Miracle-dz/UniFAR-FTUs huggingface dataset:
The gradual threshold model of ambivalence : Relating the positive and negative bases of attitudes to subjective ambivalence

This research examined the relationship between the measured (Study 1 ) and manipulated (Studies 2 and 3) positive and negative bases of attitudes and the psychological experience of attitudinal ambivalence. On the basis of these studies, the gradual threshold model of ambivalence (GTM) was advanced. The GTM holds that: (a) ambivalence increases in a negatively accelerating manner as the number of conflicting reactions (whichever of the positive or negative reactions are fewer in number) increases, (b) ambivalence is a negative function of the extent of dominant reactions, and (c) as the number of conflicting reactions increases, the impact of dominant reactions on ambivalence gradually decreases such that at some level of conflicting reactions (i.e., the threshold), the number of dominant reactions no longer has an impact on subjective ambivalence.
Educational Psychology: Theory and Practice

Chapter 1 - Educational Psychology: A Foundation for Teaching Chapter 2 - Cognitive, Language, and Literacy Development Chapter 3 - Social, Moral, and Emotional Development Chapter 4 - Student Diversity Chapter 5 - Behavioral Theories of Learning Chapter 6 - Information Processing and Cognitive Theories of Learning Chapter 7 - The Effective Lesson Chapter 8 - Student-Centered and Constructivist Approaches to Instruction Chapter 9 - Grouping, Differentiation, and Technology Chapter 10 - Motivating Students to Learn Chapter 11 - Effective Learning Environments Chapter 12 - Learners with Exceptionalities Chapter 13 - Assessing Student Learning Chapter 14 - Standardized Tests and Accountability
Price Dispersion on the Internet: Empirical Comparison of Several Commodities from the Czech Republic

The first large-scale (909 products, 79,679 individual price listings) empirical study of price dispersion based on data from the Czech Republic's online shops is presented in the paper. First, simple descriptive indicators were calculated for each product. Second, several versions of linear regression models were constructed for each of the 13 product categories and evaluated against the hypotheses. The price dispersion was regressed against its mean market price and the number of shops. For the majority of categories R2 is high or very high (the mean market price variable accounts for 86.6% to 96.7% in the price dispersion) or at least medium (from 56% to 69%). Also, price dispersion (measured by range and standard deviation) remains quite high for the majority of the analyzed product categories. The important role of pricebots for online shop owners and marketing managers and some other findings are also discussed.
Collaborative Appearance-Based Place Recognition and Improving Place Recognition Using Detection of Dynamic Objects

Collaborative Appearance-Based Place Recognition and Improving Place Recognition Using Detection of Dynamic Objects by Juan Pablo Muñoz Adviser: Scott Dexter This dissertation makes contributions to the problem of Long-Term Appearance-Based Place Recognition. We present a framework for place recognition in a collaborative scheme and a method to reduce the impact of dynamic objects on place representations. We demonstrate our findings using a state-of-the-art place recognition approach. We begin in Part I by describing the general problem of place recognition and its importance in applications where accurate localization is crucial. We discuss feature detection and description and also explain the functioning of several place recognition frameworks. In Part II, we present a novel framework for collaboration between agents from a pure appearance-based place recognition perspective. Using this framework, multiple agents can efficiently share partial or complete knowledge about places and benefit from their teamwork. This collaborative framework allows agents with limited storage and memory capacity to become useful in environment exploration tasks (for instance, by enabling remote recognition); includes procedures to manage an agent’s memory load and distributes knowledge of places across agents; allows the reuse of knowledge from one agent to another; and increases the tolerance for failure of individual agents. Part II also defines metrics which allow us to measure the performance of a system that uses the collaborative framework. Finally, in Part III, we present an innovative method to improve the recognition of places in environments densely populated by dynamic objects. We demonstrate that we can improve the recognition performance in these environments by incorporating high-level information from dynamic objects. Tests conducted using a synthetic dataset show the benefits of our
Unsupervised Detection of Network Attacks in the dark

