Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevant Factors for Implementation of Operational-level IS/ICT Processes in Small IT Organizations

Having IS/ICT processes compliant according to well known standards like COBIT or ITIL is relatively popular especially among larger organizations (to which these standard are primarily aimed). This paper discusses how standardization of processes affects or is affected by a selected set of process characteristics and tries to provide general guidelines which should be considered prior to their implementation (standards). Special attention is paid to the specifics of small IS/ICT organizations since implementation of these frameworks (intended for rather larger organizations) represents in this context more demanding endeavor.
R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems

Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.
Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
On proper simulation of phenomena influencing image formation in fluorescence microscopy

The simulation plays an important role in biomedical image analysis as it can inherently provide large collections of image data with absolute ground truth. Compared to real image data typically annotated by some expert, the computer generated data still lacks the authenticity due to the simplifications of various natural phenomena. In this paper, we focus on simulating the photobleaching effect and the uneven illumination, being simplified or even omitted from the majority of present simulation toolkits, to considerably improve the authenticity of computer generated data.
Tree Structure for Modeling Skin Lesion Growth

Tim K. Lee,∗,†,‡,§ Sina Khakabi,‡ Paul Wighton,# Harvey Lui,∗,† David I. McLean† and M. Stella Atkins†,‡ ∗Departments of Cancer Control Research and Integrative Oncology, BC Cancer Agency, Vancouver, Canada †Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, Canada ‡School of Computing Science, Simon Fraser University, Burnaby, Canada #Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA §Email: tlee@bccrc.ca
Indsatsledelse som profession: Spændingsfeltet mellem uddannelse og praksis

Artiklen stiller skarpt på udfoldelsen af en bestemt ledelsesfunktion – indsatsledelse. Gennem observation af de enkelte træningselementer i den formelle indsatslederuddannelse, samt gennem hjelmkameraoptagelser fra virkelige ulykker, analyseres ledelsesfunktionen med afsæt i indsatslederens situationsbedømmelse og ledelsesidentitet. Artiklen viser, at der opstår et gab mellem de reflekterende og refleksive metoder, der indlæres i det formelle uddannelsesprogram, over for den kompromisløse hverdagspraksis, som ledelsesfunktionen bliver udfoldet i efterfølgende. Ledelsesfunktionen i hverdagen adskiller sig fra uddannelseskonteksten ved ikke udelukkende at være rettet mod at skabe mening om selve sagen, men i lige så høj grad diskursivt forhandler den enkelte leders magtposition på skadestedet.
Robust stabilization of a class of uncertain time-delay fuzzy bilinear systems

This paper explores controller design and stability condition for a class of T-S uncertain time-delay fuzzy bilinear systems (TDFBSs). Utilizing the parallel distributed compensation (PDC) method, the fuzzy controller is designed which ensures the robust asymptotic stability of the T-S uncertain TDFBS. The stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities (LMIs). Finally, the merit of the proposed schemes are demonstrated by the simulation results.
Geometrical facial modeling for emotion recognition

Facial expressions are the facial changes in response to a person's internal emotional states, intentions, or social communications. Facial expression analysis has been an active research topic for behavioral scientists since the work of Darwin in 1872. It includes both measurement of facial motion and recognition of expression. There are two different ways to analyze facial expressions: one considers facial affect (emotion) and the other facial muscular movements. Many researchers argue that there is a set of basic emotions which were preserved during evolutive process because they allow the adaption of the organisms behavior to distinct daily situations. This paper discusses emotion recognition based on analysis of facial elements. Different feature sets are proposed to represent the characteristics of the human face and their performance is evaluated using Machine Learning techniques. The results indicate that the selected facial features represent a valid approach for emotion identification.
Genomic organization of the FMRFamide gene in Lymnaea: multiple exons encoding novel neuropeptides

Based on the sequencing of genomic and cDNA clones, we were able to determine that the FMRFamide gene consists of five exons covering at least 20 kb and predict the presence of further novel peptides. The exons are alternatively spliced: exon I (hydrophobic leader sequence) to exon II (tetrapeptides) and exon I to exons III (heptapeptides), IV, and V. A cDNA clone encoding the heptapeptides is described and has also been shown to encode further novel peptides SKPYMRFamide, HDYMRFamide, and SSFPRYamide. Analysis of the right internal parietal nerve using mass spectrometry showed that the novel peptide SKPYMRFamide was cleaved from the precursor. This peptide excites neurons, suggesting a physiological function in the CNS.
Wortmannin causes mistargeting of procathepsin D. evidence for the involvement of a phosphatidylinositol 3-kinase in vesicular transport to lysosomes

