Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Line Selection and PricingUnder the Multinomial Logit Choice

In this paper we consider a retailer's product line selection and pricing problem. We use a consumer choice model in which individuals have stochastic utility for each product and act to maximize their consumer surplus. We consider three basic problems: (1) A retailer who has a current product line with xed prices and who is considering the addition of a new model to the product line. The retailer must determine whether or not to o er the new model and, if so, at what price. (2) A retailer who has a pre-selected set of n models that she will o er. The retailer must jointly determine the optimal prices of these n models. (3) A retailer who may select any subset of models from a set of potential models. The retailer must determine how many and which products to select, and then determine the optimal price for each model. We obtain several insights into these problems by considering the case in which the consumers' reservation prices have a Gumbel distribution (i.e., under a multinomial logit (MNL) choice model). This paper provides a basis that can be built upon in order to gain a better understanding of the retailer's problem of selecting and pricing a product line under cannibalization.
SIMD Algorithms For 2-D Arrays In Shuffle Networks by Yosi

This paper studies a set of 2-D basic algorithms for SIMD perfect shuffle networks. Those algorithms where studied in [1] and [SN], but for the 1-D case, where the size of the problem N is the same as the number of processors P. For the 2-D case of N = L * P, we improve some algorithms to be "2-D efficient", by improving their running time from Ο(L *logP) to Ο(L + logP), as N exceeds P. We give nontrivial algorithms for the following operations: Row-Reduction, Parallel-Prefix, Transpose, Packing, Smoothing and Cartesian-Product.
Seamless Texture Mapping of 3 D Point Clouds

The two similar, quickly growing fields of computer vision and computer graphics give users the ability to immerse themselves in a realistic computer generated environment by combining the ability create a 3D scene from images and the texture mapping process of computer graphics. The output of a popular computer vision algorithm, structure from motion (obtain a 3D point cloud from images) is incomplete from a computer graphics standpoint. The final product should be a textured mesh. The goal of this project is to make the most aesthetically pleasing output scene. In order to achieve this, auxiliary information from the structure from motion process was used to texture map a meshed 3D structure.
Loss of Heterozygosity Drives Clonal Diversity of Phytophthora capsici in China

Phytophthora capsici causes significant loss to pepper (Capsicum annum) in China and our goal was to develop single nucleotide polymorphism (SNP) markers for P. capsici and characterize genetic diversity nationwide. Eighteen isolates of P. capsici from locations worldwide were re-sequenced and candidate nuclear and mitochondrial SNPs identified. From 2006 to 2012, 276 isolates of P. capsici were recovered from 136 locations in 27 provinces and genotyped using 45 nuclear and 2 mitochondrial SNPs. There were two main mitochondrial haplotypes and 95 multi-locus genotypes (MLGs) identified. Genetic diversity was geographically structured with a high level of genotypic diversity in the north and on Hainan Island in the south, suggesting outcrossing contributes to diversity in these areas. The remaining areas of China are dominated by four clonal lineages that share mitochondrial haplotypes, are almost exclusively the A1 or A2 mating type and appear to exhibit extensive diversity based on loss of heterozygosity (LOH). Analysis of SNPs directly from infected peppers confirmed LOH in field populations. One clonal lineage is dominant throughout much of the country. The overall implications for long-lived genetically diverse clonal lineages amidst a widely dispersed sexual population are discussed.
Analyzing multiresolution wavelet entropy of ECG with visual analytics techniques

Wavelet entropy is an analysis tool applicable to biomedical signals, including EEG and ECG. The current paper proposes an application of methods from visual analytics to time-frequency energy distribution in ECG signals, with the goal of studying heart rate variability from the main ECG band corresponding to heart rate. Such an approach can complement standard time- and frequency-domain techniques for heart rate variability assessment. Multiresolution wavelet entropy was computed from continuous wavelet transforms using windows of user-specified length and overlap. An interactive tool provides visualization of multiresolution entropy heat maps. This approach was used to compare ECG wavelet entropy in subjects during resting and stressor conditions. The interactive visual representation allows a detailed examination of wavelet entropy, revealing time-varying differences in the complexity of the underlying ECGs that are not evident in traditional measures.
Bird's-eye view of logic programming

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Comparison of classification techniques for predicting the performance of students academic environment

