Reviewerly/exHarmony huggingface dataset:
Local Influence in Constrained General Linear Models

support vector machines (SVMs) constitute one of the most popular and powerful classification methods.However, SVMs can be limited in their performance on highly imbalanced datasets.A classifier which has been trained on an imbalanced dataset can produce a biased model towards the majority class and result in high misclassification rate for minority class.For many applications, especially for medical diagnosis, it is of high importance to accurately distinguish false negative from false positive results.The purpose of this study is to successfully evaluate the performance of a classifier, keeping the correct balance between sensitivity and specificity, in order to enable the success of trauma outcome prediction.We compare the standard (or classic) SVM (C SVM) with resampling methods and a cost sensitive method, called Two Cost SVM (TC SVM), which constitute widely accepted strategies for imbalanced datasets and the derived results were discussed in terms of the sensitivity analysis and receiver operating characteristic (ROC) curves.
Electronic Flashcards as a Tool to Improve Exam Readiness

Many college classes have basic information that must be learned before students can master the more complex application and analysis of advanced ideas in the class.Students often struggle with identifying and learning these facts, terms, and principles.This paper reports on a project that evaluated the use of electronic flashcards in two undergraduate engineering classes.The study materials (online flashcards) were developed in conjunction with the university's Educational Technology Office.
Narrative Extraction, Processing and Generation for Interactive Fiction and Computer Games

Often, computer games require meaningful stories and complex worlds in order to successfully engage players. Developing a high-quality story and rich characters can be one of the hardest tasks in the game development process. Narrative is a key element in building game worlds for interactive digital entertainment. I am particularly interested in computational narrative algorithms that can analyze stories, model narrative, and generate plots to be used in various forms and game domains.
Neuro - Geometry in Managerial Decision Dynamics

New brain imaging technologies have motivated neuromanagerial studies of internal order of mind. We are only at beginning of an enterprise. Its promise suggests a fundamental change in how we think, observe and model decision in context. Brain absorbs information, recognises and frames problematic situations towards appropriate responses. Decision-making in neuromanagement is based on complexly interlinked imaging technologies with links to bandwidth of human decision. Manager often fail to design ‘rational’ decisions. Processes by which Managers reach decisions embody conflicting tenets. Question is how Manager makes decisions. How are decisions carried out in brain? Interest is on assumptions, beliefs, habits and tactics that Manager uses to make decisions. How do parts of brain govern decision-making, coordinate, face obscurity and engage in strategic simplification while deciding? What happens in brain or is activated? Is study of decision-making geometry via neuromanagement relevant? What are the limits? How does previous experience alter behaviour? There are unsolved problems in (managerial) cognition. What are the general implications? Managerial cognition plays a role. Sensing is as an important aspect and there is evidence of how evolutionary patterns are shaped by beliefs and attention allocation. How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? In this, neuromanagement seeks to explain decision-making, ability to process alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide coherent brain geometry towards decision-making? This paper attempts to explore above phenomenon to put forward a model for neuro-management decision, in which interaction are addressed through measurements of brain activity. Attempt is to model decision-making linking neuro-management of decision behaviour. This provides conceptual geometry for conducting neuro (managerial) research at intersection of neuro (managerial) science, raises a few fundamental issues and offer solution through measurements of brain activity at management levels of analysis.
VSAT Packet Security Performance Enhancement Using Modified Interlock Protocol

This study has focused on developing the Interlock protocol and on implementing it on the VSAT system to overcome the man-in-the-middle attack in the double hob topology. A new cryptography protocol has been modified into Interlock protocol by using the concept of Cipher Block Chinning mode and AES, RSA, SAH-256 algorithms. The TCP protocol is flexible and has the ability to tune its parameters depending on the environment under which it works. In this study the TCP protocol parameters had been readjusted to reach the optimum throughput when the packet was encrypted and transmitted in the VSAT environment. The results indicate an increase in the throughput of up to about 30%.
Trial-Based Heuristic Tree-search for Distributed Multi-Agent Planning

We present a novel search scheme for privacy-preserving multi-agent planning, inspired by UCT search. We compare the presented approach to classical multi-agent forward search and evaluate it based on benchmarks from the CoDMAP competition.
Exploring Personal Attributes from Unprotected Interactions

Research, so far, has shown that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable providing users' interaction data. To address these privacy concerns, users on a social networking site like Faceboook are usually left with profile settings to mark some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Faceboook page''). Although the aim of such interactions is to help users to become more social, visibilities of these interactions are beyond their profile settings and publicly accessible to everyone. The focus of this paper is to explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, we ask - are users' personal attributes predictable using only the unprotected interactions? To answer this question, we design a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge for the proposed problem. Therefore, we first provide a way to mitigate the data sparsity challenge and propose a novel attribute prediction framework using only the unprotected interactions. Experimental results on Faceboook dataset demonstrates that the proposed framework can predict users' personal attributes.
Ephemeral Photowork: Understanding the Mobile Social Photography Ecosystem

