Reviewerly/exHarmony huggingface dataset:
Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture Systems

The rapid development of algorithms for skeletal postural detection with relatively inexpensive contactless systems and cameras opens up the possibility of monitoring and assessing the health and wellbeing of humans. However, the evaluation and confirmation of posture classifications are still needed. The purpose of this study was therefore to develop a simple algorithm for the automatic classification of human posture detection. The most affordable solution for this project was through using a Kinect V2, enabling the identification of 25 joints, so as to record movements and postures for data analysis. A total of 10 subjects volunteered for this study. Three algorithms were developed for the classification of different postures in Matlab. These were based on a total error of vector lengths, a total error of angles, multiplication of these two parameters and the simultaneous analysis of the first and second parameters. A base of 13 exercises was then created to test the recognition of postures by the algorithm and analyze subject performance. The best results for posture classification were shown by the second algorithm, with an accuracy of 94.9%. The average degree of correctness of the exercises among the 10 participants was 94.2% (SD1.8%). It was shown that the proposed algorithms provide the same accuracy as that obtained from machine learning-based algorithms and algorithms with neural networks, but have less computational complexity and do not need resources for training. The algorithms developed and evaluated in this study have demonstrated a reasonable level of accuracy, and could potentially form the basis for developing a low-cost system for the remote monitoring of humans.
Enhancing Dialog Coherence with Event Graph Grounded Content Planning

How to generate informative, coherent and sustainable open-domain conversations is a non-trivial task. Previous work on knowledge grounded conversation generation focus on improving dialog informativeness with little attention on dialog coherence. In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog. We first extract event chains from narrative texts and connect them as a graph. We then present a novel event graph grounded Reinforcement Learning (RL) framework. It conducts high-level response content (simply an event) planning by learning to walk over the graph, and then produces a response conditioned on the planned content. In particular, we devise a novel multi-policy decision making mechanism to foster a coherent dialog with both appropriate content ordering and high contextual relevance. Experimental results indicate the effectiveness of this framework in terms of dialog coherence and informativeness.
Large-Scale Video Retrieval via Deep Local Convolutional Features

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.
Facial Image Denoising Using Convolutional Autoencoder Network

Noise effects can interfere the face recognition process in outdoor conditions. Therefore, image denoising topic is the classical issue in the field of image processing and computer vision subjects. In this paper, we show that the solution of denoising process using the autoencoder networks based on the ORL face database. The proposed method can support face recognition systems designed for use in an outdoor environment as the preprocessing stage and it can provide the effective results after training process.
Application Design The Data Collection Features of The Hotel Shades of Rantauprapat Using VBNET

Hotel reservation is an activity where a customer comes to the hotel to book a room and stay in a room that has been booked. Orders are made by customers, at the hotel Shades Rantaupapat the process of collecting the customers who will stay is still done manually. So that customers have to come to the hotel to make reservations directly. This reservation information system is an application that can help speed up registration or registration when at the reception. Cancellation can also be directly managed by the admin or receptionist. So as to provide other convenience in making reservations. In making this information system using a system development method in the form of a waterfall. At the time of making the programs using Visual Basic NET 2010. For databases that are used to create databases using SQL Server 2008. The application of this information system can help employees to record customers who stay overnight and manage information needs needed to support the hotel business.
A Practical Data Repository for Causal Learning with Big Data

The recent success in machine learning (ML) has led to a massive emergence of AI applications and the increases in expectations for AI systems to achieve human-level intelligence. Nevertheless, these expectations have met with multi-faceted obstacles. One major obstacle is ML aims to predict future observations given real-world data dependencies while human-level intelligence AI is often beyond prediction and seeks the underlying causal mechanism. Another major obstacle is that the availability of large-scale datasets has significantly influenced causal study in various disciplines. It is crucial to leverage effective ML techniques to advance causal learning with big data. Existing benchmark datasets for causal inference have limited use as they are too “ideal”, i.e., small, clean, homogeneous, low-dimensional, to describe real-world scenarios where data is often large, noisy, heterogeneous and high-dimensional. It, therefore, severely hinders the successful marriage of causal inference and ML. In this paper, we formally address this issue by systematically investigating existing datasets for two fundamental tasks in causal inference: causal discovery and causal effect estimation. We also review the datasets for two ML tasks naturally connected to causal inference. We then provide hindsight regarding the advantages, disadvantages and the limitations of these datasets. Please refer to our github repository ( https://github.com/rguo12/awesome-causality-data ) for all the discussed datasets in this work.
Developing Future Teachers’ Digital Culture: Challenges and Perspectives