The aim of this study is to compares some classification techniques used to predict the performance of student. It is helps to analyse the slow leaner in the semester exams that are likely study in poor which are used to improve their skill as early to achieve the goal in end semester. The task can be processed based on the several attributes to predict the performance of the student activity respectively. In this research, the paper have been focused the improvement of Prediction/ classification techniques which are used to analyse the skill expertise based on their academic performance by the scope of knowledge. Also the paper shows the comparative performance of C4.5 algorithm, AODE, Naïve Bayesian classifier algorithm, Multi Label K-Nearest Neighbor algorithm to find the well suited accuracy of classification algorithm and decision tree algorithm to analysis the performance of the students which can be experimented in Weka tool.
Self-Learning-Based Low-Quality Single Image Super- Resolution

Low-quality images are usually not only with lowresolution, but also suffer from compression artifacts, e.g., blocking artifacts. Directly performing image super-resolution (SR) to a low-quality image would also simultaneously magnify the blocking artifacts, resulting in unpleasing visual quality. In this paper, we propose a self-learning-based SR framework to simultaneously achieve low-quality single-image SR and compression artifact removal (deblocking is treated as an example in this work). We argue that individually performing deblocking first, followed by SR to an image, would usually inevitably lose some image details induced by deblocking, which may be useful for SR, resulting in worse SR result. In our method, we propose to self-learn image sparse representation for modeling the relationship between low and high-resolution image patches in terms of the learned dictionaries, respectively, for image patches with and without blocking artifacts. As a result, image SR and deblocking can be simultaneously achieved via sparse representation and MCA (morphological component analysis)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.
Intrusion detection in computer networks using Optimum-Path Forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques.
Sentence-based Summarization of Scientific Documents The design and implementation of an online available automatic summarizer

∗ In Edmundson (1969) four features are used: the title feature, the cue word feature, the location feature and the word frequency feature. The frequency method Edmundson applied used the frequency of relevant words (frequency larger than a certain threshold and not being a common word) and assigned a score to each sentence based on the frequency of the relevant words in the sentence. Because of these reasons the method of Luhn (1958) is chosen as the preferred method for calculating the frequency score of a sentence. A sentence-based automatic summarization system has been developed, which benefits from a mixture of ideas founded in the early days of automatic summarization.
From Information Management to Knowledge Management : Beyond the ' Hi-Tech Hidebound ' Systems

Beyond the 'Hi-Tech Hidebound' Systems Malhotra, Y. (2000). From Information Management to Knowledge Management: Beyond the 'Hi-Tech Hidebound' Systems. In K. Srikantaiah & M.E. D. Koenig (Eds.), Knowledge Management for the Information Professional. Medford, N.J.: Information Today Inc., 37-61. Abstract Most extant knowledge management systems are constrained by their overly rational, static and acontextual view of knowledge. Effectiveness of such systems is constrained by the rapid and discontinuous change that characterizes new organizational environments. The prevailing knowledge management paradigm limits itself by its emphasis on convergence and consensus-oriented processing of information. Strategy experts have underscored that the focus of organizational knowledge management should shift from ‘prediction of future’ [that cannot be computed] to ‘anticipation of surprise. ’ Such systems may be enabled by leveraging the divergent interpretations of information based upon the meaning-making capability of human beings. By underscoring the need for synergy between innovation and creativity of humans and the advanced capabilities of new information technologies, this article advances current thinking about knowledge management.
Magnetic Resonance

Artifacts in body MR imaging: their appearance and how to eliminate them Abstract A wide variety of artifacts can be seen in clinical MR imaging. This review describes the most important and most prevalent of them, including magnetic susceptibility arti-facts and motion artifacts, aliasing, chemical-shift, zipper, zebra, central point, and truncation artifacts. Although the elimination of some artifacts may require a service engineer, the radiologist and MR technologist have the responsibility to recognize MR imaging problems. This review shows the typical MR appearance of the described artifacts, explains their physical basis, and shows the way to solve them in daily practice.
Genetic and Phenotypic Characterization of a Salmonella enterica serovar Enteritidis Emerging Strain with Superior Intra-macrophage Replication Phenotype

Salmonella enterica serovar Enteritidis (S. Enteritidis) is one of the ubiquitous Salmonella serovars worldwide and a major cause of food-born outbreaks, which are often associated with poultry and poultry derivatives. Here we report a nation-wide S. Enteritidis clonal outbreak that occurred in Israel during the last third of 2015. Pulsed field gel electrophoresis and whole genome sequencing identified genetically related strains that were circulating in Israel as early as 2008. Global comparison linked this outbreak strain to several clinical and marine environmental isolates that were previously isolated in California and Canada, indicating that similar strains are prevalent outside of Israel. Phenotypic comparison between the 2015 outbreak strain and other clinical and reference S. Enteritidis strains showed only limited intra-serovar phenotypic variation in growth in rich medium, invasion into Caco-2 cells, uptake by J774.1A macrophages, and host cell cytotoxicity. In contrast, significant phenotypic variation was shown among different S. Enteritidis isolates when biofilm-formation, motility, invasion into HeLa cells and uptake by THP-1 human macrophages were studied. Interestingly, the 2015 outbreak clone was found to possess superior intra-macrophage replication ability within both murine and human macrophages in comparison to the other S. Enteritidis strains studied. This phenotype is likely to play a role in the virulence and host-pathogen interactions of this emerging clone.
An approach based on edge coloring of tripartite graph for designing parallel LDPC interleaver architecture

