Miracle-dz/UniFAR-FTUs huggingface dataset:
High Efficiency Integrated DC-DC Converter

This paper focus in the devolpment of a design method that allow to design high frequency Bulk DC-DC converters to be used as replacement for LDOs. To prove the method’s efficiency, it was designed a DC-DC converter, that uses an in-package inductor with 100nH that put the converter’s switching frequency at 35MHz to achieve 100mA of output current and an output voltage of 1V . The use of a small size non-overlap circuit combined with the inductors low series resistance and the precise sizing of both power transistors and gate drivers allow to achieve a maximum efficiency of 84%.
Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics

We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design. We leverage the posterior regularization framework and show that the constraint satisfaction problem can be formulated as sampling from a Gibbs distribution. The main challenges come from the black-box nature of the constraints obtained by solving complex and expensive PDEs. To circumvent these issues, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We devise two approaches for learning surrogate gradients of the black-box functions: ﬁrst, by using zero-order gradients approximations; and second, by approximating the Langevin gradients with deep neural networks. We prove the convergence of both approaches when the target distribution is log - concave and smooth. We also show the effectiveness of our approaches over Bayesian optimization in designing optimal nano-porous material conﬁgurations that achieve low thermal conductivity and reasonable mechanical stability.
Exploring the Efficiency of Software-Defined Radios in 3D Heat Mapping

Exploring the Efficiency of Software-Defined Radios in 3D Heat Mapping Andrew Scott Thomas School of Technology, BYU Master of Science A common method of connecting to the internet is a wireless network. These networks can be monitored to discover the area of their coverage, but commercial receivers don’t always provide the most accurate results. A software-defined radio was programmed to sniff wireless signals and tested against a commercial receiver and the results were compared. The results suggest that the software-defined radio performs at least as well as the commercial receiver in distance measurements and significantly better in samples taken per minute. It was determined that the software-defined radio is a viable replacement for a commercial receiver in 3D heat mapping.
Active Noise Control Headphones

In acoustic active noise control (ANC), an unwanted noise is acquired by a microphone, processed and an ”anti-noise”, with the opposite phase from the original noise, is emitted by a loudspeaker. The antinoise is superimposed to the original signal and destructive interference occurs. This paper presents the design, implementation and results of an adaptive ANC headphone system. A TMS320C6713 floating-point digital signal processor was used as the controller. Various algorithms were employed and performance has been evaluated and compared between them and with reported results in the literature.
International Journal of Instruction

In this study, we examined monitoring accuracy during in class-exams for Emirati, American and Cypriot college students. In experiment 1, 120 students made local, confidence-ratings for each multiple-choice question in a psychology exam and also estimated their performance at the end of the exam. In experiment 2, to investigate the effect of practice in monitoring accuracy, 69 students made confidence judgments in two consecutive exams. In Experiment 3, to evaluate whether rating one’s confidence aids exam performance, 172 students completed the exam either with or without providing confidence judgments. In all three cultures, confidence judgments were accurate indicators of performance, but there were significant cross-cultural differences that probably arose from regional educational practices. Additionally, global judgments were subjected to bias. Whereas American students overestimated their performance, Emiratis underestimated it. Practice alone with confidence judgments did little to improve monitoring accuracy. But there was a significant benefit for students who rated their confidence compared to those that did not. The results are discussed in light of the literature on metacognitive accuracy.
On the Numerical Convergence Properties of a Flow Solver Based on the Hybridizable Discontinuous Galerkin Method

In this project the Hybridizable Discontinuous Galerkin (HDG) method has its performance and robustness tested. For the performance analysis, a vortex-shedding flow around a cylinder is simulated and the convergence of the HDG method is compared to the convergence of the Finite Volume Method (FVM). The unsteady incompressible Navier–Stokes equations were used to simulate the flow. For a comparison with the FVM, a mesh generated by blocks was used, with second-order quadrilateral elements. The convergence with the element size and the residual of the nonlinear solver is analysed to conclude about the performance of the HDG method. The influence of the nonlinear solver used, Picard iteration or Newton’s method, is also studied. For the robustness analysis, the method is tested at high Reynolds numbers for the flows over a flat plate and around a NACA0012 airfoil. In this implementation no stabilization is added to the method. Firstly, the limits of the HDG method were found using the laminar flow over the flat plate and the airfoil. Once the limits for the method were found, a turbulence model (Spalart–Allmaras) was implemented to check its influence on the method. The effect of the linearization of the source terms is analysed as well as the influence of a user defined parameter intrinsic to the HDG method.
Suggestions for improving the effectiveness of environmental education in the maritime sector

