Miracle-dz/UniFAR-FTUs huggingface dataset:
An intelligent assistant for software information checking using a non-monotonic reasoning system

The knowledge representation of software component relations using nonmonotonic logic to assist the validity and integrity checking of software information is presented. Software components and their interconnection information are represented by axioms that exhibit the structure and behavior of the software system. Another set of axioms represents the basic truisms about a software system in general. These axioms can easily be extended to cover a wide variety of software systems architectures. In the software development and maintenance phases, information about the software system can be derived from these axioms using an automated reasoning system, and the software system itself can easily be checked against a specification of the system and compared for validity. This knowledge-based system will provide programmers with useful software information and assist the software development and maintenance process. <<ETX>>
Analysis and classification of texts for industrial machines maintenance.

Analysis and classification of texts are important tasks in data mining. Through such tasks, we are able to extract or generate relevant information from raw data that can be used to solve problems or improve existing solutions. This work addresses a solution for the problem of given a textual description for an industrial machine error, the classifier must assign a fault code for such a description. A computational solution that attenuates such a problem can contribute to streamlining the maintenance process, reducing the machine utilization downtime and reducing the cost of technical allocation errors. In this work, we tested three machine learning methods with a historical English text dataset with error descriptions and they are: Support Vector Machine (SVM), Random Forest and FastText. One important innovation of this work is a case study for the machine learning literature where the number of classes to be identified is high, reaching more than 1200 different classes. In contrast, the state-of-art in text classification described case studies with only tens of classes. The results using a 5- fold cross validation strategy and IRace for configuring the three classifiers input parameters achieved high f1-score for almost all evaluated methods, precisely the results varied from 67,5 to 94,92% in a dataset composed of more than 800k short text descriptions. The FastText proved to be faster than the others, precisely 4 times faster than SVM and 1,3 times faster than Random Forest.
On-device Structured and Context Partitioned Projection Networks

A challenging problem in on-device text classification is to build highly accurate neural models that can fit in small memory footprint and have low latency. To address this challenge, we propose an on-device neural network SGNN++ which dynamically learns compact projection vectors from raw text using structured and context-dependent partition projections. We show that this results in accelerated inference and performance improvements. We conduct extensive evaluation on multiple conversational tasks and languages such as English, Japanese, Spanish and French. Our SGNN++ model significantly outperforms all baselines, improves upon existing on-device neural models and even surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction. Through a series of ablation studies we show the impact of the partitioned projections and structured information leading to 10% improvement. We study the impact of the model size on accuracy and introduce quatization-aware training for SGNN++ to further reduce the model size while preserving the same quality. Finally, we show fast inference on mobile phones.
Automated Translation of MATLAB Code to C++ with Performance and Traceability

In this paper, we discuss the implementation and performance of m2cpp: an automated translator from MATLAB code to its matching Armadillo counterpart in the C++ language. A non-invasive strategy has been adopted, meaning that the user of m2cpp does not insert annotations or additional code lines into the input serial MATLAB code. Instead, a combination of code analysis, automated preprocessing and a user-editable metainfo file ensures that m2cpp overcomes some specialties of the MATLAB language, such as implicit typing of variables and multiple return values from functions. Thread-based parallelisation, using either OpenMP or Intel’s Threading Building Blocks (TBB) library, can also be carried out by m2cpp for designated for-loops. Such an automated and non-invasive strategy allows maintaining an independent MATLAB code base that is favoured by algorithm developers, while an updated translation into the easily readable C++ counterpart can be obtained at any time. Illustrating examples from seismic data processing are provided in this paper, with performance results obtained on multicore Sandy Bridge CPUs and Intel’s Knights-Landing Xeon Phi processor. Keywords–Code translation; Seismology; Image processing; MATLAB; C++.
A Versatile Implementation of Acoustic Analogy Based Noise Prediction Method in a General-Purpose CFD Code

This paper is concerned with incorporation of a noise prediction method based on acoustic analogy in a generalpurpose computational fluid dynamics (CFD) code based on finite-volume Navier-Stokes solver. Near-field aerodynamics can be obtained by solving, when appropriate, unsteady inviscid (Euler) equations, Navier-Stokes equations, averaged or filtered Navier-Stokes equations. Propagation of sound waves is computed using the time-domain integral formulation of Ffowcs-Williams and Hawkings. Both the flow solver and the acoustics module are fully parallelized. A forward time-projection technique is employed to effect the time-lag between emission and reception times. Some implementational issues impacting the usability are discussed, such as user-interfaces, data management, and postprocessing. Also discussed are some diagnostic tools useful to extract quantitative information on the local contributions from different parts of the emission surfaces. Several examples are presented, including simple two-dimensional cases to a more complex three-dimensional one.
Development and determinants of end-user intention: usage of expert systems

