Reviewerly/exHarmony huggingface dataset:
Graph-based Kullback-Leibler Divergence Minimization for Unsupervised Feature Selection

We live in an era of big data, in which feature selection technology is getting more and more attention. Feature selection technology is one of the important methods to reduce the dimension of data. It can select some useful features for learning tasks. The traditional feature selection methods mainly select the useful features by the scores of the features under a certain standard. However, the performance of these methods are less satisfactory in many cases because they ignore the correlation between features. For this article, we present a new unsupervised method by minimizing the Kullback-Leibler(KL) divergence based on graph matching. Firstly, we extract manifold structures from all features of the original data space by using non-negative Local Linear Embedding(NNLLE). Then, we extract manifold structure of each feature by using non-negative local linear embedding (NNLLE). We assess the importance of every feature by minimizing the KL-divergence between the graphs using all features and weighted linear combination of base graphs on each individual feature. At the same time, a global optimization algorithm based on proximal gradient descent framework is proposed. Experiments show that the proposed method is better than many existing unsupervised methods.
Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning

Handwritten recognition is a difficult task. The conventional technique relies on the character segmentation, feature extraction, and classification process. The segmentation is a tremendous challenge when there are variation of character patterns and alignments in a sentence, such as linking segments between characters in the Thai language. Promising segmentation outcome is favorable but not applicable in most applications. This work proposes a methodology for Thai handwritten recognition by applying Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The first step is text localization before feeding to the network. CNN extracts the abstract features before they are fed to RNN to learn the sequence of characters in an image. The optimization is performed with an integrated Connectionist Temporal Classification (CTC) module (to arrange the final results). A standard Thai handwritten dataset (BEST2019) and more collection are used in this study. for training and test sets. The experimental results show that the integration of CNN and RNN provides promising results of the test set with a Character Error Rate (CER) of 1.58%. For testing with the seen and unseen dataset of the final round of BEST2019 competition, the CER is at 24.53%.
SOREL: Efficient and Secure ORE-based Range Query over Outsourced Data

Outsourcing data to the cloud has become popular due to the big data challenges. However, security concerns compel the outsourced data to be encrypted before sending them to the cloud, which lowers their utility and efficiency. The range query plays a significant role in common queries. Consequently, how to efficiently support the range query over encrypted data has become an important challenge. Previously reported schemes either achieve efficiency only in a specific operation or have severe defects in scalability. To address these limitations, we propose a framework, called SOREL, which simultaneously considers security, efficiency and scalability. Specifically, we first propose a new efficient and Secure Order Revealing Encryption (SORE) scheme, which is more secure than bit-based ORE schemes. Then, by employing the proposed SORE scheme, we design a novel index within our framework SOREL to support efficient updating and query operations over encrypted data. Detailed security analysis shows that our SOREL achieves the desirable security requirements. Additionally, results from extensive evaluations indicate that i) SORE outperforms other alternative schemes by at least 8; and ii) SOREL is at least 3faster than the comparative schemes with range query operation in the best case while ensuring the competitiveness with insertion operation.
Authenticity Verification for Dynamic Social Data Outsourcing

As with the maturation and diversification of social services, social data outsourcing has become pervasive. In this context, online social networks outsource their real-time social data to a social data provider, who responses to query requests issued from data consumers. However, dishonest or malicious social data providers might tamper with collected social data by adding fake data and deleting/modifying raw data, and further return inauthentic query results to data consumers. In this article, we make the first attempt to study the problem of authenticity verification for dynamic social data. To this end, we first propose a novel authenticity verification framework, and then, propose a self-balancing hash tree scheme to tackle challenges brought on by the vast scale and dynamics of social data. To maximize the search efficiency for the self-balancing hash tree, we introduce a balance factor to define an unbalanced node as its balance factor is not chosen from a predetermined set. Furthermore, we propose three algorithms to balance unbalanced nodes in the self-balancing hash tree. Rigorous theoretical analyses prove that our framework can determinately detect any fake query results returned by data consumers. Experimental results built on a real Twitter dataset show that our scheme is efficient and effective.
Human Interaction and Networking Transitions System (HINTS) for Social User Analytics and Modeling of Offline Team Group Interaction Information

