Reviewerly/exHarmony huggingface dataset:
Efficient 3D Face Reconstruction Model Based on Dense Mesh Solution Using Rendering and Partial Search

One of the most difficult aspects of digitizing the human body is reconstructing a human face model from images. Many independent studies on face modeling methods have been published; however, the majority of the studies focus on improving the accuracy of the generated face model rather than the ability to integrate facial models with body models to create a complete human model. As a necessary consequence, the authors implement a method capable of effectively and accurately integrating with body models based on Position Map Regression Network (PRN) - a point cloud generation from an image based on a convolutional neural network (CNN).
Infection Disease Classification in Indonesian Language using Machine Learning

Infectious diseases are still the main cause of death worldwide due to their dangerous and fast-spreading natures. One way to diagnose infectious diseases quickly and accurately is to create a machine learning model that aims to assist young doctors or health practitioners. In this study, we used the support vector machine, k-nearest neighbors, and the decision tree algorithm to classify types of infectious diseases based on the similarity of signs and symptoms. These methods were trained on documents in the Indonesian Language, which has its weight extracted using the Term Frequency-Inverse Document Frequency (TF-IDF). Model training and testing were carried out using 464 data with 20 classes of infectious diseases. Test results in this study indicate that support vector machine using stopword NLTK with unigram, bigram, and trigram get anaccuracy of 87%, 81%, and 76%, followed by the K-nearest neighbors using stopword NLTK get an accuracy of 83%, 81%, and 77%. The decision tree using stopword NLTK gets an accuracy of 66%, 60%, and 53%, scoring the lowest compared to the other two.
Person Image Synthesis in Arbitrary 3D Poses Based on Part Affinity Fields

We consider the person image synthesis problem, where an output image is generated from an arbitrary source image and an arbitrary target 3D pose. Prior person image synthesizing methods usually use 2D keypoint heatmaps to represent the target pose. However, this 2D representation can be ambiguous in self-occlusion scenarios due to the lack of depth information, resulting in generating inappropriate images. To solve this problem, we propose to synthesize person image from 3D poses. We introduce an improved part affinity field representation to describe the 3D configuration of the target pose and the 2D location of the person in the pixel space. Compared to using 2D poses, our synthesized images have visually better details, such as correct self-occlusion, brightness, face direction et al. Moreover, in contrast with prior person image generators, our method predicts the difference between the source image and target image instead of the output images. This strategy allows the network to generate better foreground and significantly reduces the noise in the image background. We evaluate our method on images of fifteen different actions on Human3.6M dataset. Extensive experiments demonstrate that our method can synthesize much better person images than 2D pose-based ones. If given a sequence of desired poses, our method can produce a sequence of temporally smooth and coherent images, even on another subject and actions, which means that our method has great potential to generate high-quality person videos.
Unsupervised Controllable Generation with Self-Training

Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images. However, controllable generation with GANs remains an open research problem. Achieving controllable generation requires semantically interpretable and disentangled factors of variation. It is challenging to achieve this goal using simple fixed distributions such as Gaussian distribution. Instead, we propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training. Self-training provides an iterative feedback in the GAN training, from the discriminator to the generator, and progressively improves the proposal of the latent codes as training proceeds. The latent codes are sampled from a latent variable model that is learned in the feature space of the discriminator. We consider a normalized independent component analysis model and learn its parameters through tensor factorization of the higher-order moments. Our framework exhibits better disentanglement compared to other variants such as the variational autoencoder, and is able to discover semantically meaningful latent codes without any supervision. We empirically demonstrate on both cars and faces datasets that each group of elements in the learned code controls a mode of variation with a semantic meaning, e.g. pose or background change. We also demonstrate with quantitative metrics that our method generates better results compared to other approaches.
System Level Knowledge Analysis and Keyword Extraction in Neuroscience

