Reviewerly/exHarmony huggingface dataset:
Body Part Connection, Categorization and Occlusion Based Tracking with Correction by Temporal Positions for Volleyball Spike Height Analysis

The spike height of volleyball players is important in volleyball analysis as the quantitative criteria to evaluation players' motions, which not only provides rich information to audiences in live broadcast of sports events but also makes contribution to evaluate and improve the performance of players in strategy analysis and players training. In the volleyball game scene, the high similarity between hands, the deformation and the occlusion are three main problems that influence the acquisition performance of spike height. To solve these problems, this paper proposes a body part connection, categorization and occlusion based observation model and a temporal position based correction method. Firstly, skin pixel filter based connection detection solves the problem of high similarity between hands by judging whether a hand is connected to the spike player. Secondly, the body part categorization based observation uses the probability distribution map of hand to determine the category of each body part to solve the deformation problem. Thirdly, the occlusion part detection based observation eliminates the influence of the views with occluded body part by detecting the occluded views with a trained classifier of body part. At last, the temporal position based result correction combines the estimated results, which refers the historical positions, and the posterior result to obtain an optimal result by degree of confidence. The experiments are based on the videos of final and semi-final games of 2014 Japan Inter High School Men's Volleyball in Tokyo Metropolitan Gymnasium, which includes 196 spike sequences of 4 teams. The experiment results of proposed methods are that: 93.37% of test sequences can be successfully detected the spike height, and in which the average error of spike height is 5.96cm.
AFN: Attentional Feedback Network Based 3D Terrain Super-Resolution

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention based feedback mechanism named ‘Attentional Feedback Network’ (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.
VISUALIZATION TECHNOLOGIES IN WORLD RESEARCH

Every year the amount of information increases significantly, particularly in education, and at the same time, the possibilities of digital tools utilization for processing it changes. Scientific progress, virtualization and automation of many processes have a beneficial effect on this, and therefore there is a need for their processing and accounting, which entails an increase in computing power and data rates. The continuous flow of information is a necessary condition for the existence of modern civilization and the reason for the overload with information and media activity consciousness, which provoked the emergence of a new type of thinking based on the clip perception of messages. A person from all the variety of information grasps the brightest fragments that appeal to his consciousness and thus forms a chain of levels of information perception "image - title - text - understanding", where the information's visual component acts as a link from one to another and provides information connection. The visual component of information (visualization), based on certain associations, stereotypes of thinking, conveys the essence of significant event, fact, phenomenon, process, important for a person in time and space. Visualization is the most important step in the data analysis process. It helps to present research results in a simple and clear form, often serves as a key factor for decision-making in various fields. Although many people associate digital visualization with linear graphs and tables exclusively, in reality it is a big concept, a system of transmitting complex ideas, patterns and data through visual images. The analysis of the notions "visualization", "information visualization", "graphic image", "graph" is performed in the article. Varieties and methods of digital visualizations by different researchers are analyzed. The methods of digital visualization by Swiss researchers, who presented them in the form of a periodic table, are described. The main types of digital visualizations with examples and their possible application are described in detail.
MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textual similarity. However, the reliability of the fine-tuned text classifiers is an often underlooked performance criterion. For instance, one may desire a model that can detect out-of-distribution (OOD) samples (drawn far from training distribution) or be robust against domain shifts. We claim that one central obstacle to the reliability is the over-reliance of the model on a limited number of keywords, instead of looking at the whole context. In particular, we find that (a) OOD samples often contain in-distribution keywords, while (b) cross-domain samples may not always contain keywords; over-relying on the keywords can be problematic for both cases. In light of this observation, we propose a simple yet effective fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. When applied to various pre-trained language models (e.g., BERT, RoBERTa, and ALBERT), we demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy. Code is available at https://github.com/alinlab/MASKER.
Context-Aware Graph Convolution Network for Target Re-identification

Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks. However, many of them consider content similarity separately and fail to utilize the context information of the query and gallery sets, e.g. probe-gallery and gallery-gallery relations, thus hard samples may not be well solved due to the limited or even misleading information. In this paper, we present a novel Context-Aware Graph Convolution Network (CAGCN), where the probe-gallery relations are encoded into the graph nodes and the graph edge connections are well controlled by the gallery-gallery relations. In this way, hard samples can be addressed with the context information flows among other easy samples during the graph reasoning. Specifically, we adopt an effective hard gallery sampler to obtain high recall for positive samples while keeping a reasonable graph size, which can also weaken the imbalanced problem in training process with low computation complexity. Experiments show that the proposed method achieves state-of-the-art performance on both person and vehicle re-identification datasets in a plug and play fashion with limited overhead.
Construction of propagators for divisible dynamical maps

Divisible dynamical maps play an important role in characterizing Markovianity on the level of quantum evolution. Divisible maps provide important generalization of Markovian semigroups. Usually one analyzes either completely positive or just positive divisibility meaning that the corresponding propagators are defined in terms of completely positive or positive maps, respectively. For maps which are invertible at any moment of time the very existence of propagator is already guaranteed and hence the only issue is (complete) positivity and trace-preservation. However, for maps which are not invertible the problem is much more involved since even the existence of a propagator is not guaranteed. In this paper we propose a simple method to construct propagators of dynamical maps using the concept of generalized inverse. We analyze both time-continuous and time-discrete maps. Since the generalized inverse is not uniquely defined the same applies for the corresponding propagator. In simple examples of qubit evolution we analyze it turns out that additional requirement of complete positivity possibly makes the propagator unique.
Enabling Collaborative Data Science Development with the Ballet Framework

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, a lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to an ML performance evaluation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation

Paraphrase generation aims to generate semantically consistent sentences with different syntactic realizations.Most of the recent studies rely on the typical encoder-decoder framework where the generation process is deterministic.However, in practice, the ability to generate multiple syntactically different paraphrases is important.Recent work proposed to cooperate variational inference on a target-related latent variable to introduce the diversity.But the latent variable may be contaminated by the semantic information of other unrelated sentences, and in turn, change the conveyed meaning of generated paraphrases.In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free.Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency.So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases.The experiments show that our model outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our model can generate multiple different paraphrases by assembling different syntactic variables.
Cybersecurity Education to Create Awareness in Artificial Intelligence Applications for Developers and End Users

Abstract This new era brings new promises of technology that will bring economic and societal benefits. Artificial Intelligence is to be the disruptor for work and even military technological applications. However, developers and end-users will play keys roles in how this technology is developed and ultimately used. Among these two groups, there are cybersecurity concerns that need to be considered. In this paper, the researchers address the process of secure development and testing. Also, for the end-user appropriate methods, procedures, and recommendations are defined that can mitigate the overall use of this technology within an enterprise.
A Knowledge Graph Based Approach for Mobile Application Recommendation

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.
Blockchain-Based Security Services for Fog Computing

Fog computing is a paradigm for distributed computing that enables sharing of resources such as computing, storage and network services. Unlike cloud computing, fog computing platforms primarily support non-functional properties such as location awareness, mobility and reduced latency. This emerging paradigm has many potential applications in domains such as smart grids, smart cities, and transport management. Most of these domains collect and monitor personal information through edge devices to offer personalized services. A centralized server either at the level of cloud or fog, has been found ineffective to provide a high degree of security and privacy-preserving services. Blockchain technology supports the development of decentralized applications designed around the principles of immutability, cryptography, consistency preserving consensus protocols and smart contracts. Hence blockchain technology has emerged as a preferred technology in recent times to build trustworthy distributed applications. The chapter describes the potential of blockchain technology to realize security services such as authentication, secured communication, availability, privacy and trust management to support the development of dependable fog services.
Challenges in Real-Life Face Recognition with Heavy Makeup and Occlusions Using Deep Learning Algorithms

