Reviewerly/exHarmony huggingface dataset:
Augmented reality an economical solution for engineers and designers

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Now a days, with obscene workload and a busy life, many experts face problems which more or less result in loss of their customers or to certain overheads that immobilize the customer's satisfaction process. The software being proposed will facilitates professional from various field like in mechanical designing, interior designing and that of architects. This research proposal most likely acts as an effective tool that can reduce the gap between industrial enterprises and customers in addition to other relevant business groups. It helps to visualize architectural designs and interior designs. Before the physical implementation, a virtual model of a real environment can be designed, enabling designers to virtually implement their idea in the given workspace and then view it in a real environment, enabling designers to view their 3D visualizations on their 2D drawings this help them to initially visualize and reconstruct the design without wasting a large amount of money making it a cost-effective solution. Application is based on my observations of the user's aspirations of an augmented reality in design service, a service that combines different social media functionality, augmented reality (AR) and 3-D modeling that embraces the concept of home design, architecture and mechanical designing process. This study connects all users of relevant concerns to an augmented reality's user-intensive design. The paper provides you with the existential complexity of how the AR can be implemented to enhance the work of architects and designers. The proposed application shows two types of reality, marker-less and more marker-based. The new feature of the project is that no expensive devices are required. It can be used easily through smartphones and tablets. This is a cost-effective solution that mainly reduces the overhead accosted daily by, mechanical designers, interior designers and architects. The software displays all the features that mechanical designers, interior designers and architects may need.&lt;/span&gt;
DeepBlindness: Fast Blindness Map Estimation and Blindness Type Classification for Outdoor Scene from Single Color Image

Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues". These blindness issues may seriously affect the performance of robotic systems and could lead to unsafe decisions being made. However, existing solutions either focus on one type of blindness only or lack the ability to estimate the degree of blindness accurately. Besides, heavy computation is needed so that these solutions cannot run in real-time on practical systems. In this paper, we provide a method which could simultaneously detect the type of blindness and provide a blindness map indicating to what degree the vision is limited on a pixel-by-pixel basis. Both the blindness type and the estimate of per-pixel blindness are essential for tasks like deblur, dehaze, or the fail-safe functioning of robotic systems. We demonstrate the effectiveness of our approach on the KITTI and CUHK datasets where experiments show that our method outperforms other state-of-the-art approaches, achieving speeds of about 130 frames per second (fps).
Foundations of Info-Metrics (Modeling, Inference and Imperfect Information)

Figuratively speaking, this is a weighty book (2lbs—465 pages in paperback) that is packed with megabytes of information (no pun intended), from a plethora of sources.Oxford University Press (OUP) ...
Framework for information management through step sequencer

Data is now commonly stored in digital format because it is the most secure way to store, manage, access and share information across networks. However, there is always a risk that unauthorized users will access data and, in the worst case, that malicious hackers will modify the data. The symmetric cryptosystem is used in the approach proposed. The symmetric cryptosystem is used in the proposed method. Data is divided into two sub-blocks and each block is encrypted to provide enhanced security using three separate secret keys. This deterministic algorithm is implemented using the LabVIEW tool. One of the three keys that diffuses the one other keys provided by the information is for misunderstanding. In the proposed solution blocks the main search attacks heavily. There are two separate secret keys used and to improve the robustness of the algorithm, random rotation is applied.
YANG Schema Comparison

This document specifies an algorithm for comparing two revisions of aYANG schema to determine the scope of changes, and a list of changes,between the revisions. The output of the algorithm can be used to helpselect an appropriate revision-label or YANG semantic version numberfor a new revision. This document defines a YANG extension thatprovides YANG annotations to help the tool accurately determine thescope of changes between two revisions.
Multi-Path Policy Optimization

Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, particularly for on-policy methods. Previous exploration methods either rely on complex structure to estimate the novelty of states, or incur sensitive hyper-parameters causing instability. We propose an efficient exploration method, Multi-Path Policy Optimization (MPPO), which does not incur high computation cost and ensures stability. MPPO maintains an efficient mechanism that effectively utilizes a population of diverse policies to enable better exploration, especially in sparse environments. We also give a theoretical guarantee of the stable performance. We build our scheme upon two widely-adopted on-policy methods, the Trust-Region Policy Optimization algorithm and Proximal Policy Optimization algorithm. We conduct extensive experiments on several MuJoCo tasks and their sparsified variants to fairly evaluate the proposed method. Results show that MPPO significantly outperforms state-of-the-art exploration methods in terms of both sample efficiency and final performance.
Direct 3D Detection of Vehicles in Monocular Images with a CNN based 3D Decoder.

