Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Development Of F0 As An Acoustic Cue For Korean Stop Contrast

Korean stop contrasts (lenis, fortis, and aspirated) have been phonetically differentiated by Voice Onset Time (VOT), but with a tonogenetic sound change in progress, the role of fundamental frequency (F0) has been amplified for distinguishing between Korean stop contrasts with the loss of VOT differentiation in young adults’ production. The present study explores how F0 is perceptually acquired and how it phonetically operates in toddler speech in relation to Korean stop contrasts according to age. In order to determine the relationship between F0 developmental patterns and age in child stop production, this study uses a quantitative acoustic model to examine the word-initial stop productions of 58 Korean monolingual children aged 20 to 47 months. The production experiment confirmed that VOT is useful for distinguishing fortis stops, but F0 is required for distinguishing between lenis and aspirated stops, and this tendency is significantly related to age. As F0 becomes a determinant acoustic parameter for articulatory distinction, the role of F0 in perceptual distinction was investigated through a perceptual identification test with the F0 continuum. Children were provided with selected lenis-aspirated pairs of images in which they would point to one or the other image in response to given synthetic sounds with different F0 values. This allowed us to observe how phonetic boundaries in the F0 dimension for aspirated stops change with age. A comparative analysis between children’s production and perception of F0 indicates that articulatory skills depend on perceived F0 differences depending on the phonemic categories. Additionally, the analysis indicates that once F0 is acquired, VOT differentiation diminishes for distinction between lenis and aspirated stops, and this trade-off between VOT and F0 would occur around the age of 3 years. These findings suggest that phonemic categorization of lenis and aspirated stops should be processed in the F0 dimension and that phonemic processing in perceptual acoustic space is directly linked to phonetic discrimination between the non-fortis stops in production. This study provides experimental evidence for understanding a developmental trajectory of F0 as an acoustic cue for native phonological contrasts. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Linguistics First Advisor Mark Liberman
Exploring New Computing Paradigms for Data-Intensive Applications

ion levels Feature Extraction Low level feat. High level feat. Output Figure 1.3: Feature extraction in DL models involves multiple levels of data representation abstraction. 3. Computational power: given the large data quantity needed, DL models require high computational power and high-end machines to run (e.g. GPUs) while ML algorithms can work on low-end machines. 4. Training time: in general, the training phase of DL algorithms, unlike in ML, is highly time demanding because a large amount of parameters must be learned. 5. Results interpretation: DL models are very complex and, in turn, it is not possible to fully understand why they behave in a certain way. The same cannot be said about ML models that are based on rules that help understanding the reasoning behind their choices. These differences suggest that the choice of using DL rather than ML depends on the kind of problem to face, on the quantity and type of input data and on the computational power available. 1.2 Artificial Neural Networks Artificial Neural Networks (ANNs) are a subset of Machine Learning models. ANNs can be shallow or deep, in this case they are referred to as Deep ANNs (or simply Deep Neural Networks, DNNs). ANNs are vaguely inspired to the human brain neural network. These kind of models are used when the problem to deal with involves a large number of features or when the input dataset is complex. ANNs are heavily used nowadays in the most disparate fields. 7 1 – Motivations and background 1.2. 1 The single-layer perceptron In the human brain there are neurons and synapses: the former can be seen as processing units working in parallel, the latter are the interconnections among these units. ANNs have a similar structure. In particular, the basic processing element of an ANN is the perceptron [92], a unit that behaves analogously to the human brain perception process. A perceptron, depicted in figure 1.4. A, has inputs (xi, i = 0, . . . , n) that can either come from the outside environment or that can be the outputs of other perceptrons. Each input
Automatic analysis of online image data for law enforcement agencies by concept detection and instance search

