Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Sub-Gigahertz Reconfigurable RF Energy Harvester From −22 to 4 dBm With 99.8% Peak MPPT Power Efficiency

To overcome the low-efficiency and limited working range of the existing RF energy harvesting (EH) systems for the wireless Internet-of-Things (IoT) sensors, a novel reconfigurable system is proposed with integrated hill-climbing, maximum power point tracking (MPPT) function for wide input power from −22 to 4 dBm. A conceptual linear model with high accuracy is also proposed to analyze the rectifier efficiency for MPPT operations. The rectifier with off-chip matching is designed with a patch antenna at 915-MHz the industrial, scientific and medical (ISM) band. To further improve the end-to-end efficiency, the harvested power is used to power up the circuit block in system on a chip (SoC) directly, avoiding additional conversion loss. Our proposed reconfigurable 12-stage rectifier with matching network achieves −18.1-dBm sensitivity for 1-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> loading and 36% peak efficiency at 1 dBm. The proposed MPPT function can detect and determine the optimal rectifier stage for loading from 10 <inline-formula> <tex-math notation="LaTeX">$\text{K}\Omega $ </tex-math></inline-formula> to 1 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula>. The measured MPPT accuracy is over 87% from −22 to 4 dBm compared to external tuning conditions. The minimum stand-by power is 20 nW at 0.5 V and the overall MPPT power efficiency is over 72% with a peak value of 99.8% including dissipated power. Measurements also show the system can achieve self-startup and self-sustained functions with a 10-<inline-formula> <tex-math notation="LaTeX">$\mu \text{F}$ </tex-math></inline-formula> external capacitor buffer.
Variational Auto-Encoders for Satellite Images of Fields

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Maximilian Proll Title Variational Auto-Encoders for Satellite Images of Fields School School of Science Master’s programme Computer, Communication and Information Sciences Major Machine Learning, Data Science and Artificial Intelligence Code SCI3044 Supervisor Pekka Marttinen, Assistant Professor Advisor Santosh Hiremath, Postdoctoral Researcher Level Master’s thesis Date 20 May 2019 Pages 56 Language English
Communication Analysis of Public Forms - Discovering Multi-Functional Purposes in Citizen and Government Communication

This paper adopts a communication perspective on forms in public e-services. This perspective emphasizes that the main purpose of such forms is to facilitate communication between citizens and government agencies. The form is perceived as a tool for performing communicative actions. A communication analysis (CA) method, originally developed for systems requirements engineering, is applied on a public form; i.e. the medical certificate used in connection with a citizen’s application for a provisional driving license in Sweden. The CA method consists of a set of questions related to three communicative categories; conditions, actions, and consequences. The CA method is used in order to explore the communicative roles of forms in public e-services. As a result of the communication analysis four multi-functional purposes of citizen and government agency communication are discovered. These purposes contribute to the understanding and evaluation of forms in public e-services. The communicative roles, and their multi-functional purposes, are important design features to focus in the development of e-services and electronic forms. Besides these findings, another outcome is that the CA method has been tested in an e-government context. The communication perspective, as well as the CA method, contribute with useful insights in this context.
Repetitive Motion Planning of Free-Floating Space Manipulators

Abstract In this paper, a repetitive motion-planning scheme of free-floating space manipulators is presented. Repetitive motion means when one task ends, the end-effector pose, the joint angles and the base pose should reset (return to their initial value), which will facilitate the subsequent tasks. First, due to the lack of DOF, an order of priority to the given tasks is introduced. Second, the joint reset optimization operator, the base attitude reset optimization operator and the end-effector attitude reset optimization operator are designed. Then, the repetitive motion scheme is proposed by combining the three optimization operators above in a creative way. Finally, to make the optimization of repetitive motion obvious, the base attitude maintenance is also considered. Simulation results verify the correctness and the validity of the repetitive motion planning method of space manipulator.
Evaluating the ISO/IEC 27001 with Experts' Knowledge for Taiwanese Medical Center

