Reviewerly/exHarmony huggingface dataset:
Knowledge Graph-Based Semantic Ranking for Efficient Semantic Query

The latent domain knowledge plays an important role in helping to improve efficacy of information search. In order to utilize and mine the latent semantic knowledge behind the query, we propose a knowledge graph-based semantic ranking method for efficient semantic query, where the keywords-based syntactical query is semantically enriched by mining out the latent semantic knowledge of keywords based on knowledge graph. First, a neural network-based knowledge graph embedding model is proposed, where word embedding and entity embedding are learned and optimized using an end-to-end approach, and the semantic knowledge from knowledge graph is fused into the distributed representations of entities. Second, the word-level similarity and entity-level similarity between queries and documents are modeled by interaction matrices, and the strong matching features and soft matching features of the interaction matrices are extracted respectively. Third, we rank documents according to the relevance scores calculated by the above two features. At last, the experiments were made over the related datasets against the traditional keywords-based method, and the results show that the method proposed in this paper can effectively improve the ranking performance.
Multi-Robot Path Planning Using Medial-Axis-Based Pebble-Graph Embedding

We present a centralized algorithm for labeled, disk-shaped Multi-Robot Path Planning (MPP) in a continuous planar workspace with polygonal boundaries. Our method automatically transform the continuous problem into a discrete, graph-based variant termed the pebble motion problem, which can be solved efficiently. To construct the underlying pebble graph, we identify inscribed circles in the workspace via a medial axis transform and organize robots into layers within each inscribed circle. We show that our layered pebble-graph enables collision-free motions, allowing all graph-restricted MPP instances to be feasible. MPP instances with continuous start and goal positions can then be solved via local navigations that route robots from and to graph vertices. We tested our method on several environments with high robot-packing densities (up to 61.6% of the workspace). For environments with narrow passages, such density violates the well-separated assumptions made by state-of-the-art MPP planners, while our method achieves an average success rate of 83%.
Bilateral Knowledge Distillation for Unsupervised Domain Adaptation of Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to learn domain-invariant representations between the labeled source domain and the unlabeled target domain. Existing self- training-based UDA methods use ground truth and pseudo- labels to supervise source data and target data respectively. However, strong supervision in the source domain and pseudo- label noise in the target domain lead to some problems, such as biased predictions and over-fitting. To tackle these issues, we propose a novel Bilateral Knowledge Distillation (BKD) framework for UDA in semantic segmentation, which adopts different knowledge distillation strategies depending on the domain. Specifically, we first introduce a Source-Flow Distillation (SD) to smooth the labels of source images, which weakens the supervision in the source domain. Meanwhile, a Target-Flow Distillation (TD) is designed to extract the inter- class knowledge in the probability map output from the teacher model, which alleviates the influence of pseudo-label noise in the target domain. Considering the class imbalance in semantic segmentation, we further propose an Image-Wise Hard Pixel Mining (HPM) to address this issue without estimating class frequency in the unlabeled target domain. The effectiveness of our framework against existing state-of-the-art methods is demonstrated by extensive experiments on two benchmarks: GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.
Learning Unsupervised Disentangled Capsule via Mutual Information

CapsNet often learns entangled representations that are unfavorable for many learning tasks. In this paper, we introduce Info-CapsNet, a novel and simple unsupervised framework for learning disentangled representations automatically by mutual information. Limiting mutual information between inputs and representations can compress the information capacity of the representations and encourage the capsules to be independent and factorized. Furthermore, variational inference is used to construct a variational bound of the additional information loss and then is replaced by a constraint on the mean of the capsule, it is a very tidy and convenient algorithm to help the intend factors to implement disentangled effect. We also introduce unsupervised dynamic routing algorithm for learning from unlabeled datasets and a novel disentanglement metric that is suitable for capsule. Empirical evaluations suggest that our method achieves the state-of-the-art disentanglement performance compared to baseline on several datasets.
Improving Robustness by Enhancing Weak Subnets

Despite their success, deep networks have been shown to be highly susceptible to perturbations, often causing significant drops in accuracy. In this paper, we investigate model robustness on perturbed inputs by studying the performance of internal sub-networks (subnets). Interestingly, we observe that most subnets show particularly poor robustness against perturbations. More importantly, these weak subnets are correlated with the overall lack of robustness. Tackling this phenomenon, we propose a new training procedure that identifies and enhances weak subnets (EWS) to improve robustness. Specifically, we develop a search algorithm to find particularly weak subnets and explicitly strengthen them via knowledge distillation from the full network. We show that EWS greatly improves both robustness against corrupted images as well as accuracy on clean data. Being complementary to popular data augmentation methods, EWS consistently improves robustness when combined with these approaches. To highlight the flexibility of our approach, we combine EWS also with popular adversarial training methods resulting in improved adversarial robustness.
A Gated Deep Model for Single Image Super-Resolution Reconstruction