Vast amounts of knowledge resources are emerging from Neuroscience research, thanks to increasingly widely available imaging and analysis technologies and open data sets. Learning, processing and keeping up to date with developments however imply steep learning curves. Advances in Neuroscience research provide insights into the human brain and the mind for brain specialists, but also inform other scientific disciplines such as to complexity science, cognitive computing, medicine in general and general technology and policy sectors. With a few exceptions however, the majority of Neuroscience research outcomes can still be accessed and leveraged mostly only by highly trained neuroscientists and brain informatics researchers in their respective specialisations, as the knowledge and skill sets required to query and manipulate such data is only meaningful for individuals with specific training. This paper identifies and addresses the need to lower the cognitive barriers to accessing Neuroscience research, as it is becoming very relevant to other fields, and to widen its accessibility to a broader range of scholars of other disciplines - such as Computer Science and Information Technology for example, reducing the efforts required in tracking innovation and facilitate knowledge acquisition. Keeping up with the state of the art is a challenge in any field, yet increasing number of researchers, students and practitioners from diverse professions and with a wide range of interests and goals, and multi disciplinary and linguistic backgrounds. The approach proposed here leverages a combination of elementary core methods from semantic technology, including simple corpus and linguistic analysis techniques, and devises a low tech instrument that can be adopted irrespective of the availability of software and level of English language proficiency to acquire the necessary familiarity to handle Neuroscience topics The same method can also be leveraged in other complex knowledge domains. A set of experiments to evaluate the effectiveness of the method is described with preliminary results.
Low-Resource Neural Machine Translation Using XLNet Pre-training Model

The methods to improve the quality of low-resource neural machine translation (NMT) include: change the token granularity to reduce the number of low-frequency words; generate pseudo-parallel corpus from large-scale monolingual data to optimize model parameters; Use the auxiliary knowledge of pre-trained model to train NMT model. However, reducing token granularity will result in a large number of invalid operations and increase the complexity of local reordering on the target side. Pseudo-parallel corpus contains noise affect model convergence. Pre-training methods also limit translation quality due to the human error and the assumption of conditional independence. Therefore, we proposed a XLNet based pre-training method, that corrects the defects of the pre-training model, and enhance NMT model for context feature extraction. Experiments are carried out on CCMT2019 Mongolian-Chinese (Mo-Zh), Uyghur-Chinese (Ug-Zh) and Tibetan-Chinese (Ti-Zh) tasks, the results show that the generalization ability and BLEU scores of our method are improved compared with the baseline, which fully verifies the effectiveness of the method.
Multi-granularity Pose Fusion Network with Views for Person Re-identification

Person re-identification (re-ID), aiming to recognize a person of interest across different cameras, is a well-known challenge because of the vast variation in human poses. Existing pose-driven re-ID methods utilize keypoints, which are considered fine-grained local information, inadequate to capture global pose characteristics. In this paper we propose multi-granularity pose fusion network (MGPFNet), to address the pose variation problem. In particular we propose the use of view information in re-ID. A pose in MGPFNet is multi-granular containing the coarse-grained view and fine-grained keypoint information. Two encoding methods, namely pose concatenation encoding and pose independent encoding, are proposed to incorporate both coarse-grained views information and fine-grained keypoints information while alleviating the impact of pose variations. Extensive experiments on three benchmark datasets with views demonstrates the effectiveness of MGPFNet which outperforms state-of-the-art methods in most cases, confirming the benefit of using views in re-ID.
Latent Fine-grained Features embedding model for Unsupervised Zero-shot Learning

Abstract Zero-shot learning (ZSL) aims to identify image categories that never appear in the training set by learning a mapping from visual information to the semantic space. For existing works in recent years, it has been paying much attention to user-defined-attributes-based supervised ZSL models and manual annotation-based unsupervised ZSL methods, whilst the important drawback of user-defined attributes is ignored. User-defined attributes usually need to be precisely annotated not only for seen classes but also for unseen classes. This procedure of collecting user-defined attributes is an error-prone and time-consuming work limiting the scalability of the methods to a great extent. Moreover, user-defined attributes are semantic embedding but they are not exhaustive. In this paper, we propose a manual annotation-free unsupervised ZSL method with a great scalability which is a benefit for the large-scale ZSL tasks, just using public word vectors of categories without any dedicated attributes annotation efforts. In addition, our approach can automatically extract latent fine-grained features to reduce visual information losses caused by the absence of user-defined attributes. Extensive experimental results show that our method outperforms the previous methods among the manual annotation-free unsupervised ZSL methods.
Android Application for Posture Analysis using Tensorflow and Computer Vision

