Reviewerly/exHarmony huggingface dataset:
Designing library-based research data management services from bottom-up

Research data management (RDM) services in a research institution require the broad collaboration of multiple administration and service entities, including research office, IT office, and library. Cross-unit RDM support coordinated at the institutional level (top-down) is a common model in many pioneering universities, especially those in Europe and the United Kingdom. For institutions in which such top-down RDM initiatives are not yet present, libraries can play a pivotal role in bringing changes from bottom-up. Libraries have the expertise in many areas that are instrumental in developing RDM support, such as scholarly publishing, metadata standards, repository infrastructure, and collaborative professional culture. The chapter portraits a design approach for libraries to build RDM services grounded on locating community leverage points and utilizing existing organizational capacity. It explores ways to adapt local strategies in policies, tools, infrastructure, and outreach with references from global practices.
Independent document ranking for E-learning using semantic-based document term classification

Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods.
Branch Information Correction Network for Human Pose Estimation

The main task of human keypoint detection is to detect the position of human bone joints in pictures or videos. In the branch-based network, key points are classified according to different properties, and each branch of the network is responsible for the prediction of a certain set of keypoints. Compared with the direct prediction of the network of all the key points, the advantage of this method is that it considers the structural constraints on the human body and the internal relationship between the key points. Based on the branch network, this paper studies the information-sharing relationship and the negative transfer relationship between branches. It proposes a new branch information correction network to make full use of the complementary information on branches. The experiment proves that the method proposed in this paper can further improve the accuracy of the keypoint prediction of the human body, and can correct some key points which are easily affected by the environment.
Neural Network and Pixel Position Shuffling-Based Digital Image Watermarking

This paper proposes a new optimized watermarking method by incorporating the concept of entropy, pixel position shuffling, and neural networks to enhance the imperceptibility, security, and resistance against the attacks. Firstly, the proposed watermarking system preprocesses the host image by implementing discrete wavelet transform and extracts the low-frequency components to gain robustness. The extracted low-frequency components are further decomposed into blocks and then transformed into a discrete cosine transform coefficients for inserting the watermark information. The watermark bits are only implanted into the selected discrete cosine transform DCT blocks, and DCT blocks are selected through entropy values. The selection of DCT blocks with entropy helps in strengthening the imperceptibility of the proposed system. The watermark extraction process is optimized by employing the neural network characteristics and enhances the résistance against the attacks. The traits of pixel position shuffling are also utilized in proposed watermark embedding process for ensuring the security of proposed scheme.
Pengaruh Talent Management Dan Knowledge Management Terhadap Kinerja Karyawan Pt Afiat Industri Farmasi Cimahi

AbstrakPenelitian ini dilakukan untuk mengetahui Pengaruh Talent Management Dan Knowledge Management TerhadapKinerja Karyawan Pt Afiat Industri Farmasi Cimahi. Penelitian ini dilakukan dengan menggunakan metodekuantitatif. Jenis penelitian ini adalah deskriptif. Teknik pengambilan sampel menggunakan metode sampling jenuhkarena pada menggunakan semua populasi untuk dijadikan responden sebanyak 50 orang karyawan. Teknik analisisdata yang digunakan adalah teknik analisis deskriptif serta menggunakan metode uji f. Hasil pengujian hipotesissimultan dengan uji f, diperoleh hasil fhitung sebesar 46.353 dengan signifikansi (pvalue) sebesar 0.000, selanjutnyadibandingkan dengan ftabel dengan probabilitas 5%, df1=1 dan df2=50-2 =48, maka diperoleh hasil ftabel sebesar3.191, dikarenakan hasil fhitung>ftabel (46.353>3.191) dan signifikansi (0.000 ftabel (46,353> 3,191) andsignificance (0,000 <0.05), then Ho is rejected and Ha is accepted, so it can be stated that Talent Management andKnowledge Management together have a significant effect on Employee Performance at PT. Afiat PharmaceuticalIndustry. Based on these results, it is suggested that the company PT Afiat Pharmaceutical Industry needs to improveand pay attention to compensation and retention systems in the company. PT The Pharmacy Industry Departmentneeds to increase its knowledge in technology, work procedures, and personal knowledge of employees, and the needfor employees to continue to develop themselves in terms of knowledge especially knowledge in the field of work. Inimproving performance within employees, it is expected that employees will be involved in activities undertaken bythe company, so that it will increase the emotional closeness of employees with the company and will make employeesfeel valued and get attention from the company so that employees will have a high sense of morale and will havemore contribution to the company.Keywords: Knowledge management, talent management, employee performance
Automatic Feature Isolation in Network Protocol Software Implementations

