Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of iterative decoding for serial concatenated convolutional codes

Turbo codes include two kinds of encoding schemes, one is parallel concatenated convolutional codes (PCCC), and another is serial concatenated convolutional codes (SCCC). The SCCC can get better performance than PCCC. The analysis of acquirement of some information for SCCC decoding computation, which is different from PCCC, are presented. Finally, the decoding algorithm of SCCC is derived.
Proficient Energy Consumption Aware Model in Wireless Sensor Network

In this paper, focus is on energy aware model of wireless sensor networks in which each sensor node randomly and alternatively stays in an active or sleep mode. The active mode consists of two phases, the full-active phase and the semi-active phase. When a particular sensor node is in the full-active phase of the active mode, it may sense data packets, transmit the sensed packets, receive packets, and relay the received packets. However, when the phase of the sensor node switches from the full active phase to the semi-active phase, it is only able to transmit/relay data. When the particular sensor node is in a sleep mode, it does not interact with any other node. In this work, emphasis is also given on numerical analysis for energy aware model. An energy aware model for WSN is being also developed with active and sleep features and justified with mathematical analysis and results thereon.
A 32-b RISC/DSP microprocessor with reduced complexity

This paper presents a new 32-b reduced instruction set computer/digital signal processor (RISC/DSP) architecture which can be used as a general purpose microprocessor and in parallel as a 16-/32-b fixed-point DSP. This has been achieved by using RISC design principles for the implementation of DSP functionality. A DSP unit operates in parallel to an arithmetic logic unit (ALU)/barrelshifter on the same register set. This architecture provides the fast loop processing, high data throughput, and deterministic program flow absolutely necessary in DSP applications. Besides offering a basis for general purpose and DSP processing, the RISC philosophy offers a higher degree of flexibility for the implementation of DSP algorithms and achieves higher clock frequencies compared to conventional DSP architectures. The integrated DSP unit provides instruction set support for highly specialized DSP algorithms. Subword processing optimized for DSP algorithms has been implemented to provide maximum performance for 16-b data types. While creating a unified base for both application areas, we also minimized transistor count and we reduced complexity by using a short instruction pipeline. A parallelism concept based on a varying number of instruction latency cycles made superscalar instruction execution superfluous.
On the difficulty of making concreteness concrete

The use of labels of semantic properties like ‘concreteness’ is quite common in studies in syntax, but their exact meaning is often unclear. In this article, we compare dierent denitions of concreteness, and use them in dierent implementations to annotate nouns in two data sets: (1) all nouns with word sense annotations in the SemCor corpus, and (2) nouns in a particular lexico-syntactic context, viz. the theme (e.g. a book) in prepositional dative (gave a book to him) and double object (gave him a book) constructions. The results show that the denition and implementation used in dierent approaches dier greatly, and can considerably aect the conclusions drawn in syntactic research. A followup crowdsourcing experiment showed that there are instances that are clearly concrete or
An all-analog expandable neural network LSI with on-chip backpropagation learning

This paper proposes an all-analog neural network LSI architecture and a new learning procedure called contrastive backpropagation learning. In analog neural LSI's with on-chip backpropagation learning, inevitable offset errors that arise in the learning circuits seriously degrade the learning performance. Using the learning procedure proposed here, offset errors are canceled to a large extent and the effect of offset errors on the learning performance is minimized. This paper also describes a prototype LSI with 9 neurons and 81 synapses based on the proposed architecture which is capable of continuous neuron-state and continuous-time operation because of its fully analog and fully parallel property. Therefore, an analog neural system made by combining LSI's with feedback connections is promising for implementing continuous-time models of recurrent networks with real-time learning. >
Availability based Scoring Model for Resource Grouping in Desktop Grid Computing

Many scientific projects such as SETI@home are leveraging on enormous numbers of distributed internet-based idle desktop computers to execute their jobs in a Desktop Grid (DG) computing environment. These idle desktop computers (volunteers) are volatile, i.e., they are free to join and leave the DG systems. Besides, the volunteers might also have different resources which are heterogeneous in characteristics and capabilities. Due to the above facts, volunteers in a DG system have different levels of reliability and availability which makes scheduling of jobs more challenging. Thus, this paper presents a grouping method which assigns these heterogeneous volatile volunteers into different levels of reliability groups, according to their availability score. In order to derive the availability score model for the grouping purposes, this study uses statistical analysis of real trace data which are taken from SETI@home project. By having the proper grouping, better assignment of jobs can be achieved in a DG system. Simulation runs of the approach shows that it is feasible to volunteers are able to be grouped using the scoring model.
Real time speech classification and pitch detection

