Reviewerly/exHarmony huggingface dataset:
Outer product enhanced heterogeneous information network embedding for recommendation

With the rapid development of the internet, more and more sophisticated data can be utilized by recommendation systems to improve their performance. Such data consist of heterogeneous information networks (HINs) made up of multiple nodes and link types. A critical challenge is how to effectively extract and apply the useful HIN information. In particular, the embedding-based recommendation approach has been widely used, as it can extract affluent semantic and structural information from HINs. However, the existing HIN embedding for recommendation methods only combine user embedding and item embedding through a simple concatenation or elementwise product, which does not suffer for an efficient recommendation model. In order to extract and utilize more comprehensive and subtle information from the embedding for recommendation, we propose Outer Product Enhanced Heterogeneous Information Network Embedding for Recommendation, called HopRec. The main idea is to utilize the outer product to model the pairwise relationship between user HIN embedding and item HIN embedding. Specifically, by performing an outer product between user HIN embedding and item HIN embedding, we can obtain a two-dimensional interaction matrix. Subsequently, we can obtain a rating prediction function by integrating matrix factorization (MF), user HIN embedding, item HIN embedding and interaction matrix. The results of experiments conducted on three open benchmark datasets show that HopRec significantly outperforms the state-of-the-art methods.
Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches

Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350–2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.
Can artificial intelligence, RegTech and CharityTech provide effective solutions for anti-money laundering and counter-terror financing initiatives in charitable fundraising

Purpose Artificial intelligence has had a major impact on organisations from Banking through to Law Firms. The rate at which technology has developed in terms of tasks that are complex, technical and time-consuming has been astounding. The purpose of this paper is to explore the solutions that AI, RegTech and CharityTech provide to charities in navigating the vast amount of anti-money laundering and counter-terror finance legislation in the UK; so that they comply with the requirements and mitigate the potential risk they face but also develop a more coherent and streamlined set of actions. Design/methodology/approach The subject is approached through the analysis of data, literature and, domestic and international regulation. The first part of the paper explores the current obligations and risks charities face, these are then, in the second part, set against the examination of potential technological solutions as of August 2020. Findings It is suggested that charities underestimate the importance of the nature and size of the threat posed to them, this is significant, as demonstrated, given the growing size and impact of the sector. Technological solutions are suggested to combat the issues charities face. Originality/value The study is original because it is the first to create the notion of CharityTech and to specifically explore what technological advances can assist charities in meeting the regulatory compliance challenge.
Controllable Image Synthesis via SegVAE

Flexible user controls are desirable for content creation and image editing. A semantic map is commonly used intermediate representation for conditional image generation. Compared to the operation on raw RGB pixels, the semantic map enables simpler user modification. In this work, we specifically target at generating semantic maps given a label-set consisting of desired categories. The proposed framework, SegVAE, synthesizes semantic maps in an iterative manner using conditional variational autoencoder. Quantitative and qualitative experiments demonstrate that the proposed model can generate realistic and diverse semantic maps. We also apply an off-the-shelf image-to-image translation model to generate realistic RGB images to better understand the quality of the synthesized semantic maps. Finally, we showcase several real-world image-editing applications including object removal, insertion, and replacement.
Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning

Change Captioning is a task that aims to describe the difference between images with natural language. Most existing methods treat this problem as a difference judgment without the existence of distractors, such as viewpoint changes. However, in practice, viewpoint changes happen often and can overwhelm the semantic difference to be described. In this paper, we propose a novel visual encoder to explicitly distinguish viewpoint changes from semantic changes in the change captioning task. Moreover, we further simulate the attention preference of humans and propose a novel reinforcement learning process to fine-tune the attention directly with language evaluation rewards. Extensive experimental results show that our method outperforms the state-of-the-art approaches by a large margin in both Spot-the-Diff and CLEVR-Change datasets .
MobiPose

