Reviewerly/exHarmony huggingface dataset:
A blockchain-based keyword search scheme with dual authorization for electronic health record sharing

Electronic health record is an electronic file that records people’s medical treatment and physical examination information in an all-around way which is an important branch of building a smart medical system. The sharing of electronic health records not only helps people use medical resources anytime and anywhere to deal with tricky physical conditions but also provides valuable and true reference materials for medical practitioners in related research. However, the current electronic health record sharing schemes have the problems of electronic health record data leakage, access authorization mechanism incomplete, inflexible operation of electronic health record data, low search efficiency, and untrustworthy cloud server provider. To solve these challenging problems, we propose a new blockchain-based keyword search scheme with dual authorization for electronic health record sharing. The certificateless cryptosystem avoids certificate management and key escrow. The construction of the authorization matrix realizes the dual authorization of the identities of users and the corresponding searchable departments. Also, the matrix can manage the access rights of users. The signal value of the ciphertext index can realize the authority management of ciphertext index. The ciphertext MAC verification code stored in the blockchain can verify the legality of ciphertext, and the smart contract is used to ensure fair transactions. Therefore, our scheme is more suitable for sharing confidential and sensitive electronic health records. The security proof concludes that the proposed scheme is indistinguishability under the keyword guessing attack. Besides, the result of performance analysis and comparison simulations show that the proposed scheme has higher performance than some related schemes.
Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data

This paper proposes a novel one-day-ahead photovoltaic (PV) energy prediction scheme using on-site temperatures based on ensemble of convolutional neural networks (POST-enCNN). Even meteorological information from weather station can help predict PV energy, additional information is necessary to describe on-site weather condition. Therefore, the proposed scheme takes advantage of on-site weather data along with the meteorological information to predict PV energy generation accurately. The proposed prediction process is divided into two stages. In the first stage, the day-ahead on-site temperatures are predicted based on meteorological information and the past on-site temperatures data. Specifically, incremental trend of past on-site temperatures are analyzed and transformed into a numerical values. These values are applied to ensemble of CNNs to increase prediction accuracy of the day-ahead on-site temperatures. In the second stage, the predicted day-ahead on-site temperatures are combined with meteorological information. Finally, using the long short-term memory (LSTM) based neural network, the day-ahead PV energy generation is predicted using the combined weather information. Through the simulation results, we show that the proposed POST-enCNN achieve an 27% reduction of prediction error compared to a conventional method that predicts the day-ahead PV energy generation depending solely on meteorological information.
Mid-air gestures for in-vehicle media player: elicitation, segmentation, recognition, and eye-tracking testing

Abstract There was a problem of great distraction in traditional touch interactive operation of in-vehicle information interface, but the application of gesture recognition technology on in-vehicle system improved this problem. However, users had less experience in this new interactive mode, and the cognitive deviation of the agreement between gestures and commands could directly affect the safety of drivers. The main purpose of this paper was to obtain the user's preference mid-air gestures for the in-vehicle information interface by using the user-elicitation method. In addition, the optimized gesture recognition network structure was applied to the prototype system of in-vehicle information interaction with user-defined mid-air gestures developed by us, and the effectiveness of the system was evaluated by collecting eye movement indicators of users through the simulation of driving eye movement experiments. In the process of user elicitation, the principle of command prompt and agreement rate was introduced. According to the agreement rate elicited by users (AR = .397), we got the gesture consensus set design direction of in-vehicle information control. The experimental results of the eye movement index showed that the method of user-defined mid-air gestures can effectively improve driving safety on in-vehicle media control, and reduced the distraction of users when driving compared with the traditional touch-based method. Article highlights The user's preferred in-vehicle media control task gestures were obtained through the user elicitation method. Constructed an in-vehicle secondary task control prototype system based on gesture recognition. The eye movement experiment proved that the prototype system can reduce the user's distraction when operating the in-vehicle's secondary tasks.
Design and construction of a cost-effective, portable sign language to speech translator

