Reviewerly/exHarmony huggingface dataset:
The Digital Academic – Critical Perspectives on Digital Technologies in Higher Education

How bumpy are the online lives of university lecturers?Scientific work, like many other intellectual occupations, is becoming increasingly digital.This book summarises essays by leading scholars examining the implications, opportunities, social and political circumstances and notions of working in a contemporary university setting in light of the emergence of digital technologies.Contributors of the chapters form a critical perspective focusing on the impact of digitalisation on the future of higher education, how technology affects scientific publication protocols and university employment conditions, the day-to-day work of researchers, and ways of communicating with students and colleagues.Innovations in scientific practice have an impact on education, research administration and trigger the formulation of new perspectives.The writings present the views of professionals with expertise in education, research administration, sociology, digital humanities, media and communication.We first get a summary of a controversy sparked in August 2016 concerning an article on The Guardian's online site criticising the internet habits of university lecturers.The writing undermined the raison d'être of community profiles of educators for being an unworthy form of communication relative to university goals.This is controversial because the development of electronic mail also stems from university research, and since the introduction of mobile computing, WiFi and cloud computing, the university sector now provides opportunities for scholars from different geographic regions to communicate instantly with each other, with university staff and students and with other out-of-campus connections as well.Although modern computer technologies have gradually emerged and become widespread in academia over the past thirty years or so, relatively little research has been done on how educators use digital advances in their work.The main question is, what are the broader social, cultural and political implications and contexts of technology use?In the following chapter, it is discussed that students must become experts in research practice during PhD applications.They must produce a well-founded thesis that demonstrates that they contribute to scientific knowledge.
Floating Tunable Coupler for Scalable Quantum Computing Architectures

We propose a floating tunable coupler that does not rely on direct qubit-qubit coupling capacitances to achieve the zero-coupling condition. We show that the polarity of the qubit-coupler couplings can be engineered to offset the otherwise constant qubit-qubit coupling and attain the zero-coupling condition when the coupler frequency is above or below the qubit frequencies. We experimentally demonstrate these two operating regimes of the tunable coupler by implementing symmetric and asymmetric configurations of the coupler's superconducting pads with respect to the qubits. Such a floating tunable coupler provides flexibility in designing large-scale quantum processors while reducing the always-on residual couplings.
Changing the World by Changing the Data

NLP community is currently investing a lot more research and resources into development of deep learning models than training data.While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts.Algorithmic solutions have so far had limited success.An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals.This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world.The question is only how much thought we want to invest into that process.
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout.In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients.After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose CLUSTER2SENT, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster.CLUSTER2SENT outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators.For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset.Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.... DR: So are you taking the Monteluekast regularly?PT: Yeah, one everyday like you said.DR: Good.And is it helping?Do you have chest pains
Anthropomorphized artificial intelligence, attachment, and consumer behavior

Abstract The increasing humanization and emotional intelligence of AI applications have the potential to induce consumers’ attachment to AI and to transform human-to-AI interactions into human-to-human-like interactions. In turn, consumer behavior as well as consumers’ individual and social lives can be affected in various ways. Following this reasoning, I illustrate the implications and research opportunities related to consumers’ (potential) attachment to humanized AI applications along the stages of the consumption process.
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion

We propose an audio-driven talking-head method to generate photo-realistic talking-head videos from a single reference image. In this work, we tackle two key challenges: (i) producing natural head motions that match speech prosody, and (ii)} maintaining the appearance of a speaker in a large head motion while stabilizing the non-face regions. We first design a head pose predictor by modeling rigid 6D head movements with a motion-aware recurrent neural network (RNN). In this way, the predicted head poses act as the low-frequency holistic movements of a talking head, thus allowing our latter network to focus on detailed facial movement generation. To depict the entire image motions arising from audio, we exploit a keypoint based dense motion field representation. Then, we develop a motion field generator to produce the dense motion fields from input audio, head poses, and a reference image. As this keypoint based representation models the motions of facial regions, head, and backgrounds integrally, our method can better constrain the spatial and temporal consistency of the generated videos. Finally, an image generation network is employed to render photo-realistic talking-head videos from the estimated keypoint based motion fields and the input reference image. Extensive experiments demonstrate that our method produces videos with plausible head motions, synchronized facial expressions, and stable backgrounds and outperforms the state-of-the-art.
Radio Frequency Fingerprint Collaborative Intelligent Identification Using Incremental Learning