The study emphasises the importance of digital technology as an integral part of modern society and as the driving force of economic development, the challenges and perspectives of modern digital society. We further explore the basic concepts of digital society as 'digital culture', 'digital competence', and 'digital natives'. The primary purpose of this study is to examine future teachers' knowledge based on the questionnaire about the digital culture which was conducted among 97 future teachers from the faculty of Education at Abai national pedagogical university in Kazakhstan. The obtained data expanded our understanding of future teachers' knowledge or understanding of digital culture. We further analyse the results and suggest a solution to develop future teachers' knowledge and competence in the mentioned field.
Temporal Adaptive Alignment Network for Deep Video Inpainting

Video inpainting aims to synthesize visually pleasant and temporally consistent content in missing regions of video. Due to a variety of motions across different frames, it is highly challenging to utilize effective temporal information to recover videos. Existing deep learning based methods usually estimate optical flow to align frames and thereby exploit useful information between frames. However, these methods tend to generate artifacts once the estimated optical flow is inaccurate. To alleviate above problem, we propose a novel end-to-end Temporal Adaptive Alignment Network(TAAN) for video inpainting. The TAAN aligns reference frames with target frame via implicit motion estimation at a feature level and then reconstruct target frame by taking the aggregated aligned reference frame features as input. In the proposed network, a Temporal Adaptive Alignment (TAA) module based on deformable convolutions is designed to perform temporal alignment in a local, dense and adaptive manner. Both quantitative and qualitative evaluation results show that our method significantly outperforms existing deep learning based methods.
A Video Compression Framework Using an Overfitted Restoration Neural Network

Many existing deep learning based video compression approaches apply deep neural networks (DNNs) to enhance the decoded video by learning the mapping between decoded video and raw video (ground truth). The big challenge is to train one well-fitted model (one mapping) for various video sequences. Different with the other applications such as image enhancement whose ground truth can only be obtained in the training process, the video encoder can always get the ground truth which is the raw video. It means we can train the model together with video compression and use one model for each sequence or even for each frame. The main idea of our approach is building a video compression framework (VCOR) using overfitted restoration neural network (ORNN). A lightweight ORNN is trained for a group of consecutive frames, so that it is overfitted to this group and achieves a strong restoration ability. After that, parameters of ORNN are transmitted to the decoder as a part of the encoded bitstream. At the decoder side, ORNN can perform the same strong restoration operation to the reconstructed frames. We participate in the CLIC2020 challenge on P-frame track as the team "West-World".
An Overview of You Only Look Once: Unified, Real-Time Object Detection

This paper aims at reviewing existing YOLO architecture, its implementation and working. You only look once, is an architecture which is a regression problem. Yolo comes in different versions such as YoloV1, YoloV2 and YoloV3.The feature extractor for Yolo is Darknet. The network looks at the entire image only once. In one evaluation, a single neural network predicts bounding boxes and class probabilities directly from full images. The unified architecture is extremely fast. YOLO model processes images in real-time at 45 frames per second. Fast YOLO, an extremely fast version of Yolo, processes 155 frames per second. Yolo is better at making less localization errors as it looks at the entire image to predict objects on individual cells. This paper also provides details on the evolution and evaluation of the architecture.
Talking Head-based L2 Pronunciation Training: Impact on Achievement Emotions, Cognitive Load, and Their Relationships with Learning Performance

(2020). Talking Head-based L2 Pronunciation Training: Impact on Achievement Emotions, Cognitive Load, and Their Relationships with Learning Performance. International Journal of Human–Computer Interaction: Vol. 36, No. 16, pp. 1487-1502.
Deductive Module Extraction for Expressive Description Logics

In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.
Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.
The moderating effects of organizational commitment on the relationship between employee satisfaction and employee performance in Jordanian Islamic banks

This study aimed to analyze the effect of employee satisfaction on employee performance, and the moderating role of organizational commitment in Jordanian Islamic banks.This study was conducted using a self-administered questionnaire distributed among employees from four different Islamic banks in Jordan.A total of 303 responses were collected and analyzed using SPSS and partial least square.A significant relationship was found between employee satisfaction and employee performance.There was also a significant effect of the moderating role of organizational commitment on the relationship between employee satisfaction and employee performance.One potential reason to support this relationship is that individuals who are satisfied with their performance tend to be happier and need a commitment from the organization to improve their work performance.The finding from correlation analysis shows that implementing the idea of organizational commitment illustrates the role that it plays in achieving employee satisfaction while doing their desired work.On the other hand, this suggests that increased organizational commitment in Jordanian Islamic banks increases employee performance significantly, which can enable each bank's management to achieve its targets, missions, and visions.The study is useful for the banking sector, financial institutions and other organizations in any country that seek improvements in employee performance.
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience

When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch.Here we focus on span mismatch and show that its severity can vary from a serious error to a fully acceptable entity extraction due to the subjectivity of span annotations.For a domain-specific BERT-based NER system, we showed that 25% of the errors have the same labels and overlapping span with gold standard entities.We collected expert judgement which shows more than 90% of these mismatches are accepted or partially accepted by the user.Using the training set of the NER system, we built a fast and lightweight entity classifier to approximate the user experience of such mismatches through accepting or rejecting them.The decisions made by this classifier are used to calculate a learning-based F-score which is shown to be a better approximation of a forgiving user's experience than the relaxed F-score.We demonstrated the results of applying the proposed evaluation metric for a variety of deep learning medical entity recognition models trained with two datasets.
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

Differentiable renderers have been used successfully for unsupervised 3D structure learning from 2D images because they can bridge the gap between 3D and 2D. To optimize 3D shape parameters, current renderers rely on pixel-wise losses between rendered images of 3D reconstructions and ground truth images from corresponding viewpoints. Hence they require interpolation of the recovered 3D structure at each pixel, visibility handling, and optionally evaluating a shading model. In contrast, here we propose a Differentiable Renderer Without Rendering (DRWR) that omits these steps. DRWR only relies on a simple but effective loss that evaluates how well the projections of reconstructed 3D point clouds cover the ground truth object silhouette. Specifically, DRWR employs a smooth silhouette loss to pull the projection of each individual 3D point inside the object silhouette, and a structure-aware repulsion loss to push each pair of projections that fall inside the silhouette far away from each other. Although we omit surface interpolation, visibility handling, and shading, our results demonstrate that DRWR achieves state-of-the-art accuracies under widely used benchmarks, outperforming previous methods both qualitatively and quantitatively. In addition, our training times are significantly lower due to the simplicity of DRWR.
Principal component analysis of financial statements. A compositional approach

Financial ratios are often used in principal component analysis and related techniques for the purposes of data reduction and visualization. Besides the dependence of results on ratio choice, ratios themselves pose a number of problems when subjected to a principal component analysis, such as skewed distributions. In this work, we put forward an alternative method drawn from compositional data analysis (CoDa), a standard statistical toolbox for use when data convey information about relative magnitudes, as financial ratios do. The method, referred to as the CoDa biplot, does not rely on any particular choice of financial ratio but allows researchers to visually order firms along the pairwise financial ratios for any two accounts. Non-financial magnitudes and time evolution can be added to the visualization as desired. We show an example of its application to the top chains in the Spanish grocery retail sector and show how the technique can be used to depict strategic management differences in financial structure or performance, and their evolution over time.
Dataless Short Text Classification Based on Biterm Topic Model and Word Embeddings

Dataless text classification has attracted increasing attentions recently. It only needs very few seed words of each category to classify documents, which is much cheaper than supervised text classification that requires massive labeling efforts. However, most of existing models pay attention to long texts, but get unsatisfactory performance on short texts, which have become increasingly popular on the Internet. In this paper, we at first propose a novel model named Seeded Biterm Topic Model (SeedBTM) extending BTM to solve the problem of dataless short text classification with seed words. It takes advantage of both word co-occurrence information in the topic model and category-word similarity from widely used word embeddings as the prior topic-in-set knowledge. Moreover, with the same approach, we also propose Seeded Twitter Biterm Topic Model (SeedTBTM), which extends Twitter-BTM and utilizes additional user information to achieve higher classification accuracy. Experimental results on five real short-text datasets show that our models outperform the state-of-the-art methods, and especially perform well when the categories are overlapping and interrelated.
Real-World Automatic Makeup via Identity Preservation Makeup Net

This paper focuses on the real-world automatic makeup problem. Given one non-makeup target image and one reference image, the automatic makeup is to generate one face image, which maintains the original identity with the makeup style in the reference image. In the real-world scenario, face makeup task demands a robust system against the environmental variants. The two main challenges in real-world face makeup could be summarized as follow: first, the background in real-world images is complicated. The previous methods are prone to change the style of background as well; second, the foreground faces are also easy to be affected. For instance, the ``heavy'' makeup may lose the discriminative information of the original identity. To address these two challenges, we introduce a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity. Specifically, we disentangle the face images to two different information codes, i.e., identity content code and makeup style code. When inference, we only need to change the makeup style code to generate various makeup images of the target person. In the experiment, we show the proposed method achieves not only better accuracy in both realism (FID) and diversity (LPIPS) in the test set, but also works well on the real-world images collected from the Internet.
Role of Social Media Marketing in the Successful Implementation of Business Management

For increasing the sales, building the brands and driving the traffic of web to connect the people through platform of social media refers to as social media marketing. The social media marketing include running of social media advertisements, engaging followers, listening to followers, analyzing results and publishing content on social media profiles. Now in these days the major platforms of social media are Pinterest, Instagram, Snapchat, Twitter, Facebook, LinkedIn, YouTube and many more. These social media platform plays a major role for developing and increasing the business.
XDN: Towards Efficient Inference of Residual Neural Networks on Cambricon Chips

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine.