Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Realization of Intelligent Public-Transport Flow Monitoring System Based on Big Data

An intelligent public-transport flow monitoring system based on big data is designed and realized so that the increasingly serious bus overloading problem during holidays and festivals can be resolved. The system can achieve intelligent identification and remote inspection of persons loaded in an inter-city bus to prevent overloading. First, system design and architecture are introduced. Second, ways of video key frame extraction, face detection combining Spark with Opencv, and small file merger with MapFile, etc. are resolved under Hadoop and Spark environments. Finally, the system is proved to have good efficiency and expandability through experimental comparison.
A Novel Pareto-VIKOR Index for Ranking Scientists’ Publication Impacts: A Case Study on Evolutionary Computation Researchers

Scientists’ publication impacts ranking is an important topic in scientometrics which is performed based on various proposed criteria. One of the well-known indicators is h-index which evaluates researchers achievements based on number of citations. The h-index has utilized in many research data sources because of its appropriate properties, but similar to other assessment indicators, it has own disadvantages. hindex cannot give a fair comparison between junior and senior researches. There are two reasons for this unfair comparison: (1) h-index depends on the research period of scholars and (2) the number of received citations can be increased by time, even if researcher doesn’t publish new papers, the h-index increases. Consequently, in addition to h-index, the number of the years of academic research (called the research period) is preferable to be considered as an independent indicator, which makes us able to have a more fair evaluation. So these two objectives, maximizing h-index and minimizing research period, can be considered as a multi-criteria comparison task to assess researchers. In this paper, we propose a strategy based on Pareto dominance ranking which uses dominance concept to obtain an order for researchers. In order to complete ranking between scientists in the same rank, a multi-criteria decision making measure called VIKOR is utilized. Therefore, a total ranking measure (P-V index) is obtained using Perto front concept and VIKOR measure. The proposed method is applied on 235 researchers who are conducting research on Evolutionary Computation (EC) topic. The h-index value and the research period of scholars are collected via Google Scholar service. P-V index obtains 26 Pareto ranks for all researchers and places six EC scientists on the first Pareto front.
Events that Affect Urban Mobility Patterns: an Analysis of Beijing GPS Data

The analysis of data collected through GPS and / or smartphone logs allows the identification of human mobility patterns. This identification has been the subject of several surveys in recent years. However, certain factors can change these patterns, such as special events of great magnitude (e.g., the Olympic Games) or environmental events (e.g., an increase in air pollutants.) In this paper, we present an investigation into the impact of these events on these patterns. We analyze urban mobility data collected in Beijing between April 2009 and October 2012, we use Artificial Intelligence techniques to identify mobility patterns in these data and then check for changes in patterns during the aforementioned events. The analysis of these data has become an important tool to extract insights and to support the planning of the urban displacement in the day to day and in special events.
Moves Recognition in Abstract of Research Paper Based on Deep Learning

The purpose of this work is to explore the applicability and effectiveness of deep learning methods for the task–moves recognition in abstract of research paper. We firstly build a large corpus for moves recognition. Then we choose the traditional machine learning method SVM as a benchmark, and develop four moves recognition methods based on DNN, LSTM, Attention-BiLSTM and BERT. Finally, we design two groups of experiments with sample size 10,000 and 50,000 and then compare experimental results. The results show that most of the deep learning methods outperform the traditional machine learning method SVM especially in large-scale sample experiments, in which the BERT with a re-pre-trained model achieves the best results in both groups of experiments. Deep learning methods are proved applicable and effective for moves recognition in research paper abstracts.
Friend or Foe: Studying user Trustworthiness for Friend Recommendation in the Era of Misinformation

The social Web, represented mainly by social media sites, is characterized by enriching the life and activities of its users, thus giving rise to new forms of communication and interaction. The unlimited possibilities offered by social media sites generate new problems related to information overload, the quality of published information and the formation of new social relationships. This opens the possibility to the contamination of social media with unwanted and unreliable content (false news, rumours, spam, hoaxes), which influences the perception and understanding of events, exposing users to risks. Motivated by the large amount of heterogeneous information available on the social Web and considering the consequences of the exposure to unwanted and unreliable content on social media, the existence of accounts dedicated to sharing said content, and the rapid dispersion of both phenomena, the goal of this work is is to define a profile to describe and estimate the trustworthiness or reputation of users, to avoid making "bad" recommendations that could favour the propagation of unreliable content and polluting users. The contribution of this work lies in the provision of reliable recommendation systems based on the integration of techniques that automatically allow the detection of unreliable content and the users publishing it. The final aim is to reduce the negative effects of the existence and propagation of such content, and thus improving the quality of the recommendations.
CM & Line-Dm Noise Separation for Three-Level NPC Inverter with Connected Neutral Point for Vehicle Traction Applications

