Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiments with linear and nonlinear feature transformations in HMM based phone recognition

Feature extraction is the key element when aiming at robust speech recognition. Both linear and nonlinear data-driven feature transformations are applied to the logarithmic mel-spectral context feature vectors in the TIMIT phone recognition task. Transformations are based on principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) and multilayer perceptron network based nonlinear discriminant analysis (NLDA). All four methods outperform the baseline system which consists of the standard feature representation based on MFCCs (mel-frequency cepstral coefficients) with the first-order deltas, using a mixture-of-Gaussians HMM recognizer. Further improvement is gained by forming the feature vector as a concatenation of the outputs of all four feature transformations.
Chronic Occupational Exposure to Low Levels of Organic Vapors Can Affect Color Vision and Contrast Sensitivity

This study evaluated the level of exposure to organic vapors in chromatic and achromatic vision of 38 male gas-station workers (average of 6.82 ± 5.62 years worked). The level of exposure was found by passive method with gas chromatography. Color vision was assessed by Lanthony desaturated D15 tests (D15d; Lanthony, 1978) the Cambridge Color Test (CCT; Mollon, & Reffin, 1989), and contrast sensitivity by sinusoidal gratings at frequencies 0.2, 0.5, 1, 2.0, 5, 10 and 16 cycles per degree (cpd). The highest average daily concentrations found in gasoline (168 ppm) were below the threshold limit values (300 ppm), yet gas-station workers had a higher color-confusion index (CCI) for D15d (p < .01), predominantly for blue/yellow (75%), larger ellipse areas in the CCT (p < .05), and lower sensitivity contrast for 5, 10, and 16 cpd (p < .01). Prolonged service time correlated with the areas A1 (&rgr; = 0.46; p < .01), A2 (&rgr; = 0.63; p < .01), and 5 cpd (&rgr; = – 0.36; p < .05). Gas-station attendants with 8–20 years of service had lower performances for the areas A1 and A2 (p < .0083) than those with less exposure (1–3 years of service). The results show that chronic exposure to organic vapors can reduce the chromatic and achromatic visual perception of luminance in gas-station workers with age, even at low levels, suggesting the inadequacy of tolerance levels used for gasoline. The also highlight the need for chronic systematic long-term monitoring, and not just daily.
Pose unconstrained face recognition based on SIFT and alignment error

Pose variation is one of the key challenges for practical face recognition problem. Face recognition under well-controlled settings, like frontal face and good illumination, achieved high performance. But it fails when they are directly adopted to face recognition with large pose change. In this paper, we propose a novel framework using the combination of SIFT and alignment error (SIFT-AE) to perform pose invariant face recognition. SIFT (Scale Invariant Feature Transformation) is an effective local descriptor for face recognition under small pose change, which is scale and rotation invariant as well. However, the performance declined in case of large pose variance. To compensate this declination, Lucas-Kanade method is used to align the probe image and the gallery image, and the alignment error is deducted from the number of matching for SIFT algorithm. This alignment error provides additional information even in case of large pose change, while it is not distinctive alone. Therefore, the combination of SIFT and alignment error gains the performance for face recognition with large pose variance. Experiment results show our algorithm achieves impressive improvement compared with either SIFT or online alignment.
Robot-Assisted Laparoscopic Dismembered Pyeloplasty

Objective: Advanced laparoscopic skills limit the implementation of laparoscopic pyeloplasty to centers with extensive experience. The introduction of robotic technology into the field of minimally invasive surgery has facilitated complex surgical dissection and genitourinary reconstruction. We report our experience with robot-assisted laparoscopic pyeloplasty using the da Vinci Robotic Surgical System at 3 New York City medical centers. Methods: A review of all robot-assisted laparoscopic Anderson-Hynes dismembered pyeloplasty cases in 38 patients (21 females, 17 males) between April 2001 and January 2004 was performed. All patients had symptoms or radiographic evidence of ureteropelvic junction obstruction. Robotic assistance with the da Vinci Robotic Surgical System was used after preparation of the ureteropelvic junction with a standard laparoscopic approach. Results: The average patient age was 39.3 years (range, 15 to 69). The mean operative time and suturing time were 225.6±59.3 minutes and 64.2±14.6 minutes. The average estimated blood loss was minimal at 77.3±55.3 mL. The mean length of hospitalization was 69.6 hours (range, 28 to 310). The average use of intravenous morphine was 26.5 mg (range, 0 to 162). No intraoperative complications occurred, and open conversions were not necessary. A mean follow-up of 12.2 months revealed a success rate of 94.7% with 2/38 patients requiring further treatments. Conclusions: This combined multi-institutional series reveals that robot-assisted pyeloplasty with the da Vinci Surgical System is safe and reproducible. These intermediate results appear comparable to results with open and laparoscopic pyeloplasty repairs.
Design and Implementation of Fuzzy PID Controller for Single Link Flexible Joint Robotic System using FPGA

