Miracle-dz/UniFAR-FTUs huggingface dataset:
On Cybersecurity of Freeway Control Systems: Analysis of Coordinated Ramp Metering Attacks

This article focuses on cybersecurity of transportation systems and investigates their vulnerability to attacks on the sensing and control infrastructure. An array of different attack points, classified into physical, close-proximity, and virtual layers, are reviewed and investigated. The authors construct two benchmark scenarios which exploit these vulnerabilities to identify the potential harm of a traffic control system compromise. A more in-depth analysis is then presented on the takeover of a series of networked onramp metering traffic lights. The analysis is conducted using a methodology for precise and intelligent onramp metering attacks based on finite-horizon optimal control techniques and multi-objective optimization. The methodology is demonstrated in simulation for two examples of high-level attack objectives: congestion-on-demand, which aims to create precise pockets of congestion on a model of the I15 San Diego freeway, and catch-me-if-you-can, which attempts to aid a fleeing vehicle from chasing pursuants.
Multi-scale Contrastive Learning for Complex Scene Generation (Supplementary Material)

Table 1 shows the architectural details of MsConD discriminator. Our discriminator is built upon the backbone resnet-based discriminator used in StyleGAN2 [4]. We use branches to process the feature map at each level, where each branch consists of three components: a shared block, a discriminator head and a projection head. The shared block consists of 3 1× 1 convolutional layers with residual connections. The shared block translates a feature map in the backbone network into an intermediate feature map of the same size. The intermediate feature map is then projected into two different outputs each by a discriminator head and a projection head, where both head layers are implemented with 1× 1 convolutional layers. The discriminator head is a single convolutional layer, while the projection head consists of two convolutional layers, i.e., Conv-ReLUConv. We set the channel dimension of the projection output, i.e., Cp, as 256. We use ReLU activation for all layers in branches.
Ebaluatoia : crowd evaluation of English-Basque machine translation

This dissertation reports on the crowd-based large-scale English-Basque machine translation evaluation campaign, Ebaluatoia. This initiative aimed to compare system quality for five machine translation systems: two statistical systems, a rulebased system and a hybrid system developed within the IXA group, and an external system, Google Translate. We have established a ranking of the systems under study and performed qualitative analyses to guide further research. In particular, we have carried out initial subset evaluation, structural analysis and error analysis to help identify where we should place future analysis effort.
Performance Analysis of Text To Speech Synthesis System Using HMM And Prosody Features With Parsing For Tamil Language

This paper describes a Hidden Markov Model (HMM) based (TTS) system and prosody based (TTS) system for producing natural sounding synthetic speech in Tamil language. The (HMM) based system consists of two phases such as training and synthesis. Tamil speech is first parameterized into spectral and excitation features using Glottal Inverse Filtering (GIF). An emotions present in the input text is modeled based on the parametric features. The performance measure has been carried out with recorded speech and the (HMM) based (TTS) system. Subsequently the (TTS) system with prosodic features for generating human voice has been implemented. To produce the output of (TTS) in the same form as if it is actually spoken the prosody feature allows the synthesizer to vary the pitch of the voice. The pitch and duration play an important role to improve the naturalness of (TTS) output. The performance measure has been carried out with recorded speech and the prosody based (TTS) system. Finally the performance of (HMM) based (TTS) has been compared with prosody based (TTS) to measure the effectiveness of the system. Both (TTS) systems are used to analyze the emotions such as Happy, Fear, Neutral and Sad to improve the effectiveness of the system.
GPU-Based background illumination correction for blue screen matting

Separation of foreground objects from an almost constant backing color for video applications is still a common problem ([1],[2]). For non-realtime situations there is a wide variety of different powerful mathematical approaches that can deal with most of the matting problems. For SD/HD studio realtime keyers most solutions are not applicable due to their algorithm complexity or high effort in user interaction. Excellent hardware keyers, such as UltimatteTM work on most occasions, but even under controlled lighting in a blue-/greenscreen matting problems may occur, or creativity is limited by necessary lighting conditions. As a preprocessing algorithm for traditional chroma keying systems, we present a simple background illumination correction based approach for improving matting problems with uneven or poor lit blue-/greenscreens. Using the computational power of GPU computing (GPGPU [3]) the presented algorithm is realtime capable and offers an improvement for achievable mattes quality.
Detailed routing by sparse grid graph and minimum-area-captured path search

