Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Bandwidth Allocation and Server Scaling for Colocation Edge Computing

Multi-access Edge Computing (MEC) is recently acknowledged as one of the key pillars for the next revolution of mobile communications area, where the convergence of IT and telecommunications network provides lower latency and more computation capability for cellular base stations (BSs). Nevertheless, a huge capital and operational cost can challenge mobile operators for new BSs and MEC micro-datacenters deployment. Thus, sharing the BS infrastructure, bandwidth, and MEC micro-datacenters for the co-located mobile operators can be an economical solution to provide high performance services with lower expense by exploiting the temporal and spatial difference in traffic loads. Turning this vision to reality, we study a joint bandwidth allocation sharing and shared MEC micro-datacenter scaling in ColoMEC management problem (CMMP) and propose an algorithm based on proximal block coordinate descent technique.
Context Aware Community Detection Scheme for Identification of Community in Multi-dimensional Networks

The modern science of networks has enhanced our understanding of complex systems and ere are now growing interests in modeling ese large complex systems as multi-dimensional networks such as e World Wide Web (WWW), social networks, co-citation networks and biological networks. In order to understand and study ese kind of networks, community detection is done. There are several approaches for community structure identification from ese kind of networks. But e approaches in literature could lead to generation of communities wi noisy interactions. This paper presents a context aware community detection me od based on DBSCAN (Density Based Clustering Algori m for Applications wi Noise) approach. The result and discussions based on e experiments enhance e work. KeywordsCommunity, tensor, MultiComm, Linear Dirichlet Allocation, ContextAware Relation, DBSCAN CONTEXT AWARE COMMUNITY DETECTION SCHEME FOR IDENTIFICATION OF COMMUNITY IN MULTI-DIMENSIONAL NETWORKS Dhanya Sudhakaran PG Scholar, Dept. of CSE Sree Buddha College of Engineering Alappuzha, India dhanyasudhakaran1991@gmail.com Shini Renji Assistant Professor, Dept. of CSE Sree Buddha College of Engineering Alappuzha, India shinirenji @gmail.com
STUDY OF MECHANICAL BEHAVIOR OF DOWEL BAR IN TRANSVERSE JOINTS OF CONCRETE PAVEMENT

The objective of this study is to develop a mechanical design method for doweled joints. The mechanical behavior of dowel bars is investigated using a mechanical model (PAVE3D) developed for dowel bars in the transverse joints of concrete pavement based on the three-dimensional finite element method. In this model, a dowel bar is divided into two segments that are embedded in concrete connected by a third segment. These two types of segment are modeled by solving for a beam on an elastic foundation and for a three-dimensional beam element, respectively. The model is verified by comparing the predicted strains in the concrete slab and dowel bars with experimental data obtained from loading tests conducted on a model pavement and an actual pavement. The effects of transverse joint structure and subbase stiffness on the stresses in dowel bars and the concrete slab are investigated through numerical simulations with PAVE3D.
Paleomagnetism and rock magnetism of IODP 325 Hole M 0058 A

We have studied the rock magnetic and paleomagnetic properties of a 41 meter-long core (Hole M0058A) recovering fine calcareous sediments located seaward of Noggin Reef, off Queensland, Australia. We have deployed 1-cc samples at every 10 cm down-core and subsampled the core by means of U-channels in order to obtain a continuous record. We have also conducted magnetic granulometry analyses and Curie point determinations. The thermomagnetic analyses indicate the presence of Tipoor magnetite with Curie points from 560 o to 563 o C. Hysteresis loop experiments were carried out and the results show Mrs/Ms and Bcr/Bc ratios corresponding to Single Domain to Multi Domain (SD-MD) as well as Super Paramagnetic (SP) to Single Domain (SD, i.e. SP-PSD) ranges.
Assessing alternative futures for agriculture in Iowa ,

The contributions of current agricultural practices to environmental degradation and the social problems facing agricultural regions are well known. However, landscape-scale alternatives to current trends have not been fully explored nor their potential impacts quantified. To address this research need, our interdisciplinary team designed three alternative future scenarios for two watersheds in Iowa, USA, and used spatially-explicit models to evaluate the potential consequences of changes in farmland management. This paper summarizes and integrates the results of this interdisciplinary research project into an assessment of the designed alternatives intended to improve our understanding of landscape ecology in agricultural ecosystems and to inform agricultural policy. Scenario futures were digitized into a Geographic Information System GIS , visualized with maps and simulated images, and evaluated for multiple endpoints to assess impacts of land use change on water quality, social and economic goals, and native flora and fauna. The Biodiversity scenario, targeting restoration of indigenous biodiversity, ranked higher than the current landscape for all endpoints biodiversity, water quality, farmer preference, and profitability . The Biodiversity scenario ranked higher than the Production scenario which focused on profitable agricultural production in all endpoints but profitability, for which the two scenarios scored similarly, and also ranked higher than the Water Quality scenario in all endpoints except water quality. The Water Quality scenario, which targeted improvement in water quality, ranked highest of all landscapes in potential water quality and higher than the current landscape and the Production scenario in all but profitability. Our results indicate that innovative agricultural practices targeting environmental improvements may be acceptable to farmers and could substantially reduce the environmental impacts of agriculture in this region. Landscape Ecology 19: 357–374, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands. 357
HUSSIEN Adjusting Hot Strip Mill Level 2 Setup Using Tribological Approach to Eliminate Biting Refusal

