Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.
Subatomic-Level Solid/Fluid Boundary of Lennard-Jones Atoms: A Molecular Dynamics Study of Metal-Inert Fluid Interface

At the molecular scale, the definition of solid/fluid boundary is ambiguous since its defining precision is comparable to the size of the electron orbitals. It is important to figure out the sub-atomic-level solid/fluid boundary as the definition of the solid/fluid interface is related to estimating various properties such as slip length, Kapitza resistance, confined volume, thermodynamic properties, and material properties. In this work, molecular dynamics (MD) simulations were conducted to show the effects of the solid/fluid boundary on estimating thermodynamic properties. Our results reveal that the different definitions of solid/fluid boundary can cause a considerable impact on quantitative analysis and even qualitative analysis of a nanoscale system. The solid/fluid boundary for Lennard-Jones atoms is determined within sub-atomic precision via heat transfer MD simulations and microscopic heat flux relation. The result shows that solid/fluid boundary is slightly shifted to the fluid regime as the temperature increase. We suggested a mathematical expression of solid/fluid boundary of LJ atom that is theoretically estimated by ignoring the thermal vibration. The results presented in this work are expected to improve the accuracy of analyzing nanoscale phenomena as well as the continuum-based models for nanoscale heat and mass transport.
Faster-than-Nyquist Signal Processing Based on Unequal Error Probability for High-Throughput Wireless Communications

Faster-than-Nyquist (FTN) signal processing, which transmits signals faster than the Nyquist rate, is a representative method for improving throughput efficiency sacrificed performance degradation due to inter-symbol interference. To overcome this problem, this paper proposed FTN signal processing based on the unequal error probability to improve performance. The unequal error probability method divides encoded bits into groups according to priority, and FTN interference rates are differently applied to each group. A lower FTN interference ratio is allocated to the group to which high-priority encoded bits belong and a higher FTN interference ratio is allocated to the group to which low-priority encoded bits belong, thus performance improvement can be obtained compared to the conventional FTN method, with the same interference ratio. In addition, we applied the proposed FTN signal processing, based on the unequal error probability method, to the OFDM (orthogonal frequency division multiplexing) system in multipath channel environments. In the simulations, the performance of the proposed method was better than that of the conventional FTN method by about 0.2 dB to 0.3 dB, with an interference ratio of 20%, 30%, and 40%. In addition, in multipath channels, we confirmed that by applying the proposed unequal error probability, the OFDM-FTN method improves performance to a larger extent than the conventional OFDM-FTN method.
A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems

To overcome the real-time problem of maximum power point tracking (MPPT) for partially shaded photovoltaic (PV) systems, a novel nature-inspired MPPT controller with fast convergence and high accuracy is proposed in this paper. The proposed MPPT controller is achieved by combining salp swarm algorithm (SSA) with grey wolf optimizer (GWO) (namely, SSA-GWO). The leader structure of the GWO algorithm is introduced into the basic SSA algorithm to enhance the global search capability. Numerical simulation on 13 benchmark functions was done to evaluate the proposed SSA-GWO algorithm. Finally, the MPPT performance on PV system with the proposed SSA-GWO algorithm under static and dynamic partial shading conditions was investigated and compared with conventional MPPT algorithms. The quantitative and simulation results validated the effectiveness and superiority of the proposed method.
Performance Evaluation and Scalability of IP-based and Heuristic-based Job Scheduling Algorithm Backup Systems

Background/Objectives: One of the most important factors in the design of enterprise computer networks is data storage method. Increase the volume, recovery and security of stored information is the biggest challenge. Using backup systems is a perfect solution to solve these challenges. Traditional methods can be used to perform backups, but new methods and new backup systems are more efficient. Methods/Statistical Analysis: To analyze and compare overall backup session time, backup session processing time, overhead, fragmentation of large data and efficiency of the IP-based schedule and the heuristic-based job scheduling algorithm. Findings: My analysis show that the simple heuristic-based job scheduling algorithm with adaptive number of active DAs is close to optimal while having no additional computing overhead compared to the compute expensive IP-based solution. The simulator-based on heuristic-based job scheduling algorithm enables the analysis to minimize the number of actively used backup servers while meeting backup window constraints and data restore satisfying rates. Applications/Improvements: This algorithm is close to the results that reduce backup time by 60 percent without additional computing overhead and fragmentation of large data processing compared to IP-based methods. As well as, the algorithm can simulate the analysis of a backup system will help. The proposed approach significantly increases the performance, reliability and quality of implemented solutions.
Research on the forcefree control of cooperative robots based on dynamic parameters identification

Purpose This paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots. Design/methodology/approach Dynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control. Findings The model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag. Practical implications The entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well. Originality/value A full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.
Advances in Modal Logic

Modal logic originated in philosophy as the logic of necessity and possibility. Now it has reached a high level of mathematical sophistication and has many applications in a variety of disciplines, ranging from artificial intelligence, computer science and mathematics via natural language syntax and semantics to philosophy. It is the main source of formal languages aimed at analyzing complex notions such as common knowlwdge and formal provability. This volume presents a broad, contemporary view of the field, with contributions covering the foundations of modal logic as well as its many application areas.
A Unified Architecture for Physical and Ergonomic Human–Robot Collaboration

