Reviewerly/exHarmony huggingface dataset:
Timely-Automatic Procedure for Estimating the Endocardial Limits of the Left Ventricle Assessed Echocardiographically in Clinical Practice

In this paper, we propose an analytical rapid method to estimate the endocardial borders of the left ventricular walls on echocardiographic images for prospective clinical integration. The procedure was created as a diagnostic support tool for the clinician and it is based on the use of the anisotropic generalized Hough transform. Its application is guided by a Gabor-like filtering for the approximate delimitation of the region of interest without the need for computing further anatomical characteristics. The algorithm is applying directly a deformable template on the predetermined filtered region and therefore it is responsive and straightforward implementable. For accuracy considerations, we have employed a support vector machine classifier to determine the confidence level of the automated marking. The clinical tests were performed at the Cardiology Clinic of the County Emergency Hospital Timisoara and they improved the physicians perception in more than 50% of the cases. The report is concluded with medical discussions.
Mímir: Building and Deploying an ML Framework for Industrial IoT

In this paper we describe Mímir, a production grade cloud and edge spanning ML framework for Industrial IoT applications. We first describe our infrastructure for optimized capture, streaming and multi-resolution storage of manufacturing data and its context. We then describe our workflow for scalable ML model training, validation, and deployment that leverages a manufacturing taxonomy and parameterized ML pipelines to determine the best metrics, hyper-parameters and models to use for a given task. We also discuss our design decisions on model deployment for real-time and batch data in the cloud and at the edge. Finally, we describe the use of the framework in building and deploying an application for Predictive Quality monitoring during a Plastics Extrusion manufacturing process.
Specific Action Recognition Method based on Unbalanced Dataset

Specific Action Recognition is a mission that refers to identifying a certain type of human from a video, e.g., fighting, chasing. In the actual application, the number of negative samples is far more than positive samples. This paper focus on the specific action recognition of the human body based on extremely unbalanced dataset, and proposes A specific action recognition method that can be applied to unbalanced dataset. The input of the method are video frames. After extracting image features by 2D convolutional networks and stacking the image feature maps, the time domain features are fused by a small number of 3D convolutional networks. In order to solve the problem that dataset is imbalanced, negative videos are under-sampled firstly, then focal loss and weighted cross entropy loss are used to guide the model training. This model is trained on Kinetics-600 dataset and self-collecting specific action dataset. Finally, average accuracy on the self-collected dataset is 95.47%.
VRASP: A Virtual Reality Environment for Learning Answer Set Programming

Answer Set Programming (ASP) is a dominant programming paradigm in Knowledge Representation. It is used to build intelligent agents – knowledge-intensive software systems capable of exhibiting intelligent behaviors. It is found that ASP can also be used to teach computer science in middle and high schools. However, the current ASP systems do not provide direct support for a programmer to produce an intelligent agent that a general user can directly interact with, which may greatly compromise the potential attraction of ASP to the secondary school students. In this paper, we propose a Virtual Reality (VR) programming environment called VRASP that allows a student to produce an avatar (agent) in a virtual world that is able to answer questions in spoken natural language from a general user. The VR application is accessible from anywhere so that the students’ friends can interact with the agent. As a result, it gives the students a feeling of achievement and thus encourages them to solve problems using ASP. VRASP was evaluated with 10 users. Results of these studies show that students are able to communicate with the environment intuitively with an accuracy of 78%.
UCEJ Database Refinement and Applicability Proof

The representation by computer systems of Chinese characters is an ongoing issue: it is still impossible to use some of them on a computer. Several encoding solutions have been proposed over the years, with most notably two approaches: the unifying approach followed by Unicode which aims at covering all the glyphs known to mankind, and the non-unifying approach followed by "local" encodings such as Shift-JIS and EUC-JP in the case of Japanese. In previous works, we have proposed an unrestricted character encoding for Japanese (UCEJ) so as to address the issues faced by the other encodings. In this paper, we propose a refinement to the realisation method of the character database on which UCEJ is based, and then show the applicability of the UCEJ encoding. To this end, we first describe a proof of concept UCEJ application (viewer and converter) and finally quantitatively compare UCEJ against Unicode with respect to memory size requirements. Not only does UCEJ features essential improvements over Unicode regarding the code structure and features overall, but we show that the induced memory size overhead can be almost eliminated under certain conditions.
Performance Evaluation of Cloud Systems by Switching the Virtual Machines Power Mode Between the Sleep Mode and Active Mode