Different from global routing, detailed routing takes care of many detailed design rules and is performed on a significantly larger routing grid graph. In advanced technology nodes, it becomes the most complicated and time-consuming stage. We propose Dr. CU, an efficient and effective detailed router, to tackle the challenges. To handle a 3D detailed routing grid graph of enormous size, a set of two-level sparse data structures is designed for runtime and memory efficiency. For handling the minimum-area constraint, an optimal correct-by-construction path search algorithm is proposed. Besides, an efficient bulk synchronous parallel scheme is adopted to further reduce the runtime usage. Compared with the first place of ISPD 2018 Contest, our router improves the routing quality by up to 65% and on average 39%, according to the contest metric. At the same time, it achieves 80--93% memory reduction, and 2.5--15X speed-up.
Control and Cybernetics a Characterization of Stability and Sensitivity Properties for State-constrained Optimal Control

In a series of the recent papers of the author, it was shown that the solutions and Lagrange multipliers of state-constrained optimal control problems are locally Lipschitz continuous and directionally differentiable functions of the parameter, under usual constraint qualifications and weakened second order conditions. In this paper, it is shown that those conditions are not only sufficient, but also necessary. Thus, they consitute a characterization of Lipschitz stability and sensitivity properties for state-constrained optimal control problems.
Population connectivity of two reef fish species in northwestern Australia using otolith geochemistry: A pilot study

Sarah Hearne1,5, Mike Travers2,5, Richard Evans3,5, Alison Blythe4,5, Kate Trinajstic1,5, Jennifer McIlwain1,5, Stephen Newman2,5 1Department of Environment and Agriculture, Curtin University, Bentley, Western Australia 2Fisheries Division, Department for Primary Industries and Regional Development, Hillarys, Western Australia 3Department of Biodiversity, Conservation and Attractions, Kensington, Western Australia 4Department of Environment and Agriculture, Curtin University, Bentley, Western Australia 5Western Australian Marine Science Institution, Perth, Western Australia
Computer aided diagnosis system based on random forests for the prognosis of Alzheimer ’ s disease

Introduction: Over the last decade, a large number of computer aided diagnosis (CAD) systems have been developed by researchers in neuroimaging to study neurodegenerative diseases or other kinds of brain disorders [1,2,3]. Briefly, machine-learning (ML) techniques help doctors to distinguish groups of people (e.g. healthy vs. diseased) by automatically identifying characteristics in the images that discriminate the groups. The challenge in the modelling of CAD systems is not only to perform well in terms of prediction but also to provide relevant information about the diagnosis, such as regions of interest in the brain that are affected by the disease.
Department of Computer Science Technical Report Generalised

We present a generalisation to the Mixtures of Experts model that introduces prior localisation of the experts as part of the model structure, and as such relates them strongly to the evolutionary computation ML method known as Learning Classiﬁer Systems. While the introduced generalisation allows speciﬁcation of more complex localisation patterns, identifying good models becomes more diﬃcult. We approach this tradeoﬀ by introducing a new training schema that makes ﬁtting a single model computationally less demanding and shifts the importance to searching the space of possible model structures, guided by approximate variational Bayesian inference to ﬁt the model and ﬁnd the model evidence. We demonstrate model search for simple non-linear curve ﬁtting tasks by sampling from the model posterior, as a proof-of-concept alternative to the genetic algorithm used in Learning Classiﬁer Systems for that purpose.
Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output.
An overview of Genetic Algorithms: Pt1, Fundamentals

Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems. They are based on the genetic processes of biological organisms. Over many generations, natural populations evolve according to the principles of natural selection and \survival of the ttest", rst clearly stated by Charles Darwin in The Origin of Species. By mimicking this process, genetic algorithms are able to \evolve" solutions to real world problems, if they have been suitably encoded. For example, GAs can be used to design bridge structures, for maximum strength/weight ratio, or to determine the least wasteful layout for cutting shapes from cloth. They can also be used for online process control, such as in a chemical plant, or load balancing on a multi-processor computer system. The basic principles of GAs were rst laid down rigourously by Holland [Hol75], and are well described in many texts (e.g. [Dav87, Dav91, Gre86, Gre90, Gol89a, Mic92]). GAs simulate those processes in natural populations which are essential to evolution. Exactly which biological processes are essential for evolution, and which processes have little or no role to play is still a matter for research; but the foundations are clear. In nature, individuals in a population compete with each other for resources such as food, water and shelter. Also, members of the same species often compete to attract a mate. Those individuals which are most successful in surviving and attracting mates will have relatively larger numbers of o spring. Poorly performing individuals will produce few of even no o spring at all. This means that the genes from the highly adapted, or \ t" individuals will spread to an increasing number of individuals in each successive generation. The combination of good characteristics from di erent ancestors can sometimes produce \super t" o spring, whose tness is greater than that of either parent. In this way, species evolve to become more and more well suited to their environment. GAs use a direct analogy of natural behaviour. They work with a population of \individuals", each representing a possible solution to a given problem. Each individual is assigned a \ tness score" according to how good a solution to the problem it is. For example, the tness score might be the strength/weight ratio for a given bridge design. (In nature this is equivalent to assessing how e ective an organism is at competing for resources.) The highly t individuals are given opportunities to \reproduce", by \cross breeding" with other
Term-list Translation using Mono-lingual Word Co-occurrence Vectors

A term-list is a list of content words that characterize a consistent text or a concept. This paper presents a new method for translating a term-list by using a corpus in the target language. The method first retrieves alternative translations for each input word from a bilingual dictionary. It then determines the most 'coherent' combination of alternative translations, where the coherence of a set of words is defined as the proximity among multi-dimensional vectors produced from the words on the basis of co-occurrence statistics. The method was applied to term-lists extracted from newspaper articles and achieved 81% translation accuracy for ambiguous words (i.e., words with multiple translations).
WiPoD Wireless Positioning System based on 802 . 11 WLAN Infrastructure

Detector) which is a pure software based location determination and tracking (positioning) system. It uses empirical signal strength measurements from different wireless access points for mobile user positioning. It is designed to determine the location of users having 802.11 enabled mobile devices in an 802.11 WLAN infrastructure and track them in real time. WiPoD is the first main module in our LBS (Location Based Services) framework. We tested K-Nearest Neighbor and Triangulation algorithms to estimate the position of a mobile user. We also give the analysis results of these algorithms for real time operations. In this paper, we propose a supportable, i.e. understandable, maintainable, scalable and portable wireless positioning system architecture for an LBS framework. The WiPoD software has a multithreaded structure and was designed and implemented with paying attention to supportability features and real-time constraints and using object oriented design principles. We also describe the real-time software design issues of a wireless positioning system which will be part of an LBS framework.
Classify-normalize-classify : a novel data-driven framework for classifying forest pixels in remote sensing images

Changing atmospheric conditions often result in a data distribution shift in remote sensing images for different dates and locations making it difficult to discriminate between various classes of interest. To alleviate this data shift issue, we introduce a novel supervised classification framework, called Classify-Normalize-Classify (CNC). The proposed scheme uses a two classifier approach where the first classifier performs a rough segmentation of the class of interest (COI) in the input image. Then, the median signal of the estimated COI regions is subtracted from all image pixels values to normalize them. Finally, the second classifier is applied to the normalized image to produce the refined COI segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. The CNC framework compared favorably to benchmark masks of the PRODES program and state-of-the-art classifiers run on surface reflectance images
DSP utilization in digital color printing

This article discusses the utilization of DSP techniques to digital printing systems, particularly in transforming multidimensional signals from electronic to hardcopy formats by utilizing digital printing solutions and controlling the "marking" engine that is at the heart of the electrophotographic system. Researchers and DSP practitioners are urged to share their vision on the novel use of signal processing algorithms to analyze, manipulate, and solve problems related to high-end digital printing.
Sparse reduced-rank regression for exploratory visualization of paired multivariate datasets

