Reviewerly/exHarmony huggingface dataset:
Generating and Exploiting Probabilistic Monocular Depth Estimates

Beyond depth estimation from a single image, the monocular cue is useful in a broader range of depth inference applications and settings---such as when one can leverage other available depth cues for improved accuracy. Currently, different applications, with different inference tasks and combinations of depth cues, are solved via different specialized networks---trained separately for each application. Instead, we propose a versatile task-agnostic monocular model that outputs a probability distribution over scene depth given an input color image, as a sample approximation of outputs from a patch-wise conditional VAE. We show that this distributional output can be used to enable a variety of inference tasks in different settings, without needing to retrain for each application. Across a diverse set of applications (depth completion, user guided estimation, etc.), our common model yields results with high accuracy---comparable to or surpassing that of state-of-the-art methods dependent on application-specific networks.
See the World through Network Cameras

Millions of network cameras have been deployed worldwide. Real-time data from many network cameras can offer instant views of multiple locations with applications in public safety, transportation management, urban planning, agriculture, forestry, social sciences, atmospheric information, and more. This paper describes the real-time data available from worldwide network cameras and potential applications. Second, this paper outlines the CAM2 System available to users at https://www.cam2project.net/. This information includes strategies to discover network cameras and create the camera database, user interface, and computing platforms. Third, this paper describes many opportunities provided by data from network cameras and challenges to be addressed.
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish

This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT model to our classification task, and using the TPOT library to find the optimal pipeline. We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience. Our team placed second in subtask 2 with a shallow model found by TPOT: a~logistic regression classifier with non-trivial feature engineering.
Transformasi Pembelajaran Melalui Penggunaan Multimedia Interaktif Berbasis Game: Persepsi Guru di Sekolah Dasar

This study aims to describe: (1) teacher acceptance of game-based interactive multimedia as a learning medium; (2) teacher's understanding of game-based interactive multimedia; and (3) teacher assessment of game-based interactive multimedia. The research method used is descriptive qualitative. Data collection is done using the method of observation and interviews. Data analysis techniques used are interactive analysis consisting of data collection, data reduction, data presentation and conclusion drawing. The results of this study indicate that (1) teachers provide good acceptance of game-based interactive multimedia because learning becomes more active and student-centered; (2) interactive multimedia based on simple games so that it is easy to use to help students learn; and (3) the teacher has a good assessment of game-based interactive multimedia so that it can be used as a companion learning media both at school and at home
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization

This article investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstring domain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hillclimbing is presented for uniformly distributed equal-height instances of this class of problems. It is shown empirically that conventional niching and mating restriction techniques incorporated in an evolutionary algorithm are not sufficient to make them competitive with the hillclimbing strategy. We conjecture the reason for this behavior is the lack of structure in the space of local optima on instances of this problem class, which makes an optimization algorithm unable to exploit information from one optimum to infer where another optimum might be. When no such structure exists, it seems that the best strategy for discovering all optima is a brute-force one. Overall, our study gives insights with respect to the adequacy of hillclimbers and evolutionary algorithms for multimodal optimization, depending on properties of the fitness landscape.
Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision

We propose a learning approach for turn-level spoken language understanding, which facilitates a user to speak one or more utterances compositionally in a turn for completing a task (e.g., voice ordering). A typical pipelined approach for these understanding tasks requires non-trivial annotation effort for developing its multiple components. Also, the pipeline is difficult to port to a new domain or scale up. To address these problems, we propose an end-to-end statistical model with weak supervision. We employ randomized beam search with memory augmentation (RBSMA) to solve complicated problems for which long promising trajectories are usually difficult to explore. Furthermore, considering the diversity of problem complexity, we explore automated curriculum learning (CL) for weak supervision to accelerate exploration and learning. We evaluate the proposed approach on real-world user logs of a commercial voice ordering system. Results demonstrate that when trained on a small number of end-to-end annotated sessions collected with low cost, our model performs comparably to the deployed pipelined system, saving the development labor over an order of magnitude. The RBSMA algorithm improves the test set accuracy by 7.8% relative compared to the standard beam search. Automated CL leads to better generalization and further improves the test set accuracy by 5% relative.
Soft Subdivision Motion Planning for Complex Planar Robots

