Reviewerly/exHarmony huggingface dataset:
DP-GAN: Differentially private consecutive data publishing using generative adversarial nets

In the era of big data, increasingly massive volumes of data is generated and published consecutively for both research and commercial purposes. The potential value of sensitive information also attracts interest from adversaries and thereby arises public concern. Current research mostly focuses on privacy-preserving data publishing in a statistic manner rather than taking the dynamics and correlation of context into consideration. Motivated by this, we propose a novel idea that combining differential privacy and generative adversarial nets. Generative adversarial nets and its extensions are used to generate a synthetic dataset with indistinguishable statistic features while differential privacy guarantees a trade-off between privacy protection and data utility. By employing a min-max game with three players, we devise a deep generative model, namely DP-GAN model, for synthetic data generation while fulfilling the privacy constraints in a differentially private manner. Extensive simulation results on a real-world dataset testify the superiority of the proposed model in terms of privacy protection, data utility, and efficiency.
Deployment of Machine Learning and Deep Learning Algorithms in Detecting Cyberbullying in Bangla and Romanized Bangla text: A Comparative Study

Popularity of social media has increased rapidly and now it is very easy to interact with different persons across social media. As a result, cyberbullying towards people across social media has also increased. As cyberbullying can cause significant emotional and psychological distress, it should be detected and prevented. A lot of work has been done by researchers for cyberbullying detection in several languages except Bangla and Romanized Bangla. Therefore, we developed a model to detect cyberbullying from Bangla and Romanized Bangla texts using Machine Learning and Deep Learning algorithms. We also presented a comparative analysis among the algorithms in terms of accuracy, precision, recall, f1score and roc area. We prepared three datasets for Bangla, Romanized Bangla and combination of both from social media. The three datasets contained 5000 Bangla, 7000 Romanized Bangla and a combination of 12000 Bangla and Romanized Bangla texts. The preprocessed datasets were trained using the Machine Learning and Deep Learning algorithms and the model performances were evaluated to present the comparative analysis. Deep Learning algorithm CNN performed best for the Bangla Dataset by achieving 84% accuracy. For the other two datasets Machine Learning algorithm Multinomial Naïve Bayes performed best by achieving 84% accuracy in Romanized Bangla dataset and 80% accuracy in the combined dataset.
Cross-Positional Attention for Debiasing Clicks

A well-known challenge in leveraging implicit user feedback like clicks to improve real-world search services and recommender systems is its inherent bias. Most existing click models are based on the examination hypothesis in user behaviors and differ in how to model such an examination bias. However, they are constrained by assuming a simple position-based bias or enforcing a sequential order in user examination behaviors. These assumptions are insufficient to capture complex real-world user behaviors and hardly generalize to modern user interfaces (UI) in web applications (e.g., results shown in a grid view). In this work, we propose a fully data-driven neural model for the examination bias, Cross-Positional Attention (XPA), which is more flexible in fitting complex user behaviors. Our model leverages the attention mechanism to effectively capture cross-positional interactions among displayed items and is applicable to arbitrary UIs. We employ XPA in a novel neural click model that can both predict clicks and estimate relevance. Our experiments on offline synthetic data sets show that XPA is robust among different click generation processes. We further apply XPA to a large-scale real-world recommender system, showing significantly better results than baselines in online A/B experiments that involve millions of users. This validates the necessity to model more complex user behaviors than those proposed in the literature.
Cross-lingual Language Model Pretraining for Retrieval

Existing research on cross-lingual retrieval cannot take good advantage of large-scale pretrained language models such as multilingual BERT and XLM. We hypothesize that the absence of cross-lingual passage-level relevance data for finetuning and the lack of query-document style pretraining are key factors of this issue. In this paper, we introduce two novel retrieval-oriented pretraining tasks to further pretrain cross-lingual language models for downstream retrieval tasks such as cross-lingual ad-hoc retrieval (CLIR) and cross-lingual question answering (CLQA). We construct distant supervision data from multilingual Wikipedia using section alignment to support retrieval-oriented language model pretraining. We also propose to directly finetune language models on part of the evaluation collection by making Transformers capable of accepting longer sequences. Experiments on multiple benchmark datasets show that our proposed model can significantly improve upon general multilingual language models in both the cross-lingual retrieval setting and the cross-lingual transfer setting.
Towards Multi-Modal Conversational Information Seeking

