Miracle-dz/UniFAR-FTUs huggingface dataset:
Psycho-visual based distortion measures for monochrome image and video compression

Describes quantitative distortion measures for compressed monochrome image and video based on a psycho-visual model. The model follows the human vision perception in that the distortion as perceived by a human viewer is dominated by the compression error uncorrelated with the local features of the original image and for a video sequence the distortion is perceived from two sources, the still areas and the motion areas of a video frame. The authors have performed subjective tests to obtain ranking results for compressed images and video sequences which were compressed using different compression algorithms and compared the results with the rankings obtained using their distortion measure and other existing mean-square error based measures. They have found that their distortion measure's ranking matches the subjective ranking accurately whereas the mean-square error and its variants performed poorly in matching the subjective ranking on an average. <<ETX>>
Support Vector Machine Model Selection Using Strangeness

Model Selection is the task of choosing the best model for a particular data analysis task. It generally makes a compromise between fit with the data and the complexity of the model. Currently the most popular techniques used by practitioners are Cross-Validation (CV) and Leave-One-Out (LOO). In this study we concentrated on the Support Vector Machine (SVM) (Boser et al., 1992) model. Recently, Ozogur-Akyuz et al. (In Press), following on work by Ozogur et al. (2008), show that selecting a model whose hyperplane achieves the maximum separation from a test point obtains comparable error rates to those found by selecting the SVM model through CV. In other words, while methods such as CV involve finding one SVM model (together with its optimal parameters) that minimises the CV error, Ozogur-Akyuz et al. (In Press) keep all of the models generated during the model selection stage and make predictions according to the model whose hyperplane achieves the maximum separation from a test point. The main advantage of this approach is the computational saving when compared to CV or LOO. However, their method is only applicable to large margin classifiers like SVMs. We continue this line of research, but rather than using the distance of each test point from the hyperplane we explore the idea of using the nonconformity measure (Vovk et al., 2005; Shafer & Vovk, 2008) of a test sample to a particular label set. The nonconformity measure is a function that evaluates how ‘strange’ a prediction is according to the different possibilities available. The notion of nonconformity has been proposed in the on-line learning framework of conformal prediction (Shafer & Vovk, 2008), and is a way of scoring how different a new sample is from a bag of old samples. The premise is that if the observed samples are well-sampled then we should have high confidence on correct prediction of new samples, given that they conform to the observations. We take the nonconformity measure and apply it to the SVM algorithm during testing in order to gain a time advantage over CV and to generalise the algorithm of Ozogur-Akyuz et al. (In Press). Hence we are not restricted 1A bag is a more general formalism of a mathematical set that allows repeated elements.
Inter-/Intra-session Recurrent Neural Network for Session-based Recommender Systems

Recommender systems are useful to users of a service and to the company offering the service. Good recommendations can help users find what they are looking for faster, and they can help users discover new content. For businesses, the recommendations can improve user engagement. In recent years, research has been done on employing Recurrent Neural Networks (RNNs) within the case of recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of these experiments, the RNN could potentially improve its recommendations by utilizing information about a user’s past sessions, in addition to his interactions in the current session. A problem for session based recommenders, is how to produce accurate recommendations at the start of a session, before the system has learned much about the user’s current interests. We propose a novel approach that extends the existing RNN-based sessionbased recommender, making it able to process the user’s recent sessions, to improve recommendations. This is done by using a second RNN layer to learn from recent sessions, and predict the user’s interest in the current session. By feeding this information to the original RNN layer, the proposed solution is able to improve its recommendations. Our experiments on three different datasets, show that the proposed approach can significantly improve recommendations throughout the sessions, compared to a single RNN working only on the current session. The proposed model especially improves recommendations at the start of sessions, and is therefore able to deal with the cold start problem within sessions.
Peer-to-peer network modeling for adversarial proactive cyber defenses