Common vulnerabilities and exposures (CVEs) usually exploit design or implementation flaws of specific features in widely used network protocols. Feature isolation as a general protocol customization practice is shown to be highly promising to reduce attack surfaces in these protocols. In this work-in-progress paper, we present two program analysis based methods targeting different feature granularity to automatically identify and isolate unnecessary features in a software protocol implementation. In addition, we develop a semantic reconstruction mechanism to enforce user-specified feature access control policies. Preliminary case studies confirm that our proposed techniques can be effectively applied on real-world protocol vulnerabilities.
Lattice-Based Linearly Homomorphic Signature Scheme over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math>

In this paper, we design a new lattice-based linearly homomorphic signature scheme over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> . The existing schemes are all constructed based on hash-and-sign lattice-based signature framework, where the implementation of preimage sampling function is Gaussian sampling, and the use of trapdoor basis needs a larger dimension <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>m</mi> <mo>≥</mo> <mn>5</mn> <mi>n</mi> <mtext> </mtext> <mi mathvariant="normal">log</mi> <mtext> </mtext> <mi>q</mi> </mrow> </mfenced> </math> . Hence, they cannot resist potential side-channel attacks and have larger sizes of public key and signature. Under Fiat–Shamir with aborting signature framework and general SIS problem restricted condition <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>m</mi> <mo>≥</mo> <mi>n</mi> <mtext> </mtext> <mi mathvariant="normal">log</mi> <mtext> </mtext> <mi>q</mi> </mrow> </mfenced> </math> , we use uniform sampling of filtering technology to design the scheme, and then, our scheme has a smaller public key size and signature size than the existing schemes and it can resist side-channel attacks.
EPUR: An Efficient Parallel Update System over Large-Scale RDF Data

RDF is a standard model for data interchange on the web and is widely adopted for graph data management. With the explosive growth of RDF data, how to process RDF data incrementally and maximize the parallelism of RDF systems has become a challenging problem. The existing RDF data management researches mainly focus on parallel query, and rarely pay attention to the optimization of data storage and update. Also, the conventional parallel models for parallel query optimizations are not suitable for data update. Therefore, we propose a new design of an efficient parallel update system which is novel in three aspects. Firstly, the proposed design presents a new storage structure of RDF data and two kinds of indexes, which facilitates parallel processing. Secondly, the new design provides a general parallel task execution framework to maximize the parallelism of the system. Last but not least, parallel update operations are developed to handle incremental RDF data. Based on the innovations above, we implement an efficient parallel update system (EPUR). Extensive experiments show that EPUR outperforms RDF-3X, Virtuoso, PostgreSQL and achieves good scalability on the number of threads.
Single Image Super Resolution Using Discrete Cosine Transform Driven Regression Tree

Single image super resolution restoration is a process for solving the ill-posed problem of achieving a high resolution image from one low resolution image. This process is considered an active point of research due to the increased demands for high resolution imagery in many applications. This paper presents a proposed methodology for single image super resolution based on replacing the traditional discrete cosine transform basis. These bases are replaced by learned filters that can transfer the low resolution image from the spatial domain to high resolution coefficients in the discrete cosine domain. Accordingly, these estimated filters can then be applied to produce a high resolution image in the spatial domain through the standard inverse discrete cosine transform process. To learn these transformation filters two modifications in the decision tree algorithm are introduced to adapt the tree performance for the super resolution task. This is performed such that the node splitting decision depends on external features that minimize the regression errors. Experimental results demonstrate that the performance of the proposed algorithm is superior to that of the traditional interpolation single image super resolution method.
Frequent Semantic Trajectory Sequence Pattern Mining in Location-Based Social Networks