In genomics, transcriptomics, and related biological fields (collectively known as omics), it is common to work with n p datasets with the dimensionality much larger than the sample size. In recent years, combinations of experimental techniques began to yield multiple sets of features for the same set of biological replicates. One example is Patch-seq, a method combining single-cell RNA sequencing with electrophysiological recordings from the same cells. Here we present a framework based on sparse reduced-rank regression for obtaining an interpretable visualization of the relationship between the transcriptomic and the electrophysiological data. We use an elastic net regularization penalty that yields sparse solutions and allows for an efficient computational implementation. Using several publicly available Patch-seq datasets, we show that sparse reduced-rank regression outperforms both sparse full-rank regression and non-sparse reduced-rank regression in terms of predictive performance, and can outperform existing methods for sparse partial least squares and sparse canonical correlation analysis in terms of out-of-sample correlations. We introduce a bibiplot visualization in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons. We believe that sparse reduced-rank regression can provide a valuable tool for the exploration and visualization of paired multivariate datasets, including Patch-seq.
Title : Feasible Performance Evaluations of Digitally-Controlled Auxiliary Resonant Commutation Snubber-Assisted Three Phase Vo

This paper proposes, an efficient method to detect and discriminate mechanical defects of transformer winding based on extracting the winding frequency responses using outlier data detection and ensemble algorithms, which together constitutes an efficient hybrid method. First, the frequency response of the high voltage winding of a real transformer model (1.6 MVA) was extracted in different condition and arranged as primary data. Then, due to the high standard deviation of the characteristics and the weight of the outlier samples above the threshold of 1.1, the Local Outlier Factor (LOF) method was used to clean the samples. Finally, data mining algorithms have been used to detect and distinguish mechanical defects. Based on the results, the decision tree bagging ensemble method reported the best accuracy compared to other techniques and improved the accuracy of the decision tree with total accuracy of 92.68% by LOF. These results also showed that all methods improved accuracy by LOF. It can, therefore, be claimed that the proposed method is capable of discriminating transformer winding mechanical defects accurately.
Developing Process Mining Tools An Implementation of Sequence Clustering for ProM

The goal of process mining is to extract useful information from event logs that record the activities an organization performs. There are many process mining techniques to discover a process model, based on some event log. These techniques perform well on structured processes, but have problems with less structured ones. In this case the logs are very confusing and have high quantities of noise, making it difficult to extract useful information. The models generated for such logs tend to be difficult to read and contain unrelated behavior. In this work we present an approach that aims at overcoming these difficulties by extracting only the useful data and presenting it in an understandable manner. For this purpose sequence clustering algorithms are used to cluster the log into smaller logs (clusters) that correspond to a set of related cases. For each cluster, a model in the form of a Markov chain is presented. A preprocessing stage was also developed, to clean the log of certain irrelevant elements that complicate the models generated. The approach was fully implemented in the ProM framework and all the experiments were performed in that environment. Taking into account the results achieved for a real-world case study and the results of several experiments, we conclude that the approach is capable of dealing with complex logs, eliminating unnecessary behavior and partitioning different types of behavior into more understandable models. We also conclude that the sequence clustering algorithm provides good results when compared to other clustering methods to divide sequences in a process mining context.
EPISTEMOLOGICAL PERSPECTIVES ON MULTI-METHOD INFORMATION SYSTEMS RESEARCH

There is a continuing discussion on methodological pluralism in IS research. Several claims have been made both for and against methodological pluralism. The debate focuses mainly on discussing the relationship between research methods and IS research paradigms, especially positivism and interpretivism. Also, the literature analyzes the epistemological assumptions of research paradigms, but pays little attention to a direct association of research methods with epistemology. We argue that the potential for methodological pluralism depends heavily on the epistemological characteristics of the research methods themselves. After analyzing various arguments pro and contra methodological pluralism, we then provide an epistemological framework addressing the relevant aspects in this context. Summary of Arguments Information Systems (IS) research can be described as a rich tapestry of diverse research methods, research paradigms, and research approaches (Section 1). Multi-method research is useful and favourable for IS research. Combining different research methods in the context of multi-method research designs can provide different perspectives on a particular phenomenon (Section 2). Calls against multi-method research designs applying epistemological and ontological arguments, the discussion focuses on the indirect relationship: epistemology-paradigms-methods. We advocate that in discussing the potential for multi-method research designs it is essential to consider the direct relationship: epistemology-methods (Section 3). A disciplined methodological pluralism must discuss epistemological assumptions of the various research methods to be combined in the context of a multi-method research design. An epistemological framework can help to structure and systematize epistemological issues relevant in this field (Section 4).