Recent research on conversational information seeking (CIS) mostly focuses on uni-modal interactions and information items. This per- spective paper highlights the importance of moving towards de- veloping and evaluating multi-modal conversational information seeking (MMCIS) systems as they enable us to leverage richer context, overcome errors, and increase accessibility. We bridge the gap between the multi-modal and CIS research and provide a formal definition for MMCIS. We discuss potential opportunities and research challenges in designing, implementing, and evaluating MMCIS systems. Based on this research, we propose and implement a practical open-source framework for facilitating MMCIS research.
Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation

Recently, exploiting a knowledge graph (KG) to enrich the semantic representation of a news article have been proven to be effective for news recommendation. These solutions focus on the representation learning for news articles with additional information in the knowledge graph, where the user representations are mainly derived based on these news representations later. However, different users would hold different interests on the same news article. In other words, directly identifying the entities relevant to the user's interest and deriving the resultant user representation could enable a better news recommendation and explanation.
Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data

Facial expression recognition (FER) has attracted much attention in recent years due to its wide applications. While some progress has been achieved thanks to the emergence of deep learning, the challenge occasioned by pose variations remains. Therefore, most conventional approaches mainly perform FER under laboratory-controlled environment, and the FER in-the-wild has received relatively less attention. To implement the FER in-the-wild, the pose-invariant expression recognition model would be a possible solution but for a paucity of training data. Sufficient training data with reliable expression labels on FER tasks typically are unavailable. This paper devotes to addressing the problem of how to model pose variations in facial images, and how to leverage noisy data in the web to boost the FER performance. The proposed model is implemented in an end-to-end weakly supervised manner and enjoys several merits. First, the proposed model utilizes massive noisy labeled data to boost the performance of the FER classifier trained on a small set of clean labels. Second, we offer a novel pose modeling network to adaptively capture the discrepancy in the deep representation space of facial images under different head poses, and consequently, the pose-invariant expression representations can be learned in our model. Last, to exploit the reliable information in the noisy data, we formulate a noise modeling network, which is capable of learning the mapping from feature space to the residuals between clean labels and noisy labels. We validate the proposed approach on four public FER benchmarks: AffectNet, RAF-DB, SFEW, and BU-3DFE. Extensive experiments show that the proposed method performs favorably against state-of-the-art methods.
Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval

Due to the popularity of video contents on the Internet, the information retrieval between videos and texts has attracted broad interest from researchers, which is a challenging cross-modal retrieval task. A common solution is to learn a joint embedding space to measure the cross-modal similarity. However, many existing approaches either pay more attention to textual information, video information, or cross-modal matching methods, but less to all three. We believe that a good video-text retrieval system should take into account all three points, fully exploiting the semantic information of both modalities and considering a comprehensive match. In this paper, we propose a Hierarchical Cross-Modal Graph Consistency Learning Network (HCGC) for video-text retrieval task, which considers multi-level graph consistency for video-text matching. Specifically, we first construct a hierarchical graph representation for the video, which includes three levels from global to local: video, clips and objects. Similarly, the corresponding text graph is constructed according to the semantic relationships among sentence, actions and entities. Then, in order to learn a better match between the video and text graph, we design three types of graph consistency (both direct and indirect): inter-graph parallel consistency, inter-graph cross consistency and intra-graph cross consistency. Extensive experimental results on different video-text datasets demonstrate the effectiveness of our approach on both text-to-video and video-to-text retrieval.
Bottom-up and Layerwise Domain Adaptation for Pedestrian Detection in Thermal Images

