Reviewerly/exHarmony huggingface dataset:
Medicare fraud detection using neural networks

Access to affordable healthcare is a nationwide concern that impacts a large majority of the United States population. Medicare is a Federal Government healthcare program that provides affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that costs taxpayers billions of dollars and puts beneficiaries’ health and welfare at risk. Previous work has shown that publicly available Medicare claims data can be leveraged to construct machine learning models capable of automating fraud detection, but challenges associated with class-imbalanced big data hinder performance. With a minority class size of 0.03% and an opportunity to improve existing results, we use the Medicare fraud detection task to compare six deep learning methods designed to address the class imbalance problem. Data-level techniques used in this study include random over-sampling (ROS), random under-sampling (RUS), and a hybrid ROS–RUS. The algorithm-level techniques evaluated include a cost-sensitive loss function, the Focal Loss, and the Mean False Error Loss. A range of class ratios are tested by varying sample rates and desirable class-wise performance is achieved by identifying optimal decision thresholds for each model. Neural networks are evaluated on a 20% holdout test set, and results are reported using the area under the receiver operating characteristic curve (AUC). Results show that ROS and ROS–RUS perform significantly better than baseline and algorithm-level methods with average AUC scores of 0.8505 and 0.8509, while ROS–RUS maximizes efficiency with a 4× speedup in training time. Plain RUS outperforms baseline methods with up to 30× improvements in training time, and all algorithm-level methods are found to produce more stable decision boundaries than baseline methods. Thresholding results suggest that the decision threshold always be optimized using a validation set, as we observe a strong linear relationship between the minority class size and the optimal threshold. To the best of our knowledge, this is the first study to compare multiple data-level and algorithm-level deep learning methods across a range of class distributions. Additional contributions include a unique analysis of the relationship between minority class size and optimal decision threshold and state-of-the-art performance on the given Medicare fraud detection task.
Observation of Three-Photon Spontaneous Parametric Down-Conversion in a Superconducting Parametric Cavity

A long-sought three-photon version of spontaneous parametric down-conversion, a common technique for entangled photon generation, lays the groundwork for expanded investigations into novel types of quantum entanglements and quantum computing resources.
AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition. Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial components and a contextual feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos

In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models.
Recent Advances in Imitation Learning from Observation

Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task. Conventionally, the imitator has access to both state and action information generated by an expert performing the task (e.g., the expert may provide a kinesthetic demonstration of object placement using a robotic arm). However, requiring the action information prevents imitation learning from a large number of existing valuable learning resources such as online videos of humans performing tasks. To overcome this issue, the specific problem of imitation from observation (IfO) has recently garnered a great deal of attention, in which the imitator only has access to the state information (e.g., video frames) generated by the expert. In this paper, we provide a literature review of methods developed for IfO, and then point out some open research problems and potential future work.
Abstractive Summarization: A Survey of the State of the Art

The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.
Assessment and Ranking Framework for the English Skills of Pre-Service Teachers Based on Fuzzy Delphi and TOPSIS Methods

In this study, pre-service teaching refers to teaching English as a second language (TESL) to Malaysian students whose first language is not English.TESL prepares English-language learners to become future teachers of English as a second language.To date, no multi-criteria framework has been developed to evaluate and select the skills of pre-service teachers.This study presents a new framework to assess and rank the English skills of pre-service teachers on the basis of fuzzy Delphi and multi-criteria analysis.Three experiments were conducted.Firstly, criteria were identified from the literature review and the opinions of representative experts via the Delphi method.Secondly, 31 pre-service teachers were evaluated to determine the skills of pre-service teachers on the basis of Delphi criteria outcomes.English proficiency was tested through the English Language Testing Service and four language skill examinations.Each examination was evaluated by experts with vast experience in English teaching.Thirdly, pre-service teachers were ranked on the basis of a set of evaluated Delphi criteria outcomes through the technique for the order of preference by similarity to ideal solution (TOPSIS) method.Thereafter, the mean and standard deviation were utilized to ensure the identical systematic ranking of pre-service teachers.Findings are as follows.Twenty-five criteria from previous studies are representative as evaluated by the opinions of experts, which were gathered through interviews and a structured questionnaire.The validity of content was verified using a five-point Likert scale.With Delphi method outcomes, 14 criteria were selected and included in the final framework.The results of the proposed evaluation framework were tested on Malaysian pre-service teachers.TOPSIS is effective for solving the selection problems of pre-service teachers.In the final experiment, significant differences were recognized between the scores of groups, indicating identical ranking results.
Towards Faster and Better Federated Learning: A Feature Fusion Approach

