Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Noisy Time Series: Physiological Tremor

Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this noise. We discuss problems and possible solutions for nonlinear deterministic as well as nonlinear stochastic processes. Especially we discuss the state space model applicable for modeling noisy stochastic systems and Bock's algorithm capable for modeling noisy deterministic systems.
Feature and score level combination of subspace Gaussinas in LVCSR task

In this paper, we investigate employment of discriminatively trained acoustic features modeled by Subspace Gaussian Mixture Models (SGMMs) for Rich Transcription meeting recognition. More specifically, first, we focus on exploiting various types of complex features estimated using neural network combined with conventional cepstral features and modeled by standard HMM/GMMs and SGMMs. Then, outputs (word sequences) from individual recognizers trained using different features are also combined on a score-level using ROVER for the both acoustic modeling techniques. Experimental results indicate three important findings: (1) SGMMs consistently outperform HMM/GMMs (relative improvement on average by about 6% in terms of WER) when both techniques are exploited on single features; (2) SGMMs benefit much less from feature-level combination (1% relative improvement) as opposed to HMM/GMMs (4% relative improvement) which can eventually match the performance of SGMMs; (3) SGMMs can be significantly improved when individual systems are combined on a score-level. This suggests that the SGMM systems provide complementary recognition outputs. Overall relative improvements of the combined SGMMand HMM/GMM systems are 21% and 17% respectively compared to a standard ASR baseline.
North African Influences and Potential Bias in Case-Control Association Studies in the Spanish Population

Background Despite the limited genetic heterogeneity of Spanish populations, substantial evidences support that historical African influences have not affected them uniformly. Accounting for such population differences might be essential to reduce spurious results in association studies of genetic factors with disease. Using ancestry informative markers (AIMs), we aimed to measure the African influences in Spanish populations and to explore whether these might introduce statistical bias in population-based association studies. Methodology/Principal Findings We genotyped 93 AIMs in Spanish (from the Canary Islands and the Iberian Peninsula) and Northwest Africans, and conducted population and individual-based clustering analyses along with reference data from the HapMap, HGDP-CEPH, and other sources. We found significant differences for the Northwest African influence among Spanish populations from as low as ≈5% in Spanish from the Iberian Peninsula to as much as ≈17% in Canary Islanders, whereas the sub-Saharan African influence was negligible. Strikingly, the Northwest African ancestry showed a wide inter-individual variation in Canary Islanders ranging from 0% to 96%, reflecting the violent way the Islands were conquered and colonized by the Spanish in the XV century. As a consequence, a comparison of allele frequencies between Spanish samples from the Iberian Peninsula and the Canary Islands evidenced an excess of markers with significant differences. However, the inflation of p-values for the differences was adequately controlled by correcting for genetic ancestry estimates derived from a reduced number of AIMs. Conclusions/Significance Although the African influences estimated might be biased due to marker ascertainment, these results confirm that Northwest African genetic footprints are recognizable nowadays in the Spanish populations, particularly in Canary Islanders, and that the uneven African influences existing in these populations might increase the risk for false positives in association studies. Adjusting for population stratification assessed with a few dozen AIMs would be sufficient to control this effect.
A review of some Bayesian Belief Network structure learning algorithms

Bayesian Belief Networks (BBNs) are useful in modeling complex situations. Such graphical models help in giving better insight and understanding of the situation. Many algorithms for machine learning of BBN structures have been developed. In this paper six different algorithms have been reviewed by constructing BBN structures for two different datasets using various algorithms. Some inferences have been drawn from the results obtained from the study which may help in decision making.
Incoherent dictionaries and the statistical restricted isometry property

In this paper we formulate and prove a statistical version of the restricted isometry property (SRIP for short) which holds in general for any incoherent dictionary D which is a disjoint union of orthonormal bases. In addition, we prove that, under appropriate normalization, the spectral distrib- ution of the associated Gram operator converges in probability to the Sato-Tate (also called semi-circle) distribution. The result is then applied to various dic- tionaries that arise naturally in the setting of …nite harmonic analysis, giving, in particular, a better understanding on a conjecture of Calderbank concerning RIP for the Heisenberg dictionary of chirp like functions.
The Transmission of Productivity and Investment Shocks in the Asia Pacific Region

