Reviewerly/exHarmony huggingface dataset:
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modeling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module aggregates the boundary characteristic from low-level features and semantic information from high-level features for better-preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations, such as small object segmentation and ambiguous object boundaries. The project is available at https://github.com/Barrett-python/DuAT .
Pairwise Exemplar Clustering

Exemplar-based clustering methods have been extensively shown to be effective in many clustering problems. They adaptively determine the number of clusters and hold the appealing advantage of not requiring the estimation of latent parameters, which is otherwise difficult in case of complicated parametric model and high dimensionality of the data. However, modeling arbitrary underlying distribution of the data is still difficult for existing exemplar-based clustering methods. We present Pairwise Exemplar Clustering (PEC) to alleviate this problem by modeling the underlying cluster distributions more accurately with non-parametric kernel density estimation. Interpreting the clusters as classes from a supervised learning perspective, we search for an optimal partition of the data that balances two quantities: 1 the misclassification rate of the data partition for separating the clusters; 2 the sum of within-cluster dissimilarities for controlling the cluster size. The broadly used kernel form of cut turns out to be a special case of our formulation. Moreover, we optimize the corresponding objective function by a new efficient algorithm for message computation in a pairwise MRF. Experimental results on synthetic and real data demonstrate the effectiveness of our method.
Architectural Issues for Compositional Dialog in Games

Making a game that supports generative conversation with NPCs involves a number of technical and design challenges, ranging from designing new game mechanics to making a Prolog interpreter run under Unity. I discuss the software architecture of a game, scheduled for initial release in spring 2015, that attempts to solve some of these problems.
Undergraduate Electronics Students' Use of Home Experiment Kits for Distance Education

Robynne Hall spent 13 years in the photographic industry designing cutting edge commercial print laboratories throughout Australia, maintaining and teaching silver halide and digital printing machines.As a mature age student, she returned to study and in 2002 completed an
A Team Oriented, Case Based Approach For A Transportation Engineering Course

Teamwork and the ability to work with contemporary and emerging technologies that facilitate collaboration are critical to the engineering profession today.Team oriented, casebased methods can be effective tools for learning.The use of well designed collaborative activities can be effective for enhancing student learning.This paper summarizes both the efforts and results in developing a team oriented, case-based approach for a junior level introductory course in Transportation Engineering.The intent of this approach is to support student acquisition of knowledge and a context sensitive use of the skills necessary for success in the profession.The approach used is based on the following beliefs:1. Realistic learning activities presented as cases promote learning.2. Collaboration is vital for learning and for success in the profession.The paper includes examples of the strategies used to facilitate learning, instruments used to assess the effectiveness of the approach, and results from the experiences.In summary, some students are reluctant to participate in "team" efforts.They indicate that they enjoy the casebased approach.In general, the team oriented, cased-based approach helps students make the connection from acquiring / processing information to developing knowledge and applying the same.
Cohesion and Coherence in Technical Translation: The Case of Demonstrative Reference

This paper examines cohesion and coherence within the larger framework of an equivalence-relevant investigation based on a theoretically well-founded translation comparison and a highly refined translation corpus. Cohesion is analysed at the textual, more specifically at the sentential/supra-sentential, level taking due account of the underlying coherence operating at the text¬in-context level. The research investigates and exemplifies the English demonstrative determiner/pronoun ‘this’ as a cohesive device of reference and its German potential equivalents, and indicates trends in translation solutions. The results of this analysis will show that coherence – established by the interaction of intended sense and informed inference – is upheld in the German TT by cohesive means which, though occasionally quite different from their ST counterparts, contribute toward achieving “equivalence in difference” (Jakobson [1959]1992) at the overall text-in-context level.
Automatic Identification of Student Misconceptions and Errors for Truss Analysis

