Reviewerly/exHarmony huggingface dataset:
Cross-Modal Prototype Driven Network for Radiology Report Generation

Radiology report generation (RRG) aims to describe automatically a radiology image with human-like language and could potentially support the work of radiologists, reducing the burden of manual reporting. Previous approaches often adopt an encoder-decoder architecture and focus on single-modal feature learning, while few studies explore cross-modal feature interaction. Here we propose a Cross-modal PROtotype driven NETwork (XPRONET) to promote cross-modal pattern learning and exploit it to improve the task of radiology report generation. This is achieved by three well-designed, fully differentiable and complementary modules: a shared cross-modal prototype matrix to record the cross-modal prototypes; a cross-modal prototype network to learn the cross-modal prototypes and embed the cross-modal information into the visual and textual features; and an improved multi-label contrastive loss to enable and enhance multi-label prototype learning. XPRONET obtains substantial improvements on the IU-Xray and MIMIC-CXR benchmarks, where its performance exceeds recent state-of-the-art approaches by a large margin on IU-Xray and comparable performance on MIMIC-CXR (The code is publicly available at https://github.com/Markin-Wang/XProNet .).
Application of machine learning techniques for predicting survival in ovarian cancer

Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the treatment process and the prognosis.The ovarian cancer patients' dataset is compiled from the Surveillance, Epidemiology, and End Results (SEER) database. With the help of a clinician, the dataset is curated, and the most relevant features are selected. Pearson's second coefficient of skewness test is used to evaluate the skewness of the dataset. Pearson correlation coefficient is also used to investigate the associations between features. Statistical test is utilized to evaluate the significance of the features. Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. An interpretable method, Shapley Additive Explanations (SHAP), is applied to clarify the decision-making process and determine the importance of each feature in prediction. Additionally, DTs of the RF model are displayed to show how the model predicts the survival intervals.Our results show that RF (Accuracy = 88.72%, AUC = 82.38%) and XGBoost (Root Mean Squad Error (RMSE)) = 20.61%, R2 = 0.4667) have the best performance for classification and regression approaches, respectively. Furthermore, using the SHAP method along with extracted DTs of the RF model, the most important features in the dataset are identified. Histologic type ICD-O-3, chemotherapy recode, year of diagnosis, age at diagnosis, tumor stage, and grade are the most important determinant factors in survival prediction.To the best of our knowledge, our study is the first study that develops various ML models to predict ovarian cancer patients' survival on the SEER database in both classification and regression approaches. These ML algorithms also achieve more accurate results and outperform statistical methods. Furthermore, our study is the first study to use the SHAP method to increase confidence and transparency of the proposed models' prediction for clinicians. Moreover, our developed models, as an automated auxiliary tool, can help clinicians to have a better understanding of the estimated survival as well as important features that affect survival.
Musical Metaverse: vision, opportunities, and challenges

Abstract The so-called metaverse relates to a vision of a virtual, digital world which is parallel to the real, physical world, where each user owns and interact through his/her own avatar. Music is one of the possible activities that can be conducted in such a space. The “Musical Metaverse” (MM), the metaverse part which is dedicated to musical activities, is currently in its infancy, although is a concept that is constantly evolving and is progressing at a steady pace. However, to the best of the author’s knowledge, as of today an investigation about the opportunities and challenges posed by the MM has not been conducted yet. In this paper, we provide a vision for the MM and discuss what are the opportunities for musical stakeholders offered by current implementations of the MM, as well as we envision those that are likely to occur as the metaverse emerges. We also identify the technical, artistic, ethical, sustainability, and regulatory issues that need to be addressed so for the MM to be created and utilized in efficient, creative, and responsible ways. Given the importance and timeliness of the MM, we believe that a discussion on the related opportunities and concerns is useful to provide developers with guidelines for creating better virtual environments and musical interactions between stakeholders.
Federated Learning in Health care Using Structured Medical Data

The success of machine learning–based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.
COLD: A Benchmark for Chinese Offensive Language Detection

Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models.However, this task in Chinese is still under exploration due to the scarcity of reliable datasets.To this end, we propose a benchmark -COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset -COLDATASET and a baseline detector -COLDETECTOR which is trained on the dataset.We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources.We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models.We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues.Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier. 1
Semi-Relaxed Plan Heuristics

The currently dominant approach to domain-independent planning is planning as heuristic search, with most successful planning heuristics being based on solutions to delete-relaxed versions of planning problems, in which the negative effects of actions are ignored. We introduce a principled, flexible, and practical technique for augmenting delete-relaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work, conditional effects are used to limit the growth of the task to be linear in the number of such conjunctions, making its use for obtaining heuristic functions feasible. The resulting heuristics are empirically evaluated, and shown to be some- times much more informative than standard delete-relaxation heuristics.
Fast Phrase Search for Encrypted Cloud Storage