SUMMARY Industrial applications that involve working on and moving a heavy load or that constrain the operator to work in uncomfortable positions can take advantage of the assistance of a robotic assistant. In this paper, we propose an architecture for an ergonomic human–robot co-manipulation of objects of various shapes and weight. The object is carried by the robot and, thanks to an ergonomic planner, is positioned in the most comfortable way for the user. Furthermore, thanks to an admittance control with payload compensation, the user can easily adjust the position of the object for working on different parts of it. The proposed architecture is experimentally validated in a robotic cell including an ABB industrial robot.
Fine Grained Sentiment Classification of Customer Reviews Using Computational Intelligent Technique

Online reviews are now popularly used for judging quality of product or service and influence decision making of users while selecting a product or service. Due to innumerous number of customer reviews on the web, it is difficult to summarize them which require a faster opinion mining system to classify the reviews. Many researchers have explored various supervised and unsupervised machine learning techniques for binary classification of reviews. Compared to these techniques, fuzzy logic can provide a straightforward and comparatively faster way to model the fuzziness existing between the sentiment polarities classes due to the ambiguity present in most of the natural languages. But the fuzzy logic techniques are less explored in this domain. Hence in this paper, a fuzzy logic model based on the most popularly known sentiment based lexicon SentiWordNet has been proposed for fine grained classification of the reviews into weak positive, moderate positive, strong positive, weak negative, moderate negative and strong negative classes. Experiments have been conducted on datasets containing reviews of electronic products namely smart phones, LED TV and laptops and have shown to provide fine grained classification accuracy approximately in the range of 74% to 77%. Keyword- Sentiment analysis, Fine grained Classification, Fuzzy Logic, SentiWordNet, Online reviews
Performance Analysis of HEVC In-Loop Filter

The need of high definition video (HDV) is growing day by day. Keeping in need of HDV, the Joint Collaborative Team on Video Coding (JCTVC) developed a new video coding project known as High Efficiency Video Coding (HEVC). The upcoming HEVC is designed to serve wide range of applications. However, it suffers from visually disturbing discontinuities known as blocking artifacts to achieve high compression ratio. HEVC employs in-loop filter to suppress these blocking artifacts. This paper describes the performance analysis of HEVC in-loop filter which comprises of deblocking and sample adaptive offset (SAO) filter. Various high definition video sequences of 1080p, 720p and 480p are used for evaluation. Simulation results show that in-loop filter can suppress blocking artifacts effectively without losing objective and subjective quality of video. (Gulistan Raja, Awais Khan, Ahmad Khalil Khan, Muhammad Haroon Yousaf. Performance Analysis of HEVC In-Loop Filter. Life Sci J 2013; 10(11s): 331-336). (ISSN:1097-8135). http://www.lifesciencesite. com 61
Improving Performance in Hadoop Using Automatic and Predictive Configuration

MapReduce is an effective programming model for analyzing large-scale data. Hadoop-a distributed processing system is widely used nowadays. Improving the task parallelism can be a key point to improve the MapReduce performance in Hadoop. In this paper, we address the problem in two ways. On the one hand we can run the tasks with some dynamic configurations. On the other hand, considering of the difference of tasktracker we use mathematics method to predict the cups’ utilization of tasktracker to assign the task. Experimental results on both ways show we can improve the performance in Hadoop by improving the task parallelism. Keywordshigh performance; dynamic; prediction; big data
Additive manufacturing as an emerging technology for fabrication of microelectromechanical systems (MEMS)

Abstract The recent success of additive manufacturing processes (also called, 3D printing) in the manufacturing sector has led to a shift in the focus from simple prototyping to real production-grade technology. The enhanced capabilities of 3D printing processes to build intricate geometric shapes with high precision and resolution have led to their increased use in fabrication of microelectromechanical systems (MEMS). The 3D printing technology has offered tremendous flexibility to users for fabricating custom-built components. Over the past few decades, different types of 3D printing technologies have been developed. This article provides a comprehensive review of the recent developments and significant achievements in most widely used 3D printing technologies for MEMS fabrication, their working methodology, advantages, limitations, and potential applications. Furthermore, some of the emerging hybrid 3D printing technologies are discussed, and the current challenges associated with the 3D printing processes are addressed. Finally, future directions for process improvements in 3D printing techniques are presented.
A Reconfigurable Passive Voltage Multiplier for Wireless Mobile IoT Applications

