Reviewerly/exHarmony huggingface dataset:
Improved Convex Hull Algorithm Applied to Body Size Measurements

Abstract The Quickhull algorithm is a very efficient convex hull algorithm for many engineering application. Although it can be applied in the clothing industry, its conventional formulation requires massive computational resources when processing point clouds from concave parts of the human body. Thus, we improve the Quickhull algorithm and apply it to accurately estimate human body dimensions. Assuming body symmetry with respect to the sagittal plane, some concave points can be quickly deleted to reduce the number of recursions, enhancing the overall calculation efficiency. Compared with the traditional Quickhull algorithm, the efficiency of the improved algorithm increases by 15.1–24.4%. Additionally, the computation time of the improved Quickhull algorithm is about three-quarters that of Graham’s scan. We expect that this study provides insights on the application of body size measurement and estimation based on 3D scan data.
Salient Object Detection with Pretrained Deeplab and k-Means: Application to UAV-Captured Building Imagery

We present a simple technique that can convert a pretrained segmentation neural network to a salient object detector. We show that the pretrained network can be agnostic to the semantic class of the object of interest, and no further training is required. Experiments were run on UAV-captured aerial imagery of the “smart home” structure located in the premises of the CERTH research center. Further experiments were also run on natural scenes. Our tests validate the usefulness of the proposed technique.
WITHDRAWN: Design of Robust Pole Configuration Controller Based on Embedded Network Distributed Single Input System

Robust control methods today are primarily solutions and the optimization process is presented as a set of optimization problems depends on the mathematical properties of the objective function. The proposed method, solving a polynomial equation based on the fixed characteristic polynomial is selected, including introducing a parameter solution having a known parameter structure of the controller.The embedded controller model should be modified to include an input / output interface with an external device. The method also includes a common reference function, which allows the possibility of different unrelated may consist of a standard, multi-objective optimization strict standard, mixed sensitivity problem applications, and other closed-loop constraints. Optimized design of the controller, clustering K- means the algorithm is executed.The following control performance parameters at different time and frequency domain studies, and is uncertain process parameters deviations thereof. Controller tuning rules are used for the process using a test bench, and it also the computational complexity analysis and process algorithms dynamics and sampled non- dominant pole type different types of distribution of data point's stable solution that provides an implementation of the hypothesis test for robustness. In the best case, these control law with single-precision (32-bit) arithmetic is implemented in the embedded processor.
An Investigation of Fine-tuning Pre-trained Model for MR-to-Text Generation

Natural Language Generation (NLG) task is to generate natural language given structured data, like Meaning Representations (MRs). The generated sentences are supposed to convey all the information in MRs and be fluent and realistic as human written. The constantly emerging pre-trained models push the state-of-the-art performance of various Natural Language Processing tasks to a new level. The common practice of fine-tuning pre-trained language models for NLG is to regard it as a text-to-text generation task. That is, MRs are converted into graph structures, then the graphs are linearized as text sequences, during which lots of pre-processing and post-processing work are required. We explore different methods to organize the MRs and show that just linearizing the information in MRs achieve decent results, while complex annotation process can be omitted. Under the circumstances, further experiments demonstrate that the fine-tuned pre-trained model achieves comparable results with state-of-the-art models on the RNNLG dataset.
An efficient uncertain graph processing framework for heterogeneous architectures

Uncertain or probabilistic graphs have been ubiquitously used in many emerging applications. Previously CPU based techniques were proposed to use sampling but suffer from (1) low computation efficiency and large memory overhead, (2) low degree of parallelism, and (3) nonexistent general framework to effectively support programming uncertain graph applications. To tackle these challenges, we propose a general uncertain graph processing framework for multi-GPU systems, named BPGraph. Integrated with our highly-efficient path sampling method, BPGraph can support a wide range of uncertain graph algorithms' development and optimization. Extensive evaluation demonstrates a significant performance improvement from BPGraph over the state-of-the-art uncertain graph sampling techniques.
Approximation Ratios of RePair, LongestMatch and Greedy on Unary Strings

A grammar-based compressor is an algorithm that receives a word and outputs a context-free grammar that only produces this word. The approximation ratio for a single input word is the size of the grammar produced for this word divided by the size of a smallest grammar for this word. The worst-case approximation ratio of a grammar-based compressor for a given word length is the largest approximation ratio over all input words of that length. In this work, we study the worst-case approximation ratio of the algorithms Greedy, RePair and LongestMatch on unary strings, i.e., strings that only make use of a single symbol. Our main contribution is to show the improved upper bound of O((logn)8·(loglogn)3) for the worst-case approximation ratio of Greedy. In addition, we also show the lower bound of 1.34847194⋯ for the worst-case approximation ratio of Greedy, and that RePair and LongestMatch have a worst-case approximation ratio of log2(3).
Blockchain-Based Iris Authentication in Order to Secure IoT Access and Digital Money Spending

