Miracle-dz/UniFAR-FTUs huggingface dataset:
Production , Manufacturing and Logistics Knowledge-based framework for automated dynamic supply chain configuration

Supply chain management has gained renewed interest among researchers in recent years. This is primarily due to the availability of timely information across the various stages of the supply chain, and therefore the need to effectively utilize the information for improved performance. Although information plays a major role in effective functioning of supply chains, there is a paucity of studies that deal specifically with the dynamics of supply chains and how data collected in these systems can be used to improve their performance. In this paper I develop a framework, with machine learning, for automated supply chain configuration. Supply chain configuration used to be mostly a one-shot problem. Once a supply chain is configured, researchers and practitioners were more interested in means to improve performance given that initial configuration. However, recent developments in e-commerce applications and faster communication over the Internet in general necessitates dynamic (re)configuration of supply chains over time to take advantage of better configurations. Using examples, I show performance improvements of the proposed adaptive supply chain configuration framework over static configurations. 2004 Elsevier B.V. All rights reserved.
Customer‐perceived relationship quality and satisfaction: A case of Ethiopian Telecommunication Corporation

Purpose – The purpose of this study is to explore and assess the contribution of relationship marketing constructs namely trust, commitment, communication, and conflict handling to perceived quality of customer relationship and satisfaction in an Ethiopian mobile communications perspective.Design/methodology/approach – Descriptive research approach was used, and a sample of 350 respondents was drawn using systematic random sampling technique. Multivariate analyses (factor and multiple regressions) were applied to test the theoretical framework set for the study.Findings – All relationship marketing dimensions were found to be significant contributors to both relationship quality and customer satisfaction. Empirically, similar to other studies conducted in varying contexts, customer satisfaction was found to be a significant predictor of relationship quality in the Ethiopian mobile telecommunication sector.Research limitations/implications – The scope of the study is limited to the mobile service subscribe.
Research of methodical error of average energy consumption of microcontrollers

The article describes a new method of measuring the average power consumption of microcontrollers during the execution of the instructions, programs or their fragments. The method provides high accuracy and noise immunity. One of the disadvantages of the method is the methodical error. There is assessed its dependence on energy consumption nonlinearity by microcontroller.
EMOTION REPRESENTATION AND PERCEPTION ACROSS CULTURES

Are emotion words or emotion categories universal, or are particular emotions and emotion categories specific to certain cultures? The current review explores the answer to this question by summarizing the limited number of studies that have addressed this issue. The representation of emotion is discussed with regards to verbal and nonverbal (facial) processing, in turn. The evidence indicates that the answer is often conflicting and that issues such as methodological, linguistic, social and cultural variance have contributed to the often contradictory findings.
A Modular Model for Predicting the Impact of Process Improvements on Software Development Cycle Time

Reducing software development cycle time can be advantageous to software development organizations. Software organizations are investing time and money in process improvements aimed at reducing their cycle time. Unfortunately, the impact of process improvements on the cycle time of complex software processes is not well understood. The objective of the research being proposed is to give decision makers a model that will predict the impact a set of process improvements will have on their software development cycle time. The outcome of the research will be a modular system dynamics model of the software development environment that allows decision makers to gain insight and perform controlled experiments to answer "What if? " type questions, such as, "What kind of cycle time reduction can I expect to see if I implement inspections? " or "How much time should I spend on inspections? " This research proposal covers necessary background information, discusses existing work on this topic, describes the proposed research, and describes the approach that will be used to achieve the research objectives of this proposal.
The Role of Domain Knowledge in Requirements Elicitation: an Exploratory Study

Requirements elicitation is the first activity in the requirements engineering process. It includes learning, surfacing and discovering the requirements of the stakeholders of the developed system. The elicitation process involves actors of different roles, backgrounds and domain knowledge. Therefore, it is a communication-intensive process. Overcoming communication barriers between analysts and stakeholders, partly caused by a gap in their domain knowledge, is essential. Various elicitation techniques exist for helping analysts extract the requirements from the different stakeholders. During the elicitation process, the analysts are not limited to one specific technique and can use different techniques according to the situation, time and resources available. Analysts may have domain knowledge prior to the elicitation process. This prior knowledge may have an impact on the elicitation process, affecting the analysts’ decisions and conduct within it. This paper reports an exploratory study in which the perceived and actual effects of prior domain knowledge on the requirements elicitation process were examined. The results indicate that domain knowledge clearly affects the elicitation process and the way the analysts conduct the elicitation. The findings provide insights as to both positive and negative effects of domain knowledge on requirements elicitation, as perceived by participants with and without domain knowledge. Furthermore, these insights can be utilized in practice for supporting analysts in the elicitation process and for forming requirements analysis teams. They highlight the different contributions that can be provided by analysts with different levels of domain knowledge in requirements analysis teams and the synergy that can be gained by forming heterogeneous teams of analysts with and without domain knowledge.
Remote Protection for Mobile Apps in Cloud Computing Environments

