Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a narrative conversational agent using a component-based architecture

This paper describes a component based-model for a Narrative Conversational Agent. This project has to interact with a child in a storytelling environment as natural as possible and replace the previous version of the platform, where the actions were triggered manually by a psychologist. The prototype was carried out as a student project, and had the goal to integrate existing components and libraries along with a new model for narrative dialogue interaction.
Stellar variability on time-scales of minutes: results from the first 5 yr of the Rapid Temporal Survey

The Rapid Temporal Survey (RATS) explores the faint, variable sky. Our observations search a parameter space which, until now, has never been exploited from the ground. Our strategy involves observing the sky close to the Galactic plane with wide-field CCD cameras. An exposure is obtained approximately every minute with the total observation of each field lasting around 2 hours. In this paper we present the first 6 epochs of ob servations which were taken over 5 years from 2003‐2008 and cover over 31 square degrees of which 16.2 is within 10 � of the Galactic plane. The number of stars contained in these data is over 3.0 × 10 6 . We have developed a method of combining the output of two variability tests in order to detect variability on time-scales ranging from a few minutes to a few hours. Using this technique we find 1.2×10 5 variables ‐ equal to 4.1 per cent of stars in our data. Follow- up spectroscopic observations have allowed us to identify the nature of a fraction o f these sources. These include a pulsating white dwarf which appears to have a hot companion, a number of stars with A-type spectra that vary on a period in the range 20‐35 min. Our primary goal is the discovery of new AM CVn systems: we find 66 sources which appear to show periodic modulation on a time-scales less than 40 min and a colour consistent with the known AM CVn systems. Of those sources for which we have spectra of, none appears to be an AM CVn system, although we have 12 candidate AM CVn systems with periods less than 25 min for which spectra are still required. Although our numbers are not strongly constraining, they are consistent with the predictions of Nelemans et al.
Throughput Analysis of Some Idealized Input, Output, and Compute Overlap Configurations

Using a tutorial and expository approach, this paper assesses the performance of some simple, idealized input, output, and compute overlap configurations. Equations are gwen for oneand two-channel configurations that have one, two, and three partltmns of main storage. The objectives of the paper are to gain insight into bounds on system throughput performance and to give equations useful for checking computer simulation.
Mobile authentication using keystroke dynamics

As mobiles become a ubiquitous, they are used more and more for operations that may involve sensitive data or huge amounts of money. The mobile is also increasingly being used as the tool of preference for browsing and using the Internet. Hence, new security measures must be developed to support increased functionality of these devices, to protect users in the case of any mishap. Authentication using keystroke dynamics for mobiles is an area that has received attention recently. In this paper, we have studied some authentication techniques used earlier including relative and absolute distance measures, mean and standard deviation based methods and feature fusion methods. We provide a method for authentication using fusion techniques for our own novel mean and standard deviation based approaches, which gives low error rates.
PTAT CMOS current sources mismatch over temperature

Proportional to Absolute Temperature (PTAT) CMOS current sources are widely used in temperature sensors, bandgap references, and other temperature-compensating circuits. Most of these applications strongly rely on the accuracy of a current ratio m established with a set of 1+m PTAT current sources. However, a PTAT CMOS current source has a temperature-dependent bias point, which in turn, has a well-known effect on the mismatch of CMOS current sources. This paper analyzes the mismatching properties of PTAT current sources due to variation of the current-sources bias point (gm/IDS) with temperature, from -55°C to 125°C. After the analysis, the paper shows measurements of a precision temperature sensor without mismatch compensation to corroborate the analysis developed.
The Unemployment Rate Forecast Model Based on Neural Network

Unemployment is a critical problem in China. It is also a major factor relative with the economic development of a society. Hence, unemployment forecast model as an important way of studying economy and society should be paid more attention. An accurate predicted outcome is significant for the government to set the relative policies. In the paper, there are some influencing indicators suggested by experts. We use back-propagation neural network (BPNN) and Elman neural network to predict unemployment rate respectively, and do a comparison between them. However, before inputting the data into the neural network to calculate, there is some preprocessing should do to accelerate the neural network's computing speed, here, principal component analysis (PCA) is adopted. PCA is a kind of statistics analysis tool for condensing the results.
The Market Valuation of Biotechnology Firms and Biotechnology R&D

