Reviewerly/exHarmony huggingface dataset:
Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications

Negation and speculation are universal linguistic phenomena that affect the performance of Natural Language Processing (NLP) applications, such as those for opinion mining and information retrieval, especially in biomedical data. In this article, we review the corpora annotated with negation and speculation in various natural languages and domains. Furthermore, we discuss the ongoing research into recent rule-based, supervised, and transfer learning techniques for the detection of negating and speculative content. Many English corpora for various domains are now annotated with negation and speculation; moreover, the availability of annotated corpora in other languages has started to increase. However, this growth is insufficient to address these important phenomena in languages with limited resources. The use of cross-lingual models and translation of the well-known languages are acceptable alternatives. We also highlight the lack of consistent annotation guidelines and the shortcomings of the existing techniques, and suggest alternatives that may speed up progress in this research direction. Adding more syntactic features may alleviate the limitations of the existing techniques, such as cue ambiguity and detecting the discontinuous scopes. In some NLP applications, inclusion of a system that is negation- and speculation-aware improves performance, yet this aspect is still not addressed or considered an essential step.
Spatial mutual information based detail preserving magnetic resonance image enhancement

Medical imaging is a widespread method of envisioning the inside of the human anatomy without causing harm. In magnetic resonance imaging (MRI), poor contrast images may not support adequate information for visual reading of affected areas. As a result, image enhancement technique is essential to enhance image views and keep the image processing approach computationally low. Because of the anatomical complexity of the brain, low contrast is a challenging aspect to deal with in MRI imaging. The issue of conserving structural features while maintaining brightness is also a significant consideration. The histogram equalization (HE) based technique is frequently applied to enhance contrast in brain MRI images. A unique enhancing approach is presented to increase the brightness and contrast of the MRI picture. Spatial mutual information-based algorithm analyses a clinically gathered dataset of MRI images, producing good results. The proposed approach tested both healthy and unhealthy brain MRI pictures. Contrast and brightness improvement are the two divisions of the suggested technique. Adaptive gamma correction using weighted distribution method is applied on the value channel (V) in HSV color model. It provides the brightness gain matrix, which enhances the image brightness. Spatial mutual information methods act on the luminosity space (L) of the CIE 1976 L*a*b* color space for contrast enhancement. Finally, an efficacious brightness and contrast modification strategy for MRI images is provided, with its performance compared to several state-of-the-art approaches using a well-known performance measure.
SybilHunter: Hybrid graph-based sybil detection by aggregating user behaviors

Nowadays, online social networks (OSNs) support users in sharing their information and providing valuable data for various applications, e.g., rating and recommendation systems, search engine systems, etc. In such online social network-based applications, information quality is essential. However, the easy-to-use interactive user interfaces and free publication mechanisms facilitate malicious users to launch Sybil Attacks. They create fake identities, pollute user-generated content, and cause server information quality problems, such as buzz, rumor, spam, etc. Most existing graph-based sybil detection approaches only consider the static graph structure features and heavily rely on the prior knowledge of labeled nodes. "Limited-attack-edge" assumption, the critical assumption of these approaches has been proved invalid in many scenarios. In this paper, we propose SybilHunter, a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes' similarity according to the dynamic user behavior features to evaluate user pairs' trustworthiness and consistency. Then it constructs a weighted-strong-social (WSS) graph, based on which SybilHunter outputs sybil nodes. We simulate and evaluate our approach under a Weibo dataset. The AUC of SybilHunter achieves 0.945, which is significantly higher than typical graph-based sybil detection methods, such as SybilRank, SybilWalk, etc.
Frequency-switched photonic spiking neurons

