Reviewerly/exHarmony huggingface dataset:
Human Motion Modeling Based on Single Action Context

Motion modeling is applied in pattern recognition, image and video processing, and human-computer interaction, including motion analysis, prediction and synthesis. With the development of deep learning method, recent works focus on recurrent neural network (RNN). According to experimental results of recent works, RNN is effective for short-term motion analysis. However, RNN has distortion problem in long-term motion analysis and prediction. We find that long-term motion is always based on a explicit action context, such as walking and sitting. RNN could hardly analyze action context through short-term historical sequence, while a longer historical sequence difficulties in network fitting. We propose a encoder-decoder model with a context unit, to keep the context information of each actions. Our model obtains state-of-the-art performance on long-term human motion prediction. Furthermore, our model can synthesis composite motion by changing action context.
Implementation of Ensemble-Based Prediction Model for Detecting Sybil Accounts in an OSN

Online Social Networks (OSNs) are the leading platforms that are being used these days for a variety of social interactions generally aimed at fulfilling the specific needs of different strata of users. Normally, a user is allowed to join these social networks with little or negligible amount of antecedent verification which essentially leads to the coexistence of fake entities with malicious intentions on these social networking websites. A specific category of such accounts is known as the Sybil accounts where a malicious user pretending as an honest user, creates multiple fake identities to manipulate/harm honest users, creating an illusion of the real users in the OSN that these are real identities. In the absence of stringent control mechanisms, it is difficult to identify and remove these malicious accounts. But, as every single interaction on a social media website leaves its digital trace and a huge number of such interactions every day culminates into huge datasets, it is possible to use Machine Learning (ML) techniques to build prediction models for identifying these Sybil accounts. This paper is one such attempt where we have used ML techniques to build prediction models that can predict the presence of Sybil accounts in Twitter datasets. After preprocessing the data in these datasets, we have selected an optimal set of features using one filter method namely Correlation with Heatmap and two wrapper methods namely Recursive Feature Elimination (RFE) and Recursive Feature Elimination with Cross-Validation (RFE-CV). Then using 8 classifiers (SVM, NN, LR, DT, RF, NB, GPC, and KNN) for the classification of accounts in the datasets, we have concluded that the Decision Tree classifier gives the best prediction performance among all these classifiers. Lastly, we have used an ensemble of 6 classifiers (SVM, NN, LR, DT, RF, and KNN) by using Bagging (max voting) to achieve better results. But it can be concluded that due to the inclusion of weak learners like SVM, NN, and GPC in the ensemble, DT has given the best possible prediction outcomes.
A flexible template generation and matching method with applications for publication reference metadata extraction

Abstract Conventional rule‐based approaches use exact template matching to capture linguistic information and necessarily need to enumerate all variations. We propose a novel flexible template generation and matching scheme called the principle‐based approach (PBA) based on sequence alignment, and employ it for reference metadata extraction (RME) to demonstrate its effectiveness. The main contributions of this research are threefold. First, we propose an automatic template generation that can capture prominent patterns using the dominating set algorithm. Second, we devise an alignment‐based template‐matching technique that uses a logistic regression model, which makes it more general and flexible than pure rule‐based approaches. Last, we apply PBA to RME on extensive cross‐domain corpora and demonstrate its robustness and generality. Experiments reveal that the same set of templates produced by the PBA framework not only deliver consistent performance on various unseen domains, but also surpass hand‐crafted knowledge (templates). We use four independent journal style test sets and one conference style test set in the experiments. When compared to renowned machine learning methods, such as conditional random fields (CRF), as well as recent deep learning methods (i.e., bi‐directional long short‐term memory with a CRF layer, Bi‐LSTM‐CRF), PBA has the best performance for all datasets.
Shift of Paradigm from Model-Based to Simulation-Based

Simulation-based approach provides a very powerful, rich, and versatile paradigm for many disciplines, methodologies, and studies. Already over 170 disciplines, methodologies, and approaches are simulation-based. The article clarifies the essence and power of simulation-based approach. To reach this aim the following is done: First, as a basis, the essence of simulation which consists of experimentation and experience is covered. The historic rise of the experimentation in early 17th century as a basis of scientific method as well as the additional benefits of simulated experiments are covered. The unique advantages of coupling simulation and the working of real systems are explained. Possibilities offered by simulation to gain three types of experience are clarified. A brief history of model-based approach as well as the first discipline for which model-based approach was promoted are explained. Richness and versatility of simulation and benefits of its use are detailed. The power gained by first and higher-order synergies of simulation with several other disciplines such as system theories, systems engineering, computers, software engineering, software agents, and reliability are mentioned. The already achieved shift of paradigm from model-based to simulation-based is strongly endorsed.
Remote Sensing of River Velocity Using Drone Video and Optical Flow Algorithm

