Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Privacy-Preserving Medical Data Sharing Scheme Using Federated Learning

With the booming development of big data technology and health care applications, data in the medical field is characterized by explosive growth, and medical data is valuable, which is the privacy data of patients. However, the characteristics and storage environment of medical big data have brought great challenges to the realization of privacy protection of medical data. In order to ensure the protection of data privacy when sharing medical data, we propose a medical data privacy protection framework based on blockchain (MPBC). In this framework, we protect privacy by adding differential privacy noise into federated learning. In addition, the growing volume of medical data could make blockchain storage problematic. Therefore, a storage mode is proposed to reduce the storage burden of blockchain. The raw data are stored locally and only the hash value calculated by IPFS are stored in blockchain. To enhance the performance, a mechanism is used to validate transactions and aggregate the model. Security analysis shows that our method is a safe and effective way to implement medical data.
Protect, show, attend and tell: Empowering image captioning models with ownership protection

By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital watermarking framework that was conventionally used to protect the ownership of multimedia and video content. This paper demonstrates that the current digital watermarking framework is insufficient to protect image captioning tasks that are often regarded as one of the frontiers AI problems. As a remedy, this paper studies and proposes two different embedding schemes in the hidden memory state of a recurrent neural network to protect the image captioning model. From empirical points, we prove that a forged key will yield an unusable image captioning model, defeating the purpose of infringement. To the best of our knowledge, this work is the first to propose ownership protection on image captioning task. Also, extensive experiments show that the proposed method does not compromise the original image captioning performance on all common captioning metrics on Flickr30k and MS-COCO datasets, and at the same time it is able to withstand both removal and ambiguity attacks. Code is available at https://github.com/jianhanlim/ipr-imagecaptioning
An Improved Bounding Box Regression Loss Function Based on CIOU Loss for Multi-scale Object Detection

The regression loss function is a key factor in the training and optimization process of object detection. The current mainstream regression loss functions are Ln norm loss, IOU loss and CIOU loss. This paper proposes the Scale-Sensitive IOU(SIOU) loss, a new loss function different from the above all, which could solve the issues that the current loss functions cannot distinguish the two bounding boxes in some special cases when the target area scales in one image vary greatly during training process, thereby leading to the improper regression loss calculation and the slowing down of the optimization. An area scale regulating factor Y is added on the basis of CIOU loss to adjust the loss values of the bounding boxes, which could distinguish all the boxes quantitatively in theory thus gets a faster converging speed and better optimization. Through analysis and simulation comparison among the several loss functions, the superiority of SIOU loss is verified. Furthermore, by incorporating SIOU loss into YOLO v4, Faster R-CNN and SSD on the two mainstream aerial remote sensing datasets, i.e., DIOR and NWPU VHR-10, the detection precisions improve by 10.2% than IOU loss and 2.8% than CIOU loss respectively.
ETDNet: An Efficient Transformer Deraining Model

Rainy days usually degrade the visual effect of images and videos.At present, most deraining models for single images adopt gradual optimization or elimination to remove rain streaks, but actually with relatively low efficiency in real tasks.An efficient one-stage deraining model, Efficient Transformer Derain Network (ETDNet), is proposed to remove rain streaks in single images efficiently.A new Transformer architecture is designed to provide rich multiple scales and context information, making the model extract features in a coarse-to-fine way.Multiple expansion filters with different expansion rates are embedded to predict the suitable kernel for each pixel of the rainy image in a multi-scale way.A multi-scale Loss Function is introduced to restore features with high-fidelity and detail textures.Experiments on Rain100L, Rain100H, and SPA datasets show that compared with other models, the proposed ETDNet reaches the highest PSNR and SSIM values at the fastest speed.
SChain

