Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Insider Threats Using a Graph-Based Approach

– This paper presents the use of graph-based approaches to discovering anomalous instances of structural patterns in data that represent insider threat activity. The approaches presented search for activities that appear to match normal transactions, but in fact are structurally different. We show the usefulness of applying graph theoretic approaches to discovering suspicious insider activity in domains such as e-mail correspondences, business processes, and cybercrime. The paper then concludes with some future research that deals with the handling of dynamic graphs, as well as the implementation of these graph-based anomaly detection algorithms in a frequent subgraph miner.
Computer-Adaptive Testing: Implications for Students’ Achievement, Motivation, Engagement, and Subjective Test Experience

The present study investigated the implications of computer-adaptive testing (operationalized by way of multistage adaptive testing; MAT) and “conventional” fixed order computer testing for various test-relevant outcomes in numeracy, including achievement, test-relevant motivation and engagement, and subjective test experience. It did so among N = 12,736 Australian elementary (years 3 and 5) and secondary (years 7 and 9) school students. Multilevel modeling assessed the extent to which Level 1 (student) test condition (fixed order vs. adaptive), gender, and year group factors and Level 2 (school) socioeducational advantage, location, structure, and size factors predicted students’ test-relevant outcomes. In terms of statistically significant main effects, students in the computer-adaptive testing condition generated lower achievement error rates (i.e., higher measurement precision). Other statistically significant computer-adaptive test effects emerged as a function of year-level and gender, with positive effects of computer-adaptive testing being relatively greater for females and older students: these students achieved more highly (year 9 students), reported higher test-relevant motivation and engagement (year 9 students), and reported more positive subjective test experience (females and year 9 students). These findings (a) confirm that computer-adaptive testing yields greater achievement measurement precision, (b) suggest some positive test-relevant motivation and engagement effects from computer-adaptive testing, (c) counter claims that computer-adaptive testing reduces students’ test-relevant motivation, engagement, and subjective experience, and (d) suggest positive computer-adaptive testing effects for older students at a developmental stage when they are typically less motivated and engaged.
Digital Video Transcoding

Video transcoding, due to its high practical values for a wide range of networked video applications, has become an active research topic. We outline the technical issues and research results related to video transcoding. We also discuss techniques for reducing the complexity, and techniques for improving the video quality, by exploiting the information extracted from the input video bit stream.
The Role of Organizational Justice and Stress in Predicting Job Burnout

This study explores how job-burnout dimensions—emotional exhaustion, cynicism, and personal accomplishment—relate to distributive justice and stress. Questionnaire data were collected in a cross-sectional design from 90 information technology professionals. Structural equation modeling analysis indicated that low levels of perceived justice are associated with higher exhaustion. Next, a parallel mechanism unfolds: High levels of exhaustion are directly related to low levels of personal accomplishment. At the same time, high levels of exhaustion are associated with high levels of cynicism, which are associated with higher levels of self-accomplishment. These findings are consistent with theories, suggesting that employees may utilize cynicism as a defense mechanism in an attempt to protect the self. Cynicism also mediated the relationship between stress and personal accomplishment. Cynicism may help employees cope with exhaustion and stress and thus may protect their sense of self-worth. Theoretical and applied implications for organizations, career development, and counseling are discussed.
A multi-modal gesture recognition system using audio, video, and skeletal joint data

This paper describes the gesture recognition system developed by the Institute for Infocomm Research (I2R) for the 2013 ICMI CHALEARN Multi-modal Gesture Recognition Challenge. The proposed system adopts a multi-modal approach for detecting as well as recognizing the gestures. Automated gesture detection is performed using both audio signals and information about hand joints obtained from the Kinect sensor to segment a sample into individual gestures. Once the gestures are detected and segmented, features extracted from three different modalities, namely, audio, 2-dimensional video (RGB), and skeletal joints (Kinect) are used to classify a given sequence of frames into one of the 20 known gestures or an unrecognized gesture. Mel frequency cepstral coefficients (MFCC) are extracted from the audio signals and a Hidden Markov Model (HMM) is used for classification. While Space-Time Interest Points (STIP) are used to represent the RGB modality, a covariance descriptor is extracted from the skeletal joint data. In the case of both RGB and Kinect modalities, Support Vector Machines (SVM) are used for gesture classification. Finally, a fusion scheme is applied to accumulate evidence from all the three modalities and predict the sequence of gestures in each test sample. The proposed gesture recognition system is able to achieve an average edit distance of 0.2074 over the 275 test samples containing 2,742 unlabeled gestures. While the proposed system is able to recognize the known gestures with high accuracy, most of the errors are caused due to insertion, which occurs when an unrecognized gesture is misclassified as one of the 20 known gestures.
Local Vector-based Models for Sense Discrimination