The unsupervised detection of network attacks represents an extremely challenging goal. Current methods rely on either very specialized signatures of previously seen attacks, or on expensive and difficult to produce labeled traffic data-sets for profiling and training. In this paper we present a completely unsupervised approach to detect attacks, without relying on signatures, labeled traffic, or training. The method uses robust clustering techniques to detect anomalous traffic flows. The structure of the anomaly identified by the clustering algorithms is used to automatically construct specific filtering rules that characterize its nature, providing easy-to-interpret information to the network operator. In addition, these rules are combined to create an anomaly signature, which can be directly exported towards standard security devices like IDSs, IPSs, and/or Firewalls. The clustering algorithms are highly adapted for parallel computation, which permits to perform the unsupervised detection and construction of signatures in an on-line basis. We evaluate the performance of this new approach to discover and to build signatures for different network attacks without any previous knowledge, using real traffic traces.
EDMODO USE IN ESP WRITING: STUDENTS’ PERCEPTIONS

A growing body of literature has examined the nature of blended learning and its effects on language teaching and learning, including the use of Edmodo to promote student writing. However, this type of integration of face-to-face instruction and online learning remains relatively new for EFL contexts. Also, teaching writing at the Vietnamese contexts, particularly in the ESP tertiary education, is still largely dependent on conventional way. This paper therefore reports students’ perceptions about Edmodo use in writing classes at a Vietnamese university. This paper draws on data collected as part of a larger mixed methods project including tests and interviews over a fifteen-week semester of an ESP writing class. In this paper, the focus is the data from interviews, which explored how students perceived the effects of Edmodo use in their writing learning process. The findings indicate students’ positive perceptions about this supportive learning delivery method in writing classes. Implications for teachers and school administrators with regard to practical applications of Edmodo as a potential tool are also discussed. Article visualizations:
Advances in Visual Computing : 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part II

The two volume set LNCS 8033 and 8034 constitutes the refereed proceedings of the 9th International Symposium on Visual Computing, ISVC 2013, held in Rethymnon, Crete, Greece, in July 2013. The 63 revised full papers and 35 poster papers presented together with 32 special track papers were carefully reviewed and selected from more than 220 submissions. The papers are organized in topical sections: Part I (LNCS 8033) comprises computational bioimaging; computer graphics; motion, tracking, and recognition; segmentation; visualization; 3D mapping, modeling and surface reconstruction; feature extraction, matching, and recognition; sparse methods for computer vision, graphics, and medical imaging; and face processing and recognition. Part II (LNCS 8034) comprises topics such as visualization; visual computing with multimodal data streams; visual computing in digital cultural heritage; intelligent environments: algorithms and applications; applications; and virtual reality
Neuroradiological Changes Following Single or Repetitive Mild TBI

Objectives To test the hypothesis that there are differences in neuroradiological measures between single and repeated mild traumatic brain injury using multimodal MRI. Methods A closed-head momentum exchange model was used to produce one or three mild head injuries in young adult male rats compared to non-injured, age and weight-matched controls. Six–seven weeks post-injury, rats were studied for deficits in cognitive and motor function. Seven–eight weeks post-injury changes in brain anatomy and function were evaluated through analysis of high resolution T2 weighted images, resting-state BOLD functional connectivity, and diffusion weighted imaging with quantitative anisotropy. Results Head injuries occurred without skull fracture or signs of intracranial bleeding or contusion. There were no significant differences in cognitive or motors behaviors between experimental groups. With a single mild hit, the affected areas were limited to the caudate/putamen and central amygdala. Rats hit three times showed altered diffusivity in white matter tracts, basal ganglia, central amygdala, brainstem, and cerebellum. Comparing three hits to one hit showed a similar pattern of change underscoring a dose effect of repeated head injury on the brainstem and cerebellum. Disruption of functional connectivity was pronounced with three mild hits. The midbrain dopamine system, hippocampus, and brainstem/cerebellum showed hypoconnectivity. Interestingly, rats exposed to one hit showed enhanced functional connectivity (or hyperconnectivity) across brain sites, particularly between the olfactory system and the cerebellum. Interpretation Neuroradiological evidence of altered brain structure and function, particularly in striatal and midbrain dopaminergic areas, persists long after mild repetitive head injury. These changes may serve as biomarkers of neurodegeneration and risk for dementia later in life.
Mobile Robot Intelligence Based SLAM Features Learning and Navigation