We demonstrate SChain, a consortium blockchain that scales transaction processing to support large-scale enterprise applications. The unique advantage of SChain stems from the exploitation of both intra- and inter-block concurrency. The intra-block concurrency not only takes advantage of the multi-core processor on a single peer but also leverages the capacity of multiple peers. The interblock concurrency enables simultaneous processing across multiple blocks to increase the utilization of various peers. In our demonstration, we use real-time dashboards containing visualization based on the output of SChain to give the attendees interactive explorations of how SChain achieves intra- and inter-block concurrency.
Perceptions about the Future of Integrating Emerging Technologies into Higher Education—The Case of Robotics with Artificial Intelligence

Emerging technologies (ETs) will most likely have a strong impact on education (starting with higher education), just like they have already had in so many economic and social areas. This paper is based on the results obtained in the project “My Future Colleague Robot”, an initiative that aimed to improve the competence of university teaching staff regarding the introduction of ETs in teaching practices at university level. In this paper, we identified the strengths and weaknesses, opportunities, and threats that are related to the adoption in higher education of the combination of two ETs: robotics together with artificial intelligence (AI). Additionally, we analyzed the perceptions of university-level teaching staff about the potential of introducing ETs in education. The empirical data presented here were collected using written essays from 18 university teachers and students. Deductive and inductive approaches with thematic analysis were used for the data analysis. The findings support the idea that previous ET-related experience can support positive attitudes and the implementations of ETs in university teaching; in this study, university teachers had optimistic expectations towards ETs, accepting them as part of teaching practice development, while discussion about the negative effects of ETs was negligible.
A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning

Detecting anomalies on graph data has two types of methods. One is pattern mining that discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks in the graph's adjacency matrix. The other is feature learning that mainly uses graph neural networks (GNNs) to aggregate information from local neighborhood into node representations. However, there is a lack of study that utilizes both the global and local information for graph anomaly detection. In this article, we propose a synergistic approach that leverages pattern mining to inform the GNN algorithms on how to aggregate local information through connections to capture the global patterns. Specifically, it uses a GNN encoder to perform feature aggregation, and the pattern mining algorithms supervise the GNN training process through a novel loss function. We provide theoretical analysis on the effectiveness of the loss function, as well as empirical analysis on the proposed approach across a variety of GNN algorithms and pattern mining methods. Experiments on real-world data show that the synergistic approach performs significantly better than existing graph anomaly detection methods.
Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles

This paper describes the design and development of a cleaning robot, using adaptive manufacturing technology and its use with a control algorithm for which there is a stability proof. The authors’ goal was to fill the gap between theory and practical implementation based on available low-cost components. Adaptive manufacturing was chosen to cut down the cost of manufacturing the robot. Practical verification of the effectiveness of the control algorithm was achieved with the experiments. The robot comprises mainly three assemblies, a four-wheel-drive platform, a four-degrees-of-freedom robotic arm, and a vacuum system. The inlet pipe of the vacuum system was attached to the end effector of the robotic arm, which makes the robot more flexible to clean uneven areas, such as skirting on floors. The robot was equipped with a LIDAR sensor and web camera, giving the opportunity to develop more complex methods. A low-level proportional–integral–derivative (PID) speed controller was implemented, and a high-level controller that uses artificial potential functions to generate repulsive components, which avoids collision with obstacles. Robot operating system (ROS) was installed in the robot’s on-board system. With the help of the ROS node, the high-level controller generates control signals for the low-level controller.
Time-aware Gradient Attack on Dynamic Network Link Prediction

In network link prediction, it is possible to hide a target link from being predicted with a small perturbation on network structure. This observation may be exploited in many real world scenarios, for example, to preserve privacy, or to exploit financial security. There have been many recent studies to generate adversarial examples to mislead deep learning models on graph data. However, none of the previous work has considered the dynamic nature of real-world systems. In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP). The proposed attack method, namely time-aware gradient attack (TGA), utilizes the gradient information generated by deep dynamic network embedding (DDNE) across different snapshots to rewire a few links, so as to make DDNE fail to predict target links. We implement TGA in two ways: one is based on traversal search, namely TGA-Tra; and the other is simplified with greedy search for efficiency, namely TGA-Gre. We conduct comprehensive experiments which show the outstanding performance of TGA in attacking DNLP algorithms.
Def-IDS: An Ensemble Defense Mechanism Against Adversarial Attacks for Deep Learning-based Network Intrusion Detection