An important aspect of macroeconomic interdependence in the Asia Pacific region is the adjustment of trade and current account balances in response to changes in saving and investment rates in individual economies. Greater trade flows, reliance on imported intermediate goods as well as more integrated capital markets imply that shifts in private or public saving and investment rates in an economy in the region can potentially have large impacts on other economies. This paper explores the quantitative nature of these linkages by focusing on a number of shocks within the context of a new dynamic multi-sector global model called the Asia-Pacific G-Cubed Model (AP-GCUBED). This model integrates sectoral adjustment with macroeconomic interdependence including explicit treatment of capital flows to explore the implications of a variety of productivity and investment shocks in the Asia Pacific Region.
Connecting Storytelling and Social Wellness: A Case for Holistic Storytelling in the Elementary Classroom

Storytelling has a fundamental place in teaching practice, most noticeably in the sharing and developing of curricular content. But teachers share more than academic content with children. A critical prerequisite for meaningful, engaged learning is a strong sense of community and social wellness in the classroom. Based on an inquiry into literature and reflection on personal practice, this study asserts that the practice of storytelling fosters social wellness in the classroom and supports the healthy development of each child as a whole human being within and as part of that community. Connecting storytelling and social wellness, this inquiry offers a unique definition of “holistic storytelling. ”
On the Design and Evaluation of a Real-Time Operating System for Cache-Coherent Multicore Architectures

The uncontrolled use of the cache hierarchy in a multicore processor by real-time tasks may impact their worst-case execution times. Several operating system techniques have been recently proposed to deal with caches in a multiprocessor in order to improve predictability, such as cache partitioning, cache locking, and real-time scheduling. However, the contention caused by the cache coherence protocol and its implication for real-time tasks is still an open problem. In this paper, we present the design and evaluation of a real-time operating system for cache-coherent multicore architectures. The real-time operating system infrastructure includes real-time schedulers, cache partitioning, and cache coherence contention detection through hardware performance counters. We evaluate the real-time operating system in terms of run-time overhead, schedulability of realtime tasks, cache partitioning performance, and hardware performance counters usability. Our results indicate that: (i) a real-time operating system designed from scratch reduces the run-time overhead, and thus improves the realtime schedulability, when compared to a patched operating system; (ii) cache partitioning reduces the contention in the shared cache and provides safe real-time bounds; and (iii) hardware performance counters can detect when real-time tasks interfere with each other at the shared cache level. Scheduling, cache partitioning, and hardware performance counters together are a step-forward to provide real-time bounds in cache-coherent architectures.
Signature-based high-level simulation of microthreaded many-core architectures

The simulation of fine-grained latency tolerance based on the dynamic state of the system in high-level simulation of many-core systems is a challenging simulation problem. We have introduced a high-level simulation technique for microthreaded many-core systems based on the assumption that the throughput of the program can always be one cycle per instruction as these systems have fine-grained latency tolerance. However, this assumption is not always true if there are insufficient threads in the pipeline and hence long latency operations are not tolerated. In this paper we introduce Signatures to classify low-level instructions in high-level categories and estimate the performance of basic blocks during the simulation based on the concurrent threads in the pipeline. The simulation of fine-grained latency tolerance improves accuracy in the high-level simulation of many-core systems.
Shape-from-texture revisité : reconstruction 3D et auto-calibrage à partir d’une seule image Shape-from-Texture Revisited : 3D-reconstruction and Self-calibration from a Single Image

Dans cet article, nous revisitons le problème du shapefrom-texture. Notre première contribution est d’ordre méthodologique. Nous montrons que l’estimation de la profondeur et de la normale est équivalente à un calcul de pose. Notre deuxième contribution consiste à utiliser la redondance d’information entre la profondeur et la normale pour auto-calibrer la caméra. Nous validons ces deux contributions sur images de synthèse et sur image réelle.
Data scheduling and SAR for Bluetooth MAC

Motivated by the emerging standards for indoor pico-cellular wireless systems, such as the Bluetooth, we propose and study the scheduling policies for master driven time division duplex (TDD) wireless networks. In these networks, the frequency band is divided into time slots, and each end (i.e., master or slave) takes turns in using the time slots. In Bluetooth, a slave transmits packets in the reverse slot only after the master polls the slave in a forward slot (by sending data to it). The conventional scheduling policies such as round robin do not perform well in these systems as they are not suited to the tight coupling of the uplink-downlink. We propose new scheduling policies, (i) the priority scheme, and, (ii) the K-fairness scheme that utilize the state at the master and slaves to schedule the TDD slots effectively. Active slaves are differentiated based upon the binary information (i.e., the presence or absence of packets in a slave queue) about the master-slave queue pairs. The priority scheme achieves high throughput as compared to the packet-by-packet generalized processor sharing (PGPS) based policies while guaranteeing a minimal service to each active slave while the K-fairness policy is characterized by a tight fairness bound as well as high system throughput. We then extend these policies for scheduling variable size data in the presence of voice. Further, since Bluetooth supports variable size data packets (1, 3 or 5 slots) on the same connections, the segmentation and reassembly (SAR) can significantly impact scheduling of data packets by varying packet size distribution. We propose an intelligent SAR policy (ISAR) and compare it with the naive random-SAR in which the data packet sizes (i.e., 1, 3 or 5) are assigned probabilistically. ISAR adapts MAC packet size at the master and slave queues depending on the data arrival rates at both the queues.
Features extraction and correlation analysis of stock index

