Miracle-dz/UniFAR-FTUs huggingface dataset:
Classical model for the noise properties of distributed optical amplifiers

Adopting a new classical approach, the additive noise power generated in optical amplifiers is calculated in terms of power spectral density. The classical formalism used combines a coruscular approach to a phase-amplitude description of the optical field. The noise contributions of the input field fluctuations, including zero-point fluctuations, and of the electron momentum fluctuations at optical frequency linked to the amplifier itself, are clearly identified. The excess of noise associated to coupling or built-in losses is determinated. The well-known result of the Amplified Spontaneous Emission (ASE) is obtained for the laser amplifiers. This description is applied to linear phase-insensitive amplifiers and to inhomogeneous, nonlinear phase-insensitive Raman amplifier, pointing out the effects of gain compression and gain distribution. This new approach makes possible the treatment of the squeezed-state of light and the quadrature reduced noise amplifications.
Destination-Aware Task Assignment in Spatial Crowdsourcing: A Worker Decomposition Approach

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.
Standard Work From a Lean Theory Perspective

This paper presents a proof of concept that standard work procedures as prescribed by lean theory can be implemented within a concrete construction company. Standard work procedures and knowledge transfer utilizing the “J” programs as prescribed by Training Within Industry and lean theory can and does reduce variability in construction processes. Variability in work processes increases the probability of breakdowns (any deviation from an expected outcome), errors and negative iteration which leads to schedule and cost overruns. Standardizing work methods reduces the probability of breakdowns, thereby improving work flow, providing a basis for learning from what breakdowns do occur, and providing a basis for experimentation with alternative work method designs. Exploratory research with work standardization in a concrete construction division is presented, including the cultural and organizational issues that were overcome to change the current paradigm. Two findings from this research are: 1) obtained a better understanding of what standard work procedures are and how they differ from preconceived notions, and 2) development of standard work procedures to create a baseline for continuous improvement. Practitioners can use this research to understand how to analyze processes, improve them and transfer critical knowledge.
Condition Analysis and Forecasting in the Fashion Industry

The work considers the problem of demand for the clothing industry's goods. It shows how this problem is connected with the mathematical problem of the partition of the set. Investment decisions depend on a diagnosis based on forecasting demand in individual product groups. These groups are characterized by a number of features and even in the simplest situations (3 attributes) lead to computationally complex situations. In this situation, the recursive partitioning method can be used. This is a method related to the construction of classification trees (regression). These methods are widely used in natural, technical and economic sciences. The main direction of their applications is to support decision-making processes. The article shows how to support the construction of classification trees. The paper proposes a practical solution to the problem using the method of random partitions. The proposed method can be a complement to the recursive partitions method, or used in some situations instead. The submitted method is a practical proposal to avoid the problem of computational complexity. The numerical example shows how to replace a population of about 52 trillion by a sample of only 100. The applied method was justified by an example of a less numerous population, where the result could be verified empirically by reviewing all possibilities. Such verification is not practically possible in the case of 20 product profiles. Such a number generates a number of partitions amounting to almost 52 trillion. The article also presents the estimation of the calculation time. These results are useful from a practical point of view, although they are not optimal.
Optimal Control Method for Robot-Tracking Based on Control-Lyapunov-Function

A control Lyapunov function (CLF)-based optimal control method is proposed to solve an affine nonlinear system with input limitation for the wheeled robot. Motivated by Lyapunov stability theory, viability theory, and exact linearization and sliding mode control, the construction of the CLF uses the information of the multi-order derivatives of the system outputs. The result shows the stability depends on the controller coefficients and the input limitation. Thus, the sufficient conditions are given and proved for stable controller design. The proposed control method provides a new robust regulation map to an affine nonlinear system for high-dimensional trajectory tracking. A simulation of multi-agent wheeled robot formation is designed to illustrate the application of the proposed controller. Meanwhile, the validation and feasibility of the controller are also verified by the simulation.
Model Merging Through Mean Value Coordinates

Merging a model to another model, including the mesh and its texture, is a useful technology for generating new models. This paper introduces a novel method to merge models through Mean Value Coordinates (MVC). The method firstly computes the MVC of the vertices of a source mesh with respect to its merging boundary. As the target mesh is specified, the differences between the two meshes’ boundaries are computed. The source mesh is transformed fluently according to the differences through MVC, and then construct a new mesh with the target. With the same framework, smoothing the seaming line can be achieved by interpolating tangent vector variations. And a new texture is similarly constituted by interpolating the color differences on the boundaries to make a consistent model. This method avoids the mesh parameterizations or the solving a large linear system, but merges through computing closeform expressions. Experiments show that model merging with this method is less artifacts and faster than some other methods.
A public-key cryptosystem based on second order linear sequences

