Miracle-dz/UniFAR-FTUs huggingface dataset:
A Techno-Economic MILP Optimization of Multiple Offshore Wind Concessions

The connection of offshore wind requires expensive infrastructure. The development of such infrastructure can be calculated through the use of a techno-economic optimization routine. This paper investigates how two completely different geographic locations, Belgian North Sea and the Indian Cambay wind zones compare. The optimization is formulated as a sequential Mixed Integer linear Program (MILP) considering both AC and DC technology as well as a number of different voltage levels for both technologies. For AC connections, the possibility of mid-point compensation is included. The optimization software utilized is developed in the Julia language at CG Holdings Belgium. In the results section, two optimal topologies are described, one for each analyzed offshore wind power zone. The topologies include the optimal voltage levels, locations of Offshore Substations (OSSs) and transformer and cabling ratings for the transmission grid.
A Appendix : Summary of DA Recognition Projects

This report addresses dialogue acts (DAs), their existing applications and techniques of automatically recognizing them. Three main applications are described: in dialogue systems to determine the intention of the speaker, in dialogue systems with machine translation to resolve ambiguities in the possible translation variants and in speech recognition to reduce word recognition error rate. Several recognition techniques are described, the most frequent being: n-grams, hidden Markov models, multilayer perceptrons, naive Bayes classifiers, Bayesian networks, decision trees, memory-based learning and transformation-based learning. Each technique is described on the surface level: how it works and how it is trained. A summary of the corresponding representation methods is provided for each technique. The paper also includes examples of applying the techniques to DA recognition. The author comes to the conclusion that the technique choice is not the most important one in DA recognition. The most influencing factor is the DA tag set (its size, tag definition separability, etc). Yet the current evaluation metric doesn’t take the tag set into account. Therefore it is impossible to compare the techniques when these are applied to corpora annotated with different DA tag sets, which is most frequently the case. Dialogue acts remain an open research area, with space and need for developing new recognition techniques and methods of evaluation.
A Quantitative Description of Atmospheric Absorption and Radiation at Equilibrium Surface Temperature

An analytical theory is proposed for the earth-atmosphere system at its equilibrium surface temperature, 289.16 K. A non-linear relation is formulated between atmospheric absorption and atmospheric radiation by modifying Kirchhoff’s law on thermal radiation. For the first time, the Global Energy Balance can be realized in a wide range of atmospheric absorptivity, transmittance, and surface emissivity. It is revealed that atmospheric radiation becomes negative once the atmospheric absorptivity is below its threshold value. It is proven that the upward cumulative long-wave atmospheric radiation spontaneously increases from 3.8 W m−2 to 199.4 W m−2 as the long-wave atmospheric absorptivity increases from 0.4 to 1.0 whilst the long-wave atmospheric transmittance decreases from 0.6 to 0.1.
Targeting Copolymer Composition Distribution via Model-Based Monomer Feeding Policy in Semibatch RAFT Mini-Emulsion Copolymerization of Styrene and Butyl Acrylate

Copolymer composition distribution (CCD) is an important parameter of chain microstructure that has significant impact on the material properties of polymer products. Controlled/living radical polymerization (CLRP) has provided a great opportunity in producing polymers with predesigned CCDs through a model-based monomer feeding policy (MMFP) in homogeneous systems. In this work, the MMFP has been expanded to heterogeneous systems. Reversible addition–fragmentation transfer (RAFT) mini-emulsion polymerizations of styrene (St) and butyl acrylate (BA) were carried out with 3-benzyltrithiocarbonyl propionic acid (BCPA) used as a RAFT agent. A kinetic model was developed and correlated to the batch St/BA RAFT mini-emulsion experimental data for parameter estimation. The MMFP was then developed by combining the kinetic model with a semibatch reactor model and applied to the semibatch St/BA RAFT mini-emulsion copolymerization for the synthesis of targeted CCDs. The model agreed well with the polymerization kinet.
THE NTV-SV-PWM METHOD USING AN IMPROVED LOOK-UP TABLE FOR DC-LINK MID-POINT BALANCING APPLIED FOR OCEAN WAVE ENERGY CONVERSION SYSTEM

