Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Major Terrestrial, Celestial, and Temporal Coordinate Systems for Target Tracking

Abstract : Coordinate systems useful for those designing target tracking algorithms are discussed. Strict internationally recognized definitions of terrestrial and celestial coordinate systems are addressed, as well as a number of other lesser coordinate systems that arise when dealing with data typically used in target tracking algorithms. International organizations defining standard systems are indicated, as well as where to obtain data. Limitations in the data are demonstrated and methods for determining one s orientation using gravitation and magnetic measurements, as well as using stars, are presented. The report focuses on implementing the coordinate-system standards using freely available data and software but does not provide specific details on the strict definitions of all of the standards. More detailed information sources are provided in an extensive reference list. So readers can better reproduce the results in this report, summaries of the sources of the data and external code used in creating the plots in the report are provided. All online sources in this report were verified as of April 2015 or later. Even if high-precision global coordinate system definitions are not necessary to meet the basic requirements of a tracking network, they can be necessary in evaluating the performance of tracking algorithms in such a network.
Feature learning and clustering analysis for images classification

The problem this thesis is addressing is to improve an existing classification in 10 categories of the images captured by SEM microscopes. In particular, the challenge faced is to classify those images according to a hierarchical tree structure of sub-categories without requiring any further human labelling effort. In order to uncover intrinsic structures among the images, a procedure involving supervised and unsupervised feature learning, as well as cluster analysis is defined. Moreover, to reduce the bias introduced in the supervised phase, various strategies focusing on features of different nature and level of abstraction are analyzed.
Brain Mechanisms of Language and Semantic Processing in Sighted and Congenitally Blind Populations: A Neurobiologically Constrained Model

7 Zusammenfassung . 9 List of publications . 11 List of abbreviations . 12 1. General Introduction . 14 Semantic theories . 15 Brain representation of semantic processing . 17 Semantic processing under visually deprived condition . 21 How neurocomputational models can help . 22 Neurobiologically constrained neural network . 25 Overview of the present work . 28 2. Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex . 30 Abstract . 31. 3
Robust Optimization Framework for Proactive User Association in UDNs: A Data-Driven Approach

We study the user association problem in the context of dense networks, where standard adaptive algorithms become ineffective. This paper proposes a novel data-driven technique leveraging the theory of robust optimization. The main idea is to predict future traffic fluctuations, and use the predictions to design association maps before the actual arrival of traffic. Although, the actual playout of the map is random due to prediction error, the maps are robustly designed to handle uncertainty, preventing constraint violations, and maximizing the expectation of a convex utility function, which is used to accurately balance base station loads. We propose a generalized iterative algorithm, referred to as GRMA, which is shown to converge to the optimal robust map. The optimal maps have the intriguing property that they jointly optimize the predicted load and the variance of the prediction error. We validate our robust maps in Milano-area traces, with dense coverage and find that we can reduce violations from 25% (inflicted by a baseline adaptive algorithm) down to almost zero.
Pache: A Packet Management Scheme of Cache in Data Center Networks

The communication traffic among servers is a principal bottleneck for data center networks (DCNs), in which redundant traffic has a significant impact on the performance of DCNs. In this paper, we propose Pache, a distributed and efficient cache scheme to achieve traffic redundancy elimination in DCNs. In Pache, nodes cache packets to reduce bandwidth consumption and gain performance revenue. Each cache node manages the local cache and improves cache efficiency with counting bloom filter, hash table and Trie. Each cache node broadcasts the local cache information by a bloom filter to other servers, and servers have cache information tables (cache-table) to record them, which achieves a cache sharing mechanism. Moreover, Pache employs false positive information table (fp-table) to reduce the false positive rate caused by bloom filters. A server determines an original packet or the corresponding fingerprint packet to send by querying cache-table and fp-table. Cache node placement mechanism is also designed to select which node to cache a packet. For evaluating the performance of Pache, we execute extensive simulations and conduct a case study on Amazon EC2 platform from different aspects. The results show that Pache can eliminate about 40 percent redundant traffic on average, and only increase 10 percent runtime, and it is also a scalable and feasible cache scheme in DCNs.
Optimizing airline operations under uncertainty

