Miracle-dz/UniFAR-FTUs huggingface dataset:
Using aspects and annotations to separate application code from design patterns

Design patterns are an invaluable resource to generate effective design solutions. However, employing some of them can be cumbersome for some application classes when these are forced to mix domain- with pattern-related code. Indeed, for a number of design patterns, we view the code implementing the behaviour required by their roles as a snippet, or concern, which is not involved in the domain issues that a class is supposed to address. This paper proposes a methodology to obtain the behaviour described by some well-known design patterns, which allows better separation between domain and non-domain concerns through recourse to aspects and annotations. Indeed, application classes retain all the benefits that the design patterns are supposed to give them, while staying thoroughly separated from non-domain concerns. Moreover, fewer lines of code are needed to obtain a design pattern than in other approaches, and the propagation of changes to application code, caused by the introduction or removal of a design pattern, are greatly reduced.
AN ALGORITHM TO VERIFY LOCAL THRESHOLD TESTABILITY OF DETERMINISTIC FINITE AUTOMATA

A locally threshold testable language L is a language with the property that for some nonnegative integers k and l, whether or not a word u is in the language L depends on (1) the preex and suux of the word u of length k? 1 and (2) the set of intermediate substrings of length k of the word u where the sets of substrings occurring at least j times are the same, for j l. For given k and l the language is called l-threshold k-testable. A nite deterministic automaton is called l-threshold k-testable if the automaton accepts a l-threshold k-testable language. In this paper, the necessary and suucient conditions for an automaton to be locally threshold testable are found. We introduce the rst polynomial time algorithm to verify local threshold testability of the automaton based on this characterization.
Alternative Neural Network Based Edge Detection

The edge detection on the images is so important for image processing. There are different methods for improving edge detection. Here, it is shown that edge detection can be realized using artificial neural network (ANN) with noise. Supervised learning method with momentum is used. Laplacian edge detector is a teacher of artificial neural network. In this study, it is shown that any classical method can be used for training of ANN as edge detector. Keywords—Neural Networks, Edge Detection, Laplacian
Wireless Sensors Clustering Tradeoffs

Simone Campanoni, William Fornaciari Politecnico di Milano, DEI – Milano, Italy ABSTRACT This paper addresses the problem of designing WSNs (Wireless Sensor Networks) with particular emphasis on the choice and space distribution of the sensors set, based upon performance and cost effectiveness of both realization and in-field deployment. The starting point is a formalization of the operating environment and of the sensing goal of the WSN. Such models are the base to select a suitable set of sensor-position pairs, and to enable a sensitivity analysis of the overall network performance against parameters like cost, changing of sensor position, ageing, energy-related lifetime, faults, etc. The focus of this paper is on the mapping of the optimal WSN onto realistic architecture templates, where the sensors have to be clustered in boards, and some of them possibly can act as gateways for sensor patches (subnets). The overall design flow is sketched, and particular attention is paid to the modeling of the board cost and on the relation existing between spatial clustering of sensors and the attitude of the so modified network to still recognize the events. Some case studies are included in the paper, mainly derived from two multi-partner projects.
Survey on Incremental Information Extraction Using Relational Database

Information Extraction is the task of automatically extracting structured information from unstructured or semi-structured machine readable documents. In which extraction are expressed in the form of database query and query evaluated by using database system. Information extraction provide automated query generation components so that causual user do not have to learn query language in order to perform extraction. In Information extraction goal must be in the form of database query. Query must be evaluated and optimized by database system. Information Extraction is an active research area that uncover information from large collection of text. We performed experiments to highlight two important aspect of an information extraction system: Efficiency and Quality of extraction result. Our experiment show that query performance is efficient for real time application. Our approach is composed of two phases first initial phase for processing of text and second Extraction phase for using database query to perform extraction. Incremental Extraction approach reduce processing time 90 percent as compare to traditional approach.
Friendship between children using augmentative and alternative communication and peers: A systematic literature review

ABSTRACT Background Although research on friendship among children without any reported disability is extensive, the investigation of the characteristics of friendship among children who are using augmentative and alternative communication (AAC) has received little attention. In this systematic literature review we investigated the current research on friendship between children who use AAC under 13 years of age. Method Database searches were undertaken with English search terms. The review included 8 articles. Results The results revealed that current research lacks systematic development of knowledge. Children using AAC have a different basis for establishing and developing friendship, due to restrictions in presence, participation, interaction, and communication with peers. Conclusions Future research could pay closer attention to how children using AAC develop friendship, and how such relationships can be supported. This research could usefully incorporate the body of research on friendship among children without disability.
Molecular and functional characterization of the kstD2 gene of Rhodococcus erythropolis SQ1 encoding a second 3-ketosteroid Delta(1)-dehydrogenase isoenzyme.

