Miracle-dz/UniFAR-FTUs huggingface dataset:
Managerial creativity of high and low investors of district Anantnag union territory of Jammu and Kashmir

The present study was intended to explore the level of managerial, creativity of high and low investors. The study was carried in context of descriptive research. The sample for the present study consists of 200 respondents. Whole sample was selected by using convenient sampling technique. The collected data was processed by using purposive sampling technique. Thus, from the above reported results, it was inferred that there seems significant difference between high and low investors on their level of managerial creativity. High investors were seen with high level of managerial creativity as compared to their counterparts.
Observer Based on Sliding Mode Control for Ship Dynamic Positioning

Abstract – The ship dynamic positioning is a new technology for positioning and orientation applied to the ship or floating platform, which can resist to the influence of offshore wind, wave, and flow by automatically keep ing the setting position. The sliding mode is the way for si mple and robust control for automatic systems. In most cases , the only available system variables are the input and output ; it is necessary from those information’s, to reconstruct he state of the model to perform the control. In this paper we propose an observer based on sliding mode control for the s hip dynamic positioning.
SCREENING OF IN VITRO ANTI OXIDANT ACTIVITY OF METHANOL EXTRACT OF STEM BARK OF OROXYLUM INDICUM (L.) KURZ

DEPARTMENT OF PHARMACY REGIONAL INSTITUTE OF PARAMEDICAL AND NURSING SCIENCE (RIPANS) ZEMABAWK, P.O. 796017, AIZWAL, MIZORAM, INDIA ABSTRACTS This plant has history of many health benefits local uses in Nagaland. Therefore, the main objectives of the present work are to explore the invitro antioxidant potentiality of methanol stem bark extract from the plant Oroxylum indicum indigenous to Nagaland. The stem bark was collected and air dried at room temperature and the extraction was done by successively Soxhlet apparatus. Total phenolic, total flavonoid were analyzed. Antioxidant capacity and free radical scavenging potentialities of the plant extract were done by performing several in vitro methods, viz; 1,1-diphnyl-2-picrylhydazyl (DPPH), reducing power assay and hydrogen peroxide scavenging activity. The results of phenolic and flavonoids were found to be highest in methanolic extract of the bark of Oroxylum indicum. The study shows that the plant has potential source of antioxidant and could be used for mitigating the detrimental effect of oxidative stress and reactive oxygen species-mediated disease and thus justifies its used in traditional medicine.
PLA plasticized with low molecular weight polyesters: structural, thermal and biodegradability features

Poly(lactic acid) (PLA) was plasticized with ester oligomers having different structure, molecular weight and carboxylic acid content (AN) as end-functionalities. In particular PLA oligomers and a low molecular weight polyester of adipic acid and 1,2-propane diol (an adipate-based derivative) were used and compared. Their plasticizing capability was tested and the final structural and thermal properties of PLA matrix were evaluated by correlating the different features to the chemical and physical characteristics of these additives. SEC, DSC, TGA, tensile tests, XRD, SEM results, even after annealing, were collected, the related data analysed and evaluated with reference to additive starting properties. All the oligoesters resulted able to generate flexible compounds, but PLA oligomers provided mixtures with reduced structural and thermal stability. Finally, the most performing blend was tested for biodegradability to definitely assess the material suitable for the final application (sustainable packaging).
Title: STATISTICAL ANALYSES AND RISK ASSESSMENT OF SOME POTENTIALLY TOXIC METALS (PTMS) IN CHILDREN’s TOYS

Chemical exposure to children, especially from toys is an engineering concern. The concentration and availability of some potentially toxic metals (PTM) in children’s toys were determined in order to assess the risk posed by these metals to children. Samples of 25 toys imported from China to Nigeria were purchased. Ternary acid digestion followed by Atomic Absorption Spectrophotometry was used to determine the concentration of the PTM in the sample. Simulation of the saline and stomach acid extraction conditions were carried out to determine the concentrations of the PTM that could leach out from the toys during mouthing behaviour of children (available PTM) which involves chewing, sucking and swallowing. Total concentration of PTM in the toy ranged from 3.55 40.7, 3.21-38.2, 9.78-159, 3.55-11.2, and 36.1-106 mg/kg for Cd, Cr, Cu, and Pb respectively. Availability studies showed concentration range from 2.605. 60 mg/kg for Pb, 0.53-2.03 mg/kg for Cd and 0.15-2.88 mg/kg for Ni after saline extraction and the concentration after stomach acid extractions range from 3.337. 10 mg/kg, 1.15-3.15 mg/kg and 1.33-1.81 mg/kg for Pb, Cd and Ni respectively. Statistical analysis showed a positive correlation between the total concentration of PTM and toys made with PVC materials. Risk assessment study showed that Cd posed the highest risk, with its Hazard index (HI) as high as 4.50 for saline extraction. The study revealed that more precaution needs to be taken in the manufacture of children’s toys.
FRAMEWORK FOR DEVELOPING INTELLECTUAL SKILLS USING COLLABORATIVE LEARNING TOOLS: THE EXPERTS’ CONSENSUS

