Miracle-dz/UniFAR-FTUs huggingface dataset:
Object-Oriented Programming Strategies in C # for Power Conscious System

battery-powered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there is another significant restraint, the time to market. To be able to serve along the power constraint while can launch products in shorter production period, object-oriented programming (OOP) has stepped in to this field. Though everyone knows that OOP has quite much more overhead than assembly and procedural languages, development trend still heads to this new world, which contradicts with the target of low power consumption. Most of the prior power related software researches reported that OOP consumed much resource, however, as industry had to accept it due to business reasons, up to now, no papers yet had mentioned about how to choose the best OOP practice in this power limited boundary. This article is the pioneer that tries to specify and propose the optimized strategy in writing OOP software under energy concerned environment, based on quantitative real results. The language chosen for studying is C# based on . NET Framework 2.0 which is one of the trendy OOP development environments. The recommendation gotten from this research would be a good roadmap that can help developers in coding that well balances between time to market and time of battery.
Sub-Word Unit based Non-Audible Spe Electromyogr

In this paper we present a novel approach for a surface electromyographic speech recognition system based on sub-word units. Rather than using full word models as integrated in our previous work we propose here smaller sub-word units as prerequisites for large vocabulary speech recognition. This allows the recognition of words not seen in the training set based on seen sub-word units. Therefore we report on experiments with syllables and phonemes as sub-word units. We also developed a new feature extraction method that gains significant improvement for words and sub-word units.
ParCorFull: a Parallel Corpus Annotated with Full Coreference

ParCorFull is a parallel corpus annotated with full coreference chains that has been created to address an important problem that machine translation and other multilingual natural language processing (NLP) technologies face -- translation of coreference across languages. Our corpus contains parallel texts for the language pair English-German, two major European languages. Despite being typologically very close, these languages still have systemic differences in the realisation of coreference, and thus pose problems for multilingual coreference resolution and machine translation. Our parallel corpus covers the genres of planned speech (public lectures) and newswire. It is richly annotated for coreference in both languages, including annotation of both nominal coreference and reference to antecedents expressed as clauses, sentences and verb phrases. This resource supports research in the areas of natural language processing, contrastive linguistics and translation studies on the mechanisms involved in coreference translation in order to develop a better understanding of the phenomenon.
A Modified Vertical Mixing Parameterization for Its Improved Ocean and Coupled Simulations in the Tropical Pacific

Climate models suffer from significant biases over the tropical Pacific Ocean, including a too-cold cold tongue and too-warm temperature at the depth of the thermocline. The emergence of model biases can be partly attributed to vertical mixing parameterizations, in which there are great uncertainties in selections of functional forms and empirical parameters. In this paper, the impacts of two different vertical mixing schemes on the tropical Pacific temperature simulations are investigated using version 5 of the Modular Ocean Model (MOM5). One vertical mixing scheme is the widely used K-profile parameterization (KPP) scheme, and the other is a hybrid mixing scheme (the Chen scheme) by combining a Kraus–Turner-type bulk mixed layer (ML) model with Peters et al.’s shear instability mixing model (PGT model). It is shown that the Chen scheme works better than the KPP scheme for SST simulation but produces an exaggerated subsurface warm bias simultaneously. The better SST simulation can be attributed to the employment of the PGT model, which produces lower levels of shear instability mixing than its counterpart in the KPP scheme. Furthermore, a modified KPP scheme is presented in which its shear instability mixing model and constant background diffusivity are replaced by the PGT model and the Argo-derived background diffusivity, respectively. This new scheme is then employed into MOM5-based ocean-only and coupled simulations, demonstrating substantial improvements in temperature simulations over the tropical Pacific. The modified KPP scheme can be easily employed into other ocean models, offering an effective way to improve ocean simulations.
Higher-Order “ Perturbation ” Solutions to Dynamic , Discrete-Time Rational Expectations Models Methods and an Application to Optimal Monetary Policy

