Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor-Rich Smart Personal Photo Organization

With the popularity of visual sensor on mobile devices, that is the cameras, it becomes a habit for many people to take photos everyday and everywhere. This led to the rapid expansion in number of personal photos and becomes a problem to the users in storing and organizing them, which had not been faced before. Fortunately, cloud storage provided a comprehensive solution at the right moment, and it facilitates the synchronization and sharing of photos acquired. However, organizing this large number of personal photos is still a tedious and painful task. Common needs in photo organization may include tagging, removing duplicated or similar photos, and grouping photos into albums. In this paper, we target to provide a smart personal photo organizing application which make use of existing sensors and related technologies to help users to handle duplicated or similar photos more efficiently. For example, photos can be sorted, reorganized or searched according to different information, like geographical, time or appearance, acquired from various sensors on the mobile device. By harnessing the power of cloud computing for image analysis and face recognition algorithm, our system significantly reduce the time spent on managing photos in a neat and tidy way which reduce user frustration and enhance user experience.
Requirements of Next Generation Robotic Manipulators

Recent projections suggest that there will be considerable growth in coming years in the area of assistive robotics, in both domestic and industrial scenarios [1]. An important enabler of this is likely to be the development of flexible, multipurpose grippers/manipulators. This paper explores key challenges in the development of such manipulators, including manipulator design and implementation, control algorithms, manipulation strategies, and human-robot interaction, and identifies a roadmap to the successful deployment of next generation systems.
Ecosystem Services Provided by the Little Things That Run the World

Highest extinction risk and consequently biodiversity loss are predicted to occur in invertebrates, specifically insects, and these declines are expected to cascade onto ecosystem functioning and human well-being. Although this knowledge is intrinsically present in more traditional communities, in more urban environments, mapping ecosystem services can be an important tool to raise people’s awareness on the importance of preserving insect diversity. After an extensive revision of the available literature, we used a rule-based approach to assess the provisioning, regulating and maintenance, and cultural services delivered by insects. We followed the Common International Classification of Ecosystem Services (CICES) and identified several potential indicators that may help underpin the mapping and valuation of the services delivered by insects. From our search, we extracted a total of 73 indicators, divided as 17 Provisional indicators, 27 Regulation and Maintenance indicators, and 29 Cultural indicators. We concluded that insects are providers of services in the three major ‘Sections’ of ecosystem services defined by CICES. Despite the lack of recognition of provisioning and cultural services, the indicators provided may help to raise awareness on the importance of the little things the run the world, in order to preserve traditional and technological uses of insects and their services.
Self-Efficacy and Resilience: Mediating Mechanisms in the Relationship Between the Transformational Leadership Dimensions and Well-Being

The objective of this article is to test the roles of self-efficacy and resilience as consecutive mediators in the relationship between the four dimensions of transformational leadership and well-being (operationalized as psychosomatic complaints and psychological distress). The sample consists of 225 social services employees in Spain. Data were gathered at two time points with a time-lag of 6 months. We used path analysis to test the hypothesized model and Monte Carlo confidence intervals to check the significance of the indirect effects. Our results showed that only two of the four transformational leadership dimensions have a direct impact on self-efficacy: inspirational motivation and individualized consideration, although contrary to our hypothesis, the latter had a negative effect on self-efficacy. As for the indirect effects, self-efficacy did not mediate between the leadership dimensions and well-being, whereas resilience mediated between self-efficacy and well-being. Self-efficacy and resilience consecutively mediated between the leadership dimensions and well-being. Our findings contribute to understanding the underlying mechanisms connecting different dimensions of transformational leadership and well-being, and they highlight the importance of developing employees’ personal resources.
Multimodal sentiment analysis in social media using deep learning with convolutional neural networks