Background and Purpose: Students in higher education need intellectual skills for critical thinking, problem-solving and creating new knowledge. Social interactions during collaborative learning (CL) encourage these cognitive processes. However, instructors lack the skill to develop intellectual skills using appropriate pedagogy and CL tools. Hence, this study seeks to develop a framework for developing intellectual skills. Methodology: The Fuzzy Delphi Method (FDM) was employed to identify and determine consensus on elements to teach intellectual skills among 16 panel of experts in educational technology and continuous professional development. In the first round, semi-structured interviews were conducted among four experts and the themes emerging from the interviews were used to develop the FDM questionnaire. The questionnaire was used in the second round of the FDM to determine consensus among the experts based on calculations of the defuzzification value. Findings: The consensus among the experts is that resources for teaching intellectual skills are videos, interactive slides, animation/graphics and quizzes while the corresponding assessments are studentgenerated contents (i.e videos, infographic posters, interactive slides and designed products). Contribution: The framework with the instructional strategies, resources and assessment provides a guideline for instructors to plan instruction for developing intellectual skills and generating new knowledge. Journal of Nusantara Studies 2021, Vol 6(1) 284-308 ISSN 0127-9386 (Online) http://dx.doi. org/10.24200/jonus.vol6iss1pp284-308 285
Improving Fire Resistance of Mahogany (Swietenia macrophylla) Wood Impregnated with Mixture of Borax and Boric Acid

The study was aimed to investigate the effects of mixture of borax and boric acid concentration and different level of pressure on absorption, retention quantity, and fire resistance properties of mahogany (Swietenia macrophylla). A total of 50 samples of 9 x 19 x 1016 mm (ASTM E 69-02) were taken from mahoganylumber and then air-dried. A total of 45 samples were impregnated with aqueous solutions of borax and boric acid in three different concentrations (5, 7, and 9 %) and at three different pressure levels (5, 7.5, and 10 atm) for 2 hours pressure time, while5 samples were liftedas control. Dried impregnated samples and untreated samples were feeding in flame with lid combustion method refers to ASTM E 69-02. Interestingly, the results showed that borax and boric acid effectively improved the fire resistanceproperties(mass losses, ignition temperature, maximum temperature, and smoke and smolder production)of mahogany wood.Mixtures of borax and boric acid at a concentration level of 9% and pressure of 10 atm arerecommended to obtain the best results compare to the other interactions on combustion properties.
Opleiding Informatica Assessing the fitness of web-applications within the context of mobile phones , on performing spatially distributed , co-located , collaborative , audio-related activities

Mobile devices have advanced capabilities for audio-related activities, such as to record and to play music. Many software applications successfully integrate audio into their software, and a multitude of applications exist in which audio plays a central role, for example audio-creation or audio-listening software. However, these applications focus on individual audio performance, while when considering music, one of the most prevalent uses would be listening and playing music together. Even though each one of us carries along their mobile phone around, and is using it for listening, there isn’t a scalable solution available for any such audio activities to be done together. In other words, there are no audio applications available to successfully draw either beginners or professionals into the digital landscape of interactive, co-located audio-related activities with the aid of their mobile phone. In this thesis we research whether a typical modern mobile phone could perform sufficiently enough, to allow it’s user to participate in a collaborative audio activity, in real-time. We research available technological solutions, their use-cases, and afterwards seek how they could or cannot be applied in the context of a web-application. We target a web-application, since this we think would provide the most available software solution. We reason about the inherent latencies that could influence an audio-application in it’s performance, and we develop a solution with the aid of a signal detection approach, which consists of cross-correlating recorded audio material against target audio signals, to afterwards resolve with these measure results relevant, device-related latencies. We test our approach in a proof of concept and show that a web-application is able to successfully calibrate. However, it fails to meet accuracy requirements to achieve acceptable synchronized audio performance, due to the inherently available inaccurate of web-application audio scheduling. We define a benchmarking approach to assess an audio activity for it’s fitness for implementation within a web-application. We conclude that mobile devices are capable to participate in audio activities in real time. Web-applications however, could offer a more complete solution: Both on improving the development platform to simplify and improve the development process, as well as the performing qualities.
A Novel Provably Secure Key-Agreement Using Secret Subgroup Generator

