Reviewerly/exHarmony huggingface dataset:
Perception and Recognition of Underwater Cable Based on Deep Learning

Aiming at the problems of blurring, low contrast and high noise of underwater target data sets, this paper proposes an underwater image enhancement method based on weight fusion to enhance the image of underwater cables. This method introduces the idea of image fusion to fuse the pictures processed by the white balance algorithm and the auto level algorithm according to a certain weight. It achieves the performance of color correction and target enhancement. The characteristics of the underwater cable are clearer after the image enhancement preprocessing. In addition, an underwater target recognition system based on deep learning is established. The Faster RCNN and SSD deep learning algorithms are utilized to realize underwater cable recognition. Good recognition accuracy and performance are demonstrated through an experiment on data set of underwater cables.
Fast identification of a human skeleton-marker model for motion capture system using stochastic gradient descent method

A method to identify a human model as a rigid kinematic chain and marker arrangement, which is referred to as a human skeleton-marker model in this paper, from the data collected by an optical motion capture system is proposed. The model is utilized for convenient and accurate motion analyses based on robotics computations. A chicken-and-egg problem to find the model to estimate the whole-body posture and the whole-body posture to fit the model simultaneously is resolved by a dual-phase nonlinear least square error minimization. The computation cost mainly due to the numerical computation of the gradient of the cost function with the inverse kinematics is significantly reduced by applying the stochastic gradient descent (SGD) method. Despite only using partial samplings for the estimation of the global gradient, it does not sacrifice the accuracy since it stochastically reflects information of the overall motion sequence through the iterations.
A Simple Model of Reading Eye Movement Based on Deep Learning

At present, an increasing amount of research is being conducted on human cognitive behaviour, and reading eye-movement modelling is a research hotspot in cognitive linguistics. However, existing reading eye-movement models are complicated and require a large number of hand-crafted features. To address these issues, this paper improves upon the fixation granularity processing mode and the regression processing mode of the traditional reading eye-movement models and proposes a reading eye-movement fixation sequence labelling method to construct a simpler model. The proposed model is based on a multi-input deep-learning neural network, which takes advantage of deep learning to reduce the number of required hand-crafted features and integrates knowledge from the field of cognitive psychology to increase its accuracy. To meet the data-size requirements of the deep-learning model, this paper also proposes a reading eye-movement data augmentation method. The experimental results show that the proposed method can describe the actual process of reading eye-movement intuitively and that the simple reading eye-movement models based on this method can obtain a similar accuracy with existing models by using fewer hand-crafted features.
Laplace ℓ1 robust Student’s T-filter for attitude estimation of satellites

A Laplace ℓ 1 robust student’s T-filter is presented for satellites to estimate their attitude state despite severe measurement noise and modeling error. Although the student’s T-filter (STF) has the capability of handling measurement noise, it cannot address the unknown modeling error. It is further sensitive to the degree of freedom (DOF). Hence, the measurement covariance is updated by using the maximum correntropy criterion to accommodate the covariance of unknown modeling error in robust filtering design. Moreover, the Laplace distribution is derived to reduce the influence of the DOF parameter by forming a surrogate function of an optimization problem. Then, the majorization minimization approach is formulated to solve such an optimization problem and present the proposed filtering algorithm in the STF framework. Numerical simulation of applying the proposed attitude estimation scheme is performed by comparing it with the third/fifth-order cubature Kalman filters.
GGDs

We propose Graph Generating Dependencies (GGDs), a new class of dependencies for property graphs. Extending the expressivity of state of the art constraint languages, GGDs can express both tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. We provide the formal definition of GGDs, analyze the validation problem for GGDs, and demonstrate the practical utility of GGDs.
A Comprehensive Predictive Evaluation Model Based on T-S Fuzzy Neural Network and Regression Fitting Cross Analysis

