Miracle-dz/UniFAR-FTUs huggingface dataset:
A two-level knowledge approach to support multilingual legislative drafting

The quality of legislative drafting process at European and national levels is highly influenced by the legal drafters control over the multilingual complexity of European legislation and over the linguistic and conceptual issues involved in its transposition into national laws. The DALOS project aims at ensuring coherence and alignment in the legislative language, providing law-makers with a knowledge (ontological-linguistic) resource and knowledge management tools to support the multilingual legislative drafting process. This paper outlines the activities within DALOS, aiming at the definition of the characteristics of a the knowledge resource, at its implementation, at its integration in a legislative drafting environment for the project prototype.
Eyes Whisper Depression: A CCA based Multimodal Approach

This paper presents our work on ACM MM Audio Visual Emotion Corpus 2013 (AVEC 2013) depression recognition sub-challenge using the baseline features in accordance with the challenge protocol. We use Canonical Correlation Analysis for audio-visual fusion as well as covariate extraction for the target task. The video baseline provides histograms of local phase quantization features extracted from 4x4=16 regions of the detected face. We summarize the video features over segments of length 20 seconds using mode and range functionals. We observe that features of range functional that measure the variance tendency provides statistically significantly higher canonical correlation than mode functional features that measure the mean tendency. Moreover, when audio-visual features are used with varying number of covariates per region, the regions that were consistently found the best are the ones corresponding to two eyes and the right part of the mouth.
Weak-duality based adaptive finite element methods for PDE-constrained optimization with pointwise gradient state-constraints

Adaptive finite element methods for optimization problems for second order linear elliptic partial differential equations subject to pointwise constraints on the 2-norm of the gradient of the state are considered. In a weak duality setting, i.e. without assuming a constraint qualification such as the existence of a Slater point, residual based a posteriori error estimators are derived. To overcome the lack in constraint qualification on the continuous level, the weak Fenchel dual is utilized. Several numerical tests illustrate the performance of the proposed error estimators.
DETERMINING THE APPROPRIATE AMOUNT OF DATA FOR CLASSIFYING CONSUMERS FOR DIRECT MARKETING PURPOSES

This article examines the impact of using incremental amounts of purchasing data on the ability to classify consumers in consumer packaged goods categories for direct marketing purposes. Building on the work of Rossi, McCulloch, and Allenby (1996), who focused on the impact of three information sets—(a) demographics only, (b) demographics and one purchase made by a consumer, and (c) demographics plus an entire purchasing history of a consumer—we examine the impact of each additional purchase, starting with no purchasing information (i.e., demographics only) through 20 purchases. Using two different classiﬁcation models, a Multinomial Logit model and an Artiﬁcial Neural Network model, we examine the sensitivity of classiﬁcation accuracy to each additional purchase. We use these results in a proﬁtability analysis of a hypothetical direct marketing campaign to determine the optimal number of purchases to use for classiﬁcation in the category studied. The ﬁndings suggest an optimal number of purchasing observations exists for classiﬁcation and targeting purposes and this optimal number falls between one purchase and a “history” of purchases as studied by Rossi et al. Our ﬁndings illustrate the importance of conducting a sensitivity analysis to identify the optimal amount of purchasing data to use when classifying consumers for the purpose of a direct marketing campaign.
Botulinum toxin A in the treatment of Raynaud's phenomenon: a systematic review

Introduction The management of Raynaud's phenomenon in its most severe form is challenging, and current medical and surgical treatment methods frequently do not lead to optimal symptom control and prevention of ischemic complications. The aim of the study was to critically evaluate all existing evidence on the use of botulinum toxin A in the management of Raynaud's phenomenon. Material and methods We adopted the PRISMA methodology and searched Cochrane Library, MEDLINE, SCOPUS, EULAR and ACR congresses abstract archives for Raynaud* AND botulinum toxin OR onabotulinum. All studies that contained reports of botulinum toxin A use and its outcome in Raynaud's phenomenon were included in the review. Results Eleven studies met our inclusion criteria and involved a total of 125 patients. Two reviewers extracted data from the studies under review and achieved a consensus in their selection. The main outcomes measured were pain reduction and healing of digital ulcers. The level of evidence across studies was very low to moderate. Conclusions There is insufficient evidence to assess the efficacy of botulinum toxin A in Raynaud's phenomenon. Despite many promising reports, further research in the form of randomized controlled trials is warranted in order to investigate this new treatment method for Raynaud's phenomenon.
USER ACTIVITIES IN BUSINESS PROCESSES - A SYSTEM-BASED ANALYSIS OF SPECIALIST USERS

