Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of the STEP-NC compliant online toolpath generation for T-spline surfaces using convolutional neural network and random forest classifier

Deep implementation of principals of the intelligent STEP-NC compliant manufacturing implies enabling a CNC system with integrated CAM functionality to perform all tasks autonomously. This means that most of the responsibilities of a CAM engineer need to be delegated to the CNC system. When it comes to manufacturing of freeform surfaces, the implementation of this approach becomes very challenging. One of the most important issues is the autonomous online toolpath generation. Making a choice of optimal machining strategies and other manufacturing parameters, that might be a trivial task for an expert, turns out to be not an easy problem for a machine, and often can be hardly solved using the traditional approach of explicit programming. Therefore, this paper proposes a method to optimize toolpath generation for T-spline surfaces, in particular, the process of choosing an optimal machining strategy for a given surface region using Machine Learning. The selection model has been trained to make a choice of the appropriate freeform machining strategy (two different strategies have been considered) based on the shape of an input surface.
Effects of Progressive Visuospatial Cues on EFL Learners’ Processing of Animated Visualizations Through Mobile Device

ABSTRACT The present empirical study was to examine whether progressive snowball-like cues can benefit learners in learning the operation of a mechanical system. In particular, this study investigated the effects of the experimental condition on the retention and transfer test results and cognitive load by considering the prior knowledge as a covariate. The instructional material used in conducting the study was a self-regulatory, mobile phone-based animation depicting the kinematics of a musical instrument. A total of 131 English as a foreign language (EFL) learners recruited from three sections of a reading course participated in the experiment. First, their prior knowledge was assessed. Next, they were randomly assigned to one of three groups involving three different conditions – animation-only, entity-cued animation, and arrow-entity-cued animation. Immediately after the experimental treatment by manipulating the instructional visualizations, the participants were administered retention and transfer tests concerning the operation of the mechanical system and a cognitive load questionnaire. The experimental results indicated that progressive snowball-like cues were beneficial in identifying the causal relation between different components during the operation of a mechanical system as indicated by the learners’ retention and transfer test results. Significantly, the experimental results demonstrated that the progressive snowball-like cues helped the learners’ attention to be equally distributed to each component by reducing the dynamic components to outcompete the cues for learners’ attention.
The dynamic pipeline paradigm

Nowadays, in the era of Big Data and Internet of Things, large volumes of data in motion are produced in heterogeneous formats, frequencies, densities, and quantities. In general, data is continuously produced by diverse devices and most of them must be processed at real-time. Indeed, this change of paradigm in the way in which data are produced, forces us to rethink the way in which they should be processed even in presence of parallel approaches. To process continuous data, data-driven frameworks are demanded; they are required to dynamically adapt execution schedulers, reconfigure computational structures, and adjust the use of resources according to the characteristics of the input data stream. In previous work, we introduced the Dynamic Pipeline as one of these computational structures, and we experimentally showed its efficiency when it is used to solve the problem of counting triangles in a graph. In this work, our aim is to define the main components of the Dynamic Pipeline which is suitable to specify solutions to problems whose incoming data is heterogeneous data in motion. To be concrete, we define the Dynamic Pipeline Paradigm and, additionally, we show the applicability of our framework to specify different well-known problems.
Explaining Regressions via Alignment Slicing and Mending

Regression faults, which make working code stop functioning, are often introduced when developers make changes to the software. Many regression fault localization techniques have been proposed. However, issues like inaccuracy and lack of explanation are still obstacles for their practical application. In this work, we propose a trace-based approach to identifying not only where the root cause of a regression bug lies, but also how the defect is propagated to its manifestation as the explanation. In our approach, we keep the trace of original correct version as reference and infer the faulty steps on the trace of regression version so that we can build a causality graph of how the defect is propagated. To this end, we overcomes two technical challenges. First, we align two traces derived from two program versions by extending state-of-the-art trace alignment technique for regression fault with novel relaxation technique. Second, we construct causality graph (i.e., explanation) by adopting a technique called alignment slicing and mending to isolate the failure-inducing changes and explain the failure. Our comparative experiment with the state-of-the-art techniques including dynamic slicing, delta-debugging, and symbolic execution on 24 real-world regressions shows that (1) our approach is more accurate on isolating the failure-inducing changes, (2) the generated explanation requires acceptable manual effort to inspect, and (3) our approach requires lower runtime overhead. In addition, we also conduct an applicability experiment based on Defects4J bug repository, showing the potential limitations of our trace-based approach and providing guidance for its practical use.
Professional learning support experiences of primary school teachers in their third to fifth year of practice

