Reviewerly/exHarmony huggingface dataset:
Application of Different Techniques for Underwater Image Processing- A Systematic Review

Abstract The area of underwater image processing has gained huge attention among the researchers because of its wide application. Image processing is the approach of developing the input image quality so that it would be understood easily by users in future. Image processing develops the data content of image and changes the images visual impact on the observer. Image processing intensifies the image features and accentuates the features of image like corners, contrast to enhance the photographs display which is much helpful for study and examination. Several methods and techniques have been used by researchers worldwide in order to resolve the underwater image processing issues. This paper reviews different techniques of underwater image processing and also compares the techniques among each other. This paper discusses about the filtering method, histogram-based equalization method and particle swarm optimization techniques and compares them with their advantages.
Time-series interval prediction under uncertainty using modified double multiplicative neuron network

This paper presents a hybrid intelligent approach for constructing prediction intervals (PIs) of terrain profiles over time under uncertainty. It utilizes the double multiplicative neuron (DMN) model and the modified particle swarm optimization (MPSO) algorithm to calculate the upper and lower bounds of unknown elevations ahead on terrain profiles based on the vehicles’ track. MPSO withholds particles generating the positive PIs in the training epochs, in order to prevent the occurrence of unreasonable upside-down PIs that are brought by conventional methods. MPSO adjusts the parameters of the DMN model iteratively by minimizing the value of the proposed cost function. The fitness function aims to enhance DMN’s capability of forecasting terrain trends by integrating a trend indicator with PIs coverage probability and interval widths. This study utilizes the terrain profiles of 3 arc-seconds resolution to verify the effectiveness of the proposed MPSO-DMNT approach for one-step and multi-step PIs estimation. Experimental results demonstrate that the proposed approach (1) overcomes the limitations of the conventional PIs indicators; (2) improves the prediction accuracy for terrain trends by 18.8% in the training data and 15.4% in the testing data, and reduces the computational burden by 31.6% in the training data and 8% in the testing data over the lower upper bound estimation (LUBE) method; (3) achieves comparative coverage probability and interval widths to LUBE using a low-complexity single-layered network. The proposed hybrid approach can be used as an auxiliary decision-making tool for terrain avoidance and terrain following in flight.
The Power of Nested Parallelism in Big Data Processing  Hitting Three Flies with One Slap 

Many common data analysis tasks, such as performing hyperparameter optimization, processing a partitioned graph, and treating a matrix as a vector of vectors, offer natural opportunities for nested-parallel operations, i.e., launching parallel operations from inside other parallel operations. However, state-of-the-art dataflow engines, such as Spark and Flink, do not support nested parallelism. Users must implement workarounds, causing orders of magnitude slowdowns for their tasks, let alone the implementation effort.
Boosting Graph Similarity Search through Pre-Computation

Graph similarity search is to retrieve all graphs from a graph database whose graph edit distance (GED) to a query graph is within a given threshold. As GED computation is NP-hard, existing solutions adopt the filtering-and-verification framework, where the main focus is on the filtering phase to reduce the number of GED verifications. However, existing filtering techniques have inherently limited filtering capabilities, and suffer from a large number of GED verifications. To address the problem, in this paper, we propose a fundamentally different approach that utilizes pre-computed GEDs between data graphs in the filtering phase. Based on the approach, we develop a novel search framework Nass, which substantially reduces the verification workload. Because the efficiency of GED computation is essential in GED pre-computation, not to mention the verification of candidate graphs, we also propose an efficient GED computation algorithm as a part of Nass. We conduct extensive experiments on real datasets, and show Nass significantly outperforms the state-of-the art solutions.
The Logarithmic Dynamic Cuckoo Filter

The emergence of big data applications makes efficient representation for large-scale dynamic data sets a challenge. The state-of-the-art design, i.e., the dynamic cuckoo filter (DCF), provides extensible approximate set representation by employing a novel chain based data structure which allows appending new building cuckoo filter blocks. However, such a design needs linearly increasing computation costs and memory space when a set scales. This makes it inefficient for big data sets. In this paper, we propose a novel data structure for dynamic big data sets, called logarithmic dynamic cuckoo filter (LDCF). LDCF uses a novel multi-level tree structure and reduces the worst insertion and membership testing times from O(N) to O(1), where N is the size of the set. At the same time, LDCF reduces the memory cost of DCF as the cardinality of the set increases. Comprehensive experiment results show that LDCF significantly reduces the membership checking time and the memory space cost for large-scale datasets compared to state-of-the-art designs.
Staged-Learning: Assessing the Quality of Screen Content Images from Distortion Information