For many years, researchers have explored digital support for photographs and various methods of interaction around those photos. Services like Instagram, Facebook, and Flickr have demonstrated the value of online photographs in social media. Yet we know relatively little about these new practices of mobile social photography and in-situ sharing. Drawing on screen and audio recordings of mobile photo app use, this paper documents the ephemeral practices of social photography with mobile devices. We uncover how photo use on mobile devices is centered around social interactions both through online services, but also face-to-face around the devices themselves. We argue for a new role for the mobile photograph, supporting networks of communication through instantaneous interactions, complemented with rich, in person discussions of captured images with family and friends; photography not for careful selection and archive, but as quick social play and talk. The paper concludes by discussing the design possibilities of ephemeral communication.
Analysis on Relationship between Code Quality and Code Coverage in an XP Environment: a Case Study on the SWURV Project

The thesis used hypothesis testing and correlation analysis methods to explore the relationship between structural code coverage and the quality of software developed in an eXtreme Programming (XP) environment, via a case study of a commercial software product. We find that improving code coverage is helpful to detect residual defects, but it is not enough, and we also need other testing, like acceptance testing, in the process of XP software development to provide good quality software products. In addition, in order to investigate why the strength of association between code coverage and residual defect density is not as strong as that presented in prior work, a detailed defect root cause analysis is performed, showing that over 96% of bugs cannot be detected by improving code coverage. Based on the defect categories and distribution of defect root cause, six improvement actions are proposed for future XP projects.
Academic Threads Curriculum Integration For Specific Capabilities

This paper will begin with a discussion of learning objectives and illustrate their application within a number of different basic courses in industrial and systems engineering.It will then introduce the notion of "Academic Threads" and present a number of different situations where specific topics might be considered as appropriate for integration at a number of different points within the curriculum.A plan for the creation of an Academic Thread will be discussed.The paper will then show how the thread can be woven into the learning objectives for specific courses.A discussion of some of the strengths and weaknesses of this approach will then be presented.An extension of the idea of academic threads into more technical areas will also be presented.
Accreditation of Engineering Education in Korea

Abstract Accreditation of Engineering Education in KoreaProf. Wonjong Kim (ABEEK)Wonjong Joo is a professor of Seoul National University of Scienceand Technology (Seoul Tech) and is a vice president of ABEEK(Accreditation Board of Engineering Education in Korea) from 2012.He has been a director of Seoul Tech’s Innovation Center forEngineering Education (ICEE) & Hub Center for 5 years and is nowa president of Innovation Hub Center Council.His role in ABEEK is to lead the committee of Criteria of Accrediting EngineeringPrograms. His role in Hub Center Council is to coordinate 60 ICEE universities forsharing the outcomes of the ICEE project. He graduated from Mechanical EngineeringDepartments of Seoul National University (BS), KAIST (MS), and University of Illinoisat Chicago (Ph.D) and his research area includes interferometric measurements,automatic fringe pattern analysis, and vision inspections.
Learning With Annotation of Various Degrees

In this paper, we study a new problem in the scenario of sequences labeling. To be exact, we consider that the training data are with annotation of various degrees, namely, fully labeled, unlabeled, and partially labeled sequences. The learning with fully un/labeled sequence refers to the standard setting in traditional un/supervised learning, and the proposed partially labeling specifies the subject that the element does not belong to. The partially labeled data are cheaper to obtain compared with the fully labeled data though it is less informative, especially when the tasks require a lot of domain knowledge. To solve such a practical challenge, we propose a novel deep conditional random field (CRF) model which utilizes an end-to-end learning manner to smoothly handle fully/un/partially labeled sequences within a unified framework. To the best of our knowledge, this could be one of the first works to utilize the partially labeled instance for sequence labeling, and the proposed algorithm unifies the deep learning and CRF in an end-to-end framework. Extensive experiments show that our method achieves state-of-the-art performance in two sequence labeling tasks on some popular data sets.
Calibrating Classification Probabilities with Shape-Restricted Polynomial Regression

In many real-world classification problems, accurate prediction of membership probabilities is critical for further decision making. The probability calibration problem studies how to map scores obtained from one classification algorithm to membership probabilities. The requirement of non-decreasingness for this mapping involves an infinite number of inequality constraints, which makes its estimation computationally intractable. For the sake of this difficulty, existing methods failed to achieve four desiderata of probability calibration: universal flexibility, non-decreasingness, continuousness and computational tractability. This paper proposes a method with shape-restricted polynomial regression, which satisfies all four desiderata. In the method, the calibrating function is approximated with monotone polynomials, and the continuously-constrained requirement of monotonicity is equivalent to some semidefinite constraints. Thus, the calibration problem can be solved with tractable semidefinite programs. This estimator is both strongly and weakly universally consistent under a trivial condition. Experimental results on both artificial and real data sets clearly show that the method can greatly improve calibrating performance in terms of reliability-curve related measures.
Realtime Mobile Bandwidth Prediction Using LSTM Neural Network

