Reviewerly/exHarmony huggingface dataset:
The effect of a credit sales accounting information system on the effectiveness of account control in PT Megah Kayu Industri Bandung

The purpose of this study is to determine and study how much influence the sales accounting information system, accounting information system cash receipts on the effectiveness of control of accounts receivable. The population in this study is limited, namely employees of PT Megah Kayu Industri Bandung. The sampling technique was 30 respondents. Data collection techniques used primary and secondary data with the help of SPSS version 20. For windows. From the test results, it can be concluded that the credit sales accounting information system, the cash receipt accounting information system have a partial and joint effect on the effectiveness of controlling accounts.
Development of Nutrition Learning Models for Elementary School Children in Kendari

A nutrition learning model themed “Makananku Sehat dan Bergizi” was aimed for fourth-grade students of elementary school who used the K13 Curriculum K13 that was developed in 2013. The model accommodated the old version of nutrition concept and they were delivered withconventional model. This study aimed to design a new learning model and to discover effect of themodel on nutritional knowledge and behavior of elementary school children in Kendari. Thisstudy was a quasi experimental study using one group with pre-post test design. This study was held in 2017 in Puuwatu district, Kendari. The subjects were 15 fourth-grade teachers and 60 elementaryschool students from SD 05 Mandonga. Data related to knowledge and behavior of the students werecollected and analyzed. The pre- and post-nutrition learning models were analyzed by interview using a questionnaire. Data were statistically tested using paired t-test. Learning model that was developedwas named Model Pembelajaran Gizi Kontekstual or CTL (Contextual Teaching and Learning). There was significant improvement in terms of nutritional knowledge and behavior of the studentsbefore and after introduction to a song titled Kebiasaan Makanku and to a flipchart themed Mengenal Jenis dan Manfaat Makanan. There is a need for nutrition education for elementaryschool teachers, especially for the fourth grade. Thus, teachers would have good knowledge onnutrition and therefore, they could use it to educate their students. Eventually, it would helpimplementation of CTL model.
Research on Estimation Method of Underwater Operation Robot Motion Pose Based on Bayesian Inference

Aiming at the problem of poor estimation effect of traditional underwater robot motion pose estimation methods, a Bayesian inference-based underwater robot motion pose estimation method is proposed. According to the specific characteristic parameters of the underwater vehicle, the specific transfer functions of the forward and backward motion estimation, the direction estimation and the fixed depth estimation are obtained, and the traditional Bayesian inference estimator, fuzzy Bayesian inference estimator and parallel fuzzy Bayesian are used. Inferred composite estimator, and established simulation estimation models under various motions of the robot according to the advancing and retreating distance, steering angle and depth values. The response results show that the parallel fuzzy Bayesian inference composite estimation algorithm is better for underwater robots. Good estimate effect. Considering the actual structure and motion of the underwater vehicle, a one-dimensional slide table is introduced as the center of gravity adjustment device to compensate the pitch caused by the disturbance of the center of gravity around the longitudinal axis. The disturbance observer is used to estimate the disturbance, and the discrete attitude variable structure estimation is used to estimate the pitch angle The results show that the chattering caused by switching has a weakening effect and has a good estimation effect on the motion of the underwater vehicle.
Measuring and Testing Mutual Dependence for Functional Data

In this paper, measures of mutual independence of many-vector random processes were defined. Based on these measures, permutation tests of mutual independence of these random processes were also given. The properties of the described methods were presented using simulation studies for univariate and multivariate processes.
Recognizing Complex Activities by a Temporal Causal Network-Based Model

Complex activity recognition is challenging due to the inherent diversity and causality of performing a complex activity, with each of its instances having its own configuration of primitive events and their temporal causal dependencies. This leads us to define a primitive event-based approach that employs Granger causality to discover temporal causal dependencies. Our approach introduces a temporal causal network generated from an optimized network skeleton to explicitly characterize these unique temporal causal configurations of a particular complex activity as a variable number of nodes and links. It can be analytically shown that the resulting network satisfies causal transitivity property, and as a result, all local cause-effect dependencies can be retained and are globally consistent. Empirical evaluations on benchmark datasets suggest our approach significantly outperforms the state-of-the-art methods. In particular, it is shown that our approach is rather robust against errors caused by the low-level detection from raw signals.
Constrained Maximum-Utility Rate Optimization for Unicast-Based Streaming Applications Using Differentiated Multipaths