Previously, Rhodococcus erythropolis SQ1 kstD, encoding ketosteroid Delta(1)-dehydrogenase (KSTD1) was characterized. Surprisingly, a kstD gene deletion mutant (strain RG1) grew normally on steroids. UV mutagenesis of strain RG1 allowed isolation of strains (e.g. strain RG1-UV29) unable to perform the Delta(1)-dehydrogenation of 4-androstene-3,17-dione (AD) and 9alpha-hydroxy-4-androstene-3,17-dione (9OHAD). Functional complementation of strain RG1-UV29 with total genomic DNA of strain RG1 resulted in identification of a 1698 nt ORF (kstD2) showing clear similarity (35% identity at amino acid sequence level) with KSTD1. Expression of kstD2 in Escherichia coli resulted in high KSTD2 activity levels. Single gene deletion mutants of either kstD (strain RG1) or kstD2 (strain RG7) appeared unaffected in growth on the steroid substrates AD, 1,4-androstadiene-3,17-dione and 9OHAD. Strain RG7, but not strain RG1, showed temporary accumulation of 9OHAD during AD conversion. A kstD kstD2 double deletion mutant (strain RG8) was constructed. Strain RG8 was unable to grow on steroid substrates, had undetectable steroid Delta(1)-dehydrogenation activity and efficiently converted AD into 9OHAD. Strain SQ1 thus employs two KSTD isoenzymes in steroid catabolism. Analysis of two null mutants in KSTD2 showed that the semi-conserved Ser325 and the highly conserved Thr503 play a role in KSTD enzyme activity.
Inter Time Series Sales Forecasting

Combining forecast from different models has shown to perform better than single forecast in most time series. To improve the quality of forecast we can go for combining forecast. We study the effect of decomposing a series into multiple components and performing forecasts on each component separately. The original series is decomposed into trend, seasonality and an irregular component for each series. The statistical methods such as ARIMA, Holt-Winter have been used to forecast these components. In this paper we focus on how the best models of one series can be applied to similar frequency pattern series for forecasting using association mining. The proposed method forecast values has been compared with Holt Winter method and shown that the results are better than Holt Winter method
Hidden Variable Theories: Arguments for a Paradigm Shift

Usually the 'hidden variables' of Bell's theorem are supposed to describe the pair of Bell particles. Here a semantic shift is proposed, namely to attach the hidden variables to a stochastic medium or field in which the particles move. It appears that under certain conditions one of the premises of Bell's theorem, namely 'measurement independence', is not satisfied for such 'background-based' theories, even if these only involve local interactions. Such theories therefore do not fall under the restriction of Bell's no-go theorem. A simple version of such background-based models are Ising models, which we investigate here in the classical and quantum regime. We also propose to test background-based models by a straightforward extension of existing experiments. The present version corrects an error in the preceding version.
Centering Theory for Evaluation of Coherence in Computer-Aided Summaries

This paper investigates a new evaluation method for assessing the coherence of computer-aided summaries, justified by the inappropriacy of existing evaluation methods for this task. It develops a metric for Centering Theory (CT), a theory of local coherence and salience, to measure coherence in pairs of extracts and abstracts produced in a computer-aided summarisation environment. 100 news text summaries (50 pairs of extracts and their corresponding abstracts) are analysed using CT and the metric is applied to obtain a score for each summary; the summary with the higher score out of a pair is considered more coherent. Human judgement is also obtained to allow a comparison with the CT evaluation to assess the validity of the development of CT as a useful evaluation metric in computer-aided summarisation.
An Adaptive Object Detection Scope Algorithm Based on SIFT

For camera movement causes moving objects detecting and tracking problems under complex background, we propose an adaptive object detection scope algorithm based on SIFT features. Firstly, let camera stationary and obtain three images to detect the moving object by using three-frame-difference method, then extract the object SIFT features. Secondly, according to the location and displacement of the object in the dynamic background, we determine the detection scope which matches the object well and obtain the minimum rectangle which can surround the right matching points in the detection scope, and then update the object template. The algorithm avoids the analysis of the complex relative motion between the object and the background, and reduces mismatch points and the calculation amount. This algorithm can quickly and accurately track the object without occlusion, and performs robust in small occlusion case.
Assisted viewpoint interaction for 3d visualizations