Research shows that many barriers to pro-environmental behaviour in the maritime industry exist despite significant efforts of various stakeholders, and there is a need to put additional efforts to improve the sustainability of shipping. Education has an important role because a lack of environmental knowledge is almost always identified as a barrier to environmental concern and behaviour. However, eliminating knowledge gaps and raising concern does not necessarily translate to behaviour change, because behaviour depends on numerous factors, which moderate and mediate one another. Effectiveness of education activities may be improved if potential barriers are identified and targeted. This paper reviews determinants of pro-environmental behaviour and provides some suggestions for educators to plan and execute educational activities to increase the willingness of seafarers and shore-based personnel to adopt environmentally-friendly practices.
Recommendation Systems for News Articles at the BBC

Personalised user experiences have improved engagement in many industry applications. When it comes to news recommendations, and especially for a public service broadcaster like the BBC, recommendation systems need to be in line with the editorial policy and the business values of the organisation. In this paper we describe how we develop recommendation systems for news articles at the BBC. We present three models and describe how they compare with baseline approaches such as random and popularity. We also discuss the metrics we use, the unique challenges we face and the considerations needed to ensure the recommendations we generate uphold the trust and quality standards of the BBC.
Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system

The increasing availability of operational limited area ensemble prediction systems (LEPS) opens up new opportunities for the application of weather forecasts in agriculture and water resource management. This study aims to evaluate the performances of probabilistic daily reference crop evapotranspiration (ET0) forecasts with lead times up to 5 days and a spatial resolution of 7 km, computed by using COSMO-LEPS outputs (provided by the European Consortium for small–scale modelling, COSMO), in a region of southern Italy known for its complex topography in proximity to the Mediterranean coastline. ET0 was estimated by means of three different estimation methods, i.e. the Hargreaves-Samani (HS), Priestley-Taylor (PT) and FAO Penman-Monteith (PM) equations, in order to assess the size of the weather forecast errors with models of different accuracies. Forecasts were verified with ground-based data from 18 automatic weather stations, and for two irrigation seasons. Performances were assessed with both deterministic indices, including BIAS, RMSE, correlation coefficients and coefficients of variation of the 16-member ensemble forecasts, and probabilistic metrics, such as the Brier skill score, reliability diagrams and relative operating characteristic. ET0 forecasts with PM equation were robust and reliable, with slight sensitivity to the forecast lead time. High performances were also achieved with HS and PT equations, except for locations close to the coastline, where large systematic errors affect the numerical weather forecasts. © 2016 Elsevier B.V. All rights reserved.
Key Factors for Knowledge Management: Pilot Study in IT SMEs

Importance of knowledge management lies in the fact that it could result in empowerment of individuals and organizationitself to accomplish activities effectively through organizing knowledge. Knowledge management is a major issue forhuman resources management. Organization, culture and information technology play crucial enabler for various aspects of knowledge management. Aim of this paper is to present findings on knowledge management and factors that contribute to its development. In the empirical part, research on small IT companies is presented. Data was analyzed using PLS-SEM techniques. Findings show that that personal factors, culture and technical factors are substantial predictors of knowledgemanagement, but informational factors do not directly predict knowledge management.
6.4: Complex Correlation

As we have already seen, researchers conduct correlational studies rather than experiments when they are interested in noncausal relationships or when they are interested in causal relationships but the independent variable cannot be manipulated for practical or ethical reasons. In this section, we look at some approaches to complex correlational research that involve measuring several variables and assessing the relationships among them.
Geographical sharding in MongoDB Using Voronoi partitioning to geographically shard point data

