Reviewerly/exHarmony huggingface dataset:
Multi-Head Attention on Image Captioning Model with Bert Embedding

Image captioning models are in abundance, with various techniques and methods in place for them. One such primitive model is "Show, Attend and Tell". This model showcases how well attention mechanisms can be used to focus on necessary parts of an image to predict the next word in the caption. While this model uses soft attention mechanism, the recent development of transformer introduced the concept of multi-head attention mechanism, which was the inspiration for using it instead of soft attention. Multi-head attention was primarily introduced to work on text, but we experimented it on images and text here. We show different components of the captioning model such as the encoder (ResNet), decoder (LSTM with Bert Embeddings) and the use of multi-head attention for image and text instead of text only (as previously used in transformer). We work on the benchmark dataset of MSCOCO. We prepared a baseline and achieved better BLEU score results with our proposed model.
Research by Subject: Fair Use: Creative Commons

This guide offers basic information on using images and media in research. Reasonable use of images and media in teaching, course papers, and graduate theses/dissertations are generally covered by fair use.
Challenges in measuring the Internet for the public Interest

The goal of this paper is to offer framing for conversations about the role of measurement in informing public policy about the Internet, the barriers to gathering measurements, public policy challenges that are creating pressure for reform in this space, and recommended actions that could facilitate gathering of measurements to support policy-making.
Exploration and Effect Evaluation of Teaching Reform of Big Data Course Oriented by Professional Application

A professional application-oriented big data course teaching reform plan is proposed to solve the limitation of the big data course at the current stage, that is, unclear teaching purpose that results in a single teaching content and poor teaching effect. To adapt to the employment requirements of the big data market, professional application is the design goal of the big data course. Based on this goal, the teaching plan of the big data course is adjusted; the teaching concepts of universities are changed. The teaching content is re-selected from three aspects, namely, theory, experiment, and practice. Multimedia teaching methods are introduced to optimize teaching resources from various aspects, such as teaching environment and faculty, and to support the teaching reform of big data courses. The final innovative curriculum assessment method reflects the professional level of students. The proposed teaching reform plan is applied to actual teaching work. Through the questionnaire survey and statistical analysis of the data, the evaluation conclusion about the effect of teaching reform is drawn. After the big data course reform, students' grades on the course's teaching status increased by about 26.6 points, the number of students who think that problems exist in the content structure of theoretical courses is reduced by 38%, the comprehensive satisfaction of courses increased by 2.25%, and the comprehensive level of students' major improved. The teaching reform program of professional application-oriented big data course has a good application effect.
Comparison of Background Extraction Methods for Anomaly Detection

Background extraction is one of the pivotal tasks in automated video analysis. It forms the primary step in abnormal event detection in surveillance videos. In this paper, we present a comparative study of background extraction methods for application in traffic surveillance videos. Performance comparison is performed on real surveillance videos with different illumination and climatic conditions. Qualitative as well as quantitative evaluations on standard datasets show that adaptive mixture of Gaussians is the best among the compared algorithms.
Compression-Encryption Model for Digital Images Based on GAN and Logistic Map

The problem of digital image transmission lies on the size of the image which tends to get bigger, and how to satisfy the confidentiality aspects when images are transmitted via unsecured channels. To solve this problem, a cryptosystem model is proposed, using a combination of compression and cryptography methods. This research proposes the development and performance measurement of compression-encryption models, Which consists of Generative Adversarial Network and logistic map encryption. The test results also showed that this scheme is better than other color image compression-encryption algorithms, With the SSIM values above 0.97 and compression ratio above 94%.
Comparative Analysis between Linear Measures from Bidimensional and Three-dimensional Images of the Face for Human Identification Purpose: A Pilot Study

