Reviewerly/exHarmony huggingface dataset:
VividGraph: Learning to Extract and Redesign Network Graphs From Visualization Images

Network graphs are common visualization charts. They often appear in the form of bitmaps in articles, web pages, magazine prints, and designer sketches. People often want to modify graphs because of their poor design, but it is difficult to obtain their underlying data. In this article, we present VividGraph, a pipeline for automatically extracting and redesigning graphs from static images. We propose using convolutional neural networks to solve the problem of graph data extraction. Our method is robust to hand-drawn graphs, blurred graph images, and large graph images. We also present a graph classification module to make it effective for directed graphs. We propose two evaluation methods to demonstrate the effectiveness of our approach. It can be used to quickly transform designer sketches, extract underlying data from existing graphs, and interactively redesign poorly designed graphs.
A Tracking-By-Detection Based 3D Multiple Object Tracking for Autonomous Driving

Multiple object detection and tracking is a fundamental part of scene understanding in the application of self-driving, mobile robot and other unmanned system. Generally, 2D image-based and 3D point based are two main kind of input information and the 3D points may come from RGBD camera, RADAR or LiDAR. We will focus on the 3D LiDAR point cloud-based method in this paper. Since the depth information is lost during the process of image capturing, and vulnerable to the illumination change, 2D image-based multi-object detection and tracking (MOT) methods still exist great challenges for real application. 3D LiDAR based object detection and tracking methods show some advantages over 2D image-based methods in terms of robustness and accuracy. However, in most existing literatures, the methods of 3D object detection and tracking are usually studied separately. Therefore, a compact tracking-by-detection based 3D multiple object tracking (3D MOT) method is proposed. Following the framework of tracking-by-detection method which usually adopted in 2D image-based MOT method, a two-stage based 3D object detection is employed to improve the detection accuracy and robustness, which is followed by an efficient 3D Kalman filter and Hungarian based tracker. The experiments confirm that the presented method is efficient for real-time application and reliable in object tracking, which achieved excellent performance in 3D MOT on KITTI dataset.
Research on Disorderly Grasping System Based on Binocular Vision

In order to make up for the shortcomings of traditional sorting methods, a disordered sorting system was built based on binocular vision. The system is based on machine vision to realize that when disordered workpieces are sent into the field of view of the binocular camera by the conveyor belt, the image information of the workpiece is first collected by the binocular camera, the 3D point cloud of the workpiece is obtained, the target workpiece is identified and positioned, and the end of the manipulator is obtained. The relative pose relationship with the workpiece can guide the robotic arm to sort and grasp the workpiece in real time, realize the sorting task, can form an automated workpiece sorting detection line, and improve the efficiency of workpiece sorting.
Investigating the Efficacy of Persuasive Strategies on Promoting Fair Recommendations

Recommender systems have become an inseparable part of our daily life, like listening to music based on recommender playlists or browsing through the recommended shopping list online. Fairness in such recommender systems has gained lots of attention considering provider and system objectives along with end-user satisfaction. However, often there are trade-offs between the objectives of different stakeholders. For instance, fairness for providers can be defined as ensuring the same exposure for all providers [7]. However, less popular providers might not satisfy users as much as widely-known providers; therefore, user satisfaction might decrease significantly. Consequently, there is a need to explore methods to promote recommendations from less-known providers more effectively. Previous studies have shown that explanations and persuasive explanations are beneficial for increasing user acceptance of recommended items. However, there has been little work investigating explanations for a fairness objective. Here, we study the effect of persuasive strategies for promoting items included for the recommender’s fairness objective in a music platform. Results show empirical evidence of higher user satisfaction for the items accompanied by explanations. Our findings could guide the user interface design of two-sided marketplaces leading to a better user satisfaction rate.
A Knowledge-based Mesh Generation System for Forging Simulation

We have developed a knowledge-based system GENMAI (Artificial Intelligence Mesh GENerator) for automatically generating two-dimensional meshes, which is easily applicable to various fields of computational dynamics with structured meshes. We analyzed and modeled experts’ mesh generation procedures in the simulation processes of forging deformations, vehicle aerodynamics and engine in-cylinder flows. This paper describes the outline of GENMAI focusing on the implementation techniques of search and inference, and then shows the functional ability of GENMAI applying to the forging simulation. The characteristics of GENMAI are as follows. (1) Plural solutions can be efficiently obtained at the same time in the search phase, using the global dependency and local dependency. (2) The meta-level inference method and its knowledge representation method are very applicable to various fields of analyses. We have applied GENMAI to forging simulation, and found it to have a prospect for practical use.
Poisson disk sampling with randomized satellite points for projected texture stereo

