Reviewerly/exHarmony huggingface dataset:
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks

There is a growing demand for high-resolution (HR) medical images for both clinical and research applications. Image quality is inevitably traded off with acquisition time, which in turn impacts patient comfort, examination costs, dose, and motion-induced artifacts. For many image-based tasks, increasing the apparent spatial resolution in the perpendicular plane to produce multi-planar reformats or 3D images is commonly used. Single-image super-resolution (SR) is a promising technique to provide HR images based on deep learning to increase the resolution of a 2D image, but there are few reports on 3D SR. Further, perceptual loss is proposed in the literature to better capture the textural details and edges versus pixel-wise loss functions, by comparing the semantic distances in the high-dimensional feature space of a pre-trained 2D network (e.g., VGG). However, it is not clear how one should generalize it to 3D medical images, and the attendant implications are unclear. In this paper, we propose a framework called SOUP-GAN: Super-resolution Optimized Using Perceptual-tuned Generative Adversarial Network (GAN), in order to produce thinner slices (e.g., higher resolution in the 'Z' plane) with anti-aliasing and deblurring. The proposed method outperforms other conventional resolution-enhancement methods and previous SR work on medical images based on both qualitative and quantitative comparisons. Moreover, we examine the model in terms of its generalization for arbitrarily user-selected SR ratios and imaging modalities. Our model shows promise as a novel 3D SR interpolation technique, providing potential applications for both clinical and research applications.
Generation-Augmented Retrieval for Open-Domain Question Answering

We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision.We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR (Karpukhin et al., 2020).We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy.Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance.GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Few-Shot Named Entity Recognition: An Empirical Baseline Study

This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available.Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve model generalization ability in few-shot settings: (1) metalearning to construct prototypes for different entity types, (2) task-specific supervised pretraining on noisy web data to extract entityrelated representations and (3) self-training to leverage unlabeled in-domain data.On 10 public NER datasets, we perform extensive empirical comparisons over the proposed schemes and their combinations with various proportions of labeled data, our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned using domain labels.(ii) We create new state-of-theart results on both few-shot and training-free settings compared with existing methods.
A Survey of IoT and Blockchain Integration: Security Perspective

Blockchain has recently attracted significant academic attention in research fields beyond the financial industry. In the Internet of Things (IoT), blockchain can be used to create a decentralized, reliable, and secure environment. The use of blockchain in IoT applications is still in its early stages, particularly at the low end of the computing spectrum. As a result, the future roadmap is hazy, and several challenges and questions must be addressed. Several articles combining blockchain technology with IoT have recently been released, but they are limited to shallow technological potential discussions, with very few providing an in- depth examination of the complexities of implementing blockchain technology for IoT. Therefore, this paper aims to coherently and comprehensively provide current cutting-edge efforts in this direction. It provides a literature review of IoT and blockchain integration by examining current research issues and trends in the applications of blockchain-related approaches and technologies within the IoT security context. We have surveyed published articles from 2017 to 2021 on blockchain-based solutions for IoT security, taking into consideration different security areas and then, we have organized the available articles according to these areas. The surveyed articles have been chronologically organized in tables for better clarity. In this paper, we try to investigate the vital issues and challenges to the integration of IoT and blockchain, and then investigate the research efforts that have been conducted so far to overcome these challenges.
MixFormer: Mixing Features across Windows and Dimensions

While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs self-attention within non-overlapped windows and shares weights on the channel dimension. We propose Mix-Former to find a solution. First, we combine local-window self-attention with depth-wise convolution in a parallel design, modeling cross-window connections to enlarge the receptive fields. Second, we propose bi-directional interactions across branches to provide complementary clues in the channel and spatial dimensions. These two designs are integrated to achieve efficient feature mixing among windows and dimensions. Our MixFormer provides competitive results on image classification with EfficientNet and shows better results than RegNet and Swin Transformer. Performance in downstream tasks outperforms its alternatives by significant margins with less computational costs in 5 dense prediction tasks on MS COCO, ADE20k, and LVIS. Code is available at https://github.com/PaddlePaddle/PaddleClas.
Story Representation and Interactive Drama

When building a story-intensive game, there is always the question of how much freedom to give the player. Give the player too little, and he may feel constrained and disconnected from the character he is controlling. Give him too much freedom, and the progression of the story may lag or stop altogether. The field of interactive drama attempts to strike a balance between interaction and authorship. The story experienced in an interactive drama is dependent both on the plot content authored as well as the player&amp;rsquo;s choices in the story. Which story representation is appropriate for a particular approach to interactive drama and the relationship between that language and other elements of the architecture is a key factor in design. This paper introduces our interactive drama architecture, IDA, and addresses the requirements it has for a story representation. How those requirements are met by our representational choices is the focus of the rest of the paper.
Variational inference with Gaussian mixture model and householder flow