The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of resolution-exact algorithms, it is possible to exploit soft predicates for collision detection. The design of soft predicates is a balancing act between easily implementable predicates and their accuracy/effectivity. In this paper, we focus on the class of planar polygonal rigid robots with arbitrarily complex geometry. We exploit the remarkable decomposability property of soft collision-detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an m-gon, the complexity of this approach scales linearly in m. This contrasts with the O(m^3) complexity known for exact planners. It follows that we can now routinely produce soft predicates for any rigid polygonal robot. This results in resolution-exact planners for such robots within the general Soft Subdivision Search (SSS) framework. This is a significant advancement in the theory of sound and complete planners for planar robots. We implemented such decomposed predicates in our open-source Core Library. The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods.
Performance comparison of an AI-based Adaptive Learning System in China

Adaptive learning systems stand apart from traditional learning systems by offering a personalized learning experience to students according to their different knowledge states. Adaptive systems collect and analyse students' behavior data, update learner profiles, then accordingly provide timely individualized feedback to each student. Such interactions between the learning system and students can improve the engagement of students and the efficiency of learning. This paper evaluates the effectiveness of an adaptive learning system, "Yixue Squirrel AI" (or Yixue), on English and math learning in middle school. The effectiveness of the Yixue's math and English learning systems is respectively compared against (1) traditional classroom math instruction conducted by expert human teachers and (2) BOXFiSH, another adaptive learning platform for English language learning. Results suggest that students achieved better performance using Yixue adaptive learning system than both traditional classroom instruction by expert teachers and another adaptive learning platform.
A novel algorithm for segmentation of leukocytes in peripheral blood

In the detection of anemia, leukemia and other blood diseases, the number and type of leukocytes are essential evaluation parameters. However, the conventional leukocyte counting method is not only quite time-consuming but also error-prone. Consequently, many automation methods are introduced for the diagnosis of medical images. It remains difficult to accurately extract related features and count the number of cells under the variable conditions such as background, staining method, staining degree, light conditions and so on. Therefore, in order to adapt to various complex situations, we consider RGB color space, HSI color space, and the linear combination of G, H and S components, and propose a fast and accurate algorithm for the segmentation of peripheral blood leukocytes in this paper. First, the nucleus of leukocyte was separated by using the stepwise averaging method. Then based on the interval-valued fuzzy sets, the cytoplasm of leukocyte was segmented by minimizing the fuzzy divergence. Next, post-processing was carried out by using the concave-convex iterative repair algorithm and the decision mechanism of candidate mask sets. Experimental results show that the proposed method outperforms the existing non-fuzzy sets methods. Among the methods based on fuzzy sets, the interval-valued fuzzy sets perform slightly better than interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets.
Bank competitive landscape and competition in the banking sector in Kenya

This study investigates the evolution of competition among commercial banks in Kenya with changes in the bank competitive landscape. Employing system GMM and the performance dynamics approach, the study establishes that bank consolidation has an inverted U effect on competition in the banking sector in Kenya, growth in technology spurs competition among commercial banks in the short run but is impotent in the long run and the progressive increase in the core capital requirement for commercial banks from KES250 million in 2008 to KES1 billion in 2012 slowed competition in Kenya’s banking sector by 3.3 percentage points. Arising from the findings the study concludes that consolidation of commercial banks is a short to medium term instrument for promoting competition in the banking sector in Kenya, growth in technology is effective in promoting competition in Kenya’s banking sector in the short run rather than in the long run and blind increases in the core capital requirements can lead to undesired outcome of reduced competition in the banking sector in Kenya.Keywords: Bank competitive landscape; Intermediation efficiency; Exceptional profits
자문화기술지를 통한 무용 예술강사의 지도경험 성찰

The purpose of this research is to provide reflection of dance art instructors’ teaching experience through autoethnography as a good alternative when...
Generalized Markov Method for Ranking Supplier Performance in the Herbal Industry: A Case Study of a Herbal Village in Thailand