Roll bite during steel rolling has always been a topic of interest as it is the most critical moment for hot rolling. Higher reduction ratio causes some troubles such as the increase of the rolling force [8] and leads to critical biting conditions. The extreme environment of the steel hot mill roll bite with so many influencing process parameters makes it very difficult to find the root causes of such biting troubles.
Evaluation of Wireless Sensor Networks for Environmental Monitoring

Wireless Sensor Network (WSN) is an evolving technology enabled by improvements in technology. A WSN is a wireless network of nodes that are small in size, and poses challenges like limited power supply and processing power, and lossy radio links. These networks are often used to collect data from sensors at multiple locations. In order to increase lifetime these devices must minimize power consumption. The lossy links between poses challenges that normal computer network routing protocols does not solve very well. IPv6 Routing Protocol for LowPower and Lossy Networks (RPL) is a routing protocol designed specifically for WSN, and is made for communication flows that are normal in WSNs. RPL uses an Objective Function (OF) in order to optimize routes, which enables different optimization objectives. In this thesis a WSN using RPL is implemented and deployed. RPL specifies the use of an OF, which uses metrics in order to fulfill its optimization goals. This thesis implements RPL in TinyOS, an operating system designed specifically for embedded systems like WSNs. This implementation proposes to use LQI, an link quality indicator for 802.15. 4 radios which are often used in WSNs, as an initial value to the Estimated Transmission Count (ETX). The initial is an improvement on other implementation using ETX in that it passively estimates an ETX for all nodes. Nodes thus have an indication as to how good the link towards nodes that have not been tested is. This estimation can also be kept up to date for possible parents that are not actively tested. My test shows that proper calculation from LQI to ETX is very important for this to provide good results. The resulting implementation is used in a deployed network consisting of 13 nodes that measure temperature and humidity in the church in Laksevag. The tested values for the LQI to ETX conversion was made very conservative in order to minimize loss. This resulted in routes where the hop count was very high, compared to routes that were selected with a different calculation, which showed shorter routes in terms of hop, while still providing a high delivery ratio. The LQI to ETX values need to be set correctly in order to provide the wanted routes. This is left for future work.
Medical Image Watermarking : A Study on Image Degradation

Digital watermarking has been proposed to increase medical image security, confidentiality and integrity. Med ical image watermarking is a special subcategory of image watermarking in the sense that the images have special requirements. Particularly, watermarked medical images should not differ perceptually from their original counter parts, because the clinical reading of the images (e.g. for diagnosis) must not be affected. This paper presents a preliminary study on the degradation of medical images when embedded with different watermarks, using a variety of popular systems. Image quality is measured with a number of widely used metrics, which have been applied elsewhere in image processing. The general conclusion that arises from the results is that typical watermark embedding can cause numerical and perceptual errors in an image. The greater the robustness of a watermark, the greater the errors are likely to be. Consequently medical image watermarking remains an open area for research, and it appears that a selection of different watermarks for different medical imag e types is the most appropriate solution to the generic problem.
Application of GLCM-Based Texture Features to Remote Sensing Image Classification

In order to solve the problem of low-accuracy in the conventional classification of remote sensing image classification, a new method based on gray level co-occurrence matrix(GLCM) texture features is presented and utilized. After the principal component analysis, the first two principal components were selected to extract the texture features of different measurements based on gray level co-occurrence matrix. As a new band, the extracted texture features with the original bands were combined by supervised classification methods to classify the images. The classification result was compared with the maximum likelihood classification qualitatively and quantitatively. The research results show that a combination of texture features with spectral characteristics can enhance the accuracy of ground object classification, and prove the feasibility and effectiveness of the GLCM-based classification method presented in this paper.
An approach to damage detection based on PZT sensors and Transfer Impedance method