For efficient, and knowledge based navigation, it is essential to blend mobile robot navigation details with information and details from navigation paths-localities. In this respect, the presented scheme was focused towards building intelligence for mobile robot navigation. Intelligence was achieved by considering the navigation capabilities while the mobile robot was in motion. The adopted learning paradigm was a five layers Neuro-Fuzzy learning architecture, with to ability to create an FIS inference for enhanced navigation. To capture the enormous visual and non-visual sensory data, the mobile robot platform has fully computerinterfaced stereo vision, and reliable 3D perception system onboard the mobile platform. A Neuro-Fuzzy intelligence paradigm was used to learn navigation maps (SLAM) main visual features, distances, nature of localities as it travels within spaces. Blinding intelligence with visual maps and non-visual sensory data, has indeed resulted in improved navigation capabilities.
Mining Question and Answer Sites for Automatic Comment Generation

Code comments improve software maintainability, programming productivity, and software reliability. To address the comment scarcity issue in many projects and save developers’ time in writing comments, we propose a new, general automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings. We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open source projects. We apply AutoComment to analyze 92,140 Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment generate 102 comments automatically for 23 Java and Android projects. The number of generated comments is still low, but the user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code. One of the advantages from mining Q&A sites for automatic comment generation is that human written comments can provide information that is not explicitly in the code. In the future, we would like to focus on improving both the yield and quality of the generated comments. To improve the yield, we can replace the token-based clone detection tool with one that can detect addition and reordering of lines to increase the number of code matches. To improve the quality, we can apply advanced natural language processing techniques such as semantic role labeling to analyze the semantics of the sentences, or typed dependencies to analyze the grammatical structure of the sentences.
Limited Attention and Stock Price Drift Following Earnings Announcements and 10-K Filings

Purpose - This study aims to examine whether limited attention leads to the market underreaction to earnings announcement and 10-K filings. Design/methodology/approach - This is an empirical study involving statistical analysis of a large sample of data, obtained from Compustat, CRSP and Xignite Inc. Both portfolio analysis and multivariate regressions are used in hypotheses testing. Findings - The following key findings are presented in the paper. First, we show that among large firms, investors under-react more to the information contained in 10-K filings than earnings announcements. Second, underreaction to earnings announcements tends to be stronger for small firms than large firms. Third, we find that companies report their earnings and 10-Ks earlier when there is a higher demand for such information, and document a negative relationship between the degree of underreaction and the timeliness of such information release. Finally, we show that the recent ruling by SEC to accelerate 10-K filing has little impact on the degree of investors' underreaction to 10-K information. Research limitations/implications - The findings of this study suggest that investors' failure to devote enough attention to an economic event leads to underreaction, and the degree of underreaction is negatively correlated with the amount of investor attention. Practical implications - Investors need to periodically reassess the informational contents of economic events, and allocate their attention accordingly, in order to avoid underreaction. Originality/value - This study analyzes and the roles of limited attention in determining the degree of investor underreaction to earnings announcement and 10-K filings. The comparison of the two related but distinct financial reporting events yields interesting insights.
Constructing Integrated Corpus and Lexicon Models for Multi-Layer Annotation in OWL DL

We present a general approach to formally modelling corpora with multi-layered annotation in a typed logical representation language, OWL DL. By defining abstractions over the corpus data, we can generalise from a large set of individual corpus annotations, thereby inducing a lexicon model. The resulting combined corpus and lexicon model can be interpreted as a graph structure that offers flexible querying functionality beyond current XML-based query languages. Its powerful methods for characterising and checking consistency can be used for incremental model refinement. In addition, the formalisation in a graph-based structure offers the means of defining flexible lexicon views over the corpus data. These views can be tailored for linguistic inspection or to define clean interfaces with other linguistic resources. We illustrate our approach by applying it to the syntactically and semantically annotated SALSA/TIGER corpus, a collection of German newspaper text.
Design of Novel Low-Power and High-Efficiency Class-d Audio Amplifier

This study develops and evaluates a novel architecture for class-D CMOS power amplifier. In this new architecture all stages of class-D amplifier are designed carefully. A second order delta sigma modulation, high switching frequency and three passive feedbacks are used in modulation stage. A special filter is designed to improve the quality of this system. This paper idea is simulated using MATLAB. The simulation results show that the developed class-D CMOS power amplifier architecture has low THD (<0.001), High SNR (>80dB), and high efficiency (>95%), which show that this new architectural design for class-D amplifier has the best performance in terms of THD, and efficiency in comparison with other class-D CMOS power amplifier architectures.
Perceptions of Service Quality and Behaviour Intentions of Campus Fitness Center Users in Malaysia and Thailand