A stereo camera is fundamental for 3D sensing. We can only use simple stereo algorithms for real-time applications; however, it has low accuracy. Adding texture by projection is a solution to the problem. The system is called projected texture stereo. Projecting light patterns is essential and deeply related to the matching algorithm in the system. This article proposes a projected pattern and a new texture stereo camera system with a suitable stereo matching algorithm. Experimental results show that our system is superior to Intel RealSense, the commercially successful projected texture stereo camera.
Personalized Reader System Based on the Characteristic Information of the Recommended Object of the Resource

The current personalized recommendation technology is user-centric and can meet the individual needs of users to a certain extent. However, the traditional personalized reader system does not fully consider the user&#x0027;s personalized needs, resulting in many problems such as inconsistent with user needs and insignificant learning effects. This article first introduces the current development of personalized recommendation and personalized reader systems at home and abroad, and expounds the theoretical basis used in the personalized recommendation subsystem; then, it conducts a detailed demand analysis and design of the personalized recommendation subsystem. When a user logs in to the personalized recommendation personalized reader system, the system can obtain the user&#x0027;s personalized case information from the user case library, and then obtain the result through a comprehensive weighted analysis of multiple case information such as user cases, model cases, and similar user cases A recommendation result that meets the needs of the user.
Particle swarm optimization gray wolf fusion algorithm for solving the optimal solution of function

Abstract In order to improve the local difference problem of wolf swarm algorithm, which is easy to fall into local optimization in solving function optimization, a particle swarm grey wolf fusion algorithm is proposed based on the characteristics of particle swarm algorithm, such as fast search speed, strong ability and easy implementation. Firstly, this paper compares particle swarm optimization and wolf swarm algorithm, and introduces particle swarm optimization gray wolf fusion algorithm by analyzing the root of the problem. The experimental results show that the particle swarm wolf swarm algorithm is feasible, and the search accuracy, stability and the optimal value of the objective function are significantly improved in the solution process.
RescueCASTR: Exploring Photos and Live Streaming to Support Contextual Awareness in the Wilderness Search and Rescue Command Post

Wilderness search and rescue (WSAR) is a command-and-control activity where a Command team manages field teams scattered across a large area looking for a lost person. The challenge is that it can be difficult for Command to maintain awareness of field teams and the conditions of the field. We designed RescueCASTR, an interface that explores the idea of deploying field teams with wearable cameras that stream live video or sequential photos periodically to Command that aid contextual awareness. We ran a remote user study with WSAR managers to understand the opportunities and challenges of such a system. We found that the awareness provided by the footage could give additional confidence and comfort to Command, while reducing the need for explicit communications. However, it could also impact workers' traditional roles and shift the burden of responsibility toward Command. We conclude that, while wearable-camera footage could be beneficial to Command, they need to have the tools and means to narrow their focus within the abundance of information provided. Furthermore, camera streams should not be thought of as a replacement for more direct communications, but rather as another tool available to help Command supplement their understanding of events in the field and help them narrow their focus.
Age-uniform Feature Learning for Image-based Kinship Verification

Kinship verification based on face image is an important topic in computer vision and has many applications in practice, such as family pedigree organization, missing person search, etc. Although parent and children share certain similarities in facial images, it is still difficult to verify the kin between people with large age gap. Therefore, how to reduce the influence of age factors on verification is the key to improve the accuracy of kinship verification. To this end, we propose an Age-uniform Face Representation Learning Network (AFRLN) to verify kinship. It mainly consists of Age Uniform Network (AUN) and Verification Network (VFN). Specifically, the design of AUN utilizes the idea of generative adversarial network, which aims to transform parent and child's face images of different ages into images of the uniform age range. Then, the transformed facial images are fed into the verification network, and discriminative deep features of parent and child are obtained. Finally, the output features are fused and then kinship verification task is conducted. Our approach is tested on two publicly kinship datasets: KinFaceW-I and KinfaceW-II. Experimental results validate performance of our method.
The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition

In recent years, the large amount of debris scattered throughout the ocean is becoming one of the major pollution problems, causing extinction of species and accelerating the degradation of our planet, among other environmental issues. Since the manual treatment of this waste represents a considerably tedious task, autonomous frameworks are gaining attention. Due to their reported good performance, such frameworks generally rely on Deep Learning techniques. However, the scarcity of data coupled with the inherent difficulties of the field—debris with different shapes and colors due to long-lasting exposure to the ocean, illumination variability or sea conditions—makes detecting underwater objects a particularly challenging task. The contribution of this work to the field is double: on the one hand, we introduce a novel data collection for supervised learning—the CleanSea corpus—annotated at both the bound box and contour levels of the objects to contribute with the research and progress in the field; on the other hand, we devise and optimize a recognition model based on the reference Mask Object-Based Convolutional Neural Network for this set to establish a benchmark for future comparison and assess its performance in both simulated and real-world scenarios. Results show the relevance of the contributions as the devised model is capable of properly addressing the detection and recognition of general debris when trained with the introduced CleanSea corpus.
Hybrid Frontier Detection Strategy for Autonomous Exploration in Multi-obstacles Environment

Rapidly-exploring Random Tree (RRT) algorithm is widely used in path planning, while the RRT is inefficient for robotic exploration in large-scale environments with multi-obstacles and narrow entrances. Here, we propose a Hybrid Frontier Detection (HFD) strategy for autonomous exploration which incorporates a variable step-size random tree global frontier detector, a multi-root nodes random tree frontier detector, and a grid-based frontier detector algorithm. The proposed strategy enables a robot to quickly search for the frontier in real-time. Compared with the traditional RRT-based strategy, the exploration time and traveling length of the proposed HFD strategy are respectively decreased by over 15% and 12% in the simulation environment and decreased by over 14% and 11% under the same experimental conditions in the experimental environment. The results indicate that the HFD strategy effectively solves the problem of autonomous exploration in the environment with multi-obstacles and narrow entrances.
Fuzzy U-Net Neural Network Design for Image Segmentation

One of the problems in biomedical image analysis is the problem of nuclei segmentation. The annotation of nuclei by hand has proven itself very time consuming with varying results depending on many factors. More recently convolutional neural networks have made the problem of automatic image segmentation easier, faster and more reliable. In this article, an extension to the standard U-Net is proposed which aims at improving the quality of biomedical image segmentation. By integrating Fuzzy computations in the standard U-Net architecture we have achieved even better accuracies than the ones reached by the base architecture. The Fuzzy Layers resemble a sense of uncertainty, which is already seen in the real world. This allows for a more precise detection and instance segmentation of cellular nuclei. When the original U-Net was first developed one of its focuses was to be trainable with a small dataset. This quality has been proved useful when undertaking the task of Biomedical Image Segmentation. The model was trained with the Kaggle 2018 Dataset. The segmentation process comes right after, including the following two steps: 1) creating a prediction matrix, 2) thresholding the matrix to attain a visual result. The second step exhausts the following techniques: Manual Thresholding; Adaptive Thresholding; Gaussian Thresholding, and Otsu Thresholding. The results point out which thresholding technique, combined with a certain set of Fuzzy Layers, yields the best possible results in terms of accuracy of the models.
Do Arduinos Dream of Efficient Reasoners?

The Semantic Web of Things enhances the Internet of Things with Web technologies as well as Knowledge Graphs and reasoning. Traditional reasoners are too heavy in terms of memory footprint and/or processing time to be implementable on things. In this work, we present LiRoT, a lightweight incremental reasoner that can be embedded in constrained objects, so that reasoning on them in a fog architecture becomes possible. The focus of this work is to reduce drastically memory footprint while paying attention to processing time, hence usual optimization techniques are not fully adequate. We provide evaluations that (i) compare our system to the state of the art and (ii) show the effective benefits of the different optimizations we have implemented.
Big Data Analyzing Techniques in Mathematical House Price Prediction Model

The Chinese house market has been flourishing in the past three decades as an increasingly bigger population moves into cities. To keep the rise of house price within a proper range and acceptable to the public, the government has adopted various approaches to bring the price under control. After intermittent fluctuations, house purchasing has become a hot topic for both the media and the public. It is of great significance to study the change of house price considering all the related factors. Some scientists prefer machine learning. Machine learning is a subject which involves many subjects such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. In this paper, we will discuss the details of the machine learning algorithms, three typical models (i.e., AdaBoost, Linear Regression and KNN) and their strengths and weaknesses. In the future, the growing complexity of all the factors that influence house price will cause more and more trouble for scientists pursuing more precise results. Also, based on its own specialty that computer is still not able to think like a man, machine learning cannot achieve 100&#x0025; right, which often results in some silly outcomes.
Sensitivity-aware Distance Measurement for Boosting Metric Learning

