Reviewerly/exHarmony huggingface dataset:
DFraud³: Multi-Component Fraud Detection Free of Cold-Start

Fraud review detection is a hot research topic in recent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approach (TransE) to model the interaction of the components of a review system. However, these approaches suffer from the limitation of TransE in handling N-1 relations and the narrow scope of a single classification task, i.e., detecting fraudsters only. In this paper, we model a review system as a Heterogeneous Information Network (HIN) which enables a unique representation to every component and performs graph inductive learning on the review data through aggregating features of nearby nodes. HIN with graph induction helps to address the camouflage issue (fraudsters with genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews. In this research, instead of focusing only on one component, detecting either fraud reviews or fraud users (fraudsters), vector representations are learned for each component, enabling multi-component classification. In other words, we can detect fraud reviews, fraudsters, and fraud-targeted items, thus the name of our approach DFraud <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . DFraud <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> demonstrates a significant accuracy increase of 13% over the state of the art on Yelp.
Unveiling the Mystery of API Evolution in Deep Learning Frameworks: A Case Study of Tensorflow 2

API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract 6,329 API changes by mining API documentation of Tensorflow 2 across multiple versions and mapping API changes into functional categories on the Tensorflow 2 framework to analyze their API evolution trends. We then investigate the key reasons for API changes by referring to multiple information sources, e.g., API documentation, commits and StackOverflow. Finally, we compare API evolution in non-deep learning projects to that of Tensorflow 2, and identify some key implications for users, researchers, and API developers.
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition

Automatic speech recognition (ASR) systems for young children are needed due to the importance of age-appropriate educational technology. Because of the lack of publicly available young child speech data, feature extraction strategies such as feature normalization and data augmentation must be considered to successfully train child ASR systems. This study proposes a novel technique for child ASR using both feature normalization and data augmentation methods based on the relationship between formants and fundamental frequency (f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> ). Both the f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> feature normalization and data augmentation techniques are implemented as a frequency shift in the Mel domain. These techniques are evaluated on a child read speech ASR task. Child ASR systems are trained by adapting a BLSTM-based acoustic model trained on adult speech. Using both f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> normalization and data augmentation results in a relative word error rate (WER) improvement of 19.3% over the baseline when tested on the OGI Kids’ Speech Corpus, and the resulting child ASR system achieves the best WER currently reported on this corpus.
PD-GAN: Perceptual-Details GAN for Extremely Noisy Low Light Image Enhancement

Extremely noisy low light enhancement suffers from high-level noise, loss of texture detail, and color degradation. When recovering color or illumination for images taken in a dark environment, the challenge for networks is how to balance the enhancement for noise and texture details for a good visual effect. A single network is not suitable for solving the ill-posed problem of mapping the input image's noise to the clear target in the ground truth. To solve the problems, we pro-pose perceptual-details GAN (PD-GAN) utilizing Zero-DCE to initially recover illumination and combine residual dense-block Encoder-Decoder structure to suppress noise while finely adjusting the illumination. Besides, fractional differential gradient masks are integrated into the discriminator to enhance details. Experiment results demonstrate that PD-GAN outperforms other methods on the extremely low-light image dataset.
Policy-Based Broadcast Access Authorization for Flexible Data Sharing in Clouds

Cloud storage services allow data owners to outsource their potentially sensitive data (e.g., private genome data) to remote cloud servers in a ciphertext form. To enable data owners to further share the data encrypted in ciphertexts, many proxy re-encryption (PRE) schemes are proposed. However, most schemes only support single-recipient or coarse-grained re-encryption, which may limit the flexibility for data sharing. To address this issue, we propose a Policy-based Broadcast Access Authorization (PBAA) scheme by introducing the well-established identity-based broadcast encryption (IBBE) and key-policy attribute-based encryption into PRE. In our PBAA scheme, a data owner can apply IBBE to encrypt his data to a group of recipients. More importantly, the data owner can generate a delegation key with an access policy, and send this key to the cloud such that it can convert any initial ciphertext satisfying the access policy into a new ciphertext for a new group of recipients. With these features, cloud users can share their remote data in a secure and flexible way. Security analysis and performance evaluation show that the PBAA scheme is secure and efficient, respectively.
Scalable Multi-Query Execution using Reinforcement Learning

