Miracle-dz/UniFAR-FTUs huggingface dataset:
Issues and effort in integrating data from heterogeneous software repositories and corporate databases

Software repositories and corporate databases capture different fragments of a project's history. Software cockpits integrate the data from these repositories and databases to provide a holistic view of the project and the capability to drill-down and analyze details. By incorporating existing data, the cockpit can be used effectively from the first day it is introduced. In this paper we describe our findings from integrating several repositories and databases for a large, distributed project. We highlight common issues in data integration, report on the resulting effort for the development of software cockpits, and share our lessons learned from this data integration project.
Forest adaptation and transition in the eastern united states under climate change : a forest landscape modeling method

x CHAPTER 1.1 PREFACE.1 CHAPTER DESCRIPTIONS . 3 LITERATURE CITED . 5 CHAPTER 2.7 ESTIMATING THE POTENTIAL IMPACT OF CLIMATE CHANGE ON FOREST ECOSYSTEM TRANSITION IN THE EASTERN UNITED STATES INTRODUCTION . 7 METHODS . 10 RESULTS . 16 DISCUSSION . 18 CONCLUSION . 22 LITERATURE CITED . 23 CHAPTER 3. 40
Quickest Change Detection in Adaptive Censoring Sensor Networks

The problem of quickest change detection with communication rate constraints is studied. A network of wireless sensors with limited computation capability monitors the environment and sends observations to a fusion center via wireless channels. At an unknown time instant, the distributions of observations at all the sensor nodes change simultaneously. Due to limited energy, the sensors cannot transmit at all the time instants. The objective is to detect the change at the fusion center as quickly as possible, subject to constraints on false detection and average communication rate between the sensors and the fusion center. A minimax formulation is proposed. The cumulative sum (CuSum) algorithm is used at the fusion center and censoring strategies are used at the sensor nodes. The censoring strategies, which are adaptive to the CuSum statistic, are fed back by the fusion center. The sensors only send observations that fall into prescribed sets to the fusion center. This CuSum adaptive censoring (CuSum-AC) algorithm is proved to be an equalizer rule and to be globally asymptotically optimal for any positive communication rate constraint, as the average run length to false alarm goes to infinity. It is also shown, by numerical examples, that the CuSum-AC algorithm provides a suitable trade-off between the detection performance and the communication rate.
Bull Trout Life History, Genetics, Habitat Needs, and Limiting Factors in Central and Northeast Oregon. Annual Report 1996.

This study is part of a multi-year research project studying aspects of bull trout life history, ecology and genetics. This report covers the activities of the project in 1996. Results and analysis are presented in the following five areas: (1) analysis of the genetic structure of Oregon bull trout populations; (2) distribution and habitat use of bull trout and brook trout in streams containing both species; (3) bull trout spawning surveys; (4) summary and analysis of historical juvenile bull trout downstream migrant trap catches in the Grande Ronde basin; and (5) food habits and feeding behavior of bull trout alone and in sympatry with brook trout.
Public preferences for pasture landscapes and the role of scale heterogeneity

Despite its relevance for agricultural production, biodiversity and landscape aesthetics, grazing livestock is rarely considered in research on public landscape preferences. This paper studies public preferences for pasture usage in Germany by the means of a discrete choice experiment. The results indicate that there is a general willingness to pay (WTP) for livestock presence in landscape. The mean WTP is independent of its density. The paper discusses the implications of different econometric models and the role of preference heterogeneity on the results. The results show that a detailed analysis of the preference heterogeneity can provide deeper insights on their structure.
Evaluating CMIP5 Model Agreement for Multiple Drought Metrics

Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.
Neuroimaging Markers for Early Neurologic Deterioration in Single Small Subcortical Infarction

