Miracle-dz/UniFAR-FTUs huggingface dataset:
, Halley ( 2020 ) Personality factors in exercise addiction : a pilot study exploring the role of narcissism , extraversion , and agreeableness

Despite the increased evidence and acceptance of exercise being classed as a behavioral addiction, there is limited research examining personality characteristics within exercise addicts. The purpose of this study was to examine three personality traits (narcissism, extraversion, and agreeableness) and to examine their role in exercise addiction. The sample comprised 114 voluntary participants (74 females and 40 males) who completed the (i) Exercise Addiction Inventory, (ii) Narcissistic Personality Inventory, and (iii) Ten-Item Personality Inventory, as well as demographic questions and questions concerning their engagement and intensity levels of exercise. Results indicated a low incidence of individuals who were classed as at risk of exercise addiction (7%), but a high incidence of symptomatic individuals (75%). Results suggested that extraversion and narcissism may be underlying factors in exercise addiction with no effect for agreeableness. Exercise engagement and intensity were also related to exercise addiction. Further research examining the relationship between personality types and exercise addiction may be useful in identifying individuals at risk for developing exercise addiction.
QueryMed : An Intuitive SPARQL Query Builder for Biomedical RDF Data

We have developed an open-source SPARQL query builder and result set visualizer for biomedical data, QueryMed, that allows end users to easily construct and run translational medicine queries across multiple data sources. QueryMed is flexible enough to allow queries relevant to a wide range of biomedical topics, and can run queries across multiple SPARQL endpoints. It is designed to be accessible to users who are not familiar with the structure of the underlying ontologies used in describing the datasets or with the SPARQL query language used to query the data. The system allows users to select the data sources that they wish to use, drawing on their specialized domain knowledge to decide the most appropriate data sources to query. Users can add additional data sources if they are interested in querying endpoints that are not in the default list. After retrieval of the initial result set, query results can be filtered to improve their relevance. The system also allows the user to exploit the underlying structure of the RDF data to improve query results.
Consumer Decision-Making Styles: Relationships with Consumer Alienation

By understanding the relationship between consumer decision-making style and consumer alienation, insight can be gained into consumers and the manifestations of their wants and needs, especially as it concerns discretionary purchases (which comprise most purchases). Individuals possessing differing consumer decision-making styles are hypothesized to possess differing degrees of alienation from the marketplace. The results support several of the hypotheses. The relationships, however, appear to be more complex than hypothesized. The implications for retailers are discussed.
Identification of Incident Injuries in Hospital Discharge Registers

Background: Hospital discharge data on injuries constitute a potentially powerful data source for epidemiologic studies. However, reliable identification of incident injury admissions is necessary. The objective of this study was to develop a prediction model for identifying incident hospital admissions, based on variables derived from a hospital discharge register. Methods: There were 743,022 hospital admissions for injury in Sweden 1998–2004. Of these, 23,920 were in the county of Uppsala and 24% of these people had previous injury admissions. To determine if these admissions were new injuries or readmissions for earlier injuries, we reviewed 817 randomly selected hospital records. A prediction model for incident injury admissions was developed on the basis of patient age, type of admission (urgent or elective), time interval from the previous injury admission, main diagnosis, and department type. Results: The final prediction model showed good discrimination (c-statistic = 0.969). This model was applied to the validation dataset using the optimal cut-off level, and the resulting sensitivity and specificity were adjusted according to the proportion with a previous injury admission in each injury category. The injury with the highest proportion of possible readmissions was hip contusion (35%). Nevertheless, using the prediction model, incident hip contusions were identified with a sensitivity of 94% (95% confidence interval = 93%–95%) and a specificity of 95% (94%–97%). The accuracy was higher for all other injury categories. Conclusions: Incident injury admissions can be accurately separated from readmissions using a prediction model based on information derived from hospital discharge data.
Enhanced Unequal Clustering Scheme for Wireless Sensor Networks