Due to the rapid growth of internet and proliferation of smartphones and tablets, socialmedia platforms like Facebook and YouTube have risen to great relevance. People share all sorts of contents in such platforms, leading to huge amount of data requiring processing. Companies are trying use these plethora of data to their advantage by developing automated systems for several applications. One such application is automated customer feedback gathering from user reviews, where the fundamental problem is to mine user sentiment associated with a particular product or service. Solving such a complex problem from a huge amount of data requires efficient and effective sentiment analysis systems. In this thesis, we specifically deal with the sentiment analysis of videos, where information is available in three modalities: audio, video, and text, hence multimodal sentiment analysis. One of the biggest challenge of multimodal sentiment analysis is modality fusion. We present two novel methods of fusion multiple modalities: fusing in a hierarchical fashion using perceptrons and employing attention mechanism. We obtained improvement over simple concatenation based fusion for both of the methods. In literature, most of the works consider utterances in a video to be independent of each other. In reality, this is not quite true, since the surrounding utterances can provide context to a given utterance. We model such inter-utterance influence using Long ShortTerm Memory (LSTM), which is a special kind of recurrent neural network. This greatly improves sentiment classification performance over state-of-the-art method. We also added attention network on top of LSTM, which further improves the performance due to the amplification of relevant information. We also present a deep network based approach for personality detection from textual data, specifically essays. We use a combination of automatically extracted features and hand-crafted features. Also, elimination of emotionally-neutral sentences further improves performance. We observe improved performance over the state-of-the-art for all of the personality traits.
The Micro-Macro Constitution of Power

The focus of this work is the complex and dialectic relationship between personal, social, and institutional powers. This distinction is not new, it is even obvious or commonsensical. However, first, this multi-layered nature of power is not acknowledged in all the disciplines (for instance sociology typically rejects the idea of a connection between the personal powers and the social ones); second, in our view there is not a good analytic theory of the personal and interpersonal layers of power and of their – not so obvious – relationships with the power that we prefer to call “institutional” (where the “legal” one is just a sub-case based not simply on conventions, traditions, and social norms but on a system of laws). For example, the term “physical power” is a bit reductive, since in fact one not only needs strength, skills, and a working body, but the appropriate action plan and the relevant information, i.e. knowledge, and also the required mental abilities and conditions (motives, awareness, confidence, and so on).
Disentangling taxonomy within the Rhabditis (Pellioditis) marina (Nematoda, Rhabditidae) species complex using molecular and morhological tools

Correct taxonomy is a prerequisite for biological research, but currently it is undergoing a serious crisis, resulting in the neglect of many highly diverse groups of organisms. In nematodes, species delimitation remains problematic due to their high morphological plasticity. Evolutionary approaches using DNA sequences can potentially overcome the problems caused by morphology, but they are also affected by theoretical flaws. A holistic approach with a combination of morphological and molecular methods can therefore produce a straightforward delimitation of species. The present study investigates the taxonomic status of some highly divergent mitochondria1 haplotypes in the Rhabditis (Pellioditis) marina species complex by using a combination of molecular and morphological tools. We used three molecular markers (COI, ITS, D2D3) and performed phylogenetic analyses. Subsequently, morphometric data from nearly all lineages were analysed with multivariate techniques. We included R. (P) mediterranea and R. (R.) nidrosiensis to infer species status of the observed lineages. The results showed that highly divergent genotypic clusters were accompanied by morphological differences, and we created a graphical polytomous key for future identifications. This study indisputably demonstrates that R. (P) marina and R. (l? ) mediterranea belong to a huge species complex and that biodiversity in free-living marine nematodes may be seriously underestimated. O 2008 The Linnean Society of London, Zoological Journal of the Linnean Society, 2008, 152, 1-15. ADDITIONAL KEYWORDS: COI - D2D3 - delimitation - DNA-barcoding - holistic - ITS - Pellioditis marina - polytomous key.
A case study of simulated endoscopy: a conceptual approach based on observation of the hands-on workshops of the Colombian Association of Digestive Endoscopy

Introduction: Simulation is an educational tool that consists of artificial replication of phenomena, processes or real situations in order to achieve an established academic goal. The aim of this study is to describe the pedagogical elements and the organizational model used in activities simulating endoscopy at an internatio- nal convention of digestive endoscopy in order to compare them with the categories currently accepted for Medical Education Based on Simulation. Methods: This is a qualitative, cross-sectional study. Data were collected during five hours of simulation workshops attended by six instructors and 40 students. Data were collected through observation, notes, chec- klists, oral and visual records, interviews with instructors and student surveys. The information was analyzed and compared using five categories accepted in previous work and related to Medical Education Based on Simulation. Results: Five workstations were designed for the acquisition and development of individual technical skills. The allocation of time and the design of the workshops led to overcrowded work stations, unequal opportuni- ties, and a five hour long stretch of work. This was, stressful for both students and instructors. Nevertheless, students perceived the simulation exercises of the convention as positive activities. Conclusions: This study allows us to propose suggestions for improving activities at future events. First, it is essential that all components of the simulation be planned, designed and implemented according to esta- blished learning objectives. Second, all simulations should be accompanied by some kind of feedback. Third, it is recommended that highly structured formats be used during all stages of the simulation when participants do not have sufficient prior experience in education based on simulation.
Examining Enrollment Trends in Schools Participating in Online Learning in Newfoundland and Labrador

