Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Bayesian Optimization and Applications

Bayesian optimization is an approach for globally optimizing black-box functions that are expensive to evaluate, non-convex, and possibly noisy. Recently, Bayesian optimization has been used with great effectiveness for applications like tuning the hyperparameters of machine learning algorithms and automatic A/B testing for websites. This thesis considers Bayesian optimization in the presence of black-box constraints. Prior work on constrained Bayesian optimization consists of a variety of methods that can be used with some efficacy in specific contexts. Here, by forming a connection with multi-task Bayesian optimization, we formulate a more general class of constrained Bayesian optimization problems that we call Bayesian optimization with decoupled constraints. In this general framework, the objective and constraint functions are divided into tasks that can be evaluated independently of each other, and resources with which these tasks can be performed. We then present two methods for solving problems in this general class. The first method, an extension to a constrained variant of expected improvement, is fast and straightforward to implement but performs poorly in some circumstances and is not sufficiently flexible to address all varieties of decoupled problems. The second method, Predictive Entropy Search with Constraints (PESC), is highly effective and sufficiently flexible to address all problems in the general class of decoupled problems without any ad hoc modifications. The two weaknesses of PESC are its implementation difficulty and slow execution time. We address these issues by, respectively, providing a publicly available implementation within the popular Bayesian optimization software Spearmint, and developing an extension to PESC that achieves greater speed without significant performance losses. We demonstrate the effectiveness of these methods on real-world machine learning meta-optimization problems.
A NOTE ON THE TWO-SIDED BEHAVIOR OF N AFTER N

In a number of languages, expressions are attested that behave like quantified NPs without an obvious element of quantification (cf. Postma (1995), Heycook and Zamparelli (2003), Roodenburg (2004)). One instance of such expressions is Coordinated Bare Nouns (CBN), which Heycook and Zamparelli (2003) (hereafter, H&Z) study in some detail. They classify. CBNs into two types: those with singular nouns (Coordinated Bare Singulars; hereafter, CBS) and those with plural nouns (Coordinated Bare Plurals; hereafter, CBP), as shown in (la, b) respectively. (1) a. I have to give key and letter to the tenant, . b. Forks and knives were equally dirty. (H&Z (2003: 448)) Another instance which acts like a quantified NP without an overt quantifier is an expression with two identical nouns joined by after, as shown in (2a-c). We will henceforth call such an expression "N after N. "1
Phoneme modelling using continuous mixture densities

Deals with the use of continuous mixture densities for phenome modelling in large vocabulary continuous speech recognition. The concept of continuous mixture densities is applied to the emission probability density functions of hidden Markov models for phonemes in order to take into account phonetic-context dependencies. It is shown that the advantage of continuous mixture densities is the ability to lead to parameter estimates that are accurate and at the same time robust with respect to the limited amount of training data. Training and recognition algorithms for mixture densities in the framework of phoneme modelling are described. Recognition results for a 917-word task, requiring only 7 min of speech for training and an overlap of 43 words between training vocabulary and test vocabulary, are presented. <<ETX>>
Utilizing bias to evolve recurrent neural networks

Since architectures and weights for recurrent neural networks are difficult to design, evolutionary methods may be applied to search the space of such networks. However, for all but trivial problems, this space is very large. Hence, biases are required that guide the search. Here, we investigate solving a smaller related problem to establish such a bias. Networks are specified by trees containing operators that act on nodes (neurons) and edges (connections). We demonstrate the approach on a signal reproduction task that requires internal state. Performance on a small problem size was improved by solving a smaller problem first. By repeatedly applying the principle, versions of the problem were solved that were not solved by a direct approach.
Hardware accelerator design for tracking in smart camera

Smart Cameras are important components in video analysis. For video analysis, smart cameras needs to detect interesting moving objects, track such objects from frame to frame, and perform analysis of object track in real time. Therefore, the use of real-time tracking is prominent in smart cameras. The software implementation of tracking algorithm on a general purpose processor (like PowerPC) could achieve low frame rate far from real-time requirements. This paper presents the SIMD approach based hardware accelerator designed for real-time tracking of objects in a scene. The system is designed and simulated using VHDL and implemented on Xilinx XUP Virtex-IIPro FPGA. Resulted frame rate is 30 frames per second for 250x200 resolution video in gray scale.
Efficiently implementing synchronous groupware