Background and Purpose— Early neurological deterioration (END) occurs in ≥20% of single small subcortical infarctions (SSSIs; axial diameter ⩽20 mm in the perforator territories) and deters functional recovery. Both microvasculopathies and atherosclerosis have been proposed to independently contribute to the occurrence of END in SSSI cases. We hypothesized that the occurrence of END in SSSIs differs according to the pathological process. Methods— We collected data from 587 patients with SSSI within 48 hours of onset from a prospective stroke registry containing 4961 case records. Independent reviewers, blinded to END information, rated neuroimaging characteristics, including relevant artery stenosis (0% to 50% stenosis of the adjacent arteries on magnetic resonance angiography), branch atheromatous lesions (≥4 consecutive axial cuts or extensions from the basal surface of the pons), white matter hyperintensities, old lacunar infarctions, and cerebral microbleeds. Results— END occurred in 79 (13.5%) cases, including 6 recurrences, 68 progressions, 1 symptomatic hemorrhagic transformation, 1 others, and 3 unknowns. END increased the National Institutes of Health Stroke Scale score by 2.3±1.4 points. Patients with END showed higher frequencies of modified Rankin Scale scores of 3 to 6 after 3 months compared with patients without END (49% versus 23%). Patients with relevant artery stenosis (adjusted odds ratio, 1.91; 95% confidence interval, 1.13–3.21) and branch atheromatous lesions (adjusted odds ratio, 2.98; 95% confidence interval, 1.80–4.93) had significantly higher odds of exhibiting END. However, such an association was not detected with small vessel disease markers. Conclusions— Our analysis indicated a potential contribution of the localized atherosclerotic process to END in SSSIs. Precautionary measures might be used for SSSIs suggestive of atherosclerotic pathologies.
Effects of Mindful Eating in Patients with Obesity and Binge Eating Disorder

Introduction: Binge eating disorder (BED) is a psychiatric illness related to a high frequency of episodes of binge eating, loss of control, body image dissatisfaction, and suffering caused by overeating. It is estimated that 30% of patients with BED are affected by obesity. “Mindful eating” (ME) is a promising new eating technique that can improve self-control and good food choices, helping to increase awareness about the triggers of binge eating episodes and intuitive eating training. Objectives: To analyze the impact of ME on episodes of binge eating, body image dissatisfaction, quality of life, eating habits, and anthropometric data [weight, Body Mass Index (BMI), and waist circumference] in patients with obesity and BED. Method: This quantitative, prospective, longitudinal, and experimental study recruited 82 patients diagnosed with obesity and BED. The intervention was divided into eight individual weekly meetings, guided by ME sessions, nutritional educational dynamics, cooking workshops, food sensory analyses, and applications of questionnaires [Body Shape Questionnaire (BSQ); Binge Eating Scale (BES); Quality of Life Scale (WHOQOL-BREF)]. There was no dietary prescription for calories, carbohydrates, proteins, fats, and fiber. Patients were only encouraged to consume fewer ultra-processed foods and more natural and minimally processed foods. The meetings occurred from October to November 2023. Statistical analysis: To carry out inferential statistics, the Shapiro–Wilk test was used to verify the normality of variable distribution. All variables were identified as non-normal distribution and were compared between the first and the eighth week using a two-tailed Wilcoxon test. Non-Gaussian data were represented by median ± interquartile range (IQR). Additionally, α < 0.05 and p < 0.05 were adopted. Results: Significant reductions were found from the first to the eighth week for weight, BMI, waist circumference, episodes of binge eating, BSQ scale score, BES score, and total energy value (all p < 0.0001). In contrast, there was a significant increase in the WHOQOL-BREF score and daily water intake (p < 0.0001). Conclusions: ME improved anthropometric data, episodes of binge eating, body image dissatisfaction, eating habits, and quality of life in participants with obesity and BED in the short-term. However, an extension of the project will be necessary to analyze the impact of the intervention in the long-term.
Profile of Self-Regulated Learning on First-Year Students at Science Education Department in Online Learning

Online learning requires students to study independently and manage lectures according to the time set. Researchers need to know the criteria for self-regulated learning of first-year students at the Physics Education Department. This paper aims to determine self-regulated learning during online learning. The study used a quantitative research approach with a survey method. The Online Self-Regulated Learning Questionnaire (OLSQ) collected the data, a 20-item scale with a 5-point Likert-type response format. The sample of this study was 131 students of physics and chemistry education study programs. Each was randomly selected to see how their respective study programs implemented independent learning activities during online learning. The results showed that each indicator of independent learning in online learning received a positive response through the OLSQ instrument that had been distributed. The disciplined behavior indicator has a higher percentage, followed by the exercise self-control indicator. Generally, the pre-service students have self-regulated learning with high criteria. This result can provide a good contribution and open for further research. The implication of this research can be a reference for lecturers to improve online and self-regulated learning quality.
Estimation of distribution algorithm using factor graph and Markov blanket canonical factorization