In their review of the open-source literature related to K-12 online learning, Cavanaugh, Barbour and Clark (2009) described how much of the research in the field had focused on comparing student performance of online students with the performance of students in the traditional brick-and-mortar environment in an effort to “prove” that K-12 online learning was as effective as traditional schooling. However, historically, distance education programs were primarily designed for a select group of students, specifically those with higher aptitudes, higher achievement, and greater aspirations for post-secondary education.
Optimizing an Environmental Observatory Network Design Using Publicly Available Data

There is a need to optimize resources for large‐scale environmental monitoring efforts, especially in developing countries. We tested a flexible framework to optimize the design (i.e., selection of study sites) of an environmental observatory network (EON) using publicly available data for Mexico. This country represents a challenge for designing EONs because of its megadiversity and large climate and ecological heterogeneity. We address three pervasive challenges for designing EONs: (1) How to characterize and delineate ecologically similar areas, (2) how to set geographic priorities to establish new representative study sites, and (3) how to assess the representativeness of current and potential new study sites. We used unsupervised cluster analysis to spatially delineate ecologically similar sampling domains. We identified the most representative sites within each domain using a conditioned Latin Hypercube‐sampling strategy. Finally, we demonstrated the applicability of this approach by assessing the spatial representativeness of the eddy covariance network in Mexico (i.e., MexFlux). At least 84 distributed sampling sites are needed to represent >45% of the spatial heterogeneity of gross primary productivity (GPP) dynamics (i.e., GPP_mean and GPP_cv) and evapotranspiration (ET) dynamics (i.e., ET_mean and ET_cv) at the national level. The current array of MexFlux only represents 3% of GPP and 5% of ET dynamics spatial variability at the national‐level, while the same number of sites organized under an optimal framework nearly doubled these estimates. Our framework is based on a data‐driven approach and publicly available sources of information, so it could be applied anywhere in the world.
High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.
Effect of GPS feedback on Lactate Threshold Pacing in Intercollegiate Distance Runners

International Journal of Exercise Science 6(1) : 74-80, 2013. In their roles as coaches, the authors have observed that first-year collegiate distance runners often have difficulty running at prescribed training paces during lactate threshold (LT) training runs. Previous research has validated the accuracy of global positioning system (GPS) devices in providing distance and velocity feedback during running. The purpose of this study was to determine the efficacy of using the Garmin Forerunner 305 GPS watch (Garmin) to reduce deviations from prescribed training paces during LT runs with first-year collegiate runners. Participants were two groups of varsity cross country runners who completed a three-week LT training intervention either with (n = 5) or without (n = 6) a Garmin device. Prescribed training paces were based off an initial time-trial. In both the preand post-test runs, in which all runners ran without a Garmin device, differences were calculated between the prescribed pace and actual pace. The comparisons revealed a significant difference between the training groups in the post-test. Those runners who trained with the Garmin device had a significant decrease in pacing variability. This suggests that GPS pacing feedback appears to be an effective tool at improving LT pacing in first-year collegiate distance runners.
Enhancing of real-time lightning for outdoor augmented reality

Knowledge about illumination conditions in a real world scene has many applications, among them Augmented Reality which aims at placing virtual objects in the real world. An important factor for convincing augmentations is to use the illumination of the real world when rendering the virtual objects so they are shaded consistently and cast consistent shadows. This paper provides an overview of several lightning technics and proposes enhancements of real-time image based lightning system for outdoor augmented reality under dynamically changing illumination conditions. Proposed improvements help to increase speed and quality of rendering.
FACTORS INFLUENCING THE FIRST YEAR STUDENTS’ ADAPTATION TO NATURAL SCIENCE STUDIES IN HIGHER EDUCATION

