Reviewerly/exHarmony huggingface dataset:
The Study of Charging Mechanism of Knowledge Payment Platform Based on Tripartite Game Model

As a new form of sharing economy, knowledge payment market has developed rapidly in recent years. A reasonable and effective charging mechanism can coordinate the interest relationship between knowledge payment platform and users,maximize the interests of all parties,and promote the sustainable and healthy development of knowledge transaction.This paper constructs a tripartite game model with knowledge demanders, knowledge suppliers and knowledge payment platform as the main body, compares the effects of the quality level of knowledge product,the price of knowledge product, the level of value-added service and the proportion of transaction commission fees on the decision-making of each game subject. First , knowledge demanders will make purchasing decisions when the marginal utility of money is equal to the marginal utility of quality. At this time,the total utility of knowledge demanders is the largest. Second, in order to increase profits,knowledge suppliers will improve the quality level of knowledge products and price high-quality knowledge products with the willingness of knowledge demanders. And knowledge suppliers will purchase value-added services that will maximize their profits. Third, the knowledge payment platform will maximize its own benefits by increasing the influence on the purchasing behavior of knowledge demanders, improving the efficiency of value-added services, and implementing the commission differential charging for knowledge suppliers.
Two-Step Memory Networks for Deep Semantic Parsing of Geometry Word Problems

Semantic parsing of geometry word problems (GWPs) is the first step towards automated geometry problem solvers. Existing systems for this task heavily depend on language-specific NLP tools, and use hard-coded parsing rules. Moreover, these systems produce a static set of facts and record low precision scores. In this paper, we present the two-step memory network, a novel neural network architecture for deep semantic parsing of GWPs. Our model is language independent and optimized for low-resource domains. Without using any language-specific NLP tools, our system performs as good as existing systems. We also introduce on-demand fact extraction, where a solver can query the model about entities during the solving stage that alleviates the problem of imperfect recalls.
The Practice of the Multimedia Courseware for College Foreign Language Teaching Based on the Network Resources

With the mature development of the information technology, the network multimedia teaching is gradually integrated into the practice of the foreign language teaching in colleges and universities. In contrast, the traditional teaching mode has been unable to meet the comprehensive needs of the foreign language teaching. Therefore, making full use of the advantages of the network resources and the multimedia technology to make the multimedia courseware for college foreign language teaching will essentially promote the innovation of the college foreign language teaching practice. This paper explores the design of the college foreign language multimedia courseware based on the network resources, and makes a detailed study.
Towards Music-Driven Procedural Animation

We present our approach towards the development of a framework for the creation of music-driven procedural animations. We intend to explore the potential that elementary musical features hold for driving engaging audio-visual animations. To do so, we bring forward an integrated environment where real-time musical information is available and may be flexibly used for manipulating different aspects of a dynamic animation. In general terms, our approach consists of developing a virtual scene, populated by controllable entities, termed actors, and using scripting to define how these actors' behaviour or appearance change in response to musical information. Scripting operates by establishing associations, or mappings, between musical events, such as the ringing of notes or chords, or sound information, such as the frequency spectrum, and changes in the animation. The scenario we chose to explore is comprised of two main actors: trees and wind. Trees grow in an iterative process, and may develop leaves, while swaying in response to the wind field. The wind is represented as a vector field whose configuration and strength can be altered in real-time. Scripting then allows for synchronising these changes with musical events, providing a natural sense of harmony with the accompanying music. By having real-time access to musical information, as well as control over a reactive animation we believe to have taken a first step towards exploring a novel interdisciplinary concept with vast expressive potential.
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses

In this paper, we present and illustrate some new tools for rigorously analyzing training data selection methods. These tools focus on the information theoretic losses that occur when sampling data. We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce these losses. These are meant to act as generalizable theoretical examples of applying the field of Information Theoretic Deep Learning Theory to the fields of data selection and active learning. Both analyses yield insight into their respective methods and increase their interpretability. In the case of Transductive Experimental Design, the provided analysis greatly increases the method's scope as well.
Comparative Usability Analysis of Selected Data Entry Methods for Web Systems