Watershed models of extreme flooding events require accurate and reliable measurements of streamflow for calibration and validation. However, flow rate measurements during floods are inherently uncertain, and physical measurements of velocity during flood conditions are prohibitive in many cases. Therefore, novel methods to measure stream velocity during extreme floods must be considered. This project addresses this challenge through the development of a novel system that utilizes drones, video imaging, and optical flow algorithms to measure velocity in rivers and streams. This system was applied at a case study location on the Menomonee River in Wauwatosa, WI. To remotely sense stream velocity, a DJI Matrice 210 RTK drone equipped with a Zenmuse X5S camera was used to capture video. The video data from the drone was analyzed using an optical flow algorithm, which generates a high-resolution velocity field by computing the optical displacement of pixels between each video frame. This data was compared against in situ measurements of velocity captured with a hand-held velocimeter. Results indicate that the optical flow algorithms estimate the magnitude of surface velocity to within 12% of hand-held measurements. If fully developed, this system could transform the way that streamflow is measured and modeled.
Post-quantum Group Key Agreement Scheme

Progress in quantum technologies forces the development of new cryptographic primitives that are resistant to attacks of an adversary with a quantum computer. A large number of key establishment schemes have been proposed for two participants, but the area of group post-quantum key establishment schemes has not been studied a lot. Not so long ago, an isogeny-based key agreement scheme was proposed for three participants, based on a gradual increase in the degree of the key. We propose another principle for establishing a key for a group of participants using a tree-structure. The proposed key establishment scheme for four participants uses isogeny of elliptic curves as a mathematical tool.
3D-local oriented zigzag ternary co-occurrence fused pattern for biomedical CT image retrieval

In this letter, a new feature descriptor called three dimensional local oriented zigzag ternary co-occurrence fused pattern ( $$3D{\text{-}}LOZTCoFP$$ ) is proposed for computed tomography (CT) image retrieval. Unlike the conventional local pattern based approaches, where the relationship between the reference and its neighbors in a circular shaped neighborhood are captured in a 2-D plane, the proposed descriptor encodes the relationship between the reference and it's neighbors within a local 3D block drawn from multiscale Gaussian filtered images employing a new 3D zigzag sampling structure. The proposed 3D zigzag scan around a reference not only provides an effective texture representation by capturing non-uniform and uniform local texture patterns but the fine to coarse details are also captured via multiscale Gaussian filtered images. In this letter, we have introduced three unique 3D zigzag patterns in four diverse directions. In $$3D{\text{-}}LOZTCoFP$$ , we first calculate the 3D local ternary pattern within a local 3D block around a reference using proposed 3D zigzag sampling structure at both radius 1 and 2. Then the co-occurrence of similar ternary edges within the local 3D cube is computed to further enhance the discriminative power of the descriptor. A quantization and fusion based scheme is introduced to reduce the feature dimension of the proposed descriptor. Experiments are conducted on popular NEMA and TCIA-CT image databases and the results demonstrate superior retrieval efficiency of the proposed $$3D{\text{-}}LOZTCoFP$$ descriptor over many local pattern based approaches in terms of average retrieval precision and average retrieval recall in CT image retrieval.
Smart Streaming: A High-Throughput Fault-tolerant Online Processing System

In recent years, there has been considerable interest in developing frameworks for processing streaming data. Like the precursor commercial systems for data-intensive processing, these systems have largely not used methods popular within the HPC community (for example, MPI for communication). In this paper, we demonstrate a system for stream processing that offers a high-level API to the users (similar to MapReduce), is fault-tolerant, and is also more efficient and scalable than current solutions. Particularly, a cost-efficient MPI/OpenMP based fault-tolerant scheme is incorporated so that the system can survive node failures with only a modest degradation of performance. We evaluate both the functionality and efficiency of Smart Streaming using four common applications in machine learning and data analytics. A comparison against state-of-the-art streaming frameworks shows our system boosts the throughput of test cases by up to 10X and achieve desirable parallelism when scaled out. Additionally, the performance loss upon failures is only proportional to the share of failed resources.
Optimising word embeddings with search-based approaches

Word embeddings have rapidly become an all-purpose tool for a diverse range of real world applications. This development is nurtured by the availability and applicability of pre-trained models. However, their usage faces the risk of being inaccurate when used in domains different from the ones they were trained on.
Figure Captioning in Scholarly Literatures to Augment Search Results