Designing a Fuzzy Proportional Integral Derivative (FPID) controller that can control single link flexible joint robotic system which is nonlinear and have a complex mathematical model, led to the need of finding a way that could be efficient to hardware implementation, using Field – Programmable Gate Array FPGA (small device size, cost effective, short time to market, Reliability and Long – Term Maintenance) could solve that issue. Fuzzy Logic Controller (FLC) system is a most promising area for industrial application and by combining it with PID control algorithms features; they offer a high speed process using simple rules with the accuracy of PID tuning methods. In this paper, we have demonstrated the implementation of FPID controller using Very high speed integrated circuits Hardware Description Language (VHDL) code in FPGA, we have used Mamdani type as a FLC structure. The controller algorithm have been developed, synthesized, simulated and implemented on FPGA Spartan6, XC6SLX150T, FGG676 board.
Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation

Sustainable energy domains have become extremely important due to the significant growth in energy usage. Building multiagent systems for real-world energy applications raises several research challenges regarding scalability, optimizing multiple competing objectives, model uncertainty, and complexity in deploying the system. Motivated by these challenges, this paper proposes a new approach to effectively conserve building energy. This work contributes to a very new area that requires considering large-scale multi-objective optimization as well as uncertainty over occupant preferences when negotiating energy reduction. There are three major contributions. We (i) develop a new method called HRMM to compute robust solutions in practical situations; (ii) experimentally show that obtained strategies from HRMM converge to near-optimal solutions; and (iii) provide a systematic way to tightly incorporate the insights from human subject studies into our computational model and algorithms. The HRMM method is verified in a validated simulation testbed in terms of energy savings and comfort levels of occupants.
Expected Savings Using Loss-Minimizing Flux on IM Drives—Part I: Optimum Flux and Power Savings for Minimum Losses

The operation of variable-speed ac drives with loss-minimizing flux strategies can produce important energy savings without the need of any investment. This paper proposes algorithms for the evaluation of loss motor components and the flux level that produces the minimum-loss operating condition, covering the whole constant-torque operating region. Both scalar and vector drives are considered, although it is found that the operation of scalar- and vector-controlled drives with optimum flux (or d-axis current) gives very similar results for the same operating points. Evaluation is done using four induction motors of 4, 11, 55, and 110 kW. Results confirm that important savings can be achieved, especially in applications where motors operate with high speed and low to moderate load torque. Results will be used in a follow-up paper to evaluate the expected savings of the use of these strategies in a pulp mill and in a paper mill.
Object recognition based on gabor wavelet features

The proposed method is to recognize objects from different categories of images using Gabor features. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, salient point detection and patch extraction are used. Gabor wavelet features such as Gabor mean and variance using 2 scales and 2 orientations and 2 scales and 4 orientations are computed for every patch that extracted over the salient points taken from the original image. These features provide adequate resolution in both spatial and spectral domains. Thus extracted features are trained in order to get a learning model, tested and classified using SVM. Finally, the results obtained using Gabor wavelet features using 2 scales and 2 orientations and 2 scales and 4 orientations are compared and thus observed that the latter performs better than the former with less error rate. The experimental evaluation of proposed method is done using the Caltech database.
Towards model checking of computer games with Java PathFinder

We show that Java source code of computer games can be checked for bugs such as uncaught exceptions by the model checker Java PathFinder (JPF). To model check Java games, we need to tackle the state space explosion problem and handle native calls. To address those two challenges we use our extensions of JPF, jpf-probabilistic and jpf-nhandler. The former deals with the randomization in the source code of the game, which is a cause of the state space explosion problem. The latter handles native calls automatically. We show how JPF enhanced with our extensions can check games such as the text based game Hamurabi and a graphics based version of rock-paper-scissors.
Sequence Pattern Query Processing over Out-of-Order Event Streams

Complex event processing has become increasingly important in modern applications, ranging from RFID tracking for supply chain management to real-time intrusion detection. A key aspect of complex event processing is to extract patterns from event streams to make informed decisions in real-time. However, network latencies and machine failures may cause events to arrive out-of-order at the event processing engine. State-of-the-art event stream processing technology experiences significant challenges when faced with out-of-order data arrival including output blocking, huge system latencies, memory resource overflow, and incorrect result generation. To address these problems, we propose two alternate solutions: aggressive and conservative strategies respectively to process sequence pattern queries on out-of-order event streams. The aggressive strategy produces maximal output under the optimistic assumption that out-of-order event arrival is rare. In contrast, to tackle the unexpected occurrence of an out-of-order event and with it any premature erroneous result generation, appropriate error compensation methods are designed for the aggressive strategy. The conservative method works under the assumption that out-of-order data may be common, and thus produces output only when its correctness can be guaranteed. A partial order guarantee (POG) model is proposed under which such correctness can be guaranteed. For robustness under spiky workloads, both strategies are supplemented with persistent storage support and customized access policies. Our experimental study evaluates the robustness of each method, and compares their respective scope of applicability with state-of-art methods.
ENSEMBLE DESIGN FOR INTRUSION DETECTION SYSTEMS