Many three-dimensional visualizations are characterized by the use of a mobile viewpoint that offers multiple perspectives on a set of visual information. To effectively control the viewpoint, the viewer must simultaneously manage the cognitive tasks of understanding the layout of the environment, and knowing where to look to find relevant information, along with mastering the physical interaction required to position the viewpoint in meaningful locations. Numerous systems attempt to address these problems by catering to two extremes: simplified controls or direct presentation. This research attempts to promote hybrid interfaces that offer a supportive, yet unscripted exploration of a virtual environment. Attentive navigation is a specific technique designed to actively redirect viewers' attention while accommodating their independence. User-evaluation shows that this technique effectively facilitates several visualization tasks including landmark recognition, survey knowledge acquisition, and search sensitivity. Unfortunately, it also proves to be excessively intrusive, leading viewers to occasionally struggle for control of the viewpoint. Additional design iterations suggest that formalized coordination protocols between the viewer and the automation can mute the shortcomings and enhance the effectiveness of the initial attentive navigation design. The implications of this research generalize to inform the broader requirements for Human-Automation interaction through the visual channel. Potential applications span a number of fields, including visual representations of abstract information, 3D modeling, virtual environments, and teleoperation experiences.
Coyote Project: The Simulator

Data caches are a key component of current microprocessors for reducing memory latency. It is well known that the effectiveness of a cache memory depends not only on the hardware structure, but also on the code generated by the compiler. Various hardware and software approaches have been proposed to exploit caches efficiently. Software-controlled prefetching [12] hides the memory latency by overlapping a memory access with computation and other accesses. Another useful optimization is applying loop transformations such as tiling [4, 6, 11, 17] and data layout transformations [5, 10, 13, 14]. In all cases, a fast and accurate assessment of a program’s cache behavior at compile time is needed to make an appropriate choice of parameter values. Real-time systems rely on the assumption that tasks’ worst-case execution times (WCETs) are known. In order to get an accurate WCET, a tight worstcase memory performance (WCMP) is needed. However, cache behavior is very hard to predict, which leads to an overestimation of the WCMP, and thus for the WCET as well. Frameworks of WCET prediction are used to ensure that deadlines of tasks can be met. While the computation of WCET in the presence of instruction caches has progressed in such a way that makes it possible to obtain an accurate estimate of the WCET [1, 2, 8], there has not been much
Operator space valued Hankel matrices

If E is an operator space, the non-commutative vector valued Lp spaces Sp[E] have been defined by Pisier for any 1 ≤ p ≤ ∞. In this paper a necessary and sufficient condition for a matrix of the form (ai+j)0≤i,j with ak ∈ E to be bounded in Sp[E] is established if 1 ≤ p < ∞. This extends previous results of Peller where E = C or E = Sp. The main theorem states that if 1 ≤ p < ∞, (ai+j )0≤i,j is bounded in Sp[E] if and only if there is an analytic function φ in the vector valued Besov Space B 1/p p (E)+ such that an = b φ(n) for all n ∈ N. In particular this condition only depends on the Banach space structure of E. We also show that the norm of the isomorphism φ 7→ (b φ(i+j))i,j grows as √ p as p → ∞, and compute the norm of the natural projection onto the space of Hankel matrices.
Forecasting the Term Structure of Variance Swaps

Recently, Diebold and Li (2003) obtained good forecasting results for yield curves in a reparametrized Nelson-Siegel framework. We analyze similar modeling approaches for price curves of variance swaps that serve nowadays as hedging instruments for options on realized variance. We consider the popular Heston model, reparametrize its variance swap price formula and model the entire variance swap curves by two exponential factors whose loadings evolve dynamically on a weekly basis. Generalizing this approach we consider a reparametrization of the three-dimensional Nelson-Siegel factor model. We show that these factors can be interpreted as level, slope and curvature and how they can be estimated directly from characteristic points of the curves. Moreover, we analyze a semiparametric factor model. Estimating autoregressive models for the factor loadings we get termstructure forecasts that we compare in addition to the random walk and the static Heston model that is often used in industry. In contrast to the results of Diebold and Li (2003) on yield curves, no model produces better forecasts of variance swap curves than the random walk but forecasting the Heston model improves the popular static Heston model. Moreover, the Heston model is better than the flexible semiparametric approach that outperforms the Nelson-Siegel model.
Minimizing Post-Resolution Costs in Bank Failures *

Significant bank failures have been widespread in recent years, affecting almost every country with a banking system. A map published by the IMF in 1996 and reproduced in Figure 1 shows that only the United Kingdom, the Benelux countries and a few other countries generally with less developed banking systems escaped suffering significant banking problems and a substantial number suffered serious banking crisis. And this was before the East Asian banking and currency crises of 1997 and the continuation of the Japanese crisis. As a result, if the map were to be update, a number of additional countries would be darkened. Bank crises generate two costs to the domestic economies. One is a fiscal transfer cost and the other is a slowdown or actual decline in aggregate real output that results in a loss in output from a trend or capacity level. The fiscal cost occurs because some or all stakeholders in bank failures may be protected by the government against loss through schemes such as deposit insurance. If the bank losses are sufficiently large, the necessary funds to make the stakeholders whole are likely to come from the taxpayer. This represents a transfer payment and redistribution of income. Although it does not represent lost output directly, it is likely to do so indirectly through deadweight losses. The savings and loan association (S&L) crisis in the U.S. in the 1980s resulted in taxpayer transfers
On the multivariate analysis of animal networks