Human pose estimation is a key technique for many vision-based mobile applications. Yet existing multi-person pose-estimation methods fail to achieve a satisfactory user experience on commodity mobile devices such as smartphones, due to their long model-inference latency. In this paper, we propose MobiPose, a system designed to enable real-time multi-person pose estimation on mobile devices through three novel techniques. First, MobiPose takes a motion-vector-based approach to fast locate the human proposals across consecutive frames by fine-grained tracking of joints of human body, rather than running the expensive human-detection model for every frame. Second, MobiPose designs a mobile-friendly model that uses lightweight multi-stage feature extractions to significantly reduce the latency of pose estimation without compromising the model accuracy. Third, MobiPose leverages the heterogeneous computing resources of both CPU and GPU to execute the pose estimation model for multiple persons in parallel, which further reduces the total latency. We have implemented the MobiPose system on off-the-shelf commercial smartphones and conducted comprehensive experiments to evaluate the effectiveness of the proposed techniques. Evaluation results show that MobiPose achieves over 20 frames per second pose estimation with 3 persons per frame, and significantly outperforms the state-of-the-art baseline, with a speedup of up to 4.5X and 2.8X in latency on CPU and GPU, respectively, and an improvement of 5.1% in pose-estimation model accuracy. Furthermore, MobiPose achieves up to 62.5% and 37.9% energy-per-frame saving on average in comparison with the baseline on mobile CPU and GPU, respectively.
Analysis of Josephson junctions switching time distributions for the detection of single microwave photons

We investigate an optimal scheme for the detection of single microwave photons by a Josephson junction through the analysis of its switching times distribution. The proposed analysis is of support for the decision about the existence of the photon field, which is important in the case of rare events. We assume that the cavity and the transmission line are ideal (each photon absorbed to the cavity gives a current pulse as the output of the transmission line) and the photon source is periodic. The employed methodology consists in comparing the switching probabilities of a Josephson junction exposed to a train of current pulses, simulating a weak photon field, with that of the same device in absence of pulses. In both cases, thermal noise can induce thermal activated switchings. The investigation of the unbalance in the number of switching events in the two cases, gives an estimate of the efficiency of the detection. Furthermore, in the assumption of escapes described by Kramers model, it is possible to provide a relationship between the properties of the photons field, the quantum efficiency of the detection process, and the Josephson junctions switching features at finite temperatures.
BroadFace: Looking at Tens of Thousands of People at once for Face Recognition

The datasets of face recognition contain an enormous number of identities and instances. However, conventional methods have difficulty in reflecting the entire distribution of the datasets because a mini-batch of small size contains only a small portion of all identities. To overcome this difficulty, we propose a novel method called BroadFace, which is a learning process to consider a massive set of identities, comprehensively. In BroadFace, a linear classifier learns optimal decision boundaries among identities from a large number of embedding vectors accumulated over past iterations. By referring more instances at once, the optimality of the classifier is naturally increased on the entire datasets. Thus, the encoder is also globally optimized by referring the weight matrix of the classifier. Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage. BroadFace can be easily applied on many existing methods to accelerate a learning process and obtain a significant improvement in accuracy without extra computational burden at inference stage. We perform extensive ablation studies and experiments on various datasets to show the effectiveness of BroadFace, and also empirically prove the validity of our compensation method. BroadFace achieves the state-of-the-art results with significant improvements on nine datasets in 1:1 face verification and 1:N face identification tasks, and is also effective in image retrieval.
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation

In recent years, convolutional neural network (CNN) has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, more complex structures and advanced operations are incorporated into neural networks, which results in very long inference time. For time-critical tasks such as autonomous driving and virtual reality, real-time processing is fundamental. In order to reach real-time processing speed, a lightweight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this article. It achieves 92.55% MaxF score on KITTI road segmentation dataset. The inference time is about 9 ms per frame when running on GTX 1080 GPU. Comparing to the state-of-the-art network, RoadNet-RT speeds up the inference time by a factor of 17.8 at the cost of only 3.75% loss in accuracy. What is more, on CamVid dataset its accuracy is 92.98%. Several techniques such as depthwise separable convolution and non-uniformed kernel size convolution are optimized in the hardware accelerator design. The proposed CNN architecture has been successfully implemented on a ZCU102 MPSoC FPGA that achieves the computation capability of 331 GOPS using INT8 quantization. The system throughput reaches 196.7 frames per second with input image size of 280 × 960 . The source code is published at https://github.com/linbaiwpi/RoadNet-RT.
3D multi-view tumor detection in automated whole breast ultrasound using deep convolutional neural network