In this paper, a new key-agreement scheme is proposed and analyzed. In addition to being provably secure in the shared secret key indistinguishability model under Decisional Diffie-Hellman assumption for subgroup of matrices over GF(2) with prime order, which considered as basic security requirement, the scheme has an interesting feature; it uses exponentiations over cyclic group using hidden secret subgroup generator as a platform for the key exchange, whereby - unlike many other exponentiation based key exchange schemes - it transcends the reliance on intractability of Discrete Logarithm Problem in its security.
SynShine: Improved Fixing of Syntax Errors

Novice programmers struggle with the complex syntax of modern programming languages like Java, and make lot of syntax errors. The diagnostic syntax error messages from compilers and IDEs are sometimes useful, but often the messages are cryptic and puzzling. Novices could be helped, and instructors’ time saved, by automated repair suggestions when dealing with syntax errors. Large samples of novice errors and fixes are now available, offering the possibility of data-driven machine-learning approaches to help novices fix syntax errors. Current machine-learning approaches do a reasonable job fixing syntax errors in shorter programs, but don't work as well even for moderately longer programs. We introduce SynShine, a machine-learning based tool that substantially improves on the state-of-the-art, by learning to use compiler diagnostics, employing a very large neural model that leverages unsupervised pre-training, and relying on multi-label classification rather than autoregressive synthesis to generate the (repaired) output. We describe SynShine's architecture in detail, and provide a detailed evaluation. We have built SynShine into a free, open-source version of Visual Studio Code (VSCode); we make all our source code and models freely available.
Hierarchical Modeling for Out-of-Scope Domain and Intent Classification

User queries for a real-world dialog system may sometimes fall outside the scope of the system’s capabilities, but appropriate system responses will enable smooth processing throughout the human-computer interaction. This paper is concerned with the user’s intent, and focuses on out-of-scope intent classiﬁcation in dialog systems. Although user intents are highly correlated with the application domain, few studies have exploited such correlations for intent classiﬁcation. Rather than developing a two-stage approach that ﬁrst classiﬁes the domain and then the intent, we propose a hierarchical multi-task learning approach based on a joint model to classify domain and intent simultaneously. Novelties in the proposed approach include: (1) sharing supervised out-of-scope signals in joint modeling of domain and intent classiﬁcation to replace a two-stage pipeline; and (2) introducing a hierarchical model that learns the intent and domain representations in the higher and lower layers respectively. Experiments show that the model outperforms existing methods in terms of accuracy, out-of-scope recall and F1. Additionally, threshold-based post-processing further improves performance by balancing precision and recall in intent classiﬁcation.
A Refined Inertial DCA for DC Programming

We consider the diﬀerence-of-convex (DC) programming problems whose objective function is level-bounded. The classical DC algorithm (DCA) is well-known for solving this kind of problems, which returns a critical point. Recently, de Oliveira and Tcheo incorporated the inertial-force procedure into DCA (InDCA) for potential acceleration and preventing the algorithm from converging to a critical point which is not d (directional)-stationary. In this paper, based on InDCA, we propose two reﬁned inertial DCA (RInDCA) with enlarged inertial step-sizes for better acceleration. We demonstrate the subse-quential convergence of our reﬁned versions to a critical point. In addition, by assuming the Kurdyka-(cid:32)Lojasiewicz (KL) property of the objective function, we establish the sequential convergence of RInDCA. Numerical simulations on image restoration problem show the beneﬁt of enlarged step-size.
Analytical investigation of pre-inflationary effects in the primordial power spectrum: from General Relativity to hybrid Loop Quantum Cosmology

This work is an analytical study of the main differences between classical inflationary effects of a fast-roll regime and imprints of hybrid Loop Quantum Cosmology (LQC) on the primordial power spectrum. Effective LQC solutions of phenomenological interest typically contain a classical period of kinetic dominance prior to slow-roll inflation, with consequences on the power spectrum that here we try and tell apart from those of phenomena occuring in the vicinity of a bounce in hybrid LQC. For this comparison, we use the same criterion for the choice of a vacuum state of the primordial perturbations, both in the context of relativistic cosmology and on the LQC cosmological background. As a first approximation to the problem, our study ignores the influence of a quasi-de Sitter evolution (rather than de Sitter) in the slow-roll inflationary regime, and of a typically short transition between the kinetically dominated era and the slow-roll period. Our results show that, while the two studied types of effects can lead to a drastic power suppression in the infrared sector of the spectrum, the mode scale at which this happens is much larger for LQC than for classical relativistic cosmology, the corresponding scales being related to the spacetime curvature at the bounce and at the onset of inflation, respectively.
Calibration of wind speed ensemble forecasts for power generation

