Reviewerly/exHarmony huggingface dataset:
ROS Based Autonomous Disinfectant Mobile Robot for Hospitals

This paper presents a design of an autonomous mobile robot system, primarily focuses on hospital environments, which focuses on disinfecting the hospital's isolation ward. This paper also primarily concentrates on making this system cost efficient with minimalistic sensors. This system consists of an advance autonomous indoor navigating mobile robot and a ground station, LIDAR, an UVC disinfectant lamp, encoder motors, IMU, etc. and uses ROS to navigate in indoor autonomously, the hospital environment is a busy environment so this paper equally concentrates on dynamically environment navigation, path planning based on global planner algorithms, localisation and navigation is done using particle filter, dwa_local_planner, navfn. The autonomous mobile robot system has been tested and simulated whose results are reliable and efficient.
Practical People Counting Algorithm

In this paper, we consider the problem of people counting in video surveillance. This is an important task in video analysis, because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. The proposed methods are based on object tracking and are able to work on sparse frames, which allows them to work faster and requires minimum computing resources. We use the algorithm from [1] as a baseline, which based on object tracking by head detections. Head tracking in baseline is proved to be more robust and accurate as the heads are less susceptible to occlusions. But this approach has two disadvantages: the height of people is different, which means that people’s heads are in different planes, so the raised signal line doesn’t look so clear, and also because of this, the accuracy of people counting may decrease. In baseline, this problems were solved using head-to-body linear regression, which had to be retrained for each scene, but this complicates the use of the algorithm for practical purposes. In this paper, we propose a new neural network head-to-body regressor, which allows us to solve the mentioned problems at once. Also in this paper, we use a new visual tracking algorithm that allowed us to speed up our solution. In this work, we introduce two methods — distributed modified baseline algorithm with high people counting accuracy and a solution that can run on a single processor core. Our experimental evaluation showed that the proposed modifications are consistent.
Science Education in Slovenia

This chapter provides a short overview of the education system in Slovenia with a focus on science education. Institutions for basic and upper secondary education collaborate with universities and research centers on a national level in the development and research projects that aim to enhance students' scientific and mathematics literacy, entrepreneurship competences and encourage the use of e-learning environments. According to international student assessments, PISA and TIMSS, Slovenia ranks above the OECD average in mathematics and natural science. There are many competitions, science fairs, summer schools, and workshops organized on a national level. Based on the analysis of the study program Subject teacher at the University of Maribor, we can conclude trainee teachers obtain good theoretical knowledge in the field of natural science and adequate competences for the sensible use of ICT in teaching. In the future, greater focus should be on e-learning, e-teaching, and distance learning, as well as introducing AI and VR in education.
Understanding the Formation of Trust in Blockchain

Trust plays a crucial role in our daily lives. With the advent of blockchain technology, the issue of trust has become a more important topic to mankind. This revolutionary change in the technological context enables new forms of human, social, economic, and humane interactions. It also empowers individuals to leverage the value of their participation. The subversive nature of blockchain challenges the existing notion of trust, which is the basis of our research. This interpretive study based on interview data, using an inductive process of theory generation revealed three interconnected trusting-beliefs: Technical features, Functionality, and Valuableness as the dimensions of trust in blockchain. There is a remarkable similitude between the definitions of these dimensions and the conventional trusting beliefs. However, the latter is more expansive; they are explicitly infused and extended to delineate trust dimensions of blockchain. Furthermore, this research highlights a close bond between the trusting-beliefs and the characteristics of blockchain.
Rule Based Question Generation for Arabic Text

Question answering systems have evolved into a whole new domain. Question answering systems are of emense importance in the present scenario as well as for many purposes such as humanlike conversation, machine translation, summarization, etc. For the present work, the data is collected from the Arabic language textbook of the 5th standard of Yemen. The text is pre-processed for removing punctuation marks, stop words. The paragraphs are segmented first sentence-level and then word level. For the generation of questions, a rule based approach is used, which had a pre-requirement of properly tagged words. Thus pos tagger and NER relevant to the present domain is also developed considering linguistic aspect and rule based approach. A clear explanation of how wh-type questions are developed is given in detail. The main focus on which the work is done is types of nouns. The types of nouns have been used to generate wh-questions from the Arabic text.
Analysis of the acceleration of deep learning inference models on a heterogeneous architecture based on OpenVINO

Convolutional neural networks (CNN) are a very powerful tool for many different applications. This capability is highly demanded in the field of embedded systems for video surveillance, speech recognition, and image analysis. Due to its high computational intensity, the application of CNN is limited to real-time research areas where computational speed is extremely important. Therefore, an appropriate accelerator is required to fulfill the requirements of these limitations. On the one hand, GPUs are widely used to accelerate the CNN under high power dissipation. On the other hand, the trend for FPGA implementation is increasing rapidly due to its low power consumption and facile re-configurability. In this work, we evaluate the inference performance of 10 classification models and 9 object detection models using the OpenVINO toolkit. In addition, we analyzed the implementation of these models on the DE5a-Net DDR4 equipped with an Arria 10 GX FPGA. The results show that the performance of Full-Precision FP32 classification models on a heterogeneous architecture FPGA/CPU is on average 3.6X faster than the CPU.
Improved face recognition using convolutional neural network with unaided learning