Several deep learning-based models for single image super-resolution (SISR) have been presented in recent years. For different image content, however, the performance of each Super Resolution (SR) model varies. For example, one model may outperform others on images with high and intricate textures, while another may outperform others on images of structured scenes. We present a method that uses image content to select the most suitable model for SR reconstruction by taking advantage of each image's dominant content. The obtained results confirm that the proposed model delivers better results for scene-specific content.
Guided Spatial Propagation Network for Depth Completion

Depth completion aims to recover dense depth maps from sparse depth maps using the corresponding RGB images as guides. Learning guided convolutional network (GuideNet) is one of the state-of-the-art (SoTA) depth completion methods. In this letter, we propose a robust and efficient end-to-end guided spatial propagation network (GSPN), which further improves the effectiveness and efficiency of GuideNet through spatial propagation. Specifically, we expand the receptive field of the content-dependent guided kernels through a spatial propagation network without adding additional parameters. And the resources required by GSPN can be adjusted according to the actual situation. Furthermore, the proposed algorithm can better fuse the information from different sensors, which is one of the main problems of depth completion. We demonstrate the effectiveness of GSPN compared to other SoTA methods on KITTI depth completion and NYUv2 datasets.
A k-Anonymity-Based Robust Watermarking Scheme for Relational Database

In the era of big data, secure and controlled data publishing becomes increasingly vital. When data holders publish dataset to data demanders, data holders often (1) protect the copyright of the published dataset and (2) anonymize user’s data by k-anonymity for privacy purpose. Hence, there is a realistic demand of watermarked k-anonymity dataset for ownership. However, there are two important challenges to be addressed: the lack of primary key and the narrow bandwidth channel for watermarked k-anonymity dataset. In this paper, we try to address above challenges by proposing a k-anonymity-based robust watermarking scheme in anonymized dataset by an “one-time” way to achieve both protection of privacy and copyright. This scheme is primary key independent and meets the requirement of keeping the same form with k-anonymity. Experimental studies prove the robustness of watermarking scheme against subset deletion and subset addition attacks.
Together or Apart? Investigating a mediator bot to aggregate bot’s comments on pull requests

Software bots connect users and tools, streamlining the pull request review process in social coding platforms. However, bots can introduce information overload into developers’ communication. Information overload is especially problematic for newcomers, who are still exploring the project and may feel overwhelmed by the number of messages. Inspired by the literature of other domains, we designed and evaluated FunnelBot, a bot that acts as a mediator between developers and other bots in the repository. We conducted a within-subject study with 25 newcomers to capture their perceptions and preferences. Our results provide insights for bot developers who want to mitigate noise and create bots for supporting newcomers, laying a foundation for designing better bots.
Enhancing Traceability Link Recovery with Unlabeled Data

Traceability link recovery (TLR) is an important software engineering task for developing trustworthy and reliable software systems. Recently proposed deep learning (DL) models have shown their effectiveness compared to traditional information retrieval-based methods. DL often heavily relies on sufficient labeled data to train the model. However, manually labeling traceability links is time-consuming, labor-intensive, and requires specific knowledge from domain experts. As a result, typically only a small portion of labeled data is accompanied by a large amount of unlabeled data in real-world projects. Our hypothesis is that artifacts are semantically similar if they have the same linked artifact(s). This paper presents TRACEFUN, a new approach to enhance traceability link recovery with unlabeled data. TRACEFUN first measures the similarities between unlabeled and labeled artifacts using two similarity prediction methods (i.e., vector space model and contrastive learning). Then, based on the similarities, newly labeled links are generated between the unlabeled artifacts and the linked objects of the labeled artifacts. Generated links are further used for TLR model training. We have evaluated TRACEFUN on three GitHub projects with two state-of-the-art DL models (i.e., Trace BERT and TraceNN). The results show that TRACEFUN is effective in terms of a maximum improvement of F1-score up to 21% and 1,088%, respectively for Trace BERT and TraceNN.
Detecting Exams Fraud Using Transfer Learning and Fine-Tuning for ResNet50

Online examinations gradually become popular due to Covid 19 pandemic. Environmentally friendly, saving money, and convenient,... are some of the advantages when taking exams online. Besides its major benefits, online examinations also have some serious adversities, especially integrity and cheating. There are some existing proctoring systems that support anti-cheating, but most of them have a low probability of predicting fraud based on students’ gestures and posture. As a result, our article will introduce an online examination called ExamEdu that supports integrity, in which the accuracy of detecting cheating behaviors is 96.09% using transfer learning and fine-tuning for ResNet50 Convolutional Neural Network.
LDGAN: Latent Determined Ensemble Helps Removing IID Data Assumption and Cross-node Sampling in Distributed GANs

