Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.
Research on Financial Early Warning of Listed Companies Based on Lasso-logistic Model

The financial status is an important factor affecting the survival and development of enterprises. When we conduct financial early-warning model analysis, the selection of index variables and the estimation of model parameters directly affect the prediction accuracy of the early-warning model. Lasso is a variable selection method for shrinkage estimation. By constructing a penalty functions to realize variety selection and retaining the advantages of subset shrinkage; Lasso can be applied to time series, high-dimensional graphics discrimination and selection. In this paper, the Lasso method and the Logistic model are combined to construct an early-warning model reflecting the financial status within the enterprise. The experimental results show that the model can effectively select relatively important influencing factors and also has good predictive evokes. Keywords—Financial warning; Lasso algorithm; Logistic model; Variable selection
Mapping Suspected Prostatic Carcinoma Using Smoothed Co-occurrence Texture Features

In this paper we present a system to assist in the scoring of prostatic carcinoma in histopathology images. Cancer and non-cancer regions of interest, annotated by an expert pathologist on whole-mount slides, are used to build a training set for support vector machine clas- sication. Our strategy entails extraction of co-occurrence texture fea- tures from image tiles within these regions of interest. These features are mapped to the underlying image, and 2D smoothing is applied to these maps using mean, median, and Gaussianlters to produce new texture features. Inclusion of smoothed features in the classier improves both sensitivity and specicity when testing on regions of interest from unseen images. Heat maps generated from tile classication probabilities high- light suspect regions on full section images for further inspection by a pathologist or CAD system. Improvements in training data and inclusion of additional texture features should improve the overall performance and robustness of the underlying methodology.
State Transition Probability as the Anticipation Clue of the Course of a Struck Ball

When opponent players in a racket competition anticipate the course of a struck ball, they use the kinematic clues when the ball is struck as clues, including various contextual informations, such as the position when the ball is struck and the status of the game. This study proposes a measurement method of the state transition probability that shows the connection between two strokes as effective contextual information for increasing the accuracy of a player’s anticipation of the course of a struck ball. The state transition probability that is calculated by the proposed method shows the struck ball sequence pattern for a player, and whether characteristic differences can be found between players is examined. Two matches of opposing players in a soft tennis singles match competition were analyzed in this study. The two-dimensional actual coordinate data of the drop position of the ball was acquired from the target matches. The courts were divided into three sections from the left to right direction, the depth direction, and from the net towards the baseline. The striking of the ball within these sections was defined as one state, and the state transition probability matrix was measured. The result shows that the state transition probabilities of two players, namely, the characteristic differences in the struck ball sequence pattern, were observed, and their effectiveness as contextual information of the prediction clues was demonstrated.
Dynamic scheduling in make-to-stock production systems with setup times and random breakdowns: performance analysis and improved policies

We study the scheduling of production in a multi-item, failure-prone machine with setup times, with the objective of minimising long-run average inventory and backlog costs. We make two contributions to this problem, which has received significantly less attention compared to the standard stochastic economic lot scheduling problem (with random demand and production times). First, we adapt and implement a variety of existing scheduling policies and compare their performance through a set of numerical experiments spanning a wide range of operating conditions. This analysis allows us to identify important shortcomings of the policies, including their poor performance when items have widely different priorities (as dictated by the product of their cost and production rate parameters). Second, we leverage these insights to develop and analyse a new, easy-to-implement policy. This policy tightly controls the surplus of the highest-priority items using fixed base-stock levels and, for all other items, it determines their run lengths dynamically in such a way that long production runs become decreasingly likely with decreasing item priority. Our experiments show that our new policy greatly outperforms the benchmarking policies over a large and important set of operating regimes, comprising high-utilisation systems where the machine cycles through all items at a frequency comparable to or greater than the frequency of failure events.
A Survey and Evaluation of Voice Activity Detection Algorithms

The term Voice Activity Detector (VAD) refers to a class of signal processing methods that detects if short segments of a speech signal contain voiced or unvoiced signal data. A VAD is normally using decision rules based on selected estimated signal features. VADs play a major role as a preprocessing block in a variety of speech processing applications such as speech enhancement, speech coding and speech recognition where it is desirable to classify voiced signal parts from unvoiced. This thesis presents a thorough investigation of modern VAD algorithms that are based on energy threshold, zero crossing and other statistical measures. The selected VAD algorithms are implemented in MATLAB and evaluated using objective parameters in different noise environments. The simulation results indicate that the selected methods produce favorable results in the noise environments with SNR above 5dB. VAD based on pattern recognition approach method proved effective when compared to those based on energy threshold, zero crossing measures and statistical measures.
Circular Economy in the Triple Helix of Innovation Systems