At present, more and more researchers have focused on the study of the frequent trajectory sequence pattern mining in location-based social network (LBSN), in which the trajectories of contributing frequent patterns in users’ trajectory database must have same or similar the location coordinates and conform to the semantics and time constraints. In this paper, we focus on the study of users’ daily frequent mobile pattern. Excessive limitations on location information may limit the results of mining users’ frequent mobile pattern. Therefore, based on the frequent trajectory sequence pattern mining in LBSNs, we first define a new frequent semantic trajectory sequence pattern mining (FSTS-PM) problem that focuses on the study of mining users’ frequent mobile pattern. FSTS-PM problem does not consider the location coordinates of the trajectory points, but uses the distance and time constraints among the trajectory points in a trajectory sequence to optimize the user’s frequent mobile pattern mining results. Then, we propose the modified PrefixSpan (MP) algorithm which integrates the distance and time filtering mechanism based on the original PrefixSpan to find frequent semantic trajectory sequence pattern. Finally, the extensive experiments verify the performance of MP algorithm.
A Computational Tool for Automated Detection of Genetic Syndrome Using Facial Images

Early diagnosis of genetic syndromes has a vital importance in the prevention of any potential related health problems. Down syndrome is the most common genetic syndrome. Patients with down syndrome have a high probability of developmental disorders, like Congenital Heart Disease, which is best treated when discovered in the early stages. These patients also have particular facial characteristics that are identified by geneticists in a physical exam. However, there is subjectivity in the professional analysis, which can lead to a late diagnosis, aggravating the patient’s health condition. This paper proposes a software framework for the automatic detection of Down syndrome using facial features extracted from digital images, which could be used as a tool to help in the early detection of genetic syndromes. For training the machine learning model, we create a dataset gathering 170 pictures of children available on the internet. 50% of the pictures were of children with Down syndrome and the other 50% of healthy children. Then, we automatically identify faces and describe the images with facial landmarks. Next, we use two approaches for feature extraction. The first is a traditional computer vision approach using selected distances and angles and textures between the landmarks. The other, a deep learning approach using a Convolutional Neural Network to extract the features automatically. Then, the feature vector is fed to a Support Vector Machine with a linear kernel on both feature extraction approaches. We validate the results measuring the accuracy, sensitivity, and specificity of both feature extraction approaches using 10-fold cross-validation. The deep learning method resulted in an accuracy of 0.94, while the traditional approach achieved 0.84 of accuracy in our dataset. The results shows that the deep learning approach has a higher classification accuracy for this task, even with a small dataset.
Cryptanalysis and improvement of Wu–Cai–Wu–Zhang’s quantum private comparison protocol

In a recent paper [Int. J. Quantum Inf. 17 (2019) 1950026], the authors discussed the shortcomings in the security of a quantum private comparison protocol that we previously proposed [Int. J. Quantum Inf. 15 (2017) 1750014]. They also proposed a new protocol aimed to avoid these problems. Here, we analyze the information leaked in their protocol, and find that it is even less secure than our protocol in certain cases. We further propose an improved version which has the following advantages: (1) no entanglement needed, (2) quantum memory is no longer required, and (3) less information leaked. Therefore, better security and great feasibility are both achieved.
Architecture Design of Emotion Recognition Early Warning System Based on AE-TAN Algorithm

Abstract In order to improve the analysis effect of human emotion text data, this paper proposed NB algorithm, TAN algorithm and AE-TAN algorithm model method. In addition, three index parameters, precision, recall and F1 value, are compared and analyzed under three different algorithm model scenarios. The experimental results show that the AE-TAN algorithm is superior to the other two algorithms in Chinese text emotion analysis. Based on the AE-TAN algorithm, the architecture design scheme of emotion recognition early warning system was proposed in this paper, which has the characteristics of high concurrency and high availability, thus laying a technical foundation for the system that can quickly and accurately warn people of psychological problems.
Research Development and Landscape Artificial Intelligence in Indonesia