Cloud computing has generated much interest in the research community in recent years for its many advantages, but has also raise security and privacy concerns. The storage and access of confidential documents have been identified as one of the central problems in the area. In particular, many researchers investigated solutions to search over encrypted documents stored on remote cloud servers. While many schemes have been proposed to perform conjunctive keyword search, less attention has been noted on more specialized searching techniques. In this paper, we present a phrase search technique based on Bloom filters that is significantly faster than existing solutions, with similar or better storage and communication cost. Our technique uses a series of n-gram filters to support the functionality. The scheme exhibits a trade-off between storage and false positive rate, and is adaptable to defend against inclusion-relation attacks. A design approach based on an application's target false positive rate is also described.
A benchmarking study of classification techniques for behavioral data

The predictive power of increasingly common large-scale, behavioral data has been demonstrated by previous research. Such data capture human behavior through the actions and/or interactions of people. Their sparsity and ultra-high dimensionality pose significant challenges to state-of-the-art classification techniques. Moreover, no prior work has systematically explored the choice of methods with respect to the trade-off between classification performance and computational expense. This paper provides a contribution in this direction through a benchmarking study. Eleven classification models are compared on forty-one fine-grained behavioral data sets. Statistical performance comparisons enriched with learning curve analyses demonstrate two important findings. First, there is an inherent generalization performance versus time trade-off, rendering the choice of an appropriate classifier dependent on computation constraints and data set characteristics. Well-regularized logistic regression achieves the best AUC; however, it takes the longest time to train. L2 regularization performs better than sparse L1 regularization. An attractive generalization/time trade-off is achieved by a similarity-based technique. Second, although the data sets used are large, the learning curve results illustrate that as a direct consequence of their high dimensionality and sparseness, significant value lies in collecting and analyzing even more data. This finding is observed both in the instance and in the feature dimensions, contrasting with learning curve studies on traditional data. The results of this study provide guidance for researchers and practitioners for the selection of appropriate classification techniques, sample sizes and data features, while also providing focus in scalable algorithm design in the face of large, behavioral data.
Event Detection Through Differential Pattern Mining in Cyber-Physical Systems

Extracting knowledge from sensor data for various purposes has received a great deal of attention by the data mining community. For the purpose of event detection in cyber-physical systems (CPS), e.g., damage in building or aerospace vehicles from the continuous arriving data is challenging due to the detection quality. Traditional data mining schemes are used to reduce data that often use metrics, association rules, and binary values for frequent patterns as indicators for finding interesting knowledge about an event. However, these may not be directly applicable to the network due to certain constraints (communication, computation, bandwidth). We discover that, the indicators may not reveal meaningful information for event detection in practice. In this paper, we propose a comprehensive data mining framework for event detection in the CPS named DPminer, which functions in a distributed and parallel manner (data in a partitioned database processed by one or more sensor processors) and is able to extract a pattern of sensors that may have event information with a low communication cost. To achieve this, we introduce a new sensor behavioral pattern mining technique called differential sensor pattern (DSP) which considers different frequencies and values (non-binary) with a set of sensors, instead of traditional binary patterns. We present an algorithm for data preparation and then use a highly-compact data tree structure (called DP-Tree) for generating the DSP. An important tradeoff between the communication and computation costs for the event detection via data mining is made. Evaluation results show that DPminer can be very useful for networked sensing with a superior performance in terms of communication cost and event detection quality compared to existing data mining schemes.
Energy-efficient mapping of large-scale workflows under deadline constraints in big data computing systems

Large-scale workflows for big data analytics have become a main consumer of energy in data centers where moldable parallel computing models such as MapReduce are widely applied to meet high computational demands with time-varying computing resources. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on energy efficiency, which remains largely unexplored. In this paper, we analyze the properties of moldable jobs and formulate a workflow mapping problem to minimize the dynamic energy consumption of a given workflow request under a deadline constraint in big data systems. Since this problem is strongly NP-hard, we design a fully polynomial-time approximation scheme (FPTAS) for a special case with a pipeline-structured workflow on a homogeneous cluster and a heuristic for the generalized problem with an arbitrary workflow on a heterogeneous cluster. The performance superiority of the proposed solution in terms of dynamic energy saving and deadline missing rate is illustrated by extensive simulation results in comparison with existing algorithms, and further validated by real-life workflow implementation and experimental results in Hadoop/YARN systems.
Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks

In this paper, we propose a multi-agent based art production framework. In existing artwork creation systems, images were generated using artificial life and evolutionary computation approaches. In artificial life, swarm intelligence or Boids model, and in evolutionary computation, genetic algorithm or genetic programming are commonly used to create images. These automated artwork creation systems make it easy to create artistic images even if the users are not professional artists. Despite the high possibility of these creation systems, however, much research has not been done so far. In this paper, we propose an art production framework that generates images using multi-agents with chaotic dynamics features. Agents act on the canvas following the three rules of Boids model. In addition, each agent possesses a chaotic neural network which trained by differential evolution algorithm, so that colors can be evolved to represent a better style. As a result, we propose an art production framework for generating processing artworks that contain highly complex dynamics. Finally, we created the glitch artworks using the proposed framework, which shows a new glitch style.
A corpus study of splitting and joining sentences in translation

