Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.
Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?

Stock market prediction is a challenging issue for investors. In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. When applying CNN, 9 technical indicators were chosen as predictors of the forecasting model, and the technical indicators were converted to images of the time series graph. For verifying the usefulness of deep learning for image recognition in stock markets, the predictive accuracies of the proposed model were compared to typical artificial neural network (ANN) model and support vector machine (SVM) model. From the experimental results, we can see that CNN can be a desirable choice for building stock prediction models. To examine the performance of the proposed method, an empirical study was performed using the S&P 500 index. This study addresses two critical issues regarding the use of CNN for stock price prediction: how to use CNN and how to optimize them.
Traffic Steering for Service Function Chaining

Dynamic service function chaining (SFC) is a technique that facilitates the enforcement of advanced services and differentiated traffic forwarding policies. It dynamically steers the traffic through an ordered list of service functions. Enabling SFC capabilities in the context of a software defined networking (SDN) architecture is promising, as it takes advantage of the SDN flexibility and automation abilities to structure service chains and improve the delivery time. However, the delivery time depends also on the traffic steering techniques used by an SFC solution. This paper provides a closer look at the current SDN architectures for SFC and provides an analysis of traffic steering techniques used by the current SDN-based SFC approaches. This study presents a comprehensive analysis of these approaches using efficiency criteria. It concludes that the studied solutions are not efficient enough to be deployed in real-life networks, principally due to scalability and flexibility limitations. Accordingly, this paper identifies relevant research challenges.
Maximum margin object tracking with weighted circulant feature maps

Support vector machine (SVM) based tracking algorithms training with dense circulant samples have shown favourable performance due to its strong discriminative power and high efficiency. However, the challenges caused by the circulant sampling remain unaddressed. In this study, the authors give each training sample a weight based on their accuracy to reduce the influence of inaccurate samples. Moreover, they reform the SVM model with weighted circulant training samples. Secondly, they advocate an efficient solution by using the property of circulant matrices to solve the learning problem. Thirdly, a model update strategy is introduced to prevent the tracking models polluted by wrong samples. Experimental results on large benchmark datasets with 50 and 100 video sequences demonstrate that the authors’ tracking algorithms achieve state-of-art performance in terms of precision and accuracy. In addition, their tracker runs in real time.
A hybrid training method of convolution neural networks using adaptive cooperative particle swarm optimiser

For solving the problem that it is easy to fall into the local minimum in Convolution Neural Networks (CNN) training, a hybrid training algorithm based on heuristic algorithm is proposed. Firstly, an Adaptive Cooperative Particle Swarm Optimisation (ACPSO) is proposed, which uses a learning automata to adaptively divide the subpopulation of the Cooperative Particle Swarm Optimisation (CPSO), and makes the decision variables with strong coupling relationship enter the same subpopulation. Then, the connection weights of CNN are considered as elements in particles and the CNN is trained by ACPSO algorithm. The output of the ACPSO algorithm is applied as the initial weight of the BP algorithm for the purpose of speeding up the training speed of the CNN. The experimental results show that the ACPSO-BP algorithm has achieved good results, and the recognition rate of the CNN is improved. Thus it has the potential to be applied to other deep learning fields.
A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks

Pacing the way toward 5G has lead researchers and industry in the direction of centralized processing known from Cloud-Radio Access Networks (C-RAN). In C-RAN research, a variety of different functional splits is presented by different names and focusing on different directions. The functional split determines how many base station functions to leave locally, close to the user, with the benefit of relaxing fronthaul network bitrate and delay requirements, and how many functions to centralize with the possibility of achieving greater processing benefits. This paper presents for the first time a comprehensive overview systematizing the different work directions for both research and industry, while providing a detailed description of each functional split option and an assessment of the advantages and disadvantages. This paper gives an overview of where the most effort has been directed in terms of functional splits, and where there is room for further studies. The standardization currently taking place is also considered and mapped into the research directions. It is investigated how the fronthaul network will be affected by the choice of functional split, both in terms of bitrates and latency, and as the different functional splits provide different advantages and disadvantages, the option of flexible functional splits is also looked into.
Learning and Data-Driven Beam Selection for mmWave Communications: An Angle of Arrival-Based Approach

This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup comprising an analog beamforming (ABF) network with adjustable beamwidth followed by a zero-forcing baseband processing block. The goal is to select the optimal configuration for the ABF network based on the estimated AoAs of the various user equipments. To that aim, we consider 1) two supervised machine-learning approaches: $k$ -nearest neighbors (kNN) and support vector classifiers (SVC); and 2) a feed-forward deep neural network: the multilayer perceptron. We conduct an extensive performance evaluation to investigate the impact of the quality of CSI estimates (AoAs and powers) obtained via the Capon or MUSIC methods, fluctuations in the received power, the size of the training dataset, the total number of analog beamformers in the codebook, their beamwidth, or the number of active users. The computer simulations reveal that performance, in terms of classification accuracy and sum-rate is very close to that achievable via exhaustive search.
High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation

Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors' model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high-density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high-density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.
Experimental Research of Effervescent Atomization of High Viscosity Fluids

The purpose of this research was to investigate the dependence of effervescent spray performace on fluid properties and atomizer internal design.The spray liquid included water and six kinds of high viscosity fluids.With the phase doppler particle analyzer(PDPA),spray characteristics of various fluids were investigated.The influence of atomizer geometries and operating parameters were discussed.Increasing air-to-liquid ratio(ALR) will reduce droplets Sauter mean diameter(SMD) effectively.The largest SMD along radial is less than 120? m.The exit orifice diameter and air injection diameter affects droplets SMD significantly,while the change of air injection angle and mixing chamber length has less effects on droplets SMD.The atomization quality of non-Newtonian fluid drops when the mixing chamber length increases.With the addition of xanthan gum the droplets SMD increase dramatically.The SMD of water is 60~95? m,and the SMD of viscous fluids is 60-120? m.
Template Construction using a Patch-based Robust Estimator

In MR image analysis, template construction is often required to perform voxel-based morphometry analysis in order to create a reference used to register a population of different subjects. Usually, after a non-linear registration step used to align the MR images together, simple voxel-wise averaging is used to construct the template. The averaging approach has the advantage of being fast and simple. However this technique is not robust to registration errors, and tends to degrade small structures and contrast between tissues producing an over-smoothed result. To overcome these limitations, we propose to use a robust patch-based estimation which is less sensitive to outliers during template construction and preserves image structure continuity.
A Tutorial on IEEE 802.11ax High Efficiency WLANs

While celebrating the 21st year since the very first IEEE 802.11 “legacy” 2 Mbit/s wireless local area network standard, the latest Wi-Fi newborn is today reaching the finish line, topping the remarkable speed of 10 Gbit/s. IEEE 802.11ax was launched in May 2014 with the goal of enhancing throughput-per-area in high-density scenarios. The first 802.11ax draft versions, namely, D1.0 and D2.0, were released at the end of 2016 and 2017. Focusing on a more mature version D3.0, in this tutorial paper, we help the reader to smoothly enter into the several major 802.11ax breakthroughs, including a brand new orthogonal frequency-division multiple access-based random access approach as well as novel spatial frequency reuse techniques. In addition, this tutorial will highlight selected significant improvements (including physical layer enhancements, multi-user multiple input multiple output extensions, power saving advances, and so on) which make this standard a very significant step forward with respect to its predecessor 802.11ac.
Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.
Bidirectional aggregated features fusion from CNN for palmprint recognition

In this paper, we present a novel bidirectional aggregated features representation from convolutional neural networks (CNNs) with score-level fusion for palmprint recognition. Our method adopts the vector of locally aggregated descriptors (VLAD) to encode the convolutional features from two directions, i.e., vertical and horizontal directions, to mine both the local and global descriptions of palmprint image. Then, three score-level fusion rules are respectively employed to integrate the matching scores of the bidirectional features. We extensively evaluate the performance of convolutional features, vertical and horizontal encoding together with the score-level fusion rules through recent deep network VGG-F on the PolyU palmprint and multispectral palmprint databases. Experiments demonstrate that horizontal encoding significantly outperforms vertical encoding on red, green, blue and near-infrared (NIR) palmprint image subsets while it is slightly worse on PolyU palmprint database, moreover, the effective performance improvement can be achieved after the fusions.
Segment Routing in Software Defined Networks: A Survey

Segment routing (SR) has emerged as a promising source-routing methodology to overcome the challenges in the current routing schemes. It has received noticeable attention both in industry and academia, due to its flexibility, scalability, and applicability, especially in software defined networks. The emerging cloud services require strict service level agreements such as packet loss, delay, and jitter. Studies have shown that traditional network architectures lack the essential flexibility and scalability to offer these services. To combat this, a more flexible and agile routing paradigm of SR enables a source node to steer an incoming packet along a performance engineered path represented as an ordered list of instructions called segment list. This is encoded as a multiprotocol label switching label stack or an IPv6 address list in the packet header. This paper provides a comprehensive review of the novel SR technology by describing its architecture, operations, and key applications to date. SR paradigm can be effectively applied to a wide range of network applications, such as traffic engineering, network resiliency, network monitoring, and service function chaining, to achieve efficient network solutions. Furthermore, this paper identifies an interesting set of future research directions and open issues that can help realize the full potential of the emergent SR paradigm.
Patients' learning in cyberspace: a thematic analysis of patient-patient discussions in a chronic illness Facebook page