We propose an approach to generate neuron-like spikes of vertical-cavity surface-emitting laser (VCSEL) by multi-frequency switching. A stable temporal spiking sequence has been realized both by numerical simulations and experiments with a pulse width of sub-nanosecond, which is 8 orders of magnitude faster than ones from biological neurons. Moreover, a controllable spiking coding scheme using multi-frequency switching is designed and a sequence with 20 symbols is generated at the speed of up to 1 Gbps by experiment. Furthermore, we investigate the factors related to time delay of spiking generation, including injection strength and frequency detuning. With proper manipulation of detuning frequency, the spiking generation delay can be controlled upto 60 ns, which is 6 times longer than the delay controlled by intensity. The multi-frequency switching provides another manipulation dimension for spiking generation and will be helpful to exploit the abundant spatial-temporal features of spiking neural network. We believe the proposed VCSEL-neuron, as a single physical device for generating spiking signals with variable time delay, will pave the way for future photonic spiking neural networks.
VR Sickness Adaptation With Ramped Optic Flow Transfers From Abstract To Realistic Environments

VR sickness is a major concern for many users as VR continues its expansion towards widespread everyday use. VR sickness is thought to arise, at least in part, due to the user's intolerance of conflict between the visually simulated self-motion and actual physical movement. Many mitigation strategies involve consistently modifying the visual stimulus to reduce its impact on the user, but this individualized approach can have drawbacks in terms of complexity of implementation and non-uniformity of user experience. This study presents a novel alternative approach that involves training the user to better tolerate the adverse stimulus by tapping into natural adaptive perceptual mechanisms. In this study, we recruited users with limited VR experience that reported susceptibility to VR sickness. Baseline sickness was measured as participants navigated a rich and naturalistic visual environment. Then, on successive days, participants were exposed to optic flow in a more abstract visual environment, and strength of the optic flow was successively increased by increasing the visual contrast of the scene, because strength of optic flow and the resulting vection are thought to be major causes of VR sickness. Sickness measures decreased on successive days, indicating that adaptation was successful. On the final day, participants were again exposed to the rich and naturalistic visual environment, and the adaptation was maintained, demonstrating that it is possible for adaptation to transfer from more abstract to richer and more naturalistic environments. These results demonstrate that gradual adaptation to increasing optic flow strength in well-controlled, abstract environments allows users to gradually reduce their susceptibility to sickness, thereby increasing VR accessibility for those prone to sickness.
ARNN-QA: Adaptive Recurrent Neural Network with feature optimization for incremental learning-based Question Answering system

The evolution of internet services has raised many information retrieval systems, and the use of intelligent services for the Question Answering (QA) systems has been on ascending. QA system identifies the accurate answers briefly, and the answers are identified through natural language expressions. Developing intelligent techniques for QA has been an abiding issue, and it has been studied for over years. Though, different QA models focus on common-sense and general questions in open fields, which are incompetent in solving the more complex professional questions. Moreover, QA models are the most emerging area in computer science nowadays. It is more significant as it includes more deep learning derived questions. However, the existing models suffer from question retrieval under the incremental learning system. Hence, this paper introduces the QA system based on feature extraction, feature optimization, and learning. Initially, the text input is subjected to feature extraction, in which word to vector is used. Further, optimal feature selection is employed using the Sail Fish-based Whale Optimization Algorithm (SF-WOA). Once the feature optimization is done, the question-answering learning is developed by the Adaptive Recurrent Neural Network (A-RNN). As this is the incremental learning system, the proposed architecture adaptively adjusts the weight for the new incoming question by the proposed SF-WOA, so that it can learn new questions other than the trained data. The performance of the suggested SF-WOA-RNN in terms of remembrance is 8.2%, 6.9%, 4.5%, and 4.5% progressed than GWO-RNN WOA-RNN, PSO-RNN, and SFO-RNN, respectively while considering test case 1. Finally, the experiments are tested with a set of open-source datasets and prove that the suggested model is effective and holds better answers than the traditional models.
Human Pose Estimation Based on Lite HRNet with Coordinate Attention