An accurate silence-unvoiced-voiced classification and pitch detection algorithm is described and its implementation for real-time applications on a Texas Instruments TMS320C25 digital signal processor is evaluated. Speech classification is separated into silence detection and voice-unvoiced classification. Only the signal's energy level and zero-crossing rate are used in both classification processes. Pitch detection need only operate on voiced periods of speech. A peak picking technique is used to successively home in on the peaks that bound the pitch periods. Tests are performed on the found peaks to ensure that they are pitch-period peaks. A real-time implementation strategy is developed that combines silence detection with the signal acquisition and tightly couples voiced-unvoiced classification with pitch detection. The silence detection task is interrupt-driven and the pitch detection task loops continuously. The execution speed and accuracy results for this algorithm are shown to compare favorably with those for other such algorithms published in the literature. <<ETX>>
Comparing objects composed by abstract relationships

J.C. Gower's similarity algorithm (1971) is flexible in its ability to classify objects with diverse types of attributes. The author extends Gower's algorithm to handle composite objects using semantic data modeling. The resulting composite classification algorithm, which can be integrated with a knowledge base, facilitates the definition and use of prototype concepts. <<ETX>>
Tuning a Priority-Based Register Allocator Using Adaptive Compilation

Register allocation is a long studied optimization in compiler construction because it provides great opportunity for improving execution time. Adaptive compilation is a relatively new technique that uses repeated compilation and search to find effective parameters for compiler optimizations. We examine the priority-based graph-coloring register-allocation algorithm in the context of an adaptive compiler. The prioritybased algorithm was selected because it is well known, but little information exists on how it should be tuned to produce good results or how it compares with competing algorithms. We show that adaptive compilation can be used to improve the performance of a priority-based allocator. Aggressive tuning through adaptive compilation enables us to fairly compare against the Chaitin-Briggs algorithm for register allocation. We found the standard priority-based allocator was, on average, 16-9% worse than Chaitin-Briggs. Adaptive compilation enabled the priority-based allocator to close this performance gap and slightly outperform Chaitin-Briggs by an average of 1%.
Speech Therapy Based on Expert System

Abstract —in this paper the design and implementation of an expert speech therapy system is presented. A brief presentation of involved speech therapy process is made. Also the base for designing and implementations of this system is presented too. 1 Index Terms —artificial intelligence, expert systems, handicapped aids, medical services, speech treatment I. INTRODUCTION Nowadays in rich societies there are sometime enough human resources specialized in speech disorder therapy of the child or for the adults. Unfortunately even in this case not all persons can afford this type of personalized treatment that involve a human expert who will interact a few hours each day with each child in order to assure him a good feedback in the time of his special home speech exercises given by the speech therapy expert. As result the computer based either using a classic PC or a mobile device, seems to be suitable. Of course that will not imply the lack of a human therapist in handling child treatment but will ensure a good execution of daily home exercises that are vital for the treatment. This approach have also many advantages for treating persons from isolated communities by mixing the telepresence of the speech disorder therapist with exercises executed by the patient. After long analysis with the specialist in speech therapy the conclusion were that a classic approach it is not suitable in design of this kind of systems. The rules are not exactly quantifiable and sometimes may vary on each case. As result the design of a expert system based applications were elected to permit he specialist to give us in terms of general rules the treatment execution and surveillance. This system will work only in conjunction with a very good speech recognition therapy that is required to make comparisons between the correct work pronunciation and the child form that need to be corrected. II. S
Adaptation of visually lossless colour coding to JPEG2000 in the composite colour space

A visually lossless colour image (VLCI) coder is proposed based on the embedded block coding with optimized truncation (EBCOT) [D. Taubman, July 2000] coding structure. The VLCI coder is adapted with the contrast gain control [A. B. Watson and J. A. Solomon, 1997] model for the identification and removal of visually redundant information in the spectral domain prior to encoding. Since no additional side information are required and no modifications are involved at the decoding end, this coder is fully compliant with the EBCOT bitstream. Thus, it can be easily adapted to any JPEG2000 standard coder. The proposed coder has been designed to perform visually lossless compression independent of viewing distances. It currently operates in the Y/sub D/C/sub b/C/sub r/ composite colour domain. Simulation results of the VLCI coder has shown marked improvements over the LOCO [M. Weinberger et al., 1996] and the near-lossless LOCO (d2) coder in terms of compression ratio.
Smart Demand Response Management of Islanded Microgrid using Voltage-Current Droop Mechanism

