Reviewerly/exHarmony huggingface dataset:
Style transfer network for complex multi-stroke text

Neural style transfer has achieved success in many tasks. It is also introduced to text style transfer, which uses a style image to generate transferred images with textures and shapes consistent with the semantic content of the reference image. However, when the text structure is complex, existing methods will encounter problems such as stroke adhesion and unclear text edges. This will affect the aesthetics of the generated image and bring a lot of extra workload to the designers. This paper proposes an improved text style transfer network for complex multi-stroke texts. We use shape-matching GAN as the baseline and perform the following modifications: (1) morphological methods, erosion and dilation, are introduced in image processing; (2) the SN-Resblock is added to the structure network, and a BCEWithLogits loss is added to the texture network; (3) AdaBelief optimizer is adopted to constrain the transfer of text structure. Further, a new dataset of traditional Chinese characters is constructed to train the model. Experimental results show that the proposed method outperforms state-of-the-art methods on both simple characters and complex multi-stroke characters. It is shown that our method increases the readability and aesthetics of the text.
Building operation and maintenance scheme based on sharding blockchain

Blockchain can ensure data security and reliability during the stage of building operation and maintenance (BOM), provide reliable data for decision-making. However, existing schemes based on single-chain architecture have the problems of storage limitation and scalability, and ignore the impact of event's priority and real-time on blockchain transaction. Therefore, for BOM, this paper provides a BOM framework based on sharding blockchain (SBC-BOMF), which constructs two-layer architecture based on master-chain and multiple shards, relieves the storage pressure of blockchain nodes and improves the concurrency capability. Priority-based transaction handling strategy is designed to achieve reasonable and rapid response for multi-level transactions. Finally, an actual BOM project is taken as example to illustrate the effectiveness of proposed scheme; experiments are conducted for performance testing and evaluation. Results show that proposed scheme can effectively solve the scalability problem caused by the application of blockchain in BOM, reduce storage overhead, and realize efficient handling for blockchain transactions.
Barriers and Facilitators of Robot-Assisted Education in Higher Education: A Systematic Mixed-Studies Review

Lack of motivation and enjoyment is a challenge that many students face. Due to the current coronavirus pandemic, many lessons are not being conducted face-to-face. However, the use of robots has been proven able to alleviate this challenge. This review explores the barriers and facilitators of robot-assisted education among higher education students. Ten databases were comprehensively searched for studies in English. Both published and unpublished studies were considered without a time limit. A systematic mixed-studies review was adopted and the mixed method appraisal tool was used to assess the methodological quality of the selected articles. The qualitative and quantitative findings were then synthesized via thematic and narrative syntheses, respectively. To integrate these two sets of findings, a result-based convergent synthesis was performed. A total of 28 studies covering 1689 higher education students across 14 countries were eventually used for the analysis. Most of these studies had average to high methodological quality. Two barrier themes were identified from these studies, namely, poor audio verbatim from the robots, and disruption and restrictions from software and hardware of the robot. Meanwhile, three themes related to facilitators were identified, namely, greater engagement in learning, facilitation in remote learning, and knowledge enhancement. The use of robot-assisted education has been proven to improve student learning in higher education. Robot-assisted education is an alternate educational technique that can be utilized to supplement and augment ongoing teaching arrangements. Future studies should examine a specific type of robot in a comparable learning environment.
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing

The explosion of e-commerce has caused the need for processing and analysis of product titles, like entity typing in product titles. However, the rapid activity in e-commerce has led to the rapid emergence of new entities, which is difficult for general entity typing. Besides, product titles in e-commerce have very different language styles from text data in general domain. In order to handle new entities in product titles and address the special language styles of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing. First, we reformulate entity typing into a textual entailment problem to handle new entities that are not present during training. Second, we design a model to automatically generate textual entailment hypotheses using a continuous prompt tuning method, which can generate better textual entailment hypotheses without manual design. Third, we utilize the fusion embeddings of BERT embedding and Char-acterBERT embedding to solve the problem that the language styles of product titles in e-commerce are different from that of general domain. To analyze the effect of each contribution, we compare the performance of entity typing and textual entailment model, and conduct ablation studies on continuous prompt tuning and fusion embeddings. We also evaluate the impact of different prompt template initialization for the continuous prompt tuning. We show our proposed model improves the average F1 score by around 2% compared to the baseline BERT entity typing model.
Asymmetric Differential Privacy

Differential privacy (DP) is attracting considerable research attention as a privacy definition when publishing statistics of a dataset. This study focused on addressing the limitation that DP inevitably causes two-sided errors. For example, consider a threshold query that asks whether a counting is above a given threshold or not. An answer through the DP mechanism can cause error. This phenomenon is not desirable for sensitive analysis such as the counting of COVID-19-infected individuals (in a dataset) visiting a specific location; misinformation can result in incorrect decision-making which can increase the epidemic. To the best of our knowledge, the problem is yet to be solved. We proposed a variation of DP, namely asymmetric DP (ADP) to solve the problem. ADP can provide reasonable privacy protection and achieve one-sided errors. Finally, experiments were conducted to evaluate the utility of the proposed mechanism for the epidemic analysis using a real-world dataset. The results of study revealed the feasibility of proposed mechanisms.
WaveNets: Wavelet Channel Attention Networks