EMI standards limit the noise level for conducted disturbances on DC power cables of electric vehicles. However, designing the EMI filter requires the information about the CM and DM noise levels. Therefore, a separation of the noise is needed. This paper deals with the separation and quantification of the three-phase DM and CM noise for a three-level NPC inverter with a connected neutral point. A hardware separator, based on HF transformers was developed to separate the noise into CM and line-DM noise. The CM and line-DM noise of the NPC inverter was measured, when operating the inverter with a two-level and a three-level modulation. As expected, the CM noise is dominating, and it is shown that the three-level operation reduces the noise compared to the two-level operation by about 3 to 6 dB.
Quadtree-Based KNN Search on Road Networks

This paper proposed a novel k-nearest neighbor search algorithm based on quadtree, called the QkNN. Three steps are made in the QkNN. First, moving objects are indexed by quadtree and the cell where the query point q is located is determined. Furthermore, query scope is extended according to the smallest nearest neighbor cell around q, the cells where k-nearest neighbor moving objects are located is initially determined, the search space is effectively pruned. Finally, knearest neighbor moving objects are found by sorting the road distance between q and objects in search scope. The analysis and experimental results show that the algorithm has good performance in large amount of data and uneven distribution of moving objects.
Mind the gap – a distributed framework for enabling energy optimisation on modern smart-phones in the presence of noise, drift, and statistical insignificance

Smartphones are becoming essential to people’s everyday lives. Due to the limited battery capacity of smartphones, researchers and developers are increasingly interested in the energy efficiency of these devices and the software applications that run on them. In the most basic setting, a developer might be interested in knowing which of two program variants might consume more energy, whether this is for use in regression testing or for use in full-scale evolutionary optimisation. To perform such comparisons (tournaments) reliably, we need a model of the number of trials needed to discern between two variants to a desired level of statistical significance. To enable this, we present a conceptual framework based on tournaments which we use to compare a range of test workloads on different combinations of phones and operating systems. Our results quantify the number of trials required to resolve different variants to different levels of fidelity on a range of platforms.
Restricted Boltzmann Machine Based on Item Category for Collaborative Filtering

To improve the accuracy of recommendation, an new model is proposed in this paper, which solves the problem of data sparseness of collaborative filtering to a certain extent. On the base of Real_value Restricted Boltzmann Machine (R_RBM), the impact of item category on recommendation results are considered. The item category, as a new layer, is added to R_RBM, which aims to predict the missing rating in the matrix. Then the recommendation is produced by combining ICR_RBM and collaborative filtering. The experimental results on MovieLens show that recommended effect of new model is more efficient than traditional RBM and singular value decomposition.
Multiple Channel Model For The Prediction Of Subjective Image Quality

A model for the perception of distortions in pictures is suggested. It consists of two main parts: an adaptive input stage realized as a ROG (Ratio of Gaussian) pyramid also suited for applications in image coding and computer vision, and a further decomposition by orientation selective filters including a saturating nonlinearity acting at each point of the filter outputs. The output values for each point of each filter are regarded as feature vector of the internal representation of the input picture. The difference between the internal representations of original and distorted picture is evaluated as norm of the difference vector. Due to local nonlinearities this operation explains periodic and aperiodic masking effects.
Dirichlet Process Gaussian Mixture for Active Online Name Disambiguation by Particle Filter

The name disambiguation task partitions a collection of records pertaining to a given name, such that there is a one-to-one correspondence between the partitions and a group of people, all sharing that given name. Most existing solutions for this task are proposed for static data. However, more realistic scenarios stipulate emergence of records in a streaming fashion where records may belong to known as well as unknown persons all sharing the same name. This requires a flexible name disambiguation algorithm that can not only classify records of known persons represented in the training data by their existing records but can also identify records of new ambiguous persons with no existing records included in the initial training dataset. Toward achieving this objective, in this paper we present a non-parametric Bayesian framework that utilizes a Dirichlet Process Gaussian Mixture Model (DPGMM) as a core engine for online name disambiguation task. A Sequential Importance Sampling with Resampling (SISR) technique, also known as particle filtering, is proposed for inference to simultaneously perform online classification and new class discovery. Specifically, for each online record, we approximate its class conditional posterior distribution by a set of particles and their weights, which are updated in a sequential manner without the need to re-access previously observed records. We also propose an interactive version of our online name disambiguation method, which improves the prediction accuracy by exploiting user feedback.
Tweet Summarization of News Articles: An Objective Ordering-Based Perspective