With the outbreak of COVID-19 at the end of 2019, under the requirement of in-depth study and implementation of the overall national security concept, people's health level has become the focus of people's attention, and it is also the most basic and fundamental important indicator to reflect people's livelihood. Taking Shenzhen, a city with strong comprehensive economic level, as an example, this paper uses data processing to select six major influencing factors, such as medical treatment and environment, and uses the method of regression and fitting crossover analysis to establish the fitting curve between factors and people's health level for prediction, and obtains the regression equation. On this basis, T-S Fuzzy Neural Network (T-S FNN) is used to divide the evaluation grade of regression model, make an effective evaluation of multiple factors of people's physical health level, establish a comprehensive prediction evaluation model, and obtain the gradient grade of factors affecting people's physical health correlation and their own direct factors.
Tabletap: A Collaborative Web Application for Large Screen Surfaces Devices

Traditionally, humans use conventional tables at home, offices, and as design centers, these are elements found everywhere on which multiple tasks are performed. Such objects have the particularity to gather many individuals in order to carry out any collaborative tasks. Large Screen Surface is the digital evolution of tables, since they are large touch screens that can be set horizontally or vertically allowing multitouch interaction by many users. In recent years, interest in the development of applications for such devices has increased, since its screens offer a large visual field in high resolution; additionally, its physical configuration allows multiple users to work simultaneously in situ. This research proposes the development and implementation of Tabletap, a collaborative web application which allows multiple users to create content such as concept maps, moodboards, timelines, etc. Users have a collective library which contains visual resources such as videos, shapes, images and text input.
Grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree

This paper presents an algorithm for grouping strokes. This method includes two stages. Firstly, a set of strokes is transformed into a set of hypotheses that a group of strokes matches the pattern. For this purpose, a method for comparing small groups of strokes is proposed. Then, the set of hypotheses is selected with the use of a decision tree to get a proposition of a word.
Single image dehazing algorithm based on pyramid mutil-scale transposed convolutional network

In this paper, a novel single image dehazing method based on pyramid multi-scale transposed convolutional network (MST-Net) is proposed. Conventional haze removal algorithms based on the atmospheric scattering model may lead to some problems such as incomplete dehazing and colour distortion due to the inaccurate parameter approximations or indirect image optimization and reconstruction. Therefore, we design a real end-to-end image dehazing network to directly learn the mapping relationship between hazy images and the corresponding clear images. In this network, the cascaded feature extraction blocks extract the diversified feature information of the input images by multi-channel concatenation structure, which can effectively fuse the local features of the first convolution layer into the semantic features of subsequent layers in the block. In order to reconstruct high-quality dehazed images relieving the colour distortion, we design a multi-scale transposed convolution block to gradually expand the resolution of the obtained feature maps, and introduce skip connections from the feature extraction module to supplement the detailed information of the feature map pyramid. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze completely and achieve superior performance in subjective and objective evaluation over the other state-of-the-art methods.
Use All Your Skills, Not Only The Most Popular Ones

Reinforcement Learning (RL) has shown promising results across various domains. However, applying it to develop gameplaying agents is challenging due to sparsity of extrinsic rewards, where agents get rewards from the environments only at the end of game levels. Previous works have shown that using intrinsic rewards is an effective way to deal with such cases. Intrinsic rewards allow to incorporate basic skills in agent policies to better generalize over various game levels. In a gameplay, it is common that certain actions (skills) are observed more often than others, which leads to a biased selection of actions. This problem boils down to a normalization issue in formulating the skill-based reward function. In this paper, we propose a novel solution to this problem by taking into account the frequency of all skills in the reward function. We show that our method improves the performance of agents by enabling them to select effective skills up to 2.5 times more frequently than that of the state-of-the-art in the context of the match-3 game Candy Crush Friends Saga.
Brightness in Cinema: When is it too bright?