This paper sheds light on how and why the stock market values high technology by examining the pricing of 606 biotechnology firms that were publicly traded at some time during the period 1989:q1-2000:q3. Contrary to the common view that the primary value drivers of biotechnology are "soft" variables such as intellectual human capital, patents, strategic alliances and joint ventures, I show that simple balance sheet, income statement and statement of cash flows data explains some 70% of the variance in biotech firms' equity market values within a log-linear regression framework. Given the size and economic importance of RD the growth rate in RD the scale of RD the human capital of the firm's employees; and the age of the firm. Using financial statement proxies for these factors, I find that the elasticity of biotech firms' equity market values with respect to R&D is significantly larger the earlier is the R&D expenditure in the value chain, and the greater is the growth rate in R&D spending. The value elasticity of R&D is also reliably decreasing in the scale of R&D expenditures, and the more mature is the firm. However, it appears unrelated to proxies for both the quantity and quality of employee human capital.
Data-Flow Analysis for Interrupt-Driven Microcontroller Software

Enhancing traditional data-flow analysis with new techniques for dealing with concurrency and volatile data provides significant additional precision on interrupt-driven microcontroller code. This dissertation presents a whole-program analysis for C code that generalizes and extends traditional abstract interpretation techniques. Interatomic concurrent data-flow (ICD), a novel technique, allows the analysis to understand interrupt-driven concurrent control flow and increase the amount of trackable shared data. The analysis leverages system information to track data through volatile variables when safe to do so. Several subanalyses concurrently run inside the main analysis in order to further increase their precision through synergy. The analysis supports "conditional X propagation," a generalization of the well-known conditional constant propagation algorithm where X is a supplied abstract value domain. The main contributions of this dissertation are a presentation of new data-flow analysis techniques, an implementation description (cXprop), and evaluations of the new techniques. Three examples illustrate the utility of the new techniques in the analysis. First, cXprop reduces microcontroller code size by an average of 12%, static data size by an average of 2.5%, estimated dynamic data size by an average 1.8%, and duty cycle by an average of 8.3%. Second, a platform for introducing memory safety to legacy microcontroller applications utilizes cXprop as a race detector and whole-program optimizer. CXprop enables the platform to be sound in the presence of interrupt-driven concurrency and practical on tiny microcontroller-based systems. Third, an extension of cXprop performs offline compression for on-chip RAM. The analysis provides information about limited bitwidth use for statically allocated global variables. A transformation then reduces the variables to the limited bitwidth, resulting in an average savings of 22% RAM.
Providing an Architecture Framework for Cyberjustice

The paper illustrates how architecture can be used to show the contribution and the use of technology in the legal system. The models created enable the rapid identification of the stakeholders, their objectives, the technologies they use, and their goals. Such understanding helps decision makers ensure that the tools and processes enabled by information technology (IT) are aligned with the goals of the legal system. The preliminary framework developed in the paper shows the feasibility and the contribution of such models on a larger scale.
Prognostic Value and Clinical Impact of 18FDG-PET in the Management of Children with Burkitt Lymphoma after Induction Chemotherapy

Objective: Burkitt lymphoma (BL) is a rare and aggressive form of B-cell lymphoma that is curable using intensive chemotherapy. Obtaining a complete response (CR) at the end of induction chemotherapy is a major prognostic factor. This study retrospectively evaluates the potential impact of 18FDG-PET in the management of children with BL after induction chemotherapy, and the prognostic performance of the Deauville criteria. Methods: Nineteen children with BL treated according to the French LMB2001 protocol between 2005 and 2012 were included. 18FDG-PET and conventional imaging (CI) were performed after induction chemotherapy to confirm CR. 18FDG-PET was interpreted according to Deauville criteria with follow-up and/or histology as the gold standard. Results: 18FDG-PET was negative in 15 cases, in agreement with CI in 9/15 cases. The six discordant cases confirmed to be negative by histology, were considered as true negative for 18FDG-PET. Negative predictive value (NPV) of CI and 18FDG-PET were 73 and 93%, respectively. The 5-year progression-free survival (PFS) was significantly higher in patients with negative 18FDG-PET than those with positive 18FDG-PET (p = 0.011). Conclusion: 18FDG-PET interpreted using Deauville criteria can help confirm CR at the end of induction chemotherapy, with a prognostic impact on 5-year PFS. Its high NPV could limit the use of residual mass biopsy. Given the small size of our population, these results need to be confirmed by future prospective studies on a larger population.
An improved neural network algorithm based on artificial bee colony algorithm and its application in sewage treatment