In this work we use two approaches to prevent double spending and secure IoT transaction through Blockchain and information fusion techniques: the first method is based on Smart Contracts; while the second method is implemented by a novel Blockchain system. Both methods use a hybrid RSA and biometric codes fusion in order to have an encrypted, taking account of the privacy, identity of the spender. In Blockchain a contract between the parties is stipulated and the biometric fused code are kept. The used coin pack is tagged, so a future double spending or any illegal action on the IoT network or Smart city becomes forbidden and prosecutable legally. The second part of the work will present a novel Blockchain where a transaction can be signed only if the user is successfully authenticated though his biometric fused key.
A responsiveness model for privacy conservation against antagonist and fake user in cyber- physical and online social networks

Abstract The enormous numbers of users are interacted with Social networking sites throughout the world. They are consistently engaging with Social Medias are Twitter, Instagram and Facebook have an incredible impression and rarely objectionable consequences for daily life. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). This paper proposes to perform a review of techniques used for detecting spammers on social media like Twitter, Facebook, Instagram, etc. Furthermore, a taxonomy of alerting spam message methodologies is presented that classifies the techniques based on their ability to detect: fake content, spam message or junk mail depends on either url or trendy news, and identifying unauthenticated users. The proposed techniques are also compared based on historical features that comprise the characteristics of user profile, wish-list, content usage, methodology used, and physical context, logging activities. This research paper contributes the study on identifying appropriate users against fake users and spam or junk mail detection using Adversary-Privacy evaluation model that helpful to preserving private data in Cyber-Physical Social Systems.
Indonesian Teachers Perspective towards the Use of Authentic Materials on Students Communicative Competence in EFL Classroom

In this digital era, learning materials that include adaptation materials like authentic materials occupy a focal place in the success of the EFL teaching and learning context. Thus, teachers have an onus to adapt the teaching materials and do more effort in improving the teaching materials as materials designers, developers, evaluators, and explorers to cater to students' needs for developing students' English ability to achieve the learning goals and curriculum objective. This study investigated how Indonesian secondary teachers viewed authentic materials in EFL classrooms whether they employed authentic materials as one of EFL materials adaptation or considering the importance of reallife authentic language exposure in the 21st-century learning. The data were collected through interview with three English teachers as the participants. The findings indicated that two out of three teachers utilized authentic materials as their main and only sources in their teaching.
The Implementation of the Amora Learning Model to Improve Students’ Digital Literacy Skill of Indonesia University of Education

Abstract 4.0 industrial revolution known as the era of digitalization in various sectors of life. Literacy skills in the industrial revolution era 4.0, not only emphasize reading, writing and arithmetic skills, but also emphasize the importance of digital literacy skills. This study aims to examine the effectiveness of the amora learning model in increasing digital literacy of students. This quasi-experimental study uses a pretest-posttest control group design. The research instrument uses a digital literacy ability test that has been validated by an expert. Hypothesis testing using the variance test used. The increase in digital literacy ability is determined based on the results of the N-gain test. The results showed that there was an increase in digital literacy of students in both classes. The experimental class has a higher increase in digital literacy than the control class. Digital literacy skills are increasing in both classes. This shows that the amora learning model applied successfully increases digital literacy of students in foundation education courses.
History of library websites

The history of foreign and Russian libraries’ websites in 1996—2017 is analyzed on the basis of professional publications and web-archive. The author analyzed the websites of Russian libraries, i. e. RASL Library for Natural Sciences, All-Russia Library for Foreign Literature, Russian State Library, Russian National Library, State National Pedagogical Library, Russian National Public Library for Science and Technology, Central Scientific Medical Library, State Public Scientific and Technological Library of the RAS Siberian Branch and divisions, Central Scientific Agricultural Library, Russian Academy of Sciences Library, Far Eastern State Scientific Library, Nizhny Novgorod Regional Universal Scientific Library, Novosibirsk State Regional Scientific Library. The number of websites was limited due to many changed domain names which disables the analysis. The archive copies of the selected libraries’ home pages obtained through the Wayback Machine service were analyzed. The first (1996–1998) home pages of Russian and foreign libraries are compared and distinctions are identified. The dynamics of Russian libraries’ websites up to 2017 is characterized. The author concludes that the libraries have been regularly changing design, content, representation forms, and navigation and retrieval systems, which is often not welcome by their users. The libraries have to introduce and promote efficiently their content and resources with the focus on user friendliness. Internet technologies and libraries’ representing themselves on the World Wide Web have changed library practice, and the libraries have to think over how to design the website and how to assess it.
Neural User Embedding from Browsing Events

