Miracle-dz/UniFAR-FTUs huggingface dataset:
Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

A novel method for music video recommendation is presented in this paper. The contributions of this paper are two-fold. (i) The proposed method constructs a network, which not only represents relationships between music videos and users but also captures multi-modal features of music videos. This enables collaborative use of multi-modal features such as audio, visual, and textual features, and multiple social metadata that can represent relationships between music videos and users on video hosting services. (ii) A novel scheme for link prediction considering local and global structures of the network (LP-LGSN) is newly derived by fusing multiple link prediction scores based on both local and global structures. By using the LP-LGSN to predict the degrees to which users desire music videos, the proposed method can recommend users’ desired music videos. The experimental results for a real-world dataset constructed from YouTube-8M show the effectiveness of the proposed method.
Research on Vibration Characteristics and Stress Analysis of Gearbox Housing in High-Speed Trains

Gearbox housing in high-speed trains has two kinds of excitation sources: the internal excitation of gear mesh excitation and the external excitation that contains the wheel polygonization and the input torque. To study the effect of the torque on the vibration and stress of the gearbox housing, a multibody dynamic model is established by the Simpack, in which the elastic deformation of the wheelset and the gearbox housing are considered. A traction drive system is modeled by the MATLAB/Simulink to calculate the traction torque, which is transformed to drive the high-speed train to overcome the basic resistance and run with a constant speed. The accuracy of the model is verified by comparing the simulation results with the measurement results. Based on the electromechanical model, the vibration acceleration of the gearbox housing in the case of without torque, with ideal torque, and with harmonic torque is calculated and analyzed, as well as its dynamic stress. The results show that the traction torque plays a role in amplifying the vibration acceleration and dynamic stress. When the fatigue damage of the gearbox housing is calculated, it will obtain a more accurate result to consider the effect of the traction torque.
Design, Installation, and Testing of a Paratransit Bus with Electrified Accessories

Urban transit agencies across the United States operate several types of services, inluding paratransit operations and fixed route fleets. The paratransit service operations are unique, since they do not run fixed routes and have relatively long passenger stops. During these stops, passengers onboard the vehicle must be kept comfortable, requiring operation of the heating, ventilation, and air conditioning (HVAC). In a conventional bus configuration, the engine must be idling to power the HVAC compressor and other accessories. Thus, the engine idle time in a paratransit fleet is a substantial portion of the daily operation. This paper describes a research program with goals of lowering paratransit bus emissions, improving fuel economy, and reducing engine idle time. The program concept is electrification of the bus accessories using a high voltage battery and alternator system. With the vehicle engine running, the high voltage battery is charged via the engine driven high voltage alternator. During vehicle stops, the engine can be turned off to reduce emissions, increase fuel economy, and reduce idle time while still having HVAC for passenger comfort powered from the high voltage battery. This paper will describe the modeling, design, installation, and testing phases of the vehicles.
On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches

The training of machine learning models for imbalanced data classification is a challenging task. Several metrics have been used to assess the performance of the classifiers. Each metric is appropriate for a class of problems, and some users often do not have a clear notion of which metric to use. In such cases, it is desirable that the chosen objective function provides good overall performance for most of the existing metrics. Here, three neuroevolutionary approaches based on Cartesian Genetic Programming are used in order to investigate the impact of optimizing accuracy, G-mean, Fβ-score, and the area under the Receiver Operating Characteristic (ROC) curve when creating classifiers based on Artificial Neural Networks applied to imbalanced data classification problems. The results suggest that the optimization of G-mean and Fβ-score generate models that present a superior overall performance in all metrics.
A Novel Data Augmentation Method for Sea Ice Scene Classification of Arctic Aerial Images

In this paper, a novel data augmentation method was proposed for supervised sea ice scene classification with arctic aerial images. Ice-type classification of sea ice scenes in a region is useful for instant navigation. However, some types of sea ice scenes are difficult to collect. The small number of available samples usually limits the performance in classification. Inspiring by the transfer learning method, the training samples are augmented with simulated sea ice images. Considering the distribution characteristic of sea ice scene, simulation samples are synthesized by algebraic operations on the respective regions of interest from two true samples. One of the two true samples can be an additional image, which is more easily collected than others, such as full ice and snow. Simultaneously, it introduces new information. Generalization and error-correction capability of deep neural networks for training samples make the proposed method feasible. The experiments on true sample sets, simulation sample sets, and mixed sample sets were implemented. Finally, the effectiveness of our data augmentation method was demonstrated, which improved the accuracy of sea ice classification.
Efficient Similarity Search on Quasi-Metric Graphs

