Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting to dynamically changing noise during learning of heart sounds: an AIS-based approach using systemic computation

Real world machine learning, where data is sampled continuously, may in theory be classifiable into distinct and unchanging categories but in practice the classification becomes non-trivial because the nature of the background noise continuously changes. Applying distinct and unchanging categories for data ignores the fact that for some applications where the categories of data may remain constant, the background noise constantly changes, and thus the ability for a supervised learning method to work is limited. In this work, we propose a novel method based on an Artificial Immune System (AIS) and implemented on a systemic computer, which is designed to adapt itself over continuous arrival of data to cope with changing patterns of noise without requirement for feedback, as a result of its own experience.
Efficient Large-Scale Structure From Motion by Fusing Auxiliary Imaging Information

One of the potentially effective means for large-scale 3D scene reconstruction is to reconstruct the scene in a global manner, rather than incrementally, by fully exploiting available auxiliary information on the imaging condition, such as camera location by Global Positioning System (GPS), orientation by inertial measurement unit (or compass), focal length from EXIF, and so on. However, such auxiliary information, though informative and valuable, is usually too noisy to be directly usable. In this paper, we present an approach by taking advantage of such noisy auxiliary information to improve structure from motion solving. More specifically, we introduce two effective iterative global optimization algorithms initiated with such noisy auxiliary information. One is a robust rotation averaging algorithm to deal with contaminated epipolar graph, the other is a robust scene reconstruction algorithm to deal with noisy GPS data for camera centers initialization. We found that by exclusively focusing on the estimated inliers at the current iteration, the optimization process initialized by such noisy auxiliary information could converge well and efficiently. Our proposed method is evaluated on real images captured by unmanned aerial vehicle, StreetView car, and conventional digital cameras. Extensive experimental results show that our method performs similarly or better than many of the state-of-art reconstruction approaches, in terms of reconstruction accuracy and completeness, but is more efficient and scalable for large-scale image data sets.
Cooperative Household Models

We set out a general framework for cooperative household models, based on Samuelson's idea of a household welfare function, but extending it to incorporate the key insight from Nash bargaining models – the idea that the household’s preference ordering over the utility profiles of its members depends on their wage rates (or prices more generally) and non-wage incomes. Applying reasonable general restrictions on the effects of changes in these variables allows derivation of the general implications of cooperative models.
Application of relation analysis to a small Java software

Relation analysis is a method to find logical couplings in a software by examining a version control log to see which files change together regularly. However, the process can be twisted into finding, for example, illogical couplings between modules, modules with too much influence or burden with responsibility. Here we explain how the relation analysis was applied to a course information system written in Java to find out hidden connections between the modules inside the software.
Estimation and Control of Quality of Service in Demand Dispatch

It is now well known that flexibility of energy consumption can be harnessed for the purposes of grid-level ancillary services. In particular, through distributed control of a collection of loads, a balancing authority regulation signal can be tracked accurately, while ensuring that the quality of service (QoS) for each load is acceptable on average. Subject to distributed control approaches advocated in recent research, the histogram of QoS is approximately Gaussian, and consequently, each load will eventually receive poor service. Statistical techniques are developed to estimate the mean and variance of QoS as a function of the power spectral density of the regulation signal. It is also shown that additional local control can eliminate risk. The histogram of QoS is truncated through this local control, so that strict bounds on service quality are guaranteed. While there is a tradeoff between the grid-level tracking performance (capacity and accuracy) and the bounds imposed on QoS, it is found that the loss of capacity is minor in typical cases.
Three Experiments to Analyze the Nature of the Heat Spreader

In this paper, we describe ongoing work to investigate the properties of the heat spreader, and its implication on architecture research. In specific, we conduct two experiments to quantify the heat distribution across the surface of a spreader during normal operation. The first experiment uses T-type thermocouples, to find the temperature difference across different points on the spreader. We observe about a 6 degree celsius temperature difference on average. In the second experiment, we try to capture the temperature gradients using an infrared camera. However, this experiment was inconclusive because of some practical constraints such as the low emissivity of the spreader. We conclude that to properly model the spreader, it is necessary to conduct detailed finite element simulations. We describe a method to accurately measure the thermal conductivity of the heat spreader such that it can be used to compute the steady state temperature distribution across the spreader.
Interaction-Based Recommendations for Online Communities