One of the ideas for structural health monitoring (SHM) systems built is based on analysis of small displacements propagation excited in the element by a network of PZT piezoelectric actuators. Structural damage can result in observable changes of the signal generated by the network sensors, due to elastic wave interaction with damage. There are two approaches how to utilize PZT sensors for SHM purposes. One of the approaches follows closely classical ultrasonic testing. In that case, short pulse excitation of PZT transducers is used, thus guided wave packets can be scattered on different elements of structure, eventually also on the damage itself. The disturbance of the scattered wave field is the basis of damage detection and evaluation. In the different approach, the so called electromechanical impedance (EMI) method, harmonic excitation of PZT is used, thus steady elastic waves are excited in the structure. As in guided waves approach, the signal can be gather in the pulse echo scheme, i.e. when single transducer is used both as a actuator and the receiver of waves, as well in the pitch – catch scheme, when a pair of transducers, the generator and the sensor, are used. For the latter EMI approach, the term Transfer Impedance Approach is sometimes used. In the paper, an approach for damage detection and localization with use of network of PZT sensors excited with harmonic signals in broad frequency spectrum is presented. In particular, some signal characteristics – called Damage Indices (DI’s) used for structure assessment are proposed and their properties are discussed. The DI’s have the property to maintain all of the information about the signals. That property manifests the robustness of the proposed approach, in the sense that if a damage is detectable by PZT sensors, then it should be revealed by the proposed DIs.
Non-Negative Orthogonal Greedy Algorithms

Orthogonal greedy algorithms are popular sparse signal reconstruction algorithms. Their principle is to select atoms one by one. A series of unconstrained least-square subproblems of gradually increasing size is solved to compute the approximation coefficients, which is efficiently performed using a fast recursive update scheme. When dealing with non-negative sparse signal reconstruction, a series of non-negative least-squares subproblems have to be solved. Fast implementation becomes tricky since each subproblem does not have a closed-form solution anymore. Recently, non-negative extensions of the classical orthogonal matching pursuit and orthogonal least squares algorithms were proposed, using slow (i.e., non-recursive) or recursive but inexact implementations. In this paper, we revisit these algorithms in a unified way. We define a class of non-negative orthogonal greedy algorithms and exhibit their structural properties. We propose a fast and exact implementation based on the active-set resolution of non-negative least-squares and exploiting warm start initializations. The algorithms are assessed in terms of accuracy and computational complexity for a sparse spike deconvolution problem. We also present an application to near-infrared spectra decomposition.
The Impact of Economic Attitudes on Natural and Social Corporate Responsibility – A Comparative Study of Lithuania and Slovenia

This paper reports on research that examines the state of corporate social responsibility (CSR) and the impact of economic attitudes on natural and social CSR in Lithuania and Slovenia. The study exposes CSR and organizational behavior theories and analyzes answers from 159 Lithuanian and 183 Slovenian business students considered as future employees. The authors established a model to examine the impact of economic CSR – considered through a “primary concern for economic results” and “devoting resources for CSR” to “natural CSR” and “social CSR. ” In Lithuania interest in social CSR prevails, while in Slovenia interest is focused on natural CSR. The economic aspect of CSR is poorly appreciated in both countries. Associations between CSR aspects reveal that favoring economic results has no significant influence on natural and social CSR in the Lithuanian sample. In the Slovenian sample there is a significant and negative association between the concern for economic results and natural and social CSR. A positive and significant impact of devoting resources to natural and social CSR in both countries exists, but the impact is stronger for Slovenia than Lithuania. Devoting resources contributes more to a concern for social rather than natural CSR in both samples. Economic CSR explains significantly more variance in social CSR than in natural CSR. The findings could help improve and develop CSR behavior in organizations and among their future employees. They could also benefit higher education organizations and society.
Global Optimization Networks

We consider the problem of estimating a good maximizer of a black-box function given noisy examples. To solve such problems, we propose to fit a new type of function which we call a global optimization network (GON), defined as any composition of an invertible function and a unimodal function, whose unique global maximizer can be inferred in $\mathcal{O}(D)$ time. In this paper, we show how to construct invertible and unimodal functions by using linear inequality constraints on lattice models. We also extend to \emph{conditional} GONs that find a global maximizer conditioned on specified inputs of other dimensions. Experiments show the GON maximizers are statistically significantly better predictions than those produced by convex fits, GPR, or DNNs, and are more reasonable predictions for real-world problems.
Exploration of the Gulf of Mexico Oil Spill with the Sentry Autonomous Underwater Vehicle

We report on the use robotic assets in the investigation of subsea hydrocarbon plumes caused by the blowout, on 20 April 2010, and subsequent sinking of theDeepwater Horizon drilling platform in the Gulf of Mexico. We employed conventional oceanographic sampling techniques along wit h the Sentry autonomous underwater vehicle (AUV) to confirm the existence of a coherent subsea hydrocarbon plume, then to ma p the plume’s spatial extent out to 35 km down-current from the well head, and finally to collect targeted water samples from within the plume itself for later laboratory analysis. In th is paper we focus on the techniques used to coordinate sampling activities between the AUV and conventional instrumentati on: geo-referenced navigation of all data, integrative visual ization of multi-modal and multi-platform data, real-time telemet ry, visualization, and analysis of data, and the real-time adap tation of vehicle trajectory in response. Our results demonstratethat when initial characterization is poor, limited human interaction and feedback can accelerate the study, and improve the analy sis, of evolving environmental phenomena. We discuss several le ssons learned, particularly as they apply to the future development of limited-interaction autonomy in subsea robotics. Using real data collected during the Deepwater Horizonexpedition, we present simulations of semi-automated data interpretation and sampling plan adaptation comparable to the real-time actions taken by us during the expedition itself.
Green ICT Policy Maturity for Actualization of Sustainable Computing in Developing Nations