demonstration Human Interaction and Networking Transitions System (HINTS) for Social User Analytics and Modeling of Offline Team Group Interaction Information Share on Authors: Ngoc Doan Tilburg University Tilburg UniversityView Profile , Dan Hudson Tilburg University, The Netherlands Tilburg University, The NetherlandsView Profile , Travis J. Wiltshire Tilburg University, The Netherlands Tilburg University, The NetherlandsView Profile , Philia Lijdsman Bricklayers V.O.F, The Netherlands Bricklayers V.O.F, The NetherlandsView Profile , Stijn Wever Bricklayers V.O.F, The Netherlands Bricklayers V.O.F, The NetherlandsView Profile , Martin Atzmueller Osnabrück University, Germany Osnabrück University, GermanyView Profile Authors Info & Claims UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationJune 2021 Pages 71–74https://doi.org/10.1145/3450614.3461689Online:22 June 2021Publication History 0citation33DownloadsMetricsTotal Citations0Total Downloads33Last 12 Months33Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development of a Neural Network Algorithm to Detect Soldier Load from Environmental Speech

The objective was to develop a model based on speech input that can identify when team members need adaptive autonomous assistance. Human teams often adjust their behavior to work cohesively and effectively as a team. Similarly, it is beneficial for autonomous agents to be able to adaptively adjust to team needs. We constructed a convolutional recurrent neural network model based on those developed for the recognition of emotion from speech. Audio recordings from a recent field exercise were used to train and validate the model. These data were labeled according to whether the speech occurred during an engagement (engaged, neutral, or no-speech). The model classified more than 99% of the training, validation, and test sets correctly. This information will allow us to design systems in which autonomous agents can prioritize, assist with, and take autonomous control of tasks.
Virtual Reality Utilization as a Character– building of Children in Waste Problems

Abstract This paper aims to explain the benefits of Virtual Reality (VR) technology as an alternative solution to limited facilities in building children’s awareness of waste problems. The method used is a descriptive method that explains how Indonesians treat waste and validates the effectiveness of using VR to develop an awareness of waste. The results showed that children are now more interested in using VR in education to care about the earth’s environment. This can happen because it is interactive, but the immersive effect impacts the effectiveness of the learning experience felt by children. The use of Virtual Reality is the main solution with the latest technology in overcoming waste problems in Indonesia by building character since childhood.
So What’s the Plan? Mining Strategic Planning Documents

In this paper we present a corpus of Russian strategic planning documents, RuREBus. This project is grounded both from language technology and e-government perspectives. Not only new language sources and tools are being developed, but also their applications to e-government research. We demonstrate the pipeline for creating a text corpus from scratch. First, the annotation schema is designed. Next texts are marked up using human-in-the-loop strategy, so that preliminary annotations are derived from a machine learning model and are manually corrected. The amount of annotated texts is large enough to showcase what insights can be gained from RuREBus.
Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation

The benefits of open data were realised worldwide since the past decades, and the efforts to move more data under the license of open data intensified. There was a steep rise of open data in government repositories. In our study, we point out that privacy is one of the consistent and leading barriers among others. Strong privacy laws restrict data owners from opening the data freely. In this paper, we attempted to study the applied solutions and to the best of our knowledge, we found that anonymity-preserving algorithms did a substantial job to protect privacy in the release of the structured microdata. Such anonymity-preserving algorithms argue and compete in objectivethat not only could the released anonymized data preserve privacy but also the anonymized data preserve the required level of quality. K-anonymity algorithm was the foundation of many of its successor algorithms of all privacy-preserving algorithms. l-diversity claims to add another dimension of privacy protection. Both these algorithms used together are known to provide a good balance between privacy and quality control of the dataset as a whole entity. In this research, we have used the K-anonymity algorithm and compared the results with the addon of l-diversity. We discussed the gap and reported the benefits and loss with various combinations of K and l values, taken in combination with released data quality from an analyst’s perspective. We first used dummy fictitious data to explain the general expectations and then concluded the contrast in the findings with the real data from the food technology domain. The work contradicts the general assumptions with a specific set of evaluation parameters for data quality assessment. Additionally, it is intended to argue in favour of pushing for research contributions in the field of anonymity preservation and intensify the effort for major trends of research, considering its importance and potential to benefit people.
Feedback-based Graph Convolutional Networks