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.
Applying Software Product Lines to Build Autonomic Pervasive Systems

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. This paper introduces an approach for the design of pervasive SPLs that is based on model driven development (MDD) and Variability Modeling principles. Variability models are interpreted at run-time to reconfigure pervasive systems according to fluctuations in the environment. This approach helps to improve Pervasive SPLs to produce software that adapts itself in an autonomic way. We have developed an adaptive pervasive system for smart homes to validate this approach.
Fabrication of microfluidic channel using micro end milling and micro electrical discharge milling

This paper discusses the fabrication of microfluidic channels using micro end milling and micro electrical discharge (ED) milling on metals and polymers. The width of the microchannels fabricated by micro end milling was 100-800 µm with 1-2 aspect ratio. The average surface roughness values were 100-200 nm and 80-120 nm on metals and polymers respectively. The end milling was found to form more burrs on metallic surface. Micro ED milling, on the other hand, was capable to produce high aspect ratio microchannels on metallic materials without forming any burrs. Using this micro ED milling process, microchannels of 120 µm width and 8-9 aspect ratio were machined with 40-50 nm average surface roughness. Micro swiss-roll combustor mold cavity was machined by using micro ED milling.
Design and Implementation of Context-Awareness Simulation Toolkit for Context learning

The study deals with the most important elements of ubiquitous computing, that is, the toolkit to acquire, express and safely use the context information. To do so, we introduce CAST (context-awareness simulation toolkit) and show how it works. CAST generates users and devices in a virtual home domain, designates their relation and creates virtual context information. The created context information is reused by the request of application and put into use for context learning. Particularly, we have given a consideration to security in the process of context creation and its consumption. That is, we applied SPKI/SDSI to test if the created context information was valid information and if the application that called for the context had legitimate authority to do so. CAST not only captures virtual context information, but it also guarantees the safe sharing of the context information requested by the application
Snubber Circuit for High-Power Gate Turn-Off Thyristors

A snubber circuit calculation model for high-power gate turn-off thyristors (GTO's) was proposed to consider problems which must be investigated for snubber circuit design. The calculated GTO waveforms showed good agreement with experimental values obtained by a GTO chopper circuit for both a resistive and inductive load case. Problems to be considered for the snubber design, such as voltage spike reduction, maximum GTO anode current, and switching power, were discussed using the calculation model. Design criteria for the snubber circuit were successfully established, introducing allowable maximum voltage spikes to avoid failures due to current crowding during the GTO interval and excessive voltage applied over maximum blocking voltage rating. Minimum switching power loss dissipated inside the GTO and the snubber resistor was also calculated, taking the design criteria into consideration. As results of the calculations, the snubber circuit stray inductance was found to play an important role in optimizing the minimum switching power loss, especially for large anode current and large stray inductance in a main circuit. 1983.
Bio-inspired Center Pattern Generator based control models

This paper presents an overview of the Central Pattern Generator (CPG) based control models as well as their applications to various multi-joint or multi-DOF robots. The motivation is to provide a relevant and practical guide to the existing literature for engineers with an interest in the emerging CPG-based biocybernetics approach. The CPGs function as a biological neural network with the great capability of producing stable rhythmic patterns that are robust against external perturbations. Some conventional models to emulate CPG network are firstly reviewed, and their merits and faults are analyzed. Combined the current stage of CPG-based locomotion control with its critical issues, a two-phase control architecture is formed to accomplish autonomous motions in dynamic environments. The developing prospect of the CPG-based control is finally outlined in the context of bio-robotics.
A Research of Image Difference Algorithm Based on the Correlation of Color Attributes

The CIE color difference formula was developed using simple color patches in controlled viewing conditions. They are not suitable for predicting color differences of spatially complex images. In order to quantify color differences between the original and reproduced images, we present an image difference algorithm based on image contents. This formula uses the Contrast Sensitivity Functions (CSF) to reduce the spatial complexity. The CSF serves to remove information that is imperceptible to the human visual system. This algorithm determines the color attribute weights by means of the correlation analysis between image pairs. Psychophysical experiments confirmed that the metric correlates strongly with the human perception. The proposed algorithm can be used to assess the performance of color image compressions, color image enhancement algorithms, gamut mapping algorithms and so on.
Türkiye'de döviz piyasası müdahalelerinin sterilizasyon maliyeti

