Reviewerly/exHarmony huggingface dataset:
Heterogeneous MacroTasking (HeMT) for Parallel Processing in the Cloud

Using tiny tasks (microtasks) has long been regarded an effective way of load balancing in parallel computing systems. When combined with containerized execution nodes pulling in work upon becoming idle, microtasking has the desirable property of automatically adapting its load distribution to the processing capacities of participating nodes - more powerful nodes finish their work sooner and, therefore, pull in additional work faster. As a result, microtasking is deemed especially desirable in settings with heterogeneous processing capacities and poorly characterized workloads. However, microtasking does have additional scheduling and I/O overheads that may make it costly in some scenarios. Moreover, the optimal task size generally needs to be learned. We herein study an alternative load balancing scheme - Heterogeneous MacroTasking (HeMT) - wherein workload is intentionally skewed according to the nodes' processing capacity. We implemented and open-sourced a prototype of HeMT within the Apache Spark application framework and conducted experiments using the Apache Mesos cluster manager. It's shown experimentally that when workload-specific estimates of nodes' processing capacities are learned, Spark with HeMT offers up to 10% shorter average completion times for realistic, multistage data-processing workloads over the baseline Homogeneous microTasking (HomT) system.
Dynamic Mitigation of Catastrophic Forgetting Using the Sampling Network

The catastrophic forgetting in transfer learning makes a neural network lose the performance on previously learned datasets when dealing with large amounts of data. Predictive Elastic Weight Consolidation (PEWC) reduces the catastrophic forgetting by extracting only images with relatively more incorrect network predictions, but uses static sampling technique. PEWC also includes images in the training data which can be correctly classified by the network, leaving the possibility for further reduction of the training data. In this paper, we additionally apply a sampling network that extracts images dynamically without sorting, so that only images whose predictions are similarly inaccurate in general are used for training. In the experiment, our method achieved a similar level of mitigation of catastrophic forgetting while learning less data than PEWC.
Throwing Motion For Yo-yo Forward-pass Operation Based On Wave Propagation

In this research, a yo-yo throwing motion for the forward-pass operation based on wave propagation is considered. Some simulation results have been provided to show the effectiveness of the method. For the motion design, a model integrating a robot arm and a yo-yo is derived. An optimized yo-yo throwing operation is obtained through an optimization process with the integrated model and the concept of wave propagation referencing to human throwing motion called a kinematic chain. As a result, the torque required at robot joints to realize the motion is reduced than the previous method, and the velocity of the end effector is maximized at the release point for throwing motion.
On a Relation between Knowledge-of-Exponent Assumptions and the DLog vs. CDH Question

Knowledge-of-exponent assumptions (KEAs) are a somewhat controversial but nevertheless commonly used type of cryptographic assumptions. While traditional cryptographic assumptions simply assert that certain tasks (like factoring integers or computing discrete logarithms) cannot be performed efficiently, KEAs assert that certain tasks can be performed efficiently, but only in certain ways. The controversy surrounding those assumptions is due to their non-falsifiability, which is due to the way this idea is formalised, and to the general idea that these assumptions are “strong”. Nevertheless, their relationship to existing assumptions has not received much attention thus far. In this paper, we show that the first KEA (KEA1), introduced by Damgård in 1991, implies that computing discrete logarithms is equivalent to solving the computational Diffie-Hellman (CDH) problem. Since showing this equivalence in the standard setting (i.e., without the assumption that KEA1 holds) is a longstanding open question, this indicates that KEA1 (and KEAs in general) are indeed quite strong assumptions.
Focus and retain: Complement the Broken Pose in Human Image Synthesis

Given a target pose, how to generate an image of a specific style with that target pose remains an ill-posed and thus complicated problem. Most recent works treat the human pose synthesis tasks as an image spatial transformation problem using flow warping techniques. However, we observe that, due to the inherent ill-posed nature of many complicated human poses, former methods fail to generate body parts. To tackle this problem, we propose a feature-level flow attention module and an Enhancer Network. The flow attention module produces a flow attention mask to guide the combination of the flow-warped features and the structural pose features. Then, we apply the Enhancer Network to re-fine the coarse image by injecting the pose information. We present our experimental evaluation both qualitatively and quantitatively on DeepFashion, Market-1501, and Youtube dance datasets. Quantitative results show that our method has 12.995 FID at DeepFashion, 25.459 FID at Market-1501, 14.516 FID at Youtube dance datasets, which outperforms some state-of-the-arts including Guide-Pixe2Pixe, Global-Flow-Local-Attn, and CocosNet.
Adaptively Truncating Gradient for Image Quality Assessment

