Reviewerly/exHarmony huggingface dataset:
Integrated Modalities And Multi-Level Granularity: Towards A Unified Video-Text Retrieval Framework

Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. Recent researches handle different issues of this task such as exploiting multi-modal video cues, hierarchical reasoning, and learning pre-trained models. The implementations of these approaches vary a lot, which brings difficulty for the further research. Therefore, in this paper, we provide a unified video-text retrieval framework that has following features: 1) a modular design for easy modification of different structures of deep learning models; 2) training and test pipelines of the state-of-the-art (SOTA) models that leverage hierarchy cues and interactions between different levels of granularity and different video modalities; 3) support for various benchmark datasets; 4) demo exhibitions and well tested and documented. We hope our unified framework useful and efficient for the further research.
P‐85: Improvement of the Transmittance of ADS Mode in 8K Display

One of the main factors for popularization of 8K TV is the drastically deteriorated transmittance. We proposed several ways to improve the transmittance of ADS mode in 8K display, such as modifying the traditional pixel layout and adopting new materials. The transmittance gap between 4K and 8K has narrowed.
Framework for Differentially Private Data Analysis with Multiple Accuracy Requirements

Organizations who collect sensitive data, such as hospitals or governments, may want to share the data with others. There could be multiple applications or analysts that want to use this data. Directly releasing the data could violate the privacy of individual data contributors. To address this privacy concern, differential privacy [1,2] has arisen as a popular technique for allow for sensitive data analysis. It frequently works through the addition of randomized noise to the output of the analysis, which is controlled through the privacy parameter or budget ε. This noise affects the utility of the analyses, where a smaller budget allocation results in larger noise values, and some applications may set accuracy requirements on the output to restrict the amount of noise added [3,9,10].
Towards Semantic Web Services Density Clustering Technique

With the rapid evolution of SOA (Service Oriented Architecture) and wide techniques of web services, the web service semantic has attracted the most attention during recent years. The WS (Web Service) has become a promising technology for the development, deployment, and integration of Internet applications. The large-scale deployment of web services brings great value to web service customers; however, the large-scale, dynamic, and heterogeneous web services make it difficult to discover and compose WS. Therefore, as one of the challenges to interpreting relationships of semantics is to understand the functional associations between ontology concepts. Several approaches have been described to extract relationships between ontology concepts using term-matching methods. However, it is necessary to have flexible and efficient methods to ensure functional relationships. To facilitate the discovery the relevance of a service to a query, there are many solutions based on the use of Semantic Web technologies to develop homogeneous service descriptions to reason on and to support precise and flexible discovery. In this paper, we proposed an approach to measure semantic similarity by combining Latent Semantic Analysis Similarity (LSA) and IO-MATCHING semantic matching. The density clustering services method is treated as a pre-processing method for semantic matching between the web service and the user requirements. The results of the simulation show a high accuracy to cluster semantically web services.
Auto-Precision Scaling for Distributed Deep Learning

It has been reported that the communication cost for synchronizing gradients can be a bottleneck, which limits the scalability of distributed deep learning. Using low-precision gradients is a promising technique for reducing the bandwidth requirement. In this work, we propose Auto Precision Scaling (APS), an algorithm that can improve the accuracy when we communicate gradients by low-precision floating-point values. APS can improve the accuracy for all precisions with a trivial communication cost. Our experimental results show that for many applications, APS can train state-of-the-art models by 8-bit gradients with no or only a tiny accuracy loss (<0.05%). Furthermore, we can avoid any accuracy loss by designing a hybrid-precision technique. Finally, we propose a performance model to evaluate the proposed method. Our experimental results show that APS can get a significant speedup over state-of-the-art methods. To make it available to researchers and developers, we design and implement CPD (Customized-Precision Deep Learning) system, which can simulate the training process using an arbitrary low-precision customized floating-point format. We integrate CPD into PyTorch and make it open-source ( https://github.com/drcut/CPD ).
Knowledge-Driven Network for Object Detection

