Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study

Problem definition: We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which are made available by smart city infrastructure, such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. Academic/practical relevance: As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. Methodology: We formalize the bike lane planning problem in view of the cyclists’ utility functions and derive an integer optimization model to maximize the utility. To capture cyclists’ route choices, we develop a bilevel program based on the Multinomial Logit model. Results: We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route-choice-based planning model as a mixed-integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Managerial implications: Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists’ route choices. The proposed framework drives the data-driven urban-planning scheme in smart city operations management.
An IBSMF Optimization Technique used Distribution system with Renewable Energy Sources

In this paper, the interconnection of renewable energy sources is proposed for power quality improvement of renewable energy sources (RESs) with an energy storage system. The proposed system is the combined execution of Improved Bat Search Algorithm (IB SA) through Moth Flame Optimization Algorithm (MFOA) named as IBSMFO. The searching behaviour of the bats is modified with efficient neighborhood search functions like crossover & mutation. In the proposed method, the MFOA has handled the searching behavior of the IBSA in perspective of the minimum error objective function. The objective of the proposed IBSMFO approach was to enhance the power quality as for the real and reactive power variations. To accomplish the objective, MFOA is optimized for minimize power variations as well as the operational cost of the RESs in light of weekly and daily forecast of data for grid electricity charge, electrical load and environmental parameter. The proposed IBSMFO procedure deals with the performance of the energy storage apparatus as far as the enhancement of power in the entire system. By at that point, the proposed system is executed in MATLAB/SIMULINK working model and the execution is surveyed with the current procedure.
Automatic Alignment Methods for Visual and Textual Data with Narrative Content

Visual and linguistic tools are the most common medium for exchanging information, self-expression, and storytelling since the ancient times. The advancing technology and humanity enriched these media by introducing novel forms such as digital images, digital videos, online books, blogs, etc. which are tremendously increasing in quantity. Today, we are at a point where we see various manifestations of the same story for which joint analysis for the purposes of summarization, archiving, and automatic meta-data annotation becomes crucial. This leads to challenges in aligning multiple facet stories which would alleviate the difficulties in comprehensive understanding for a joint analysis. Traditional approaches usually require complicated pre-processing steps (e.g., shot segmentation, speech/scene/face recognition and tracking), define a similarity metric between the sequence elements, and perform the alignment with standard techniques based on dynamic programming. Thus, they suffer from the limitations caused by the pre-processing steps, and the inherent drawbacks of Markov assumptions. In this thesis, we focus on aligning multi-modal data, specifically in visual and textual form, which is a fundamental step to learn and analyze correspondences between different manifestations of the same story. To achieve this, we build upon recent advances in deep and recurrent neural networks which provide efficient vectorial and contextual representations of the modalities to be aligned. Our label-based method for automatic alignment of video with narrative sentences proposes a highly efficient alignment technique that does not require heavy pre-processing steps, while enabling a fine level of granularity in the alignment result. Then, we develop an end-to-end differentiable neural architecture that addresses the limitations of the two-stage solutions by optimizing the similarity metric specifically for the alignment task while supporting one-to-one, oneto-many, skipping unmatched elements, and non-monotonic alignment. Expanding on this neural architecture, we develop a sequential spatial phrase grounding network that formulates grounding of multiple phrases as a sequential and contextual process allowing many-to-many matching. In a large variety of experiments, we show that using neural methods for multi-modal data alignment bear potential for more interesting research and applications by alleviating the large manpower that would be needed otherwise.
{P}Net: privacy-preserving personalization of AI-based models by anonymous inter-person similarity networks