Data centers are cost-effective infrastructures for storing large volumes of data and hosting large scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. In this chapter, we study energy savings of data centers by consolidation and switching off of those virtual machines which are not in use. According to this policy, c virtual machines continue serving the customer until the number of idle servers attains the threshold level d; then d idle servers take synchronous vacation simultaneously, otherwise these servers begin serving the customers. Numerical results are provided to demonstrate the applicability of the proposed model for the data center management in particular, to quantify theoretically the tradeoff between the conflicting aims of energy efficiency and Quality of Service (QoS) requirements specified by cloud tenants.
Peer adjacent mapping with optimum parameters for fractal image compression on medical images

In order to reduce fractal image compression (FIC) computational complexity, this paper presents a novel approach based on peer adjacent and mean difference method in mapping the domain and range blocks. It allows the substitution of the costly process of the domain and range mapping with straight-forward peer adjacent schemes. The optimum parameters are suggested in terms of quadtree threshold, range sizes and number of iterations to fine-tune the output. Simulations on real computed radiography images show that the proposed method with optimum parameters yields a considerable peak signal-to-noise ratio (PSNR) value above 48 dB, reducing the runtime by as much as 19.32 s with the highest compression ratio is 20.03. Comparison with the standard FIC method confirms that our method not only accelerated the domain-range mapping procedure but also provided a high compression ratio with remarkable visual quality.
Analysis of Junior High School students’ data literacy in Ciamis with local potential kampung adat kuta

Abstract This study aims to analysis the profile of data literacy ability by utilizing the local potential in Kampung Adat Kuta for junior high school students in Ciamis. This study used quantitative descriptive study with the test method. Subjects were 235 junior high school students in the academic year of 2019/2020 determined using purposive sampling techniques. The description test instrument was used to measure data literacy with four indicators, namely: exploring data, selecting data, converting data and maintaining decisions or making new decisions; this instrument has been validated by several lecturers. Data analysis generated was quantitative percentage. From the results of this study, test instruments for data literacy in junior high schools in Ciamis showed that the highest indicator was exploring data (39.32%), while the poorest was maintaining or making new decisions from data (29.47%). The results of this study indicate that the data literacy ability of secondary school students in Ciamis falls into the category of poor, so we need attention from educators to overcome the poor data literacy ability by practicing data literacy ability.
Learning Context-Sensitive Strategies in Space Fortress

Research in deep reinforcement learning (RL) has coalesced around improving performance on benchmarks like the Arcade Learning Environment. However, these benchmarks do not emphasize two important characteristics that are often present in real-world domains: requirement of changing strategy conditioned on latent contexts, and temporal sensitivity. As a result, research in RL has not given these challenges their due, resulting in algorithms which do not understand critical changes in context, and have little notion of real world time. This paper introduces the game of Space Fortress as a RL benchmark which specifically targets these characteristics. We show that existing state-of-the-art RL algorithms are unable to learn to play the Space Fortress game, and then confirm that this poor performance is due to the RL algorithms' context insensitivity. We also identify independent axes along which to vary context and temporal sensitivity, allowing Space Fortress to be used as a testbed for understanding both characteristics in combination and also in isolation. We release Space Fortress as an open-source Gym environment.
$\mathbb{Z}_{q}(\mathbb{Z}_{q}+u\mathbb{Z}_{q})-$ linear skew constacyclic codes

In this paper, we study skew constacyclic codes over the ring $\mathbb{Z}_{q}R$ where $R=\mathbb{Z}_{q}+u\mathbb{Z}_{q}$, $q=p^{s}$ for a prime $p$ and $u^{2}=0.$ We give the definition of these codes as subsets of the ring $\mathbb{Z}_{q}^{\alpha}R^{\beta}$. Some structural properties of the skew polynomial ring $ R[x,\Theta]$ are discussed, where $ \Theta$ is an automorphism of $R.$ We describe the generator polynomials of skew constacyclic codes over $\mathbb{Z}_{q}R,$ also we determine their minimal spanning sets and their sizes. Further, by using the Gray images of skew constacyclic codes over $\mathbb{Z}_{q}R$ we obtained some new linear codes over $\mathbb{Z}_{4}$. Finally, we have generalized these codes to double skew constacyclic codes over $\mathbb{Z}_{q}R$.
Few-Shot Learning Based on Attention Relation Compare Network

In the field of computer vision, great progress has been made in the classification of few-shot images recently. However, challenges in improving classification accuracy and training stability remain. To address this issue, a novel meta learning method aiming at few-shot was proposed in this paper, which is a relationship comparison network based on attention mechanism. The network is an end-to-end network, consisting of three folds: feature coding, feature combination, and relationship coding. Firstly, the attention mechanism is introduced into the feature coding module and spectral normalization is used to normalize the weight parameters of the convolutional layer of the module, resulting in better extraction of key feature information from few-shot images and enhanced training stability of the attention mechanism comparison network. Secondly, in order to compare the feature information of the sample set and the query set, the feature information of the extracted sample set and the query set is combined into a new feature information in the feature combination module. Besides, the relationship coding module can learn a depth measurement method to calculate the relationship score of combined feature information and realize the classification of few-shot images. The performance of Attention Relation Compare Network Meta-Learning was evaluated on few-shot data set, namely the Omniglot and Mini ImageNet. It was indicated that our method excelled other few-shot learning method in accuracy and efficiency and improve the training stability to some extent . Experiments show that our method produces very competitive results when compared to the state-of-the-art method, and our model also exhibits the training stability.
Uniform parallel-machine scheduling to minimize the number of tardy jobs in the MapReduce system

The MapReduce model has become increasingly important in machine scheduling and big data processing. Each job consists of one map task and one reduce task. The map task can be split into several parts and processed on several machines simultaneously, while the reduce task has to be processed on one of the machines and must start after the completion of the map task. The processing of reduce task cannot be interrupted. In this paper, we consider the MapReduce problem of minimizing total number of tardy jobs on uniform parallel machines with arbitrary release times and due date. We formulate a mixed integer linear programming (MILP) model for this problem and develop an improved grey wolf algorithm (IGWO) using differential perturbation strategy to obtain a near-optimal solution. The effectiveness of the model and the improved grey wolf algorithm evaluated by computational experiments on a series of randomly generated instances. The numerical results indicate that the improved grey wolf algorithm outperforms PSO for the problem.
Machine learning in Python with no strings attached

Machine-learning frameworks in Python, such as scikit-learn, Keras, Spark, or Pyro, use embedded domain specific languages (EDSLs) to assemble a computational graph. Unfortunately, these EDSLs make heavy use of strings as names for computational graph nodes and other entities, leading to repetitive and hard-to-maintain code that does not benefit from standard Python tooling. This paper proposes eliminating strings where possible, reusing Python variable names instead. We demonstrate this on two examples from opposite ends of the design space: Keras.na, a light-weight wrapper around the Keras library, and , a new embedding of Stan into Python. Our techniques do not require modifications to the underlying library. Avoiding strings removes redundancy, simplifies maintenance, and enables Python tooling to better reason about the code and assist users.
Online Ambiguity Resolution in Bilingual Lexical Access

This chapter reports on three experiments using the cross-modal lexical priming paradigm to explore whether interlingual homographs (i.e., words with competing semantic and overlapping orthographic representations) are activated selectively or nonselectively. The literature is somewhat controversial when it comes to the question of how bilinguals process ambiguous language. While the majority of studies suggest language nonselectivity, some research seems to indicate selective bilingual lexical access depending on the user’s linguistic needs and demands. In Experiment 1, which serves as a baseline, Spanish-English bilinguals listened to sentences in which a critical prime (e.g., trial) was associated with the English meaning of a homograph target (cases). In Experiment 2, participants were presented with homograph-translation primes (e.g., the stimulus married is presented before cases). Experiment 3, aside from the homograph-translation priming from Experiment 2, included a Spanish language mode induction variable presented at the beginning of the experiment. Results point to the effects of proficiency and priming in modulating language coactivation.
Video-Data Compression Using Wavelet Analysis

This research work focuses on researching and evaluating modern tendencies in video-compression, estimating possibilities and potential of wavelet-transform, transform at oriented basis specifically, in the context of visual data compression. For this purpose several types of wavelets (Haar, Daubechies and oriented basis) and cosine transform were compared and analyzed. Possible improvements on existing video coding standards featuring transform at oriented basis were proposed. Results regarding effectiveness of reviewed methods are presented.
A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

Reference values of erythrocyte sedimentation rate (ESR) are the key to interpret ESR blood test in clinic. The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression (MLR) model and the artificial neural network (ANN). These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited. This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning (CBR) since knowledge domain of ESR and geographical factors is weak. Overall 224 local normal ESR values of China that calculated from 13 623 samples were obtained, and the corresponding natural geographical factors and location that include altitude, sunshine hours, relative humidity, temperature, precipitation, annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China. CBR was used to predict the unseen local reference ESR values with cases. The average absolute deviation (AAD), mean square error (MSE), prediction accuracy (PA), and Pearson correlation coefficient (r) between the observed and estimated data of proposed model is 33.07%, 9.02, 66.93% and 0.78, which are better than those of ANN and MLR model. The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models. The predicted values are very close to the observed values. Model results show significant agreement of cases data. Consequently, the model is used to predict the unseen local reference ESR with natural geographical factors and location. In spatial, the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan, Chongqing, Guangxi and Guizhou provinces, and the reference ESR values are greater than 23 mm/60 min. The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei, Henan, Shaanxi, Shanxi, Jilin and Heilongjiang provinces, and the reference ESR values are greater than 18 mm/60 min. The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang, and the reference ESR values are lower than 5 mm/60 min.
An Effective Content Based image Retrieval Utilizing Color Features and Tamura Features

In In today's real life applications complexity of multimedia contents is significantly increased. This is highly demanding the development of effective retrieval systems to satisfy human desires. In practice, appropriate computer vision and image processing techniques are usually employed to obtain image visual features. Central to functional Content Based Image Retrieval (CBIR) system is effective indexing and fast searching of images based on the visual features. Effective indexing is also essential to make CBIR system scalable for large image databases and incorporation of advanced technique. Usually, CBIR system cannot be developed with only one type of visual features. As such, a combination of different visual features at different levels is needed to identify and classify images in different contexts. In this paper, we propose CBIR system based on color and texture features the color features are represented by Color moments and HSV color histogram and texture feature is represented by Tamura. Detailed experimental analysis is carried out using precision and recall for the two datasets: colored Brodatz textures and KTH TIPS. The Analysis is also performed to compare of the proposed method with the existing similar best methods
Research on User Experience of VR Games Based on ISM Theory

Abstract This paper explores the starting point of VR (virtual reality) game experience by 5 “W” of care (What, Why, Who, Where and When), uses STEEP method to analyze the problems in different aspects of the user experience, and invites two senior people to evaluate 11 important factors. Then, the factor relationship hierarchy analysis graph is obtained with the ISM theoretical operation, and the VR game users’ problems and goals that urgently need to change are obtained. The main issue in the research is that the quality of the game story determines the experience of the users, and the side issues are the sound effect, art effect, and special effect.
Modulation spectrum augmentation for robust speech recognition

Data augmentation is a crucial mechanism being employed to increase the diversity of training data in order to avoid overfitting and improve robustness of statistical models in various applications. In the context of automatic speech recognition (ASR), a recent trend has been to develop effective methods to augment training speech data by warping or masking utterances based on their waveforms or spectrograms. Extending this line of research, we make attempts to explore novel ways to generate augmented training speech data, in comparison to the existing state-of-the-art approaches. The main contribution of this paper is at least two-fold. First, we propose to warp the intermediate representation of the cepstral feature vector sequence of an utterance in a holistic manner. This intermediate representation can be embodied in different modulation domains by performing discrete Fourier transform (DFT) along the either the time- or the component-axis of a cepstral feature vector sequence. Second, we also develop a two-stage augmentation approach, which successively conduct perturbation in the waveform domain and warping in different modulation domains of cepstral speech feature vector sequences, to further enhance robustness. A series of experiments are carried out on the Aurora-4 database and task, in conjunction with a typical DNN-HMM based ASR system. The proposed augmentation method that conducts warping in the component-axis modulation domain of cepstral feature vector sequences can yield a word error rate reduction (WERR) of 17.6% and 0.69%, respectively, for the clean-and multi-condition training settings. In addition, the proposed two-stage augmentation method can at best achieve a WERR of 1.13% when using the multi-condition training setup.