Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of a dynamic numerical approach in predicting flashover critical voltage

Glass insulators, due to its diverse characteristics, were used widely around the world for high voltage transmission lines. Surfaces of the insulators are exposed to high electrical, mechanical, and thermal stresses over the period of service. Accumulation of contamination distort stresses distribution along the insulators that may lead to flashover under severe condition. In this paper, Obenaus pollution model has been adopted to propose a dynamic mathematical modelling to determine flashover critical voltage with regard to parameters such as pollution conductivity, arc length, and width of layer of contamination on the surface of glass insulator. In addition, laboratory experimental works have been carried out according to IEC60305 to validate the results from numerical approach, which indicate a good agreement.
Speaker-independent word recognition using dynamic programming neural networks

A description is given of speaker-independent word recognition based on a new neural network model called the dynamic programming neural network (DNN), which can treat time-sequence patterns. DNN is based on the integration of a multilayer neural network and dynamic-programming-based matching. Speaker-independent isolated Japanese digit recognition experiments were carried out using data uttered by 107 speakers (50 speakers for training and 57 speakers for testing). The recognition accuracy was 99.3%, suggesting that the model can be effective for speech recognition. <<ETX>>
Taking simulation semantics out of the laboratory: towards an interactive and multimodal reappraisal of embodied language comprehension

abstract Recent embodied theories of meaning known as ‘simulation semantics’ posit that language comprehension engages, or even amounts to, mental simulation. What is meant here by ‘language comprehension’, however, deviates from the perspectives on interpersonal communication adhered to by researchers in social psychology and interactional linguistics. In this paper, we outline four alternative perspectives on comprehension in spoken interaction, each of which highlights factors that have remained largely outside the current purview of simulation theories. These include perspectives on language comprehension in terms of (i) striving for inter-subjective conformity; (ii) recognition of communicative intentions; (iii) prediction and anticipation in a dynamic environment; and (iv) integration of multimodal cues. By contrasting these views with simulation theories of comprehension, we outline a number of fundamental differences in terms of the kind of process comprehension is assumed to be (passive and event-like versus active and continuous), as well as the kind of stimulus that language is assumed to be (comprising unimodal units versus being multimodal and distributed across conversational turns). Finally, we discuss potential points of connection between simulation semantics and research on spoken interaction, and touch on some methodological implications of an interactive and multimodal reappraisal of simulation semantics.
Sensar_SecureTM Iris Identification System

The system uses an active vision paradigm. Customdesigned image pyramid-generatinghardware, developed in conjunction with Sarnoff Corporation, generates stereo disparity images in real-time, yielding the average distance to the subject’s head (figure 2). The depth map is also used to restrict the system’s attention to the closest person out of a possible queue of people. A face-template matching algorithm is then used to determine the (X,Y) position in image space of the subject’s eyes, even if the subject is wearing glasses (figures 3, 4). The eye position, along with the depth of the subject’s eye region (Z), is mapped to an (X,Y,Z) location in real 3-D space with calibration tables specific to each stereo camera pair. The eye’s 3-D location is used to control a pan/tilt mirror and a fixed focal-length lens to precisely center and focus a high-resolution image of the subject’s right or left eye [4]. The system locates the eye in the high-resolution image Figure 2. Stereo image and depth map
Text Orientation Detection from Document Image of Indian Scripts

This paper describes an effective and robust technique to determine orientation of text perceiving in document image as well as restoring it to right orientation. Described technique is useful in scripts of Hindi, Bengali, and Punjabi languages. It is based on pattern recognition in document. Specific pattern has been decided by extensive study of different scripts. On the basis of specific pattern, area of text of interest in document is determined and after analysis of these text area, orientation and skew of the document is determined. After that, document is restored to right orientation by rotating the document in reverse direction. This technique is also useful in those documents where text is coming with images, and document is in multicolumn. Orientation is determined by partitioning whole document into sub blocks of fixed size. Histogram Analysis of text pattern is done on each sub blocks to find orientation of document.
Fast Measurement of Individual Head-Related Transfer Functions