Time series exists in lots of fields, therefore data mining in time series has important research value. Considering correlation analysis is the foundation of time series data mining, the paper concentrates on the topic. We choose robust Dynamic Time Warping (DTW) distance and propose the improvement for standard DTW algorithm to deal with its large computing time cost: extracting the feature points according to fluctuation at first and organizing the features in a binary tree. It reduces the dimension and meanwhile reserves trend information. DTW with a computing window is then employed on the feature sequence. Experiments on three datasets and two scenarios in Shanghai Stock Market closed price series show that, the new method is much faster with keeping high accuracy as well.
UAV-based remote sensing for vegetation cover estimation using NDVI imagery and level sets method

Vegetation is a key component of nearly all global ecosystems, having a role of regulating various biogeochemical cycles in nature, as well as maintaining the energy balance at the earth's surface and atmospheric boundary layer. Therefore, it is of wide importance to constantly monitor the changes in vegetation cover and structure, especially those that are due to human influence. Backed up by many global initiatives for vegetation preservation and monitoring, it is necessary to have a system to collect and analyse different vegetation indices and products. One of the most important parameters of calculating many of these indices, is the amount of the vegetation cover in a certain area. This paper proposes a compact system for estimating vegetation in a certain area. The proposed system consists of a small UAV platform with the ability of performing autonomous flights and recording the videos of the ground cover using a camera with a modified infrared filter lens for obtaining the composite NDVI videos. The video is analysed and processed to extract the most significant frames that form a mosaic representing the area recorded by the camera. Finally, a contour is calculated forming the boundary around the area on the mosaic image that contains pixels that represent vegetation or photosynthetic activity. The contour and the area inside the boundary can be used for segmentation and estimation of other vegetation indices.
Energy balance of thermal hydrolysis and anaerobic digestion on waste activated sludge

This paper considers the energy balance of thermal hydrolysis pretreatment and anaerobic digestion. The application of anaerobic digestion is limited by low biodegradability. Thermal hydrolysis is an effective method to improve the anaerobic performance. The main concern of the technique is high-energy demand. The heat requirement and biogas production is calculated in the paper, showing that the system is energy self-sufficient.
Classical Tools for Constructive Proof

In this paper, we compare program development by extraction from a constructive proof with direct veriication of a function. Motivated by the development of a decision procedure for intuitionistic propositional logic, we have taken rst steps toward considering how classical type systems might be used in support of constructive systems. To this end we explore two statements of theorems asserting the existence of the tableau decision procedure. The constructive proof of one has a tableau decision procedure as its extraction; the other, if proved classically by providing a explicit witness, results in a veriied decision procedure. The systems considered are the classical type theory of the PVS system and the constructive type theory of the Nuprl system. Since lambda terms encode constructive proofs, the question of what the explicit witness of the PVS theorem might prove on the constructive side is also considered.
Rule based Production Systems for Automatic Code Generation in Java

Unified modeling language is being used as a premier tool for modeling the user requirements. These CASE tools provide an easy way to get efficient solutions. This paper presents a natural language processing based automated system for generating code in multi-languages after modeling the user requirements based on UML. UML diagrams are first generated by analyzing the given business scenario provided by the user. A new model is presented for analyzing the natural languages and extracting the relative and required information from the given requirement notes by the user. User writes the requirements in simple English in a few paragraphs and the designed system has conspicuous ability to analyze the given script. After compound analysis and extraction of associated information, the designed system draws various UML diagrams as activity diagrams, sequence diagrams, class diagrams and uses cases diagrams. The designed system has robust ability to create code automatically without external environment. The designed system provides a quick and reliable way to generate UML diagrams and generate respective code to save the time and budget of both the user and system analyst.
Towards Metamorphic Virus Recognition Using Eigenviruses

