Reviewerly/exHarmony huggingface dataset:
Deep Iterative Frame Interpolation for Full-frame Video Stabilization

Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization that can generate video frames without cropping and low distortion. The proposed framework utilizes frame interpolation techniques to generate in between frames, leading to reduced inter-frame jitter. Once applied in an iterative fashion, the stabilization effect becomes stronger. A major advantage is that our framework is end-to-end trainable in an unsupervised manner. In addition, our method is able to run in near real-time (15 fps). To the best of our knowledge, this is the first work to propose an unsupervised deep approach to full-frame video stabilization. We show the advantages of our method through quantitative and qualitative evaluations comparing to the state-of-the-art methods.
A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images

Computed Tomography (CT) has become an important way for examining the critical anatomical organs of the human temporal bone in the diagnosis and treatment of ear diseases. Segmentation of the critical anatomical organs is an important fundamental step for the computer assistant analysis of human temporal bone CT images. However, it is challenging to segment sophisticated and small organs. To deal with this issue, a novel 3D Deep Supervised Densely Network (3D-DSD Net) is proposed in this paper. The network adopts a dense connection design and a 3D multi-pooling feature fusion strategy in the encoding stage of the 3D-Unet, and a 3D deep supervised mechanism is employed in the decoding stage. The experimental results show that our method achieved competitive performance in the CT data segmentation task of the small organs in the temporal bone.
Dependability in edge computing

Edge computing holds great promise, and almost as many challenges in deployment.
Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks

Convolutional neural networks (CNNs) have been widely adopted for computer vision applications. CNNs require many multiplications, making their use expensive in terms of both computational complexity and hardware. An effective method to mitigate the number of required multiplications is via the Winograd algorithm. Previous implementations of CNNs based on Winograd use the 2-D algorithm F(2 × 2,3 × 3), which reduces computational complexity by a factor of 2.25 over regular convolution. However, current Winograd implementations only apply when using a stride (shift displacement of a kernel over an input) of 1. In this article, we presented a novel method to apply the Winograd algorithm to a stride of 2. This method is valid for one, two, or three dimensions. We also introduced new Winograd versions compatible with a kernel of size 3, 5, and 7. The algorithms were successfully implemented on an NVIDIA K20c GPU. Compared to regular convolutions, the implementations for stride 2 are 1.44 times faster for a 3 × 3 kernel, 2.04× faster for a 5 × 5 kernel, 2.42× faster for a 7 × 7 kernel, and 1.73× faster for a 3 × 3 × 3 kernel. Additionally, a CNN accelerator using a novel processing element (PE) performs two 2-D Winograd stride 1, or one 2-D Winograd stride 2, and operations per clock cycle was implemented on an Intel Arria-10 field-programmable gate array (FPGA). We accelerated the original and our proposed modified VGG-16 architectures and achieved digital signal processor (DSP) efficiencies of 1.22 giga operations per second (GOPS)/DSPs and 1.33 GOPS/DSPs, respectively.
Enabling HPC Workloads on Cloud Infrastructure Using Kubernetes Container Orchestration Mechanisms

Containers offer a broad array of benefits, including a consistent lightweight runtime environment through OS-level virtualization, as well as low overhead to maintain and scale applications with high efficiency. Moreover, containers are known to package and deploy applications consistently across varying infrastructures. Container orchestrators manage a large number of containers for microservices based cloud applications. However, the use of such service orchestration frameworks towards HPC workloads remains relatively unexplored. In this paper we study the potential use of Kubernetes on HPC infrastructure for use by the scientific community. We directly compare both its features and performance against Docker Swarm and bare metal execution of HPC applications. Herein, we detail the configurations required for Kubernetes to operate with containerized MPI applications, specifically accounting for operations such as (1) underlying device access, (2) inter-container communication across different hosts, and (3) configuration limitations. This evaluation quantifies the performance difference between representative MPI workloads running both on bare metal and containerized orchestration frameworks with Kubernetes, operating over both Ethernet and InfiniBand interconnects. Our results show that Kubernetes and Docker Swarm can achieve near bare metal performance over RDMA communication when high performance transports are enabled. Our results also show that Kubernetes presents overheads for several HPC applications over TCP/IP protocol. However, Docker Swarm's throughput is near bare metal performance for the same applications.
Design Framework on Tertiary Education System in Indonesia Using Blockchain Technology

The objective of the research is to study the blockchain technology impact on the higher education system in Indonesia. Blockchain technology is widely used in other industrial aspects, as well as global utilization and acceptance in education sectors. Few, however, have been found in Indonesia private and public schools, as well as tertiary institutions. Indonesian community and governments struggle to improve the quality of education nationwide and also to face challenging disruption wave of industrial revolution 4.0. In line with RIRN (Rencana Induk Riset Nasional), the government is concentrating on investing in human capital and social capital. Finding an education solution using blockchain technology could be a highly probable solution to national education challenges. Further will be to develop a blockchain technology framework using an artificial intelligence platform that is best suited for Indonesia tertiary education.
Gamma spectrum denoising method based on improved wavelet threshold