The research activities for maximum energy absorption from wave energy converters (WECs) are very often limited to hydrodynamic control and performance of the power take-off (PTO) which mostly simplifies the power electronics conversion systems. There are only few studies that show deep analysis of connecting the generated voltage to the grid by using the most developed power electronics technologies. The main objective of this study is (i) to develop a full wave-to-grid model to show all conversion stages from ocean waves to the electricity network, (ii) to combine the WEC with neutral point clamped (NPC) inverter which uses advanced nearest three vector–space vector–pulse width modulation (NTV-SV-PWM) method based on a look-up table that can build up a sinusoidal voltage waveform at the inverter output with the least number of switching in the power circuit and the best possible harmonic spectrum without the need for any big filtering.
High-Tech Functional and Professional Skills, Generic Job Satisfaction, and Training Needs of the Pakistani University Librarians: An Empirical Appraisal

Since modern librarians are urged to acquire technological skills, thus objectives of this study are: 1) to examinewhether or not the acquisition of such skills affects theirgeneric job satisfaction (GJS) and; 2)toidentifythe training needs.To this end, twelve research hypotheses were formulated indicating the relationship of high-tech functional and professional skillswith GJS. The findings revealedsignificant effects of high-tech functional and professional skills on GJS and accordingly, all hypotheses (H1-H12) are supported. In addition, Information Assurance and Security Skills were established as the principal predictor of GJS whereas Library Content Management System skills as theweakest. Additionally, training needs analysis establisheda 76.3percent deficit, 19 percentsurplus, and 21.2 percentjob-best fit. This study has diverse implications: 1) practical implications-national high-tech functional and professional competencies model is neededto enable librariansto acquire neededskills; 2) social implicationsresults emphasize librariansto enhance the current level of professional skills to perform contemporarily that perhaps augment their social image and; 3) theoretical implications-the integrated research model of this study can be utilizedbyfuture researcherseither for crossvalidation of findings of this study or to improve the topic scope.
Solidarity Business Model for Micro-Businesses that Allows Economic Reactivation Due to COVID-19 in Colombia

One of the main most notable problems that the situation that the world is going through with COVID-19 has generated is the increase in the shortage of personal protection elements for the face, such as masks, which are the means of protection primary for the general population. This is due to the fact that each country did not have the current situation the world is going through, but micro-enterprises in the textile manufacturing sector can reinvent themselves to support the crisis so that they can manufacture and market said personal protection elements, thinking about a common good, contributing to a solidarity economy founded to achieve a common benefit. The methodology used was mixed, where it has both qualitative and quantitative components, with a type of descriptive research in order to contribute with a supportive business model for microapparel companies that allows economic reactivation from the manufacture and commercialization of protection elements personal to face, in order to contribute to the health emergency present by COVID 19.
How Does the Adoption of Ad Blockers Affect News Consumption?

Ad blockers allow users to browse websites without viewing ads. Online news publishers that rely on advertising income tend to perceive users’ adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads—which users presumably dislike—may affect users’ online news consumption behavior in positive ways. Using 3.1 million visits from 79,856 registered users on a news website, this research finds that ad blocker adoption has robust positive effects on the quantity and variety of articles users consume. Specifically, ad blocker adoption increases the number of articles that users read by 21.0%–43.2%, and it increases the number of content categories that users consume by 13.4%–29.1%. These effects are stronger for less-experienced users of the website. The increase in news consumption stems from increases in repeat visits to the news website, rather than in the number of page impressions per visit. These postadoption visits tend to start from direct navigation to the news website, rather than from referral sources. The authors discuss how news publishers could benefit from these findings, including exploring revenue models that consider users’ desire to avoid ads.
Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and communicates with its neighbors to seek for the global optimum. In this article, we develop a modified variance reduction technique to deal with the variance introduced by stochastic gradients. Combining gradient tracking and variance reduction techniques, this article proposes a distributed stochastic algorithm, gradient tracking algorithm with variance reduction (GT-VR), to solve large-scale non-convex finite-sum optimization over multiagent networks. A complete and rigorous proof shows that the GT-VR algorithm converges to the first-order stationary points with $O({1}/{k})$ convergence rate. In addition, we provide the complexity analysis of the proposed algorithm. Compared with some existing first-order methods, the proposed algorithm has a lower $\mathcal {O}(PM\epsilon ^{-1})$ gradient complexity under some mild condition. By comparing state-of-the-art algorithms and GT-VR in numerical simulations, we verify the efficiency of the proposed algorithm.
Microwave study of hydration of cement with different waters

