Reviewerly/exHarmony huggingface dataset:
A Mechatronics Design Approach of a Low-Cost Smart Reconnaissance Robot

Mobile robots, now-a-days, are majorly used in various fields such as service robotics, military, education, and surveillance. Human surveillance does have limitations as it is difficult for humans to monitor inaccessible areas and also, they get tired. Recent approaches in surveillance include the use of static cameras to monitor a particular location. But they do not cover the dead spot areas and also people try to avoid the camera’s sight. In order to overcome these drawbacks, surveillance is being replaced by mobile robots. In this paper, we presented a low-cost Smart Reconnaissance Robot (SRR) that is able to wander around an environment, capture images, and perform live streaming of video both in daytime and also at night using a camera deployed on the robot. Initially, the robot is modeled and simulated using webots robot simulation software and its performance is analyzed. Then, it is compared with the performance of the robot developed using Arduino microcontroller and other sensors. The user can control the robot’s action using a mobile phone or laptop through Internet of Things (IoT) and also can view the video information transmitted by the robot using a wi-fi module. Thus, this robot can be used in domestic or household environments where the user can remotely view any abnormal events or interpretation of human events.
Development of the Learning Logic Gate for Optimization of Neural Networks at the Hardware Level

The problem of implementation of fully hardware neural networks based on programmable logic circuits is considered. A method for minimizing the hardware costs of artificial neural networks is proposed. The model of the learning logic gate network is given. For programmable logic gate array a model of a learning logic gate is offered. On the base of the methods of algebra of logic the decomposition of a two-layer gate neural network with a trained hidden layer is proposed. Models of learning logic gates based on conjunction and disjunction functions are being developed. The intellectual properties of such models are shown. A method of mapping to the basis of learning logic gate networks is proposed. The results of discrete optimization of network parameters are presented. It is shown that the genetic algorithm has the lowest training error. A conclusion is drawn on the applicability of the obtained models when constructing optimal combinatorial circuits for neural network processing.
Vision Based Pose Detection of Joint Bearing of Helicopter Automatic Tilter

In order to detect the motion state of the joint bearing on the helicopter automatic tilter, this paper uses vision to detect the position and orientation of the joint bearing of the helicopter automatic tilter by setting the visual cooperation target on the reference position of the bearing outer ring and the fuselage. In this paper, the composition and measurement principle of the measurement system are described in detail. Aiming at the eccentricity problem between the ellipse center of the ring type cooperative target and the center of the real image, an improved pose algorithm based on the center extraction is proposed to reduce the eccentricity error. By setting the visual cooperation target on the reference position of the fuselage, the reference coordinates for the bearing position and pose are provided, and the camera position does not need to be calibrated, so the feasibility of the measurement system is improved. In this paper, through simulation and experiment, the theoretical correctness of the improved pose calculation method based on the extraction of the center of the circle is illustrated from the simulation point of view, and the feasibility of the measurement scheme and pose algorithm is illustrated from the experimental point of view.
Application of Computer Multimedia Technology in Training and Teaching of Nursing Staff in Operating Room

With the progress and continuous development of science and technology, computer multimedia technology has achieved rapid development, multimedia teaching means in all aspects of the application have made revolutionary progress. In this context, the application of computer multimedia technology in the surgical nursing industry should also be put on the agenda. This article unifies the computer multimedia technology in the training operating room application development process, to the traditional old nurse takes the new nurse to lead the teaching way, the revolutionary computer multimedia technology is used in the operating room to the new staff on the job training. The specific methods of this study are as follows: by using the Office software in the computer multimedia to establish the teaching folder, using PowerPoint software to edit the operating room courseware for pre-post training and teaching, using Photoshop software and Flash software to process the operating room specialized operation pictures and operation process, and using Dreamweaver software to make the release platform of training video. The research of this paper realizes the innovation of the training means of computer multimedia technology in the operating room nursing personnel training industry and improves the training efficiency through the above practices. The study of this paper has proved that the application of computer multimedia technology in operating room nursing staff training teaching is an efficient and practical means of teaching.
Named Entity Recommendations to Enhance Multilingual Retrieval in Europeana.eu

In the past years significant research efforts were invested towards the usage of Named Entity recommendation for improving information retrieval in large and heterogeneous data repositories. Such technology is employed nowadays to better understand user's search intention, to improve search precision and to enhance user experience in web portals. Within the current paper we present a case study on recommending Named Entities for enhancing multilingual retrieval in Europe's digital platform for cultural heritage. The challenges of designing an entity auto-suggestion service able to effectively support users searching for information in Europeana.eu are described in the paper together with a preliminary experimental evaluation and the outline indicating the directions for future development.
HAMLET - A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection

Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning. Traditional multi-armed bandit strategies look to the history of observed rewards to identify the most promising arms for optimizing expected total reward in the long run. When considering limited time budgets and computational resources, this backward view of rewards is inappropriate as the bandit should look into the future for anticipating the highest final reward at the end of a specified time budget. This work addresses that insight by introducing HAMLET, which extends the bandit approach with learning curve extrapolation and computation time-awareness for selecting among a set of machine learning algorithms. Results show that the HAMLET Variants 1-3 exhibit equal or better performance than other bandit-based algorithm selection strategies in experiments with recorded hyperparameter tuning traces for the majority of considered time budgets. The best performing HAMLET Variant 3 combines learning curve extrapolation with the well-known upper confidence bound exploration bonus. That variant performs better than all non-HAMLET policies with statistical significance at the 95% level for 1,485 runs.
Security Enhancement on ECC Dynamic Point Encoding for IoT

In this paper, a security enhancement on ECC dynamic point encoding is proposed to prevent a duplicate message frequency analysis attack by using an ASCII value, a sequence number, and an initial value to map a character to a point on an Elliptic Curve. This encoding is compared with 3x3 matrix based encoding and ECC dynamic point encoding with 4 performance metrics, which are a frequency analysis, a duplicate message frequency analysis, encoding and decoding time, and memory usage. The experiment is done on a Raspberry Pi, which is an IoT device. The results show that all encodings can prevent the frequency analysis attack but only the proposed encoding can prevent the duplicate message frequency analysis attack. The encoding time for the proposed encoding is the longest due to more steps to calculate but the decoding time for all encodings do not show significantly differences. The memory usages for all encodings are also similar.
Object Detection by Integrating Scene-Level Semantic Information and Border Regression Reinforcement

The improvement of object detection performance is mostly focused on the extraction of local information near the region of interest in the image, which results in detection performance in this area being unable to achieve the desired effect. First, a depth-wise separable convolution network(D_SCNet-127 R-CNN) is built on the backbone network. Considering the importance of scene and semantic informations for visual recognition, the feature map is sent into the branch of the semantic segmentation module, region proposal network module, and the region proposal self-attention module to build the network of scene-level and region proposal self-attention module. Second, a deep reinforcement learning was utilized to achieve accurate positioning of border regression, and the calculation speed of the whole model was improved through implementing a light-weight head network. This model can effectively solve the limitation of feature extraction in traditional object detection and obtain more comprehensive detailed features. The experimental verification on MSCOCO17, VOC12, and Cityscapes datasets shows that the proposed method has good validity and scalability.
Colorization of Grayscale Images Based on Automatic Content Association

In view of solving the color distortion and nonconformity with subjective feeling in gray image coloring, a new coloring algorithm for gray image based on automatic content correlation is proposed in this paper. First, the feature points of the color reference image is extracted and extended to the 3×3 neighborhood region. Then, a feature set consistent with integrated visual saliency is constructed. Subsequently, the source gray image is matched by structural similarity index measurement (SSIM) one by one in the way of line scanning. And the Cb and Cr of the feature points with the highest matching degree is transferred to the gray image. Finally, the color image is reconstructed in the YCbCr color space and then converted to the RGB color space. The method proposed in this paper does not need user interaction. Compared with other coloring algorithms, it is verified by experiments that our algorithm can effectively improve the visual effect of gray image coloring.
Cloud Allocation and Consolidation Based on a Scalability Metric

In this paper, we present a new allocation and resource consolidation system based on a scalability metric. According to cloud computing principles, the end users rent computing and big data analytic services with a pay-as-you-go cost model. However, when users’ data size increases or when the application stresses the memory or requires more computing power, they need to scale their rental to achieve approximately the same performance, such as task completion time and normalized system throughput. In this paper, we propose to delegate the responsibility to scale-up and scale-out the cloud system to a new component of the cloud orchestrator. The decision is taken on a metric that quantifies the scalability of the cloud system consistently under different system expansion configurations. The scalability metric is defined as a ratio between the new and the current situation, over the size of the i-th workload in terms of CPU cores and a performance metric. The considered metrics are the waiting time in the queue and the average resource (cores) usage rate during task execution. To validate our approach, we conduct experiments by emulation, on a real platform. The experimental results demonstrate the validity of the proposed general automatic strategies for cloud allocation and consolidation.
A Deep Learning Method Based Self-Attention and Bi-directional LSTM in Emotion Classification

