Reviewerly/exHarmony huggingface dataset:
Sparker: Efficient Reduction for More Scalable Machine Learning with Spark

Machine learning applications on Spark suffers from poor scalability. In this paper, we reveal that the key reasons is the non-scalable reduction, which is restricted by the non-splittable object programming interface in Spark. This insight guides us to propose Sparker, Spark with Efficient Reduction. By providing a split aggregation interface, Sparker is able to perform split aggregation with scalable reduction while being backward compatible with existing applications. We implemented Sparker in 2,534 lines of code. Sparker can improve the aggregation performance by up to 6.47 × and can improve the end-to-end performance of MLlib model training by up to 3.69 × with a geometric mean of 1.81 × .
An Ensemble Hypergraph Learning Framework for Recommendation

Recommender systems are designed to predict user preferences over collections of items. These systems process users’ previous interactions to decide which items should be ranked higher to satisfy their desires. An ensemble recommender system can achieve great recommendation performance by effectively combining the decisions generated by individual models. In this paper, we propose a novel ensemble recommender system that combines predictions made by different models into a unified hypergraph ranking framework. This is the first time that hypergraph ranking has been employed to model an ensemble of recommender systems. Hypergraphs are generalizations of graphs where multiple vertices can be connected via hyperedges, efficiently modeling high-order relations. We perform experiments using four datasets from the fields of movie, music and news media recommendation. The obtained results show that the ensemble hypergraph ranking method generates more accurate recommendations compared to the individual models and a weighted hybrid approach.
A Robust watermarking using RDWT and Schur with Firefly optimization

In this paper, firefly optimization is applied to balance robustness and imperceptibility in image watermarking. First of all, the original image is partitioned into 4 × 4 blocks, and later Redundant Discrete Wavelet Transform (RDWT) and Schur applied for obtaining transform coefficients. The highly correlated pixels in the first column are collected from each 4 × 4 block and are amended by blindly concealing the watermark using quantization index modulation. These quantized values are amended with the randomized matrix and quantization factor randomly selected from the firefly algorithm to obtain the best performance in terms of robustness without compromising the quality of the image. Experimental results show that the proposed scheme maintains improved imperceptibility, and the watermark can still extract from a seriously distorted image.
Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision

The deep learning methods supervised by annotating different regions of histopathology images (patch-level labels) have achieved promising outcomes in assisting pathologic diagnosis. However, most clinical data only contains label information for the whole slide image (WSI-level labels), so the methods supervised by WSI-level labels are more necessary than the ones supervised by patch-level labels. Additionally, various methods supervised by WSI-level labels ignore the contextual relations among patches extracted from a WSI, making incorrect predictions for some patches in a WSI and further misclassifying the WSI. In this paper, we propose to utilize an interpretable dual encoder network with a context-capturing RNN module to capture the contextual relations among all patches extracted from a WSI. Besides, we propose to utilize a feature attention module to weigh the importance of each patch automatically. More importantly, visualization of weight for each patch in a WSI demonstrates that our approach matches the concerns of pathologists. Furthermore, extensive experiments demonstrate the superiority of the interpretable dual encoder network.
Modelling bi-static uncertainties in sequential Monte Carlo with the GLMB model

Bi-static sensing, where the transmitter and receiver of sensors are separately located, underlies a wide range of collaborative sensing systems. Bi-static detections generally feature a signal time-of-flight (ToF) and an angle-of-arrival (AoA). The current practice in multi-object tracking uses the bi-static geometry to map these pairs onto a selected coordinate frame and filter the mapped detections with a noisy range-bearing (i.e., a mono-static) sensor model. However, the uncertainties in ToFAoA pairs are not equivalently captured by this model, and the sensing geometry may result in significant degradation of the modelling accuracy. We introduce bi-static likelihood and false alarm models together with Monte Carlo (MC) computational methods to accurately capture the uncertainties involved and use them within Bayesian filtering. We demonstrate the efficacy of our proposed model in simulations with multiple objects using a sequential MC version of the generalised labelled multi-Bernoulli (GLMB) track filter. We compare the filtering performance with the conventional approximation mentioned above.
A comparative study of deep learning approaches for Chinese Sentence Classification

One of the most commonly used natural language processing technologies is text classification. Spam detection, news text classification, information retrieval, emotion analysis, and intention judgment, among other applications, are all popular text classification applications [25]. Text classification is the process of assigning pre-defined class labels to text documents in order to shape semantic classes. Engineering, medical science, life science, social sciences and humanities, marketing, and government are only a few of the real-world applications. Machine learning and deep learning algorithms have recently become common and efficient methods for dealing with text classification problems involving labeled data [26]. The primary goal of text classification is to automatically assign texts to pre-defined categories based on their content. In this study, we will conduct a comparative study of the accuracies of different deep learning methods that include Bidirectional Encoder Representations from Transformers (BERT), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Region-based convolutional neural networks and compare the effectiveness of these deep-learning approaches in classifying Chinese news title text using the THUCNews dataset.
Constructions of Beyond-Birthday Secure PRFs from Random Permutations, Revisited

In CRYPTO 2019, Chen et al. showed how to construct pseudorandom functions (PRFs) from random permutations (RPs), and they gave one beyond-birthday secure construction from sum of Even-Mansour, namely SoEM22 in the single-key setting. In this paper, we improve their work by proving the multi-key security of SoEM22, and further tweaking SoEM22 but still preserving beyond birthday bound (BBB) security. Furthermore, we use only one random permutation to construct parallelizable and succinct beyond-birthday secure PRFs in the multi-key setting, and then tweak this new construction. Moreover, with a slight modification of our constructions of tweakable PRFs, two parallelizable nonce based MACs for variable length messages are obtained.
Weighted ensemble with angular feature learning for facial expression recognition

Ensemble learning using a set of deep convolutional neural networks (DCNNs) as weak classifiers has become a powerful tool for face expression. Nevertheless, training a DCNNS-based ensemble is not only time consuming but also gives rise to high redundancy due to the nature of DCNNs. In this paper, a novel DCNNs-based ensemble method, named weighted ensemble with angular feature learning (WDEA), is proposed to improve the computational efficiency and diversity of the ensemble. Specifically, the proposed ensemble consists of four parts including input layer, trunk layers, diversity layers and loss fusion. Among them, the trunk layers which are used to extract the local features of face images are shared by diversity layers such that the lower-level redundancy can be largely reduced. The independent branches enable the diversity of the ensemble. Rather than the traditional softmax loss, the angular softmax loss is employed to extract more discriminant deep feature representation. Moreover, a novel weighting technique is proposed to enhance the diversity of the ensemble. Extensive experiments were performed on CK+ and AffectNet. Experimental results demonstrate that the proposed WDEA outperforms existing ensemble learning methods on the recogntion rate and computational efficiency.
Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the whole image with required scale. In MDBC method, the suitable box size selection and Under Counting Shifting rule computation handles over counting problem. An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification. The extracted features are fed to K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) categorizes the mammograms into normal, benign, and malignant. The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%, whereas the existing FD, GLCM, Texture and Shape feature method has 91% accuracy.
User Authentication in Cipher Domain for a Cloud Based Electronic Health Record Access System

User authentication is an important aspect of Electronic Health Record (EHR) access system due to the sensitive nature of EHR information. A new method of user authentication, based on integer matrix keys is presented. The authentication process takes place in the cipher domain without decrypting the user query. In the proposed scheme, a Fog Computing Unit (FCU) that receives the encrypted query from a user verifies the access privilege of the user and blocks the access if the user is unauthorized or has been revoked. Our method is especially suited when the overall user base has a large number of revoked users who may attempt to access the EHR data in sly.
THC: Practical and Cost-Effective Verification of Delegated Computation

Homomorphic cryptography is used when computations are delegated to an untrusted third-party. However, there is a discrepancy between the untrustworthiness of the third-party and the silent assumption that it will perform the expected computations on the encrypted data. This may raise serious privacy concerns, for example when homomorphic cryptography is used to outsource resource-greedy computations on personal data (e.g., from an IoT device to the cloud). In this paper we show how to cost-effectively verify that the delegated computation corresponds to the expected sequence of operations, thus drastically reducing the necessary level of trust in the third-party. Our approach is based on the well-known modular extension scheme: it is transparent for the third-party and it is not tied to a particular homomorphic cryptosystem nor depends on newly introduced (and thus less-studied) cryptographic constructions. We provide a proof-of-concept implementation, THC (for trustable homomorphic computation), which we use to perform security and performance analyses. We then demonstrate its practical usability, in the case of a toy electronic voting system.
Proposing a Semantic Analysis based Sanskrit Compiler by mapping Sanskrit's linguistic features with Compiler phases