The Triple Helix concept of innovation systems holds that consensus space among industry, government and university is required to bring together their competences to achieve enhanced economic and social development on a systemic scale. In line with this argument, this article analyses empirically how the concept of circular economy is conceived in the institutional spheres of “industry”, “government” and “university”. Innovation systems are constantly being reconstructed through knowledge production and communication, which is reflected in how concepts develop in the different spheres. By applying natural language processing tools to key contributions from each of the three spheres (the “Triple Helix”), it is shown that, although institutional backgrounds do contribute to differing conceptualizations of circular economy, there is a substantial but limited conceptual consensus space, which, according to the Triple Helix, should open new opportunities for innovations. The consensus space shared across the three spheres focuses on materials and products and sees circular economy as a way to create new resources, businesses and products from waste. The industry sphere highlights business opportunities on global scale, which are also evident in the government sphere. The government sphere connects circular economy to waste-related innovation policies targeted at industrial renewal, economic growth, investments and jobs. The university sphere, in turn, focuses on production and environmental issues, waste and knowledge, and is rather distinct from the two other spheres. The importance of the differing conceptions of circular economy is based on the logic of Triple Helix systems. Accordingly, sufficient consensus between the Triple Helix spheres can advance the application of the concept of circular economy beyond the individual spheres to achieve systemic changes.
Synthetic Control Estimation Beyond Comparative Case Studies: Does the Minimum Wage Reduce Employment?

Abstract Panel data are often used in empirical work to account for fixed additive time and unit effects. The synthetic control estimator relaxes the assumption of additive fixed effects for comparative case studies in which a treated unit adopts a single policy. This article generalizes the synthetic control estimator to estimate parameters associated with multiple discrete or continuous explanatory variables, jointly estimating the parameters and synthetic controls for each unit. I apply the estimator to study the disemployment effects of the minimum wage, estimating that increases in the minimum wage reduce employment.
44 Validated Planets from K2 Campaign 10

We present 44 validated planets from the 10th observing campaign of the NASA K2 mission, as well as high-resolution spectroscopy and speckle imaging follow-up observations. These 44 planets come from an initial set of 72 vetted candidates, which we subjected to a validation process incorporating pixel-level analyses, light curve analyses, observational constraints, and statistical false positive probabilities. Our validated planet sample has median values of = , Porb = days, = K, and J = mag. Of particular interest are four ultra-short period planets ( day), 16 planets smaller than 2 , and two planets with large predicted amplitude atmospheric transmission features orbiting infrared-bright stars. We also present 27 planet candidates, most of which are likely to be real and worthy of further observations. Our validated planet sample includes 24 new discoveries and has enhanced the number of currently known super-Earths ( ≈ 1–2 ), sub-Neptunes ( ≈ 2–4 ), and sub-Saturns ( ≈ 4–8 ) orbiting bright stars (J = 8–10 mag) by ∼4%, ∼17%, and ∼11%, respectively.
An Auction Based Task Dispatching and Pricing Mechanism in Bike-sharing

As a green and low-carbon transportation way, bike-sharing provides lots of convenience in the daily life. However, how to dispatch bikes efficiently is a key issue in such a system. The bike-sharing platform can hire workers and pay to incentivize them to accomplish the dispatching tasks. However, there exist multiple workers competing for the dispatching tasks, and they may strategically report their task accomplishing costs (private information known by themselves) in order to make more profits, which may result in inefficient task dispatching. In this paper, we first design a dispatching algorithm named GDY-MAX to allocate tasks to workers. Furthermore, we design a strategy proof mechanism under the budget constraint to allocate tasks and determine the payments to workers. We theoretically prove that our mechanism can satisfy the properties of incentive compatibility, individual rationality and budget balance. Furthermore we run extensive experiments to evaluate our mechanism based on a Mobike dataset. The results show that our approaches can make better performance than benchmark approaches.
Polymorphisms in the interleukin-10 gene and Graves ’ disease : a meta-analysis

