Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Audio Recording Devices From Background Noise

In this paper we present a study on identification of audio recording devices from background noise, thus providing a method for forensics and copyright disputes related. The audio signal is the sum of speech signal and noise signal. Since usually, it's the speech which is regarded as the information to be passed that people care about, a great amount of researches have been dedicated to getting higher SNR. So there are many speech enhancement algorithms to improve the quality of the speech signals, which is generally can be seen much like reducing the noise. However, we make the underlying hypothesis that the noise can be regarded as the intrinsic fingerprint traces of an audio recording device in a way. These digital traces can be characterized and identified by new machine learning techniques. Therefore, the noise can serve as the intrinsic features for the identification. As for the classifier, a Softmax classifier and an MLP classifier are used and compared. The identification accuracy is up to 93\% among nine different devices, and shows the method of getting feature vector from the noise of each device and identifying with deeplearning techniques is viable, and well-preformed.
A Toolsuite for Testing Real-Time Ada Applications

Software testing is one of the most expensive phases in the software life cycle. Even for the best commercial grade software, it seldom contains fewer than 100 faults per million lines of code. These faults result in failures and costs for customers which software manufactures are motivated to avoid. It is not unusual for manufacturers to spend 40% or more of their software budgets on testing in an effort to eliminate these faults. Hence, it is very important to provide a solution, supported by tools, which not only can reduce the cost but also improve the quality.In this paper, we describe our effort in developing an Ada program instrumentation environment, Ada-PINE, for testing real-time Ada programs. This tool can help developers and testers in many ways such as determining how well the software has been tested, displaying the code that has not been executed, and improving the code coverage in the most effective way. It can also conduct test set minimization with respect to code coverage and select effective fault-revealing regression tests.
Barbecued Opakapaka: Using Semantic Preferences for Ontology Population

This paper investigates the use of semantic preferences for ontology population. It draws on a new resource, the Pattern Dictionary of English Verbs, which lists semantic categories expected in each syntactic slot of a verb pattern. Knowledge of semantic preferences is used to drive and control bootstrapped pattern extraction techniques on the EnClueWeb09 corpus with the aim of identifying common nouns belonging to twelve semantic types. Evaluation reveals that syntactic patterns perform better than lexical and surface patterns, at the same time raising issues about assessing ontology population candidates out of context.
A linear time algorithm for high quality mesh simplification

High resolution 3D range scanning as well as iso-surface extraction have introduced densely and uniformly sampled models that are difficult to render at an interactive rate. To remove excessive details and produce meshes of various resolutions for different kinds of applications, the study of fast and high quality polygonal mesh simplification algorithms has become important. In this paper, we propose a new linear time algorithm that can achieve fast and high quality mesh simplification. In the new algorithm, we pipeline the cost computation, optimization, and edge collapse, and use a small constant-sized replacement selection min-heap instead of a large greedy queue to effectively reduce the runtime complexity to linear complexity. Compared to previous works, our new algorithm has at least three advantages. First, the new algorithm is runtime efficient. Second, the new algorithm is memory efficient. Third, the algorithm is capable of generating competitive high quality outputs.
Adaptive Frequency Counting over Bursty Data Streams

We investigate the problem of frequent itemset mining over a data stream with bursty traffic. In many modern applications, data arrives at a system as a continuous stream of transactions. In many cases, the arrival rate of transactions fluctuates wildly. Traditional stream mining algorithms, such as Lossy Counting (LC), were generally designed to handle data streams with steady data arrival rates. We show that LC suffers significant loss of accuracy when the data stream is bursty. We propose the Adaptive Frequency Counting algorithm (AFC) to handle bursty data. AFC has a feedback mechanism that dynamically adjusts the mining speed to cope with the changing data arrival rate. Through extensive experiments, we show that AFC outperforms LC under bursty traffics in terms of the accuracy of the set of frequent itemsets
Bodies of Knowledge

As different in approach and range as they undoubtedly are, these four books all share an interest in medieval representational practices out of which complex ideas and theories concerning bodies — indeed, out of which different bodies — emerge. Katharine Park’s Secrets of Women is a wonderfully rich study of anatomy and dissection in northern Italy as it developed from the fourteenth to the sixteenth century; Carole Rawcliffe’s Leprosy in Medieval England offers a dense and detailed examination of the treatment and theories surrounding leprosy in late medieval England; Nicola McDonald’s wide-ranging collection of essays explores a plethora of medieval obscenities and related issues; and Patrizia Bettella’s The Ugly Woman examines the role of this reviled figure in Italian poetry from the Middle Ages to the Baroque.
Tolerating memory latency through push prefetching for pointer-intensive applications

