Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems

Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.
Aggressive Maneuvering of a Quadrotor with a Cable-Suspended Payload

This work examines the system of a quadrotor carrying a cable-suspended payload. As a motivating application, we demonstrate the “load-transport maneuver”, where the quadrotor picks up an object with a cable mechanism, transports it through an environment, and drops it into a target location, mimicking the process of Christmas tree harvesting by humanpiloted helicopters. We derive the coordinate-free dynamics for the system, which we model as a hybrid dynamical system, explore its differential flatness properties, and develop nonlinear controllers for its subsystems. We then present a trajectory generation technique that accounts for the switches of the hybrid system. Finally, we provide experimental validation on a quadrotor platform.
Policy Framework for Education Development in Nepal

This exploration of challenges and barriers to inclusion in Nepal elaborates a conceptual framework for education development in a diverse society. As Nepal is a highly diverse, caste-based, multi-ethnic, and multi-linguistic society with very low development indicators, the article focuses on barriers to education and related issues across different socioeconomic groups. A systematic review of the relevant literature forms the basis for the design of a practical approach to education development for this diverse society in light of education policy trends in Nepal since 1950. The five proposed steps for education policy formulation and implementation include an in-depth analysis of the existing situation and outcome assessments. The proposed approach will enable local governance institutions to design and implement pragmatic provisions for education development at local level in the context of a new constitution that mandates local government management of school education.
Development of W based materials for fusion power reactors

W-2Y, W-1Y2O3 and W-2Y2O3 materials have been fabricated using two different powder metallurgy methods. W-2Y and W-1Y2O3 have been fabricated using mechanical alloying and hot isostatic pressing (HIPping) while W-2Y2O3 has been produced in collaboration with Plansee Company by sintering and hot forging method. Transmission electron microscopy observations reveal that W-2Y, W-1Y2O3 materials have nanometer size grains while W-2Y2O3 material has micrometer size grains. In both cases, the particles are distributed inhomogeneously inside the grains. Berkovich micro-hardness and 3 point bending tests confirm that the nano-grains W-2Y, and W-1Y2O3 materials are stiff and show no ductility up to 1000 °C. In case of W2Y2O3, the Berkovich micro-hardness value is found to be 4.9 GPa and the three point bending tests confirm that W-2Y2O3 shows ductility behaviour from 400°C and above. Relation between the fabrication techniques, the microstructure and mechanical properties in W-2Y, W-1Y2O3 and W-2Y2O3 materials is reported.
LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries

Algorithms for calculating the string edit distance are used in e.g. information retrieval and document analysis systems or for evaluation of text recognizers. Text recognition based on CTC-trained LSTM networks includes a decoding step to produce a string, possibly using a language model, and evaluation using the string edit distance. The decoded string can further be used as a query for database search, e.g. in document retrieval. We propose to closely integrate dictionary search with text recognition to train both combined in a continuous fashion. This work shows that LSTM networks are capable of calculating the string edit distance while allowing for an exchangeable dictionary to separate learned algorithm from data. This could be a step towards integrating text recognition and dictionary search in one deep network.
Acoustic Scene Classification using Combination of Spatio-Temportal and MFCC Features

Acoustic scene classification (ASC) is an active research area because it is also one of the challenging problems. Several studies have been carried out in this area, but there are still challenging tasks. The main challenging task is to identify features which are relevant to scene classification due to heterogeneity in nature of scene audio and shared acoustic between different scene types. The purpose of this paper is to propose acoustic scene classification task of TUT Acoustic scenes 2016 dataset. The proposed system consists of two main parts: feature extraction and classification. For decomposing the audio signal, Discrete Wavelet Transform (DWT) 1D is used to be more effective because the decomposition was done very effectively. The audio signal is split into frames and some kind of mathematical transform is applied in order to extract the feature vector from frames. The extracted features are classified by using convolutional neural network (CNN)
Liu Interconnection Network Models Compute Node Models MPI Model PPT Parallel Architecture Workload Archives Workload Models Job Submission Parallel Application Repository Job Waiting Queue ( s ) Job Scheduling Job Execution Scheduling Algorithms Task Mapping Running Jobs Finished Jobs