Human posture is an important body part that indicates the fundamental structure of the human body. Hence, the aim of this project is to develop an android application that can analyze on the human body posture by capturing an image of the body and provide guide to improve it. It is decided that using Microsoft Azure AI: Custom Vision and Tensorflow as the machine learning approaches for this project is reasonable. The development of this project used the Rapid Application Development (RAD) model which consists of the requirement planning that required the collection requirements for the project from the users. The user design that needed to build prototype for deciding system design, the construction phases that focus on coding and testing, while lastly the cutover phase that last test will be conducted, and the project will be published. The expected outcome of this project is that the application can conveniently help the people that needed to improve their body posture in a more simple and effective way. In the future, this project is hoped to be enhanced with more features and reliable functionalities that improve the effectiveness of improving human body posture.
Venn Diagrams with “Most”: A Natural Logic Approach

This note exposes a little-known fact originally proposed by Nicholas Rescher in 1965, that the generalized second-order quantifier “Most” and the rules governing its behavior can be incorporated into Euler-Venn diagrams with an iconic notion of an arrow and its head and vane extensions and contractions. The objective is then to analyse this work further and to link it with the related but independently developed recent work in the area of natural logic.
The Design of the SBMPTN Tryout System on the Official Website of the Tutoring Institute “Road to PTN”

The design of the Road to PTN Tutoring Institution official website serves to assist students in preparing for the SBMPTN, as well as complete information from Road to PTN’s social media; LINE, Instagram, Youtube and Twitter. On the website that is built there are several main features, those are simulation, a user can buy a tryout class by filling up the balance (top up) using a bank ATM, the user can also download the test results, see the rank results, and get the results of the talent interest analysis. Several predictions of campus rank and department are displayed in the premium tryout class including the leaderboard rank profile (top 10). Beside that there is information on the article menu which contains tips and motivation. Issues published are made via the admin dashboard including confirmation of user activity. In the tests carried out on the Road To PTN official website, users can use all the available features including, login, create an account, set up accounts, fill in balances, withdraw balances, perform simulations, view rank results, download test results and create articles or read articles. And the admin has been able to process the data on the website. This is evidenced by the results of user testing functional testing and admin functional testing which results in all features being successfully used as expected.
An Overview on State-of-Art in Mid-Air Writing and Recognition Systems

Moving hand in the air with an intention to convey a character, symbol, or word is a dynamic gesture. These gestures are characterized by projected trajectories of the hand movement. Automated recognition of these air trajectories is open research due to inherent complexities. Research works in this area range from isolated character trajectory recognition to continuous word trajectory recognition. Attempts have been made to develop in-air writing systems in order to mimic natural pen-paper writing. At the other end, algorithms have been reported which presume the existence of unwanted, ligature strokes within the legitimate strokes. Right from the acquisition of the trajectories to the spotting of the legitimate gesture boundaries is principal challenges. This paper gives a comprehensive overview of various schemes employed for developing Mid-Air Writing (MAW) systems. An intensive walk through the literature suggests that most of the concerned schemes are variations to Hidden Markov Model (HMM), Dynamic Time Warping (DTW), and Longest Common Subsequence (LCS) techniques. However, some novel schemes targeting MAW systems have also been discussed in this paper.
White light interference microscopy and cell counting with wide field of view

We report classification between normal and anemic erythrocytes by determining cell counts computationally using Circular Hough transform algorithm in matlab and quantifying phase map which are important for early diagnosis of diseases.
Design of Distributed Server Management Platform in Data Center

With the in-depth construction of the energy Internet of Things, the continuous and rapid development of cloud computing and edge computing, a large number of data centers will be deployed accordingly and the corresponding energy consumption will increase sharply. According to the actual production and operation needs of the power information system, this paper studies and designs a set of distributed server energy management platform for the data center based on the detailed demand analysis. Through the trial operation of this platform within the company, it can be seen that the platform can realize the dynamic collection and control of important indicators such as server temperature and power in the data center, so that operation and maintenance personnel can more reasonably and accurately manage the server business scheduling in the data center, while reducing the energy consumption of the data center and improving the energy efficiency management ability.
Recognition of Hygiene Activities by Means of Multimodal Sensors