Recent advances in display technologies enable home and cinema display systems to reach higher peak luminance and perceived Full Screen White (FSW) levels while also improving on black level capabilities. This next generation of imaging technology will help creators deliver content of higher visual quality, increasing the overall Quality of Experience (QoE) of viewers. However, there is growing concern in the cinema industry that possible “mismanagement” or “misuse” of the new luminance capabilities may result in viewers' visual discomfort. A typical example of potential “misuse” is abrupt temporal brightness transitions during a scene change or even within the same scene. Prior studies, which were conducted in home environments, reported that displaying a higher amount of light could potentially lead to viewers' visual discomfort. However, no study has been conducted in cinema environment. In this work, we quantify the human observer tolerance to temporal brightness transitions in a cinema environment by performing four subjective experiments. The different experiments evaluate the impact of various distributions of light on screen, namely full screen, highlights, noise and real images. Results of the experiments indicate that the light distribution as well as the peak luminance values have minimum impact on viewers' tolerance of temporal brightness transitions. Indeed, observers reported visual discomfort only when the total amount of light on screen varied significantly. Specifically, for average on-screen luminance equal to both 0.1 or 1 cd/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , viewers rated as “slightly annoying” and “annoying” transitions to average on-screen luminance as low as 35 cd/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 75 cd/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively. It is to be noted that viewers reported visual discomfort at a similar transition intensity for both saturated colors and white. The results of our study could provide useful insight to content creators, for example providing a warning that temporal transitions may cause visual discomfort.
Evaluating Similarity Measures for Dataset Search

Dataset search engines help scientists to find research datasets for scientific experiments. Current dataset search engines are query-driven, making them limited by the appropriate specification of search queries. An alternative would be to adopt a recommendation paradigm ("if you like this dataset, you'll also like..."). Such a recommendation service requires an appropriate similarity metric between datasets. Various similarity measures have been proposed in computational linguistics and informational retrieval. The goal of this paper is to determine which similarity measure is suitable for a dataset search engine. We will report our experiments on different similarity measures over datasets. We will evaluate these similarity measures against the gold standards which are developed for Elsevier DataSearch, a commercial dataset search engine. With the help of F-measure evaluation measure and nDCG evaluation measure, we find that Wu-Palmer Similarity, a similarity measure which is based on hierarchical terminologies, can score quite good in our benchmarks.
Efficient Discovery of Bent Function Using Reed-Muller Subsets

Bent functions are functions that have the biggest distance from all linear binary functions. Attacks against cryptosystems can be defeated when these systems use bent functions. Generalisation of bent functions is still unknown and when they have many variables, it is very difficult to find them. Therefore, the construction of bent functions is done by using discovery of them within a subset of functions. Thus, this paper describes a method for creating groups of functions in Reed-Muller (RM) domain within which efficient detection of bent functions is performed. As follows, the search space for discovery is limited, and it can be defined for various subsets of Boolean functions. Experimental results confirm that the time required for discovery of bent function when using RM subsets are smaller than when using general random discovery.
Towards family‐centered health technologies that support distributed families on sustainable healthy practices together

Abstract Family relationships can be resources to foster individuals' healthy behaviors. However, in many families, different factors can prevent members in supporting one another around positive health outcomes. A systematic understanding of these factors is crucial for developing appropriate solutions to reduce barriers on family collaboration in health promotion. In this paper, we focus on one factor that affects family support in healthy living: geographic distance. We present a formative study composed by two research explorations which aimed to examine distributed family members' needs and challenges to engage in healthy living together. First, an interview study helped us understand families' practices in engaging in health conversations. Then, findings from focus group discussions provided necessary knowledge to explore the use of technology to support conversations around health topics by distributed families. We draw on our findings to present practical recommendations for researchers working towards supporting intergenerational families on healthy practices collectively when living apart.
Multiple Classifier of Traffic Accident Based on Matter-Element Analysis

Abstract The paper establishes a new multiple classifier for grading the traffic accident based on the Hard Decision Model and the parallel topological structure. Logistic Regression, decision tree(DT) and BP Neural Network are selected as the base classifiers and matter-element analysis is employed as the fusion algorithm to improve the traditional topological structure. Matter-Element analysis has potential to increase the objectivity and dynamic in the criterion setting of the multiple classifier by combining the confidence coefficient of the base classification results. The accuracies of different base classifiers are used for weight factors calculation instead of the analytic hierarchy process (AHP) or the fuzzy synthetic evaluation (FSE) to avoid the influence of human factors. 200 recordings of the traffic accident are selected as a case study for methodology verification. The results show that compared with the base classifiers, the capacity to identify the class of traffic accident can be strengthened apparently by the multiple classifier based on confidence coefficient and Matter-Element Analysis Meanwhile, this classifier provides the best opportunity to avoid many bad conditions of single model, such as overfitting and under-fitting.
Profiling JVM for AI Applications Using Deep Learning Libraries

