Miracle-dz/UniFAR-FTUs huggingface dataset:
Using latent semantic analysis to grade brief summaries: some proposals

In this paper, we present several proposals in order to improve the LSA tools to evaluate brief summaries (less than 50 words) of narrative and expository texts. First, we analyse the quality of six different methods assessing essays that have been widely employed before (Foltz et al., 2000). The second objective is to analyse how new algorithms inspired by some authors (Denhiere et al., 2007) that try to emulate human behaviour to improve the reliability of LSA with human graders when assessing short summaries, compared with standard LSA use in expository text. Finally, we present an assessment method to combine LSA as a semantic computational linguistic model with ROUGE-N as a lexical model, to show how combining different automatic evaluation systems (LSA and ROUGE) can improve the quality of assessments in different academic levels.
Information Theoretic Face Detection : A Survey

This paper surveys the current applications of Information Theory to detecting faces in arbitrary images. We cover techniques using the methods, models, and measures of Principal Component Analysis (PCA) for constructing eigenfaces, distance from face space (DFFS), Kullback relative information, Most Important Pixels (MIP), Markov processes, and Markov Random Fields (MRF). Both view-based and modelbased methods are discussed as well as how the mutual information among faces explains psychological observations.
On-chip caches built on multilevel spin-transfer torque RAM cells and its optimizations

It has been predicted that a processor's caches could occupy as much as 90% of chip area a few technology nodes from the current ones. In this article, we investigate the use of multilevel spin-transfer torque RAM (STT-RAM) cells in the design of processor caches. We start with examining the access (read and write) scheme for multilevel cell (MLC) STT-RAM from a circuit design perspective, detailing the read and write circuits. Compared to traditional SRAM caches, a multilevel cell (MLC) STT-RAM cache design is denser, fast, and requires less energy. However, a number of critical architecture-level issues remain to be solved before MLC STT-RAM technology can be deployed in processor caches. We shall offer solutions to the issue of bit encoding as well as tackle the write endurance problem. In particular, the latter has been neglected in previous works on STT-RAM caches. We propose a set remapping scheme that can potentially prolong the lifetime of a MLC STT-RAM cache by 80× on average. Furthermore, a method for recovering the performance that may be lost in some applications due to set remapping is proposed. The impacts of process variations of the MLC STT-RAM cell on the robustness of the memory hierarchy is also discussed, together with various enhancement techniques, namely, ECC and design redundancy.
Diffusion of Competing Innovations: The Effects of Network Structure on the Provision of Healthcare

Medical innovations, in the form of new medication or other clinical practices, evolve and spread through health care systems, impacting on the quality and standards of health care provision, which is demonstrably heterogeneous by geography. Our aim is to investigate the potential for the diffusion of innovation to influence health inequality and overall levels of recommended care. We extend existing diffusion of innovation models to produce agent-based simulations that mimic population-wide adoption of new practices by doctors within a network of influence. Using a computational model of network construction in lieu of empirical data about a network, we simulate the diffusion of competing innovations as they enter and proliferate through a state system comprising 24 geo-political regions, 216 facilities and over 77,000 individuals. Results show that stronger clustering within hospitals or geo-political regions is associated with slower adoption amongst smaller and rural facilities. Results of repeated simulation show how the nature of uptake and competition can contribute to low average levels of recommended care within a system that relies on diffusive adoption. We conclude that an increased disparity in adoption rates is associated with high levels of clustering in the network, and the social phenomena of competitive diffusion of innovation potentially contributes to low levels of recommended care.
Microscopic and Macroscopic Schema Theories for Genetic Programming and Variable-length Genetic Algorithms with One-Point Crossover, their Use and their Relations with Earlier GP and GA Schema Theories

A few schema theorems for GP have been proposed in the literature in the last few years. One of their main weaknesses is that they provide only a lower bound for the expected value of the number of instances of a given schema H at the next generation, E[m(H; t+ 1)℄, rather than an exact value. This paper presents new theoretical results for GP with one-point crossover which overcome this problem. Firstly, we give an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Thanks to this formulation we are then able to provide in improved version for an earlier GP schema theorem in which some (but not all) schema creation events are accounted for, thus obtaining a tighter bound for E[m(H; t+ 1)℄. Then, we extend the microscopic schema theorem to obtain an exact formulation of E[m(H; t + 1)℄ in terms of macroscopic quantities. In this formulation E[m(H; t+1)℄ is a function of the selection probabilities of the schema itself and of a set of lower-order schemata which one-point crossover uses to build instances of the schema. This result makes the effects and the mechanisms of schema creation explicit, unlike previous work which concentrated on schema survival and disruption. This supports the existence of building blocks in GP which, however, are not necessarily all short, low-order or highly fit. Also, the macroscopic schema theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, only to mention some possibilities.
Bayesian Inference Via Classes of Normalized Random Measures

