Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the Performance of Machine Learning Algorithms for Diagnosing Diabetes in Individuals Idemudia

Application of machine learning algorithms for the diagnosis of diabetes have become a trending research area, as effort to improve current techniques and methods used byhealth care institutions to determine the occurrence of diabetes in individuals is now given more attention than before. This study attempts to evaluate the performance of five (5) machine learning models on diabetic dataset using Python to predict the incidence of diabetes. Pima Indian diabetes dataset from UCI machine learning repository was used for the study. To ensure quality evaluation of the algorithms, a second dataset provided by Dr. John Schorling of the department of Medicine, University of Virginia was used for double evaluation. Result shows that Naive Bayes algorithm performs better when used for the prediction of diabetes.
Multielectron transportation of polyoxometalate-grafted metalloporphyrin coordination frameworks for selective CO2-to-CH4 photoconversion

Abstract Photocatalytic CO2 reduction into energy carriers is of utmost importance due to the rising concentrations of CO2 and the depleting energy resource. However, the highly selective generation of desirable hydrocarbon fuel, such as methane (CH4), from CO2 remains extremely challenging. Herein, we present two stable polyoxometalate-grafted metalloporphyrin coordination frameworks (POMCFs), which are constructed with reductive Zn-ϵ-Keggin clusters and photosensitive tetrakis(4-carboxylphenyl)porphyrin (H2TCPP) linkers, exhibiting high selectivity (>96%) for CH4 formation in a photocatalytic CO2-reduction system. To our knowledge, the high CH4 selectivity of POMCFs has surpassed all of the reported coordination-framework-based heterogeneous photocatalysts for CO2-to-CH4 conversion. Significantly, the introduction of a Zn-ϵ-keggin cluster with strong reducing ability is the important origin for POMCFs to obtain high photocatalytic selectivity for CH4 formation, considering that eight MoV atoms can theoretically donate eight electrons to fulfill the multielectron reduction process of CO2-to-CH4 transformation.
GENERALIZED KY FAN MINIMAX INEQUALITIES FOR SET-VALUED MAPPINGS

In this paper, by virtue of the Ky Fan section theorem, the Kakutani-FanGlicksberg fixed point theorem and two nonlinear scalarization functions, we investigate some generalized Ky Fan minimax inequalities for set-valued mappings, in which the minimization and the maximization of set-valued mappings are taken in the sense of vector optimization.
A Testbed for research and development of SDN applications using OpenFlow

Network technologies have been dominated by traditional paradigms resulting from the TCP/IP model and local networks, centered on traditional switching and routing concepts. The current network complexity at the data center, local and operator levels present new management challenges and flexibility requirements. The SDN paradigm emerged to tackle these challenges. The main goal of SDN is to separate the control plane from the data plane, which are usually tied together in conventional network devices, in such way that these can be managed, controlled and monitored by custom applications, enabling increased network flexibility independently of proprietary solutions. OpenFlow is a communication protocol based on the SDN paradigm, which defines a communication between the data plane and the control plane. This project aims to create a testbed in order to facilitate the comprehension about Programmable Network.
A Effective Feature Construction Method for Fall Detection using Smartphone

Recent years, smartphone based fall detection solutions have become research hotspots. These previous algorithms always analyze two types of data (accelerometer and gyroscope) and detect fall event on activities of daily life (ADL) of people which does not consider the case on physical exercise, such as, running etc. In this paper, we propose an effective feature construction method to convert a continuously device motion record to a feature vector which can define the occurrence of a fall event accurately. Base on those feature vectors, a heuristic fusion approach is adopted to extract the fall events on ADL with running. Our method runs on four types of refined and unbiased data (Attitude, RotationRate, Gravity and UserAcceleration) providing by iPhone’s Core Motion framework. And 15 volunteers were employed to simulate fall events. The empirical results have demonstrated that the proposed method is effective and reliable on ADL with physical exercise.
Identification of Brain Tumor using Gabor Wavelets Techniques

In the field of medical science is biomedical application are used for Medical image analysis, it is used for detect abnormalities for human bodies through examine image. Magnetic resonance (MR) images are one of the excellent tools to identify the tumor growth in brain but in this case accurate brain image segmentation is a not easy and time consuming procedure. In this paper we propose a process for automatic brain tumor diagnostic system with Gabor Wavelets techniques. This method includes three phases to detect a brain tumor. In the first phase, input data sets of MR image are required for detection of brain tumor. In Second phase preprocessing, image is in the form of noise and artifact this image images is converted into Gray image. In the third phase, threshold segmentation is done on the sharpened image. This experiments demonstrate that technique is accurately identifies and segments the brain tumor in MR images.
Portable Parallel Performance via Multi-Dimensional Homomorphisms

