Reviewerly/exHarmony huggingface dataset:
Visualization of Traffic Data: A Survey of Methods and Datasets

With the rapid development of cities, massive and complex traffic data is being generated and collected. The traffic data is not intuitive and cannot highlight key information about urban traffic conditions. However, traffic data visualization can directly correlate users with the data, and support users to interact with data in a convenient and visual way. Then realize the feedback of blending user wisdom and machine intelligence. This paper investigates a structured survey of the state of the art in the visualization of traffic data. First, we reviewed five representative traffic data visualization methods including WebVRGIS based traffic analysis and visualization system, TripMiner, IoV distributed architecture, SMASH architecture, and LDA-based topic modelling. Meanwhile, we analyzed the traffic datasets that applied in each method. Then we summarize these methods from seven aspects: scalability, data storage, data update, interactivity, reliability, data anomaly detection, and spatiotemporal visualization. In addition, we make a detailed comparative analysis of the key capabilities of five representative traffic data visualization methods in processing traffic big data. Finally, we conclude that the SMASH architecture performs better in processing high speed and large flow traffic data. Moreover, we propose a novel direction for optimizing traffic data visualization techniques.
An Estimation of hourly average solar radiation using artificial neural network in the city of Surabaya

Abstract To maximize the effectiveness of solar energy systems installed on the ship, the panels need to be positioned on their optimal inclination angle. To determine this angle, information on hourly average solar radiation is required in addition to position and direction data. However, the amount of hourly solar radiation is not continuously measured in Indonesia. This study proposed a method to estimate the hourly solar radiation for any month. This is based on Artificial Neural Network (ANN) with the month and the hour required for the estimate and the previous daily average solar radiation as the input. The proposed ANN method was validated by comparing the estimation results with the measured hourly average solar radiation in Surabaya from May to July 2020. Its effectiveness was affirmed with the coefficient of determination (R 2 ) of 0.983 or higher for each of the three observed months.
Using Diverse Neural Networks for Safer Human Pose Estimation: Towards Making Neural Networks Know When They Don’t Know

In recent years, human pose estimation has seen great improvements by the use of neural networks. However, these approaches are unsuitable for safety-critical applications such as human-robot interaction (HRI), as no guarantees are given whether a produced detection is correct or not and false detections with high confidence scores are produced on a regular basis. In this work, we propose a method to identify and eliminate false detections by comparing keypoint detections from different neural networks and assigning a 'Don't know' label in the case of a mismatch. Our approach is driven by the principle of software diversity, a technique recommended by the safety standard IEC 61508-7 [1] for dealing with software implementation faults. We evaluate our general concept on the MPII human pose dataset [2] using available ground truth data to calculate a suitable threshold for our keypoint comparison, reducing the number of false detections by approx. 61%. For the application at runtime, where no ground truth data is available, we introduce a method to calculate the needed threshold directly from keypoint detections. In further experiments, it was possible to reduce the number of false detections by approx. 75%. Eliminating keypoints by comparison also lowers the correct detection rate, which we maintained above 75% in all experiments. As this effect is limited and non-critical regarding safety we believe that the proposed approach can lead the way to a safe use of neural networks for human pose estimation in the future.
Design of New One-Time Pad Encryption Using Non-Uniform Cellular Automata With Parity Bit

This paper attempts to propose a one-time pad block encryption using non-uniform cellular automata for the public cloud which satisfies avalanche diffusion and confusion properties. It is found that by combining non-uniform and reversible cellular automata mechanisms, a robust master key in the public cloud could be generated. The simulation results show that the proposed method has large key space and protects the traditional brute force and chosen plaintext attacks in addition to having better execution time for accessing cloud data. The theoretical analysis confirmed that the proposed cellular automata–based random key generation solution was marked by acceptable security.
EMPLOYING DEEP LEARNING TO ENABLE VISUAL EXPLORATION OF EARTH SCIENCE EVENTS

Earth science data archives have significantly increased in size due to the number of advanced sensors and science missions. In the meantime, Earth science data systems have not taken advantage of data driven technologies to provide advanced search capabilities. This paper discusses a machine learning-based approach, an enabling data driven technology, to detect Earth science events from image archives. The automated event detection is cataloged in an event database that provides a novel way to explore large archives of data. In addition, a phenomena portal to visually explore events and contextual information is discussed.
Applying Delaunay Triangulation Augmentation for Deep Learning Facial Expression Generation and Recognition