A practical and feasible solution for LDPC decoder is to design partially-parallel hardware architecture. These architectures are efficient in terms of area, cost, flexibility and performances. However, this type of architecture is complex to design since concurrent read and write accesses to data have to be performed at each time instance without any conflict. To solve this memory mapping problem, we present in this paper, an original approach based on a tripartite graph modeling and a modified edge coloring algorithm to design parallel LDPC interleaver architecture.
Doing It Your Way: How Individual Movement Styles Affect Action Prediction

Individuals show significant variations in performing a motor act. Previous studies in the action observation literature have largely ignored this ubiquitous, if often unwanted, characteristic of motor performance, assuming movement patterns to be highly similar across repetitions and individuals. In the present study, we examined the possibility that individual variations in motor style directly influence the ability to understand and predict others’ actions. To this end, we first recorded grasping movements performed with different intents and used a two-step cluster analysis to identify quantitatively ‘clusters’ of movements performed with similar movement styles (Experiment 1). Next, using videos of the same movements, we proceeded to examine the influence of these styles on the ability to judge intention from action observation (Experiments 2 and 3). We found that motor styles directly influenced observers’ ability to ‘read’ others’ intention, with some styles always being less ‘readable’ than others. These results provide experimental support for the significance of motor variability for action prediction, suggesting that the ability to predict what another person is likely to do next directly depends on her individual movement style.
A very fast algorithm for RAM compression

Compressed virtual memory systems have been suggested, and in some cases implemented, to improve the effectiveness of use of physical RAM. However, most proposals and/or implementations are based on adaptive compression algorithms which achieve good compression ratios, but are slow compared to a local disk. Hence, they can only give some advantage with very slow (e.g. network-mounted) swap devices. In this paper we show that in many cases memory pages contain highly compressible data, with a very large amount of zero-valued elements. This suggests the replacement of slow, adaptive compression algorithms with very fast ones based on static Huffman codes.We present one such algorithm which, paired with a careful layout of the data, is able to compress 4KB pages at 40MB/s even when implemented in software on an inexpensive Pentium 100 system. We also show that the algorithm can achieve interesting compression ratios despite its simplicity.Since the compression/decompression speed of our algorithms exceeds disk bandwidth, its use in a compressed VM system can lead to both memory savings and speed improvements in servicing page faults. In this paper we discuss some possible applications of the algorithm in a compressed VM system.
Towards Usable Solutions to Graphical Password Hotspot Problem

Click based graphical passwords that use background images suffer from hot-spot problem. Previous graphical password schemes based on recognition of images do not have a sufficiently large password space suited for most Internet applications. In this paper, we propose two novel graphical password methods based on recognition of icons to solve the hotspot problem without decreasing the password space. The experiment we have conducted that compares the security and usability of proposed methods with earlier work (i.e. Passpoints) shows that hotspot problem can be eliminated if a small increase in password entrance and confirmation times is tolerable.
Modeling the AIDS Epidemic: Planning, Policy, and Prediction

This volume presents applied mathematical and statistical modelling research that may help focus and improve major decision-making in planning, resource allocation, and public health policy for the current AIDS health crisis. In addition to important models developed in academia, this text presents models developed and currently used by public health practitioners, thus providing truly broad-based coverage of AIDS modelling efforts. 30 detailed chapters examine how mathematical and statistical tools are being successfully applied to many AIDS-related issues, including forecasting, evaluation of behavioural intervention programs, natural history and disease progression, clinical trials for treatment, field studies of incidence and prevalence of the disease, characterization of the disease transmission process, and evaluation of the societal costs and benefits of alternative policy decisions.
A CAPTCHA with Tips Related to Alphabets Upper or Lower Case