Introduction: Photo-anthropometry is a method of facial image comparison that consists of taking measurements on images. The objective of this study was to verify if facial measurements obtained from a two-dimensional (2D) image can be applied for the purpose of human identification when compared with measurements obtained from a three-dimensional (3D) image. Materials and Methods: In this cross-sectional research, a convenience sample was formed by 3D and 2D images of 12 participants. In these images, 35 linear measurements were taken between landmarks. The 2D images were obtained in different angles and norms (left and right sides, and front sides), and the measures were categorized into vertical, lateral, and lip regions. The data were organized in Excel® spreadsheets (Microsoft Corp., Redmond, Washington, USA) and submitted to descriptive statistics. Results: The vertical measurements in lateral norms were more divergent than the measurements of 3D images, whereas the measurements of the lip region showed less differences in all norms. In the lateral norms, vertical measures such as nasion–pogonion were underestimated by 14.35 mm, whereas this same measure was overestimated by 7.20 mm in the frontal norm. In the lip region, the most underestimated measures were crista philtri (left)–cheilion (left) at 5.95 mm and crista philtri (right)–cheilion (right) at 5.45 mm, and the most overestimated was cheilion (right)–cheilion (left) at 4.38 mm, all in the frontal norm. Conclusion: The facial measurements obtained in 2D images can be underestimated or overestimated depending on the angle and norm of each image.
Towards Managing Firm Performance through Enterprise Risk Management Practices and Staff Competence: An Empirical Study from the Banking Sector

This study was aimed to measure the impact of enterprise risk management practices on firm performance following the moderation of staff competence. The present study proposed five hypotheses, three direct and two moderating. For measuring hypotheses and objectives, the current research targeted bank officers in the Kingdom of Bahrain's banking sector. A total final sample of 349 was used in primary analyses selected through simple random sampling. Current research shows significant positive effects of risk culture and risk knowledge sharing on the firm`s (financial and non-financial) performance of banks in the Kingdom of Bahrain. Similarly, the first moderation strengthens the relationship between risk knowledge sharing and firm performance through staff competence. In addition, the second moderation hypothesis does not strengthen the relationship between risk culture and firm performance with the moderating effect of staff competence. The current research findings are supported under the resource-based view with several theoretical and practical implications for researchers and industry practitioners.
Learning Predictive Models for Efficient Policy Learning

Author(s): Xu, Huazhe | Advisor(s): Darrell, Trevor | Abstract: For an intelligent agent to interact with the environment efficiently, it must have the ability to predict, plan and generalize. This thesis studies how an intelligent agent can learn to predict future observations and leverage the predictive models for efficient policy learning and generalization. The four instances in this thesis are on high-fidelity video prediction, video prediction that handles multi-modal data distribution, predictive model-based reinforcement learning, and model-based zero-shot policy generalization. In the first case, we use a model that disentangles motion and appearance to predict high-fidelity images. We find this method can alleviate the blurry artifact and shape deformation inherited in previous methods. In the second case, we propose to use an example-guided model in the face of the multi-modal distribution of real-world data. The proposed method can predict diverse, multi-modal data that can also generalize well. In the third instance, we propose a model-based reinforcement learning method with theoretical guarantees. Specifically, we propose a novel value discrepancy loss for predictive model training. We experimentally also prove such framework and loss will significantly improve sample efficiency. Finally, we propose a method that learns both the dynamics model as well as the value of regions for zero-shot policy generalization. We show that this approach can generalize without finetuning to novel tasks. This thesis proposes several methods toward learning and using better predictive models to achieve policies efficiently.
Contrastive pooling segmentation network

Producing a better segmentation mask is crucial in scene understanding. Semantic Segmentation is a vital task for applications such as autonomous driving, robotics, medical image understanding. Efficient high and low-level context manipulation is a key for competent pixel-level classification. The image’s high-level feature map helps in the better spatial configuration of the objects for Segmentation, while the low-level features help to discern the boundaries of the objects in the segmentation map. In our implementation, We use a two bridged network. The first bridge manipulates the subtle differences between images and produces a vector to understand the low-level features in the input images. The second bridge produces global contextual aggregation from the image while gathering a better understanding of the image’s high-level features. The backbone network is the dialated residual network which helps to avoid the attrition of the size of the image during feature extraction. We train our network on the Cityscapes dataset and ADE20k dataset and compare our results with the State-of-the-Art models. The initial experiments have yielded an initial mean IoU of 70.1% and pixel accuracy of 94.4% on the cityscapes dataset and 34.6% on the ADE20K dataset.
Telemetry Data Analysis on sEMG Major Muscle Groups in Three-step Leap over Stumps of 400m Obstacle

