Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric driven based generation and transformation of MBD mid-tolerance model 1

A parametrically driven Model Based Definition (MBD) mid-tolerance model is proposed. First, the definition, vital significance and meaning of MBD mid-tolerance model are put forward. On the basis of comprehensively elaborating parameter design technology, one-to-one corresponding mathematical relation between feature constraint dimension and feature driving parameter is analyzed and mathematical relation between engineering dimension and feature driving parameter together with feature constraint dimension is further explained. Then mathematical expression between engineering dimension and parameter variable is established. Meanwhile, through analyzing the interrelation among dimensions by taking advantage of dimension chains, mathematical expressions between non-marking engineering dimension and driving parameter is established. Also the change law between marking engineering dimension and non-marking engineering dimension is specified and theoretical basis for generation and transformation of mid-tolerance model is provided. On this basis, a detailed process and specific algorithm of generation and transformation of mid-tolerance is proposed. Finally, a case used to verify the thought is feasible and relevant algorithm is proved to be accurate.
Fuzzy Logic Based Control of a Dual Rotor MIMO

The objective of this paper is to introduce to the simulation community. This tool is also called fuzzy set theory. This paper is contains brief discussion of the current trends in simulation Introduction to the fuzzy set is limited to the finite number of the element. In this paper attention will be devoted to the fuzzy logic. Fuzzy logic which lends itself to growth in the simulation of situation that arise in real life either because of variation of environment or because of the variation of the available data. Fuzzy controller is used to solve non-linearity of pitch AND yaw angles of Twin Rotor MIMO system (DRMS). FIS and Rule base were used to easier the controller and refine the response. Simulation results under MATLAB proved the improvement of response using fuzzy logic controller. Keywords-fuzzy logic, Rule base, Fuzzy interference system (FIS) Control system, MATLAB
Mushrooms – a human health booster , a skin health promoter and even a cancer therapeutic adjuvant

Science technology has confirmed in vitro and in vivo the ancient Asian beliefs on mushrooms being a rich source of bioactive compounds with therapeutic properties in diverse diseases, besides its value as an edible resource. So far, innumerous health valuable agents were isolated, which constitutes an incentive to keep researching for new bioactive metabolites and new applications. The most isolated and studied compounds have been polysaccharides, especially β-glucans. These compounds, extracted from the mushroom fruit body, cultured mycelium or culture filtrates, have been revealing beneficial applications such as enhancement of the immune system, allowing the prevention and recovering from various infections and diseases; antioxidant activity, helping to prevent the action of free radicals on cells; antitumor properties, influencing positively cancer progression and also showing activity as adjuvants in the recovery from the cancer drug therapies side effects. Accordingly, mushrooms can constitute a tasteful and healthy food source, a health promoting agent (nutriceuticals), and a source of extracts applicable in pharmacological and cosmeceutical formulations, improving health quality and nourishing and improving skin appearance. However, since there is a lack of regulatory affairs, quality control, and establishment of appropriate dosages, mushroom nutriceuticals should be used with precaution and further studies would be of most importance to fully understand the functional compounds of mushrooms and its mechanisms of action.
Intensified Sentiment Analysis of Customer Product Reviews Using Acoustic and Textual Features

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.
IDENTIFICATION OF UNKNOWN COEFFICIENT IN TIME FRACTIONAL PARABOLIC EQUATION WITH MIXED BOUNDARY CONDITIONS VIA SEMIGROUP