The multipath-based method can effectively improve the video streaming quality. However, it suffers a limitation on the realistic deployment and needs global optimization to improve the network accommodation capability. In this article, we study a novel multipath-based streaming rate optimization solution for unicast-based video applications. The proposed solution employs a differentiated multipath delivery model, which uses a main path built at the network layer and multiple auxiliary paths built at the application layer. Because of the application layer-based implementation, auxiliary paths overcome the aforementioned limitation on deployment. Based on the proposed multipath delivery model, we study the global multipath-based streaming rate optimization problem, which strives for an effective tradeoff among video streaming quality, network accommodation capability and video delivery stability. The studied optimization problem is complicated, and we solve it approximately using an approximate algorithm and a convex optimization-based technique.
Motivation Structure and Risk Prevention and Control of Youth Participating In Network Mass Disturbances in the Age of “We Media”

Abstract This paper investigates and studies the motivation structure and risk prevention and control of contemporary youth in network mass disturbances. This study selected more than 1200 college students from 5 colleges and universities in Hebei province as the research objects through questionnaire survey and interview. Network group event is a special group event which is different from the common group event in the field and the subject and object of the event. The results show that the frequent occurrence of network mass disturbances is driven by internal motives and external causes. Based on this, this paper starts from the balance of government, youth and network operators. To explore the construction of a diversified network of mass incidents prevention and control system rational reference Suggestions.
Development of a Memory-Based Homing Algorithm Using a Tracking Camera for an Autonomous Cart

Transportation is a key sector in various fields and highly affects the efficiency of industries and other related organizations. Especially, in warehouses, the transport of goods from one point to another is dominated by manually operated carts or forklifts, which makes it labor-intensive work. In this paper, to reduce the burden of workers engaged in the transportation of goods and materials, a new memory-based system integrated into a wheeled robot was developed. It records a route previously taken through the use of a tracking camera and an IMU sensor. The saved motion trajectories are later used as a reference by the robot for its autonomous movement when carrying a burden. The developed system was confirmed through experiments by the developed hardware and software systems. As a result, a reasonable result was obtained when tested in an indoor environment.
Real Time Full Duplex Gesture Vocalizer for Mute People

Making a smart full Duplex interacting system of communication for Mute people. Using this system, normal people can communicate with Mute person by speaking in normal way and the mute person will be able to express what he/she wants to say. Hence the system is termed as full Duplex mode of communication system. When the system is used by mute person, input to the system will be the sign language and output will be in the form of audio(voice) that means the system will vocalize the gesture. When the system is used by normal person, input to the system will be his/her voice and output will be appropriate sign language image which will be understandable to the mute person. In this way this system helps to reduce the time consumption required to communicate between a normal and mute person. This proposed system aims to lower the barrier in communication.
The Application of Computer Technology in Urban Survey

Abstract The computer measurement control system ensures the safe and normal operation of the equipment in urban survey. If we want the equipment to be able to contribute its own role in a long period of time, we must timely maintain the safety performance of the computer measurement control system to ensure its stability and security. Make the equipment better for urban measurement to provide more efficient, more favorable, more accurate service. With the development of computer technology, the experience of using computer technology in surveying and mapping circles at home and abroad shows that it has great advantages to process survey information by computer. In this paper, several of the most common computer technology used in urban survey are selected and discussed.
WITHDRAWN: Preliminary study of embedding two-level reinforcement learning to enhance the functionality of setting objectives compared with machine learning

The system designer needs to provide complete knowledge about the resources and ability, tasks and their particular implementation specifications in order to prepare the scenarios for tasks. For instance, hospitals need to follow stringent rules when planning doctors for COVID-19 patients so as to ensure that every patient receives adequate care while maintaining comfortable doctor planning. These concerns need to be profoundly learned, so that the impact of various mapping scenarios can be effectively analysed. An efficient asset mapping strategy task maximises the resource use under restrictions while reducing the time needed for a task which maximises the effectiveness of the task execution. A 2-stage strengthening training algorithm is proposed in this work for task planning. The algorithm uses a profoundly intensive learning phase to construct a deployable resource mapping strategy. Relevant execution breaks are reassessed and the plan is reassessed on the basis of the incremental learning from such break points. A real-time parametric testing of the resources and tasks is done in order to perform incremental learning and a new strategy is developed.
Convolutional neural network based evil twin attack detection in WiFi networks

