Miracle-dz/UniFAR-FTUs huggingface dataset:
Forthcoming in Mathematical Programming MAXIMIZING A CLASS OF SUBMODULAR UTILITY FUNCTIONS

Given a finite ground set N and a value vector a ∈ R , we consider optimization problems involving maximization of a submodular set utility function of the form h(S) = f (∑ i∈S ai ) , S ⊆ N , where f is a strictly concave, increasing, differentiable function. This function appears frequently in combinatorial optimization problems when modeling risk aversion and decreasing marginal preferences, for instance, in risk-averse capital budgeting under uncertainty, competitive facility location, and combinatorial auctions. These problems can be formulated as linear mixed 0-1 programs. However, the standard formulation of these problems using submodular inequalities is ineffective for their solution, except for very small instances. In this paper, we perform a polyhedral analysis of a relevant mixed-integer set and, by exploiting the structure of the utility function h, strengthen the standard submodular formulation significantly. We show the lifting problem of the submodular inequalities to be a submodular maximization problem with a special structure solvable by a greedy algorithm, which leads to an easily-computable strengthening by subadditive lifting of the inequalities. Computational experiments on expected utility maximization in capital budgeting show the effectiveness of the new formulation.
A comparison of map neuron labeling approaches for unsupervised self-organizing feature maps

The self-organizing map (SOM) is an unsupervised neural network approach that reduces a high-dimensional data set to a representative and compact two-dimensional grid. In so doing, a SOM reveals emergent clusters within the data. Research has shown that SOMs lend themselves to visual and computational analysis for exploratory and data mining purposes. However, an important requirement for many SOM interpretations is the characterization of the mappsilas emergent clusters. This process is often addressed by either a manual or automated map neuron labeling approach. This paper discusses techniques for the labeling of the unsupervised, supervised and semi-supervised variants of the SOM, and proposes some new methods. It also presents empirical results characterizing the performance of two automated labeling approaches for fully unsupervised SOMs when applied for example classification of experimental data sets.
B2C eCommerce Strategy and Market Structure: The Survey Based Approach

This paper follows two objectives: (i) It demonstrates the merits of the survey based approach to B2C eCommerce characteristics and company strategy, and (ii) it presents empirical evidence of the crucial importance of size and marketing investment in B2C eCommerce markets. It presents econometric estimates of the effects of company characteristics and company strategies on the performance of Viennese B2C eCommerce companies in 2001. We provide econometric analysis of three dependent variables in turn: (i) number of B2C eCommerce customers in 2000, (ii) number of B2C eCommerce employees in January 2001 and (iii) revenue growth rate in 2001. The models do explain the data quite well: Size as well as endogenous sunk costs emerge as the main success factors. Furthermore, the results of nonparametric tests are presented. They mostly confirm the econometric evidence. We also show that the quantitative results are consistent with the qualitative results of the surveys. Finally, we argue that the survey based approach to B2C eCommerce is a method that provides reliable and consistent data, and that it complements the approach based on prices and consumer behavior commonly applied.
An Efficient Random Beamforming Method based on Beam Subset Selection

In the finite number of users, the proposed beam subset selection method improves the throughput performance of the orthogonal random beamforming (ORBF), which is a scheme to achieve asymptotic sum capacity for a downlink multiuser multiple-input/multiple output (MIMO) system using partial channel information in the infinite number of users. We show that there exists an optimal number of random beams to maximize the sum throughput for given number of users. When the number of random beams is less than the number of antennas, selecting the random beams to maximize signal- to-interference-plus-noise ratios (SINRs) provides additional SINR gain to ORBF. Through the simulation results, we show the performance improvement by the proposed beam subset selection.
Biological-inspired model for hybrid-order chromatic texture segregation

In this study, a computational model for chromatic texture segregation is developed. The model attempts to simulate the visual processing characteristics by mimicking the visual perception. According to Hering's opponent theory, we deal with color information in a color space with three opponent axes. The algorithm extracts 1st-order features by a Gaussian filter and 2nd-order features by a set of Gabor filters as so called Gabor wavelets. The hybrid-order features are combined at a common site to detect the boundary. The model is very intuitive and physiological relevant such that it reserves opportunities for further approaches.
Crowd collectiveness measure via graph-based node clique learning