In autonomous driving, the detection of objects like surrounding vehicles based on monocular RGB images is usually performed by 2D bounding box detectors. The resulting 2D objects can be used for a first coarse 3D position estimate but for a precise location, additional sensor data has to be taken into account. For further use in sensor fusion systems and environment maps it is preferable to detect objects, their orientation and dimensions directly in 3D coordinates. To address this 3D object detection task, we propose a direct 3D bounding box estimator which is realized as CNN decoder module and can be connected to most 2D object detectors like SSD[1], OverFeat[2], YOLO[3] and RetinaNet[4] or directly to CNN feature extractors like VGG [2] and ResNet [5]. The 3D parameters of the objects such as dimension and orientation are directly predicted by the CNN module. To successfully train this complex MultiNet architecture, a combination and modification of current loss functions is proposed. The fastest of the proposed network module combinations is capable of detecting objects in 3D camera coordinates at a frame rate of 28 fps.
On the Development of Spart (sports art)-A Proposed Method

Research & Investigations in Sports Medicine On the Development of Spart (sports art)-A Proposed Method Daniel Shorkend* Wizo School of Design, Isreal *Corresponding author:Daniel Shorkend, Wizo School of Design, Isreal Submission: September 26, 2019;Published: October 21, 2019 DOI: 10.31031/RISM.2019.05.000616 ISSN 2578-0271 Volume5 Issue4
Analysis of an Attractor Neural Network Model for Working Memory: A Control Theory Approach

Working Memory (WM) is a general-purpose cognitive system responsible for temporaryholding information in service of higher order cognition systems, e.g. decision making. Inthis thesis we focus on ...
Data Curation Workshop: Tips and Tools for Today

The current state of research data is like a disorganized photo collection: a mix of formats scattered across different media without a lot of authority control.That is changing as the need to make data available to researchers across the world is becoming recognized.Researchers know that their data needs to be maintained and made accessible, but often they do not have the time or the inclination to get involved in all of the details.This provides an excellent opportunity for librarians.Data curation is the process of preparing data to be made available in a repository with the goal of making it FAIR: Findable, Accessible, Interoperable, and Reusable.This workshop walks attendees through the steps in the process and gives them hands-on experience in data curation activities.
Android-based online cattle card system for recording quality cattle in Semarang regency

Abstract The manual of cattle card system that contains the identity of each livestock has been made by the Ministry of Agriculture and Fisheries. The Informatics Engineering Study Program of Semarang State University through a research, had contributed to changing the manual of cattle card system into an Android-based online Information System (IS). The research by applying Science and Technology was carried out in Semarang Regency. The problem was how to change the manual of cattle card system into an Android-based online IS. Implementation of the research in Semarang Regency. The aim of this activity was that the recording of quality cattle in Semarang Regency could be changed from the manual of Cattle Card System to online Information Systems. The research activities were observations, interviews, collaborative activities, programming based on Android where the system was developed using the waterfall model, and triangulation.The result, an Android-based online IS prototype had been created to record quality cattle in Semarang Regency. This Android-based online IS had been registered to obtain intellectual property rights.
Agent-Based Turing-Complete Transactions Integrating Feedback Within a Blockchain System

The proposed system relates generally to blockchain implementations, and is suited for, but not limited to, use with the Bitcoin blockchain. It can be used for the implementation of automated processes such as device/system control, process control, distributed computing, storage, and others. The system provides a solution which uses a blockchain to control a process executing on a computing resource. In a preferred implementation, the computing resource, running simultaneously and in parallel to the blockchain, manages a loop-based operation. The computing resource continuously monitors the state of the blockchain and any other off-blockchain input data or source. The execution of the loop is influenced by the state of the blockchain. Each iteration of the loop that is executed by the computing resource is recorded in a transaction that is written to the blockchain. It is stored as a hash within the transaction’s metadata. If the computing resource finds a transaction which contains a hash relating to the loop, it accesses the relevant portion of code. The loop contains a conditional statement which enables the computing resource to decide which action to take. The condition may be dependent upon the state of the blockchain or any other data source. The action can be any type of action, on or off the blockchain. Thus, the combination of the computing resource and the blockchain provides a solution which is (at least partially) Turing complete.
VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differs from the label of the target image. Furthermore, we show that JLF is more suitable for the VoteNet framework as a label fusion method than plurality voting. Lastly, we use Platt scaling to calibrate the probabilities of our new model. Results on LPBA40 3D MR brain images show that our proposed method can achieve better performance than VoteNet.
CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation

Accurately locating the start and end time of an action in untrimmed videos is a challenging task. One of the important reasons is the boundary of action is not highly distinguishable, and the features around the boundary are difficult to discriminate. To address this problem, we propose a novel framework for temporal action proposal generation, namely Continuous Multi-stage Network (CMSN), which divides a video that contains a complete action instance into six stages, namely Backgroud, Ready, Start, Confirm, End, Follow. To distinguish between Ready and Start, End and Follow more accurately, we propose a novel loss function, Variable Margin Cosine Loss (VMCL), which allows for different margins between different categories. Our experiments on THUMOS14 show that the proposed method for temporal proposal generation performs better than the state-of-the-art methods using the same network architecture and training dataset.
Pengembangan Sistem Rekruitmen Karyawan Perusahaan Mitra UPT Kewirausahaan Dan Pengembangan Karir Universitas Lampung

Recruitment is the process of finding and the best-qualified candidate work in a company or agency.There are various recruitment methods such as via employee recommendations, university collaboration, job vacancy, and jobsfair.In this paper an online company employee recruitment will be made using the black box testing method with the Equivalence Partitioning technique and the Likert scale.The data is taken from company users and job seekers.System displays job vacancies in accordance with the minimum level of education, gender and applicant's GPA.Job seekers fill out the Curriculum Vitae (CV) on the system as a company assessment for acceptance of applicants.The system can also provide announcements for applicants who have successfully passed a company.The system has been tested with black box testing with technique Equivalence Partitioning and get valid results for each test case, and for testing using a Likert scale gets very good results with a value of 87.05%.
A New Dimension to Plate Tectonics

New tools to model and visualize subduction zones in 3-D are providing researchers with insights into the gaps inherent in the theory of plate tectonics.
A Parallel Fuzzy Method to Reduce Mixed Gaussian-Impulsive Noise in CT Medical Images

A fuzzy logic filter to remove mixed Gaussian and impulsive noise from CT medical images is presented. The method performs a weighted average operation where a fuzzy rule-based model is defined to compute the coefficients. In addition, a parallel filter based on this method is presented. Implementation of the parallel algorithm on multi-core platform using OpenMP is presented. Efficiency is measured in terms of execution time and in terms of PSNR over medical CT images. Experimental results show that mixed Gaussian-impulsive noise is reduced efficiently and image details are preserved. The multi-core implementation allows reducing mixed Gaussian-impulse noise in real-time.
AN ANALYSIS ON STUDENT’S ERROR OF PASSIVE VOICE IN THE SIMPLE PRESENT

This research is aimed to analyze and to know an error of the passive voice in the simple present, passive is needed in daily speaking and writing activity. In this research, the writers use a qualitative method and took 25 students as a sample of this research. While the result of this research, among others: 1. there are 5 students who error on the use of verb, the students are looked confused to choose between Verb 2 and Also Verb 3 (Past Participle), the student cannot distinguish between irregular verb, regular verb and Verb 3 (Past Participle), 2. 4 students’ error on the use to be they can distinguish to be in the passive voice with the use to be in past tense, 3. 6 students’ error in the use to be and verb on passive voice of simple present.&#x0D; Penelitian ini bertujuan untuk menganalisis dan mengetahui kesalahan passive voice pada simple present, passive voice sangat dibutuhkan dalam kegiatan berbicara dan menulis sehari-hari. Dalam penelitian ini penulis menggunakan metode kualitatif dan mengambil 25 siswa sebagai sampel penelitian. Adapun Hasil penelitian ini antara lain: 1. ada 5 siswa yang melakukan kesalahan dalam penggunaan kata kerja, siswa terlihat bingung untuk memilih antara kata kerja 2 dan kata kerja 3 (Past Participle), siswa tidak dapat membedakan antara kata kerja tidak teratur, kata kerja reguler dan Kata kerja 3 (Past Participle), 2. Ada 4 siswa yang melakukan kesalahan dalam menggunakan to be, para siswa tidak dapat membedakan to be passive voice dan past tense 3. Ada 6 siswa yang salah dalam penggunaan menjadi dan kata kerja pada passive voice.
USE OF SENSOR TECHNOLOGY TO MAP THE SOCIAL NETWORKS OF PEOPLE LIVING WITH DEMENTIA: A FEASIBILITY STUDY