The information available on-line and off-line, from open as well as from private sources, is growing at an exponential rate and places an increasing demand on the limited resources of Law Enforcement Agencies (LEAs). The absence of appropriate tools and techniques to collect, process, and analyze the volumes of complex and heterogeneous data has created a severe information overload. If a solution is not found, the impact on law enforcement will be dramatic, e.g. because important evidence is missed or the investigation time is too long. Furthermore, there is an uneven level of capabilities to deal with the large volumes of complex and heterogeneous data that come from multiple open and private sources at national level across the EU, which hinders cooperation and information sharing. Consequently, there is a pertinent need to develop tools, systems and processes which expedite online investigations. In this paper, we describe a suite of analysis tools to identify and localize generic concepts, instances of objects and logos in images, which constitutes a significant portion of everyday law enforcement data. We describe how incremental learning based on only a few examples and large-scale indexing are addressed in both concept detection and instance search. Our search technology allows querying of the database by visual examples and by keywords. Our tools are packaged in a Docker container to guarantee easy deployment on a system and our tools exploit possibilities provided by open source toolboxes, contributing to the technical autonomy of LEAs.
A Fuzzy Logic Based Clustering Algorithm for WSN to Extend the Network Lifetime

A mobile sensor network is a set of physically distributed sensor nodes. Sensor node is a small wireless device with limited battery life, radio transmission range and storage size. Sensor nodes perform the task of collecting data, processing of data, monitoring of environment etc. Mobility of sensors can be effectively used to improve the target coverage quality and network connectivity in randomly deployed mobile sensor networks. Appropriate cluster head (CH) selection can reduce the energy consumption. Most famous hierarchical routing protocol is Low energy adaptive clustering hierarchical (LEACH), in which CH is selected on rotation basis based on a probabilistic threshold value. In this process only CH are allowed to send information to base stations (BS). But in this method a SCH i.e. super cluster head is chosen among the CHs who can send the information to mobile base stations by choosing suitable fuzzy descriptors like base station mobility, clusters centrality and remaining battery power. Fuzzy inference engine is used to select the chance of CHs to be SCH. We have derived the results from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol as it provides better stability and better lifetime to network. Keywords —Wireless Sensor Network (WSN), Super Cluster Head (SCH), Fuzzy Logic
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm.
Early Weak Fault Diagnosis of Rolling Bearing Based on Duffing System and LMCD

The early weak fault signals of bearings are usually hidden in a large amount of noise, and the components of the noise are more complicated. Therefore, it is difficult to extract the early weak fault features of bearings using conventional time-frequency methods. In this paper, the traditional Holms-Duffing system has been improved to enhance the anti-noise performance of the system, and then the Duffing system can be used to detect the signal in a broadband frequency through time transformation. At the same time, the four Duffing systems array model has been used to solve the misjudgment problem of the Duffing system caused by the phase difference. When the Duffing system is in a large period state, the complexity of the displacement sequence is smaller than that in the chaotic state. According to this characteristic, a method for diagnosing weak bearing faults based on the Duffing system and LMC difference entropy(LMCD) is proposed. In this paper, simulation signal with strong noise components and the real fault signals have been used to verify the suggested method. The proposed methods in this paper have certain practical value in the field of fault diagnosis.
A spatiotemporal registration method between roadside lidar and camera

With the rapid development of intelligent transportation, it is of great significance to improve road traffic safety and traffic efficiency by tracking road users in real time. In this paper, a roadside multi-source data acquisition platform is designed based on two main sensors, lidar and camera, to perform time synchronization between lidar data and camera data. The point cloud data and image data of the same frame are the research objects. Based on Zhang Zhengyou’s calibration method and the plane target model theory, the camera internal parameters and the joint external parameters of the lidar and camera are respectively calibrated to obtain the internal and external parameter matrix. The error is 0.09 pixels, and the external parameter calibration error is 0.22 pixels. The registration test of lidar and camera under different roadside layout conditions is designed to verify the adaptability of the registration method. When the camera is arranged and installed, the distance between the horizontal and vertical directions should not exceed 90 cm.
Signature and Date-Based Document Image Retrieval