Intrusion Detection problem is one of the most promising research issues of Information Security. The problem provides excellent opportunities in terms of providing host and network security. Intrusion detection is divided into two categories with respect to the type of detection. Misuse detection and Anomaly detection. Intrusion detection is done using rule based, Statistical, and Soft computing techniques. The rule based measures provides better results but the extensibility of the approach is still a question. The statistical measures are lagging in identifying the new types of attacks. Soft Computing Techniques offers good results since learning is done using the training, and during testing the new pattern of attacks was also recognized appreciably. This paper aims at detecting Intruders using both Misuse and Anomaly detection by applying Ensemble of soft Computing Techniques. Neural networks, Support Vector Machines and Naïve Bayes Classifiers are trained and tested individually and the classification rates for different classes are observed. Then threshold values are set for all the classes. Based on this threshold value the ensemble approach produces result for various classes. The standard kddcup’99 dataset is used in this research for Misuse detection. Shonlau dataset of truncated UNIX commands is used for Anomaly detection. The detection rate and false alarm rates are notified. Multilayer Perceptrons, Naïve Bayes classifiers and Support vector machines with three kernel functions are used for detecting intruders. The Precision, Recall and FMeasure for all the techniques are calculated. The cost of the techniques is estimated using the cost measures. The Receiver Operating Characteristic (ROC) curves are drawn for all the techniques. The results show that Support Vector Machines and Ensemble approach provides better detection rate of 99% than the other algorithms.
Caenorhabditis elegans as a Model System for Studying Drug Induced Mitochondrial Toxicity

Today HIV-1 infection is recognized as a chronic disease with obligatory lifelong treatment to keep viral titers below detectable levels. The continuous intake of antiretroviral drugs however, leads to severe and even life-threatening side effects, supposedly by the deleterious impact of nucleoside-analogue type compounds on the functioning of the mitochondrial DNA polymerase. For detailed investigation of the yet partially understood underlying mechanisms, the availability of a versatile model system is crucial. We therefore set out to develop the use of Caenorhabditis elegans to study drug induced mitochondrial toxicity. Using a combination of molecular-biological and functional assays, combined with a quantitative analysis of mitochondrial network morphology, we conclude that anti-retroviral drugs with similar working mechanisms can be classified into distinct groups based on their effects on mitochondrial morphology and biochemistry. Additionally we show that mitochondrial toxicity of antiretroviral drugs cannot be exclusively attributed to interference with the mitochondrial DNA polymerase.
PERFORMANCE OF SMART ANTENNAS WITH ADAPTIVE COMBINING AT HANDSETS FOR THE cdma2000 SYSTEM

In this paper, we present simulation results for the performance gain of smart dual antennas with adaptive combining at handsets for the forward link in the cdma2000 system. The adaptive algorithm based on the normalized least-mean-square (N-LMS) algorithm is applied, in which antenna weights are recursively obtained. The system was modeled with the SPW of Cadence. Our simulation results show that the performance gain (in terms of the reduction of the frame error rate) of a smart dual antenna system over a single antenna system lies in the range of 0.8 dB to 2.2 dB depending on the mobile velocity.
Annotating a Large Representative Corpus of Clinical Notes for Parts of Speech

[We report of the procedures of developing a large representative corpus of 50,000 sentences taken from clinical notes. Previous reports of annotated corpus of clinical notes have been small and they do not represent the whole domain of clinical notes. The sentences included in this corpus have been selected from a very large raw corpus of ten thousand documents. These ten thousand documents are sampled from an internal repository of more than 700,000 documents taken from multiple health care providers. Each of the documents is de-identified to remove any PHI data. Using the Penn Treebank tagging guidelines with a bit of modifications, we annotate this corpus manually with an average inter-annotator agreement of more than 98%. The goal is to create a parts of speech annotated corpus in the clinical domain that is comparable to the Penn Treebank and also represents the totality of the contemporary text as used in the clinical domain. We also report the output of the TnT tagger trained on the initial 21,000 annotated sentences reaching a preliminary accuracy of above 96%. ]
Using Geometric Blur for Point Correspondence