Prefetching is often used to overlap memory latency with computation for array-based applications. However, prefetching for pointer-intensive applications remains a challenge because of the irregular memory access pattern and pointer-chasing problem. In this paper, we proposed a cooperative hardware/software prefetching framework, the push architecture, which is designed specifically for linked data structures. The push architecture exploits program structure for future address generation instead of relying on past address history. It identifies the load instructions that traverse a LDS and uses a prefetch engine to execute them ahead of the CPU execution. This allows the prefetch engine to successfully generate future addresses. To overcome the serial nature of LDS address generation, the push architecture employs a novel data movement model. It attaches the prefetch engine to each level of the memory hierarchy and pushes, rather than pulls, data to the CPU. This push model decouples the pointer dereference from the transfer of the current node up to the processor. Thus a series of pointer dereferences becomes a pipelined process rather than a serial process. Simulation results show that the push architecture can reduce up to 100% of memory stall time on a suite of pointer-intensive applications, reducing overall execution time by an average 15%.
Collective Ontology-based Information Extraction using Probabilistic Graphical Models

Information Extraction (IE) is a process of extracting structured data from unstructured sources. It roughly consists of subtasks named entity recognition, relation extraction and coreference resolution. Researchers have primarily focused just on one subtask or their combination in a pipeline. In this paper we introduce an intelligent collective IE system combining all three subtasks by employing conditional random fields. The usage of same learning model enables us to easily communicate between iterations on the fly and to correct errors during iterative process execution. In addition to the architecture we introduce novel semantic and collective feature functions. The system’s output is labelled according to an ontology and new instances are automatically created during runtime. The ontology as a schema encodes a set of constraints, defines optional manual rules or patterns and with instances provides semantic gazetteer lists. The proposed framework is being developed during ongoing PhD research. It’s main contributions are intelligent iterative interconnection of the selected subtasks, extensive use of context-specific features and parameterless system that can be guided by an ontology. Some preliminary results combining just two subtasks already show promising results over traditional approaches.
Term Structure Modelling Under Alternative Official Regimes

Monetary authorities exercise control of domestic short term interest rates. We argue that models of the term structure of interest rates must take into account the consequences of this control if they are to capture important empirical features of interest rate dynamics. In particular, previous one or two factor models of the term structure may not adequately describe the short rate process. Interest rate regimes in the United Kingdom, the United States, France and Germany are described. A common characteristic is the presence of "non-effective" official interest rates used for signalling purposes. We construct a term structure model, consistent with general equilibrium, that captures, in idealised form, some important features of these regimes. The paper concludes with an illustrative example of a regime in which the short rate is constrained to lie within an officially controlled corridor.
Leveraging Cognitive Features for Sentiment Analysis

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.
Social behaviours in dog-owner interactions can serve as a model for designing social robots

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A Static Tasks Assignment Model For Grid Computing

Tasks assignment in grid computing is a challenge for most researchers and developers of these types of systems. This is due to characteristics inherent to grid infrastructures, namely, heterogeneity, dynamicity and scalability. It becomes much more complex when it comes to assign tasks with precedence constraints represented by a Directed Acyclic Graph. The task assignment problem is well known to be NP-complete. In this paper, we present and evaluate a dependent task assignment strategy for Grids. Our goal is two folds: first we reduce , whenever possible, the average response time of tasks submitted to the grid, and secondly, we reduce the transfer cost inducing by the tasks transfer respecting the dependency constraints.
The Subgraph Similarity Problem

Similarity is a well known weakening of bisimilarity where one system is required to simulate the other and vice versa. It has been shown that the subgraph bisimilarity problem, a variation of the subgraph isomorphism problem where isomorphism is weakened to bisimilarity, is NP-complete. We show that the subgraph similarity problem and some related variations thereof still remain NP-complete.
Location-dependent emotional memory for natural communication of partner robots

This paper deals with an emotional model based on location-dependent emotional memory and location-dependent physical memory for partner robots to realize natural communication with people. First we discuss the functions of emotion in social interaction, and we propose an emotional model. Furthermore, the episode memory is related with the change of feelings. Therefore, we propose a map building method based on the emotional model. Next, we propose a behavior control method based on emotional model. Finally, we show several experimental results of the proposed method, and discuss the availability of the emotional model based on location-dependent memory.
LDA-based Topic Modelling in Text Sentiment Classification: An Empirical Analysis

