Miracle-dz/UniFAR-FTUs huggingface dataset:
OOCE: Object-Oriented Communication Engine for SoC Design

For decades, software middlewares have tackled with the heterogeneity and interconnectivity problems in computer networks with success. These problems are recurrent in the design of complex systems-on chip with a large number of components of different nature (including SW and HW modules). In this paper we present an object- oriented communication engine (OOCE) based on the architectural concepts used in software middlewares that unifies the inter-communication interface for both HW and SW elements. The infrastructure provided by the OOCE introduces a low-overhead abstraction layer that can be easily used to implement several parallel programming models. An implementation of this approach has been made for the Xilinx-V2Pro platform.
The Resegregation of Suburban Schools: A Hidden Crisis in American Education.

"The United States today is a suburban nation that thinks of race as an urban issue, and often assumes that it has been largely solved," write the editors of this groundbreaking and passionately argued book. They show that the locus of racial and ethnic transformation is now clearly suburban and illustrate patterns of demographic change in the suburbs with a series of rich case studies. The book concludes by considering what kinds of strategies school officials and community leaders can pursue at all levels to improve opportunities for suburban low-income students and students of colour, and what ways address the challenges associated with demographic change.
Pitch detection and voicing/unvoicing decision of Arabic speech signal by HOS-polycesptre

One of the most important issues in Speech Analysis is the extraction of a set of parameters that can, hopefully, help understand the speech process. Depending on the nature of the sound, these parameters could be the fundamental frequency (Pitch) and the harmonics (or formants). The first and second order statistics (autocorrelation, power spectrum, cepstrum) have proved its efficiency for detecting the pitch in signals without noise. The performances diminish, however, when signals are corrupted with noise. Research targeted towards development of methods that can resist to noise. In recent years new tools have been developed referred to as higher order statistics (HOS). These statistics have solved problems that have not been resolved by the first and second order statistics and to extract more information from the signal. In this article we use higher order statistics, in particular polycepstrale analysis for the detection of the fundamental frequency (pitch).
BCI and Physiological Computing for Computer Games : Differences , Similarities & Intuitive Control

This paper is concerned with how Physiological Computing (PC) and Brain-Computer Interfaces (BCI) may be used to enhance computer games. It is argued that PC offers a greater range of possibilities for future development. An analysis of both approaches revealed that PC functions at the meta-level of the Human-Computer Interaction (HCI) whilst BCI offer a novel communication channel within the HCI. It is argued that both offer distinct possibilities for intuitive control for both singleand multi-player games. It is concluded that PC and BCI may be combined for the next generation of computer games.
A simple semantic kernel approach for SVM using higher-order paths

The bag of words (BOW) representation of documents is very common in text classification systems. However, the BOW approach ignores the position of the words in the document and more importantly, the semantic relations between the words. In this study, we present a simple semantic kernel for Support Vector Machines (SVM) algorithm. This kernel uses higher-order relations between terms in order to incorporate semantic information into the SVM. This is an easy to implement algorithm which forms a basis for future improvements. We perform a serious of experiments on different well known textual datasets. Experiment results show that classification performance improves over the traditional kernels used in SVM such as linear kernel which is commonly used in text classification.
Secure universal designated verifier identity-based signcryption

In 2003, Steinfeld et al. introduced the notion of universal designated verifier signature UDVS, which allows a signature holder, who receives a signature from the signer, to convince a designated verifier whether he is possession of a signer's signature; at the same time, the verifier cannot transfer such conviction to anyone else. These signatures devote to protect the receiver's privacy, that is, the receiver may want to prove to any designated verifier who he is in possession of such signature signed by the known signer but reluctant to disclose it. Moreover, the receiver also does not want the verifier to be able to convince anyone that he is in possession of such signature. In the existing UDVS schemes, a secure channel is required between the signer and the signature holder to transfer the signature. This paper, for the first time, proposes the notion of universal designated verifier signcryption without this secure channel by combining the notions of UDVS and signcryption. We give the formal definitions and a concrete construction of universal designated verifier identity-based signcryption scheme. We also give the formal security proofs for our scheme under the random oracle model. Copyright © 2013 John Wiley & Sons, Ltd.
The wiretap channel with generalized feedback: Secure communication and key generation

It is well-known that feedback does not increase the capacity of point-to-point memoryless channels, however, the advantage it poses to secure communications is not fully understood yet. In this work, an achievable scheme for the wiretap channel with generalized feedback is presented. This scheme employs the feedback signal to generate a secret key shared between the legitimate users, which is then used to encrypt the message to be sent. It is shown that the resulting achievable rate recovers previous results, thus it can be seen as a generalization and unification of several results in the field.
Assessing the financial state of construction enterprises