1 MOTIVATION AND RELATEDWORK Achieving portable program performance on modern architectures, e.g., multi-core CPU and Graphics Processing Unit (GPU) is hard: while approaches such as OpenCL [7] provide portability of program code across a range of hardware architectures, they do not guarantee portability of performance, e.g., a parallel program yielding high performance on a multi-core CPU can yield poor performance on a GPU and vice versa. This is because different hardware architectures usually differ significantly in their characteristics, e.g., GPUs provide a high number of cores but small caches while multi-core CPUs have a low number of cores and big caches. Performance differs also across input sizes [17]. For example, a high-performance implementation of GEneral Matrix-Matrix Multiplication (GEMM) targeting big square input matrices – the usual input of numerical applications [8] – differs significantly from a GEMM implementation optimized for small matrices, e.g., as used in the important application area of deep learning [18]. This is because high performance on big square input matrices is achieved by computing each element of the result matrix in parallel [6]; however, for high performance on small matrix sizes, the computation of each result element itself has to be parallelized as well [17] to increase the degree of parallelism and consequently to better utilize modern parallel hardware with many cores. Several state-of-the-art approaches aim at providing portability of performance. However, they are mostly limited to restricted combinations of: 1) applications, 2) hardware architectures, and/or 3) input sizes. For example, the popular libraries NVIDIA cuBLAS [13] and Intel MKL [5] provide high performance for linear algebra routines on NVIDIA GPUs or Intel multi-core CPUs, respectively. However, these libraries cannot be used for application classes that are different from linear algebra and for hardware architectures that are not NVIDIA GPU or Intel CPU, e.g., AMD CPU/GPU and ARMmobile processors. In contrast, the CLBlast library [12] targets linear algebra routines on various architectures, but it is only optimized for large input sizes and usually provides lower performance
A critical view on measurements of noise : Improvements on noise annoyance predictions by psychoacoustics

Zusammenfassung Die A-bewertete Schalldruckpegelmessung erlaubt zuverlässige Voraussagen, wenn es darum geht, aurale Schädigungen des menschlichen Gehörs durch hohe Schalldruckpegel abzuschätzen und entsprechende Maßnahmen zu veranlassen. Da aber eine Messung des A-bewerteten Schalldruckpegels weder die spektrale Zusammensetzung noch die zeitliche Struktur eines Schallereignisses berücksichtigt, verliert diese Messung bei geringeren Schalldruckpegeln hinsichtlich der von Menschen wahrgenommenen Geräuschqualität und der damit verknüpften Lärmbelästigung zunehmend an Aussagekraft. Hier bietet die Psychoakustik weitergehende Methoden an, den Zusammenhang zwischen Schall ereignis und Hörereignis zu verstehen. Mit den bislang standardisierten psychoakustischen Kenngrößen wie Lautheit und Schärfe stehen erste geeignete Messverfahren zur Verfügung, um die akustische Belastung durch Umweltgeräusche gehörgerecht zu bewerten. Insbesondere bei der Beurteilung von Verkehrslärm ist die in der DIN 45631/A1 postulierte Perzentillautheit N5 besser geeignet, das menschliche Geräuschempfinden zu beschreiben als der äquivalente Dauerschallpegel, auf dessen Grundlage oft voreilige und sogar falsche Schlussfolgerungen bezüglich der Lärmbelästigung getroffen werden.
A DEEP REINFORCEMENT LEARNING APPROACH TO USINGWHOLE BUILDING ENERGYMODEL FOR HVAC OPTIMAL CONTROL

Whole building energy model (BEM) is difficult to be used in the classical model-based optimal control (MOC) because of its high-dimension nature and intensive computational speed. This study proposes a novel deep reinforcement learning framework to use BEM for MOC of HVAC systems. A case study based on a real office building in Pennsylvania is presented in this paper to demonstrate the workflow, including building modeling, model calibration and deep reinforcement learning training. The learned optimal control policy can potentially achieve 15% of heating energy saving by simply controlling the heating system supply water temperature.
Beware the downgrading of secure electronic mail

Researchers have investigated the usability challenges of end-to-end encryption for electronic mail while users seem to place little value into the confidentiality of their mail. On the other hand, users should see value in protection against phishing. Designing mail apps so that they help users resist phishing attacks has received less attention. A well-known and widely implemented mechanism can be brought to bear on this problem - digital signatures. We investigated contemporary mail apps and found that they make limited use of digital signatures to help users detect phishing mail. We developed and studied an opinionated user interface design that steers users towards safe behaviors when confronted with phishing mail. In our study with 18 participants we found that the control group was phishable whereas the experimental group remained safe.
Development of a Tightly Coupled Vision/GNSS System

