Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Jigsaw-puzzle and Graphic Organizer Instructional Strategies on Biology Students’ Performance in Abia State

The study investigated the effects of jigsaw-puzzle and graphic organizer instructional strategies on Biology students’ performance in Ukwa West Local Government Area, Abia State. Three groups pre-test post-test control group non-equivalent quasi-experimental design was employed in the study. Two objectives, two research questions and two hypotheses guided the study. One hundred and fifty-one (151) senior secondary school one (SSS1) students out of a total population of 450 Biology students formed the sample size of the study. Biology Performance Test on growth (BPTOG) was used for gathering data. The instrument was tested using test re-test method and was validated by two experts in the Department of Curriculum Studies and Educational Technology, University of Port Harcourt, Nigeria. A reliability index of 0.75 was obtained using Pearson’s Product Moment Correlation. The data was analyzed using mean and standard deviation to answer the research questions while analysis of covariance (ANCOVA) was used to test the hypotheses at 0.05 level of significance. The result of the study showed that Jigsaw-Puzzle and Graphic Organizer are effective instructional strategies for enhancement of students’ academic performance in growth as a concept in Biology. It was recommended that Biology teachers should adopt Jigsaw-Puzzle and Graphic Organizer Instructional Strategies to teach the concept of growth in the class.
Predicting Short Versus Long Hospital Stay for Navy Personnel with a Diagnosed Mental Health Problem: A Replication

Abstract : The purpose of the present study was to examine the relationship between mental health diagnosis (psychotic and nonpsychotic), certain hospital factors (i.e., year of hospitalization, direct versus transfer admission, first versus multiple admission) and length of hospital stay for active duty, enlisted Navy personnel. The sample (N=30,340) consisted of all hospitalized cases of active duty, enlisted Navy personnel between 1981 and 1984, inclusive, with a mental health problem as the primary diagnosis. A small percentage of mental health problem cases accounted for a disproportionate number of total hospital days, and longer hospital stays were associated with psychotic diagnoses, Air Force, Army, or Navy medical transfers, and recent years of hospitalization. Nonpsychotic cases, direct admissions, and first admissions were more likely to indicate longer hospital stays during the second two-year period of the study than the first two-year period of the study. It was concluded that hospital policies can increase or decrease hospital costs and that different hospital practices or rules apply to nonpsychotic versus psychotic cases. Keywords: Mental problems; Diagnosis medicine; Length of hospital stay.
An Electronic Health Tool to Prepare for the First Orthopedic Consultation: Use and Usability Study

Background The use of electronic health (eHealth) technology to prepare patients with hip or knee osteoarthritis (OA) for their first orthopedic consultation seems promising. Exploration of the use and usability of an educational eHealth tool may highlight potential modifications that could increase patient engagement and effectiveness. Objective This study aimed to (1) identify the use and usability of a stand-alone educational eHealth tool for patients with suspected hip or knee OA, (2) explore whether the recorded questions in the eHealth tool were in line with an existing widely used question prompt list, and (3) investigate whether user characteristics are related to use and usability. Methods We used data from 144 participants in the intervention group of a randomized controlled trial, who were asked to use the educational eHealth tool to prepare for their upcoming first orthopedic consultation. We defined users and nonusers based on whether they had opened the tool at least once. Users were characterized as active or superficial depending on the extent of their use of the tool. The recorded questions for the consultation preparation were categorized into themes fitting 3 predefined questions or in a remaining category. Usability was measured using the System Usability Scale (SUS, 0-100). Data were collected including the patient demographic and clinical characteristics, knowledge of OA, and internet and smartphone usage in daily life. The characteristics associated with users and nonusers were analyzed using a multivariable logistic regression analysis. Results A total of 116/144 (80.6%) participants used the educational eHealth tool, of whom 87/116 (75.0%) were active users. Of the three components of the tool (information, my consultation, and medication), medication was the least used (34%). On the basis of recorded questions of the users, the fourth predefined question could be proposed. The mean (SD) SUS score was 64.8 (16.0). No difference was found between the SUS scores of superficial and active users (mean difference 0.04, 95% CI −7.69 to 7.77). Participants with a higher baseline knowledge of OA (odds ratio [OR] 1.2, 95% CI 1.0 to 1.4) and who used the internet less frequently in their daily life (OR 0.6, 95% CI 0.5 to 0.9) were more likely to use the educational eHealth tool. We found no differences between the demographics and clinical characteristics of the superficial and active users. Conclusions Based on the results of this study, it can be concluded that the use of an educational eHealth tool to prepare patients with hip and knee OA for the first orthopedic consultation is feasible. Our results suggest some improvements that should be made to the content of the tool to improve its usability. No clear practical implications were found to support the implementation of the educational eHealth tool in specific subgroups. Trial Registration Netherlands Trial Register NTR6262; https://www.trialregister. nl/trial/6262
Using Intelligent Clustering to Implement Geometric Computation for Electoral Districting