Abstract The financial state of construction enterprises can be described by a set of criteria evaluating their commercial activity from various perspectives. The most important of them are solvency and financial risk, including the ratio of current assets to current liabilities, the ratio of current assets minus stocks to current liabilities, the ratio of equity to liabilities, the ratio of assets to liabilities and the ratio of current assets to equity. These values indicate how efficiently various resources, i. e. finances, materials, manpower, etc. are used at an enterprise. The criteria characterizing the financial state of an enterprise from various perspectives may change in different directions. Moreover, these criteria are multidimensional. Therefore, a problem of finding a unified approach to describe this conflicting situation arises. The particular criteria describe the particular aspects of a complex object. To obtain an integrated estimate of a considered object, multicriteria evaluation met.
Recognizing Textual Entailment Based on WordNet

Textual entailment recognition (RTE) is one of the fundamental problems in many natural language processing applications. This paper proposes a new method for lexical entailment measure which is based on exploiting the information in the WordNet glosses. Further we perform textual entailment recognition based on this method and cast the RTE problem to be a classification problem. The experimental result on RTE challenge data sets indicates that our method is promising.
CD8+ T Cell Responses to Plasmodium and Intracellular Parasites

Parasitic protozoa are major threats to human health affecting millions of people around the world. Control of these infections by the host immune system relies on a myriad of immunological mechanisms that includes both humoral and cellular immunity. CD8+ T cells contribute to the control of these parasitic infections in both animals and humans. Here, we will focus on the CD8+ T cell response against a subset of these protozoa: Plasmodium, Toxoplasma gondii, Leishmania and Trypanosoma cruzi, with an emphasis on experimental rodent systems. It is evident a complex interaction occurs between CD8+ T cells and the invading protozoa. A detailed understanding of how CD8+ T cells mediate protection should provide the basis for the development of effective vaccines that prevent and control infections by these parasites.
Predicting gene function in S. cerevisiae and A. thaliana using hierarchical multi-label decision tree ensembles

• S. cerevisiae (13 datasets) and A. Thaliana (12 datasets) • two of biology’s model organisms • most genes are annotated, ideal for testing purposes • method can be applied to other organisms • Data • based on sequence statistics, phenotype, secondary structure, homology, microarray data,. • Evaluation: precision-recall • precision: percentage of predicted functions that are correct • recall: percentage of actual functions predicted by the algorithm • Results Predictions in Functional Genomics • Our focus is on decision trees • Advantages: fast to build, noise-resistant, fast to apply, accurate predictions, easy to interpret, . • General framework: predictive clustering trees (PCTs) Predictive Clustering Trees • Classification: a common machine learning task • E.g., Given: genes with known function Task: predict function for new genes • Special case: hierarchical multi-label classification (HMC) • gene can have multiple functions • functions are organized in a hierarchy • tree (e.g., MIPS FunCat) • DAG (e.g., Gene Ontology) Hierarchical Multi-Label Classification (HMC) for Gene Function Prediction
Grey Kmeans algorithm and its application to the analysis of regional competitive ability

Mining and discovering clusters from tremendous data is a useful analysis work for many applications like economics, medicine, engineering, etc. As a widely applied clustering method, Kmeans has the merits of fast running and moderate clustering quality. However, the traditional Euclidean measure has its own inefficiency. In this paper, a new clustering method that integrates the grey relational analysis from grey theory into Kmeans algorithm is proposed to overcome the shortcomings of traditional Kmeans. By applying to the analysis of reginal competitive ability of regions in China, the new algorithm proved to be an effective and efficient method.
Event Logs for the Analysis of Software Failures: A Rule-Based Approach

Event logs have been widely used over the last three decades to analyze the failure behavior of a variety of systems. Nevertheless, the implementation of the logging mechanism lacks a systematic approach and collected logs are often inaccurate at reporting software failures: This is a threat to the validity of log-based failure analysis. This paper analyzes the limitations of current logging mechanisms and proposes a rule-based approach to make logs effective to analyze software failures. The approach leverages artifacts produced at system design time and puts forth a set of rules to formalize the placement of the logging instructions within the source code. The validity of the approach, with respect to traditional logging mechanisms, is shown by means of around 12,500 software fault injection experiments into real-world systems.
From Individual Contribution to Group Learning: the Early Years of Apache Web Server