Generating and recognizing facial expressions has numerous applications, however, those are limited by the scarcity of datasets containing labeled nuanced expressions. In this paper, we describe the use of Delaunay triangulation combined with simple morphing techniques to blend images of faces, which allows us to create and automatically label facial expressions portraying controllable intensities of emotion. We have applied this approach on the RafD dataset consisting of 67 participants and 8 categorical emotions and evaluated the augmentation in a facial expression generation and recognition tasks using deep learning models. For the generation task, we used a deconvolution neural network which learns to encode the input images in a high-dimensional feature space and generate realistic expressions at varying intensities. The augmentation significantly improves the quality of images compared to previous comparable experiments and it allows to create images with a higher resolution. For the recognition task, we evaluated pre-trained Densenet121 and Resnet50 networks with either the original or augmented dataset. Our results indicate that the augmentation alone has a similar or better performance compared to the original. Implications of this method and its role in improving existing facial expression generation and recognition approaches are discussed.
Generative Audio and Real-Time Soundtrack Synthesis in Gaming Environments

An important yet oft-overlooked front in the scope of interactive media, audio technologies have remained relatively stagnant compared to groundbreaking advancements made in fields such as visual fidelity and virtual reality. This paper explores the use of generative audio within a gaming environment, examining how dynamically-rendered audio can modify the creative pipeline, offer greater flexibility for audio designers, and improve the overall immersion of games and interactive media. A prototype generative audio engine is created, allowing for various musical parameters like tempo and pitch to be changed at runtime. Additionally, bidirectional linking between gameplay and music is explored, allowing player inputs to influence the soundtrack and the soundtrack to trigger or quantize player inputs. The final result, while somewhat limited in scope, demonstrates the potential of partially generative soundtracks to provide greater variety and freedom for audio engineers.
Searchable encryption scheme with limited search scope for group users

Aiming at the problem that most existing searchable encryption cannot effectively prevent information leakage from the internal user department, we propose a new multi-user searchable encryption scheme for group users to limit the search range. Searchable encryption can solve the problem of convenient use of data stored in a cloud server in a secret state. This solution divides users into different user groups and builds a segmented index. When users search, they first find the corresponding keywords, and then match the ID of the file that meets the access range through the user group ID to obtain the file Storage address. The searchable encryption scheme proposed in this paper for group users to limit the search range can improve the fine-grained user search range in multi-user searchable encryption and define different search ranges for different user groups. Two users who are not in the same user group cannot search for files uploaded by members of the other group, which reduces the risk of internal leakage of data stored in the cloud server.
Research on the Teaching Plan of Modern English Reading Based on Data Exchange and Sharing Mode

Abstract With the development of educational informatization and modern technology, information technology (hereinafter referred to as IT) has been gradually applied to modern English Reading (hereinafter referred to as English Reading) teaching program. As the core part of knowledge management, knowledge sharing has been widely used in enterprises, which requires us to strengthen knowledge innovation and adapt to social development. Therefore, knowledge sharing has attracted many educational experts to introduce it into the field of education. With the widespread application of IT, sharing mode based on data exchange has become an important research topic in current teaching. Through IT, teachers improve the interest of the classroom, which stimulates students’ interest in learning. However, there are still many problems in the mode of data exchange and sharing, which will affect the teaching effect. Firstly, this paper analyzes the feasibility of data exchange and sharing mode. Then, this paper analyzes the design process of English Reading teaching plan. Finally, this paper puts forward the related computer technology.
CPWF: Cross-Platform Website Fingerprinting Based on Multi-Similarity Loss

Website Fingerprinting (WF) attacks aim to identify the website label of anonymous website traffic trace. WF attacks are suitable for anonymous network scenario because it only needs to analyze the information contained in the packet header without analyzing the content of the packet payload. WF attacks are important means to combat anonymous cyber-crimes. However, with anonymous networks are widely deployed on different devices such as PC and mobile devices, the sources of anonymous website traffic have become more diverse, and differences in devices will cause differences in anonymous website traffic. Existing WF attacks only use anonymous website traffic collected from the same device to train the classifier, and the performance will be significantly reduced while it is trying to identify the mixed anonymous website traffic generated by different devices. In this study, we propose cross-platform website fingerprinting (CPWF) attack based on multi-similarity loss. First, we use the multi-similarity loss to train a deep learning-based website fingerprint extraction model. The model is able to extract a feature set for anonymous website traffic classification, ignoring the differences caused by different devices, so that attackers can use the anonymous website traffic collected from the single terminal device to train the classifier, the classifier capable of identifying anonymous website traffic collected from all devices effectively.
Multimodal Behavior Analysis of Human-Robot Navigational Commands

