Reviewerly/exHarmony huggingface dataset:
Nexus between students’ attitude towards self-learning, Tencent APP usability, mobile-learning, and innovative performance

The present study examined Tencent APP's effective novelty in technology as a brand new online learning process during the Covid-19 global public health emergency among students in China and beyond. Mobile learning is a pedagogical tool intuitive for distance online. Aimed to empirically explore international student's effective usability of the Tencent APP during the stay-at-home phenomenon. This study adopted a cross-sectional survey to examine 1083 international students during the period of Covid-19 lockdown. The quantitative approach analyzed the collected data using Amos ver.25. Majority of the respondents were male (65.4%) and female respondents were (34.6%). Our results found attitude did not make any difference with mobile learning behavior, likewise, innovative performance. However, perceived self-learner usefulness (PSLU), and Tencent APP alongside mobile learning were significant. Mobile-learning behavior (MLB) mediation strengthened innovativeness. Tencent APP strengthens the interaction between mobile learning behavior and innovative performance to enhance the efficiency of the technology. Tencent APP was easy to communicate and measurably been one of the Millennium Development Goals witnessed technology that ensures institutional integrated transformation to ensure online learning during the pandemic.
Span-level Emotion Cause Analysis with Neural Sequence Tagging

This paper addresses the task of span-level emotion cause analysis (SECA). It is a finer-grained emotion cause analysis (ECA) task, which aims to identify the specific emotion cause span(s) behind certain emotions in text. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more "emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two benchmark datasets demonstrate the effectiveness of the proposed models.
DFLLR: Deep Feature Learning With Latent Relationship Embedding for Remote Sensing Image Retrieval

For deep networks, accurate image similarities cannot be well characterized with limited iterations, so the latent relationships between images can be embedded to enhance image retrieval performance. In this article, we propose a method named DFLLR to learn deep image features and accurate image similarities for remote sensing image retrieval (RSIR) simultaneously. First, the AlexNet is employed to extract high-level semantic features. Second, to obtain accurate image similarities, latent relationships between images are constructed with manifold learning and embedded in the AlexNet model with fully connected layers; in this way, the latent relationships and image features can be jointly learned. Third, to boost the RSIR performance further, the constraints of central and margin for jointly learning latent relationships and image features are integrated into our DFLLR. The central constraint is used to reduce the discrepancy of the latent relationships at the intraclass level and enhance the accuracies of image features. Moreover, the margin constraint is designed to enhance the accuracies of the latent relationships by maximizing the manifold margin between the latent relationships at the intraclass and interclass levels. To validate our method, we perform comprehensive experiments on three publicly available remote sensing image datasets, and the results demonstrate that it significantly outperforms other state-of-the-art methods.
The Weight-Based Sum Codes in the Residue Ring by Arbitrary Modulus for Synthesis of Self-Checking Digital Computing Systems

The authors of the paper consider the features of the weight-based sum code in the residue ring by arbitrary modulus. For the first time, the error detection characteristics by this codes class and some patterns of using specific modules and weight coefficient values have been established. It is shown that the number of different weight-based codes is limited, but there are a large number of ways to construct them for each value of the bits number in data vectors. An algorithm for obtaining the bits of the check vectors of the considered codes is presented. Examples of their construction are given, and calculating the number of undetectable errors in data vectors is described. The code properties under study are determined, the account of which is useful in solving problems of technical diagnostics. An example of such a task is the synthesis of self-checking digital devices and devices with controllable architecture. The analysis results of the considered code detecting abilities for the built-in control circuits of combinational benchmarks are presented. The results obtained in this work are universal and are not focused on using only one element base of the implemented devices.
Frequency-Supervised MR-to-CT Image Synthesis

This paper strives to generate a synthetic computed tomography (CT) image from a magnetic resonance (MR) image. The synthetic CT image is valuable for radiotherapy planning when only an MR image is available. Recent approaches have made large strides in solving this challenging synthesis problem with convolutional neural networks that learn a mapping from MR inputs to CT outputs. In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images. To address this common limitation, we introduce frequency-supervised deep networks to explicitly enhance high-frequency MR-to-CT image reconstruction. We propose a frequency decomposition layer that learns to decompose predicted CT outputs into low- and high-frequency components, and we introduce a refinement module to improve high-frequency reconstruction through high-frequency adversarial learning. Experimental results on a new dataset with 45 pairs of 3D MR-CT brain images show the effectiveness and potential of the proposed approach. Code is available at https://github.com/shizenglin/Frequency-Supervised-MR-to-CT-Image-Synthesis.
Uncertainty-Aware Self-Training for Semi-Supervised Event Temporal Relation Extraction