It is a common organisational practice nowadays to store and maintain large digital databases in an effort to move towards a paperless office. Large quantities of administrative documents are often scanned and archived as images (e.g. the ‘Tobacco’ dataset [1]) without adequate indexing information. Consequently, such practices have created a tremendous demand for robust ways to access and manipulate the information that such images contain. Manual processing (i.e. indexing, sorting or retrieval) of documents from these huge collections need substantial human effort and time. So, automatic processing of documents is required for office automation. In this context, Document Image Analysis (DIA) has enjoyed many decades of popularity as a research area to address these issues because of its huge application potential in many fields such as academics, banking and in industry. A document repository available for analysis in such a domain contains a large collection of heterogeneous documents. Automatic analysis of such large document database has been an interesting and challenging research field for many years, specifically due to the diverse layouts and contents. One way to efficiently search and retrieve documents from a large repository is to fully convert the documents to an editable representation (i.e. through Optical Character Recognition) and index them based on their content. There are many factors (e.g. high cost, low document quality, non-text components, etc.) which prohibit complete conversion of a document to an editable form. Hence, other components of a document, namely signatures, dates, logos, stamps/seals, etc. are worthy consideration for indexing, without the requirement for complete OCR.
GLOBALLY-APPLICABLE PREDICTIVE WILDFIRE MODEL A TEMPORAL–SPATIAL GIS BASED RISK ANALYSIS USING DATA DRIVEN FUZZY LOGIC FUNCTIONS

Abstract. This study (van den Dool, 2017) is a proof of concept for a global predictive wildfire model, in which the temporal–spatial characteristics of wildfires are placed in a Geographical Information System (GIS), and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada), Europe (Spain), and Asia (Mongolia), and are downscaled to the highest resolution (3-arc second). The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model) and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1) by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.
The extended commutation cell : a path towards flexible multilevel power processing

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Interoperability Testing for The GridRPC API Specification

This document proposes a process of interoperability testing of the GridRPC API specification (GFD-R.52), which is submitted to the Global Grid Forum (GGF) recommendation track. Three independent implementations from different code bases, Ninf-G, GridSolve and DIET, are used for showing sufficient successful operational experiences. A strong belief that the specification is mature and will be useful is indicated through all the results of the test suites.
Indefinites and the Operators they Depend on: From Japanese to Salish

1. Towards a typology of indefinites It is hard to imagine what contemporary theories of indefinites might look like if Barbara Partee had never organized that famous Amherst workshop on indefinites in November 1978. In the preface of her dissertation, Irene Heim quotes Partee's description of the workshop topic as follows: 1. University of Washington in Seattle. I thank the audience of SALT for challenging comments, and the organizers, in particular Toshiyuki Ogihara, for a conference that was one of the most enjoyable and best organized I have ever attended. Gennaro Chierchia and an anonymous reviewer conveyed generous comments that were so thought provoking that only the insistence of the editors of this volume could extract a final version of this paper from me before the book had to go to press. I certainly took those comments to heart, and even if I could not see how to follow all the advice I got in a single article, the issues raised will continue to influence my work in the area of quantification. Getting comments of this kind just shows what a great community the community of semanticists is. Barbara's intellectual generosity and fairness is bound to have had a profound influence in that arena, too. footnotes and references to point to important issues discussed there. I plan to address those issues in more depth in a separate paper. 2 " One standard view among logicians is that indefinite noun phrases like 'a tall man' are not referring expressions, but quantifier phrases, like 'every man', 'no man', and 'most men'. Yet in many respects, indefinite noun phrases seem to function in ordinary language much like definite noun phrases or proper names, particularly with respect to the use of pronouns in discourse. This may be simply a matter of sorting out semantics from pragmatics, but there is not to our knowledge any currently available theory that simultaneously characterizes the logical or truth-functional properties of indefinite noun phrases and accounts for their 'discourse-reference' properties…. " 2 Irene Heim (1982) and Hans Kamp (1981) took up Partee's challenge and proposed theories that simultaneously accounted for the quantificational and discourse reference properties of indefinites. They argued that the quantificational behavior of indefinites was an illusion. It derived from overt or non-overt operators present in semantic representations, or alternatively, from the mechanics of the semantic interpretation procedure itself. According to Heim and Kamp, indefinites introduced mere …
TRANSFORMASI CERITA RAKYAT JAMARUN KE PERTUNJUKAN “CAHAYA MEMINTAS MALAM/THE LIGHT WITHIN A NIGHT”

