Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data

Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS trajectories and Point of Interest (POI) data. In the framework, functional regions are first generated based on the distribution of POIs by the DBSCAN clustering algorithm. A Weighted Term Frequency-Inverse Document Frequency (WTF-IDF) method is proposed to identify function values in each region. Then, the Origin-Destination (OD) of trips between functional regions is extracted from GPS trajectories to detect anomalous urban mobility patterns. Mobility vectors are established for each time interval based on the OD of trips and are classified into clusters by the mean shift algorithm. Abnormal urban mobility patterns are identified by processing the mobility vectors. A case study in the city of Wuhan, China, is conducted; the experimental results show that the proposed method can effectively identify daily and hourly anomalous urban mobility patterns.
One Year of the OpenCitations Corpus - Releasing RDF-Based Scholarly Citation Data into the Public Domain.

Reference lists from academic articles are core elements of scholarly communication that permit the attribution of credit and integrate our independent research endeavours. Hitherto, however, they have not been freely available in an appropriate machine-readable format such as RDF and in aggregate for use by scholars. To address this issue, one year ago we started ingesting citation data from the Open Access literature into the OpenCitations Corpus (OCC), creating an RDF dataset of scholarly citation data that is open to all. In this paper we introduce the OCC and we discuss its outcomes and uses after the first year of life.
Heart Disease Prediction Using Machine Learning Algorithms

Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached. Therefore, it needs accurate diagnosis at accurate time. Health care industry produced huge amount of data every day related to patients and diseases. However, this data is not used efficiently by the researchers and practitioners. Today healthcare industry is rich in data however poor in knowledge. There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making. Increasing research on heart disease predicting systems, it become significant to summarize the completely incomplete research on it. The main objective of this research paper is to summarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and Artificial Neural Networks techniques improve the accuracy of the heart disease prediction system in different scenarios. In this paper commonly used data mining and machine learning techniques and their complexities are summarized.
Online judge MySQL for learning process of database practice course

Database course is the main courses in the department of informatics and computer science. This course aims to provide knowledge to students to build and manage data on Database Management Systems (DBMS) like MySQL. Therefore, the implementation of database processing practices in one of the DBMS such as MySQL is very important as basic skills in the field of informatics. With the growing number of informatics students, lectures cannot correct the results of student exercises quickly. For that, we need a system that can help lecturers to make corrections of query exercise on MySQL automatically, which is called Online Judge MySQL. This application was developed using NodeJS to execute queries that users input and ReactJS to build its interfaces. Testing is done by using this application for the online exam in several classes simultaneously. The results show that this application can correct the test results quickly and lightly.
The convolutional codesаnalysis technique on the optimum block codes grounds

The convolutional codes analysis and synthesis technique is proposed. This technique is optimal according to near Shannon's bound criterion and guarantees a given bit error rate from message source. The proposed technique is based on the well-known optimum block codes synthesis method. As a result, equivalent parameters set of convolutional codes is received. This set corresponds the optimal block code. The algorithm for synthesis an optimal convolutional code is proposed. Examples of optimal convolutional codes analysis and synthesis in the channel with a given energy are presented.
The scheduling of power transaction with Improved swarm optimization algorithm

The application characteristics of power industry are very consistent with the technology mode of cloud computing. Task and resource scheduling is one of the basic problems in the cloud computing environment. It is a big challenge to find the optimal solution in a limited time because of the large solution search space. To meet the need of the higher requirements for quality of service and response in the power trading cloud computing platform, a resource scheduling method is proposed based on Improved Particle Swarm Optimization (PSO) algorithm. Firstly, weight of particle is optimized. And then dual fitness function for particle selection strategy is proposed not only aiming at minimum completion time, but also considering quality of computing measurement. Simulation experiments show that the algorithm has a significant improvement in task completion time and resource usage.
Validity, reliability and utility of connected objects as a tool for clinicians