Collectiveness motions of crowd systems have attracted a great deal of attentions in recently years. In this paper, we try to measure the collectiveness of a crowd system by the proposed node clique learning method. The proposed method is a graph based method, and investigates the influence from one node to other nodes. A node is represented by a set of nodes which named a clique, which is obtained by spreading information from this node to other nodes in graph. Then only nodes with sufficient information are selected as the clique of this node. The motion coherence between two nodes is defined by node cliques comparing. The collectiveness of a node and the collectiveness of the crowd system are defined by the nodes coherence. Self-driven particle (SDP) model and the crowd motion database are used to test the ability of the proposed method in measuring collectiveness.
Distributed Search on Large NoSQL Databases

This work focuses on performance and scalability of different policies for solving queries on large noSQL databases with clusters. Distribution of data and queries are amongst the main problems, given the distributed nature of clusters: basically a set of networked computers. The basic centralized model (for both, data and processing) is used as a departure point and different distributed configurations are experimented with, in order to determine several guidelines for performance improvement. Apache Solr has been used for database management and search server. The current contents of the Wikipedia in Spanish (with about 4.5 GB) have been used as an example of a NoSQL database for experimentation.
Bank efficiency evaluation using a neural network-DEA method

In the present time, evaluating the performance of banks is one of the important subjects for societies and the bank managers who want to expand the scope of their operation. One of the non-parametric approaches for evaluating efficiency is data envelopment analysis(DEA). By a mathematical programming model, DEA provides an estimation of efficiency surfaces. A major problem faced by DEA is that the frontier calculated by DEA may be slightly distorted if the data is affected by statistical noises. In recent years, using the neural networks is a powerful non-parametric approach for modeling the nonlinear relations in a wide variety of decision making applications. The radial basis function neural networks (RBFNN) have proved significantly beneficial in the evaluation and assessment of complex systems. Clustering is a method by which a large set of data is grouped into clusters of smaller sets of similar data. In this paper, we proposed RBFNN with the K-means clustering method for the efficiency evaluation of a large set of branches for an Iranian bank. This approach leads to an appropriate classification of branches. The results are compared with the conventional DEA results. It is shown that, using the hybrid learning method, the weights of the neural network are convergent.
Parallel white noise generation on a GPU via cryptographic hash

A good random number generator is essential for many graphics applications. As more such applications move onto parallel processing, it is vital that a good parallel random number generator be used. Unfortunately, most random number generators today are still sequential, exposing performance bottlenecks and denying random accessibility for parallel computations. Furthermore, popular parallel random number generators are still based off sequential methods and can exhibit statistical bias. In this paper, we propose a random number generator that maps well onto a parallel processor while possessing white noise distribution. Our generator is based on cryptographic hash functions whose statistical robustness has been examined under heavy scrutiny by cryptologists. We implement our generator as a GPU pixel program, allowing us to compute random numbers in parallel just like ordinary texture fetches: given a texture coordinate per pixel, instead of returning a texel as in ordinary texture fetches, our pixel program computes a random noise value based on this given texture coordinate. We demonstrate that our approach features the best quality, speed, and random accessibility for graphics applications.
Small distortion and volume preserving embeddings for planar and Euclidean metrics

A finite metric space, (S,d) , contains a finite set of points and a distance function on pairs of points. A contraction is an embedding, h, of a finite metric space (S, d) into Rd where for any u, v E S, the Euclidean (&) distance between h(u) and h(v) is no more than d(u, v). The distortion of the embedding is the maximum over pairs of the ratio of d(u, w) and the Euclidean distance between h(u) and h(v). Bourgain showed that any graphical metric could be embedded with distortion O(logn). Linial, London and Rabinovich and Aumman and Rabani used such embeddings to prove an O(log k) approximate max-flow min-cut theorem for k commodity flow problems. A generalization of embeddings that preserve distances between pairs-of points are embeddings that preserve volumes of larger sets. In particular, A (k, c)-volume respecting embedding of n-points in any metric space is a contraction where every subset of k points has within an ck-’ factor of its maximal possible k l-dimensional volume. Feige invented these embeddings in devising a polylogarithmic approximation algorithm for the bandwidth problem using these embeddings. Feige’s methods have subsequently been used by Vempala for approximating versions of the VLSI layout problem. Feise showed that a (k, O(10,g~‘~ n,/m)) volume r&ecting embedding‘ eksted. ” Be -recently found improved (k, 0( mdk log k + log n)) volume respecting embeddings. For metrics arising from planar graphs (planar metrics), we give (k,O(m)) volume respecting contractions. As a corollary, we give embeddings for Permission to makkr digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. XC’99 Miami Beach Florida Copyright ACM 1999 I-581 13-068-6/99/06. $5.00 planar metrics with distortion O(e). This gives rise to an O(e)-approximate max-flow min-cut theorem for multicommodity flow problems in planar graphs. We also give an improved bound for volume respecting embeddings for Euclidean metrics. In particular, we give an (k,O(dog klog D)) volume respecting embedding where D is the ratio of the largest distance to the smallest distance in the metric. Our results give improvements for Feige’s and Vempala’s approximation algorithms for planar and Euclidean metrics. For volume respecting embeddings, our embeddings do not degrade very fast when preserving the volumes of large subsets. This may be useful in the future for approximation algorithms or if volume . respecting embeddings prove to be of independent interest.
Comparing Pre Commit Reviews and Post Commit Reviews Using Process Simulation