The study was designed to determine the perceptions of service quality and future intention to use campus fitness centers in Universiti Putra Malaysia (UPM) and Mahasarakham University, Thailand. Data were collected using questionnaires distributed to 450 campus fitness center users comprising of faculty and staff from Mahasarakham university (n=250) and one university in UPM (n=200) were selected to participate as subjects in the study. The questionnaire consisted of items based on the Likert-scale Service Quality Assessment Scale (SQAS) developed by Lam, Zhang and Jensen (2005). Behavioral intention is based on three questions modified from Cunningham and Kwon (2003) and Zeithaml et al. (1996). Results showed significant differences (p<.05) in perceptions of overall fitness facility service quality and overall program service quality between users in UPM and Mahasarakham universities. The results showed a statistically significant difference (p<.05) in terms of behavior intentions of users in UPM and Mahasarakham University with those in UPM reporting higher intentions to renew their membership and recommend the recreation center to other people. Based on the results, it was concluded that good quality service would influence fitness center users at both universities to recommend the fitness center to other people and influence them to renew their membership.
Information fusion based techniques for HEVC

Aiming at the conflict circumstances of multi-parameter H.265/HEVC encoder system, the present paper introduces the analysis of many optimizations' set in order to improve the trade-off between quality, performance and power consumption for different reliable and accurate applications. This method is based on the Pareto optimization and has been tested with different resolutions on real-time encoders.
A SUPER VOXEL-BASED RIEMANNIAN GRAPH FOR MULTI SCALE SEGMENTATION OF LIDAR POINT CLOUDS

Abstract. Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.
GRAPHIC SYMBOLS RECOGNITION USING FLEXIBLE MATCHING OF ATTRIBUTED RELATIONAL GRAPHS

Graph representation and graph matching have been successfully applied to a large number of problems in computer vision and pattern recognition. Concerning graph matching, the classical algorithms of graph isomorphism seems useless when the image is degraded with noise or vectorial distortion. This paper introduce a novel similarity measure to recognize symbols by performing inexact matching of attributed graphs. In the proposed approach, symbols are encoded by attributed graphs, whose nodes represent structural primitives like quadrilaterals and whose edges represent mutual relationships between these primitives. To be invariant of rotation and scaling, relative information about primitives are associated as attributes on the nodes and edges. Considering a mapping between two graphs, a similarity function is formulated, that use the numerical values of the attributes to calculate a similarity score. This new similarity measure has many desirable properties such as discrimination power, invariant to affine transformations, and robustness to noise or vectorial distortions.
How are Ambulance Personnel Prepared and Supported to Withhold or Terminate Resuscitation and Manage Patient Death in the Field? A Scoping Review

Introduction Research shows that ambulance personnel can find it challenging to withhold or terminate resuscitation and manage patient death in the field. This scoping review provides a synthesis of published research that has addressed three main questions. Each relates to ambulance personnel enacting decisions to withhold or terminate resuscitation and manage patient death: How are they prepare; what supports and coping strategies are utilised; and what preparation and support needs have been identified? Methods Using Arksey and O'Malley's five-stage scoping review framework this review identifies what is known and what further research is needed in this area. Results Sixteen methodologically heterogenous papers were located. Half of the studies were conducted in the United States, with the remainder in Canada, Singapore, Spain, Sweden and the United Kingdom. Ambulance personnel receive little formal training and feel unprepared to manage situations where resuscitation is unsuccessful or unwarranted, particularly communicating with family, breaking bad news and managing death scenes. These aspects of termination of resuscitation are so challenging that distancing and avoidance are sometimes used as coping strategies, even when medical futility has been established. In addition to formal counselling for personal loss and stressors, ambulance personnel value downtime and informal peer debriefing, following difficult resuscitation decisions and patient deaths. Conclusion Ambulance personnel feel inadequately prepared to withhold or terminate resuscitation and manage patient death in the field. They want more opportunities to learn about death notification, communication with bereaved families and safe, sensitive management of death scenes. More research is needed to determine how best to prepare and support ambulance personnel for the challenges of resuscitation decision-making and patient death.
Police Culture and Perceived Service Value: Officer Perspectives on Psychological Services Utilization

Police Culture and Perceived Service Value: Officer Perspectives on Psychological Services Utilization by Kymberli Copeland Barker MPhil, Walden University, 2019 MA, Argosy University, 2012 BS, University of North Carolina at Chapel Hill, 1996 Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Psychology Forensic Specialization Walden University August 2019