehicle logo. Our training policy is valid method for small-scale objects. The lightweight network can solve the equilibrium problem of detecting precision and speed.
Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers' preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates.
Generative Adversarial Networks-Based Imbalance Learning in Software Aging-Related Bug Prediction

Software aging refers to a problem of performance decay in the software systems, which are running for a long period. The primary cause of this phenomenon is the accumulation of run-time errors in the software, which are also known as aging-related bugs (ARBs). Many efforts have been reported earlier to predict the origin of ARBs in the software so that these bugs can be identified and fixed during testing. Imbalanced dataset, where the representation of ARBs patterns is very less as compared to the representation of the non-ARBs pattern significantly hinders the performance of the ARBs prediction models. Therefore, in this article, we present an oversampling approach, generative adversarial networks-based synthetic data generation-based ARBs prediction models. The approach uses generative adversarial networks to generate synthetic samples for the ARBs patterns in the given datasets implicitly and build the prediction models on the processed datasets. To validate the performance of the presented approach, we perform an experimental study for the seven ARBs datasets collected from the public repository and use various performance measures to evaluate the results. The experimental results showed that the presented approach led to the improved performance of prediction models for the ARBs prediction as compared to the other state-of-the-art models.
Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems

One challenge for the modern recommendation systems is the Tyranny of Majority — the generated recommendations are often optimized for the mainstream trends so that the minority preference groups remain discriminated. Moreover, most modern recommendation techniques are characterized as black-box systems. Given a lack of understanding of the dataset characteristics and insufficient diversity of represented individuals, such approaches inevitably lead to amplifying hidden data biases and existing disparities. In this research, we address this problem by proposing a novel approach to detecting and describing potentially discriminated user groups for a given recommendation algorithm. We propose a Bias-Aware Hierarchical Clustering algorithm that identifies user clusters based on latent embeddings constructed by a black-box recommender to identify users whose needs are not met by the given recommendation method. Next, a post-hoc explainer model is applied to reveal the most important descriptive features that characterize these user segments. Our method is model-agnostic and does not require any a priori information about existing disparities and sensitive attributes. An experimental evaluation on a synthetic dataset and two real-world datasets from different domains shows that, compared with other clustering methods and arbitrarily selected user groups, our method is capable of identifying underperforming segments for different recommendation algorithms, and detect more severe disparities.
Bi-Directional Pyramid Network for Edge Detection

Multi-scale representation plays a critical role in the field of edge detection. However, most of the existing research focuses on one of two aspects: fast training and accurate testing. In this paper, we propose a novel multi-scale method to resolve the balance between them. Specifically, according to multi-stream structures and the image pyramid principle, we construct a down-sampling pyramid network and a lightweight up-sampling pyramid network to enrich the multi-scale representation from the encoder and decoder, respectively. Next, these two pyramid networks and a backbone network constitute our overall architecture, a bi-directional pyramid network (BDP-Net). Extensive experiments show that compared with the state-of-the-art model, our method could improve the training speed by about one time while retaining a similar test accuracy. Especially, under the single-scale test, our approach also reaches human perception (F1 score of 0.803) on the BSDS500 database.
A gating context-aware text classification model with BERT and graph convolutional networks

Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF–IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrate the graph embedding with BERT embedding by using a GCN with gating mechanism to enable the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF–IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF–IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline.
Sanitizable Access Control System for Secure Cloud Storage Against Malicious Data Publishers

Cloud computing is considered as one of the most prominent paradigms in the information technology industry, since it can significantly reduce the costs of hardware and software resources in computing infrastructure. This convenience has enabled corporations to efficiently use the cloud storage as a mechanism to share data among their employees. At the first sight, by merely storing the shared data as plaintext in the cloud storage and protect them using an appropriate access control would be a nice solution. This is assuming that the cloud is fully trusted for not leaking any information, which is impractical as the cloud is owned by a third party. Therefore, encryption is mandatory, and the shared data will need to be stored as a ciphertext using an appropriate access control. However, in practice, some of these employees may be malicious and may want to deviate from the required sharing policy. The existing protection in the literature has been explored to allow only legitimate recipients to decrypt the contents stored in the cloud storage, but unfortunately, <i>no existing work</i> deals with issues raised due to the presence of malicious data publishers. Malicious data publishers construct data following the given policy, but the ciphertexts can actually be decrypted by unauthorized users without valid keys, or simply, anyone else who is unauthorized. The impact of the involvement of malicious data publishers is detrimental, as it may damage intellectual properties from the corporations. Therefore, it remains an elusive research problem on how to enable a sound approach to resolve the issue when malicious data publishers are involved in the system, which is a very practical question. In this work, we present <i>a new direction of research</i> that can cope with the presence of malicious data publishers. We resolve the aforementioned problem by proposing the notion of Sanitizable Access Control System (SACS), which is designed for a secure cloud storage that can also resist against malicious data publishers. We define the threat model and its formal security model, as well as its design and scheme which is based on <inline-formula><tex-math notation="LaTeX">$q$</tex-math><alternatives><mml:math><mml:mi>q</mml:mi></mml:math><inline-graphic xlink:href="susilo-ieq1-3058132.gif"/></alternatives></inline-formula>-Parallel Bilinear Diffie-Hellman Exponent Assumption. We provide the security proof of our construction as well as its performance analysis. We believe that this work has opened a new area of research which has never been explored before, even though it is very practical. Therefore, this work will enhance the adoption of secure cloud storage in practice.
Network embedding enhanced intelligent recommendation for online social networks

With the fast development of Internet technology, more and more online social networks are changing our daily life. Whether or not an accurate recommendation can be provided for each user in the massive amount of information directly affects the user's enthusiasm for receiving network services and the user experience effect, which in turn determines the user's participation and loyalty to network applications. However, most previous methods only use a single network topology information and ignore other auxiliary information (such as user content information). Moreover, how to deal with large scale network is a challenging task. To tackle these challenges, we propose a topic-aware network embedding approach for providing intelligent recommendation services. Specifically, we first extract the network topology based on the constructed social network. Then, we extract the topic information based on the context released by the users with the help of topic model. Finally, a topic-aware network embedding framework is utilized for recommendation. Experimental results on two-widely used dataset demonstrate that our method can achieve the best performance.
Fake Banknote Recognition Using Deep Learning

Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strategy is more appropriate (custom or by transfer learning) in terms of classifier performance and inference times for massive data applications. This paper presents a comparison of the two design strategies in various types of architecture. For the transfer learning (TL) strategy, the most appropriate freezing points in CNN architectures (sequential, residual and Inception) are identified. In addition, a custom model based on an AlexNet-type sequential CNN is proposed. Both the TL and the custom models were trained and compared using a Colombian banknote dataset. According to the results, ResNet18 achieved the best accuracy, with 100%. On the other hand, the network with the shortest inference times was the proposed custom network, since its performance is up to 6.48-times faster in CPU and 16.29-times faster in GPU than the inference time with the models by transfer learning.