The following topics are dealt with: mobile robots; optimisation; position control; nonlinear control systems; biomechanics; adaptive control; motion control; stability; manipulators; autonomous aerial vehicles.
Method validation of As, Cd, Cr, Cu, Mn, Ni, Se, and Zn metals in Citarum River sediment using Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)

Abstract The Citarum River has a volcanic catchment area in West Java Province. It is also one of the nationally strategic rivers in tropical Indonesia due to its roles in water supply and in power generation. However, the Citarum River has experienced a decline in quality in which one of the causes is pollutants containing heavy metals. This research is aimed to validate the ICP-OES method in analyzing metals in sediment. Several parameters have been taken into account and evaluated for the validation of method, namely: linearity, the minimum detection limit, the limit of quantification, accuracy, and precision. The linearity of As, Cd, Cr, Cu, Mn, Ni, Se, and Zn was found satisfactory with coefficient of correlation (r) &gt; 0.9995. The precision of all metals was calculated as relative standard deviation (RSD) and was found that the RSD value was &lt; 2/3 CV Horwitz. The recovery value of all metals was found between 80-95%. The limit of detection (LOD) value for all metals was below 10 ug/L except for Zn and Mn. The method was validated using CRM PACS-3. Metal concentrations in the Citarum River sediment for all stations after being compared with the quality standard of ANZECC are still below the threshold values.
An Adversarial Attack with Fusion of Polarization for Unmanned Scenes

With the rise of artificial intelligence, the emergence of unmanned vehicles can alleviate traffic congestion and reduce the risk of traffic accidents, in which image recognition has become one of the key technologies. Yet, with the advent of the concept of adversarial examples, many works have proved that the existence of adversarial examples has huge hidden danger in the field of scene recognition. Currently, in unmanned scene recognition, polarization images are widely used because they can robustly describe important physical properties of the object. However, most of the researches on adversarial examples are based on RGB images, and few people studied polarization-based imaging. Therefore, this paper proposes an adversarial attack with fusion of polarization for unmanned scenes. Theoretically, we analyze that polarization images have better effects on adversarial example attacks than RGB images. Experimentally, we evaluate the performance of the proposed model by generating adversarial examples attack scene recognition classification model. The experiment results show that compared with RGB images, polarization images are less vulnerable to attack and have better effects on robustness, which can improve the security of unmanned scenes. And it can reduce the successful attack rate of adversarial examples by up to 9.4%.
Consortium Blockchain-based Decentralized Energy Trading Mechanism for Virtual Power Plant

As a new data structure, blockchain has the characteristics of decentralization, high security and information disclosure, which integrates and innovates a variety of computer technologies. Virtual power plant (VPP) is able to increase the operational flexibility of the energy resources and plays an important role in the aggregation of distributed generation resources. For the problem of scheduling difficulty and low transaction efficiency of VPP, this paper proposes a VPP transaction mechanism based on blockchain, which considers the stability on matching supply and demand and priority of renewables. The consortium blockchain is used to allow qualified users to participate in the energy transaction. A market trading platform for VPP based on peer-to-peer (P2P) network in the environment of Ethereum is designed. The incentive mechanism is added to improve the transaction efficiency and the transaction process of VPPis simulated. It shows that the blockchain-based VPP trading mechanism has excellent economy and traceability.
A Heuristic Evaluation of Usability for Environmental Control Units’ Eye-Tracking Interfaces at Veterans Affairs Spinal Cord Injuries and Disorders Centers

Heuristic evaluation is a valid method for assessing interface usability. Results derived from these methods are useful in identifying the most impactful design elements influencing usability and user experience of a system. By establishing the features in need of most improvement, designers can more effortful consider their approaches for iterative efforts to improve usability and user experience. This work used established design principles (i.e., Shneiderman’s Golden Rules and Nielsen’s Heuristics) to analyze the inpatient environmental control units deployed at VA Spinal Cord Injuries and Disorders (SCI/D) Centers to assist with task completion, foster independence, and improve the quality of life in veterans with spinal cord injuries and disorders. Analyses revealed many usability issues in the current interface, varying in severity from minor to catastrophic. Specific design recommendations to address these issues are provided.
Multilabel Sentiment Prediction by Addressing Imbalanced Class Problem Using Oversampling