At present little is known of the biochemical machinery controlling transport of newly synthesized lysosomal hydrolases from the trans- Golgi network (TGN) to endosomes. The demonstration that Vps34p (a protein required for targeting soluble hydrolases to the vacuole in Saccharomyces cerevisiae) is a phosphatidylinositol 3-kinase (PI3-K) suggested the possibility that a homologous enzyme might be involved in the equivalent step in mammalian cells. Using the PI3-K inhibitors wortmannin and LY294002, I provide evidence to support this hypothesis. Treatment of K-562 cells with wortmannin induced secretion of procathepsin D, with half-maximal inhibition of accurate targeting to lysosomes at 10-20 nM. Kinetic analysis indicated that a late Golgi (TGN) step was affected, and that other constitutive vesicular transport events were not. The M6P recognition signal was still generated in the presence of wortmannin suggesting that the drug was directly inhibiting export of the receptor-ligand complex from the TGN, while removal of the drug led to a rapid restoration of accurate sorting. At the concentrations used, wortmannin and LY294002 are presently accepted to be specific inhibitors of PI3-K. I conclude that these data implicate such an enzyme in the trafficking of M6P-receptor- ligand complexes from the TGN towards lysosomes.
Effect of Herbal Prescriptions in Accordance with Pattern Identification in Acute Cerebral Infarction Patients: Based on Fire-Heat Pattern

Objectives. This study was conducted to verify the necessity of corresponding prescription to the diagnosed pattern in acute cerebral infarction patients. Methods. We studied cerebral infarction patients hospitalized within 30 days after the ictus. Forty-four clinical indicators, Motricity Index (MI) score, Scandinavian Stroke Scale (SSS) score, and herbal prescriptions were checked twice, two weeks apart. The probability of each pattern was calculated based on the clinical indicators. Changes in MI score, SSS score, and the probability of fire-heat pattern were compared between the pattern-prescription correspondence group and the noncorrespondence group. Results. Increments of MI score and SSS score in the correspondence group were significantly greater than those of the noncorrespondence group (p = 0.003, p = 0.001) while the baseline score of the two groups showed no significant difference. Probability of fire-heat pattern decreased significantly in the correspondence group (p = 0.013) while the noncorrespondence group showed no significant difference after the treatment. Conclusion. Acute cerebral infarction patients who are diagnosed as fire-heat pattern showed better improvement in dysfunctions caused by the disease when they took the pattern corresponding prescriptions. This study provides evidence for the necessity and usefulness of pattern identification in Traditional Korean Medicine.
What is disputed on the web?

We present a method for automatically acquiring of a corpus of disputed claims from the web. We consider a factual claim to be disputed if a page on the web suggests both that the claim is false and also that other people say it is true. Our tool extracts disputed claims by searching the web for patterns such as "falsely claimed that X" and then using a statistical classifier to select text that appears to be making a disputed claim. We argue that such a corpus of disputed claims is useful for a wide range of applications related to information credibility on the web, and we report what our current corpus reveals about what is being disputed on the web.
Recommendations using Absorbing Random Walks

Collaborative filtering attempts to find items of interest for a user by utilizing the preferences of other users. In this paper we describe an approach to filtering that explicitly uses social relationships, such as friendship, to find items of interest to a user. Modeling user-item relations as a bipartite graph we augment it with user-user (social) links and propose an absorbing random walk that induces a set of stationary distributions, one per user, over all items. These distributions can be interpreted as personalized rankings for each user. We exploit sparsity of both user-item and user-user relationships to improve the efficiency of our algorithm.
Cisplatin-Associated Ototoxicity: A Review for the Health Professional

