Miracle-dz/UniFAR-FTUs huggingface dataset:
Search Model of the Region With the Maximum Coverage Value Based on Trajectory Data

The wide application of mobile terminals has given rise to a large number of trajectory data. These data record spatio-temporal mobility of mobile objects and have important value for urban planning, traffic congestion detection, and other applications. In view of the important reference value of the trajectory data for commercial location selection, this paper proposes the search model of the region with the maximum coverage value based on trajectory data, which aims to maximize the sum of weights of sampling points covered by circular regions. The model considers the difference between the sampling points of different users and the interaction between the sampling points of the same user so as to adapt to different application scenarios such as location selection of signal stations and location selection of shopping malls. In order to further improve computing performance, this paper proposes two distributed schemes for this model. Finally, the extensive experiments on three real data sets demonstrated that the distributed schemes outperformed the centralized scheme and the application scenarios of the two schemes are summarized based on the experimental results.
Development of an effective formulation for an acne treatment cream withOcimum basilicumusing invasomes

Cosmeceutical formulations of Ocimum basilicum are designed in an effective base to medicate topical acne-causing inflammation using an advanced drug delivery system. Acne vulgaris is a common disease found not only in young people but also in adults. The present study suggests the use of O. basilicum as a cream for the treatment of acne. Anti-acne creams formulated from crude herbal extracts have long been used for their antimicrobial and antioxidant activities to prevent the inflammation that causes acneiform eruption. This study proposes that, along with the formulation of a micro-emulsion, the use of invasomes will offer an effective advanced drug delivery system that promises improved efficacy and stability in treating acne with O. basilicum.
Modelling and simulation of a smartgrid architecture for a real distribution network in the UK

As part of the inteGRIDy project, funded by the European Commission, an investigation is carried out on a real distribution network, where high penetrations of distributed generations (DGs) exist, in the UK. In this study, a model of this network is built. In this model, additional energy storage systems are located in the network close to DGs to represent future smartgrid architecture. This architecture is proposed to reduce the power import and export between this network and the grid. Four test cases are designed to explore the impacts of DG and the benefits of ESSs.
Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs

Verberne, Johannes MSAE, Embry-Riddle Aeronautical University, May 2019. Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs. Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances. CONTROL LAWS DEVELOPMENT FOR DISTURBANCE REJECTION 1
Mobile Application Services Based Upon Open Urban Government Data

Mobile applications (m-apps), which are based upon open urban data, make up new ways of governmental services and information. In a large-scale study, we analyzed 471 m-apps of 24 metropolitan regions all over the world, regarding their implementation and usage development. For instance, cities like Sydney, New York, or Berlin host so-called hackathons (hacker marathons) to actively push the development of m-apps based on government data. We model a typology of these m-apps and present a unique overview of the variety of types, used eco-systems, and developers. For Android mapps the success was measurable by the amount of counted downloads. Finally, we argue that we are still in the beginning of citizen-oriented e-government and need more research in this field.
A Study on Fine-Grained User Behavior Analysis in Web Search

Web search engines are now used by people with diverse search intents for various purposes. Some tasks they conduct with the search engines cannot be fulfilled just by issuing a single search query or browsing a single document. There are several characteristics common to such complex search tasks. The first two are related to search intents: (1) even if the same query is issued to a search engine, the desired information may differ among searchers, since the search intents behind the query depend on their search contexts; and (2) searchers’ intents can also shift from one to another during a series of their searches on a particular topic. The remaining two concern search outcomes: (3) as complex tasks require searchers to collect more than one relevant document, browsing a single relevant document does not necessarily indicate the success of their search sessions; and (4) this requirement also makes it difficult for searchers to decide when to stop searching. This thesis aims to establish methodologies for understanding diverse intents of searchers and for appreciating their search outcomes in order to help searchers complete complex tasks efficiently and effectively. Our key idea for achieving this aim is analyzing fine-grained user behavior data, where we use the phrase “user behavior” in a broader sense so that it can include search interaction data between searchers and search systems. More specifically, we address the following four research topics in this thesis: 1. Estimating Attractive Terms from Gazing Behavior We address the problem of estimating hidden intents of Web search users from their observable search behavior. To this end, we focus on searchers’ gazing behavior on their browsed Web pages and assume that terms drawing a high degree of the attention of a searcher are likely to be relevant to his/her topical intent. On the basis of this assumption, we propose a method of estimating a set of weighted terms that clarify the searcher’s ambiguous topical intent. Through a laboratory experiment, the proposed method is evaluated from both qualitative and quantitative perspectives. 2. Predicting Query Reformulation from Current Search Behavior
lmplementing inertial sensors to quantify whole-body orientation in the roll plane