Pedestrian detection is a canonical problem for safety and security applications, and it remains a challenging problem due to the highly variable lighting conditions in which pedestrians must be detected. This article investigates several domain adaptation approaches to adapt RGB-trained detectors to the thermal domain. Building on our earlier work on domain adaptation for privacy-preserving pedestrian detection, we conducted an extensive experimental evaluation comparing top-down and bottom-up domain adaptation and also propose two new bottom-up domain adaptation strategies. For top-down domain adaptation, we leverage a detector pre-trained on RGB imagery and efficiently adapt it to perform pedestrian detection in the thermal domain. Our bottom-up domain adaptation approaches include two steps: first, training an adapter segment corresponding to initial layers of the RGB-trained detector adapts to the new input distribution; then, we reconnect the adapter segment to the original RGB-trained detector for final adaptation with a top-down loss. To the best of our knowledge, our bottom-up domain adaptation approaches outperform the best-performing single-modality pedestrian detection results on KAIST and outperform the state of the art on FLIR.
High-Resolution Remote Sensing Image Captioning Based on Structured Attention

Automatically generating language descriptions of remote sensing images has become an emerging research hot spot in the remote sensing field. Attention-based captioning, as a representative group of recent deep learning-based captioning methods, shares the advantage of generating the words while highlighting corresponding object locations in the image. Standard attention-based methods generate captions based on coarse-grained and unstructured attention units, which fails to exploit structured spatial relations of semantic contents in remote sensing images. Although the structure characteristic makes remote sensing images widely divergent to natural images and poses a greater challenge for the remote sensing image captioning task, the key of most remote sensing captioning methods is usually borrowed from the computer vision community without considering the domain knowledge behind. To overcome this problem, a fine-grained, structured attention-based method is proposed to utilize the structural characteristics of semantic contents in high-resolution remote sensing images. Our method learns better descriptions and can generate pixelwise segmentation masks of semantic contents. The segmentation can be jointly trained with the captioning in a unified framework without requiring any pixelwise annotations. Evaluations are conducted on three remote sensing image captioning benchmark data sets with detailed ablation studies and parameter analysis. Compared with the state-of-the-art methods, our method achieves higher captioning accuracy and can generate high-resolution and meaningful segmentation masks of semantic contents at the same time.
TPrune

The invention of Transformer model structure boosts the performance of Neural Machine Translation (NMT) tasks to an unprecedented level. Many previous works have been done to make the Transformer model more execution-friendly on resource-constrained platforms. These researches can be categorized into three key fields: Model Pruning, Transfer Learning, and Efficient Transformer Variants. The family of model pruning methods are popular for their simplicity in practice and promising compression rate and have achieved great success in the field of convolution neural networks (CNNs) for many vision tasks. Nonetheless, previous Transformer pruning works did not perform a thorough model analysis and evaluation on each Transformer component on off-the-shelf mobile devices. In this work, we analyze and prune transformer models at the line-wise granularity and also implement our pruning method on real mobile platforms. We explore the properties of all Transformer components as well as their sparsity features, which are leveraged to guide Transformer model pruning. We name our whole Transformer analysis and pruning pipeline as TPrune. In TPrune, we first propose Block-wise Structured Sparsity Learning (BSSL) to analyze Transformer model property. Then, based on the characters derived from BSSL, we apply Structured Hoyer Square (SHS) to derive the final pruned models. Comparing with the state-of-the-art Transformer pruning methods, TPrune is able to achieve a higher model compression rate with less performance degradation. Experimental results show that our pruned models achieve 1.16×–1.92× speedup on mobile devices with 0%–8% BLEU score degradation compared with the original Transformer model.
Deep reinforcement learning assisted edge-terminal collaborative offloading algorithm of blockchain computing tasks for energy Internet

