nils-herrmann/clean_scirepeval huggingface dataset:
An Improved Approach for Text-Independent Speaker Recognition

This paper presents new Speaker Identification and Speaker Verification systems based on the use of new feature vectors extracted from the speech signal. The proposed structure combine between the most successful Mel Frequency Cepstral Coefficients and new features which are the Short Time Zero Crossing Rate of the signal. A comparison between speaker recognition systems based on Gaussian mixture models using the well known Mel Frequency Cepstral Coefficients and the novel systems based on the use of a combination between both reduced Mel Frequency Cepstral Coefficients features vectors and Short Time Zero Crossing Rate features is given. This comparison proves that the use of the new reduced feature vectors help to improve the system’s performance and also help to reduce the time and memory complexity of the system which is required for realistic applications that suffer from computational resource limitation. The experiments were performed on speakers from TIMIT database for different training durations. The suggested systems performances are evaluated against the baseline systems. The increase of the proposed systems performances are well observed for identification experiments and the decrease of Equal Error Rates are also remarkable for verification experiments. Experimental results demonstrate the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches requiring lengthy calculation. GMM; speaker verification; speaker recognition; speaker identification
Real-time wireless communication with per-packet deadlines

The last decades’ tremendous advances in wireless communications have been driven mainly by personal communications. Radio resource allocation mechanisms for optimizing key metrics, such as average throughput and delay, for such traffic are by now rather well-developed. However, with the increased interest in wireless machine-to-machine communication, e.g. for industrial control or monitoring of large-scale infrastructures, new challenges emerge. The performance of an estimator or closed-loop control system that operates over an unreliable wireless network depends on the full latency and loss distributions and not only on their averages. For these applications, more suitable performance metrics are per-packet guarantees on latency and reliability (on-time delivery). This thesis studies optimal forwarding of deadline-constrained traffic over lossy multi-hop networks. We assume a routing topology in the form of a directed graph with packet loss on links described by finite-state Markov chains, and focus on a single transient packet scenario. Two problems are considered: maximizing the probability that packets are delivered within their specified deadline; and minimizing the expected energy cost with a guaranteed probability of on-time delivery. The first problem can be formulated as a finite-horizon Markov decision process (MDP), while the second problem is a finite-horizon constrained Markov decision process (CMDP). A general dynamic programming framework that solves a weighted sum of reliability and energy maximization problem is proposed. The maximum deadline-constrained reliability problem is solved by studying a simplification of the general dynamic programming. The minimum energy optimal policy is a random selection between two deterministic and computable forwarding policies, each of which can be found via a dynamic programming framework. Particular instances with Bernoulli and Gilbert-Elliot loss models that admit numerically efficient solutions are discussed. Finally, we show the application of the technique for a co-design of forwarding policies and controller for wireless control. Based on the recent result on the monotonicity of the optimal control loss, the problem of minimum control loss and the problem of minimum energy cost with a guaranteed control performance can be solved optimally.
Component Selection for Component based Software Engineering

Component selection is not an easy task in Component Based Software Engineering .and it is very difficult to select component for CBSE. Component Based Software Engineering (CBSE) is a concerned with the assembly of pre-existing software components that leads to a software system that responds to client-specific requirements. This paper presents an approach for defining evaluation criteria for reusable software components. We introduce taxonomy of factors that influence selection, describe each of them, and present a hierarchical decomposition method for deriving reuse goals from factors and formulating the goals into an evaluation criteria hierarchy. It also presents a summary of the common problems in reusable off-the-shelf software selection, describes the method .It also indicates that the evaluated aspects of the method are feasible, improve the quality and efficiency of reusable software selection. In this paper the selection of component is done on the basis of the cost of the component which is calculated on the basis of the quality attributes of the component. The approach used for selecting the component is a part of OTSO method that has been developed for reusable component selection process.
Biocybernetic system evaluates indices of operator engagement in automated task

A biocybernetic system has been developed as a method to evaluate automated flight deck concepts for compatibility with human capabilities. A biocybernetic loop is formed by adjusting the mode of operation of a task set (e.g., manual/automated mix) based on electroencephalographic (EEG) signals reflecting an operator's engagement in the task set. A critical issue for the loop operation is the selection of features of the EEG to provide an index of engagement upon which to base decisions to adjust task mode. Subjects were run in the closed-loop feedback configuration under four candidate and three experimental control definitions of an engagement index. The temporal patterning of system mode switching was observed for both positive and negative feedback of the index. The indices were judged on the basis of their relative strength in exhibiting expected feedback control system phenomena (stable operation under negative feedback and unstable operation under positive feedback). Of the candidate indices evaluated in this study, an index constructed according to the formula, beta power/(alpha power + theta power), reflected task engagement best.
Affine Parallelization Using Dependence and Cache Analysis in a Binary Rewriter