Battery-less wireless systems generate their supply voltage from the incoming RF signal, and need to deal with the problem of generating sufficient voltage level required by their various blocks from weak input signals, while maintaining high efficiency. The presented voltage multiplier (VM) seeks to maximize the power conversion efficiency (PCE) by changing the RF-to-DC converter topology in the VM based on the incoming signal. Besides topology, the number of stages in the VM can change for the desired output dc voltage from the varying RF input signal. A prototype is fabricated in 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> standard CMOS process, operating at 13.56 MHz with a 2 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> load. The core element in the VM yields a peak PCE of 76% with the cross-coupled topology at low input power, while the hybrid topology provides 70% PCE at higher input power. This reconfigurable VM can be used in RFID or Internet-of-Things (IoT) mobile applications to regulate power against changes in the distance between the power source and the target.
Epidemic spread model based on cellular automata

Based on the theory of complexity adaptive system,we build a cellular automata model to simulate the complex process of epidemic spread.Based on this model,we simulate the spread process of SARS successively.Then several factors,such as move of people,whether being treated in time,which have great effects on the propagation of epidemic are analyzed,and whose effects on epidemic are studied.Some suggestion to deal with this kind of problem is given finally.
Synchronization: A Universal Concept in Nonlinear Sciences

Preface 1. Introduction Part I. Synchronization Without Formulae: 2. Basic notions: the self-sustained oscillator and its phase 3. Synchronization of a periodic oscillator by external force 4. Synchronization of two and many oscillators 5. Synchronization of chaotic systems 6. Detecting synchronization in experiments Part II. Phase Locking and Frequency Entrainment: 7. Synchronization of periodic oscillators by periodic external action 8. Mutual synchronization of two interacting periodic oscillators 9. Synchronization in the presence of noise 10. Phase synchronization of chaotic systems 11. Synchronization in oscillatory media 12. Populations of globally coupled oscillators Part III. Synchronization of Chaotic Systems: 13. Complete synchronization I: basic concepts 14. Complete synchronization II: generalizations and complex systems 15. Synchronization of complex dynamics by external forces Appendix 1. Discovery of synchronization by Christiaan Huygens Appendix 2. Instantaneous phase and frequency of a signal References Index.
Local reasoning for Java

This thesis develops the local reasoning approach of separation logic for common forms of modularity such as abstract datatypes and objects. In particular, this thesis focuses on the modularity found in the Java programming language. We begin by developing a formal semantics for a core imperative subset of Java,Middleweight Java (MJ), and then adapt separation logic to reason about this subset. However, a naive adaption of separation logic is unable to reason about encapsulation or inheritance: it provides no support for modularity. First, we address the issue of encapsulation with the novel concept of an abstract predicate, which is the logical analogue of an abstract datatype. We demonstrate how this method can encapsulate state, and provide a mechanism for ownership transfer: the ability to transfer state safely between a module and its client. We also show how abstract predicates can be used to express the calling protocol of a class. However, the encapsulation provided by abstract predicates is too restrictive for some applications. In particular, it cannot reason about multiple datatypes that have shared read-access to state, for example list iterators. To compensate, we alter the underlying model to allow the logic to express properties about read-only references to state. Additionally, we provide a model that allows both sharing and disjointness to be expressed directly in the logic. Finally, we address the second modularity issue: inheritance. We do this by extending the concept of abstract predicates to abstract predicate families. This extension allows a predicate to have multiple definitions that are indexed by class, which allows subclasses to have a different internal representation while remaining behavioural subtypes. We demonstrate the usefulness of this concept by verifying a use of the visitor design pattern.
Wavelet-Local binary pattern based face recognition

Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.
Design and implementation of an unmanned aerial vehicle with self-propulsive wing

The article aims to prove the effectiveness of the proposed unmanned air vehicle design (The Propulsive Wing) through numerical and experimental means. The propulsive wing unmanned air vehicle is a completely new class of unmanned air vehicle, making disruptive changes in the aircraft industry. It is based on a distributed cross-flow electric fan propulsion system. When the fan starts to operate, the flow is drawn from the suction surface, provided by energy through the fan and expelled out of the airfoil trailing edge (TE). This causes a significant lift increase and drag reduction with respect to ordinary aircrafts, making it perfect for applications requiring low cruise speed such as firefighting, agriculture, and aerial photography. In this early stage of the investigation, our main aim is to prove that this design is applicable and the expected aerodynamic and propulsion improvements are achievable. This is done through a two-dimensional computational fluid dynamics investigation of the flow around an airfoil with an embedded cross-flow fan near its TE. A scaled wind tunnel model of the same geometry used in the computational fluid dynamics investigation was manufactured and used to perform wind tunnel testing. The computational fluid dynamics and wind tunnel results are compared for validation. Furthermore, an unmanned air vehicle model was designed and manufactured to prove that the propulsive wing concept is flyable. The article shows that the aerodynamic forces developed on the cross-flow fan airfoil are not only functions of Reynolds number and angle of attack as for standard airfoils but also function of the fan rotational speed. The results show the great effect of the rotational speed of fan on lift augmentation and thrust generation through the high momentum flow getting out of the fan nozzle. Wind tunnel tests show that the suction effect of the fan provides stall free operation up to very high angles of attack (40 degrees) leading to unprecedented values of lift coefficient up to 5.8. The flight test conducted showed the great potential of the new aircraft to perform the expected low cruise speed and high angles of attack flight.