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.
A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection

Interior Search Algorithm (ISA) is a recently proposed metaheuristic inspired by the beautification of objects and mirrors. However, similar to most of the metaheuristic algorithms, ISA also encounters two problems, i.e., entrapment in local optima and slow convergence speed. In the past, chaos theory has been successfully employed to solve such problems. In this study, 10 chaotic maps are embedded to improve the convergence rate as well as the resulting accuracy of the ISA algorithms. The proposed Chaotic Interior Search Algorithm (CISA) is validated on a diverse subset of 13 benchmark functions having unimodal and multimodal properties. The simulation results demonstrate that the chaotic maps (especially tent map) are able to significantly boost the performance of ISA. Furthermore, CISA is employed as a feature selection technique in which the aim is to remove features which may comprise irrelevant or redundant information in order to minimize the classification error rate. The performance of the proposed approaches is compared with five state-of-the-art algorithms over 21 data sets and the results proved the potential of the proposed binary approaches in searching the optimal feature subsets.
Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks

As a trending and interesting research topic, in recent years, researchers have been adopting the blockchain in the wireless ad-hoc environment. Owing to its strong characteristics, such as consensus, immutability, finality, and provenance, the blockchain is utilized not only as a secure data storage for critical data but also as a platform that facilitates the trustless exchange of data between independent parties. However, the main challenge of blockchain application in an ad-hoc network is which kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation appropriately while maintaining the genuine characteristics of a blockchain. In this paper, we propose the blockchain-based trust management system with a lightweight consensus algorithm in a mobile ad-hoc network (MANET). The proposed scheme provides the distributed trust framework for routing nodes in MANETs that is tamper-proof via blockchain. The optimized link state routing protocol (OLSR) is exploited as a representative protocol to embed the blockchain concept in MANETs. As a securely distributed and trusted platform, blockchain solves most of the security issues in the OLSR, in which every node is performing the security operation individually and in a repetitive manner. Additionally, using predefined principles, the routing nodes in the proposed scheme can collaborate to defend themselves from the attackers in the network. The experimental results show that the proposed consensus algorithm is suitable to be used in the resource-hungry MANET with reduced validation time and less overhead. Meanwhile, the attack detection overhead and time also decrease because the repetitivity of the process is reduced while providing a scalable and distributed trust among the routing nodes.
Deep pancreas segmentation with uncertain regions of shadowed sets

Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, location, shape and size among pancreas images. It becomes even worse with cases of pancreatic cancer. Besides, as an inevitable phenomenon, some of the slices have disconnected topology in pancreas part. All these problems lead to high segmentation uncertainties and make the results inaccurate. Existing pancreas segmentation methods rarely achieve sufficiently accurate and robust results especially for cancer cases. To tackle these problems, we propose a 2D deep learning-based method which can involve uncertainties in the process of segmentation iteratively. The proposed method describes the uncertain regions of pancreatic MRI images based on shadowed sets theory. The results are further corrected through increasing the weights of uncertain regions in iterative training. We evaluate our approach on a challenging pancreatic cancer MRI images dataset collected from the Changhai Hospital, and also validate our approach on the NIH pancreas segmentation dataset. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of the Dice similarity coefficient of 73.88% on cancer MRI dataset and 84.37% on NIH dataset respectively.
Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography

Aerial filming is constantly gaining importance due to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose a deep reinforcement learning agent which supervises motion planning of a filming drone by making desirable shot mode selections based on aesthetical values of video shots. Unlike most of the current state-of-the-art approaches that require explicit guidance by a human expert, our drone learns how to make favorable viewpoint selections by experience. We propose a learning scheme that exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both a hand-crafted reward function as well as reward from direct human input. We then deploy the same agent on a real DJI M210 drone in order to test the generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shot quality of our methods. Videos of the system in action can be seen at https://youtu.be/qmVw6mfyEmw.
Deep face recognition with clustering based domain adaptation

Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images gathered from Internet are different from test images because of different lighting condition, pose and image quality. These factors increase domain discrepancy between training (source domain) and testing (target domain) database and make the learnt models degenerate in application. Meanwhile, due to lack of labeled target data, directly fine-tuning the pre-learnt models becomes intractable and impractical. In this paper, we propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes. Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally. Specifically, it first learns a more reliable representation for clustering by minimizing global domain discrepancy to reduce domain gaps, and then applies simplified spectral clustering method to generate pseudo-labels in the domain-invariant feature space, and finally learns discriminative target representation. Comprehensive experiments on widely-used GBU, IJB-A/B/C and RFW databases clearly demonstrate the effectiveness of our newly proposed approach. State-of-the-art performance of GBU data set is achieved by only unsupervised adaptation from the target training data.
Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSet

At present, in the mainstream sentiment analysis methods represented by the Support Vector Machine, the vocabulary and the latent semantic information involved in the text are not well considered, and sentiment analysis of text is dependent overly on the statistics of sentiment words. Thus, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed in this paper for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived from the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information involving the probability characteristics can be used as the classification characteristics, along with the improvement of the effect of classification for support vector machine, and the problem of ignoring the latent semantic characteristics in text sentiment analysis can be addressed. The results show that the effect of the method proposed in this paper, compared with the comparison method, is obviously improved.
THE EFFECT OF CUSTOMER EMPOWERMENT AND CUSTOMER ENGAGEMENT ON MARKETING PERFORMANCE: THE MEDIATING EFFECT OF BRAND COMMUNITY MEMBERSHIP

This study aimed at examining the mediating role of brand Community membership on the effect of customer empowerment and customer engagement on marketing performance. The population of the study contained students of universities in the North Region of Jordan. A total of 1320 questionnaires were administered to sample members, and 895 questionnaires were returned completed. Data analysis indicated a significant partial mediation of brand community in the effect of customer empowerment on customer satisfaction but not in the effect of customer engagement and customer loyalty.Interestingly, the results confirmed that employee engagement is more important for organisations to ensure customer loyalty. The effect of customer empowerment on customer satisfaction was mediated by the customer’s membership to the brand community. Thereupon, organisations should ensure put their customers’ engagement as a priority. Further research is required to investigate the predictability of both customer satisfaction and engagement using different samples from several settings.
Principled reward shaping for reinforcement learning via lyapunov stability theory

Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effectively accelerate the training. Furthermore, the shaped reward function leads to convergence guarantee via stochastic approximation, an invariant optimality condition using Bellman Equation and an asymptotical unbiased policy. Moreover, sufficient RL benchmarks have been experimented to demonstrate the effectiveness of our proposed method. It has been verified that our proposed method substantially accelerates the convergence process as well as improves the performance in terms of a higher accumulated reward.
Does BERT need domain adaptation for clinical negation detection?

Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue.We examine neural unsupervised domain adaptation methods, introducing a novel combination of domain adaptation with transformer-based transfer learning methods to improve negation detection. We also want to better understand the interaction between the widely used bidirectional encoder representations from transformers (BERT) system and domain adaptation methods.We use 4 clinical text datasets that are annotated with negation status. We evaluate a neural unsupervised domain adaptation algorithm and BERT, a transformer-based model that is pretrained on massive general text datasets. We develop an extension to BERT that uses domain adversarial training, a neural domain adaptation method that adds an objective to the negation task, that the classifier should not be able to distinguish between instances from 2 different domains.The domain adaptation methods we describe show positive results, but, on average, the best performance is obtained by plain BERT (without the extension). We provide evidence that the gains from BERT are likely not additive with the gains from domain adaptation.Our results suggest that, at least for the task of clinical negation detection, BERT subsumes domain adaptation, implying that BERT is already learning very general representations of negation phenomena such that fine-tuning even on a specific corpus does not lead to much overfitting.Despite being trained on nonclinical text, the large training sets of models like BERT lead to large gains in performance for the clinical negation detection task.
Sentiment strength detection with a context-dependent lexicon-based convolutional neural network

Sentiment strength detection is an essential task in sentiment analysis, wherein the sentiment strength of subjective text is automatically determined. Sentiment analysis has numerous applications in different sectors, including business and social domains. In this study, we present a model to effectively extract the features and strength of sentiment from words and text using a context-dependent, lexicon-based convolutional neural network. To build this convolutional neural network, the model is trained using the sentiment polarity for each word from a co-occurrence pattern of words and labels. Then, a context-dependent lexicon is generated from the corpus, which is used to generate positive and negative sentiment word embeddings. Positive sentiment word embeddings, negative sentiment word embeddings, and the pre-trained word embeddings are input to a 3-channel convolutional neural network (CNN) to predict the strength of the sentiments. Moreover, with the trained convolutional neural network model, we can obtain a learned sentiment strength-specific word embedding, which generates a sentiment strength-specific lexicon (SSS-Lex) that contains word associations and sentiment intensity scores. To validate the effectiveness of sentiment strength detection in the proposed model, we evaluate the model using six real-world datasets. Furthermore, to evaluate the sentiment strength-specific lexicon, we compare it with seven existing lexicons in three evaluation tasks from the SemEval-2015 and SemEval-2016 competitions. Experimental results indicate that the proposed model can predict the sentiment strength of documents more effectively than the baseline methods, and that the SSS-Lex is of higher quality than the existing lexicons.
A New Image Captioning Approach for Visually Impaired People

Automatic caption generation in natural language to describe the visual content of an image has attracted an increasing amount of attention in the last decade due to its potential applications. It is a challenging task to generate captions with proper linguistics properties as it requires an advanced level of image understanding that goes far beyond image classification and object detection. In this paper, we propose to use the Stanford CoreNLP model to generate a caption after images are trained using VGG16 deep learning architecture. The visual attributes of images are extracted with the VGG16, which conveys richer content, and then they are fed into the Stanford model for caption generation. Experimental results on the MSCOCO dataset show that the proposed model significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.