This paper presents the design and construction of a cost-effective, portable sign language to speech translator. The system is designed to work with American Sign Language fingerspelling (which facilitates translation in any Latin alphabet-based language) and aims to translate a pangram into speech. The system consists of three subsystems, namely, gesture detection, gesture classification, and text-to-speech subsystems and relies on a power bank for its power supply. The gesture detection subsystem makes use of five flex sensors, each placed on the finger of a glove, as well as a three-axis accelerometer. Gesture classification is achieved through a supervised machine learning approach — five different algorithms are compared to determine the best configuration for this system. Overall, a support vector machine with a radial basis function kernel and a penalty parameter of 10.0 performs best in the context of this investigation. Using the best-case sensor configuration, and the best-performing machine learning classifier, the system achieves a practical repeatability of 85.51%. Text-to-speech translation of the classified gestures is performed using the eSpeak engine. Overall, the system costs under $35 which is lower when compared to similar systems investigated, that cost at least $100. The final system meets the initial goals of sign language to speech translation, portability and cost-effectiveness.
<scp>SEntFiN</scp> 1.0: <scp>Entity‐aware</scp> sentiment analysis for financial news

Abstract Fine‐grained financial sentiment analysis on news headlines is a challenging task requiring human‐annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a human‐annotated dataset of 10,753 news headlines with entity‐sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity‐relevant sentiments using a feature‐based approach rather than an expression‐based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon‐based and pretrained sentence representations and five classification approaches. Our experiments indicate that lexicon‐based N‐gram ensembles are above par with pretrained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain‐specific BERT) achieve the highest average accuracy of 94.29% and F1‐score of 93.27%. Further, using over 210,000 entity‐sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.
Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation

Neural language models show vulnerability to adversarial examples which are semantically similar to their original counterparts with a few words replaced by their synonyms. A common way to improve model robustness is adversarial training which follows two steps—collecting adversarial examples by attacking a target model, and fine-tuning the model on the augmented dataset with these adversarial examples. The objective of traditional adversarial training is to make a model produce the same correct predictions on an original/adversarial example pair. However, the consistency between model decision-makings on two similar texts is ignored. We argue that a robust model should behave consistently on original/adversarial example pairs, that is making the same predictions (what) based on the same reasons (how) which can be reflected by consistent interpretations. In this work, we propose a novel feature-level adversarial training method named FLAT. FLAT aims at improving model robustness in terms of both predictions and interpretations. FLAT incorporates variational word masks in neural networks to learn global word importance and play as a bottleneck teaching the model to make predictions based on important words. FLAT explicitly shoots at the vulnerability problem caused by the mismatch between model understandings on the replaced words and their synonyms in original/adversarial example pairs by regularizing the corresponding global word importance scores. Experiments show the effectiveness of FLAT in improving the robustness with respect to both predictions and interpretations of four neural network models (LSTM, CNN, BERT, and DeBERTa) to two adversarial attacks on four text classification tasks. The models trained via FLAT also show better robustness than baseline models on unforeseen adversarial examples across different attacks.
An End-to-End Cascaded Image Deraining and Object Detection Neural Network

While the deep learning-based image deraining methods have made great progress in recent years, there are two major shortcomings in their application in real-world situations. Firstly, the gap between the low-level vision task represented by rain removal and the high-level vision task represented by object detection is significant, and the low-level vision task can hardly contribute to the high-level vision task. Secondly, the quality of the deraining dataset needs to be improved. In fact, the rain lines in many baselines have a large gap with the real rain lines, and the resolution of the deraining dataset images is generally not ideal. Meanwhile, there are few common datasets for both the low-level vision task and the high-level vision task. In this letter, we explore the combination of the low-level vision task with the high-level vision task. Specifically, we propose an end-to-end object detection network for reducing the impact of rainfall, which consists of two cascaded networks, an improved image deraining network and an object detection network, respectively. We also design the components of the loss function to accommodate the characteristics of the different sub-networks. We then propose a dataset based on the KITTI dataset for rainfall removal and object detection, on which our network surpasses the state-of-the-art with a significant improvement in metrics. Besides, our proposed network is measured on driving videos collected by self-driving vehicles and shows positive results for rain removal and object detection.
A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

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A novel hand gesture detection and recognition system based on ensemble-based convolutional neural network

Nowadays, hand gesture recognition has become an alternative for human-machine interaction. It has covered a large area of applications like 3D game technology, sign language interpreting, VR (virtual reality) environment, and robotics. But detection of the hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks, but CNN architectures suffer from some problems like high variance during prediction, overfitting problem and also prediction errors. To overcome these problems, an ensemble of CNN-based approaches is presented in this paper. Firstly, the gesture portion is detected by using the background separation method based on binary thresholding. After that, the contour portion is extracted, and the hand region is segmented. Then, the images have been resized and fed into three individual CNN models to train them in parallel. In the last part, the output scores of CNN models are averaged to construct an optimal ensemble model for the final prediction. Two publicly available datasets (labeled as Dataset-1 and Dataset-2) containing infrared images and one self-constructed dataset have been used to validate the proposed system. Experimental results are compared with the existing state-of-the-art approaches, and it is observed that our proposed ensemble model outperforms other existing proposed methods.
Autoregressive Linguistic Steganography Based on BERT and Consistency Coding