A BSTRACT Sentiment analysis is the process of identifying the subjective information in the source materials towards an entity. It is a sub-field of text and web mining. Web is a rich and progressively expanding source of information. Sentiment analysis can be modelled as a text classification problem. Text classification suffers from the high dimensional feature space and feature sparsity problems. The use of conventional representation schemes to represent text documents can be extremely costly especially for the large text collections. In this regard, data reduction techniques are viable tools in representing document collections. Latent Dirichlet allocation (LDA) is a popular generative probabilistic model to represent collections of discrete data. In this regard, this paper examines the performance of LDA in text sentiment classification. In the empirical analysis, five classification algorithms (Naïve Bayes, support vector machines, logistic regression, radial basis function network and K-nearest neighbor algorithms) and five ensemble methods (Bagging, AdaBoost, Random Subspace, voting and stacking) are evaluated on four sentiment datasets.
An approach to obtain weighted graphs of words based on phoneme detection

In this paper, we present an approach for phoneme detection and phonetic classification that can be used as a basis for dierent speech processes, such as phoneme boundary detection, acoustic-phonetic decoding or word-graph construction with acoustic confidence scores. The phonetic classifier that has been developed is based on a phase of acoustic vector clustering in the space of acoustic characteristics, and on a second phase for the association of the acoustic classes with the phonetic units by means of conditional probabilities. We also present methods to build graphs of linguistic units (phonemes, words or semantic units). Phoneme graphs can be applied to acoustic-phonetic decoding tasks or as a preceding step to obtain word graphs. Word graphs can be used in recognition tasks, or they can be converted into graphs of semantic units to be used for understanding tasks. Some recognition experiments and understanding experiments are also presented for a restricted-semantic task about geographical queries.
Progression of Mouse Skin Carcinogenesis Is Associated with Increased Erα Levels and Is Repressed by a Dominant Negative Form of Erα

Estrogen receptors (ER), namely ERα and ERβ, are hormone-activated transcription factors with an important role in carcinogenesis. In the present study, we aimed at elucidating the implication of ERα in skin cancer, using chemically-induced mouse skin tumours, as well as cell lines representing distinct stages of mouse skin oncogenesis. First, using immunohistochemical staining we showed that ERα is markedly increased in aggressive mouse skin tumours in vivo as compared to the papilloma tumours, whereas ERβ levels are low and become even lower in the aggressive spindle tumours of carcinogen-treated mice. Then, using the multistage mouse skin carcinogenesis model, we showed that ERα gradually increases during promotion and progression stages of mouse skin carcinogenesis, peaking at the most aggressive stage, whereas ERβ levels only slightly change throughout skin carcinogenesis. Stable transfection of the aggressive, spindle CarB cells with a dominant negative form of ERα (dnERα) resulted in reduced ERα levels and reduced binding to estrogen responsive elements (ERE)-containing sequences. We characterized two highly conserved EREs on the mouse ERα promoter through which dnERα decreased endogenous ERα levels. The dnERα-transfected CarB cells presented altered protein levels of cytoskeletal and cell adhesion molecules, slower growth rate and impaired anchorage-independent growth in vitro, whereas they gave smaller tumours with extended latency period of tumour onset in vivo. Our findings suggest an implication of ERα in the aggressiveness of spindle mouse skin cancer cells, possibly through regulation of genes affecting cell shape and adhesion, and they also provide hints for the effective targeting of spindle cancer cells by dnERα.
University of Pennsylvania Working Papers in Linguistics Nonactive Voice in Hebrew and Elsewhere: between Unaccusativity and Agentivity Nonactive Voice in Hebrew and Elsewhere: between Unaccusativity and Agentivity Nonactive Voice in Hebrew and Elsewhere: between Unaccusativity and Agentivity

Two verbal templates in Modern Hebrew allow for any kind of verbal construction, except for a simple transitive verb. Unaccusatives, reflexives, reciprocals and unergatives that take an obligatory indirect object are all attested, but transitive verbs are not allowed. I discuss what the morphology of these templates actually signals, given that external arguments and internal arguments are both possible. Working in Distributed Morphology, I propose that a number of functional heads conspire to produce the existing alternations in argument structure, with implications for theories of anticausativization, reflexivization and reciprocalization.
A probabilistic approach for on-line positioning in nano manipulations