Evil Twin Attack (ETA) refers to attackers use a device to impersonate a legitimate hotspot. To address the problem of ETAs in the WiFi network, a Convolutional Neural Network (CNN) attack detection method is proposed. The method uses the preamble of the WiFi signal as the feature and uses it to train a CNN based classification model. Next, it uses the trained model to detect the potential ETA device by the inconsistent of the identity it claims and the signal feature. Experiments based on the commercial hardware demonstrate that the proposed method can effectively detect the Evil Twin Attack.
Improving the Quality of Functional Literacy of Primary Schoolchildren in the Process of Extracurricular Activities

The article discusses the problem of organizing extracurricular activities of younger schoolchildren by a teacher, focused on the formation of their experience of using the skills of functional literacy in various imaginary situations. The author reveals the special importance of director’s play in improving the quality of functional literacy, gives examples of playful imaginary situations that are used in class with fourth grade students as part of an integrated extracurricular activity course, designed to develop their mathematical literacy and achieve higher meta-subject learning outcomes.
Heuristic Tentacle Algorithm for Local Path Planning Based on Obstacles Clustering Concept

This paper introduces a heuristic tentacle algorithm for local path planning of unmanned skid-steering vehicle. Mobility, safety and economy are three mainly focused aspects in the navigation of unmanned ground vehicles. Critical skidding and slipping often occur during the turning motion, which effect the vehicle's motion apparently. So vehicle kinematics are discussed and applied to construct the cluster of tentacles. Several path assessment criteria named obstacle avoidance, terrain roughness and distance to the global path are discussed. Based on the multi-density clustering processed in the global path planning, heuristic method is introduced to guide to search in sparse region. The simulation analysis shows the generated local path can avoid the obstacles along the global path. Simultaneously. the global path can be smoothed through kinematic aware tentacle algorithm.
Second Order Bifurcating Methodology for Neural Network Training and Topology Optimization

This work proposes a second-order methodology that minimizes the global loss error over a neural network (NN) training process of fully connected layers, based on the usage of vertical and horizontal tangent parabolas to the error derivative. This methodology expands the search area of zero-crossings in the error derivative function without restrictions, therefore quantifying the need for a larger or a smaller number of neurons in a given fully connected layer in order to optimally converge to the solution of a given training database. During training, the number of neurons in a layer converges to the number of roots of the derivative of the error function, e.g. when two neurons converge to the same root, both will merge into a single neuron; additionally, every neuron improves its position to better cover the training data distribution, or otherwise, it will be split into two neurons of needed, depending on its derivative function on each training iteration. The proposed routine removes neurons that are not in the error’s minimum value neighborhood, reducing computational costs and therefore optimizing the model architecture, since it adjusts the NN topology embedded in the same training process, without the cost of having to train multiple topologies over an exhaustive trial-and-error approach.
The Mask Of Objects In Intellectual Irrigation Systems

Finding and tracking moving objects in a video stream is an important task for a different class of tasks that need to allocate moving objects on a complex background. Image segmentation based on mask selection is considered for solving this class of problems of rotation and scale of the image has little effect on the amount of calculations. In addition, the contours completely determine the shape of the image, are slightly dependent on color and brightness and contain the necessary information for further classification of the object. This approach makes it possible not to consider the internal points of the image and thereby significantly reduce the amount of information processed by moving from the analysis of the function of two variables to the function of one variable. The consequence of this is the possibility of ensuring the operation of the processing system on a time scale closer to real. Despite the GIS (Geographic Information Systems/Geospatial Information Systems) have been provided with several applications to manage the two-dimensional geometric information and arrange the topological relations among different spatial primitives, most of these systems have limited capabilities to manage the three-dimensional space. Other tools, such as CAD systems, have already achieved a full capability of representing 3D data. Despite the GIS (Geographic Information Systems/Geospatial Information Systems) have been provided with several applications to manage the two-dimensional geometric information and arrange the topological relations among different spatial primitives, most of these systems have limited capabilities to manage the three-dimensional space. Other tools, such as CAD systems, have already achieved a full capability of representing 3D data. Despite the GIS (Geographic Information Systems/Geospatial Information Systems) have been provided with several applications to manage the two-dimensional geometric information and arrange the topological relations among different spatial primitives, most of these systems have limited capabilities to manage the three-dimensional space. Other tools, such as CAD systems, have already achieved a full capability of representing 3D data.
Defocused Image Splicing Localization by Distinguishing Multiple Cues between Raw Naturally Blur and Artificial Blur

