Reviewerly/exHarmony huggingface dataset:
Classification of Metaphase Chromosomes Using Deep Convolutional Neural Network

Karyotype analysis has important clinical significance in the diagnosis, treatment, and prognosis of diseases such as birth defects and hematological tumors. Identifying chromosomes and their structure variations from G-banded metaphase images is an important process in karyotyping, and also is the most difficult one. Automatic chromosome classification becomes urgent in recent years since more and more samples of patients are subject to medical test such as bone marrow biopsy. With the development of artificial intelligence, convolutional neural networks (CNNs) have shown good performance in image recognition. In this study, a CNN with 6 convolutional layers, 3 pooling layers, 4 dropout layers, and 2 fully connected layers was trained using the labeled data set to classify chromosomes into 24 classes through softmax activation function mapping. The classifier gave an accuracy of 93.79% for chromosome identification. The result demonstrated that the CNN has potential application value in chromosome classification and will contribute to the construction of an automatic karyotyping platform.
Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts

The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts.
Jointly Learning to Label Sentences and Tokens

Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size. Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations. In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens. The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations. Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input–output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.
Sentence Boundary Detection in Legal Text

In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.
An Input-aware Factorization Machine for Sparse Prediction

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference

User-generated textual data is rich in content and has been used in many user behavioral modeling tasks.However, it could also leak user private-attribute information that they may not want to disclose such as age and location.User's privacy concerns mandate data publishers to protect privacy.One effective way is to anonymize the textual data.In this paper, we study the problem of textual data anonymization and propose a novel Reinforcement Learning-based Text Anonymizor, RLTA, which addresses the problem of private-attribute leakage while preserving the utility of textual data.Our approach first extracts a latent representation of the original text w.r.t. a given task, then leverages deep reinforcement learning to automatically learn an optimal strategy for manipulating text representations w.r.t. the received privacy and utility feedback.Experiments show the effectiveness of this approach in terms of preserving both privacy and utility.
Online Time-Optimal Trajectory Planning for Robotic Manipulators Using Adaptive Elite Genetic Algorithm With Singularity Avoidance

In this paper, a new method of online planning high smooth and time-optimal trajectory for robotic manipulators that applies an adaptive elite genetic algorithm with singularity avoidance (AEGA-SA) is presented. The strategy is designed as a combination of the time-optimal trajectory planning with quintic polynomial in Cartesian space. For improving optimization performance, elitist group and adaptive adjustment mechanisms are used based on genetic algorithm (GA) framework. In the meantime, GA is combined with singularity avoidance mechanism to avoid the singularities appearing in the trajectory, improves the recognition capability of optimum solution. Experimental results show that, the proposed approach is more effective and better performance than the original GA and its variants, with ensuring a both smooth and efficiency performance for the robotic manipulators.
Joint Transaction Relaying and Block Verification Optimization for Blockchain Empowered D2D Communication

Device to device (D2D) communication is a promising paradigm to support data sharing in the fifth generation (5G) small cell networks. However, D2D communication is more vulnerable to security and privacy threats due to direct wireless connection between the proximity devices. In this paper, we exploit blockchain technology to propose a new distributed and secure data sharing framework named D2D blockchain, where we deploy a set of Access Points (APs) to verify end users' transactions. Besides, in order to enable the end users to receive the required data on time in the D2D blockchain, we propose two new schemes for the transaction confirmation procedure including a relay-assisted transaction relaying scheme and a Delegated Proof-of-Stake (DPoS) based lightweight block verification scheme. Furthermore, to incentivize the relay devices to help in relaying transactions in congested areas, and to encourage the verifiers to contribute their resources in block verification, we design a two-stage contract theory based joint optimization scheme. To measure the quality of the relayed transaction and the verified block, we formulate two models: value of transaction relaying (VoTR) and value of block verification (VoBV), and then design the optimal contract for each relay device and each verifier. Security analysis and numerical results illustrate that the proposed D2D blockchain and the designed joint optimization scheme are secure and efficient for data sharing among end users.
“Data Stewardship Wizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning

The Data Stewardship Wizard is a tool for data management planning that is focused on getting the most value out of data management planning for the project itself rather than on fulfilling obligations. It is based on FAIR Data Stewardship, in which each data-related decision in a project acts to optimize the Findability, Accessibility, Interoperability and/or Reusability of the data. The background to this philosophy is that the first reuser of the data is the researcher themselves. The tool encourages the consulting of expertise and experts, can help researchers avoid risks they did not know they would encounter by confronting them with practical experience from others, and can help them discover helpful technologies they did not know existed. In this paper, we discuss the context and motivation for the tool, we explain its architecture and we present key functions, such as the knowledge model evolvability and migrations, assembling data management plans, metrics and evaluation of data management plans.
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction

With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings. In line with such interest, we propose a novel method that assists us in investigating the extent to which pre-trained LMs capture the syntactic notion of constituency. Our method provides an effective way of extracting constituency trees from the pre-trained LMs without training. In addition, we report intriguing findings in the induced trees, including the fact that pre-trained LMs outperform other approaches in correctly demarcating adverb phrases in sentences.
Acquiring Knowledge from Pre-Trained Model to Neural Machine Translation

Pre-training and fine-tuning have achieved great success in natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an Apt framework for acquiring knowledge from pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable knowledge from pre-trained models to improve the NMT. Experimental results on WMT English to German, German to English and Chinese to English machine translation tasks show that our model outperforms strong baselines and the fine-tuning counterparts.
Black, white or grey magic? Our future with artificial intelligence