This paper addresses usability problems of data entry techniques and patterns in web systems and web sites. In order to carry out a comparative analysis of the solutions a dedicated web application was developed as an experimental tool for performing usability testing. The application was designed in the form of a test divided into scenarios and exercises on filling up selected models. Furthermore, the application recorded usability metrics, questionnaires, open and closed questions, as well as users’ personal data. 40 participants, who took part remotely in the experiment, were split into two distinct groups and completed two separate tests that differed in input data and tested solutions. Some measures referring to the effectiveness, efficiency, and user satisfaction were collected. The results were thoroughly analyzed and the recommendations for the usage of particular models were formulated.
Development of Unmanned Aerial Vehicle (UAV) for Dropping Object Accurately Based on Global Positioning System

The paper presents design of Unmanned Aerial Vehicle (UAV) for dropping object accurately based on Global Positioning System (GPS). It can be used for many applications such as dropping life vests for victims of a ship accident, dropping food for flood victims as well as any other disasters that difficult to be reached by a ground vehicle, and also can be used for dropping water on a forest fire. The key issue here is how the UAV can drop an object precisely on the determined position. The proposed UAV is designed for dropping objects up to four. The electronic subsystems of UAV consist of Radio Remote Controller Transmitter Receiver, Pixhawk Flight Controller, electronic speed controller, twin of brushless motor, GPS, and servo motors. We design the dropping object mechanism using four servo motors as the actuator for close and release the gates. The dropping object mechanism is installed around the Center of Gravity (COG) of the UAV to minimize the COG changing effect. The position of the dropped object can be selected by utilizing mission planner software. We add formulas to adjust the position by considering the UAV speed, altitude, mass of the object and wind speed. The designed UAV has been successfully realized, tested, and joined the Tubitak International UAV Competition in Turkey in September 2018. It has dropping object accuracy of 4 meters when the altitude is 40m and UAV speed is 12 m/s.
PROPRIEDADE DOS DADOS E CIÊNCIA ABERTA

The practice of providing open access to articles, adopted in Brazil and several other countries, still faces resistance from many commercial publishers abroad (Packer & Santos, 2019a). Recently, some of them have switched to a hybrid model (open and closed access) as a more balanced path. Transparency in the peer review process is also being discussed: The digital library SciELO recommends a “gradual increase of transparency and openness [...] with the disclosure of the identities of authors and reviewers during the evaluation process” (Packer & Santos, 2019b). Even more controversial is the policy of open access to research data of articles published in scientific publications. The Blog SciELO em Perspectiva has several texts discussing this trend, which have been questioned by many actors involved in the production and publication of scientific articles, including authors, universities, editors, and publishers.
Research on Fault Location of Distribution Network Based on MRSVD

Based on the existing fault location of distribution network, a novel traveling wave head detection method based on MRSVD decomposition and symmetric difference energy operator is proposed in this paper. Multi-resolution singular value decomposition (MRSVD) is a method that combines matrix binary recursive structure principle and singular value decomposition method. In this paper, the original signal is separated from the high frequency detail components by MRSVD method, and then the energy of teager is calculated to obtain the precise traveling wave arrival time. Then, a multi-terminal traveling wave fault location scheme is proposed, and the fault location is obtained by layer by layer calculation. Finally, the accuracy of the algorithm is verified by MATLAB simulation. Experiments on setting different fault parameters show that the method has good accuracy and practicability.
An Optimized Ant Colony Algorithm for Text Edge Extraction

The deep learning-based detection model has achieved outstanding results in the field of natural scene text detection. However, the lack of image detail information in the depth feature will result in the inseparable text objects in the detection results cannot be separated, and will affect subsequent text recognition. Precision. In order to solve this problem, this paper proposes an optimized ant colony algorithm for extracting text edges, and combines the optimized ant colony algorithm with the depth detection model to achieve the purpose of optimizing text detection and recognition accuracy. The algorithm uses the gradient information of image partition to initialize the position of ant colony, and optimizes the process of ant colony searching for edges according to the characteristics of text objects. An adaptive pheromone dissipation coefficient is used to deal with the ant colony movement process, which effectively solves the traditional ant. Group algorithms are slow to converge and sensitive to noise. This paper will conduct experiments on the standard text detection sets of ICDAR 2011 and ICDAR 2013. The results show that combining the optimized ant colony algorithm with the FCN-based deep text detection model can not only effectively improve the accuracy of the original deep text detection model. The text recognition accuracy can be further optimized.
Product Attribute Extraction Based on Affinity Propagation Clustering Algorithm and Pointwise Mutual Information Pruning

