Reviewerly/exHarmony huggingface dataset:
Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.
Improving the procurement process in the corporation

The article examines the problems of the Russian sphere of circulation, including the supply and marketing of finished products in integrated industrial structures, considers traditional and innovative methods of procurement management in corporations, reveals the disadvantages and advantages of the system of supplying goods and services using the example of PJSC RSC Energia, cost calculations are given for centralized and decentralized procurement models, a number of measures are proposed to improve the supply system, much attention is paid to the use of a combined corporative logistics system based on the JELS model.
Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks

Backpropagation (BP) is the most widely used algorithm for the training of deep neural networks (DNN) and is also considered a de facto standard algorithm. However, the BP algorithm often requires a lot of computation time, which remains a major challenge. Thus, to reduce the time complexity of the BP algorithm, several methods have been proposed so far, but few do not apply to the BP algorithm. In the meantime, a new DNN algorithm based on nonnegative matrix factorization (NMF) has been proposed, and the algorithm has different convergence characteristics from the BP algorithm. We found that the NMF-based method could lead to rapid performance improvement in DNNs training, and we developed a technique to accelerate the training time of the BP algorithm. In this paper, we propose a novel training method for accelerating the BP algorithm by using an NMF-based algorithm. Furthermore, we present a technique to boost the efficiency of our proposed method by concurrently training DNNs with the BP and NMF-based algorithms. The experimental results indicate that our method significantly improves the training time of the BP algorithm.
FPLP3D: Security robot for face recognition in the workplace environment using face pose detection assisted controlled FACE++ tool position: A three-dimensional robot

BACKGROUND: In recent years, several tracker systems have been developed to monitor a 3-dimensional skull position for facial action whereas, various tracker systems simultaneously analyze the single sequence of video, which can be provided with low-quality cameras and less security. Initially, implementing a 2-D face detector and an unrepentance system has been suggested; furthermore, it has been improved using an integrated 3-D face initialized scheme for the real-time tracker in the present face recognition systems. OBJECTIVES: To overcome the present setbacks of the conventional systems, Face Pose Detection assisted controlled FACE++ tool position of Three-Dimensional Robot (FPLF3D) has been proposed in this article. Furthermore, the suggested proposed configuration has a high-end monitoring approach, which is used to improve the reliability of the robot’s human-machine contact in the workplace environment for security assistance. Additionally, the robot’s direction can be controlled by the operator’s head position assessment of the camera (or any active viewing system) using a three-dimensional robot. RESULTS: Besides, the applications that are imitated by headers like telepresence, computer-generated reality, and video competitions will directly take advantage of the strategies introduced in this paper. CONCLUSION: Finally, real video tests at the lab-scale level show the accuracy and usefulness of the approaches proposed in this research outperform the existing methods used for tracking.
Slider Swatch: an inconspicuous switch design solution for electronic textiles

In this paper we would like to explain our approach to the development of this swatch, from the idea to the possible applications of the final product. We will explain how we came up with the basic idea, which questions we asked ourselves and what kind of materials we used to achieve the desired result. Furthermore, we will also explain what problems we had to deal with during the development process and how we found a way to solve them. Moreover, we will have a look at how our Swatch can be integrated into everyday life and what possibilities its use could offer. It is important to us to convey our way of thinking and working during the process, so we also added a little step by step guide that tells how the swatch is made.
SmartLED: Smartphone-Based Covert Channels Leveraging the Notification LED

The widespread adoption of smartphones make them essential in daily routines. Thus, they can be used to create a covert channel without raising suspicions. To avoid detection, networkless communications are preferred. In this paper, we propose SmartLED, a mechanism to build covert channels leveraging a widely available smartphone feature - its notification LED. The secret is encoded through LED blinks using Manhattan encoding. SmartLED is assessed in real-world indoor and outdoor scenarios, considering different distances up to 5 meters. Our results show that the best performance is achieved in dark settings - 34.8 s. are needed to exfiltrate a 7-byte password to a distance of 1 m. Remarkably, distance does not cause a great impact on effective transmission time and shorter blinks do not lead to substantially greater transmission errors.
Intelligent Sanitary Napkin Coin Operated Dispensing System