The development of consumer grade connected objects has the potential to change the way we detect and manage health in many diverse domains, such as cardiovascular, respiratory, neurological disease, diabetes, sleep disorders and post-operative care. Some of the most significant advantages that connected objects could provide to the medical field include early diagnosis of diseases and exacerbations, improved patient autonomy and behavior, and remote communication with healthcare providers. However, comprehensive studies on the validity and reliability of connected objects in medicine are still limited. More importantly, prospective trials exploring their impact on health outcomes are lacking. Nevertheless, connected tools offer low cost, accessibility and availability across a large community of users, allowing collection of big data in free-living settings. Continuous development in technology added to machine learning systems will further improve connected objects performance in medicine. With the development of this new technology arise new challenges to our society. Regulatory authorities must revise their approach to address digital health. Confidentiality and personal data are increasingly difficult to protect from cybercriminals. On the other hand, insuring patients’ privacy without limiting the development of new potential beneficial technologies is challenging. An opportunity for improvement or a threat to our society, no matter what our opinion on connected objects is, we will have to adapt to them, as they are already everywhere.
Visual Distortions in 360° Videos

Omnidirectional (or 360°) images and videos are emergent signals being used in many areas, such as robotics and virtual/augmented reality. In particular, for virtual reality applications, they allow an immersive experience in which the user can interactively navigate through a scene with three degrees of freedom, wearing a head-mounted display. Current approaches for capturing, processing, delivering, and displaying 360° content, however, present many open technical challenges and introduce several types of distortions in the visual signal. Some of the distortions are specific to the nature of 360° images and often differ from those encountered in classical visual communication frameworks. This paper provides a first comprehensive review of the most common visual distortions that alter 360° signals going through the different processing elements of the visual communication pipeline. While their impact on viewers’ visual perception and the immersive experience at large is still unknown—thus, it is an open research topic—this review serves the purpose of proposing a taxonomy of the visual distortions that can be encountered in 360° signals. Their underlying causes in the end-to-end 360° content distribution pipeline are identified. This taxonomy is essential as a basis for comparing different processing techniques, such as visual enhancement, encoding, and streaming strategies, and allowing the effective design of new algorithms and applications. It is also a useful resource for the design of psycho-visual studies aiming to characterize human perception of 360° content in interactive and immersive applications.
Data Transmissions Using Hub Nodes in Vehicular Social Networks

Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a “social” one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.
Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms

Agriculture is the backbone of every economy. In a country like India, which has ever increasing demand of food due to rising population, advances in agriculture sector are required to meet the needs. To add to it, the present economic conditions and government policies of India are such that it necessitates the adoption of Precision farming or smart farming. It will enable the farmers to maximize their crop yields and minimize the input costs as well as the losses due to reasons like uncertain rainfall, droughts etc. from this model. For Predicting weather forecasting we will use machine learning Algorithms like Linear Regression, Decision tree.
Optimal Control of Vehicle Dynamics for the Prevention of Road Departure on Curved Roads

Run-off-Road crashes are often associated with excessive speed in curves, which may happen when a driver is distracted or fails to compensate for reduced surface friction. This work introduces an Automated Emergency Cornering (AEC) system to protect against the major effects of over-speeding on curves, especially lateral deviation leading to lane or road departure. The AEC architecture has two levels: an upper level to perform motion planning, based on the optimal control of a nonlinear particle model, and a lower level to distribute the resulting two-dimensional acceleration reference to the available actuators. The lower level adopts the recently introduced Modified Hamiltonian Algorithm (MHA), which continuously adjusts the priority between mass-centre acceleration and yaw moment demands derived from lateral stability targets. AEC makes use of a high precision map and triggers control interventions based on vehicle kinematic states and detailed road geometry. To avoid false-positive interventions, AEC is triggered only when excessive road departure is predicted for the optimal particle motion. AEC then takes control of steering and individual wheel brake actuators to perform autonomous motion control for speed and path curvature at the limits of available friction. The AEC system is tested and evaluated using the high-fidelity simulation software CarMaker.
LMS Adaptive Filtering of Drilling Tool Vibration Signal