Emerging proactive applications need user data to power their underlying AI algorithms. However, both the training and inference tasks are typically performed in the provider's cloud, leading to multiple privacy issues to the data subject. Current privacy-preserving concepts are neither practicable for AI algorithms nor promise mutual benefits for both parties. To address these issues, we propose {P}Net---a novel two-level approach for privacy-preserving personalization, which exploits 'divisible' AI algorithms and anonymous inter-person similarity measurements. In short, {P}Net splits the training task into a cloud-based general model learning process and 'local' personalization steps in which the general 'black-box' model is subsequently adapted to individuals (level 1). Based on anonymously-contributed model modifications (patches) resulting from the first level, {P}Net further allows new users to request a community model---representing the general model personalized by crowd-sourced patches from other similar users (level 2). Our experiments show that {P}Net outperforms existing techniques especially when only a few user data is labeled. With {P}Net, new users can now directly benefit from personalized applications in a practical and privacy-preserving way with reduced labeling effort.
Influence of Time and Risk on Response Acceptability in a Simple Spoken Dialogue System

We describe a longitudinal user study conducted in the context of a Spoken Dialogue System for a household robot, where we examined the influence of time displacement and situational risk on users’ preferred responses. To this effect, we employed a corpus of spoken requests that asked a robot to fetch or move objects in a room. In the first stage of our study, participants selected among four response types to these requests under two risk conditions: low and high. After some time, the same participants rated several responses to the previous requests — these responses were instantiated from the four response types. Our results show that participants did not rate highly their own response types; moreover, they rated their own response types similarly to different ones. This suggests that, at least in this context, people’s preferences at a particular point in time may not reflect their general attitudes, and that various reasonable response types may be equally acceptable. Our study also reveals that situational risk influences the acceptability of some response types.
Empowering Empirical Research in Software Design: Construction and Studies on a Large-Scale Corpus of UML Models

Context: In modern software development, software modeling is considered to be an essential part of the software architecture and design activities. The Unified Modeling Language (UML) has become the de facto standard for software modeling in industry. Surprisingly, there are only a few empirical studies on the practices and impacts of UML modeling in software development. This is mainly due to the lack of empirical data on real-life software systems that use UML modeling. Objective: This PhD thesis contributes to this matter by describing a method to build and curate a big corpus of open-source-software (OSS) projects that contain UML models. Subsequently, this thesis offers observations on the practices and impacts of using UML modeling in these OSS projects. Method: We combine techniques from repository mining and image classification in order to successfully identify more than 24.000 open source projects on GitHub that together contain more than 93.000 UML models. Machine learning techniques are also used to enrich the corpus with annotations. Finally, various empirical studies, including a case study, a user study, a large-scale survey and an experiment, have been carried out across this set of projects. Result: The results show that UML is generally perceived to be helpful to new contributors. The most important motivation for using UML seems to be to facilitate collaboration. In particular, teams use UML during communication and planning of joint implementation efforts. Our study also shows that the use of UML modeling has a positive impact on software quality, i.e. it correlates with lower defect proneness. Further, we find out that visualisation of design concepts, such as class role-stereotypes, helps developers to perform better in software comprehension tasks.
Resource Allocation for Energy Efficiency and QoS Provisioning

In this paper, we formulate and solve two Energy Efficiency (EE) problems, namely the Power Minimization Problem (PMP) and the Maximization of Energy Efficiency Problem (MEEP), for a wireless system using power and frequency resource allocation considering Quality of Service (QoS) requirements and multiple services. Despite those problems are nonlinear, they can be converted into Integer Linear Problems (ILPs). Therefore, the optimal solution for both PMP and MEEP can be obtained by well-known methods. Additionally, we propose two fast suboptimal algorithms as to avoid the high computational complexity of obtaining optimal solution for MEEP. Our results show that the MEEP has a better trade-off between transmitted data rate and power saving than the PMP solution. Moreover, the suboptimal algorithms present good performance compared to the optimal solution for moderated loads but with a much lower computational complexity, thus achieving a remarkable trade-off between performance and computational complexity.
THE EFFECTS OF COMBINED DOCOSAHEXAENOIC ACID AND VITAMIN E SUPPLEMENTS ON SPERMATOGRAM IN ASTHENOZOOSPERMIC MALES: A RANDOMIZED, TRIPLE-BLIND, PLACEBO-CONTROLLED CLINICAL TRIAL

