Miracle-dz/UniFAR-FTUs huggingface dataset:
On the relationship between standard problem and model-matching problem for multidimensional systems with BIBO stability

In this paper, we consider the multidimensional system with bounded-input-bounded-output (BIBO) stability. It is not known yet whether every stabilizable plant always admits its doubly coprime factorization or not. Even so, in this paper, we show the equivalence between the standard control problem and the model-matching problem.
Norm creation, spreading and emergence: A survey of simulation models of norms in multi-agent systems

Norms in human societies are expectations of behaviours of the individuals. In human societies, there are several types of norms such as moral norms, social norms and legal norms (laws). In multi-agent systems, software agents are modelled as possessing characteristics and behaviour borrowed from human societies. In order to design and develop robust artificial agent societies, it is important to understand different approaches proposed by researchers by which norms can spread and emerge within agent societies. This paper makes three contributions to the study of norms. Firstly, based on the simulation research on norms, we propose a life-cycle model for norms. Secondly, we discuss different mechanisms used by researchers to study norm creation, identification, spreading, enforcement and emergence. We also discuss the strengths and weaknesses of each of these mechanisms. Thirdly, in the context of identifying the desired characteristics of the simulation models of norms we discuss the research issues that need to be addressed.
A novel method for multi-sensory data fusion in multimodal human computer interaction

Multimodal User Interaction (MMUI) technology aims at building natural and intuitive interfaces allowing a user to interact with computer in a way similar to human-to-human communication, for example, through speech and gestures. As a critical component in MMUI, Multimodal Input Fusion explores ways to effectively interpret the combined semantic interpretation of user inputs through multiple modalities. This paper presents a novel approach to multi-sensory data fusion based on speech and manual deictic gesture inputs. The effectiveness of the technique has been validated through experiments, using a traffic incident management scenario where an operator interacts with a map on a large display at a distance and issues multimodal commands through speech and manual gestures. The description of the proposed approach and preliminary experiment results are presented.
The Impact of FDI on Economic Growth: An Analysis for the Transition Countries of Central and Eastern Europe

Solow Growth Accounting and Stylised Facts. - Capital Deepening through FDI in an Economic Growth Model. - Estimating the Effect of FDI on Economic Growth for 13 Countries of Central and Eastern Europe. - The Determinants of FDI - What Can the Transition Countries Do to Attract FDI? - Conclusion.
Cryptic diversity and population structure at small scales : 1 The freshwater snail Ancylus ( Planorbidae , Pulmonata ) in 2 the Montseny mountain range 3 4 Author names and affiliations

2 3 Anthropogenic impacts like intensified land use and climate change are severe threats to 2 4 freshwater biodiversity and effective biodiversity monitoring is therefore one of the most 2 5 urgent tasks. This is however often hampered by the lack of knowledge regarding the 2 6 number and ecology of species. Molecular tools have shown many freshwater taxa to 2 7 comprise morphologically cryptic species, which often occur in sympatry on a small 2 8 geographic scale. Here, we studied the freshwater snail Ancylus fluviatilis (MÜLLER, 1774) 2 9 species complex in the Iberian Montseny Mountains. We hypothesised 1) that several 3 0 species of A. fluviatilis sensu lato occur in the Montseny, 2) that different Ancylus species 3 1 seldom co-occur in syntopy due to different ecological demands or interspecific competition, 3 2 and 3) that species show a pattern of strong population structure within streams or 3 3 catchments due to ecological preferences or local adaptation. We barcoded 180 specimens 3 4 from 36 sites in the Montseny for the cytochrome c oxidase subunit I (COI) barcoding gene 3 5 and molecularly identified two Ancylus species. These species seldom occurred in syntopy 3 6 and a species distribution modelling approach showed differing bioclimatic preferences of the 3 7 species. One species mainly occurs in cooler, higher altitude streams while the second 3 8 species occurs in lower-altitude areas with higher temperatures. Tests of population structure 3 9 showed that both species possibly do not disperse well in the study area and that 4 0 populations within species are likely adapted to certain bioclimatic conditions in different 4 1 regions of the Montseny. Our results highlight the need to incorporate molecular techniques 4 2 into routine monitoring programmes.
Robust Example-based Dialog Retrieval using Distributed Word Representations and Recursive Autoencoders (第5回対話システムシンポジウムおよび一般)