As an advanced artificial intelligence technology, error back-propagation (BP) neural network algorithm has been widely applied to electronics, communications, automation and other fields. However, traditional BP neural network algorithm has the disadvantages, such as inclination to stick into local optima, and slow convergence, which exert a great impact on the processing performance, and also limit its further application. Artificial bee colony (ABC) algorithm has been developed as a new heuristic algorithm in recent years based on foraging behavior, which is also proposed to be a global optimization algorithm. As a simple and reliable algorithm, ABC algorithm is easy to be implemented. In this paper, an optimized BP neural network algorithm based on ABC algorithm is proposed so as to integrate the advantages of both algorithms. Simulation result shows that compared with the conventional BP neural network algorithm, the new algorithm has gained a significant development not only in prediction accuracy but also in convergence speed.
Optimized fuzzy logic control strategy of hybrid vehicles under different driving cycle

This paper focuses on the control strategy of Hybrid Vehicles under different driving cycle. FLC is mainly based on the process knowledge and intuition. In comparison, the Adaptive Neural-Fuzzy Inference System (ANFIS) is a modeling method, which primarily based on data. It is presented here that the membership functions and rules of FLC could be optimized once ANFIS is trained by actual driving cycle data collected from software ADVISOR. Then the FLC controller block in ADVISOR is rewritten by the optimized membership functions according to the ANFIS training. And we choose two different driving cycles for comparison to improve the effectiveness of the method. Some simulation results are compared and discussed: the optimized FLC exhibits better performance in terms of fuel consumption and pollutants emission.
Gay-Straight Alliances: Making Ontario Schools Safe and Inclusive

Gay-straight alliances (GSAs) have become widespread in Ontario schools and, starting in 2012, all schools are required to permit students to form GSAs. While American research suggests that GSAs have a positive impact on school safety and inclusion, there is little research on the impact of GSAs in Ontario schools. This study, based on a survey of 30 educators working with GSAs, suggests that policy changes in Ontario have had a positive impact on school climate for lesbian, gay, bisexual, transgender, and queer (LGBTQ) students, and that GSAs contribute to the development of safer and more inclusive schools. The next phase of the research will probe more deeply by increasing the number of respondents and conducting interviews with 14 participants.
Offline Handwritten Signature Verification using Zernike Moments

In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. The moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. The features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve Az equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.
Hydrodynamic based hybrid dynamic load balancing

Balanced load distribution is especially important to attain optimal use of existing computational resources in distributed and parallel applications. In dynamic load balancing (DLB), surplus workload in nodes overwhelmed with work is transferred to relatively free nodes during run time. While in iterative DLB methods, the load reaches to its final execution node through several iteration steps, the execution node is selected directly in one step in direct methods. However, direct methods require immense system state information to perform selection. In this paper, we present two new hybrid dynamic load balancing (HLB) methods that take advantage of both direct and iterative methods. HLB aims to consume minimum possible system resources to balance common workload distributions by using an iterative method, the hydrodynamic approach (HA). Besides, HLB intends to solve the problems derived from exceptional instantaneous load rises by using a direct method which requires only little system knowledge. The excess workload is shared directly with some non-neighboring nodes which are selected randomly or from a fixed distribution list. The experimental results designate that the hybrid methods outrun other iterative methods in terms of performance and whole system utilization.
HRDBMS: Combining the Best of Modern and Traditional Relational Databases

HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regarding query optimization, while also taking advantage of the scalability of Big Data platforms. The system uses an execution framework that is tailored for relational processing, thus addressing some of the performance challenges of running SQL on top of platforms such as MapReduce and Spark. These include excessive materialization of intermediate results, lack of a global cost-based optimization, unnecessary sorting, lack of index support, no statistics, no support for DML and ACID, and excessive communication caused by the rigid communication patterns enforced by these platforms.
A Powerful Statistical Method for Identifying Differentially Methylated Markers in Complex Diseases