The deep understanding of online users on the basis of their behavior data is critical to providing personalized services to them. However, the existing methods for learning user representations are usually based on supervised frameworks such as demographic prediction and product recommendation. In addition, these methods highly rely on labeled data to learn user-representation models, and the user representations learned using these methods can only be used in specific tasks. Motivated by the success of pretrained word embeddings in many natural language processing (NLP) tasks, we propose a simple but effective neural user-embedding approach to learn the deep representations of online users by using their unlabeled behavior data. Once the users are encoded to low-dimensional dense embedding vectors, these hidden user vectors can be used as additional user features in various user-involved tasks, such as demographic prediction, to enrich user representation. In our neural user embedding (NEU) approach, the behavior events are represented in two ways. The first one is the ID-based event embedding, which is based on the IDs of these events, and the second one is the text-based event embedding, which is based on the textual content of these events. Furthermore, we conduct experiments on a real-world web browsing dataset. The results show that our approach can learn informative user embeddings by using the unlabeled browsing-behavior data and that these user embeddings can facilitate many tasks that involve user modeling such as user-age prediction and -gender prediction.
The Power Control Method of Data Center Based on Cloud- Edge Collaboration

Abstract Data centers have the characteristics of huge energy consumption and strong power fluctuations. To solve the problem of unstable supply and demand such as frequent power fluctuations in the micro-grid of the data center using renewable energies, this paper establishes a controllable load model of server clusters based on the task migration mechanism by analyzing the various tasks and characteristics of large cloud data centers, as well as edge computing nodes. Through the collaborative optimization control of edge and cloud computing tasks, and dynamically adjusting and migrating the server cluster load, a new data center tie-line power control method is proposed. The results of calculation examples show that the method can achieve power smoothing of large-scale cloud data center tie lines through cloud-edge collaborative control.
Impulsive Noise Removal from Gray-Scale Video Sequences via Adaptive Thresholding

In this paper, we propose a novel method to reconstruct a video sequence corrupted by impulsive noise. Images and videos can become sparse through transformations such as discrete cosine transform, Wavelet, and contourlet. We can employ this redundancy in the images and videos and the sparsity of the impulsive noise in the observation domain to recover the noisy video sequence. By introducing the augmentation of the vectorization of frames, we convert the video denoising problem to image denoising and employ the temporal information in our algorithm as well. We then compare our method with the baseline 3-D median filter for random-valued impulsive noise removal from a gray-scale video sequence. Simulation results show that our algorithm is simple and very fast, which makes it a suitable choice for online applications. Moreover, its reconstruction quality is far better than that of the classical method, 3-D median filter.
Teaching Archaeology in VR

The authors discuss their experience at Duke University and, more specifically, at the Dig@Lab, a core research unit of the CMAC (Computational Media Art and Culture) program in the Department of Art, Art History, and Visual Studies. This community of scholars and students represents a new branch of experimental teaching in digital humanities with the participation of students and faculty from the humanities, engineering, computer science, neuroscience, and visual media. In particular, the Dig@Lab studies the impact of virtual reality in cyberarchaeology and virtual museums.
Assessment of Blockchain Technology Adoption Factors and Scenarios Within the Economy of Latvia

The aim of this paper is to assess blockchain technology adoption factors and indicate the most likely scenario for blockchain technology adoption in the economy of Latvia. The study employs the Analytic Hierarchy process (AHP) method for expert assessment of technological, organisational, market and institutional factors influencing blockchain technology adoption in the economy of Latvia in 4 stakeholder-based scenarios. The most likely scenario for facilitating blockchain technology adoption in the economy of Latvia in the experts' opinion is the initiative of technology developers, which covers development of blockchain solutions in such areas as payments, crypto/token economy, digital identity management, etc. An aggregated expert opinion has identified 2 most significant factors for blockchain technology adoption in the economy of Latvia – regulatory environment (20%) and innovation system (16%), with the 3rd most important factor substantially lagging behind – market dynamics (8%). Therefore, it can be concluded that blockchain technology adoption in the economy of Latvia can be facilitated through strengthening blockchain innovation system and crypto regulation, specifically taking into account that blockchain solutions of technology developers in Latvia predominantly utilize public blockchain networks, which are connected to crypto-currencies and tokenized virtual assets. It is also necessary to raise awareness of top managers and policy-makers in Latvia about blockchain technology benefits in order to create local market demand for blockchain solutions.
Analysis of a Secured System for Command Control