Human pose estimation is an essential task in computer vision, applied in motion recognition, motion capture, augmented reality, etc. The emergence of Lite HRNet balances computational complexity with high precision, using channel attention mechanism instead of complex convolution while maintaining high resolution, thus possessing less computation and higher precision. The use of the channel attention mechanism results in the loss of the position information that is crucial to generating spatially selective attention maps. Accordingly, in this paper, we implement an improved pose estimation method based on the coordinate attention mechanism and Lite HRNet. Our method adds the coordinate information embedding to the original approach instead of expensive point-by-point convolutions. In this way, more information can be reserved in the case of less computation. The proposed method is evaluated on the COCO dataset, and the experiments show that this method can achieve a better balance in terms of accuracy of calculation quantities.
Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model

Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography.A total of 2,208 mammography phantom images were collected for periodic accreditation of the scanner from 1,755 institutions. The dataset was randomly split into training (1,808 images) and testing (400 images) datasets with subgroups (76 images) for the multi-reader study. To develop an interpretable model that contains two deep learning networks in series, five processing steps were performed: mammography phantom detection, phantom object detection, post-processing, score evaluation, and a report with a comment about ambiguous results.For phantom detection, the accuracy and mean intersection over union (mIOU) were 1.00 and 0.938 in the test dataset, respectively. During phantom object detection, a total of 6,369 out of 6,400 objects were detected as the correct object class, and the accuracy and mIOU were 0.995 and 0.813, respectively. The predicted score for each object showed a consensus of 97.40% excluding ambiguous points and 59.10% for ambiguous points of the groups.The interpretable deep learning model using large-scale data from multiple centers shows high performance and reasonable object scoring, successfully validating the reliability and feasibility of mammography phantom image quality management.
dcbench

The development workflow for today's AI applications has grown far beyond the standard model training task. This workflow typically consists of various data and model management tasks. It includes a "data cycle" aimed at producing high-quality training data, and a "model cycle" aimed at managing trained models on their way to production. This broadened workflow has opened a space for already emerging tools and systems for AI development. However, as a research community, we are still missing standardized ways to evaluate these tools and systems. In a humble effort to get this wheel turning, we developed dcbench, a benchmark for evaluating systems for data-centric AI development. In this report, we present the main ideas behind dcbench, some benchmark tasks that we included in the initial release, and a short summary of its implementation.
Optical and optoelectronic neuromorphic devices based on emerging memory technologies

As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.
Bi-discriminator GAN for tabular data synthesis

This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preprocessing scheme and a novel conditional term using the χβ2 distribution for the generator network to more effectively capture the input sample distributions. Additionally, we implement straightforward yet effective architectures for discriminator networks aiming at providing more discriminative gradient information to the generator. Our experimental results on four benchmarking public datasets corroborates the superior performance of our GAN both in terms of likelihood fitness metric and machine learning efficacy.
Reconfigurable Network-on-Chip based Convolutional Neural Network Accelerator

Convolutional Neural Networks (CNNs) have a wide range of applications due to their superior performance in image and pattern classification. However, the performance of CNNs comes at the price of high computational load and memory bandwidth usage. Hardware acceleration has become the primary way to tackle this ever-increasing complexity of CNNs. Most of the recent accelerators arrange processing units (PEs) as a many-core accelerator architecture, with the inter-PE connections tailored to the specific dataflow of the CNN layers. The performance of such accelerators is maximized if the input feature map and filter size/dimension matches that of the underlying accelerator. However, current fixed-size accelerator structures lead to sever resource underutilization because the same structure is used to compute CNN layers of varying dimensions. In this paper, we tackle this problem by presenting RC-CNN, a reconfigurable accelerator architecture for CNNs that can adapt the structure of accelerator to the size and dataflow pattern of the running CNN layer. RC-CNN relies on a reconfigurable on-chip interconnection fabric that can organize a sub-set of accelerator's PEs as a PE set with the same size/dimension of the target CNN layer and customize the inter-PE connections for the layer's dataflow pattern. Since the area/energy overhead does not justify using a full-fledged packet-switched network in accelerators with fine-grained PEs, we use a reconfigurable network with very simple switches in order to efficiently implement the dynamic reconfiguration capability for many-core fine-grained CNN accelerators. Experimental results show that, based on the CNN size and accelerator structure, RC-CNN yields 37% higher PE utilization over a baseline design, on average. It also improves the PE utilization of the state-of-the-art CNN accelerators we selected for comparison purpose by 18%, on average. The results show that these improvements translate to 9%–41% increase in the accelerator's throughput. Further, RC-CNN reduces the network latency and energy consumption by 28% and 22%, respectively, compared to the state-of-the-art utilization-aware methods that employ packet-switched networks-on-chip.
Privacy-Preserving Deep Sequential Model with Matrix Homomorphic Encryption