Purpose Expert systems (ES) design emulates expertise with the intention that the ES be used by non-experts. This study aims to predict end-user intention and use of ES by proposing a research model that extends the basic components of the unified theory of acceptance and use of technology (UTAUT) by including additional relevant factors to ES, including the expert domain, perceived relevance, reliability, quality of ES and management support, which directly and indirectly influence the end-user intention to use an expert system. Design/methodology/approach A structural equation model (SEM), using LISREL, was used to test the measurement and structural models using a sample of 205 end-users of expert systems in the USA. These users of expert systems come from a variety of domains. The factors include both internal and external factors for the individual level of analysis design of this study. Findings The results showed behavioral intention had the strongest effect on usage, followed by perceived relevance. With respect to the factors that impact intention, perceived relevance had the strongest total effect, followed by attitude. For attitude, effort expectancy had the strongest total effect, followed by management support and perceived relevance. Research limitations/implications The results of this study should assist decision-makers in planning training and communications about the use of expert systems so that the expert systems will be used as intended. Originality/value The originality of this work resides in the addition of external factors to the UTAUT model that helps provide advice to practitioners in the support needed to insure expert system implementation success.
Multi-grained Attention with Object-level Grounding for Visual Question Answering

Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.
Attentive Devices for Groupware Systems

In this paper we present a combined perspective over groupware systems and attentive user interfaces to address the balance between the rising needs for collaboration with the rising costs of not paying attention and being interrupted. We introduce a framework that derives attentive devices from groupware mediation modes to take into account attentional phenomena. Finally, we demonstrate the framework’s role in an attentive brainstorming tool and provide preliminary results from a pilot experiment.
Embedded wavelet packet image coder using set partitioning

In this paper, we propose an embedded wavelet packet image coder in which a new model for parent-child relationships using set partitioning is considered. By the wavelet packet transform, energy compaction of the image is achieved, and an embedding approach on set partitioning in packet trees is then adopted for the coding of the wavelet packet transform coefficients. It is shown that the proposed technique achieves good compression results and allows a perfect rate control.
A Robust Three-Factor User Authentication Scheme based on Elliptic Curve Cryptography and Fuzzy Extractor

A three-factor user authentication is appropriate to ensure a high degree of authentication. Fan and Lin proposed a typical three-factor authentication scheme, which requires token, password, and fingerprint. The scheme does not allow authentication in the absence of any of the three factors. Unfortunately, Fan and Lin's scheme is associated with security risks such as vulnerability to insider attacks, stolen-verifier attacks, and message modification attacks. Yeh et al. proposed a three-factor user authentication, which overcomes such pitfalls and improves security and performance using elliptic curve cryptography. We found that Yeh et al.'s scheme is still vulnerable to user impersonation attacks and server masquerading attacks. We propose a robust three-factor authentication scheme entailing server smart cards, elliptic curve cryptography, and a fuzzy extractor that address the foregoing flaws and result in enhanced security. The proposed scheme is resistant to various attacks and improves system performance. BAN logic is used to prove that the scheme establishes a secure channel.
A UWB Vivaldi Antenna for Time-Domain Radar Applications

This paper presents a UWB tapered Vivaldi Antenna for time domain radar applications. The proposed antenna has a hyperbolic cosine aperture profile which produces a smoother transition between the feeding slotline and the curved profile of the taper. Measurements results show that the impedance bandwidth is 0.8-9.93 GHz for the S11 < 10 dB with an average peak gain in the end-fire direction greater than 3 dBi. These results are in good agreement with those obtained in simulation.
A variability taxonomy to support automation decision-making for manufacturing processes

Abstract Although many manual operations have been replaced by automation in the manufacturing domain in various industries, skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realize the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realize the potential. This article describes a taxonomy of variability when considering the automation of manufacturing processes. Three industrial case studies were analyzed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making.
Two Stage Interleaved Boost Converter Design and Simulation in CCM and DCM

The concept of interleaving, or more generally that of increasing the effective pulse frequency of any periodic power source by synchronizing several smaller sources and operating them with relative phase shifts, has several advantages. We have a source and a load and the converter in between can have many stages in parallel. Among these the Interleaved Boost Converter has many applications. Here the concept of interleaving and the design and simulation of the Interleaved Boost Converter in both the CCM and DCM modes are discussed. Keywords—Interleaving, Boost Converter, CCM, DCM.
Revisiting automated project management in the digital age – a survey of AI approaches

In this decade, remarkable progress has been made in the field of artificial intelligence (AI). Inspired by well-known services of cognitive assistance systems such as IBM Watson, Apple's Siri or Google Duplex, AI concepts and algorithms are widely discussed regarding their automation potentials in business, politics and society. At first glance, project management (PM) seems to be less suitable for automation due to the inherent uniqueness of projects by definition. However, AI is also creating new application possibilities in the PM area, which will be explored in this contribution by involving an extensive literature review as well as real-world examples. The objective of this article is to provide a current overview of AI approaches and available tools that can be used for automating tasks in business project management.
Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension

Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer. In this paper, we introduce the Dynamic Self-attention Network (DynSAN) for multi-passage reading comprehension task, which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. The core module of the dynamic self-attention is a proposed gated token selection mechanism, which dynamically selects important tokens from a sequence. These chosen tokens will attend to each other via a self-attention mechanism to model long-range dependencies. Besides, convolutional layers are combined with the dynamic self-attention to enhance the model’s capacity of extracting local semantic. The experimental results show that the proposed DynSAN achieves new state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets. Further ablation study also validates the effectiveness of our model components.
MODELING REFLECTION PROPERTIES OF ROAD SURFACES BY DATABASE METHOD

This paper proposes a new method to model r-tables without an analytical expression, but by database method. A so-called ‘knowledge base’ for road surfaces is setup, which is constructed by verified r-tables. The data model is trained and built up by some selected r-tables, then predictions are made for the other r-tables in the knowledge base, and prediction errors are then analysed to verify the model. In this paper, 287 r-tables of dry road surfaces is tested. The model is built up by its r-tables with even number indices. The r-tables with odd number indices are then predicted. Results show that 90% of the prediction errors are below 15%, and a great number of examples are distributed around 5%. Only two worst cases are of error around 50%. These results are much better than those we obtained by analytical expression methods. By this database model, if we have a large enough data base with reliable r-tables, covering road surfaces of different materials, service and weather conditions, we can make predictions for most road surfaces with a few measured value, then make a more precise road/street lighting design.
A comparison of human observer LROC and numerical observer ROC for tumor detection in SPECT images

The authors have compared performances of humans in a localization ROC (LROC) study to performances of a channelized Hotelling observer (CHO) in a "signal-known- exactly" (SKE) ROC study. The task was tumor detection in simulated Ga-67 scans of the chest. The study variables tested by the observer studies were different filtering strategies created by varying the dimensionality and cut-off frequency of a 5th-order Butterworth filter used to smooth the images. Image reconstruction involved filtered backprojection (FBP) with multiplicative Chang attenuation correction. A total of 35 tumor locations were used. Human LROC results for 4 participants were acquired from a preliminary study of 140 images per strategy and a main study of 204 images per strategy. The LROC ratings are given as areas under the LROC curve. For the ROC study, a constant-Q channel model was used, with parameters determined from a previous comparison of human and CHO performance in a SKE ROC study. The CHO was applied to 200 noise realizations per location and strategy. The CHO ratings of the filtering strategies are given as areas under the ROC curve averaged over location. Correlation between the ROC and LROC rankings of the strategies indicates that the CHO may be of use in LROC study design.
Estimation of time-varying multipath channel parameters using chirp signals

We propose a method for estimating the parameters of linear time-varying multipath channels, based on the transmission of consecutive chirp signals having different sweep rates. We show that the channel parameter estimation problem can then be recast as a harmonics-in-noise problem. We estimate the number of paths and the parameters of each path, and then we use our estimates to predict the future channel evolution. We compute the variance of the proposed estimator, and show that performance is close to the Cramer-Rao bound, at least as far as the estimates of the Doppler frequency shifts are concerned.
On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements

Cellular-network operators are becoming increasingly interested in knowing the Quality of Experience (QoE) of their customers. QoE measurements represent today a main source of information to monitor, analyze, and subsequently manage operational networks. In this paper, we focus on the analysis of YouTube QoE in cellular networks, using QoE and distributed network measurements collected in real users' smartphones. Relying on YoMoApp, a well-known tool for collecting YouTube smartphone measurements and QoE feedback in a crowdsourcing fashion, we have built a dataset covering about 360 different cellular users around the globe, throughout the past five years. Using this dataset, we study the characteristics of different QoE-relevant features for YouTube in smartphones. Measurements reveal a constant improvement of YouTube QoE in cellular networks over time, as well as an enhancement of the YouTube video streaming functioning in smartphones. Using the gathered measurements, we additionally investigate two case studies for YouTube QoE monitoring and analysis in cellular networks: the machine-learning-based prediction of QoE-relevant metrics from network-level measurements, and the modeling and assessment of YouTube QoE and user engagement in cellular networks and smartphone devices. Last but not least, we introduce the YoMoApp cloud dashboard to openly share smartphone YouTube QoE measurements, which allows anyone using the YoMoApp smartphone app to get immediate access to all the raw measurements collected at her devices.
A 320-fs RMS Jitter and – 75-dBc Reference-Spur Ring-DCO-Based Digital PLL Using an Optimal-Threshold TDC

This paper presents a ring-type, digitally controlled oscillator (DCO)-based integer- ${N}$ digital phase-locked loop (DPLL) that can achieve low jitter and low reference spur concurrently. In order to minimize the quantization error, while consuming a small amount of power, this work presents an optimal-threshold (OT) time-to-digital converter (TDC). The thresholds of the OT TDC and the phase-correction gain of the loop are corrected continuously in the background. The PLL was fabricated in a 65-nm CMOS process and its measured rms jitter integrated from 1 kHz to 100 MHz and the reference spur of a 2.4-GHz frequency were 320 fs and −75 dBc, respectively. Through measurement, they were verified to be maintained robustly over temperature and supply variations. The active area was 0.055 mm2, and the power consumption was 6.0 mW.