Code review in practice is often performed change-based, i.e. using the code changes belonging to a task to determine which code to review. In previous studies, it was found that two variations of this process are used in industry: Pre commit review (review-then-commit) and post commit review (commit-then-review). The choice for one of these variants has implications not only for practitioners deciding on a code review process to use, but also for the development of review tools and for experimentation with review processes. In some situations, a specific variant is clearly preferable due to the nature of the development process or team. In other situations, there are conflicting opinions, and both variants have proponents arguing for their method of choice. So we asked: Are there practically relevant performance differences between pre commit and post commit reviews, and how are these differences influenced by contextual factors? To assess this question, we designed a parametric discrete event simulation model of certain agile development processes. We validated this model with practitioner's feedback and in part also with empirical data from industry. Our analysis of the simulation results indicates that the best choice does depend on the context, but also that there are many situations with no practically relevant difference between both choices. We identified the main influencing factors and underlying effects and condensed our findings into heuristic rules.
Sensing the Internet through crowdsourcing

Portolan is a crowdsourcing-based system aimed at building an annotated graph of the Internet: the smartphones of participating volunteers are used as mobile monitors to collect measures about the network that surrounds them, then results are conveyed on a central server where they are aggregated. Thus, differently from all the other Internet monitoring systems, which are based on fixed infrastructure, Portolan rely on a multitude of mobile sensing nodes. While this paves the way to the opportunity of having detailed and geo-referenced measures, the design of the systems has to take into account additional difficulties such as scalability, coordination and smartphones' reachability. Besides describing the Portolan's architecture, this paper also shows some preliminary results that confirm the validity of the proposed approach.
Screening of hepatocyte proteins binding with C-terminally truncated surface antigen middle protein of hepatitis B virus (MHBst167) by a yeast two-hybrid system

The function of middle hepatitis B surface protein C-terminally truncated at amino acid position 167 (MHBst167) is not currently clear. This study aimed to screen and identify the proteins that interact with MHBst167 in hepatocytes using a yeast two-hybrid system, and to explore the effects of MHBst167 in the development of hepatocellular carcinoma and precancerous diseases of the liver. The MHBst167 gene was amplified by polymerase chain reaction (PCR) and cloned into a pGEM-T vector. The target region was sequenced and the constructed bait plasmid, pGBKT7-MHBst167, was transformed into AH109 yeast cells. The transformed AH109 cells were then mated with Y187 yeast cells containing the fetal liver cDNA library plasmid using a yeast two-hybrid system. The false positives were eliminated and the true positive clones were selected by PCR and sequencing analysis. The pGBKT7-MHBst167 bait plasmid was successfully constructed and 66 clones grew in the selective synthetic defined media lacking leucine, tryptophan, histidine and adenine. Fifty-two clones were identified following X-α-Gal selection and segregation analysis. Seven proteins were found to be expressed that could interact with MHBst167 in hepatocytes by the yeast two-hybrid system. These results have provided novel insights into the biological functions of MHBst167.
Empirical Investigation of the Impact of Organizational Culture on Human Resource Management

The study investigated empirically the impact of organizational culture on human resource management using the CUBS project (MSH/AFRICARE) in Nigeria as the case study. In order to achieve the research objectives, one research questions and hypotheses regarding organizational culture and human resource management were formulated. The study employed quantitative method. The case study organization was selected because the majority of studies conducted in Nigeria on similar or related topics were conducted on business or public organizations. It was in the light of the above that the present study opted for an NGO organization. The respondents of the study comprised the staff of CUBS and heads of civil service organizations (CSOs) who were recipients of CUBS funding across seven states in Nigeria. The outcome of the quantitative analysis reveals that organizational culture has a positive and significant impact on human resource management, and that organizational culture determines human resource management practice in the organization. The findings of this study have practical implications for organizational leaders, managers and employees, and their organizations, particularly NGOs in Nigeria, besides offering scope for further research in the area of organizational development
Probabilistic Programming Concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years.
Bangladeshi Sign Language Recognition employing Neural Network Ensemble