While binaural technology applications gained in popularity in recent years, the majority of applications still use non-individual Head-Related Transfer Functions (HRTFs) from artificial heads. These datasets enable a reasonably good spatial localization which works especially well when using an additional visual cue. However, certain applications, for example research of spatial hearing or hearing attention, require an physically exact and realistic binaural signal. Moreover, it was shown in many experiments that there is a substantial gain from the use of individual HRTFs, for example in localization tasks. The limiting factor that prohibits the widespread use of individual HRTFs is the acquisition of such data. A substantial hardware requirement obstructs a more universal usage. Even for institutions that allow individual measurements, the measurement time that is required, and that the subjects are required to remain motionless made most measurements unfeasible in the past. This time requirement has recently been reduced by the use of parallelization in the measurement signal which lead to the development of fast measurement systems capable of acquiring individual and spatially dense HRTF. This thesis provides a objective and subjective evaluation of such a system that is designed with the goal of little disturbance of the measurements in mind. The construction is detailed, followed by both an objective and subjective evaluation. A detailed investigation into additional distortion of the sound field introduced by the system itself is presented and it is shown that the system performs comparably to a conventional system in terms of sound source localization. Furthermore, a method is introduced and evaluated to further reduce the measurement time by using continuous rotation during the measurement. This method is used to reduced the measurement duration from eight minutes to three minutes without audible differences. The introduced methods are also used to reducing additional errors from subject movement. It is shown that this movement can be reduced by a visual feedback system to a level that can be compensated efficiently.
AvoPlot: An extensible scientific plotting tool based on matplotlib

AvoPlot is a simple-to-use graphical plotting program written in Python and making extensive use of the matplotlib plotting library. It can be found at http://code.google. com/p/avoplot/ . In addition to providing a user-friendly interface to the powerful capabilities of the matplotlib library, it also offers users the possibility of extending its functionality by creating plug-ins. These can import specific types of data into the interface and also provide new tools for manipulating them. In this respect, AvoPlot is a convenient platform for researchers to build their own data analysis tools on top of, as well as being a useful standalone program.
Elliptic Curve Cryptography on PocketPCs

The commercial use of small mobile computer devices by enterprise and government organizations is on the rise as wireless networking is becoming very popular and evolving very fast. Elliptic Curve Cryptography (ECC) seems very useful for providing a high level of security on these devices with small key sizes compared to the traditional public-key cryptographic systems. In this work we implement the National Institute of Standards and Technology (NIST) recommended ECC algorithms on Pocket PCs. The programs are written in Java since a vast array of Internet applications service infrastructure is designed around Java technology. We show that Elliptic Curve Digital Signature Algorithm (ECDSA) can run in a suitable time with sufficient level of security.
Towards textual data augmentation for neural networks: synonyms and maximum loss

Data augmentation is one of the ways of dealing with labeled data scarcity and overfitting. Both these problems are crucial for modern deep learning algorithms which require massive amounts of data. The problem is better explored in the context of image analysis than for text. This work is a step forward to close this gap. We propose a method for augmenting textual data when training convolutional neural networks for sentence classification. The augumentation is based on the substitution of words using a thesaurus as well as the Princeton WordNet. Our method improves upon the baseline in almost all cases. In terms of accuracy the best of the variants is 1.2% (pp.) better than the baseline.
OWL (On‐line Webstories for Learning): a unique web‐based literacy resource for primary/elementary children

Abstract Technology resources created for use by children have often been drill and practise or game‐like in approach. For a literacy resource, the use of narrative provides an opportunity for children to become more immersed in an environment rich in print and illustrations that supports the development of reading skills while encouraging the enjoyment of reading. This paper will examine the rationale for the incorporation of narrative in the OWL (On‐line Webstories for Learning) resource, and how the narrative line is supported by on‐line and off‐line activities.
Current mode proportional resonant controlled multi input–SEPIC-re-boost-system

The intention of this paper is to identify a suitable controller for closed loop multi converter system for multiple input sources and to improve time response of high-gain-step up-converter. Closed-loop Multi Converter System (MCS) is utilized to regulate load-voltage. This effort recommends suitable-controller for closed-two loop-controlled-SEPIC-REBOOST Converter fed DC motor. The estimation of the yield in open-two loop and closed- two-loop-circuit has been done using MATLAB or Simulink. Closed-two loop-control of Multi Converter System with Propotional+Integral (PI)- Propotional+Integral (PI) and Proportional+Resonant (PR) - Proportional+Resonant (PR) Controllers are investigated and their responses are evaluated in conditions of rise time, peak time, settling time and steady state error. It is seen that current-mode PR-PR controlled MCS gives better time domain response in terms of motor speed. A Prototype of MCS has been fabricated in the laboratory and the experimental-results are authenticated with the simulation-results.
Optimal and efficient time series classification with burrows-wheeler transform and spectral window based transformation

