Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Science Research: A Suitable Approach to Scope and Research IT Service Catalogs

Based on the best-practice framework ITIL, an IT service catalog is a system which displays all live IT services but does not manage service requests. Moreover, an IT service catalog also shows how IT services are technically realized. When implementing an IT service catalog as part of the IT service management, firms often struggle with service catalog implementation projects as it requires portfolio-wide information and knowledge about service design, service operations, the customers' use of services, service level agreements and service costing. There is a lack of existing literature on how to design an IT service catalog to fit the individual context and service offerings of companies. This concise paper proposes an approach based on design science for defining the initial design requirements for IT service catalogs. Using this approach in practice resulted in the identification and scoping of the design objectives of IT service catalogs to the needs of individual firms. Moreover, the explorative knowledge on how to design an IT service catalog has been validated by following rigorous research guidelines set out by the design science research method.
A Governance Metamodel for Industry 4.0 Service Collaborations

In Industry 4.0 scenarios, the presence of formalized and enforced governance rules helps to clarify how core and external contributors should collaborate to co-design and deliver key subsystems of the finished product. This clarity diminishes the time for establishing collaborations and facilitates tasks execution. However, current governance rules are usually more implicit than explicit and often are subject to timeconsuming negotiation before a new collaboration starts. The concept of Industry 4.0 demands higher levels of dynamism; therefore, we present the importance of governance rules in such digital marketplaces. In this short paper, we lay out the foundation for enabling the explicit definition and enforcement of governance rules by proposing a specialized Abstract Syntax Metamodel for specifying governance rules. With such a model, we aim to aid demand-driven production and logistics service collaborations between suppliers towards enabling a quicker and more predictable creation and execution of the tasks involved.
Sentence Compression for Arbitrary Languages via Multilingual Pivoting

In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.
Roles in Local Communities and Global Position in Social Media

An organization of any society often takes on a complex structure, where many different communities may be identified which include or overlap one another. A significant question is how local activities affect its global position and also facilitate gaining position in other local communities. We discuss a problem of how a local group supports the user in achieving a significant global position. In this paper we present a model of the system containing descriptions of users and roles as well as results of analyses using data gathered from the blog portal Salon24. We describe relationships between global roles played by users in the whole social portal and local roles in given identified communities. Additionally, other useful characteristics of users of such analyzed portals are presented, such as: numbers of groups to which users with given roles belongs or the total numbers of global and local roles.
Laser Seam Welding optimization using Inductive Transfer Learning with Artificial Neural Networks

Transfer Learning aims at transferring knowledge from an already learned task to a different, but related task, in order to accelerate the learning process of the latter. This concept can be applied to manufacturing systems where process models that map process parameters into process quality are used to optimize the calibration phase of new unseen products at the shop-floor. However, these process models often require a great amount of experiments, which normally is costly and impractical for most manufacturing systems. The present work explores a Laser Seam Welding scenario with 3 different product variants where the problem is training one of the process models with a reduced amount of labeled data. Artificial Neural Networks (ANNs) were used to model these processes and Inductive Transfer Learning is then used to tackle the proposed problem. Ultimately, this approach was compared to traditional machine learning where no transfer occurs and a model is trained only using the small amount of labeled data. The results revealed that for all the Laser Seam Welding processes the trained models performed better when using Inductive Transfer.
Transmission Power Analysis and Control of the DC Transformer in Hybrid AC/DC Microgrid

In a hybrid AC/DC microgrid, the DC transformer (DCT) is more competitive than the conventional line frequency AC transformer (ACT) to connect the AC and DC bus operating with bidirectional interlinking converter (BIC) due to the high frequency, high power density and adjustable characteristics of the DCT. However, the hybrid AC/DC microgrid application often poses a series of specific challenges to the DCT design, such as the system energy management requirement, efficiency, volume, galvanic isolation and power balance challenges, etc. In this paper, a symmetrical CLLC resonant dual active bridge (DAB) converter has been employed to realize the DCT topology. Meanwhile, a systematic design method of the DCT including transmission power analysis, power control and operation mode analysis have also been presented with the consideration of the hybrid AC/DC microgrid application. Finally, all of the theoretical analysis has been experimentally verified in a 6-kVA hybrid AC/DC microgrid.
Multi-Objective Optimization Platform of AC-DC Converters Components

The paper presents application of two selected algorithms for an online platform for multicriteria optimization of AC/DC converters. The purpose of the platform is to speed up selection of components to satisfy the four main objectives in power electronic circuits: total power loses, weight, volume and total price. The system generates a pareto front for the objectives considering several constraints. It uses a discrete optimization scheme searching the database of available components. The database of components contains magnetic elements, semiconductors and capacitors used in a few typical configurations of ACDC converters. The paper focuses on presenting a concept of the system which allows to put together simulators of different components calculating objectives with modern multicriteria optimization algorithms (NSGAIII, SPEA2). The system proved to be very effective even for large databases of components composed of tens of thousands of items.
ZIP-Code Classification Using Spatial and Crowdsourced Data