paper proposes a Bangladeshi sign language recognizer (BdSLR), an initiative to recognize sign language of Bangladeshi deaf and mute (D&M) people. Although all over the world, the D&M people are a part of the community, the communication between the general and the D&M people becomes tough when interaction is required. Moreover in different races, the D&M people use different sign languages. In this regard BdSLR has been developed that can interpret Bangladeshi sign language into Bengali text and vice versa. In BdSLR, the inputs of Bangladeshi sign language have been taken by webcam and later on recognized by efficient Neural Network Ensemble (NNE). Without any major modification, BdSLR can be used as an interpreter for the sign languages of other races. In the Proposed BdSLR the use of an efficient NNE technique converges the training time faster and recognizes with good generalization ability.
Comparison of Brain Development in Sow-Reared and Artificially Reared Piglets

Introduction Provision of adequate nutrients is critical for proper growth and development of the neonate, yet the impact of breastfeeding versus formula feeding on neural maturation has to be fully determined. Using the piglet as a model for the human infant, our objective was to compare neurodevelopment of piglets that were either sow-reared (SR) or artificially reared (AR) in an artificial setting. Methods Over a 25-day feeding study, piglets (1.5 ± 0.2 kg initial bodyweight) were either SR (n = 10) with ad libitum intake or AR (n = 29) receiving an infant formula modified to mimic the nutritional profile and intake pattern of sow’s milk. At study conclusion, piglets were subjected to a standardized set of magnetic resonance imaging (MRI) procedures to quantify structure and composition of the brain. Results Diffusion tensor imaging, an MRI sequence that characterizes brain microstructure, revealed that SR piglets had greater (P < 0.05) average white matter (WM) (generated from a piglet specific brain atlas) fractional anisotropy (FA), and lower (P < 0.05) mean and radial and axial diffusivity values compared with AR piglets, suggesting differences in WM organization. Voxel-based morphometric analysis, a measure of white and gray matter (GM) volumes concentrations, revealed differences (P < 0.05) in bilateral development of GM clusters in the cortical brain regions of the AR piglets compared with SR piglets. Region of interest analysis revealed larger (P < 0.05) whole brain volumes in SR animals compared with AR, and certain subcortical regions to be larger (P < 0.05) as a percentage of whole brain volume in AR piglets compared with SR animals. Quantification of brain metabolites using magnetic resonance spectroscopy revealed SR piglets had higher (P < 0.05) concentrations of myo-inositol, glycerophosphocholine + phosphocholine, and creatine + phosphocreatine compared with AR piglets. However, glutamate + glutamine levels were higher (P < 0.05) in AR piglets when compared with SR animals. Conclusion Overall, increases in brain metabolite concentrations, coupled with greater FA values in WM tracts and volume differences in GM of specific brain regions, suggest differences in myelin development and cell proliferation in SR versus AR piglets.
Effects of a Lifestyle-Based Physical Activity Intervention on Medical Expenditure in Japanese Adults: A Community-Based Retrospective Study

Background. This study aimed to investigate whether a lifestyle-based physical activity program could contribute to reduced medical expenditure. Methods. The study participants were 60 adults aged 63.1 (standard deviation, 4.4) years in the intervention group; the case-control group consisted of 300 adults who were randomly selected from Japan's national health insurance system. This community-based retrospective study incorporated a 3-year follow-up. Results. The total and outpatient medical expenditure in the intervention group were significantly lower than in the control group: total expenditure, $US640.4/year; outpatient expenditure, $369.1/year. The odds ratio for outpatient visiting was 6.47-fold higher in the control than in the intervention group. Conclusion. Our study suggests that a health program to promote physical activity can result in reduced total medical expenditure, outpatient medical expenditure, and possibly also inpatient medical expenditure.
Combining Rule-Based Methods and Latent Semantic Analysis for Ontology Structure Construction

We report on a pilot study of techniques for organizing terms derived from relevant corpora into semantic clusters. We combine rule-based techniques and an unsupervised statistical method to semantically cluster terms extracted from medical reports. The rule-based approach identifies lexicosyntactic patterns suggestive of parent-child relations and coordination structures suggestive of sibling-sibling relations. Latent semantic analysis (LSA) is applied to those relations to weed spurious pairs. Two domain experts evaluated the results. Applying LSA as a filter to the relations extracted by rules did not improve the precision for either parent-child relations (52%) or sibling-sibling relations (71%).