Today, nearly all general-purpose computers are parallel, but nearly all software running on them is serial. Bridging this disconnect by manually rewriting source code in parallel is prohibitively expensive. Hence, automatic parallelization is an attractive alternative. We present a method to perform automatic parallelization in a binary rewriter. The input to the binary rewriter is the serial binary executable program and the output is a parallel binary executable. The advantages of parallelization in a binary rewriter versus a compiler include: (i) applicability to legacy binaries whose source is not available; (ii) applicability to library code that is often distributed only as binary; (iii) usability by end-user of the software; and (iv) applicability to assembly-language programs. Adapting existing parallelizing compiler methods that work on source code to binaries is a significant challenge. This is primarily because symbolic and array index information used by existing parallelizing compilers to take parallelizing decisions is not available from a binary. We show how to adapt existing parallelization methods to binaries without using symbolic and array index information to achieve equivalent source parallelization from binaries. Results using our x86 binary rewriter called SecondWrite are presented in detail for the dense-matrix and regular Polybench benchmark suite. For these, the average speedup from our method for binaries is 27.95X versus 28.22X from source on 24 threads, compared to the input serial binary. Super-linear speedups are possible due to cache optimizations. In addition our results show that our method is robust and has correctly parallelized much larger binaries from the SPEC 2006 and OMP 2001 benchmark suites, totaling over 2 million source lines of code (SLOC). Good speedups result on the subset of those benchmarks that have affine parallelism in them; this subset exceeds 100,000 SLOC. This robustness is unusual even for the latest leading source parallelizers, many of which are famously fragile.
Optimal dimensioning of active cell balancing architectures

This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.
A GLOBAL MEASURE OF PERCEIVED STRESS

To evaluate validity of the Greek version of a global measure of perceived stress PSS−14 (Perceived Stress Scale – 14 item).  The original PSS−14 (theoretical range 0−56) was translated into Greek and then back-translated. One hundred men and women (39±10 years old, 40 men) participated in the validation process. Firstly, participants completed the Greek PSS−14 and, then they were interviewed by a psychologist specializing in stress management. Cronbach’s alpha (α) evaluated internal consistency of the measurement, whereas Kendall’s tau-b and Bland & Altman methods assessed consistency with the clinical evaluation. Exploratory and Confirmatory Factor analyses were conducted to reveal hidden factors within the data and to confirm the two-dimensional character of the scale.  Mean (SD) PSS−14 score was 25(7.9). Strong internal consistency (Cronbach’s α = 0.847) as well as moderate-to-good concordance between clinical assessment and PSS−14 (Kendall’s tau-b = 0.43, p<0.01) were observed. Two factors were extracted. Factor one explained 34.7% of variability and was heavily laden by positive items, and factor two that explained 10.6% of the variability by negative items. Confirmatory factor analysis revealed that the model with 2 factors had chi-square equal to 241.23 (p<0.001), absolute fix indexes were good (i.e. GFI=0.733, AGFI=0.529), and incremental fix indexes were also adequate (i.e. NFI=0.89 and CFI=0.92).  The developed Greek version of PSS−14 seems to be a valid instrument for the assessment of perceived stress in the Greek adult population living in urban areas; a finding that supports its local use in research settings as an evaluation tool measuring perceived stress, mainly as a risk factor but without diagnostic properties.
Deep Learning for Time Series Modeling CS 229 Final Project Report

Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can cause “brown outs,” while excess supply ends in waste. In an industry worth over $1 trillion in the U.S. alone , almost 9% of GDP , even marginal improvements can have a huge impact. Any plan toward energy efficiency should include enhanced utilization of existing production. Energy loads provide an interesting topic for Machine Learning techniques due to the availability of large datasets that exhibit fundamental nonlinear patterns. Using data from the Kaggle competition “Global Energy Forecasting Competition 2012 Load Forecasting”  we sought to use deep learning architectures to predict energy loads across different network grid areas, using only time and temperature data. Data included hourly demand for four and a half years from 20 different geographic regions, and similar hourly temperature readings from 11 zones. For most of our analysis we focused on short term load forecasting because this will aide online operational scheduling.
Transforming XML documents to OWL ontologies: A survey

The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology (TBox), and ontology population is related to an individual (Abox). In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL (Ontology Web Language) or RDF (Resource Description Framework). These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs (classes, predicates, simple or complex data types, etc.) and XML constructs (elements, attributes, element lists, etc.). Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.
Convolutional Monte Carlo Rollouts in Go