Information security is a management problem, not a technology one. Experience indicates that technology cannot provide all the answers to the problems posed by people in the context of information security management (ISM). Since the improvements of information security managements are crucial for all healthcare organizations. The information they possess is very personal, while the trust between the patient and the hospital is one basic factor for quality care. Therefore hospital information security and privacy are major issues that cannot be ignored. For the information security management, ISO/IEC 27001 is the most important standards and it plays an important role while the organizations are considering strengthening their security management. This research uses case study methods to observe and understand the information security management system of our research subject. We used the nominal group technique (NGT) that was developed on the basis of “ISO/IEC 27001”to develop models that could verify their information security management systems. The primary data was collected in the NGT and uses a structured group process to elicit and prioritize answers to a carefully articulated question. The chosen expert panel consists of the following interest groups. Based on the result of this study, we found that NGT represents a workable research tool in hospital information security management to capture multifaceted and enriched view about risk factors selecting. Finally, it not only can realize the more accurate potential risk incident by utilize the ISO/IEC 27001, but also achieved the objective for self-assessed management of hospital information security.
MCDASH: Refinement Based Property Verification for Machine Code

This paper presents MCDASH, a refinement-based model checker for machine code. While model checkers such as SLAM, BLAST, and DASH have each made significant contributions in the field of verification/flaw-detection, their use has been restrict ed to programs for which source code is available. This paper discusses several challenges that arise when working with machine code, and explains how they are addressed in MCDASH. Unlike previous model checkers, MCDASH does not require the usual preprocessing steps of (a) building control-flow graphs, and (b) performing poin ts-to analysis (or alias analysis); nor does MCDASH require type information to be supplied. The paper also describes how we extended MCDASH to check properties of self-modifying code. MCDASH is built using language-independent meta-tools that generate the implementations of the required analysis components from descriptions of an instruction set’s syntax and semant ics. It has been instantiated for Intel x86 and PowerPC.
Process Mining Approach Towards Optimization of ERP Business Processes: A Case Study of Healthcare

Today enterprises are growing in size supporting globally distributed and vigorous businesses involving huge number of events. Enterprise Resource Planning (ERP) systems have a particular way of recording activities in the form of event logs. Business Intelligence aims to improve decision making processes and efficiency of the systems for that, organizations having ERP systems apply process mining techniques to extract information from event logs and analyze the actual business processes underlying the organizations. In this paper we have focused on one of the three features of process mining application i.e. discovery of process model with control flow perspective. Case study of a Pakistani Enterprise has been presented to analyze the results of three applied discovery algorithms Alpha Miner, Fuzzy Model and Heuristic Miner have determined the most frequent behaviors underlying its complaint monitoring process. To have so-called ideal process model discovery, preprocessing is the most essential and initial phase. ProM framework and DISCO tool has been used to analyze and visualize the results of process mining techniques.
Transform & Learn: A data-driven approach to nonlinear model reduction

This paper presents Transform & Learn, a physics-informed surrogate modeling approach that unites the perspectives of model reduction and machine learning. The proposed method uses insight from the physics of the problem—in the form of partial differential equation (PDE) models—to derive a state transformation in which the system admits a quadratic representation. Snapshot data from a high-fidelity model simulation are transformed to the new state representation and subsequently are projected onto a low-dimensional basis. The quadratic reduced model is then learned via a least-squares-based operator inference procedure. The state transformation thus plays two key roles in the proposed method: it allows the task of nonlinear model reduction to be reformulated as a structured model learning problem, and it parametrizes the machine learning problem in a way that recovers efficient, generalizable models. The proposed method is demonstrated on two PDE examples. First, we transform the Euler equations in conservative variables to the specific volume state representation, yielding low-dimensional Transform & Learn models that achieve a 0.05% relative state error when compared to a high-fidelity simulation in the conservative variables. Second, we consider a model of the Continuously Variable Resonance Combustor, a single element liquid-fueled rocket engine experiment. We show that the specific volume representation of this model also has quadratic structure and that the learned quadratic reduced models can accurately predict the growing oscillations of an unstable combustor.
Evaluation of the boundary layer dynamics of the TM5 model