One of the main research areas in Bayesian Nonparametrics is the proposal and study of priors which generalize the Dirichlet process. Here we exploit theoretical properties of Poisson random measures in order to provide a comprehensive Bayesian analysis of random probabilities which are obtained by an appropriate normalization. Specifically we achieve explicit and tractable forms of the posterior and the marginal distributions, including an explicit and easily used description of generalizations of the important Blackwell-MacQueen Polya urn distribution. Such simplifications are achieved by the use of a latent variable which admits quite interesting interpretations which allow to gain a better understanding of the behaviour of these random probability measures. It is noteworthy that these models are generalizations of models considered by Kingman (1975) in a non-Bayesian context. Such models are known to play a significant role in a variety of applications including genetics, physics, and work involving random mappings and assemblies. Hence our analysis is of utility in those contexts as well. We also show how our results may be applied to Bayesian mixture models and describe computational schemes which are generalizations of known efficient methods for the case of the Dirichlet process. We illustrate new examples of processes which can play the role of priors for Bayesian nonparametric inference and finally point out some interesting connections with the theory of generalized gamma convolutions initiated by Thorin and further developed by Bondesson.
Two-dimensional pilot-aided channel estimation for wireless OFDM systems over severe frequency-selective fading environments

Orthogonal frequency division multiplexing (OFDM) technique is effective and powerful in high data rate digital transmission due to its spectral efficiency, robustness in multipath propagation environments and ability to cope with intersymbol interference. Channel estimation is a crucial problem in coherent OFDM systems, and the various estimation techniques with particular pilot arrangements have been investigated recently. In this paper, a two-dimensional pilot-aided channel estimation technique is developed for wireless OFDM communications under severe frequency-selective fading channels. By applying least-square (LS) estimation for the leading pilot symbol in time domain, the channel statistics can be obtained and preserved. And then, LS estimation is also applied for the following regular pilot tones in the OFDM data symbol. Based on the variation of the two LS estimates in the time domain, a novel two-dimensional pilotaided channel estimation algorithm is presented. From the simulation results, the proposed channel estimation scheme has superior performance in the severe frequency-selective fading channels, especially in high SNR. Even if the mobile speed rises, the proposed algorithm still achieves excellent performance.
Introduction to the dna package

In this vignette, a description is given for an R (R Development Core Team, 2014) package dna which implements statistical tests for differential network analysis described in Gill, Datta, and Datta (2010) based on connectivity scores. Built-in options for computing the connectivity scores include partial least squares (PLS), principal components, ridge regression, and correlation; the package also provides the option for the user to specify a custom method for computing the scores. An example of using the package with a custom method based on the least absolute shrinkage and selection operator (LASSO) is presented with code in Section 4. Once the method for computing the scores has been selected, the following three types of tests are implemented by the dna package.
Community Detection with Similarity Transition

Analysis of social networks, in particular discovering communities within networks, has been a focus of recent work and has a variety of applications in many fields. Usually a network is converted to a graph, from which a set of dense subgraphs are identified and regarded as distinct communities. This paper presents a method for identifying a set of dense subgraphs in a given graph. We exploit the idea of matrix column or row similarities, in order to find the vertices of concern in the graph. We apply punishment to the relation matrix and use it to complement the adjacency matrix several times according to the diameter of the graph. Furthermore, not every participating node in the network is required to belong to a certain community. The effectiveness of the proposed method is demonstrated in several artificial and real-life examples.
Detecting data misuse by applying context-based data linkage