Applying graph convolutional networks to efficient nodes classification is still a hot spot in the research area of artificial intelligence. In order to fully exploit the neuron information in the graph model, we propose a feedback-based graph neural network, where the graph network takes the information of the known labels and the output of the model as the targets to optimize the network. Then, a top-down manner is adopted to select neurons associated with the target and remove the ones with negative contribution weights through a gating mechanism. The experimental results on benchmark datasets show that our proposed networks remarkably outperform the comparison methods.
A Social Media Mining Approach for Monitoring Customer Complaints in the Early Stage of Product Launch

This study suggests a social media mining approach for monitoring critical complaints in the early stage of product launch via word embedding, clustering and sentiment analysis. This approach selectively uses social media product reviews created in the beginning stage of product launch. Word embedding and clustering are used to identify customer complaint factors from reviews. Next, sentence-level sentiment analysis is conducted based on the understanding of review writing pattern, thereby calculating urgency and severity in a quantitative manner. Finally, a customer-stated complaint portfolio map is developed, showing critical complaint factors. In the case study of smartphone ‘Galaxy S10’, the critical complaint factors related to Samsung pay connection error, and fingerprint recognition are identified, which have been rapidly improved since the product launch, indeed. We expect that this approach contributes to identifying critical customer complaints in the early stage, in addition to enabling the prompt response to customer complaints.
Face Detection with YOLO on Edge

Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learning methods require huge computational power such as graphical processing units. These computational requirements make using such methods unsuitable for deployment on platforms with limited resources, such as edge devices. In this paper, we present an experimental framework to reduce the model’s size systematically, aiming at obtaining a small-size model suitable for deployment in a resource-limited environment. This was achieved by systematic layer removal and filter resizing. Extensive experiments were carried out using the “You Only Look Once” model (YOLO v3-tiny). For evaluation purposes, we used two public datasets to assess the impact of the model’s size reduction on a common computer vision task such as face detection. Results show clearly that, a significant reduction in the model’s size, has a very marginal impact on the overall model’s performance. These results open new directions towards further investigation and research to accelerate the use of deep learning models on edge-devices.
Train the Neural Network by Abstract Images

Abstract Like the textbook for students’ learning, the training data plays a significant role in the network’s training. In most cases, people intend to use big-data to train the network, which leads to two problems. Firstly, the knowledge learned by the network is out of control. Secondly, the space occupation of big-data is huge. In this paper, we use the concepts-based knowledge visualization [33] to visualize the knowledge learned by the model. Based on the observation results and information theory, we make three conjectures about the key information provided by the dataset. Finally, we use experiments to prove that the artificial abstracted data can be used in networks’ training, which can solve the problem mentioned above. The experiment is designed based on Mask-RCNN, which is used to detect and classify three typical human poses on the construction site.
Fast and Efficient Parallel Alignment Model for Aligning both Long and Short Sentences

Recently, demand for fast and efficient translation system been widely seen. However, translation model are dependent parallel corpora. However, it is challenging to obtain large parallel corpora for resource starved language such as Kannada-Telugu pair. The existing Giza++ based word alignment and Moses phrase based alignment model are efficient for aligning only short sentences. However, for longer sentence the accuracy of model degrades. For performing alignment for longer sentences, neural based alignment has been presented in recent times. However, these models are trained using fixed vector length. Thus, induces memory and training overhead. For overcoming research challenges, this work presents a parallel alignment model using recurrent neural network (RNN). Further, to utilize memory efficiently and minimize training time parallel execution of RNN under GPU is considered. For improving alignment accuracy presented a cost function by combing statistical and neural based alignment method. Experiment are conducted to evaluate the proposed alignment model in terms of accuracy, Word alignment error (WAE), memory utilization, and computation time.
Research and Design of Wearable Intelligent Umbrella to Help People in Rainy Days