Traditional electoral districting is mostly carried out by artificial division. It is not only time-consuming and labor-intensive, but it is also difficult to maintain the principles of fairness and consistency. Due to specific political interests, objectivity is usually distorted and controversial in a proxy-election. In order to reflect the spirit of democracy, this study uses computing technologies to automatically divide the constituency and use the concepts of “intelligent clustering” and “extreme arrangement” to conquer many shortcomings of traditional artificial division. In addition, various informational technologies are integrated to obtain the most feasible solutions within the maximum capabilities of the computing system, yet without sacrificing the global representation of the solutions. We take Changhua County, Taiwan as an example of complete electoral districting, and find better results relative to the official version, which obtained a smaller difference in the population of each constituency, more complete and symmetrical constituencies, and fewer regional controversies. Our results demonstrate that multidimensional algorithms using a geographic information system could solve many problems of block districting to make decisions based on different needs.
Text Genre and Training Data Size in Human-like Parsing

Domain-specific training typically makes NLP systems work better. We show that this extends to cognitive modeling as well by relating the states of a neural phrase-structure parser to electrophysiological measures from human participants. These measures were recorded as participants listened to a spoken recitation of the same literary text that was supplied as input to the neural parser. Given more training data, the system derives a better cognitive model — but only when the training examples come from the same textual genre. This finding is consistent with the idea that humans adapt syntactic expectations to particular genres during language comprehension (Kaan and Chun, 2018; Branigan and Pickering, 2017).
3D Audio-Visual Speaker Tracking with A Two-Layer Particle Filter

Audio-visual speaker tracking in 3D space is a challenging problem. Although the classical particle filter based methods have shown effectiveness in audio-visual speaker tracking, the performance degrades considerably when the measurements are disturbed by noise. To this end, a novel two-layer particle filter is proposed for 3D audio-visual speaker tracking. Firstly, two groups of particles, which are generated from the audio and video streams respectively, are propagated independently in the audio layer and visual layer. Then, the audio and visual likelihoods are combined in an adaptive sigmoid function, which can adjust particle weights according to the confidence of two modalities. Finally, an optimal particle set selected from two groups of particles is proposed to determine the speaker position and reset the particle positions in the next frame. Experiments on AV16.3 database show that our method outperforms the trackers using individual modalities and the existing approaches in the 3D space and on the image plane.
One-Shot Object Detection with Co-Attention and Co-Excitation

This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel {\em co-attention and co-excitation} (CoAE) framework that makes contributions in three key technical aspects. First, we propose to use the non-local operation to explore the co-attention embodied in each query-target pair and yield region proposals accounting for the one-shot situation. Second, we formulate a squeeze-and-co-excitation scheme that can adaptively emphasize correlated feature channels to help uncover relevant proposals and eventually the target objects. Third, we design a margin-based ranking loss for implicitly learning a metric to predict the similarity of a region proposal to the underlying query, no matter its class label is seen or unseen in training. The resulting model is therefore a two-stage detector that yields a strong baseline on both VOC and MS-COCO under one-shot setting of detecting objects from both seen and never-seen classes.
A Clinical Study and an analysis of Symptom Check List-90-Revision on Psychiatric Patients Visiting the Emergency Room in an Oriental Medicine Hospital

Objective : The aims of this study were to show the clinical aspects and to investigate psychiatric traits by SCL-90-R in patients who visited Oriental Medicine Hospital Emergency Room. Method : We studied twenty patients who visited Daejeon University Oriental Medicine Hospital from May, 2003 to September, 2003. Results : 1. The ratio of male to female patients was 1:3 and age distribution was most frequent in 30s&40s. 2. The most frequent cause of illness was stress in occupation and overwork. The greater part of patients came to hospital in 12hours after an attack. 3. In chief problem, somatic symptoms such as headache, dizziness, numbness and chest discomfort were most frequent. In diagnosis, somatization disorder was most frequent. 4. After treatment, most of symptoms were improved. 5. Psychiatric traits by SCL-90-R correlated with somatization and anxiety.
Detecting and Mitigating Bias in Machine Learning Image Data through Semantic Description of the Attention Mechanism: The use-case Gender Bias in Profession Prediction from Images