Background: Considering the decrease of omega-3 fatty acids in spermatozoa of asthenozoospermic men, the aim of the present study was to investigate the effects of combined docosahexaenoic acid (DHA) and vitamin E supplements on spermatogram in asthenozoospermic males. Methods: In this randomized, triple-blind, placebo-controlled clinical trial, out of 275 men who referred to Avicenna infertility clinic, fifty asthenozoospermic males, defined as less than 50% sperm motility or less than 25% with rapid progressive motility, were randomly assigned to one of two groups according to the stratified blocked randomization. Participants in the intervention group took daily 465 mg of DHA and 600 IU of vitamin E; and those in the control group took daily two placebos for 12 weeks. Sperm characteristics, dietary intakes, anthropometric measurements and physical activity were measured at the baseline and at the end of the study. Statistical analyses were performed using the SPSS software, the statistical tests being analysis of covariance, Student's t-test, paired-samples t-test, Wilcoxon and Mann-Whitney. Results: Out of 50 participants, 22 men in the intervention group and 20 men in the control group completed the protocol of the study. Number of sperms, sperm concentration, percentage of motile sperms and percentage of motile sperms with a straight direction increased in the intervention group, as compared with the control values (p<0.05). Conclusion: According to this research, combined DHA and vitamin E supplements led to increasing sperm concentration and sperm motility; however no significant changes occurred in sperm morphology and vitality in asthenozoospermic men. DHA and vitamin E, as an antioxidant, may improve sperm motility.
Autopilot Design for Software-in-the-Loop Validation of Fixed-wing UAV Guidance Laws

UnmannedAerial Vehicles(UAVs) have multi-domain applications and fixed-wing UAVs are a widely used class. There is ongoing research on topics in view to optimize the control and guidance of UAVs. This work explores the design, implementation and Software-in-the-Loop validation of an autopilot using adaptive guidance laws with emphasis on formation control of multiple fixed-wing UAVs. The work is done on Raspberry Pis in C++ which can be interfaced to standard autopilots as companion computers. The work splits a mission given by the user into primitive missions and uses an adaptive vector field approach for following it. For formation control, the work implements a discretized version of the Model Reference Adaptive Control synchronisation laws for multi-agent systems. Simulations are done in a distributed setting with a server program designed for the purpose. The server program handles the user inputs and configurations of the UAVs.
Research on Link Prediction Based on Similarity Index Algorithm

For social network with no rights and no directions, link prediction has lower computational complexity and higher link prediction accuracy by using similarity metrics. The existing local information-based similarity metrics are mostly calculated by the common neighbors of the pair of nodes to be predicted when performing link prediction, but these methods do not consider the influence of the relationship between the common neighbors. Aiming at this problem, this paper proposes a new link prediction algorithm based on the existing local information-based similarity measurement method, which not only considers the number and degree of common neighbors between the pairs of nodes to be predicted, but also considers the relationship between common neighbors. In order to verify the effectiveness of the algorithm, the proposed algorithm is compared with the original algorithm and other methods based on the similarity measure of local information in this paper. The experimental results demonstrate the effectiveness of the algorithm.
Exploring the Response Mechanism of Remote Sensing Images in Monitoring Fixed Assets Investment Project in Terms of Building Detection

Fixed assets investment is a driving factor in facilitating urbanization and economic growth. The local governments have spent a lot of budgets on the construction of fixed assets. However, such investment lacks a scientific and objective mechanism to supervise the construction process continuously to guarantee on-time delivery. Owing to the development of remote sensing technology, the availability of high spatial resolution images makes it possible to visualize the construction process continuously. By synthesizing the amount of fixed assets investment, we can build a reasonable monitoring system to supervise the fixed assets projects mutually in both terms of visual construction and statistical money spent. However, as far as we know, there is not much work exploring the methods in monitoring fixed assets investment yet. We collected the continuous investment records of fourteen fixed assets projects from the year 2015 to 2017 and the corresponding GaoFen satellite images in nine-time nodes. Semantic segmentation deep learning technology is applied to detect buildings from the high spatial resolution images. The monitoring system is built by regression between the ratio of investment and the ratio of building area at nine-time nodes. Compared with the regression model from the ratio of investment and that of ground truth building area, our model achieves an RMSE of 0.0136 in the test samples. It indicates the strong potential applicability of remote sensing images in supervising the reasonability of the construction process of fixed assets and the investment allocation.
Spectrum aggregation for dynamic spectrum access based cognitive networks

