Reviewerly/exHarmony huggingface dataset:
Adaptive Multi-Domain Learning for Outdoor 3d Human Pose and Shape Estimation

It is an extremely challenging task to estimate 3D human pose and shape in outdoor scenes for which we can hardly obtain precise ground truth data for training. Previous methods usually use multiple datasets collected at different scenes to train their models, including those collected in laboratories with precise ground truth and those collected at outdoor scenes with estimated or even no ground truth. Since data from different scenes are included in training, it is necessary to handle the domain difference problem, which unfortunately has never been considered by previous works. In this paper, we first point out this problem and then address it via a novel cascade multi-domain learning module (CMDL), where multiple adapters are employed to extract more discriminative features for different domains. We show that our method with CMDL outperforms previous methods in outdoor scenes. In principle, the proposed CMDL module can be easily applied on top of any arbitrary 3D human pose and shape approach.
Robust Speaker Identification System Based on Variational Bayesian Inference Gaussian Mixture Model and Feature Normalization

Voice is an important human feature for identifying an individual in normal human’s communication. Automatic speaker recognition (ASR) mechanisms can, therefore, be considered a client-friendly form of biometric type that is used in applications such as banking, forensics, teleconferencing, and so on. This paper presents a text-independent speaker identification system based on Variational Bayesian Gaussian Mixture Model (VBGMM). Four types of features which are: MFCCs, derivatives of MFCCs, Log Filter-bank Energies and Spectral Sub-band Centroids, in addition to feature normalization have been used in the proposed system. The performance evaluation of proposed system is compared with the traditional Gaussian Mixture Model (GMM). The two modeling techniques with different types of covariance (Diagonal and Tied) are examined using the TIMIT and Arabic corpus. The recognition rates for the two datasets indicate that VBGMM is superior to GMM particularly when using data normalization for the extracted features. The recognition rates that achieved in this experiment were 98.3% and 93.3% for the TIMIT and the Arabic corpus respectively.
Research on equipment fault detection based on boundary-based OCC-KELM

In response to the short installation time of active equipment, the lack of various fault samples, the difficulty of obtaining, and the low accuracy of existing algorithms for fault detection, the kernel ELM is used as the basic modeling framework, and the boundary-based threshold selection criteria are used; Combine extreme learning with one-class detection, and propose BB-OCKELM detection model. Based on the kernel ELM, and at the same time integrating the idea of one-class detection, the constraint optimization problem of KELM is realized under the constraint of lp-norm, and the output function expression of a unified one-class KELM classifier is derived; for the convenience of fault detection Implementation, based on BB-OCKELM to define the statistical inspection volume and detection threshold. The proposed method is applied to four common data sets commonly used in the field of machine learning and the fault detection application of a certain type of equipment. The experimental results show that the proposed method is suitable for comparison with SVDD, PCA, OC-SVM, and OC-KELM. For different public data sets, the proposed method can effectively balance missed and false alarms, and significantly improve the accuracy of fault detection when the time cost is equivalent.
Sparse Flow Adversarial Model For Robust Image Compression

Existing learned-based image compression methods have shown impressive performance. However, most of them rely on the consistency of distribution between training images and test images, which limits the robustness of the trained model. In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). SFAM employs a deep generative framework to learn a reversible and stable mapping between image distributions, thus it can work in varied scenes for robust compression. Moreover, a sparse adversarial map is introduced into SFAM, to constrain the SFAM to generate more sparser features for efficient compression. Extensive experiments are conducted on different datasets, in which the effectiveness and robustness of the proposed method is verified. Meanwhile, SFAM is trained only once and it can work well on three different datasets, which also proves the robustness of the proposed SFAM.
A strategic model-based of micro-moment data in smart cities especially internet of things