Machine Learning models are increasingly used to assist or replace humans in a variety of decision-making domains. However, a lot of concerns have been raised about the impact of these decisions on people’s lives. In this work we focus on two main problems. The first one is that there might be discrimination between different groups of people with respect to their protected attributes in the aforementioned Machine Learning decision making applications. The second one is that there is lack of methods that actually interpret and explain the predictions of these Machine Learning systems which are then used to help decision making. Particularly, we focus on a specific aspect of these decision making applications, namely in Machine Learning training data for the problem of classification. Most research tackles solely individual aspects like trying to detect and mitigate bias and does not pay any attention to explain their reasoning in a human interpretable way. Also, the main method that they use is to balance the distribution of the training data with respect to the protected attribute. However, as we show, this is not always the solution to the problem. On the contrary, we study concurrently three steps (detection, semantic interpretation and mitigation of bias) to overcome these shortcomings and limitations and show that there is the presence of specific visual clues that leads to that bias. Finally, we perform an extensive evaluation of our method in order to verify its efficiency and effectiveness on the particular use-case Gender Bias in Profession Prediction from Images.
Better Exploration with Optimistic Actor-Critic

Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address this problem both theoretically and empirically. On the theoretical side, we identify two phenomena preventing efficient exploration in existing state-of-the-art algorithms such as Soft Actor Critic. First, combining a greedy actor update with a pessimistic estimate of the critic leads to the avoidance of actions that the agent does not know about, a phenomenon we call pessimistic underexploration. Second, current algorithms are directionally uninformed, sampling actions with equal probability in opposite directions from the current mean. This is wasteful, since we typically need actions taken along certain directions much more than others. To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function. This allows us to apply the principle of optimism in the face of uncertainty to perform directed exploration using the upper bound while still using the lower bound to avoid overestimation. We evaluate OAC in several challenging continuous control tasks, achieving state-of the art sample efficiency.
Self-attention with Functional Time Representation Learning

Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and interpretability, self-attention is gradually becoming a key component in event sequence models. However, like most other sequence models, self-attention does not account for the time span between events and thus captures sequential signals rather than temporal patterns. Without relying on recurrent network structures, self-attention recognizes event orderings via positional encoding. To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces. By constructing the associated translation-invariant time kernel function, we reveal the functional forms of the feature map under classic functional function analysis results, namely Bochner's Theorem and Mercer's Theorem. We propose several models to learn the functional time representation and the interactions with event representation. These methods are evaluated on real-world datasets under various continuous-time event sequence prediction tasks. The experiments reveal that the proposed methods compare favorably to baseline models while also capture useful time-event interactions.
Turning Talk into “Rationales”: Using the Extended Case Method for the Coding and Analysis of Semi-Structured Interview Data in ATLAS.ti

This article focuses on the tool-driven analysis of qualitative data in ATLAS.ti collected as part of an empirical investigation into how American voters legitimate their positions on direct democratic economic policy. To carry out this research, semi-structured interviews were conducted from 2013 to 2015 with 120 Arizonan respondents. Participants were interviewed about why they voted the way they did on four economic policy ballot measures that appeared on the Arizona state ballot from 2008–2012 related to narcotic decriminalization and medicalization, education funding, immigration and labor markets, and consumer protection. The principle of “reconstruction” from Michael Burawoy’s “Extended Case Method” (ECM) was applied in the coding and analysis of these data in ATLAS.ti. Reconstruction starts with a theory and then looks for anomalous cases as a way of theory building. The theories that were used to generate the project’s research question were also used to guide a review of the empirical literature in order to create a deductive thematic codebook for each ballot measure. Open coding later allowed for “reconstructed” theory by adding new codes to the initial codebook and modifying others. These issue-specific thematic codes are referred to as “rationales”. Through this process of coding, the author contributed to theories of American political ideology and voter legitimation by identifying theoretically anomalous “rationales” in the data and then using these rationales to expand the theories that were used to generate the initial thematic codebook.
A Causal View on Robustness of Neural Networks

We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data. Based on this view, we design a deep causal manipulation augmented model (deep CAMA) which explicitly models possible manipulations on certain causes leading to changes in the observed effect. We further develop data augmentation and test-time fine-tuning methods to improve deep CAMA's robustness. When compared with discriminative deep neural networks, our proposed model shows superior robustness against unseen manipulations. As a by-product, our model achieves disentangled representation which separates the representation of manipulations from those of other latent causes.
Neural Diffusion Model for Microscopic Cascade Study