Word sense discrimination aims at automatically determining which instances of an ambiguous word share the same sense. A fully unsupervised technique based on a high dimensional vector representation of word senses was proposed by Sch¨utze [10]. While this model was assumed to be Gaussian, results were only reported for the K-means approximation. In this work, a local vector-based model of reduced dimensionality which is linguistically coherent and can be computed for multivariate Gaussian mixtures is proposed. Several practical experiments are conducted on the New York Times News 1997 corpus. They show the advantages of unrestricted Gaussian models compared to K-means. The correct discrimination rate is further increased when using regularized Gaussian models as proposed in [2].
Responses of Aquatic Bacteria to Terrestrial Runoff: Effects on Community Structure and Key Taxonomic Groups

Organic fertilizer application is often touted as an economical and effective method to increase soil fertility. However, this amendment may increase dissolved organic carbon (DOC) runoff into downstream aquatic ecosystems and may consequently alter aquatic microbial community. We focused on understanding the effects of DOC runoff from soils amended with compost, vermicompost, or biochar on the aquatic microbial community of a tropical reservoir. Runoff collected from a series of rainfall simulations on soils amended with different organic fertilizers was incubated for 16 days in a series of 200 L mesocosms filled with water from a downstream reservoir. We applied 454 high throughput pyrosequencing for bacterial 16S rRNA genes to analyze microbial communities. After 16 days of incubation, the richness and evenness of the microbial communities present decreased in the mesocosms amended with any organic fertilizers, except for the evenness in the mesocosms amended with compost runoff. In contrast, they increased in the reservoir water control and soil-only amended mesocosms. Community structure was mainly affected by pH and DOC concentration. Compared to the autochthonous organic carbon produced during primary production, the addition of allochthonous DOC from these organic amendments seemed to exert a stronger effect on the communities over the period of incubation. While the Proteobacteria and Actinobacteria classes were positively associated with higher DOC concentration, the number of sequences representing key bacterial groups differed between mesocosms particularly between the biochar runoff addition and the compost or vermi-compost runoff additions. The genera of Propionibacterium spp. and Methylobacterium spp. were highly abundant in the compost runoff additions suggesting that they may represent sentinel species of complex organic carbon inputs. Overall, this work further underlines the importance of studying the off-site impacts of organic fertilizers as their impact on downstream aquatic systems is not negligible.
Greater Food-Related Stroop Interference Following Behavioral Weight Loss Intervention

Objective Individuals who have successfully lost and maintained weight have slower reaction times on food-related Stroop tasks, indicating greater cognitive interference to food stimuli compared to obese and normal weight individuals. It remains unclear whether this interference is a preexisting characteristic of weight loss maintainers or if food-interference changes in obese individuals as they lose weight. Method To examine potential changes in food-related interference, a food-Stroop paradigm was used to measure responses to food versus non-food words in 13 obese women before and after a 12-week behavioral weight loss program. Results Participants achieved a mean weight loss of 5.12 kg through the behavioral weight loss program. Their reaction time to food words became significantly slower (p<0.001) and they made significantly more errors (p<0.01) following treatment. Discussion These findings suggest that through behavioral weight loss treatment obese individuals experience increased interference toward food words, which may reflect increased salience of food-related cues. Future research is needed to determine whether increases in interference are related to better weight loss and maintenance.
Impaired Osteoblast Differentiation in Annexin A2- and -A5-Deficient Cells