Network intrusion detection plays an important role in the Internet of Things systems for protecting devices from security breaches. Facing challenges of the rapidly increasing amount of diverse network traffic, recent research has employed end-to-end deep learning-based intrusion detectors for automatic feature extraction and high detection accuracy. However, deep learning has been proved vulnerable to adversarial attacks that may cause misclassification by imposing imperceptible perturbation on input samples. Though such vulnerability is widely discussed in the image processing domain, very few studies have investigated its perniciousness against network intrusion detection systems (NIDS) and proposed corresponding defense strategies. In this paper, we try to fill this gap by proposing Def-IDS, an ensemble defense mechanism specially designed for NIDS, against both known and unknown adversarial attacks. It is a two-module training framework that integrates multi-class generative adversarial networks and multi-source adversarial retraining to improve model robustness, while the detection accuracy on unperturbed samples is maintained. We evaluate the mechanism over CSE-CIC-IDS2018 dataset and compare its performance with the other three defense methods. The results demonstrate that Def-IDS is able to detect various adversarial attacks with better precision, recall, F1 score, and accuracy.
Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN

Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>l</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msup> </mml:math> norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.
Coherent Ising Machines with Optical Error Correction Circuits

Abstract A coherent Ising machine (CIM) comprising a network of open‐dissipative quantum oscillators with optical error correction circuits is proposed. In the proposed network, the squeezed/anti‐squeezed vacuum states of the constituent optical parametric oscillators establish quantum correlations below the threshold through optical mutual coupling and collective symmetry breaking is induced above the threshold as a decision‐making process. This initial search process is followed by a chaotic solution search step facilitated by optical error correction feedback. The particular algorithm used by the proposed network is derived from the truncated Wigner stochastic differential equation. As an optical hardware technology, the proposed CIM has several unique features, including programmable all‐to‐all Ising coupling in the optical domain, directional coupling ()‐induced chaotic behavior, and the ability to operate at low power at room temperature. The proposed CIM is evaluated in terms of its performance and how this scales at different problem sizes. It is shown that the various CIMs discussed in this paper are effective at solving many problem types, although the optimal algorithm depends on the problem case. It is further shown that the proposed optical implementations can be achieved with low energy consumption on thin‐film LiNbO 3 platforms.
Multi-Stream Fusion Network With Generalized Smooth L<sub>1</sub> Loss for Single Image Dehazing

Single image dehazing is an important but challenging computer vision problem. For the problem, an end-to-end convolutional neural network, named multi-stream fusion network (MSFNet), is proposed in this paper. MSFNet is built following the encoder-decoder network structure. The encoder is a three-stream network to produce features at three resolution levels. Residual dense blocks (RDBs) are used for feature extraction. The resizing blocks serve as bridges to connect different streams. The features from different streams are fused in a full connection manner by a feature fusion block, with stream-wise and channel-wise attention mechanisms. The decoder directly regresses the dehazed image from coarse to fine by the use of RDBs and the skip connections. To train the network, we design a generalized smooth L1 loss function, which is a parametric loss family and permits to adjust the insensitivity to the outliers by varying the parameter settings. Moreover, to guide MSFNet to capture the valid features in each stream, we propose the multi-scale supervision learning strategy, where the loss at each resolution level is computed and summed as the final loss. Extensive experimental results demonstrate that the proposed MSFNet achieves superior performance on both synthetic and real-world images, as compared with the state-of-the-art single image dehazing methods.
Bayesian feature interaction selection for factorization machines