This article presents a semigroup approach for the mathematical analysis of the inverse coefficient problem of identifying the unknown coefficient k(x) in the linear time fractional parabolic equation Dαt u(x, t) = (k(x)ux)x, 0 < α ≤ 1, with mixed boundary conditions u(0, t) = ψ0(t), ux(1, t) = ψ1(t). Our aim is the investigation of the distinguishability of the input-output mapping Φ[·] : K → C[0, T ], via semigroup theory. This work shows that if the null space of the semigroup Tα,α(t) consists of only zero function, then the input-output mapping Φ[·] has distinguishability property. Also, the value k(0) of the unknown function k(x) is determined explicitly. In addition to these the boundary observation f(t) can be shown as an integral representation. This also implies that the mapping Φ[·] : K → C[0, T ] can be described in terms of the semigroup. AMS (MOS) Subject Classification. 35R11. 1. PRELIMINARIES The inverse problem of determining unknown coefficient in a linear time fractional parabolic equation by using over measured data have attracted considerable interest from engineers and scientist recently. The generalizations of ordinary and partial differential equations are called fractional differential equations and they are used for modeling various processes such as fluid mechanics, viscoelasticity and electromagnetic polymerheology, regular variation in thermodynamics, biophysics, blood flow phenomena, aerodynamics, electrodynamics of complex medium. This kind of problems are very useful in engineering, physics and applied mathematics. They help us to model complex phenomena. Various numerical methods have been developed to overcome the determination of the unknown coefficient(s) [19, 20, 21, 23]. Luchko extended the classical maximum principle and uniqueness of solution for nonlinear fractional differential equation [15, 16]. In this study, we focus our attention on the inverse problem of determining unknown coefficient k(x) in a one dimensional time fractional parabolic equation by Received April 2, 2015 1056-2176 $15.00 c ©Dynamic Publishers, Inc. 342 E. OZBILGE AND A. DEMIR semigroup approach. In our analysis we will make extensive use of semigroup and noisy free measured output data. We first obtain the unique solution of this problem, written in terms of semigroup with respect to the eigenfunctions of a corresponding Sturm-Liouville eigenvalue problem under certain conditions. As the next step, the noisy free measured output data is used to introduce the input-output mapping Φ[·] : K → C[0, T ]. Finally we investigate the distinguishability of the unknown coefficient via the above input-output mapping Φ[·]. Semigroup approach is an analytical approach for inverse problems of identifying unknown coefficients in parabolic problems. The inverse problem of unknown coefficients in a quasi-linear parabolic equations was studied by Demir and Ozbilge [5, 6, 10, 12, 13, 14]. Consider now the following initial boundary value problem:
Holistic Action Transform Harsh Goyal Supervisors :

Robots can be expensive and fragile. It can also take them a long time to exeute instructions in the physical world. These are some reasons why a simulation environment is often used as a proxy for the real world when having a robot learn how to perform a task well. However, this carries the risk of the robot learning a skill which overfits to the simulator. This overfitting leads to the learnt skill not performing well in the real world. That is why there is previous work which makes efforts to automatically modify the simulator to minimize differences between the simulator and the real environment. Intuitively, a simulator models an environment well if, for each skill under consideration, there is a correlation between the performances of the physical agent and the simulation agent on the given task. In this work, we introduce Holistic Action Transform (HAT), which lets us encode this intuition about what it means to be a good simulator into the measures of differences between reality and simulation that we minimize with simulator optimization. We then evaluate HAT in a simple “Linear Dynamical System” domain. We establish that minimizing these measures of differences in this domain can increase the correlation between performances of agents in the two environments, which enables the simulation agent to learn skills that transfer well to the real world. Finally, we make empirical comparisons between HAT and previous work. We find that HAT is able to match the best possible performance of the Grounded Action Transform (GAT) on this domain, and perform better than (GAT) for some training datasets.
International immigration effect on internal out-migration patterns in the German states between 1993 and 2016

Internal migration has frequently been subject of empirical research. This study attempts to find a relationship between international immigration and internal out-migration in all German states, covering the time period between 1993 and 2016. The underlying theories were established by Card et al. (2008), Schlömer (2012), Florida (2002) and Chiswick and Miller (2015). These were used to develop a modified version of the gravity model. The dataset was received from the Federal Statistical Office of Germany (Statistisches Bundesamt). The regressions were executed using a fixed effects model and a pooled OLS as a robustness check. The empirical findings suggest no evidence of a statistically significant effect of international immigration on internal out-migration patterns in the covered period. Control variables suggest policymakers to focus on other factors when the effects of immigration policies on internal out-migration are being considered.
Stain Standardization Capsule: A pre-processing module for histopathological image analysis

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing use of digital histopathological images, the deep learning methods are likely developed based on the data from multiple medical centers. This requirement makes it a challenge task to normalize the color variance of histopathological images from different medical centers. In this paper, we proposed a novel color standardization module named stain standardization capsule (SSC) based on the paradigm of capsules network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The SSC module is light and can be trained endto-end with the application-driven CNN model. The proposed method was validated on two public datasets and compared with the state-of-theart methods. The experimental results have demonstrated that the SSC module is effective in color normalization for histopathological images and achieves the best performance in the compared methods.
Job Matching in the UK : Determinants and Implications of Underskilling and Overskilling

