Reviewerly/exHarmony huggingface dataset:
SpanNER: Named Entity Re-/Recognition as Span Prediction

Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction.Despite its preliminary effectiveness, the span prediction model's architectural bias has not been fully understood.In this paper, we first investigate the strengths and weaknesses when the span prediction model is used for named entity recognition compared with the sequence labeling framework and how to further improve it, which motivates us to make complementary advantages of systems based on different paradigms.We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems' outputs.We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners.We make all code and datasets available: https:// github.com/neulab/spanner,as well as an online system demo: http://spanner.sh.Our model also has been deployed into the EXPLAINABOARD (Liu et al., 2021) platform, which allows users to flexibly perform the system combination of top-scoring systems in an interactive way: http://explainaboard. nlpedia.ai/leaderboard/task-ner/.
Light Field Super-Resolution with Zero-Shot Learning

Deep learning provides a new avenue for light field super-resolution (SR). However, the domain gap caused by drastically different light field acquisition conditions poses a main obstacle in practice. To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve the reference view with examples extracted solely from the input low-resolution light field itself. Given highly limited training data under the zero-shot setting, however, we observe that it is difficult to train an end-to-end network successfully. Instead, we divide this challenging task into three sub-tasks, i.e., pre-upsampling, view alignment, and multi-view aggregation, and then conquer them separately with simple yet efficient CNNs. Moreover, the proposed framework can be readily extended to finetune the pre-trained model on a source dataset to better adapt to the target input, which further boosts the performance of light field SR in the wild. Experimental results validate that our method not only outperforms classic non-learning-based methods, but also generalizes better to unseen light fields than state-of-the-art deep-learning-based methods when the domain gap is large.
Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.
Blockchain and 6G: The Future of Secure and Ubiquitous Communication

Future communication will be characterized by ubiquitous connectivity and security. These features will be essential requirements for the efficient functioning of futuristic applications. In this article, in order to highlight the impact of blockchain and 6G on future communication systems, we categorize these application requirements into two broad groups. In the first category, called Requirement Group I (RG-I), we include the performance-related needs on data rates, latency, reliability and massive connectivity, while in the second category, called Requirement Group II (RG-II), we include the security-related needs on data integrity, non-repudiability, and auditability. With blockchain and 6G, network decentralization and resource sharing would minimize resource under-utilization, thereby facilitating RG-I targets. Furthermore, through appropriate selection of blockchain type and consensus algorithms, RG-II needs of 6G applications can also be readily addressed. Through this study, the combination of blockchain and 6G emerges as an elegant solution for secure and ubiquitous future communication.
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks. While reinforcement learning (RL) based agents can generate a stroke sequence step by step for this task, it is not easy to train a stable RL agent. On the other hand, stroke optimization methods search for a set of stroke parameters iteratively in a large search space; such low efficiency significantly limits their prevalence and practicality. Different from previous methods, in this paper, we formulate the task as a set prediction problem and propose a novel Transformer-based framework, dubbed Paint Transformer, to predict the parameters of a stroke set with a feed forward network. This way, our model can generate a set of strokes in parallel and obtain the final painting of size 512 × 512 in near real time. More importantly, since there is no dataset available for training the Paint Transformer, we devise a self-training pipeline such that it can be trained without any off-the-shelf dataset while still achieving excellent generalization capability. Experiments demonstrate that our method achieves better painting performance than previous ones with cheaper training and inference costs. Codes and models are available on https://github.com/wzmsltw/PaintTransformer.
Herding intensity and volatility in cryptocurrency markets during the COVID-19

This paper investigates whether herding is present before and during the COVID-19 pandemic, analyzing intraday data of Bitcoin and eight altcoins. The herding intensity measure of Patterson and Sharma (2006) is calculated for the first time for cryptocurrency markets. Furthermore, we employed a novel Granger causality methodology with a Fourier approximation to determine the relationship between herding and volatility, considering the structural breaks. Our results indicate a significant herding behavior, concentrating during the COVID-19 outbreak. The causality test results show that herding has a significant effect on market volatility. Our results do not support the efficient market hypothesis.
A Brief History of AI: How to Prevent Another Winter (A Critical Review)

Artificial intelligence has witnessed exponential growth in the past decade. Advances in computing power and the design of sophisticated artificial intelligence algorithms have enabled computers to outperform humans in a variety of tasks. Yet, artificial intelligence's path has never been smooth, having essentially fallen apart twice in its lifetime after periods of popular success. We provide a brief rundown of artificial intelligence's evolution, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another winter.
Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.
Small Object Detection via Precise Region-Based Fully Convolutional Networks