Finding a good model and efficiently estimating the distribution is still an open challenge in estimation of distribution algorithms (EDAs). Factorization encoded by models in most of the EDAs are constrained. However for optimization of many real-world problems, finding the model capable of representing complex interactions without much computational complexity overhead is the key challenge. On the other hand factor graph which is the most natural graphical model for representing additively decomposable functions is rarely employed in EDAs. In this paper we introduce Factor Graph based EDA (FGEDA) which learns factor graph as the model and estimate the probability distribution represented by the learned factor graph using Markov blanket canonical factorization. The class of factorization that is employed for approximation of distribution in FGEDA is expanded relative to famous EDAs. We have used matrix factorization for learning the factor graph of the problem based on the pairwise mutual information between pair of variables. Gibbs sampling and BB-wise crossover are used to generate new samples. Empirical evaluation as well as theoretical analysis of the approach show the efficiency and power of FGEDA in the optimization of functions with complex interactions. It is showed experimentally that FGEDA outperform other well-known EDAs.
Page content rank: an approach to the web content mining

Methods of web data mining can be divided into several categories according to a kind of mined information and goals that particular categories set: Web structure mining (WSM), Web usage mining (WUM), and Web Content Mining (WCM). The objective of this paper is to propose a new WCM method of a page relevance ranking based on the page content exploration. The method, we call it Page Content Rank (PCR) in the paper, combines a number of heuristics that seem to be important for analysing the content of Web pages. The page importance is determined on the base of the importance of terms which the page contains. The importance of a term is specified with respect to a given query q and it is based on its statistical and linguistic features. As a source set of pages for mining we use a set of pages responded by a search engine to the query q. PCR uses a neural network as its inner classification structure. We describe an implementation of the proposed method and a comparison of its results with the other existing classification system – PageRank algorithm.
A Data Glove with Tactile Feedback for fMRI of Virtual Reality Experiments

Virtual reality (VR) technology is increasingly recognized as a useful tool for the assessment and rehabilitation of neurologic and psychiatric disorders. The hope that VR can accurately mimic real-life events is also of great interest in basic neuroscience, to identify the brain activity that underlies complex behavior by combining VR with techniques such as functional magnetic resonance imaging (fMRI). Toward these applications, in this study we designed and validated an fMRI-compatible data glove with a built-in vibratory stimulus device for tactile feedback during VR experiments. A simple VR-fMRI experiment was performed at 3.0 Tesla on four young healthy adults involving touching a virtual object with and without tactile feedback. The usefulness of the data glove was subsequently assessed using a series of questionnaires, behavioral performance, and the resulting activation images. Questionnaire scores indicated positive opinions with respect to the data glove, the tactile feedback, and the experimental paradigm. All subjects felt comfortable in the scanner during the VR experiment and were able to perform all aspects of the tasks successfully and with reasonable accuracy. In addition, activation maps showed the anticipated modulations in motor, somatosensory, and parietal cortex. These results support that tactile feedback enhances the realism of virtual hand-object interactions, and that the tactile data glove is suitable for use in other VR-fMRI research applications (e.g., VR physical therapy for stroke recovery).
MixER: Mixup-Based Experience Replay for Online Class-Incremental Learning

Continual learning in the online class-incremental setting aims to learn new classes continuously from a consistent data stream while retaining the knowledge of old classes to prevent catastrophic forgetting. Traditional replay-based methods store and use old-class data to achieve this. However, they often overlook the representation shift caused by the incoming data streams, which leads to suboptimal classification accuracy. In this study, we propose a solution for mitigating representation shifts by incorporating asymmetric mixup training into the replay method. Our approach is based on the concept that mixup-based training enhances the stability of model predictions and gradient norms between training samples. Our method differs from typical mixup augmentation, which is uniformly applied to all data. Instead, it selectively targets the old data stored in the memory buffer, deliberately excluding the classes from the newly incoming data. This approach enables the model to learn new data while preserving the representation of the old data. Moreover, our experiments demonstrate the effectiveness of the proposed method, which not only enhances the performance of replay-based methods but can also be seamlessly integrated as an additional compatible module into various replay-based techniques. Evaluation on the CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets demonstrates that our approach surpasses existing replay-based methods. It addresses the limitations of conventional replay techniques and offers a potential solution for continual learning scenarios. Our source code is publicly available at https://github.com/laymond1/MixER.
LAFICNN: A Novel Convolutional Adaptive Fusion Framework for Fault Diagnosis of Rotating Machinery