In wireless sensor networks, the lifetime of a sensor network is a hottest research topic. Clustering provides an efficient method for increasing the network lifetime. Existing clustering algorithms selects the cluster head with high residual energy and rotates the cluster head periodically to distribute the energy among the nodes in each cluster. Cluster head has the responsibility for collecting data from its cluster members and then it forwards them to the base station. As a result, each node spends more energy for re-electing the cluster head frequently and gathering data from its cluster members. The hot spot problem and distributing the energy consumption among the nodes are the major issues in clustering. To mitigate the hot spot issue and preserve energy for multihop data forwarding, an enhanced clustering scheme namely EUCS is proposed. It constructs the clusters of unequal size and clusters closer to the base station have smaller in size. The cluster head can be re-elected when the energy level of current cluster head reaches to its predefined threshold value which reduces the frequent re-election of cluster head. In addition, to preserve some more energy, data from the sensor nodes are directly transferred to the nearest cluster heads which forwards them to the base station to avoid data gathering in the cluster head. The simulation results show that our algorithm increases the network lifetime effectively. General Terms Wireless Sensor Networks, Clustering.
WHUSUM: Wuhan University at the Update Summarization Task of TAC 2009

This paper describes the system WHUSUM we developed to participate in the update summarization task of TAC 2009. Given a topic and corresponding topic statement, this year's task is to write 2 summaries (one for Document Set A and one for Document Set B) that meet the information need expressed in the topic statement. In order to generate a topic-oriented summary for Set A, We present a co-training based strategy to select the topic relevant sentences from two abundant views and adopt a graph-based ranking algorithm (i.e. GRASSHOPPER) to achieve both information richness and content diversity in the generated summary. Furthermore, to capture the novel information in Set B and remove the possible redundant information in historical Document Set A, we propose two approaches to encourage novelty. One is to incorporate similarity between sentences in historical set and current set in the prior ranking of GRASSHOPPER. Another is to directly rank sentences for Document Set B first, and then to adjust their ranking scores based on the content comparison between the relevant sentence sets in A and B. The official evaluation results show that our system gets competitive performance in general topic-oriented summarization task and ranks in the middle among 52 submitted systems in update summarization task, which demonstrate that there is still large room to improve the novelty detection mechanism of the system.
Fast functional simulation with a dynamic language

Simulation of large computational systems-on-a-chip (SoCs) is increasing challenging as the number and complexity of components is scaled up. With the ubiquity of programmable components in computational SoCs, fast functional instruction-set simulation (ISS) is increasingly important. Much ISS has been done with straightforward functional models of a non-pipelined fetch-decode-execute iteration written in a low-to-mid-level C-family static language, delivering mid-level efficiency. Some ISS programs, such as QEMU, perform dynamic binary translation to allow software emulation to reach more usable speeds. This relatively complex methodology has not been widely adopted for system modeling. We demonstrate a fresh approach to ISS that achieves performance comparable to a fast dynamic binary translator by exploiting recent advances in just-in-time (JIT) compilers for dynamic languages, such as JavaScript and Lua, together with a specific programming idiom inspired by pipelined processor design. We believe that this approach is relatively accessible to system designers familiar with C-family functional simulator coding styles, and may be generally useful for fast modeling of complex SoC components.
A NMF-based Collaborative Filtering Recommendation Algorithm

Recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering (CF) is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The performance of CF systems degrades with increasing number of customers and items. To reduce the dimensionality of filtering databases and to improve the performance, non-negative matrix factorization (NMF) is applied to CF. The experiment results show that NMF-based CF can improve the performance of CF systems in both the recommendation quality and efficiency
If Eyes are the Window to Our Soul, What Role does Eye-Tracking Play in Accounting Research?

Recent advances in technology have increased the accessibility and ease in using eye-tracking as a research tool. These advances have the potential to benefit behavioral accounting researchers' understanding of the cognitive processes underlying individuals' judgments, decisions, and behaviors. However, despite its potential and wide use in other disciplines, few behavioral accounting studies use eye-tracking. The purpose of this paper is to familiarize accounting researchers with eye-tracking, including its advantages and limitations as a research tool. We start by providing an overview of eye-tracking and discussing essential terms and useful metrics, as well as the psychological constructs they proxy. We then summarize eye-tracking research across research domains, review accounting studies that use eye-tracking, and identify future research directions across accounting topics. Finally, we provide an instructional resource to guide those researchers interested in using eye-tracking, including important considerations at each stage of the study. JEL Classifications: M41; C91.
Even-skipped, acting as a repressor, regulates axonal projections in Drosophila