The correlation between interleukin-10 (IL-10) common polymorphisms (rs1800896 A>G, rs1800872 A>C and rs3021097 C>T) and Gravesdisease (GD) has been widely studied; however, the findings remain conflicting. The objective of this meta-analysis was to address the relationship of polymorphisms in the IL-10 gene with the risk of GD. PubMed, China National Knowledge Infrastructure (CNKI) and Chinese BioMedical Disc (CBM) databases were systematically searched by two reviewers independently to identify relevant studies before March19, 2016. Pooled odds ratios (ORs) and their 95% confidence intervals (CIs) for the correlation of IL-10 gene polymorphisms with GD susceptibility were calculated by Stata 12.0 software. Tests for publication bias, sensitivity analyses, and heterogeneity were also assessed in our meta-analysis. A total of six publications comprising sixteen case-control studies concerning the IL-10 rs1800896 A>G, rs1800872 A>C and rs3021097 C>T polymorphisms were included in our study. The pooled results suggested that IL-10 rs1800896 A>G polymorphism was correlated with an increased GD risk (G vs. A: OR = 1.58, 95% CI = 1.14-2.20, P = 0.007; GG vs. AA: OR = 5.01, 95% CI = 2.81-8.94, P<0.001; GG+AG vs. AA: OR = 1.75, 95% CI = 1.24-2.48, P = 0.001 and GG vs. AA+AG: OR = 3.84, 95% CI = 2.32-6.37, P<0.001). In a stratified analysis by ethnicity, an increased GD risk was found among Asians and Caucasians. However, for IL-10 rs1800872 A>C and rs3021097 C>T polymorphisms, the results showed that they were not associated with GD risk. In summary, these findings demonstrate that IL-10 rs1800896 A>G polymorphism may be a risk factor for GD.
Influence of Thermal Shrinkage on Protective Clothing Performance during Fire Exposure: Numerical Investigation

The thermal shrinkage of protective clothing during fire exposure plays a crucial rule in reducing the clothing protective performance. The transversal reduction in the fabric perimeter around the body due to the fabric thermal shrinkage causes a dynamic reduction in the air gap between the clothing and the body. This leads to a dynamic change in the heat transfer modes within the gap. Despite of its influential effect on the clothing performance, the thermal shrinkage of protective clothing during fire exposure has not been yet addressed in the literature. This can be attributed to the absence of a gap model that can capture the reciprocal change in heat transfer modes within the gap due to clothing shrinkage. This paper develops a finite volume model to investigate the influence of the fabric thermal shrinkage on protective clothing performance. A special attention was drawn to the model of the air gap between the clothing and skin as it responds directly to the clothing thermal shrinkage. The influence of a variation in the fabric shrinkage rate and the overall reduction in the fabric dimensions was investigated. The paper demonstrates that the clothing protective performance continuously decreases with the reduction in the fabric dimensions while the decay in the clothing protective performance is limited to small shrinkage rates of the fabric. Moreover, this decay in the clothing performance vanishes at high shrinkage rates of the fabric.
Accuracy and precision of phytonematode sampling plans

Sampling accuracy and precision are basic to nematode research and rational management decision. Taylor's Power Law is used to determine sample size. The published power law parameters are used here differently to define levels of reliability associated with a fixed, cost-determined, sample size. Such levels may provide the decision-maker with more informative data on sampling methods and their reliabilities. Instead of solving for unknown sample number, the rearranged equation to solve for sampling accuracy permitted the use of exact Student’s t-values. The t-values for confidence limits and standard normal variate were used. Both statistical techniques resulted in identical confidence band, in terms of being below, at, or above the economic threshold level, in the majority of cases. Reckoning with new trends in collecting and processing samples, a 99% confidence interval above action threshold of Tylenchulus semipenetrans population with mean = 2500 at a precision of 80% or more would be attainable.
Optimizing High-Performance Weighted Extreme Learning Machine with DAPM for Imbalance Credit Card Fraud Detection