Developing synchronous groupware applications is a complex task due to the many distributed systems issues that must be tackled in addition to realizing the application's semantics. High-level groupware toolkits offer design-level architectures that hide such distributed systems issues, greatly simplifying the development task. End users demand good responsiveness when interacting with synchronous groupware. In the presence of slow networks, developers need control over the distributed implementation to achieve good responsiveness. High-level toolkits do not offer such control as it compromises the programming model. We introduce the concept of semantics-preserving architecture annotations to offer developers the abstractions of high-level toolkits while still providing the control of low-level toolkits. Annotations are applied directly to the design-level architecture and give hints to the toolkit, specifying how to map the design-level architecture to a distributed implementation. By combining different types of annotations, a single design-level architecture can be mapped to many different implementations, allowing developers to tune the performance of applications. To demonstrate the usefulness of architecture annotations, we incorporate them into an existing high-level groupware toolkit called Clock. We propose four types of annotations, giving control over distribution, caching, concurrency control, and replication. Performance evaluation experiments show that annotations can have a dramatic impact on responsiveness, allowing instantaneous group interactions to take place over slow wide-area-networks. The results indicate that different applications require different combinations of annotations for best performance.
TOWARDS EVOLUTIONARY DESIGN OF COMPLEX SYSTEMS INSPIRED BY NATURE

This paper presents first steps towards evolutionary design of complex autonomous systems. The approach is inspired in modularity of human brain and principles of evolution. Rather than evolving neural networks or neural-based systems, the approach focuses on evolving hybrid networks composed of heterogeneous sub-systems implementing various algorithms/behaviors. Currently, the evolutionary techniques are used to optimize weights between predefined blocks (so called Neural Modules) in order to find an agent architecture appropriate for given task. The framework, together with the simulator of such systems is presented. Then, examples of agent architectures represented as hybrid networks are presented. One architecture is hand-designed and one is automatically optimized by means of evolutionary algorithm. Even on such a simple experiment, it can be observed how the evolution is able to pick-up unexpected attributes of the task and exploit them when designing new architecture.
Highlighting User Related Advice

Research on explanation techniques for expert systems has demonstrated 由at (1) explanations are most effective when 由ey address the user' s needs and (2) it is necess缸Y to augment explanations with information 由at is missing from the expe口 system's reasoning. It is our thesis 由at explanation content can a1so be improved by removing extraneous information from the system's reasoning and reorganizing 由e remainder to emphasize user concerns. To test our ideas , we have developed an interactive natural language problem-solving system called ADVISOR which advises students on course selection. Previously, we have repo口ed on our methodology for deriving user goa1s from the discourse, representing different points of view in 由e knowledge base and inferring user-oriented advice with a rule-based system 由at employs information from the appropriate perspective to address user goa1s. In this paper, we describe a model for pruning an explanation to highlight the role of the user's goal. 叽le model is part of ADVISOR's natural language generation component We demonstrate its efficacy with ex缸nples of different advice that ADVISOR provides for the same query in the context of different goals. WORD COUNT: 3487 (3118 in text, 377 in display). In addition, Figure 1 contains a diagram of approximately 1/2 page. TOPIC: Natural Language
Securing Images in Cloud using Hyper Chaos with User Authentication

Security in the cloud is the top most concern. To achieve trust and preserve the privacy of data stored in third-party cloud storage has emerged as a key research area. To achieve this, several different techniques have been proposed based on cryptography. Secret sharing schemes and chaos based encryption have also been considered to address these issues of trust and privacy by various researchers. In this paper, we are proposing a new scheme to encrypt the private and sensitive images of users in the third party storage named as “Securing images in cloud using hyper chaos with user authentication" to solve the problem of authenticating users. Chaos based encryption with image captcha authentication using VCS is a major advantage of this scheme. Chaos based encryption is a very secure method for encryption. Many researchers have used the above technique but user authentication or authorization is not mentioned in their schemes. To increase more security we have included the authentication in the scheme. In this paper the authentication process is explored in depth and only the overview of encryption process is given. For securing the authentication logistic map is used to divide and shuffle the original image captcha into many blocks. The use of visual cryptography technique is explored to preserve the privacy of image captcha. Part of the image share will be kept with the image owner such that the original image captcha can be revealed only when both (user, owner) of the shares are simultaneously available. The individual share images do not reveal the identity of the original image captcha. Once the original image captcha is revealed after merging different shares, the user will be considered as authenticated user. Then only the encrypted key for the images will be transferred to the user. Dynamically generating the Captcha image by the system is one of the major advantage of the system.
Application of Kalman filter in agricultural economic forecasting

