Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method of Scheduling Tasks in Cloud Computing

Task scheduling and energy efficiency seem to be the necessary design requirements for current computing systems in recent years. It extends from single servers to data centers and clouds, as they consume large amounts of electrical power. For this reason, an effective energy management for cloud data centers is essential. At present, many researchers have focused and implemented biologically-based calculations as a desirable paradigm for addressing heterogeneity and the growth of energy crisis with skill and no added complications. Similarly, for our work, we selected biological behavior of Korean insects and chosen FFO-based migration method. The benchmark for choosing it is the rapid convergence and global optimization. In addition, the notion of limiting the overall increase in power increases with respect to new VM migration and never before used for the VM migration method. In the energy consumption scenario by VM migration, a FFO-based linear model is formulated that executes an FFO algorithm that is able to solve the power consumption problem with the firefly attraction feature. In other words, this paper proposes a virtual energy virtualization migration technique that emits live VMs from an active node to another active node. The proposed technique uses the biography-inspired worn-out optimization technique to find the best node for over-migrating VMs to achieve energy efficiency in cloud data centers. This optimizes energy efficiency through the optimal migration of VMs, thereby improving the level of resource utilization.
Analogy-based model for software project effort estimation

Accurate effort estimation of software development plays an important role to predict how much effort should be prepared during the works of a software project so that it can be completed on time and budget. Some sectors, e.g. banking sectors, were renowned fields of software projects, not only due to its huge size of project, but also extremely expensive and takes a long time to completion. Project estimation is essential for software development project able to run on time and budget with maximum quality. This study aims to investigate the accuracy of software project effort estimation with the Analogy method using three parameters: Euclidean, Manhattan and Minkowski distance. Analogy based estimation consists several stage included similarity measure, analogy adaptation, estimation calculation and model evaluation. The results showed that the best combination of Analogy methods was using Manhattan distance with an accuracy of 50% MMRE, 28% MdMRE and Pred(25) 48%. Thus, we can concluded that this model can be used to predict accurately.
An Algorithmic Approach to Develop Auto Translation System - Bengali to English

In today's globalized world, keeping aware and staying ahead on all sorts of issues from health, environment and energy to political affairs, security and the financial crisis has become a mammoth task. Breaking down language barriers means first-hand access to relevant information that helps to keep the citizen, the policy maker, governmental authorities, the private sector, industry, etc. better informed, especially on fast changing and impacting issues. In the last decade, the working method in Computational Linguistics has evolved to achieve an ultimate goal gradually. Fifteen years back, most of the researches focused on selected example of sentences. Now a day, the information access and utilization of large text is at a common stage. The final output of this research would be like below: Definition of the Bengali’s word/information need; Selection of the Bengali’s natural information sources to be used; Translation of the Bengali user’s query expressed in natural language into the targeting language(English/German/French) of the information source, if necessary; Translation of the Bengali Expression from the Targeting(Foreign) Language to the query language of each information system; Implementation of Bengali Expressions obtained from the query language; Assessment of results by the user and the redefinition of the query expressions if the results are not relevant; and Selecting and obtaining the documents that respond to the user’s needs.
A sharp interface evolutionary model for shape memory alloys

We show the existence of an energetic solution to a quasistatic evolutionary model of shape memory alloys. Elastic behavior of each material phase/variant is described by polyconvex energy density. Additionally, to every phase boundary, there is an interface‐polyconvex energy assigned, introduced by M. Šilhavý in . The model considers internal variables describing the evolving spatial arrangement of the material phases and a deformation mapping with its first‐order gradients. It allows for injectivity and orientation‐preservation of deformations. Moreover, the resulting material microstructures have finite length scales.
Adaptive KCF learning with fused infrared information

In the land battlefield environment, the tracking of personnel target is mainly affected by the complex environment, like smog, rain, shadows and partial shelter from the woods. These elements make the effect of kernelized correlation filters (KCF) target tracking based on visible light very unsatisfactory, because the brightness, color and rich texture information mainly included in visible light imaging are polluted. The infrared imaging system, due to its sensitivity to heat source, can be perceived in the dark and has low dependence on the surrounding environment. However, limited by its own characteristics, the single infrared imaging system loses some visible light information, such as light intensity, texture and color, and the image resolution is low. Considering the characteristics of visible light and infrared imaging are complementary, it’s reasonable to fuse integral channel features (ICF) of infrared gray image into HOG features of visible light images, and adjust model update rate corresponding to the degree of occlusion of target in the infrared image, to achieve more robust tracking effect.
Text Classification of installation Support Contract Topic Models for Category Management