1 European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra (Va), Italy 2 Max-Planck-Institute for Biogeochemistry, Jena, Germany 3 ICOS Carbon Portal, ICOS ERIC at Lund University, Sweden 4 SRON Netherlands Institute for Space Research, Utrecht, Netherlands, 5 Institute for Marine and Atmospheric Research Utrecht, Utrecht, University, Utrecht, Netherlands 6 MAQ, Wageningen University and Research Centre, Wageningen, Netherlands, 7 Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Netherlands 8 Institut für Umweltphysik, Heidelberg University, Germany 9 Energy research Center Netherlands (ECN), Petten, Netherlands 10 Royal Holloway, University of London (RHUL), Egham, UK 11 Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France 12 Royal Netherlands Meteorological Institute (KNMI), Netherlands 13 Centrum voor Isotopen Onderzoek (CIO), Rijksuniversiteit Groningen, Netherlands 14 Atmospheric Chemistry Research Group, University of Bristol, UK 15 Department of Meteorology, Pennsylvania State University, State College, PA, USA
Selection process of sustainable production indicators using eco-quality function deployment

Sustainable production has to become a necessity for all industries for last decades. While the industry has to improve the quality of the product, they also need to consider three essential indicators (economic, social and environment). To concern for sustainable production, there are many indicators that can be set. Every industry may have different indicators with others depends on their policy, goal, and circumstances. The objective of this research is to propose a practical approach to determine appropriate sustainable production indicators. The selection process of sustainable production indicators will be challenging. The proposed method used a framework of eco-quality function deployment. The construction of house of quality in this concept is derived from three main perspectives of sustainable development such as, economic, social and environment. The assessment of eco-quality function deployment involves expert judgments. The linguistic terms in fuzzy concept is applied. The result showed that the combination of fuzzy eco-QFD could be a useful tool for the selection process of sustainable production indicators that is flexible enough to be modified and can accommodate many varieties of indicators.
Thermal and energy aware routing in wireless body area networks

Unlike many other Internet of Things applications, in addition to the limited power of Internet of Medical Things devices, the safety aspect and reliability are the major concerns while designing wireless body area network routing solutions. In fact, the heat dissipation from Internet of Medical Things devices and changes in body posture cause tissue damage and frequent wireless links breakage, respectively. Hence, designing an efficient routing solution is extremely challenging in wireless body area network. Considering the safety aspects of human body, the power constraint of Internet of Medical Things devices, and topological variations in wireless body area network environment, this article presents a thermal-aware, energy-efficient, and reliable routing protocol named thermal and energy aware routing. In the given perspective, thermal and energy aware routing considers the weighted average of three costs while selecting the routing path: energy consumption, heat dissipation, and link quality (between communicating nodes). The proposed protocol is validated by a comparison with a state-of-the-art wireless body area network routing protocol. Simulation results demonstrate that the proposed protocol is efficient in terms of energy consumption, thermal impact, and packet reception rate.
Evolving Coauthorship Modeling and Prediction via Time-Aware Paired Choice Analysis

Coauthorship prediction is challenging yet important for academic collaboration and novel research topics discovery. The challenges lie in the dynamics of social or organizational relationships, changing preferences of suitable collaborators, and the evolution of research interests or topics. However, most current approaches and systems developed so far are mainly based on past coauthorships from a static viewpoint and do not capture the above evolving characteristics in coauthoring. Accordingly, this paper proposes a time-aware approach to capture the evolving coauthorships from online academic databases in terms of capturing the dynamics of social relationships and research interests. In particular, in order to understand the underlying factors influencing researchers to make choices of coauthors, we incorporate choice modeling based on utility theory. More specifically, our model conducts a series of pairwise choices over a poset induced by a utility function so as to learn the preference over all candidate coauthors. To complete the model inference, a gradient-based algorithm is devised to efficiently learn the model parameters for large-scale data. Finally, extensive experiments conducted on a real-world dataset show that our approach consistently outperforms other state-of-the-art methods.
Negative Lexically Constrained Decoding for Paraphrase Generation

Paraphrase generation can be regarded as monolingual translation. Unlike bilingual machine translation, paraphrase generation rewrites only a limited portion of an input sentence. Hence, previous methods based on machine translation often perform conservatively to fail to make necessary rewrites. To solve this problem, we propose a neural model for paraphrase generation that first identifies words in the source sentence that should be paraphrased. Then, these words are paraphrased by the negative lexically constrained decoding that avoids outputting these words as they are. Experiments on text simplification and formality transfer show that our model improves the quality of paraphrasing by making necessary rewrites to an input sentence.
GSM Digital Cellular Communication System