This paper studies the application of Kalman filter in agricultural economic forecasting. At first it introduces the multivariate Bayesian dynamic linear model with the Kalman filter, then gives some knowledge about subjective intervention. At last it gives an application in agricultural economic forecasting. In this example a bivariate Bayesian dynamic linear model is built for the quarterly data about pork production. Kalman filter is applied here to make dynamic forecasting. In addition, the fluctuation in the pork production is analyzed according to the expert's experience. These would help us have deep comprehension about the development of pork production.
Millimeter Wave Ring Oscillator Using Carbon Nano-Tube Field Effect Transistor in 150 GHz and Beyond

Carbon Nano-Tube Field Effect Transistors (CNTFETS) are the competitor of the conventional MOSFET technology due to their higher current drive capability, ballistic transport, lesser power delay product, higher thermal stability, and so on. Based on these promising properties of CNTFETs, a CNTFET-based millimeter wave ring oscillator operating around 150 GHz and beyond is introduced here in 32 nm technology node. To prevent overestimation, the CNT interconnects between transistors are also included in simulation, which are assumed to be a single layer of ballistic metallic CNTs in parallel. For the sake of simplicity in RF design, the oscillator is based on CNTFET-based inverters. The inverters with DC gain of 87.5 dB are achieved by proper design with the non-loaded delay around 0.6 ps, which is at least one order of magnitude better than the same 32 nm MOSFET-based inverters. The oscillator’s average power consumption is as low as 40 μW with the fundamental harmonic amplitude of around –6.5 dB. These values are, based on our knowledge, for the first time reported in the literature in CNTFET-based oscillator designs. Also, on the average, the performance of the designed oscillator is 5 - 6 times better than MOSFET-based designs.
Analysis of bandpass sigma-delta modulator architectures

This work analyses two architectures of bandpass sigma delta modulators, in order to find their sensitivity against operational amplifier finite voltage gain, having in mind a switched capacitor realization of both architectures. Expressions for the ideal signal to noise ratio and dynamic range of both architectures are found, as well as a model of the degradation of the mentioned parameters due to finite values of the voltage gain. The proposed model is then compared with nonlinear high level simulations including the effect of finite voltage gain.
CMOS camera with on-chip signal processing for optical correlators

The design and simulation results of a 64 X 64 pixels smart CMOS photodiode sensor array are presented. The chip is capable of capturing an image as well as performing real time on-chip signal processing on 3 X 3 kernel array of the image. The size of the optical system is significantly reduced by integrating the signal processing circuits on chip. It is particularly suitable for applications such as optical correlators or image processing system. The chip is designed using 0.8 micrometers standard CMOS process technology. It is incorporated with processing circuitry to implement low-pass and high-pass image filters as well as the processing algorithm required by a 1/f binary phase optical correlation system.
An Efficient and Fast Algorithm for Routing Over the Cells

A linear time algorithm for routing over the cells is presented. The algorithm tries to reduce maximum channel density by routing some connections over the cells. The algorithm first defines a new scheme for channel representation and formulates the problem based on an intersection graph derived from the new scheme. Then, a feasible independent set of the intersection graph is found for routing some subnets over the cells. The algorithm is implemented and evaluated with several well known benchmarks. In comparison with previous research, our results are satisfactory, and the algorithm takes substantially less CPU time than those of previous works. For Deutsch's difficult example, the previous algorithms take about 29.25 seconds on an average but our new algorithm needs only 5.6 seconds.
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Learning and generalization in a coulombian network