Abstract The reduction of power of the autonomous microgrid is proposed in this paper. The concept utilized is Conservative Voltage Reduction (CVR). The active and reactive power consumption of the microgrid decreases and a power reserve of the system increased. The voltage–current droop mechanism is used to lessen the voltage of the DG and it reduces the consumption of power on the consumer side. This reduced power is used to cater more demands. The control strategy is used to cater the greater number of loads at the time of power shortage. The DG taken in this paper is photovoltaic (PV) system and perturb & observe MPPT is used. The interleaved boost converter is used as DC- DC converter as it lessens the ripple of the output voltage and inputs current. The simulation is done on MATLAB platform and results are validated at different loading conditions and comparison has been done with P-f/Q-V mechanism.
Face Recognition Via Sparse Representation

Recent years have witnessed rapid developments of sparse representation in various classification tasks, including face recognition (FR). The success of sparse representation-based classification (SRC) on FR greatly boosts the research of sparsity-based classification techniques. However, many problems are still pending to be further addressed. For example, what is the role of l0 or l1 norm sparsity in it? How to design a robust representation fidelity term to handle various outliers? How to extract effective features to improve the accuracy and efficiency of SRC? In this article, we aim to answer these questions via three aspects: Role of sparse coefficient: Several works have questioned the role of sparse coefficients. Especially, a collaborative representation-based classification has been proposed for FR, which use l2-norm to regularize the coding coefficient, with similar accuracy to SRC but much faster speed. However, there is also some work that defends the sparsity of coding coefficients when an appropriate dictionary is used. Regularization on the representation residual: Robust models with effective representation terms have been developed to handle various outliers in face images. For instance, by assuming the coding residual and the coding coefficient are respectively independent and identically distributed, regularized robust coding was proposed based on maximum a posterior estimation. Moreover, structured representation models were also proposed to make use of the image structure information. Extended sparse representation model: Extended sparse models for FR include misalignment robust models, undersampled models, and multifeature models. These FR approaches via sparse representation have achieved very promising results. Keywords: face recognition; sparse representation
A Hybrid Hypergraph Partitioning Algorithm for Scientific Computing

Hypergraph partitioning is an increasingly important and widely studied research topic in parallel scientific computing. In this paper, we present a multiway hypergraph partitioning algorithm, mixed simulated annealing algorithm for global optimization and tabu search algorithm for local optimization. Experiments on the benchmark suite of several unstructured meshes show that, for 2-, 4-, 8-, 16-and 32-way partitioning, the quality of partition produced by our algorithm are on the average 6% and the maximum 17% better than those produced by partitioning software hMETIS in term of the cutsize metric.
Automating XML Pipelines Through Rules

This paper presents XPR XML Pipeline Rules, a novel approach on defining, maintaining, and using XML pipelines for multiplestep document format conversion and automated detection. XPR introduces a template-based approach for reusable blocks of processing operations, as well as an abstraction for detecting document formats based either on XPath or schema validation. With XPR, there is no need to hand code different pipelines for each required document conversion. Therefore, it helps developers on maintenance and extensibility scenarios. A use case is presented, illustrating the drawbacks of current XML pipeline technologies, and how XPR can be used complementary to these by overcoming their limitations.
Networks based on Kanerva's sparse distributed memory: results showing their strengths and limitations and a new algorithm to design the location matching layer

Kanerva's sparse distributed memory model consists of a fixed nonlinear mapping, called location mapping, followed by a single layer of adaptive dot-product step-threshold links. Various networks of this type are tested on three tasks in order to discover the circumstances in which this type of network provides an efficient solution. The networks provide more competitive performance when the dimensionality of the input patterns is fairly low. A location pruning technique is reported which improves the design of the location matching mappings. The resulting network is extensively tested on large pattern classification tasks to demonstrate the benefits of the algorithm. The experiments show that the main benefit of location matching networks is their ability, using the location pruning algorithm, to train in roughly 1/2 to 1/10 of the training iterations required by single or double layer adaptive networks on the same tasks. <<ETX>>
A Spatial Analysis System for Integrating Data, Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.
Crystal structure of (E)-2,4-dichloro-6-(((4-methoxy-2-nitrophenyl)imino)methyl)phenol, C14H10Cl2N2O4