One of the most oft cited reasons for the UK’s lack of competitiveness is skills shortages. Yet the issue is normally discussed in the framework of a pool of inadequately skilled, unemployed labour who cannot hope to fill the job requirements of firms. In this paper we consider job‐skills mismatches of those individuals fortunate enough to have jobs and question whether firms are failing to utilise the skills of their existing labour force. Further, we also consider whether firms are hiring workers not capable of fulfilling their job requirements without adequate training provision.
Peer Support for Professional Learning : Rewards and Challenges

This paper addresses the quality of professional learning of student teachers in their teaching practicum. It begins with an identification of the problem for this Action Learning Project. Peer support is considered a promising human resource to enhance professional learning and to tackle the problem of facing the traditional culture of learning to teach. For this project, strategies to promote peer support were developed and implemented in action learning cycles over the student teachers' practicum period. During this time, the researcher also engaged in a parallel process of action learning, by organising student teachers' course work on campus and supervising them during the practicum. Over the two-year period of the project, a procedural manual was developed and revised in two cycles of Action Learning to guide peer support in the teaching practicum. The manual has been used by different cohorts of student teachers in both the University of Hong Kong and the Hong Kong Institute of Education. This paper draws data from the University of Hong Kong for evaluation of peer support as a resource for enhancing the quality of professional learning. The discussion focuses on the rewards and the challenges for the process. The conclusion identifies implications from the insights of this project for higher education. The Problem: Facing the Traditional Culture of Learning to Teach The teaching practicum for student teachers is a critical period, during which the integration of theory and practice is expected to take place under the guidance of university supervisors and the school-site support of collaborating teachers. However, the effect of the conventional structure of the teaching practicum has been a concern amongst teacher educators (e.g. Stones, 1987; Calderhead, 1987; Alexander, Muir, & Chant, 1992). Guidance is mostly limited to occasional supervisory observation and post-lesson discussion. This limitation is of concern and raises the following challenging questions for both teachers and students: • How do student teachers synthesise learning experiences from course work and field experience? • What support do student teachers need in the process of professional learning? • How do student teachers reach a stage that the challenges in teaching can be met with a sense of self-efficacy? • How do student teachers strengthen themselves to move beyond survival and become educators? One must question whether traditional classroom supervisory visits, which focus on evaluative observation, can address these questions adequately. The process of learning to teach during the teaching practicum can be neglected when evaluation must rely on infrequent observations of student teachers. Teaching is more complicated than a proficient demonstration of desirable
The Dynamics of Software Composition Analysis

Developers today use significant amounts of open source code, surfacing the need for ways to automatically audit and upgrade library dependencies, and giving rise to the subfield of Software Composition Analysis (SCA). SCA products are concerned with three tasks: discovering dependencies, checking the reachability of vulnerable code for false positive elimination, and automated remediation. The latter two tasks rely on call graphs of application and library code to check whether vulnerability-specific sinks identified in libraries are used by applications. However, statically-constructed call graphs introduce both false positives and false negatives on real-world projects. In this paper, we develop a novel, modular means of combining call graphs derived from both static and dynamic analysis to improve the performance of false positive elimination. Our experiments indicate significant performance improvements.
Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Big data systems are being increasingly adopted by the enterprises exploiting big data applications to manage data-driven process, practices, and systems in an enterprise wide context. Specifically, big data systems and their underlying applications empower enterprises with analytical decision making (e.g., recommender/decision support systems) to optimize organizational productivity, competitiveness, and growth. Despite these benefits, big data applications face some challenges that include but not limited to security and privacy, authenticity, and reliability of critical data that may result in propagation of false information across systems. Data provenance as an approach and enabling mechanism (to identify the origin, manage the creation, and track the propagation of information etc.) can be a solution to above mentioned challenges for data management in an enterprise context. Data provenance solution(s) can help stakeholders and enterprises to assess the quality of data along with authenticity, reliability, and trust of information on the basis of identity, reproducibility and integrity of data. Considering the wide spread adoption of big data applications and the needs for data provenance, this paper focuses on (i) analyzing state-of-the-art for holistic presentation of provenance in big-data applications (ii) proposing a bio-inspired approach with underlying algorithm that exploits human thinking approach to support data provenance in Wireless Sensor Networks (WSNs). The proposed ‘Think-and-Share Optimization’ (TaSO) algorithms modularizes and automates data provenance in WSNs that are deployed and operated in enterprises. Evaluation of TaSO algorithm demonstrates its efficiency in terms of connectivity, closeness to the sink node, coverage, and execution time. The proposed research contextualizes bio-inspired computation to enable and optimize data provenance in WSNs. Future research aims to exploit machine learning techniques (with underlying algorithms) to automate data provenance for big data systems in networked environments.
Mixing in pictorial and multimodal metaphors