Significant increases in foreign exchange reserves in developing countries and Turkey have been observed in the 2000s. The proposal to build up reserves as a precaution against the financial crises since the second half of the 1990s has also brought interventions to the foreign exchange markets. However, the fact that intervention to the foreign exchange market created contrasting effects with regard to inflation targeting applications of central banks made the sterilization policy is inevitable. Therefore, the cost of sterilization is also added to the cost of possession of high foreign exchange reserves. In this study, the level of sterilization of foreign exchange market interventions, and the cost of the sterilization policy have both been calculated.
Brain–Machine Interfaces: Your Brain in Action

An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, each being associated to a particular intention or mental task. Hence, adaptation is a key component of a BMI, because, on the one side, users must learn to control their neural activity so as to generate distinct brain patterns, while, on the other side, machine learning techniques (mathematical ways to pick patterns out of complex data) ought to discover the individual brain patterns characterizing the mental tasks executed by the user. In essence, a BMI is a two-learner system.
Abnormal Traffic Detection Using Intelligent Driver Model

We present a novel approach for detecting and localizing abnormal traffic using intelligent driver model. Specifically, we advect particles over video sequence. By treating each particle as a car, we compute driver behavior using intelligent driver model. The behaviors are learned using latent dirichlet allocation and frames are classified as abnormal using likelihood threshold criteria. In order to localize the abnormality; we compute spatial gradients of behaviors and construct Finite Time Lyaponov Field. Finally the region of abnormality is segmented using watershed algorithm. The effectiveness of proposed approach is validated using videos from stock footage websites.
Preprocessing of Fingerprint Images Captured with a Digital Camera

Reliable touch-less fingerprint recognition still remains a challenge nowadays as the conventional techniques that used to preprocess the optical or capacitance sensor acquired fingerprint image, for segmentation, enhancement and core point detection, are inadequate to serve the purpose. The problems of the touch-less fingerprint recognition are the low contrast between the ridges and the valleys in fingerprint images, defocus and motion blur. However, it imposes a competency for a further advancing of this biometric modality since it frees from the problems in terms of hygienic, maintenance and latent fingerprints. In this paper, the digital camera is opted as the device to capture the fingerprint image in RGB format and the procedures to segment, enhance and detect the core point of the fingerprint image are outlined
On the Feasibility of Key Compromise Impersonation Attacks Against the Elliptic Curve Version of the MTI/C0 Key Agreement Protocol

In a recent conference (ICSSSM 2010), Eun-Jun Yooni and Kee-Young proposed the two-pass elliptic curve Diffie-Hellman key agreement protocol EECKE-1N that according to the authors is more efficient that ECKE-1N. In this paper we point out that the protocol proposed by the aforementioned authors is in fact the elliptic curve version of the MTI/C0 family of key agreement protocols which suffer from a well-known vulnerability against a particular type of key-compromise impersonation attack.
Keyword Combination Extraction in Text Categorization Based on Ant Colony Optimization

Due to the increasing number of documents in digital form, the automated text categorization (TC) has become more and more promising in the last ten years. A TC system can automatically assign a document with the most suitable category, but the reason for such an assignment is usually unknown by users. To make the TC system be interpretable, it is necessary to select a group of keywords, or termed a keyword combination, to describe each text category. In this paper, we propose a novel algorithm, keyword combination extraction based on ant colony optimization (KCEACO), to search the optimal keyword combination of a target category. By extending the traditional feature selection techniques, an evaluation function is designed for evaluating a keyword combination. This function takes into account the relationships among different keywords. Experimental results show that KCEACO can efficiently find the optimal keyword combination from a large number of candidate combinations.
In-Process Embedding of Piezo Sensors and RFID Transponders into Cast Parts for Autonomous Manufacturing Logistics