Telecommunication standards bodies and vendors all over the world are seeking ways of continuing the evolution of mobile communication technology on to its next generation. Providing ubiquitous broadband access to all mobile users distributed in a given area with a fragmented spectrum remains a challenging task. This thesis contributes to the global efforts by extending bandwidth by means of Spectrum Aggregation (SA) in Next Generation Mobile Networks (NGMN). First of all, the motivation behind SA for NGMN is investigated. SA is introduced to create large virtual carrier bandwidths for data hungry users by aggregating fragmented segments of spectrum. By using SA, multiple carriers with different bandwidths, dispersed within intra or inter-bands, can be simultaneously utilised to provide higher data rates, better coverage and simplified multi-band traffic management, resulting in an enhanced user quality of experience. However, SA functionality introduces new challenges for transceiver architecture and Radio Resource Management (RRM) functionality of the network. Secondly, spectrum assignment for cognitive radio networks with SA is investigated. For this purpose, an aggregation-based spectrum assignment is formulated as an integer optimisation problem. The problem is solved using a genetic algorithm to exploit the discontinuity of the available spectrum and to achieve higher capacity gains. The formulation not only considers interference to primary users but also takes into account co-channel interference among secondary users and maximum aggregation span. Simulation results clearly show that the proposed algorithm outperforms state-of-the-art solutions in terms of spectrum utilisation and higher convergence rate. Thirdly, RRM in Long-Term Evolution (LTE) networks with SA is considered. It is shown that the optimal solution of RRM in different scenarios can be achieved by solving the relaxed optimisation problem. The optimum RRM algorithm with reduced complexity is proposed and compared with the current solutions in literature. The proposed algorithm optimally assigns component carriers, resource blocks, modulation and coding scheme values based on users' channel state information and SA capabilities. Simulation results clearly show that the proposed algorithm outperforms current solutions in terms of cell throughput and user throughput fairness. Last but not least, resource allocation with SA for orthogonal frequency-division multiplexing based networks is studied. A joint resource allocation problem with SA functionality is formulated and sub-optimal solutions with low computational complexity are proposed. Simulation results verify that the proposed algorithms achieve near optimum throughput and exploit the multi-carrier diversity of wireless systems.
Building Hierarchies of Retail Centers Using Bayesian Multilevel Models

The perceived quality of urban environments is intrinsically tied to the availability of desirable leisure and retail opportunities. In this article, we explore methodological approaches for deriving indicators that estimate the willingness to pay for retail and leisure services offered by retail centers. Most often, because the quality of urban environments cannot be qualified by a natural unit, the willingness to pay for an urban environment is explored through the lens of the residential housing market. Traditional approaches control for individual characteristics of houses, meaning that the remaining variation in the price can be unpacked and related to the availability of local amenities or, equivalently, the willingness to pay. In this article, we use similar motivations but exchange housing prices for residential properties with property taxes paid by nondomestic properties to glean hierarchies of retail centers. We outline the applied methodological steps that include very recent, nontrivial contributions from the literature to estimate these hierarchies and provide clear instructions for reproducing the methodology. Using the case study of England and Wales, we undertake a series of econometric experiments to rigorously assess retail center willingness to pay (RWTP) as a test of the methods reviewed. We build intuition toward our preferred specification, a Bayesian multilevel model, that accounts for the possibility of a spatial autoregressive process. Overall, the applied methodology describes a blueprint for building hierarchies of retail spaces and addresses the limited availability of spatial data that measure the economic and social value of retail centers. Key Words: econometrics, retail geography, spatial statistics.
Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.