The small volume of the existing screen content images (SCIs) database with human ratings restricts the training processes of no-reference (NR) image quality assessment models based on traditional machine learning and deep learning. In this letter, we propose an NR model called the multi-task distortion-learning network to jointly analyse the distortion types and distortion degree of SCIs to be the prior knowledge for predicting the SCIs quality. Specifically, we first generate sufficient distorted SCIs labelled with the distortion type and degree, which does not need much effort to conduct subjective scoring experiments. Then, relying on these data, we pre-train a multi-task learning network to obtain strong prior knowledge about assessing the image quality. Finally, we further jointly train a quality assessment network with an attention module that simulates the mechanism of processing visual signals in the human eyes. The experimental results on the public SCIs databases show that the proposed model is competitive against other state-of-art approaches and achieves better consistency with the human vision system.
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos

In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and LiOTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset. Our project is available at https://sijiesong.github.io/co-grounding.
Social Network Analysis Based on Combining Probabilistic Models with Graph Deep Learning

Social network analysis (SNA) has emerged as a key technique in modern sociology and has gained achievements in demography, communication research, development studies, sociolinguistics, and computer science. Social network data is often in the form of graphs, a significant amount of research on graph to addressing node and graph classification and graph fulfillment has been applied to the SNA. The paper presents the Bayesian graph deep learning framework for the case of classified mixed node random block models to classify the topic of social posts as nodes by creating a homogeneous graph with links between them. The experimental results show that the Bayesian formulation provides better performance in topic classification in social during the training process.Dinh, Xuan Truong Van Pham, Hai
S3M: Siamese Stack (Trace) Similarity Measure

Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application's bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information. In this paper, we present S3M ("extreme") - the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.
Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction

This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.
Single-Shot Freestyle Dance Reenactment

The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unseen during training. The method combines three networks: (i) a segmentation-mapping network, (ii) a realistic frame-rendering network, and (iii) a face refinement network. By separating this task into three stages, we are able to attain a novel sequence of realistic frames, capturing natural motion and appearance. Our method obtains significantly better visual quality than previous methods and is able to animate diverse body types and appearances, which are captured in challenging poses.
Camera Distances and Shot Sizes in Cinematic Virtual Reality

This paper describes the impact of different camera distances in cinematic virtual reality. For our findings, we took a closer look at proxemics, the study on how humans behave regarding space and distances. We explored the four proxemics distances (intimate, personal, social, public) and adapted them to camera distances in cinematic virtual reality. For the user study, a stereoscopic movie with a speaking person was produced for four different distances. The results show that different distances cause different feelings in viewers similar to shot sizes in traditional movies. In our scenario - a person in a museum is speaking about an exhibit - the personal distance was preferred by the participants. As an outcome of this work, the proxemics distances were put in relation to well-known shot sizes used in traditional filmmaking.
A subgraph-based knowledge reasoning method for collective fraud detection in E-commerce

Fraud detection is essential for e-commerce platforms to maintain a fair business environment. Many existing works propose manually designed methods such as label propagation and dense block mining rules on built user-item graphs to detect fraud behaviours, but they are always heuristic and thus have limited performance. Other learning-based methods can either handle only the fraud detection problem well in the transductive scenario when there is only structural information or require rich content features to obtain a good inductive ability. Considering that content features are not always available in practice and there are usually many fraudulent behaviours that belong to newly emerging users and items, how to learn effective inductive rules with structures only is still underexplored. In this paper, we propose a subgraph-based method named SubGNN for collective fraud detection. In SubGNN, first, we extract the subgraphs around the given edges (user behaviours) to be tested. Then, we remove nodes' global IDs so that SubGNN is entity-independent. Finally, by learning knowledge reasoning rules on extracted heterogeneous subgraphs using our proposed relational graph isomorphism network (R-GIN), a powerful graph neural network (GNN) model, SubGNN can achieve precise fraud detection. Experiments are conducted on publicly available Amazon and Yelp datasets and a newly collected Taobao dataset. The results clearly show the advantages and prospects of our method. When using SubGNN to detect fraudulent transactions on Taobao, the precision is higher than 0.99 and more than 90% of fraud samples are recalled.
MetaSeg: A survey of meta-learning for image segmentation