In this work, we present a MCTS-based Go-playing program which uses convolutional networks in all parts. Our method performs MCTS in batches, explores the Monte Carlo search tree using Thompson sampling and a convolutional network, and evaluates convnet-based rollouts on the GPU. We achieve strong win rates against open source Go programs and attain competitive results against state of the art convolutional net-based Go-playing programs.
Using Associative Classifiers for Predictive Analysis in Health Care Data Mining

Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims to discover a small set of rule in the database that forms an accurate classifier. In this paper, we introduce the combined approach that integrates association rule mining and classification rule mining called Associative Classification (AC). This is new classification approach. The integration is done by focusing on mining a special subset of association rules called classification association rule (CAR). And then classification is being performed using these CAR. Using association rule mining for constructing classification systems is a promising approach. Given the readability of the associative classifiers, they are especially fit to applications were the model may assist domain experts in their decisions. Medical field is a good example was such applications may appear. Consider an example were
Noise adaptive soft-switching median filter

Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%
A Cross-Type Thermal Wind Sensor With Self-Testing Function

A 2-D smart flow sensor with self-testing function was designed, fabricated and tested in this paper. It is composed of cross-structure heaters and four symmetrically located sensing parts. When the flow angle changes in the clockwise direction, the temperature differences among the four parts, namely Mode 1 given by T13(T24) and Model 2 given by T12(T14) will be Sine(Cosine) functions of wind direction. Further study shows that the magnitude of mode 1 is 1.414 times that of Mode 2, and the phase of Mode 2 leads Mode 1 by 45°. By using the fixed phase gap between the two modes, the self-test function can be realized without additional components. In order to achieve a high sensitivity and robust sensor, thermal insulated substrate was introduced to fabricate calorimetric flow sensor instead of silicon wafers. The proposed sensor was fabricated on the Pyrex7740 substrate using single liftoff process. Finally, a wind tunnel test was carried out in constant power (CP) mode, and the test results show reasonable agreement with the simulation curves.
The Conjunction of Fog Computing and the Industrial Internet of Things - An Applied Approach

Industrial applications benefit from cloud computing deployments in terms of scalability, manageability, and cost. However, cloud-only implementations do not meet the requirements of industrial applications such as latency, network traffic reduction, and reliability. Fog computing is a promising approach to overcome the limitations of cloud computing in terms of realtime and network utilization. Fog computing already found its application in various areas such as Augmented Reality and Smart Cities. This paper presents two case studies which show the applicability of the fog paradigm for Industrial Internet of Things (IIoT) applications. IIoT applications are characterized by a high degree of integration and flexibility. The FEAt case study shows how the availability of a system can be increased by using a fog computing architecture and at the same time low latency for the analysis of sensor data can be achieved. In the second case study, FARADAY, we optimize bandwidth utilization in an existing industry environment by applying a fog-based solution. We show, that fog computing is applicable for industrial applications, helps to overcome problems associated with cloud deployments, and leads to reliable applications.
Forecasting Gold Prices Using Multiple Linear Regression Method

Forecasting is a function in management to assist decision making. It is also described as the process of estimation in u k own future situations. In a more general term it is commonly known as prediction which refers to estima on of time series or longitudinal type data. Gold is a precious yellow commodity once used as mo ney. It was made illegal in USA 41 years ago, but is now once again accepted as a potential curre ncy. The demand for this commodity is on the rise.  Objective of this study was to develop a forecasti ng model for predicting gold prices based on economic factors such as inflation, currency pri ce movements and others. Following the melt-down of US dollars, investors are putting their money in to gold because gold plays an important role as a stabilizing influence for investment portfolios. Du e to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to de velop a model that reflects the structure and patte rn of gold market and forecast movement of gold price. Th e most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (M LR) model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted mo el of MLR will be used to predict the future gol d prices. A naive model known as “forecast-1” was con sidered to be a benchmark model in order to evaluate the performance of the model.  Many factors determine the price of gold and based on “a hunch of experts”, several economic factors h ad been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Mone y Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were considered to have influence on the prices. Paramet er estimations for the MLR were carried out using Statistical Packages for Social Science package (SP SS) with Mean Square Error (MSE) as the fitness function to determine the forecast accuracy.  Two models were considered. The first model considered all possible independent variables . The model appeared to be useful for predicting the price of gold with 85.2% of sample variations i monthly gold prices explained by the model. The second model considered the following four independ ent variables the (CRB lagged one), (EUROUSD lagged one), (INF lagged two) and (M1 lagged two) t o be significant. In terms of prediction, the second model achieved high level of predictive accu ra y. The amount of variance explained was about 70% and the regression coefficients also provide a me ns of assessing the relative importance of individual variables in the overall prediction of g old price.