Miracle-dz/UniFAR-FTUs huggingface dataset:
An Incentive-Aware Job Offloading Control Framework for Multi-Access Edge Computing

This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users’ behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.
Underwater acoustic communications and adaptive signal processing

This dissertation proposes three new algorithms for underwater acoustic wireless communications. One is a new tail-biting circular MAP decoder for full tail-biting convolution (FTBC) codes for very short data blocks intended for Internet of Underwater Things (IoUT). The proposed algorithm was evaluated by ocean experiments and computer simulations on both Physical (PHY) and Media access control (MAC) layers. The ocean experimental results show that without channel equalization, the full tail-biting convolution (FTBC) codes with short packet lengths not only can perform similarly to zero-tailing convolution (ZTC) codes in terms of bit error rate (BER) in the PHY layer. Computer simulation results show that the FTBC codes outperform the ZTC codes in terms of MAC layer metrics, such as collision rate and bandwidth utilization, in a massive network of battery powered IoUT devices. Second, this dissertation also proposes a new approach to utilizing the underwater acoustic (UWA) wireless communication signals acquired in a real-world experiment as a tool for evaluating new coding and modulation schemes in realistic doubly spread UWA channels. This new approach, called passband data reuse, provides detailed procedures for testing the signals under test (SUT) that change or add error correction coding, change bit to symbol mapping (baseband modulation) schemes from a set of original experimental data.
INFN Tier–1: a distributed site

The INFN Tier-1 center at CNAF has been extended in 2016 and 2017 in order to include a small amount of resources (∼ 22 kHS06 corresponding to ∼ 10% of the CNAF pledges for LHC in 2017) physically located at the Bari-ReCas site (∼ 600 km distant from CNAF). In 2018, a significant fraction of the CPU power (∼ 170 kHS06, equivalent to ∼ 50% of the total CNAF pledges) is going to be provided via a collaboration with the PRACE Tier-0 CINECA center (a few km from CNAF), thus building a truly geographically distributed (WAN) center. The two sites are going to be interconnected via an high bandwidth link (400-1200 Gb/s), in order to ensure a transparent access to data residing on CNAF storage; the latency between the centers is small enough not to require particular caching strategies. In this contribution we describe the issues and the results of the production configuration, focusing both on the management aspects and on the performance provided to end-users.
Co-Clustering Ensembles Based on Multiple Relevance Measures

Co-clustering aims at discovering groups of both objects and features from a given data matrix. Co-clustering ensembles can produce robust co-clusters by combining multiple base co-clusterings. However, current co-clustering ensemble solutions either ignore the constraints resulting from feature-to-feature and object-to-object relevance information, or ignore feature-to-object relevance information. In this paper, we advocate that all three information sources contribute to the achievement of good consensus solutions, and propose a co-clustering ensemble (CoCE) approach based on multiple relevance measures. CoCE first evaluates the quality of base co-clusters and consequently measures feature-to-object relevance. The latter, along with feature-to-feature and object-to-object relevance measures, contribute to the definition of a hybrid graph. The consensus process uses the resulting hybrid graph; it's formulated as a trace minimization problem and introduces a block-wise matrix multiplication technique to perform the optimization. Experimental results on various datasets show that CoCE not only frequently outperforms other related co-clustering ensembles, but also has reduced runtime cost and is more robust to poor base co-clusterings.
Review of various Artificial Intelligence Techniques and its applications

With the upgrading needs of automation and prediction requirements in the industries, automation for improving the quality of the product and prediction of the product cycle to make product more economical. Now a days the use of artificial intelligence techniques becoming crucial to apply in the industries which makes the products more reliable, robust and economical. This paper deals and explains the different techniques of artificial intelligence. Also covers the review of the different applications of these techniques in the industries. This paper covers the review of Artificial intelligence techniques applied in the different fields like civil engineering, biomedical engineering, mechanical engineering, electrical and electronics engineering and many more.
Structural Equation Modeling: A Bayesian Approach