This paper focuses on the loose and tight integration of a stereo-vision system with a Global Navigation Satellite System (GNSS) based navigation system. A simplified vehicular dynamic model was chosen and implemented in order to gain maximum advantage from the vision sensor’s information. Data collected in open-sky as well as urban environments was processed in post-mission with the University of Calgary’s GSNRx™ software GNSS receiver. The results include performance measures of the loose and tight integration approaches using high-quality GPS-synchronized computer vision cameras, which are compared to the output of a GNSS-only solution as well as a tactical-grade reference trajectory. More than 20,000 images and 30 minutes of raw GNSS measurements in a dynamic scenario with a total travelled distance of more than 10 km were collected, processed and analyzed, and the resulting errors in the trajectories were compared.
Bandwidth Measurement in 4 G Networks

Existing available bandwidth estimation tools were mainly designed for fixed IP backbone networks and desktops with assumption of First In First Out(FIFO) Principle. While LTE supports high data rates of 100 Mbps, challenge is to adapt available bandwidth algorithms for quick and non-intrusive measurements on an Android OS based device supporting different MIMO configurations. This Master thesis presents real time, two way available bandwidth measurement tool using Two Way Active Measurement Protocol (TWAMP) for Android OS based devices to measure high speed LTE networks. The tool performance is verified against FTP throughput measurement with effect of variation in constant bit rate UDP cross traffic and load on the server on endto-end measurements. The results from emulation and tests in a commercial LTE network show that we can achieve available bandwidth estimations on both the uplink and downlink in real time. This opens up wide possibility to include various existing available bandwidth techniques and tools in mobile application to be used over a wireless link. Further, possible suggestions to achieve better available bandwidth estimations with native application development has been proposed.
Storytelling Platform Based on Emotional Levels

This paper presents the results of an ongoing design research project whose aim is to explore ways of sharing digital memories and experiences based on emotional levels. In this research we designed and tested a platform that records and graphs the emotional arousal underwent by the protagonist of a given experience. Furthermore, it also organizes the digital videos and pictures shot by the protagonist chronologically and layers them on top of the emotional graph. As a result, this platform assists the protagonist to recount vividly the experience by adding narrative clues to the arranged digital content displayed on a communal interactive table. The platform design is based on two main ideas: the first is the synchronization of recorded content with the biometric emotional level registered at the exact moment it was captured. The second is the use of tangible objects for storing and recalling digital memories and stories in a similar way that heirlooms and logbooks do. Consequently, this platform is mainly composed of an input biometric sensor, an interactive display table, and a presentation software. After several tests, a mid-resolution prototype proved to ease the assembly of multimedia presentations by organizing numerous digital assets through the lens of actual emotional arousal. In addition, this organizational technique appears to boost the performative storytelling experience by enabling simultaneous media channels (video and images) to be displayed on the table depending on the specific time or emotional level the protagonist wishes to show.
Enhancing Nutrient Efficiency Through Genetic Selection: Opportunities and Challenges

Introduction Intensive animal agriculture has lead to public and legislative concern about environmental and health risks from manure (Meyer and Mullinax, 1999). Solutions to nutrient management issues require an understanding of the flow of nutrients on livestock farms (Koelsch and Lesoing, 1999). Of the nutrients present in manure, nitrogen, phosphorus, sodium, potassium, copper and zinc cause the greatest concern (NRC, 1998). Nutrient inputs to the farm are purchased feed, fertilizer, purchased animals, nitrogen fixed by crops and nitrogen in irrigation water. Nutrients may exit the farm as managed outputs such as animals, milk, or crops. The difference between nutrient inputs and managed nutrient outputs is the imbalance and is an indication of environmental risk. A systems effort that incorporates all components of the farming enterprise is needed to minimize environmental risk. Engineering solutions for the handling, treatment and application of manure and systems of cropping and soils management have received most of the scientific attention (NCSU, 1999). Improving the efficiency of nutrient utilization by animals is also a feasible approach. Feeding programs that influence nutrient excretion have also been extensively studied and described (Prince et al., 2000; Galyean, 2001; Satter et al., 2002). Genetic selection is another method that can be utilized to improve efficiency of nutrient utilization by animals thereby decreasing amount of nutrients excreted in urine and feces and reducing total nutrient output from the farming enterprise and increasing efficiency of production. The objective of this article is to review genetic opportunities to influence nutrient efficiency for concentrated animal feeding operations.
High-Speed Smooth Cyclical Motion of a Hydraulic Cylinder