For distributed sensor systems using neural networks, each sub-network has a different electromagnetic environment, and these recognition accuracy is also different. In this paper, we propose a distributed sensor system using incremental learning to solve the problem of radio frequency fingerprint identification. First, the intelligent representation of the received signal is linearly fused into a four-channel image. Then, convolutional neural network is trained by using the existing data to obtain the preliminary model of the network, and decision fusion is used to solve the problem in the distributed system. Finally, using new data, instead of retraining the model, we employ incremental learning by fine-tuning the preliminary model. The proposed method can significantly reduce the training time and is adaptive to streaming data. Extensive experiments show that the proposed method is computationally efficient, and also has satisfactory recognition accuracy, especially at low signal-to-noise ratio (SNR) regime.
Towards Understanding the Spectral Bias of Deep Learning

An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions. The priority of learning functions with low complexity might be at the core of explaining the generalization ability of neural networks, and certain efforts have been made to provide a theoretical explanation for spectral bias. However, there is still no satisfying theoretical result justifying the underlying mechanism of spectral bias. In this paper, we give a comprehensive and rigorous explanation for spectral bias and relate it with the neural tangent kernel function proposed in recent work. We prove that the training process of neural networks can be decomposed along different directions defined by the eigenfunctions of the neural tangent kernel, where each direction has its own convergence rate and the rate is determined by the corresponding eigenvalue. We then provide a case study when the input data is uniformly distributed over the unit sphere, and show that lower degree spherical harmonics are easier to be learned by over-parameterized neural networks. Finally, we provide numerical experiments to demonstrate the correctness of our theory. Our experimental results also show that our theory can tolerate certain model misspecification in terms of the input data distribution.
Multiobjective Reinforcement Learning-Based Neural Architecture Search for Efficient Portrait Parsing

This article dedicates to automatically explore efficient portrait parsing models that are easily deployed in edge computing or terminal devices. In the interest of the tradeoff between the resource cost and performance, we design the multiobjective reinforcement learning (RL)-based neural architecture search (NAS) scheme, which comprehensively balances the accuracy, parameters, FLOPs, and inference latency. Finally, under varying hyperparameter configurations, the search procedure emits a bunch of excellent objective-oriented architectures. The combination of two-stage training with precomputing and memory-resident feature maps effectively reduces the time consumption of the RL-based NAS method, so that we complete approximately 1000 search iterations in two GPU days. To accelerate the convergence of the lightweight candidate architecture, we incorporate knowledge distillation into the training of the search process. This also provides a reasonable evaluation signal to the RL controller that enables it to converge well. In the end, we conduct full training with outstanding Pareto-optimal architectures, so that a series of excellent portrait parsing models (with only approximately 0.3M parameters) is received. Furthermore, we directly transfer the architectures searched on CelebAMask-HQ (Portrait Parsing) to other portrait and face segmentation tasks. Finally, we achieve the state-of-the-art performance of 96.5% MIOU on EG1800 (portrait segmentation) and 91.6% overall <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score on HELEN (face labeling). That is, our models significantly surpass the artificial network on the accuracy, but with lower resource consumption and higher real-time performance.
A Comprehensive Survey on Security and Privacy for Electronic Health Data

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.
Dialogue State Tracking with a Language Model using Schema-Driven Prompting

Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots.Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers.Recently, good results have been obtained using more general architectures based on pretrained language models.Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots.We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of indomain knowledge.Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: Multi-WOZ 2.1 and M2M.The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.
A Comparative Study on Cyber Threat Intelligence: The Security Incident Response Perspective