With the progressing amount of data every day, Time series classification acts as a vital role in the real life environment. Raised data volume for the time periods will make hard for the researchers to examine as well as assess the data. Therefore time series classification is taken as a significant research problem for the examining as well as identifying the time series dataset. On the other hand the previous research might carry out low in case of existence of weak classifiers. It is solved by introducing the Weak Classifier aware Time Series Data Classification Algorithm (WCTSD). In this proposed technique, with the help of the Burrows-Wheeler Transform (BWT), primarily frequency domain based data transformation is carried out. After that, by means of presenting the technique called spectral window based transformation, time series based data transformation is performed. With the help of the Hybrid K Nearest Neighbour, Hybrid decision tree algorithm, Linear Multiclass Support Vector Machine, these transformed data is classified. Here, to enhance the classification accuracy, the weak classifier is eliminated by utilizing hybrid particle swarm with firefly algorithm. In the MATLAB simulation environment, the total implementation of the presented research technique is carried out and it is confirmed that the presented research technique WCTSD results in providing the best possible outcome compared to the previous research methods.
Cost-based Optimization of Graph Queries

Many applications require efficient management and querying of graph structured data. For example, Systems Biology studies metabolic pathways and gene regulation networks modeled as directed graphs. These graphs consist of tens of thousands of molecules and interactions between them. To get a better understanding of these networks biologists need to query the networks and extract information. In this paper we propose a framework for cost-based optimization of graph queries in relational database management systems. The result of graph queries is a set of subgraphs that is selected from the data graph based on conditions on nodes and paths posed in the query. We present the pathway query language as syntax to express graph queries. We show how to utilize techniques of classical cost-based query optimization to optimize graph queries. To handle graph specific predicates, such as the existence of paths, we propose new operators. In addition we describe two implementations of path operators in more detail and give an overview of future work.
Relevance vector machine based fault classification in wind energy conversion system

This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility.
Hodor: Intra-Process Isolation for High-Throughput Data Plane Libraries

As network, I/O, accelerator, and NVM devices capable of a million operations per second make their way into data centers, the software stack managing such devices has been shifting from implementations within the operating system kernel to more specialized kernel-bypass approaches. While the in-kernel approach guarantees safety and provides resource multiplexing, it imposes too much overhead on microsecondscale tasks. Kernel-bypass approaches improve throughput substantially but sacrifice safety and complicate resource management: if applications are mutually distrusting, then either each application must have exclusive access to its own device or else the device itself must implement resource management. This paper shows how to attain both safety and performance via intra-process isolation for data plane libraries. We propose protected libraries as a new OS abstraction which provides separate user-level protection domains for different services (e.g., network and in-memory database), with performance approaching that of unprotected kernel bypass. We also show how this new feature can be utilized to enable sharing of data plane libraries across distrusting applications. Our proposed solution uses Intel’s memory protection keys (PKU) in a safe way to change the permissions associated with subsets of a single address space. In addition, it uses hardware watchpoints to delay asynchronous event delivery and to guarantee independent failure of applications sharing a protected library. We show that our approach can efficiently protect highthroughput in-memory databases and user-space network stacks. Our implementation allows up to 2.3 million library entrances per second per core, outperforming both kernellevel protection and two alternative implementations that use system calls and Intel’s VMFUNC switching of user-level address spaces, respectively.
Study and analysis of intrusion detection system using random forest and linear regression

The cyber security is the challenging job in present network system. There are number of existing Intrusion Detection Systems are available to overcome the issues, in this paper we proposed the linear regression and random forest technique is used. The latest UNSW-NB15 dataset is used for analyzing the proposed methods. Selecting significant features and removing irrelevant features by using proposed learning methods as well as identifying the best method by evaluating the results obtained.
Hierarchical Hybrid Memory Management in OS for Tiered Memory Systems