"Mixed metaphors" in language use two or more different source domains to predicate something about the same target domain in a short stretch of discourse. This often leads to unintendedly humorous results and is usually considered bad style. Given that metaphors may be expressed pictorially or multimodally as well as verbally, one may ask whether non-verbal modalities can also give rise to metaphors of the "mixed" kind. If so, would such instances be considered odd, humorous, or stylistically awkward? And what, if anything, would make such "mixed metaphors" different from metaphoric blends with three input spaces (one target and two sources)? The provisional conclusion is: we should, for the time being, not adopt "mixed pictorial/multimodal metaphor" as a technical term; but the discussion provides leads for further research from which both metaphor theory and multimodal discourse analysis will benefit.
Score ! ! Time ? ? ? ? ? ? ? ? ?

Take a brief pause from reality and imagine that you are the first-year coach of a professional basketball team. It is the first game of the season, and the score is tied with only seconds remaining. You call a timeout, consider your multitude of strategic options, and then decide to set up a play for the guy that everyone just calls C. The play begins, C gets the ball, and though double-covered by two hard-nosed defenders, gets off a long shot. Swish! The ball goes through the net, you win the game, and C is carried off the court on the shoulders of his teammates. You have now learned some valuable information that will help throughout the season: C is a clutch performer.
Generalized Tonnetze and Zeitnetze, and the topology of music concepts

The music-theoretic idea of a Tonnetz can be generalized at different levels: as a network of chords relating by maximal intersection, a simplicial complex in which vertices represent notes and simplices represent chords, and as a triangulation of a manifold or other geometrical space. The geometrical construct is of particular interest, in that allows us to represent inherently topological aspects to important musical concepts. Two kinds of music-theoretical geometry have been proposed that can house Tonnetze: geometrical duals of voice-leading spaces and Fourier phase spaces. Fourier phase spaces are particularly appropriate for Tonnetze in that their objects are pitch-class distributions (real-valued weightings of the 12 pitch classes) and proximity in these space relates to shared pitch-class content. They admit of a particularly general method of constructing a geometrical Tonnetz that allows for interval and chord duplications in a toroidal geometry. This article examines how these duplications can relate to important musical concepts such as key or pitch height, and details a method of removing such redundancies and the resulting changes to the homology of the space. The method also transfers to the rhythmic domain, defining Zeitnetze for cyclic rhythms. A number of possible Tonnetze are illustrated: on triads, seventh chords, ninth chords, scalar tetrachords, scales, etc., as well as Zeitnetze on common cyclic rhythms or timelines. Their different topologies – whether orientable, bounded, manifold, etc. – reveal some of the topological character of musical concepts.
Comparative analysis of poverty incidence and patterns in Ghana

This paper uses two sets of Living Standards Surveys to determine poverty incidence and patterns within the different ecological zones of Ghana. To ascertain changes in the standards of living of households, the headcount, poverty gap index, Foster, Greer and Thorbecke (FGT) class of indices and the Sen’s index were employed to measure the incidence and intensity of poverty and the effects of various household characteristics on the standard of living of the household. The results show that the incidence of extreme poverty increased between the two survey periods. The poor who were mostly found in the rural areas were involved basically in forestry and agriculture-related activities and across levels of the education system they had little or no education. Poverty incidence was also higher in the 2orthern Savanna area compared to the Forest area, Coastal and 2orthern Savanna areas.
Water in the Cloud: Understanding Water Chemistry via the Internet of Things

Water treatment is one of those essential elements of modern life that is taken for granted by the general population. We describe the ability to remotely understand and improve water chemistry using IoT devices attached to the cloud. The commercial benefits of this IoT connectivity are substantial, including: reduced chemical usage, improved chemical inventory management, safer operation (fewer and less critical alarms), reduced maintenance costs, and higher quality water.