Generative Adversarial Networks (GANs) have received a lot of attention due to their powerful generative ability, and many related studies have been carried out. Among them, deploying GANs in distributed scenarios has become a hot research topic due to the sharp increase in the capacity of training data. However, distributed GANs face many challenges, such as different data distribution of each node, limitation of cross-node data sampling, etc. The previous methods defaulted to some strong assumptions to alleviate these problems, but the performance would be degraded once they encountered a natural scene. In this paper, we propose Latent Determined Generative Adversarial Network (LDGAN), a network introducing latent determined ensemble to guide the training of the generator without specific assumptions. LDGAN measures the difference in the latent space of data distribution at each node and uses this as the weight of the feedback information of each discriminator, and there is no need for any cross-node data sampling and the independent and identical distribution (iid) data assumption. Our experiments show that on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, the images generated by LDGAN are more realistic and diverse, and the achieved Fréchet Inception Distance (FID) is 15.0%, 22.8%, and 15.3% smaller than the state-of-the-art models, respectively.
A Study on Human Image Segmentation for Evaluation of Segmentation Methods

In this paper we pose the question of how well segmentation of images done by humans are well suited to be used as ground truth in training and evaluating image segmentation methods. In general we show that humans prefer image segmentation done by other humans, even though there are cases, where subjects reject segmentation done by other humans. In fact we found cases in which humans gave a higher marks to image segmentation by the algorithms then of those done by humans. We also show that humans do not like segmentation methods that tend to produce regions having (approximately) same sizes, pixel-wise. They preferred segmentation with variation in region size.
Motion Transformer for Unsupervised Image Animation

Image animation aims to animate a source image by using motion learned from a driving video. Current state-of-the-art methods typically use convolutional neural networks (CNNs) to predict motion information, such as motion keypoints and corresponding local transformations. However, these CNN based methods do not explicitly model the interactions between motions; as a result, the important underlying motion relationship may be neglected, which can potentially lead to noticeable artifacts being produced in the generated animation video. To this end, we propose a new method, the motion transformer, which is the first attempt to build a motion estimator based on a vision transformer. More specifically, we introduce two types of tokens in our proposed method: i) image tokens formed from patch features and corresponding position encoding; and ii) motion tokens encoded with motion information. Both types of tokens are sent into vision transformers to promote underlying interactions between them through multi-head self attention blocks. By adopting this process, the motion information can be better learned to boost the model performance. The final embedded motion tokens are then used to predict the corresponding motion keypoints and local transformations. Extensive experiments on benchmark datasets show that our proposed method achieves promising results to the state-of-the-art baselines. Our source code will be public available.
Robust Graph Convolutional Clustering With Adaptive Graph Learning

Graph-based clustering learns underlying data representation by employing topological graph structure. Recently, Graph Convolutional Network (GCN)-based clustering methods have accumulated great attentions and achieved great performance. Its performance is seriously determined by the quality of pre-provided graph, which is usually constructed by predefined model (such as k-Nearest-Neighbor). However, the graph may be inaccurate due to the noises and fixed graph limits flexibility of model learning. In this paper, we propose a Robust Graph Convolutional Clustering (RGCC) method, which adaptively learns a clean and accurate graph from original graph. Specifically, adaptive graph with low-rank and sparse structures be learned during the optimization process, which can better encode structural information of data than fixed graph. Then, to explore the local connectivity of data, graph Laplacian constraint is introduced. Thus, optimal graph relationships and discriminative representation of data could be simultaneously learned, which improves the flexibility of the RGCC model. By designing a self-supervised clustering module, it can self-supervise the node representations learning and thus explore the better clustering structure. Experimental results on several benchmark databases reveal the superiority of the proposed RGCC approach.
Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams

Near-real-time monitoring and classification of business process event streams is becoming more and more prominent. This also includes ensuring data quality for the application of downstream online process mining activities and therefore identify and classify incorrect process behavior of incoming event streams in an online setting, what is considered too little in existing approaches. In this paper, we present an online classification approach that supports monitoring and anomaly detection in event streams at the event level. Possible process drifts can be handled by an online learning workflow. By integrating two explanatory components, the results of the online classification are made transparent and comprehensible. Through a technical experiment, the performance of the classification approach is evaluated based on different data sets. Thereby, the classification model achieves an average F1 score of 0.877 with an average processing time of ∼15 ms per event.
Performance Comparison of Several Robot Visual Servo Path Planning Approaches