Empirical data based on actual system use is in very short supply. How many transactions do ERP system users execute? In which business processes do most users still play an active role? Answers to these questions are provided in the analysis of 77 user organizations, the subject of this publication. For the first time, these questions can be answered objectively, based on data collected with system-based analyses of live post-implementation SAP systems. The analyses examined activities of over 253,000 users and the data was extracted using RBE Plus, a reverse business engineering tool. Areas of intense usage and user types have been identified objectively, and the findings pinpoint specific sub-processes that reveal potential for improvement.
RELATE: a research training network on engineering and provisioning of service-based cloud applications

The RELATE Initial Training Network (ITN), funded by the EU in the Marie Curie Actions programme, is a multidisciplinary training network of European academic and industrial partners working together to train academic researchers and next generation experts in the area of engineering and provisioning of service-based Cloud applications. In this paper, we present the up-to-date goals and strategy of the RELATE project and give an overview of the ongoing research activities within the training network.
Judicious power loading for a cognitive relay scenario

This paper introduces a power loading strategy for relays to be used in a cognitive scenario. A cognitive relay node allows unlicensed (secondary) users to concurrently operate with licensed (primary) users, thereby improving spectral efficiency. The dynamic spectrum access in an OFDM-based cognitive relay network may lead to unwanted interference to the primary user from the side lobes of the secondary users. Two judicious algorithms are formulated by means of which the cognitive relay meets the twofold challenge of achieving optimum system throughput while ensuring minimum interference to the primary. The simulation results demonstrate that the suggested algorithms exhibit enhanced channel capacity for the secondary users as compared to traditional power allocation methods.
A Survey of Routing Protocols in Mobile Ad Hoc Network

Mobile Ad hoc Network (MANET) is characterized by mobile hosts, dynamic topology, multi-hop wireless connectivity and infra-structure less ad hoc environment. On demand routing protocols for ad hoc networks discover and maintain routes on a reactive, "as-needed" basis. The nodes in the MANET are typically powered by batteries which have limited energy reservoir. Sometimes it becomes very difficult to recharge or replace the battery of nodes; in such situation energy conservations are essential. Also nodes move away without giving any notice to its cooperative nodes, causing changes in network topology and thus, these changes may significantly degrade the performance of a routing protocol. Hence the energy consumption and lifetime of the node and link becomes an important issue in MANET. This paper evaluates the performance of various adhoc routing protocols such as DSDV, AODV, DSR, TORA and AOMDV in terms of energy efficiency and showed that AOMDV is better in comparison with all the above protocols. The simulation is carried out using network simulator NS-2.35 under Linux platform. The performance metrics used for evaluation are packet delivery ratio, latency, throughput, energy consumption, overhead and packet received.
Fast and accurate identification of implicit enterprise users in social media

There are a large number of implicit enterprise users in social media, who register as ordinary users but act like enterprise ones. It is a fundamental task to eliminate this type of users from the user set since their existing will severely affect the performance of many applications like network demographic investigation and targeted advertisement. Despite of its importance, this problem is surprisingly unexplored. In this paper, we present a novel two-stage framework to identify the implicit enterprise users. In the first stage, we detect this type of users by only using the profile information. In the second stage, we improve the classification accuracy by integrating more information from the user's contents. Such a framework meets well the scenario in reality: 1) due to the very nature of preprocessing, the identification of implicit enterprise users needs to be handled quickly with little cost, and 2) if enough resources such as microblog data and training/test time are provided, the problem is expected to be solved more precisely. We conduct extensive experiments on a real data set consisting of 400 users (200 ordinary users and 200 implicit enterprise users, respectively) in Sina Weibo, one of the largest social media in China. The result reaches an accuracy of 81.75% when using only profile features and is further improved to 86.00% when the content features are combined.
Self-interest or the greater good: How political and rational dynamics influence the outsourcing process

Purpose – The purpose of this paper is to provide an understanding of the influence of political goals and behaviour on the outsourcing decision process and outcomes. Design/methodology/approach – The research used an exploratory longitudinal case-based approach. Eight outsourcing projects in three telecommunications companies were analysed from the initial decision to the outcome of the case. Findings – The authors show how political goals and behaviours influence the outsourcing decision process and inductively develop four political goals: personal reputation, attainment, elimination and control. The authors also identify three dynamic outsourcing paths: the personal reputation path, which leads to successful outcomes; the short-term attain and eliminate path leading to unsuccessful outcomes; and the destabilised path, which leads to mixed outcomes. All of these can be tested in other empirical settings. Research limitations/implications – The implications for outsourcing literature are that political .
The Magnitude of Switching Costs for Corporate Antivirus Software Switching Decision

