Reviewerly/exHarmony huggingface dataset:
Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network

Organ localization is an essential preprocessing step for many medical image analysis tasks such as image registration, organ segmentation and lesion detection. In this work, we propose an efficient method for multiple organ localization in CT image using 3D region proposal network. Compared with other convolutional neural network based methods that successively detect the target organs in all slices to assemble the final 3D bounding box, our method is fully implemented in 3D manner, thus can take full advantages of the spatial context information in CT image to perform efficient organ localization with only one prediction. We also propose a novel backbone network architecture that generates high-resolution feature maps to further improve the localization performance on small organs. We evaluate our method on two clinical datasets, where 11 body organs and 12 head organs (or anatomical structures) are included. As our results shown, the proposed method achieves higher detection precision and localization accuracy than the current state-of-theart methods with approximate 4 to 18 times faster processing speed. Additionally, we have established a public dataset dedicated for organ localization on http://dx. doi.org/10.21227/df8g-pq27. The full implementation of the proposed method have also been made publicly available on https://github.com/superxuang/caffe_3d_faster_rcnn.
Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges

Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions.To form consensus about perceptions, issues, and challenges of AI in primary care.A three-round Delphi study was conducted. Round 1 explored experts' viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups.PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care.While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.
MOF-BC: A memory optimized and flexible blockchain for large scale networks

The inherent immutability offered by Blockchains (BC) ensures resilience against modification or removal of the stored data. In large scale networks like the Internet of Things (IoT), however, this feature significantly increases BC storage size and raises privacy concerns. In this paper, we propose a Memory Optimized and Flexible BC (MOF-BC) that enables the IoT users and service providers to remove or summarize their transactions and age their data and thus exercise their “right to be forgotten”. To increase privacy, a user may employ multiple keys for different transactions. However, to facilitate removal of the stored transactions in the future, all keys would need to be stored which complicates key management and storage. MOF-BC introduces the notion of a Generator Verifier (GV) which is a signed hash of a Generator Verifier Secret (GVS). The GV changes for each transaction to provide privacy yet is signed by a unique key, thus minimizing the information that needs to be stored. A flexible transaction fee model and a reward mechanism is proposed to incentivize users to participate in optimizing memory consumption. We propose MOF-BC as a generalized solution, which can be implemented on top of any existing or future BC instantiation. Qualitative security and privacy analysis demonstrates that MOF-BC is resilient against several security attacks. Evaluation results show that MOF-BC decreases BC memory consumption by up to 25% and the cost incurred by users by more than two orders of magnitude compared to conventional BC instantiations.
Development of the Teacher Digital Competence Validation of DigCompEdu Check-In Questionnaire in the University Context of Andalusia (Spain)

We are currently witnessing a moment in history in which sustainable education practices are being principally modified by the proliferation of technologies and their wider use in every level of society, which makes necessary their integration into education contexts. This is found in the crosshairs of different institutions, which propose a series of competency frameworks, such as DigCompEdu. This framework provides suggestions for the competences that educators should be trained on under pedagogic-didactic criteria. The present work intends to measure the reliability and validity of the questionnaire DigCompEdu Check-In with the participation of 2262 professors from different public Andalusian universities. The known-groups method was utilized to elucidate if the tool is able or not to discriminate different variables of interest between known groups. The study reveals that the instrument has high indices of reliability, globally, and in the different dimensions that comprise it. Furthermore, it verifies that the instrument is sufficiently robust to discriminate the subjects who are clearly differentiated by variables related with technology. Along this line, the recommendation is given to continue working on the creation of new instruments focused on the mastery of this competency.
AI-enabled future crime