Making deep neural networks available as a service introduces privacy problems, for which homomorphic encryption of both model and user data potentially offers the solution at the highest privacy level. However, the difficulty of operating on homomorphically encrypted data has hitherto limited the range of operations available and the depth of networks. We introduce an extended CKKS scheme MatHEAAN to provide efficient matrix representations and operations together with improved noise control. Using the MatHEAAN we developed a deep sequential model with a gated recurrent unit called MatHEGRU. We evaluated the proposed model using sequence modeling, regression, and classification of images and genome sequences. We show that the hidden states of the encrypted model, as well as the results, are consistent with a plaintext model.
An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens’ life styles and hence, contribute to reducing the crime rates accordingly.
Hunter

In order to protect user privacy in Machine Learning as a Service (MLaaS), a series of ingeniously designed privacy-preserving frameworks have been proposed. The state-of-the-art approaches adopt Homomorphic Encryption (HE) for linear function and Garbled Circuits (GC)/Oblivious Transfer (OT) for nonlinear operation to improve computation efficiency. Despite the encouraging progress, the computation cost is still too high for practical applications. This work represents the first step to effectively prune privacy-preserving deep learning models to reduce computation complexity. Although model pruning has been discussed extensively in the machine learning community, directly applying the plaintext model pruning schemes offers little help to reduce the computation in privacy-preserving models. In this paper we propose Hunter, a structured pruning method that identifies three novel HE-friendly structures, i.e., internal structure, external structure, and weight diagonal to guide the pruning process. Hunter outputs a pruned model that, without any loss in model accuracy, achieves a significant reduction in HE operations (and thus the overall computation cost) in the privacy-preserving MLaaS. We apply Hunter in various deep learning models, e.g., AlexNet, VGG and ResNet over classic datasets including MNIST, CIFAR-10 and ImageNet. The experimental results demonstrate that, without accuracy loss, Hunter efficiently prunes the original networks to reduce the HE Perm, Mult, and Add operations. For example, in the state-of-the-art VGG-16 on ImageNet with 10 chosen classes, the total number of Perm is reduced to as low as 2% of the original network, and at the same time, Mult and Add are reduced to only 14%, enabling a significantly more computation-efficient privacy-preserving MLaaS.
Economic Analysis of Decentralized Exchange Market with Transaction Fee Mining

Blockchain-based Web 3.0, denoting the next-generation Internet, has attracted attention from academia and industry. However, the open-source application and the decentralized storage of users' interaction data break down the algorithm and data barriers, resulting in more fierce competition among service providers. To cope with the competition, decentralized exchanges (DEXs), the financial infrastructures of Web 3.0, adopt the transaction fee mining mechanism, which refunds the transaction fees to users in the form of governance tokens. However, the ratio of governance tokens a DEX decides to give to users would affect the enthusiasm of users to participate, which has not been discussed yet. In this paper, we establish the DEX market with transaction fee mining and formulate our model based on the Hotelling model. Besides, we propose a two-stage game to formulate the interaction between DEXs and users and derive the equilibriums under different conditions of two parameters: the transaction cost difference and users' stickiness. We show that though the service provider with a lower transaction cost can win the market, users' stickiness can offset the market advantage. Thus, incentivizing users with transaction fee mining has become a crucial strategy in the duopoly competition.