The Thailand herbal market expected to grow 10 percent a year, but it was growing more than 30 percent in 2017. According to the import herbs as raw materials around USD 485. There are more than 11,861 Acres of herbal farms, but it is not enough for the Thai herbal market. The government also plans to promote upstream through downstream. The ministry of public health plans to promote strategies as applying providing physician and traditional Thai treatment simultaneously to patients in a community hospital by producing the products by the hospitals. Besides, major suppliers for herbs are farmers. This study is aimed to measure supplier performance of farmers in Thapthim Siam 05 village, Her Royal Highness Princess Chulabhorn Valayaluk project. Supplier selection criteria are viewed and selected from published international journals. They are cost, productivity, quality, and time. In addition, an in-depth interview of suppliers, and quantitative data are obtained. Markov Chain and Power Method are employed to evaluate and rank their performances. Policy recommendations are discussed to reduce their production cost and increase productivity.
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods.
Image analysis of the tree test using wavelet transform, singular value decomposition, and Fourier transform

In this study, we proposed three image analysis methods (wavelet transform, singular value decomposition, and Fourier transform) to evaluate drawings of the tree test quantitatively, and demonstrated the analyses to three images of the tree test drawn by schizophrenic patients. Wavelet analysis suggested that information about the position of drawn trees (direction, depth and width of drawn lines) can be captured. Fourier analysis suggested that information about the direction and depth of drawn lines can be captured. Singular value decomposition suggested that information about the position and direction of drawn lines can be captured. Further research is needed to consider the features of mathematical image analysis in detail, and apply them to analysis of the tree test.
Embedding entanglement generation within a measurement-feedback coherent Ising machine

We present a new scheme to efficiently establish entanglement between optical modes in a time-multiplexed coherent Ising machine (CIM) by means of nonlocal measurement and feedback. We numerically simulate and evaluate the generation of steady-state entanglement in a system with nearest-neighbor interactions on a 1D ring, and we compare the results to those of a conventional CIM with all-optical interactions mediated by delay lines. We show that the delay-line architecture has a fundamental limit on accessible entanglement that our measurement-based scheme can substantially surpass. These results motivate the study of non-classical correlations in CIMs with levels of entanglement previously considered to be experimentally impractical.
Constructions of New Entanglement-Assisted Quantum Codes

Abstract Entanglement-assisted quantum error-correcting codes are considered as a new research direction of quantum coding theory. In this work, based on cyclic codes, we present the constructions of new entanglement-assisted quantum error-correcting codes. Several new families of nonbinary entanglement-assisted quantum error-correcting codes with length n=2(q-1) can be efficiently constructed.
Comparison of Local and Non-local Deep Feature on Malignancy Characterization of Hepatocellular Carcinoma

The malignancy of hepatocellular carcinoma (HCC) is of much importance for the liver cancer prognosis which greatly influences the patient survival. With the development of deep learning, deep feature extracted from deep network shows superior ability in characterizing HCC. However, deep feature focuses on the local area of image and taking the noise into consideration, we are inspired by the non-local means in traditional image processing. Then a non-local block is proposed and we decided to extract the non-local deep feature to characterize HCC. In this work, we only use the arterial phase image because other phases in HCC are poor in characterization and we just ignore the impact. Also, only the axial view is used for the same reason. In this work, we study three kinds of nonlocal network and each is divided into net with residual and without residual as well. Experimental results show that the nonlocal deep feature outperforms the local deep feature and the network with residual outperforms that without residual.
On the weight distribution of second order Reed-Muller codes and their relatives

The weight distribution of second order $q$-ary Reed-Muller codes have been determined by Sloane and Berlekamp (IEEE Trans. Inform. Theory, vol. IT-16, 1970) for $q=2$ and by McEliece (JPL Space Programs Summary, vol. 3, 1969) for general prime power $q$. Unfortunately, there were some mistakes in the computation of the latter one. This paper aims to provide a precise account for the weight distribution of second order $q$-ary Reed-Muller codes. In addition, the weight distributions of second order $q$-ary homogeneous Reed-Muller codes and second order $q$-ary projective Reed-Muller codes are also determined.
Knowledge Management System Approach of Police Performance Management in Regional Police of Yogyakarta Special Region, Indonesia

