Reviewerly/exHarmony huggingface dataset:
Predicting length of stay in hospitals intensive care unit using general admission features

According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. Therefore, accurate prediction of patient LOS may aid the healthcare specialists to take medical decisions and allocate medical team and resources. As well, the patient and insurance companies may use this prediction to manage their budget. In this paper, a framework for predicting patient LOS in the ICU using different machine learning (ML) techniques is proposed. Unlike most of the previous studies, this study relies on general medical features collected on patient admission regardless of the patient diagnosis. This provide a broad scope and cover all patients making this approach general and easy to use. The prediction accuracy of the proposed approach was recorded to be very high and different for each ML technique. For example, the best prediction accuracy was achieved by fuzzy with accuracy reach 92%, while classification tree managed to achieve a prediction accuracy of 90% coming in the second place.
Blockchain-Aided Privacy-Preserving Outsourcing Algorithms of Bilinear Pairings for Internet of Things Devices

Bilinear pairing is a fundamental operation that is widely used in cryptographic algorithms (e.g., identity-based cryptographic algorithms) to secure IoT applications. Nonetheless, the time complexity of bilinear pairing is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{3})$ </tex-math></inline-formula> , making it a very time-consuming operation, especially for resource-constrained IoT devices. Secure outsourcing of bilinear pairing has been studied in recent years to enable computationally weak devices to securely outsource the bilinear pairing to untrustworthy cloud servers. However, the state-of-art algorithms often require to precompute and store some values, which results in storage burden for devices. In the Internet of Things, devices are generally with very limited storage capacity. Thus, the existing algorithms do not fit the IoT well. In this article, we propose a secure outsourcing algorithm of bilinear pairings, which does not require precomputations. In the proposed algorithm, the outsourcer side’s efficiency is significantly improved compared with executing the original bilinear pairing operation. At the same time, the privacy of the input and output is ensured. Also, we apply the Ethereum blockchain in our outsourcing algorithm to enable fair payments, which ensures that the cloud server gets paid only when he correctly accomplished the outsourced work. The theoretical analysis and experimental results show that the proposed algorithm is efficient and secure.
A soft measurement approach of wastewater treatment process by lion swarm optimizer-based extreme learning machine

Some key variables in the wastewater treatment process, such as the biochemical oxygen demand (BOD5) and the chemical oxygen demand (COD), are difficult to perform timely and accurate measurement by hardware means. In view of this issue, we introduce a novel soft measurement approach. In the proposed approach, the sparse principal component analysis (SPCA) is used for dimensionality reduction of datasets, and construction of the soft measurement model is done by an improved extreme learning machine (ELM) algorithm, the lion swarm optimizer-based extreme learning machine (LSO-ELM). In the LSO-ELM, the connection weight matrix from the input layer to the hidden layer and the bias vector of the hidden layer are optimized by the LSO. The optimization of these two parameters has greatly improved the predictive ability of original ELM. We illustrate the LSO-ELM approach on Benchmark Simulation Model 1 (BSM1) and the results show that it has excellent performance on the prediction of BOD5 and COD. The proposed approach will greatly improve the quality of soft measurement in wastewater treatment process.
DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems

Deep Learning is emerging as an effective technique to detect sophisticated cyber-attacks targeting Industrial Control Systems (ICSs). The conventional approach to detection in literature is to learn the "normal" behaviour of the system, to be then able to label noteworthy deviations from it as anomalies. However, during operations, ICSs inevitably and continuously evolve their behaviour, due to e.g., replacement of devices, workflow modifications, or other reasons. As a consequence, the accuracy of the anomaly detection process may be dramatically affected with a considerable amount of false alarms being generated. This paper presents DAICS, a novel deep learning framework with a modular design to fit in large ICSs. The key component of the framework is a 2-branch neural network that learns the changes in the ICS behaviour with a small number of data samples and a few gradient updates. This is supported by an automatic tuning mechanism of the detection threshold that takes into account the changes in the prediction error under normal operating conditions. In this regard, no specialised human intervention is needed to update the other parameters of the system. DAICS has been evaluated using publicly available datasets and shows an increased detection rate and accuracy compared to state of the art approaches, as well as higher robustness to additive noise.
CARAFE++: Unified Content-Aware ReAssembly of FEatures

