Miracle-dz/UniFAR-FTUs huggingface dataset:
Large System Performance and Distributed Scheme of Downlink Beamforming in F-RANs With Distributed Antennas

This paper addresses the performance of downlink beamforming in fog radio access networks (F-RANs) with distributed antennas. The wireless channel is modeled as Rician fading. We analyze the asymptotic behavior of the signal-to-interference-plus-noise ratio (SINR) and the data rate of maximum ratio transmission (MRT) and regularized zero-forcing (RZF) beamformers in the large system limit, where the numbers of antennas and users go to infinity while keeping a fixed ratio. In some practical F-RAN environment, it is difficult to perform centralized processing and share channel state information (CSI) globally, so distributed beamforming schemes are of interest. Therefore, we propose the local zero-forcing (ZF) beamformer that relies only on local CSI. The simulation results show that the asymptotic results of MRT and RZF are accurate for finite system size, and the local ZF beamformer provides a tradeoff between MRT and RZF in terms of CSI sharing complexity and interference control ability.
Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network

While most heart arrhythmias are not immediately harmful, they can lead to severe complications. In particular, atrial fibrillation, the most common arrhythmia, is characterized by fast and irregular heart beats and increases the risk of suffering a stroke. To detect such abnormal heart conditions, we propose a system composed of two main parts: a smart vest with two cooperative sensors to collect ECG data and a neural network architecture to classify heart rhythms. The smart vest uses two dry bi-electrodes to record a single lead ECG signal. The biopotential signal is then streamed via a gateway to the cloud where a neural network detects and classifies the heart arrhythmias. We selected an architecture that combines convolutional and recurrent layers. The convolutional layers extract relevant features from sliding windows of ECG and the recurrent layer aggregates them for a final softmax layer that performs the classification. Our neural network achieves an accuracy of 87.50% on the dataset of the challenge of Computing in Cardiology 2017.
Large-Scale Social Multimedia Analysis

The Internet is abundant with opinions, sentiments, and reflections of the society about products, brands, and institutions hidden under tons of irrelevant and unstructured data. This work addresses the contextual augmentation of events in social media streams in order to fully leverage the knowledge present in social multimedia by making three major contributions. First, a global study of the Twitter Firehose is presented. To our knowledge this is the first study of this kind and comprehension providing valuable insights about variability of tweets with respect to multimedia content. The results for more than one billion tweets show the great potential of the stream for many application domains. As a second key contribution, a fully automated system was developed for the augmentation of social multimedia with contextual information on a large scale. The system trawls multimedia content from Twitter and performs a multi-modal analysis on it. The analysis considers temporal, visual, textual, geographical, and user-specific dimensions. Third, we present a near-duplicate detection approach based on deep learn- ing to detect the most frequent images being propagated through Twitter during events
Lifelong Optimization with Low Regret

In this work, we study a problem arising from two lines of works: online optimization and lifelong learning. In the problem, there is a sequence of tasks arriving sequentially, and within each task, we have to make decisions one after one and then suffer corresponding losses. The tasks are related as they share some common representation, but they are different as each requires a different predictor on top of the representation. As learning a representation is usually costly in lifelong learning scenarios, the goal is to learn it continuously through time across different tasks, making the learning of later tasks easier than previous ones. We provide such learning algorithms with good regret bounds which can be seen as natural generalization of prior works on online optimization.
Intensity Invariant Stereo Matching and Disparity Refinement

A intensity invariant measure of pixel dissimilarity that compares two pixels is proposed. Horizontal and vertical differences of the image are utilized for dissimilarity measure. The method is intensity invariant for stereo matching. Bad matches arising from illumination change between two cameras are eliminated as much as possible. Moreover, some parameters of belief propagation algorithm for stereo matching can be figure out due to introducing this measure. Disparity gradient constraint is utilized to refine disparity map and extrapolate the disparities into those regions where disparities are not produced properly. Experiments on real images demonstrate that our method is distinctly robust to changes in camera gain and bias or illumination.
MonaLIA 1.0 preliminary study on the coupling learning and reasoning for image recognition to enrich the records of in the Joconde database