Objective: The purpose of the research is to study the sEMG characteristics of associated major muscle groups (agonist muscles) in three-step leap over stumps of 400m obstacle and the relationship between them and muscle performance. Methods: This research tests and analyzes the original electromyography, iEMG and iEMG% of seven male cadets who did quite well in 400m obstacle test by using the surface electromyography telemetry and anatomical analysis of the action, combining with sEMG synchronal video collecting data analysis. Results: The order of muscle power that functions in the right lower limb muscle groups and rectus abdominis is the following: tibialis anterior muscle and quadriceps generate power first, followed by rectus abdominis muscles, gastrocnemius, hamstrings, and gluteus maximus in sequence. The iEMG and iEMG% order from large to small is: gastrocnemius, tibialis anterior, quadriceps, hamstrings, rectus abdominis muscle, and gluteus maximus. Conclusion: The completion of three-step leap over stumps mainly depends on the muscle power of lower limbs and the arm strength of forward movement. Actively stretching and extending ankle and knee joints ensure the major muscle power to successfully push off the ground. In the movement of arms, pulling the thighs forward and tightening the knees also generate essential muscle power in the action. Therefore, during the training exercise, the muscle strength of triceps surae of the lower limbs, group before the calf muscles, quadriceps, and iliopsoas muscles should be greatly improved.
The Butterfly Effect of the March 1 Memorandum: The Hood Event

Terrorist attacks in the United States on September 11, 2001, shook the entire world. These attacks in the United States, the superpower of the world, worried the world and were a harbinger of a new order. After attacks, states such as Iraq, Iran, Korea supported terrorism were considered devil states, and the US fought these states and global terrorism. In this context, Saddam Hussein has designated the axis of evil and the US wanted to make an operation. In this process, Turkey became a crucial ally for the United States. The US wanted to use Turkish territories. However, the Turkish government sent a memorandum to the Parliament, but it rejected this memorandum. Refusal of the memorandum was a harbinger of chaos and confusion. For example, the staff of the 11 Turkish Armed Forces, which established headquarters in Sulaymaniyah on July 4, 2003, was arrested and led away with hoods over their heads. This event, known as the Hood event in Turkey, has serious damage to relations between the US. As Edward N. Lorenz stated in the Butterfly Effect approach, everything in the universe could not be calculated and caused an insignificant event to occur unpredictable big events. In this framework, this study will be discussed with Lorenz’s Butterfly Effect Approach, also known as Chaos Theory.
A Hybrid Factor Graph Model for Biomedical Activity Detection

For activity detection on biomedical time-series data, biomedical signals are modeled as a switching linear dynamical system with random variables, including discrete and continuous dynamics. We present a formalism for representing a system's joint probability density function as a hybrid factor graph. Solving inference problems is based on belief propagation using message passing. Inference results yield the activity estimations in terms of probability distributions instead of binary decisions. This work builds on previous efforts to consolidate factor graphs as unifying representations for signal processing algorithms. We show that the formalism can be successfully applied to detect activities in surface electromyography data acquired during walking. The modularity of factor graphs enables the straightforward adoption and extension of the formalism expanding its scope of application.
Image and Data Analysis Techniques

This chapter describes image and data analysis techniques for PSP and TSP. For quantitative measurements, cameras should be calibrated to establish the accurate relationship between the image plane and the 3D object space (Euclidean space) and then map data in images onto a surface grid in the object space. Analytical camera calibration techniques, especially the Direct Linear Transformation (DLT) and an optimization calibration method, are discussed. For PSP and TSP measurements, an ideal camera should have a linear response to the luminescent radiance. A simple effective technique is described to determine the radiometric response function of a camera. The self-illumination of PSP and TSP may cause a significant error near a conjuncture of surfaces when a strong exchange of the radiative energy occurs between neighboring surfaces. The numerical methods for correcting the self-illumination are described and the errors associated with the self-illumination are estimated.
Application of Internet of Things and Remote Sensing Development in Smart Rural Construction

