Miracle-dz/UniFAR-FTUs huggingface dataset:
Using conversation analysis to understand how agreements, personal experiences, and cognition verbs function in online discussions

In this conceptual paper, we propose that insights from conversation analysis (CA) may provide a useful approach for scholars interested in online learning by focusing first on how learners themselves orient to performing social actions online. We further propose that gaining a better understanding of what conversational moves are actually doing in online discussion can help researchers and educators better assess the learning that takes place there. Through worked examples, we demonstrate the ways that three conversational features (agreements, personal experiences and stories, and cognition verbs) tend to function in online institutional talk. We illustrate how these conversational features can accomplish a variety of actions in online academic discussions, including social affiliation, displaying expertise, and distancing oneself from claims. Recognizing how these features are interactionally deployed can help scholars better understand the functions of learners’ talk. CA is a practical methodology for educators who may be reluctant to impose interactional frameworks on student discussions for learning.
Evaluating Behavioral Biometrics for Continuous Authentication: Challenges and Metrics

In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can sometimes lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.
Perspective: Growing Up or Growing Out: Growth Faltering in Early Childhood and Subsequent Risk of Overweight.

All of us believe in a world where every person can grow to their full potential and, in turn, contribute to society in their own way. As nutritionists, our specific concerns are with the food we provide to children and the policies that we advocate to promote good nutrition. Historically, nutritionists working in lowand middle-income countries have been concerned with measuring the extent of, and developing strategies to prevent, growth faltering and other manifestations of undernutrition. In some locations, particularly in the context of armed conflict or weather-related famines, acute wasting is a public health emergency, which requires specific responses from the global public health community; this is not the focus of the present article, which focuses on linear growth and overweight and obesity, and their interrelations, within contexts of children living their usual lives. In recent years, the worlds of “undernutrition” and “overnutrition” have been intersecting. We have become increasingly aware of the spread of the obesity epidemic from high-income countries to lowand middle-income economies, predominantly in adults but, increasingly, in children. We have also become aware of the changing pattern of regional, ethnic, and socioeconomic disparities in obesity and its consequent morbidities within countries. Linear growth velocity in childhood and adolescence is a monotonic, nonlinear, function of age. Most representations of growth velocity characterize it as a smooth curve—over
Brucellar Testicular Abscess Presenting as a Testicular Mass: Can Color Doppler Sonography be used in Differentiation?

SUMMARY Brucellosis is an endemic disease in various regions of the world. Testicular abscess is a very rare complication of brucellosis which can be misdiagnosed as a testicular mass and may lead to unnecessary orchiectomy. To our knowledge there are only eight reported cases in the literature of a brucellar testicular abscess. We present a case of testicular abscess due to brucellosis diagnosed with serologic tests and color Doppler sonography, and treated with antibiotics and fine needle aspiration.
The Dragon vs . The Eagle : A Case of Mergers

With the rise of China’s global presence, mergers conducted by domestic bidders have begun to raise political and economic concern. This paper directly examines this topical debate by comparatively assessing the performance of inexperienced Chinese bidders undertaking mergers against the experienced merger market of the US. The work shows that US bidders follow a market timing strategy while Chinese bidders have sought to capitalise upon deals conducted in low valuation markets, most likely to signal that target firms are also in a lowvaluation state. This value strategy is reaping great rewards for Chinese firms and overall, Chinese bidders significantly and positively outperform those in the US. With this evidence, it is apparent that experience does not always make the difference. JEL Classification: G14; G34.
Image Compression Using Discrete Wavelet Transform

This Project presents an approach towards MATLAB implemention of the Discrete Wavelet Transform (DWT) for image compression. The design follows the JPEG2000 standard and can be used for both lossy and lossless compression. In order to reduce complexities of the design linear algebra view of DWT has been used in this concept.With the use of more and more digital still and moving images, huge amount of disk space is required for storage and manipulation purpose. For example, a standard 35-mmphotograph digitized at 12μm per pixel requires about 18 Mbytes of storage and one second of NTSC-quality color video requires 23 Mbytes of storage. JPEG is the most commonly used image compression standard in today’s world. But researchers have found that JPEG has many limitations. In order to overcome all those limitations and to add on new improved features, ISO and ITU-T has come up with new image compression standard, which is JPEG2000.
Sagan Summer School 2015 : GlobalPFE A simple model for planetary population synthesis