Abstract A review was conducted to identify possible applications of artificial intelligence and related technologies in the perpetration of crime. The collected examples were used to devise an approximate taxonomy of criminal applications for the purpose of assessing their relative threat levels. The exercise culminated in a 2-day workshop on ‘AI &amp; Future Crime’ with representatives from academia, police, defence, government and the private sector. The workshop remit was (i) to catalogue potential criminal and terror threats arising from increasing adoption and power of artificial intelligence, and (ii) to rank these threats in terms of expected victim harm, criminal profit, criminal achievability and difficulty of defeat. Eighteen categories of threat were identified and rated. Five of the six highest-rated had a broad societal impact, such as those involving AI-generated fake content, or could operate at scale through use of AI automation; the sixth was abuse of driverless vehicle technology for terrorist attack.
Voxel-based three-view hybrid parallel network for 3D object classification

Three-dimensional models are widely used in the fields of multimedia, computer graphics, virtual reality, entertainment, design, and manufacturing because of the rich information that preserves the surface, color and texture of real objects. Therefore, effective 3D object classification technology has become an urgent need. Previous methods usually directly convert classic 2D convolution into 3D form and apply it to objects with binary voxel representation, which may lose internal information that is essential for recognition. In this paper, we propose a novel voxel-based three-view hybrid parallel network for 3D shape classification. This method first obtains the depth projection views of the three-dimensional model from the front view, the top view and the side view, so as to preserve the spatial information of the three-dimensional model to the greatest extent, and output its predicted probability value for the category of the three-dimensional model, and then combining the three-view parallel network with voxel sub-network performs weight fusion, and then uses Softmax for classification. We conducted a series of experiments to verify the design of the network and achieved competitive performance in the 3D object classification tasks of ModelNet10 and ModelNet40.
A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems

In most recommender systems, the data of user feedbacks are usually represented with a set of discrete values, which are difficult to exactly describe users’ interests. This problem makes it not easy to exactly model users’ latent preferences for recommendation. Intuitively, a basic idea for this issue is to predict continuous values through a trained model to reveal users’ essential feedbacks, and then make use of the generated data to retrain another model to learn users’ preferences. However, since these continuous data are generated by an imperfect model which are trained by discrete data, there exists a lot of noise among the generated data. This problem may have a severe adverse impact on the performance. Towards this problem, we propose a novel Enhanced Collaborative Autoencoder (ECAE) to learn robust information from generated soft data with the technique of knowledge distillation. First, we propose a tightly coupled structure to incorporate the generation and retraining stages into a unified framework. So that the generated data can be fine tuned to reduce the noise by propagating training errors of retraining network. Second, for that each unit of the generated data contains different level of noise, we propose a novel distillation layer to balance the influence of noise and knowledge. Finally, we propose to take both predict results of generation and retraining network into account to make final recommendations for each user. The experimental results on four public datasets for top-N recommendation show that the ECAE model performs better than several state-of-the-art algorithms on metrics of MAP and NDCG.
Forecasting cryptocurrency returns and volume using search engines

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers.
Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs

Multilingual knowledge graphs constructed by entity alignment are the indispensable resources for numerous AI-related applications. Most existing entity alignment methods only use the triplet-based knowledge to find the aligned entities across multilingual knowledge graphs, they usually ignore the neighborhood subgraph knowledge of entities that implies more richer alignment information for aligning entities. In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs. NAEA devises an attention mechanism to learn neighbor-level representation by aggregating neighbors' representations with a weighted combination. The attention mechanism enables entities not only capture different impacts of their neighbors on themselves, but also attend over their neighbors' feature representations with different importance. We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models.
A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans' daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.
Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction

Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network and is known for its wide range of implementations using different physical technologies. Large reservoirs are very hard to obtain in conventional computers, as both the computation complexity and memory usage grow quadratically. We propose an optical scheme performing reservoir computing over very large networks potentially being able to host several millions of fully connected photonic nodes thanks to its intrinsic properties of parallelism and scalability. Our experimental studies confirm that, in contrast to conventional computers, the computation time of our optical scheme is only linearly dependent on the number of photonic nodes of the network, which is due to electronic overheads, while the optical part of computation remains fully parallel and independent of the reservoir size. To demonstrate the scalability of our optical scheme, we perform for the first time predictions on large spatiotemporal chaotic datasets obtained from the Kuramoto-Sivashinsky equation using optical reservoirs with up to 50 000 optical nodes. Our results are extremely challenging for conventional von Neumann machines, and they significantly advance the state of the art of unconventional reservoir computing approaches, in general.
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is proposed in an end-to-end learning manner. Our main idea is to first localize potential target centers in 3D search area embedded with target information. Then point-driven 3D target proposal and verification are executed jointly. In this way, the time-consuming 3D exhaustive search can be avoided. Specifically, we first sample seeds from the point clouds in template and search area respectively. Then, we execute permutation-invariant feature augmentation to embed target clues from template into search area seeds and represent them with target-specific features. Consequently, the augmented search area seeds regress the potential target centers via Hough voting. The centers are further strengthened with seed-wise targetness scores. Finally, each center clusters its neighbors to leverage the ensemble power for joint 3D target proposal and verification. We apply PointNet++ as our backbone and experiments on KITTI tracking dataset demonstrate P2B's superiority (~10%'s improvement over state-of-the-art). Note that P2B can run with 40FPS on a single NVIDIA 1080Ti GPU. Our code and model are available at https://github.com/HaozheQi/P2B.
Self-Guided Contrastive Learning for BERT Sentence Representations

Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers.In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations.Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors.Moreover, we redesign the contrastive learning objective (NT-Xent) and apply it to sentence representation learning.We demonstrate with extensive experiments that our approach is more effective than competitive baselines on diverse sentence-related tasks.We also show it is efficient at inference and robust to domain shifts.
Survey of Immersive Analytics

Immersive analytics (IA) is a new term referring to the use of immersive technologies for data analysis. Yet such applications are not new, and numerous contributions have been made in the last three decades. However, no survey reviewing all these contributions is available. Here we propose a survey of IA from the early nineties until the present day, describing how rendering technologies, data, sensory mapping, and interaction means have been used to build IA systems, as well as how these systems have been evaluated. The conclusions that emerge from our analysis are that: multi-sensory aspects of IA are under-exploited, the 3DUI and VR community knowledge regarding immersive interaction is not sufficiently utilised, the IA community should focus on converging towards best practices, as well as aim for real life IA systems.
Exploiting Persona Information for Diverse Generation of Conversational Responses

In human conversations, due to their personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with persona information to a chatbot, how to exploit the information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on persona-based conversational models successfully make use of predefined persona information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit persona information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging persona-based responses than baseline approaches.
Exploratory Combinatorial Optimization with Reinforcement Learning

Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With such tasks often NP-hard and analytically intractable, reinforcement learning (RL) has shown promise as a framework with which efficient heuristic methods to tackle these problems can be learned. Previous works construct the solution subset incrementally, adding one element at a time, however, the irreversible nature of this approach prevents the agent from revising its earlier decisions, which may be necessary given the complexity of the optimization task. We instead propose that the agent should seek to continuously improve the solution by learning to explore at test time. Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph. Experimentally, we show our method to produce state-of-the-art RL performance on the Maximum Cut problem. Moreover, because ECO-DQN can start from any arbitrary configuration, it can be combined with other search methods to further improve performance, which we demonstrate using a simple random search.
Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN

Traffic congestion due to vehicular accidents seriously affects normal travel, and accurate and effective mitigating measures and methods must be studied. To resolve traffic accident compensation problems quickly, a vehicle-damage-detection segmentation algorithm based on transfer learning and an improved mask regional convolutional neural network (Mask RCNN) is proposed in this paper. The experiment first collects car damage pictures for preprocessing and uses Labelme to make data set labels, which are divided into training sets and test sets. The residual network (ResNet) is optimized, and feature extraction is performed in combination with Feature Pyramid Network (FPN). Then, the proportion and threshold of the Anchor in the region proposal network (RPN) are adjusted. The spatial information of the feature map is preserved by bilinear interpolation in ROIAlign, and different weights are introduced in the loss function for different-scale targets. Finally, the results of self-made dedicated dataset training and testing show that the improved Mask RCNN has better Average Precision (AP) value, detection accuracy and masking accuracy, and improves the efficiency of solving traffic accident compensation problems.
A trust architecture for blockchain in IoT

Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.