Many industry leaders predict that the industrial Internet will create unprecedented levels of growth and productivity over the coming decades. Business leaders, governments, academic environments, and technology vendors are working hard together to realize and restrain this powerful potential. In this research, the combined method has been used. In the qualitative part, the foundation data method or grounded theory with a systematic approach which attributed to Strauss and Corbin has been used for data analysis and has been designed with three open, axial and selective coding techniques in Maxqda model software. Then, the questionnaire was designed in 6 dimensions: causal, axial phenomenon, prevailing context, intervening conditions and consequences in 50 items to confirm the dimensions, components and indicators. The statistical population is consist of 35 managers and basic technology senior experts in the Internet of Things (IOT) field, marketing and information technology experts who have been selected by Snow Ball method (chain reference) to the theoretical saturation limit. In the quantitative part, after identifying the relevant dimensions, components and indicators, the questionnaire was provided to managers and experts. The results were confirmed by independent t-test. Prioritization of dimensions and components has been done by AHP technique. Reliability of interviews with Cohen's Kappa coefficient and inter-rater reliability method, content validity of the questionnaire using content validity ratio (CVR), content validity index (CVI), apparent validity with item impact scores and reliability with Cronbach's alpha and halving method has been approved.
Improving Social Trend Detection Based on User Interaction and Combined with Keyphrase Extraction Using Text Features on Word Graph

Recently, by the explosion of information technology, the valuable and available data exponentially increases in various social media platforms which allow us to exploit and attain convenient information and transform it into knowledge. This means that prominent topics are extracted on time in the social media community by leveraging the proper techniques and methods. Although there are various novel approaches in this area, they almost ignore the factors of the user interactions. Besides, since the enormous size of the textual dataset is distributed to any languages and the requirements for trending detection in a specific language, most of the proposed methods concentrating on English. In this paper, we proposed a graph-based method for the Vietnamese dataset in which graph nodes represent posts. More specifically, the approach combines the user interactions with a word graph representation which then is extracted to the topic trends by the RankClus Algorithm. By applying our proposal in several Facebook and Twitter datasets, we introduce dominantly dependable and coherent results.
Collision Attack Framework on RIPEMD-128

In the last few years, the hash functions are the very common and essential primitives implemented in lots of fields. RIPEMD-128, a member of MD-family hash function, has been found collisions on the full round. In this paper, we further analyze the property about the security against collision attack of RIPEMD-128. The collision attack framework for RIPEMD-128 is proposed in order to provide ideas for other hash functions. The framework consists of three parts namely choosing a message word difference insertion, constructing differential characteristics and collision search phase. Cryptographic techniques including message modification is also introduced to reduce the complexity.
Information space and time and its social dominants

Information space and time in their socio-cultural dimension act as knowledge components of being, and, consequently, the most important regulator of social relations. At the same time, the social and cultural dominants of the information space and time are determined by the creative activity of a person. In principle, a person is always focused on knowledge, where its elements interact in a comprehensive way, and not on information, in which there is a separation of its elements from the spiritually observed cross-section of reality, and thus from reality, which does not lend itself to the construction of connections, and the construction somewhat reduces the elasticity of the structure of information space and time.
Data Preprocessing Method For The Analysis Of Incomplete Data On Students In Poverty

Data mining is the focus of big data applications in various fields. Data pre-processing is a crucial step in the data mining process. With the development of the information society and the application of databases, the educational data has seen explosive growth, and the data on poor students has become informative. However, the actual student financial aid management system collects the data on poor students which generally has problems such as missing values, attributes redundancy, and noise. To solve this problem, we proposed a novel method called DPBP to preprocess data. The proposed DPBP approach consists of four stages: the preparation of data, the scoping of characteristics, the combination of characteristics, and the filtering of missing number. Firstly, we prepare the dataset by extracting data. Next, the characteristic range is limited by choosing experimental results of feature selection algorithm. Then, third stage performs feature combination to obtain the feature decomposition sets. Finally, based on accuracy and missing number, we gain the optimal dataset. Series of experiments result show that our proposed method significantly improves the data quality and stability.
The Method for Evaluating Rope-jumping Posture

Nowadays as living standard has greatly improved, most of people pay more attention to their health. As a result, rope-jumping has become one of the main ways to maintain health because of its advantages of consuming extra nutrition and improving physical fitness. However, improper posture and excessive amount of training may increase the burden of the body or even cause damage on the body. Therefore, in this paper a method of using a single waist-fixed accelerometer to quantify rope-jumping movement was investigated and evaluated. Based on the analysis of the acceleration trend of a single rope jump, the parameters and corresponding calculation methods for evaluating the motion posture were proposed, and by collecting and analyzing 10 groups of detected sensor data of the standard posture and the high jump posture respectively, the proposed parameters were proved that the difference between two kinds of posture could be well distinguished. This indicates that proposed parameters can be applied in recognizing other rope-jumping posture and furthermore used in correcting wrong jumping posture.
Evaluating Automated System Interventions Against Email Harassment