The study of information diffusion or cascade has attracted much attention over the last decade. Most related works target on studying cascade-level macroscopic properties such as the final size of a cascade. Existing microscopic cascade models which focus on user-level modeling either make strong assumptions on how a user gets infected by a cascade or limit themselves to a specific scenario where “who infected whom” information is explicitly labeled. The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data. Also, the methods which focus on specific scenarios cannot be generalized to a general setting where the diffusion graph is unobserved. To overcome the drawbacks of previous works, we propose a Neural Diffusion Model (NDM) for general microscopic cascade study. NDM makes relaxed assumptions and employs deep learning techniques including attention mechanism and convolutional network for cascade modeling. Both advantages enable our model to go beyond the limitations of previous methods, better fit the diffusion data and generalize to unseen cascades. Experimental results on diffusion identification task over four realistic cascade datasets show that our model can achieve a relative improvement up to 26 percent against the best performing baseline in terms of F1 score.
Prevalence of Salmonella Isolated from Pigs, Pig carcasses, Water and Workers at Slaughterhouses in Khon Kaen Province

Objective—To determine prevalence and serovars of Salmonella isolated from pigs, pig carcasses, water and workers at three slaughterhouses in Khon Kaen province. Materials and Methods—During February - April 2009, 545 samples from pigs (210), carcasses (210), water (41) and workers (84) were collected from three slaughterhouses in Khon Kaen province. All samples were examined for Salmonella isolation and identification by ISO 6597:2002 and Modified method. Results—Overall prevalences from three slaughterhouses of Salmonella isolated from pigs, pig carcasses, water and workers were 27.14%, 36.67%, 19.51% and 10.71%, respectively. In each slaughterhouse A, B and C the prevalences from pigs, pig carcasses, water and workers were 20%, 18.33%, 8.33% and 10.34% for slaughterhouse A; 41.33%, 50.67%, 53.85% and 12% for slaughterhouse B; and 18.67%, 37.33%, 0% and 10% for slaughterhouse C, respectively. The most prevalent serovars from all samples in each slaughterhouse were S. enterica subsp. ser enterica 4,5,12:i:- (48.15%) for slaughterhouse A, S. Rissen (35.44%) for slaughterhouse B and S. Rissen (44.44%) for slaughterhouse C. Conclusion—This study indicated high prevalence of Salmonella contamination in all sample types from the slaughterhouses. Isolation of similar Salmonella serovars from different type of samples within the slaughterhouse may be due to cross-contamination during the slaughtering processes. Therefore, effective measures are required for prevention and control of Salmonella contamination in pork. KKU Vet J. 2011;21(1):33-40. http://vmj.kku. ac.th/
Interactive maps, productivity and user experience: A user study in the e‐mobility domain

E‐mobility has been gaining importance in recent years in Germany, with the National Platform for E‐mobility infrastructure in Germany is expected to change fast, therefore many charging stations will appear. These need to be managed, and the aim of this work is to explore how web applications could best support productivity and user experience in this context. We compared a text‐form‐based approach to a map‐based approach for the addition and update of locations and chargepoints. We found that the map‐based approach yields better learning effects and offers a better compromise between pragmatic and hedonic properties. The results are useful for a better understanding of the impact of interactive maps on work and user experience in the e‐mobility domain and beyond.
Sais: Self-Adaptive Identification of Security Bug Reports

Among various bug reports (BRs), security bug reports (SBRs) are unique because they require immediate concealment and fixes. When SBRs are not identified in time, attackers can exploit the vulnerabilities. Prior work identifies SBRs via text mining, which requires a predefined keyword list and trains a classifier with known SBRs and non-security bug reports (NSBRs). The former approach is not reliable, because (1) as the contexts of security vulnerabilities and terminology of SBRs change over time, the predefined list will become out-dated; and (2) users may have insufficient SBRs for training. We introduce a semi-supervised learning-based approach, Sais, to adaptively and reliably identify SBRs. Given a project's BRs containing some labeled SBRs, many more NSBRs, and unlabeled BRs, Sais iteratively mines keywords, trains a classifier based on the keywords from the labeled data, classifies unlabeled BRs, and augments its training data with the newly labeled BRs. Our evaluation shows that Sais is useful for identifying SBRs.