Green ICT Policy encompasses the frameworks an organization has developed and put in place to apply environmental sustainability criteria throughout its value chain. The lack of appropriate green policy alignment to application of ICT uptake challenges the reaping of the benefits often stated of ICT. Multiple case study design with case from different sectors of the economy selected based on information richness, accessibility, size as well as diversity in application of ICT was employed. Triangulation of data collection and findings interpretation was utilised. The study established that green ICT policy maturity was very low. In effect, calling for purposed policy set up on green ICT application by developing nations to ensure ICT benefits are realised while limiting environmental degradation.
Livestock Donation and Child Health : Evidence from Heifer International , Rwanda

This paper evaluates the impact of dairy cow and meat goat donations on the chronic and acute nutritional status of children 0-5 years of age. The data was collected from household-level surveys in two regions of Rwanda participating in Heifer International’s livestock donation program. The results show that donations of dairy cows significantly improve the mean height-for-age of children while having no significant effect on the mean weight-for-height. Donations of meat goats were found to have no significant effect on any of the examined measures of child malnutrition. The author would like to thank the staff at Heifer Project International, Rwanda, for their support and coordination of the research; Sarah Pedersen, Rosemary Rawlins, Timothy Ong, Denise Mutabazi, Docus Karungi, Evariste Kayiranga and Enid for their tireless efforts in the field; Bruce Wydick and Christopher Barrett for helpful comments and input into this research.
Deep Learning Inverse Multidimensional Projections

We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based on a projected training set, and next use this mapping to infer the mapping on arbitrary points. We compare our method with two recent inverse projection techniques on three datasets, and show that our method has similar or higher accuracy, is one to two orders of magnitude faster, and delivers result that match well known ground-truth information about the respective high-dimensional data
WUST at the NTCIR-14 FinNum Task

This paper describes our system in the FinNum task at NTCIR-14, and the FinNum task is dedicated to identifying the category of a numeral from the financial social media data, i.e. tweet, for fine-grained numeral understanding. In NTCIR-14, the FinNum task contains two subtasks, the first one, named Subtask1 in this paper, is to classify a numeral into seven categories, i.e. Monetary, Percentage, Option, Indicator, Temporal, Quantity, and Product/Version Number, and the second one, named Subtask2 in this paper, extends the Subtask1 to the subcategory level, which classifies financial numerals into seventeen classes. In our system, we first complete the Subtask1, and then, on the basis of the seven categories, we separately classify numerals into corresponding subcategories according to the category. Our submitted system constructs the classification model based on Support Vector Machines (SVM) to identify the categories of numerals. In additional experiments, we adopt the Bidirectional Encoder Representations from Transformers (BERT) model to act as a multi-classifier, and the experimental results show that the BERT model is superior to the SVM model.
Too Tight - Too Loose: Controlling Lessons from a Failed IS Development Project

Information Systems Development outsourcing (ISD-outsourcing) projects are notorious for not providing agreed deliverables within the stipulated time and budget. More alarmingly, it is estimated that 19% of ISD-outsourcing projects fail outright (Hastie and Wojewod 2015). This study investigates an ISD-outsourcing failure case which was attributed to issues in the requirements engineering process. Adopting a ‘control theory’ perspective and leveraging case study approach, we examine how the weaknesses in control portfolio contributed to project failure. We attribute the failure not to the absence of control portfolios, rather (i) employing particular control mechanisms incorrectly depending on the phase of requirements engineering process, (ii) dominance of a particular control mechanism, and (iii) insufficiency of particular control mechanisms.
directed polymers 1 Directed Polymers in Random Environment a

Directed polymers in random environment can be thought of as a model of statistical mechanics in which paths of stochastic processes interact with a quenched disorder (impurities), depending on both time and space. We review a number of results which have been obtained during the last fifteen years. Our topics will include: 1Discrete and continuous models, related models; 2Weak disorder regime and strong disorder regime; 3Localization phenomenon at strong disorder; 4Diffusivity at weak disorder; 5Large deviations for a Brownian model. We will use a thermodynamics approach, a martingale approach, and we prove that the transition between delocalized and localized phases is characterized by equality or strcit inequality between the quenched and the annealed free energy.