Email and other forms of electronic communication are becoming increasingly more essential to our everyday lives. However, with this growth comes the paralleled increased risk of email harassment, exacerbated by the current lack of platform support for managing these harmful messages. This paper explores different interfaces for the automated detection and management of email harassment using artificial intelligence in order to investigate what degree of platform intervention email users prefer when navigating their email platform. Through conducting a study involving three different email platform prototypes based on the Gmail platform, we evaluate how varying levels of platform intervention affect users’ perceived sense of safety, agency, and trust with their email platform. Our primary findings suggest that users generally benefited from each of the system intervention strategies and desired higher intervention features when combating email harassment, as well as ways of managing this intervention based on their unique preferences.
Video Surveillance System for Helmet Recognition Based on SSD Target Detection Algorithm

Video surveillance technology is derived from a branch of artificial intelligence. It can have a computer to automatically analyze the video image source, identify and extract useful key information from it, and automatically control the machine to perform corresponding operations. This article is a video surveillance system based on the SSD target detection algorithm to identify specific data for hard hats. The aim is to be able to patrol and respond to incidents in a more intelligent way without having to use human resources to stay in front of the monitor.
MicroNet: Realizing Micro Neural Network via Binarizing GhostNet

Binary Neural Networks (BNNs) are regarded as very effective approaches to reduce the high computational and memory cost of deep convolutional neural networks; however, most of the previous works only focus on the binarization of traditional large-scale networks. In this paper, to further decrease the computational and memory complexity, we propose to binarize the lightweight network: GhostNet, which can generate feature maps with fewer operations. We first modify the ghost block for reducing the information loss caused by the binarization. Moreover, to enhance the performance of binarized GhostNet, some training schemes are introduced including gradient approximation and new activation layers. Various experiments on CIFAR10 dataset show that, compared with other methods, the MicroNet can achieve better performance with fewer computational and memory cost.
A Study of Mobile Augmented Reality for Motor Nerve Deficits in Anatomy Education

Augmented reality applications for anatomy education have seen a large growth in their literature presence as an educational technology. However, the majority of these new anatomy applications limit their educational scope to the labelling of anatomical structures and layers, and simple identification interactions. There is a strong need for expansion of augmented reality applications, in order to give the user more dynamic control of the anatomy material within the application. To meet this need, the mobile augmented reality (AR) application, InNervate AR, was created. This application allows the user to scan a marker for two distinct learning modules; one for labelling and identification of anatomy structures, the other one for interacting with the radial nerve as it relates to the movement of the canine forelimb. A formal user study was run with this new application, which included the Crystal Slicing test for measuring visual-spatial ability, the TOLT test to measure critical thinking ability and both a pre- and post- anatomy knowledge assessment. Data analysis showed a positive qualitative user experience overall, and that the majority of the participants demonstrated an improvement in their anatomical knowledge after using InNervate AR. This implies that the application may prove to be educationally effective. In future, the scope of the application will be expanded, based on this study’s analysis of user data and feedback, and educational modules for all of the motor nerves of the canine forelimb will be developed.
Improving Quality of Use-Case Models by Correlating Defects, Difficulties, and Modeling Strategies

Use case (UC) models play an essential role in software specification since they describe system functional requirements. A UC model should be free of defects due to its relevance and impact throughout the software development life cycle. However, inspections in UC models frequently identify defects related to modelers’ difficulties in different activities during the modeling process. The quality of a UC model is usually analyzed based on quality criteria such as ambiguity and inconsistency. Several strategies in the literature assist use case modeling in mitigating defects, but these strategies do not identify which potential defects they aim to prevent or eliminate. In this context, we proposed a correlation between UC modeling difficulties and strategies to mitigate these difficulties based on UC’s quality criteria. In this paper, we describe each strategy contained in the correlation and present, in detail, the controlled experiment that assesses the correlation effectiveness, including the discrete data collected in analyzing the participants’ models and statistical analysis performed in these data. Besides, we also propose a mechanism to guide in elaborating checklists to identify defects in UC models focusing on quality criteria. Through a controlled experiment, we evaluate the Antipattern strategy, and the results showed a clear indication that this strategy mitigates the difficulties in which it is related according to the correlation. Besides, the UC models developed in the experiment were evaluated using the checklist generated based on the proposed mechanism.
The Discussion Corpus toward Argumentation Quality Assessment in Multi-Party Conversation