The dielectric constant and electrical conductivity of laboratory prepared cement paste, using X-band microwave technique in the frequency range 8.5-11.5 GHz have been studied. The cement was mixed with different waters, in a water to cement ratio (w/c) of 0.3, 0.4 and 0.5. With the help of dielectric constant and electrical conductivity, the hydration reactions of the cement pastes have been studied.
Geodesic Distance Computation via Virtual Source Propagation

We present a highly practical, efficient, and versatile approach for computing approximate geodesic distances. The method is designed to operate on triangle meshes and a set of point sources on the surface. We also show extensions for all kinds of geometric input including inconsistent triangle soups and point clouds, as well as other source types, such as lines. The algorithm is based on the propagation of virtual sources and hence easy to implement. We extensively evaluate our method on about 10000 meshes taken from the Thingi10k and the Tet Meshing in the Wild data sets. Our approach clearly outperforms previous approximate methods in terms of runtime efficiency and accuracy. Through careful implementation and cache optimization, we achieve runtimes comparable to other elementary mesh operations (e.g. smoothing, curvature estimation) such that geodesic distances become a “first‐class citizen” in the toolbox of geometric operations. Our method can be parallelized and we observe up to 6× speed‐up on the CPU and 20× on the GPU. We present a number of mesh processing tasks easily implemented on the basis of fast geodesic distances. The source code of our method is provided as a C++ library under the MIT license.
Source Seeking by Dynamic Source Location Estimation

This paper focuses on the problem of multi-robot source-seeking, where a group of mobile sensors localizes and moves close to a single source using only local measurements. Drawing inspiration from the optimal sensor placement research, we develop an algorithm that estimates the source location while approaches the source following gradient descent steps on a loss function defined on the Fisher information. We show that exploiting Fisher information gives a higher chance of obtaining an accurate source location estimate and naturally leads the sensors to the source. Our numerical experiments demonstrate the advantages of our algorithm, including faster convergence to the source than other algorithms, flexibility in the choice of the loss function, and robustness to measurement modeling errors. Moreover, the performance improves as the number of sensors increases, showing the advantage of using multi-robots in our source-seeking algorithm. We also implement physical experiments to test the algorithm on small ground vehicles with light sensors, demonstrating success in seeking a moving light source.
A SOLVENT DRIES UP

A SHORTAGE of acetonitrile is leaving chemists around the U.S. and beyond wondering how long their supplies will last and what their options will be if stocks run dry. There are good reasons why the situation is making chemists feel vulnerable. Thousands of them use the polar solvent in high-performance liquid chromatography. It is also used in pharmaceutical synthesis and in the extraction of butadiene from streams of C 4 hydrocarbons. Laboratory chemical suppliers have been allocating acetonitrile to existing customers or not selling it at all. “The market is beyond short,” says Jerry Richard of Purification Technologies, a Chester, Conn. -based firm that buys acetonitrile in bulk, purifies it, and sells it to laboratory chemical suppliers. “You have people scrambling around trying to get material. My phone is ringing off the hook. ” Richard says the heart of the problem is that acetonitrile goes into applications that are healthy and growing. But its production is tied to .
Leveraging Memory Enhanced Conditional Random Fields with Gated CNN and Automatic BAPS Features for Chinese Named Entity Recognition

Named Entity Recognition (NER) is an essential task in Natural Language Processing. Memory Enhanced CRF (MECRF) integrates external memory to extend Conditional Random Field (CRF) to capture long-range dependencies with attention mechanism. However, the performance of pure MECRF for Chinese NER is not good. In this paper, we enhance MECRF with Stacked CNNs and gated mechanism to capture better word and sentence representation for Chinese NER. Meanwhile, we combine both character and word information to improve the performance. We further improve the performance by importing common before and common after vocabularies of named entities as well as entity prefix and suffix via feature mining. The BAPS features are then combined with character embedding features to automatically adjust the weight. The model proposed in this research achieve 91.67% tagging accuracy on the online social media data for Chinese person name recognition, and reach the highest F1-score 92.45% for location name recognition and 90.95% overall recall rate in SIGHAN-MSRA dataset.
Discovering the Latent Writing Style from Articles: A Contextualized Feature Extraction Approach