The mass-approach in higher education promotes the broadening of students’ number and diversity therefore the question about the young people’s readiness to adapt to studies becomes more and more topical. Students who enrol in the natural science faculties of the University of Latvia (UL) are motivated and have high assessment in biology and chemistry as school subjects. However, the studies already during the 1st term prove that students encounter problems to get adjusted to the study process. In order to find out the key factors that determine the adaptation of the first-year students to studies, an e-questionnaire was developed and 79 students from the Faculty of Chemistry and Faculty of Biology were surveyed. Institutional and personal factors that are focused on the application of transferable skills are mainly those that affect the students’ adaptation in the first term of studies. At the same time such issues of the academic factor as students’ prior knowledge in biology, chemistry, physics and mathematics and their skill to learn independently are intrinsic for students’ successful studies. Key words: adaptation to studies, first year natural science students, institutional, personal and academic factors, transferable skills.
An Exploration of Multi-agent Learning Within the Game of Sheephead

In this paper, we examine a machine learning technique presented by Ishii et al. [7] used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of Sheephead as a partially observable Markov decision process (POMDP). This model will allow us to estimate the hidden state information inherent within the game of Sheephead. By first estimating this information, we restore the Markov property needed to model the problem as a Markov decison problem. We then solve the problem as Ishii et al. did by using a reinforcement learning technique based on the actor-critic algorithm [13]. Though our results were positive, they were skewed by a rules-based implementation of part of the algorithm. Future research will be needed complete this implementation via a learning-based action predictor. Future research should also include testing against human subjects thus removing the rules-based bias inherent in the current algorithm. Given increased processing power, disk space, and improved AI techniques such as the techniques described above, complex multi-agent learning problems which once proved difficult may find solutions
A neo-Schumpeterian perspective on the analytical macroeconomic framework : the expanded reproduction system

This study aims to introduce a new analytical macroeconomic framework, the expanded reproduction system, that combines the accumulated wisdom of several contemporary economic models while also compensating for their shortcomings. This new framework may be used to study macroeconomic phenomena from both the supply and demand side over a number of different time intervals. Furthermore, as we account for both new product and productivity innovations, we are able to account for both qualitative and quantitative developments within the economy.
Corrosion resistance and surface characterization of miniscrews removed from orthodontic patients

* Corresponding author. Correio eletrónico: marcelosbahia@outlook.com (Marcelo Santos Bahia). http://doi.org/10.24873/j.rpemd. 2019.05. 445 1646-2890/© 2017 Sociedade Portuguesa de Estomatologia e Medicina Dentária. Published by SPEMD. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). rev port estomatol med dent cir maxilofac. 2019;60(1) :1-7
A general theory of the firm: From Knight to relationship marketing

The purpose of this paper is to build an integrative framework for our understanding of the firm which would embrace all alternative theories in the field into one logical scheme. There are multiple views of the firm â€“ microeconomic view, transactional costs view, legal view, organizational theory view, resource-based view, knowledge-based view, relationship marketing and others. The main idea of this paper is that all arguments of these approaches may be systematized and built into one integrative framework which may be called a general theory of the firm.
Structured Decision Making to Meet a National Water Quality Mandate

Water quality criteria are necessary to ensure protection of ecological and human health conditions, but compliance can require complex decisions. We use structured decision making to consider multiple stakeholder objectives in a water quality management process, with a case study in the Three Bays watershed on Cape Cod, Massachusetts. We set a goal to meet or exceed a nitrogen load reduction target for the watershed and four key objectives: minimizing economic costs of implementing management actions, minimizing the complexity of permitting management actions, maximizing stakeholder acceptability of the management actions, and maximizing the provision of ecosystem services (recreational opportunity, erosion and flood control, socio‐cultural amenity). We used multi‐objective optimization and sensitivity analysis to generate many possible solutions that implement different combinations of nitrogen‐removing management actions and reflect tradeoffs between the objectives. Results show technological advances in controlling household nitrogen sources could provide lower cost solutions and positive impacts to ecosystem services. Although this approach is demonstrated with Cape Cod data, the decision‐making process is not specific to any watershed and could be easily applied elsewhere.