i ACKNOWLEDGEMENTS . iii LIST OF TABLES . vi LIST OF FIGURES . vii CHAPTER ONE INTRODUCTION INTRODUCTION . 1 RATIONALE . 3 RESEARCH AIMS AND QUESTIONS . 5 THESIS ORGANISATION . 5 CHAPTER TWO LITERATURE REVIEW INTRODUCTION . 7 SUPPORT FOR PROFESSIONAL LEARNING . 11 CONCLUSION . 21 CHAPTER THREE METHODOLOGY AND METHODS INTRODUCTION . 23 METHODOLOGY . 23 SAMPLE SELECTION . 26 RESEARCH METHODS . 28 DATA ANALYSIS . 31 THE VALIDITY AND RELIABILITY OF RESULTS . 34 ETHICAL CONSIDERATIONS . 37 CONCLUSION . 38 CHAPTER FOUR FINDINGS AND DATA ANALYSIS: FOCUS GROUP DISCUSSIONS INTRODUCTION . 40 STAGE 1: FOCUS GROUP DISCUSSIONS . 41 FINDINGS AND ANALYSIS . 42 CONCLUSION . 65 CHAPTER FIVE FINDINGS AND DATA ANALYSIS: INTERVIEWS INTRODUCTION . 66 STAGE 2: SEMI-STRUCTURED INTERVIEWS . 67 FINDINGS AND ANALYSIS . 68 CONCLUSION . 91
A benchmark for graph neural networks for computer network modeling

Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. RouteNet is a Graph Neural Network that aims to provide this lacking functionality which models network schemes and is able to predict the key performance indicators. Thanks to its GNN architecture that operates over graph-structured data, RouteNet reveals an unprecedented ability to learn and model the complex relationships among topology, routing and input traffic in networks. In previous related works it was proved that RouteNet was able to generalize multiple computer networks by training the model with various topologies of different sizes. This thesis aims to prove how RouteNet is able to model a computer network, but also, and more importantly, wants to provide the ground work for routing sampling creation, which allows to create enough routing combinations to provide a representative sample from which RouteNet would be able to be trained by only simulating a single topology. Finally myself and the research team of RouteNet would like to present the benchmark for computer network modeling using graph neural networks in the form of RouteNet (the model) and the datasets used. Proving the two first objective would permit future research to be done much faster, since simulation and training of multiple topologies is a time costly procedure. It would also allow us to model any other network by only using the routing samples from one simulated network.
LCHR-TSV: Novel Low Cost and Highly Repairable Honeycomb-Based TSV Redundancy Architecture for Clustered Faults

Due to the winding level of the thinned wafers and the surface roughness of silicon dies, the quality of through-silicon vias (TSVs) varies during the fabrication and bonding process. If one TSV exhibits a defect during its manufacturing process, the probability of multiple defects occurring in the TSVs neighboring the faulty TSV increases, i.e., the TSV defects tend to be clustered, which significantly reduces the yield of 3-D integrated circuit. To resolve the clustered TSV faults, router-based, ring-based, group-based, and cellular-based redundant TSV (RTSV) architectures were proposed. However, the repair rate is low and the hardware overhead as well as delay overhead is high. In this article, we propose a honeycomb-based RTSV architecture to utilize the area and delay more efficiently as well as to maintain high yield. The simulation results show that the proposed architecture has a 99.84% repair rate for uniform faults and an 81.42% repair rate for highly clustered faults. The proposed design achieves a 51.66% reduction of hardware overhead compared with the router-based design and a 20.69%, 46.93%, 34.17%, and 11.15% reduction of total delay compared with ring-based, router-based, group-based, and cellular-based methods, respectively.
A randomized, double-blind, placebo-controlled, phase III study in patients (Pts) with metastatic adenocarcinoma of the colon or rectum receiving first-line chemotherapy with oxaliplatin/5-fluorouracil/leucovorin and PTK787/ZK 222584 or placebo (CONFIRM-1)