As the dynamic measurement of attitude parameter of the steerable drilling tool under vibration may be not accurate, the Least Mean Square (LMS) adaptive filtering algorithm is adopted to filter the influence of drilling tool vibration on the attitude measurement in this paper. The simulation results show that the measurement error of inclination angle after LMS adaptive filtering can be less than 0.1°, and the measurement error of tool face angle is less than 6°, which could effectively improve the attitude measurement accuracy of vertical steerable drilling tools; the results of inverse analysis on actual drilling data show that the actual measurement error of inclination angle after LMS adaptive filtering is about 3°, which is much smaller than that before filtering. It shows that LMS adaptive filtering can effectively filter the vibration signal of drilling tools and greatly improve the dynamic measurement accuracy of the tool attitude.
Research on Small Target Recognition Technology Based on Deep Learning

All small target recognition has always been a difficult problem in the field of target recognition. Traditional target detection methods have met a bottleneck. With the development of deep learning, the field of target recognition has witnessed a boom based on deep learning. According to the characteristics of the small target recognition, this paper briefly introduces the traditional target recognition method, and studies the methods of target detection based on the deep learning. It mainly summarized the development of research on target detection algorithm based on deep learning. Finally, it summarized the influence of the depth study on target recognition and the trend of development in the future.
Review of Intent Detection Methods in the Human-Machine Dialogue System

Spoken language understanding is an important part of the human-machine dialogue system, intent detection is a sub-task of spoken language understanding, and it is very important. The accuracy of intent detection is directly related to the performance of semantic slot filling, and it is helpful to the following research of the dialogue system. Considering the difficulty of intent detection in human-machine dialogue system, the traditional machine learning method cannot understand the deep semantic information of user’s discourse. This paper mainly analyzes, compares and summarizes the deep learning methods applied in the research of intent detection in recent years, and further considers how to apply deep learning model to multi-intent detection task, so as to promote the research of multi-intent detection methods based on deep neural network.
Joint Access and Backhaul Power Consumption Optimization in Heterogeneous Mobile Broadband Networks

Capacity overprovisioning in mobile networks leads to underutilization of network resources and thus inefficient operation from a power consumption perspective. In addition, as operators constantly evolve their networks to denser heterogeneous layouts, with overlaying macrocellular base stations coexisting with numerous smaller cells, to cope with upcoming increase in peak mobile data traffic, the energy consumption of mobile networks will continue to grow in the next years. In this context, although backhaul connections can be responsible for tremendous power consumption, the corresponding study is usually neglected in most of the recent energy related literature. An approach for fast and low cost connectivity of dense microcellular sites is the deployment of wireless in-band backhaul links via existing macrocellular base stations. In this paper, we consider this wireless in-band backhaul approach, where access and backhaul links share the same frequency spectrum, and examine joint energy efficient resource optimization on both links. We introduce an iterative low-complexity polynomial backhaul-aware heuristic, which directs traffic to the most energy efficient network resources, and compare it against i) an exhaustive search method that provides optimum results, Journal of Green Engineering, Vol. 6 4, 337–368. doi: 10.13052/jge1904-4720.641 c © 2017 River Publishers. All rights reserved. 338 G. Kyriazis and A. Rouskas ii) a previously proposed reference algorithm, and iii) ordinary Full Operational Topology (FOT) network, where no energy aware technique is applied. From the results obtained, we conclude that our heuristic achieves high energy efficiency gains in all scenarios examined, while wireless in-band backhaul method proved a solid choice for this purpose.
Many-Valued Concept Lattices for Backing Composite Web Services

A Web service is a software functionality accessible through the network.Web services are intended to be composed to realize domain-specific applications. Achieving a required functionality by a service composition necessitates the discovery of a collection of Web services out of the enormous service space. Each service from this service space must be examined to verify its functionality, which makes the discovery task neither efficient nor practical. Moreover, when a service in a composition becomes unavailable or functionally broken, the whole composition may become broken too. Therefore, a functionally equivalent service must be discovered, in order to replace the broken service, thus spending more time and effort. In this paper, we propose an approach that facilitates the discovery of a web service and the identification of its candidate substitutes. We use many-valued concept lattices to classify web services, depending on the similarity estimated between their operations. This classification enables the identification of a needed web service as well as its possible alternatives. Thus, a Web service composition can be achieved more easily and can be supported with backup services, to recover the functionality of a broken service.
Toward an Optimal Anomaly Detection Pattern in Wireless Sensor Networks