The purpose of this study was to identify the need of knowledge management and the method of implementing knowledge management in police performance. This research was conducted in the regional police of Yogyakarta Special Region. The approach of research used mixed method research. The method used a survey and description of the analysis. The result of this study showed that the perspective of Yogyakarta Special Region community on police performance had a positive trend, as indicated by level of public confidence in the performance of members of the Yogyakarta special region police at 93.19%, this could be indicated as a positive impact of the knowledge management system approach. The knowledge management system approach will transform secret knowledge into explicit knowledge and will also drive innovation. The implications in this study identified that police performance could be influenced by the knowledge management system approach.
Automatic Intracranial Brain Segmentation from Computed Tomography Head Images

Fast and automatic algorithm to segment Brain (intracranial region) from computed tomography (CT) head images using combination of HU thresholding, identification of intracranial voxels through ray intersection with cranium, special binary erosion and connected components per slice.
‘Explain It to a Beginner!’ Effects of Model Instruction on Learning with Eye Movement Modeling Examples

Eye-movement modeling example (EMME) videos show the expert’s eye-movements (i.e., the focus of attention) while h/she demonstrates how to perform a task. EMMEs often have found to guide learners’ attention and foster their learning. One potential influencing factor on EMME’s effectiveness might be its concrete design. However, clear design guidelines on how to create an optimal EMME are missing. For instance, some (expert) models received the instruction to behave ‘didactically’ when creating an EMME. Recently, we (Authors, in prep.) found that the instruction to behave didactically clearly changes (programming) experts' non-verbal behavior and thus, the EMME videos. Consequently, our next study uses EMME videos about code debugging to investigate the effect of model instruction (non-didactical EMME vs. didactical EMME) on novices’ learning and video evaluation. Ideas on the study design will be presented at the ICO NSS19. Our findings may provide researchers and practitioners with guidelines on how to create more effective EMME videos.
Building bridges between Open Science policy and practice

There is increasing recognition of the scope of cultural change needed to realize the European vision for Open Science policy. Key challenges include contemporary modes of recognition and reward that are out of step with principles of open science [1] and identification of numerous disincentives, at the individual level, regarding adoption of increased openness [2]. These obstacles raise questions about implementation of top-down policy in general, and the European Commission's open science policy in particular: Where does the European notion of Open Science come from? In what ways is open science policy informed by the science of science? And what role is there for ongoing, bottom-up contributions to open scholarship? This project examines foundational European policy documents in relation to published research on open science, and outlines a bridge to better understanding of openness practices through recognition[3] of those already doing open science. [1] European Commission. 2017. “Evaluation of Research Careers Fully Acknowledging Open Science Practices; Rewards, Incentives and/or Recognition for Researchers Practicing Open Science.” Directorate-General for Research and Innovation Open Science and ERA policy. https://ec.europa.eu/research/openscience/pdf/os_rewards_wgreport_final.pdf [2] Leonelli, Sabina. 2017. “European Commission: MLE on Open Science: Altmetrics and Rewards – Incentives and Rewards to Engage in Open Science Activities.” Brussels: Directorate-General for Research and Innovation. https://rio.jrc.ec.europa.eu/en/file/11619/download?token=uiOzNZM_ [3] the Openness Profile is presently under development by the Knowledge Exchange working group on Open Scholarship and Research Evaluation - http://knowledge-exchange.info
Morphological Networks for Image De-raining

Mathematical morphological methods have successfully been applied to filter out (emphasize or remove) different structures of an image. However, it is argued that these methods could be suitable for the task only if the type and order of the filter(s) as well as the shape and size of operator kernel are designed properly. Thus the existing filtering operators are problem (instance) specific and are designed by the domain experts. In this work we propose a morphological network that emulates classical morphological filtering consisting of a series of erosion and dilation operators with trainable structuring elements. We evaluate the proposed network for image de-raining task where the SSIM and mean absolute error (MAE) loss corresponding to predicted and ground-truth clean image is back-propagated through the network to train the structuring elements. We observe that a single morphological network can de-rain an image with any arbitrary shaped rain-droplets and achieves similar performance with the contemporary CNNs for this task with a fraction of trainable parameters (network size). The proposed morphological network(MorphoN) is not designed specifically for de-raining and can readily be applied to similar filtering / noise cleaning tasks. The source code can be found here this https URL