Nervous system-specific eve mutants were created by removing regulatory elements from a 16 kb transgene capable of complete rescue of normal eve function. When transgenes lacking the regulatory element for either RP2+a/pCC, EL or U/CQ neurons were placed in an eve-null background, eve expression was completely eliminated in the corresponding neurons, without affecting other aspects of eve expression. Many of these transgenic flies were able to survive to fertile adulthood. In the RP2+a/pCC mutant flies: (1) both RP2 and aCC showed abnormal axonal projection patterns, failing to innervate their normal target muscles; (2) the cell bodies of these neurons were positioned abnormally; and (3) in contrast to the wild type, pCC axons often crossed the midline. The Eve HD alone was able to provide a weak, partial rescue of the mutant phenotype, while both the Groucho-dependent and -independent repressor domains contributed equally to full rescue of each aspect of the mutant phenotype. Complete rescue was also obtained with a chimeric protein containing the Eve HD and the Engrailed repressor domain. Consistent with the apparent sufficiency of repressor function, a fusion protein between the Gal4 DNA-binding domain and Eve repressor domains was capable of actively repressing UAS target genes in these neurons. A key target of the repressor function of Eve was Drosophila Hb9, the derepression of which correlated with the mutant phenotype in individual eve-mutant neurons. Finally, homologues of Eve from diverse species were able to rescue the eve mutant phenotype, indicating conservation of both targeting and repression functions in the nervous system.
Plasma Lipoproteins and Apolipoproteins as Predictors of Cardiovascular Risk and Treatment Benefit in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER)

Background— Statins are important in vascular disease prevention in the elderly. However, the best method of selecting older patients for treatment is uncertain. We assessed the role of plasma lipoproteins as predictors of risk and of treatment benefit in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER). Method and Results— The association of LDLc and HDLc with risk was examined in the 5804 70- to 82-year-old subjects of PROSPER. Baseline LDLc showed no relation to risk of the primary end point in the placebo group (P=0.27), nor did on-treatment LDLc in the pravastatin group (P=0.12). HDLc was inversely associated with risk in subjects on placebo (P=0.0019) but not in those on pravastatin (P=0.24). Risk reduction on pravastatin treatment was unrelated to baseline LDLc (P=0.38) but exhibited a significant interaction with HDLc (P=0.012). Subjects in the lowest 2 quintiles of HDLc (<1.15 mmol/L) had a risk reduction of 33% (hazard ratio, 0.67; 95% confidence limits, 0.55, 0.81; P<0.0001), whereas those with higher HDLc showed no benefit (RR, 1.06; 95% confidence limits, 0.88, 1.27; P=0.53). During follow-up, there was no relation between achieved level of LDLc or HDLc and risk. However, the change in the LDLc/HDLc ratio on statin treatment appeared to account for the effects of therapy. Conclusions— In people >70 years old, HDLc appears to be a key predictor of risk and of treatment benefit. Findings in PROSPER suggest that statin therapy could usefully be targeted to those with HDLc <1.15 mmol/L or an LDLc/HDLc ratio >3.3.
Manual Ventilation and Sustained Lung Inflation in an Experimental Model: Influence of Equipment Type and Operator’s Training

Aim To compare the influence of devices for manual ventilation and individual experience on the applied respiratory mechanics and sustained lung inflation. Methods A total of 114 instructors and non-instructors from the Neonatal Resuscitation Program of the Brazilian Society of Pediatrics participated in this study. Participants ventilated an intubated manikin. To evaluate respiratory mechanics and sustained lung inflation parameters, a direct comparison was made between the self-inflating bag and the T-shaped resuscitator (T-piece), followed by an analysis of the effectiveness of the equipment according to the participants’ education and training. Results A difference between equipment types was observed for the tidal volume, with a median (interquartile range) of 28.5 mL (12.6) for the self-inflating bag and 20.1 mL (8.4) for the T-piece in the instructor group and 31.6 mL (14) for the self-inflating bag and 22.3 mL (8.8) for the T-piece in the non-instructor group. Higher inspiratory time values were observed with the T-piece in both groups of professionals, with no significant difference between them. The operator’s ability to maintain the target pressure over the 10 seconds of sustained lung inflation was evaluated using the area under the pressure-time curve and was 1.7-fold higher with the use of the T-piece. Inspiratory pressure and mean airway pressure applied during sustained lung inflation were greater with the self-inflating bag, as evaluated between the beginning and the end of the procedure. Conclusion The T-piece resulted in lower tidal volume and higher inspiratory time values, irrespective of the operator’s experience, and increased the ease of performing the sustained lung inflation maneuver, as demonstrated by the maintenance of target pressure for the desired period and a higher mean airway pressure than that obtained using the self-inflating bag.
Predicting Bankruptcy with Support Vector Machines