Based on Lucas functions, an improved version of the Diffie-Hellman distribution key scheme and to the ElGamal public key cryptosystem scheme are proposed, together with an implementation and computational cost. The security relies on the difficulty of factoring an RSA integer and on the difficulty of computing the discrete logarithm.
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.
Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloudbased services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing
Software Challenges in Heterogeneous Computing: A Multiple Case Study in Industry

One way to improve the performance of embedded systems is through heterogeneous platforms, i.e., using hardware containing more than one type of processor, like CPU + GPU or CPU + FPGA. This approach has shown improved performance, particularly in the domain of artificial intelligence, in which computationally demanding models must be trained and executed. However, these computational environments pose various challenges to software engineering, since applications must be designed and developed differently while accounting for target hardware architectures that are inherently different. Companies interested in migrating to heterogeneous platforms must be aware of the changes to the software development processes that are required to accommodate such solution. In this paper, we conducted a multiple case study that aims to discover the challenges and common practices when developing software for heterogeneous platforms in industrial contexts. First, we organized semi-structured interviews with companies on the automotive, automation, and telecommunication domains. Then, we analyzed and structured the data in order to create a classification describing the software engineering challenges faced by companies using heterogeneous platforms. The companies involved in this study are in different stages of maturity concerning the use of heterogeneous platforms. Thus, the classification takes into consideration these levels to describe the challenges accordingly.
Searching source code fragments using incremental clustering

Plagiarism is becoming an increasingly serious problem in academic environment. In this paper, we deal with a specific kind of plagiarism: source code plagiarism. In this case, there is no software available for detecting plagiarism on a larger scale (hundreds of student submissions every year). We propose algorithms for source code parsing and processing as a part of a complex system for plagiarism detection. A source code vectorization using characteristic vectors is a vital piece of the whole process, and k‐means algorithm helps with the classification and clustering of vectors. Student assignments are submitted regularly, and any plagiarism detection system needs to handle them as they come. For this reason, we propose a modified incremental k‐means algorithm and a method for determining the number of clusters. We also consider methods for vector search among clusters and suggest the use of conditional entropy to select the important vector elements used in the search algorithm. Our results show how the proposed algorithms and methods work on real student submissions.
Driver-Behavior-Based Adaptive Steering Robust Nonlinear Control of Unmanned Driving Robotic Vehicle With Modeling Uncertainties and Disturbance Observer

In this paper, an adaptive steering robust nonlinear control method based on driver behavior is presented for an unmanned driving robotic vehicle (UDRV), in order to achieve accurate and stable steering control of UDRV. Considering modeling uncertainties and unknown nonlinear external disturbances, a UDRV nonlinear dynamics model is established. A driver behavior model is established considering a coordinate transformation model, a driver virtual path planning model, and a driver desired yaw rate model. On the basis of this, an adaptive steering robust nonlinear controller for UDRV is presented, consisting of an adaptive robust backstepping controller and a nonlinear disturbance observer (NDO). The NDO is designed to compensate for modeling uncertainties and unknown nonlinear external disturbances. The adaptive steering robust nonlinear controller for UDRV is designed through backstepping method and NDO compensation. The stability of control system is proved. Finally, double lane change experiments are conducted. Comparison analysis results among the proposed control method, other existed control method, and human driver demonstrate the effectiveness of the proposed method.
Instruction of Learning Strategies: Effects on Conceptual Learning, and Learning Satisfactions.

This study has investigated the effects of learning strategy instruction on conceptual learning, and student satisfactions in an introductory physics course at university level. In this study, pretest-posttest and quasi-experimental design with a non-equivalent control group was used. A total of 36 sophomore students majoring in mathematics teaching in a four-year pre-service primary teacher education program in Turkey participated. There was one control group and one experimental group; namely, the summarizing group. The summarizing group (n=18) received physics instruction with summarizing strategy instruction and the control group (n=18) received physics instruction in line with traditional teaching methods. Data were
Proteus: A Lecturer-Friendly Adaptive Tutoring System.