The MonaLIA 1.0 project is a preliminary study on the coupling of learning methods (Deep Neural Networks) and knowledge-based methods (Semantic Web) for image recognition and the enhancement of descriptive documentary records. The approach is applied and evaluated on the collection and data in the Joconde database in order to identify the possibilities and challenges offered by this coupling in assisting in the creation and maintenance of such an annotated collection.
Local LUT Upsampling for Acceleration of Edge-preserving Filtering

Edge-preserving filters have been used in various applications in image processing. As the number of pixels of digital cameras has been increasing, the computational cost becomes higher, since the order of the filters depends on the image size. There are several acceleration approaches for the edge-preserving filtering; however, most approaches reduce the dependency of filtering kernel size to the processing time. In this paper, we propose a method to accelerate the edge-preserving filters for high-resolution images. The method subsamples an input image and then performs the edge-preserving filtering on the subsampled image. Our method then upsamples the subsampled image with the guidance, which is the high-resolution input images. For this upsampling, we generate per-pixel LUTs for high-precision upsampling. Experimental results show that the proposed method has higher performance than the conventional approaches.
New absolute dates as a contribution to the study of the Late Bronze Age Chronology in the Central Balkans

The paper presents three Late Bronze Age absolute dates from contexts located in the territory of western Serbia and northern Republic of Macedonia, and discusses them together with further absolute dates from the same period, which will be published shortly by one of the authors and other colleagues. On the basis of these dates the Late Bronze Age chronology is discussed together with cultural aspects of the groups representative of this period in the Central Balkans.
Image aesthetic assessment using a saliency symbiosis network

Abstract. As a result of people taking more and more pictures in their lives, image assessment technology, which can automatically help people choose high quality pictures quickly, has become particularly important. Most algorithms use peak signal-to-noise ratio (PSNR) to assess image quality. However, images with high scores on PSNR are not as beautiful as individuals think. Image aesthetic assessment technology can come closer to human aesthetic standards. We report on a method named saliency symbiosis network for image aesthetic assessment. This is significant because we improved the conventional convolutional neural networks (CNN) method, which gets very close to the human visual mechanism after adding saliency features in CNN. Owing to considering limitations of CNN input size, we also proposed a pooling strategy to improve the ability of the model to accept arbitrary input sizes. Afterward, we propose an effective mean Huber loss function, which becomes less sensitive to outliers and can quickly train the model to being optimal. The experiment results proved that the proposed method, by using very small training data, performed the highest accuracy in image aesthetic assessment and classification.
A Compiler-assisted locality aware CTA Mapping Scheme

General purpose GPU (GPGPU) is an effective many-core architecture that can yield high throughput for many scientific applications with thread-level parallelism. However, several challenges still limit further performance improvements and make GPU programming challenging for programmers who lack the knowledge of GPU hardware architecture. In this paper, we design a compiler-assisted locality aware CTA (cooperative thread array) mapping scheme for GPUs to take advantage of the inter CTA data reuses in the GPU kernels. Using the data reuse analysis based on the polyhedron model, we can detect inter CTA data reuse patterns in the GPU kernels and control the CTA mapping pattern to improve the data locality on each SM. The compiler-assisted locality aware CTA mapping scheme can also be combined with the programmable warp scheduler to further improve the performance. The experimental results show that our CTA mapping algorithm can improve the overall performance of the input GPU programs by 23.3% on average and by 56.7% when combined with the programmable warp scheduler.
Digital workflow for the accurate computation of the geometric properties of bamboo culms for structural applications