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.
Decreased reaction time variability is associated with greater cardiovascular responses to acute stress

Abstract Cardiovascular (CV) responses to mental stress are prospectively associated with poor CV outcomes. The association between CV responses to mental stress and reaction times (RTs) in aging individuals may be important but warrants further investigation. The present study assessed RTs to examine associations with CV responses to mental stress in healthy, older individuals using robust regression techniques. Participants were 262 men and women (mean age = 63.3 ± 5.5 years) from the Whitehall II cohort who completed a RT task (Stroop) and underwent acute mental stress (mirror tracing) to elicit CV responses. Blood pressure, heart rate, and heart rate variability were measured at baseline, during acute stress, and through a 75‐min recovery. RT measures were generated from an ex‐Gaussian distribution that yielded three predictors: mu‐RT, sigma‐RT, and tau‐RT, the mean, standard deviation, and mean of the exponential component of the normal distribution, respectively. Decreased intraindividual RT variability was marginally associated with greater systolic (B = −.009, SE = .005, p = .09) and diastolic (B = −.004, SE = .002, p = .08) blood pressure reactivity. Decreased intraindividual RT variability was associated with impaired systolic blood pressure recovery (B = −.007, SE = .003, p = .03) and impaired vagal tone (B = −.0047, SE = .0024, p = .045). Study findings offer tentative support for an association between RTs and CV responses. Despite small effect sizes and associations not consistent across predictors, these data may point to a link between intrinsic neuronal plasticity and CV responses.
Physical based Lighting-Simulation of Human Skin

Simulating the visual appearance and lighting of human skin i a difficult task, which has been addressed by researchers for several years now. With the availability of high-performance, programmable graphic-boards, it is possible to use techniques formerly only available for of fline-rendering. This paper presents solutions to enhance visual quality of human skin and overall appearance of human body-parts by using existing computer-graphicstechniques as well as through enhancing offline-techniques to real-time appliance. Thus, subsurface scattering, soft shadows, aging and emotional changes in the appearance of th face are approximated to get results in real-time at interactive frame rates for more immersive virtual environ ments such as e-learning, cultural heritage and computer games.
A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier

Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.
The propagation parameters on RFID-localization accuracy

While in the near future everything will be tagged with Radio Frequency Identification (RFID) tags, the localization of these tags in their environment is becoming an important feature for many RFID-based ubiquitous computing applications. The problem being tackled here relates to the problem of object location in wireless sensor networks, it is a specific problem in localization. Localization primarily refers to the detection coordinates of a node or an object. This work presents an approach named multidimensional scaling for estimating the location of unknown RFID tags, within the area to facilitate locating all tags in a RFID network and reduce the localization cost and environment complexity. It measures the distances between the reader and tags using the log normal distance path loss propagation model for indoor environment based on Received Signal Strength (RSS) measurement. The main advantage of this technique is reduction the number of expensive RFID readers and it can better tolerate noises. However, signals in indoor environments are generally harshly impaired because of multipath propagation and the variable factors in the surrounding environment, resulting in the tags have very limited capabilities which pose many challenges for positioning them. This work presents also the effect of propagation parameters on the localization accuracy by varying these parameters to observe the probability distribution of estimated distance, where the propagation parameters in indoor environments make working with signal strength measurements challenging.
Voting for Deceptive Opinion Spam Detection

Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam fictitious reviews that have been deliberately written to sound authentic, to deceive the readers. Existing approaches mainly focus on developing automatic supervised learning based methods to help users identify deceptive opinion spams. This work, we used the LSI and Sprinkled LSI technique to reduce the dimension for deception detection. We make our contribution to demonstrate what LSI is capturing in latent semantic space and reveal how deceptive opinions can be recognized automatically from truthful opinions. Finally, we proposed a voting scheme which integrates different approaches to further improve the classification performance.