With the rapid development of e-commerce platforms, how to quickly sort out and summarize a large number of product reviews on e-commerce websites has become an urgent problem. Product attribute extraction is one of the methods to solve this problem. Automatic extraction of product attributes is one of the important research contents in the field of natural language processing. A product attribute extraction method based on affinity propagation clustering algorithm and pointwise mutual information pruning is proposed to extract product attributes comprehensively and rapidly. Firstly, candidate product attribute sets are obtained from the corpus according to predefined syntactic rules. Then, the candidate product attribute sets are represented by word vectors and calculating the similarity of attribute words to cluster the candidate attribute sets. Thus the candidate attribute clusters are obtained. At last, the effectiveness of the method is proved in a Chinese product review.
Research on DOA Estimation Method Based on RM-FOCUSS Improved Algorithms

Compressed sensing theory is a new research direction in the field of modern signal processing, and direction of arrival (DOA) estimation is a research hotspot of this theory. In practical engineering applications, the traditional DOA estimation algorithm usually needs to collect a large number of independent snapshot data, and the estimation accuracy is low and the calculation amount is large when the observation data and dictionary matrix contain noise at the same time. In order to solve the above problems effectively, this paper presents an improved algorithm based on Regularized-FOCaI Underdetermined System Solver to Multiple Measurement Vectors (RM-FOCUSS). The algorithm obtains higher estimation accuracy and angle resolution by iterating between sparse solution and dictionary error. The simulation results verify the reliability of the algorithm.
Decentralized Incremental Fuzzy Reinforcement Learning for Multi-Agent Systems

We present a new incremental fuzzy reinforcement learning algorithm to find a sub-optimal policy for infinite-horizon Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). The algorithm addresses the high computational complexity of solving large Dec-POMDPs by generating a compact fuzzy rule-base for each agent. In our method, each agent uses its own fuzzy rule-base to make the decisions. The fuzzy rules in these rule-bases are incrementally created and tuned according to experiences of the agents. Reinforcement learning is used to tune the behavior of each agent in such a way that maximum global reward is achieved. In addition, we propose a method to construct the initial rule-base for each agent using the solution of the underlying MDP. This drastically improves the performance of the algorithm in comparison with random initialization of the rule-base. We assess the performance of our proposed method using several benchmark problems in comparison with some state-of-the-art methods. Experimental results show that our algorithm achieves better or similar reward when compared with other methods. However, from the runtime point of view, our method is superior to all previous methods. Using a compact fuzzy rule-base not only decreases the amount of memory used but also significantly speeds up the learning phase.
Tactile Sensor on Cane Handle for Gait Phase Analysis

Low cost devices such as accelerometers, gyroscopes, or force sensing resistors have been exploited to build wearable systems for gait analysis. These sensors are sometimes mounted on a cane if it is required. This paper proposes the use of a tactile sensor on the cane handle to obtain information about gait phases. Moreover, since the sensor is located at the human-device interface, any shaping of the force at this interface that anticipates the movement can be registered. Experimental results from an instrumented cane are presented to illustrate this behavior that can be exploited in applications that involve closed loop control.
Soft Sensor Design of Solar Irradiance Using Multiple Linear Regression

Solar irradiance is power per area received from the sun. Solar irradiance is an important variable that affects the solar panel output. Monitoring of solar irradiance can use a pyranometer sensor. However, the drawbacks of the sensor are expensive and less efficient if that is applied to solar power generation systems requiring a lot of sensors. The problem can be solved by soft sensors which can be designed using multiple linear regression models. The model is developed using training data from solar cell output. The variations given is the type of input to build the model. The model input comprises solar cell output voltage, solar cell output current, and a combination of both. Data testing is done by using multiple correlation tests, multiple linear regression significance tests, and multiple linear regression coefficient significance tests. The best design of the soft sensor is a combination of current and voltage as input models. It has the smallest mean square error value of 7.887 × 10-24 for training data and 3.876 for test data.
Tutorial on Software Testing &amp; Quality Assurance for Machine Learning Applications from research bench to real world