Effectively targeting a heterogeneous student population is a common challenge in academic courses. Most traditional learning material targets the ? average student,? and is suboptimal for students who lack certain prior knowledge, or students who have already attained some of the course objectives. Student-activating learning material supports effective training. Adaptive systems may help to support learning for a heterogeneous group of students. Development of adaptive learning material, however, is usually a complex task not easily done by the average lecturer. An adaptive tutoring system is therefore designed that requires little knowledge and skills from lecturers: Proteus. Proteus provides adaptive navigation on a micro scale based on a set of closed questions. The questions are used both to measure, as well as to stimulate student progress. Entering questions in Proteus requires little effort from lecturers. Proteus is tested and evaluated in a Bachelor of Science (BSc) course with 91 students and is received very well.
Reverse Engineering of Printed Electronics Circuits: From Imaging to Netlist Extraction

Printed electronics (PE) circuits have several advantages over silicon counterparts for the applications where mechanical flexibility, extremely low-cost, large area, and custom fabrication are required. The custom (personalized) fabrication is a key feature of this technology, enabling customization per application, even in small quantities due to low-cost printing compared with lithography. However, the personalized and on-demand fabrication, the non-standard circuit design, and the limited number of printing layers with larger geometries compared with traditional silicon chip manufacturing open doors for new and unique reverse engineering (RE) schemes for this technology. In this paper, we present a robust RE methodology based on supervised machine learning, starting from image acquisition all the way to netlist extraction. The results show that the proposed RE methodology can reverse engineer the PE circuits with very limited manual effort and is robust against non-standard circuit design, customized layouts, and high variations resulting from the inherent properties of PE manufacturing processes.
THE IDENTIFICATION OF MISSING PERSONS MAKING USEOF DNA PROFILES

This paper tackles the issue of identifying missing persons through probability and decision. Evidence is represented by measurements of DNA loci on an Unknown individual and on familial donors requiring to track down their missing relatives. The inferential problem is solved by reviewing, extending and commenting two different approaches recently appeared in the literature. Stemming from a more general contribution we recently provided we explain how to achieve a short-list of individuals to be further scrutinized with a view to finally ascertain if one of them is the recovered Unknown.
Modeling and Mapping Location-Dependent Human Appearance

OF DISSERTATION Modeling and Mapping Location-Dependent Human Appearance Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person’s sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship between human appearance, location, and time. Additionally, we build interactive maps that allow for inspection and demonstration of what our models have learned.
Incorporating Weather Impact in Railway Traffic Control

Abnormal weather events can have significant impacts on the safety and operational performance of the railways. In Great Britain, weather related train delays run into 1 to 2 million of minutes each year. With the rapid advances in weather forecasting and emerging information technology, the weather forecasting data can be utilised to improve the performance of train control models in dealing with weather events. In this thesis, the forecasted moving weather fronts are map in terms of their temporal and spatial coverage, as well as the corresponding speed restrictions and/or track blockages according to the severity of the weather fronts, onto the railway lines. This enables the control models to consider multiple disruptions in advance of them commencing, instead of dealing with them one by one after they have commenced. Then the proactive train control methods are proposed, i.e. mixed integer liner programming (MILP) and genetic algorithm (GA) for single-track rescheduling in adverse condition, and an MILP model for simultaneous train rerouting and rescheduling model, taking into account forecasted severe weather perturbations. In the models, the forecasted moving weather perturbations on different parts of the rail network are represented as individual constraints, whereby, trains travelling through the adversely impacted zones follow reduced speed limits and in the severely impacted zones where the tracks are blocked, trains need to be rerouted or wait until the blockage disappears. The case studies indicate: a) compared with existing control methods our rescheduling methods have shown to make significant reduction in total train delays (in the case studies examined, an average 21% reduction in delays); b) within the timescale considered, the further ahead the weather forecast information is considered, the less the overall delay tends to be; c) under severe weather disruptions (with track blockage), the proposed rerouting and rescheduling model is shown to be able to effectively and efficiently find a cost effective route and timetable.
Ranking of Potential Questions

Questions are an integral part of discourse. They provide structure and support the exchange of information. One linguistic theory, the Questions Under Discussion model, takes question structures as integral to the functioning of a coherent discourse. This theory has not been tested on the count of its validity for predicting observations in real dialogue data, however. In this submission, a system for ranking explicit and implicit questions by their appropriateness in a dialogue is presented. This system implements constraints and principles put forward in the linguistic literature.
Network Optimization for Improved Performance and Speed for SDN and Security Analysis of SDN Vulnerabilities

Software defined Network (SDN) has shown a great interest since its emergence. The extreme efficiency in comparison with traditional networks and its agility introduced by separating control and data planes provided many benefits to the market. Yet, there are many challenges accompanied with this agility, leading to many security concerns and threats such as Denial of Service (DoS) and Flooding attacks and many other threats. This paper aims at assessing the effectiveness of this technology via comparisons against traditional networks using standard comparison parameters such as Throughput and Round Trip Time (RTT) and to measuring speed and efficiency of the new underlying technology. Security analysis is carried out to exploit SDN vulnerabilities by implementing successful attacks using primitive SDN Toolkit.