Zoning Improvement Plan (ZIP) Code polygon maps, obtained from different data sources, do not match, thus creating uncertainty in spatial analysis. In this dissertation, we want to combine multiple source of ZIP-code polygon data into a coherent system that maps a given location to a ZIP-code. For this purpose, we want to harness the wisdom of the crowd by combining various polygon map data sets with various sources of volunteered geographical information. The system that we want to build will employ traditional classification methods to map a given spatial coordinate to a distribution of ZIP-codes using the (not publicly available) United States Postal Service (USPS) map as an authoritative ground truth. In our first studies, we train a Naïve Bayes classifier using multiple (publicly available) ZIP Code polygon maps. In addition, we enrich our classification by using a lazy K-Nearest Neighbor classifier to predict the ZIP Codes for a given location using Twitter. Feeding the result of this classifier to the Naïve Bayes classification our experimental evaluation shows an improvement in classification accuracy, compared to using only map data.
A New Accurate and Fast Homography Computation Algorithm for Sports and Traffic Video Analysis

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes.
A New Standby Structure Integrated with Boost PFC Converter for Server Power Supply

In the standby stage of the server power supply, the flyback converter has been widely used due to its simple structure and low cost. However, it suffers from high voltage stress on the primary switch and large transformer size. Thus, the flyback converter in the standby stage degrades the efficiency and power density of the server power supply. To relieve these drawbacks, this paper presents a new standby structure where the standby stage is integrated with the boost PFC stage. In the proposed standby structure, since the primary side of the flyback converter is integrated into the boost PFC stage, the proposed standby structure can eliminate high voltage stress and large transformer. Furthermore, the proposed standby structure helps the boost PFC stage to achieve soft switching operation. Therefore, the proposed standby structure can improve the efficiency and power density of the server power supply. The validity of the proposed standby structure is confirmed by a prototype with 90-264Vrms AC line, 750W main output, and 12V/2A standby output.
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification

This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.
Automated patch extraction via syntax- and semantics-aware Delta debugging on source code changes

Delta debugging (DD) is an approach to automating the debugging activities based on systematic testing. DD algorithms find the cause of a regression of a program by minimizing the changes applied between a working version and a faulty version of the program. However, it is still an open problem to minimize a huge set of changes while avoiding any invalid subsets that do not result in testable programs, especially in case that no software configuration management system is available. In this paper, we propose a rule-based approach to syntactic and semantic decomposition of changes into independent components to facilitate DD on source code changes, and hence to extract patches automatically. For analyzing changes, we make use of tree differencing on abstract syntax trees instead of common differencing on plain texts. We have developed an experimental implementation for Java programs and applied it to 194 bug fixes from Defects4J and 8 real-life regression bugs from 6 open source Java projects. Compared to a DD tool based on plain text differencing, it extracted patches whose size is reduced by 50% at the cost of 5% more test executions for the former dataset and by 73% at the cost of 40% more test executions for the latter, both on average.
Divergent lncRNA GATA3-AS1 Regulates GATA3 Transcription in T-Helper 2 Cells

Long non-coding RNAs (lncRNAs) possess a diverse array of regulatory functions including activation and silencing of gene transcription, regulation of splicing, and coordinating epigenetic modifications. GATA3-AS1 is a divergent lncRNA gene neighboring GATA3. GATA3 is considered the master regulator of TH2 lineage commitment enabling TH2 effector cells to efficiently transcribe genes encoding cytokines IL-4, IL-5, and IL-13. Here, we show that the GATA3-AS1 lncRNA is selectively expressed under TH2 polarizing conditions and is necessary for efficient transcription of GATA3, IL5, and IL13 genes, while being sufficient for GATA3 transcription. GATA3-AS1 is required for formation of permissive chromatin marks, H3K27 acetylation and H3K4 di/tri-methylation, at the GATA3-AS1-GATA3 locus. Further, GATA3-AS1 binds components of the MLL methyltransferase and forms a DNA-RNA hybrid (R-loop) thus tethering the MLL methyltransferase to the gene locus. Our results indicate a novel regulatory function for a divergent lncRNA and provide new insight into the function of lncRNAs in T helper cell differentiation.
It’s going to be okay: Measuring Access to Support in Online Communities

People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one’s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement.
Compact Personalized Models for Neural Machine Translation

We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture–combining a state-of-the-art self-attentive model with compact domain adaptation–provides high quality personalized machine translation that is both space and time efficient.
Dual Half-Bridge LLC Resonant Converter with Hybrid-Secondary-Rectifier (HSR) for Wide-Ouput-Voltage Applications