Are new technologies driving us towards dystopia or utopia – or something in between? Marketers not only have a stake in finding out the answer to this question but also a direct (and indirect) influence on determining the answer. This commentary discusses recent developments in artificial intelligence (AI) and robotics, and how these are influencing marketers, consumers and society by either leading to a dystopian (black magic), utopian (white magic) or ‘dualopian’ future (grey magic). We argue that the net effect is squarely in the grey and that marketers have a unique power to leverage AI and robotics developments for good. The commentary concludes with 24 research questions forming an agenda for future research under three streams: marketing power, understanding the bias and ethics revisited.
Manipulating the Perceived Shape and Color of a Virtual Limb Can Modulate Pain Responses

Changes in body representation may affect pain perception. The effect of a distorted body image, such as the telescoping effect in amputee patients, on pain perception, is unclear. This study aimed to investigate whether distorting an embodied virtual arm in virtual reality (simulating the telescoping effect in amputees) modulated pain perception and anticipatory responses to pain in healthy participants. Twenty-seven right-handed participants were immersed in virtual reality and the virtual arm was shown with three different levels of distortion with a virtual threatening stimulus either approaching or contacting the virtual hand. We evaluated pain/discomfort ratings, ownership, and skin conductance responses (SCRs) after each condition. Viewing a distorted virtual arm enhances the SCR to a threatening event with respect to viewing a normal control arm, but when viewing a reddened-distorted virtual arm, SCR was comparatively reduced in response to the threat. There was a positive relationship between the level of ownership over the distorted and reddened-distorted virtual arms with the level of pain/discomfort, but not in the normal control arm. Contact with the threatening stimulus significantly enhances SCR and pain/discomfort, while reduced SCR and pain/discomfort were seen in the simulated-contact condition. These results provide further evidence of a bi-directional link between body image and pain perception.
Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

At present, human body moving target detection and recognition algorithms based on deep learning have made breakthrough progress. However, in some applications with high real-time requirements, the existing deep learning real-time detection and recognition network is difficult to achieve high detection accuracy. Therefore, how to achieve accurate positioning and recognition of human moving targets while ensuring real-time detection is still an urgent problem in this field. Based on the single shot multi-box detector (SSD) real-time detection network, this paper proposes a real-time detection positioning and recognition network based on multi-scale feature fusion (IMFF-SSD), which improves the positioning accuracy and identification accuracy. First, this article analyzes the multi-scale features extracted from the SSD network. It combines the position-sensitive information provided by low-level detail features with the context information provided by high-level semantic features through feature fusion, which effectively improves positioning accuracy of the target prediction layer in the SSD network. Secondly, a feature embedded prediction structure is designed to strengthen the semantics of target features without changing the spatial resolution of the SSD prediction layer, and embed low-scale detailed features in high-semantic features for collaborative prediction of targets. This improves the accuracy of the SSD network's recognition of human moving targets at all scales. The experimental results show that by combining the above two improvements, the real-time monitoring and recognition network based on multi-scale feature fusion proposed in this paper has achieved a greater degree of improvement in positioning accuracy and motion recognition accuracy than the original SSD, which is better than some current the human body moving object detection and recognition algorithm has great advantages.
Artificial Intelligence Trends Based on the Patents Granted by the United States Patent and Trademark Office

This paper analyzes artificial intelligence (AI)-related patents that were granted by the U.S. Patent and Trademark Office (USPTO) between January 2008 and December 2018. The study used both the USPTO patent classification systems and a keyword-based search to obtain AI-related patent information. By considering the AI-related patents, growth in AI-related research and development (R&D) activities, as well as AI trends during the period, are investigated. The leading countries and companies in AI and widely used techniques and applications of AI in each region are identified in order to evaluate the level of attention paid to AI in different countries and industries.
Two-Phase Hypergraph Based Reasoning with Dynamic Relations for Multi-Hop KBQA

Multi-hop knowledge base question answering (KBQA) aims at finding the answers to a factoid question by reasoning across multiple triples. Note that when human performs multi-hop reasoning, one tends to concentrate on specific relation at different hops and pinpoint a group of entities connected by the relation. Hypergraph convolutional networks (HGCN) can simulate this behavior by leveraging hyperedges to connect more than two nodes more than pairwise connection. However, HGCN is for undirected graphs and does not consider the direction of information transmission. We introduce the directed-HGCN (DHGCN) to adapt to the knowledge graph with directionality. Inspired by human's hop-by-hop reasoning, we propose an interpretable KBQA model based on DHGCN, namely two-phase hypergraph based reasoning with dynamic relations, which explicitly updates relation information and dynamically pays attention to different relations at different hops. Moreover, the model predicts relations hop-by-hop to generate an intermediate relation path. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our model.
SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields

The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution.
Knowledge Graphs: An Information Retrieval Perspective

The aim of this survey is to bridge two important components of modern information access: information retrieval (IR) and knowledge graphs (KGs). Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. The authors provide an overview of the literature on KGs in the context of IR and the components required when building IR systems that leverage KGs. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, they consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using IR techniques. They summarize research work, group related approaches, and discuss challenges shared across tasks at the interface of IR and KGs. In Knowledge Graphs: An Information Retrieval Perspective, the authors present an extensive overview of tasks related to KGs from an IR perspective, provide a thorough review for each task, and present discussions on common issues that are shared among the tasks. They discuss common issues that appear across the tasks that consider and identify future directions for addressing them. They also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area.