In today’s world women cleanliness or hygiene is of very important and to be taken care by them. Women in India are still afraid or shy of buying napkins from outside like shops, medicals and from other stores. A permanent solution to this particular problem by installing a napkin dispensing system in different places like schools, colleges and public places. Existing coin operated dispensing system has many drawbacks because of no mechanism available for the person to refill the napkins about to know the status of how many napkins available in the dispensing system. So that the person need to check the availability of napkins in the dispensing system manually in order to refill it. This work mainly aims to install an automatic napkin dispenser in schools, colleges, toilets and other public places which can keep track of availability of the napkins. A vending machine is an automated machine that provides a particular item to the consumer after a specified amount is inserted into a machine. The same concept we have used and come up with a machine called intelligent sanitary napkin dispenser, this machine provides sanitary napkin dispenser consumer after a specified amount inserted in to the machine. Another drawback in the current system is that the coins can be operated or duplicated in a fashion such that napkin can be obtained without coin. In order to tackle this problem and to provide round the clock quality “Sanitary Napkin Dispenser” is used. Arduino provides a factor that breaks the functions of the microcontroller into a more available package .After designing the project and implementation results have to be checked. The expected outcome of this designed system is to ensure the consultant person has to know about the status of the napkins.
Unified Identity Authentication System Based on Blockchain

In traditional centralized application construction and data storage modes, the application servers and the users are not in equal position in user information management. The application server can store and abuse the user's information under the circumstances that the users are not aware, and the user cannot revoke the authorization from the application server. Therefore, this paper proposes an identity management system combined with blockchain technology, which can return the management right of user information to users. This system adopts a three-layer architecture composed of blockchain layer, virtual chain layer and storage layer, and encapsulates all functions in each layer. It has good flexibility, and is easy to add new technologies, with good extensibility. The blockchain records the user's information state to realize the consensus and integrity of the user's data. The virtual chain layer is responsible for the main logical functions and encapsulates the user's request into a blockchain transaction, maintaining a good portability. The storage layer is responsible for the actual file storage and is responsible for file routing, backup, and query. Comprehensively speaking, based on the above three layers' architecture, the system implements the user's privacy granularity control, the user file's external storage, and combines the zero-knowledge proof to update the user's private key. In view of the application environment of this system, the access time delay and efficiency of the P2P storage system are simulated. It solves the problems of centralized application construction and data storage mode under which users cannot fully control personal information and privacy protection.
Web Application for Automobile Services

A vehicle breakdown is the mechanical failure of a motor vehicle in such a way that the underlying problem prevents the vehicle from being operated at all, or impedes the vehicle's operation so much, that it is very difficult, nearly impossible, or dangerous to operate. Vehicle breakdowns have various causes. Depending on the nature of the problem, the vehicle may or may not need to be towed to an automobile repair shop. To aid such inconveniences building an application that facilitates the user to contact the nearby mechanic shops and choose the services required is an optimal solution. The workshop must be able to display their services and cost on their profile. Users must be able to register and login to cart the required services. The users must then be able to confirm the location, service time slot and complete the checkout process. In case, the services aren’t available the user must be able to view the nearby parking locations, towing options or book a cab.
Super-resolution Generative Adversarial Networks Based on Attention Model

The purpose of super-resolution reconstruction is to reconstruct a high-resolution image from one or more low resolution images. In this paper, we propose an improved super-resolution generative adversarial networks based on the attention model. The attention model can be used to extract the important features and suppress the unimportant features, so as to ensure the quality of network reconstruction and optimize the network structure of the generator in the generative adversarial networks (GAN). The experimental results show that on the Set5 datasets, we could use less residual block to train and the spending time of reconstruction is less by using the attention model. That also means the fewer parameters and the less calculation. The images selected on Set14 and BSD100 datasets are reconstructed, and the reconstructed time is also shortened.
Enhanced Stochastic Gradient Descent with Backward Queried Data for Online Learning