Abstract Because the people with upper limb disability will encounter travel problems in rainy days, researchers designed and made wearable intelligent umbrellas, so as to improve the rain-shield efficiency of the disabled when traveling in rainy days. People with upper limb disabilities will suffer from poor balance and low rain-shield efficiency when holding umbrellas with one hand. This umbrella can be used as a backpack for people with upper limb disabilities. When traveling in rainy days, the umbrella will automatically adjust the orientation of the umbrella surface according to the wind speed sensor on the other side of the backpack to improve the rain-shield efficiency. The design of umbrella is based on physical experiments, and the conclusion of rain-shield efficiency is also drawn from experiments, which ensures the strong scientific nature of the research. At present, the conclusion of the experiment verifies that umbrellas can be used in daily life and can run normally under turning, walking, turning and other actions. This study can improve the travel convenience of disabled people and improve their social identity.
Recursive MaxSquare: Cache-friendly, Parallel, Scalable in situ Rectangular Matrix Transposition

An in situ rectangular matrix transposition algorithm is presented based on recursively partitioning an original rectangular matrix into a maximum size square matrix and a remaining rectangular sub-matrix. To transpose the maximum size square sub-matrix, a novel cache-friendly, parallel (multithreaded) and scalable in-place square matrix transposition procedure is proposed: it requires a total of Θ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /2) simple memory swaps, a single element temporary storage per thread, and does not make use of complex index arithmetic in the main transposition loop. Recursion is used to transpose the remaining rectangular sub-matrix. Dubbed Recursive MaxSquare, the novel proposed rectangular matrix in-place transposition algorithm uses a generalization of the perfect shuffle/unshuffle data permutation to stitch together the recursively transposed square matrices. The shuffle/unshuffle permutations are shown to be efficiently decomposed using basic vector/segment swaps, exchanges and/or cyclic shifts (rotations). A balanced parallel cycles-based transposition is also proposed for comparison.
활성화 함수에 따른 유출량 산정 인공신경망 모형의 성능 비교

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN’s structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.
Comparative Analisys of Texts by F. M. Dostoevsky and Other Authors Using Decision Trees

The paper is devoted to the research of the publicistic style of F. M. Dostoevsky compared to other authors (M. M. Dostoevsky, N. N. Strakhov, I. N. Shill, A. A. Golovachev, A. Grigoriev, Y. P. Polonsky) based on articles in the magazines "Time" and "Epoch" in the period 1861-1865 in pre-revolutionary spelling. Using the distribution of parts of speech and other features of text fragments (the length of such non-intersecting fragments is 1000 words), decision trees with different classification parameters and quality were constructed. Decision trees are easily interpreted as opposed to neural networks. The analysis of these graph-theoretic models has shown good results for works in Russian. It allows the researcher to set and solve various tasks of the text attribution.
A Comparative Study of Diagnostic Methods of Malaria - Microscopy Versus Rapid Diagnostic Test Kits in a Tertiary Care Hospital, Rajasthan

BACKGROUND Malaria is one of the most prevalent parasitic diseases in the world including India. Majority of malarial cases are found in countries where cost-effectiveness, diagnostic test performance ease, and trained personnel, are the most important considerations. We wanted to compare the two methods of rapid diagnostic tests (RDTs) and microscopy in the diagnosis of malaria parasite infection. METHODS We studied 2,723 blood samples of patients who presented with signs and symptoms of malaria from out patient department (OPD) and various wards of Pacific Institute of Medical Science, Udaipur, Rajasthan, from Oct 2018 to Oct 2019. All samples obtained were first tested by RDTs and then the same samples were used to make peripheral blood film (PBF) for microscopy. RESULTS During the 1-year period, rapid card test method showed 178 positives for malarial parasite. Of these, 64 cases were positive for P. vivax and 108 cases for P. falciparum. Peripheral blood smear method showed 160 cases positive for malarial parasite. Of these, 55 cases were positive for P. vivax and 99 cases for P. falciparum and 6 cases of co-infection. PBF failed to detect 9 cases of P. vivax and 9 cases of P. falciparum which were positive by rapid card test. So, 18 cases reported PBF negative. Among these patients, there were 83 females and 95 males. Most affected age group was 16 – 30 years followed by 31 – 45 years. Maximum samples were from the month of October 19 with a positivity rate 35.4 %. CONCLUSIONS RDTs based on malaria antigen (whole blood) method is as specific and more sensitive than microscopy (which is being considered as the gold standard method). Peripheral blood smear method still remains superior for accurate species differentiation, quantitation of parasite and maintaining a permanent record. KEYWORDS Microscopy, Malaria, Rapid Diagnostic Tests