Object detection is a challenging computer vision task with numerous real-world applications. In recent years, the concept of the object relationship model has become helpful for object detection and has been verified and realized in deep learning. Nonetheless, most approaches to modeling object relations are limited to using the anchor-based algorithms; they cannot be directly migrated to the anchor-free frameworks. The reason is that the anchor-free algorithms are used to eliminate the complex design of anchors and predict heatmaps to represent the locations of keypoints of different object categories, without considering the relationship between keypoints. Therefore, to better fuse the information between the heatmap channels, it is important to model the visual relationship between keypoints. In this paper, we present a knowledge-driven network (KDNet)—a new architecture that can aggregate and model keypoint relations to augment object features for detection. Specifically, it processes a set of keypoints simultaneously through interactions between their local and geometric features, thereby allowing the modeling of their relationship. Finally, the updated heatmaps were used to obtain the corners of the objects and determine their positions. The experimental results conducted on the RIDER dataset confirm the effectiveness of the proposed KDNet, which significantly outperformed other state-of-the-art object detection methods.
Computer-mediated communication in DIY discourse

Do-It-Yourself discourse (DIY discourse) is communication aimed at teaching laymen to do professional processes and exchanging experiences in professional spheres. It is a very called-for tool for non-vocational training. DIY discourse comprises non-verbal, verbal, and polycode texts. In this research we analyzed general characteristics of DIY discourse. On the first stage of our research we selected articles from www.wikihow.com that teach the lay audience how to do different things. On the second stage of the research we analyzed genres that occur in the computer-mediated environment and polycode texts that represent the computer-mediated communication (CMC) in the DIY discourse. From a semiotic point of view, non-verbal texts are an emblem of a professional process, verbal texts provide a symbol side, and polycode texts combine an emblem and a symbol. We have found out that DIY discourse comprises several genres with different semiotic codes. It can have non-verbal, verbal and polycode format. We have come to the conclusion that polycode texts are the specific feature of computer-mediated communication. They are widely employed in DIY discourse to achieve a more profound comprehension of the professional processes.
Estimation of Classification Rules From Partially Classified Data

We consider the situation where the observed sample contains some observations whose class of origin is known (that is, they are classified with respect to the g underlying classes of interest), and where the remaining observations in the sample are unclassified (that is, their class labels are unknown). For class-conditional distributions taken to be known up to a vector of unknown parameters, the aim is to estimate the Bayes’ rule of allocation for the allocation of subsequent unclassified observations. Estimation on the basis of both the classified and unclassified data can be undertaken in a straightforward manner by fitting a g-component mixture model by maximum likelihood (ML) via the EM algorithm in the situation where the observed data can be assumed to be an observed random sample from the adopted mixture distribution. This assumption applies if the missing-data mechanism is ignorable in the terminology pioneered by Rubin (). An initial likelihood approach was to use the so-called classification ML approach whereby the missing labels are taken to be parameters to be estimated along with the parameters of the class-conditional distributions. However, as it can lead to inconsistent estimates, the focus of attention switched to the mixture ML approach after the appearance of the EM algorithm (Dempster et al. ). Particular attention is given here to the asymptotic relative efficiency (ARE) of the Bayes’ rule estimated from a partially classified sample. Lastly, we consider briefly some recent results in situations where the missing label pattern is non-ignorable for the purposes of ML estimation for the mixture model.
57‐3: <i>Invited Paper:</i> Diversification of Processing Requirements for Large Format IoT Displays: 8K, Programmatic and LED Walls

Large format IoT displays support a range of exciting visual experiences which are encountered in our day‐to‐day lives. Four processor roles, media player, display controller, touch controller and CV/AI module, leverage four processor types, scalar, vector, spatial and matrix, to power these experiences. Algorithm characteristics such as arithmetic, memory access and data‐dependence complexity influence the suitability of an architecture to a workload. Including, GPUs to rendering, CPUs to interactive application, and FPGAs to custom video pipelines. Programmatic advertising, touch‐less kiosks and CODEC enhancements to minimize bitrate of 8K video streams can require inference (AI) workloads. Matrix (I PU) type processors are designed to address this workload type. The narrow pitch LED wall display market is growing at 10x the overall video wall display market rate and leverages spatial FPGA processing in LED controllers, also requiring 8K resolution at smaller surface area sizes as pixel pitch reduces. Requirements on each heterogenous processor type is concluded to be growing with the combination of market trends.
Improving Adaptive Monitoring with Incremental Runtime Model Queries