This thesis implements a novel peer-to-peer network simulator that integrates coevolutionary algorithms in order to model adversarial attack and defense dynamics in networks. Modeling this behavior is desirable as it allows for network designers to better develop network defense strategies against adaptive cyber attackers. By developing a network simulator that implements a peer-to-peer protocol, we were able to control the environment and abstract away many of the complex details that would normally arise from using a live network. Because of this environment, we were able to design attack and defense models and grammars, construct arbitrary network topologies, and rapidly test adversarial behavior using the integrated coevolutionary algorithms. Second, the thesis implements the integration of the coevolutionary algorithms with a more complex, proprietary emulator that implements an advanced version of Chord. Our experiments with this system start to investigate the effectiveness of peer-to-peer networks as defenders as well as elucidate the issues of integrating coevolutionary algorithms in a real-world system. Thesis Supervisor: Una-May O’Reilly Title: Principal Research Scientist Thesis Supervisor: Erik Hemberg Title: Research Scientist
A Human Motor Control-Inspired Control System for a Walking Hybrid Neuroprosthesis

The purpose of this research is to develop a human motor control-inspired control system for a hybrid neuroprosthesis that combines functional electrical stimulation (FES) with electric motors. This device is intended to reproduce gait for persons with spinal cord injuries (SCI). Each year approximately 17,000 people suffer from an SCI in the U.S. alone, of which about 20% of them are diagnosed with complete paraplegia. Currently, there is a lot of interest in gait restoration for subjects with paraplegia but the existing technologies use either solely FES or electric motors. These two sources of actuation both have their own limitation when used alone. Recently, there have been efforts to provide a combination of the two means of actuation, FES and motors, into gait restoration devices called hybrid neuroprostheses. In this dissertation the derivation and experimental demonstration of control systems for the hybrid neuroprosthesis are presented. Particularly, the dissertation addresses technical challenges associated with the real-time control of a FES such as nonlinear muscle dynamics, actuator dynamics, muscle fatigue, and electromechanical delays (EMD). In addition, when FES is combined with electric motors in hybrid neuroprostheses, an actuator redundancy problem is introduced. To address the actuator redundancy issue, a synergy-based control framework is derived. This synergy-based framework is inspired from the concept of muscle synergies in human motor control theory. Dynamic postural synergies are developed and used in the feedforward path of the control system for the walking hybrid neuroprosthesis. To address muscle fatigue, the stimulation levels are gradually increased based on a model-based fatigue estimate. A dynamic surface control technique, modified with a delay compensation term, is used to address the actuator dynamics and EMD in the control derivation. A Lyapunov-based stability approach is used to derive the controllers and guarantee their stability. The outcome of this research is the development of a human motor control-inspired control framework for the hybrid neuroprosthesis where both FES and electric motors can be simultaneously coordinated to reproduce gait. Multiple experiments were conducted on both able-bodied subjects and persons with SCI to validate the derived controllers.
A survey of image synthesis and editing with generative adversarial networks

This paper presents a survey of image synthesis and editing with Generative Adversarial Networks (GANs). GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing applications. This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.
Inferring social relationship in mobile social networks using tempo-spatial information

With the advancement of the Internet technology, network communication technology and mobile social applications, mobile social networks present the booming development momentum. Meanwhile, due to the popularity of mobile terminals such as smart phones and variety of PADs, it is feasible to obtain relatively accurate tempo-spatial information from mobile terminal holders. Therefore, inferring social relationships among those people on the basis of their historical tempo-spatial information becomes one of the hottest research fields. In this paper, we aim to infer social relationship among users according to their historical location information via the location-based service which is provided by many on-line social platforms. To achieve this goal, we firstly obtain historical location records and their corresponding timestamps which can be efficiently collected from selected on-line social platforms. Then, we infer the potential social relationship among users according to their historical tempo-spatial information by applying the social relationship inferring method proposed in this paper. Finally, by analyzing the experimental results, we make a set of helpful observations.
Automatic Registration of Phalanges Regions in CR Images Based on Scale-Invariant Salient Region Features

As two common orthopedic diseases, there are rheumatoid arthritis (RA) and osteoporosis. And early detection and early treatment are important for these diseases. However, some problem such as mass screening on data sets, mis-diagnosis are still remained in visual screening. In order to solve these problems and reduce the burden to physicians, needs of an automatic diagnosis system capable of performing quantitative analysis is anticipated. In this paper, we carry out the development of a registration method of phalanges regions from CR images of the hand to detect temporal changes of phalanges regions by alignment between previous image and current one. The proposed method is carried out registration of phalanges regions using salient region features (SRF) in image. Specifically, SRF are detected from phalanges regions. Then, rigid registration technique is performed using deformation amount based on SRF. We applied our method on 84 pairs of CR temporal images of phalanges regions.
An Overview of Knowledge Discovery Databaseand Data mining Techniques