In a broad sense, the construction of smart villages includes the infrastructure layer and the smart application layer. The infrastructure layer should be the construction of power grid, followed by the construction of the Internet of Things and communication network, and finally the construction of the corresponding data platform. The construction of the smart application layer consists of five smart application layers: ecology, industry, governance, local customs and people's livelihood. In this paper, feature points are extracted and matched, and then the image of the test area is obtained by iterative Mosaic of the overall optimization. Experimental results show that the average detection accuracy of Faster R-CNN on UAV remote sensing images reaches 90.29%, which can meet the detection requirements.
Phase-error mitigation in optical interferometric imaging

Random phase errors due to atmospheric fluctuations are a ubiquitous challenge for ground based optical imaging interferometers. We present methods for dealing with these atmospheric phase errors to improve image reconstruction algorithms. The first method utilizes a scale-and-linear-phase-invariant error metric during nonlinear optimization. This method is prone to stagnation in local minima. The second method is a global linear phase correction that is applied prior to image reconstruction, either in fringe processing or as a simple preprocessing step to image reconstruction. This phase calibration method, like the baseline bootstrapping concept, is possible only with certain beam combination configurations and requires multispectral measurements. This phase correction is coarse but provides a solution within the capture range of the nonlinear optimization of the first method. Using both methods results in a simplified image reconstruction algorithm that produces a high-fidelity reconstruction.
Intrinsic Rewards for Reinforcement Learning Within Complex 2D Environments

In this paper, we propose an approach to train an intelligent agent using reinforcement learning in order to draw on a two-dimensional grid. Painting is a creative art, and it will take human beings years to learn how to draw. In the training process, we build grid environments with obstacles and challenges resembling abstract art and then place the agent in different environments to reach the goal. In phase I, We propose using intrinsic rewards based on the state of the model to stimulate the agent’s exploration desire and to increase adaptability in complex environments. In phase II, we prototype a rendering pipeline to translate the agent’s movement during the training process into a painting. Our results show the intrinsic reward method increased the agent’s ability to learn in environments of moderate complexity. The rendering pipeline prototype was evaluated in a single round of crowd sourced evaluation and steps to further improve outlined.
A sparse coding approach to up-sampling and extrapolating 2-dimensional computational fluid dynamics simulations

We demonstrate through unsupervised machine learning that spatiotemporal latent representations optimized for sparse coding are able to support up-sampling of decimated frames, interpolation of missing frames, and extrapolation to future frames of computational fluid dynamics (CFD) simulations via linear regression. Specifically, we optimize an overcomplete bases of convolutional kernels for the sparse reconstruction of local x,y,z velocities, which are then inferred from decimated data streams and used to reconstruct the entire stream. The input data we use here is a series of time steps extracted as 2D slices from 3D CFD simulations. The simulation input is decimated by removing every-other pixel in every odd frame and by removing every even frame entirely, resulting in a 75% decimation that exposes the sparse coding model to only 25% of the original data. Reconstructions generated by sparse inference utilize features that capture higher-order structures using a Locally Competitive Algorithm to learn the corresponding physics. The quality of reconstructions against the original data can be quantified by the absolute difference between pixels via PSNR in which a higher score indicates a reconstruction that is more faithful to the original input. This study observes the up-sampled, interpolated, and extrapolated reconstructions compared against their inputs for both 4-frame, 6-frame, and 8-frame architecture. Our results suggest that sparse coding networks may offer a competitive method for up-sampling, interpolating and extrapolating from lower to higher resolution CFD simulations.
Design and Realization of Production Practice System for Tandem Hot Strip Rolling Based on VR

The production practice of tandem hot strip rolling is an important part of the practical teaching related to steel production. Due to the complex environment, many safety hazards and so on, it is impossible to effectively carry out production practice. To solve these problems, this paper designs and develops a production practice system for tandem hot strip rolling based on VR technology. This system builds 3D models of the building and equipment of the production workshop, simulates the production process, and includes 4 teaching functions such as 3D roaming, audio introduction, equipment perspective and exploded diagram of equipment, making it possible to carry out large-scale production practice teaching and effectively improving practice efficiency and teaching effectiveness.
Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ε</mi> </math> -differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.