Air Force Installation Contracting Agency manages nearly 18 percent of total Air Force spend, equating to approximately 57 billion dollars. To improve strategic sourcing, the organization is beginning to categorize installation-support spend and assign accountable portfolio managers to respective spend categories. A critical task in this new strategic environment includes the appropriate categorization of Air Force contracts into newly created, manageable spend categories. It has been recognized that current composite categories have the opportunity to be further distinguished into sub-categories leveraging text analytics on the contract descriptions. Furthermore, upon establishing newly constructed categories, future contracts must be classified into these newly constructed categories in order to be strategically managed. This research proposes a methodological framework for using Latent Dirichlet Allocation to sculpt categories from the natural distribution of contract topics, and assesses the appropriateness of supervised learning classification algorithms such as Support Vector Machines, Random Forests, and Weighted K-Nearest Neighbors models to classify future unseen contracts. The results suggest a significant improvement in modeled spend categories over the existing categories, facilitating more accurate classification of unseen contracts into their respective sub-categories.
Improving Large Scale Application Performance via Data Movement Reduction

The compute capacity growth in high performance computing (HPC) systems is outperforming improvements in other areas of the system for example, memory capacity, network bandwidth and I/O bandwidth. Therefore, the cost of executing a floating point operation is decreasing at a faster rate than moving that data. This increasing performance gap causes wasted CPU cycles while waiting for slower I/O operations to complete in the memory hierarchy, network, and storage. These bottlenecks decrease application time to solution performance, and increase energy consumption, resulting in system under utilization. In other words, data movement is becoming a key concern for future HPC system-design. Data volume reduction techniques (e.g. lossless data compression, information hiding approaches, di↵erence-based patches etc.) have been useful in many contexts to reduce data movement. In this thesis, I study the use of such techniques to reduce data movement in the context of current and future HPC environments. I trade
An approach for fabricating high performance inductors on low resistivity substrates

Porous Si layers up to 250 /spl mu/m in thickness are used to isolate spiral inductors from low resistivity substrates. Wafer curvature and SIMS analysis are done to address the manufacturability issue of porous Si. Spiral inductors with a single level Al on 2-inch, p-type substrates of 0.008 /spl Omega/-cm resistivity are demonstrated with Q=5.0 at 1.8 GHz for an L of 9 nH. Large inductors with L/spl sim/150 nH have been shown with the first resonance frequency at 1 GHz. The expected performance potential as well as factors that could be limiting the Q are discussed.
On the chiral rings in N=2 and N=4 superconformal algebras

We study the chiral primary rings of N=2 and N=4 superconformal algebras (SCA’s) constructed over triple systems. The chiral primary states of N=2 SCA’s realized over Hermitian Jordan triple systems are given. Their coset spaces G/H are Hermitian-symmetric and can be compact or noncompact. In the noncompact case under the requirement of unitarity of the representations of G, we find an infinite discrete set of chiral primary states associated with the holomorphic discrete series representations of G and their analytic continuation. A further requirement that the corresponding N=2 module be unitary truncates this infinite set to a finite subset. There are no chiral primary states associated with the other unitary representations of noncompact groups. Remarkably, the only noncompact groups G that admit holomorphic discrete series unitary representations are such that their quotients G/H with their maximal compact subgroups H are Hermitian-symmetric. The chiral primary states of N=2 SCA’s constructed over th.
Efficient Sketching Algorithm for Sparse Binary Data

Recent advancement of the WWW, IOT, social network, e-commerce, etc. have generated a large volume of data. These datasets are mostly represented by high dimensional and sparse datasets. Many fundamental subroutines of common data analytic tasks such as clustering, classification, ranking, nearest neighbour search, etc. scale poorly with the dimension of the dataset. In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm – BinSketch (Binary Data Sketch) – for sparse binary datasets. BinSketch preserves the binary version of the dataset after sketching and maintains estimates for multiple similarity measures such as Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same sketch. We present a theoretical analysis of our algorithm and complement it with extensive experimentation on several real-world datasets. We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking. Our proposed algorithm offers a comparable accuracy while suggesting a significant speedup in the dimensionality reduction time, with respect to the other candidate algorithms. Our proposal is simple, easy to implement, and therefore can be adopted in practice.
The design of metamaterial cloaks embedded in anisotropic medium