Factorization machines are a generic supervised method for a wide range of tasks in the field of artificial intelligence, such as prediction, inference, etc., which can effectively model feature interactions. However, handling combinations of features is expensive due to the exponential growth of feature interactions with the order. In nature, not all feature interactions are equally useful for prediction. Recently, a large number of methods that perform feature interaction selection have attracted great attention because of their effectiveness at filtering out useless feature interactions. Current feature interaction selection methods suffered from the following limitations: (1) they assume that all users share the same feature interactions; and (2) they select pairwise feature interactions only. In this paper, we propose novel Bayesian variable selection methods, targeting feature interaction selection for factorization machines, which effectively reduce the number of interactions. We study personalized feature interaction selection to account for individual preferences, and further extend the model to investigate higher-order feature interaction selection on higher-order factorization machines. We provide empirical evidence for the advantages of the proposed Bayesian feature interaction selection methods using different prediction tasks.
Predictors of using social media to purchase drugs in New Zealand: Findings from a large-scale online survey

There are increasing reports of social media being used to buy and sell illegal drugs internationally. Studies of social media drug markets to date have largely involved general explorations of social media drug transactions in select countries. Social media drug markets may operate differently for different drug types and in different country contexts.To identify predictors of the use of social media to purchase cannabis, methamphetamine, LSD and ecstasy/MDMA in New Zealand using a large-scale online survey sample.The annual New Zealand Drug Trends Survey (NZDTS), an online convenience survey promoted via targeted Facebook™ campaign, was completed by 23,500 respondents aged 16+. Logistic regression models were fitted to identify demographic, drug use and drug market predictors of reporting the use of social media to purchase cannabis, methamphetamine, LSD and ecstasy/MDMA.Fifty-one percent of the sample had purchased cannabis, methamphetamine, ecstasy/MDMA or LSD in the past six months, of which 22% had done so via social media (n = 2,650). Cannabis was the drug type most purchased from social media among drug purchasers (24%), followed by ecstasy/MDMA (13%). Sixty percent of social media purchasers had used Facebook™, 48% Snapchat™, and 20% Instagram™. The leading advantages of social media purchasing were "high convenience" (74%), and "fast transaction speed" (43%). Younger age was a significant predictor of social media purchasing for all drug types (particularly among 16-17 year olds). Purchasing from someone identified as a "drug dealer" was a predictor of social media purchasing among respondents for all drug types.Social media drug markets may have significant implications for drug purchasing by youth through providing greater access to supply and breaking down age-related barriers between social supply and commercial drug markets. Our findings highlight the importance of incorporating social media platforms into youth drug prevention and harm minimisation strategies.
Balanced Neural Architecture Search and Its Application in Specific Emitter Identification

Since the performance of a single neural network can vary unexpectedly corresponding to different classification tasks, the network with fixed structure may lack the flexibility and often lead to overfitting or underfitting. To deal with the limitation of the fixed neural network structure on classifying radar signals in different electromagnetic environments, we propose an automatic network architecture search (NAS) mechanism and apply it in specific emitter identification (SEI). In this paper, a ‘`block-cell’' structure and a controller based recurrent neural network (RNN) are utilized to design models automatically according to external environment. Like some state-of-the-art neural networks, the combination of some operations like convolutional layers and pooling layers with various sizes, builds the search space. A progressive strategy is utilized to design child models from simple to complicated. The controller then evaluates child models and chooses the most promising ones as candidates to build the final network. A balance function, which considers both the model accuracy and efficiency when designing networks, is also proposed to replace the validation accuracy as the reward to feed back to the controller. Simulations and experiments indicate that the proposed NAS mechanism with the balance function outperforms the conventional algorithms in classifying radar signals in different environments.
Biden vs Trump: Modeling US General Elections Using BERT Language Model

Social media has played a crucial role in shaping the worldview during election campaigns.It has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots.Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections, can indicate the election outcome.Recently, there has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants known as Bidirectional Encoder Representations from Transformers (BERT).Motivated by these innovations, we develop a framework to model the US general elections.We investigate if sentiment analysis can provide a means to predict election outcomes.We use the LSTM and BERT language models for Twitter sentiment analysis leading to the US 2020 presidential elections.Our results show that sentiment analysis can form a general basis for modelling election outcomes where the BERT model indicates Biden winning the elections.