The growth of dataset sizes in the Big Data era has created the need for horizontally scalable Database Management Systems (DBMS), like MongoDB, Apache Cassandra and many others, generally known as NoSQL DBMSs. These systems need to be able to be configured in ways that best fit the data that they’ll contain, partitioning the data (Sharding) in an contextaware way so that queries can be executed as efficiently as possible. Spatial data (associated with a location) availability has been steadily growing as more devices get geolocation capabilities, making it important for NoSQL DBMSs to be able to handle this type of data. While NoSQL DBMSs are capable of sharding, not all (MongoDB included), can do it based on location or do it in an efficient way. Following the work by João Pedro Martins Graça in his Master’s thesis [1], exploring the various spatial partitioning policies, this work aims to implement and evaluating the practical usefulness of the proposed policy GeoSharding, based on Voronoi indexing, on the popular open-source project MongoDB. Benchmark results show that this policy can have a large positive impact in performance when used with datasets of large amounts of small documents, such as sensor data or statistical geography information.
Invention, Intension and the Extension of the Computational Analogy

This short philosophical discussion piece explores the relation between two common assumptions: first, that at least some cognitive abilities, such as inventiveness and intuition, are specifically human and, second, that there are principled limitations to what machine-based computation can accomplish in this respect. In contrast to apparent common wisdom, this relation may be one of informal association. The argument rests on the conceptual distinction between intensional and extensional equivalence in the philosophy of computing: Maintaining a principled difference between the processes involved in human cognition, including practices of computation, and machine computation will crucially depend on the requirement of intensional equivalence. However, this requirement was neither part of Turing's expressly extensionally defined analogy between human and machine computation, nor is it pertinent to the domain of computational modelling. Accordingly, the boundaries of the domains of human cognition and machine computation might be independently defined, distinct in extension and variable in relation.
Autonomous Driving of Competition Robot Navigation and Decision Making

The deployment of self-driving vehicles is underway which enables the research for novel autonomous navigation techniques and the necessity of validation of current systems. Motion planning and decision-making modules for an autonomous vehicle are proposed in this master thesis, which, from a robotics background, evolve to valid applicability in a real car. As such, it is suggested a Robot Operating System (ROS) implementation of three trajectory generation techniques based on lane corridors, clothoids and splines, which are followed by a ROS local planner. This navigation stack is applied in a 1/8 scale RC car model with the objective of participating in the Portuguese Robotics Open 2018, organized by Sociedade Portuguesa de Robótica (SPR). The process of building the customized robot platform, from scratch, is documented along this work, also focusing on porting it to ROS which does not support car-like robots by default. From 3D modeling to simulation and reality, this master thesis solution can handle all the proposed competing scenarios, with a high-speed approach, by delivering a map based motion planning, given an absolute localization pose. Some tools for rapid robot deployment are developed such as trajectory editors and suggested such as graphical state machines so that the car can operate in distinct scenarios from the competition ones.
Recommendation Systems for IT Recruitment

Recruitment processes have increasingly become dependent on the internet. Companies post job opportunities on their websites or on online job boards and candidates search and apply online. There are inefficiencies in the process: candidates are overloaded with opportunities and companies have trouble reaching interesting candidates. The viability of applying Recommender Systems to Online IT Recruitment is the focus of this work. The IT field is a particularly good candidate for this research due to a shortage of talent and because most of IT recruitment processes already have an online component. Six different Recommender Systems were tested, using the available data: a binary rating of applications, job descriptions, and candidate profiles. The implemented models use a wide range of strategies: Collaborative Filtering, Content Based Filtering, Item-to-Item and User-to-User neighborhood models, Matrix Factorization, and a graph based approach. Based on the results of six different base models an hybrid model was built to combine their strengths and fight their limitations. The resulting system generates valuable recommendations to 60% of the tested users. While the system is not ready to be used autonomously it is useful as a supporting tool. These results pave the way for further studies in this area, showing that Recommender Systems can bring value to Online IT Recruitment.
Bridging the Educational Gap between Requirements Holders and Requirements Engineers

Requirements engineering is still hindered by misunderstandings between requirements holders and requirements engineers. The mutual understanding can be improved by providing common cognitive basis for both professional groups. Higher priority of Business Modelling module in curricula of potential requirements holders and potential requirements engineers can form a cognitive background for better communication between both partners in requirements engineering. The module should consist of several business modelling methodologies that provide understanding of relationships between business and computer systems in different contexts; illustrate a wide scope of issues in business modelling and relationships between them; as well as provide insight in cognitive frameworks exploited by requirements engineers and requirements holders.
A Humanized Anti-Ganglioside GM2 Antibody, BIW-8962, Exhibits ADCC/CDC Activity against Multiple Myeloma Cells and Potent Anti- Tumor Activity in Mouse Xenograft Models.