Objective: The present study describes the validity, feasibility and reliability of a new system of wearable inertial motion sensors to measure body segments orientation in the roll plane, especially useful to evaluate lateropulsion for stroke patients. lmplementation: We consistently explained the process of sensor selection and tested their validity. After selecting the body-segments and the relative placement of sensors, we established a calibration procedure and tested reliability of first measurements in a laboratory environment. As the results were excellent, we focused on the feasibility of the procedure to determine the ideal protocol for patients routine testing. We finally selected clinically useful data and established a smart data processing workflow. Application: During implementation, we observed several interesting gait and posture behaviors, especially discussed for stroke patients suffering from a bias in their verticality perception. We computed a first reliability analysis on 6 patients with promising results on average inclination and maximum reached angle of body segments for static and dynamic tasks (ICCs 0.77 - 0.95). Measuring instability of each task using standard deviation of measures showed a poor reliability except for sit-to-stand task (ICC = 0.71 (0.37;0.88)). Prospects: This protocol will be carried out in two upcoming national research protocols about lateropulsion (SCALA) and virtual reality in rehabilitation (VIRGIL). Conclusion: Our new method demonstrates a feasible, cheap, portable, easy to use, clinically useful and reliable tool in measuring body segments orientations in the roll plane especially for stroke patients.
Feeding Concentrates: Supplements for Dairy Cows

This book presents strategies for feeding energy and protein supplements to pasture-fed dairy cows and examines the potential economic benefits. Effective supplementary feeding of concentrates is critical to the success of all dairy farms. This book is a substantially revised edition of 'Feeding Concentrates: Supplements for Dairy Cows' DRDC 1993. It focuses on feeding concentrates to pasture fed cows to achieve high milk production per cow per hectare, and will assist farmers to decide which supplements give the best results in their particular situation. The benefits that arise from supplementary feeding include higher stocking rates, promotion of growth in heifers and young cows; better body condition score and increased lactation length when pasture is less available; improved pasture use; reduced cost per tonne of pasture eaten; flexibility to increase milk production when milk prices are high; and increased milk protein content when the energy content in pasture is low. This edition has thoroughly reviewed the issues and clearly documents the results of research particularly for grains supplementation. The summaries and recommendations in each chapter will be particularly helpful to dairy farmers in making best management decisions relating to concentrate feeding.
Back Pain: A Spectral Clustering Approach

We used a spectral clustering algorithm to find clusters among medical patients with lower back pain symptoms, and then we assessed the health outcomes within each cluster. First, we mapped all of the variables onto [0,1] intervals. This allowed us to compute a similarity score between every pair of patients, using an adaptation of Pearson correlation. We then calculated the spectral (eigen) decomposition of this similarity matrix, and we used the first few eigenvectors to create a low-dimensional subspace. Finally, we performed k–means clustering in this new subspace to find four clusters. We compared the cluster means and variances for each recovery assessment variable to differentiate the health outcomes for each cluster. Lastly, we highlighted the identifying symptoms of each patient cluster by inspecting any variable whose within–cluster average is extraordinarily low or high, relative to the other clusters.
Resource accounting and reservation in Java Virtual Machine

The Java programming language was designed to developed small application for embedded devices, but it was a long time ago. Today, Java application are running in many platforms ranging from smartphones to enterprise servers. Modern pervasive middleware is typically implemented using Java because of its safety, flexibility, and mature development environment. However, the Java virtual machine specification has not had a major revised since 1999 [50]. It was designed to execute only a single application per instances, and thus it does not provide resource accounting or per-application resource reservations in the sense most middlewares consider application. Often, current pervasive middlewares are thus unable to reserve resources for critical applications, which may cause these applications to crash or hang up when there are not enough resources. Moreover, contemporary middlewares are unable to provide resource accounting. It makes impossible to apply adaptation policies to optimize resource use. Several researches had addressed these important issues. As result of these efforts some Java specification requests (JSR) have emerged. We consider there are seven JSRs relate to monitoring and to resource accounting and reservation: three for the Java Management eXtension API (JSRs 3, 160, 255), two for Metric Instrumentation (JSRs 138, 174) and two for resource-consumption management (JSRs 121, 284). The Java Management extension API only addresses monitoring and management: it does not define specific resource accounting or reservation strategies. JSRs 138 and 174 define monitors for the Java Virtual Machine. They are coarse grained, monitoring the number of running threads, the memory used, the number of garbage collections and so on. They monitor the entire virtual machine, not specific component so, they are useless to most middleware. Based on the Multitasking Virtual Machine [21], JSR 121 defines isolated JVM instances running in the same OS process, each instance with its own resources. Based on JRes [23], JSR 284 defines resource accounting for Java threads and processes. Together, JSRs 121 and 284 permit resource accounting and reservation for applications. However, the notion of application in JSR 121 is similar to a process at the OS level and it is incompatible with the notion of application of pervasive middlewares like OSGi [4], which relies on a notion of component.
Dual Capacity Upper Bounds for Binary-Input Single-Tap ISI Channels

The capacity of noisy channels with memory and finite input has been difficult to characterize explicitly in many cases. In this paper, we consider single-tap binary-input Gaussian channels with inter-symbol interference (ISI). The dual capacity method is used to obtain upper bounds using Markov test distributions. The bound, expressed as a single-letter optimization problem, is solved numerically. For higher memory, the notion of cycle basis is used to provide an exponential reduction in the computational complexity of the optimization problem. Bounds were obtained for the important case of the dicode channel, and these are better than the previously known upper bounds and are close to achievable rates over a wide range of SNRs.
Improved Continuous Fast Terminal Sliding Mode Control With Extended State Observer for Speed Regulation of PMSM Drive System