Annexins are a class of calcium-binding proteins with diverse functions in the regulation of lipid rafts, inflammation, fibrinolysis, transcriptional programming and ion transport. Within bone, they are well-characterized as components of mineralizing matrix vesicles, although little else is known as to their function during osteogenesis. We employed shRNA to generate annexin A2 (AnxA2)- or annexin A5 (AnxA5)-knockdown pre-osteoblasts, and determined whether proliferation or osteogenic differentiation was altered in knockdown cells, compared to pSiren (Si) controls. We report that DNA content, a marker of proliferation, was significantly reduced in both AnxA2 and AnxA5 knockdown cells. Alkaline phosphatase expression and activity were also suppressed in AnxA2- or AnxA5-knockdown after 14 days of culture. The pattern of osteogenic gene expression was altered in knockdown cells, with Col1a1 expressed more rapidly in knock-down cells, compared to pSiren. In contrast, Runx2, Ibsp, and Bglap all revealed decreased expression after 14 days of culture. In both AnxA2- and AnxA5-knockdown, interleukin-induced STAT6 signaling was markedly attenuated compared to pSiren controls. These data suggest that AnxA2 and AnxA5 can influence bone formation via regulation of osteoprogenitor proliferation, differentiation, and responsiveness to cytokines in addition to their well-studied function in matrix vesicles.
A Methodology and Architecture Embedding Quality Assessment in Data Integration

Data integration aims to combine heterogeneous information sources and to provide interfaces for accessing the integrated resource. Data integration is a collaborative task that may involve many people with different degrees of experience, knowledge of the application domain, and expectations relating to the integrated resource. It may be difficult to determine and control the quality of an integrated resource due to these factors. In this article, we propose a data integration methodology that has embedded within it iterative quality assessment and improvement of the integrated resource. We also propose an architecture for the realisation of this methodology. The quality assessment is based on an ontology representation of different users’ quality requirements and of the main elements of the integrated resource. We use description logic as the formal basis for reasoning about users’ quality requirements and for validating that an integrated resource satisfies these requirements. We define quality factors and associated metrics which enable the quality of alternative global schemas for an integrated resource to be assessed quantitively, and hence the improvement which results from the refinement of a global schema following our methodology to be measured. We evaluate our approach through a large-scale real-life case study in biological data integration in which an integrated resource is constructed from three autononous proteomics data sources.
Extraction of Sensitive Bands for Monitoring the Winter Wheat (Triticum aestivum) Growth Status and Yields Based on the Spectral Reflectance

To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R2, TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat.
Syntactic Parsing as a Step for Automatically Augmenting Semantic Lexicons

This paper investigates how, and to what extent the flexibility and robustness of a partial parser can be utilized to automatically extend existing semantic lexicons. Our work is based on the observation that members of a semantic group are often surrounded by other members of the same group in text. Given a few category members we collect surrounding contexts and try to identify other words that also belong to the same group. Large quantities of partially parsed corpora is an important ingredient for the enrichment and further development of the semantic resources.
Self-supervised road detection from a single image

In this paper, we study the problem of detecting road from a single image without priori knowledge of the general structure or the appearance of road surface. In contrast with the road, the background is more cluttered and heterogeneous, therefore different statistical models can be built for the road and the background based on a roughly selected initial road mask, respectively. Then the self-learned statistical models will re-label each pixel in the input image base on a likelihood ratio classifier. This classification based algorithm can detect difficult urban roads without tracking results from image to image. The performance of the proposed algorithm is evaluated on two benchmark databases. Compared with some state-of-the-art vision-based road detection approaches, the proposed algorithm is simple and efficacious.
Quantitative evaluation of systematic imaging error due to uncertainty in tissue optical properties in high-density diffuse optical tomography

In MRI-guided diffuse optical tomography of the human brain function, three-dimensional anatomical head model consisting of up to five segmented tissue types can be specified. With disregard to misclassification between different tissues, uncertainty in the optical properties of each tissue type becomes the dominant cause of systematic error in image reconstruction. In this study we present a quantitative evaluation of image resolution dependence due to such uncertainty. Our results show that given a head model which provides a realistic description of its tissue optical property distribution, high-density diffuse optical tomography with cortically constrained image reconstruction are capable of detecting focal activation up to 21.81 mm below the human scalp at an imaging quality better than or equal to 1.0 cm in localization error and 1.0 cm3 in FVHM with a tolerance of uncertainty in tissue optical properties between +15% and -20%.
Effects of Short-Term Cognitive Remediation on Cognitive Dysfunction in Partially or Fully Remitted Individuals with Bipolar Disorder: Results of a Randomised Controlled Trial