Federated learning enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT devices. However, the leading optimization algorithm in such settings, i.e., federated averaging, suffers from heavy communication cost and inevitable performance drop, especially when the local data is distributed in a Non-IID way. In this paper, we propose a feature fusion method to address this problem. By aggregating the features from both the local and global models, we achieve a higher accuracy at less communication cost. Furthermore, the feature fusion modules offer better initialization for newly incoming clients and thus speed up the process of convergence. Experiments in popular federated learning scenarios show that our federated learning algorithm with feature fusion mechanism outperforms baselines in both accuracy and generalization ability while reducing the number of communication rounds by more than 60%.
A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks

With the success of deep learning in a wide variety of areas, many deep multi-task learning (MTL) models have been proposed claiming improvements in performance obtained by sharing the learned structure across several related tasks. However, the dynamics of multi-task learning in deep neural networks is still not well understood at either the theoretical or experimental level. In particular, the usefulness of different task pairs is not known a priori. Practically, this means that properly combining the losses of different tasks becomes a critical issue in multi-task learning, as different methods may yield different results. In this paper, we benchmarked different multi-task learning approaches using shared trunk with task specific branches architecture across three different MTL datasets. For the first dataset, i.e. Multi-MNIST (Modified National Institute of Standards and Technology database), we thoroughly tested several weighting strategies, including simply adding task-specific cost functions together, dynamic weight average (DWA) and uncertainty weighting methods each with various amounts of training data per-task. We find that multi-task learning typically does not improve performance for a user-defined combination of tasks. Further experiments evaluated on diverse tasks and network architectures on various datasets suggested that multi-task learning requires careful selection of both task pairs and weighting strategies to equal or exceed the performance of single task learning.
Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation

Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes.We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text.We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.
A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.
Secure SVM Training Over Vertically-Partitioned Datasets Using Consortium Blockchain for Vehicular Social Networks

Machine learning (ML) techniques are expected to be used for specific applications in Vehicular Social Networks (VSNs). Support vector machine (SVM) is one of the typical ML methods and widely used for its high efficiency. Due to the limitation of data sources, the data collected by different entities usually contain attributes that are quite different. However, in some real-world scenarios, when training an SVM classifier, many entities face the same problem that they are lacking in data with adequate attributes. Thus multiple entities are required to share data to combine a dataset with diverse attributes and then jointly train a comprehensive classifier. However, data privacy concerns are raised because of data sharing. To sovle the problem, we propose a privacy-preserving SVM classifier training scheme over vertically-partitioned datasets posessed by multiple data providers. In our scheme, we utilize consortium blockchain and threshold homomorphic cryptosystem to establish a secure SVM classifier training platform without a trusted third-party. We keep lots of training operations locally over original data and necessary interactions between participants are protected by the threshold Paillier and consortium blockchain. Security analysis proves that our scheme can preserve the privacy of the original data and the training intermediate values. Extensive experiments indicate that our scheme has high efficiency and no accuracy loss.
Blockchain-Based Hierarchical Trust Networking for JointCloud

The Internet of Things (IoT) is gradually becoming mature and has already entered our daily life, which interconnects more machines and makes communication more convenient and more intelligent. Massive IoT devices produce innumerable data which need to be analyzed in joint cloud computation (JointCloud) with diversified services. However, due to the weak security of IoT devices, the existing JointCloud architecture hardly provides a secure trusted trade environment for users, which affects severely the application in the IoT network. In this article, we propose a hierarchical trust networking architecture based on permissioned blockchain to implement JointCloud (HTJC). The proposed Hyperledger fabric-based architecture has a better performance than those based on Ethereum in latency. By introducing the credit bonus-penalty strategy (CBPS), HTJC can solve the trust problem and provide users with a secure trusted trade environment. The availability of the proposed architecture is evaluated and compared to the existing models. The numerical results show that the HTJC can defend distributed denial-of-service (DDoS) attacks and provide users with a trusted and effective trade platform.
A Fast Chaotic Image Encryption Scheme With Simultaneous Permutation-Diffusion Operation