Splicing is one common type of forgery that maliciously changes the image contents. To make the forged image more realistic, blurring operations may be conducted to partial image regions or splicing edges to promise visual consistency. Revealing the blurring inconsistency among the whole image regions contributes to the splicing detection. However, for the defocused image already containing blur inconsistency, the existing methods cannot work well. Splicing detection and localization in defocused image is a challenging problem. In this paper, we overcome this problem by distinguishing multiple cues between raw naturally blur and artificial blur. Firstly, after the overlapped image blocks partition, three kinds of feature sets are extracted based on posterior probability map, noise histogram and derivative co-occurrence matrix. Then, an effective classifier is trained to determine the blur property of each pixel. Finally, a localization map refinement is proposed by fusing color segmentation probability map to improve the quality of the locating result. Experimental results demonstrate that the proposed method is very effective to detect splicing for the defocused images. The localization accuracy also outperforms the existing methods.
Is There Evidence of Antitrust Harm in the House Judiciary Committee’s Hot Docs?

The House Judiciary Committee released a large number of business documents on July 29, 2020 alongside the Antitrust Subcommittee’s hearing on “Online Platforms and Market Power.” The hearing featured the chief executive officers of four large U.S. technology companies. Jeff Bezos of Amazon, Sudhar Pichai of Google, Mark Zuckerberg of Facebook, and Tim Cook of Apple gave testimony and answered questions on a range of topics over five hours. The business documents are a subset of the documents collected from an ongoing antitrust investigation by the House Committee. We read the files and analyze them below. In my assessment, these “hot docs,” particularly combined with market evidence, reveal episodes of rapid growth and innovation in digital markets, as opposed to obvious instances of anticompetitive conduct.
A Content-based Intelligent Mobile Video Censoring System

With the development of information technology, mobile video on-demand and live streaming prevails in the young population. But the content of mobile video may contain some bloody and violent scenes. Teenagers, among all consumers, are more susceptible the negative influence of violence in videos. The censorship targeting at the online contents including images and video is an important yet arduous task. The adoption of AI-based censoring technique can help lower the operation cost for Internet content providers and save the work labor in censorship while preventing video consumers from being affected by unhealthy information. This paper presents an embedded system for content-based video censoring based on the deep learning technique. The proposed system is capable of filtering video content automatically, where an algorithm is developed that utilizes the power of Intel IoT development suite and the Intel Neural compute stick. Efforts has been made to optimize the real-time content censoring of the system. Experimental results show that, when applied to the video-on-demand IoT applications, the proposed system can speed up the video processing significantly and it can be effective in shielding young audience from violent videos at mobile devices in a real-time manner.
Multimodal Word Sense Disambiguation in Creative Practice

Language is ambiguous; many terms and expressions can convey the same idea. This is especially true in creative practice, where ideas and design intents are highly subjective. We present a dataset-Ambiguous Descriptions of Art Images (ADARI)-of contemporary workpieces, which aims to provide a foundational resource for subjective image description and multimodal word disambiguation in the context of creative practice. The dataset contains a total of 240k images labeled with 260k descriptive sentences. It is additionally organized into sub-domains of architecture, art, design, fashion, furniture, product design and technology. In subjective image description, labels do not necessarily correspond to well-defined entities i.e. cars, quantitative attributes such as the color red, or actions like playing. For example, the ambiguous label dynamic is a qualitative attribute of an extensive amount of objects and thus, the data's variance is high. To understand this complexity, we analyze the ambiguity and relevance of text with respect to images using the state-of-the-art pre-trained BERT model for sentence classification. We provide a baseline for multi-label classification tasks and demonstrate the potential of multimodal approaches for understanding ambiguity in design intentions. We hope that ADARI dataset and baselines constitute a first step towards subjective label classification.
Whole-Game Motion Capturing of Team Sports: System Architecture and Integrated Calibration

This paper discusses the application of video motion capturing technology (VMocap) to a competitive team sports game. The setting introduces a specific set of constraints: large scale markerless motion capturing, big recording volume, transmitting and processing gigabytes of data, operation without interfering with players or distracting spectators and staff, etc... In this paper, we present how we tackled and successfully solved all of these constraints. That enabled us to analyze the sportsmen without any intrusions, while giving their peak performance, hence opening a new field for Mocap application. International volleyball game was recorded in full length with the described system. During the course of the event, we compressed 54TB of raw image data real-time, capturing 6 hours of high framerate video per camera, without disturbing any of the game operations. Using the data, we were able to reconstruct the motion, muscle activity and behavior of the athletes present on the court.