Traditional recurrent neural network cannot achieve parallelism, while convolutional neural network cannot be used to process variable-length sequence samples directly. In this study, we combined the bidirectional short-time memory (Bi-LSTM) model with the self-attention to form the SA-BiLSTM method, to further improve the performance of the emotion classification model. The SA-BiLSTM method obtains the attention probability distribution by calculating the correlation between the intermediate state and final state. The SA-BiLSTM method weights the state of each moment differently to ensure that the problem of information redundancy is solved while retaining valid information and the accuracy of text classification is improved by optimizing the text feature vector. Experimental results on three different data sets show that the performance of SA-BiLSTM algorithm outperforms the six emotion classification methods by the accuracy, loss rate, time and other performance indicators of the classification model.
Multi-Step Quantization Of A Multi-Scale Network For Crowd Counting

Crowd counting is one of the most important tasks in visual surveillance applications since it provides useful information such as the number of crowds and their distribution. However, it is very challenging due to severe occlusions, large geometrical deformations, and high visual clutter. To tackle this problem, we propose a novel CNN-based crowd density estimation network consisting of a backbone, decoder, and mapper, and also a multi-step quantization scheme to train the network more effectively. As a backbone network, ResNet is adopted, then the decoder and mapper are added to deal with multi-scale problems of crowd counting and to generate high-resolution density maps. Finally, a multi-step quantization scheme discretizes the continuous space of both predictions and ground truth density maps, and it reduces the search scope of the network and raises their matching ratio. As a result, our method outperforms recent methods in four major datasets.
Stereo Matching Based on Improved Minimum Spanning Tree

The existing local stereo matching algorithms have some problems such as high noise sensitivity, low matching accuracy in occlusion regions, and long matching time, while the traditional Minimum Spanning Tree (MST) algorithm can simplify the cost aggregation step and improve the matching accuracy. However, for texture-less regions and edge regions, the matching effect of MST is still not ideal. In order to overcome these shortcomings, a stereo matching algorithm based on Improved Minimum Spanning Tree (IMST) is proposed in this paper. Firstly, based on the MST algorithm, our method transforms the original image into the image of minimum spanning tree, the cost aggregation of each tree node can be represented by the weight value and the matching cost of this node, and verifies the simplicity of the MST algorithm; Secondly, based on bilateral filtering, the grey similarity factor is redesigned to enhance the adaptive ability of the algorithm to the noise standard deviation to optimize the aggregation cost based on the MST; Thirdly, a separable bilateral filter is used to improve the processing speed while preserving the edge-preserving effect of the algorithm in the disparity refinement phase. Finally, based on the Middlebury Stereo Datasets, the pro-posed algorithm is compared with the Minimum Spanning Tree algorithm (MST) algorithm in three aspects: disparity map effect, matching accuracy and processing time. Compared with other algorithms, the proposed algorithm has better matching accuracy and shorter processing time.
Analysis of Algorithms K-Means and Apriori for Data Mining

Cloud computing can be understood as an environment given to user from any application residing online stored in cloud server and executed using browser. Hence, it is important to maintain user data proficiently. This paper discusses data mining techniques and algorithms for cloud computing. This paper also discusses the necessity of data mining and elaborates the use of K-means and Apriori algorithms and their implementation. It also draws comparison between both the algorithms.
Evaluating Quality of Statistical Disclosure Control Methods – VIOLAS Framework

VIOLAS Framework (Volatile Index of Liable Accuracy for Statistical Disclosure Controls) defines an assessment methodology for evaluating quality of statistical disclosure control methods by measuring the key factors determining the effectiveness of such methods. This paper describes the base model and explains how the framework may be used and extended by additional measures that may be desirable in particular IT environments or for specific data sets.
Taming Reasoning in Temporal Probabilistic Relational Models.

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by restoring a lifted representation, we present temporal approximate merging (TAMe), which incorporates (i) clustering for grouping submodels as well as (ii) statistical significance checks to test the fitness of the clustering outcome. In exchange for faster runtimes, TAMe introduces a bounded error that becomes negligible over time. Empirical results show that TAMe significantly improves the runtime performance of inference, while keeping errors small.
Comparative Study of Noise Robustness in Visual Attention Models