: Based on our previous work on example-based chat-oriented dialog systems that utilize a human-to-human conversation. Though promising, our previous simple retrieval techniques resulting a weakness on handling an out of vocabulary (OOV) database queries. In this paper we discuss an approach to increase the robustness of example-based dialog response retrieval. We employ a recursive neural network paraphrase identiﬁcation technique to achieve a good performance on ﬁnding a good response in dialog-pair database. To achieve that, we remodel our previous dialog-pair database into distributed word representation. We apply an recursive autoencoders and dynamic pooling algorithm between user utterance and database to decide whether they have a same meaning or not, even when they composed in diﬀerent word and structure. These distribute representations and recusive autoencoders have the potential to enhance our retrieval techniques and reduce confusion in example matching, especially when handling an OOV cases. We also present the system performance evaluation based on objective and subjective metrics.
Combination of Multiple Handwritten Text Line Recognition Systems with a Recursive Approach

In this paper we propose a novel method to combine the results of multiple text line recognition systems. The method uses a recursive approach and re-examines those parts in a text line which have been rejected based on the initial combination of the base recognisers’ results. By means of the new method, the search space can be reduced, and therefore more accurate recognition results can be expected. Experiments conducted on the IAM database show that the proposed method is able to improve the recognition rate compared to a standard combination scheme.
The Function of Rho-Associated Kinases ROCK1 and ROCK2 in the Pathogenesis of Cardiovascular Disease

Rho-associated kinases ROCK1 and ROCK2 are serine/threonine kinases that are downstream targets of the small GTPases RhoA, RhoB, and RhoC. ROCKs are involved in diverse cellular activities including actin cytoskeleton organization, cell adhesion and motility, proliferation and apoptosis, remodeling of the extracellular matrix and smooth muscle cell contraction. The role of ROCK1 and ROCK2 has long been considered to be similar; however, it is now clear that they do not always have the same functions. Moreover, depending on their subcellular localization, activation, and other environmental factors, ROCK signaling can have different effects on cellular function. With respect to the heart, findings in isoform-specific knockout mice argue for a role of ROCK1 and ROCK2 in the pathogenesis of cardiac fibrosis and cardiac hypertrophy, respectively. Increased ROCK activity could play a pivotal role in processes leading to cardiovascular diseases such as hypertension, pulmonary hypertension, angina pectoris, vasospastic angina, heart failure, and stroke, and thus ROCK activity is a potential new biomarker for heart disease. Pharmacological ROCK inhibition reduces the enhanced ROCK activity in patients, accompanied with a measurable improvement in medical condition. In this review, we focus on recent findings regarding ROCK signaling in the pathogenesis of cardiovascular disease, with a special focus on differences between ROCK1 and ROCK2 function.
An Incremental Clustering Algorithm for the Topology Adjustment of Location Databases

How to effectively organize and store the profile of moving objects in a mobile environment, where then can effectively lower the paging and update cost, is an important problem in location management. Combining data mining into the mobile environment is a challenging research task, which has broad applications. Zone partition can effectively optimize the topology of location databases and efficiently reduce the cost of location paging and location update. But with the evolving time, the mobile users’ moving patterns may change, so the original partitions may not match the current moving patterns. Thus one of the important problems, which need to be solved, is how to partition the zones dynamically. Clustering method can solve the static zone partition well, but face with the dynamic zone partition problem. If the clustering method is still used to solve this problem, it means that the zones are partitioned again from scratch, which doesn’t utilize the original partitions and need great cost. In this paper an incremental clustering method is provided to solve the dynamic zone partition problem, which adjusts the original zone partitions with less cost and guarantees all the conditions needed for zone partition problem in the meanwhile.
Improving Word Alignment Based on Extended Inversion Transduction Grammar

We propose a fusion of Inversion Transduction Grammar model with IBM-style notation of fertility to improve wordaligning performance. In our approach, binary context-free grammar rules on the source language, accompanied with orientation preferences on the target, and fertilities of words are leveraged to construct a syntax-based statistical translation model. Our model, inherently possessing the characteristic of ITG restrictions and allowing for many consecutive words aligned to one and vise versa, outperforms original ITG model and GIZA++ not only in alignment error rate (23% and 14% error reduction) but in consistent phrase error rate (13% and 9% error reduction) as well. Better performance in these two evaluation metrics will lead to better phrase-based machine translation with great possibility.
Combination of Brain-Computer Interface Training and Goal-Directed Physical Therapy in Chronic Stroke: A Case Report

