Reviewerly/exHarmony huggingface dataset:
How Can National Identity Card Reduce Authentication Risks in Enterprise Attendance Management System?

The attendance process is widespread today in Indonesia. One everyday use case is recording attendance at an enterprise. However, the existing attendance system still poses some risks. One of the biggest causes is when the smart card used does not have a good quality guarantee and does not use an official Indonesian identity card, namely e-KTP. This issue causes the wasteful use of smart cards. In addition, this is not under the Law of the Republic of Indonesia. Therefore, this research focuses on utilizing the e-KTP as an authentication factor in recording attendance. In addition, this study uses two methods: survey methods and theoretical methods. As a result, the proposed mechanism system has fewer steps than the current enterprise’s (2 out of 3). With the e-KTP, the conclusion obtained indicate that the proposed mechanism has successfully mitigated all technical existing risks (R2, R4, and R5). User Agreement Testing also shows the same results, participant’s perspectives of R1 and R3 scenarios more feel comfortable with e-KTP as the proposed ID cards in their enterprise. This study has been founding a new mechanism approach that is handled 100% risk (5 risks are covered), with all of the enterprise respondent’s responses that showed the agreement about the more efficiency and effectiveness of the proposed scheme that using e-KTP than the enterprise’s smartcard owned.
Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm

Successful and efficient path planning is essential for autonomous mobile robot. Traditional Rapidly-Exploring Random Tree(RRT) algorithm usually suffer from long path planning time and strong randomness in specific environment such as depression trap and narrow channel. In order to tackle such problem, in this paper a bidirectional fast search algorithm based on violent matching and regression analysis is proposed for fire-fighting robot. Specifically, by using violent matching strategy, the path can be searched directly when there are few obstacles. In addition, the regression analysis reduces the probability of sampling nodes appearing in the same area. Meanwhile, a bidirectional RRT algorithm is employed to improve the path search efficiency. Finally, the redundant nodes in the random tree are discarded by the method of node backtracking, which reduces the robot control complexity. Several comparison experiment case studies are carried out on Rviz, and the results validate the effectiveness and advantages of our proposed approach.
An object recognition method based on deep learning

Deep learning's self-learning capabilities are used to identify objects. Based on the traditional CNN convolutional neural network, this paper constructs a new network model, in which the 9 linear stacked inception modules are embedded. The use of inception modules reuses more features, improves the width of feature tensors, optimizes the training speed and improves the detection accuracy. Firstly, food picture data source is constructed and preprocessed. Then, some images are used to train the model, and the optimization algorithm SGDM is used to update the parameters, and the influence of learning rate on the training effect of the model is compared. Finally, the model is tested by testing the food pictures in the test data set, and the simulation based on MATLAB shows that the model improves the efficiency and accuracy of object recognition. It reflects the potential and advantages of deep learning in feature extraction.
A Pragmatic Regulatory Framework for Artificial Intelligence

The application of AI technology in different sectors can intrude on the data subjects' privacy rights. While the data protection laws attempt to regulate the use and processing of personal data, these laws obstruct the growth and development of AI technology. Current regulations are unable to cope with the AI revolution due to the pacing problem and Collingridge dilemma. In view of the regulatory gaps and the complexity of technology, there is a strong justification to regulate AI technology. It is increasingly important to safeguard privacy without encumbering AI technology with regulatory requirements that will hinder its progress. With the convergence of AI and blockchain technology, privacy challenges are exacerbated. In this chapter, several types of regulations will be analysed to decipher a suitable regulatory framework for AI. This is to ensure effective regulation of AI and to allow AI to flourish with the use and application of blockchain features.
Research on UAV Task Assignment Based on Hybrid Particle Swarm Algorithm

Aiming at the task planning of UAVs at the tactical level, this paper uses particle swarm algorithm as the basis and uses A* algorithm to complete the voyage estimation between tasks. In the task allocation stage, the initial planning of the coordinated route between UAVs is completed simultaneously to realize the tight coupling of task allocation and route planning. When considering more obstacle threats, or even sudden threats, the algorithm can effectively complete the coordinated task assignment of multiple drones to more targets. And the accuracy is greatly improved, the total execution cost is small, and the load between machines is reasonable.
Entanglement-assisted multiple-access channels: capacity regions and protocol designs