Today’s businesses environment is forcing companies to become increasingly more efficient in applying Internet technology to conduct transactions. AS the possibility of infection by computer virus is much greater now than ever before, businesses search for appropriate corporate antivirus software to safeguard their computer systems. This paper considers corporate antivirus software switching as one of the major security selection problem and proposes possible avenues for software switching decision and management. In conceptual model, we draw upon switching costs where transaction costs, learning costs, and artificial costs were examined as main costs for software switching decision. Our findings shown only two out of three types of switching costs have influence over corporate antivirus software switching decisions. Despite the existence of switching costs, businesses continue to repeat software switching because the perceived risks of security threats are much greater than the switching cost itself. Furthermore, we examine various approaches to the cost of switching and then propose an index map to evaluate switching decision. Five sets of propositions are advanced to help guide this research.
Dynamic time-slot allocation for QoS enabled networks on chip

MP-SoCs are expected to require complex communication architectures such as NoCs. This paper presents, to our knowledge, the first algorithm to dynamically perform routing and allocation of guaranteed communication resources on NoCs that provide QoS with TDMA techniques. We test the efficiency of our algorithm by allocating the communication channels required for an application composed of a 3D pipeline and an MPEG-2 decoder/encoder video chain on a 16 node MP-SoC. Dynamism in the communication is created by the 3D application. On a StrongARM processor clocked at 200 MHz, the allocation time for one time-slot takes about 1000 cycles per hop in the connection. We show that central time-slot allocation algorithms are practical for small-scale MP-SoC systems. Indeed, our algorithm can compute the allocation of 40 connections for a complex scene of the 3D pipeline in 450 to 900 /spl mu/s, depending on the slot table size.
DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking

Defining hand-crafted feature representations needs expert knowledge, requires timeconsuming manual adjustments, and besides, it is arguably one of the limiting factors of object tracking. In this paper, we propose a novel solution to automatically relearn the most useful feature representations during the tracking process in order to accurately adapt appearance changes, pose and scale variations while preventing from drift and tracking failures. We employ a candidate pool of multiple Convolutional Neural Networks (CNNs) as a data-driven model of different instances of the target object. Individually, each CNN maintains a specific set of kernels that favourably discriminate object patches from their surrounding background using all available low-level cues. These kernels are updated in an online manner at each frame after being trained with just one instance at the initialization of the corresponding CNN. Given a frame, the most promising CNNs in the pool are selected to evaluate the hypothesises for the target object. The hypothesis with the highest score is assigned as the current detection window and the selected models are retrained using a warm-start back-propagation which optimizes a structural loss function. In addition to the model-free tracker, we introduce a class-specific version of the proposed method that is tailored for tracking of a particular object class such as human faces. Our experiments on a large selection of videos from the recent benchmarks demonstrate that our method outperforms the existing state-of-the-art algorithms and rarely loses the track of the target object.
Hey, You Have a Problem: On the Feasibility of Large-Scale Web Vulnerability Notification

Large-scale discovery of thousands of vulnerable Web sites has become a frequent event, thanks to recent advances in security research and the rise in maturity of Internet-wide scanning tools. The issues related to disclosing the vulnerability information to the affected parties, however, have only been treated as a side note in prior research. In this paper, we systematically examine the feasibility and efficacy of large-scale notification campaigns. For this, we comprehensively survey existing communication channels and evaluate their usability in an automated notification process. Using a data set of over 44,000 vulnerable Web sites, we measure success rates, both with respect to the total number of fixed vulnerabilities and to reaching responsible parties, with the following high-level results: Although our campaign had a statistically significant impact compared to a control group, the increase in the fix rate of notified domains is marginal. If a notification report is read by the owner of the vulnerable application, the likelihood of a subsequent resolution of the issues is sufficiently high: about 40%. But, out of 35,832 transmitted vulnerability reports, only 2,064 (5.8%) were actually received successfully, resulting in an unsatisfactory overall fix rate, leaving 74.5% of Web applications exploitable after our month-long experiment. Thus, we conclude that currently no reliable notification channels exist, which significantly inhibits the success and impact of large-scale notification.
Supply chain flexibility and firm performance