In the last decades, wind power became the second largest energy source in the EU covering 16% of its electricity demand. However, due to its volatility, accurate short range wind power predictions are required for successful integration of wind energy into the electrical grid. Accurate predictions of wind power require accurate hub height wind speed forecasts, where the state-of-the-art method is the probabilistic approach based on ensemble forecasts obtained from multiple runs of numerical weather prediction models. Nonetheless, ensemble forecasts are often uncalibrated and might also be biased, thus require some form of post-processing to improve their predictive performance. We propose a novel flexible machine learning approach for calibrating wind speed ensemble forecasts, which results in a truncated normal predictive distribution. In a case study based on 100m wind speed forecasts produced by the operational ensemble prediction system of the Hungarian Meteorological Service, the forecast skill of this method is compared with the predictive performance of three different ensemble model output statistics approaches and the raw ensemble forecasts. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts, and from the four competing methods, the novel machine learning based approach results in the best overall performance.
String diagram rewrite theory II: Rewriting with symmetric monoidal structure

Abstract Symmetric monoidal theories (SMTs) generalise algebraic theories in a way that make them suitable to express resource-sensitive systems, in which variables cannot be copied or discarded at will. In SMTs, traditional tree-like terms are replaced by string diagrams, topological entities that can be intuitively thought of as diagrams of wires and boxes. Recently, string diagrams have become increasingly popular as a graphical syntax to reason about computational models across diverse fields, including programming language semantics, circuit theory, quantum mechanics, linguistics, and control theory. In applications, it is often convenient to implement the equations appearing in SMTs as rewriting rules. This poses the challenge of extending the traditional theory of term rewriting, which has been developed for algebraic theories, to string diagrams. In this paper, we develop a mathematical theory of string diagram rewriting for SMTs. Our approach exploits the correspondence between string diagram rewriting and double pushout (DPO) rewriting of certain graphs, introduced in the first paper of this series. Such a correspondence is only sound when the SMT includes a Frobenius algebra structure. In the present work, we show how an analogous correspondence may be established for arbitrary SMTs, once an appropriate notion of DPO rewriting (which we call convex) is identified. As proof of concept, we use our approach to show termination of two SMTs of interest: Frobenius semi-algebras and bialgebras.
Model-order reduction for hyperbolic relaxation systems

Abstract We propose a novel framework for model-order reduction of hyperbolic differential equations. The approach combines a relaxation formulation of the hyperbolic equations with a discretization using shifted base functions. Model-order reduction techniques are then applied to the resulting system of coupled ordinary differential equations. On computational examples including in particular the case of shock waves we show the validity of the approach and the performance of the reduced system.
Unsupervised Layered Image Decomposition into Object Prototypes

We present an unsupervised learning framework for decomposing images into layers of automatically discovered object models. Contrary to recent approaches that model image layers with autoencoder networks, we represent them as explicit transformations of a small set of prototypical images. Our model has three main components: (i) a set of object prototypes in the form of learnable images with a transparency channel, which we refer to as sprites; (ii) differentiable parametric functions predicting occlusions and transformation parameters necessary to instantiate the sprites in a given image; (iii) a layered image formation model with occlusion for compositing these instances into complete images including background. By jointly learning the sprites and occlusion/transformation predictors to reconstruct images, our approach not only yields accurate layered image decompositions, but also identifies object categories and instance parameters. We first validate our approach by providing results on par with the state of the art on standard multiobject synthetic benchmarks (Tetrominoes, Multi-dSprites, CLEVR6). We then demonstrate the applicability of our model to real images in tasks that include clustering (SVHN, GTSRB), cosegmentation (Weizmann Horse) and object discovery from unfiltered social network images. To the best of our knowledge, our approach is the first layered image decomposition algorithm that learns an explicit and shared concept of object type, and is robust enough to be applied to real images.
Continuous normalizing flows on manifolds

Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately, current approaches are only available for the most basic geometries and fall short when the underlying space has a nontrivial topology, limiting their applicability for most realworld data. Using fundamental ideas from differential geometry and geometric control theory, we describe how the recently introduced Neural ODEs and continuous normalizing flows can be extended to arbitrary smooth manifolds. We propose a general methodology for parameterizing vector fields on these spaces and demonstrate how gradient-based learning can be performed. Additionally, we provide a scalable unbiased estimator for the divergence in this generalized setting. Experiments on a diverse selection of spaces empirically showcase the defined framework’s ability to obtain reparameterizable samples from complex distributions.
BERT Meets Relational DB: Contextual Representations of Relational Databases

In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be used in a variety of settings like auto-completion of tables, fully-neural query processing of relational joins queries, seamlessly handling missing values, and more. Current work is restricted to working with just single table, or using pretrained embeddings over an external corpus making them unsuitable for use in real-world databases. In this work, we look into ways of using these attention-based model to learn embeddings for entities in the relational database. We are inspired by BERT style pretraining methods and are interested in observing how they can be extended for representation learning on structured databases. We evaluate our approach of the autocompletion of relational databases and achieve improvement over standard baselines.