The requirement for a Natural Language to be used as a programming language has been discussed for many years, yet we still are finding answers to the same question. Most of the current work in this area is limited to English and a few other oriental languages, as a result of which we are susceptible to ambiguity in its interpretation due to the weak grammatical structure of these languages. Although there are thousands of languages spoken worldwide and we haven't tested all of them, we still can get a strong contender for this argument. To answer this question, this paper proposes the use of Sanskrit language as a programming language which has the most substantial ancient roots and offers a robust grammatical foundation, which will help us achieve a natural language to be used as a computational language. This paper deals with an approach to demonstrate how Sanskrit could be a solid contender to be a natural language used as a programming language by mapping it to the compiler's phases.
Exploring learning outcomes of science experiments using physical instrument and substances assisted by digital entities

Physical experiments on real-world systems are proposed as a means to raise learning outcomes and Arduino microcontroller is highlighted as an appropriate tool to perform them. However, experimentation on a real system may encounter various barriers. Therefore, a learning environment was developed in order to perform Arduino experiments on digital entities when the real ones are inaccessible. That was consisted of an Arduino-based pH meter from which measurements were transmitted to three different digital entities: a shoal of goldfish, an ancient Greek temple and an ancient Greek statue. In order to evaluate the learning outcomes of teaching through physical experiments on digital entities, a pre- to post-comparison was conducted on three Greek Junior High Schools. In each school, students were divided into two subgroups. The one group was taught about Acids and Bases through physical Arduino experiments by demonstration, using real instruments and substances, with a simultaneous observation of their effects on the digital entities in real-time, while the other group was taught through the same Arduino experiments by demonstration without the use of the digital entities. The results have demonstrated that students of the former group exhibited greater learning gains regarding Declarative Knowledge than those of the latter one.
Fine-grained image classification method based on generating adversarial networks with SIFT texture input

A fine-grained image classification method based on generating adversarial networks with SIFT (Scale Invariant Feature Transform) texture input is proposed to improve the recognition ratio of fine-grained image classification by deep learning. For the phenomenon of data sets that require a large amount of labeled information for strong supervised learning, active learning capabilities of generative and adversarial networks and excellent image modeling capabilities for target classification images are used to achieve active learning of image features. Then the difficulty of data set construction and the computational complexity are reduced, and the disturbance to the network that may be caused by manually set labeled boxes is lessened. The input method of generating the adversarial network to is fixed to balance the authenticity and diversity of the generated samples. The idea of image restoration is considered. The random input method of the generative adversarial network that combines image feature points and random noise to is used to reduce the training difficulty of the generative and adversarial network. Experiments results show that our method outperformances the current deep learning methods in fine-grained image classification.
Integrated smart farming system in developing potential products of the village

The village as an area that rests on local potential always needs development in product innovation and a sustainable market. In the world of agriculture, digital technology has been developing from nurseries to post-harvest. Included in the integrated agricultural system from upstream to downstream. Integrated smart farming system as an agricultural system that combines all components needed in agriculture (include the digitalization) were attracting young farmer’s interest. This paper aims to describe an integrated agriculture model in Bali. The research was conducted by a descriptive analysis method of existing conditions. The results showed there was an upstream stage of agricultural development carried out through technology for seeding and breeding to produce more quality seedlings. The process involves young farmers who act as an organization that bridges farmers through funding for farmers, quality control, stock management, and farmer organizing. The downstream stage focuses on post-harvest processing, so farmers can sell their crops to market to meet retail and export needs. As the conclusion, the development of potential products through the use of the Integrated Smart Farming System can provide economic added value and encourage the economic growth of villages.
Image Web Crawler Towards Machine Learning

In today’s world, we have tremendous amount of data in the world of Web. This data is present in the various places or Web sites in the forms of hyperlink and URL. Web crawler is used as full text search engine which helps to navigate the Web text. They can also be used for the image/media search. So, in this work, we have developed a Web crawler which extracts data and takes care of the IP address of host and produces crawling results; all the top ranked results will be displayed to the user.
Vote Secrecy and Voter Feedback in Remote Voting – Can We Have Both?