We solve the entanglement-assisted (EA) classical capacity region of quantum multiple-access channels with an arbitrary number of senders, which is conjectured by Hsieh, Devetak and Winter. As an example, we consider the bosonic thermal-loss multiple-access channel and solve the rate region enabled by an entanglement source composed of sender-receiver pairwise two-mode squeezed vacuum states. The EA rate region is strictly larger than the capacity region without entanglement-assistance, therefore also larger than the Yen-Shapiro rate-region of Gaussian encoding or coherent-state encoding. When the senders have equal low brightness, we also numerically find that the two-mode squeezed vacuum source is optimal at a corner rate point. With two-mode squeezed vacuum states as the source and phase modulation as the encoding, we also design practical receiver protocols to realize the entanglement advantages. In the parameter region of a large noise background, the receivers can enable a simultaneous rate advantage of 82.0% for each sender with binary phase-shift keying. Due to teleportation and superdense coding, our results for EA classical communication can be directly extended to EA quantum communication at half of the rates.
Personalized Cryptographic Protocols - Obfuscation Technique Based on the Qualities of the Individual

Most of the cryptographic protocols nowadays, mainly asymmetrical, have a generated user personal key as their basis, known only to them but still vulnerable to theft. There can be proposed an innovative solution, which hashes the cipher with chosen key and uses some personal characteristics of the person generating it (e.g., by voice sampling or biometrics, encoded as a bit vector). Such a private key would be usable only by its creator, making the design much safer and authenticating him personally, making the non-repudiation of encoded material possible. Also, with the proposed solution, another avenue of research has been opened by using such a technique in steganography - encoding information in the user-provided picture, secured by his biometric sequences (quality vectors). In both cases, the cryptographic protocols take on truly unique sequences, used instead of traditional salt values, also providing a layer of truly personal touch to an otherwise purely theoretical construct. It may find many applications in the growing world of IoT where people and their unique needs have to be incorporated into the growing web of security protocols.
Design and Implementation of Auxiliary Application of Middle School Geography Textbook Based on Augmented Reality Technology

In order to overcome the problems of the poor three-dimensional display effect and weak interaction of traditional middle school geography textbook, an auxiliary application for middle school geography textbooks based on augmented reality technology (AR) was designed. Application according to the teaching content in the middle school geography textbook, based on augmented reality technology, the Unity3D game engine and 3D modeling software are used to combine 3D models, animations and videos with the pictures and text in the geography textbook to achieve the integration of virtual and reality. After downloading the application through mobile devices, students scan the illustrations in the textbooks, watch the displayed 3D models and animations, and interact with the displayed UI interfaces and models, so as to intuitively understand geographic knowledge, improve learning efficiency, and cultivate learning interest. The AR geography textbook auxiliary application also adds AR virtual button interaction and 360° panoramic interaction in the interactive design, which enriches the interactive functions of the AR geography textbook auxiliary application. Through investigation and research, the application of geography teaching in middle school is helpful to improve students’ learning enthusiasm. However, there are still problems with higher application design and development requirements and greater difficulty in teaching management, and further exploration and research are needed.
Stable Line and Circle Detection Method in Noise Image for Machine Vision

It is not easy to stably and precisely detect geometric features in noise image in machine vision as it could be used in calibration center detection for robot monocular vision or robotic grasping operation. A new stable line and circle detection method in noise image is proposed in this paper. First, a region of interest (ROI) of line or circle is detected using a template matching method of the stable feature. Then, a 2D feature detection problem can be converted to 1D by projection lines sampled vertical to the detection line, and the sampled pixels along the projection line are projected to the sample line. Second, the local noise scale and filtering variance are computed from pixels along the sample line, and the edge point is detected from the first order derivative and the outliers are rejected. Finally, the line or circle is fitted by using a linear least square method. To verify the proposed method, lots of synthetic images are tested and compared with the state-of-the-art method. The experimental results show that our method is better than the advanced commercial method.
Local-Minimum-Free Artificial Potential Field Method for Obstacle Avoidance

In this paper we present a new formulation of the local minimum problem which characterizes the artificial potential field method for obstacle avoidance. Our approach consists of detecting the local-minimum situation by measuring the angle formed by the two force vectors (repulsive force and attractive force vectors). Afterward, we suggest adding a new vector expressed in term of the angle formed by two vectors. The modified APF method we are suggesting ensures a local minimum-free obstacle-free path planning algorithm. The developed algorithm is verified through MATLAB platform simulations. One robot is put in some position moves within a 50 × 50 m2 area to reach a goal point while avoiding three stationary obstacles. Four different initial configurations are simulated. The robot dynamics are represented by first-order integrator model. The obtained trajectories showed a clear effectiveness of the proposed algorithm.
Embedding Simulated Annealing within Stochastic Gradient Descent