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time databases that optimize queries for efficient serial execution. Sharing work across queries presents an opportunity to reduce the total cost of processing and therefore improve throughput with increasing query load. Systems can share work either by assessing all opportunities and restructuring batches of queries ahead of execution, or by inspecting opportunities in individual incoming queries at runtime: the former strategy scales poorly to large query counts, as it requires expensive sharing-aware optimization, whereas the latter detects only a subset of the opportunities. Both strategies fail to minimize the cost of processing for large and ad-hoc workloads. This paper presents RouLette, a specialized intelligent engine for multi-query execution that addresses, through runtime adaptation, the shortcomings of existing work-sharing strategies. RouLette scales by replacing sharing-aware optimization with adaptive query processing, and it chooses opportunities to explore and exploit by using reinforcement learning. RouLette also includes optimizations that reduce the adaptation overhead. RouLette increases throughput by 1.6-28.3x, compared to a state-of-the-art query-at-a-time engine, and up to 6.5x, compared to sharing-enabled prototypes, for multi-query workloads based on the schema of TPC-DS.
Motion-supervised Co-Part Segmentation

Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful object parts. To this end, our method relies on pairs of frames sampled from the same video. The network learns to predict part segments together with a representation of the motion between two frames, which permits reconstruction of the target image. Through extensive experimental evaluation on publicly available video sequences we demonstrate that our approach can produce improved segmentation maps with respect to previous self-supervised co-part segmentation approaches.
eyeSay: Eye Electrooculography Decoding with Deep Learning

Consumer electronics that can decode eye movement-induced bio-potential will enable many practices, from voice-free communication, attention tracking, to virtual or augmented reality. We propose a novel deep learning-enabled approach for eye Electrooculography decoding, towards voice-free communication for patients with amyotrophic lateral sclerosis. We have designed a multi-stage convolutional neural network to decode eye dynamics. Our approach and promising results will directly contribute to voice-free communications for patients, and greatly advance the ubiquitous eye EOG-based smart health area.
Design and Simulation of Next-Generation Augmented Reality User Interfaces in Virtual Reality

We present a methodology for the simulation of next-generation Augmented Reality (AR) User Interfaces (UIs) within immersive Virtual Reality (VR). We use a user-centered model to support design decisions for specialized operations in high stakes fields, and present augmented reality user interface designs for two use cases in public safety: a law enforcement traffic stop and a firefighting search and rescue scenario. By utilizing VR to simulate AR, we can design and evaluate the benefits of idealized UIs that are unencumbered by hardware limitations. We discuss the trade-offs of Virtual Reality as a medium for simulation and training of next-generation Augmented Reality User Interfaces.
Piezoelectric Touch Sensing-Based Keystroke Dynamic Technique for Multi-User Authentication

In the era of information technology, secure access to electronic systems is important for protecting private information. Among various authentication techniques, keystroke authentication methods based on users’ touch behavior have received increasing attention. In this paper, we present a technique for obtaining high multi-user authentication accuracy by utilizing users’ touch time and force information, which were obtained from a piezoelectric touch panel. After feeding a support vector machine (SVM) with users’ touch features, a high classification accuracy of 97% is achieved, validating the feasibility of the proposed technique for achieving highly secure multi-user authentication, hence advancing the development of security techniques.
Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model

This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.
Automated Test Assembly in R: The eatATA Package

Combining items from an item pool into test forms (test assembly) is a frequent task in psychological and educational testing. Although efficient methods for automated test assembly exist, these are often unknown or unavailable to practitioners. In this paper we present the R package eatATA, which allows using several mixed-integer programming solvers for automated test assembly in R. We describe the general functionality and the common work flow of eatATA using a minimal example. We also provide four more elaborate use cases of automated test assembly: (a) The assembly of multiple test forms for a pilot study; (b) the assembly of blocks of items for a multiple matrix booklet design in the context of a large-scale assessment; (c) the assembly of two linear test forms for individual diagnostic purposes; (d) the assembly of multi-stage testing modules for individual diagnostic purposes. All use cases are accompanied with example item pools and commented R code.
A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data

The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.
VR platform enabling crowdsourcing of embodied HRI experiments – case study of online robot competition