Nanomanipulation and nanoassembly using atom force microscopy (AFM) is a potential and promising technology for nanomanufacturing. Precise position of the tip of AFM is important to increase the accuracy and efficiency on fabricate complex nanostructures. However at the nano-scale, it is difficult to acquire the tip position expressed by the coordinate in real time due to PZT nonlinearity and thermal drift through the general measure. In this paper, a probabilistic approach incorporating a Kalman filter based localization algorithm is introduced into the on-line estimation of the tip position expressed by probability distribution known as probability density function. A probabilistic motion model of AFM tip is introduced that consists of a PZT dynamic model based on the Prandtl-Ishlinskii (PI) model, and motion error distribution obtained from calibration experiments. An observation model by using a local scanning algorithm is proposed and the change of uncertainty distribution on scanning landmarks, e.g. nano-particles, near the target position is analyzed. Some experiment results are included for showing the motion error distribution and a simulation result is presented to illustrate the validity of the proposed method.
Fiscal policy and private saving behavior in developing countries

Most conventional policy analyses assume that of the fiscal variables, only current taxes affect private saving or consumption decisions, since these decisions are based on a notion of disposable or after-tax income. The large and growing fiscal deficits in both developed and developing countries in recent years have revived interest in the links between fiscal policy variables and private sector saving and investment decisions. Recent theoretical studies have shown that in view of the possibility of future taxation following increases in the current stock of debt, the financing of a given path of government expenditures through government debt would have some effects on private sector saving as would financing through taxation. It has been shown that one important reason for a deviation from an equivalence of the two forms of government financing is the possibility of different planning horizons for the government and individual consumers. This paper provides an empirical approach based on an optimizing model of consumer behavior in a rational expectations setting to test the equivalence proposition. Evidence from sixteen developing countries provides empirical support for the equivalence proposition that there is no difference between consumers and the government.
An ergodic BSDE approach to forward entropic risk measures: representation and large-maturity behavior

Using elements from the theory of ergodic backward stochastic differential equations (BSDEs), we study the behavior of forward entropic risk measures in stochastic factor models. We derive general representation results (via both BSDEs and convex duality) and examine their asymptotic behavior for risk positions of large maturities. We also compare them with their classical counterparts and provide a parity result.
Explaining Psychological Statistics (2 Chapter 13 – Section D F versus Q: Different Approaches to Controlling Type I Errors with Multiple Comparisons

In section B of this chapter, I mentioned that when using Tukey’s HSD “it is unusual to find a pair of means that are significantly different when the overall ANOVA is not significant” (p. 375; EPS, 2e). However, it is also possible, though unusual, to find no significant differences with the HSD procedure when the ANOVA is significant. Tukey’s HSD is based on a different distribution from the ANOVA, and though it always yields similar results, there is some room for discrepancies with respect to statistical significance, as I will demonstrate in the following examples.
An affective design for jenang packaging in Indonesia

Affective design is today’s focus in the packaging design development process. It’s very important to develop the affective packaging that can make the customer feels good by seeing it. Affective design can support other qualities of the product, and influence customer purchasing decisions. Furthermore, it should be the main focus in product marketing especially Small and Medium Entreprises (SMEs) product. Low ability in packaging matters is one of the problems in most SMEs in Indonesia. The improvement of SMEs product packaging design should be done and it will increase the SMEs product sales. This research proposed a framework to design an affective packaging design based on customer needs. Jenang packaging is used as a research object. The objectives of this research are (1) to determine the relevant design elements of Jenang packaging and (2) to formulate the Jenang packaging design. Relief algorithm was used to extract the relevant design elements and resulted in six most important elements (shape, brand, color of brand name, image, material, and size). Association Rule Mining (ARM) is used to formulate the affective Jenang packaging design. Based on ARM, top ten rules are resulted and can be used to be the guideline for SMEs to improve their Jenang product packaging. The SMEs owner must choose the rules that have positive emotion. For example, if SMEs owners want to develop Jenang packaging design with curvy shape, the brand name should be traditional and packaging material is plastic.
Infection with Possible Novel Parapoxvirus in Horse, Finland, 2013

A horse in Finland exhibited generalized granulomatous inflammation and severe proliferative dermatitis. After euthanization, we detected poxvirus DNA from a skin lesion sample. The virus sequence grouped with parapoxviruses, closely resembling a novel poxvirus detected in humans in the United States after horse contact. Our findings indicate horses may be a reservoir for zoonotic parapoxvirus.