Path planning is essential in improving performance and robustness of robot visual servo controllers under real-world constraints. This paper compares the performances of three major visual servo path planning approaches, including optimization-based, potential field-based, and sampling-based approaches, in which both image and camera paths are under consideration. These approaches are employed to a collaborative robot with 7 degrees of freedom equipped with an eye-in-hand camera in a high-fidelity simulation platform, where field of view constraints, workspace constraints, and obstacles in the camera workspace are considered during comparisons. The pros and cons of each approach are clarified by analyzing performance indices such as computing time and camera path length. The optimization-based approach can generate an optimal path, but the convexity of the optimization problem and the optimizer for solving it affect computational efficiency. The potential field-based approach has the fastest computational speed in complex environments, but the generated path may oscillate, dramatically increasing the path length. The sampling-based approach has strong randomness, which leads to a most irregular path.
Scenario-based Requirements Engineering for Complex Smart City Projects

Various stakeholders with different backgrounds are involved in Smart City projects. These stake-holders define the project goals, e.g., based on participative approaches, market research or innovation management processes. To realize these goals often complex technical solutions must be designed and implemented. In practice, however, it is difficult to synchronize the technical design and implementation phase with the definition of moving Smart City goals. We hypothesize that this is due to a lack of a “common language” for the different stakeholder groups and the technical disciplines. We address this problem with scenario-based requirements engineering techniques. In particular, we use scenarios at different levels of abstraction and formalization that are connected end-to-end by appropriate methods and tools. This enables fast feedback loops to iteratively align technical requirements, stakeholder expectations, and Smart City goals. We demonstrate the applicability of our approach in a case study with different industry partners.
Balancing Run-Length Straight-Line Programs

It was recently proved that any SLP generating a given string w can be transformed in linear time into an equivalent balanced SLP of the same asymptotic size. We show that this result also holds for RLSLPs, which are SLPs extended with run-length rules of the form $$A \rightarrow B^t$$ for $$t>2$$ , deriving $$\texttt {exp}(A) = \texttt {exp}(B)^t$$ . An immediate consequence is the simplification of the algorithm for extracting substrings of an RLSLP-compressed string. We also show that several problems like answering RMQs and computing Karp-Rabin fingerprints on substrings can be solved in $$\mathcal {O}(g_{rl})$$ space and $$\mathcal {O}(\log n)$$ time, $$g_{rl}$$ being the size of the smallest RLSLP generating the string, of length n. We extend the result to solving more general operations on string ranges, in $$\mathcal {O}(g_{rl})$$ space and $$\mathcal {O}(\log n)$$ applications of the operation. In general, the smallest RLSLP can be asymptotically smaller than the smallest SLP by up to an $$\mathcal {O}(\log n)$$ factor, so our results can make a difference in terms of the space needed for computing these operations efficiently for some string families.
Reinforcement-Mining: Protecting Reward in Selfish Mining

Selfish mining is notorious for receiving additional rewards disproportionate to the attacker’s mining power in Proof-of-Work (PoW) consensus-based blockchain, e.g., Bitcoin. Unfair reward distribution may cause partial honest miners to quit blockchain mining, which will seriously weaken the security of the PoW blockchain since the security is guaranteed by strong mining power. Various efforts have been proposed to alleviate this problem, but are generally expensive to implement, e.g., upgrading the blockchain backbone protocol. In this work, we propose a method, named Reinforcement-Mining, to protect honest miners’ mining rewards to mitigate the harm of selfish mining. The key insight of Reinforcement-Mining is to employ a deep reinforcement learning framework to choose the optimal policy for honest miners to protect their rewards when the blockchain suffers from a selfish mining attack. Experiments on mining reward and chain quality property are conducted respectively. The analysis of experiment results demonstrates that our approach moderates the unfair reward distribution of selfish mining and improves the chain quality property of the blockchain. The proposed method may be still far from practical application, however, it provides a new perspective for defense against selfish mining.
Analysis of Deep Learning-Based Image Steganalysis Methods Under Different Steganographic Algorithms

In this paper, we present an experiment to evaluate the performance of deep-learning-based steganalysis methods under different steganographic algorithms and payloads. We designed and implemented a simple and efficient convolutional neural network (CNN) steganalysis model. This model combines SRM [10] filters and 2D Gabor filters to initialize the CNN's filters in the feature extraction layers. The extracted features are passed to a fully connected neural network classifier to detect images containing hidden messages. We generated several datasets using different steganographic algorithms and payloads to train and test our CNN model. We designed several experiments to evaluate the detection rate of the proposed model under separate training-testing conditions.