The use of corpora in translation studies has risen dramatically over recent years, contributing towards a growing body of empirical research focussing not only on what differentiates translated from non-translated language, but also on the changes or shifts that translators make from source to target texts. Most of these studies are centred on sub-sentential elements, such as the contrastive use of particular lexis or grammar. However, translation shifts can transcend the level of the sentence. For example, sentences can be split or joined in translation, or there can be complex shifts that combine the two. While there is some research on sentence splitting, there do not seem to be many studies about sentence joining, or indeed sentence splitting and joining together. This study seeks to address this gap. Using a bi-directional parallel corpus of Portuguese and English fiction, over 90,000 source-text sentences and their corresponding text in translation were analysed from a quantitative perspective, and a closer look was taken at a sample of over 1,000 parallel text segments involving sentence joining and splitting. The main findings were that in both translation directions (1) there was a strong tendency for sentence preservation, (2) the differences between sentence splitting and joining were not significant, and (3) changes in sentence boundaries were predominantly associated with the standardisation or normalisation of syntax and a tendency for explicitation.
On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule

This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour classifier (kNN) called Simultaneous Weighting of Attributes and Neighbours (SWAN). Unlike other weighting methods, SWAN presents the ability of adjusting the contribution of the neighbours and the significance of the features of the data. The optimization process focuses on the search of two real-valued vectors. One of them represents the votes of neighbours, and the other one represents the weight of each feature. The synergy between the two sets of weights found in the optimization process helps to improve significantly, the classification accuracy. The results on 35 datasets from the UCI repository suggest that SWAN statistically outperforms the other weighted kNN methods.
Learning domain-invariant feature for robust depth-image-based 3D shape retrieval

In recent years, 3D shape retrieval has been garnering increased attention in a wide range of fields, including graphics, image processing and computer vision. Meanwhile, with the advances in depth sensing techniques, such as those used by the Kinect and 3D LiDAR device, depth images of 3D objects can be acquired conveniently, leading to rapid increases of depth image dataset. In this paper, different from most of the traditional cross-domain 3D shape retrieval approaches that focused on the RGB-D image-based or sketch-based shape retrieval, we aim to retrieve shapes based only on depth image queries. Specifically, we proposed to learn a robust domain-invariant representation between 3D shape and depth image domains by constructing a pair of discriminative neural networks, one for each domain. The two networks are connected by a loss function with constraints on both inter-class and intra-class margins, which minimizes the intra-class variance while maximizing the inter-class margin among data from the two domains (depth image and 3D shape). Our experiments on the NYU Depth V2 dataset (with Kinect-type noise) and two 3D shape (CAD model) datasets (SHREC 2014 and ModelNet) demonstrate that our proposed technique performs superiorly over existing state-of-the-art approaches on depth-image-based 3D shape retrieval task.
A Novel Technique to Develop Cognitive Models for Ambiguous Image Identification Using Eye Tracker

Human behavior can be analyzed using Eye tracker. Thus, it is used for revealing the cognitive processes for object identification. Cognitive process is the mental ability for identification of what our eyes see. Vision with 20/20 sometimes may not reveal the purpose. In this study, ambiguous images are taken to observe the cognitive process in participants. During the perception of an object, a participant uses goal-directed search for identifying various objects. Dense gaze coordinates provide the region of interests and are considered as the target regions for object identification in ambiguous images. These data are used to develop cognitive models for identification of ambiguous images. Features such as, eye fixation, pupil diameter, fixation durations, moments of inertia, and polar moments are used for developing the cognitive model. Three different feature selection methods along with six different classifiers are used for the task of classification. The selection of a subset of features using hypothesis testing performed well, compared to principal component analysis based dimensionality reduction method. This study could be used in detecting whether a participants is lying or not while perceiving an ambiguous image.
Adaptive Quantized Estimation Fusion Using Strong Tracking Filtering and Variational Bayesian

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.
Can word vectors help corpus linguists?

Two recent methods based on distributional semantic models (DSMs) have proved very successful in learning high-quality vector representations of words from large corpora: word2vec and GloVe. Once trained on a very large corpus, these algorithms produce distributed representations for words in the form of vectors. DSMs based on deep learning and neural networks have proved efficient in representing the meaning of individual words. In this paper, I assess to what extent state-of-the-art word-vector semantics can help corpus linguists annotate large datasets for semantic classes. Although word vectors suggest exciting opportunities for resolving semantic annotation issues, there is still room for improvement in terms of the representation of polysemy, homonymy, and multiword expressions.
A new approach to map-assisted Bayesian tracking filtering

This paper presents a new non-linear filter designed to track targets following a road network, taking advantage of the road map information. The algorithm is based on a Bayesian Multiple Hypotheses modelling of movement process, postulating and evaluating different hypotheses on the segments being followed by the target after road junctions. Then, the along-road tracking is carried out, for each hypothesis, by a longitudinal IMM filter capable of tracking target movements along straight roads, circular segments, and generic curvilinear segments defined through Bézier curves. The algorithm also includes a lateral drift estimator, which tracks the lateral motion of the target with respect to road axis, to be able to estimate target piloting error and especially to track targets in wide roads. The paper completely describes the filter and associated measurement preprocessing procedures, and also includes a comparative evaluation of the proposed filter with other filtering methods in the literature.