Cyber Threat Intelligence (CTI) is threat information intended for security purposes.However, use for incident response demands standardization.This study examines the broader security incident response perspective.Introducing 18 core concepts, we assist efforts to establish and assess current standardization approaches.We further provide the reader with a detailed analysis of 6 incident response formats.While we synthesize structural elements, we point to characteristics and show format deficiencies.Also, we describe how core concepts can be used to determine a suitable format for a given use case.Our surveys' findings indicate a consistent focus on incident response actions within all formats.Besides, playbooks are used to represent procedures.Different use cases suggest that organizations can leverage and combine multiple formats.Finally, we discuss open research challenges to fully realize incident response potentials.
A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.
Digital Competence in Higher Education: Students’ Perception and Personal Factors

University students are expected to have the appropriate digital competence to face the demands of the changing educational model and to meet the challenges of the future work. This paper describes university students’ perceptions of digital competence and analyzes the impact of personal factors on digital competence in a sample of 5164 students from all majors in the first and fourth year of their studies at Gansu Agricultural University (China). A quantitative methodology was followed, employing a non-experimental method and the survey technique to collect data. The results obtained show that students’ perceptions of digital competencies in terms of information and data literacy, communication and collaboration, and safety were positive. Furthermore, there were significant differences in students’ self-perceptions of digital competence related to gender, grade level, area of residence, and prior relevant training in the DigComp framework-based instrument. The development of key competency areas for digital competence, such as the creation of digital content, should be promoted, along with helping students to gain knowledge when dealing with everyday technological issues. The need for training related to the use of ICT and digital competencies was also highlighted, as well as supporting the promotion of female students in selected areas of digital competence and assisting lower grade students and students from rural areas in digital competence development.
Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification

Fine-grained visual classiﬁcation (FGVC) is much more challenging than traditional classiﬁcation tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary - the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a consistent block convolution that encourages the network to learn the category-consistent features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and demonstrate the proposed method consistently outperforms existing alternatives or delivers competitive results. Codes are available at https://github.com/PRIS-CV/PMG-V2.
Systematic and Critical Review of RSA Based Public Key Cryptographic Schemes: Past and Present Status

Nowadays, enormous data is being generated by the interconnected digital world, and the need to secure data is highly demanding. Advancement in technologies and new innovative methods applied by the attackers play an instrumental role in breaching data security. Public key Cryptography provides a set of cryptographic algorithms in achieving data security through confidentiality, integrity and authentication. Among all cryptographic algorithms in general and public key cryptography in particular, RSA is one of the most widely used and applied algorithms. Since its inception, it is commonly being adopted in securing data across different domains such as cloud, image etc. Despite its importance and wide application, no such systematic and extensive survey exists in the literature. A systematic and thorough study of RSA based cryptography is presented in this work covering several domains. All the available works in this direction are divided into 11 different categories, viz, Hybrid, Parallel, Cloud, Image, Multiple-Keys, Chinese-Remainder-Theorem-based, Digital-Signatures, K-Nearest-Theorem-based, Batch, Wireless, and Core-Modifications. This study aims to explore RSA-based cryptosystems, either modifications in core RSA or application of enhanced RSA across different domains, systematically categorizing in various categories and eventually providing finding and indications. As a result, this study will guide researchers and practitioners in understanding the past and present status of RSA cryptography and the possibility of its applications in other domains.
AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

Abstract We propose A ria NN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol (with a trusted dealer) leverages function secret sharing, a recent lightweight cryptographic protocol that allows us to achieve an efficient online phase. We design optimized primitives for the building blocks of neural networks such as ReLU, MaxPool and BatchNorm. For instance, we perform private comparison for ReLU operations with a single message of the size of the input during the online phase, and with preprocessing keys close to 4 × smaller than previous work. Last, we propose an extension to support n -party private federated learning. We implement our framework as an extensible system on top of PyTorch that leverages CPU and GPU hardware acceleration for cryptographic and machine learning operations. We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet. We show that computation rather than communication is the main bottleneck and that using GPUs together with reduced key size is a promising solution to overcome this barrier.
Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.