Feature reassembly, i.e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e.g., residual networks and feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose unified Content-Aware ReAssembly of FEatures (CARAFE++), a universal, lightweight, and highly effective operator to fulfill this goal. CARAFE++ has several appealing properties: (1) Unlike conventional methods such as pooling and interpolation that only exploit sub-pixel neighborhood, CARAFE++ aggregates contextual information within a large receptive field. (2) Instead of using a fixed kernel for all samples (e.g. convolution and deconvolution), CARAFE++ generates adaptive kernels on-the-fly to enable instance-specific content-aware handling. (3) CARAFE++ introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation, and image inpainting. CARAFE++ shows consistent and substantial gains on mainstream methods across all the tasks with negligible computational overhead. It shows great potential to serve as a strong building block for modern deep networks.
Zero-Shot Learning on 3D Point Cloud Objects and Beyond

Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However, despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. In this paper, we identify some of the challenges and apply 2D Zero-Shot Learning (ZSL) methods in the 3D domain to analyze the performance of existing models. Then, we propose a novel approach to address the issues specific to 3D ZSL. We first present an inductive ZSL process and then extend it to the transductive ZSL and Generalized ZSL (GZSL) settings for 3D point cloud classification. To this end, a novel loss function is developed that simultaneously aligns seen semantics with point cloud features and takes advantage of unlabeled test data to address some known issues (e.g., the problems of domain adaptation, hubness, and data bias). While designed for the particularities of 3D point cloud classification, the method is shown to also be applicable to the more common use-case of 2D image classification. An extensive set of experiments is carried out, establishing state-of-the-art for ZSL and GZSL on synthetic (ModelNet40, ModelNet10, McGill) and real (ScanObjectNN) 3D point cloud datasets.
Pchatbot: A Large-Scale Dataset for Personalized Chatbot

atural language dialogue systems raise great attention recently. As many dialogue models are data-driven, high-quality datasets are essential to these systems. In this paper, we introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively. To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization, deduplication, segmentation, and filtering. The scale of Pchatbot is significantly larger than existing Chinese datasets, which might benefit the data-driven models. Besides, current dialogue datasets for personalized chatbot usually contain several persona sentences or attributes. Different from existing datasets, Pchatbot provides anonymized user IDs and timestamps for both posts and responses. This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history. Our preliminary experimental study benchmarks several state-of-the-art dialogue models to provide a comparison for future work. The dataset can be publicly accessed at Github: https://github.com/qhjqhj00/Pchatbot.
EnglishBot: An AI-Powered Conversational System for Second Language Learning

Today, many students learn to speak a foreign language by listening to and repeating pre-recorded materials due to the lack of practice opportunities with human partners. Leveraging recent advancements in AI, Speech, and NLP, we developed EnglishBot, a language learning chatbot that converses with students interactively on college-related topics and provides adaptive feedback. We evaluated EnglishBot against a traditional listen-and-repeat interface with 56 Chinese college students through two six-day user studies under both voluntary and fixed-usage conditions. Students' fluency improved more with EnglishBot as evaluated by the IELTS grading standard for voluntary learning. EnglishBot users also showed higher engagement and voluntarily spent 2.1 times more time interacting with EnglishBot. Our results suggest that conversational interfaces may benefit foreign learners' oral language learning, particularly under casual learning settings.
Detecting facial manipulated videos based on set convolutional neural networks

With the boom of artificial intelligence, facial manipulation technology is becoming more simple and more numerous. At the same time, the technology also has a large and profound negative impact on face forensics, such as Deepfakes. In this paper, in order to aggregate multiframe features to detect facial manipulation videos, we solve facial manipulated video detection from set perspective and propose a novel framework based on set, which is called set convolutional neural network (SCNN). Three instances of the proposed framework SCNN are implemented and evaluated on the Deepfake TIMIT dataset, FaceForensics++ dataset and DFDC Preview datset. The results show that the method outperforms previous methods and can achieve state-of-the-art performance on both datasets. As a perspective, the proposed method is a fusion promotion of single-frame digital video forensics network.
Generalized Label Enhancement with Sample Correlations

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods.
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding

Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text.AWT is the first end-to-end model to hide data in text by automatically learning -without ground truth- word substitutions along with their locations in order to encode the message. We empirically show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of attacks.
Mixture of Informed Experts for Multilingual Speech Recognition

When trained on related or low-resource languages, multilingual speech recognition models often outperform their monolingual counterparts. However, these models can suffer from loss in performance for high resource or unrelated languages. We investigate the use of a mixture-of-experts approach to assign per-language parameters in the model to increase network capacity in a structured fashion. We introduce a novel variant of this approach, ‘informed experts’, which attempts to tackle inter-task conflicts by eliminating gradients from other tasks in these task-specific parameters. We conduct experiments on a real-world task with English, French and four dialects of Arabic to show the effectiveness of our approach. Our model matches or outperforms the monolingual models for almost all languages, with gains of as much as 31% relative. Our model also outperforms the baseline multilingual model for all languages by up to 9% relative.
A neuralized feature engineering method for entity relation extraction

Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively.
Nonblind Image Deblurring via Deep Learning in Complex Field

Nonblind image deblurring is about recovering the latent clear image from a blurry one generated by a known blur kernel, which is an often-seen yet challenging inverse problem in imaging. Its key is how to robustly suppress noise magnification during the inversion process. Recent approaches made a breakthrough by exploiting convolutional neural network (CNN)-based denoising priors in the image domain or the gradient domain, which allows using a CNN for noise suppression. The performance of these approaches is highly dependent on the effectiveness of the denoising CNN in removing magnified noise whose distribution is unknown and varies at different iterations of the deblurring process for different images. In this article, we introduce a CNN-based image prior defined in the Gabor domain. The prior not only utilizes the optimal space-frequency resolution and strong orientation selectivity of the Gabor transform but also enables using complex-valued (CV) representations in intermediate processing for better denoising. A CV CNN is developed to exploit the benefits of the CV representations, with better generalization to handle unknown noises over the real-valued ones. Combining our Gabor-domain CV CNN-based prior with an unrolling scheme, we propose a deep-learning-based approach to nonblind image deblurring. Extensive experiments have demonstrated the superior performance of the proposed approach over the state-of-the-art ones.
Sentiment Analysis for Youtube Videos with User Comments: Review

Sentiment analysis is a process that discovers the user opinions and views against any service or a product. YouTube is one of the most popular videos sharing platforms obtaining millions of views. These receive several comments, containing valuable information that helps in improving the rating levels of the uploaded content. These comments are utilized by using natural language processing techniques and machine learning techniques. There are many attempts had been proposed scholarly with two (positive or negative), three (two with neutral) or multiple (happy, sad, fear, surprise and anger) classes. However, it is challenging to choose the best accurate model. Therefore, there had been attempts to use sentiment analysis on YouTube comments in identifying the polarity as well. This research paper investigates the sentiment analysis methods and techniques that can be used on the YouTube content. Additionally, it explains and categorizes these approaches which are useful in researches in data mining and sentiment analysis.
Improved CBSO: A distributed fuzzy-based adaptive synthetic oversampling algorithm for imbalanced judicial data

Imbalanced data problem is a big challenge for judicial data analysis since it often leads to a low accuracy of the data classification. Synthesizing new samples by means of oversampling is a useful method to handle this problem. However, most oversampling algorithms have been obtained regardless of noise samples and the data distribution has not been fully taken into consideration. For this purpose, an improved cluster-based synthetic oversampling algorithm, namely distributed fuzzy-based adaptive synthetic oversampling (DFBASO) algorithm, is proposed by simultaneously considering the distribution of inter-class, the distribution of intra-cluster and the characteristic of noise samples. The proposed DFBASO algorithm is equipped with: 1) fuzzy c-means (FCM) clustering algorithm application for samples of minority and majority classes; 2) weighted distribution based on two factors including the inter-class distance and the cluster capacity; and 3) a mixed synthetic method under different distribution cases of intra-cluster. Finally, the judicial data set and eight public data sets are utilized to show the effectiveness and universal applicability of the proposed DFBASO algorithm for the imbalanced data classification.
GANPU: An Energy-Efficient Multi-DNN Training Processor for GANs With Speculative Dual-Sparsity Exploitation

This article presents generative adversarial network processing unit (GANPU), an energy-efficient multiple deep neural network (DNN) training processor for GANs. It enables on-device training of GANs on performance- and battery-limited mobile devices, without sending user-specific data to servers, fully evading privacy concerns. Training GANs require a massive amount of computation, and therefore, it is difficult to accelerate in a resource-constrained platform. Besides, networks and layers in GANs show dramatically changing operational characteristics, making it difficult to optimize the processor's core and bandwidth allocation. For higher throughput and energy efficiency, this article proposed three key features. An adaptive spatiotemporal workload multiplexing is proposed to maintain high utilization in accelerating multiple DNNs in a single GAN model. To take advantage of ReLU sparsity during both inference and training, dual-sparsity exploitation architecture is proposed to skip redundant computations due to input and output feature zeros. Moreover, an exponent-only ReLU speculation (EORS) algorithm is proposed along with its lightweight processing element (PE) architecture, to estimate the location of output feature zeros during the inference with minimal hardware overhead. Fabricated in a 65-nm process, the GANPU achieved the energy efficiency of 75.68 TFLOPS/W for 16-bit floating-point computation, which is 4.85 × higher than the state of the art. As a result, GANPU enables on-device training of GANs with high energy efficiency.
Towards Automatic Collaboration Analytics for Group Speech Data Using Learning Analytics

Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.