In this work, we present an approach for the recognition of hygiene activities by means of fusion sensors. On the one hand, hygiene habits are related to the correct behavior and functional status of people. Therefore, the monitoring of these activities represent a research outcome with immediate practicality, such as guaranteeing the correct functional status of people while Covid-19 confinement. In the proposed approach, multi sensorial devices are integrated in the toilet to recognize hygiene activities. First, an architecture of non-invasive sensors and data processing methods, which is a key element to guarantee the privacy of inhabitants specifically in the space of toilets, is proposed. The type of sensor proposed in this work are audio, ambient and inertial sensors. A fog computing approach to recognize patterns in real-time close to the sensor which are collected is introduced. The case studies presented here are: i) audio recognition to provide water and human event detection, ii) inertial sensors determine hand washing movements by wristband devices, iii) methane and hydrogen sensors to detect defecation, and iv) humidity sensor to detect showering.
Automatic Docking Design of Flange Pipe Based on Spatial Information

With the development of ship industry, pipeline system has been more and more widely used. However, the pipeline site space is narrow. Mechanical and electrical equipments are of all kinds. The pipeline cable is densely distributed. All problems mentioned above seriously affects the pipeline construction. In order to solve the problem of pipeline docking, the method of making model and comprehensive lofting is usually adopted. However, this method is technically backward. The operation is inconvenient due to the narrow onsite space. The required period is long. With low information utilization and accuracy, the method of making model and comprehensive lofting has a great influence on the development cycle and design quality. In order to solve these problems, this paper presents a design method and system of flange pipe docking. A binocular camera is used to obtain the image of the pipe joint. The center position of the flange surface and array holes on the flange surface are extracted from the image, which is bias of subsequent work. First, the path of the intermediate connecting pipe is calculated. Then, according to the pipe standards of intermediate connection pipe and production specification of enterprise, the model of intermediate connecting pipe is automatically generated by using secondary development technology of CAD. In this paper, the binocular vision technology is combined with pipeline docking design, which significantly improves the utilization rate of field space information and design efficiency, and solves the problems of inconvenient operation and safety risk of traditional profiling method.
Unsupervised dimensionality reduction based on fusing multiple clustering results

The majority of the classical dimensionality reduction methods can be unified into a graph-embedding-based framework. A fixed graph constructed in a high-dimensional space has been extensively employed in the graph-embedding-based dimensionality reduction methods. However, a fixed graph often cannot characterize the structure of high-dimensional data owing to the curse of dimensionality. To solve this problem, we combine graph construction and dimensionality reduction into a coherent framework. Thus, the constructed graph can be updated dynamically in dimensionality reduction. In the existing methods based on the coherent framework, graphs are usually constructed by a type of neighborhood relationship and single clustering result. This study proposes an unsupervised dimensionality reduction method guided by fusing multiple clustering results. In the proposed method, multiple clustering results are first obtained by the k-means algorithm, and then a graph is constructed using a weighted co-association matrix of fusing the clustering results to capture data distribution information. Based on the graph, we present an objective function of combining graph construction and dimensionality reduction to implement mutual guidance between them. Numerical experiments on real data sets illustrate that the proposed method achieves significant improvement over some representative and state-of-the-art unsupervised dimensionality reduction methods.
System Level Knowledge Representation for Metacognition in Neuroscience

Neuroscience lies at the heart of a wide range of novel scientific and technological advances, from neurotechnology to computing and the cognitive sciences. Observing and studying the human brain allows glimpses into the most valuable resource resulting from evolution: the human mind. However abundant and widely available, neuroscience research data are knowledge and resource intensive to acquire and maintain, demanding a high level of skill and expertise to be produced, accessed and applied. As in all complex knowledge domains, reasoning with neuroscience relies on implicit metacognitive processes, and metacognition relies on high level cognitive models. This paper proposes a System Level Knowledge Representation schema as an abstraction for neuroscience data aimed at supporting explicit metacognition as well as knowledge organisation, understandability and to ease the cognitive load of processing systems neuroscience data.
Topic Modelling and Clustering of Disaster-Related Tweets using Bilingual Latent Dirichlet Allocation and Incremental Clustering Algorithm with Support Vector Machines for Need Assessment

The occurrence of various types of disasters are thoroughly described in social media websites like Twitter which can be a useful source of data. This research aimed to solve the problem of bilinguality of data which caused a complexity that yielded inaccurate results during clustering. The researchers were able to develop a system which adds a bilingual topic model, Bilingual Latent Dirichlet, to an existing system. This research was able to compare in terms of precision, recall, accuracy, and area under the curve metrics, the clustering accuracy of the incremental clustering module with and without the bilingual topic model, while also classifying the needs that the Tweet will require.