LBA3 Background: PTK787/ZK 222584 (PTK/ZK) is a novel, oral, small molecule, anti-angiogenesis compound that blocks tyrosine kinase signaling from all known vascular endothelial growth factor (VEGF) receptors. Phase I/II studies of PTK/ZK as a single agent showed that it is safe and well tolerated. Dose response was observed by DCE-MRI. An expanded phase I/II study evaluating escalating doses of PTK/ZK in combination with oxaliplatin/5-FU/LV as first-line therapy of metastatic colorectal cancer (CRC) demonstrated that chemotherapy and PTK/ZK could be safely administered as combination therapy. An overall response rate of 49% was observed; an additional 44% of Pts achieved stable disease. Methods: A multinational, randomized double-blind, placebo controlled phase III study has recently been completed to evaluate PTK/ZK in combination with the FOLFOX-4 regimen (oxaliplatin/5-FU/LV) in Pts with metastatic adenocarcinoma of the colon or rectum. Pts were included if they had histologically documented metastati.
Effect of Combined Therapy on Colistin Resistant Pseudomonas aeruginosa: An in Vitro Study

Pseudomonas aeruginosa is known for its antibiotic resistance to the clinicians. The infections caused by this pathogen are hard to treat because of its highly versatile property to mutate and acquire drug resistance. Pseudomonas aeruginosa also possesses intrinsic property of resistance to certain antibiotics like tetracyclines. However; in a practice to overcome the problem of multi drug resistance, clinicians restored the use of some antibiotics that were previously been used to treat the Pseudomonal infections; but they were discontinued because of its toxic effects. Colistin is an example of one such antibiotic. Use of Colistin was barred for its neurotoxicity. However in recent clinical trials, Colistin was reintroduced to fight with this superbug. Sadly in recent years, Pseudomonas aeruginosa developed resistance to Colistin as well. Therefore combined therapy is an alternate and suitable treatment to overcome the infections caused by multidrug-resistant Pseudomonas aeruginosa. The present study is an in vitro study; in which we tested synergy between two antibiotics namely streptomycin and Colistin on 29 clinical isolates of P. aeruginosa collected from hospitals in Jazan city KSA. The combination of two drugs showed synergistic activity on 55.1% of tested strains, while 20.6% strains had partial synergy, whereas indifferent synergy was observed in 13.8% strains and the 6.8% of strains had additive synergy. In addition to this, the drugs when combined also showed antagonism on one strain (3.44%). The present study showed synergistic action on Colistin-resistant Pseudomonas aeruginosa to greater extent (55.1%) by the two tested drugs. Hence Colistin and streptomycin can be used as a suitable combination therapy (in vivo) to treat multidrug resistant P. aeruginosa infections.
Prototipo de aplicación móvil para tratamiento de señales del fonendoscopio electrónico FonoDX

This document have the process of design and development of a mobile application for the graphic, record and comparison of phonocardiographic sounds with the purpose of give a tool to give a second opinion in a diagnostic. With the requirements propose for the development of this application, it was make the path of a methodological process, guide for the process of creation of the application, where it can be find record of previous projects where similar studies happen or have part of the development that is describe in this document. For the design, a study is made about the user, and an evaluation methodology is set to get feedback and plan future changes to the application. Also is detail how the process of interface design was made and the references taken for this,
A Software-Defined Management System for IP-Enabled WSNs

Software-defined networking (SDN) offers potential pathways to overcome the management complexity of the Internet of Things (IoT). Previous studies have often been limited to software simulations or general proposals only. In this article, we design and evaluate an SDN-based management system for wireless sensor networks (WSNs) using IPv6 over low-power wireless personal area networks (6LoWPAN). The framework is described in detail covering different data-, control-, and application-plane implementations, and includes a novel addressing scheme and packet format. It also uses a centralized routing protocol, located at the SDN controller, based on the shortest path algorithm. We compare our approach with the routing protocol for low-power and lossy networks (RPL), which uses a distributed routing protocol. Hardware tests were carried out in a dynamic environment, with multiple sources of interference for different payload sizes to evaluate the impacts and practicality of SDN in WSNs. The performance comparison shows that the proposed SDN management system for IP-enabled WSNs using a centralized routing protocol outperforms the RPL protocol in terms of round-trip time, jitter, memory consumption, and packet loss rate (PLR), despite the control overhead introduced.
Building Knowledge Graphs : Processing Infrastructure and Named Entity Linking