Twitter has become an essential platform for the news media sources to disseminate news. The opinions expressed through Twitter can be mined by news media sources to obtain users’ reactions centered around different news articles. A comprehensive summary of the users’ reactions with respect to a news article can be crucial due to various reasons like: 1) understanding the sensitivity/importance of the news; 2) obtaining insights about the diverse opinions of the readers with respect to the news; and 3) understanding the key aspects that draw the interest of the readers. However, the selected summary tweets must fulfill multiple objectives, like relevance to the news article, diversity among the selected tweets, and should cover the entire spectrum of opinions expressed through the tweets. Existing methods primarily attempt to identify a set of relevant tweets from which the summary tweets are selected that maintains the diversity and coverage requirements. However, the noise and the nontemporal behavior of the article-specific tweets make the identification of such relevant tweets extremely difficult, resulting in poor summary quality. In this paper, through empirical investigations, we show that initially identifying the diverse opinions can lead to better identification of the relevant tweets, i.e., following a specific ordering of the objectives can lead to the improved summary. We, subsequently, propose a tweet summarization technique that follows such a specific ordering. Validation of our proposed approach for 800 news articles with 2.1 billion related tweets shows that the proposed approach produces 11.6%–34.8% improvement in summary quality as compared to existing state-of-the-art techniques.
Direct Localization of Multiple Noncircular Sources With a Moving Nested Array

This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and coprime (CP) array are available in the existing literature. Sparse arrays have larger apertures than ULAs, which enhances degrees of freedom (DOFs). However, the existing sparse CP arrays have limited DOF and also have poor detecting ability. In this paper, the physical sensors of NA are rearranged to obtain a difference sum NA (DSNA) and second-order super NA (II-SNA). The rearranged NA sensors are seen to increase array aperture. The noncircular characteristics of the sources are also used to improve accuracy in positioning. To obtain high DOF in NA, DSNA, and II-SNA, the covariance matrices are vectorized. The coherency of the virtual array is resolved by applying the spatial smoothing technique. Finally, the SDF-based DPD is used to establish the cost function and the target is localized. The simulation results are provided for the NA, DSNA, and II-SNA and are compared with the existing CP array DPD algorithm. The results show that the proposed method shows significant enhancement in localization accuracy. The Cramer–Rao lower bound (CRLB), complexity, and performance comparisons are also described in this paper.
SketchSegNet+: An End-to-End Learning of RNN for Multi-Class Sketch Semantic Segmentation

We investigate the problem of stroke-level sketch segmentation, which is to automatically assign strokes of a given sketch with semantic labels. Solving the problem of sketch segmentation opens the door for fine-grained sketch interpretation, which can benefit many novel sketch-based applications, including sketch recognition and sketch-based image retrieval. In this paper, we propose an approach for multi-class sketch semantic segmentation by considering it as a sequence-to-sequence generation problem. Specifically, an end-to-end learned network SketchSegNet+, built on recurrent neural networks (RNN), is presented to translate a sequence of strokes into a sequence of semantic labels. In addition, a large-scale stroke-level sketch segmentation dataset is constructed for the first time, which is composed of 150K annotated free-hand human sketch selected from QuickDraw. The dataset will be released publicly. The experimental results of stroke-level sketch semantic segmentation on this novel dataset and the SPG dataset demonstrate the effectiveness of our approach.
Cultivating Intellectual Property Education in the Electronics Engineering Curriculum: A Case Study in Integrated Circuit Design