OPTIMIZING AIRLINE OPERATIONS UNDER UNCERTAINTY Özge Şafak Aydıner Ph.D. in Industrial Engineering Advisor: Mehmet Selim Aktürk Co-Advisor: Özlem Çavuş İyigün June 2019 Fluctuations in passenger demand, airport congestion, and high fuel costs are the main threats to airlines’ profit, thereby need to be carefully addressed in airline scheduling problems. This study takes an advantage of aircraft cruise speed control in several scheduling problems to keep the cost of fuel manageable. We first generate a flight schedule by integrating strategic departure time decisions, tactical fleeting and routing decisions and more operational flight timing decisions under stochastic demand and non-cruise times. Our model differs from the existing studies by including aircraft cruise speed decisions to compensate for increase in non-cruise time variations due to the airport congestion. To efficiently solve the problem, we provide a scenario group-wise decomposition algorithm. Then, we consider a new problem which aims to accommodate new flights into an existing flight schedule in a short time. We suggest some operational changes such as controlling the aircraft cruise speed, re-timing flight departures and swapping aircraft to open up time for new flights. However, nonlinear fuel cost function, and binary assignment and swapping decisions significantly increase the computational burden of solving scheduling problems. In this thesis, we propose strong mixed integer conic quadratic formulations. Finally, we extend the problem by including a strategic decision to lease an aircraft for introducing new flights. More importantly, we consider the effects of departure time decisions on the probability distribution of random demand. We propose a bounding method based on scenario group-wise decomposition for stochastic programs with decision dependent probabilities.
Welfare effects of network neutrality in mobile Internet market

ABSTRACT The network neutrality can enhance the profitability of content providers but reduce carrier platforms’ profits and consumer surplus. What is more, network neutrality can improve the total social welfare level when the external utility of mobile phone users to content providers is greater than that of content providers to those users. In addition, it is also found that both profitability of content providers and pricing power of carrier platforms for mobile phone users are directly proportional to the basic utility that carriers themselves can provide.
Paving the Way towards an Armenian Data Cube

Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.
Continuous User Authentication via Random Forest

Random forest is an important technique in modern machine learning. It was ﬁrst proposed in 1995, and with the increasing of the compute power, it became a popular method in this decade. In this paper, we present the background knowledge of random forest and also have comparison with other machine learning methods. We also have some discussion of data preprocessing and feature extraction. Then we study the application of random forest to a real world problem - an authentication system which for continuous user authentication approach for web services based on the user’s behavior. The dataset we used for evaluate is given by Workiva company, and the result demonstrates that random forest is eligible for the system and achieve a fair classiﬁcation accuracy.
Study Activities That Foster Generative Learning: Notetaking, Graphic Organizer, and Questioning

Fourth graders were asked to read a text and either to fill in a compare-and-contrast graphic organizer, answer a set of structured questions, take notes, or simply read the text. Both the graphic organizer and questioning groups outperformed the read-only group on a comprehension test (d = 1.24 and 1.22, respectively) and a memory test (d = 0.54 and 0.53, respectively). No significant differences were found between the notetaking and read-only groups on the comprehension test (d = 0.30) or the memory test (d = 0.20). Results showed more integrative eye movements between paragraphs for the graphic organizer (d = 1.53) and the questioning groups (d = 1.90) than the read-only group, but not for the notetaking group (d = −0.06). On all three measures, the graphic organizer group and the questioning group exhibited a generative learning strategy, whereas the notetaking and read-only groups exhibited a linear learning strategy.
Spectral efficiency evaluation of full‐duplex mode of communications based on SLNR approach

Full‐duplex (FD) mode of communication with efficient transmission scheme is a promising approach for 5G wireless systems by improving the spectral efficiency. This can be attained by making use of various precoding approaches. We propose a new co‐channel interference (CCI)‐aware improvement to signal‐to‐leakage‐and‐noise ratio (SLNR) technique and a suppression filter at the receiver to whiten the interference for the downlink channel. As well, for the uplink (UL) communication, we propose a self‐interference (SI)‐aware enhancement to SLNR scheme and designing a precoder using self‐interference plus noise covariance matrix. The total spectral efficiency is obtained from the sum‐rates of both downlink and uplink communication systems. Simulation results verify that the spectral efficiency (SE) of FD using the proposed scheme performs well relative to the half‐duplex system for all Rician factor and for small powers at the base station (BS) and UL communication channel users. Moreover, as the number of users grows, which entails that as the number of receiving antennas greater than the number of antennas at the BS the SLNR scheme still works, nonetheless, zero‐forcing (ZF) and block‐diagonalization (BD) precoding schemes failed. This is due to the fact that designing a precoder based on SLNR scheme supports multiple numbers of antennas at the base station and users compared with ZF and BD by compromising the interference and noise. However, for the cases of ZF and BD approaches failed due to both schemes require the number of transmit antennas at the BS to be larger than the sum of the receiving antennas at all users.
Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings.
Artificial Intelligent in Healthcare

BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data
Energy Efficient Chip-to-Chip Wireless Interconnection for Heterogeneous Architectures

Heterogeneous multichip architectures have gained significant interest in high-performance computing clusters to cater to a wide range of applications. In particular, heterogeneous systems with multiple multicore CPUs, GPUs, and memory have become common to meet application requirements. The shared resources like interconnection network in such systems pose significant challenges due to the diverse traffic requirements of CPUs and GPUs. Especially, the performance and energy consumption of inter-chip communication have remained a major bottleneck due to limitations imposed by off-chip wired links. To overcome these challenges, we propose a wireless interconnection network to provide energy-efficient, high-performance communication in heterogeneous multi-chip systems. Interference-free communication between GPUs and memory modules is achieved through directional wireless links, while omnidirectional wireless interfaces connect cores in the CPUs with other components in the system. Besides providing low-energy, high-bandwidth inter-chip communication, the wireless interconnection scales efficiently with system size to provide high performance across multiple chips. The proposed inter-chip wireless interconnection is evaluated on two system sizes with multiple CPU and multiple GPU chips, along with main memory modules. On a system with 4 CPU and 4 GPU chips, application runtime is sped up by 3.94×, packet energy is reduced by 94.4%, and packet latency is reduced by 58.34% as compared to baseline system with wired inter-chip interconnection.
Predictive modelling approach to data-driven computational psychiatry

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.
Design, virtual prototyping and test of a regenerative shock absorber for race cars

This communication deals with energy regenerative damping. A two quadrants electro-hydrostatic damper is designed, simulated, manufactured and tested. The proposed damper involves a single rod linear jack, a charging accumulator, a manifold block with four check valves mounted as a full hydraulic rectifier and a hydraulic motor connected to an electrical generator. The proposed top level modelling is performed in order to point out in a simple and efficient way the contribution of both functional and parasitic effects such as inertia, friction, valves cracking pressure and flow rate pressure gradient. On basis these results, the initial design is modified to reduce the motor flow and to admit more conventional components for the demonstrator. A design exploration is run for the components sizing with special consideration to off-the-shelf references. Then, a virtual prototype is built within the LMS-AMESim simulation environment to verify the preliminary design and to assess the damper transient behaviour. Finally, the experimental results measured from the individual test of the first prototype are displayed and analyzed.
Planting the Seed of Positive Human-IoT Interaction

ABSTRACT We present a visual interface for communicating the internal state of a coffee machine via a tree metaphor. Nature-inspired representations have a positive impact on human well-being. We also hypothesize that representing the coffee machine as a tree stimulates emotional connection to it, which leads to better maintenance performance. The first study assessed the understandability of the tree representation, comparing it with icon-based and chart-based representations. An online survey with 25 participants indicated no significant mean error difference between representations. A two-week field study assessed the maintenance performance of 12 participants, comparing the tree representation with the icon-based representation. Based on 240 interactions with the coffee machine, we concluded that participants understood the machine states significantly better in the tree representation. Their comments and behavior indicated that the tree representation encouraged an emotional engagement with the machine. Moreover, the participants performed significantly more optional maintenance tasks with the tree representation.
Gap quality of employee attendance system application using technology acceptance model approach and fuzzy

The employee attendance system application in an educational institution occupies an important role in providing evaluation and improving performance in addition to being able to improve the transparency of employee and institutional relations. In its use, the employee attendance system must be able to accommodate the needs of its users, namely employees and staffing. This research evaluates user satisfaction over system applications using the Technology Acceptance Model approach. The evaluation was conducted by assessing the level of perception and satisfaction level of 40 users, in this case, employees. The research variable used is perceived usefulness, perceived ease of use (level of ease of use received), and user satisfaction (level of user satisfaction). Computing process was done by fuzzy methods. The results showed that some quality of the application could not meet the expectations of employees at this time. The only variable that met the performance and expectation was perceived ease. Key Words : TAM, Employee Attendance System Aplication, GAP Nad Fuzzy Method