The new continuous fast terminal sliding mode control (CFTSMC) is adopted in this paper for the speed regulation of permanent magnet synchronous motor drive applications. The CFTSMC method has a capability to converge the error faster, provide higher tracking accuracy and robust against the load disturbances. However, the CFTSMC method needs high switching gain during the strong disturbances, which introduces the chattering and steady state variation to the system. To address these issues, a composite CFTSMC technique based on the extended state observer (ESO) is proposed in this paper. The ESO is used to estimate the system disturbances for feed-forward compensation. The ESO-based CFTSMC method needs smaller switching gain than the CFTSMC method. Moreover, the ESO-based CFTSMC method has a strong robustness against load disturbances, better convergence rate, and better tracking accuracy than the CFTSMC and conventional continuous sliding mode control method. The superiority of the proposed control method is verified by the Lyapunov function and confirmed by the simulation and experimental results.
The Outperformance Probability of Mutual Funds

We propose the outperformance probability as a new performance measure, which can be used in order to compare a strategy with a specified benchmark, and develop the basic statistical properties of its maximum-likelihood estimator in a Brownian-motion framework. The given results are used to investigate the question of whether mutual funds are able to beat the S&P 500 or the Russell 1000. Most mutual funds that are taken into consideration are, in fact, able to beat the market. We argue that one should refer to differential returns when comparing a strategy with a given benchmark and not compare both the strategy and the benchmark with the money-market account. This explains why mutual funds often appear to underperform the market, but this conclusion is fallacious.
Mid-long Term Optimized Scheduling System of Cascaded Hydropower Plants Based on Three-layer B/S Structure

This paper introduces a mid-long term optimized operations system for cascaded hydropower plants,which is based on three-layer browse/server/database framework and uses Internet and database technologies.The object of design and development of this system,system’s structure and function,working environment,key technologies,normalization and standardization design are emphasized.A computer’s software that is advanced,suitable,reliable and friend-user interface is developed by using Web Service and ASP.NET. The software has been applied in many cascaded hydropower plants and the results are satisfactory.
Autonomous IoT Device Identification Prototype

In this paper, we demonstrate a prototype implementation to help identifying the types of IoT devices being connected to a home network. Our solution is based on a supervised classification algorithm (decision tree) trained on 33 IoT devices using relevant information extracted from network traffic. Our demo shows that our proposal is effective to automatically identify the types of IoT devices.
Software Formal Modeling and Verification Method Based on Time STM

State transition matrix(STM), designed for modeling software system, is a table-based modeling language in which the front-end is expressed in the table form and the back-end has strict formalized definition. At present, however, STM has no time semantics, which greatly limits the application of this method in real-time embedded software modeling. In order to solve this problem, this paper proposes a time STM(TSTM) modeling method attained by adding time semantics and constraint for each cell in STM, making it suitable for describing real-time system behavior. In addition, a time computation tree logic(TCTL) model checking method is presented based on bounded model checking(BMC) technology for verification of time and logic properties of TSTM model. At last, the effectiveness of the proposed method is validated by modeling and verifying certain type train control software.
A Customer Behavior Analysis Algorithm Based on Swarm Intelligence

A customer behavior analysis algorithm based on swarm intelligence is proposed. Firstly, customer consumption patterns are randomly projected on a plane. Then, clustering analysis is processed by a clustering method based on swarm intelligence with different swarm similarity coefficients. Finally, the clustering customer groups with various consume characteristics are collected from the plane by a recursive algorithm. A parallel strategy is also proposed. It improves the scalability of the algorithm. The data of telecom mobile customer consumption are used in the experiment. The results are compared with the results obtained by other clustering methods such as k-means algorithm and self-organizing maps algorithm. The comparison shows that this customer behavior analysis algorithm based on swarm intelligence meets the demands of customer clustering and classifying of customer relationship management. Especially, on the aspect of master customer analysis and one to one sell analysis, the algorithm shows the advantages of visualization, self-organization and clusters with distinct characteristics.
Stochastic Analysis of UDNs With Resource Capacity and User Scheduling

In this paper, we investigate the impact of antenna elevation, resource capacity and user scheduling on the performance of ultra-dense networks (UDNs). Using tools from stochastic geometry (SG), we extend a recently introduced definition of coverage probability by inducing a generic thinning that can capture BSs with available resource capacity to transmit users data. Analytical results are then derived for the coverage probability and the average achievable rate, where we obtain closed-form expressions allowing to assess UDNs performance in a more tractable and meaningful fashion compared to the conventional definition of coverage probability. Moreover, we show that the average rate under the new definition requires only the computation of a two-fold numerical integral rather than a four-fold integral in the previous works, which is expected to significantly reduce computational complexity. Comparing the obtained results, we find that the impact of parameters, such as resource capacity, BS transmit power as well as the implementation complexity of scheduling schemes are irrelevant as network density increases, which suggests new insights into the role of these parameters in UDNs.
Web video classification with visual and contextual semantics

On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.