Open Source Software (OSS) groups experience many benefits and challenges with respect to the core group's effectiveness. In order to capitalize on the benefits and minimize the challenges, OSS groups must leam not only on the individual level, but also on the group level. OSS groups leam by integrating individual contributions into the group's product and processes. This paper reports on the characteristics of the learning process in OSS groups. The study utilized an embedded single case study design that observed and analyzed group learning processes in the Apache Web server OSS project. The study used learning opportunity episodes (LOE) as the embedded unit of analysis and developed and utilized three content analytic schemes to describe the characteristics of the learning process and the factors affecting this process.
Mutual SIP Authentication Scheme Based on ECC Samaneh

this scheme vulnerable against some attacks, such as off-line password guessing attacks, replay attacks, impersonate attacks and etc. So, many researches proposed various schemes to secure the SIP authentication. In the year 2012, Tang et al. proposed a SIP authentication using elliptic curve cryptography (ECC), but their scheme is insecure against off-line password guessing and DoS attacks. We proposed an ECC-based authentication scheme for SIP to overcome such security problems and analysis of security of the ECC-based protocol indicates that our scheme is appropriate for the applications with higher security requirement.
A Lagrange Multiplier and Hopfield-Type Barrier Function Method for the Traveling Salesman Problem

A Lagrange multiplier and Hopfield-type barrier function method is proposed for approximating a solution of the traveling salesman problem. The method is derived from applications of Lagrange multipliers and a Hopfield-type barrier function and attempts to produce a solution of high quality by generating a minimum point of a barrier problem for a sequence of descending values of the barrier parameter. For any given value of the barrier parameter, the method searches for a minimum point of the barrier problem in a feasible descent direction, which has a desired property that lower and upper bounds on variables are always satisfied automatically if the step length is a number between zero and one. At each iteration, the feasible descent direction is found by updating Lagrange multipliers with a globally convergent iterative procedure. For any given value of the barrier parameter, the method converges to a stationary point of the barrier problem without any condition on the objective function. Theoretical and numerical results show that the method seems more effective and efficient than the softassign algorithm.
Autonomic Prioritization of Enterprise Transactions Based on Bid-Price Controls

We study the business-driven prioritization of workflows in enterprise transaction processing systems. Today, IT services are increasingly shared amongst workflows, and static prioritization is applied during phases of high system workload to mitigate the business impact of service queuing and resulting workflow response-time increase. Static prioritization rules can only be optimal for one particular workflow definition and demand mix scenario, while in practice workflow definitions are continuously re-engineered and demand varies over time. We propose a technique to automatically adapt workflow priorities aimed at minimizing overload-related costs resulting from workflow delays. We continuously align maximum service buffer lengths and bid-prices reflecting current marginal opportunity costs of invoking a shared service. New prioritization is derived by comparing expected costs when queuing a workflow with its current opportunity costs, computed as the sum of bid-prices of invoked services. Simulation outcomes based on industry data illustrate that the approach dominates standard queuing disciplines as well as static prioritization.
A cluster-based approach for search and exploration of tag spaces

Although Semantic Web technology is increasingly becoming more and more important, tagging remains a popular method to describe Web resources. Therefore it is important to address the issues that are found in current tagging search engines, such as Flickr. We find that the free nature of tagging results in many issues for tag search engines, such as synonyms, homonyms, syntactic variations, etc. The Semantic Tag Clustering Search (STCS) framework deals with these issues by detecting syntactic variations of tags and by clustering semantically related tags. We evaluate our framework using Flickr data from 2009 and compare the STCS framework to two previously introduced tag clustering techniques.
Effect of Visual Input and Foam Rubber on Leg-Joint Angles and Sway of Center of Pressure during Stepping on the Spot

Dynamic balance has frequently been evaluated by using center of pressure (COP) during stepping movement. COP sway may be greater during stepping on foam rubber, which is a kind of external load. This study aimed to examine the effect of leg-joint angles and COP sway during stepping on a spot in 13 young male adults (mean age 25.6 ± 4.8 years, mean height 170.6 ± 5.2 cm, mean mass 69.1 ± 8.2 kg), with or without foam rubber and with eyes open or closed. When subjects stepped on foam rubber placed on two force plates (right and left), while matching a tempo of 100 bpm for 20 s under the above four conditions, characteristics such as mean angles of hip, knee, and ankle joints and COP sway were measured. In the results of two-way ANOVA (rubber condition × eye condition) and post-hoc tests, the hip-joint angle was significantly greater with eyes open, regardless of with or without foam rubber. The knee-joint angle was significantly greater in eyes closed with off-foam-rubber than on-foam-rubber. The ankle-joint angle was significantly greater in off- foam-rubber, regardless of eyes open or closed, and with eyes open than with eyes closed, regardless of with or without foam rubber. The total trace length and velocity were significantly greater in off-foam-rubber with eyes open, and on-foam-rubber with eyes closed than on-foam-rubber with eyes open. The other sway parameters were significantly greater with eyes open than with eyes closed regardless of with or without foam rubber. The hip-joint angle was significantly greater with eyes open regardless of with or without foam rubber. In conclusion, the COP sway during stepping with a stipulated tempo is less with eyes open than eyes closed, but the motion of knee and ankle joints is greater with eyes open. The sway distance and velocity during stepping with eyes open and motion of knee and ankle joints during stepping with eyes closed are both affected by the foam-rubber load. When stepping on the foam rubber, movement of the knee joints relates to distance and velocity of COP regardless of open or closed eyes. The findings in this study will be used to evaluate balance functions that are closely related to basic movements.
3D Reconstruction From Non-Euclidian Distance Fields