Introduction Cognitive dysfunction is common in bipolar disorder (BD) but is not sufficiently addressed by current treatments. Cognitive remediation (CR) may improve cognitive function in schizophrenia but no randomised controlled trial has investigated this intervention in BD. The present study aimed to investigate the effects of CR on persistent cognitive dysfunction in BD. Method Patients with BD in partial remission with cognitive complaints were randomised to 12 weeks group-based CR (n=23) or standard treatment (ST) (n=23). Outcomes were improved verbal memory (primary), sustained attention, executive and psychosocial function (secondary) and additional measures of cognitive and psychosocial function (tertiary). Participants were assessed at baseline and weeks 12 and 26. Results Of the 46 randomised participants five dropped out and one was excluded after baseline. CR (n=18) had no effect on primary or secondary measures of cognitive or psychosocial function compared with ST (n=22). However, CR improved subjective sharpness at week 12, and quality of life and verbal fluency at week 26 follow-up (tertiary outcomes). Although the trial turned out to have suboptimal statistical power for the primary outcome analysis, calculation of the 95% confidence interval showed that it was highly unlikely that an increase in sample size would have rendered any beneficial effects of CR vs. ST on the verbal memory. Conclusions Short-term group-based CR did not seem to improve overall cognitive or psychosocial function in individuals with BD in full or partial remission. The present findings suggest that that longer-term, more intensive and individualised CR may be necessary to improve cognition in BD. Trial Registration ClinicalTrials.gov NCT01457235
Iterative detection of linear-dispersion codes

In this article we present how iterative linear detection can be applied to a multiple input multiple output (MIMO) system with linear-dispersion (LD) codes. The LD codes are space-time (ST) mappings that maximize the mutual information between the transmitted and received signal. They consider the data symbols as well as their complex conjugates and thus require two complex minimum mean squared error (MMSE) filters for linear detection known as widely linear (WL) filters. We determine different LD codes and show that the overall performance of the system can be significantly improved over standard approaches
Identifying Protective Factors of Posttraumatic Stress Disorder, Depression, and Self-Reported Health Outcomes of Residential Fire Survivors

Posttraumatic Stress Disorder (PTSD) has been demonstrated as the primary pathway through which morbidity and mortality is achieved post-trauma. However, less is known about protective factors to PTSD, depression, and self-reported health outcomes of adults following a traumatic event. Through examination of residential fire survivors, the current project evaluated the predictive validity of protective factors of PTSD as they relate to PTSD, depression, and somatic health outcomes. Additionally, the project collapsed the three outcomes variables into a unified health construct and evaluated protective factors ability to predict health. It was hypothesized the peritraumatic emotionality, social support, and resource loss would predict PTSD, depression, and somatic health. Additionally, it was predicted that peritraumatic emotionality, social support, and resource loss would predict a unified construct of health. Participants were assessed via self-report and semi-structured interviews approximately four months post-fire. Results of the current project demonstrated strong associations amongst peritraumatic emotionality and resource loss for many of the outcome variables. However, social support was not found to be a predictor of any of the outcomes variables. When evaluating the unified health construct, resource loss was found to significant predict a resilient group of trauma survivors four months post-fire. The present study suggests lower peritraumatic emotionality and lower sustained resource loss are significant protective factors for resiliency from trauma.
What Influences CS Faculty to Adopt Teaching Practices?

Despite widespread development, research, and dissemination of teaching and curricular practices that improve student retention and learning, faculty often do not adopt them. This paper describes the first findings of a two-part study to improve understanding of adoption of teaching practices and curriculum by computer science faculty. The paper closes with recommendations for designers and developers of teaching innovations hoping to increase their chance of adoption.