Purpose – To explore the relationship between the dimensions of supply chain flexibility and firm performance in a sample of automotive suppliers.Design/methodology/approach – Empirical survey of a representative sample of 126 Spanish automotive suppliers during the months of September and October 2003. Data gathered through a mail survey to purchasing managers by using a structured questionnaire. Spearman correlation coefficients were used to analyse the relationship between the different supply chain flexibility dimensions, between supply chain flexibility dimensions and firm performance dimensions, and between supply chain flexibility dimensions and environmental uncertainty dimensions. A multivariate analysis studied the determinants of supply chain flexibility.Findings – The research has found a positive relation between a superior performance in flexibility capabilities and firm performance, although flexibility dimensions are not equally important for firm performance. On the other hand, the result.
Principles and realization strategies of multilevel transaction management

One of the demands of database system transaction management is to achieve a high degree of concurrency by taking into consideration the semantics of high-level operations. On the other hand, the implementation of such operations must pay attention to conflicts on the storage representation levels below. To meet these requirements in a layered architecture, we propose a multilevel transaction management utilizing layer-specific semantics. Based on the theoretical notion of multilevel serializability, a family of concurrency control strategies is developed. Suitable recovery protocols are investigated for aborting single transactions and for restarting the system after a crash. The choice of levels involved in a multilevel transaction strategy reveals an inherent trade-off between increased concurrency and growing recovery costs. A series of measurements has been performed in order to compare several strategies. Preliminary results indicate considerable performance gains of the multilevel transaction approach.
Stability of Networked Control Systems Subject to a Multiple-packet Transmission Policy

This paper is concerned with stability analysis of discrete-time networked control systems under a multiple-packet transmission policy. The packet dropping probability of the communication network is assumed to be known, or bounded by a given constant. Necessary and sufficient conditions for stability are obtained for the above two cases. For the second case, a formula is derived for a measure of the robustness of a system against packet dropping, termed packet dropping margin. A design method is proposed for improving the margin subject to the constraint that the system has a set of prescribed nominal closed-loop poles
Rapid Activation of Rac GTPase in Living Cells by Force Is Independent of Src

It is well known that mechanical forces are crucial in regulating functions of every tissue and organ in a human body. However, it remains unclear how mechanical forces are transduced into biochemical activities and biological responses at the cellular and molecular level. Using the magnetic twisting cytometry technique, we applied local mechanical stresses to living human airway smooth muscle cells with a magnetic bead bound to the cell surface via transmembrane adhesion molecule integrins. The temporal and spatial activation of Rac, a small guanosine triphosphatase, was quantified using a fluorescent resonance energy transfer (FRET) method that measures changes in Rac activity in response to mechanical stresses by quantifying intensity ratios of ECFP (enhanced cyan fluorescent protein as a donor) and YPet (a variant yellow fluorescent protein as an acceptor) of the Rac biosensor. The applied stress induced rapid activation (less than 300 ms) of Rac at the cell periphery. In contrast, platelet derived growth factor (PDGF) induced Rac activation at a much later time (>30 sec). There was no stress-induced Rac activation when a mutant form of the Rac biosensor (RacN17) was transfected or when the magnetic bead was coated with transferrin or with poly-L-lysine. It is known that PDGF-induced Rac activation depends on Src activity. Surprisingly, pre-treatment of the cells with specific Src inhibitor PP1 or knocking-out Src gene had no effects on stress-induced Rac activation. In addition, eliminating lipid rafts through extraction of cholesterol from the plasma membrane did not prevent stress-induced Rac activation, suggesting a raft-independent mechanism in governing the Rac activation upon mechanical stimulation. Further evidence indicates that Rac activation by stress depends on the magnitudes of the applied stress and cytoskeletal integrity. Our results suggest that Rac activation by mechanical forces is rapid, direct and does not depend on Src activation. These findings suggest that signaling pathways of mechanical forces via integrins might be fundamentally different from those of growth factors.
Pindyck : Irreversibility , Uncertainty , and Investment

My thanks to Prabhat Mehta for his research assistance, and to Ben Bernanke, Vittorio Corbo, Nalin Kulatilaka, Robert McDonald, Louis Serven, Andreas Solimano, and two anonymous referees for helpful comments and suggestions. Financial support was provided by MIT's Center for Energy Policy Research, by the World Bank, and by the National Science Foundation under Grant No. SES-8618502. Any errors are mine alone.