Bamboo is one of the most promising sustainable construction materials due to the large endemic natural reserves prevalent in the Southern Hemisphere. However, industrialised materials, such as concrete, steel and aluminium have overshadowed the application of natural bamboo culms, due to the high-quality assurance achieved over decades refining the production processes of structural elements manufactured from the former. As a result, the physical, geometric and mechanical properties of these industrialised structural elements are quantifiable, predictable and in agreement with international standards. This research presents the details of a digital workflow to quantify the inherent geometric variability of bamboo culms as part of a new quality assurance process for this natural structural element. This workflow relies on the use of a mid-range, commercially available structured-light 3D scanner to accurately capture a point cloud of the bamboo geometry and generate a corresponding polygon mesh. Digital models of three different bamboo species were validated through comparison with key physical measurements finding that the adoption of these digital models can significantly improve the accuracy and efficiency of manual methods due to the complex irregularities found in bamboo culms. This work demonstrates the benefits of adopting a non-destructive, reverse-engineering approach to quantify the geometric properties of bamboo compared to traditional tools and methods. Overall, this research shows the potential of digital technologies to support the adoption of this natural material allowing for the re-assessment of design workflows and providing an opportunity for bamboo to compete with industrialised materials.
Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Spintronic three-terminal magnetic-tunnel-junction (3T-MTJ) devices have gained considerable interest in the field of neuromorphic computing. Previously, these devices required external circuitry to implement the leaking functionality that leaky integrate-and-fire (LIF) neurons should display. However, the use of external circuitry results in decreased device efficiency. We previously demonstrated lateral inhibition with a 3T-MTJ neuron that intrinsically performs the leaking, integrating, and firing functions; however, it required the fabrication of a complex multilayer structure. In this paper, we introduce an anisotropy gradient to implement a single-layer intrinsically leaking 3T-MTJ LIF neuron without the use of any external circuitry. This provides the leaking functionality with no hardware cost and reduced fabrication complexity, which increases the device, circuit, system, and cost efficiency.
Learning Invariant Representations with Kernel Warping

Invariance is an effective prior that has been extensively used to bias supervised learning with a \emph{given} representation of data. In order to learn invariant representations, wavelet and scattering based methods ``hard code'' invariance over the \emph{entire} sample space, hence restricted to a limited range of transformations. Kernels based on Haar integration also work only on a \emph{group} of transformations. In this work, we break this limitation by designing a new representation learning algorithm that incorporates invariances \emph{beyond transformation}. Our approach, which is based on warping the kernel in a data-dependent fashion, is computationally efficient using random features, and leads to a deep kernel through multiple layers. We apply it to convolutional kernel networks and demonstrate its stability.
Construction Scheme of a Scalable Distributed Stream Processing Infrastructure Using Ray and Apache Kafka

The spread of various sensors and the development of cloud computing technologies enable the accumulation and use of large numbers of live logs in ordinary homes. To operate a service that utilizes sensor data, it is difficult to install servers and storage in ordinary homes and to analyze the collected data from sensors. Those data are typically transmitted from sensors to a cloud and analyzed in the cloud. However, services that involve moving image analysis must transfer large amounts of data continuously and require high computing power for analysis. Hence, it is highly difficult to process them in real time in the cloud using a conventional stream data processing framework. In this research, we propose a construction scheme for a highly efficient distributed stream processing infrastructure that enables scalable processing of moving image recognition tasks according to the amount of data that are transmitted from sensors. We implement a prototype system of the proposed distributed stream processing infrastructure using Ray and Apache Kafka, which is a distributed messaging system, and we evaluate its performance. The experimental results demonstrate that the proposed distributed stream processing infrastructure is highly scalable.
LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites

An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models’ size and high memory usage associated with real-time segmentation are the main challenges for mobile robotics systems that have limited computing resources. To overcome these challenges, this paper presents an efficient semantic segmentation method named LNSNet (lightweight navigable space segmentation network) that can run on embedded platforms to determine navigable space in real-time. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance. LNSNet is faster, has fewer parameters and less model size, while provides similar accuracy compared to existing models. A new pixel-level annotated dataset for real-time and mobile navigable space segmentation in construction environments has been constructed for the proposed method. The results demonstrate the effectiveness and efficiency that are necessary for the future development of the autonomous robotics systems.
A Survey on Classification of Concept Drift with Stream Data

Usually concept drift occurs in many applications of machine learning. Detecting a concept drift is the main challenge in a data stream because of the high speed and their large size sets which are not able to fit in main memory. Here we take a small look at types of changes in concept drift. This paper discusses about methods for detecting concept drift and focuses on the problems with existing approaches by adding STAGGER, FLORA family, Decision tree methods, meta-learning methods and CD algorithms. Furthermore, classifier ensembles for change detection are discussed.
Traffic accident analysis based on C4.5 algorithm in WEKA

At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.
Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning

Despite many years of improvements to it, TCP still suffers from an unsatisfactory performance. For services dominated by short flows (e.g., web search and e-commerce), TCP suffers from the <italic>flow startup problem</italic> and cannot fully utilize the available bandwidth in the modern Internet: TCP starts from a conservative and static initial window (<italic>IW</italic>, 2–4 or 10), while most of the web flows are too short to converge to the best sending rate before the session ends. For services dominated by long flows (e.g., video streaming and file downloading), the congestion control (<italic>CC</italic>) scheme manually and statically configured might not offer the best performance for the latest network conditions. To address these two challenges, we propose <italic>TCP-RL</italic>, which uses reinforcement learning (<italic>RL</italic>) techniques to dynamically configure <italic>IW</italic> and <italic>CC</italic> in order to improve the performance of TCP flow transmission. Basing on the latest network conditions observed at the server side of a web service, <italic>TCP-RL</italic> dynamically configures a suitable <italic>IW</italic> for short flows through group-based <italic>RL</italic>, and dynamically configures a suitable <italic>CC</italic> scheme for long flows through deep <italic>RL</italic>. Our extensive experiments show that for short flows, <italic>TCP-RL</italic> can reduce the average transmission time by about 23%; and for long flows, compared with the performance of 14 <italic>CC</italic> schemes, <italic>TCP-RL</italic>’s performance ranks top 5 for about 85% of the 288 given static network conditions, whereas for about 90% of conditions, its performance drops by less than 12% compared with that of the best-performing <italic>CC</italic> schemes for the same network conditions.
Markov Properties of Discrete Determinantal Point Processes

Determinantal point processes (DPPs) are probabilistic models for repulsion. When used to represent the occurrence of random subsets of a finite base set, DPPs allow to model global negative associations in a mathematically elegant and direct way. Discrete DPPs have become popular and computationally tractable models for solving several machine learning tasks that require the selection of diverse objects, and have been successfully applied in numerous real-life problems. Despite their popularity, the statistical properties of such models have not been adequately explored. In this note, we derive the Markov properties of discrete DPPs and show how they can be expressed using graphical models.
A non-redundant benchmark for symmetric protein docking

Symmetric proteins play important roles in many biological processes, such as signal transduction and molecular transportation. Therefore, determining the symmetric oligomeric structure of subunits is crucial to investigate the molecular mechanism of the related processes. Due to the high cost and technical difficulties associated with many experimental methods, computational approaches, such as molecular docking, have played an important complementary role in the determination of symmetric complex structures, in which a benchmark data set is pressingly needed. In the present work, we develop a comprehensive and non-redundant benchmark for symmetric protein docking based on the structures in the Protein Data Bank (PDB). The diverse dataset consists of 251 targets, including 212 cases with cyclic groups symmetry, 35 cases with dihedral groups symmetry, 3 cases with cubic groups symmetry, and 1 case with helical symmetry. According to the conformational changes in the interface between bound and unbound structures, the 251 targets were classified into three groups: 176 "easy", 37 "medium", and 38 "difficult" cases. A preliminary docking test on the targets of cyclic groups symmetry with MZDOCK indicated that symmetric multimer docking remains challenging. The benchmark will be beneficial for the development of symmetric protein docking algorithms. The proposed benchmark data set is available for download at http://huanglab.phys.hust.edu. cn/SDBenchmark/.
A Framework for Real-Time Sentiment Analysis of On-line Micro-texts with Sentiment Drift Detection Method

Nowadays, the Twitter data streams have become most trusted on-line micro-texts (tweets) for sensing and monitoring the public opinion for any entity (event, topic or product). The real-time monitoring of Twitter data can be done by performing the real-time sentiment analysis of the tweets. However, unlike to the sentiment analysis of static textual data in which the aggregated sentiment score remains static, the aggregated sentiment score of Twitter data changes very frequently with the time. We believe that the accurate and timely identification of those change points where the sentiment drifts have occurred can strengthen the real-time decision-making process. This paper offers a framework for the real-time sentiment analysis of on-line micro-texts with sentiment drift detection method. Furthermore, this paper also demonstrates the proposed framework with a case study; that utilized a dataset of tweets related to US election 2016, collected using Twitter API.
The Research on Algorithm of Flame Field Fast Compression

D graphics technology is one of the important research direction in the field of graphics currently. 3D graphics technology has very broad application prospects, and it has very important application value in the field of scientific research, military, education, industry, medical, entertainment and many other fields. But the data volume of high resolution 3D graphics is huge, and the storage, transmission and real-time display higher requirements are put forward. In recent years, people have shown strong interest in the research of 3D graphics compression techniques. In this article, the 3D graphics compression technology and the combination of industrial flame are be combined to introduces a compression method for three-dimensional flame.