In the regional distribution network, microgrid is often used to build local energy system to realize regional autonomy in the process of power generation, transmission, and consumption. Applying blockchain technology in microgrid can meet the needs of security and privacy in energy transactions, and can conduct secure point-to-point transactions between anonymous entities. However, blockchain nodes will generate numerous computing-intensive tasks in the process of mining, and cause high delay in energy transaction. Therefore, we take advantage of mobile edge computing (MEC) technology and propose an edge-terminal collaborative mining task processing framework to increase the computing ability of the blockchain system. This framework includes three working modes: local computing, user collaboration and edge node collaboration. Particularly, the trust value of collaborative user nodes is considered to avoid security threats caused by malicious nodes. Furthermore, we establish a delay-and-throughput-based blockchain computing task offloading model, and use asynchronous advantage actor-critic (A3C) algorithm to jointly optimize offloading decision, transmission power allocation, block interval and size configuration. Simulation results show that, compared with Only-MEC and Fixed-BlockSize algorithms, the proposed algorithm can reduce the average delay by 1.7% and 2.5%, and improve the average transaction throughput by 12.1% and 28.5% respectively.
Convolutional Neural Network for Crowd Counting on Metro Platforms

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.
Online bagging of evolving fuzzy systems

Evolving fuzzy systems (EFS) have received increased attention from the community for the purpose of data stream modeling in an incremental, single-pass and transparent manner. To date, a wide variety of EFS approaches have been developed and successfully used in real-world applications which address structural evolution and parameter adaptation in single EFS models. We propose a specific ensemble scheme of EFS to increase their robustness in predictive performance on new stream samples. Our approach relies on an online variant of bagging in which various EFS ensemble members are generated from online bags, that is, the members are updated based on a specific probabilistic online sampling technique, and this with guaranteed convergence to classical sampling in batch bagging. The autonomous pruning of ensemble members is undertaken to omit undesired members with atypically higher errors than other members. We propose two variants, hard pruning where undesired members are deleted forever from the ensemble, and soft pruning where members receive weights to calculate the overall ensemble prediction, according to their single performance; thus, members who are undesired at a certain point of time may be dynamically recalled at a later stage. The autonomous evolution of new ensemble members is carried out whenever a drift in the stream is detected, based on a significantly worsening performance indicator, measured in terms of the Hoeffding inequality. Newer members typically represent the drifted state better and are thus up-weighed compared to older members within an advanced (weighted) calculation of the overall ensemble prediction. The new approach termed online bagged EFS (OB-EFS) was successfully evaluated and compared with single EFS models and related SoA approaches on four data streams from real-world applications (containing various noise levels, drifts and new operating conditions) and showed significantly lower prediction error trend lines.
Exponential convergence of a proximal projection neural network for mixed variational inequalities and applications

This paper proposes a novel proximal projection neural network (PPNN) to deal with mixed variational inequalities. It is shown that the PPNN has a unique continuous solution under the condition of Lipschitz continuity and that the trajectories of the PPNN converge to the unique equilibrium solution exponentially under some mild conditions. In addition, we study the influence of different parameters on the convergence rate. Furthermore, the proposed PPNN is applied in solving nonlinear complementarity problems, min–max problems, sparse recovery problems and classification and feature selection problems. Finally, numerical and experimental examples are presented to validate the effectiveness of the proposed neurodynamic network.
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes’ identification accuracy in the Ethereum networks.
Mobile Robot Path Planning Algorithm Based on Improved A Star

Abstract With the rapid development of artificial intelligence, path planning has become one of the key technologies in the application of mobile robots. Although the A star(A*)path planning algorithm can plan a collision-free path, there are still shortcomings such as the path is not optimal, too many redundant points, and not smooth enough. Aiming at the deficiencies of the A* algorithm, an improved A* algorithm is proposed. Firstly improved A* algorithm divides the distance between adjacent nodes in the path planned by A* algorithm. Then we use the two-way optimization method to delete useless nodes, short the path length and reduce the number of inflection points. Finally, a cubic spline function is introduce d to smooth the path. The simulation results show that the improved A* algorithm is effectively optimized in terms of path length, number of inflection points and smoothness.