In this paper, a secure and fast chaotic image encryption scheme with simultaneous permutation-diffusion operation is proposed. We combine permutation and diffusion processes into a whole, namely, simultaneous permutation and diffusion operation (SPDO). This can solve the problem of traditional encryption scheme in which the permutation and diffusion are two independent processes, that leads attackers to crack the two processes separately. In SPDO, the initial value of the current Sine-Sine chaotic map is related to the secret keys and the previous encrypted pixels' values. In this case, the proposed scheme can generate dynamic key streams and indexes that are related to plaintext, which improves the sensitivity to plaintext for the encryption scheme. In addition, the pixel values are processed by row and column (row-level and column-level) during the encryption procedure. Thus, the proposed scheme presents lower time complexity and faster running speed compared with bit-level or pixel-level image encryption schemes, which makes the proposed scheme be conducive to the batch transmission and real-time transmission of digital images. The simulation results and security analysis show that our scheme can resist common attacks, such as statistical attack, differential attack, chosen plaintext attack and other comprehensive attacks.
Self-Attention Network for Skeleton-based Human Action Recognition

Skeleton-based action recognition has recently attracted a lot of attention. Researchers are coming up with new approaches for extracting spatio-temporal relations and making considerable progress on large-scale skeleton-based datasets. Most of the architectures being proposed are based upon recurrent neural networks (RNNs), convolutional neural networks (CNNs) and graph-based CNNs. When it comes to skeleton-based action recognition, the importance of long term contextual information is central which is not captured by the current architectures. In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3. Our SAN variants has the impressive capability of extracting high-level semantics by capturing long-range correlations. We have also integrated the Temporal Segment Network (TSN) with our SAN variants which resulted in improved overall performance. Different configurations of Self-Attention Network (SAN) variants and Temporal Segment Network (TSN) are explored with extensive experiments. Our chosen configuration outperforms state-of-the-art Top-1 and Top-5 by 4.4% and 7.9% respectively on Kinetics and shows consistently better performance than state-of-the-art methods on NTU RGB+D.
Transferable two-stream convolutional neural network for human action recognition

Human-Robot Collaboration (HRC), which enables a workspace where human and robot can dynamically and safely collaborate for improved operational efficiency, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC, as it helps identify current human action and provides the basis for future action prediction and robot planning. While Deep Learning (DL) has demonstrated great potential in advancing human action recognition, effectively leveraging the temporal information of human motions to improve the accuracy and robustness of action recognition has remained as a challenge. Furthermore, it is often difficult to obtain a large volume of data for DL network training and optimization, due to operational constraints in a realistic manufacturing setting. This paper presents an integrated method to address these two challenges, based on the optical flow and convolutional neural network (CNN)-based transfer learning. Specifically, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Subsequently, transfer learning is investigated to transfer the feature extraction capability of a pretrained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed the effectiveness of the developed method.
Blockchain-based applications in shipping and port management: a literature review towards defining key conceptual frameworks

Purpose The purpose of this paper is to examine and to categorize the tendencies of blockchain-based applications in the shipping industry and supply chain as well as the interrelations between them, including possible correlation of found categories with theoretical background and existing concepts. This study also explores whether blockchain can be adopted into existing maritime shipping and port document workflow management. Design/methodology/approach The current study builds a conceptual framework through a systematic project review carried along with scientific and grey literature, published in journals and conference proceedings during the past decade and giving information or proposals on an issue. Findings The results showed that reviewed projects can be compiled into three main conceptual areas: document workflow management, financial processes and device connectivity. However, having clear interlinkages, none of the reviewed projects consider all three areas at once. Concepts associated with maritime document workflow received broad support among the reviewed projects. In addition, reviewed projects unintentionally follow the similar goals that were laid down within port management scientific projects before the introduction of blockchain technology. Originality/value This study contributes to research by revealing a consistent framework for understanding the blockchain applications within maritime port environment, a less-studied part of blockchain implementation in the supply chain field. Moreover, this work is the first to find out conceptual intersections and correlations between existing projects, mapping current tendencies and potentially increasing knowledge about the field.
Improving Event Detection via Open-domain Trigger Knowledge

Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.