With the popularity of mobile access Internet and the higher bandwidth demand of mobile applications, user Quality of Experience (QoE) is particularly important. For bandwidth and delay sensitive applications, such as Video on Demand (VoD), Realtime Video Call, Games, etc., if the future bandwidth can be estimated in advance, it will greatly improve the user QoE. In this paper, we study realtime mobile bandwidth prediction in various mobile networking scenarios, such as subway and bus rides along different routes. The main method used is Long Short Term Memory (LSTM) recurrent neural network. In specific scenarios, LSTM achieves significant accuracy improvements over the state-of-the-art prediction algorithms, such as Recursive Least Squares (RLS). We further analyze the bandwidth patterns in different mobility scenarios using Multi-Scale Entropy (MSE) and discuss its connections to the achieved accuracy.
Flexible camera-projector calibration using superposed color checkerboards

Camera-projector calibration is essential in structured-light systems for three-dimensional imaging. A prevalent difficulty for calibration is that two different checkerboards must be captured. First, a physical checkerboard on the reference plane is required to obtain the camera parameters. Second, the physical checkerboard must be removed because it interferes with the projected pattern required to obtain the projector parameters. These requirements can be reduced if the camera is previously calibrated or specific arrangements are considered. However, the calibration procedure is very impractical and susceptible to misalignment errors. In this paper, a flexible camera-projector calibration method without any constraint on the position and orientation of the camera, the projector, and the reference plane, is proposed. For this, a physical checkerboard of a specific color is placed on the reference plane while the projector device projects another checkerboard with a different color. From an image captured by the camera, the two superposed checkerboards are recovered using the color information. In this way, the homographies required for calibration of both the camera and the projector are obtained simultaneously. The performance of the proposed method is verified experimentally by measuring the three-dimensional surface of three objects. Because of its high flexibility and accuracy, the proposed method is useful for in situ calibration of structured-light systems for three-dimensional imaging applications.
Gradient Visualization for General Characterization in Profiling Attacks

In Side-Channel Analysis (SCA), several papers have shown that neural networks could be trained to efficiently extract sensitive information from implementations running on embedded devices. This paper introduces a new tool called Gradient Visualization that aims to proceed a post-mortem information leakage characterization after the successful training of a neural network. It relies on the computation of the gradient of the loss function used during the training. The gradient is no longer computed with respect to the model parameters, but with respect to the input trace components. Thus, it can accurately highlight temporal moments where sensitive information leaks. We theoretically show that this method, based on Sensitivity Analysis, may be used to efficiently localize points of interest in the SCA context. The efficiency of the proposed method does not depend on the particular countermeasures that may be applied to the measured traces as long as the profiled neural network can still learn in presence of such difficulties. In addition, the characterization can be made for each trace individually. We verified the soundness of our proposed method on simulated data and on experimental traces from a public side-channel database. Eventually we empirically show that the Sensitivity Analysis is at least as good as state-of-the-art characterization methods, in presence (or not) of countermeasures.
GraphOne: A Data Store for Real-time Analytics on Evolving Graphs.

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed GraphOne, a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView, to enable data access at two different granularities of data ingestions (called data visibility) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that GraphOne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. GraphOne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. GraphOne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.
A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Abstract Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system using a convolutional neural network (CNN) was developed to identify regions of interest (ROIs) and to detect C4d positive and negative peritubular capillaries (PTCs) in giga-pixel immunostained slides. The performance of faster R-CNN was evaluated using optimal parameters of the novel method to enlarge the size of labeled masks. Fifty and forty pixels of the enlarged size images showed the best performance in detecting C4d positive and negative PTCs, respectively. Additionally, the feasibility of deep-learning-assisted labeling as independent dataset to enhance detection in this model was evaluated. Based on these two CNN methods, a fully automated system for renal allograft rejection was developed. This system was highly reliable, efficient, and effective, making it applicable to real clinical workflow.
Salient object detection employing a local tree-structured low-rank representation and foreground consistency

We propose a local tree-structured low-rank representation (TS-LRR) model to detect salient objects under the complicated background with diverse local regions, which is problematic for most low-rank matrix recovery (LRMR) based salient object detection methods. We first impose a local tree-structured low-rank constraint on the representation coefficients matrix to capture the complicated background. Specifically, a primitive background dictionary is constructed for TS-LRR to promote its background representation ability, and thus enlarge the gap between the salient objects and the background. We then impose a group-sparsity constraint on the sparse error matrix with the intention to ensure the saliency consistency among patches with similar features. At last, a foreground consistency is introduced to identically highlight the distinctive regions within the salient object. Experimental results on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model over the state-of-the-art methods.
Empirical curvelet based fully convolutional network for supervised texture image segmentation

In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapted to a given dictionary of textures. We then show that the output of these filters can be used to build efficient texture descriptors utilized to finally feed deep learning networks. Our approach is finally evaluated on several datasets and compare the results to various state-of-the-art algorithms and show that the proposed method dramatically outperform all existing ones.