Stochastic gradient descent (SGD) is one of the preferred online optimization algorithms. However, one of its major drawbacks is its predisposition to forgetting previous data when optimizing through a data stream, also known as catastrophic interference. In this paper, we attempt to mitigate this drawback by proposing a new low-cost approach which incorporates backward queried data with SGD during online training. Under this new approach, we propose that for every new training sample through the data stream, the neural network is optimized using the corresponding backward queried data from the initial dataset. After comparing our algorithm to SGD, we see substantial improvements in the performance of the neural network with two different MNIST datasets (Fashion and Kuzushiji), demonstrating that our proposed algorithm can be a potential alternative to online SGD.
Research on software credibility algorithm based on deep convolutional sparse coding

Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the algorithm from two aspects: factor processing based on deep convolution neural network and trust classification based on sparse representation.
Education Game Indonesian Old Museum Explorer using Fuzzy State Machine

Abstract Indonesia has a diverse culture from the form of creation, work, and intention, but over time Indonesian culture is eroded by the sophistication of the times. Using entertainment media such as games will be able to provide knowledge about culture through the game and can develop morale, using 3D interactive design of an object such as historical heritage will look close to real and with the Artificial Intelligence (AI) used in the opponent will make the game that is played becomes fun or not monotonous. This study uses the Fuzzy State Machine (FuSM) method which aims to shape character behaviour like human like behaviour. The results that can be generated from FuSM are behaviour that changes according to the conditions and rules that have been determined. This is where the AI that is formed will not always be the same in the same situation, all will be different depending on the conditions and rules that exist.
Cyber Crime and Children in Digital Era

Cyberspace is a virtual world or online world. It is an artificial world created by computers or internet enabled devices or components to communicate. It is digital platform where users interact and communicate with one another with the help of internet. The cybercrime is a rapid growing area of crime in the world. It is defined as a crime in which computers or internet enabled devices to commit an offenses which is categorized into two types such as property crimes (hacking, spamming, identity theft, fraud and copyright infringement) and crimes against the person ( child pornography, sexual abuse of children, cyber stalking, cyber bullying). Crimes in the virtual world is serious threat to the people as cybercriminals uses computer technology to access the personal and professional information or documents of person or organization for evil or malevolently reasons. The aim of the paper is to render the psychology of child predation and child pornography online in India. This paper will discuss about the different measures which should be taken to protect and educate the children about cyber bullying and cyber stalking in India. It will also highlight the legal intervention, effects on victims, punishment and preventative measures for a broad range of cybercrimes. The study will also understand the psychology behind the cybercrimes such as child sex offences and how such crimes can be prevented. The aim of the study is to discuss the role of government and policy makers for the rehabilitation of victims against cybercrimes. The paper will suggest some measures for the holistic development and security of children.
Generation of Airborne Synthetic Aperture Radar Video from Stripmap and Spot mode images and Frame Rate Analysis

VideoSAR is the latest technology, where the radar system enables continuous data collection and processes SAR imagery as a sequence of images continuously when the radar platform either flies by or circles the region of interest. In this paper, the Synthetic Aperture Radar video is generated from Strip map mode and Spot light mode of SAR images. In case of strip map images to video conversion, the continuous image sequence is formed by mosaicing of multiple image strips and or by overlapping of batches. In spot mode, the target of interest is fixed and platform with radar flies by or in circular path and looks at same region of interest. Two to five spot images are considered per second to form the Spot video for 24 frames per second. Interpolation techniques are performed in between the spot images to make it into 24 frames. The results of the airborne Strip map and Spot mode SAR videos are given in the paper. The frame rates also analyzed for the airborne radar of the different velocities, ranges and resolution of images to produce better SAR video.
NBVC: A Benchmark for Depth Estimation from Narrow-Baseline Video Clips

