Reviewerly/exHarmony huggingface dataset:
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.
Transfer Learning for Digital Heritage Collections: Comparing Neural Machine Translation at the Subword-level and Character-level

Transfer learning via pre-training has become an important strategy for the efficient application of NLP methods in domains where only limited training data is available.This paper reports on a focused case study in which we apply transfer learning in the context of neural machine translation (French-Dutch) for cultural heritage metadata (i.e.titles of artistic works).Nowadays, neural machine translation (NMT) is commonly applied at the subword level using byte-pair encoding (BPE), because word-level models struggle with rare and out-of-vocabulary words.Because unseen vocabulary is a significant issue in domain adaptation, BPE seems a better fit for transfer learning across text varieties.We discuss an experiment in which we compare a subword-level to a character-level NMT approach.We pre-trained models on a large, generic corpus and fine-tuned them in a two-stage process: first, on a domain-specific dataset extracted from Wikipedia, and then on our metadata.While our experiments show comparable performance for character-level and BPEbased models on the general dataset, we demonstrate that the character-level approach nevertheless yields major downstream performance gains during the subsequent stages of fine-tuning.We therefore conclude that character-level translation can be beneficial compared to the popular subword-level approach in the cultural heritage domain.
The Effect Of Gamified Student Response System On Students’ Perception and Achievement

Rapid advances in technology affect many areas of our lives, one of which is education. In the local Indonesian context, teachers and schools sometimes still see technology as a threat to the teaching and learning process. Many schools forbid their students to bring their devices to school. Some modern schools are trying to embrace technology by allowing students to carry and use their tools at school and provide free WiFi at school. However, it does not mean that teachers are ready to adapt and use technology in their classrooms. To get the benefits of information technology advances in teaching and learning process at schools; we need to find a technology that is easy for the teacher to use and fun for the students to learn it. Kahoot is a form of technology in the Student Response System (SRS) with a gamification approach that can increase student involvement in the class-room. This study aims to look at the students' perceptions of Kahoot and see the effect on non-cognitive aspects, namely Likeness and Interesting, and cognitive aspects, namely Achievement. As many as 153 students were involved in this study consisting of 127 students in the experimental class and 26 students in the control class. We implemented Kahoot in chemistry lessons for three months in the experimental class then surveyed to see their perceptions. We also compare the academic score of chemistry lesson from both classes before and after the implementation. The results show that Kahoot has a positive effect on student perceptions and non-cognitive aspects, but the application of Kahoot has no direct impact on the cognitive elements observed, namely Achievement.
Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter

This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP) with the information of the target aspect angle, and target classification is implemented through the data correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-target JTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTC method into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter with Bayesian theory. To further tackle the complex integrals’ calculation involved in targets state and class estimation, we finally provide the sequential Monte Carlo (SMC) implementation of the proposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC method is validated by simulation results under the application scenario of maritime ship surveillance.
Multiset Canonical Correlations Analysis With Global Structure Preservation

This paper considers unsupervised dimensionality reduction of multi-view data, where locality preserving canonical correlation analysis and a new locality-preserving canonical correlation analysis are two typical effective methods. However, they ignore the global structure while considering the local structure of data, and are sensitive to noises because of the relationship of neighbors based on the Euclidean distance. In this paper, we propose a novel multi-view dimensionality reduction method: multiset canonical correlations analysis based on low-rank representation. Our model introduces the cross-view similarity matrix to consider the correlation of all different points in cross views, which makes it not only preserve the local structure but also the global structure of data. And the cross-view similarity matrix is constructed by using low-rank representation, which can make the model more robust. In addition, a parameter ß is introduced to adjust the importance of the correlation of different sample points, enhancing the generalization ability for different datasets. Experiments on four multi-view datasets show our proposed method has better performance than the related methods.
A Systematic Literature Review of Application Development to Realize Paperless Application in Indonesia: Sectors, Platforms, Impacts, and Challenges