DNA methylation is an important epigenetic modification that regulates transcriptional expression and plays an important role in complex diseases, such as cancer. Genome-wide methylation patterns have unique features and hence require the development of new analytic approaches. One important feature is that methylation levels in disease tissues often differ from those in normal tissues with respect to both average and variability. In this paper, we propose a new score test to identify methylation markers of disease. This approach simultaneously utilizes information from the first and second moments of methylation distribution to improve statistical efficiency. Because the proposed score test is derived from a generalized regression model, it can be used for analyzing both categorical and continuous disease phenotypes, and for adjusting for covariates. We evaluate the performance of the proposed method and compare it to other tests including the most commonlyused t-test through simulations. The simulation results show that the validity of the proposed method is robust to departures from the normal assumption of methylation levels and can be substantially more powerful than the t-test in the presence of heterogeneity of methylation variability between disease and normal tissues. We demonstrate our approach by analyzing the methylation dataset of an ovarian cancer study and identify novel methylation loci not identified by the t-test.
User generic model for writer verification using multiband image scanner

Writer verification plays an important role in biometrics and forensics area. The familiar use of handwritten signatures in various network applications, e.g., credit card authentication by handwriting through digital terminal, increases the needs for the automated writer verification methods. However, there is still room for improvement in the performance conducted by such methods compared to human beings. Since the applications sometimes result in grave consequences, further research to improve the performance of automated writer verification is required. Especially, problems caused by the limited samples have to be solved. In practice, a sufficient number of references per writer are often unavailable. In this paper, we will report the advantage of the user generic model with pen pressure information in such situations. The experimental results which use samples from 54 volunteers show the decrease of error rate from 10.0% to 4.6%.
A Process Framework for Product Quality Achievement

In current software engineering practice, there is generally a rather limited connection between software processes and product quality, because the processes are defined at a level of abstraction above the specific product being delivered. This tutorial shows how that connection can be strengthened through the introduction of product-specific processes that identify and capture the collection of tasks needed to produce the specific product. It presents a process framework for product quality achievement that has recently been published as ISO/IEC TS 30103, and implemented successfully on several projects at an IT service organization. The key ideas include establishment of quality specifications for each configuration item, instantiating the organizational processes to the particular product context based on the targeted quality attributes using institutional knowledge, and tracking consistency relationships among project artifacts as concurrent development proceeds. The framework is likely to be of particular interest to project organizations that operate in multiple domains, where there is significant variation in quality goals and the techniques required to achieve quality.
A fast algorithm and hardware implementation for rate-distortion optimization in JPEG2000

A fast algorithm for T2 encoder in JPEG2000 suitable for hardware implementation is presented in this paper, based on the elaborate analysis of ratedistortion optimization algorithm. By reducing calculative complexity, the difficulty of hardware implementation for T2 encoder is reduced, and the parallelizability of the JPEG2000 hardware system is enhanced. The experimental results show that the final code stream is accorded with the standard format of JPEG2000 and reconstructed image quality decreases little. The system has been implemented on FPGA.
3-01-30 Redesigning the IT Organization for the Information Age

Organizational design is a critical facilitator of strategic information age initiatives. Yet traditional IT structures are misaligned with the information age because they cannot cope with continual change and the rapid horizontal introduction and utilization of gregarious information technologies. An organizational structure that combines the ideas of minibusinesses and the internal marketplace can provide a dynamic balance between stability and productivity and flexibility and innovation, replace a history of episodic and misguided restructuring with continuous restructuring, and position the IT organization to fulfill its strategic potential.
Fast indexing for blocked array layouts to improve multi-level cache locality

One of the key challenges computer architects and compiler writers are facing, is the increasing discrepancy between processor cycle times and main memory access times. To overcome this problem, program transformations that decrease cache misses are used, to reduce average latency for memory accesses. Tiling is a widely used loop iteration reordering technique for improving locality of references. In this paper, we further reduce cache misses, restructuring the memory layout of multi-dimensional arrays, that are accessed by tiled instruction code. In our method, array elements are stored in a blocked way, exactly as they are swept by the tiled instruction stream. We present a straightforward way to easily translate multi-dimensional indexing of arrays into their blocked memory layout using simple binary-mask operations. Indices for such array layouts are now easily calculated based on the algebra of dilated integers, similarly to morton-order indexing. Actual experimental results on three different hardware platforms, using 5 benchmarks, illustrate that execution time is greatly improved when combining tiled code with tiled array layouts and binary mask-based index translation functions. Both TLB and L1 cache misses are concurrently minimized, for the same tile size, thus, applying the proposed layouts, locality of references is greatly improved. Finally, simulations using the Simplescalar tool, verify that our enhanced performance is due to the considerable reduction of cache misses in all levels of memory hierarchy.