Over the past years, various anomaly detection techniques for Wireless Sensor Networks (WSNs) have been proposed in the literature. These detectors are generally only effective against particular security threats and thus many detectors have to be executed to ensure high-level security in WSNs. Due to the hardware limitations, WSNs cannot execute all of these detectors simultaneously. Thus, WSNs have to appropriately schedule when to execute a particular detector and when to execute the others. This paper presents an optimized solution by providing a unified framework that combines the usage of a costly yet highly-accurate detector, called an oracle detector, and a low cost and less-accurate detector, called a partial detector. A detection pattern is created and optimized using a first-order approximation method. Simulation results show that combining several detectors into an optimized pattern is essential to provide low execution time overhead.
Off the Chain: Blockchain Technology—An Information Organization System

ABSTRACT Blockchain technology allows data to be effectively organized, stored, and shared. Its unique decentralized authentication system means no central database exists to store information. All information is shared by a network of nodes and is continuously updated. Blockchain is currently being used across several industries due to its unique features: tamper-proof records, authenticity, transparency, and elimination of third-party brokers. These features offer potential for applications in libraries, archives, and museums as well.
Learning Haptic Exploration Schemes for Adaptive Task Execution

The recent generation of compliant robots enables kinesthetic teaching of novel skills by human demonstration. This enables strategies to transfer tasks to the robot in a more intuitive way than conventional programming interfaces. Programming physical interactions can be achieved by manually guiding the robot to learn the behavior from the motion and force data. To let the robot react to changes in the environment, force sensing can be used to identify constraints and act accordingly. While autonomous exploration strategies in the whole workspace are time consuming, we propose a way to learn these schemes from human demonstrations in an object targeted manner. The presented teaching strategy and the learning framework allow to generate adaptive robot behaviors relying on the robot’s sense of touch in a systematically changing environment. A generated behavior consists of a hierarchical representation of skills, where haptic exploration skills are used to touch the environment with the end effector, and relative manipulation skills, which are parameterized according to previous exploration events. The effectiveness of the approach has been proven in a manipulation task, where the adaptive task structure is able to generalize to unseen object locations. The robot autonomously manipulates objects without relying on visual feedback.
Adaptive ARX Rate Controller for multi hop MANET

TCP is a well-known transport protocol developed for wired networks. But, TCP does not work well in multi-hop MANET due to burst nature. It further leads to severe intra-flow interference which reduces the throughput. In this paper, by using classical control theory, a rate based congestion control algorithm is proposed. For multi-hop MANET the number of outstanding packets in the end-to-end path should not exceed the upper bound of bandwidth delay product that is equal to h/4 where h refers to Round trip hop-count. This value has been taken as reference in our algorithm. Our algorithm regulates its sending rate based on the feedback received from plant (Multi-Hop MANET) to meet the objective point. The proposed system is estimated using ARX model where the output obtained from our system is verified against the estimated values using MATLAB coding. We also evaluate our algorithm against TCP using ns2.35 Simulator in terms of Throughput, Delay, Contentions and Packet Drop Rate
Representación de la variabilidad en líneas de productos de software empleando redes de Petri

Se define variabilidad como la posibilidad que posee un sistema de ser extendido, cambiado, localizado, o configurado para su uso en un contexto particular. Su especificación en una línea de productos de software (LPS) es una actividad central, donde se establecen las familias de productos con sus varian-tes, y dependencias. Una forma de definir la variabilidad de una LPS es a través de un modelo de características (“features”, FM). Sin embargo, las familias de productos obtenidas pueden presentar ciertos problemas de inviabilidad, esto es, reglas de inclusión contradictorias que resultan en características imposibles de ser incorporadas en ningún producto. Tales inconvenientes pueden provenir tan-to de errores durante la elaboración inicial del FM, como por cambios introdu-cidos para alcanzar nuevas necesidades. En este trabajo se propone una herramienta basada en redes de Petri para representar y analizar los FMs y detectar los problemas antes mencionados.