Fraudulent activities associated with financial transactions are observed in the present scenario, especially with the use of credit cards, at a fast rate. As banking services are rising in digitalization and mobile banking is on the increase in structured written requests, credit card payments rates rise all year, with billions of transactions detected as unfair. For financial institutions to maintain the goodwill of their customers a fraud detection system requiring different detection strategies is therefore extremely important. Researchers and practitioners, using different algorithms, have proposed many methods for fraud detection to find the pattern of fraud. Data mining (DM) algorithms were influential to detect fraudulent transactions by combating fraudsters' attacks on the classic frameworks for preventing fraud. The paper aims to classify fraudulent transactions by Weighted Extreme Learning Machine (WELM) classifiers of 2 Artificial Neural networks (ANN) and 3 separate data sets of Credit Card Fraud (CCF). We use a high-performance Weighted Extreme Learning Machine (HPWELM). The efficiency of the classifiers is calculated based on accuracy, precision, recall & G-mean. The research work has been implemented in Python 3.6. The results are represented in form of tables & snapshots. Results demonstrate that accuracy of the HPWELM classifier has achieved a remarkable improvement in the training and testing phases of the algorithm.
Investigation Concerning the Excitation Loss of Synchronous Generators in a Stand-Alone Ship Power Plant

The protection systems of ship generators enable them to eliminate potential failures that pose a significant threat to the safety of the crew and the use of the ship. However, due to the fact that marine classification societies do not require the protection of generators against the loss of excitation, such protection is only used sporadically. This article presents an LOE (loss of excitation) analysis of ship generators that operate in parallel. This analysis is supported by simulations and experimental research. The test results show that the positions of the operating points of each generator are interrelated, and changes in the excitation of one (damaged) generator cause automatic changes in the excitation, as well as changes in electromagnetic and energy processes, in the second (efficient) generator. An LOE in one generator causes a dangerous increase in armature currents in both generators. The results of this study prove that the lack of LOE protection at lower levels of excitement in one of the generators causes (by activating the required protection) the efficient generator to be switched off first. The main conclusion of this article is that the introduction of the use of security measures against LOE should be obligatory and legally required.
Exploring effectuation in early-stage ventures using the C-K design theory

We have little understanding of why and how entrepreneurs sometimes continue to innovate within their ventures. The discovery perspective within entrepreneurship assumes a relatively linear and casual logic, once an initial business or product opportunity is identified. However, recent entrepreneurial decision making theory around effectuation emphasizes a logic of design as a means for determining future action. To date this logic has not been studied through innovation design theory lenses. The purpose of this article is therefore to explore how nascent technology ventures continue to behave entrepreneurially post conceptualization. The C-K (Concept-Knowledge) design theory is used to illustrate series of enacted decisions in four technology venture cases. The cases show how the ventures change direction based on deliberate and designed actions, resulting not only in more innovative opportunities but also allowing the ventures to become more robust and thus adaptable to change. The article adds insight to how effectuation is carried out as a logic of design.
Framing : More than language

The word framing is frequently used in politics. I define framing in this article as emphasizing aspects of a (perceived) reality in a message, with the goal to promote a certain valuation of a problem, or solution. In almost every (political) debate examples of this kind of framing can be identified. For instance in the debate around CO2 Capture and Storage (CCS), a technology used to capture and store the greenhouse gas CO2 in an underground geological formation. Opponents of CCS frequently use the frame: ‘CO2-dumping. ’
Hydrothermal synthesis of zeolite A using blast furnace slag

Alkali hydrothermal synthesis of zeolite A using blast furnace (BF) slag was investigated. The preliminary experiment was conducted in use of synthetic slag consisting of SiO2, Al2O3, and CaO powders, and it was made clear that the most optimum slag compositions to synthesize zeolite A were the molar ratio of Si to Al (Si/Al) of 1 and reduction of CaO content down to 15 mass%, and that hydrothermal treating conditions were the temperature range from 328 to 358 K, NaOH solution of 1 M (=mol/L) and the ratio of the volume of NaOH solution to total mass of slag (Vsol/Wslag) of 15 (mL/g). It was also found that synthetic slag with such a higher content of CaO as 40 % resulted in formation of tobermorite and hydrogarnet. In the experiment using BF slag, zeolite A could be successfully synthesized by optimizing both conditions of the compositions of raw material powders and hydrothermal treatment as noted above, where optimization of the compositions of raw material powders such as Si or Al content were performed by the suitable amount of addition of SiO2 powder or NaAlO2 powder as a source of Si and Al. The ball milling type reaction vessel containing numerous small SiC balls which was first adopted in this hydrothermal treating study was confirmed to be very effective for acceleration of synthetic reaction rate, shortening markedly the time period needed for fully synthesis of zeolite A. Temperature dependence of heat capacity of zeolite A powder synthesized in use of BF slag was measured after absorption of vapor at the ambient temperature, showing endothermic behavior with the peak at the temperature of around 473 K.