The use of concept inventories in mechanics education has great potential to identify areas where interventions are either working or not working for particular concepts.Concept inventories are validated measures and easy to implement.Where intended interventions are not working, there is potential for enhancing student learning.This process of implementing concept inventories to quickly measure the effectiveness of a teaching intervention is shown through a case study with Mechanix.Mechanix is a sketch recognition tool that tutors students on drawing free-body diagrams (FBDs) and truss problems.It is being developed at Texas A&M University.Students sketch their answers on tablet computers as they would normally on paper or with a mouse and a standard computer monitor.This provides a system with a low learning curve.Mechanix is able to provide immediate and intelligent feedback to the students; it tells them if they are missing any components of the FBD and if their answers are correct.The program tells the students whether their solved reaction forces or member forces are correct or not without actually providing the answers.The concept inventories indicate the concepts that Mechanix is already more effective at teaching than tradition lecture only.Since Mechanix captures the students answers in real-time, their errors and misconceptions can easily be identified and corrected if appropriate feedback is built into Mechanix.Mechanix is still in development and its feedback system can be further refined to more precisely target the concepts the students should be learning.Future versions of Mechanix will automatically provide instructions with a description of the errors their students are making and the concepts they may be having difficulty with.This will be provide in an easy to use interface where professors can quickly obtain a real-time update on their students' performance and then adjust their teaching approach and examples as needed.The concept inventories are an effective tool for determining which concepts are most difficult for the students and should be targeted for feedback within the Mechanix software.The concepts inventories provide quick identification of concepts students do not understand.Results from the concept inventories indicate that for the Statics Concept Inventory, almost all pre-scores are at the level of random guessing.This means that professors using the Statics Concept Inventory for students who have not had statics, it may not be necessary to do a pre-test.Our results do indicate that students may be familiar with the direction of forces at pin and slot joints.The concept inventories are indicating that Mechanix needs to better support students in learning how to separate bodies for free body diagram and the directions of forces at pin-in-slots.
Erasure Codes with a Hierarchical Bundle Structure

This paper presents a proof of the existence of computationally fast probabilistic erasure codes at distance ∈ from being MDS, namely the decoding algorithm is able with high probability to reconstruct the n letter message from any set of (1+∈)n letters. It can either be fixed rate or a rateless LT code [10] in that any number of code letters can be produced and each is produced independently of the others. We also decrease the minimum packet size from many to one letter. The key ingredient is a scheme Hierarchical Bun- dle/Bin (HB) which splits the message into a hierarchy of disjoint bundles and produces coded packets about each bundle. We show a correspondence of this to a particular game having to do with randomly throwing balls into a hierarchy of bins. The “information” that does not over flow from a smaller bin, contributes to the next larger bin that it is contained in. We prove matching upper and lower bounds on the cost of this game and provide the implementation details. This analysis is somewhat analogous to the evolution of the “ripple” in the LT decoding analysis [10]. The bundle size corresponds to the degree of the packet, therefore, smaller bundles tend to reduce encoding/decoding complexity, but packets coming from larger bundles ensure the approximately-MDS constraint, by ensuring more coverage. Our HB scheme with largest block size b requires encoding and decoding time O(∈b2) rather than the O(b2) needed for Reed-Solomon codes. This scheme HB (together with Spielman’s expanders) gives a probabilistic code with running time O(∈-1 ln(∈-1)n). Alon and Luby [5, 6] simultaneously developed a deterministic version but their running time is O(∈-4n). Both our and Alon’s results have since been completely subsumed by the latest generation of Shokrollahi and Luby’s Raptor codes [13, 14, 11].
Implementation of Face Recognition Algorithm on Field Programmable Gate Array (FPGA)

The aim of this study is to implement an algorithm for face recognition, based on fast fourier transform (FFT), on the field programmable gate array (FPGA) chip. Implemented program included the initialization process of two single-IP-core ROM blocks, each with an image of a human face, which are sent to the real components of two-channel IP CoreFFT block. The result of classification could be displayed in the form of either a word “yes” or “no” on the seven-segment display or the information about the reference to the folder with the found match face. Due to the lack of memory on the chip, the results are discussed based on the results obtained by the simulation, whilst the implemented part of the system included displaying images on VGA monitor and result of the algorithm shown on seven-segment display or realized as a software solution in Matlab. The results show 79% accuracy and the advantage of presented system lies in the possibility of working with images in real time. The results obtained in this study can be a good starting point in the implementation of complex algorithms for face recognition using all the benefits that FPGAs offer.
Cutting out the middleman: a case study of blockchain technology induced reconfigurations in the Swiss financial services industry