At the 2015 Sagan Summer Workshop you will have the opportunity to experiment with a toy population synthesis model called GlobalPFE which stands for Global Planet Formation and Evolution model. It is based on the original Ida and Lin (2004) model, but it also includes elements of several other models like Alibert et al. (2005); Mordasini et al. (2009a); Dittkrist et al. (2014). Recent reviews of population synthesis methods are found in Benz et al. (2014) and Mordasini et al. (2015).
Segmental "anchorage" and the French late rise

We examined the tonal alignment and scaling patterns of the start and end points of the French late rise, using a rate mani pulation paradigm. Our findings call into question aspects of the segmental anchoring hypothesis: the low starting point of t he late rise was not stably anchored to a segmental landmark, and for some speakers, F 0 excursion size varied across rates. The position of the peak of the late rise was found to vary across syllable structures. To account for the observed patterns, we propose the notion of an “anchorage,” that is, a region withi n which an intonational turning point can anchor. For the peak of the French late rise, this anchorage stretches from just bef ore the end of the vowel of the last full syllable of the accentual phrase to the end of the phrase.
Personality Matters! Improving The User Experience of Chatbot Interfaces - Personality provides a stable pattern to guide the design and behaviour of conversational agents

Recent advances in machine learning has contributed to the rebirth of the chatbot. Lately we have seen a rise in chatbot technology being made available on the web and on our mobile devices, and recent reports states that 57 % of companies have implemented or are planning to implement a chatbot in the near future. Chatbots are therefore a big part of an AI powered future, however recent reviews find chatbots to be perceived as unintelligent and non-conversational. Such findings have not slowed down the rapid implementation of chatbots online, and the same mistakes seems to be repeated over and over again. Chatbot services have been found to save companies an estimate of $8 billion by 2022, and extends to customer service tasks, product purchasing, shopping assistants, recommender systems, service or product support. This explains why so many are eager to implement their own chatbots, but the reviews make one wonder whether we now are forcing users to adopt technology which they find frustrating and useless. Chatbots are becoming an extension of the services companies provide, therefore ensuring a great user experience is important not only for a company’s brand image, but also for the users of their services. Existing literature regarding how humans perceive conversational agents have found that personality can offer a stable pattern to how the chatbot is perceived, and add consistency to the user experience. This master thesis project investigated how we could improve the user experience of chatbots through personality. The thesis is twofold, the first part of the thesis consists of a detailed description of the personality framework developed and implemented to build a chatbot prototype. The framework combines techniques from user-centred design, branding, and personality theory, to build user-centred chatbots through a design process with a basis in personality. The thesis also offers a method to test and evaluate the modelled personality in regards to whether it is perceived as intended by the designer. The second part of the thesis consists of an experiment to investigate whether personality has an improved effect on the user experience of chatbot interfaces. The experiment found that the chatbot personality built using the personality framework had a significant improved effect on the user experience.
Deep Reduced PEDOT Films Support Electrochemical Applications: Biomimetic Color Front

Most of the literature accepts, despite many controversial results, that during oxidation/reduction films of conducting polymers (CPs) move from electronic conductors to insulators. Thus, engineers and device’s designers are forced to use metallic supports to reoxidize the material for reversible device work. Electrochromic front experiments appear as main visual support of the claimed insulating nature of reduced CPs. Here, we present a different design of the biomimetic electrochromic front that corroborates the electronic and ionic conducting nature of deep reduced films. The direct contact PEDOT metal/electrolyte and film/electrolyte was prevented from electrolyte contact until 1 cm far from the metal contact with protecting Parafilm®. The deep reduced PEDOT film supports the flow of high currents promoting reaction induced electrochromic color changes beginning 1 cm far from the metal-polymer electrical contact and advancing, through the reduced film, toward the metal contact. Reverse color changes during oxidation/reduction always are initiated at the film/electrolyte contact advancing, under the protecting film, toward the film/metal contact. Both reduced and oxidized states of the film demonstrate electronic and ionic conductivities high enough to be used for electronic applications or, as self-supported electrodes, for electrochemical devices. The electrochemically stimulated conformational relaxation model explains those results.
Does Emotional Intelligence Effect Leaders Strategic Decision Making?