Going paperless is an ideal form of the information era with the advantages of being time-efficient, environmentally friendly, proper documentation management, and it is an important step to improve the perception of the organization in the environmental field. From the environmental perspective, paperless is a concrete step to reduce the use of trees for paper. The paperless concept has been proposed by the government and has been legally guaranteed, so various sectors have begun to implement the paperless concept such as in the government, education, and industry sectors. However, there has been limited research that studies how many sectors implement paperless applications, the platforms that are used to develop paperless applications, the impacts of using paperless applications and the challenges for Indonesia. Therefore, this study aims to find out more details in the use of paperless applications in terms of sectors, platforms, impacts, and challenges for Indonesia. The data used in this study are articles of journal accredited by Sinta discussing the development of paperless applications in the government, education, and industry sectors from 2010 to 2019. The data are analyzed using the Systematic Literature Review method (SLR). The results of this study indicate that the sector that constantly develops paperless applications is the education sector, while the dominant platform used to develop paperless applications is the website. The impact of using paperless applications has a positive impact both in terms of performance, budget savings, and solving environmental problems generated by paper waste. Paperless applications are the solution in the digital era in supporting environmental preservation. The challenge is how the government makes regulations to support paperless applications in all agencies and provides financial support to sectors in which the use of paper is classified as significant but lacks funds in implementing paperless applications. Paperless applications must also be easy to use, and users must be provided continuous training so that paperless applications can be implemented easier.
IIU

Inverted index serves as a fundamental data structure for efficient search across various applications such as full-text search engine, document analytics and other information retrieval systems. The storage requirement and query load for these structures have been growing at a rapid rate. Thus, an ideal indexing system should maintain a small index size with a low query processing time. Previous works have mainly focused on using CPUs and GPUs to exploit query parallelism while utilizing state-of-the-art compression schemes to fit the index in memory. However, scaling parallelism to maximally utilize memory bandwidth on these architectures is still challenging. In this work, we present IIU, a novel inverted index processing unit, to optimize the query performance while maintaining a low memory overhead for index storage. To this end, we co-design the indexing scheme and hardware accelerator so that the accelerator can process highly compressed inverted index at a high throughput. In addition, IIU provides flexible interconnects between modules to take advantage of both intra- and inter-query parallelism. Our evaluation using a cycle-level simulator demonstrates that IIU provides an average of 13.8\times× query latency reduction and 5.4\times× throughput improvement across different query types, while reducing the average energy consumption by 18.6\times×, compared to Apache Lucene, a production-grade full-text search framework.
A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images

This paper investigates the problem of human pose estimation (HPE) from single 2-dimensional (2D) still images using a convolutional neural network (CNN). The aim was to train the CNN to analyze a 2D input image of a person to determine the person's pose. The CNN output was given in the form of a tree-structured graph of interconnected nodes representing 2D image coordinates of the person's body joints. A new data-driven tree-based model for HPE was validated and compared to the traditional anatomy-based tree-based structures. The effect of the number of nodes in anatomy-based tree-based structures on the accuracy of HPE was examined. The tree-based techniques were compared with non-tree-based methods using a common HPE framework and a benchmark dataset. As a result of this investigation, a new hybrid two-stage approach to the HPE estimation was proposed. In the first stage, a non-tree-based network was used to generate approximate results that were then passed for further refinement to the second, tree-based stage. Experimental results showed that both of the proposed methods, the data-driven tree-based model (TD_26) and the hybrid model (H_26_2B) lead to very similar results, obtaining 1% higher HPE accuracy compared to the benchmark anatomy-based model (TA_26) and 3% higher accuracy compared to the non-tree-based benchmark (NT_26_A). The best overall HPE results were obtained using the anatomy-based benchmark with the number of nodes increased from 26 to 50, which also significantly increased the computational cost.
Variables that Influence the Intention to Persist in Vocational Education

This study explored variables that influence students' intention to persist in vocational education.The variables considered included self-regulation strategies, school participation, self-determination, school identification and teacher autonomy.These variables have been previously studied in other contexts but not in vocational education.Data collection involved self-report surveys with 277 vocational learners (age 15) from three vocational schools in Bangkok, Thailand.Pearson's correlation was used to test possible relationship among variables.Regression analysis identified the variables that were significant.School participation, self-determination and school identification were the three variables identified that influenced intention to persist.Self-regulation strategies, promotion of task-related discussion and teacher autonomy support did not have a significant influence on the intention to persist in school.
A new tech platform as innovative teaching model at high schools in Georgia

Modern high schools are facing challenges brought by technological revolution. Today Leading Universities have already started to use Artificial Intelligence (AI) as an instrument in teaching, that will enhance education quality, emphasize individual approach and create 24/7 available students’ assistant and virtual space. World leading universities also successfully apply AI in planning of entrepreneurial activities. This directly links science with the factors facilitating the growth of country’s economy.&#x0D; The aim of the research was to show the opportunities and challenges for the use tech platform as innovative teaching model in Georgia.&#x0D; Both qualitative and quantitative methods were used during conducting research. Experiment was held, questionnaires and in-depth interviews were used.&#x0D; The research proved the readiness of Georgia for the innovative platform implementation as a mean of learning quality improvement. It’s planned to use platform in other courses too.
Small weight codewords of projective geometric codes

We investigate small weight codewords of the p-ary linear code Cj,k(n,q) generated by the incidence matrix of k-spaces and j-spaces of PG(n,q) and its dual, with q a prime power and 0⩽j<k<n. Firstly, we prove that all codewords of Cj,k(n,q) up to weight (3−O(1q))[k+1j+1]q are linear combinations of at most two k-spaces (i.e. two rows of the incidence matrix). As for the dual code Cj,k(n,q)⊥, we manage to reduce both problems of determining its minimum weight (1) and characterising its minimum weight codewords (2) to the case C0,1(n,q)⊥. This implies the solution to both problem (1) and (2) if q is prime and the solution to problem (1) if q is even.
LDA Model and Network Embedding-Based Collaborative Filtering Recommendation

The most classic algorithm in the recommender system domain is the collaborative filtering algorithm, but it often suffers from data sparsity and item cold-start problems. Moreover, most traditional collaborative filtering algorithms only consider the user's rating of the item, which greatly limits the accuracy of the recommendation. This paper takes movie recommendation as the research object, and proposes a collaborative filtering recommendation model based on LDA topic model and network embedding, which improves the dependability of the model. The model first uses the LDA topic model to extract the text features of users and movies from texts, then builds the association network through the association features between users, and learns the network features using node2vec and GraphGAN. Finally, the two parts are combined to compute the similarity between users to recommend movies using the collaborative filtering algorithm. Results of the experiment on the real dataset of the Douban website demonstrate that movie recommendation with the user characteristics mined by the LDA model and GraphGAN combined can achieve the best effect, which is much higher than the collaborative filtering based on user ratings or using only LDA theme. This proves that network embedding can promote the accuracy of recommendationation, while ensuring the dependability of the model. The method proposed in this paper is effective.
Multi-Scale Weighted Fusion Attentive Generative Adversarial Network for Single Image De-Raining

With the rapid development of outdoor vision system, removing rain streaks from a single image has attracted considerable attention as rain streaks can affect the quality of the image taken in rainy days, and interrupt the key information, which will greatly reduce the use value of the image, thus affecting the performance of traffic, safety monitoring and other facilities. Although the deep learning methods have achieved satisfying performance in single image de-raining, there are still two problems: First, the rain streaks contained in one dataset we can use are limited, and in the case of real rainy days, the rain streak density is diverse, it is impossible to accurately classify them. Therefore, the existing rain removal models cannot remove rain streaks properly for images with different rain streak density which attend to over or under rain removal. Secondly, the results of single image after rain removal model often appear the phenomenon of variegated spots, image contrast saturation change and even unsmooth rain streak after rain removal. We use a three-way multi-scale weighted fusion module to enhance the feature extraction, and then generate an attention map through the improved spatial attentive module to accurately locate the location of the rain streaks. After the combination of the two, we will obtain the foreground information of the rain streaks. Through the characteristic of mutual game in the training mechanism of GAN, we can enhance the rain streak location recognition and effectively remove the rain at the same time. Through the training mechanism of the GAN network game, we can enhance the rain line location recognition and effectively remove the rain at the same time. Experiments show that our network achieves superior performance, it has high generalization for different rain streak density, and ensures that the contrast and saturation of the image are not changed.
Improved Security Bounds for Generalized Feistel Networks

We revisit the security of various generalized Feistel networks. Concretely, for unbalanced, alternating, type-1, type-2, and type-3 Feistel networks built from random functions, we substantially improve the coupling analyzes of Hoang and Rogaway (CRYPTO 2010). For a tweakable blockcipher-based generalized Feistelnetwork proposed by Coron et al. (TCC 2010), we present a coupling analysis and for the first time show that with enough rounds, it achieves 2n-bit security, and this provides highly secure, double-length tweakable blockciphers.
Loss Functions Forcing Cluster Separations for Multi-class Classification Using Deep Neural Networks