Channel Attention reigns supreme as an effective technique in the field of computer vision. However, the proposed channel attention by SENet suffers from information loss in feature learning caused by the use of Global Average Pooling (GAP) to represent channels as scalars. Thus, designing effective channel attention mechanisms requires finding a s olution t o enhance features preservation in modeling channel inter-dependencies. In this work, we utilize Wavelet transform compression as a solution to the channel representation problem. We first t est wavelet transform as a standalone channel compression method. We prove that global average pooling is equivalent to the recursive approximate Haar wavelet transform. With this proof, we generalize channel attention using Wavelet compression and name it WaveNet. Implementation of our method can be embedded within existing channel attention methods with a couple of lines of code. We test our proposed method using ImageNet dataset for image classification t ask. O ur m ethod o utperforms t he baseline SENet-34, and SOTA FcaNet-34. Our code implementation is publicly available at https://github.com/hady1011/WaveNet-C.
Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks

Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.
Optimal blind colour image watermarking based on adaptive chaotic grasshopper optimization algorithm

This paper proposes an effective optimal blind colour image watermarking based on Triangular Vertex Transform (TVT), Lifting Wavelet Transform (LWT) and Schur decomposition. Firstly, the RGB channels of the colour host image are processed with TVT to attain U, V, and W coefficients. The image quality is preserved by applying LWT on the W coefficients. As the middle frequency (HL and LH) components of the LWT wavelet domain achieve much robustness, these two components are split into 4×4 nonoverlapping blocks further it is processed with Schur decomposition for improving perceptual transparency. The optimal solution attained by an Adaptive Chaotic Grasshopper Optimization Algorithm (ACGOA) is used as the embedding factor to ensure the embedding strength of the watermark. The confidentiality of the watermark is improved by 2D Logistic-Modulated-Sine-Coupling-Logistic Chaotic Map (LSMCL). Subsequently, each bit of encrypted watermark image is inserted in the highest eigenvalue of upper triangular matrix of Schur decomposition using the embedding factor β obtained by ACGOA to accomplish the watermarked image. The watermark recovery is performed using the embedding factor β and the decryption process is done using LSMCL. The efficiency of the ACGOA based scheme is estimated and the highest Peak signal-to-noise ratio (PSNR) value achieved is 54.2987 dB. The Normalized Cross-Correlation (NCC) results show better reliability for different attacks including geometric attacks and the NCC value obtained is close to 1.
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series

Online social networks are actively involved in removing malicious social bots due to their role in spreading low-quality information. However, most of the existing bot detectors are supervised classifiers incapable of capturing the evolving behavior of sophisticated bots. Here we propose MulBot, an unsupervised bot detector based on multivariate time series (MTS). For the first time, we exploit multidimensional temporal features extracted from user timelines. We manage the multidimensionality with an LSTM autoencoder, which projects the MTS in a suitable latent space. Then, we perform a clustering step on this encoded representation to identify dense groups of very similar users – a known sign of automation. Finally, we perform a binary classification task achieving f1-score =0.99, outperforming state-of-the-art methods (f1-score ≤ 0.97). Not only does MulBot achieve excellent results in the binary classification task, but we also demonstrate its strengths in a novel and practically-relevant task: detecting and separating different botnets. In this multiclass classification task we achieve f1-score =0.96. We conclude by estimating the importance of the different features used in our model and by evaluating MulBot’s capability to generalize to new unseen bots, thus proposing a solution to the generalization deficiencies of supervised bot detectors.
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System

Knowledge graph (KG) enhanced recommendation has demonstrated improved performance in the recommendation system (RecSys) and attracted considerable research interest. Recently the literature has adopted neural graph networks (GNNs) on the collaborative knowledge graph and built an end-to-end KG-enhanced RecSys. However, the majority of these approaches have three limitations: (1) treat the collaborative knowledge graph as a homogeneous graph and overlook the highly heterogeneous relationships among items, (2) lack of design to explicitly leverage the rich side information, and (3) overlook the rich knowledge in user preference.To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec). In particular, we focus on modeling the rich, heterogeneous semantic relationships among items and construct several collaborative Meta-KGs to explicitly depict the relatedness of the items under the guidance of meta-knowledge. In addition to the knowledge obtained from KG, we leverage user knowledge that extracts from user preference to construct the Meta-KGs. The constructed Meta-KGs can capture the knowledge from both the knowledge graph and user preference. Furthermore. we utilize a light convolution encoder to recursively integrate the item relationship in each collaborative Meta-KGs. This scheme allows us to explicitly gather the heterogeneous semantic relationships among items and encode them into the representations of items. In addition, we propose channel attention to fuse the item and user representations from different Meta-KGs. Extensive experiments are conducted on four real-world benchmark datasets, demonstrating significant gains over the state-of-the-art baselines on both regular and cold-start recommendation settings.
ScaDLES: Scalable Deep Learning over Streaming data at the Edge