In the past several years, remarkable achievements have been made in the field of object detection.Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection.In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets).To address this problem, we propose an improved region-based fully convolutional network (R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps, thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context (MS COCO) training dataset was oversampled.Results showed an accuracy improvement of 3.7% for object detection tasks and an increase of 6.0% for small objects.
Self-support Few-Shot Semantic Segmentation

Existing few-shot segmentation methods have achieved great progress based on the support-query matching framework. But they still heavily suffer from the limited coverage of intra-class variations from the few-shot supports provided. Motivated by the simple Gestalt principle that pixels belonging to the same object are more similar than those to different objects of same class, we propose a novel self-support matching strategy to alleviate this problem, which uses query prototypes to match query features, where the query prototypes are collected from high-confidence query predictions. This strategy can effectively capture the consistent underlying characteristics of the query objects, and thus fittingly match query features. We also propose an adaptive self-support background prototype generation module and self-support loss to further facilitate the self-support matching procedure. Our self-support network substantially improves the prototype quality, benefits more improvement from stronger backbones and more supports, and achieves SOTA on multiple datasets. Codes are at https://github.com/fanq15/SSP .
Semi-Supervised Salient Object Detection Using a Linear Feedback Control System Model

To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.
Optimal Probabilistic Storage and Retrieval of Unitary Channels

We address the question of quantum memory storage for quantum dynamics. In particular, we design an optimal protocol for N→1 probabilistic storage and retrieval of unitary channels on d-dimensional quantum systems. If we access the unknown unitary gate only N times, the optimal success probability of perfect single-use retrieval is N/(N-1+d^{2}). The derived size of the memory system exponentially improves the known upper bound on the size of the program register needed for probabilistic programmable quantum processors. Our results are closely related to probabilistic perfect alignment of reference frames and probabilistic port-based teleportation.
99DOTS

Ensuring that patients adhere to prescribed medication remains an important challenge in global health. While technology has been utilized to monitor and improve adherence, solutions to date have been too costly for large-scale deployment in developing regions. This paper describes 99DOTS, a low-cost approach for tracking adherence using a combination of paper packaging and low-end mobile phones. Every day, patients reveal an unpredictable phone number behind the pills and send a free call to that number to indicate that drugs were dispensed and taken. Within five years of its inception, 99DOTS has become a standard of care for tuberculosis in India and has enrolled over 200,000 patients. We provide a holistic account of the project's evolution, including its iterative design, scaled implementation, and lessons learned along the way. We hope this account will serve as a useful case study for anyone seeking to establish and scale new low-cost technologies for a global audience.
Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.
Velocity Obstacle Approaches for Multi-Agent Collision Avoidance

This paper presents a critical analysis of some of the most promising approaches to geometric collision avoidance in multi-agent systems, namely, the velocity obstacle (VO), reciprocal velocity obstacle (RVO), hybrid-reciprocal velocity obstacle (HRVO) and optimal reciprocal collision avoidance (ORCA) approaches. Each approach is evaluated with respect to increasing agent populations and variable sensing assumptions. In implementing the localized avoidance problem, the author notes a problem of symmetry not considered in the literature. An intensive 1000-cycle Monte Carlo analysis is used to assess the performance of the selected algorithms in the presented conditions. The ORCA method is shown to yield the most scalable computation times and collision likelihood in the presented cases. The HRVO method is shown to be superior than the other methods in dealing with obstacle trajectory uncertainty for the purposes of collision avoidance. The respective features and limitations of each algorithm are discussed and presented through examples.
A Universal Intensity Standardization Method Based on a Many-to-One Weak-Paired Cycle Generative Adversarial Network for Magnetic Resonance Images

In magnetic resonance imaging (MRI), different imaging settings lead to various intensity distributions for a specific imaging object, which brings huge diversity to data-driven medical applications. To standardize the intensity distribution of magnetic resonance (MR) images from multiple centers and multiple machines using one model, a cycle generative adversarial network (CycleGAN)-based framework is proposed. It utilizes a unified forward generative adversarial network (GAN) path and multiple independent backward GAN paths to transform images in different groups into a single reference one. To preserve image details and prevent resolution loss, two jump connections are applied in the CycleGAN generators. A weak-pair strategy is designed to fully utilize the prior knowledge of the organ structure and promote the performance of the GANs. The experiments were conducted on a T2-FLAIR image database with 8192 slices from 489 patients. The database was obtained from four hospitals and five MRI scanners and was divided into nine groups with different imaging parameters. Compared with the representative algorithms, the peak signal-to-noise ratio, the histogram correlation, and the structural similarity were increased by 3.7%, 5.1%, and 0.1% on average, respectively; the gradient magnitude similarity deviation, the mean square error, and the average disparity were reduced by 19.0%, 15.7%, and 9.9% on average, respectively. Experiments also showed the robustness of the proposed model with a different training set configuration and effectiveness of the proposed framework over the original CycleGAN. Therefore, the MR images with different imaging settings could be efficiently standardized by the proposed method, which would benefit various data-driven applications.
High resolution hydrogeochemical survey and estimation of baseline concentrations of trace elements in surface water of the Itacaiúnas River Basin, southeastern Amazonia: Implication for environmental studies