The variational auto-encoder (VAE) is a powerful and scalable deep generative model. Under the architecture of VAE, the choice of the approximate posterior distribution is one of the crucial issues, and it has a significant impact on tractability and flexibility of the VAE. Generally, latent variables are assumed to be normally distributed with a diagonal covariance matrix, however, it is not flexible enough to match the true complex posterior distribution. We introduce a novel approach to design a flexible and arbitrarily complex approximate posterior distribution. Unlike VAE, firstly, an initial density is constructed by a Gaussian mixture model, and each component has a diagonal covariance matrix. Then this relatively simple distribution is transformed into a more flexible one by applying a sequence of invertible Householder transformations until the desired complexity has been achieved. Additionally, we also give a detailed theoretical and geometric interpretation of Householder transformations. Lastly, due to this change of approximate posterior distribution, the Kullback–Leibler distance between two mixture densities is required to be calculated, but it has no closed form solution. Therefore, we redefine a new variational lower bound by virtue of its upper bound. Compared with other generative models based on similar VAE architecture, our method achieves new state-of-the-art results on benchmark datasets including MNIST, Fashion-MNIST, Omniglot and Histopathology data a more challenging medical images dataset, the experimental results show that our method can improve the flexibility of posterior distribution more effectively.
Efficient Quantization for Neural Networks with Binary Weights and Low Bitwidth Activations

Quantization has shown stunning efficiency on deep neural network, especially for portable devices with limited resources. Most existing works uncritically extend weight quantization methods to activations. However, we take the view that best performance can be obtained by applying different quantization methods to weights and activations respectively. In this paper, we design a new activation function dubbed CReLU from the quantization perspective and further complement this design with appropriate initialization method and training procedure. Moreover, we develop a specific quantization strategy in which we formulate the forward and backward approximation of weights with binary values and quantize the activations to low bitwdth using linear or logarithmic quantizer. We show, for the first time, our final quantized model with binary weights and ultra low bitwidth activations outperforms the previous best models by large margins on ImageNet as well as achieving nearly a 10.85× theoretical speedup with ResNet-18. Furthermore, ablation experiments and theoretical analysis demonstrate the effectiveness and robustness of CReLU in comparison with other activation functions.
Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models

Mixed effects models are widely used to describe heterogeneity in a population. A crucial issue when adjusting such a model to data consists in identifying fixed and random effects. Testing the nullity of the variances of a subset of random effects can help to investigate this issue. Some authors have proposed to use the likelihood ratio test and have established its asymptotic distribution in some particular cases. Extending the existing results, a likelihood ratio test procedure is studied, to test that the variances of any subset of the random effects are equal to zero in nonlinear mixed effects model. More precisely, the asymptotic distribution of the test statistics is shown to be a chi-bar-square distribution, that is to say a mixture of chi-square distributions, and the corresponding weights are identified. In particular, it is highlighted that the limiting distribution depends strongly on the presence of correlations between the random effects. The finite sample size properties of the test procedure are illustrated through simulation studies and the test procedure is applied to two real datasets of dental growth and of coucal growth.
A Walk Among the Data

We examine the potential for immersive unit visualizations—interactive virtual environments populated with objects representing individual items in a dataset. Our virtual reality prototype highlights how immersive unit visualizations can allow viewers to examine data at multiple scales, support immersive exploration, and create affective personal experiences with data.
Sentiment Analysis of Weibo Comment Texts Based on Extended Vocabulary and Convolutional Neural Network

In the era of big data and Internet, social network platforms, blogs, and recommender systems generate thousands of subjective information every day. The emotional content of these information may be related to books, characters, commodities, activities and so on. Analyzing and mining subjective emotional information is conducive to personal decision making, enterprise reform, and government’s public opinion regulation. In this paper, based on Weibo comment texts, we use the network term and the wiki Chinese data set to expand the original vocabulary, train word embeddings and realize the sentence-level sentiment classification based on the convolution neural network. At the same time, an optimization method according to the statement length of pooling layer is put forward. The method is proved to be effective with high accuracy on our data set.
A Study of Academic Search Scenarios and Information Seeking Behaviour

An important contribution in the development of interactive information retrieval as a research discipline has been the specification of information seeking models. A variety of such models have been documented, some of which apply generally to a broad set of search settings, and others which are specific to settings such as academic search. Within the domain of academic search, it is unclear to what extent searchers employ the strategies specified in such models when faced with different types of information needs (ranging from fact verification to knowledge discovery). Using an online questionnaire that presented four different academic search scenarios, we collected data on the self-reported likelihood of researchers (professors, graduate students) to use specific strategies from each of five different information seeking models. Preliminary analysis of data from a pilot study (n=10) has revealed differences in which of the strategies are employed depending on the type of search scenario as well as the level of expertise of the searcher.
Face Detection and Expression Recognition Using Haar Cascade Classifier and Fisherface Algorithm

Facial expression recognition is the process of identifying the expression that is displayed by a person, and it has several applications in the fields of medicine, human–computer interaction others; where recognition of expressions displayed on a face is of vital. The process mainly comprises face detection and expression recognition using Haar classifier and using Fisherface based on Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction, respectively. The dataset from which the faces were presented to the classifiers yielded a precision of 96.3% with a recognition speed of 8.2 s. An improvement in recognition accuracy of 3.4% is observed by this algorithm from other algorithms, viz. eigenfaces, LBPH recognizer, and artificial neural network; although with a drawback of incorrect recognition in cases of uneven illumination or low-light conditions. This result may be considered as efficient both with respect to accuracy and speed of recognition of the facial expressions.
SECURE : Efficient resource scheduling by swarm in cloud computing

Cloud computing is providing resources to customers based on application demand under service level agreement (SLA) rules. Service providers are concentrating on providing a requirement based resource to fulfill the quality of service (QoS) requirements. But, it has become a challenge to cope with service-oriented resources due to uncertainty and dynamic demand for cloud services. Task scheduling is an alternative to distributing resource by estimating the unpredictable workload. Therefore, an efficient resource scheduling technique needs to distribute appropriate virtual machines (VMs). Swarm intelligence, involving a metaheuristic approach, is suitable to handle such uncertainty problems meticulously. In this research paper, we present an efficient resource scheduling technique using ant colony optimization (ACO) algorithm, with an objective to minimize execution cost and time. The comparative analysis of results has been demonstrated that the proposed scheduling algorithm performed better as compared to existing algorithms. Thus, the proposed resource scheduling algorithm can be used to improve the efficacy of cloud resources.
Reliability of the Most Common Objective Metrics for Light Field Quality Assessment

Light field imaging is a promising technology for 3D computational photography. As Light Field images are represented for multiple views, their subjective evaluation is a very demanding task. Hence, identifying reliable objective quality assessment methodologies plays a very important role. In this paper six objective quality metrics; PSNR-Y, PSNR-YUV, SSIM-Y, MSSSIM-Y, FSIM-Y and HDRVDP2-Y are assessed for five state-of-the-art codecs at various bit-rates. Moreover, the metrics are computed in the linear, perceptually uniform and perceptual quantizer spaces. The results are compared against those of a subjective study and is concluded that the average FSIM-Y is the most reliable metric. The paper also introduces maps of the objective metrics to evaluate the quality dispersion among the different light field image views.
: Dynamic clustering for tracking evolving environments

Evolving environments challenge researchers with non stationary data flows where the concepts – or states – being tracked can change over time. This requires tracking algorithms suited to represent concept evolution and in some cases, e.g. real industrial environments, also suited to represent time dependent features. This paper proposes a unified approach to track evolving environments that uses a two-stages distance-based and density-based clustering algorithm. In this approach data samples are fed as input to the distance based clustering stage in an incremental, online fashion, and they are then clustered to form μ-clusters. The density-based algorithm analyses the micro-clusters to provide the final clusters: thanks to a forgetting process, clusters may emerge, drift, merge, split or disappear, hence following the evolution of the environment. This algorithm has proved to be able to detect high overlapping clusters even in multi-density distributions, making no assumption about cluster convexity. It shows fast response to data streams and good outlier rejection properties.
A Survey of Genetic Programming and Its Applications

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA).In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically.Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA.These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest.The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on.But one of the most significant uses of GAs is the automatic generation of programs.Technically, the GP solves problems automatically without having to tell the computer specifically how to process it.To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures.Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated.GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc.This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.
A quality of experience evaluation system and research challenges for networked virtual reality-based teleoperation applications

Teleoperation applications are designed to assist humans in operating complex mechanical systems. Interfaces to teleoperation systems have always been challenging. Recently the potential of virtual reality (VR) has been a topic of interest, particularly with the availability of head mounted displays and interaction controller devices. As a result, research into the viability of VR as a technology to support remote operation and improved human machine interaction has emerged. It is assumed that VR will offer the user a more immersive and natural experience when operating a virtual representation of a mechanical system. To achieve this, there are a number of research challenges that need to be addressed. In this short paper, we introduce and discuss key challenges for VR-based teleoperation systems. Since the key focus of our work is understand user quality of experience (QoE) of VR-based teleoperation applications, the design and implementation of an implicit and explicit QoE Evaluation system is also presented.
Experimental investigation of linear-optics-based quantum target detection

The development of new techniques to improve measurements is crucial for all sciences. By employing quantum systems as sensors to probe some physical property of interest allows the application of quantum resources, such as coherent superpositions and quantum correlations, to increase measurement precision. Here we experimentally investigate a scheme for quantum target detection based on linear optical measurment devices, when the object is immersed in unpolarized background light. By comparing the quantum (polarization-entangled photon pairs) and the classical (separable polarization states), we found that the quantum strategy provides us an improvement over the classical one in our experiment when the signal to noise ratio is greater than 1/40, or about 16dB of noise. This is in constrast to quantum target detection considering non-linear optical detection schemes, which have shown resilience to extreme amounts of noise. A theoretical model is developed which shows that, in this linear-optics context, the quantum strategy suffers from the contribution of multiple background photons. This effect does not appear in our classical scheme. By improving the two-photon detection electronics, it should be possible to achieve a polarization-based quantum advantage for a signal to noise ratio that is close to 1/400 for current technology.
Taking stock of legal ontologies: a feature-based comparative analysis

Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated.