Miracle-dz/UniFAR-FTUs huggingface dataset:
Image character recognition using deep convolutional neural network learned from different languages

This paper proposes a shared-hidden-layer deep convolutional neural network (SHL-CNN) for image character recognition. In SHL-CNN, the hidden layers are made common across characters from different languages, performing a universal feature extraction process that aims at learning common character traits existed in different languages such as strokes, while the final softmax layer is made language dependent, trained based on characters from the destination language only. This paper is the first attempt to introduce the SHL-CNN framework to image character recognition. Under the SHL-CNN framework, we discuss several issues including architecture of the network, training of the network, from which a suitable SHL-CNN model for image character recognition is empirically learned. The effectiveness of the learned SHL-CNN is verified on both English and Chinese image character recognition tasks, showing that the SHL-CNN can reduce recognition errors by 16-30% relatively compared with models trained by characters of only one language using conventional CNN, and by 35.7% relatively compared with state-of-the-art methods. In addition, the shared hidden layers learned are also useful for unseen image character recognition tasks.
The shape of the utility function under risk in the loss domain and the "ruinous losses" hypothesis: some experimental results

This paper reports some preliminary experimental results as regards the shape of the utility function for losses when elicited over a wide interval of consequences. Individual utility functions are elicited using the trade-off method, which, unlike standard elicitation procedures, is robust to probability weighting (and avoids most cognitive biases). Even though most utility functions exhibit the usual convex shape, nearly 25% of them appear to be inverse-S shaped, with convexity over moderate losses changing to concavity as losses grow. Though not conclusive (due mainly to the small size of our subject pool), this result brings some new support to the old idea that ruinous or unacceptable losses may induce some abrupt change in the shape of the utility function. Most importantly, it paves the way for more systematic investigation of the "ruinous losses" hypothesis.
VCP Phosphorylation-Dependent Interaction Partners Prevent Apoptosis in Helicobacter pylori-Infected Gastric Epithelial Cells

Previous studies have demonstrated that valosin-containing protein (VCP) is associated with H. pylori-induced gastric carcinogenesis. By identifying the interactome of VCP overexpressed in AGS cells using a subtractive proteomics approach, we aimed to characterize the cellular responses mediated by VCP and its functional roles in H. pylori-associated gastric cancer. VCP immunoprecipitations followed by proteomic analysis identified 288 putative interacting proteins, 18 VCP-binding proteins belonged to the PI3K/Akt signaling pathway. H. pylori infection increased the interaction between Akt and VCP, Akt-dependent phosphorylation of VCP, levels of ubiquitinated proteins, and aggresome formation in AGS cells. Furthermore, phosphorylated VCP co-localized with the aggresome, bound ubiquitinated proteins, and increased the degradation of cellular regulators to protect H. pylori-infected AGS cells from apoptosis. Our study demonstrates that VCP phosphorylation following H. pylori infection promotes both gastric epithelial cell survival, mediated by the PI3K/Akt pathway, and the degradation of cellular regulators. These findings provide novel insights into the mechanisms of H. pylori infection induced gastric carcinogenesis.
Lab on the Web—Looking at Different Ways of Experiencing Electronic Experiments*

Students who perform a laboratory exercise via the Internet display decisive differences in how they approach this resource: either as just a way to gather data, as illustrations of theory or as a tool for reflection and testing of hypotheses. From more than 200 written student log books, student interviews and video recorded lab sessions we conclude that the students' approaches to measurements conducted through the Internet are very similar to that of measurements conducted with the students present in local classroom labs. Possible measures to support students in adopting a desirable approach to measurements are discussed.
A frame-based dialogue management approach

This paper is focused on our approach to hierarchical frame-based dialogue management. As we show, even when dealing with this simple technique, the manager exhibits complex skills, as for example acceptance of references to historical entities or maintenance of context causality (by utilizing application of a journaling system). Our research goal is to create a generic and easy-to-use manager. At the end of this paper, future work is outlined, as the manager is still under development.
Finding Relevant Answers in Question Answering System Contest

We have investigated the potential use of question answering systems and participated in the Question Answering Challenge (QAC) of National institute of informatics Test Collection for Information Retrieval systems (NTCIR). In this paper, we describe our question answering system, the preliminary results of our experiments to the contest and some possible improvement to question answering systems.
Authenticity Is in the Eye of the Beholder: The Exploration of Audiences’ Lay Associations to Authenticity Across Five Domains

Existing research has proposed multiple definitions to authenticity. Instead of such a definitional approach, this article takes a bottom-up approach and provides an exploratory assessment of lay associations to authenticity. I conducted a survey that asked participants to list the words they associate with authentic restaurants, people, paintings, brands, and organizations. I find that there is substantial variance among individuals in their associations to authenticity, and the meanings they evoke also change with the domain of evaluation. People also vary in the level of importance they place on authenticity across the different domains. I discuss the implications of these findings for authenticity research and practice in psychology, marketing, management, and sociology.
Teallach: a model-based user interface development environment for object databases

Model-based user interface development environments show promise for improving the productivity of user interface developers, and possibly for improving the quality of developed interfaces. However, model-based techniques have rarely been applied to the important area of database interfaces. This lack of experience with data intensive systems may have led to model-based projects failing to support certain requirements that are essential in data intensive applications, and has prevented database interface developers from benefiting from model-based techniques. This paper presents a model-based user interface development environment for object databases, describing the models it supports, the relationships between these models, and the tool used to construct interfaces using the models.
Russian regions in growth and crisis : equalization or stratification ?

The questions of regional economic development in Russia and its interaction to economic growth during the last decade are considered in the paper. It is shown, that intense increase of regional income differentiation for the last years of economic growth is going hand by hand with strong evidence of regional convergence for the most part of economic space. This specific case of disparities – Quasi-divergence – is provided thanking with few leader regions. Most part of Russian regional space converges with the rate of around 2% per year. On the base of distribution dynamic approach of D.Quah we build ergodic distribution of regional GRP per capita, and show that most part of regional space tends to concentrate to the interval from 1/4 to the average. We also find some evidence of a trade-off between economic growth and regional variation in the Russian macroregions. During the general convergence tendency the short time regional inequality increases are found. These inequality shocks – short time (1-2 years) increases are produced by very high growth rates (accelerations). During next years such risings of differences then follow back to the decrease tendency. On the base of macroeconomic forecasts it was estimated that modern crisis affect for Russian regional economic disparities to increase way. The potential policy implications of this analysis are also discussed in the paper.
Attentional Recruitment of Inter-Areal Recurrent Networks for Selective Gain Control

There is strong anatomical and physiological evidence that neurons with large receptive fields located in higher visual areas are recurrently connected to neurons with smaller receptive fields in lower areas. We have previously described a minimal neuronal network architecture in which top-down attentional signals to large receptive field neurons can bias and selectively read out the bottom-up sensory information to small receptive field neurons (Hahnloser, Douglas, Mahowald, & Hepp, 1999). Here we study an enhanced model, where the role of attention is to recruit specific inter-areal feedback loops (e.g., drive neurons above firing threshold). We first illustrate the operation of recruitment on a simple example of visual stimulus selection. In the subsequent analysis, we find that attentional recruitment operates by dynamical modulation of signal amplification and response multistability. In particular, we find that attentional stimulus selection necessitates increased recruitment when the stimulus to be selected is of small contrast and of small distance away from distractor stimuli. The selectability of a low-contrast stimulus is dependent on the gain of attentional effects; for example, low-contrast stimuli can be selected only when attention enhances neural responses. However, the dependence of attentional selection on stimulus-distractor distance is not contingent on whether attention enhances or suppresses responses. The computational implications of attentional recruitment are that cortical circuits can behave as winner-take-all mechanisms of variable strength and can achieve close to optimal signal discrimination in the presence of external noise.
Unsupervised identification and prediction of foothold robustness

This paper addresses the problem of evaluating and estimating the mechanical robustness of footholds for legged robots in unstructured terrain. In contrast to approaches that rely on human expert knowledge or human defined criteria to identify appropriate footholds, our method uses the robot itself to assess whether a certain foothold is adequate or not. To this end, one of the robot's legs is employed to haptically explore an unknown foothold. The robustness of the foothold is defined by a simple metric as a function of the achievable ground reaction forces. This haptic feedback is associated with the foothold shape to estimate the robustness of untouched footholds. The underlying shape clustering principles are tested on synthetic data and in hardware experiments using a single-leg testbed.
Upper and Lower Bounds for Competitive Online Routing on Delaunay Triangulations

Consider a weighted graph G where vertices are points in the plane and edges are line segments. The weight of each edge is the Euclidean distance between its two endpoints. A routing algorithm on G has a competitive ratio of c if the length of the path produced by the algorithm from any vertex s to any vertex t is at most c times the length of the shortest path from s to t in G. If the length of the path is at most c times the Euclidean distance from s to t, we say that the routing algorithm on G has a routing ratio of c.We present an online routing algorithm on the Delaunay triangulation with competitive and routing ratios of 5.90. This improves upon the best known algorithm that has competitive and routing ratio 15.48. The algorithm is a generalization of the deterministic 1-local routing algorithm by Chew on the L1-Delaunay triangulation. When a message follows the routing path produced by our algorithm, its header need only contain the coordinates of s and t. This is an improvement over the currently known competitive routing algorithms on the Delaunay triangulation, for which the header of a message must additionally contain partial sums of distances along the routing path.We also show that the routing ratio of any deterministic k-local algorithm is at least 1.70 for the Delaunay triangulation and 2.70 for the L1-Delaunay triangulation. In the case of the L1-Delaunay triangulation, this implies that even though there exists a path between two points x and y whose length is at most 2.61|[xy]| (where |[xy]| denotes the length of the line segment [xy]), it is not always possible to route a message along a path of length less than 2.70|[xy]|. From these bounds on the routing ratio, we derive lower bounds on the competitive ratio of 1.23 for Delaunay triangulations and 1.12 for L1-Delaunay triangulations.
Knowledge-poor and Knowledge-rich Approaches for Multilingual Terminology Extraction

In this paper, we present two terminology extraction tools in order to compare a knowledge-poor and a knowledge-rich approach. Both tools process single and multi-word terms and are designed to handle multilingualism. We run an evaluation on six languages and two di erent domains using crawled comparable corpora and hand-crafted reference term lists. We discuss the three main results achieved for terminology extraction. The rst two evaluation scenarios concern the knowledge-rich framework. Firstly, we compare performances for each of the languages depending on the ranking that is applied: speci city score vs. the number of occurrences. Secondly, we examine the relevancy of the term variant identi cation to increase the precision ranking for any of the languages. The third evaluation scenario compares both tools and demonstrates that a probabilistic term extraction approach, developed with minimal e ort, achieves satisfactory results when compared to a rule-based method.
Set-Based Bi-level Optimisation for QoS-Aware Service Composition in Ubiquitous Environments

Service composition is a widely used method in ubiquitous computing that enables accomplishing complex tasks required by users based on elementary (hardware and software) services available in ubiquitous environments. To ensure that users experience the best Quality of Service (QoS) with respect to their quality needs, service composition has to be QoS-aware. Establishing QoS-aware service compositions entails efficient service selection taking into account the QoS requirements of users. A challenging issue towards this purpose is to consider service selection under global QoS requirements (i.e., Requirements imposed by the user on the whole task), which is of high computational cost. This challenge is even more relevant when we consider the dynamics, limited computational resources and timeliness constraints of ubiquitous environments. To cope with the above challenge, in this paper we present QASSA, an efficient service selection algorithm that provides the appropriate ground for QoS-aware service composition in ubiquitous environments. The contribution of QASSA is three-fold. First, it formulates service selection under global QoS requirements as a set-based optimisation problem, benefiting from recent proposals in the domain of multi-objective optimisation. Second, QASSA resolves this problem in an efficient way using clustering techniques, namely the K-Means algorithm. Third, QASSA is devised in two versions, viz., centralised and distributed versions, so that it can be executed on top of centralised and decentralised infrastructures in ubiquitous environments. Results of experimental studies are presented to illustrate the timeliness and optimality of QASSA.
Risks of metabolic syndrome in students of the faculty of health sciences.