Emotional intelligence is important for a leader nowadays as it could improves leader in strategic decision making, problem solving and cooperate with others. The objective of this study is to examine the effects of emotional intelligence on strategic decision making among leaders in Government Link Companies (GLCs). Data were collected using questionnaire. Stratified random sampling were used to select the sample for this study. Collected data were analysed using Partial Least Square Structural Equation Model (PLS-SEM) approach. The result revealed that there is a significant effects between the emotional intelligence and strategic decision making. The implications of this study could be used as a guide for leaders in managing and controlling their emotions when making a strategic decision for their organization. Theoretically, this theory use which is leader member exchange theory, is significant since the outcomes will contribute to the body of knowledge in the field of leadership, and GLCs in Malaysia.
Modeling Variation in Motion Data

We present a new method to model and synthesize variation in human motion. Given a small amount of input motion data, we learn a generative model that is able to synthesize new output motion variations that are statistically similar to the input data. The new variations retain the features of the original data examples, but are not exact copies. Our model does not require timewarping or synchronization of similar sequences of motions. We learn a Dynamic Bayesian Network model from the input data that enables us to capture properties of conditional independence in the data, and build a multivariate probability distribution of it. We present synthesis results across a range of different types of motions, and demonstrate novelty and aesthetic appeal of the new variations generated with respect to the input motions. Our technique can synthesize new motions efficiently and has a small memory requirement.
A THEORY OF COMPLEXITY, CONDITION, AND ROUNDOFF

We develop a theory of complexity for numerical computations that takes into account the condition of the input data and allows for roundoff in the computations. We follow the lines of the theory developed by Blum, Shub and Smale for computations over $\mathbb{R}$ (which in turn followed those of the classical, discrete, complexity theory as laid down by Cook, Karp, and Levin, among others). In particular, we focus on complexity classes of decision problems and, paramount among them, on appropriate versions of the classes $\mathsf{P}$, $\mathsf{NP}$, and $\mathsf{EXP}$ of polynomial, nondeterministic polynomial, and exponential time, respectively. We prove some basic relationships between these complexity classes, and provide natural NP-complete problems.
Genome-Wide Analysis of Gene Expression in Primate Taste Buds Reveals Links to Diverse Processes

Efforts to unravel the mechanisms underlying taste sensation (gustation) have largely focused on rodents. Here we present the first comprehensive characterization of gene expression in primate taste buds. Our findings reveal unique new insights into the biology of taste buds. We generated a taste bud gene expression database using laser capture microdissection (LCM) procured fungiform (FG) and circumvallate (CV) taste buds from primates. We also used LCM to collect the top and bottom portions of CV taste buds. Affymetrix genome wide arrays were used to analyze gene expression in all samples. Known taste receptors are preferentially expressed in the top portion of taste buds. Genes associated with the cell cycle and stem cells are preferentially expressed in the bottom portion of taste buds, suggesting that precursor cells are located there. Several chemokines including CXCL14 and CXCL8 are among the highest expressed genes in taste buds, indicating that immune system related processes are active in taste buds. Several genes expressed specifically in endocrine glands including growth hormone releasing hormone and its receptor are also strongly expressed in taste buds, suggesting a link between metabolism and taste. Cell type-specific expression of transcription factors and signaling molecules involved in cell fate, including KIT, reveals the taste bud as an active site of cell regeneration, differentiation, and development. IKBKAP, a gene mutated in familial dysautonomia, a disease that results in loss of taste buds, is expressed in taste cells that communicate with afferent nerve fibers via synaptic transmission. This database highlights the power of LCM coupled with transcriptional profiling to dissect the molecular composition of normal tissues, represents the most comprehensive molecular analysis of primate taste buds to date, and provides a foundation for further studies in diverse aspects of taste biology.
Option Decision Trees with Majority Votes