Humans are experts at quickly and accurately Identifying the most visually noticeable foreground object in the scene,. Several models of saliency detection are now available that have shown successful applications in various fields. Using visual attention techniques, we examine whether the effects of noise have an influence on their recognition rate. To answer this question, we select three recent computational techniques of visual saliency: Saliency Detection Using human saliency map technique, Saliency Detection Using Contourlet Transform technique (CT) and Saliency Detection Using low-rank and Structured sparse Matrix Decomposition (LSMD) technique, and we have evaluated the performance techniques based on the MAE, WF, AUC, PR curves and F-measure c. While the experimental results on the testing set show that LSMD technique has a better performance compared to the other techniques.
Extending Ontology Alignment Evaluation Data Sets for Evaluation of Bulgarian Language – Labelled Ontology Alignment

Mapping tacks may require mapping ontologies, labelled in various natural languages. OAEI proposes test sets for evaluation of alignments mainly in English. Only MultiFarm track in conference domain is adapted for evaluation of alignments in ten natural languages and Bulgarian language is not included. In this paper we propose and discuss an adaptation of MultiFarm test set for evaluation of alignment between Bulgarian language - labelled ontologies and alignments of multilingual ontologies.
MSE-optimal measurement dimension reduction in Gaussian filtering

We present a framework for measurement dimension reduction in Gaussian filtering, defined in terms of a linear operator acting on the measurement vector. This operator is optimized to minimize the Cramér-Rao bound of the estimate's mean squared error (MSE), yielding a measurement subspace from which elements minimally worsen the filter MSE performance, as compared to filtering with the original measurements. This is demonstrated with Kalman filtering in a linear Gaussian setting and various non-linear Gaussian filters with an on-line adaption of the operator. The proposed method improves computational time in exchange for a quantifiable and sometimes negligibly worsened MSE of the estimate.
Multidimensional medical data modeling based on fuzzy cognitive maps and k-means clustering

The paper concerns the use of fuzzy cognitive maps and k-means clustering to solve the problem of modeling multidimensional medical data. A fuzzy cognitive map is a recurrent neural network that describes the analyzed phenomenon in the form of key concepts and causal relationships between them. It is an effective tool for modeling decision support systems and is widely used in medicine. The aim of this paper is to analyze the use of fuzzy cognitive maps with k-means clustering to model decision support systems based on multidimensional data related to Parkinson’s disease. K-means method was applied to group the data, and then a separate fuzzy cognitive map was built for each cluster to increase forecasting accuracy. The learning process was realized with the use of the previously developed Individually Directional Evolutionary Algorithm. The obtained results confirm that the analyzed approach provides much better forecasting accuracy than the standard approach based on one model.
Computer Generated Holography with Depth-based View Synthesis

Depth-based view synthesis with a dozen of sparsely-spaced cameras is a viable solution for single-shot digital holography of nahrral sceneiy. We ouftrerform shearlet approaches that require around fifty input views for reaching high-quality holographic stereograms.
User Experience Research for the Next Decade in Education and Visual Arts

The purpose of this study is to investigate how UX-related subjects have been studied in both Education and Visual Arts between 2000 and 2019 to identify the trends and the relationship between the two disciplines. This study collected peer-reviewed journal articles found in Penn State University's online library that included User Experience or UX in their titles. These articles were then analyzed to identify what subjects were studied and how they were related within the two disciplines. The data allowed visualization of frequencies and relationships between subjects using Gephi 0.9.2, freeware software for visualizing data. The findings depicted the mainstreams of the research subjects and relationships between subjects in Education and Visual Arts. Our findings would support researchers seeking to study a future research UX-related subject in the Education and Visual Arts as well as in the multidiscipline.
A Secure and Efficient Smart Contract Execution Scheme

As a core technology of the blockchain, the smart contract is receiving increasing attention. However, the frequent outbreak of smart contract security events shows that improving the security of smart contracts is essential. How to guarantee the privacy of contract execution and the correctness of calculation results at the same time is still an issue to be resolved. Using secure multi-party computation (SMPC) technology to implement smart contracts is considered to be one of the potential solutions. But in the existing SMPC based contract execution schemes, a problem has been ignored, that is, the attacker can perform the same process as the reconstructor to recover the secret, which leads to the leakage of users' privacy. Therefore, in order to solve this problem in the process of smart contract operation, an improved homomorphic encryption algorithm is proposed in this paper, which has a relatively small public key size, short ciphertext length, and high encryption efficiency. Then, a contract execution scheme integrated with SMPC and homomorphic encryption (SMPC-HE for short) is further proposed, which is able to guarantee the privacy of contract execution and the correctness of the calculation results at the same time, and also makes smart contract execution fairer. Finally, our scheme is proved secure, efficient and has low space overhead by theory and experiment results.