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, a s w ell a s independent and identically distributed (IID) data across all nodes. However, these assumptions don’t necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. Computing on the edge suffers from both systems and statistical heterogeneity. Systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can’t train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD.
A tool for assisting in the forensic investigation of cyber-security incidents

The exponential growth of networking capabilities including the Internet of Things (IoT), has led to an outburst of cyberattacks. Many well-documented cyber-attacks have targeted critical energy infrastructures as well as any kind of cloud-based IT platforms. Early examination of critical systems’ vulnerabilities, as well as previous cyber-security incidents, are of utmost importance to prevent new ones. A thorough investigation to examine the context of the cyber-security breach can reveal facts about the source of the attack, the profile of the attacker, the resources, and the skills required and can further reveal mitigations for preventing the attack from re-appearing in the future. To safeguard critical energy infrastructures, many forensic approaches have been developed to collect, analyze, and digitalize evidence assisting in the in-depth investigation of an incident. However, up to now, the many open-source vulnerability data sources which have been developed to provide valuable information for a cyber-attack are yet to be employed to assist in forensic investigation. This paper introduces the Automated Forensic Tool, a platform that employs machine learning algorithms to combine different vulnerability data sources for facilitating the forensic procedure while minimizing the time and effort needed. A use case is also demonstrated that displays how the tool can be used towards assisting the forensic investigation of cyber-security incidents on an energy infrastructure, but the tool can also be applied to other critical energy and IT infrastructures with minor adaptations.
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations

Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain. Most existing CDRSs rely on overlapping users or items to connect domains and transfer knowledge. However, matching users is an arduous task and may involve privacy issues when data comes from different companies, resulting in a limited application for the above CDRSs. Some studies develop CDRSs that require no overlapping users and items by transferring learned user interaction patterns. However, they ignore the bias in user interaction patterns between domains and hence suffer from an inferior performance compared with single-domain recommender systems. In this paper, based on the above findings, we propose a novel CDRS, namely semantic clustering enhanced debiasing graph neural recommender system (SCDGN), that requires no overlapping users and items and can handle the domain bias. More precisely, SCDGN semantically clusters items from both domains and constructs a cross-domain bipartite graph generated from item clusters and users. Then, the knowledge is transferred via this cross-domain user-cluster graph from source to the target. Furthermore, we design a debiasing graph convolutional layer for SCDGN to extract unbiased structural knowledge from the cross-domain user-cluster graph. Our Experimental results on three public datasets and a pair of proprietary datasets verify the effectiveness of SCDGN over stateof-the-art models in terms of cross-domain recommendations.
Expanding Sparse LiDAR Depth and Guiding Stereo Matching for Robust Dense Depth Estimation

Dense depth estimation is an important task for applications, such as object detection, 3-D reconstruction, etc. Stereo matching, as a popular method for dense depth estimation, has been faced with challenges when low textures, occlusions or domain gaps exist. Stereo-LiDAR fusion has recently become a promising way to deal with these challenges. However, due to the sparsity and uneven distribution of the LiDAR depth data, existing stereo-LiDAR fusion methods tend to ignore the data when their density is quite low or they largely differ from the depth predicted from stereo images. To provide a solution to this problem, we propose a stereo-LiDAR fusion method by first expanding the sparse LiDAR depth to a semi-dense depth with RGB image as reference. Then, based on the semi-dense depth, a varying-weight Gaussian guiding method is proposed to deal with the varying reliability of guiding signals. A multi-scale feature extraction and fusion method is further used to enhance the network, which shows superior performance over traditional sparse invariant convolution methods. Experimental results on different public datasets demonstrate our superior accuracy and robustness over the state of the arts.
Improved Step-Size Schedules for Proximal Noisy Gradient Methods

Noisy gradient algorithms have emerged as one of the most popular algorithms for distributed optimization with massive data. Choosing proper step-size schedules is an important task to tune in the algorithms for good performance. For the algorithms to attain fast convergence and high accuracy, it is intuitive to use large step-sizes in the initial iterations when the gradient noise is typically small compared to the algorithm-steps, and reduce the step-sizes as the algorithm progresses. This intuition has been confirmed in theory and practice for stochastic gradient descent. However, similar results are lacking for other methods using approximate gradients. This paper shows that the diminishing step-size strategies can indeed be applied for a broad class of noisy gradient algorithms. Our analysis framework is based on two classes of systems that characterize the impact of the step-sizes on the convergence performance of many algorithms. Our results show that such step-size schedules enable these algorithms to enjoy the optimal rate. We exemplify our results on stochastic compression algorithms. Our experiments validate fast convergence of these algorithms with the step decay schedules.