By using coordinate transformation method, this paper obtains an useful equation of designing meta-material cloaks embedded in anisotropic medium. This equation is the generalization of what was introduced early by Pendry et al (2006 Science 312 1780) and can be more widely used. As an example of its applications, this paper deduces the material parameter equation for cylinder cloaks embedded in anisotropic medium, and then offers the numerical simulation. The results show that such a cylinder cloak has perfect cloaking performance and therefore verifies the method proposed in this paper.
A Self-Adaptive Virtual Network Embedding Algorithm Based on Software-Defined Networks

Network virtualization provides a promising tool to allow multiple virtual networks (VNs) to run on a shared substrate network (SN) simultaneously. VN embedding (VNE) is one of the key technologies of network virtualization. The main goal of VNE is to effectively map VN requests to the SN, which is efficiently utilizes the network resources. The emergence of software defined networks provides a platform for network virtualization to be used and promoted. In a real environment, the resource requirements of tenants are generally different. A single VN mapping algorithm can not effectively handle the multi-demand problem of tenants. We propose a self-adaptive VNE algorithm. VN requests are divided into different types by an adaptive algorithm, we use an integer linear programming formulation to solve VNE problem. This paper considers three different types of VN requests. Type 1 VN requests for high bandwidth requirements, type 2 VN requests for low latency requirements, and type 3 VN requests for high bandwidth requirements and latency requirements. The simulation results show that the virtual network embedding algorithm proposed in this paper can make full use of the SN resources and improve the overall revenue, while effectively dealing with the multi-demand problem of tenants.
Multiple invasion speeds in a competition-diffusion model with three species

We investigate the spreading properties of a three-species competition-diffusion system, which is non-cooperative. We apply the Hamilton-Jacobi approach, due to Freidlin, Evans and Souganidis, to establish upper and lower estimates of spreading speed of the slowest species, in terms of the spreading speed of two faster species, and show that the estimates are sharp in some situations. The spreading speed will first be characterized as the free boundary point of the viscosity solution for certain variational inequality cast in the space of speeds. Its exact formulas will then be derived by solving the variational inequality explicitly. To the best of our knowledge, this is the first theoretical result on three-species competition system in unbounded domains.
Bayesian inference of stellar parameters based on 1D stellar models coupled with 3D envelopes

Stellar models utilizing 1D, heuristic theories of convection fail to adequately describe the energy transport in superadiabatic layers. The improper modelling leads to well-known discrepancies between observed and predicted oscillation frequencies for stars with convective envelopes. Recently, 3D hydrodynamic simulations of stellar envelopes have been shown to facilitate a realistic depiction of superadiabatic convection in 1D stellar models. The resulting structural changes of the boundary layers have been demonstrated to impact not only the predicted oscillation spectra but evolution tracks as well. In this paper, we quantify the consequences that the change in boundary conditions has for stellar parameter estimates of main-sequence stars. For this purpose, we investigate two benchmark stars, Alpha Centauri A and B, using Bayesian inference. We show that the improved treatment of turbulent convection makes the obtained 1D stellar structures nearly insensitive to the mixing length parameter. By using 3D simulations in 1D stellar models, we hence overcome the degeneracy between the mixing length parameter and other stellar parameters. By lifting this degeneracy, the inclusion of 3D simulations has the potential to yield more robust parameter estimates. In this way, a more realistic depiction of superadiabatic convection has important implications for any field that relies on stellar models, including the study of the chemical evolution of the Milky Way Galaxy and exoplanet research.
Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging. We address these issues with a new model-based reinforcement learning algorithm, Safety Augmented Value Estimation from Demonstrations (SAVED), which uses supervision that only identifies task completion and a modest set of suboptimal demonstrations to constrain exploration and learn efficiently while handling complex constraints. We then compare SAVED with 3 state-of-the-art model-based and model-free RL algorithms on 6 standard simulation benchmarks involving navigation and manipulation and a physical knot-tying task on the da Vinci surgical robot. Results suggest that SAVED outperforms prior methods in terms of success rate, constraint satisfaction, and sample efficiency, making it feasible to safely learn a control policy directly on a real robot in less than an hour. For tasks on the robot, baselines succeed less than <inline-formula><tex-math notation="LaTeX">$\text{5}\%$</tex-math></inline-formula> of the time while SAVED has a success rate of over <inline-formula><tex-math notation="LaTeX">$\text{75}\%$</tex-math></inline-formula> in the first 50 training iterations. Code and supplementary material is available at <uri>https://tinyurl.com/saved-rl</uri>.
A new neural-network-based model for measuring the strength of a pseudorandom binary sequence