Figures convey useful information, such as trends, proportions, and values, in a concise format. People can understand these attributes at a glance, but machine process them difficultly. When searching for figures, the end-user is presented with the caption that does not contain enough information to interpret the figure. In the paper, we propose a novel end-to-end framework for scholarly figure captioning. In the figure parsing module, figures are localized, classified, and analyzed. The plotted data and its association with the legend entries are extracted. In text processing module, the figure-related sentences are identified and measured with the sentence’s relevance to the figure. The sentence subset with the optimum size is selected considering a balance between information content and the size of the generated caption. The final complete captions enable a variety of current exciting applications, such as figure search engine and figure query answering. Empirical experiments show that our proposed framework can effectively generate captions for figures under several metrics.
Object Identification in Satellite Imagery and Enhancement Using Generative Adversarial Networks

Ship detection from satellite images is an essential application for sea security, port traffic control, disaster management, and rescue operations which incorporates traffic surveillance, illicit fisheries, oil spills, and observation of ocean contamination. Significant challenges for this method include cloud, tidal wave, and even the variability of ship sizes. In this paper, we introduce a framework for ship detection from low-resolution satellite images using the best combination of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) with respect to image enhancement and training time reduction, as well as high accuracy. The operations of the above proposed method has been done on the Kaggle open source (“Ships in Satellite Imagery”) dataset.
UPA: An Automated, Accurate and Efficient Differentially Private Big-Data Mining System

In the era of big-data, individuals and institutions store their sensitive data on clouds, and these data are often analyzed and computed by MapReduce frameworks (e.g., Spark). However, releasing the computation result on these data may leak privacy. Differential Privacy (DP) is a powerful method to preserve the privacy of an individual data record from a computation result. Given an input dataset and a query, DP typically perturbs an output value with noise proportional to sensitivity, the greatest change on an output value when a record is added to or removed from the input dataset. Unfortunately, directly computing the sensitivity value for a query and an input dataset is computationally infeasible, because it requires adding or removing every record from the dataset and repeatedly running the same query on the dataset: a dataset of one million input records requires running the same query for more than one million times. This paper presents UPA, the first automated, accurate, and efficient sensitivity inferring approach for big-data mining applications. Our key observation is that MapReduce operators often have commutative and associative properties in order to enable parallelism and fault tolerance among computers. Therefore, UPA can greatly reduce the repeated computations at runtime while computing a precise sensitivity value automatically for general big-data queries. We compared UPA with FLEX, the most relevant work that does static analysis on queries to infer sensitivity values. Based on an extensive evaluation on nine diverse Spark queries, UPA supports all the nine evaluated queries, while FLEX supports only five of the nine queries. For the five queries which both UPA and FLEX can support, UPA enforces DP with five orders of magnitude more accurate sensitivity values than FLEX. UPA has reasonable performance overhead compared to native Spark. UPA's source code is available on https://github.com/hku-systems/UPA.
Real-Time Object Detection with Simultaneous Denoising using Low-Rank and Total Variation Models

The detection of objects in video scenes is the most prominent research topic in computer vision. It is the result of a wide variety of applications, such as virtual reality and intelligent surveillance systems, so that the system is based mainly on the detection of objects or moving objects. Due to the great success of the Foreground/Background Separation algorithms by decomposing the low-order matrix recently, we propose a new real-time incremental algorithm based on Low Rank and Total Variation (TV) model while simultaneously eliminating various noise. In this research, the proposed algorithms applied to solve multiple problems, such as Dynamic Background, Variation Background, with time. Also, we used real or online videos that will allow the adaptive modeling method to automatically remove noise and detect foreground (or intruder) on such scenes. The background modeling challenges in videos do not involve environmental differences such as lighting or weather changes. To check the effectiveness and efficiency of the proposed algorithms, we have experimented with real-time videos. Analytical experiments and results show the ability and efficiency of our method as well as the low computational cost of our proposed algorithms.
Robust visual tracking method based on Harris Hawks algorithm

Nowadays Object tracking is a very attractive field of research in computer vision. In this context, researches have focused on two lines: object presentation and searching algorithm. Compared to object presentation a few attention is paid to searching algorithms, and it is on which focused the present paper. Harris hawks algorithm was found to provide a good searching ability with a high precision compared to other metaheuristic methods. In this paper we apply for the first time as a searching method Harris Hawks algorithm, in the context of single object tracking. A promising results, were provided by the proposed algorithm compared to 11 state of art algorithms.
Accelerating Towards Larger Deep Learning Models and Datasets – A System Platform View Point