Due to the ubiquity of web browsers and the dramatic development of dynamic web pages, web applications become the most popular computing model for providing services over the Internet. Unfortunately, this type of systems is vulnerable to the attacks issued by automated programs. CAPTCHA, a challenge-response test, is the most widely used mechanism to protect web application systems from attacks issued by computer programs. By considering the maintenance cost, most of the CAPTCHA systems employ the text-based challenge in which a string is represented as an image. A user is classified as a human by a text-based CAPTCHA if the user can respond a challenge with the exact string shown in the image. One drawback of a text-based CAPTCHA system is that the string is possibly recognized by a computer program equipped with OCR function. Obfuscating text images can strength the robustness of CAPTCHAs. But, it also degrades the usability of the systems. In this paper, we propose a new text-based CAPTCHA mechanism in which each challenge is associated with a tip providing humans enough information to recognize the alphabets in a highly distorted text image. Therefore, our system provides a balance between the effectiveness and human success rates of text-based CAPTCHAs.
Yield and Speedup Improvements in Extensible Processors by Allocating Extra Cycles to Some Custom Instructions

In this article, we investigate the application of different techniques for mitigating the impact of process variations on the custom functional unit (CFU) of extensible processors. The techniques include using extra cycles for the CFU and extending the clock period for the extensible processor. The former technique is based on providing an extra clock cycle to those custom instructions (CIs) that have timing yields smaller than one. For this purpose, we make use of a lookup table (LUT) for each fabricated processor. Based on a post-fabrication analysis, the need for an extra clock cycle for some CIs is determined. Consequently, the CI timing violations are prevented, and all manufactured extensible processors will work with a predefined clock cycle time. To study the effect of the objective function (used during the CI selection phase) on the efficacy of the suggested architectural technique, we investigate three different objective functions. In the second technique, the clock period extension is used to guarantee a design yield of one. Our results demonstrate that combining both techniques helps increase the speedup achieved by the extensible processor. To assess the efficacies of the proposed methods, several benchmarks from different application domains are used. Results of the study reveal that the suggested techniques provide considerable improvements in the speedups of the extensible processors when compared to those of approaches that do not consider the impact of process variations.
Short-term moderate weight loss and resistance training do not affect insulin-stimulated glucose disposal in postmenopausal women.

OBJECTIVES Moderate weight loss and exercise have been proposed as important tools in the treatment and prevention of type 2 diabetes. Therefore, we tested the hypothesis that short-term (4 weeks) moderate energy restriction (-750 kcal/day) would result in a significant increase in insulin-stimulated glucose disposal (40 mU x m(-2) x min(-1) hyperinsulinemic-euglycemic clamp) in moderately overweight postmenopausal women and that when combined with resistance training (RT) an even greater effect would be seen. RESEARCH DESIGN AND METHODS Older women were randomly assigned to energy restriction (WLoss group; n = 9) or energy restriction plus RT (RT + WLoss group; n = 10). RESULTS For the WLoss versus the RT + WLoss groups, changes in body weight (-3.0 +/- 0.2 kg vs. -3.2 +/- 0.3 kg), fat mass (FM) (-3.0 +/- 0.3 kg vs. -3.2 +/- 0.3 kg), and percent body fat (BF) (-2.1 +/- 0.4 vs. -2.4 +/- 0.3%) were not different between groups. Muscle mass (group-by-time interaction, P = 0.04) was preserved in RT + WLoss (0.40 +/- 0.40 kg) and reduced in WLoss (-0.64 +/- 0.18 kg). There were no changes in fat-free mass (FFM) and waist-to-hip ratio in either group. Whole body glucose disposal (WLoss 6.14 +/- 0.57 vs. 6.03 +/- 0.53, RT + WLoss 5.85 +/- 0.60 vs. 6.09 +/- 0.56 mg/kg of FFM/min) did not change in either group. CONCLUSIONS The results of this study demonstrate that short-term energy restriction resulting in moderate decreases in body weight (4.0 +/- 0.3%) and FM (8.2 +/- 0.7%) did not improve insulin-stimulated glucose disposal. The addition of RT to the hypoenergetic diet preserved muscle mass but provided no synergistic effect on insulin action. These results suggest that a greater change in body weight or FM may be necessary to observe a significant improvement in insulin action.
Ontology Population from the Web: An Inductive Logic Programming-Based Approach

The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extraction and classification of instances of the concepts and relations defined by an ontology. Developing IE rules for OP is an intensive and time-consuming process. Thus, an automated mechanism, based on machine-learning techniques, able to convert textual data from web pages into ontology instances may be a crucial path. This paper presents an inductive logic programming-based method that automatic induces symbolic extraction rules, which are used for populating a domain ontology with instances of entity classes. This method uses domain-independent linguistic patterns for retrieving candidate instances from web pages, and a WordNet semantic similarity measure as background knowledge to be used as input by a generic inductive logic programming system. Experiments were conducted concerning both the instance classification problem and a comparison with other popular machine learning algorithms, with encouraging results.