We present a benchmark for online, video-based depth estimation, a problem that is not covered by the current set of benchmarks for evaluating 3D reconstruction, which focus on offline, batch reconstruction. Online depth estimation from video captured by a moving camera is a key enabling technology for compelling applications in robotics and augmented reality. Inspired by progress in many aspects of robotics due to benchmarks and datasets, we propose a new benchmark called NBVC for evaluating methods for online depth estimation from video. Our benchmark is composed of short video sequences with corresponding high-quality ground truth depth maps, derived from the recent Tanks and Temples dataset. We are hopeful that our work will be instrumental in the development of learning-based algorithms for online depth estimation from video clips, and will also lead to improvements in conventional approaches. In addition to the benchmark, we present a superpixel-based plane sweeping stereo algorithm and use it to investigate various aspects of the problem. The paper contains our initial findings and conclusions.
Oil Tank Detection in Satellite Images via a Contrario Clustering

Monitoring oil stocks provides valuable insights on the balance between production and demand of petroleum products. The identification of oil depots is important for estimating storage capacities and measuring oil stocks. To achieve this purpose, we present an oil tank detector. As oil tanks are generally circular, we use as a first step a circle detection algorithm. However, this approach tends to generate a considerable amount of false detections, as circular shapes are not necessarily tanks. To reduce these false detections, we take advantage of the fact that oil tanks are generally densely grouped together, and filter out isolated detections using a clustering algorithm. The clustering method uses the a contrario framework which gives a way to control the number of false detections per image. The method is illustrated on Sentinel-2 images.
Utilizing Keywords in Source Code to Improve Code Summarization

The source code summary is a short description in natural language that describes its functionality. The purpose of code summaries is to help developers understand the codes in software projects, and the automatic generation of source code summaries plays an important role in many software engineering activities. Inspired by observations that keyword information is often used to guide text summarization, we propose a novel code summarization model called CosKeywords, which can focus on the key information in the source code during the encoding process. To evaluate CosKeywords, we conducted extensive experiments on two existing datasets. Experiment results show that CosKeywords can outperform the state-of-the-art models on all datasets in terms of BLEU4 and METEOR.
Management of Transport and Logistics System Based on Predictive Cognitive and Fuzzy Models

The article offers an approach to solving particular controlling problems that arise in the management of cargo transportation. The proposed approach makes it possible to predict the possibility of emergency situations during transportation and the duration of cargo transportation, on the basis of which the optimal route can be selected. To predict the occurrence of accidents, vehicle failures, and driver errors, a fuzzy cognitive model is constructed that allows taking into account a large number of heterogeneous factors. The forecast model for estimating the duration of transportation is based on the results of a survey of experts, and fuzzy logic is used to formalize expert estimates. A fuzzy hierarchical model is used to reduce the number of fuzzy products. The article also provides a methodological example showing the results of applying the cognitive approach to specific data.
Comparing Architectures of Neural Networks for an Integration in Enterprise Systems: A Retail Case Study

While standard software has largely established itself on the market for business software, the unified integration of machine learning algorithms in standard software has received little attention so far. Business standard software serving as main component of an enterprise system is traditionally divided into software modules, such as CRM and SRM. This paper examines the architecture of neural networks (NN) with the aim of integrating them into business standard software. For this purpose, two optimization goals are chosen, the demand forecast and the delivery forecast. The delivery forecast has the aim to make recommendations for article replenishment deliveries for retail stores. These two optimization goals are traditionally implemented in different standard software modules, CRM and SRM, which differ in the nature of their task: Analytical (demand forecast) and transactional (article deliver forecast). Two different architectures of NNs are investigated. It turns out that a modularization of the NN with respect to their forecasting goal combined with a meaningful linking of the networks leads to a significant forecast precision improvement.
Separability of evolving W state in a noise environment

Abstract Entanglement is an important resource for quantum information processing. We provide a new entanglement witness to detect the entanglement of an evolving W state. Our results show that the new entanglement witness matches the evolving W state better than other witnesses or methods. The new witness significantly improves the performance of entanglement detection for some three-qubit states.