Blockchain-technology promises to have far-reaching economic and social implications, which are not yet foreseeable in its extent. It threatens to disintermediate many well-established sectors of the economy, and incumbent businesses might be overtaken by ambitious newcomers. The financial services industry is particularly ‘ripe for disintermediation’ since blockchain-technology has with Bitcoin and other cryptocurrencies its first real-world use case. Incumbent businesses have to react if they do not wish to perish. However, technological evolution also affects the State and other governmental bodies: institutional frameworks or territorial arrangements can become obsolete or detrimental to business activity and may need to be amended. This chapter aimed to examine ongoing technology-induced reconfigurations in the financial services industry through a four lenses framework. The technology, actors and their discourses, as well as the regulatory environment and affected territories, need to be considered all at once. We have applied this framework to the case of Switzerland: a country with an influential financial services industry that has seen better days. We have found that (1) the Swiss Confederation has swiftly adapted its institutional framework to the new reality in order to foster competition and innovation; (2) there are some raucous controversies between challengers and incumbent businesses. However, a tacit compromise allows them to co-exist; (3) four ‘crypto-clusters’ are emerging. These are located in traditional banking centers (Zurich, Zug, Lake Geneva region, Chiasso) that are seeking to strengthen their international visibility and improve their competitiveness.
Some Theoretical Links Between Shortest Path Filters and Minimum Spanning Tree Filters

Edge-aware filtering is an important pre-processing step in many computer vision applications. In the literature, there exist several versions of collaborative edge-aware filters based on spanning trees and shortest path heuristics which work well in practice. For instance, tree filter (TF) which is recently proposed based on a minimum spanning tree (MST) heuristic yields promising results in many filtering applications. However, links between the tree-based filters and shortest path-based filters are faintly explored. In this article, we introduce an edge-aware generalization of the TF termed as UMST filter based on a subgraph generated by edges of all MSTs. The major contribution of this paper is establishing theoretical links between filters based on MSTs and filters based on geodesics via power watershed framework. More precisely, we show that union of minimum spanning trees (UMSTs) filter can be obtained as the limit of shortest path filters (SPFs). Intuitively, TF can be viewed as an approximate limit of the SPFs. We propose and provide a detailed analysis of two different implementations of the UMST filter based on shortest paths. Further, we establish empirically with the help of denoising experiments that TF is an approximate limit by showing that TF and one of our approximations yield similar results.
A Tour of Unsupervised Deep Learning for Medical Image Analysis

Interpretation of medical images for diagnosis and treatment of complex disease from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis. Unlike supervised learning which is biased towards how it is being supervised and manual efforts to create class label for the algorithm, unsupervised learning derive insights directly from the data itself, group the data and help to make data driven decisions without any external bias. This review systematically presents various unsupervised models applied to medical image analysis, including autoencoders and its several variants, Restricted Boltzmann machines, Deep belief networks, Deep Boltzmann machine and Generative adversarial network. Future research opportunities and challenges of unsupervised techniques for medical image analysis have also been discussed.
SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.
Generating Trimap for Image Matting Using Color Co-Fusion

In this paper, we propose a simple and efficient approach to generate a high-quality trimap from the input image. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte from the input image. An attentively created trimap is required to acquire an accurate alpha matte. This is a very tedious task and may even become impractical for some applications. The accuracy of trimap-based approach reduces drastically as the trimap becomes thicker and coarser. We use the co-fusion-based method to generate a high-quality trimap in less time, which assists in obtaining accurate and fast results. The experimental results demonstrate that our method produces good quality trimap, which results in an accurate matte estimation. We validate our results by replacing our generated trimap by manually created trimap while using the same image matting algorithm.
Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming

Process Monitoring for Quality is a manufacturing quality philosophy aimed at defect detection through binary classification that is founded on big data and big models. Genetic Programming (GP) algorithms have been successfully applied by following the big models learning paradigm for rare quality event detection (classification). Since it is a bias-free technique unmarred by human preconceptions, it can potentially generate better solutions (models) compared with the best human efforts. However, since GP uses random search methods based on Darwinian philosophy of "survival of the fittest", hundreds, or even thousands of models need to be created to find a good solution. In this context, model selection becomes a critical step in the process of finding the final model to be deployed at the plant. A three-objective optimization model selection criterion ( $$3D-GP$$ ) is introduced for analyzing highly/ultra unbalanced data structures. It uses three competing attributes – prediction, separability, complexity – to project candidate models into a three-dimensional space to select the final model that solves the posed tradeoff between them the best.
Machine Learning Algorithms

You do not need a background in algebra and statistics to get started in machine learning. However, be under no illusions, mathematics is a huge part of machine learning. Math is key to understanding how the algorithm works and why coding a machine learning project from scratch is a great way to improve your mathematical and statistical skills. Not understanding the underlying principles behind an algorithm can lead to a limited understanding of methods or adopting limited interpretations of algorithms. If nothing else, it is useful to understand the mathematical principles that algorithms are based on and thus understand best which machine learning techniques are most appropriate.
An investigation into the relationship between paracycling athletes and their prosthetics technology: a proposed design framework

Objective: Product attachment has been stated as an emotional relationship an end-user may develop with a tangible product or artefact. The objective of this study is to investigate this relationship with athletes who possess limb absence and utilise assistive sports technology competitively.Method: Five elite paracyclists were surveyed using a modified 31 question product attachment survey. The survey comprised the ability to capture both closed-ended and open-ended data. The survey design itself was derived from three previously validated product relationship questionnaires.Results: Four elite athletes with limb absence did not provide any firm evidence or indication to support the concept of a non-physical relationship with their prosthetic device. However, some respondents had (or wished) to incorporate some form of aesthetic-based prosthetic personalisation or customisation, as long as this did not impact on the prostheses functional performance. Furthermore, a thematic analysis of the participant's responses yielded a four-point assistive technology design philosophy framework. The emerging thematic areas were 1) The identification of the factors that influence performance in the athletes chosen sport; 2) To consider an ‘appearance follows performance’ approach; 3) To conduct sports specific trials of the prosthetic limb; and 4) To identify any need for prostheses decorative personalisation.Conclusions: The survey revealed some anecdotes of a sports technology to user relationship but this will require further exploration with different and larger sample populations. Use of the proposed four-point framework may help inform practitioners of what considerations could provide greater end-user satisfaction when designing and developing specialised prosthetic limbs for elite-level sport.Implications for rehabilitationWhilst the formal characteristics of product attachment were not broadly identified in this study, some indications may give credence for prosthetists to consider an “appearance follows performance” approach to sports prostheses design. This may improve end-user satisfaction with their assistive technology.The inclusion of prosthetic post-manufacture decoration and personalization would seem to be desirable to the end-user.By adopting the feedback given in this study, undertaking trials conducted at a race-specific intensity of the athlete end-user may reduce the need to perform post-manufacture ad-hoc prostheses modifications.
Navigating Mental Models of Risk and Uncertainty within the Ocean Forecast System: An Oregon Case Study

Abstract This case study explores how to add value to regional ocean condition forecast information by bringing awareness to the processes that govern decision-making and outcomes within the system. A modified mental models research approach is applied to examine differences and similarities in perceptions of risk and comfort with uncertainty between two interdependent communities, the ocean “data provider” and “end user,” and how these perceptions impact accessibility and usefulness of data products. In this study, data providers are academic and agency scientists from institutions that provide ocean condition forecasts to public end users (n = 17). End users are members of the Oregon commercial-fishing community (n = 16). Comparisons reveal key differences and similarities related to the nature of each profession that impact perceptions of scale in time and space and reveal the ways that cumulative and intersecting risks and uncertainties act as key drivers in decision-making. Implications for expanding the current understanding of how ocean forecasts are produced and used include 1) highlighting the value of optimizing ocean forecast delivery tools based on end-user needs and information-seeking processes already in place, 2) identifying structural and cultural barriers within the data-provider network that prevent them from doing so, and 3) demonstrating the value of learning about both producers and users of scientific information and suggesting potential ways to structure cooperation and strengthen relationships between them by working toward a common desired outcome.