Programmed acknowledgment of individuals is a difficult issue which has gotten a lot of consideration during ongoing years because of its numerous applications in severalfields. Face acknowledgment is one of those difficult issues and cutting-edge, there is no method that gives a strong answer for all circumstances. This paper presents another procedure for human face acknowledgment. This strategy utilizes a picture- based methodology towards man-made consciousness by eliminating repetitive information from face pictures through picture pressure utilizing the two-dimensional discrete cosine change((2D-DCT) PCA (Principle Component Analysis) The DCT (Discrete Cosine Transform) separates highlights from facepictures dependentons kintone.Highlightvectorsarebui It by figuring DCTcoefficients. A Convolutional neural network(CNN) utilizing an unaided learning method is utilized to arrange DCT-based component vectors into gatherings to recognize if the subject in the information picture is “available” or “not present” in the picture information base. Face acknowledgment with CNN is completed by characterizing power estimations of grayscale pixels into various gatherings. Assessment was acted in MATLAB utilizing a picture information base of 25 face pictures, containing five subjects and each subject having 5 pictures with various outward appearances. Subsequent to preparing for roughly 850 ages the framework accomplishedan acknowiedgmentpaceof81.36 % for 10sequentialpreliminar ies. The fundamental preferred position of this method is its fast handling capacity and low computational necessities, as far as both speed and memoryuse.
Deep learning for accident avoidance in a hostile driving environment

Road traffic accidents are the major cause of accidental deaths in India. Though there are several methods available for accident avoidance, deep learning-based accident avoidance is gaining much attention nowadays. The proposed accident avoidance algorithm involves major steps, like vehicle detection and vehicle localization by semantic segmentation, and finally recognition of the segmented objects. The novelty of the work relies on the driver to make clear decision on driving by knowing the nature of the distant objects on the go. Though many systems were developed to recognize objects, invention is still going on to find more accurate results. The conventional machine learning-based object detection and recognition algorithms may be in need of prominent features for producing better results. This requires laborious work done by the researcher. Even then, the algorithm may fail to produce better results if there are small ambiguities in the image. As a further step ahead, deep neural networks are utilized for object recognition, and many inventions have been made in the last few years. As computer vision has reached its pinnacle and also active research in this field, several outstanding results have been achieved in this field of research. The invention of AlexNet, ZF Net, VGG Net, and more are used for many research works based on the requirement. We here adopted pretrained architectures for object detection and classification by transfer learning and the results obtained are promising and comparable to the existing methods.
Construction of food digital ID and intelligent monitoring platform based on blockchain traceability and GPS locationing

Construction of food digital ID and intelligent monitoring system based on blockchain traceability technology is studied in this manuscript. This paper has two aspects of novelty: (1) The blockchain integration and enhancement. The decentralization of the blockchain and the mechanism of mutual verification of the block owners achieve effective proof of the transactions, the latest models are used to improve the efficiency. (2) The digital ID system design. After checking that it is correct, it encrypts the product information with its private key, digitally signs the product information and stores it in the server. (3) The implementation of the monitoring system. Distributed systems must consider the issue of data release when multiple consumers request multiple data sources at the same time, the sysmte is implemented with the coding theories. For the verification of the models, the method is simulatedand the results are convincing.
An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection

One of the most challenging scenarios of data analysis is prediction using time series data. As the underlying causal relationships of the data shift over time, a classification model trained on data at earlier points within the course starts to yield incorrect predictions on the current data. This phenomenon in machine learning is called concept drift. Within biomedical data, one of the molecular networks that changes significantly over a time is the metabolome. Using metabolomics analysis in biomedical applications produces an ideal tool in preventive healthcare, the pharmaceutical industry, and even ecology engineering. This study provides an innovative perspective on the analysis of metabolomics datasets using the concept of drift detection. The evaluation is based on two main objectives. The first objective is connected to the concept drift detection in available metabolomics datasets, and the second objective is to provide the assessment of commonly used machine learning tools for the best general detection approach in metabolomics datasets. The application of concept drift to metabolomics data has never been carried out before and is an original take on the analysis of highly dynamic molecular networks.
Resident user load classification method based on improved Gaussian mixture model clustering

In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.
Faster classification using compression analytics