Background. There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements. Methods . A 67-year-old hemiplegic patient with no active finger extension was trained with a brain—computer interface (BCI) combined with a specific daily life—oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient’s affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of μ-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed. Results. The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased μ-oscillations in the ipsilesional motor cortex. Conclusion. This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.
Improved Channel Estimation for PC-Based Software Radio DVB-T Receiver

In the paper, we propose an improved discrete Fourier transform (DFT)-based channel estimation for a PC-based software DVB-T receiver testbed. The proposed channel estimation algorithm includes an adaptive low pass filter whose passband get wider when higher moving speed. The image quality of the proposed scheme is significantly better than that of the traditional scheme in realistic mobile channels (not simulated mobile channels).
Hedging with CO2 allowances: the ECX market

We investigate and empirically estimate optimal hedge ratios, for the first time, in the EU ETS carbon market. Minimum variance hedge ratios are conditionally estimated with multivariate GARCH models, and unconditionally by OLS and the naive strategy for the European Climate Exchange (ECX) market in the period 2005-2009. Also, utility gains are considered in order to take into account risk-return considerations. Empirical results indicate that dynamic hedging provides superior gains (in reducing the variance portfolio) compared to those obtained from static hedging, when adjustment costs are not taken into account. Moreover, results improve when the leptokurtic characteristics of the data are into consideration through distributions. Results are always compared in and out of sample, suggesting also that utility gains increase with investor's increased preference over risk.
Modelling the Dynamics of Public Attention towards Environmental Issues

Public attention, traced over time, often displays seemingly paradox behaviour: Contrary to what one might expect, public concern is seldom hig hest when the environmental conditions are the wors t. Rather, concern most often rises when conditions ha ve already become better. Furthermore, public atten tion dynamics frequently shows self-reinforcing beh aviour resulting in distinct cycles, e.g. due to so me trigger event. In this paper, public attention is v iewed as a social macro-level phenomenon that is so ught to be explained by the interaction of a multitude o f single actors, namely individual citizens, the pr ss, and politicians. First, a causal model based on rationa l choice theory is constructed in order to elucidat e the mechanisms according to which public attention dyna mics develop and to address the question of when and why public attention rises and falls. Key varia bles include the acuteness and visibility of the is sue at stake as well as the ability to “solve” the underly ing problem. Self-amplifying behaviour of agent int eraction on different time scales adds to the complexit y of the model. In a second step, an agent-based co mputer model is constructed from the conceptual rati on l choice model. It allows to reproduce the basic features of typical issue-attention cycles such as tho e analysed in empirical case studies. The model elucidates the causal mechanism and clearly displays t he emergent structure, i.e. the typical, complex pa tterns of attention cycles. It thus serves to test and val idate the conceptual model. In addition, it allows to incorporate additional conceptual refinements such as si multaneously tracking attention towards multiple is sues. However, further research is needed to elucid ate the mechanism(s) according to which public atte ntion declines.
Reactant Minimization in Sample Preparation on Digital Microfluidic Biochips

Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant's blood, DNA evidence collected from crime scenes, or costly reagents, are extremely valuable, their usage should be minimized whenever possible. In this paper, we propose a two-phased reactant minimization algorithm (REMIA), for sample preparation on digital microfluidic biochips. In the former phase, REMIA builds a reactant-minimized interpolated dilution tree with specific leaf nodes for a target concentration. Two approaches are developed for tree construction; one is based on integer linear programming (ILP) and the other is heuristic. The ILP one guarantees to produce an optimal dilution tree with minimal reactant consumption, whereas the heuristic one ensures runtime efficiency. Then, REMIA constructs a forest consisting of exponential dilution trees to produce those aforementioned specific leaf nodes with minimal reactant consumption in the latter phase. Experimental results show that REMIA achieves a reduction of reactant usage by 32%-52% as compared with three existing state-of-the-art sample preparation approaches. Besides, REMIA can be easily extended to solve the sample preparation problem with multiple target concentrations, and the extended version also effectively lowers the reactant consumption further.
An incremental ensemble of classifiers as a technique for prediction of student's career choice

The ability to predict the career of students can be beneficial in a huge number of different techniques which are connected with the education structure. Student's marks and the result of some kind of psychometric test on students can form the training set for the supervised data mining algorithms. As the student's data in the educational systems is increasing day by day, the incremental learning properties are important for machine learning research. Against to the classical batch learning algorithm, incremental learning algorithm tries to forget unrelated information while training new instances. These days, combining classifiers is nothing but taking more than one opinion contributes a lot, to get more accurate results. Therefore, a suggestion is an incremental ensemble of three classifiers namely Naïve Bayes, K-Star, SVM using voting scheme. The ensemble technique proposed in this paper is compared with the incremental algorithms, without any ensemble concept, for the student's data set and it was found that the proposed algorithm gives better accuracy.
Different structure based control system of the PUMA manipulator with an arm exoskeleton