Recently, many studies in NLP, especially argument mining, have focused on revealing what a good argument is with written texts. However, there are a few studies to challenge argumentation quality assessment in face-to-face conversations due to a lack of corpora, as compared with written texts. Therefore, we construct a multimodal multi-party discussion corpus toward argumentation quality assessment in multi-party conversation. The corpus consists of 10 multi-party discussions (200 minutes) in total. Each participant is annotated for 6 scores about rhetoric, which is one of the classes of argumentation quality. In this paper, we explain the procedure of our corpus construction and report the results of the annotation.
SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis

We describe Spatial Voxel-Net (SVNet) and Multi-View Voxel-Net (MVNet), a cascade of two novel deep learning architectures for calibrated multi-view stereopsis that reconstructs complicated outdoor 3D models accurately. Both networks use a sequence of RGB images based on ordered camera poses in a coarse-to-fine fashion. SVNet extracts summarized features and analyzes the spatial relationship among a block of 3D voxels using 3D convolutions, then predicts block-level occupancy information. MVNet then receives the occupancy information together with RGB images to predict the final voxel-level occupancy information. SMVNet is an end-to-end trainable network, which can reconstruct complex outdoor 3D models and be applied to large-scale datasets in a parallel fashion without the need of estimating or fusing multiple depth maps, typical of other approaches. We evaluated SMVNet on the complex outdoor Tanks and Temples dataset, in which outperformed two well-known state-of-the-art MVS algorithms.
Prediction of Category of Scientific Article by Graph Convolution

The convolution method that uses the IDs of citing paper and cited paper is known to improve the prediction performance of category of papers. This paper proposes a "word convolution" method that uses not only the IDs of the cited and citing papers, but also the words that appear in those papers. The proposed method improves the prediction performance (accuracy) 7% for the core dataset and 12% for the citeseer dataset and gives the same performance for the pubmed dataset compared with the state-of-the-art method.
2D-FRFT Based Frequency Shift-Invariant Digital Image Encryption

In this paper, we study the property of frequency shift in two-dimensional Fractional Fourier Transform (2D-FRFT) domain. Based on the mathematical verification and computer simulations, it is demonstrated that the magnitude of reconstruction from phase information satisfies frequency shift-invariant in 2D-FRFT domain, improving the robustness of image encryption. Experiments are implemented to verify the effectiveness of this property against the frequency shift attack in 2D-FRFT domain, improving the robustness of image encryption.
Introduction

introduction Share on Introduction Authors: Parisa Eslambolchilar Search about this author , Andreas Komninos Search about this author , Mark D. Dunlop Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 1–13https://doi.org/10.1145/3447404.3447406Online:23 February 2021Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks0 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
A Study on Dispatched Nursing during the Japanese Colonial Era

This study aimed to explore the characteristics of dispatched nursing, a type of private nursing called Hasutsu, during the Japanese colonial period in Korea. This specialized and professional nursing was a form of a dispatched nursing service provided by nurses associations.This study reviewed a literature on visiting nursing, a literature on dispatched nursing, focusing on data collected during the Japanese colonial period from August 1910 to August 1945. Study design was a document research study of historical records.The dispatched nursing service was established by the Japanese nurses in Korea. More than 60 nurses associations operated to meet the demands in the modern Korean nursing period. It was found that some associations tried to raise the nursing fees. Also, the practice of employing visiting nurses who did not have a license was criticized severely in the newspapers. Considering that the data was limited in this study, the existence of Korean nurses association employing Korean visiting nurses has not been specifically identified.The findings reveal some aspects of dispatched nursing provided by the nurses associations in that era. The nurses associations developed their own specialized service independently by dispatching nurses to fulfill the needs of people not only with cheaper nursing fares than hospitals but with convenient nursing care services, including customized care for infectious diseases. A study of modern historical records of Korean nurses would facilitate deeper research into Korean nursing history.
Do materials matter? How surface representation affects presence in virtual environments

This article reports the impact of different visual realism of materials applied to objects on perceived presence during an assembly task. The results of the experiment show that there is a significant difference between the more realistic scene and one where the surfaces of objects have been replaced with simpler, CAD-inspired visualizations. Despite these difference, both scenarios reach high values for presence and acceptance. Therefore, less detailed and less realistic rendering of surfaces might be sufficient to obtain a high presence and acceptance level in scenarios, which focus on manual tasks, if the associated drop in presence can be tolerated.