The Global System for Mobile Communication (GSM) is an international standard for wireless, cellular, and digital telecommunication networks. This article describes the basic features and history of GSM, the functionality of network components and their interworking, the services offered to GSM subscribers, voice and data transmission over the radio interface (frequencies, modulation, channels, multiple access, coding), networking-related topics (e.g., protocols for mobility management and handover), and security aspects. Keywords: wireless and mobile communications; cellular networks; digital communication; GSM
Adaptive boundary detection using 'live-wire' two-dimensional dynamic programming

An adaptive boundary detection algorithm that uses two-dimensional dynamic programming (DP) is presented. The algorithm is less constrained than previous one-dimensional dynamic programming algorithms and allows the user to interactively determine the mathematically optimal boundary between a user-selected seed point and any other dynamically selected free point in the image. Interactive movement of the free point by the cursor causes the boundary to behave like a live wire as it adapts to the new minimum cost path between the seed point and the currently selected free point. The algorithm can also be adapted or customized to learn boundary-defining features for a particular class of images. Adaptive 2-D DP performs well on a variety of images. It accurately detects the boundaries of low contrast objects, which occur with intravenous injections, as well as those found in noisy, low SNR images. <<ETX>>
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text

The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.
Social media in conflicts and crises

ABSTRACT The growing importance of social media in conflicts and crises is accompanied by an ever-increasing research interest in the crisis informatics field in order to identify potential benefits and develop measures against the technology’s abuse. This special issue sets out to give an overview of current research on the use of social media in conflicts and crises. In doing so, it focuses on both good and malicious aspects of social media and includes a variety of papers of conceptual, theoretical and empirical nature. In six sections, the special issue presents an overview of the field, analytical methods, technical challenges, current advancements and the accepted papers before concluding. Specific topics range from cyber deception over information trustworthiness to mining and near-real-time processing of social media data.
Detection of dim targets in digital infrared imagery by morphological image processing

We describe the development and the implementation of a new nonlinear image processing methodology to detect dim targets in infrared images. The methodology is based on mathematical morphology. In a first phase, the image is filtered in order to enhance positively contrasted, isotropic objects with a specified size. Then, a simple motion analysis is carried out, and finally, detection is performed by thresholding. A complete study of the algorithm performance on NATO test sequences is presented. A comparison is made with the Holmes filter and the scale-subtraction filter, a wavelet-based method.
Mathematical Modeling for Optimal System Testing under Fixed-cost Constraint

Abstract : Testing of complex systems is a fundamentally difficult task, whether locating faults (diagnostic testing) or implementing upgrades (regression testing). Branch paths through the system increase as a function of the number of components and interconnections, leading to exponential growth in the number of test cases for exhaustive examination. In practice, the typical cost for testing in schedule or in budget means that only a small fraction of these paths are investigated. Given some fixed cost, then, which tests should we execute to guarantee the greatest information returned for the effort? In this work, we develop an approach to system testing using an abstract model flexible enough to be applied to both diagnostic and regression testing, grounded in a mathematical model suitable for rigorous analysis and Monte Carlo simulation. Early results indicate that in many cases of interest, a good, though not optimal solution to the fixed-constraint problem (how many tests for budget x? ) can be approached as a simple best-next strategy (which test returns the highest information per unit cost? ). The goal of this modeling work is to construct a decision-support tool for the Navy Program Executive Office Integrated Warfare Systems (PEO IWS) offering quantitative information about cost versus diagnostic certainty in system testing.
The location management scheme using mobility information of mobile users in wireless mobile networks

Since the latest wireless communication networks allow dynamic relocation of a mobile terminal, the location management mechanism tracking the location of a mobile terminal is required to deliver incoming calls. To provide an efficient location management mechanism, we propose a location management scheme using mobility information of mobile users in wireless mobile networks. In this scheme, the location registration process caches location information of visited cells to reduce the duplicated registrations, the paging process exploits the locality of user movement patterns to predict the paging area and, therefore, to reduce the paging cost in location search procedure. The performance of the proposed scheme is evaluated by an analytical model, and is compared with that of Wan's basic velocity paging method.
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts

In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework. Existing why-QA methods retrieve “answer passages” that usually consist of several sentences. These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our proposed “Adversarial networks for Generating compact-answer Representation” (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model. We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.