Abstract Objective image quality assessment (IQA) aims to develop computational models to predict the perceptual image quality consistent with subjective evaluations. As image information is presented by the change in intensity values in the spatial domain, the gradient, as a basic tool for measuring the change, is widely used in IQA models. However, does the change measured by the gradient actually correspond to the change perceived by the human visual system (HVS)? To explore this issue, in this paper, we analyze how the ability of the HVS to perceive changes is affected by the upper threshold, and we propose an IQA index based on an adaptively truncating gradient. Specifically, the upper threshold at each pixel in an image is adaptively determined according to the image content, and the adaptively truncating gradient is obtained by retaining the part of the gradient magnitude that is less than the upper threshold and truncating the part that is greater than the upper threshold. Then, the distorted image quality is calculated by comparing the similarity of the adaptively truncating gradient between a reference image and the distorted image. Experimental results on six benchmark databases demonstrate that the proposed index correlates well with human evaluations.
Differential Evolution with Taguchi Crossover Using Polar Coordinates

This paper compares the performance of two differential evolution algorithms. The algorithms are identical except that one of the algorithms is implemented in rectangular coordinates and the other algorithm is implemented using both rectangular and a cascade of terms in polar coordinates. Both algorithms use an elitist, ranking, random selection method and both two and three level Taguchi crossover. To compare the performance of the algorithms, both algorithms are applied to 13 commonly used global numerical optimization test functions, including a spherical, three hyper-ellipsoid, the sum of different powers, Rastrigin’s, Schwefel’s, Griewank’s, Rosenbrock’s valley, Styblinski-Tang, Ackley’s Path, Price-Rosenbrock, and Eggholder’s functions. The test results show that the algorithm that is implemented using both rectangular and polar coordinates performed better than the algorithm implemented using only rectangular coordinates.
Property Analysis of Adversarially Robust Representation

In this paper, we address the open question: “What do adversarially robust models look at?” Recently, it has been reported in many works that there exists the trade-off between standard accuracy and adversarial robustness. According to prior works, this trade-off is rooted in the fact that adversarially robust and standard accurate models might depend on very different sets of features. However, it has not been well studied what kind of difference actually exists. In this paper, we analyze this difference through various experiments visually and quantitatively. Experimental results show that adversarially robust models look at things at a larger scale than standard models and pay less attention to fine textures. Furthermore, although it has been claimed that adversarially robust features are not compatible with standard accuracy, there is even a positive effect by using them as pre-trained models particularly in low resolution datasets.
Research on Object-oriented Exploration Algorithm

In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaningful while it is similar to attribute exploration. It can explore the implication relationship between objects. But for the existing work, almost all are attribute exploration, object-oriented exploration is rare. Research on object-oriented exploration has great practical value, such as object classification, retrieval and excavation. In order to address this problem, this article takes the object as the main research point in formal concept analysis based on related knowledge of traditional attribute exploration, and proposes theories of object-oriented exploration. According to these related theories and the framework of attribute exploration algorithm, an object-oriented exploration algorithm is proposed in this paper. The algorithm can be used to explore and understand the relationship between objects, which facilitates to understand better the relationship between objects and attributes.
Human Skeleton Estimation Method Focusing on the Difference of Human Position and Action from an Extremely Low-Resolution FIR Image Sequence

Elderly monitoring systems are gaining attention in the modern aging society. For the purpose, Far-InfraRed (FIR) sensors are often used, because they can avoid privacy concerns and are robust to environmental lightings. The authors have previously proposed several methods for human skeleton estimation from an extremely low-resolution FIR image sequence whose resolution is 16 × 16 pixels. For more accurate estimation, this paper proposes a method that is robust to variations of human positions and actions in the FIR sequences. Specifically, to extract features robust to the human positions from the images by using a Convolutional Neural Network (CNN), a global max-pooling layer is inserted into the last layer instead of multiple pooling layers which are not suitable for low-resolution inputs. Also, a network with two branches is introduced that focuses on capturing spatial and temporal information respectively. Moreover, the network has a weighted sum mechanism of their outputs, which depends on the human actions. For evaluation, a dataset was created by capturing action sequences of a human at various positions in the FIR images. Through an experiment, we confirmed that the human motion can be smoothly estimated and that the estimation accuracy is improved by the proposed method.
The study of perception bias toward affect emoticon on SNS among individuals with emotion dysregulation