Similarity search in metric spaces finds similar objects to a given object, which has received much attention as it is able to support various data types and flexible similarity metrics. In real-life applications, metric spaces might be combined with graphs, resulting in geo-social network, citation graph, social image graph, to name but a few. In this paper, we introduce a new notion called quasi-metric graph that connects metric data using a graph, and formulate similarity search on quasi-metric graphs based on the combined similarity metric considering both the metric data similarity and graph similarity. We propose two simple efficient approaches, the best-first method and the breadth-first method, which traverse the quasi-metric graph following the best-first and the breadth-first paradigms, respectively, and utilize the triangle inequality to prune unnecessary evaluation. Extensive experiments with three real datasets demonstrate, compared with several baseline methods, the effectiveness and efficiency of our proposed methods.
Scalable and Accurate Estimation of Room-Level People Counts from Multi-Modal Fusion of Perimeter Sensors and WiFi Trajectories

Estimating the number of people in rooms and zones within commercial buildings are gaining enormous attention for facilitating various domain applications. However, the deployment of state-of-art counting sensors such as camera technologies can be economically in-viable for individual rooms or zones in large commercial and public buildings. Such sensors are also known to be highly intrusive within building deployments. In this paper, we propose a multi-modal fusion method that leverages the accuracy of camera technologies for estimating building-level counts and the non-intrusive and scalability of wireless fidelity (WiFi) trajectory data to estimate room-level counts. This multi-modal fusion method disaggregates the obtained building-level counts by applying a series of data cleaning methods and a two-step probabilistic method. We evaluate the disaggregation method with datasets from a large teaching building, and we benchmark its performance with a state-of-art estimation algorithm and count estimates from raw WiFi trajectories. The obtained evaluation results highlight that the disaggregation algorithm outperforms other estimation methods by a minimum ratio of 35% for all room cases using the Normalized Root Mean Squared Error (NRMSE) metric.
Digital Curation at Work: Modeling Workflows for Digital Archival Materials

This paper describes and compares digital curation workflows from 12 cultural heritage institutions that vary in size, nature of digital collections, available resources, and level of development of digital curation activities. While the research and practice of digital curation continues to mature in the cultural heritage sector, relatively little empirical, comparative research on digital curation activities has been conducted to date. The present research aims to advance knowledge about digital curation as it is currently practiced in the field, principally by modeling digital curation workflows from different institutional contexts. This greater understanding can contribute to the advancement of digital curation software, practices, and technical skills. In particular, the project focuses on the role of open-source software systems, as these systems already have strong support in the cultural heritage sector and can readily be further developed through these existing communities. This research has surfaced similarities and differences in digital curation activities, as well as broader sociotechnical factors impacting digital curation work, including the degree of formalization of digital curation activities, the nature of collections being acquired, and the level of institutional support for various software environments.
Parallel Best Order Sort for Non-dominated Sorting: A Theoretical Study Considering the PRAM-CREW Model

In the current paper we focus on parallelization of non-dominated sorting which is an essential step in Pareto-based multi-objective evolutionary algorithms. The parallel approaches can help to reduce the overall execution time of multi-objective evolutionary algorithms. Although there have been some proposals to parallelize non-dominated sorting algorithms, most of them have focused on the fast non-dominated sort algorithm proposed by Deb et al. This paper explores the scope of parallelism in a recently proposed approach known as Best Order Sort, which was proposed by Roy et al. We focus on two different ways of achieving parallelism in Best Order Sort. The time and space complexity of these two parallel schemes is also analyzed theoretically considering the PRAM CREW model.
Simulation Based on Java Language - Efficiency Optimization of Domain Name System