BIW-8962 is a humanized anti-ganglioside GM2 (GM2) monoclonal antibody, produced by Poteligent technology to enhance ADCC activity. GM2 is expressed on many cancer cells including multiple myeloma (MM), small cell lung cancer and glioma cells. In this study, we evaluated the anti-myeloma activity of BIW-8962 in preclinical myeloma models both in vitro and in vivo. Expression of GM2 was analyzed in 15 human MM cell lines by FCM. Eleven out of 15 MM cell lines had positive surface expression of GM2. GM2 as a potential target was then verified in primary MM samples obtained from patients. Eleven out of 15 samples were positive for GM2. We then used two GM2 positive MM cell lines (U266B1 and KMS-11) and evaluated ADCC and CDC activity of BIW-8962 in vitro. BIW-8962 exhibited a potent ADCC and less potent CDC activity. In vivo anti-tumor activity of BIW-8962 was then examined using the standard subcutaneous xenograft model; KMS-11 was inoculated in the flank of SCID mice. BIW-8962 (intravenously administered biweekly for 3 weeks) exhibited a potent anti-tumor activity from as low a dose level as 0.1 mg/kg. Furthermore, in a more clinically relevant model, in which OPM-2/GFP (GM2 positive MM cell line) cells were intravenously inoculated into SCID mice with preferentially tumor growth within the bone marrow microenvironment, BIW-8962 (intravenously administered biweekly for 4 weeks, 10 mg/kg) suppressed OPM-2/GFP cell growth and serum M protein elevation, demonstrating in vivo anti-myeloma effect of BIW-8962. Our preclinical investigations rationalize clinical evaluation of BIW-8962 in patients with MM. Currently BIW-8962 is being investigated in a Phase 1 study in patients with multiple myeloma.
PARADISE - Preventive Advertising fRAud Detection Intelligent SystEm

—The capacity of click fraud to impact business models based on online advertisements emerged as a topic of discussion.Part of Google’s ﬁnancial success through the years was due to the rate per click online advertisement model; however, nowadays, this model has been heavily threatened by fraudulent clicks. Invalid clicks in advertisements, I.e., clicks that do not correspond to a real interest in the advertisement, deviate funds to scammmers. In order to solve this issue some solutions were presented. Unfortunately, the conducted tests revealed some difﬁculty to target these attacks. In this paper we introduce a new approach to detect fraudulent clicks, by creating a modular system, able to test the user’s browser by accessing the website in order to identify if it’s a legitimate one or not, and also to test the user’s behavior. Additionally, the invalid clicks identiﬁcation process can be improved by extraction and processing raw data from ad network database.
Development of the ISTnanosat-1 Ground Segment

In this report we propose a solution for the ground segment of the ISTNanosat-1. The ISTNanosat-1 is a CubeSat, a type of artificial satellite, currently under development by students and faculty from IST along with amateur radio experts from AMRAD and AMSAT-CT. As with any spacecraft, it will need to be monitored, controlled and results from scientific experiments have to be retrieved. The ground segment is the necessary infrastructure on Earth that will permit these tasks. The ISTNanosat-1 Ground Segment presents a system capable of telemetry retrieval and issuing instructions to the satellite. All of the relevant information is then shown to the users using a web application. Furthermore, properly authorized users have the capability to issue the aforementioned instructions. The developed ground segment is composed by multiple ground stations controlled by Core server. In this centralized design, the Core communicates with the satellite trough the ground stations, which serve as gateways, using the designed and implemented ISTNanosat Command Protocol. In the Core, several services permit for the for data and error retrieval, command and diagnostics execution and security of the ground-space segment link.
Infrastructure and Recommendation System for Banking Products

—Banks have a wide range of products to offer, and with that comes an overwhelmingness of choice for the client. Our challenge is to mitigate that and help both clients - to ﬁnd the best offers - and banks - to increase product sales. We propose a Recommender System using an XGBoost model and the Multi-armed Bandit. The infrastructure of the Recommender System will be supported by Apache Kafka, which will be responsible to receive the bank’s database events and send them pre-processed to the a database so that the recommendation system can use them in almost real-time. The evaluation of the Recommender System showed that it learned to give meaningful recommendations. The infrastructure built helped improve the development of applications that want to consume the bank’s database.