We propose a new metaheuristic training scheme for Machine Learning that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way. Our idea is to define a discrete neighborhood of the current SGD point containing a number of “potentially good moves” that exploit gradient information, and to search this neighborhood by using a classical metaheuristic scheme borrowed from Discrete Optimization. In the present paper we investigate the use of a simple Simulated Annealing (SA) metaheuristic that accepts/rejects a candidate new solution in the neighborhood with a probability that depends both on the new solution quality and on a parameter (the temperature) which is modified over time to lower the probability of accepting worsening moves. Computational results on image classification (CIFAR-10) are reported, showing that the proposed approach leads to an improvement of the final validation accuracy for modern Deep Neural Networks such as ResNet34 and VGG16.
Graph-Based Object Detection Enhancement for Symbolic Engineering Drawings

The identification of graphic symbols and interconnections is a primary task in the digitization of symbolic engineering diagram images like circuit diagrams. Recent approaches propose the use of Convolutional Neural Networks to the identification of symbols in engineering diagrams. Although recall and precision from CNN based object recognition algorithms are high, false negatives result in some input symbols being missed or misclassified. The missed symbols induce errors in the circuit level features of the extracted circuit, which can be identified using graph level analysis. In this work, a custom annotated printed circuit image set, which is made publicly available in conjunction with the source code of the experiments of this paper, is used to fine-tune a Faster RCNN network to recognise component symbols and blob detection to identify inter-connections between symbols to generate a graph representation of the extracted circuit components. The graph structure is then analysed using graph convolutional neural networks and node degree comparison to identify graph anomalies potentially resulting from false negatives from the object recognition module. Anomaly predictions are then used to identify image regions with potential missed symbols, which are subject to image transforms and re-input to the Faster RCNN, which results in a significant improvement in component recall, which increases to 91% on the test set. The general tools used by the analysis pipeline can also be applied to other Engineering Diagrams with the availability of similar datasets.
Adaptability of Simple Classifier and Active Learning in Music Emotion Recognition

research-article Share on Adaptability of Simple Classifier and Active Learning in Music Emotion Recognition Author: Xiaoxiao Yu Beijing University of Technology, China Beijing University of Technology, ChinaView Profile Authors Info & Claims ICECC 2021: The 4th International Conference on Electronics, Communications and Control EngineeringApril 2021 Pages 13–19https://doi.org/10.1145/3462676.3462679Online:07 September 2021Publication History 0citation30DownloadsMetricsTotal Citations0Total Downloads30Last 12 Months30Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Dynamic Hand Gesture Recognition using Doppler Sonar and Deep Learning

This paper explores the use of deep learning to recognise dynamic hand gestures using a sonar system. The study begins by designing and building a Doppler sonar system. The sonar system designed for this study was a multi-static system containing one transmitter and three receivers. The sonar system can measure the Doppler frequency shifts caused by dynamic hand gestures. Since the system uses three receivers, three different Doppler frequency channels are measured. Three additional differential frequency channels are formed by computing the differences between the frequency of each of the receivers. These six channels are used as inputs to the deep learning models. Two different deep learning algorithms are explored to classify hand gestures: a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). Six basic hand gestures, two in each x- y- and z-axis, and two rotational hand gestures are recorded using both left and right hand at different distances. Ten-Fold cross-validation was used to evaluate the networks' performance and classification accuracy. The CNN was able to classify all the gestures with an accuracy of at least 98%. The LSTM was able to classify the six basic gestures with an accuracy of at least 98% but with the addition of the two rotational gestures, the accuracy drops to 41%. This result is acceptable since the basic gestures are more commonly used gestures than rotational gestures.
CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication

Attributed to the ever-increasing large image datasets, Convolutional Neural Networks (CNNs) have become popular for vision-based tasks. It is generally admirable to have larger-sized datasets for higher network training accuracies. However, the impact of dataset quality has not to be involved. It is reasonable to assume the near-duplicate images exist in the datasets. For instance, the Street View House Numbers (SVHN) dataset having cropped house plate digits from 0 to 9 are likely to have repetitive digits from the same/similar house plates. Redundant images may take up a certain portion of the dataset without consciousness. While contributing little to no accuracy improvement for the CNNs training, these duplicated images unnecessarily pose extra resource and computation consumption. To this end, this paper proposes a framework to assess the impact of the near-duplicate images on CNN training performance, called CE-Dedup. Specifically, CE-Dedup associates a hashing-based image deduplication approach with downstream CNNs-based image classification tasks. CE-Dedup balances the tradeoff between a large deduplication ratio and a stable accuracy by adjusting the deduplication threshold. The effectiveness of CE-Dedup is validated through extensive experiments on well-known CNN benchmarks. On one hand, while maintaining the same validation accuracy, CE-Dedup can reduce the dataset size by 23%. On the other hand, when allowing a small validation accuracy drop (by 5%), CE-Dedup can trim the dataset size by 75%.
Similarity-Based Heterogeneous Graph Attention Network for Knowledge-Enhanced Recommendation