Abstract For older adults living with dementia, social network quality influences health outcomes. However, current social network measurement methods are time consuming and mentally draining for people living with dementia. This study aimed to accurately measure social networks using sensor technology. Bluetooth and radio-frequency identification (RFID) sensors were used to collect social network data in a simulation of a falling nursing home resident living with dementia. Participants wore sensors on their clothing, and video recordings were compared to sensor data. Bluetooth data reflected general direction of movement and instances of idling but were neither precise or accurate. RFID data was accurate after applying data filters. Both systems detected multiple sensors simultaneously. The Bluetooth system is not feasible for clinical use, but the RFID system shows potential for clinical application and accurate measurement of social network factors as interaction frequency and duration.
Predicting Unobserved Space For Planning via Depth Map Augmentation

Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robot's environment. On MAVs this is commonly achieved using low-cost sensors, such as stereo or RGB-D cameras. These sensors may fail to provide depth measurements in textureless or IR-absorbing areas and have limited effective range. In path planning, this results in inefficient trajectories or failure to recognize a feasible path to the goal, hence significantly impairing the robot's mobility. Recent advances in deep learning enables us to exploit prior experience about the shape of the world and hence to infer complete depth maps from color images and additional sparse depth measurements. In this work, we present an augmented planning system and investigate the effects of employing state-of-the-art depth completion techniques, specifically trained to augment sparse depth maps originating from RGB-D sensors, semi-dense methods and stereo matchers. We extensively evaluate our approach in online path planning experiments based on simulated data, as well as global path planning experiments based on real world MAV data. We show that our augmented system, provided with only sparse depth perception, can reach on-par performance to ground truth depth input in simulated online planning experiments. On real world MAV data the augmented system demonstrates superior performance compared to a planner based on very dense RGB-D depth maps.
Crowd Video Captioning

Describing a video automatically with natural language is a challenging task in the area of computer vision. In most cases, the on-site situation of great events is reported in news, but the situation of the off-site spectators in the entrance and exit is neglected which also arouses people's interest. Since the deployment of reporters in the entrance and exit costs lots of manpower, how to automatically describe the behavior of a crowd of off-site spectators is significant and remains a problem. To tackle this problem, we propose a new task called crowd video captioning (CVC) which aims to describe the crowd of spectators. We also provide baseline methods for this task and evaluate them on the dataset WorldExpo'10. Our experimental results show that captioning models have a fairly deep understanding of the crowd in video and perform satisfactorily in the CVC task.
Multisensor Fusion Localization using Extended H∞Filter using Pre-filtered Sensors Measurements.

Localization system is considered one of the main components of an intelligent vehicle, and it presents a criticality from the safety and performance point of view, which requires high accuracy and precision. However, the performance of these systems is affected by sensor limitations and, more importantly, noise. The localization problem can be solved by fusing the information from multiple sensors, and the multisensor fusion problem is typically solved using probabilistic methods. Among these methods, Kalman filters are the most widely used. Although Kalman filters are the most widely used, the noise models of the sensors fused are assumed to be Gaussian, and the noise statistics must be known beforehand, leading to a sub-optimal performance in systems with unknown noise characteristics. Accordingly, this paper presents a robust localization system, based on the extended $\mathcal{H}_{\infty}$ filter, in order to solve the localization problem in intelligent self-driving vehicles by fusing different localization sources. The proposed approach is validated through several real-world experiments under different scenarios, and the performance of the proposed method is compared with the Extended Kalman filter under the same conditions. The obtained results demonstrate that the proposed approach outperforms the Extended Kalman filter, and validates the use of the extended $\mathcal{H}_{\infty}$ filter as a multisensor fusion approach for localization systems.
Chirality Nets for Human Pose Regression

We propose Chirality Nets, a family of deep nets that is equivariant to the "chirality transform," i.e., the transformation to create a chiral pair. Through parameter sharing, odd and even symmetry, we propose and prove variants of standard building blocks of deep nets that satisfy the equivariance property, including fully connected layers, convolutional layers, batch-normalization, and LSTM/GRU cells. The proposed layers lead to a more data efficient representation and a reduction in computation by exploiting symmetry. We evaluate chirality nets on the task of human pose regression, which naturally exploits the left/right mirroring of the human body. We study three pose regression tasks: 3D pose estimation from video, 2D pose forecasting, and skeleton based activity recognition. Our approach achieves/matches state-of-the-art results, with more significant gains on small datasets and limited-data settings.