Cisplatin is an effective drug used in the treatment of many cancers, yet its ototoxic potential places cancer patients, exposed to this drug, at risk of hearing loss, thus negatively impacting further on a patient's quality of life. It is paramount for health care practitioners managing such patients to be aware of cisplatin's ototoxic properties and the clinical signs to identify patients at risk of developing hearing loss. English peer-reviewed articles from January 1975 to July 2015 were assessed from PubMed, Science Direct, and Ebscohost. Seventy-nine articles and two books were identified for this review, using MeSH terms and keywords such as “ototoxicity”, “cisplatin”, “hearing loss”, and “ototoxicity monitoring”. This review provides an up-to-date overview of cisplatin-associated ototoxicity, namely, its clinical features, incidence rates, and molecular and cellular mechanisms and risk factors, to health care practitioners managing the patient with cancer, and highlights the need for a team-based approach to complement an audiological monitoring programme to mitigate any further loss in the quality of life of affected patients, as there is currently no otoprotective agent recommended routinely for the prevention of cisplatin-associated ototoxicity. It also sets the platform for effective dialogue towards policy formulation and strengthening of health systems in developing countries.
Tropical Thermostats and Low Cloud Cover

Abstract The ability of subtropical stratus low cloud cover to moderate or amplify the tropical response to climate forcing such as increased CO2 is considered. Cloud radiative forcing over the subtropics is parameterized using an empirical relation between stratus cloud cover and the difference in potential temperature between 700 mb (a level that is above the trade inversion) and the surface. This relation includes the empirical negative correlation between SST and low cloud cover and is potentially a positive feedback to climate forcing. Since potential temperature above the trade inversion varies in unison across the Tropics as a result of the large-scale circulation and because moist convection relates tropospheric temperature within the convecting region to variations in surface temperature and moisture, the subtropical potential temperature at 700 mb depends upon surface conditions within the convecting region. As a result, subtropical stratus cloud cover and the associated feedback depend upon the.
Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist's image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden's J statistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.
Biological Sequence Alignment on Graphics Processing Units

Sequence alignment is a common and often repeated task in molecular biology. The need for speeding up this treatment comes from the rapid growth rate of biological sequence databases. In this paper we present a new approach to high performance biological sequence database scanning on graphics processing units. Using modern graphics processing units for high performance computing is facilitated by their enhanced programmability and motivated by their attractive price/performance ratio and incredible growth in speed. To derive an efficient mapping onto this type of architecture, we have reformulated the Smith-Waterman dynamic programming algorithm in terms of computer graphics primitives. This results in an implementation with significant runtime savings on two standard off-theshelf computer graphics cards. To our knowledge this is the first reported mapping of biological sequence alignment onto a graphics processing unit.
Erim Report Series Research in Management How and Why Decision Models Influence Marketing Resource Allocations Bibliographic Data and Classifications

We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be " good? " ; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes. Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes.
A Retrospective Assessment of Occupational Exposure to Elemental Carbon in the U.S. Trucking Industry

Background: Despite considerable epidemiologic evidence about the health effects of chronic exposure to vehicle exhaust, efforts at defining the extent of risk have been limited by the lack of historical exposure measurements suitable for use in epidemiologic studies and for risk assessment. Objectives: We sought to reconstruct exposure to elemental carbon (EC), a marker of diesel and other vehicle exhaust exposure, in a large national cohort of U.S. trucking industry workers. Methods: We identified the predictors of measured exposures based on a statistical model and used this information to extrapolate exposures across the cohort nationally. These estimates were adjusted for changes in work-related conditions over time based on a previous exposure assessment of this industry, and for changes in background levels based on a trend analysis of historical air pollution data, to derive monthly estimates of EC exposure for each job and trucking terminal combination between 1971 and 2000. Results: Occupational exposure to EC declined substantially over time, and we found significant variability in estimated exposures both within and across job groups, trucking terminals, and regions of the United States. Average estimated EC exposures during a typical work shift ranged from < 1 μg/m3 in the lowest exposed category in the 1990s to > 40 μg/m3 for workers in the highest exposed jobs in the 1970s. Conclusions: Our results provide a framework for understanding changes over time in exposure to EC in the U.S. trucking industry. Our assessment should minimize exposure misclassification by capturing variation among terminals and across U.S. regions, and changes over time.
A Non-rigid Feature Extraction Method for Shape Recognition

This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
Interval-valued regression and classication models in the framework of machine learning

We present a new approach for constructing regression and classication models for interval-valued data. The risk functional is considered under a set of probability distributions, resulting from the application of a chosen inferential method to the data, such that the bounding distributions of the set depend on the regression and classication parameter. Two extreme (‘pessimistic’ and ‘optimistic’) strategies of decision making are presented. The method is applicable with many inferential methods and risk functionals. The general theory is presented together with the specic optimisation problems for several scenarios, including the extension of the support vector machine method for interval-valued data.