In recent years, automated whole breast ultrasound (ABUS) has drawn attention to breast disease detection and diagnosis applications. However, reviewing ABUS volumes is a time-costing task and some subtle tumors may be missed. In this paper, a 3D multi-view tumor detection method is proposed for ABUS volumes. Firstly, a layer connected feature extraction network is designed for Faster R-CNN. Then, orthogonal multi-view slices are reconstructed and detected using this modified Faster R-CNN to extract 2D candidates. Finally, a 3D multi-view position analysis scheme is designed to fuse 2D detection results and get final 3D bounding boxes. The performance of this proposed method is evaluated on a data set of 158 volumes from 75 patients by 5-fold cross-validation. Experimental results show that our method achieves a sensitivity of 95.06% with 0.57 false positives (FPs) per volume. Compared with existing detection methods, the proposed method is more effective and general.
Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Of late, the research world has been vigorously involved in, inventing strategy and techniques to improve the spontaneity of Human Computer Interaction (HCI). Gesture recognition is one of the most probable techniques in this area. The eventual aim here is to introduce an intelligent system for hand gesture recognition in both static and dynamic area, which is still a challenging point due to the lag of valuable beneficial methods. The main intent of this paper is to implement an efficient hand gesture recognition model considering both static and dynamic datasets for Indian Sign Languages (ISL). In static type, images are taken for processing, whereas video frames are used for processing the dynamic type. The proposed recognition model involves five main steps “(a) Image pre-processing, (b) gesture segmentation, (c) Feature extraction, (d) Optimal Feature Selection, and (e) Recognition”. In the pre-processing phase, greyscale conversion and histogram equalization are performed. The pre-processed image is subjected to the segmentation process, where the Active Contour model and Canny Edge Detection is implemented. In the feature extraction phase, both the contour image, and the edge detected image is deployed, in which Histogram of Oriented Gradients (HOG) features are extracted from the contour image, and Edge Oriented Histogram (EOH) features are extracted from edge detected images. To reduce the dimension of HOG, and EOH features, Principle Component Analysis (PCA) is applied. Further, the region props features are extracted for both contour and edge detected image. Finally, all these features are summed, and the optimal feature selection process performs here to select the unique feature giving different information with less correlation. Finally, the recognition classifier called Neural Network (NN) is adopted, where the new training algorithm is used to update network weight. Dynamic Time Warping (DTW) method helps to remove the repeated frames in the video and to reduce the time consumption of testing. In both feature selection and classification, a hybrid algorithm Deer Hunting-based Grey Wolf Optimization (DH-GWO) is used for selecting the features and weight update in NN as well. Hence, the integration of a hybrid meta-heuristic algorithm is highly efficient for recognizing the characters for images and words for videos with high recognition accuracy.
DeepLandscape: Adversarial Modeling of Landscape Videos

We build a new model of landscape videos that can be trained on a mixture of static landscape images as well as landscape animations. Our architecture extends StyleGAN model by augmenting it with parts that allow to model dynamic changes in a scene. Once trained, our model can be used to generate realistic time-lapse landscape videos with moving objects and time-of-the-day changes. Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way. We propose simple but necessary modifications to StyleGAN inversion procedure, which lead to in-domain latent codes and allow to manipulate real images. Quantitative comparisons and user studies suggest that our model produces more compelling animations of given photographs than previously proposed methods. The results of our approach including comparisons with prior art can be seen in supplementary materials and on the project page https://saic-mdal.github.io/deep-landscape/ .
Fast Database Joins and PSI for Secret Shared Data

We present a scalable protocol for database joins on secret shared data in the honest-majority three-party setting. The key features of our protocol are a rich set of SQL-like join/select queries and the ability to compose join operations together due to the inputs and outputs being generically secret shared between the parties. Provided that all joins operate on unique primary keys, no information is revealed to any party during the protocol. In particular, not even the sizes of intermediate joins are revealed. All of our protocols are constant-round and achieve O(n) communication and computation overhead for joining two tables of n rows.
Resolution Switchable Networks for Runtime Efficient Image Recognition