Constructs a two layer modular network in which each layer can be trained independently. The first layer connection weights are determined by the learning algorithm of the coulombian network. For the second layer the authors use the perceptron algorithm. To generate the first layer the authors use a sequential constructive process that is able to make a linearly separable transformation of the input patterns, so the perceptron can always make a successful separation. As a result the authors obtain both a tendency to generate a minimal network and an improved generalization ability. The authors apply this particular architecture to some classification problems with discrete and continuous inputs, analyzing learning and generalization behavior.
From Powerful Ideas to PowerPoint

When computer literacy was almost synonymous with programming, the programming-in-education advocates were divided into two camps: those who supported BASIC as the language of choice and those who supported the Logo language. BASIC, developed at Dartmouth and with a long history of use in educational settings, had a head start, a strong 'installed base' of users, master teachers, and teaching materials. Logo, developed in Cambridge-based research centres and the Massachusetts Institute of Technology, had been built from the start with child users in mind. It presented itself as the language that could best use programming to transmit programming's most powerful ideas.
Voice activity detection using a periodicity measure

The paper describes a voice activity detector (VAD) that can operate reliably in SNRs down to 0 dB and detect most speech at −5 dB. The detector applies a least-squares periodicity estimator to the input signal, and triggers when a significant amount of periodicity is found. It does not aim to find the exact talkspurt boundaries and, consequently, is most suited to speech-logging applications where it is easy to include a small margin to allow for any missed speech. The paper discusses the problem of false triggering on nonspeech periodic signals and shows how robustness to these signals can be achieved with suitable preprocessing and postprocessing.
Investigating the effect of microarray preprocessing methods on tumor classification

Gene expression data from microarrays are a helpful tool for performing tumor classification. Before any analysis can be performed, however, the raw data has to be preprocessed. This thesis investigates how four different preprocessing methods, including RMA, GCRMA, MAS5 and PLIER, affect the classification outcome. The study is conducted by performing classifications of microarray data from three datasets using two gene selection methods, SVM-RFE and t-statistic, and two classification methods, SVM and Random forest. The main conclusion of the study is that there are only slight differences in classification outcomes when performing classification using data preprocessed with the four preprocessing methods.
Application of Data Mining to Genetic Linkage Analysis

Genetic linkage analysis tools, which are used to determine the genetic distance and order among genetic markers, take too much computational cost. To improve the e ciency, we are investigating the applicability of data mining to linkage analysis from both statistical and practical aspects. In this paper, we report the results of the investigation and discuss the problems.
Implementing Web-Based Intelligent Tutoring Systems in K-12 Settings: A Case Study on Approach and Challenges

Technologies for learning environments are a vital and growing market for software development and utilization. A team of designers and developers created a Web-based intelligent tutoring system to teach a reading comprehension strategy for elementary and middle school students. Our approach to designing ITSS. combined the waterfall method of technology design with prescriptions for creating cognitive tutors. This article reports on each phase of the process: analysis, design, development, and implementation of the system and the challenges and solutions at each stage.
A DOMAIN SPECIFIC LANGUAGE FOR THE DESCRIPTION AND THE SIMULATION OF SYSTEMS OF INTERACTING SYSTEMS

Systems of technological networks (so-called Critical Infrastructures) might undergo strong perturbations leading to the reduction of their functionalities (crisis scenarios). Under perturbed conditions, infrastructures deliver their specific services in a range from a partial reduction up to a complete loss. Systems of complex technological networks (electrical, telecommunications, roads and railways, oil, gas and water pipelines etc.) are known to display large interdependences (a fault on one system spreads over the others); for this reason, it is relevant to devise simulators and decision support systems able to capture the complexity of such a "system of systems". The paper presents parts of the outcomes of a larger scientific project aiming at realizing a Decision Support System to predict the occurrence of undesired events and their consequences. In particular, we present a conceptual modeling approach consisting of a declarative language and a software implementation for the description and simulation of elements of Crisis Scenarios. The software application is conceived for risk managers, i.e. operators with a deep knowledge of critical infrastructure scenarios, but not necessarily with high-level IT or mathematical skill.