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Sensitivity-aware Distance Measurement for Boosting Metric LearningHaoliang Liu, Tan Yu, and Ping LiHaoliang Liu, Tan Yu, and Ping Lipp.576 - 584Chapter DOI:https://doi.org/10.1137/1.9781611977172.65PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract To enhance the effectiveness of metric learning, many methods weight pairs by hard pair mining. Coarsely, hard pair mining consists of two steps. In the first step, it measures the hardness of each pair. After that, in the second step, it assigns higher weights to hard pairs when constructing the loss function. Existing deep metric learning methods focus on the second step. A variety of methods have been developed to weight each pair based on the measured hardness. In contrast, the first step is rarely exploited. We believe that the measurement of hardness in the first step is the foundation for weighting in the second step. Without a reliable hardness measurement, even the best weighting method cannot achieve the optimal performance. The hardness of a pair in existing metric learning methods is simply measured by the distance between two samples' features in the pair, which we believe may not be reliable. In this work, we propose a sensitivity-aware distance measurement (SDM) to enhance the reliability of hardness measurement. SDM augments each sample into a set of samples, and the hardness of a pair is determined by the distance between two samples' augmented sets. The proposed SDM is simple and orthogonal to hard pair mining strategies. It can be plugged in existing methods to improve their effectiveness. Systematic experiments are conducted by plugging SDM in several mainstream distance metric learning methods on three benchmarks for two retrieval tasks. The improvement achieved by SDM demonstrates its effectiveness and versatility. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:Metric Learning, Distance, Sensitivity
NutriChain: Secure and Transparent Midday Meals Using Blockchain and IoT

The midday meal program is an initiative by the government and Non-Governmental Organization (NGO) to tackle malnutrition among younger generations, enrolled in educational institutions aiming to provide a nutritious meals. It is a large scale food assistance program with an overwhelming budget. Despite government authorities trying their best to make it a success, many problems ranging from poor food quality, meals not being served as per guidelines, and food not delivered are encountered. The present implementation can be improved if there is transparency in the system with proper distribution of powers from a handful to all stakeholders involved in the process. Blockchain being an immutable, shared, and distributed ledger without the control of any single centralized authority can address these shortcomings. The proposed work NutriChain has implemented the idea of blockchain and Internet of things (IoT)-based distribution network by storing meal preparation and distribution data on Ethereum-based permissioned blockchain. Each batch of meal will be identified by unique identity and every successful and unsuccessful transfer of a batch of meal from one stakeholder to another will be recorded as a transaction on blockchain. The nutrition value along with the temperature, photographs, and weight of the meal is recorded by the IoT sensors as IoT metrics score. All meal details are stored on the database, and its hash along with aggregated IoT metrics score is stored as append-only blocks of transaction on blockchain. The initial research suggests that the blockchain and IoT-based approach in solving the chosen use case is both feasible and promising.
Automatic Machine Translation Evaluation using a Source and Reference Sentence with a Cross-lingual Language Model

As the performance of machine translation has improved, the need for a human-like automatic evaluation metric has been increasing. The use of multiple reference translations against a system translation (a hypothesis) has been adopted as a strategy to improve the performance of such evaluation metrics. However, preparing multiple references is highly expensive and impractical. In this study, we propose an automatic evaluation method for machine translation that uses source sentences as additional pseudo-references. The proposed method evaluates a translation hypothesis via regression to assign a real-valued score. The model takes the paired source, reference, and hypothesis sentences together as input. A pre-trained large-scale cross-lingual language model encodes the input to sentence vectors, with which the model predicts a human evaluation score. The results of experiments show that our proposed method exhibited stably higher correlation with human judgements than baseline methods that solely depend on hypothesis and reference sentences, especially when the hypotheses were very high- or low-quality translations.
Improving the FAIRness of Health Studies in Germany: The German Central Health Study Hub COVID-19

The German Central Health Study Hub COVID-19 is an online service that offers bundled access to COVID-19 related studies conducted in Germany. It combines metadata and other information of epidemiologic, public health and clinical studies into a single data repository for FAIR data access. In addition to study characteristics the system also allows easy access to study documents, as well as instruments for data collection. Study metadata and survey instruments are decomposed into individual data items and semantically enriched to ease the findability. Data from existing clinical trial registries (DRKS, clinicaltrails.gov and WHO ICTRP) are merged with epidemiological and public health studies manually collected and entered. More than 850 studies are listed as of September 2021.