Android app repackaging has become a serious problem currently. To protect apps from tampering, code obfuscation, watermarking and other techniques have been designed. However, most of these approaches could only provide inadequate protection. Meanwhile, it is possible to achieve remote tamper-resistance in recent, especially for networking apps, with the development of cloud computing technology and the spread of networks. In this paper, we propose a novel remote protection mechanism for mobile apps in cloud computing environments. In the proposed mechanism, the app should download and execute some code from the cloud when some important function is called. Tamper proofing code which could check the app is tampered or not is embedded in the downloaded code. Furthermore, the code can only run correctly one time and is continuous changing, which makes it difficult for an adversary to analyze and modify.
Measuring Drug and Alcohol Use Among College Student-Athletes†

type="main"> Few issues in athletics today receive more attention than drug and alcohol usage, especially when it comes to college athletics. We seek to address self-report biases related to drug usage and heavy drinking. We employ an experimental measurement technique. Our results suggest that a greater percentage of student-athletes from a major conference knowingly engage in these two behaviors than self-reports indicate. Specifically, we find 37 percent of respondents seem to have taken banned performance-enhancing drugs (compared to 4.9 percent who directly admit to doing so when asked), and 46 percent seem to have consumed more than five drinks in a week (compared to about 3 percent who openly admit to doing so). We provide evidence for the extent of self-underreporting when it comes to drug and alcohol usage among college athletes. That said, future work is needed to accurately pinpoint specific substances and the frequency with which they are taken; for example, it could be the percentage of individuals using banned substances stems from consuming significant concentrations of caffeine (e.g., multiple cups of coffee).
POPULATION SIZE CHANGES RESHAPE GENOMIC PATTERNS OF DIVERSITY

Abstract Elucidating the forces responsible for genomic variation is critical for understanding evolution. Under standard conditions, X-linked diversity is expected to be three-quarters the level of autosomal diversity. Empirical data often deviate from this prediction, but the reasons for these departures are unclear. We demonstrate that population size changes can greatly alter relative levels of X-linked and autosomal variation: population size reductions lead to particularly low X-linked diversity, whereas growth elevates X-linked relative to autosomal diversity. Genetic variation from a diverse array of taxa supports an important role for this effect in accounting for population differences in the ratio of X-linked to autosomal diversity. Consideration of this effect may improve the inference of population history and other evolutionary processes.
Understanding the dynamics of test-driven development

Test-driven development (TDD) has been the subject of several software engineering experiments. However the controversial results about its effects still need to be contextualized. This doctoral research will show how TDD could be better assessed by studying to what extent developers follow its cycle and for what kind of development tasks. This knowledge is foreseen to be beneficial for software industries willing to adopt or adapt TDD.
Multi-view Communication Visualization for Wireless Network Simulations

In this paper, we present a novel multi-view visualization tool for trace files generated by network simulations, currently supporting the ns-2 and ShoX trace formats. As the first tool in this area, it shows simultaneously three synchronized views (or perspectives) of the current network state in one window: (a) An enriched text view showing events line-by-line, marked up using colors and symbols in order to improve readability, (b) the 2-D bird’s eye view known from other simulators, (c) a newly proposed view depicting the development of network state as a graph focusing on the visualization of communication patterns on intra- and internode level. Using this synchronized multi-view, as well as, our novel graph view, we aim at increasing the speed of the analysis and evaluation processes during the development of communication protocols.
Analysis about Application of Learning System into Interactive Digital Art Implementation

The rapid growth of science and technology influences to Digital Arts. Digital art is no more unfamiliar genre in art field. We can see many kinds of digital arts in our surroundings. High technology makes digital art more brilliant and various. Interactivity is very important factor in digital art. Some interactive digital art works are look like a game. Many digital artists make their art works like game using reactions of spectators. For examples, touching, sound, moving and so on. Many kinds of interactive control elements are used in interactive arts. Some interactive artworks are called game art, because they have strong disposition of games. Usually, we define game art when ¡®goal' and 'match' concept is used in interactive art. Interactive art and game are very similar genre. One of important things are spectators and they are immersed to the interactive artwork when the artwork has dispositions of games. Most digital artists design their artwork using their own philosophical thinking. However, we tried to apply the learning system that is related to the game to interactive artworks. In the artistic point of view, we analyzed ¡®Insight Learning', ¡®Reinforcement Learning',' Neural Network', and ¡®Fuzzy' in some digital interactive artworks. These learning systems are typical learning system in game fields.
Convolutional Data : Towards Deep Audio Learning from Big Data ( Abstract )