Things such as organizations, persons, or locations are ubiquitous in all texts circulating on the internet, particularly in the news, forum posts, and social media. Today, there is more written material than any single person can read through during a typical lifespan. Automatic systems can help us amplify our abilities to find relevant information, where, ideally, a system would learn knowledge from our combined written legacy. Ultimately, this would enable us, one day, to build automatic systems that have reasoning capabilities and can answer any question in any human language.In this work, I explore methods to represent linguistic structures in text, build processing infrastructures, and how they can be combined to process a comprehensive collection of documents. The goal is to extract knowledge from text via things, entities. As text, I focused on encyclopedic resources such as Wikipedia.As knowledge representation, I chose to use graphs, where the entities correspond to graph nodes. To populate such graphs, I created a named entity linker that can find entities in multiple languages such as English, Spanish, and Chinese, and associate them to unique identifiers. In addition, I describe a published state-of-the-art Swedish named entity recognizer that finds mentions of entities in text that I evaluated on the four majority classes in the Stockholm-Umea Corpus (SUC) 3.0. To collect the text resources needed for the implementation of the algorithms and the training of the machine-learning models, I also describe a document representation, Docria, that consists of multiple layers of annotations: A model capable of representing structures found in Wikipedia and beyond. Finally, I describe how to construct processing pipelines for large-scale processing with Wikipedia using Docria. (Less)
Frequency-Domain Modeling and Analysis of Injection-Locked Oscillators

This article presents a complete frequency-domain model for injection-locked oscillators (ILOs), which is applicable to all ILO topologies. Based on the essence of the phase accumulation within ILOs, an equivalent linear frequency-domain feedback loop is constructed. By introducing an injection factor that depicts the proportion of the phase shift perturbation with respect to the injection phase disturbance, the phase transfer function (PTF)/noise transfer function (NTF) and tracking bandwidth equation of ILOs are formulated. In addition, a general single transient simulation-based injection factor-extraction procedure is proposed. Theoretical analysis for the injection factor based on both traditional vector diagram and the impulse sensitivity function is also demonstrated. Based on a ring oscillator-based pulse ILO fabricated in a 65-nm CMOS process, the deduced PTF/NTF, tracking bandwidth equation, and their properties are verified using the simulated results and measured phase noise, respectively.
Tooth Brush and Brushing Technique

During the last century or so, tooth brushes were crafted with bone, wood or ivory handles that held the stiff bristles of hogs, boars or other animals. The nylonbristled tooth-brush, as we know it today, was invented in 1938. A healthy mouth is important for many aspects of life, including eating well, absence of dental pain, personal relationships and feeling good about your health. Tooth brushing, the most widespread means of cleaning teeth and maintaining gingival health, is greatly affected by technique and brushing time, both factors that are difficult to influence. Proper toothbrush care is important to our oral health. Because a worn tooth brush is less effective in cleaning, the American Dental Association recommends replacing your tooth brushes every three to four month or sooner if the bristles become frayed.
Creating Primary Care Access for Mental Health Care Clients in A Community Mental Health Setting

Successful support of persons living with a mental illness in the community is challenged by the lack of primary care accessible to this population. The Canadian Mental Health Association–Windsor Essex County Branch explored options to provide mental and physical health care, initially creating an integrated primary care clinic and later a larger community health clinic co-located with its mental health care services and staffed by a multidisciplinary team. A retrospective review of 805 charts and a client satisfaction survey were conducted in 2001 to evaluate this service. Findings indicate that access to primary care and mental health care co-located at a community-based clinic has reduced the number of emergency room visits and admissions, and length of stay in hospital, for individuals with moderate to serious mental illness. A client survey in January 2008 supports these preliminary findings.
4D BIM for the Management of Infrastructure Projects

Building information modelling has become a core topic in the architectural engineering and construction (AEC) industry, and its benefits have been realised over different phases of project construction. Adoption of nD BIM in the domain of infrastructure projects has provided challenges and is yet evolving. This chapter reviews the adoption of Building information modelling in the management of infrastructure projects. The use of nD planning (4D, 5D, 6D, 7D, and 8D planning) in infrastructure planning and management is discussed through Mapping n-D BIM with different applications in infrastructure projects. 4D BIM models are developed integrating the 3D models with the schedule and they support multiple construction management tasks. The implementation of 4D planning and management in infrastructure projects is demonstrated with the help of two case studies.
Adaptive Resource Management for Mobile Multiprocessors through Computational Self-Awareness

Author(s): Donyanavard, Bryan | Advisor(s): Dutt, Nikil D | Abstract: Runtime resource management for many-core systems is increasingly complex.The complexity can be due to diverse workload characteristics with conflicting demands or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner.To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this thesis, I explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system will reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.