The principle of secrecy is one of the most important tools to guarantee a voting process without undue influence to the voter. However, the concepts of the secret ballot and secret vote have strong ties to voting in a controlled environment in the polling station, and remote voting methods like postal voting or Internet voting need to employ special measures and approaches to achieve similar results. At the same time, limited options of observing the tallying process remotely potentially undermines the trust in remote voting. This paper looks at possible ways of giving the voter some feedback and assurance in the integrity of their vote, at the same time adhering to the freedom of voting principle. The Estonian Internet voting system is used as a model case for evaluation of a possible feedback channel architecture.
Research on CNN-Based Malicious Traffic Identification Method

Today's Internet environment is full of all kinds of normal and malicious traffic, how to identify the feature categories of malicious traffic plays a crucial role in network management and security. With the rapid growth of modern Internet traffic, classical machine learning methods are limited by efficiency and functionality and are no longer sufficient to deal with large and complex network traffic. Thus, we propose a convolutional neural network-based traffic identification method. Firstly, the full traffic dataset for testing is preprocessed with one-hot coding. Secondly, a convolutional neural network model is built for testing, a softmax classifier is used to detect and classify the normal traffic and various malicious traffic, and finally, obtain the classification results of each traffic type. The experimental tests on the publicly available dataset show that the detection accuracy of malicious traffic is close to 99%, and the loss value is less than 1%.
한국어교육능력검정시험의 한국문화 영역에 대한 비판적 고찰

The purpose of this study was to analyzed the validity of the past questions in the “Korean Culture” area of the Korean Language Education Test in to guidelines for the test(question’s ratio and content aspects). And examined whether the questions were appropriate for evaluating Korean teachers cultural teaching competency. As a result of review, it was found that the large deviation in the ratio of the questions in each subject and there were many questions that did not meet the intention of the test. Also, most of the contents did not reflect evaluation of the ‘intercultural perspective’, which is the purpose of cultural education in the Korean language curriculum. Accordingly, this study proposes as follows. First, the Korean culture area showed different aspects from the standard of the examination in both question’s ratio and content aspects and was still biased toward traditional culture, which has not reflected the current trend in the Korean language education field. Therefore, proposed to reduce the area of Korean culture, which was about only simple knowledge questions, and proposed reconsideration of current subject classification. Second, existing multiple-choice questions hard to check identify the intercultural perspective, reasoning ability, and understanding of multiculture of Pre-service Teachers. Therefore, emphasized the need to reform the question type. Third, suggested to develop items that can evaluate the affective competence and functional competence among Korean culture teaching competencies. (Pusan National University)
Binary Multi-view Image Re-ranking

Conventional subspace-based multi-view re-ranking methods essentially handle the Euclidean feature space transformation and tend to be inefficient when dealing with large-scale data, since the cost of computing the similarity between the query item and the database item is prohibitively high. Inspired by Hashing technique, in this paper, we propose an efficient binary multi-view image re-ranking strategy in which the original multi-view features are projected onto a compact Hamming subspace. With the intrinsic structure of the original multi-view Euclidean feature space maintained, the resulting binary codes are consistent with the original multi-view features in similarity measure. Furthermore, coupled with the discriminative learning mechanism, our method leads to compact binary codes with sufficient discriminating power for accurate image re-ranking. Experiments on public benchmarks reveal that our method achieves competitive retrieval performance comparable to the state-of-the-art and enjoys excellent scalability in large-scale scenario.
An Implementation of Stereoscopic Visualization of Medical Images Based on VTK

With the continuous development of medical technology and computer technology, computer technology into medicine has become the current trend of development of social science. Combining computer vision with medical images for proper processing can make medical images more clearly presented to doctors and researchers, providing important scientific basis for its research. Through continuous research on 3D reconstruction technology and binocular stereo vision technology, we will combine the two to realize the further visualization toolkit (VTK) images after medical 3D reconstruction based on the analysis of the feasibility of the stereo imaging technology. Thereby, the visual effect of the image is effectively enhanced.