In this paper, we proposed a novel approach to control the famous PUMA manipulator with an arm exoskeleton which has been developed to enable force-feedback teleoperation and has totally different structures and dimensions from PUMA. We presented the hardware configuration and software implementation of the whole system, and then a particular issue was associated with workspace matching, which proved to be the key step for different structure based master/slave manipulator control. Finally, the conclusion was discussed, as well as the prospect of future work.
An Approach Towards the Verification of Component-Based Systems via Model Checking

Verification of software has received a lot of attentions of the software engineering community, specially modular verification of component-based software. However, to realize such an ideal component-based software paradigm, one of the key issues is to ensure that those separately specified and implemented components do not conflict to each other when composed the component consistency issue. A potential solution to the above issue is modular verification of component-based software via model checking. The main goal in this thesis is to combine the best advantages of model checking and component-based development. Currently there are many approaches have been proposed in modular verification of component-based software [2, 4, 7, 8, 10, 11, 22]. In [10, 11, 22], modular verification is rather closed. It is not prepared for future changes. If a component is added to the system, the whole system of many existing components and the new component must be re-checked altogether. For this reason, the “state space explosion problem” will occur when it checks complex software. The approach in [2, 4, 7, 8] focuses on checking a system composed of two components; M1 and M2 which satisfies the property p without composing M1 with M2. For this goal, this technique finds an assumption A such that it is strong enough for M1 to satisfy p and weak enough to be discharged by M2. From these, the composition
Improving the Performance of Protograph LDPC Codes by Using Different Transmission Energies

Irregular low-density parity-check (LDPC) codes constructed from small protographs are one of the most powerful LDPC codes. In this paper, we show that the performance of LDPC codes based on small protographs can be further improved by using different transmission energies for every variable node of the protograph. Thus, a protograph is now described by a set of variable nodes, a set of check nodes, edges connecting variable nodes and check nodes, and the transmission energy used for every variable node, which is called the energy distribution. We optimize the energy distribution of protographs by choosing the energy distribution with the highest threshold calculated with a generalization of the discretized density evolution. Furthermore, we show by simulations that the performance of long LDPC codes based on protographs can be improved as expected from the thresholds.
Research on E-Commerce Security Issues

With the rapid development of e-commerce, security issues are arising from people's attention. The security of the transaction is the core and key issues of the development of e-commerce. This paper about the security issues of e-commerce activities put forward solution strategy from two aspects that are technology and system, so as to improve the environment for the development of E-commerce and promote the further development of E-commerce.
Return-Oriented Programming Attack on the Xen Hypervisor

In this paper, we present an approach to attack on the Xen hypervisor utilizing return-oriented programming (ROP). It modifies the data in the hypervisor that controls whether a VM is privileged or not and thus can escalate the privilege of an unprivileged domain (domU) at run time. As ROP technique makes use of existed code to implement attack, not modifying or injecting any code, it can bypass the integrity protections that base on code measurement. By constructing such kind of attack at the virtualization layer, it can motivate further research work towards preventing or detecting ROP attack on the hypervisor.
Design of a novel dual-frequency microstrip patch antenna for WLAN applications

A novel dual-frequency microstrip patch antenna for WLAN applications is presented and demonstrated. By embedding two pairs of arc shaped slots with a narrow slot protruding at each side, dual-band operation, with its operating mode centered at about frequencies 2.4 GHz and 5.2 GHz, has been obtained. The proposed antenna has good gain and its impedance bandwidths defined by VSWR=2 can meet the requirements of dual ISM bands.
Subtree Extractive Summarization via Submodular Maximization

This study proposes a text summarization model that simultaneously performs sentence extraction and compression. We translate the text summarization task into a problem of extracting a set of dependency subtrees in the document cluster. We also encode obligatory case constraints as must-link dependency constraints in order to guarantee the readability of the generated summary. In order to handle the subtree extraction problem, we investigate a new class of submodular maximization problem, and a new algorithm that has the approximation ratio 12 (1 − e − 1 ) . Our experiments with the NTCIR ACLIA test collections show that our approach outperforms a state-of-the-art algorithm.