In computer vision applications, point correspondence plays an important role. Here, we present a technique called geometric blur to find point correspondences between two different images, even in the presence of affine distortions. We compare the results of this technique with other prevalent techniques for finding point correspondences, such as SSD (Sum of Squared Differences) with uniform Gaussian blur. Experimental results are shown on various face images and other object images. Index Terms — point correspondences, geometric blur, interest points, object detection and recognition, SIFT
Activation of miR-21 by STAT3 Induces Proliferation and Suppresses Apoptosis in Nasopharyngeal Carcinoma by Targeting PTEN Gene

The present study is to investigate the role of microRNA-21 (miR-21) in nasopharyngeal carcinoma (NPC) and the mechanisms of regulation of PTEN by miR-21. Fifty-four tissue samples were collected from 42 patients with NPC and 12 healthy controls. Human NPC cell lines CNE-1, CNE-2, TWO3 and C666-1 were used for cell assays. To investigate the expression of miR-21, RT-PCR was employed. RT-PCR, Western blotting, and immunohistochemistry were used to measure the expression of STAT3 mRNA and STAT3 protein. To test the effect of miR-21 on the cell growth and apoptosis of NPC cells in vitro, transfection of CNE1 and CNE2 cell lines and flow cytometry were performed. TUNEL assay was used to detect DNA fragmentation. To validate whether miR-21 directly recognizes the 3′-UTRs of PTEN mRNA, luciferase reporter assay was employed. miR-21 expression was increased in NPC tissues compared with control and the same result was found in NPC cell lines. Notably, increased expression of miR-21 was directly related to advanced clinical stage and lymph node metastasis. STAT3, a transcription factor activated by IL-6, directly activated miR-21 in transformed NPC cell lines. Furthermore, miR-21 markedly inhibited PTEN tumor suppressor, leading to increased AKT activity. Both in vitro and in vivo assays revealed that miR-21 enhanced NPC cell proliferation and suppressed apoptosis. miR-21, activated by STAT3, induced proliferation and suppressed apoptosis in NPC by targeting PTEN-AKT pathway.
Perceptual learning in patients with macular degeneration

Patients with age-related macular degeneration (AMD) or hereditary macular dystrophies (JMD) rely on an efficient use of their peripheral visual field. We trained eight AMD and five JMD patients to perform a texture-discrimination task (TDT) at their preferred retinal locus (PRL) used for fixation. Six training sessions of approximately one hour duration were conducted over a period of approximately 3 weeks. Before, during and after training twelve patients and twelve age-matched controls (the data from two controls had to be discarded later) took part in three functional magnetic resonance imaging (fMRI) sessions to assess training-related changes in the BOLD response in early visual cortex. Patients benefited from the training measurements as indexed by significant decrease (p = 0.001) in the stimulus onset asynchrony (SOA) between the presentation of the texture target on background and the visual mask, and in a significant location specific effect of the PRL with respect to hit rate (p = 0.014). The following trends were observed: (i) improvement in Vernier acuity for an eccentric line-bisection task; (ii) positive correlation between the development of BOLD signals in early visual cortex and initial fixation stability (r = 0.531); (iii) positive correlation between the increase in task performance and initial fixation stability (r = 0.730). The first two trends were non-significant, whereas the third trend was significant at p = 0.014, Bonferroni corrected. Consequently, our exploratory study suggests that training on the TDT can enhance eccentric vision in patients with central vision loss. This enhancement is accompanied by a modest alteration in the BOLD response in early visual cortex.
Compiled Query Execution Engine using JVM

A conventional query execution engine in a database system essentially uses a SQL virtual machine (SVM) to interpret a dataflow tree in which each node is associated with a relational operator. During query evaluation, a single tuple at a time is processed and passed among the operators. Such a model is popular because of its efficiency for pipelined processing. However, since each operator is implemented statically, it has to be very generic in order to deal with all possible queries. Such generality tends to introduce significant runtime inefficiency, especially in the context of memory-resident systems, because the granularity of data commercial system, using SVM. processing (a tuple) is too small compared with the associated overhead. Another disadvantage in such an engine is that each operator code is compiled statically, so query-specific optimization cannot be applied. To improve runtime efficiency, we propose a compiled execution engine, which, for a given query, generates new query-specific code on the fly, and then dynamically compiles and executes the code. The Java platform makes our approach particularly interesting for several reasons: (1) modern Java Virtual Machines (JVM) have Just- In-Time (JIT) compilers that optimize code at runtime based on the execution pattern, a key feature that SVMs lack; (2) because of Java’s continued popularity, JVMs keep improving at a faster pace than SVMs, allowing us to exploit new advances in the Java runtime in the future; (3) Java is a dynamic language, which makes it convenient to load a piece of new code on the fly. In this paper, we develop both an interpreted and a compiled query execution engine in a relational, Java-based, in-memory database prototype, and perform an experimental study. Our experimental results on the TPC-H data set show that, despite both engines benefiting from JIT, the compiled engine runs on average about twice as fast as the interpreted one, and significantly faster than an in-memory