This study investigated the perception bias on affective emoticons in individuals with emotion dysregulation and a maladaptive regulation strategy. A total of 289 students from the university completed 1) the Cognitive Emotional Regulation Questionnaire, including both adaptive and maladaptive cognitive emotional strategies, 2) the Difficulties in Emotion Regulation Scale, 3) an emoticon rating in terms of perception intensity, based on 6 basic emotions. Twenty-six emoticons from KakaoTalk, a popular messenger application in South Korea, were used after arousal whereupon valence ratings were obtained. The results revealed that individuals with emotion dysregulation significantly displayed stronger perception to ambiguous affect emoticons in anger and fear emotions, as compared to the control. These findings suggest that individuals with emotion dysregulation may be more influenced by negative affect stimuli which then leads to daily negative affect bias.
Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.
토론 기반 Flipped Learning 운영에 대한 대학생 영어 학습자의 인식조사 사례 연구

The Journal of Studies in Language 35.1, 007-021. The current study aims to explore the model of team-teaching based on the flipped learning class in an English linguistics class. Also, the study plans to investigate the EFL learners’ overall perceptions toward the flipped learning class. The data were collected from various sources to ensure the reliability of the study. The following were elicited from the current study. First, flipped learning courses can be divided into three parts, including pre-activity, in-class activity, and post-activity. Second, it is important for instructors to offer various activities to foster learners’ participation. Third, instructors should consider students’ current linguistic levels when preparing pre-class activities. (Hannam University)
Sequential Coordinates Conversion and Decoupled Linear Tracking for Airborne AESA Radars

This paper deals with the problem of tracking a maneuvering target using an airborne electronically scanned array radar. Conventional target-tracking filters produce biased target state estimates because the angular motion of a radar platform complicates the cross-correlations between the converted measurements (CMs). As a practical alternative to this issue, the target tracking filter is designed in the predicted line-of-sight Cartesian coordinate system (PLCCS). Provided that the a priori state estimation error is small enough, the statistics of the CMs in PLCCS can be approximated accurately despite using the first and second moments of the radar measurement noises only. The resulting debiasing term leads to a satisfactory target tracking performance of the proposed filter. Furthermore, by exploiting the diagonality of the noise covariance matrix derived in the PLCCS, the proposed tracking filter is decoupled into each axis to induce a suitable real-time implementation. Through simulations, the enhanced performance of the suggested method is demonstrated.
Bare Quantum Simultaneity Versus Classical Interactivity in Communication Complexity

A relational bipartite communication problem is presented that has an efficient quantum simultaneous-messages protocol, but no efficient classical two-way protocol.
Search evolution for ease and speed: A call to action for what’s been lost

In recent years, leading website search engines have abandoned vital search features supporting complex information needs, evolving instead for the marketplace and for users seeking speedy answers to easy questions. The consequences are troubling, for researchers and for information science educators, with concerns ranging from the very relevance of search results and the unknowing of what is missing, to the novice searcher’s waning ability to frame potent queries and to learn ways to refine results. We report on a grounded theory study of search experiences of information professionals and graduate students (n = 20) that contributes a holistic understanding of web searching, using its findings both to frame what is lacking in the design evolution of search engines for complex information needs and to outline a way forward. One goal of the study was to evaluate an established model of web searching, called Net Lenses, a theoretical framework shown to be highly relevant during the study’s grounded theory secondary literature review. The original Net Lenses research used phenomenography to identify variation in the web search experiences of university students (n = 41), evidencing four categories according to the characteristics of searcher awareness, approach to learning, response to obstacles and search outcomes. This study validated the model and led to an expanded version, Net Lenses 2.0, with five categories of search experience, reflecting the complex information needs of more advanced searchers. This resultant Net Lenses 2.0 model is discussed with its implications for search engine design, for advanced searchers and also for learning-to-search modes, much needed by searchers seeking to develop their abilities. The study’s implications coalesce in a call to action for more inclusive search interface design, and an agenda is put forth for how information researchers, educators and literacy advocates can move forward in their intersecting domains.
Optimal Feature Selection for Support Vector Machine Classifiers

Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership is unknown. One of the most successful methods for binary classification is the support vector machine classifier that aims at finding a hyperplane in the feature space separating the training objects of the two classes. However, the accuracy of this classifier in predicting the correct classes strongly depends on the features selected for determining the hyperplane. In this paper, we propose the first exact approach, which is based on mixed-integer quadratic programming and delayed constraint generation, to identify an optimal set of relevant features for determining the hyperplane. The results of a computational experiment demonstrate that the proposed approach is able to successfully select an optimal set of relevant features in a short running time even for classification tasks with over 10,000 objects and 100 features.
An Interpreter Implementation of STRIPS Plans Used in Knowledge-Managed Tutoring Systems

The techniques for retrieving, representing and processing knowledge used in the Intelligent Tutoring Systems (ITS) are not suitable and efficient enough for representing both domain knowledge and knowledge for managing the training modules. One of the weaknesses of ITSs is their lack of global teaching strategy for the overall training courses. The use of planning techniques in the realisation of the global teaching strategy provides opportunities for developing comprehensive, long training courses which take into account the individual needs and characteristics of learners. Such an approach preserves the flexibility of ITSs and incorporates the advantages of the global plan of the training course. This article presents an implementation of a plan interpreter used in the popular STRIPS planning system. The study also outlines the general structure of both the operators and the plan in the STRIPS system. In addition, it discusses the interaction between the main classes used in the realisation of the interpreter. The article presents a detailed example of the planning techniques application in the realisation of the global teaching strategy and demonstrates the main elements incorporated in the sequence of actions leading to the development and implementation of the training course plan.
Combining Spot Instances Hopping with Vertical Auto-scaling To Reduce Cloud Leasing Cost

Cost reduction is one of the main goals for many organizations. That is why cloud computing is considered as a valid option due to its flexibility. The cloud computing components and the way it was built requires having some extra resources to be used for elasticity feature. Such resources were offered for leasing with reduced cost but with the ability to reassigning to a different user at any moment. Eliminating the interrupting issue allowed to utilize the extra resources to minimize the leasing cost even more. Moreover, the auto-scaling feature is considered as another way that could be used to minimizes the costs. In this paper, a framework has been proposed to minimize the leasing costs. The framework utilizes the cloud unused resources and at the same time implementing the vertical auto-scaling feature. The proposed framework eliminates the associated risks that came with using the spot instances (another name for extra cloud resources) by migrating between different markets. The framework evaluation results were obtained from a simulation process after using real data taken from Amazon AWS. The result shows a cost reduction that reaches up to 78% and 52% compared with on-demand and spot plans accordingly.
Maintaining Accuracy and Efficiency in Electronic Health Records Using Deep Learning

After the advent of deep learning, traditional statistical techniques have become inferior in several domains such as computer vision, natural language processing and audio processing. Deep learning provides numerous advantages and aggrandizes healthcare industry when integrated with huge electronic health records data produced from healthcare organizations. However, deep learning is regarded as high expressive applications that need to be implemented with strenuous effort in healthcare and another reason is that it lacks accuracy. In this research work, an effective deep learning method is applied to process longitudinal electronic health records data which enhances the accuracy. Based on the results obtained from evaluation, the proposed method achieves optimal accuracy.
Cooperative Multi-agent Inverse Reinforcement Learning Based on Selfish Expert and its Behavior Archives

This paper explores the multi-agent inverse reinforcement learning (MAIRL) method which enables the agents to acquire their cooperative behaviors based on selfish expert behaviors (i.e., it is generated from the viewpoint of a single agent). Since such selfish expert behaviors may not derive cooperative behaviors among agents, this paper tackles this problem by archiving the cooperative behaviors found in the learning process and by replacing the original expert behaviors with the archived one at a certain interval. For this issue, this paper proposes AMAIRL (Archive Multi-Agent Inverse Reinforcement Learning). Through the intensive simulations of the maze problem for our method, the following implications have been revealed: (1) AMAIRL is superior to MaxEntIRL in terms of finding cooperative behavior; (2) AMAIRL requires a long interval period to acquire the cooperative behaviors. In particular, AMAIRL with the long interval can find the cooperative behaviors that are hard to be found in AMAIRL with the short interval.