The paper presents a simulator designed specifically for evaluating job scheduling algorithms on large-scale HPC systems. The simulator was developed based on the Performance Prediction Toolkit (PPT), which is a parallel discrete-event simulator written in Python for rapid assessment and performance prediction of large-scale scientific applications on supercomputers. The proposed job scheduler simulator incorporates PPT’s application models, and when coupled with the sufficiently detailed architecture models, can represent more realistic job runtime behaviors. Consequently, the simulator can evaluate different job scheduling and task mapping algorithms on the specific target HPC platforms more accurately.
A Wearable Hybrid Haptic Feedback Stimulation Device for Upper Limb Prostheses

190205-3737-IJMME-IJENS © October 2019 IJENS I J E N S Abstract— A sense of touch restored by upper limb prostheses can greatly help patients with upper limb amputation to perform activities of daily living. In this work, a novel hybrid haptic feedback stimulation system for upper limb prostheses has been designed, developed and evaluated. The wearable device consists of one servomotor and two vibration motors, which function as a pressure feedback display and a vibration feedback display, respectively. The pressure display conveys the sensing of the contact pressure and its strength level, while the vibration display gives an indication about the continuity of the contact pressure. An evaluation on sensation and response has been conducted with healthy subjects. The results showed that the subjects can identify the touch, the start of touch, and the end of touch accurately. In particular, 96 %, 95 % and 88 % of the healthy subjects are able to distinguish the grasp, the pressure level, and the slipping objects, respectively. In addition, the usability tests demonstrated that the system is easy to setup and comfortable to use. The device is able to convey real-time haptic feedback to the amputees without the risk of surgery, brain confusion and intensive pre-training. This solution contributes to making the haptic feedback stimulation a common functionality in upper limb prostheses in the near future.
An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.
Towards Linking the Digital and Real World with OpenThingMap

Currently digital inventories of outdoor features evolve such as OpenStreetMap. A digital inventory of real-world things is still missing, but is required to enable full search and interaction in the digital world. This article presents an approach to link any real world object to a georeferenced digital representation, thus suggests a concept for location within the areas of Web of Things, Ubiquitous Computing and Ambient Technology. The approach is twofold based on a) OpenFloorMap as an inventory for buildings and b) OpenThingMap as an inventory for things. The inventories are based on a lightweight data model and are populated through the knowledge of the crowd using advanced mobile devices.
Towards better interactions with a 3D deformable model with your hands

In this work, we present a low-cost and modular prototype ded icated to study 3D interactions in order to easily deform a 3D m odels using hands. This is done using a simple colored glove cou pled with a 3D motion recovery engine and 3D visualization. We present different interaction schemes that we want to stu dy using real interactions rather than a wizard of oz. This work is a first step towards different questions on 3D interactions we want to experimentaly address.
Gender differences in L 2 Motivation : A reassessment

Along with aptitude, motivation is the primary determiner of learning outcomes in second language (L2) learning. Widely regarded as an activity especially suited to girls, empirical studies from different sociocultural contexts have, with few exceptions, revealed systematic gender differences in L2 motivation. In particular, gender differences are most apparent in relation to establishing an affinity with other L2 speakers, the ability and willingness to identify with the values associated with L2 ethnolinguistic communities and a lack of ethnocentricity. Together these attributes have been categorized as integrativeness (Gardner, 1985). Explanations for observed gender differences vary and, other than a general recognition of the impact of social norms and gender role expectations, no overarching theoretical explanation has yet been attempted. Given the recent paradigm shift in the conceptualization of L2 motivation from a social psychological approach based on identifications with other groups of speakers, to one based on the learner’s internal identification of a future language speaking ‘self’, a timely opportunity is presented to review previous findings. Following an initial discussion of the paradigm shift in L2 motivation theory and the role of gender in conceptions of the self, the literature on the gender gap in integrativeness is reviewed through the lens of self-related theories. A tentative explanation for observed differences that synthesizes the results of previous research and is theoretically consistent with a self approach is proposed. Drawing on the work of, amongst others, Jordan, Kaplan, Miller, Stiver and Surrey (1991), Markus and Kitayama (1991) and Cross and Madson (1997) it is suggested that gender differences can be understood in relation to processes involving the construction and construal of selves, where the selves of males are characterized by independence whilst those of females emphasize interdependence.
Exploiting Sparsity in Supervised Learning