This paper is a further study of DNS. First, it makes a comprehensive comparison of TCP and UDP transmission, and puts forward personal opinions on the concept of DNS resource records and caches, and completes a feasible implementation plan. Based on the preparation of DNS program in Java, it uses DNS to simulate the socket. Extraction of two aspects of the data is used for comparison, and the IPV6 in the simulation program only keeps IPV4, query access speed optimization. At the same time, this paper proposes an optimization scheme for DNS server security resolution and establishes a set of perfect address resolutions. According to big data analysis and DNS protection module, IP address data is classified according to security, and DNS server security is optimized.
A Variation of Quicksort for Model Checking with Promela and SPIN

Because of the speed and memory efficiency of sorting, quicksort is often the best choice in software development. However, in model checking with SPIN, it is difficult to implement quicksort using Promela. Regular recursion-based implementations are not suitable for Promela due to the lack of function definition. Even Promela does not have stack in its data structures, increasing the difficulty to do quicksort. This paper proposes a variation of quicksort for Promela without using recursion and stack. The variation of quicksort can be applied to SPIN-based model checking requiring a quicksort algorithm. Other computer programs can also use this variation, if recursion, function definition and stack are not available or banned.
A hybrid model based on CNN and Bi-LSTM for urban water demand prediction

Water demand forecast is the basis of urban intelligent water supply, because the system is limited by nonlinear changes in the process of water consumption, the traditional prediction model has great impact in accuracy and stability. Even small changes in temperature and holidays periods can lead to abnormal changes in urban water use. To solve these problems, a hybrid model combining convolutional neural network and bidirectional long and short term memory network was adopted in this study. Corresponding corrective model is established for special situations such as weather natural changes and holidays. In order to extract the features of water quantity and climate data, these features are input into the Bi-LSTM network to predict the usage of urban water. This paper carries out a correlation analysis of historical water data and climatic factors that cause an impact on the usage of urban water. The previous five days water usage data and the daily maximum temperature were selected as the basis for the holiday correction model and the temperature correction model. Comparing the different models before and after correcting the deviation, the prediction results have been improved. The present work was compared with results of long-term and short-term memory networks (LSTM), bidirectional long-term memory networks (Bi-LSTM), CNN, sparse autoencoder (SAEs), and CNN-LSTM, hence the prediction error is reduced by using the CNN-Bi-LSTM model. Finally, under the same training period, the training time and convergence of the six models were analyzed. The training time of CNN-Bi-LSTM is less than LSTM, Bi-LSTM, CNN, and CNN-LSTM, but larger than SAEs. The training convergence of CNN-Bi-LSTM was set in 125 times, which is smaller than the training times of the other five models.
RBFA: Radial Basis Function Autoencoders

We are introducing a new variation of the existing autoencoder called Radial Basis Function Autoencoders (RBFA). This version employs radial symmetric functions, in the first step of encoding, to map the input data vectors into a new form. The transformation which relies on the similarity between the input data examples and predefined center points features forces neurons in the first hidden level to respond similarly to similar patterns of the data. This basic idea of radial transformation (activation) can be interpreted as regularization on the hidden neurons which are penalized based on the similarity criterion so only a fraction of them will become highly activated at one time. The paper also introduces a new method for defining Gaussian radial function’s parameters using Particle Swarm Optimization (PSO). Results indicate that the accuracy of four different classifiers was significantly affected by the number of layers in the first hidden level of the network. RBFA has shown promising results compared to other state of the art autoencoders using different datasets including high-dimensional small-sized medical data. Optimizing the radial function parameters improved the classification accuracy of the four classifiers in some cases.
Lighthouse: Enabling Landmark-Based Accurate and Robust Next Generation Indoor LBSs on a Worldwide Scale

A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide worldwide robust and accurate location based services (LBSs). The proposed Lighthouse landmarks have the nice properties of being abundant in space, hardware-and carry position-independent, can be computed efficiently from a single scan, are confined to a small area of space, do not restrict the user movement path, and do not require any calibration. We show that the positioning error of the Lighthouse landmarks is bounded and present the different extensions that allow it to handle practical situations including the noisy wireless channel, different AP transmit powers, obstacles in the environment, among others. We further present efficient algorithms for extracting them from WiFi scans. We have implemented and evaluated Lighthouse using thousands of surveys collected from different cities worldwide over a six months period. Our results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is less than 3.6 meters. This accuracy is robust over time, different phones hardware, phone carrying positions, and parameters configurations; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs on a worldwide scale.
Traffic Congestion Prediction by Spatiotemporal Propagation Patterns

Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.
Blind Image Quality Assessment Model Based on Deep Convolutional Neural Network

As learning-based models become predominant in image processing field, especially in image classification and object detection, image quality assessment (IQA) has been a hot issue since the quality of train images is of great importance fro model performance. This paper proposes a novel blind IQA model evolved from VGG-16[1], a state-of-the-art classification deep convolutional neural network(CNN). It modifies the classification network to apply it to regression task by keeping only one neuron in the last fully-connected layer, the output of which is considered as the quality assessment metric after being normalized to a score between 0 and 1 via Sigmoid function. And experimental results demonstrate that this model achieves a superb assessment performance on TID-2013 dataset[2].
SSVisual: Intelligent Start-Stop System

In order to reduce fuel consumption, many vehicles are equipped with idle start-stop systems. However, due to complex environment in the real world, vehicles with start-stop systems often experience short-term idling and frequent start-stops. It may greatly accelerate equipment deterioration hence reduce driving comfort. To resolve this problem, we propose SSVisual, an intelligent start-stop system by utilizing collected traffic information. Novel approaches are developed to effectively detect the states of traffic lights and traffic conditions based on image recognition techniques. Given the collected information, SSVisual can determine if it is necessary to shut down the engine at the current idle speed. Moreover, SSVisual can be used for both online and offline environments to visualize the performance of different strategies for research purpose.
Security and Reliability Performance Analysis of Cooperative Multi-Relay Systems With Nonlinear Energy Harvesters and Hardware Impairments

In this paper, we investigate the reliability and security performance of cooperative multi-relay systems, where both source and relay nodes are energy-constrained nonlinear energy harvesters, scavenging energy from a power beacon nearby. Our analysis is based on practical model since residual hardware impairments (RHIs) and channel estimation errors (CEEs) are considered. Aiming at improving the system efficiency, three representative relay selection strategies are considered: 1) random relay selection (RRS); 2) suboptimal relay selection (SRS); and 3) optimal relay selection (ORS). To characterize the security performance of the considered strategies, we derive closed-form analytical expressions of the reliability and security in terms of outage probability (OP) and intercept probability (IP). We further discuss the asymptotic expressions and scaling laws of OP with the number of relays. The IP is analyzed for non-colluding and colluding scenarios. The numerical results illustrate that: 1) There is a tradeoff between reliability and security, that is when the outage constraint is relaxed, the IP can be enhanced, and vice versa; ii) The outage performance of the ORS and SRS schemes outperform RRS, indicating that relay selection can enhance reliability performance; iii) There are error floors for the OP due to the CEEs; iv) Colluding eavesdroppers can enhance eavesdropping attacks by sharing their intercepted information; and v) Although RHIs and CEEs have deleterious effects on the OP, they can protect the information transmission against eavesdropping attacks.
Adaptive Multi-optimiser Cooperative Co-evolution for Large-Scale Optimisation

Large-scale optimisation is of great importance as most real-world optimisation problems involve a large number of decision variables. Cooperative co-evolution (CC) approaches are successful in this field as they can tackle thousands of decision variables. CC methods consist of decomposition, optimisation of decomposed sub-components and cooperative combination based on the ‘divide-and-conquer’ mechanism. Sub-components are optimised cooperatively before the merge. Most of existing CCs optimise different sub-components using the same optimisation algorithm. In this study we propose an adaptive approach based on multi-optimiser cooperative co-evolution for large-scale optimisation problems. As different sub-components may have different characteristics and different contributions towards the global fitness function, they should to be handled differently considering the diversity and the quality of each sub-component. To achieve this effect our proposed approach incorporates four different optimisers adaptively according to the relative contribution. In each optimisation cycle, an adaptive selection strategy is used to allocate the most suited optimiser to each sub-component using quality-diversity measure and the historical performance of the algorithms. The performance of the proposed approach has been evaluated on large-scale optimisation benchmark problems. The results confirmed the effectiveness of the proposed adaptive approach as it can achieve comparable or better performance on almost of test cases compared to state-of-the-art methods.