Deep Learning has become a popular approach for unsupervised feature learning [3]. It is now used extensively for object, face and speech recognition, as well as other classification tasks [1, 4]. The success achieved by deep learning is said to be due to the fact that these models comprise several layers of representation, with each higher layer expected to learn increasingly complex features of the given data. One example of such models is the Deep Belief Network (DBN) [5], a generative probabilistic model having a Restricted Boltzmann Machine (RBM) as fundamental building block. In particular, Convolutional Deep Belief Networks (cvDBN) have been very successful at audio classification tasks [2, 3]. In cvDBNs, the weights are shared between multiple units of each RBM [1, 2]. Convolutional approaches have been used for learning of invariant local features from high-dimensional data. Recently, cvDBNs have been shown capable of learning high-level features from audio spectrograms [3]. The cvDBNs map the input space to the space of latent variables by applying convolution of the audio spectrogram with the weight matrix. The convolutional networks in [3] use the entire spectrogram (following PCA whitening) as input to the RBM, thus requiring either the use of a fixed audio length or sampling that converts the timedomain signals into fixed-length spectrograms. Moreover, the length of the audio data is usually very large, which can make training [4, 5] computationally difficult in the case of big data applications. As pointed out in [3], it remains an interesting problem to apply deep learning to larger data sets and more challenging tasks. Further, we note that when applied to music genre classification, the first layer features performed the best overall [3] (using 300 nodes with a filter length of 10). 1
Parenchymal breast density estimation with the use of statistical characteristics and textons

Breast parenchymal density has been found to be a strong indicator for breast cancer risk but the process of classification by the radiologists has been proved to be quite subjective. Furthermore, recent studies have shown that the effectiveness of most modern CAD systems used for breast abnormality detection diminishes greatly when parenchymal breast tissue density is high. Therefore the existence of a system to provide automatically accurate and objective estimation of the parenchymal breast density category is of great importance. In the present work we present a method to perform breast density classification based on local textural characteristics of the image, known as textons. We make the assumption that the mammographic image comprises a “document” with a number of “visual words” and the “document” “topic” is the wanted density category. This approach allows the use of techniques originating from text classification which have been proved to be very effective. The proposed system achieved classification rates of 98.3% and 93.2% for two categories and four categories, respectively.
Fusion of Time of Flight Camera Point Clouds

Recent time of flight cameras deliver range images (2.5D) in realtime, and can be considered as a significant improvement when compared to conventional (2D) cameras. However, the range map produced has only a limited extent, and suffers from occlusions. In this paper, we investigate fusion methods for partially overlapping range images, aiming to address the issues of lateral field of view extension (by combining depth images with parallel view axes) and occlusion removal (by imaging the same scene from different viewpoints).
Diversified Remote Code Execution Using Dynamic Obfuscation of Conditional Branches

Information leakage via timing side-channel attacksis one of the main threats that target code executing on remoteplatforms such as the cloud computing environment. Theseattacks can be further leveraged to reverse-engineer or eventamper with the running code. In this paper, we propose asecurity obfuscation technique, which helps making the generatedcode more resistant to these attacks, by means of increasinglogical complexity to hinder the formulation of a solid hypothesisabout code behavior. More importantly, this software solutionis portable, generic and does not require special setup orhardware or software modifications. In particular, we considermangling the control-flow inside a program via converting arandom set of conditional branches into linear code, using ifconversiontransformation. Moreover, our method exploits thedynamic compilation technology to continually and randomlyalter the branches. All of this mangling should diversify codeexecution, hence it becomes difficult for an attacker to infertiming correlations through statistical analysis. We extend theLLVM JIT compiler to provide for an initial investigation of thisapproach. This makes our system applicable to a wide varietyof programming languages and hardware platforms. We havestudied the system using a simple test program and selectedbenchmarks from the standard SPEC CPU 2006 suite withdifferent input loads and experimental setups. Initial results showsignificant changes in program's control-flow and hence datadependences, resulting in noticeable different execution timeseven for the same input data, thereby complicating such attacks.More notably, the performance penalty is within reasonablemargins.
Correlation and prediction of trunk fat mass with four anthropometric indices in Chinese males