The COVID-19 pandemic has made it difficult to conduct research involving human-robot interactions (HRIs). As a result, many robot competitions have been canceled in the past year. To address this problem, we propose a cloud-based VR platform for the crowdsourcing of embodied cognition with multiple human users and a virtual robot. The feasibility of the platform is evaluated from the latency of the interactive events and the motions of avatars, based on the number of users logging in to the VR scene and the distance between the local and server environments. We also discuss the feasibility of our system when dealing with large-scale datasets in embodied HRI experiments.
Hardware Implementation of Tanh Exponential Activation Function using FPGA

The most active research area for Field Programmable Gate Arrays is the Convolution Neural Network (CNN), and the gist of any CNN is an activation function. Therefore, various non-linear activation functions are required for deeper CNN. In this paper, we aim to implement the Tanh Exponential (TanhExp) activation function on Artix-7 and Zynq-7000. To this end, we will use the piecewise linear approximation and the second-order polynomial approximation while using the IEEE754 2008 floating-point representation. We present an investigation of the required hardware resources. We also evaluate the efficiency of each method of approximation and its derivative.
Improved Instance Discrimination and Feature Compactness for End-to-End Person Search

Person search aims to locate and retrieve specific pedestrians in scene images, including two subtasks, pedestrian detection and person re-identification. Recently, triplet loss has been widely used in person re-identification, which effectively improves the pedestrian features embedding and achieves superior performance. However, forming triplet in the person search is not an easy task. Most of the existing end-to-end person search methods are based on Faster R-CNN. The training process of person re-identification part is affected by the detector. It is difficult to form pedestrian triplets within a limited batch size. Also, there are many pedestrian identities in the person search dataset, but each pedestrian identity only has a few samples. It is difficult to learn a robust pedestrian feature representation for person search. To resolve the problem discussed above, a novel Feature Compactness (FC) Loss for the person search is designed, which efficiently improves the inter-class discrimination and intra-class compactness of pedestrian features embedding without the need for positive or negative pairs. Besides, we propose a pedestrian attention module (PAM) to help the network focuses more on pedestrian information and suppresses irrelevant background information. Our method achieves comparable performance on two benchmarks, CUHK-SYSU and PRW, and achieves 91.96% of mAP and 93.34% of rank1 accuracy on CUHK-SYSU.
Progressive back-projection network for COVID-CT super-resolution

Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem.In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution.The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for × 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for × 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for × 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets.The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges.
Collaborative augmentation and simplification of text (CoAST): pedagogical applications of natural language processing in digital learning environments

Abstract The digitisation of higher education is raising significant questions about the impact of artificial intelligence and automation on teaching and learning environments, highlighting the need to investigate how teachers and students can work with new educational technologies in complementary ways. This paper reports results from a pilot study of the collaborative augmentation and simplification of text (CoAST) system, which is online software designed to facilitate the engagement of university students with theoretically-sophisticated academic texts. CoAST offers a digital learning interface that uses natural language processing algorithms to identify words that can be difficult to understand for readers at different ability levels. Course lecturers use their pedagogical content knowledge to add brief annotations to identified words. The software was trialed using a quasi-experimental design with (1) 23 undergraduate Education Studies students and (2) 23 digital and technology solutions students. Results suggest that CoAST offers a digital learning environment that can effectively mediate and enhance pedagogical relationships between teachers, students, and complex theoretical texts.
Survey of security supervision on blockchain from the perspective of technology

Due to decentralization, immutability, circulation, anonymity, blockchain technology has become a hot topic but also a hotbed of various cyber-crimes. Many perpetrators attack blockchain to steal cryptocurrencies or use anonymous addresses to conduct illicit financial transactions or receive ransoms while hiding their identities. Since blockchain technology has not developed for a long time, the security issues in this area cannot be well resolved through its own mechanisms, which brings great challenges to protect the security of blockchain and users. Although there are some protective measures to prevent attackers from attacking, most of them are proposed after attacks, and it is impossible to find the masterminds behind modern cyber-crimes, so it is necessary to continuously monitor suspicious nodes or users. In this paper, we first present a systematic overview of blockchain technology and security issues according to the four-layer structure, and explain the problem of security supervision of blockchain. Subsequently, we divide the key technologies for security supervision of blockchain into three aspects: node discovery technology on the network layer, data analysis technology of transaction records on the transaction layer, and network traffic analysis technology on the application layer. In terms of each aspect, we summarize the studies from various angles according to its characteristics. In the end, we discuss the relationship between blockchain and traditional law. Moreover, we present the challenges of security supervision and possible future research directions in this field.