Convolutional neural network (CNN)-based approaches have been widely developed and applied in intelligent diagnosis. However, only some CNN models make sufficient use of various favorable information in images, limiting adequate feature extraction. Moreover, existing models are still dominated by single-channel types, with limited multisensor collaborative diagnostic framework development. To alleviate the above problems, we propose a diagnostic framework called lightweight adaptive fusion improved CNN (LAFICNN). Specifically, a global spatial attention (GSA) mechanism is first developed, which considers the nature of images and enhances the model’s attention to the long-range dependencies of signals. Then, a novel Conv Block that can better utilize the spatial information in the image is designed, and a single-channel lightweight improved CNN (LICNN) is constructed. In addition, an adaptive fusion module (AFM) is proposed, which can adaptively assign learnable weights to features from different branches at a shallow layer of the network without increasing the training parameters. Finally, the LAFICNN is proposed by embedding the AFM into the LICNN to realize multisensor collaborative diagnosis. The experimental results on a rolling bearing dataset show that LAFICNN improves the performance by 0.8%–4.2% compared to the existing works. The stability of the LAFICNN is verified using a gearbox dataset.
Preference for and learning of amino acids in larval Drosophila

ABSTRACT Relative to other nutrients, less is known about how animals sense amino acids and how behaviour is organized accordingly. This is a significant gap in our knowledge because amino acids are required for protein synthesis − and hence for life as we know it. Choosing Drosophila larvae as a case study, we provide the first systematic analysis of both the preference behaviour for, and the learning of, all 20 canonical amino acids in Drosophila. We report that preference for individual amino acids differs according to the kind of amino acid, both in first-instar and in third-instar larvae. Our data suggest that this preference profile changes across larval instars, and that starvation during the third instar also alters this profile. Only aspartic acid turns out to be robustly attractive across all our experiments. The essentiality of amino acids does not appear to be a determinant of preference. Interestingly, although amino acids thus differ in their innate attractiveness, we find that all amino acids are equally rewarding. Similar discrepancies between innate attractiveness and reinforcing effect have previously been reported for other tastants, including sugars, bitter substances and salt. The present analyses will facilitate the ongoing search for the receptors, sensory neurons, and internal, homeostatic amino acid sensors in Drosophila. Summary: Drosophila larvae show positive preference but for a few individual amino acids. The preference partially changes depending on larval stage and deprivation. In learning experiments, all 20 tested amino acids were equally rewarding.
Virtual face image generation for illumination and pose insensitive face recognition

Face recognition has attracted much attention in the past decades for its wide potential applications. Much progress has been made in the past few years. However, specialized evaluation of the state-of-the-art of both academic algorithms and commercial systems illustrates that the performance of most current recognition technologies degrades significantly due to the variations of illumination and/or pose. To solve these problems, providing multiple training samples to the recognition system is a rational choice. However, enough samples are not always available for many practical applications. It is an alternative to augment the training set by generating virtual views from one single face image, that is, relighting the given face images or synthesize novel views of the given face. Based on this strategy, this paper presents some attempts by presenting a ratio-image based face relighting method and a face re-rotating approach based on linear shape prediction and image warp. To evaluate the effect of the additional virtual face images, primary experiments are conducted using our face specific subspace method as face recognition approach, which shows impressive improvement compared with standard benchmark face recognition methods.
Make Hay While the Crowd Shines: Towards Efficient Crowdsourcing on the Web

Within the scope of this PhD proposal, we set out to investigate two pivotal aspects that influence the effectiveness of crowdsourcing: (i) microtask design, and (ii) workers behavior. Leveraging the dynamics of tasks that are crowdsourced on the one hand, and accounting for the behavior of workers on the other hand, can help in designing tasks efficiently. To help understand the intricacies of microtasks, we identify the need for a taxonomy of typically crowdsourced tasks. Based on an extensive study of 1000 workers on CrowdFlower, we propose a two-level categorization scheme for tasks. We present insights into the task affinity of workers, effort exerted by workers to complete tasks of various types, and their satisfaction with the monetary incentives. We also analyze the prevalent behavior of trustworthy and untrustworthy workers. Next, we propose behavioral metrics that can be used to measure and counter malicious activity in crowdsourced tasks. Finally, we present guidelines for the effective design of crowdsourced surveys and set important precedents for future work.
Effect of Transmit and Receive Antenna Configuration on the Throughput of MIMO UMTS Downlink

In this contribution, throughput measurements of DSTTD-SGRC (Double Space Time Transmit Diversity with Sub Group Rate Control), a candidate for the MIMO extension of UMTS, are presented. The system throughput is compared for feasible transmit and receive antenna arrays. Emphasis is placed on a quad inverted-F antenna which is a realistic receive antenna array that could be built into a mobile phone. The size of this array incorporating four individual antennas is only 34×34 mm. Although due to the small size high spatial correlation between the antennas might be expected, the measurements show that the quad inverted-F antenna performs very well. The throughput is comparable to a linear array with an antenna spacing of 0. 5λ.