The paper presents a study on the performance of a secured system for command and control that monitors the variation of skin electrical conductivity, and can be used in motion control using joy-stick systems, where firm control is required and where any wrong controls or accidental movements (flight control, remote control robots or automatic systems, etc.) must be eliminated.
CRNet:3D Face Reconstruction with Contour Map Regression Network

2D face images represent faces with incomplete information. 3D face reconstruction from a single 2D image is a challenging problem with application value. The single feature extraction method distorts the generated 3D face image. In this paper, we use contour-based face segmentation method to reconstruct 3D face image. We focus on the edge and contour information of the face when using contour lines to segment the face. Different from the global 3D face reconstruction method, we combine the global and local face information to carry out 3D face reconstruction. Our method: First of all, we do contour segmentation for human faces and extract the features of the segmented images. Second, we learn the local binary features of each keypoint in a complete face image, then combine the features and use linear regression to detect the keypoints. Thirdly, we use Convolutional Neural Networks to learn the regression 3D Morphable Model coefficient and significantly improve the quality and efficiency of reconstruction. We regressed the coefficients of the 3D deformable model from 2D images to present face alignment for 3D face reconstruction. We carry out feature mapping between 2D face and 3D face image, and monitor and verify 3D face model through mapping relationship. Our method can not only reconstruct face images from all angles, but also reduce face deformities. We made face images fit better under different expressions and postures.
Contextual Image Parsing via Panoptic Segment Sorting

Real-world visual recognition is far more complex than object recognition; there is stuff without distinctive shape or appearance, and the same object appearing in different contexts calls for different actions. While we need context-aware visual recognition, visual context is hard to describe and impossible to label manually. We consider visual context as semantic correlations between objects and their surroundings that include both object instances and stuff categories. We approach contextual object recognition as a pixel-wise feature representation learning problem that accomplishes supervised panoptic segmentation while discovering and encoding visual context automatically. Panoptic segmentation is a dense image parsing task that segments an image into regions with both semantic category and object instance labels. These two aspects could conflict each other, for two adjacent cars would have the same semantic label but different instance labels. Whereas most existing approaches handle the two labeling tasks separately and then fuse the results together, we propose a single pixel-wise feature learning approach that unifies both aspects of semantic segmentation and instance segmentation. Our work takes the metric learning perspective of SegSort but extends it non-trivially to panoptic segmentation, as we must merge segments into proper instances and handle instances of various scales. Our most exciting result is the emergence of visual context in the feature space through contrastive learning between pixels and segments, such that we can retrieve a person crossing a somewhat empty street without any such context labeling. Our experimental results on Cityscapes and PASCAL VOC demonstrate that, in terms of surround semantics distributions, our retrievals are much more consistent with the query than the state-of-the-art segmentation method, validating our pixel-wise representation learning approach for the unsupervised discovery and learning of visual context.
Unsupervised Anomaly Detection of Retail Stores Using Predictive Analysis Library on SAP HANA XS Advanced

The retail industry is quite exposed to fraudulent situations. Daily, thousands of transactions are processed, which may include some frauds difficult to detect, mainly when the perpetrators are the own employees at the retail stores. Large retailers with several stores across different locations may have considerable difficulty in detecting frauds involving their cashiers since they have to take into account different contexts of operation. To reduce fraud losses, retailers get an overview of the transactions in each store to filter the ones that look suspicious deviating from what would be normal. Data mining algorithms can be useful to detect anomalies, differentiating the normal from the abnormal. This study adopted the k-Means clustering algorithm for anomaly detection on a sample of 90 stores in a large food retail chain, revealing the existence of some outliers in the data. The anomaly detection process was fully implemented in SAP HANA XS Advanced using the Predictive Analysis Library (PAL). In the end, it was possible to identify the stores with abnormal behavior and conclude for the usefulness and ease of use of such a library, despite some lack of documentation to use it.
Information Extraction Research Review

Abstract Information extraction is to extract the required specific entity, event, relationship and other information from a large number of texts and store it in a structured form, which occupies an important position in the field of natural language. Information extraction helps people get the information they need efficiently, avoids a lot of complicated work, and saves manpower and material resources. In this paper, named entity recognition and so on are described and summarized in detail, as well as the prospect of information extraction.