Maximum order complexity is an important tool for measuring the nonlinearity of a pseudorandom sequence. There is a lack of tools for predicting the strength of a pseudorandom binary sequence in an effective and efficient manner. To this end, this paper proposes a neural-network-based model for measuring the strength of a pseudorandom binary sequence. Using the Shrinking Generator (SG) keystream as pseudorandom binary sequences, then calculating the Unique Window Size (UWS) as a representation of Maximum order complexity, we demonstrate that the proposed model provides more accurate and efficient predictions (measurements) than a classical method for predicting the maximum order complexity.
Wake Word Detection Using Recurrent Neural Networks

Wake word detection is the process of continuously listening for some specific keyword or phrase which, when uttered, wakes up a system and initiates communication between the system and a user. This is a necessity for interaction with virtual assistants such as Google Now, Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa. The goal of this project was to implement and evaluate a wake word detection algorithm based on deep learning. Two systems were implemented using recurrent neural networks which were trained as binary classifiers designed to return a confidence score indicating whether a wake word was uttered or not within a speech frame. The first system was based on a commonly used approach in which features are extracted from a sliding window of fixed length. The second system was more experimental, and exploited some of the capabilities of recurrent neural networks in order to eliminate the need for overlapping and fixed-length windows. Both systems used GRU networks with 65,154 trainable parameters, and thus had a similar memory footprint. However, the experimental system cut the computation cost by nearly 80 % compared to the sliding window based system, while also reducing the number of false detections. The results were highly encouraging, though there is still room for improvement. The first step would be using a larger and more diverse data set, and using a higher ratio of negatives to positives. Added layers, tweaking of parameters, regularization techniques or using a different type of cell in the network might also have a positive effect.
Improved evaluation of deep-level transient spectroscopy on perovskite solar cells reveals ionic defect distribution.

One of the key challenges for future development of efficient and stable metal halide perovskite solar cells is related to the migration of ions in these materials. Mobile ions have been linked to the observation of hysteresis in the current--voltage characteristics, shown to reduce device stability against degradation and act as recombination centers within the band gap of the active layer. In the literature one finds a broad spread of reported ionic defect parameters (e.g. activation energies) for seemingly similar perovskite materials, rendering the identification of the nature of these species difficult. In this work, we performed temperature dependent deep-level transient spectroscopy (DLTS) measurements on methylammonium lead iodide perovskite solar cells and developed a extended regularization algorithm for inverting the Laplace transform. Our results indicate that mobile ions form a distribution of emission rates (i.e. a distribution of diffusion constants) for each observed ionic species, which may be responsible for the differences in the previously reported defect parameters. Importantly, different DLTS modes such as optical and current DLTS yield the same defect distributions. Finally the comparison of our results with conventional boxcar DLTS and impedance spectroscopy (IS) verifies our evaluation algorithm.
Financial Time Series Forecasting Using Empirical Mode Decomposition and FNN: A Study on Selected Foreign Exchange Rates

The exchange rate, an economic indicator of the country is the relative price of one country’s currency in terms of another country’s currency. The stability of the exchange rate is important for a stable economic growth. Exchange rate series are non-linear and non-stationary. The fluctuations in the forecasting exchange rate are very important to the economy of the country. Researchers have proposed many hybrid machine learning models to get a more accurate forecast. This study proposes a hybrid forecasting model using Empirical Mode Decomposition (EMD) and Feedforward Neural Network (FNN) for foreign exchange rates forecasting and comparing its performance with widely used Non-linear Autoregressive (NAR) and Support Vector Regression (SVR) models. EMD is used to decompose the original non-linear and non-stationary series into several Intrinsic Mode Functions (IMFs) and one residual. The hybrid model is then used to forecast the exchange rate with IMFs and residual obtained as inputs. Empirical results obtained from forecasting daily exchange rates of Sri Lankan Rupees to Euro and Yen showed that the proposed EMD-FNN model outperforms NAR and SVR models without time series decomposition.
Fast Spread in Controlled Evolutionary Dynamics

We study a controlled evolutionary dynamics that models the spread of a novel state in a network where the exogenous control aims to quickly spread the novel state. We estimate the performance of the system by analytically establishing upper and lower bounds on the expected time needed for the novel state to replace the original one. Such bounds are expressed as functions of the control policy adopted and of the network structure, and establish fundamental limitations on the system's performance. Leveraging these results, we classify network structures depending on the possibility of achieving a fast spread of the novel state (i.e., complete replacement in a time growing logarithmically with the network size) using simple open-loop control policies. Finally, we propose a feedback control policy that using little knowledge of the network and of the system's evolution at a macroscopic level, allows for a substantial speed up of the spreading process, guaranteeing fast spread on topologies where simple open-loop control policies are not sufficient. Examples and simulations corroborate our findings.