We explore various aspects of sparsity in the context of supervised learning. In particular, we review sparse coding techniques used to exploit sparsity in the input space for a supervised classification task. We empirically evaluate the importance of enforcing sparsity in the weight space through regularization. We use a compressed sensing framework which exploits sparsity in the output space for a multilabel classification problem. We perform experiments to quantify the effect of different sources of sparsity on the classification accuracy and performance. We also outline some promising directions for future work.
Opinion propagation on social networks: a mathematical standpoint

These lecture notes address mathematical issues related to the modeling of opinion propagation on networks of the social type. Starting from the behavior of the simplest discrete linear model, we develop various standpoints and describe some extensions: stochastic interpretation, monitoring of a network, time continuous evolution problem, charismatic networks, links with discretized Partial Differential Equations, nonlinear models, inertial version and stability issues. These developments rely on basic mathematical tools, which makes them accessible at an undergraduate level. In a last section, we propose a new model of opinion propagation, where the opinion of an agent is described by a Gaussian density, and the (discrete) evolution equation is based on barycenters with respect to the Fisher metric.
COLD ATOMS AND BOSE – EINSTEIN CONDENSATE Quantum Correlations Among Superradiant Bose – Einstein Condensate Atoms 1

Quantum correlations among atoms in superradiant Bose–Einstein condensates are discussed. It is shown that atoms in the superradiant atomic condensate can exhibit continuous variable quantum entan glement analogous to Einstein–Podolsky–Rosen (EPR) type quantum correlations. Comparison to quan tum entanglement in the Dicke model in thermal equilibrium is provided. DOI: 10.1134/S0030400X10030185 s, g ~
The Swap Curve

A swap curve is a representation of the relationship of interest rates in the swaps market. It is constructed via a bootstrap procedure with benchmark deposit rates, Eurodollar futures prices, and swap rates as inputs. Described in the paper are the method for constructing the swap curve, and the numerous adjustments needed in the process. INTRODUCTION The interest rate swaps market has grown rapidly over the past decade due to the growing popularity and constant innovation of interest rate derivatives. Just as the Treasury yield curve is used to value the Treasury securities, the swaps market employs its own valuation vehicle, the swap curve. Not only is it used for swap valuation, it is also used for pricing swap-related products, including forward rate agreements, Eurodollar futures contracts, caps, swaptions, and their variants. There are noticeable advantages to the swap curve over other curves. First, swaps are actively traded over a wide maturity range, with benchmark maturities of 1, 2, 3, 5, 7, 10, 15, 20, and 30 years in the U.S. Such a wide span assures that a swap curve constructed with them covers a large maturity spectrum of interest rates. Second, new swaps are being created constantly, which ensures that benchmark swap maturities do not vary over time. In contract, the benchmark Treasury maturities decrease as time lapses and are updated only as new Treasuries are issued, a roll effect not desired in curve building. Third, benchmark swaps are zero-cost contracts, and their supply is nearly infinite, not limited by the amount of issuance. Because of this, new swaps do not carry any extra supply-demand premium, compared with the frequent richness of recently issued on-the-run Treasuries over older issues. The levels of swap rates are, therefore, not distorted due to financing issues such as the short squeeze present in the Treasuries. A swap curve is a representation of the relationship of rates in the swap markets and their maturity, and is used for the valuations of swap-related instruments. It is usually constructed with so-called benchmark swaps and certain related contracts. An interest rate swap is a contract to exchange fixed and floating coupon payments between two counterparties for a specified period. Both coupon payments are based on a notional amount, which is analogous to the principal amount of a bond and can be thought of as being exchanged at the swap maturity. These two streams of cash flows are referred to as the fixed leg and the floating leg of the swap, respectively. The fixed-leg payments resemble the cash flows of a regular bond. However, unlike a bond
EyeDROID: Gaze tracking component for Android SPCL-Autumn 2014