Linguistic steganography (LS) conceals the presence of communication by embedding secret information into a text. How to generate a high-quality text carrying secret information is a key problem. With the widespread application of deep learning in natural language processing, recent algorithms use a language model (LM) to generate the steganographic text, which provides a higher payload compared with many previous arts. However, the security still needs to be enhanced. To tackle with this problem, we propose a novel autoregressive LS algorithm based on BERT and consistency coding, which achieves a better trade-off between embedding payload and system security. In the proposed work, based on the introduction of the masked LM, given a text, we use consistency coding to make up for the shortcomings of block coding used in the previous work so that we can encode arbitrary-size candidate token set and take advantages of the probability distribution for information hiding. The masked positions to be embedded are filled with tokens determined by an autoregressive manner to enhance the connection between contexts and therefore maintain the quality of the text. Experimental results have shown that, compared with related works, the proposed work improves the fluency of the steganographic text while guaranteeing security, and also increases the embedding payload to a certain extent.
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion

Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera. This means that the information of each instant GS frame is partially, yet sequentially, embedded into the row-dependent distortion. Inspired by this fact, we address the challenging task of reversing this process, i.e., extracting undistorted GS frames from images suffering from RS distortion. However, since RS distortion is coupled with other factors such as readout settings and the relative velocity of scene elements to the camera, models that only exploit the geometric correlation between temporally adjacent images suffer from poor generality in processing data with different readout settings and dynamic scenes with both camera motion and object motion. In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task. Grounded on the symmetric and complementary nature of dual reversed distortion, we develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time. Extensive experimental results demonstrate that IFED is superior to naive cascade schemes, as well as the state-of-the-art which utilizes adjacent RS images. Most importantly, although it is trained on a synthetic dataset, IFED is shown to be effective at retrieving GS frame sequences from real-world RS distorted images of dynamic scenes. Code is available at https://github.com/zzh-tech/Dual-Reversed-RS .
Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.
Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

This work digs into a root question in human perception: can face geometry be gleaned from one's voices? Previous works that study this question only adopt developments in image synthesis and convert voices into face images to show correlations, but working on the image domain unavoidably involves predicting attributes that voices cannot hint, including facial textures, hairstyles, and backgrounds. We instead investigate the ability to reconstruct 3D faces to concentrate on only geometry, which is much more physiologically grounded. We propose our analysis framework, Cross-Modal Perceptionist, under both supervised and unsupervised learning. First, we construct a dataset, Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes, making supervised learning possible. Second, we use a knowledge distillation mechanism to study whether face geometry can still be gleaned from voices without paired voices and 3D face data under limited availability of 3D face scans. We break down the core question into four parts and perform visual and numerical analyses as responses to the core question. Our findings echo those in physiology and neuroscience about the correlation between voices and facial structures. The work provides future human-centric cross-modal learning with explainable foundations. See our project page.
Distributed Learning With Sparsified Gradient Differences

A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications. In this paper, we devise a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> radient <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> escent method with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> parsification and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> rror <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> orrection ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GD-SEC</b> ) to improve the communications efficiency in a general worker-server architecture. Motivated by a variety of wireless communications learning scenarios, GD-SEC reduces the number of bits per communication from worker to server with no degradation in the order of the convergence rate. This enables larger scale model learning without sacrificing convergence or accuracy. At each iteration of GD-SEC, instead of directly transmitting the entire gradient vector, each worker computes the difference between its current gradient and a linear combination of its previously transmitted gradients, and then transmits the sparsified gradient difference to the server. A key feature of GD-SEC is that any given component of the gradient difference vector will not be transmitted if its magnitude is not sufficiently large. An error correction technique is used at each worker to compensate for the error resulting from sparsification. We prove that GD-SEC is guaranteed to converge for strongly convex, convex, and nonconvex optimization problems with the same order of convergence rate as GD. Furthermore, if the objective function is strongly convex, GD-SEC has a fast linear convergence rate. Numerical results not only validate the convergence rate of GD-SEC but also explore the communication bit savings it provides. Given a target accuracy, GD-SEC can significantly reduce the communications load compared to the best existing algorithms without slowing down the optimization process.