Metamorphic viruses are considered the most dangerous of all computer viruses. Unlike other computer viruses that can be detected statically using static signature technique or dynamically using emulators, metamorphic viruses change their code to avoid such detection techniques. This makes metamorphic viruses a real challenge for computer security researchers. In this thesis, we investigate the techniques used by metamorphic viruses to alter their code, such as trivial code insertion, instructions substitution, subroutines permutation and register renaming. An in-depth survey of the current techniques used for detection of this kind of viruses is presented. We discuss techniques that are used by commercial antivirus products, and those introduced in scientific researches. Moreover, a novel approach is then introduced for metamorphic virus recognition based on unsupervised machine learning generally and Eigenfaces technique specifically which is widely used for face recognition. We analyze the performance of the proposed technique and show the experimental results compared to results of well-known antivirus engines. Finally, we discuss the future and potential enhancements of the proposed approach to detect more and other target viruses.
Physiologically interactive gaming with the 3D agent Max

Physiologically interactive (or affective) gaming refers to research on the evocation and detection of emotion during game play [21]. In this paper, we first describe the two building blocks of our approach to affective gaming. The building blocks correspond to two independently conducted research strands on affective human–computer interaction: one on an emotion simulation system for an expressive 3D humanoid agent called Max, which was designed at the University of Bielefeld [13, 2]; the other one on a real-time system for empathic (agent) feedback that is based on human emotional states derived from physiological information, and developed at the University of Tokyo and the National Institute of Informatics [19]. Then, the integration of both systems is motivated in the setting of a cards game called Skip-Bo that is played by a human game partner and Max. Physiological user information is used to enable empathic feedback through non-verbal behaviors of the humanoid agent Max. With regard to the new area of Conversational Informatics we discuss the measurement of human physiological activity in game interactions and non-verbal agent behavior.
A control performance benchmark subject to output variance/covariance upper bound and pole placement constraint

User-specified benchmark has been reported in the literature for control loop performance assessment. A structured closed-loop response which is subject to simultaneous output covariance upper bound and pole placement constraint is proposed in this paper and served as an improved user-specified benchmark against which the existing controller performance can be compared. An LMI-based approach is used to formulate this problem and a cone complementarity linearization (CCL) algorithm is applied to find a global solution. An associated model approximation problem is solved in the sense of H2-norm in contrast to the use of H∞-norm of the previous work to obtain an optimal solution. The results are evaluated in a dry process rotary cement kiln example.
Introducing Triple Play for Improved Resource Retrieval in Collaborative Tagging Systems

Collaborative tagging systems (like Flickr, del.icio.us, citeulike, etc.) are becoming more popular with passage of time. Users share their resources on tagging systems, and add keywords (called tags) to these resources. Users can search resources using these tags. But as the user gives more tags for search, he might not get sufficient search results, because the resources might not be tagged with all the related tags. We introduce the method Triple Play, which smoothes the tag space by user space for improved retrieval of resources. As a part of Triple Play, we also propose two new vector space models for collaborative tagging systems, SmoothVSM Dense and SmoothVSM Sparse. These vector space models exploit the user-tag co-occurrence relationship to overcome the problem of missing information in tagging systems. Finally we apply Latent Semantic Analysis to different vector space models and analyze the results. Initial experimentation show that using additional information available in tagging systems helps in improving search in tagging systems.
A Transparent IPv6 Multi-protocol Gateway to Integrate Building Automation Systems in the Internet of Things

The future Internet of Things (IoT) should enable machine-to-machine interaction for devices out of numerous domains. Recent developments and standards focus on how to deploy IP directly on devices and investigate application protocols that fit the constrained environments, whereas research on the integration of widely deployed legacy devices of technologies like BACnet, Lon Works and KNX is still neglected. For a success of the ambitions towards an IoT we identify it of highest importance to research various integration styles for non-IP based devices already deployed in home and building automation. Therefore, this paper contributes an overview of various possible integration styles, provides a concrete multi-protocol integration architecture and presents evaluation results of a proof of concept implementation.
A robust method for automatic player detection in sport videos

In the last years, pattern recognition approaches using machine learning techniques to construct object detectors have encountered great success in the domain of visual detection. However, the application of automatic and robust player detection in sport videos is a particularly chal lenging task because of the small size, and high variability in shape and appearance of the objects to detect, and the influence of noise due to motion blur and video coding. In this paper, we present a sport player detection method based on convolutional neural networks and compare it to the well-known Adaboost approach which is known to perform well in other contexts. Compared to traditional methods, these approaches do not use background substraction after camera motion estimation. Experiments showed that both methods are very robust and effective and that convolutional neural networks perform better with an average gain of 4% on various datasets.