Nowadays system’s user experience can be considerably improved by tracking the gaze of the user since an explicit input can be transformed into implicit or extended, which encourages the user focus to be on the desired task rather than the interaction with the system. Gaze tracking systems are usually implemented on stationary processing computers because heavy processing is needed. With the emergence of mobile and wearable devices a new possibility for gaze tracking has appeared alongside. However, implementing a gaze tracking system on such devices implies new challenges not currently present on stationary ones, such as mobility and limited available resources. This paper presents EyeDroid, a low cost mobile video-based gaze tracking system designed to be used with a USB head mounted camera. Unlike other gaze tracking systems, EyeDroid performs all its processing workload in a mobile device and sends the resulting coordinates of the incoming video streaming to a network client. For this reason, a stationary processing server is not needed, which encourages mobility when used along wearable and mobile devices.
Classification with Margin Constraints: A Unification with Applications to Optimization

This paper introduces Classification with Margin Constraints (CMC), a simple generalization of cost-sensitive classification that unifies several learning settings. In particular, we show that a CMC classifier can be used, out of the box, to solve regression, quantile estimation, and several anomaly detection formulations. On the one hand, our reductions to CMC are at the loss level: the optimization problem to solve under the equivalent CMC setting is exactly the same as the optimization problem under the original (e.g. regression) setting. On the other hand, due to the close relationship between CMC and standard binary classification, the ideas proposed for efficient optimization in binary classification naturally extend to CMC. As such, any improvement in CMC optimization immediately transfers to the domains reduced to CMC, without the need for new derivations or programs. To our knowledge, this unified view has been overlooked by the existing practice in the literature, where an optimization technique (such as SMO or PEGASOS) is first developed for binary classification and then extended to other problem domains on a case-by-case basis. We demonstrate the flexibility of CMC by reducing two recent anomaly detection and quantile learning methods to CMC.
A Cognitive Linguistic Study of Metaphors in Selected Advertisements by some Financial Institutions in Ghana

The paper examines selected advertisements by some financial institutions in Ghanaian magazines within the frameworks of the Conceptual Metaphor Theory (CMT), put forward by Lakoff and Johnson (1980, the Pictorial Metaphor Theory by Forceville (1996 & 2007) and the Social Semiotics Theory put forward by Kress and van Leeuwen (2006). The purpose is to identify the underlying conceptual metaphors and also to explore both the verbal and visual mechanisms for the realisation of metaphors. Metaphors are often said to be grounded in culture and can hence serve as a good resource for the investigation of cultural beliefs expressed in language (Lakoff and Johnson, 1980 & 1999; Lakoff, 1987 & 1993; Kövecses, 2002 & 2005 and Özçalişkan, 2003). Thus, the paper also addresses how the conceptual metaphors identified inform us about the cultural beliefs and values of Ghanaians in general. The conceptual metaphors SERVICES OR PRODUCTS ARE FOOD, A PURPOSEFUL LIFE IS A JOURNEY and MONEY IS LIQUID were found to be underlying the selected advertisements by the financial institutions in Ghana. The approach and findings substantiate the claim that metaphors can occur verbally and multimodally (Lakoff and Johnson, 1980; Forceville, 2007; Kress and van Leeuwen 2006).
Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems With Multi-Access Schemes

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [ ${O}(1/{V}), {O}({V})$ ] and introduces a control parameter ${V}$ to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.