Data mining is used to find or generate new useful information’s from large amount of data base. It is a process of extracting previously unknown and processable information from large databases and using it to make important business decisions. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining and knowledge discovery techniques to understand user behavior better, to improve the service provided, and to increase the business opportunities Of an overview of knowledge discovery database and data mining.
An Overview of Problems and Approaches in Machine Intelligence

Robotics is an interdisciplinary research field leveraging on control theory, mechanical engineering, electronic engineering and computer science. It aims at designing machines able to perceive, move around and interact with their environment in order to perform useful tasks. Artificial Intelligence (AI) is an area of computer science, overlapping with but significantly distinct from robotics. Its purpose is to understand intelligence, through effective computational models, design and experiment with systems which implement these models. There is a significant convergence between Robotics and AI. Their intersection, qualified here as Machine Intelligence, is critical for both areas. Robots implement the so-called " perception-decision-action " loop; the intelligence or decision making part is central in that loop for tackling more variable and complex environments and tasks. On the other hand, AI is moving from abstract intelligence, such as in playing chess, to addressing embodied intelligence. This paper introduces the reader to some of the research issues and approaches in Machine Intelligence. It surveys the state of the art in key issues such as planning and acting deliberately on the basis of tasks and world models, learning these models, and organizing the sensory-motor and cognitive functions of a robot into resilient and scalable architectures.
Simulation Vs. Real Time Control; With Applications To Robotics And Neural Networks

Simulations are often used to model real physical systems prior to electrical, mechanical, and computer hardware development. This allows engineers and scientists to experiment with various concepts before committing time and effort into hardware. Simulations can also be run concurrently with real-time systems to build knowledge of the environment that the real-time system is operating in, then provide feedback to the system to optimize its performance. For both of these types of simulations, the simulation must accurately model the real physical system. A comparison of simulations to real-time controlled physical systems is illustrated in this paper using several simple robotic and artificial Neural Network examples. The robotics examples show how real-time control of mobile robots and robotic-arms, and the resultant governing equations and software algorithms can provide several interesting simulation problems to overcome if the simulation is to accurately model the physical system. The Neural Network example demonstrates how computational speed and numerical precision can become an issue when comparing simulations to real-time Neural Network hardware. In general, comparison of simulations to real physical systems often enhances understanding of the underlying governing principles and equations, and results in simulations that accurately model the real world.
Developing a usable metric toolkit for a commercial environment

Dictates of prevalent development cultures often prevent important progress in the development of software quality metrics being used in commercial software environments. Comparison of data between projects is discouraged due to differences in development environments. Programmers decline to count errors, employees regard error statistics as performance appraisal weapons, and costs to set up baselines are high. The author shows how to alleviate these problems and presents some experiences in two commercial projects conducted in separate corporations, thereby illustrating how simple quality metrics can be developed and used. >
A proposal for modeling intersections in traffic systems by using adaptive fuzzy Petri nets

It can be observed that the number of vehicles, accidents and congestion increase proportionally with the development of a city. A vehicular traffic system behaves like a discrete event system, and due to variations that affect the level of congestion, modeling and controlling this system becomes a complex task. Petri Nets are one of the most powerful tools for modeling graphically and mathematically. Some systems are characterized by little information, inaccurate data and / or permanent changes with regard to the model of the system, which makes modeling and control difficult. This has led to modeling techniques that apply adaptation techniques and human knowledge representation through bio-inspired computing systems such as Neural Networks and Fuzzy Logic. These techniques will be harnessed in this work in terms of an approximated model for learning in a discrete concurrent system by using Fuzzy Petri Nets to represent knowledge through the application of inference and adaptive rules in a chaotic environment, like it can be found in a traffic system.
Rigel: A DSL Use Case for Trace Analysis

Thanks to the DSLs is possible introduce compile optimizations in a flexible, scalable and simple way. However, some years ago to implement a DSL was a huge effort because it was necessary develop some complex features such as: an interpreter,a compiler, an application generator. Recently, all this effort has been reducing thanks to the development of a DSL platform that make the implementation of a DSL simple. To illustrate the potential of this platform, we have developed Rigel an use case of DSLs for trace analysis.
Basic Approaches to Semantic Web Services : A Comparative Study