Figure 1: Left: Naive reconstruction by linear interpolation of 2D Euclidian distances to aliased (i.e. binary) input contours of a two-headed dragon. Note the semi-transparent image plane corresponding to one of the horizontal sampling planes. Middle: Our improved spline interpolation of Non-Euclidian distances. Right: Hight-field representations of the two distance fields shown as semi-transparent planes. Green is the Euclidian distance and red is the non-Euclidian distance. Note how the smoothing suppresses the medial-axis on the red surface.
Computing Fractal Dimension of Signals using Multiresolution Box-counting Method

In this paper, we have developed a method to compute fractal dimension (FD) of discrete time signals, in the time domain, by modifying the box-counting method. The size of the box is dependent on the sampling frequency of the signal. The number of boxes required to completely cover the signal are obtained at multiple time resolutions. The time resolutions are made coarse by decimating the signal. The loglog plot of total number of boxes required to cover the curve versus size of the box used appears to be a straight line, whose slope is taken as an estimate of FD of the signal. The results are provided to demonstrate the performance of the proposed method using parametric fractal signals. The estimation accuracy of the method is compared with that of Katz, Sevcik, and Higuchi methods. In ddition, some properties of the FD are discussed.
Epigenetic Silencing of miR-338-3p Contributes to Tumorigenicity in Gastric Cancer by Targeting SSX2IP

MicroRNA has been recently recognized as playing a prominent role in tumorigenesis and metastasis. Here, we report that miR-338-3p was epigenetically silenced in gastric cancer, and its down-regulation was significantly correlated with gastric cancer clinicopathological features. Strikingly, restoring miR-338-3p expression in SGC-7901 gastric cancer cells inhibited proliferation, migration, invasion and tumorigenicity in vitro and in vivo, at least partly through inducing apoptosis. Furthermore, we demonstrate the oncogene SSX2IP is a target of miR-338-3p. We propose that miR-338-3p functions as a tumor suppressor in gastric cancer, and the methylation status of its CpG island could serve as a potential diagnostic marker for gastric cancer.
Researching the Changes of CO2 Stock Based on the PLSR Method in Shaanxi

It's more and more important to effectively control and reduce the emission of as global warming. Shaanxi joins the unit to control and reduce the emission of for it's an industrializing economy. According to the gap theory, this paper selects GDP , population, energy intensity and forest area as independent variables to analyze their effects on the stock of in Shaanxi. The PLSR (partial least square regression model) is developed through the cross-sectional data from 1996 to 2008. The result shows that the standardized PLSR model established by three principle components makes the effect of every independent variable to the stock of in Shaanxi becoming more precise. In addition, =1.178, =0.022, =0.164and =-0.099. Consequently, GDP plays an important role in the stock of in Shaanxi, Energy intensity follows, and the forest area has a negative correlation with it.
A Lagrangean Decomposition Approach for Robust Combinatorial Optimization ?

We address robust versions of combinatorial optimization problems, specializing on the discrete scenario case and the uncorrelated ellipsoidal uncertainty case. We present a branch and bound-algorithm for the min-max variant of these problems which uses lower bounds obtained from Lagrangean decomposition, allowing to separate the uncertainty aspect in the objective function from the combinatorial structure of the feasible set. For addressing the unrestricted binary subproblems arising in the uncorrelated ellipsoidal uncertainty case and in the twoscenario case, we devise fast combinatorial algorithms, where in the latter case we resort to solving a relaxation of the problem. In an extensive experimental evaluation we apply this approach to the robust versions of knapsack, shortest path, and minimum spanning tree problems. The results show that our approach clearly outperforms other solution methods in the uncorrelated ellipsoidal uncertainty case, as well as in the twoscenario case when the underlying problem is not given by a compact linear description.
Keyword weight adjusting schema based on Domain Repository

Webpage keyword is widely used in personalized search and recommendations. The accuracy of keyword is significant to the quality of search and recommendation result. Current keyword extraction methods' accuracy is not high. In order to make up for the shortage of present technology, a new keyword weight adjusting schema based on Domain Repository has been proposed in this paper. Three steps: 1) Extract original keywords by word frequency statistics. 2) Match original keywords with concepts in Domain Repository in this page's field. 3) Increase the weight of words more related to this field then abstract its meaning to build the weighted keywords. Extensive experiments show that this method has better resource description accuracy than traditional statistical keyword extracting method.