Casting is the most important manufacturing technology for production of metalbased near net shape components. Their enhancement with electronic components for advanced functional abilities with additional assembling technologies is state-ofart for today’s cast parts, but the conventional way of application is complex or costly, particularly for large-scale series production like in automotive applications. Such kind of smart and intelligent cast parts are able to sense their loadings, compressive and tensile stresses or vibrations for health monitoring of the structure, to control the distortion of their material actively or to retain and transfer product-related data, especially in autonomous manufacturing logistics. This approach shows a new technology method being investigated for an in-process embedding of electronic components into the cast part. The specific of this method is that the electronic functional components such as sensors, actuators or RFID transponders get integrated directly during the casting process. In this way the embedded electronic is protected from contamination, damage or loss – during subsequent mechanical machining processes on the casting, during assembly steps and in operational use. Furthermore, the connection of sensor and actuator elements to the material structure can be improved
The opportunity of using eSpeak as text-to-speech synthesizer for Albanian language

The development of the Text-to-Speech (TTS) field is not at the same level in all countries and for all languages. This was obviously conditioned by the differences in speaking and writing in different languages, but also and by the economic justification for research in national terms. Therefore it is evident that the advancement of this field for local languages has not made any significant progress in comparison with the English language. In this context, within this paper is explored the possibility of using the existing TTS converters dedicated for English language [3], for the Albanian language. In particular eSpeak is treated as a compact open source software for speech synthesizer in English, which uses the method of synthesis formant and supports the synthesis of some other words in other languages. The research is focused on two aspects, in determining the understanding of words generated by this system and in determining the feasibility of using eSpeak for generating of speech in Albanian.
Building a Large English-Chinese Parallel Corpus from Comparable Patents and its Experimental Application to SMT

The paper provides an account on the augmentation of a Chinese-English patent parallel corpus consisting of about 160K sentence pairs, which has been enlarged by about 45 times to more than 7 million sentence pairs mostly by the means of “harvesting” comparable patents from the Web. First, based on a large corpus of English-Chinese comparable patents, more than 22 million bilingual sentence pair candidates have been mined, of which we extract more than 7 million high-quality parallel sentences, which to our best knowledge is the largest parallel sentence corpus in the patent domain. Based on 1 million parallel sentences extracted from the abstract and claims sections, some interesting preliminary SMT results are also reported here. Last by not least, the method and approach proposed here should be applicable to other languages, which shows a novel way on how to reduce the data acquisition bottleneck in multilingual language processing.
Effects of selected family characteristics on interrelated components of household asset portfolios

approved: Geraldine I. Olson The effects of selected family characteristics on interrelated components of household asset portfolios over a three-year time period were investigated. Specifically, this study attempted to conceptually define mental accounts, to identify own-adjustment and cross-adjustment characteristics of these mental accounts, to explore influences of selected family characteristics on these mental accounts, and to examine substantial effects of income on family portfolio behavior. Based on the behavioral life-cycle hypothesis, consumer demand theory, household production theory, and the stock adjustment hypothesis, a family portfolio behavior model was formulated for studying family saving behavior as reflected in household asset portfolios. A tobit model was utilized to estimate ownand cross-adjustment coefficients of the portfolio components, and short-term and equilibrium effects of family characteristics. The data were from the Survey of Consumer Finances conducted in 1983 and 1986. Findings strongly support the mental account hierarchy hypothesis which was reflected in the ownand crossadjustment coefficients estimated. In addition, family income and education of the household head showed positive influences on various mental accounts. Age of the household head, employment status, family life cycle stage, house mortgage, home value, other assets, and other debts showed effects on some mental accounts. Income had a substantial influence on family portfolio behavior. The behavior of middle-income families was more consistent with the hypothesis of a mental account hierarchy than the other income groups, which implies diverse preferences for asset characteristics and varying financial needs of families at different income levels. This study has contributed to the body of knowledge of family saving behavior and increased the understanding of adaptivity and dynamics of family saving behavior. The research findings could be utilized by family finance educators and consultants, financial service marketers, and public policy makers in working successfully with different family types, marketing various financial instruments, and designing effective savings policies. In addition, this study has provided empirical evidence to assess existing theoretical models and to inspire the building of new theories. ® Copyright by Jing-jian Xiao July 23, 1991 All Rights Reserved Effects of Selected Family Characteristics on Interrelated Components of Household Asset Portfolios