About the Author. Preface. Chapter 1. Introduction. Chapter 2. Some Basic Structural Equation Models. Chapter 3. Covariance Structure Analysis. Chapter 4. Bayesian Estimation of Structural Equation Models. Chapter 5. Model Comparison and Model Checking. Chapter 6. Structural Equation Models with Continuous and Ordered Categorical Variables. Chapter 7. Structural Equation Models with Dichotomous Variables. Chapter 8. Nonlinear Structural Equation Models. Chapter 9. Two-level Nonlinear Structural Equation Models. Chapter 10. Multisample Analysis of Structural Equation Models. Chapter 11. Finite Mixtures in Structural Equation Models. Chapter 12. Structural Equation Models with Missing Data. Chapter 13. Structural Equation Models with Exponential Family of Distributions. Chapter 14. Conclusion. Index.
Driver and Pedestrian Trust Analysis On Integration of Autonomous Vehicles to Infrastructure of Turkey

Teknolojideki gelismeler ile birlikte, bir cok sektorde teknolojik devrim yasanmistir. Otomotiv sektoru, bu teknolojik devrimlerin basinda gelmektedir. Otonom arac teknolojisi ve bu teknoloji kapsaminda bulunan sensor, kamera, radar ve karar alma mekanizmalarinin gelisimi, her sirket icin otonom araclarin tasarimini ve gelistirilmesini mumkun kilmistir. Bu calismada 107 katilimci tarafindan internet uzerinden gerceklestirilen anket ile surucu ve yaya / yolcu guveni analiz edilmistir. Ayrica, ayni anket ve ayni katilimcilar ile kucuk bir otonom arac piyasasi analizi yapilmistir. Ankete katilanlarin % 60,7'si otonom arac ve sistemleri hakkinda temel bilgilere sahipken,% 10,3'u daha once hic bilgiye sahip degildir. Trafikteki otonom araclarin varligi, katilimcilarin% 73,8'ini rahatsiz etmedigi halde, % 6,5'I icin problem olabilmektedir. Katilimcilarin% 63,5'i otonom arac ile ayni seritte seyir edebilirken,% 9,6'si bunu tercih etmemektedir. Yaya olarak karsidan gecmek isteyen katilimcilarin % 60,7'si otonom araclara guvenmekte. Ankete katilanlarin % 56,1'i otonom arac ureten diger marka yerine yerli uretilen otonom arac kullanmayi tercih ederken,% 66,3'u ise klasik otomobil yerine otonom arac kullanmayi tercih etmektedir. Bu analize gore, Turkiye'deki toplumun cogunlugu otonom araca karsi olumlu bir bakis acisina sahiptir.
Path Generation Method for Aero- Engine Free-Form Surface Blade in Laser Solid Forming

Blade is the critical and extensively used component of aero-engine, which machining quality directly affects engine performance. Due to its flexibility, laser solid forming (LSF) from powders is widely used to synthesis novel materials, build three-dimensional parts, repair damage part and form surface coating in advanced manufacturing. To repair damaged parts or form surface coating, especially for aero-engine free-form surface blade, process of numerical control or robotic LSF is applied in industry. Nevertheless, on account of the complex geometry of the free-form surface, it’s quite a challenge to generate proper laser beam paths according to the process requirements of LSF. To address this problem, a path generation method based on the differential geometry is presented. The generated paths can ensure the overlap requirements between deposited tracks as well as paths shifts between adjacent layers. The proposed method has been applied to two examples to verify their validity, and there exists a great potential for the applications in aero-engine manufacturing industry, especially in the LSF repair or surface coating of free-form surface blade.
A Real-Time Patient Monitoring Framework for Fall Detection

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct. ” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.
The Roles of Phonological Recoding, Semantic Radicals and Writing Practice in Orthographic Learning in Chinese

ABSTRACT The present study tested the self-teaching hypothesis in orthographic learning in Chinese and examined the roles of semantic radicals and writing practice. Twenty-four Mandarin-speaking third graders read and comprehended eight two-sentence stories in a read-twice condition and eight in a read-write condition. Each story contained two pseudo-characters exposed to children four times. The pseudo-character was varied on the availability of a phonetic radical (presence vs. absence of phonological recoding) and the transparency of a semantic radical (transparent vs. opaque). Orthographic learning was measured via Orthographic Choice and Character Writing posttests at two time points, immediately and again after seven days. Children demonstrated robust orthographic learning across all conditions. Phonological recoding facilitated children’s character writing. Writing practice enhanced phonological recoding and benefitted their orthographic recognition. Semantically transparent radicals improved children’s orthographic recognition. Our findings suggest that phonological recoding, semantic information and writing practice may jointly contribute to children’s orthographic learning.
Advances in the definition of fragility models for the development of risk-targeted hazard maps