ABSTRAKCerita rakyat Jamarun merupakan folklor lisan yang berasal dari Cianjur, Jawa Barat. Di tengah keberadaannya yang mulai terlupakan dalam ingatan masyarakat, cerita rakyat Jamarun diolah menjadi pertunjukan oleh tiga kelompok teater dari dua negara (Indonesia dan Australia): Mainteater Bandung, Teater Lakon, dan La Trobe University Student Theatre and Film. Cerita rakyat Jamarun dan pertunjukan Cahaya Memintas Malam/The Light Within A Night menjadi data dalam penelitian ini. Teori yang digunakan dalam penelitian, yaitu teori tentang transformasi yang dikemukakan oleh Riffaterre. Berdasarkan hasil pembahasan ditemukan adanya konversi dan ekspansi. Konversi tidak terjadi dalam tataran alur dan tokoh di pertunjukan CMM. Alur dan tokoh yang terdapat dalam CMM justru lebih kompleks daripada yang terdapat dalam cerita rakyat Jamarun. Cerita rakyat Jamarun menyandarkan pada penceritaan naratif, sedangkan pertunjukan CMM menggunakan rentetan dialog sebagai penyampai cerita. Cara ungkap naratif memungkinkan cerita tersampaikan lebih singkat, sedangkan cerita yang menyandarkan pada dialog membutuhkan uraian lebih luas. Ekspansi cerita rakyat Jamarun ke pertunjukan CMM tampak melalui pengembangan alur, tokoh, latar, dan wacana. Pengembangan tersebut ditandai dengan munculnya adegan-adegan baru yang terkait dengan wacana yang terkandung dalam cerita rakyat Jamarun dan yang berhubungan dengan dua budaya, Indonesia dan Australia. Selain itu, didapatkan pengetahuan bahwa perbedaan latar belakang budaya tidak dapat membatasi kerja kreatif. Perbedaan budaya justru mampu memotivasi untuk menemukan perspektif dan gaya ungkap yang tidak terduga. Pemilihan cerita rakyat sebagai poros utama cerita, mampu mengenal lebih jauh cara berpikir masyarakatnya. Kata kunci: cerita rakyat jamarun, folklor, pertunjukan cahaya memintas malam, transformasi.
Investigating the Sound Change in the New Zealand English Nurse Vowel /ᴈ:/

Although there have been many studies of New Zealand English (NZE) vowels, little has been written about the nurse vowel /ɜ:/. This study uses data from three groups of Māori speakers of NZE born between 1871 and 1992 to analyse changes to the nurse vowel over time. Although all these speakers are bilingual in English and Māori, we show that they are representative of NZE speakers generally. Analyses are carried out on formant frequency, vowel length, lip-rounding and vocal tract shape. The vowel space position measure (VSM) is used to analyse first and second formant movements together. The nurse vowel in NZE has risen so that it is now close and front in the vowel space, and apparently in danger of being confused with the goose vowel. We conclude by considering the factors that apparently keep the vowels apart and the potential effects of such a merger.
3D Reconstruction from a Single Still Image Based on Monocular Vision of an Uncalibrated Camera

we propose a framework of combining Machine Learning with Dynamic Optimization for reconstructing scene in 3D automatically from a single still image of unstructured outdoor environment based on monocular vision of an uncalibrated camera. After segmenting image first time, a kind of searching tree strategy based on Bayes rule is used to identify the hierarchy of all areas on occlusion. After superpixel segmenting image second time, the AdaBoost algorithm is applied in the integration detection to the depth of lighting, texture and material. Finally, all the factors above are optimized with constrained conditions, acquiring the whole depthmap of an image. Integrate the source image with its depthmap in point-cloud or bilinear interpolation styles, realizing 3D reconstruction. Experiment in comparisons with typical methods in associated database demonstrates our method improves the reasonability of estimation to the overall 3D architecture of image’s scene to a certain extent. And it does not need any manual assist and any camera model information.