Abstract C14H10Cl2N2O4, monoclinic, P21/c, a = 14.2599(10) Å, b = 6.9970(4) Å, c = 15.0779(10) Å, β = 108.245(2)°, V = 1428.79(16) Å3, Z = 4, Rgt(F) = 0.0355, wRref(F2) = 0.0860, T = 150(2) K.
Statistical analysis of diffusion systems with invariants

Abstract The aim of the paper is the construction and numerical solution of stochastic differential equations whose trajectories are located on a given smooth manifold with probability 1. Second order cylindrical surfaces, i.e., elliptic, hyperbolic, and parabolic cylinders serve as examples of such manifolds for the tree-dimensional space (the phase space is two-dimensional). Classes of stochastic differential equations are constructed for these surfaces and linear equations with multiplicative noise are marked in these classes. The results of modelling were used to estimate the deviations of numerical solutions from the manifold. A comparative analysis of considered examples was carried out for accuracy of eight numerical solution methods for stochastic differential equations.
Transmitter diversity for OFDM systems with mobile wireless channels

Transmitter diversity is an effective technique used to improve wireless communication performance. We investigate transmitter diversity using space-time coding for OFDM systems in high-speed wireless data applications. We develop channel parameter estimation approaches, which are crucial for the decoding of the space-time codes, and derive the MSE bounds of the estimators. The overall receiver performance using such a transmitter diversity scheme is demonstrated by extensive computer simulations. For an OFDM system with two transmitter antennas and two receiver antennas with transmission efficiency as large as 1.475 bits/sec/Hz, the required signal-to-noise ratio is only about 7 dB for a 146 bit error rate and 9 dB for a 10% word error rate assuming channels with two-ray, typical urban, and hilly terrain delay profiles, and a 40 Hz Doppler frequency. In summary, combining OFDM with transmitter diversity using space-time coding is a promising technique for highly efficient data transmission over mobile wireless channels.
Fabrication of bilayer resin-bonded fixed abrasive wires using the pultrusion process

Abstract The fabrication of resin-bonded abrasive composite materials with constant cross-section was achieved by wire pultrusion wherein a piano wire of diameter 120 μm used as a core was pulled through a surface pretreatment, resin bath and into a die orifice and oven, where the impregnated resin slurry was cured at a moderate temperature. The bilayer resin-bonded fixed diamond wire with a diameter of about 191–205 μm with an inner layer of phenolic-modified epoxy resin and an outer layer of phenolic resin through the pultrusion method was presented. The results showed that when liquid resin slurry with higher diamond concentration during the pultrusion process was used, a larger wire diameter, less diamond spacing and higher diamond protrusion were obtained.
Will Procurement Officials be Biased to Disregard Procurement Rules in Favor of a Low-priced, Albeit Defective, Bid?

Abstract To what extent are professional decision-makers in the field of public procurement susceptible to cognitive biases? Recent research found a bias in favor of the lower bidder when ranking competing bids (Dekel and Schurr 2014, “Cognitive Biases in Government Procurement – an Experimental Study with Real Bid Evaluators,” 10(2) Review of Law and Economics 169–200). In the present research we examine this question regarding another stage of the public procurement process – the qualification stage. To this end, we conducted a series of experiments with the participation of procurement officials in situations that closely resemble their daily work. Our main finding is that even though procurement officials are susceptible to a cognitive bias when they have to score competing bids, they overcome that bias when asked to decide whether to qualify faulty or questionable bids. We cautiously ascribe this difference to the different types of decision-making involved, and suggest further explorations of these insights.
The Contribution of Large Banking Institutions to Systemic Risk: What Do We Know? A Literature Review

Abstract Against the background of the global financial crisis, we review recent literature on the debate about “too big to fail”. This is (still) one of the key issues in banking literature since it determines the conditions for adequate banking regulation, financial stability and economic welfare. Analyzing 30 papers from 2009 to 2017, our work focusses on the impact of large banks on systemic risk. Large financial institutions can affect systemic risk by either contributing to systemic risk or being extremely exposed to sources of systematic risk and contagion. We find a considerable number of theoretical and empirical studies providing evidence that against the background of the constitution of present-day real financial systems, bank size is a key predictor for systemic risk and that the largest banks disproportionately contribute to overall risk. This relationship is found in samples of different composition, for various periods and with different measures covering diverse aspects of systemic risk.