Current seismic design codes require buildings to be designed for a given ground motion intensity level that is determined from a prescribed return period. The implicit assumption is that this design criterion ensures that the probability of collapse of buildings, although unknown, is at least uniform. This is unfortunately not the case. Uncertainties in the fragility of the structure and on the shape of the local hazard curve often lead to an associated seismic risk level that is not only sitespecific but also structure-specific, thus invalidating the previous hypothesis. A new approach for the definition of the design ground shaking, commonly called risk-targeted hazard mapping, aims at estimating the ground motion intensity that leads to a uniform distribution of risk or collapse probability. An essential aspect of this methodology is that for the collapse probability at the design ground motion intensity must be known. This parameter, and the associated dispersion, can be constrained by analysing sets of structures designed according to the same criteria. In this study, a large number of structures designed according to the most up-to-date seismic regulation in Europe has been analyzed. The structures were designed for increasing levels of ground motion intensity (PGA ranging from 0.05g to 0.4g). Tridimentional finite element models have been created and nonlinear dynamic analyses have been performed in order to assess the buildings’ seismic performance. Several statistical analyses were performed to estimate boundaries for the collapse probability at the design ground motion, as well as its dispersion. A comprehensive study on the influence of these parameters on the final risk metrics (e.g. annual average loss) was also included. Finally, using the derived fragility parameters we present a suggestion for risk-targeted hazard mapping in Europe.
Identification and Estimation of Marginal Treatment Effect Through Social Networks

This thesis develops a method for examining the role peer effects play in treatment effect models. We focus on the scenario where peer effects are important in determining the treatment decision. Identification of both the treatment decision and the associated marginal treatment effect is explored. In particular, exogenous and endogenous peer effects are used as instruments to identify marginal treatment effects. A Bayesian estimation procedure is presented, utilising a network formation model to adjust for unobserved peer effects. The performance of the model and the estimation procedure is analysed through a Monte Carlo experiment. The proposed method is then applied to estimate the effect of high school peers on the decision to attend college, and the return to education associated with such a decision using the Add Health data-set. Both the Monte Carlo experiment and the empirical application underscore the importance of accounting for the presence of peer effects in treatment effects models, and allows us to consider policy implications of peer effects.
Educating healthcare compliance professionals: Identification of competencies

Abstract This study aims to identify business competencies required of healthcare compliance professionals for the purpose of developing university-based curricula for the field. A survey was distributed to members of the Health Care Compliance Association. Information was captured on respondents’ ranking of required competencies for the field. Required competencies fell into four general categories: 1. Specific healthcare compliance knowledge; 2. Skills needed to implement the requirements of the profession; 3. Business leadership skills; and 4. Traditional business skills. The data provide a useful foundation to design and implement a university-based curriculum for the demanding and complex healthcare compliance field.
Compilation of bottom-up evaluation for a pure logic programming language

Abstraction in progranuning languages is usually achieved at the price of run time efficiency. This thesis presents a compilation scheme for the Starlog logic programming language. In spite of being very abstract, Starlog can be compiled to an efficient executable form. Starlog implements stratified negation and includes logically pure facilities for input and output, aggregation and destructive assignment. The main new work described in this thesis is (1) a bottom-up evaluation technique which is optimised for Starlog programs, (2) a static indexing structure that allows significant compile time optimisation, (3) an intermediate language to represent bottom-up logic programs and (4) an evaluation of automatic data structure selection techniques. It is shown empirically that the performance of compiled Starlog programs can be competitive with that of equivalent hand-coded programs.ion in progranuning languages is usually achieved at the price of run time efficiency. This thesis presents a compilation scheme for the Starlog logic programming language. In spite of being very abstract, Starlog can be compiled to an efficient executable form. Starlog implements stratified negation and includes logically pure facilities for input and output, aggregation and destructive assignment. The main new work described in this thesis is (1) a bottom-up evaluation technique which is optimised for Starlog programs, (2) a static indexing structure that allows significant compile time optimisation, (3) an intermediate language to represent bottom-up logic programs and (4) an evaluation of automatic data structure selection techniques. It is shown empirically that the performance of compiled Starlog programs can be competitive with that of equivalent hand-coded programs.
Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience

The success of the flipped classroom (FC) is effectively reliant on the level of student engagement with the preparatory activities prior to attending face-to-face teaching sessions. Information about the nature and level of student engagement with these activities can help instructors make informed decisions regarding how to best support student learning. Despite the comprehensive data collection enabled by the increasing presence of education technologies, few studies have used these data to investigate the range of strategies students employ in FC models. This study addresses this deficit by proposing an analytical approach that allows for exploring, first, the strategies students use to interact with online preparation activities; and, second, evolution of those strategies over the duration of a course delivered with a FC pedagogy. The proposed approach identified eight learning strategies and six trajectories of strategy change over the duration of the course, with statistically significant effects of strategy change trajectories on academic performance.
CNN-based Symbol Recognition and Detection in Piping Drawings

Piping is an essential component in buildings, and its as-built information is critical to facility management tasks. Manually extracting piping information from legacy drawings that are in paper, PDF, or image format is mentally exerting, time-consuming, and error-prone. Symbol recognition and detection are core problems in the computer-based interpretation of piping drawings, and the main technical challenge is to determine robust features that are invariant to scaling, rotation, and translation. This thesis aims to use convolutional neural networks (CNNs) to automatically extract features from raw images, and consequently, to locate and recognize symbols in piping drawings. In this thesis, the Spatial Transformer Network (STN) is applied to improve the performance of a standard CNN model for recognizing piping symbols, and the Faster Region-based Convolutional Neural Network (Faster RCNN) is adopted to exploit its capacity in symbol detection. For experimentation, the synthetic data are generated as follows. Two datasets are generated for symbol recognition and detection, respectively. For recognition, eight types of symbols are synthesized based on the geometric constraints between the primitives. The drawing samples for detection are manually sketched using AutoCAD MEP software and its piping component library, and seven types of symbols are selected from the piping component library. Both sets of samples are augmented with various scales, rotations, and random noises. The experiment for symbol recognition is conducted and the accuracies of the recognition accuracy of the CNN + STN model and the standard CNN model are compared. It is observed that the spatial transformer layer improves the accuracy in classifying piping symbols from 95.39% to 98.26%. For the symbol detection task, the experiment is conducted using a public implementation of Faster RCNN. The mean Average Precision (mAP) is 82.8% when Intersection over Union (IoU) threshold equals to 0.5. Imbalanced data (i.e., imbalanced samples in each class) led to a decrease in the Average Precision in the minority class. Also, the symbol library, the small dataset, and the complex backbone network limit the generality of the model. Future work will focus on the collection of larger set of drawings and the improvement of the network’s geometric invariance.
Pulmonary Nodule Classification with 3D Convolutional Neural Networks

Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classiﬁcation of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a sensibility of 84.83%. Moreover, our results have shown that the ﬁrst slices of a nodule provide the best results and only ﬁve nodule slices are enough for a 3D CNN achieve its best results.
A measurable approach for risk justification of explicit and tacit knowledge assessment

Knowledge has become a central organizing principle in society to the extent that knowledge management has become a mainstream activity in organizations. Nevertheless, knowledge-related risks remain relatively neglected in the risk management domain. Whilst knowledge reduces uncertainty and the associated risks, the increased knowledge intensity in organizations also represents a risk factor that has to be assessed. The paper describes and validates an organizational risk assessment approach that considers knowledge-related and knowledge management risks in an integrated manner. The approach makes it possible to calculate risk ratings in terms of vulnerability and likelihood for 50 threats to all activities and phases of the knowledge life cycle. These risk ratings are plotted against 24 potential risks in the human, organizational, and technical domains. To impress on management the significance of these knowledge-related risks, the risk ratings are transformed to approximated financial figures. The approach is applied to 10 Slovenian organizations, two of which are discussed in detail in the paper, to demonstrate that it can be successfully used in a wide variety of organizations. It is concluded that the approach offers a way to assess both knowledge-related and knowledge-management-related risks, that the costs that individual risks potentially hold can be approximated, and that for a diversity of organizations mitigation strategies can be suggested for the identified risks.