To increase our understanding of the relationships of trunk fat mass (FMtrunk) and four anthropometric indices in Chinese males, 1090 males aged 20–40 years were randomly recruited from the city of Changsha, China. Waist circumference (WC) and hip circumference (HC) were measured using standardized equipment, and three other anthropometric indices of BMI, waist:hip ratio (WHR) and conicity index (CoI) were calculated using weight, height, HC and WC. FMtrunk (in kg) was measured using a Hologic QDR 4500 W dual-energy X-ray absorptiometry scanner. There was an increasing trend of FMtrunk, %FMtrunk (percentage of FMtrunk) and BMI, WC, WHR, CoI in successively older age groups (e.g. the mean FMtrunk values were 4·63 (sd 2·58), 5·39 (sd 2·74), 5·93 (sd 2·82), 6·57 (sd 2·94) in four 5-year age groups, respectively). FMtrunk and %FMtrunk were significantly correlated with four anthropometric indices with the Pearson's correlation coefficients ranging from 0·25 to 0·86. Principal component analysis was performed to form three principal components that interpreted over 99·5% of the total variation of four related anthropometric indices in all age groups, with over 65% of the total variation accounted by principal component 1. Multiple regression analyses showed that three principal components explained a greater variance (R2 70·0–80·1%) in FMtrunk than did BMI or WC alone (R2 57·8–74·1%). The present results suggest that there is an increasing trend of FMtrunk and four anthropometric indices in successively older age groups; that age has important effects on the relationships of FMtrunk and studied anthropometric indices; and that the accuracy of predicting FMtrunk using four anthropometric indices is higher than using BMI or WC alone.
Economic Profitability and the Accounting Rate of Return

The internal rate of return (IRR) is a widely used benchmark for assessing the reliability of the accounting rate of return (ROA) as a measure of economic profitability. We turn this reasoning process on its head by demonstrating that a suitable (weighted average) aggregation of ROAs better captures what is generally meant by economic profitability than does the IRR. We show that average ROA when compared with the cost of capital, will always correctly signal economic profitability in the sense that it will correspond exactly with what would be obtained from a net present value calculation. We also show that average ROA can be used to make meaningful inter-firm comparisons of profitability, when due allowance is made for differences in investment scale. Using this framework, we show how average ROA can be used to assess economic profitability for a truncated time series where the opening and closing capital stocks provided by the accounting system do not adequately represent the firm´s initial and ending endowments of resources. Finally, we suggest how this approach can be put to practical use in assessing profitability of firms on an on-going basis.
Location Prediction of Mobility Management Using Soft Computing Techniques in Cellular Network

This work describes the neural network technique to solve location management problem. A multilayer neural model is designed to predict the future prediction of the subscriber based on the past predicted information of the subscriber. In this research work, a prediction based location management scheme is proposed for locating a mobile terminal in a communication without losing quality maintains a good response. There are various methods of location management schemes for prediction of the mobile user. Based on individual characteristic of the user, prediction based location management can be implemented. This work is purely analytical which need the past movement of the subscriber and compared with the simulated one. The movement of the mobile target is considered as regular and uniform. An artificial neural network model is used for mobility management to reduce the total cost. Single or multiple mobile targets can be predicted. Among all the neural techniques multilayer perceptron is used for this work. The records are collected from the past movement and are used to train the network for the future prediction. The analytical result of the prediction method is found to be satisfactory.
Functional Analysis of Three Plasmids from Lactobacillus plantarum

ABSTRACT Lactobacillus plantarum WCFS1 harbors three plasmids, pWCFS101, pWCFS102, and pWCFS103, with sizes of 1,917, 2,365, and 36,069 bp, respectively. The two smaller plasmids are of unknown function and contain replication genes that are likely to function via the rolling-circle replication mechanism. The host range of the pWCFS101 replicon includes Lactobacillus species and Lactococcus lactis, while that of the pWCFS102 replicon also includes Carnobacterium maltaromaticum and Bacillus subtilis. The larger plasmid is predicted to replicate via the theta-type mechanism. The host range of its replicon seems restricted to L. plantarum. Cloning vectors were constructed based on the replicons of all three plasmids. Plasmid pWCFS103 was demonstrated to be a conjugative plasmid, as it could be transferred to L. plantarum NC8. It confers arsenate and arsenite resistance, which can be used as selective markers.
Artificial neural network fusion: Application to Arabic words recognition

The study of multiple classifler systems has become recently an area of intensive research in pattern recognition in order to improve the results of single classiflers. In this work, two types of features com- bination for handwritten Arabic literal words amount recognition, using neural network classiflers are discussed. Difierent parallel combination schemes are presented and their results compared with a single classifler benchmark using a complete feature set.