BACKGROUND Metabolic syndrome is highly prevalent in the adult population worldwide. Education may play an important role in preventing metabolic syndrome in young adults, especially those who are attending university. Such adults are at a critical point in their lives and make their own lifestyle choices that can affect their future health. AIMS The aims of this study were to determine the metabolic syndrome risk levels of students from the Faculty of Health Sciences. STUDY DESIGN Survey design study. METHODS In a questionnaire developed by the researchers to collect data in accordance with the relevant literature, the scale of the risk of metabolic syndrome was assessed. A stepwise logistic regression analysis was performed to determine the risks. RESULTS Important risk factors for metabolic syndrome were found to be gender, weight gain, "stress eating" excessive amounts of food, sleeping for more than 8 hours a day, feeling tired after sleep, belonging to a divided family, and eating whilst working on the computer. CONCLUSION The students from the Faculty of Health Sciences, particularly because they are trained in the health sector, are expected to have more information about the risk factors of metabolic syndrome, and take necessary precautions to prevent it.
Performance evaluation of partial deployment of Breadcrumbs in content oriented networks

In recent years, much work has been devoted to developing protocols and architectures for supporting the growing trend of data-oriented services. One drawback of many of these proposals is the need to upgrade or replace all the routers in order for the new systems to work. Among the few systems that allow for gradual deployment is the recently-proposed Breadcrumbs technique for distributed coordination among caches in a cache network. Breadcrumbs uses information collected locally at each cache during past downloads to support in-network guiding of current requests to desired content. Specifically, during content download a series of short-term pointers, called breadcrumbs, is set up along the download path. Future requests for this content are initially routed towards the server which holds (a copy of) this content. However, if this route leads the request to a Breadcrumbs-supporting router, this router re-directs the request in the direction of the latest downloaded, using the aforementioned pointers. Thus, content requests are initially forwarded by a location ID (e.g., IP address), but encountering a breadcrumb entry can cause a shift over to content-based routing. This property enables the Breadcrumbs system to be deployed gradually, since it only enhances the existing location-based routing mechanism (i.e. IP-based routing). In this paper we evaluate the performance of a network where Breadcrumbs is only partially deployed. Our simulation results show Breadcrumbs performs poorly when sparsely deployed. However, if an overlay of Breadcrumbs-supporting routers is setup, system performance is greatly improved. We believe that the reduced load on servers achieved with even a limited deployment of Breadcrumbs-supporting routers, combined with the flexibility of being able to deploy the system gradually, should motivate further investigation and eventual deployment of Breadcrumbs.
Navigation and Guidance Planning for Air Vehicles

Path planning is the art of deciding which route to take, based on and expressed in terms of the current internal representation of the terrain. Path finding also involves the execution of this theoretical route, by translating the plan from the internal representation in terms of physical movement in the environment. During combat, transfers of military units in the shortest period and with least damage are vitally important. In this study, navigation planning problems, described in semi-dynamic and fully-dynamic scenarios, have been solved using evolutionary algorithm approach. 3-D graph is used to mode the environment. In the first scenario, graph node and edge deletion and cost update operations are applied and the impact of the different parameters of genetic algorithm on the solution has been investigated. In the second scenario, in an environment with possibility of combat, the aim is to find the most secure shortest path by employing genetic algorithm approach dynamically, in case any combat information arrives during the transfer of units.
Hand gesture recognition for automatic tap system

This paper presents a technique for real time human hand gesture recognition system for automatic tap operation. Hand gesture recognition based machines have been widely developed in recent years. Most of the techniques use skin color models due to its robustness and simplicity. In this work there are two major steps for achieving this goal: firstly, the detection of skin color and secondly, the classification of a gesture into valid and invalid hand gesture for the operation of the water tap. The proposed system uses Support Vector Machine and neural network for classification of the hand gesture which in turn operates the tap.
Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier

The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu. edu.cn/software/hpfp.
Time-optimal path following for robots with trajectory jerk constraints using sequential convex programming

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator a convex reformulation of this optimal control problem has been derived previously [1]. However, the bang-bang nature of the time-optimal trajectories results in near-infinite jerks in joint space and operational (Cartesian) space. For systems with un-modeled flexibilities, this usually results in excitation of the resonant frequencies, hence in unwanted vibrations and acceleration peaks, contributing to a tracking error. These vibrations can be reduced by imposing jerk constraints on the trajectory [2]. However, these jerk constraints destroy the convexity of the time-optimal control problem. The present paper proposes an efficient sequential convex programming (SCP) approach to solve the corresponding non-convex optimal control problem by writing the non-convex jerk constraints as a difference of convex (DC) functions. We illustrate the developed approach by means of experiments with a seven d.o. f. robot. Furthermore, numerical simulations illustrate the fast convergence of the proposed method in only a few SCP iterations, confirming the efficiency and practicality of the proposed framework.