A survey on engineering students’ attitude toward intellectual property (IP) education has been conducted in several China’s universities, revealing the need for more effective teaching and learning methods. To improve the students’ IP awareness and the capability of innovative learning, a small module of patent and circuit design case study has been included in an engineering course, namely, radio-frequency integrated circuit (IC) design, which is offered to senior undergraduate or postgraduate students in a top engineering university in China. Along with the instruction of the RFIC design techniques, the concepts of the IP for the ICs are presented, followed by the analysis of innovative circuits to explore potential legal issues. The feedback of the students suggests that the curriculum is considerably helpful to enrich the knowledge of engineering students in both the design technique and IP awareness.
ParaNF: Enabling Delay-Balanced Network Function Parallelism in NFV

In Network Function Virtualization (NFV), multiple network functions cooperate to provide various network services. To reduce the end-to-end latency through a chain of network functions, research hotspots have turned to complete NF parallelism frameworks. However, several issues remain in them such as the manual dependency analysis on NFs and the excessive parallelism for NFs. Therefore, in this paper, we present ParaNF, an effective delay-balanced NF parallelism framework. ParaNF mainly consists of two logical components. First, the ParaNF orchestrator conducts a dynamic dependency analysis to find out which NFs can be parallelized and then conducts a delay-balanced NF parallelism optimization strategy. Second, the ParaNF infrastructure performs light-weight, dynamic packet copying and merging guided by an efficient label mechanism to support high-performance NF parallelism. We implement a ParaNF prototype with DPDK. Our evaluations show that ParaNF not only realizes the line-speed packet processing, but also achieves significant reduction in latency by up to 47% than the traditional SFC and 35% than OpenBox.
Use of Uncertainty with Autoencoder Neural Networks for Anomaly Detection

Autoencoders neural networks are nonlinear dimension reduction models widely used in the field of anomaly detection. Conventionally, the reconstruction error is considered as a score function allowing the discrimination between the normal data and the outliers. Recent advances in calculating uncertainty from neural networks open new perspectives in the field of anomaly detection. We study, for given models and different concentrations of anomalies, several score functions. We compare the standard score function based on the standard error, a score based on the error resulting from the Bayesian approximation, as well as score functions directly including the uncertainty. This paper empirically demonstrates how including uncertainty in the score function is likely to improve the performance of an autoencoder-based anomaly detection model.
Toward Efficient Processing of Spatio-Temporal Workloads in a Distributed In-Memory System

Location-based services (LBS) are a widely adopted technology that produces large volumes of spatio-temporal data at high velocity. Spatial data is also being generated from many other geo-spatial applications. To address the challenge of data volume, a number of big spatial data management systems have emerged that are based on the MapReduce paradigm. Recent projects have developed spatial data systems using Spark's distributed in-memory architecture. These projects, which include GeoSpark, SpatialSpark, and LocationSpark, do not support the high update rates required by LBS applications. Alternatively, systems such as MD-HBase support data updates, but are hindered by the performance characteristics of HBase, which is a disk-oriented framework. We present DISTIL+, a distributed spatio-temporal data processing system designed for high velocity location data. Our system achieves high update throughput and low query latency by leveraging the APGAS (Asynchronous Partitioned Global Address Space) architecture to build a multi-level distributed in-memory index. We present extensive experimental evaluation of our system, comparing several indexing and data placement schemes, as well as competing systems. Our results show that DISTIL+ excels at supporting high throughput location updates, and low latency spatio-temporal range queries and kNN queries, while offering better performance than existing approaches.
Finite-Time and Fixed-Time Synchronization Criteria for Discontinuous Fuzzy Neural Networks of Neutral-Type in Hale’s Form

This paper aims to study the impact of discontinuous activations, neutral-type operators, and mixed time delays on the finite-time and fixed-time robust synchronization of fuzzy neural networks. By using functional differential inclusions theory, inequality technique, and the non-smooth analysis of the Lyapunov-Krasovskii functional, a simple switching adaptive controller and a switching state-feedback controller are designed, some new criteria are obtained to achieve the finite-time and fixed-time synchronization of the proposed drive-response systems. Besides, the upper bound of the settling time of finite-time synchronization is estimated, and the settling time of fixed-time synchronization can be given in advance. In spite of many previous results on the synchronization of fuzzy neutral-type neural network and stability analysis of neural networks of neutral-type in Hale’s form with continuous activation functions, few references on the stability and synchronization control analysis of the neural network like the form of the neural network model addressed in this paper can be cited. From this point of view, the neural network model considered and the theoretical results of this paper are more generalized and inclusive. Finally, the simulation examples and remarks have been shown to verify the correctness and advantages of our main results.