The emerging hybrid DRAM-NVM architecture is challenging the existing memory management mechanism at the level of the architecture and operating system. In this paper, we introduce Memos, a memory management framework which can hierarchically schedule memory resources over the entire memory hierarchy including cache, channels, and main memory comprising DRAM and NVM simultaneously. Powered by our newly designed kernel-level monitoring module that samples the memory patterns by combining TLB monitoring with page walks, and page migration engine, Memos can dynamically optimize the data placement in the memory hierarchy in response to the memory access pattern, current resource utilization, and memory medium features. Our experimental results show that Memos can achieve high memory utilization, improving system throughput by around 20.0 percent; reduce the memory energy consumption by up to 82.5 percent; and improve the NVM lifetime by up to 34X.
Happiness Measurement Via Classroom Based on Face Tracking

Teaching and learning are important factors in our life, so, increasing the happiness during class cause raising the ability of learning. This research focused on the evaluation of the learning process via classroom before and after applying the proposed method. One of the big challenge during classroom lesson, the students after a short period of time (about 10-15 minutes) at the beginning of the session, turn off their minds and remain absolutely out of understanding. Our goal is to revitalize the classroom environment so that you are always attentive to the lecture. The proposed approach aims to track and detect the emotional facial expression and transient from emotional facial expression to raise the happy expression in order to reach the classroom happiness. Image processing issue is an important part of this approach in which based on face recognition and tracking, then evaluates the students’ happiness according to the proposed mode that located in the classroom. This model is implemented from Raspberry Pi device, tracking camera and high definition screen. The implemented algorithm allows you to continue of face tracing and make rising of happiness as possible. Increasing happiness is an important factor in making students more attractive and more understanding for the classroom lecture. This approach is implemented to cover both software and hardware, so, it is acceptable to work in real time.
A switched-current class AB ΣΔ-modulator

A class AB memory cell for SI-applications is presented. Important advantages of the class AB memory cell as linearity, low-consumption and high dynamic range are discussed. A fast system-level table-based simulation is used to optimize the circuit design. Simulation results for a second order CA-modulator are presented to show the high performance of the cell.
An improved model for gas-liquid flow pattern prediction based on machine learning

Flow pattern of a multi-phase flow refers to the spatial distribution of the phase along transport conduit when liquid and gas flow simultaneously. The determination of flow patterns is a fundamental problem in two-phase flow analysis, and an accurate model for gas-liquid flow pattern prediction is critical for any multiphase flow characterization as the model is used in many applications in petroleum engineering. The objective of this study is to present a new model based on machine learning techniques and more than 8000 laboratory multi-phase flow tests. The flow pattern is affected by fluid properties, in-situ flow rates of liquid and gas, and flow conduit geometry and mechanical properties. Laboratory data since 1950s have been collected and more than 8000 data points had been obtained. However, the actual flow conditions are significantly different with any laboratory settings. Therefore, several dimensionless variables are derived to characterize these data points first. Then machine learning techniques were applied on these dimensionless variables to develop the flow pattern prediction models. Applying hydraulic fundamentals and dimensional analysis, we developed dimensionless numbers to reduce number of freedom dimensions. These dimensionless variables are easy to use for upscaling and have physical meanings. We converted the collected data from actual laboratory measurement to the variations of these dimensionless variables. Machine learning techniques on the dimensionless variable significantly improved their predictive accuracy. Currently the best matching on these laboratory data was about 80% using the most recently developed semi-analytical models. Using machine learning techniques, we improved the matching quality to more than 90% on the experimental data. This paper applies machine learning techniques on flow pattern prediction, which has tremendous practical usages and scientific merits. The developed model is better than current existing semi-analytical or classical correlations in matching the laboratory database.
POSTECH at NTCIR-5: Combining Evidences of Multiple Term Extractions for Mono-lingual and Cross-lingual Retrieval in Korean and Japanese

This paper describes methodologies for NTCIR-5 CLIR involving Korean and Japanese, and reports the ocial result as well as retrieval results using NTCIR-3 and NTCIR-4 data. We participated in four tasks: K-K and J-J monolingual tracks and K-J and J-K cross-lingual tracks. Unlike English, in Asian languages such as Korean and Japanese term extraction is nontrivial because of segmentation ambiguities. In this regard, we prepared multiple term representations for documents and queries, of which ranked results are merged to generate final ranking. In preliminary experiments using NTCIR-3 and NTCIR-4 data, our model showed the best performances for description queries in Korean and Japanese. In oine results using NTCIR-5 data, our methodology in Korean showed the best performance by archieving 0.5680 for description queries and 0.6159 for others.