Online support groups play an increasingly important role in patients' lived experience of chronic illness. The objective of this study was to explore how learning takes place from patients' interactions in an online chronic illness support group. Qualitative data consisted of 1,478 messages posted to a publicly accessible non-illness specific Facebook support page. Data was analysed using inductive thematic analysis. Four themes were identified from the analysis. This paper presents two themes, information sharing and dis(trust). Employing Wenger's communities of practice theoretical framework (Wenger, 1998), themes are discussed in terms of learning, participation in practices and identity. Online support groups can be pivotal to learning in illness. Patients' online information sharing provides them with opportunities for informal learning about their condition to take place. These online interactions lead to patients developing trust for one another and distrust for the medical care system.
ChamelIoT: An Agnostic Operating System Framework for Reconfigurable IoT Devices

This letter proposes ChamelIoT, an agnostic operating system (OS) framework for reconfigurable Internet of Things (IoT) devices. ChamelIoT is bringing a reconfigurable hardware OS stack supported by a semantically enriched infrastructure that aims at offering an easy-to-use tool for building (mainly) low-power and low-cost IoT sensors with performance, real-time, and power consumption advantages.
Predicting rainfall using neural nets

One of the most crucial factors that can help in making strategic decisions and planning in countries that rely on agriculture in some ways is successfully predicting rainfall. Despite its clear importance, forecasting rainfall up until now remains a big challenge owing to the highly dynamic nature of the climate process and its associated seemingly random fluctuations. A wide variety of models have been proposed in literature to predict rainfall among which statistical models have been one of the most relatively successful. In this paper, we propose a novel rainfall forecasting model using focused time-delay neural networks (FTDNNs). In addition, we also contribute in comparing rainfall forecasting performances, using FTDNNs, for different prediction time scales, namely: monthly, quarterly, bi-annually and yearly. We present the optimal neural network architecture parameters automatically found for each of the aforementioned time scales. Our models are trained to perform one-step-ahead predictions and we demonstrate and evaluate our results, measured by mean absolute percent error, on the rainfall dataset obtained from Malaysian Meteorological Department (MMD) for close to a thirty year period. For test data, we found that the most accurate result was obtained by our method on the yearly rainfall dataset (94.25%). For future work, dynamic meteorological parameters such as sunshine data, air pressure, cloudiness, relative humidity and wet bulb temperature can be integrated as additional features into the model for even higher prediction performance.
Developing social entrepreneurs through distance education: the value of commitment and interactivity with the learning community

Given that the creation of successful interactions and collaboration networks develops into sustainable and successful social entrepreneurial organisations, learning communities can serve as an ideal environment to train students in social entrepreneurship. This paper describes massive open online learning communities as an appropriate methodology for the distance teaching of social entrepreneurship and how the students on a massive online open course in social entrepreneurship commit to the learning community generated in this virtual environment. Data from a pioneer social entrepreneurship massive open online learning communities were analysed using three statistical methodologies. The analysis demonstrates that a student's commitment to the distance learning community is directly related to his degree of participation, the rewards received, his correspondence with the other students and recognition from the other members of the community.
Energy Efficiency and Renewable Energy Management with Multi-State Power-Down Systems

A power-down system has an on-state, an off-state, and a finite or infinite number of intermediate states. In the off-state, the system uses no energy and in the on-state energy it is used fully. Intermediate states consume only some fraction of energy but switching back to the on-state comes at a cost. Previous work has mainly focused on asymptotic results for systems with a large number of states. In contrast, the authors study problems with a few states as well as systems with one continuous state. Such systems play a role in energy-efficiency for information technology but are especially important in the management of renewable energy. The authors analyze power-down problems in the framework of online competitive analysis as to obtain performance guarantees in the absence of reliable forecasting. In a discrete case, the authors give detailed results for the case of three and five states, which corresponds to a system with on-off states and three additional intermediate states “power save”, “suspend”, and “hibernate”. The authors use a novel balancing technique to obtain optimally competitive solutions. With this, the authors show that the overall best competitive ratio for three-state systems is 9 5 and the authors obtain optimal ratios for various five state systems. For the continuous case, the authors develop various strategies, namely linear, optimal-following, progressive and exponential. The authors show that the best competitive strategies are those that follow the offline schedule in an accelerated manner. Strategy “progressive” consistently produces competitive ratios significantly better than 2.