Compression analytics have gained recent interest for application in malware classification and digital forensics. This interest is due to the fact that compression analytics rely on measured similarity between byte sequences in datasets without requiring prior feature extraction; in other words, these methods are featureless. Being featureless makes compression analytics particularly appealing for computer security applications, where good static features are either unknown or easy to circumvent by adversaries. However, previous classification methods based on compression analytics relied on algorithms that scaled with the size of each labeled class and the number of classes. In this work, we introduce an approach that, in addition to being featureless, can perform fast and accurate inference that is independent of the size of each labeled class. Our method is based on calculating a representative sample, the Fréchet mean, for each labeled class and using it at inference time. We introduce a greedy algorithm for calculating the Fréchet mean and evaluate its utility for classification across a variety of computer security applications, including authorship attribution of source code, file fragment type detection, and malware classification.
MANE: Organizational Network Embedding With Multiplex Attentive Neural Networks

Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.
Design and Analysis of Blockchain-Based Resale Marketplace

Medury,Lalith Ghosh,SiddharthaResale and exchange of items carry high risk and uncertainty. Fraudulent sellers and misleading item listings cause huge losses to consumers of digital resale marketplaces. Blockchain technology and smart contracts can help minimize the risk and uncertainty associated with resale marketplaces. The decentralized network of blockchain establishes trust by acting as a neutral third party. And the smart contracts guarantee that both parties will uphold the terms of sale. In this work, we propose the design of a blockchain-based decentralized and trustworthy marketplace. This design focuses on minimizing risk and uncertainty by using smart contracts, micro-escrows, and smart lock secured point-of-exchange. Additionally, zero-knowledge proofs are employed in the design enabling sellers to disclose the partial location of the point-of-exchange (PoE) while protecting their privacy.
Aggregation Enhanced Graph Convolutional Network for Graph Classification

Limited by the irregularity of graph topological structure and the sequence independence of nodes, existing graph neural networks usually generate graph-level representation by simple aggregation or sorting of node features for graph classification. These models are usually not deep enough to extract more abstract semantic information. Once the network deepens, it is easy to cause over-smoothing. To solve this problem, we propose a simple and efficient graph convolutional neural network based on DenseNet called AEGCN (Aggregation Enhanced Graph Convolutional Network) for graph clas-sification. We build a local extrema function named ELEConv (Enhanced Local Extrema Convolution) to reduce the noise in graphs, and then generate a large number of reusable feature maps through dense links. Extensive experiments on four real-world datasets validate that AEGCN not only alleviates the over-smoothing problem, but also has an advanced graph classification effect.
Characteristic Overview of Digital Image Forensics Tools

With the increase in the development of technology also there is increase in using the technology for performing crimes. There is increase in misusing the Digital Information and performing crime using digital information. Digital forensics is used to investigate the crime performed using the digital device. There are many tools used to perform digital forensics. Digital image forensics is a branch of digital forensics. Digital images are losing their originality due to various tools and techniques that are available online to modify the original image. These modified images are misused by the criminals. Digital image forensics deals with identifying the digital image forensics. There are many digital forensics tool that can be applied to analyze the digital image and find out the image forgeries. In this paper, a brief explanation on digital forensics, digital image forensics and tools used for digital forensics are presented.
Color Image Denoising via Tensor Robust PCA with Nonconvex and Nonlocal Regularization

Tensor robust principal component analysis (TRPCA) is an important algorithm for color image denoising by treating the whole image as a tensor and shrinking all singular values equally. In this paper, to improve the denoising performance of TRPCA, we propose a variant of TRPCA model. Specifically, we first introduce a nonconvex TRPCA (N-TRPCA) model which can shrink large singular values more and shrink small singular values less, so that the physical meanings of different singular values can be preserved. To take advantage of the structural redundancy of an image, we further group similar patches as a tensor according to nonlocal prior, and then apply the N-TRPCA model on this tensor. The denoised image can be obtained by aggregating all processed tensors. Experimental results demonstrate the superiority of the proposed denoising method beyond state-of-the-arts.
Efficient Recognition for MQAM Signal Using Feature Extraction

In modern communication systems, modulation recognition technology occupies an extremely important position and plays a vital role in both military and civilian fields. However, at present, many technologies have high time complexity and require strictly synchronous down conversion. In this paper, the widely used Multiple Quadrature Amplitude Modulation (MQAM) signal is studied for inter-class classification. Due to the problem that the number of signal elements is different in recognition, based on the high-order cumulant feature and constellation feature theory, this paper proposes a joint recognition scheme combining subtraction clustering. At the same time, for the phase mismatch problem in subtractive clustering, we propose a high-speed and accurate constellation rotation calculation method. Our algorithm can be used for 8QAM, 16QAM, 32QAM, and 64QAM Signal recognition, and the recognition rate is more than 95% under the condition of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SNR}=13\ \text{dB}$</tex> . At the same time, the running time of the proposed algorithm is about 39.1% faster than the current mainstream algorithm, indicating that it can precisely and efficiently identify QAM signals in class.