Detecting data leakage/misuse poses a great challenge for organizations. Whether caused by malicious intent or an inadvertent mistake, data leakage/misuse can diminish a company's brand, reduce shareholder value, and damage the company's goodwill and reputation. This challenge is intensified when trying to detect and/or prevent data leakage/misuse performed by an insider with legitimate permissions to access the organization's systems and its critical data. In this paper we propose a new approach for identifying suspicious insiders who can access data stored in a database via an application. In the proposed method suspicious access to sensitive data is detected by analyzing the result-sets sent to the user following a request that the user submitted. Result-sets are analyzed within the instantaneous context in which the request was submitted. From the analysis of the result-set and the context we derive a "level of anomality". If the derived level is above a predefined threshold, an alert can be sent to the security officer. The proposed method applies data-linkage techniques in order to link the contextual features and the result-sets. Machine learning algorithms are then employed for generating a behavioral model during a learning phase. The behavioral model encapsulates knowledge on the behavior of a user; i.e., the characteristics of the result-sets of legitimate or malicious requests. This behavioral model is used for identifying malicious requests based on their abnormality. An evaluation with sanitized data shows the usefulness of the proposed method in detecting data misuse.
Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study

Background There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one). Objectives We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk. Methods We conducted a population-based case–control study of NHL in four National Cancer Institute–Surveillance, Epidemiology, and End Results centers—Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington—from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL. Results The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa. Conclusions An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk. Citation Czarnota J, Gennings C, Colt JS, De Roos AJ, Cerhan JR, Severson RK, Hartge P, Ward MH, Wheeler DC. 2015. Analysis of environmental chemical mixtures and non-Hodgkin lymphoma risk in the NCI-SEER NHL Study. Environ Health Perspect 123:965–970; http://dx.doi. org/10.1289/ehp.1408630
Patterns of allozyme diversity in the threatened plant Erigeron parishii (Asteraceae).

Thirty-one occurrences of Erigeron parishii, a narrowly endemic plant threatened by mining, were sampled for allozyme diversity. This taxon held considerable genetic variation at the 14 allozyme loci surveyed. Species (e.g., alleles per locus [A] = 4.3 and proportion of polymorphic loci [P] = 0.64) and population (e.g., A = 2.15 [SD = 0.3] and P = 0.53 [SD = 0.12]) genetic diversity measures were higher than expected for narrowly endemic plant taxa. The proportion of polymorphic loci and numbers of alleles per locus indicated that E. parishii has not experienced severe or long-lasting population bottlenecks. Within-population f indicated low to moderate levels of inbreeding. Populations were only moderately differentiated (theta-p = 0.12), suggesting either that there is substantial gene flow among populations or that populations have not been isolated long enough to detect effects of genetic drift. There was no significant differentiation among populations in different vegetation types nor was there a relationship between genetic distance and geographic distance among sites. Continued fragmentation by mining activities would isolate populations, disrupting gene flow, exacerbating loss of diversity, and increasing inbreeding in the remaining fragments. Protection of large, interconnected populations throughout the range of the taxon is warranted to maintain processes that have sustained the observed diversity.
Adaptive Probability-Based Power Management Strategies (Tech Report# UCI-ICS-01-58)

Dynamic Power Management (DPM) is an important technique to reduce power consumption in embedded and portable systems. Power hungry devices such as disk drives, network interfaces, and other peripheral devices are often designed with multiple power saving states, and control knobs are provided for changing their power states under the operating system control. DPM strategies are “online” strategies since they must make decisions about the timing of transitioning to lower power consumption states during idle periods without knowing when the next request for service will arrive. In this paper, we present a novel approach to designing adaptive online DPM strategies. This approach dynamically learns the probability distribution of idle period lengths from recent request patterns. A probability-based scheme then uses this information to optimize power saving actions. We present experimental results comparing our strategy with various strategies in the literature, including our previous work. Our study includes measuring power usage as well as the additional latency introduced from the delay in powering back up when a new request for service arrives. Our strategy exhibits the lowest power consumption among all the online algorithms. The other algorithms which come close to matching its performance in power all suffer at least an additional 40% latency on average. Meanwhile, the algorithms which have comparable latency to our method all use at least 25% more power on average. Thus, our probability-based DPM strategy is the most successful algorithm in balancing power usage as well as latency incurred.
Identifying interesting topology structure of membrane protein interaction networks based on intelligent agents