Runtime models are often employed in different forms in self-adaptive software. They reflect, due to the causal connection, the current state of the adaptable software. Runtime model querying can be used to check whether the runtime model indicates the need for an adaptation or collect the information necessary to decide which adaptation should be performed. Given a set of runtime model queries, a natural question is how the effort to obtain and maintain the required information at runtime can be reduced. Besides the general need to reduce the overhead resulting from self-adaptation concerning its environmental impact, also restricted resources may make this a particularly relevant optimization. Two opportunities for effort reduction are the query evaluation and the necessary system state sensing. In this paper we consider both opportunities by investigating how our approach for adaptive monitoring with architecture runtime models can be improved through a better integration with an enhanced mechanism for incremental querying. We outline how incremental queries in this context can be optimized to better support adaptive monitoring. We compare different approach variants and present first very promising evaluation results that indicate that the optimized incremental queries have the potential to substantially reduce the monitoring effort and query time.
Fast Solvers for Minimal Radial Distortion Relative Pose Problems

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.
Using Principal Component Analysis and Online Sequential Extreme Learning Machine Approach for Transient Electromagnetic Nonlinear Inversion

The traditional artificial neural network based on gradient descent method result in low computational efficiency and local convergence for transient electromagnetic inversion. To solve the these problems, a hybrid approach combining principal component analysis (PCA) and online sequential extreme learning machine (OSELM) is proposed in this paper (PCA-OSELM) and is applied in the transient electromagnetic inversion. First, a principal component analysis method is introduced to reduce the dimension of vertical magnetic field data and improves the computational efficiency. Then, the new samples obtained from the data sets are added to the training samples as the next update information to establish the OSELM prediction models, so that improve the inversion accuracy. Finally, the inversion results of the two typical layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed approach can well solve the modeling nonlinear problem that high-dimensional data generated by transient electromagnetic method. Moreover, compared with other nonlinear inversion methods (OSELM, ELM), the PCA-OSELM achieves more accurate, better generalization ability and higher computational efficiency, which can provide new ideas for the application of neural networks in geophysical inversion.
News Video Story Segmentation with Multi-Modality Features

In this paper, we propose a detection-based method about the news video story segmentation. First, we extract the frames contained news title caption. Since every news story must have a counterpart title caption, and the news title caption always appears after the corresponding news story has begun for a few minutes. Then we can define a possible scope of story boundary candidates according to the time points of news title captions appearing. Finally, we select all the ends of the sentences in the possible scope as our boundary candidates. In this way, the story segmentation task can be converted into a boundary/non-boundary classification problem. To address this problem, we extract multimodal features, including lexical, acoustic, and visual features, and adopt a random forest trained with these features to accomplish the binary classification task. Several experiments are conducted to evaluate the performance of our proposed method, and the results suggest that the fusion of multi-modality features is beneficial to improve the performance of story boundary classification.
Findings for Flexible Digital Model (MFD) Courses to enhance women participation in Industrial and Systems Engineering Courses