Extracting event temporal relations is an important task for natural language understanding. Many works have been proposed for supervised event temporal relation extraction, which typically requires a large amount of human-annotated data for model training. However, the data annotation for this task is very time-consuming and challenging. To this end, we study the problem of semi-supervised event temporal relation extraction. Self-training as a widely used semi-supervised learning method can be utilized for this problem. However, it suffers from the noisy pseudo-labeling problem. In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. Specifically, UAST utilizes (1) Uncertainty Estimation module to compute the model uncertainty for pseudo-labeling unlabeled data; (2) Sample Selection with Exploration module to select informative samples based on uncertainty estimates; and (3) Uncertainty-Aware Learning module to explicitly incorporate the model uncertainty into the self-training process. Experimental results indicate that our approach significantly outperforms previous state-of-the-art methods.
Incremental Graph Convolutional Network for Collaborative Filtering

Graph neural networks (GNN) recently achieved huge success in collaborative filtering (CF) due to the useful graph structure information. However, users will continuously interact with items, which causes the user-item interaction graphs to change over time and well-trained GNN models to be out-of-date soon. Naive solutions such as periodic retraining lose important temporal information and are computationally expensive. Recent works that leverage recurrent neural networks to keep GNN up-to-date may suffer from the "catastrophic forgetting'' issue, and experience a cold start with new users and items. To this end, we propose the incremental graph convolutional network (IGCN) --- a pure graph convolutional network (GCN) based method to update GNN models when new user-item interactions are available. IGCN consists of two main components: 1) a historical feature generation layer, which generates the initial user/item embedding via model agnostic meta-learning and ensures good initial states and fast model adaptation; 2) a temporal feature learning layer, which first aggregates the features from local neighborhood to update the embedding of each user/item within each subgraph via graph convolutional network and then fuses the user/item embeddings from last subgraph and current subgraph via incremental temporal convolutional network. Experimental studies on real-world datasets show that IGCN can outperform state-of-the-art CF algorithms in sequential recommendation tasks.
Text-Aware Single Image Specular Highlight Removal

Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. Therefore, in this paper, we first raise and study the text-aware single image specular highlight removal problem. The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images. To tackle this challenging problem, we first collect three high-quality datasets with fine-grained annotations, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-stage network, which contains a highlight detection network and a highlight removal network. The output of highlight detection network provides additional information about highlight regions to guide the subsequent highlight removal network. Moreover, we suggest a measurement set including the end-to-end text detection and recognition evaluation and auxiliary visual quality evaluation. Extensive experiments on our collected datasets demonstrate the superior performance of the proposed method.
HASHTAG: Hash Signatures for Online Detection of Fault-Injection Attacks on Deep Neural Networks

We propose Hashtag, the first framework that enables high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance. Recent literature in fault-injection attacks shows the severe DNN accuracy degradation caused by bit flips. In this scenario, the attacker changes a few weight bits during DNN execution by tampering with the program's DRAM memory. To detect runtime bit flips, Hashtag extracts a unique signature from the benign DNN prior to deployment. The signature is later used to validate the integrity of the DNN and verify the inference output on the fly. We propose a novel sensitivity analysis scheme that accurately identifies the most vulnerable DNN layers to the fault-injection attack. The DNN signature is then constructed by encoding the underlying weights in the vulnerable layers using a low-collision hash function. When the DNN is deployed, new hashes are extracted from the target layers during inference and compared against the ground-truth signatures. Hashtag incorporates a lightweight methodology that ensures a low-overhead and real-time fault detection on embedded platforms. Extensive evaluations with the state-of-the-art bit-flip attack on various DNNs demonstrate the competitive advantage of Hashtag in terms of both attack detection and execution overhead.
Efficient Approach for Block-Based Copy-Move Forgery Detection