Rapid progress in Machine Learning (ML) has seen a swift translation to real world commercial deployment. While research and development of ML applications have progressed at an exponential pace, the required software engineering process for ML applications and the corresponding eco-system of testing and quality assurance tools which enable software reliable, trustworthy and safe and easy to deploy, have sadly lagged behind. Specifically, the challenges and gaps in quality assurance (QA) and testing of AI applications have largely remained unaddressed contributing to a poor translation rate of ML applications from research to real world. Unlike traditional software, which has a well-defined software testing methodology, ML applications have largely taken an ad-hoc approach to testing. ML researchers and practitioners either fall back to traditional software testing approaches, which are inadequate for this domain, due to its inherent probabilistic and data dependent nature, or rely largely on non-rigorous self-defined QA methodologies. These issues have driven the ML and Software Engineering research communities to develop of newer tools and techniques designed specifically for ML. These research advances need to be publicized and practiced in real world in ML development and deployment for enabling successful translation of ML from research prototypes to real world. This tutorial intends to address this need.
Design of Parallel Pipelined Architecture For Wavelet Based Image Compression Using Daubechie’s

In this project, we have a tendency to be visiting exhibit VLSI design for 2D Daubechies wavelet primarily based compression. The Daubechies wavelet transform provides mean values that compress the image so it takes up a lot of less space for storing, and thus transmits quicker electronically and in progressive levels of detail. Element worth are protected by the scaling issue from prodigious their limits. To implement the system numerous levels of scaling factors given and spitted into two level. These values enforced for numerous levels of 2D-DWT. We have a tendency to store the worthiest in buffer technique and simulate the output of VLSI system. The parallel-pipelined design obtained by RTL schematic. This buffer memory design employed for input, output memory storage. This buffer memory design is employed for input output memory storage. Compared to different typical technique the performance of this design is extremely economical in compression ratio, PSNR (Peak Signal to Noise Ratio), MSE (Mean sq. Error) and Time. This design is extremely economical to handle any image size. The proposed design has high compression quality as well as high speed and area-efficiency. Thisd esign is applicable for high-speed image compression and memory storage applications.
Learning Agility from Dancers – Experience and Lesson Learnt

Being on of the youngest field of human endeavours, software development absorbed features of other, older fields, especially engineering, mathematics, and economics. However, being software the product of the creation and being based on a systematic discipline and technical excellence of the participants (the developers), there could be also very interesting interconnections with artistic disciplines. In this paper, we concentrate on the similarities with dance. Dance has a long tradition of instructions and development. The goal of this work is to find interesting points of contact is to identify dancesport methods that could provide the basis for new approaches or practices for software development.
Object detection for Verification Based Annotation

In this paper, we examine the properties of an object detector for Verification Based Annotation (VBA), where annotation is performed by having machine annotations checked and verified (and corrected, in this work) by a human annotator. We show that with a few modifications from standard practice, a convolutional neural network based object detector can be a robust aid to annotation, without a large degree of parameter tweaking. We previously annotated a variety of small-scale datasets which we attempt to validate some of our ideas and assumptions for an object detector used for VBA.Our approach is to use high-resolution images, training on image crops (as opposed to the usual practice of resizing input images to a fixed resolution), and find this method successful, being more accurate and robust than down-scaling. A particular interest is the effect of localisation noise and systematic bias in annotations. We characterise the impact on object detection performance and compare to human levels, we find noise has a large impact, especially with fewer training examples.
MAXIM: Mixed-reality Automotive Driving XIMulation

In-lab driving simulators have been favored by human-vehicle interaction researchers and interface designers due to their capabilities in unfettered construction of surrounding environments. On-road driving simulators, meanwhile, were developed for experiments in a real-world context. We introduce new methods and tools for simulating driving via virtual vehicle in real-world environments. The proposed system fully exploits the benefits of vivid sensory information in real road environments without sacrificing the unlimited potential of environment construction for it. In this paper, we explain the system design of the platform, and we verify its capabilities as a driving simulator through a pilot study (N=10). The proposed platform is perceived as highly realistic and can easily be augmented to test new urban landscapes and futuristic interfaces before occupancies.
Deep Feature Interaction Embedding for Pair Matching Prediction

Online dating services have become popular in modern society. Pair matching prediction between two users in these services can help efficiently increase the possibility of finding their life partners. Deep learning based methods with automatic feature interaction functions such as Factorization Machines (FM) and cross network of Deep & Cross Network (DCN) can model sparse categorical features, which are effective to many recommendation tasks of web applications. To solve the partner recommendation task, we improve these FM-based deep models and DCN by enhancing the representation of feature interaction embedding and proposing a novel design of interaction layer avoiding information loss. Through the experiments on two real-world datasets of two online dating companies, we demonstrate the superior performances of our proposed designs.