We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so that additional complexity of little utility is limited. Option Decision Trees can reduce the error of decision trees on real-world problems by combining multiple options, with the motivation similar to that of voting algorithms that learn multiple models and combine the predictions. However, unlike voting algorithms, an Option Decision Tree provides a single structured classi er (one decision tree), which can be interpreted more easily by humans. Our results show that for the tested problems, we can achieve signi cant reduction in error rates for trees restricted to two levels of option nodes at the top. When very large Option Decision Trees are built, Option Decision Trees outperform Bagging in reducing error, although the trees are much larger and cannot be reasonably interpreted by humans.
Evolving Extremal Epidemic Networks

The susceptible, infected, removed model for epidemics assumes that the population in which the epidemic takes place is well mixed. This strong assumption can be relaxed by permitting the epidemic to spread only along the links of a contact network or graph. This study uses evolutionary computation to search for graphs that exhibit one of two extreme behaviors: maximum epidemic duration or maximal number of individuals catching the disease. The focus of the paper is on comparison of two representations for evolvable networks. The first makes local expansions of the network specified by a linear chromosome. The second, a permutation-based representation, joins a large cycle with another cycle specified by the permutation. The linear chromosome representation, based on iterated simplexification, yields inferior results in both fitness measures but creates networks with a structure more like a personal contact network. Location of such behaviorally extreme networks will provide a set of test cases for intervention strategies as well as providing conjectures to focus standard mathematical investigation of the types of networks that yield extreme behavior. This study also proposes a testing protocol for network representations for epidemic modeling
Reliability, Sufficiency, and the Decomposition of Proper Scores

Scoring rules are an important tool for evaluating the performance of probabilistic forecasting schemes. In the binary case, scoring rules (which are strictly proper) allow for a decomposition into terms related to the resolution and to the reliability of the forecast. This fact is particularly well known for the Brier Score. In this paper, this result is extended to forecasts for finite–valued targets. Both resolution and reliability are shown to have a positive effect on the score. It is demonstrated that resolution and reliability are directly related to forecast attributes which are desirable on grounds independent of the notion of scores. This finding can be considered an epistemological justification of measuring forecast quality by proper scores. A link is provided to the original work of DeGroot and Fienberg (1982), extending their concepts of sufficiency and refinement. The relation to the conjectured sharpness principle of Gneiting et al. (2005a) is elucidated.
Enhancing learning in the context of Street football: a case for Nonlinear Pedagogy

ABSTRACT Background: Street football can be understood as the most natural way to learn football and it can be a great starting point to develop perceptual, decisional, tactical and motor skills. Importantly, players involved in Street football may develop a strong emotional bond to the game through their experiences playing in an informal setting and eventually this could support their participation in a more structured playing environment. However, nowadays, talented children have reduced opportunities to play the game in this informal environment and instead, are mostly experiencing the game through their participation in organised settings such as in youth football clubs and academies. Importantly, such structured and formal environments afford the children a more rigid and less representative learning environment, which could potentially inhibit the development of creativity and adaptability among these young players. Purpose: The current paper aims to discuss the importance of Street football to players’ development and the role of a Nonlinear Pedagogy (NLP) framework to support the design of a player-centred and game-based approach that can contribute to enhance learning of creative and adaptive behaviours in football. Discussion: Pedagogical principles of NLP (representative learning design, information-movement couplings, manipulation of constraints, exploratory learning and reducing conscious control of movement) underpinned by concepts from Ecological Dynamics and working through pedagogical channels (instruction, feedback and practice) can facilitate the design of representative training tasks to enhance learning. NLP emphasises a learner-centred approach and highlights the critical role that the practitioner plays as a facilitator in providing learners with meaningful practice that leads to effective transfer of game-play behaviours. Street football affords features that capture some of the above key pedagogical principles and presents a suitable platform to support the acquisition of game-play behaviour that is meaningful for the individual learner. Conclusions: Through a recognition of the current need to design a football learning environments that capture key elements of Street football, we highlight the relevance of NLP as a pedagogical framework to underpin a learner-centred and game-based approach, supporting practitioners with a set of pedagogical principles. Thus, we propose that through this representative learning environment we can enhance learning in football and contribute to the development of intelligent and creative players.