This study aims to find out the map of the development of Artificial Intelligence Research in Indonesia. The development of a study will be interrelated with research that has been done before. The existence of methods, infrastructure, data and others become a consideration when conducting research. The success of a study is supported by the existence of these things. The method used is to browse through the Lens.org website with keywords Artificial Intelligence, Machine Learning, Deep Learning, Artificial Neural Network, Convolution Neural Network, Natural Language Processing, Computer Vision, Backpropagation, and Support Vector Machine. Data sources were also taken from surveys conducted using the Google form platform, where the data from the survey results are still in the form of preliminary results. Data from these two sources are exported and processed into several parts, namely the number of publications, the type of publication, the most active institutions, open access publications, the most publications cited and the application of artificial intelligence technology in life aspects. The most published category is Support Vector Machine. The types of publications that are widely publicized are journal articles and conference proceedings. Institutions that have the most publications in each field are generally Telkom University, University of Indonesia, Bandung Institute of Technology and Sepuluh November Institute of Technology. The most cited paper is in the support vector machine category. Based on these data, Artificial Intelligence Research in Indonesia can be said to have developed rapidly.
Digital Rock Image Enhancement via a Deep Learning Approach

Digital Rock Images have been widely used for rock core analysis in petroleum industry. And it has been noticed that the resolution of Digital Rock Images are not fine enough for complex real-world problems. We propose Deep Neural Networks to increase resolution and quality for Digital Rock Images. The results demonstrate that our proposed method indeed can produce Digital Rock Images of higher resolution. And the proposed method for two-dimensional images has potential value to extend to 3D situation which means a lot for three-dimensional Digital Rock reconstruction.
A Real Time Vision System Based on Deep Learning for Gesture Based Human Machine Interaction

The use of gestures is one of the principal ways of communication among human beings when other forms, such as speech, are not possible. Taking this as a basis, the use of gestures has become also a principal form of human machine interaction in many different fields, ranging from advanced industrial setups where robots are commanded by gestures, to the use of hands to remotely control multimedia devices present at home. The majority of the systems for gesture detection are based on computer vision, either color images, depth images or point clouds, and have to overcome the inherent problems of image processing: light variations, occlusions or change of color. To overcome all these problems, recent developments using deep learning techniques have been presented, using Convolutional Neural Networks. This work presents a hand gesture recognition system based on Convolutional Neural Networks and RGB images that is robust against environmental variations, fast enough to be considered real time in embedded interaction applications, and that overcomes the principal drawbacks of the state of the art hand gesture recognition systems presented in previous works.
Mapping the Structure and Evolution of Genetic Algorithm Research

Genetic algorithm is a kind of random search algorithm that draws on the natural selection and natural genetic mechanism. It is widely used in many different disciplines. This paper uses the scientific measurement analysis system to summarize the research topics and current status of genetic algorithms. 17551 articles from 2010 to 2019 were investigated from the Web of Science Core Collection. Introduced the largest author co-citation network and the most influential journals. The disciplines and topics involved in genetic algorithm research were identified. A visualization network of genetic algorithm knowledge structure is established. A more in-depth study of citations has been carried out to explore emerging areas and hotspots of related research. The results of this study provide practitioners with a better understanding of genetic algorithms and help researchers do further research.
Enriched Feature Representation and Combination for Deep Saliency Detection

One of the most challenging issue in visual saliency detection is to discover and integrate meaningful features through deep neural networks. Saliency detection model should be carefully designed to extract sufficient features from different levels and reorganize them into the final prediction. In this paper, we propose an efficient saliency detection framework by introducing multi-scale representation and multi-level combination to deep convolutional neural networks. The main idea of our proposed model is to optimize intra-level feature extraction and inter-level feature combination, so that both saliency semantic and object details can be correctly preserved in final saliency maps. The model utilizes parallel dilated convolutions and pyramid pooling structures to enhance local details and acquire multi-scale feature representation. Feature maps of different resolutions are integrated by performing hierarchical combination in the encoder and decoder parts respectively. As a result, the model can better retain detail information during feature extraction and locate salient regions for saliency map recovery. Experimental results show that our model achieves state-of-the-art performance on several representative datasets.