From the individual to species level, it is common for animals to have connections with one another. These connections can exist in a variety of forms; from the social relationships within an animal society, to hybridisation between species. The structure of these connections in animal systems can be depicted using networks, often revealing non-trivial structure which can be biologically informative. Understanding the factors which drive the structure of animal networks can help us understand the costs and benefits of forming and maintaining relationships. Multivariate modelling provides a means to evaluate the relative contributions of a set of explanatory factors to a response variable. However, conventional modelling approaches use statistical tests which are unsuitable for the dependencies inherent in network and relational data. A solution to this problem is to use specialised models developed in the social sciences, which have a long history in modelling human social networks. Taking predictive multivariate models from the social sciences and applying them to animal networks is attractive given that current analytical approaches are predominantly descriptive. However, these models were developed for human social networks, where participants can self-identify relationships. In contrast, relationships between animals have to be inferred through observations of associations or interactions, which can introduce sampling bias and uncertainty to the data. Without appropriate care, these issues could lead us to make incorrect or overconfident conclusions about our data. In this thesis, we use an established network model, the multiple regression quadratic assignment procedure (MRQAP), and propose approaches to facilitate the application of this model in animal network studies. Through demonstrating these approaches on three animal systems, we make new biological findings and highlight the importance of considering data-sampling issues when analysing networks. Additionally, our approaches have wider applications to animal network studies where relationships are inferred through observing dyadic interactions.
Locality Awareness in Chord Based Structured Peer to Peer Networks

In peer-to-peer (P2P) overlay networks, the mechanism of a peer randomly joining and leaving a network, cause a topology mismatch between the overlay and the underlying physical topology. This cause a large volume of redundant traffic in the underlying physical network as well as an extra delay in message delivery in the overlay network. Topology mismatch occurs because overlay networks are not aware of their underlying physical networks. Chord is P2P overlay protocol used several P2P network. In this paper, we present a mechanism to enhance efficiency of Chord based P2P network by exploiting information of underlying physical network (i.e. TCP/IP). Our mechanism is backed by a theoretical analysis on address space of Chord protocol. Finally, using simulation, we examine the efficiency of proposed mechanism and show that the mechanism significantly increases efficiency of the Chord protocol.
Extraction of determinants of postoperative length of stay from operation records

Secondary use of clinical text data are gaining much attention in improving the quality and the efficiency of medical treatment. Although there is some case studies of medical-examination text data, there are not many examples fed back to the medical-examination spot. The present paper analyses the operation records of total hip arthroplasty. We extracted feature words that characterize the two peaks which appeared in distribution of postoperative hospital days using SVM (support vector machine) and FS (feature selection). The models gained by optimal FS attained 60% accuracy as prediction performance. We applied logistic regression analysis to estimate postoperative length of stay from the extracted feature words. Most words were not statistically significant except two words.
Transcutaneous electrical nerve stimulation at the PC-5 and PC-6 acupoints reduced the severity of hypotension after spinal anaesthesia in patients undergoing Caesarean section.

BACKGROUND Despite prophylactic measures, hypotension remains a common side-effect of spinal anaesthesia for parturients. Electroacupuncture at the Neiguan (PC-6) and Jianshi (PC-5) points influences haemodynamics. We thus hypothesized that transcutaneous electrical nerve stimulation (TENS) at traditionally used acupuncture points would reduce the severity of hypotension after spinal anaesthesia in patients undergoing Caesarean section. METHODS After obtaining approval from the local ethics committee and written informed patient consent, 36 singleton parturients undergoing Caesarean section under spinal anaesthesia were randomized into three groups. The control group received no treatment, and the acupoint and non-acupoint groups received TENS at the PC-5 and PC-6 points of both arms and non-acupoints of both shoulders, respectively. RESULTS The median (range) of the lowest recorded systolic blood pressure was significantly higher in the acupoint group compared with the other groups and that of the non-acupoint group was higher than that of the control group [control, 70 (68-82) mm Hg; acupoint, 94 (84-109) mm Hg; non-acupoint, 81 (70-92) mm Hg: P<0.001]. Significantly more parturients in the control and non-acupoint groups experienced hypotension [control, 10 (83%); acupoint, 4 (33%); non-acupoint, 10 (83%): P=0.013]. More ephedrine was required to maintain arterial blood pressure in the control and non-acupoint groups. CONCLUSIONS TENS on the traditional acupuncture points reduced the severity and incidence of hypotension after spinal anaesthesia in parturients.