Digital education has reached enormous advances and acceptance in the last decade. COVID 19 has triggered a boom in the digital education implementation. Those courses that never had been delivered in a digital format, suddenly were forced to be prepared and presented as digital education. The nature of different courses might facilitate this new reality, depending if they are theoretical, numerical or mixed. This paper has evaluated a set of characteristics deployed in different engineering courses transformed into digital courses, depending on its nature. On the other hand, this analysis had a focus on the women participation in engineering courses and their evaluations of such digital course’s characteristics. The main purpose of the present paper is to answer the question of what characteristics within the course methodology design, works and are evaluated better by students. A survey was designed and applied to the 87% of the Industrial and Systems Engineering program population in Tecnologico de Monterrey, Sonora Mexico. Data was statistically analyzed to find if there were any difference between female and male opinions about the course characteristics and within each gender in each kind of course. Also, a correlation analysis was made to identify how the elements influenced each other and if they should be combined depending on the desired result. An additional analysis took place to identify the level of participation of female and male students before and during pandemic time. Findings reveal interesting coincidences and differences in evaluating some of those digital course’s characteristics depending on the gender of students and course nature, such as women participation and the differences in perception of competitiveness among women and men. Some of these findings are: 1) In mixed subjects, male students identify a higher academic environment that encourages more competitiveness than female students. 2) Another finding is that men seem to have greater motivation to attend class sessions in numerical subjects. 3) Among female students a negative correlation between the frequency of the instructor request of a final reflection analysis of the session with considering a competitiveness academic environment. 4) Talking about mixed courses, all female students evaluated with the highest score in the question about motivation to be present in the session and the identification of moments and activities that allowed them to share opinions. To reinforce women participation in Industrial and Systems Engineering digital courses, those identified characteristics shall be included and developed in the course methodology design.
Magic Layouts: Structural Prior for Component Detection in User Interface Designs

We present Magic Layouts; a method for parsing screen-shots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn a prior over mobile UI layouts, encoding common spatial co-occurrence relationships between different UI components. Conditioning region proposals using this prior leads to performance gains on UI layout parsing for both hand-drawn UIs and app screen-shots, which we demonstrate within the context an interactive application for rapidly acquiring digital prototypes of user experience (UX) designs.
Rapid Real-Time Collision Detection for Large-Scale Complex Scene Based on Virtual Reality

In the virtual environment, all kinds of geometric models are composed of basic geometric elements such as triangular patches and tetrahedrons. The key of its detection is to test the intersection of different geometric model elements. Based on the two-dimensional projection method, this paper presents a fast test method of intersection between basic geometric elements. By changing the internal data structure of the virtual object, an optimization algorithm is proposed by using the spatial decomposition method and spatial-temporal correlation. It is expected that this algorithm will sacrifice a small amount of storage space to improve the real-time performance of the algorithm. Not only that, it can also deal with the collision detection of multiple geometric models colliding at the same time. The experimental results show that this method can improve the efficiency of collision detection to a certain extent.
Software Defect Prediction using KPCA & CSANFIS

Identifying Bugs/Defects in the early stages of software life cycle reduces the effort required in software development. A lot of research has been progressed in predicting software defects using machine learning approaches. In software defect prediction, there are mainly two problems, dimensionality reduction and class Iimbalance. In this paper, we are addressing dimensionality reduction using Kernal Principle Component Analysis and Class Imbalance problem using Cost sensitive Class Imbalance Problem. Kernal Principle Component Analysis transforms non linear high dimensional data into low dimensional space.Cost Sensitive Adaptive Neuro Fuzzy Inference System assigns weights to samples based on class imbalance ratio to alleviate biasing in classification towards majority class. The performance of proposed methodology is measured using Area under ROC Curve (AuC) values. We performed experimentation on Software Defect datasets downloaded from NASA Dataset repository and observed Auc values are increased with our proposed methodology by 5-6%.
Detecting Low-Rank Regions in Omnidirectional Images

Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirectional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome.
Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos

Nowadays, the interaction between humans and robots is constantly expanding, requiring more and more human motion recognition applications to operate in real time. However, most works on temporal action detection and recognition perform these tasks in offline manner, i.e. temporally segmented videos are classified as a whole. In this paper, based on the recently proposed framework of Temporal Recurrent Networks, we explore how temporal context and human movement dynamics can be effectively employed for online action detection. Our approach uses various state-of-the-art architectures and appropriately combines the extracted features in order to improve action detection. We evaluate our method on a challenging but widely used dataset for temporal action localization, THUMOS'14. Our experiments show significant improvement over the baseline method, achieving state-of-the art results on THUMOS'14.