A high resolution systematic geochemical mapping at regional-scale is considered to be the best available method to estimate geochemical baseline levels of trace elements in stream water, and is an essential part of environmental risk assessment. This methodology was applied in a project in the Itacaiúnas River Basin, southeastern Amazon, Brazil, which includes several mines of the Carajás Mineral Province (as part of the Itacaiúnas Geochemical Mapping and Background Project, ItacGMBP). A total of 1429 samples (including 55 duplicates) were collected in 2017 at 900 sites at one sample per microbasin, during both dry and rainy periods. The analyses of 34 elements were carried out using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). In general, the waters are slightly alkaline and are classified as mixed Ca-Na-HCO3 type, indicating that they are mainly influenced by silicate rock weathering. Most metal concentrations in the water are low, except Fe and Mn. Seasonality explains differences in metal concentrations, with higher values being obtained in the rainy season. Baseline threshold values (BTVs) were calculated separately for both seasons by employing different statistical methods: iterative 2σ and DF preferentially delivered a more restrictive or conservative levels, which can be represenative of the natural BTVs (NBTVs), considering as the least degraded with low or no significant level of anthropogenic influence; and 98th percentile provides the ambient BTVs (ABTVs), which consists of natural plus diffuse anthropogenic input in the defined area. The ABTVs of Fe and Mn significantly exceed the WHO (1998) and CONAMA 357/05 limits. Spatial distribution indicates that Fe and Mn are not strictly related to geologic setting, rather they are highly influenced by specific local land use as well as deep weathering of the catchment and intense leaching and run-off during the rainy season. However, higher Mn occurrence in the dry period results from redox cycling of Fe and Mn via biogeochemical processes. The ABTVs of Ni, Cr, and V are controlled by bedrock lithology (geologic setting), mainly associated with mafic-ultramafic rocks; Cu is associated with two large hydrothermally mineralized copper belts. The estimation of baseline levels of As, Se, and Pb were highly limited due to of the large number of samples with results less than detection limits, in this case the 95th percentile was used for their BTVs. Geochemical data as well as anomalous values for most of the metals indicate that anthropogenic influence from point sources is highly negligible in the basin, except at a few points, where high NO3- has been observed, probably due to more intense human and livestock activity. This study demonstrates that site-specific geochemical baseline assessment is a crucial factor when evaluating surficial water conditions in a large basin.
Let the robots do it! – Taking a look at Robotic Process Automation and its potential application in digital forensics

The challenges of tackling increasing caseloads, large volumes of digital data and maintaining examination efficiency in order to adhere to tight criminal justice system deadlines persist. As the field looks towards techniques for improving efficiency, forms of automation are both simultaneously touted as a potential solution, whilst also attracting criticism. The potential for techniques which mechanise parts of the digital forensic examination process, and do it reliably, is great, however developing the capability to do this remains a challenge. This work provides an introductory discussion to Robotic Process Automation, a form of service task automation. Its potential application is debated and two case studies are offered demonstrating potential areas of applicability. An objective evaluation is offered, debating whether technology has a place in improving efficiency in this field.
MNN: Multimodal Attentional Neural Networks for Diagnosis Prediction

Diagnosis prediction plays a key role in clinical decision supporting process, which attracted extensive research attention recently. Existing studies mainly utilize discrete medical codes (e.g., the ICD codes and procedure codes) as the primary features in prediction. However, in real clinical settings, such medical codes could be either incomplete or erroneous. For example, missed diagnosis will neglect some codes which should be included, mis-diagnosis will generate incorrect medical codes. To increase the robustness towards noisy data, we introduce textual clinical notes in addition to medical codes. Combining information from both sides will lead to improved understanding towards clinical health conditions. To accommodate both the textual notes and discrete medical codes in the same framework, we propose Multimodal Attentional Neural Networks (MNN), which integrates multi-modal data in a collaborative manner. Experimental results on real world EHR datasets demonstrate the advantages of MNN in terms of both robustness and accuracy.