We present an algorithm and software routines for computing nthorder approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizing-agent model with nominal price rigidities, subject to a fiscal policy that is stochastic, suboptimal, and exogenous to the central bank. JEL Classification: C61, C63, E37 Version 0.1 October 7, 2004 This manuscript was prepared for presentation at the 2003 AEA Meetings in a session on perturbation methods and applications. Our work is preliminary, incomplete, and ongoing. The views expressed in this paper, and all errors and omissions, should be regarded as those solely of the authors, and do not necessarily represent those of the individuals or groups listed above, the Federal Reserve System, or its Board of Governors.
Managing Authorization with RESTful XML

The potentials of REST offers new ways for communications between louse coupled entities featured through the Web of Things [12]. The binding of the disjunct components of this architecture creates security issues, such as the centralized authorization techniques respecting the independence of the underlying entities. This results in the question how authorization is performed respecting the flexibility of REST without any knowledge about the underlying resources. Nevertheless, possible knowledge about these resources should enable the authorization workflow to offer finer-granular permissions on substructures of the resources. With our new approach we named Hecatewe offer a framework to assure simplified handling while keeping the potentials and flexibility of REST . We have designed an architecture based on XML with a flexible authorization mechanism on the one hand and optional resource-awareness on the other hand. The flexibility within the authorization work-flow bases on permission sets respecting the HTTPverbs. Additional in-depth knowledge of the entity optionally extends these permissions with resource-aware filters. Hecate offers not only great benefits because of its flexibility, but also because of the optional extensibility proved within the two reference implementations. With Hecate, we show that a centralized authorization mechanism combining independence and optional resource-based filtering extends the flexibility of REST rather than restricting it. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. WS-REST 2011, March 2011; Hyderabad, India Copyright 2011 ACM 978-1-4503-0623-2/11/03 . $10.00.
Lower bounds on the run time of the univariate marginal distribution algorithm on OneMax-DTU Orbit (06/12/2018)

DTU Orbit (06/12/2018) Lower bounds on the run time of the univariate marginal distribution algorithm on OneMax The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of Ω(μ√n + n log n), where μ is the population size, on its expected run time is proved. This is the first direct lower bound on the run time of the UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation of distribution algorithms in general.
TORINO Repository ISTITUZIONALE

This works presents an integrated 0.18 µ m CMOS 2-PPM demodulator based on a switched capacitor network for an Energy Detection Impulse-Radio UWB receiver. The circuit has been designed using a top-down methodology that allows to discover the impact of low-level non-idealities on system-level performance. Through the use of a mixed signal simulation environment, performance ﬁgures have been obtained which helped evaluate the inﬂuence at system-level of the non-idealities of the most critical block. Results show that the circuit allows the replacement of the ADC typically employed in Energy Detection receivers and provides about inﬁnite equivalent quantization resolution. The demodulator achieves 190 pJ/bit at 1.8V.
Studying the long-term quality of life outcomes of patients with depression after TMS treatment , the group of

US Food and Drugs Administration (FDA) in 2008. While the method is already applied in the clinic, mostly in cases where repeated attempts at conventional therapy have failed, research into its benefits continues. In a recent randomised clinical trial involving suicidal inpatients at a military hospital, George’s group, now at the Medical University of South Carolina at Charleston, USA, showed that the treatment is feasible and safe for this particularly vulnerable group of patients. However, the suicidal thoughts subsided almost as quickly in the placebo control group (given a sham treatment) as in the treatment group. None of the patients died from suicide in the six months after the treatment (Brain Stimulation (2014) 7, 421–431). Studying the long-term quality of life outcomes of patients with depression after TMS treatment, the group of Sarah “Holly” Lisanby at Duke University carried out a randomised, sham-controlled trial and found that the quality of life improvements after TMS compared to sham treatment were statistically significant and remained so at a follow-up investigation six months after the treatment (Brain Stimulation (2014) 7, 219–225). Other treatments that may be used in difficult cases include electroconvulsive therapy (ECT, formerly known as electroshock therapy) and deep brain stimulation, which involves implantation of electrodes to deliver electrical impulses to specific areas of the brain. Compared to these measures of last resort, TMS has the marked advantage of being non-invasive and free of major side effects. Overall, the science of addressing the most difficult cases of depression is still very much in flux, as is our understanding of why so many people get depressed in the first place. Important discussions are to be had about how healthy or sane our Western culture is, where many millions aspire to the gilded lifestyle of celebrities, even if the very same celebrities are driven to despair.
IMPROVING SPECTRAL EFFICIENCY AND COVERAGE CAPACITY OF 5G NETWORKS

A BSTRACT . As the number of internet user is increasing exponentially, demand of bandwidth, speed and carrier frequencies are also raising proportionally. To meet the users demand, existing 4G is not enough, hence the requirement of high speed and superior quality internet leads 5G network. The technology of 5G network needs huge number of devices and unmatched numbers of antennae which must support many number of new applications with lots of enhanced features in terms of speed, data rate, latency, coverage capacity, spectral efﬁciency etc. This paper reviewed some of the general aspects 5G network and focus on two important features namely spectral efﬁciency and coverage capacity. The critical analysis of a proposed combined technology is presented which suggests a grouping of micro cell, joint transmission coordinated multipoint (JT CoMP) and massive MIMO to improve both the spectral efﬁciency and the coverage capacity with less intricacy. This paper also investigates grouping of cells termed as cooperation area (CA) the interference and its reduction in 2G networks in Long Term Evolution (LTE).
Differences in Salmonella enterica serovar Typhimurium strain invasiveness are associated with heterogeneity in SPI-1 gene expression

Most studies on Salmonella enterica serovar Typhimurium infection focus on strains ATCC SL1344 or NTCC 12023 (ATCC 14028). We have compared the abilities of these strains to induce membrane ruffles and invade epithelial cells. S. Typhimurium strain 12023 is less invasive and induces smaller membrane ruffles on MDCK cells compared with SL1344. Since the SPI-1 effector SopE is present in SL1344 and absent from 12023, and SL1344 sopE mutants have reduced invasiveness, we investigated whether 12023 is less invasive due to the absence of SopE. However, comparison of SopE+ and SopE− S. Typhimurium strains, sopE deletion mutants and 12023 expressing a sopE plasmid revealed no consistent relationship between SopE status and relative invasiveness. Nevertheless, absence of SopE was closely correlated with reduced size of membrane ruffles. A PprgH–gfp reporter revealed that relatively few of the 12023 population (and that of the equivalent strain ATCC 14028) express SPI-1 compared to other S. Typhimurium strains. Expression of a PhilA–gfp reporter mirrored that of PprgH–gfp in 12023 and SL1344, implicating reduced signalling via the transcription factor HilA in the heterogeneous SPI-1 expression of these strains. The previously unrecognized strain heterogeneity in SPI-1 expression and invasiveness has important implications for studies of Salmonella infection.
des publications du CNRC

Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many computer programs have been designed to extract various molecular biology findings from Medline abstracts or full text articles. The present article describes the range of text mining techniques that have been applied to scientific documents. It divides ‘automated reading’ into four general subtasks: text categorization, named entity tagging, fact extraction, and collection-wide analysis. Literature mining offers powerful methods to support knowledge discovery and the construction of topic maps and ontologies. An overview is given of recent developments in medical language processing. Special attention is given to the domain particularities of molecular biology, and the emerging synergy between literature mining and molecular databases accessible through Internet.
Graph Hawkes Neural Network for Future Prediction on Temporal Knowledge Graphs

The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work generalizing the Hawkes process to a neurally self-modulating multivariate point process enables the capturing of more complex and realistic influences of past events on the future. However, this approach is limited by the number of event types, making it impossible to model the dynamics of evolving graph sequences, where each possible link between two nodes can be considered as an event type. The problem becomes even more dramatic when links are directional and labeled, since, in this case, the number of event types scales with the number of nodes and link types. To address this issue, we propose the Graph Hawkes Neural Network that can capture the dynamics of evolving graph sequences and predict the occurrence of a fact in a future time. Extensive experiments on large-scale temporal relational databases, such as temporal knowledge graphs, demonstrate the effectiveness of our approach.
Automated Detection of Client-State Manipulation Vulnerabilities

Web application programmers must be aware of a wide range of potential security risks. Although the most common pitfalls are well described and categorized in the literature, it remains a challenging task to ensure that all guidelines are followed. For this reason, it is desirable to construct automated tools that can assist the programmers in the application development process by detecting weaknesses. Many vulnerabilities are related to Web application code that stores references to application state in the generated HTML documents to work around the statelessness of the HTTP protocol. In this article, we show that such client-state manipulation vulnerabilities are amenable to tool-supported detection. We present a static analysis for the widely used frameworks Java Servlets, JSP, and Struts. Given a Web application archive as input, the analysis identifies occurrences of client state and infers the information flow between the client state and the shared application state on the server. This makes it possible to check how client-state manipulation performed by malicious users may affect the shared application state and cause leakage or modifications of sensitive information. The warnings produced by the tool help the application programmer identify vulnerabilities before deployment. The inferred information can also be applied to configure a security filter that automatically guards against attacks at runtime. Experiments on a collection of open-source Web applications indicate that the static analysis is able to effectively help the programmer prevent client-state manipulation vulnerabilities. The analysis detects a total of 4,802 client-state parameters in ten applications, whereof 4,437 are classified as safe and 241 reveal exploitable vulnerabilities.
Tree Edit Models for Recognizing Textual Entailments , Paraphrases , and Answers to Questions : Appendix with Experimental Details

This document provides additional details about the experiments described in (Heilman and Smith, 2010). Note that while this document provides information about the datasets and experimental methods, it does not provide further results. If you have any further questions, please feel free to contact the first author. The preprocessed datasets (i.e., tagged and parsed) will be made available for research purposes upon request.
Bulletin of Electrical Engineering and Informatics

Arselan Ashraf, Ali Sophian, Amir Akramin Shafie, Teddy Surya Gunawan, Norfarah Nadia Ismail Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia Department of Mechatronics Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia School of Civil Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia
Correcting real-word spelling errors by restoring lexical cohesion

Spelling errors that happen to result in a real word in the lexicon cannot be detected by a conventional spelling checker. We present a method for detecting and correcting many such errors by identifying tokens that are semantically unrelated to their context and are spelling variations of words that would be related to the context. Relatedness to context is determined by a measure of semantic distance initially proposed by Jiang and Conrath (1997). We tested the method on an artiﬁcial corpus of errors; it achieved recall of 23 to 50% and precision of 18 to 25%.
Aalborg Universitet Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model

In this paper, we propose a method for restoring the missing or corrupted observations of non-stationary sinusoidal signals which are often encountered in music and speech applications. To model non-stationary signals, we use a time-varying sinusoidal model which is obtained by extending the static sinusoidal model into a dynamic sinusoidal model. In this model, the in-phase and quadrature components of the sinusoids are modelled as first-order Gauss-Markov processes. The inference scheme for the model parameters and missing observations is formulated in a Bayesian framework and is based on a Markov chain Monte Carlo method known as Gibbs sampler. We focus on the parameter estimation in the dynamic sinusoidal model since this constitutes the core of model-based interpolation. In the simulations, we first investigate the applicability of the model and then demonstrate the inference scheme by applying it to the restoration of lost audio packets on a packet-based network. The results show that the proposed method is a reasonable inference scheme for estimating unknown signal parameters and interpolating gaps consisting of missing/corrupted signal segments.
Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.
Growth, Complexity, and Generativity of Digital Platforms: The Case of Otto.de

We study the growth, complexity, and generativity of a digital platform—Otto.de. Through a longitudinal analysis of the company’s 65 GitHub open source repositories over a period of 6.5 years, we find a) support for a superlinear growth pattern, b) a structural split in the platform into two clusters, and c) indication that more active repositories will be more generative and in turn more popular. These findings have implications for scholars studying platform evolution as well as for companies opening up their internal IT platforms
Business Plan: Consulting Firm focused on Manufacturing Digitalization

The goal of this paper outlines a business framework for the development of a digitization consulting firm focused on the manufacturing sector. The firm’s goal is to help organizations in Michigan leverage technology to keep their business vibrant and workers skilled. The primary services for the firm will be related to the implementation of products and services within Industry 4.0, the cyber-physical smart factory industrial revolution. The beginning of the paper explains the ecosystem surrounding digitalization as well as the nine technologies (ex: big data, simulation, additive manufacturing) driving the innovation. The framework for this business plan uses the business model canvas to explain the key relationships and structures of the firm. An analysis of the plan uses SWOT to judge the proposed firm against its competitors as well as assess potential risks.