Although image processing and forensic computing are different fields, they have been involved in the same computer science research areas such as image forgery detection, in recent years. Image forgery detection is a new branch of image processing due to increased image manipulation tools. Thus, we proposed a new block-based image forgery detection method within this framework. In this research, we applied the latest and easiest application feature extraction method used in a new iris recognition system, called rotation invariant neighborhood-based binary pattern, on the block-based image forgery detection system. To the best of our knowledge, this is the first work that applies to block-based copy-move forgery detection systems. The proposed method has been evaluated for different block sizes on a well-known image database (CoMoFod) in the literature. Experimental studies showed that our method forgery detection accuracy rate incentive results are higher than the state-of-the-art block-based forgery detection methods.
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox

Learning to rank system has become an important aspect of our daily life. However, the implicit user feedback that is used to train many learning to rank models is usually noisy and suffers from user bias (i.e., position bias). Thus, obtaining unbiased model using biased feedback has become an important research field for IR. Existing studies on unbiased learning to rank (ULTR) can be generalized into two families-algorithms that attain unbiasness with logged data, offline learning, and algorithms that achieve unbiasness by estimating unbiased parameters with real-time user interactions, namely online learning. While there exist many algorithms from both families, there lacks a unified way to compare and benchmark them. As a result, it can be challenging for researchers to choose the right technique for their problems or for people who are new to the field to learn and understand existing algorithms. To solve this problem, we introduced ULTRA, which is a flexible, extensible, and easily configure ULTR toolbox. Its key features include support for multiple ULTR algorithms with configurable hyper parameters, a variety of built-in click models that can be used separately to simulate clicks, different ranking model architectures and evaluation metrics, and simple learning to rank pipeline creation. In this paper, we discuss the general framework of ULTR, briefly describe the algorithms in ULTRA, detail the structure, and pipeline of the toolbox. We experimented on all the algorithms supported by ULTRA and showed that the toolbox performance is reasonable. Our toolbox is an important resource for researchers to conduct experiments on ULTR algorithms with different configurations as well as testing their own algorithms with the supported features.
On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters

This research focuses on analyzing the robustness of different regression paradigms under regressand noise, which has not been examined in depth in the specialized literature. Furthermore, their synergy with fourteen noise preprocessing techniques adapted from the field of classification, known as noise filters, is studied. In order to do this, several noise levels are injected into the output variable of 20 real-world datasets. They are used to evaluate the performance of each regression algorithm with and without the employment of noise filters. The results obtained allow building a robustness ranking of the regression methods to regressand noise. This provides interesting findings, such as some learning paradigms change their well-know behavior with noise in classification problems when they are applied to regression data. On the other hand, the usage of noise filters improves the performance of regression methods, showing different synergies depending on the regression paradigm and filter employed.
Selection Performance Using a Smartphone in VR with Redirected Input

We present a method to track a smartphone in VR using a fiducial marker displayed on the screen. Using WebRTC transmission protocol, we capture smartphone touchscreen input and the screen contents, copying them to a virtual representation in VR. We present two Fitts’ law experiments assessing the performance of selecting targets displayed on the virtual smartphone screen using this method. The first compares direct vs. indirect input (i.e., virtual smartphone co-located with the physical smartphone, or not), and reveals there is no difference in performance due to input indirection. The second experiment assesses the influence of input scaling, i.e., decoupling the virtual cursor from the actual finger position on the smartphone screen so as to provide a larger virtual tactile surface. Results indicate a small effect for extreme scale factors. We discuss implications for the use of smartphones as input devices in VR.
CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection

The goal of multi-view subspace clustering is to explore a common latent space where the multi-view data points lying on. Myriads of subspace learning algorithms have been investigated to boost the performance of multi-view clustering, but seldom exploiting both the multi-view consistency and multi-view diversity, let alone taking them into consideration simultaneously. To do so, we lodge a novel multi-view subspace clustering via cross-view diversity detection (CDD). CDD is able to exploit these two complementary criteria seamlessly into a holistic design of clustering algorithms. With the consistent part and diverse part being detected, a pure graph can be derived for each view. The consistent pure parts of different views are further fused to a consensus structured graph with exactly k connected components where k is the number of clusters. Thus we can directly obtain the final clustering result without any postprocessing as each connected component precisely corresponds to an individual cluster. We model the above concerns into a unified optimization framework. Our empirical studies validate that the proposed model outperforms several other state-of-the-art methods.
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving

Detection of moving objects is a very important task in autonomous driving systems. After the perception phase, motion planning is typically performed in Bird's Eye View (BEV) space. This would require projection of objects detected on the image plane to top view BEV plane. Such a projection is prone to errors due to lack of depth information and noisy mapping in far away areas. CNNs can leverage the global context in the scene to project better. In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input. To the best of our knowledge, such a dataset does not exist and we create an extended KITTI - raw dataset consisting of 12.9k images with annotations of moving object masks in BEV space for five classes. The dataset is intended to be used for class agnostic motion cue based object detection and classes are provided as meta-data for better tuning. We design and implement a two-stream RGB and optical flow fusion architecture which outputs motion segmentation directly in BEV space. We compare it with inverse perspective mapping of state-of-the-art motion segmentation predictions on the image plane. We observe a significant improvement of 13 % in mIoU using the simple baseline implementation. This demonstrates the ability to directly learn motion segmentation output in BEV space. Qualitative results of our baseline and the dataset annotations can be found in https://sites.google.com/view/bev-modnet.
TRG-DAtt: The Target Relational Graph and Double Attention Network Based Sentiment Analysis and Prediction for Supporting Decision Making

The management of public opinion and the use of big data monitoring to accurately judge and verify all kinds of information are valuable aspects in the enterprise management decision-making process. The sentiment analysis of reviews is a key decision-making tool for e-commerce development. Most existing review sentiment analysis methods involve sequential modeling but do not focus on the semantic relationships. However, Chinese semantics are different from English semantics in terms of the sentence structure. Irrelevant contextual words may be incorrectly identified as cues for sentiment prediction. The influence of the target words in reviews must be considered. Thus, this paper proposes the TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information to support decision making. First, dependency tree-based TRG is introduced to independently and fully mine the semantic relationships. We redefine and constrain the dependency and use it as the edges to connect the target and context words. Second, we design dependency graph attention network (DGAT) and interactive attention network (IAT) to form the DAtt and obtain the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information. Next, the target emotional enhancement features obtained by the DGAT are input to the IAT. The influence of each target word on the review can be obtained through the interaction. Finally, the target emotional enhancement features are weighted by the impact factor to generate the review's emotional features. In this study, extensive experiments were conducted on the car and Meituan review data sets, which contain consumer reviews on cars and stores, respectively. The results demonstrate that the proposed model outperforms the existing models.
Does Sovrin Network Offer Sovereign Identity?

The Sovrin Network is an emerging public service utility based on the Self-Sovereign Identity standard aimed at providing sovereign digital identity for all. However, sovereignty is independence with regard to liberty of action, therefore, any sovereign identity should offer similar liberty to its users. Consequently, it is important to understand the meaning of sovereignty in terms of digital identity and what advantages there are for users in terms of its control, through utilisation of the sovereign identity provided by the Sovrin Network. This paper examines the questions, what does sovereignty mean in terms of digital identity and does the Sovrin Network offer sovereign identity. To understand the meaning of sovereignty, this paper assesses sovereignty in different contexts such as in the real world, cyberspace and digital identity. Following a discussion on the meaning of sovereignty in different contexts, an analysis of the type of digital identity provided by the Sovrin Network and the type of sovereignty is performed. Furthermore, it discusses the possible constraints on sovereignty of the Sovrin Network.
IoT for COVID-19 Indoor Spread Prevention: Cough Detection, Air Quality Control and Contact Tracing

Since the beginning of 2020, COVID-19 pandemic has influenced a variety of aspects related to our everyday activities. From entertainment and education to healthcare and transportation, most of the existing processes and routines have been reshaped in order to comply with safety guidelines regarding the COVID-19, either indoors or outdoors. In this paper, it is explored how cost-effective IoT devices in synergy with state-of-the-art embedded machine learning and blockchain can be adopted with aim to reduce the spread of this coronavirus indoors. The focus is on two main aspects: contact tracing and air quality control. As outcome of this research, prototypes are developed and presented, leveraging RFID for person identification, blockchain for contact tracing records, smartphone apps for notifications and deep learning-based cough detection executed on affordable IoT devices.