Many traditional machining operations involve object motion, either of a part or a tool. Changes in their velocity, including starting and stopping, result in the creation of shock forces that act upon the structural and operating members of a machine. These forces depend largely on the inertia of the moving members. At high loads, where hydraulic actuation is predominantly used, smooth velocity changes resulting in low shock forces are not easily realizable due to the low compressibility of hydraulic liquids. High-speed cyclical motion may be a cause of further unwanted vibrations. This research describes and implements a number of computer-generated and computer-controlled smooth cyclical trajectories designed for high-speed highload devices. A computer-controlled servo-valve is used to execute high-speed cyclical motion of a hydraulic cylinder. A comparative study of various implemented high-speed smooth trajectories is presented.
Rainbow options

INTRODUCTION A rainbow is an option on a basket that pays in its most common form, a nonequally weighted average of the assets of the basket according to their performance. The number of assets is called the number of colours of the rainbow. At maturity, the assets are sorted by their performance. For instance, a rainbow call with weights 50%, 30%, 20%, with a basket including an equity stock index, a bond index and a foreign currency, pays 50% of the best return (at maturity) between the equity stock index, the bond index and the foreign currency, 30% of the second best and 20% of the third best. One of the common features of rainbow options is to take an average for the underlying, as the Asian version of the rainbow is often cheaper than the vanilla one. Also exotic rainbow are packaged for retail business as structured notes with callable and putable feature as well as capital guarantee features. A callable rainbow note allows the note issuer to call back the note while a putable one allows the note buyer to sell it back. Callable putable versions are becoming also common as they allow flexibility on both sides.
EasyChair Preprint No 198 Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming

Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such ad-hoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
IZA Discussion Paper No . 753 April 2003 ABSTRACT Unemployment Equilibrium and Onthe-Job Search

Unemployment Equilibrium and On-the-Job Search This paper uses the search and matching framework to explore the impact of employed job search on the labour market. The specific features of our model are endogenous employed job search, flows in and out of the labour force, endogenous job destruction and heterogenous job creation. Also, job flows and workers flows do not coincide as we allow for job-to-job flows and labour force entries and exits. Employed job search is shown to have a substantial impact on unemployment dynamics but a negligible one on the level of unemployment. More on-the-job search leads to lower unemployment inflow and outflow, i.e. a more stagnant unemployment pool. With employed job search, the stock of vacancies is more cyclically sensitive, the unemployment outflow less cyclically sensitive and the unemployment inflow more cyclically sensitive than without employed job search. With our model, the impact of a change in unemployment benefit does not only occur through the conventional decrease in the unemployment outflow rate, but also through an increase in the unemployment inflow rate. The calibrated version of our model replicates well the cyclical behaviour of job and worker flows observed in the data. JEL Classification: J64
A Study on Design Practices with Tacit Knowledge Consequences of consumer perception

It h as b een n oted in m any f ace p erception researches that p eople r eliably an d automatically make personality inferences from facial expression despite little evidence for their accuracy. Because of that a facial expression is a silent social signal, influencing mate choice and other social judgment. The selective sensitivity of human to features in the human face that convey information on s ex, a ge, e motions, and i ntentions. A r ecent s tudy ha s r eported that the hum an ability can be applied to not only conspecifics, but also artificial objects. S. Windhager et al. found that p eople tend to p erceive th e w orld p rimarily in social way, also th at p eople actually ascribe characteristics concordantly for most of the dimensions analyzed. There remains a question: does gendered p erception make a d ifference? I n consumer r esearches, g ender is in troduced as k ey consideration when s egmenting c onsumers a long the t ransactional/relational continuum with culture. Consulting f irm A. T. K earney estimated that females d etermine 80% of consumption, purchase 60 % of c ars a nd own 40% of all s tocks as o f 2008. L ittle wonde r that fe male’s consumption becomes more and more important in all over the industries. This subject deserves more than a passing notice. This paper builds on de sign practices with tacit knowledge aiming to explore whether gendered perception makes a difference on automotive fronts or not. One hundred thirty-nine models r epresenting th irty-five b rands au tomotive f ronts w ere u sed a s s timuli. T he subject rated each stimulus on 2 traits relating with human face. Among the stimuli, twenty-six car fronts on the m ale-/female li ke t rait a nd, s ixteen on the adult-/child-like trait, s howed t he significant by gender. In other words, around 80% of the pictures did not revealed the significant. It re mains an uns ettled question: why g endered pe rception m akes exceptions in the automotive fronts. To find the answer of this question will be the further direction of this research.