The introduction of knowledge graphs (KG) has improved the accuracy and interpretability of recommendations. However, the performance of KG-based recommender system is still limited due to the lack of valid modeling of user/item similarity and effective constraints on user/item embeddings learning. In addition, common sampling and propagation methods for homogeneous graphs do not apply to KGs due to their heterogeneity. In this work, we propose Similarity-based Heterogeneous Graph Attention Network (SHGAT), which learns both the collaborative similarity and knowledge similarity of items by pre-training item embeddings with user-item interaction data and knowledge propagation in the KG. Meanwhile, users are represented by the items they have interacted with, thus establishing similarity between users and strengthening the learning of item embeddings. Besides, we design an importance sampling and aggregation method based on attention mechanism for heterogeneous graphs. We apply the proposed model on two real-world datasets, and the empirical results demonstrate that SHGAT significantly outperforms several compelling state-of-the-art baselines.
Efficient Recommendation System Using Latent Semantic Analysis

During the last decade, recommender system has been a significant part of our lives and we constantly depend on them to provide relevant and interesting results, be it “videos recommended for you” by YouTube or watch “movies recommended for you” by IMDb. There are many approaches based on the latest machine learning algorithms for finding recommendations. The efficiency of these algorithms is still a big research question. In this paper, the authors proposed an efficient recommendation using latent semantic analysis approaches. Here, authors improve the accuracy and efficiency of the basic approaches, i.e., content-based and collaborative filtering-based systems, by using singular value decomposition (SVD) and its other variant SVD++, and also perform a detailed experiment using MovieLens dataset and hence, show significant improvement, in comparison with typical K-Nearest Neighbor (KNN)-based recommender systems, in both accuracy metrics, i.e., mean absolute error and root mean square error.
Risk Identification Based on Architectural Patterns

We present a novel approach for the identification of risks for IT-based systems, where we base risk identification on the system architecture, in particular, the architectural principles a system is built on. Such principles can be expressed as architectural patterns, which are amenable to specific risks. We represent those risks – concerning e.g. safety, security or fault tolerance – as Risk Issue Questionnaires (RIQs). A RIQ enumerates the typical risks associated with a given architectural pattern. Risk identification proceeds by identifying the architectural patterns contained in a system architecture and processing the associated RIQs, i.e., for each issue in the RIQ it has to be assessed whether it is relevant for the system under analysis or not. We present an example of a RIQ, a RIQ-driven risk identification method, an application example, and the results of an initial experiment evaluating the RIQ method.
A Deep Learning Pipeline Incorporating Level Set for Vascular Segmentation and Topological Analysis

In the rat’s liver cancer chemotherapy model, it is significant to quantify the process of the time-dependent release of any anticancer drug, for which an essential component is to extract drug-diffused vessels and analyze their topology. In this work, we proposed a novel pipeline for segmenting vessels and extracting their skeletons for subsequent topological analyses. First, we used UNet to obtain coarse vessel segmentation as an initial result. Due to the vital importance of the vascular topology, we manually corrected the coarse segmentation to a minimum degree. Then, level-set was used to further refine the vessel segmentation. Finally, we extracted the vascular skeleton to analyze its topological structure. Experimental results identified the robust performance of the proposed pipeline.
Impact of Data Quality on Day-ahead Photovoltaic Power Production Forecasting

The main challenge of integrating large shares of variable generation remains the accuracy improvement of day-ahead forecasts and the establishment of applicable robust performing practices based on acquired data. To that end, the quality of the data utilized for constructing and verifying the performance of forecasting models is an important influential factor. This work aims to present the impact of data quality and different training regimes on the performance of day-ahead photovoltaic (PV) power production forecasting models. Specifically, a comparative performance evaluation was performed by training an optimally constructed forecasting model with high- and low-quality data acquired from a test-bench PV system. The forecasting model trained with high-quality data exhibited an absolute error reduction of 3.25% compared to the same model trained with low-quality data. Finally, the application of the proposed data correction methodology demonstrated that training with high-quality data provides higher performance accuracy improvements of up to 2.98% for overcast daily patterns.