We study a Face Recognition problem caused by unforeseen in the training set variations in face images, related to artistic makeup and other occlusions. Existing Artificial Neural Networks (ANNs) have achieved a high recognition accuracy; however, in the presence of significant variations, they perform poorly. We introduce a new data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup face data, and then examine the performance of the ANNs under different conditions. In our experiments, the recognition accuracy has decreased when the test images include an unseen type of the makeup and occlusions, happened in a real-world scenario. We show that the fusion off the training set with several heavy makeup and other occlusion images can improve the performance.
Target-Oriented Deformation of Visual-Semantic Embedding Space

Multimodal embedding is a crucial research topic for cross-modal understanding, data mining, and translation. Many studies have attempted to extract representations from given entities and align them in a shared embedding space. However, because entities in different modalities exhibit different abstraction levels and modality-specific information, it is insufficient to embed related entities close to each other. In this study, we propose the Target-Oriented Deformation Network (TOD-Net), a novel module that continuously deforms the embedding space into a new space under a given condition, thereby providing conditional similarities between entities. Unlike methods based on cross-modal attention applied to words and cropped images, TOD-Net is a post-process applied to the embedding space learned by existing embedding systems and improves their performances of retrieval. In particular, when combined with cutting-edge models, TOD-Net gains the state-of-the-art image-caption retrieval model associated with the MS COCO and Flickr30k datasets. Qualitative analysis reveals that TOD-Net successfully emphasizes entity-specific concepts and retrieves diverse targets via handling higher levels of diversity than existing models.
Fiction Popularity Prediction Based on Emotion Analysis

In addition to bringing us knowledge, books also bring us emotional experiences. How do the emotional fluctuations brought by books affect readers’ evaluation of them? What is the difference in emotional fluctuations between books of different popularity? In this paper, we model and analyse the emotional fluctuations of different fiction books with different popularity and study the feasibility of predicting the popularity of fiction books using emotional fluctuations and recurrent neural networks. A new dataset is also generated to support this research and other related researches. Our proposed method obtained the best accuracy of 73.4% for predicting the popularity of fiction books and 41.4% for predicting genres. Some interesting data insights are also extracted from the dataset.
Impact of Work Ethics and Work Environment on Project Success with the Moderating Role of Job Engagement

There are many projects going on in global business.Work ethics and work environment is very important because employee's behavior strongly contribute to the success of the project in global business.The objective of this study to examine the impact of work ethics work environment with moderating role of job engagement on project success in projectbased organization of Pakistan.Survey method is used to collect data from the managers along with their employees from different organizations in Islamabad and Rawalpindi, Pakistan.Managers are selected because they are considered the most knowledgeable personnel of the organizations who can provide reliable data.A structured questionnaire was distributed among 250 targeted individuals' through covariance sampling technique.The questionnaire was followed by the interview to overcome the ambiguities.The response rate was 96.4% which shows the interest of the targeted population and the importance of the study.To check the reliability, we applied internal consistency reliability, correlation.Furthermore, to test the posit hypothesis, was used regression analysis as a statistical analysis technique for measuring the nature and intensity of relationship between work ethics, working environment, work engagement and project success.The study shows that there is a positive direct relation between independent and dependent variables like work ethics, work environment, work engagement and project success.The study concludes that work ethics and work environment is very important for project success.The current study leads to the solid awareness towards the identification of work ethics, working environment, work engagement and their association with the project success.
Performance Optimization System for Hadoop and Spark Frameworks

Abstract The optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal tradeoff is a challenge, as a high compression factor may underload Input/Output but overload the processor. The paper aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyzes.
Imaging three-dimensional single-atom arrays all at once

Simultaneous imaging of a three-dimensional distribution of point sources is presented. In a two-lens microscope, the point-spreads on the quasi-image plane, which is located between the Fourier and image planes, are spatially distinct, so a set of Fresnel lenslets can perform individual wave-front shaping for axial and lateral rearrangements of the images. In experiments performed with single atoms and holographically programmed lenslets, various three-dimensional arrangements of point sources, including axially aligned atoms, are successfully refocused on the screen, demonstrating the simultaneous and time-efficient detection of the three-dimensional holographic imaging. We expect that non-sequential real-time measurements of three-dimensional point sources shall be in particular useful for quantum correlation measurements and in situ tracking of dynamic particles.