In Deep Neural Networks (DNNs), the learning process (updating the parameters) is driven by the gradients of a loss function; thus, the loss function does not only measures the "goodness" of classification, but also determines the form of representations. In this paper we propose two loss functions for Deep Neural Networks (DNNs), namely Multi-class Discriminant Analysis Loss Function and Maximum Scatter to Closest Neigh-bor Class loss function, which share the same idea, i.e., they minimize the within-class variance (scatter) and maximize the minimum between-class distance. They overcome the problem of overlapping class in the Linear Discriminant Analysis (LDA)-based loss functions and can be used to multi-class classification tasks. We show that our proposed loss functions achieve state-of-the-art accuracy in a medical image classification task. Particularly, in the Optical Coherence Tomography (OCT) image dataset, we got nearly perfect (99.6%) test accuracy.
Pixel-Level Dehazed Image Quality Assessment Based on Dark Channel Prior and Depth

Image dehazing has attracted wide attention as a promising technology to recover the hazy image to a clear image. As many algorithms are proposed to conduct single image dehazing, the way of how to evaluate the dehazed image quality effectively has become an important issue in the field of image dehazing. In this paper, we propose a novel reduced-reference Pixel-level Dehazed Image Quality Assessment (PDIQA) method by incorporating dark channel information. The proposed method can accurately measure the effect of image dehazing, and its pixel-by-pixel dehazing evaluation can guide the dehazing algorithms (DHA) to achieve the removal of residual haze. To evaluate different dehazing algorithms objectively, we firstly build a real-world dataset including 15 real-world hazy images combined with 75 dehazed results from 5 different state-of-the-arts dehazing algorithms. Then, we carry out a subjective quality evaluation study on this dataset. A large number of experiments verify the effectiveness of the proposed method.
Sociological Analysis of Educational Strategies in the System of Higher Education in Kazakhstan

The purpose of this study is to identify the educational strategies in higher education based on an analysis of students’ motives. The sociological study was conducted using a complex of quantitative and qualitative methods employing a questionnaire survey participated by students, graduate and doctoral students of national and state universities of Kazakhstan. The in-depth interviews were attended only by graduate and doctoral students.&#x0D; The findings unravelled that the leading motive for education was the desire of students to obtain a diploma. The acquisition of a formal university graduation certificate is important in the opinion of students. However, neither the content or qualifications nor the mastery of the future speciality, which they are going to acquire in the course of study, are not significant motives for the young generation. In the view of modern Kazakhstan youth, to be an educated person means to offer oneself in the labour market profitably.&#x0D; The existence of a link between the requirements for the future profession and goals in the higher education system was also confirmed in the course of study. The results obtained in the course of research make it possible to head over to a new level of substantiation of educational strategies and create prerequisites for the improvement of the efficiency of the educatory process.
Random Sampling and Size Estimation Over Cyclic Joins.

Computing joins is expensive, and often unnecessary when the output size is large. In 1999, Chaudhuri et al. [Surajit Chaudhuri et al., 1999] posed the problem of random sampling over joins as a potentially effective approach to avoiding computing the join in full, while obtaining important statistical information about the join results. Unfortunately, no significant progress has been made in the last 20 years, except for the case of acyclic joins. In this paper, we present the first non-trivial result on sampling over cyclic joins. We show that after a linear-time preprocessing step, a join result can be drawn uniformly at random in expected time O(IN^ρ/OUT), where IN^ρ is known as the AGM bound of the join and OUT is its output size. This result holds for all joins on binary relations, as well as certain joins on relations of higher arity. We further show how this algorithm immediately leads to a join size estimation algorithm with the same running time.
Persian Text Classification using naive Bayes algorithms and Support Vector Machine algorithm

One of the several beneﬁts of text classiﬁcation is to automatically assign document in predeﬁned category is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector results from traditional feature selection methods, most of the proposed text classiﬁcation methods for this purpose lack performance and accuracy. Many algorithms have been implemented to the problem of Automatic Text Categorization that’s why, we tried to use new methods like Information Extraction, Natural Language Processing, and Machine Learning. This paper proposes an innovative approach to improve the classification performance of the Persian text. Naive Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability. we have compared the Gaussian, Multinomial and Bernoulli methods of naive Bayes algorithms with SVM algorithm. for statistical text representation, TF and TF-IDF and character-level 3 (3-Gram) [6,9] were used. Finally, experimental results on 10 newsgroups.