With the growth of the Internet, the ready accessibility and generation of online information has created the issue of determining how accurate or truthful that information is. The rapid speed of information generation makes the manual filter approach impossible; hence, there is a desire for mechanisms to automatically recognize and filter unreliable data. This research aimed to create a method for distinguishing vendor-sponsored reviews from customer product reviews using real-world online forum datasets. However, the lack of labelled sponsored reviews makes end-to-end training difficult; many existing approaches rely on lexicon-based features that may be easily manipulated by replacing word usages. To avoid this word manipulation, we derived a graph-based method for extracting latent writing style patterns. Thus, this work proposes a Contextualized Affect Representation for Implicit Style Recognition framework, namely CARISR. Transfer learning architecture was also adapted to improve the model’s learning process with weakly labeled data. The proposed approach demonstrated the ability to recognize sponsored reviews through comprehensive experiments using the limited available data with 70% accuracy.
Supporting Evidence Retrieval for Answering Yes/No Questions

This paper proposes a new n-gram matching approach for retrieving the supporting evidence, which is a question related text passage in the given document, for answering Yes/No questions. It locates the desired passage according to the question text with an efficient and simple n-gram matching algorithm. In comparison with those previous approaches, this model is more efficient and easy to implement. The proposed approach was tested on a task of answering Yes/No questions of Taiwan elementary school Social Studies lessons. Experimental results showed that the performance of our proposed approach is 5% higher than the well-known Apache Lucene search engine.
Improvements to Deep-Learning-based Feasibility Prediction of Switched Ethernet Network Configurations

Graph neural network (GNN) is an advanced machine learning model, which has been recently applied to encode Ethernet configurations as graphs and predict their feasibility in terms of meeting deadlines constraints. Ensembles of GNN models have proven to be robust to changes in the topology and traffic patterns with respect to the training set. However, the moderate prediction accuracy of the model, 79.3% at the lowest, hinders the application of GNN to real-world problems. This study proposes improvements to the base GNN model in the construction of the training set and the structure of the model itself. We first introduce new training sets that are more diverse in terms of topologies and traffic patterns and focus on configurations that are difficult to predict. We then enhance the GNN model with more powerful activation functions, multiple channels and implement a technique called global pooling. The prediction accuracy of ensemble of GNNs with a combination of the suggested improvements increases significantly, up to 11.9% on the same 13 testing sets. Importantly, these improvements increase only marginally the time it takes to predict unseen configurations, i.e., the speedup factor is still from 50 to 1125 compared to schedulability analysis, which allows a far more extensive exploration of the design space.
A Mobile-assisted Edge Computing Framework for Emerging IoT Applications

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.
Response Time Analysis of Parallel Real-Time DAG Tasks Scheduled by Thread Pools

Modern high-end embedded systems nowadays have to process enormous amounts of data. In order to speed up the computations and fully exploit the resources of the underlying hardware architectures, software developers can avail parallelism frameworks such as Intel Threading Building Blocks or compiler extensions as OpenMP. They ease the development of parallel applications by providing interfaces for common parallel design patterns and by internally distributing the work among the workers of a thread pool. However, such frameworks and compiler extensions do not yet support the stringent timing requirements of real-time systems and therefore, an adaption of their computation model to the sector of real-time systems needs to be conducted. In this paper, we address the problem of scheduling parallel real-time directed acyclic graphs tasks on multiprocessor architectures where the subtasks are dispatched among and executed by the workers of a thread pool. In contrast to existing work in the state-of-the-art, we limit the maximum parallelism of real-time tasks not by the number of processors in the system, but by the number of worker threads used in the thread pool of each real-time application. For this model, we derive a worst-case response time analysis for task sets scheduled by a preemptive global fixed-priority scheduler. In order to evaluate the performance of our response time analysis, we further perform schedulability tests on generated task sets and compare the results to existing feasibility analyses in the current state-of-the-art.
Computer Science Unplugged: A Systematic Literature Review

The computer science (CS) unplugged approach intends to teach CS concepts and computational thinking skills without employing any digital tools. The current study conducted a systematic literature review to analyze research studies that conducted investigations related to implementations of CS unplugged activities. A systematic review procedure was developed and applied to detect and subsequently review relevant research studies published from 2010 to 2019. It was found that 55 research studies (17 articles + 38 conference proceedings) satisfied the inclusion criteria for the analysis. These research studies were then examined with regard to their demographic characteristics, research methodologies, research results, and main findings. It was found that the unplugged approach was realized and utilized differently among researchers. The majority of the studies used the CS unplugged term when referring to “paper–pencil activities,” “problem solving,” “storytelling,” “games,” “tangible programming,” and even “robotics. ”