Sentiment analysis has become an essential tool for Social Network data analysis. With the application of machine learning sentiment analysis has become even more accurate. It has been possible to classify text sentiments into multiple labels. The performance of such machine learning models highly depends on the data being used to train model. However, studies have indicated that data collected from Social networks may highly imbalanced. Motivated by this, the current study proposed a minority class oversampling method to address the class imbalance problem in multi label sentiment analysis using Imbalanced Ratio per Label and the Mean Imbalanced Ratio. Two well known classifiers have been engaged to establish the ingenuity of the proposed method. Experimental results revealed that the proposed method can significantly improve the performance of classifiers in multilabel sentiment analysis.
Comparative Analysis of Machine Learning techniques for Network Traffic Classification

Abstract Network traffic classification is the operation of giving appropriate identification to the every traffic flowing through a network. Several methods have been applied in the past to achieve network traffic classification including port-based, payload-based, behavior based and so on. These methods have been found to one limitation or the other. Nowadays, attention is now on Machine Learning(ML) methods that rely on the statistical properties of the traffic flows generated. However, ML methods do not perform well when confronted with large-scale traffic data having large number of features and instances. Feature selection is employed to remove non-relevant and redundant features before passing the data to ML classifiers. In this study, network traffic classification using ML methods is demonstrated from two perspectives: one that involves feature selection and one that does not. A number of performance metrics are considered including runtime, accuracy, recall, precision and F- score. The experimental results indicate that the classification without features has an average accuracy and runtime of 94.14% and 0.52 seconds respectively. On the other hand, the method with feature selection has accuracy of 95.61% and average of 0.25 seconds for the runtime. The improvement obtained reflects the importance of applying only relevant and non-redundant features to the ML methods. Thus it recommended that feature selection be included in the network classification process to guarantee an optimal accuracy result.
An Improved Convolutional Neural Network for 3D Unsupervised Medical Image Registration

In recent years, convolutional neural networks have been widely used in medical image registration. VTN network has been the most popular architecture in medical registration community. Although the structure has outstanding overall performance in medical image registration, the VTN architecture seems to own drawbacks in focusing on the areas of interest and extracting features efficiently. Therefore, we propose an improved version called CIRVTN based on the traditional VTN model where global Residual paths, the Inception module and the CBAM attention module are introduced. The introduction of global Residual path can not only alleviate the problem of gradient disappearance, but also improve the reusability of medical image features. Upon introducing the Inception and CBAM attention modules, the adaptability of the network to the diversities in shapes and locations of tissue contents in medical images are improved. Extensive experiments on three professional medical liver datasets showed that the network proposed in this paper outperforms the traditional VTN both in Dice score and computing efficiency.
Efficient Distributed MST Based Clustering for Recommender Systems

This paper presents the Distributed Kruskal Algorithm for Minimum Spanning Tree (MST) based clustering to be used in the context of recommendation engines. The algorithm can operate over large graph data sets distributed over a number of machines. The operation of the algorithm is evaluated by comparing both the quality of the cluster configurations produced, and the accuracy of the predictions, with non-MST based clustering approaches. The results indicate that the proposed approach produces comparable recommendations at much lower storage, hence runtime, costs.
Discriminative locality-constrained sparse representation for robust face recognition

Abstract In this paper, a new joint sparse representation method called discriminative locality-constrained sparse representation (DLSR) is proposed for robust face recognition. DLSR incorporates locality and label information of training samples into the framework of sparse representation. Locality information can distinguish dissimilarity between samples and plays an important role in image classification. Compared with the existing methods, DLSR contains more discriminative information of samples and can obtain more discriminative recognition results. Due to the use of l2-norm regularization, DLSR can obtain a closed-form solution. This makes it computationally very efficient. Experimental results based on the benchmark face databases ORL have shown that DLSR can achieve more promising performance than some state-of-the-art methods.
Enhanced OpenStack Cloud for Network Function Virtualization

The expense of telecommunications infrastructure has always been a heavy burden for telecom operators. With the network function virtualization, network functions on the dedicated network equipment can be virtualized and deployed on virtual machines. These virtual machines are usually hosted by the x86 computer so that the capital expenditures can be reduced. However, the penalty of virtualization is the performance degradation which is unacceptable to telecom operators who require a high quality of service. This paper attempts to address this problem and examine the enhanced techniques on OpenStack such as the impact of CPU Pinning and Hugepage on a virtual machine which is launched on the OpenStack. Experimental results show that the enhanced techniques can effectively improve the computing performance of the virtual machine with low overhead.