Deep Learning (DL) is a rapidly evolving field under the umbrella of Artificial Intelligence (AI) with proven real-world use cases in supervised and unsupervised learning tasks. As the complexity of the learning tasks increases, the DL models become deeper or wider with millions of parameters and use larger datasets. Neural networks like AmoebaNet with 557M parameters and GPT-2 with 1.5 billion parameters are some of the recent examples of large models. DL trainings are generally run on accelerated hardware such as GPUs, TPUs or FPGAs which can satisfy the high computational demands of the neural network training. But accelerators are limited in their memory capacities. Larger the models, larger the memory required while training them. Hence, large DL models and large datasets cannot fit into the limited memory available on GPUs. However, there are techniques designed to overcome this limitation like compression, using CPU memory as a data swap, recomputations within the GPUs etc. But the efficiency of each of these techniques also depends on the underneath system platform capabilities. In this paper we present the observations from our study of training large DL models using data swap method on different system platforms. This study showcases the characteristics of large models and presents the system viewpoint of large deep learning model training by studying the relation of the software techniques to the system platform used underneath. The results presented in the paper show that for training large Deep Learning models, communication link between CPU and GPU is critical and the training performance can be improved by using a platform with high bandwidth link for this communication. The results presented are based on two DL models, 3DUnetCNN model for medical image segmentation and DeepLabV3+ model for semantic image segmentation.
A fast GA for automatically evolving CNN architectures

Convolutional neural networks (CNNs) have been proved to be effective models to solve a series of challenging computer vision tasks. However, designing CNN architectures with good performance is still a challenging task. Automatic CNN architecture search algorithms have been proposed in recent years which can find competitive CNN architectures without manual intervention. But automatic search algorithms usually consume considerable computational time and resources. In addition, they only use deep blocks and ignore wide blocks of CNNs, which limits the performance of evolved CNNs. In order to address the above issues, a fast approach for automatically evolving CNN architectures based on deep and wide blocks (FAE-CNN) is proposed through genetic algorithms in this paper. In FAE-CNN, a refined fitness evaluation method based on divided datasets is designed with the purpose to speed up the running time. By introducing the Inception Block, FAE-CNN can evolve optimal CNN architectures from both deep and wide directions. Experimental results on CIFAR10 and CIFAR100 show that FAE-CNN can automatically design CNNs with flexible architectures and better performance in a very short running time.
Target Seeking and Obstacle Avoidance of Omni Robot in an Unknown Environment

Recently, growth in demand for robots has been emerged in various aspects of our life. Mobile robot researchers focus on the target seeking and obstacle avoidance in hazardous environments. Due to the difficulty in finding the exact model for the mobile robot "Robotino®", the decision to use fuzzy logic controller (FLC) capabilities to introduce a safe Robotino® target seeking without any human intervention is made. In this paper, the design and implementation of the position control using two fuzzy logic controllers for Robotino® is presented. Matlab is used to implement the two controllers. Furthermore, the Straight-Line Equation (SLE) is used to make Robotino® reaching its target. By using three infrared sensors and one ultrasonic sensor, the real time experiment results show that Robotino® can detect and avoid any obstacle may be found on its route.
HyperFDA

This paper introduces Hyper-FDA: the application of the meta-heuristic "Fractal Decomposition Algorithm" (FDA) to the optimization of the hyperparameters of deep neural network architectures. FDA is a metaheuristic that was recently proposed to solve high dimensional continuous optimization problems. This approach is based on a geometric fractal decomposition which divides the search space while looking for the optimal solution. The presented approach uses bi-level optimization to separate the architecture search composed of discrete parameters from hyperparameters optimization with continuous parameters. The architecture search is conducted by a random walker aiming to generate chained-structured architectures. The best ones are selected to be fined tuned using FDA. The benchmark CIFAR-10 is used to test the approach and results are among the state of the art considering the low number of parameters and the chained-structured architecture of the network. The encouraging results are emphasized by the fact that the entire process was conducted using low computational power with only 3 GPUs.
The Status, Hot Topics in the Field of Electronic Health Records: A Literature Review Based on Lda2vec

Electronic health records(EHRs) has become very popular in last few years. To describe the current landscape of EHR-related research,based on lda2vec and co-occurrence analysis, this study focuses on its research areas and topic distribution. It is found that studies about population health and risk prediction have grown rapidly, and much attention has been paid to the application of AI in medicine. Moreover, the standards for knowledge representation and information sharing have been basically improved.