In contemporary times, artificial intelligence (AI) techniques are widely used in development of software applications. Most of the AI-based applications are designed to perform complex tasks such as medical diagnosis, data analytic, human assistants, etc. The performance of such applications depends on development environment. Virtual machines are commonly being used for such development environments. These act as middlewares that support necessary tools for execution of applications. Java Virtual Machine (JVM) is one of the popular virtual environment which is used for several applications. It provides a secure, flexible, and fast execution environment. Therefore, we choose JVM to explore its suitability for AI applications. In this paper, we analyze JVM performance for different AI applications which include deep learning libraries. We use a profiling tool visualVM which profiles JVM performance for running applications. Our goal is to explore key strengths of JVM for AI applications. This in-depth analysis of JVM may help the developer community to choose an appropriate environment for AI applications development.
On the Effects of File-level Information on Method-level Bug Localization

CONTEXT: Automated bug localization is expected to help developers reducing effort and has been studied for years. One of the major approaches for this topic is Information Retrieval (IR) based bug localization. It takes a bug report and gives a ranking list of suspicious software modules based on the text similarity. Method-level bug localization is more desirable than file-level to reduce developers' cost to examine the ranking list. A challenge is that methods have little information inside. OBJECTIVE: To examine whether method-level bug localization can be improved with file-level information around a method. METHOD: An empirical evaluation was conducted with public data sets. A bug localization method BLUiR was used as a testbed. RESULTS: We found the class name of a method contributed to improving method-level bug localization. The comments outside the method often improved the performance but sometimes degraded it. The other elements hardly improved the performance. CONCLUSIONS: It is worth to add the class name for improving method-level bug localization. The comments are also worth trying.
Adaptive recommendation for question decomposition in Web-based investigative learning

The Web allows learners to search for a huge number of Web resources/pages to investigate any question in a self-directed way. In Web-based investigative learning, learners need to decompose a question into sub-questions to create a learning scenario for elaborate investigation. In our previous work, we have developed a cognitive tool called interactive Learning Scenario Builder (iLSB). However, we have found that some learners finish investigative learning with insufficient question decomposition even if they use iLSB. This suggests the necessity of promoting question decomposition, which requires iLSB to recommend sub-questions to be decomposed in an adaptive way. This paper proposes a method for diagnosing insufficiency of learning scenario created by learners to extract and recommend keywords representing sub-questions to be decomposed by means of Linked Open Data (LOD), which have some relevance to questions decomposed within the scenario. This paper also reports a case study whose main purpose was to ascertain whether such an adaptive recommendation could promote wider and deeper question decomposition. The results suggest that it contributes to promoting learning scenario creation.
PSU: A Framework for Dynamic Software Updates in Multi-threaded C-Language Programs

A Dynamic Software Update (DSU) system enables an operator to modify a running program without interrupting its execution. However, creating a DSU system to allow programs written in the C programming language to be modified while they are executing is challenging. This paper presents the Portable Software Update (PSU) system, a new framework that allows the creation of C-language DSU programs. PSU offers a simple programming interface to build DSU versions of existing C programs. Once a program is built using PSU, updates can be applied by background threads that have negligible impact on the execution of the program. PSU supports multi-threaded and recursive programs without the use of safe points or thread blocking. PSU uses function indirection to redirect DSU functions calls to the newest version of the function code. Once a DSU function is invoked in a PSU program, it executes to completion using the version of the function that was active when it was invoked. However, if a new version is installed, any future calls to the same function always execute the newest version. This simple mechanism allows for quick loading of updates in PSU. PSU unloads obsolete version of DSU functions after they are no longer executing. This mechanism makes PSU the first DSU system for C-language programs that is able to unload older versions of code. This efficient use of resources enables many patches to be applied to a long-running application. A suite of specialized custom synthetic programs, and a DSU-enabled version of the MySQL database storage engine, are used to evaluate the overhead of the DSU-enabling features. The MySQL storage engine maintains over 95% of the performance of the non-DSU version and allows the entire storage engine to be updated while the database continues executing. PSU includes a simple and straightforward process for the modification of the storage engine that enables DSU.