Big data-driven deep learning methods have been widely used in image or video segmentation. However, in practical applications, training a deep learning model requires a large amount of labeled data, which is difficult to achieve. Meta-learning, as one of the most promising research areas in the field of artificial intelligence, is believed to be a key tool for approaching artificial general intelligence. Compared with the traditional deep learning algorithm, meta-learning can update the learning task quickly and complete the corresponding learning with less data. To the best of our knowledge, there exist few researches in the meta-learning-based visual segmentation. To this end, this paper summarizes the algorithms and current situation of image or video segmentation technologies based on meta-learning and point out the future trends of meta-learning. Meta-learning has the characteristics of segmentation that based on semi-supervised or unsupervised learning, all the recent novel methods are summarized in this paper. The principle, advantages and disadvantages of each algorithms are also compared and analyzed.
CCCC: Corralling Cookies into Categories with CookieMonster

Browser cookies are ubiquitous in the web ecosystem today. Although these cookies were initially introduced to preserve user-specific state in browsers, they have now been used for numerous other purposes, including user profiling and tracking across multiple websites. This paper sets out to understand and quantify the different uses for cookies, and in particular, the extent to which targeting and advertising, performance analytics and other uses which only serve the website and not the user add to overall cookie volumes. We start with 31 million cookies collected in Cookiepedia, which is currently the most comprehensive database of cookies on the Web. Cookiepedia provides a useful four-part categorisation of cookies into strictly necessary, performance, functionality and targeting/advertising cookies, as suggested by the UK International Chamber of Commerce. Unfortunately, we found that, Cookiepedia data can categorise less than 22% of the cookies used by Alexa Top20K websites and less than 15% of the cookies set in the browsers of a set of real users. These results point to an acute problem with the coverage of current cookie categorisation techniques.
Towards Understanding People’s Experiences of AI Computer Vision Fitness Instructor Apps

The recent rise in on-device AI computer vision and dialogue systems has facilitated a growing number of AI fitness related instructional apps. However, these technologies have yet to be explored within the HCI community. To investigate this domain we recruited 12 participants and asked them to engage with five recently launched AI fitness instructor apps. We interviewed participants and thematically analysed transcripts to understand their experience and expectations of these technologies. Our qualitative analysis outlines five main themes focusing on; limitations of computer vision, visual feedback, dialogue with the AI, adapting to the user, and working out with the instructor. Based upon our findings we present five design considerations for designers that relate to three key areas: feedback and motivation, personalising the experience, and building a relationship with the AI. We contribute a first look into people's initial experiences with on-device AI fitness instructor applications and we provide design considerations to guide future contextually-aware AI research in this domain.
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm.
Towards User-Driven Neural Machine Translation

A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text.For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs).However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of userbehavior annotated parallel dataset.To fill this gap, we introduce a novel framework called user-driven NMT.Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion.Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.Experimental results confirm that the proposed user-driven NMT can generate user-specific translations.
Probabilistic Robust Path Planning for Nonholonomic Arbitrary-Shaped Mobile Robots Using a Hybrid A* Algorithm

To guarantee safe motion planning, the underlying path planning algorithm must consider motion uncertainties and uncertain state information related to static, and dynamic obstacles.This paper proposes novel hybrid A* (HA*) algorithms that consider the uncertainty in the motion of a mobile robot, position uncertainty of static obstacles, and position and velocity uncertainty of dynamic obstacles.Variants of the HA* algorithm are proposed wherein a soft constraint is used in the cost function instead of chance constraints for probability guarantees.The proposed algorithm offers a tradeoff between the traveling distance and safety of paths without pruning additional nodes.Furthermore, this paper introduces a method for considering the shape of a mobile robot for probabilistic safe path planning.The proposed algorithms are compared with existing path planning algorithms and the performance of the algorithms is evaluated using the Monte Carlo simulation.Compared with the related probabilistic robust path planning algorithms, the proposed algorithms significantly improved safety without excessively increasing travel distance and computational time.The results also showed that dynamic obstacles were safely avoided, which is in contrast to the conventional HA* algorithm that has a high probability of collision.In addition, considering the shape of the robot in the proposed probabilistic approach led to safer paths overall.