We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference. Thus the running speed can be selected to meet various computational resource limits. Networks trained with the proposed method are named Resolution Switchable Networks (RS-Nets). The basic training framework shares network parameters for handling images which differ in resolution, yet keeps separate batch normalization layers. Though it is parameter-efficient in design, it leads to inconsistent accuracy variations at different resolutions, for which we provide a detailed analysis from the aspect of the train-test recognition discrepancy. A multi-resolution ensemble distillation is further designed, where a teacher is learnt on the fly as a weighted ensemble over resolutions. Thanks to the ensemble and knowledge distillation, RS-Nets enjoy accuracy improvements at a wide range of resolutions compared with individually trained models. Extensive experiments on the ImageNet dataset are provided, and we additionally consider quantization problems. Code and models are available at https://github.com/yikaiw/RS-Nets .
Student-Centered and ICT-Enabled Learning Models in Veterinarian Programs: What Changed with COVID-19?

COVID-19 highlighted higher education’s resistance to implementing the student-centered and ICT (information and communication technology)-enabled learning practices as incentivized by the Bologna Declaration. ICTs were crucial to keep the students in educational programs during the outbreak; however, some students were left out, increasing socioeconomic differences, and many teachers needed the training to provide online lectures effectively. Despite those barriers, the current context could be an opportunity to invest in new teaching and learning practices. The main goal of this research is to analyze how teachers and students from veterinary programs in Portugal adapted to the digital environment, and perceive whether this change imposed by COVID-19 could provide teachers and higher institutions with new solutions to implement effective student-centered and ICT-enabled learning models. Results show that veterinary programs are based on student-centered practices by nature, but that the use of ICTs is still incipient. Teachers, students, and members from the Professional Order claim that the worst change brought by COVID-19 was the cancellation of hands-on sessions, impoverishing the students’ education during the time of mandatory confinement. Actors say that it is important to invest in innovative teaching and learning practices enabled by ICTs in the future.
Rank-based self-training for graph convolutional networks

Graph Convolutional Networks (GCNs) have been established as a fundamental approach for representation learning on graphs, based on convolution operations on non-Euclidean domain, defined by graph-structured data. GCNs and variants have achieved state-of-the-art results on classification tasks, especially in semi-supervised learning scenarios. A central challenge in semi-supervised classification consists in how to exploit the maximum of useful information encoded in the unlabeled data. In this paper, we address this issue through a novel self-training approach for improving the accuracy of GCNs on semi-supervised classification tasks. A margin score is used through a rank-based model to identify the most confident sample predictions. Such predictions are exploited as an expanded labeled set in a second-stage training step. Our model is suitable for different GCN models. Moreover, we also propose a rank aggregation of labeled sets obtained by different GCN models. The experimental evaluation considers four GCN variations and traditional benchmarks extensively used in the literature. Significant accuracy gains were achieved for all evaluated models, reaching results comparable or superior to the state-of-the-art. The best results were achieved for rank aggregation self-training on combinations of the four GCN models.
A Trinal-Code Shifted Differential Chaos Shift Keying System

In the differential chaos shift keying (DCSK) system, half of the symbol period is used to transmit the reference signal, resulting in the poor energy efficiency. The existing DCSK-based systems also inherit this disadvantage, restricting the development of DCSK-based systems significantly. To tackle this issue completely, we propose a trinal-code shifted differential chaos shift keying (TCS-DCSK) system, where information bits are sorely transmitted by specific indices of Walsh codes and thus this system removes the reference signal. Comparison results of energy efficiency show that the TCS-DCSK system achieves higher energy efficiency in contrast to its competitors. In addition, we derive the bit error rate (BER) expressions of the TCS-DCSK system over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Finally, the BER performance of the TCS-DCSK system is compared to that of other systems. It is shown that the TCS-DCSK system can obtain considerable performance gain compared to other state-of-the-art chaotic communication systems.