It has been recognized that due to the autonomy and heterogeneity of Web services and the Web itself, new approaches should be developed to describe and advertise Web services. The most notable approaches rely on the description of Web services using semantics. This new breed of Web services, termed semantic Web services (SWSs) aim to improve the possibilities for automated discovery, composition and invocation of Web Services by providing ontology-based service descriptions expressed in a formal language. Several approaches have been driving the development of Semantic Web Service frameworks such as OWL-S (Ontology Web Language for Services), WSMO (Web Service Modeling Ontology) and IRS-III (Internet Reasoning Service). This paper focuses on framework of different approaches of Semantic Web Services and also contrast these approaches to SWS according to the proposed dimension.
An experimentally-based DC model for the Bi-MOS structure and its adaptation to a circuit simulation environment

This paper presents an experimentally-based model that can accurately describe the hybrid-mode operation of deep submicron LDD p-MOSFETs in a circuit environment. Unified equations for the MOS, bipolar, and space charge currents of the Bi-MOS structure were derived and later adapted to meet the circuit simulation requirements.
Design of a rapidly acquiring and noise immune PLL for communication systems

Phase-locked loops (PLLs) are extensively used for carrier synchronization. For signals with slowly varying phase and frequency, the best performance is achieved with PLLs. However, PLLs cannot provide rapid acquisition with high probability when acquiring signals with high a priori phase and frequency uncertainty because of the hang-up phenomenon. This paper presents a new approach to the synthesis of PLLs employing the indirect nonlinear filtering theory. Through Monte Carlo simulations, the use of this approach is shown to allow the creation of a PLL devoid of the hang-up phenomenon. It also provides a wider pull-in range, more rapid acquisition without degrading the estimation accuracy in the tracking mode, and a lower probability of losing lock than the traditional algorithms of the PLL do.
Preclusion Measures for Protecting P2P Networks from Malware Spread

Malicious software or Malware is the software developed with malicious intentions. Hackers use it for spoiling computer programs or to get access to sensitive information. The detection of such malware can be done by writing program which can understand the dynamics of malware. Towards this end this paper presents an analytical model which can effectively characterize the true nature of malware and how it spreads in P2P networks such as Gnutella. The proposed model is compartmental model which involves derivation of network conditions and system parameters in such a way that under those parameters and conditions the underlying P2P network reaches a malware free equilibrium. The proposed model can also perform evaluation of strategies such as quarantine used to control malware spread. Afterwards the model has been enhanced and tested with networks of smart cell phones. The empirical results revealed that the proposed model is effective and useful. Index Terms: Malware, peer-to-peer networks, compartmental model, Bit Torrent and Time to Live (TTL).
FUZZY LOGIC CONTROLLER FOR SMART HOME LIGHTING CONTROL

Background. Modern high-tech automation systems are able to provide unmanned productive and efficient management of smart home functions. These systems should provide control of temperature, light level, humidity and air pollution for a comfortable stay in the building. In particular, fuzzy logic controller has the potential for application in intelligent systems of lighting control. Objective.Te aim of the paper is to design the two-channel lighting control system in smart home that provides control of lighting source power and of their spectral characteristics. Methods. The lighting control system is based on fuzzy inference and provides forming the base of fuzzy production rules, fuzzification of input values, aggregation of truth of sub conditions of each rule, activation of conclusions and defuzzification process that generates an output signal to control the smart home functional devices. Results. The crisp values of light source power with different spectral characteristics and output signal that controls the transparency of windows have been obtained in result of representation of input data of different types using linguistic variables and fuzzy production rules for the current values of natural light and time of day. It is shown the possibility to change the sensitivity of the control systems in different ranges of illumination deviation from optimal values. Conclusions. The lighting control method in buildings based on fuzzy logic controller enables to get the quantitative values of power of light sources with different spectral characteristics taking into account the individual characteristics of residents.
A model-based method for disclosure limitation of business microdata

Summary. We briefly define disclosure limitation. We outline a new method for disclosure limitation, illustrating our approach on microdata from the Community Innovation Survey of manufacturing and services sector enterprises. Our method builds regression models for the continuous variables to be protected. Some of the fitted values from these models are then shrunk before being released. We also review the microaggregation approach to disclosure limitation. We briefly describe a method based on principal components analysis for defining broader categories for the variable geographical area. We discuss how to assess both the amount of protection offered and the error induced by a disclosure limitation method. We find that for the four 'Nomenclature of economic activities in the European Union' main economic activities considered the new method offers more protection than microaggregation and very often leads to a smaller error.