In this paper, a dual half-bridge (HB) LLC resonant converter with a new hybrid secondary rectifier (HSR) is proposed for wide-output-voltage applications. In the proposed converter, the HSR can operate like a voltage doubler rectifier or voltage quadrupler rectifier according to the switching strategy of the dual HB LLC resonant converter. For low output voltage range, the dual HB LLC resonant converter are controlled by 180 degree phase-shifted gate signals. Thus, it can achieve low voltage gain with a voltage doubler rectifier. Whereas, in high output voltage range, in-phase gate signals are applied to the dual HB LLC resonant converter. Thus, the proposed converter can obtain high voltage gain with a voltage quadrupler rectifier. As a result, the proposed converter is able to be designed with narrow switching frequency variation, which results in high efficiency over the entire output voltage range. The validity of the proposed converter is confirmed by a 750W prototype with 400V input, 100-300V output.
Large-scale Exploration of Neural Relation Classification Architectures

Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our ‘Man for All Seasons’ approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.
Scalable Construction of Approximate Multipliers With Formally Guaranteed Worst Case Error

Approximate computing exploits the fact that many applications are inherently error resilient. In order to reduce power consumption, approximate circuits such as multipliers have been employed in these applications. However, most current approximate multipliers are based on $ad~hoc$ circuit structures and, for automated circuit approximation methods, large efficient designs are difficult to find due to the increased search space. Moreover, existing design methods do not typically provide sufficient formal guarantees in terms of error if large approximate multipliers are constructed. To address these challenges, this brief introduces a general and efficient method for constructing large high-quality approximate multipliers with respect to the objectives formulated in terms of the power-delay product and a provable error bound. This is demonstrated by means of a comparative evaluation of approximate 16-bit multipliers constructed by the proposed method and other methods in the literature.
Monolithic Doped-Semiconductor Platform for Optical Devices in the Infrared

The ability to control light in the infrared is central to improving sensing, spectroscopy, communication, and directed-energy technologies. In this presentation, we demonstrate a platform for flat optical devices based on selectively doped semiconductors for monolithic diffractive, plasmonic, and gradient-index devices in the infrared.
Process-Aware Model based IDSs for Industrial Control Systems Cybersecurity: Approaches, Limits and Further Research

Since the beginning of the century, Industrial Control Systems (ICS) are sujbected to attacks targetting their actuator controls. Although active research has been done, most of the approaches fail to secure them from strikers with high automation knowledge due to their environment and requirements. This paper provides a ICS comprehensive view, outlines the existing works and presents the authors' cybersecurity vision as well as the approach developed by G-SCOP Center of Research. From its deficiencies, further investigation is pointed out by giving the orientation for designing behavioral model based Intrustion Detection System (IDS) based on the equipment degradation and addressing one of the limitation of the current approach on a single action chain.
Practices and tools for better software testing

Automated testing (hereafter referred to as just `testing') has become an essential process for improving the quality of software systems. In fact, testing can help to point out defects and to ensure that production code is robust under many usage conditions. However, writing and maintaining high-quality test code is challenging and frequently considered of secondary importance. Managers, as well as developers, do not treat test code as equally important as production code, and this behaviour could lead to poor test code quality, and in the future to defect-prone production code. The goal of my research is to bring awareness to developers on the effect of poor testing, as well as helping them in writing better test code. To this aim, I am working on 2 different perspectives: (1) studying best practices on software testing, identifying problems and challenges of current approaches, and (2) building new tools that better support the writing of test code, that tackle the issues we discovered with previous studies. Pre-print: https://doi.org/10.5281/zenodo.1411241
Control IC for Boost-Flyback Converter for Energy Harvesting Applications

Conventionally, boost converter is used for high step-up energy harvesting system, but it is needed to be operated in discontinuous conduction mode (DCM) because of the limitation of the topology. Therefore, it causes more conduction loss and current stress on the switch. In this thesis, the control IC for boost-flyback converter in energy harvesting applications is proposed. It can greatly increase the voltage conversion ratio with the cascode of boost converter and flyback converter. In addition, this topology can recycle the energy of the leakage inductance produced from coupled inductor to load. Thus, the efficiency can be increased and the stress on the main switch can be reduced. Finally, The boost-flyback converter with input voltage of 0.2-0.3 V, output voltage of 5 V and output power of 20 mW is implemented to verify the feasibility of the proposed controller.
Distributed Power Sharing Strategy for Islanded Microgrids without Frequency and Voltage Deviations

This paper introduces a distributed power sharing strategy for the droop-controlled microgrids in which the power sharing errors and frequency/voltage deviations are completely eliminated based on the distributed cooperative control. The proposed strategy requires only a spare communication network in which the DG unit uses local and its neighbors’ information to execute the control algorithm. In the proposed strategy, a virtual-impedance-regulator based on the consensus algorithm is developed to eliminate the power sharing errors in the islanded microgrid in spite of the load condition changes, and a modified droop controller based on the neighbors’ power information is also introduced to cope with the frequency and voltage deviation issue. The proposed method does not use any information such as the feeder impedances, or the detailed microgrid configuration. The feasibility and effectiveness of the proposed method are also verified through digital simulation on an islanded microgrid system.