Recently, a collective effort from multiple research areas has been made to understand membrane protein and its interaction network. These researches require the ability to simulate particular biological systems as cells, organs, organisms and communities. In this paper, a novel membrane protein interaction network simulator is proposed by combining intelligent agents. We consider a set of active computational components interacting with each other and with an external environment. Then, as a demonstration, we apply membrane protein interaction network simulator into constructing membrane protein interaction network. Interactions among small membrane protein interaction networks can emerge a large membrane protein interaction network which has its remarkable ability to accomplish a complex function or task. We also discuss intelligent agents based dynamic and collective performance of membrane protein interaction network. Based on the proposed approach, we find out hub proteins, which should be valuable for deciphering unknown protein functions, determining protein complexes, and researching medicine.
Optical identification of XMM sources in the CFHTLS

We present optical spectroscopic identifications of X-ray sources in ∼3 square degrees of the XMM-Large Scale Structure survey (XMM-LSS), also covered by the Canada France Hawaii Telescope Legacy Survey (CFHTLS), obtained with the AAOmega instrument at the Anglo Australian Telescope. In a flux limited sample of 829 point like sources in the optical band with g 6 22 mag and the 0.5 − 2 keV flux (f0.5−2keV) > 1 × 10 erg cm s, we observed 695 objects and obtained reliable spectroscopic identification for 489 sources, ∼ 59% of the overall sample. We therefore increase the number of identifications in this field by a factor close to five. Galactic stellar sources represent about 15% of the total (74/489). About 55% (267/489) are broadline Active Galactic Nuclei (AGNs) spanning redshifts between 0.15 and 3.87 with a median value of 1.68. The optical-to-X-ray spectral index (αox) of the broad-line AGNs is 1.47 ± 0.03, typical of optically-selected Type I quasars and is found to correlate with the rest frame X-ray and optical monochromatic luminosities at 2 keV and 2500 Å respectively. Consistent with previous studies, we find αox not to be correlated with z. In addition, 32 and 116 X-ray sources are, respectively absorption and emission-line galaxies at z < 0.76. From a line ratio diagnostic diagram it is found that in about 50% of these emission line galaxies, the emission lines are powered significantly by the AGN. Thirty of the XMM sources are detected at one or more radio frequencies. In addition, 24 sources have ambiguous identification: in 8 cases, two XMM sources have a single optical source within 6 of each of them, whereas, 2 and 14 XMM sources have, respectively, 3 and 2 possible optical sources within 6 of each of them. Spectra of multiple possible counterparts were obtained in such ambiguous cases.
Knowledge management, learning and trust in e-business operations

It is argued that enterprise knowledge surfaces through organisational learning. Knowledge management is regarded as a critical system through which knowledge from separate core business functions can be integrated in order to create new business models that enable business to be conducted via e-commerce. Trust is identified as a key underpinning condition in enterprise cultures without which knowledge sharing may be constrained. Evidence from ongoing empirical research suggests that some UK enterprises my not yet be leveraging knowledge management effectively in order to become e-businesses.
Scaling Exponent Determined by a Bio-Signal Computation for the Healthy and Diseased Heartbeat

We analyzed heartbeat-intervals by using our own program of detrended fluctuation analysis (DFA). “Alternans” is an arrhythmia exhibiting alternating amplitude or alternating interval from heartbeat to heartbeat, which was first described in 1872 by Traube. Recently, alternans was finally recognized as the harbinger of a cardiac disease because physicians noticed that an ischemic heart exhibits alternans. To quantify irregularity of the heartbeat including alternans, we used the DFA and revealed that the alternans rhythm lowers the scaling exponent. We conclude that the scaling exponent calculated by the DFA reflects a risk for the “failing” heart. The scaling exponents could determine whether the subjects are under sick or healthy conditions on the basis of cardiac physiology.
Major legal systems in the world

The application of legal system varies from country to country and is largely shaped by the unique history of a particular country. Generally, the most widespread legal systems in the world are the Civil Law, the Common Law and the Religious Law. This chapter discusses the general characteristics of the above three major legal systems in the world.
Exploring Voting Anomalies Using a Demand Revealing Random Price Voting Mechanism

Recent papers show that in group decisions individuals have social preferences for efficiency and equity. However, the effect of social preferences on voting, the predominant funding mechanism for public goods, has not been thoroughly examined. This study investigates whether voting decisions are affected by the distribution of net benefits associated with a proposed public program using a new Random Price Voting Mechanism (RPVM). Theoretical and econometric analysis of experimental results presented in the paper suggest that observed differences from selfish voting are caused by a concern for social efficiency, and that voting may be more efficient than previously thought.