Reviewerly/exHarmony huggingface dataset:
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.
Skeleton-Contrastive 3D Action Representation Learning

This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton representations and various skeleton augmentations via a noise contrastive estimation. In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner. In addition, we contribute several skeleton-specific spatial and temporal augmentations which further encourage the model to learn the spatio-temporal dynamics of skeleton data. By learning similarities between different skeleton representations as well as augmented views of the same sequence, the network is encouraged to learn higher-level semantics of the skeleton data than when only using the augmented views. Our approach achieves state-of-the-art performance for self-supervised learning from skeleton data on the challenging PKU and NTU datasets with multiple downstream tasks, including action recognition, action retrieval and semi-supervised learning. Code is available at https://github.com/fmthoker/skeleton-contrast.
The Microeconomics of Cryptocurrencies

Since its launch in 2009 much has been written about Bitcoin, cryptocurrencies, and blockchains. While the discussions initially took place mostly on blogs and other popular media, we now are witnessing the emergence of a growing body of rigorous academic research on these topics. By the nature of the phenomenon analyzed, this research spans many academic disciplines including macroeconomics, law and economics, and computer science. This survey focuses on the microeconomics of crypto-currencies themselves. What drives their supply, demand, trading price, and competition amongst them? This literature has been emerging over the past decade and the purpose of this paper is to summarize its main findings so as to establish a base upon which future research can be conducted. (JEL D82, E42, G12)
Quantum computing with graphene plasmons

Abstract Among the various approaches to quantum computing, all-optical architectures are especially promising due to the robustness and mobility of single photons. However, the creation of the two-photon quantum logic gates required for universal quantum computing remains a challenge. Here we propose a universal two-qubit quantum logic gate, where qubits are encoded in surface plasmons in graphene nanostructures, that exploits graphene's strong third-order nonlinearity and long plasmon lifetimes to enable single-photon-level interactions. In particular, we utilize strong two-plasmon absorption in graphene nanoribbons, which can greatly exceed single-plasmon absorption to create a “square-root-of-swap” that is protected by the quantum Zeno effect against evolution into undesired failure modes. Our gate does not require any cryogenic or vacuum technology, has a footprint of a few hundred nanometers, and reaches fidelities and success rates well above the fault-tolerance threshold, suggesting that graphene plasmonics offers a route towards scalable quantum technologies.
Blockchains for Secure Digitized Medicine

Blockchain as an emerging technology has been gaining in popularity, with more possible applications to utilize the technology in the near future. With the offer of a decentralized, distributed environment without the need for a third trusted party (TTP), blockchains are being used to solve issues in systems that are susceptible to cyberattacks. One possible field that could benefit from blockchains that researchers have been focusing on is healthcare. Current healthcare information systems face several challenges, such as fragmented patient data, centralized systems which are viewed as single points of attacks, and the lack of patient-oriented services. In this paper, we investigate and analyze recent literature related to the use of blockchains to tackle issues found in modern healthcare information systems. This is done to understand issues that researchers commonly focus on, to discover remaining areas of concern in any proposed solution, and to understand the possible directions of the integration of blockchains in healthcare and personalized medicine. Background information regarding blockchains and existing healthcare information systems is reviewed, followed by the methodology used in the preparation of this review, where the research questions to consider are stated. Afterwards, an analysis of the results is provided, concluding with a discussion of the remaining issues that need to be focused on, and how blockchains could benefit the healthcare sector and empower personalized medicine.
Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera

Unmanned aerial vehicles (UAVs) have been widely used in urban traffic supervision in recent years. However, the detection, tracking, and geolocation of moving vehicle based on the airborne platform suffer from small object sizes, complex scenes, and low-accuracy sensors. To address these problems, this paper develops a framework for moving vehicle detecting, tracking, and geolocating based on a monocular camera, a GPS receiver, and inertial measurement units (IMUs) sensors. First, the method based on YOLOv3 was employed for vehicle detection due to its effectiveness and efficiency for small object detection in complex scenes. Then, a visual tracking method based on correlation filters is introduced, and a passive geolocation method is presented to calculate the GPS coordinates of the moving vehicle. Finally, a flight control method in terms of the previous image processing results is introduced to lead the UAV that is following the interesting moving vehicle. The proposed scheme has been built on a DJI M100 platform on which a monocular camera and a microcomputer Jetson TX1 are added. The experimental results show that this scheme is capable of detecting, tracking, and geolocating the interesting moving vehicle with high precision. The framework demonstrates its capacity in automatic supervision on target vehicles in real-world experiments, which suggests its potential applications in urban traffic, logistics, and security.
Security Enhancement for Mobile Edge Computing Through Physical Layer Authentication

In this paper, we investigate the security threats in mobile edge computing (MEC) of Internet of things, and propose a deep-learning (DL)-based physical (PHY) layer authentication scheme which exploits channel state information (CSI) to enhance the security of MEC system via detecting spoofing attacks in wireless networks. Moreover, three gradient descent algorithms are adopted to accelerate the training of deep neural networks, which enables smaller computation overheads and lower energy consumptions. In addition, the maximum likelihood function of multi-user authentication method is derived, which explains why cross entropy is chosen as the loss function. The vectorization cost function is also derived. The mini batch scheme and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization are adopted to improve training accuracy and avoid over-fitting, respectively. Moreover, the simulation and experimental results show that the DL-based PHY-layer authentication approaches can distinguish multiple legitimate edge nodes from malicious nodes and attacker by CSIs, effectively. Our proposed method supports a better performance compared with the traditional hypothesis test based method.
Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere

Task scheduling in the cloud platform seems to be the most significant issue to guarantee that cloud connectivity adequately and efficiently meets the requirements of customers. Scheduling is basically the method of mapping or assigning tasks after taking into account job features to accessible funds. An effective scheduling protocol should comply with user needs and aids a service provider perform excellent quality of service (QoS) in order to boost general application efficiency. Cloud computing is an evolving computational paradigm with a broad range of self-reliant and economically diverse computational structures. Task scheduling is a significant move to enhance cloud computing general efficiency. Task scheduling is also important in order to decrease power utilization and enhance service providers ' profitability through a reduction in handling moment. In this paper we suggest a chaotic squirrel search algorithm (CSSA) to optimally multitask scheduling in an Infrastructure as a Service (IaaS) cloud atmosphere. The methods continuously generate job plans that render the current approaches more cost-effective. In order to guarantee greater global convergence, the early eco system was produced with messy optimisation for the efficient eco-system. The suggested chaotic squirrel search algorithm was ultimately synthesised with the messy local search to enable the exploring authority to complement Squirrel search algorithm (SSA) algorithms. Other QoS conditions such as compatibility and safety for very big cases can be expanded to cover the suggested technique. A cloud simulator toolkit takes into consideration the strategy and compares the outcomes with scheduling algorithms so that ideal outcomes for several goals are achieved.
Evolving artificial neural networks with feedback

Neural networks in the brain are dominated by sometimes more than 60% feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use transfer entropy in the feed-forward paths of deep networks to identify feedback candidates between the convolutional layers and determine their final synaptic weights using genetic programming. This adds about 70% more connections to these layers all with very small weights. Nonetheless performance improves substantially on different standard benchmark tasks and in different networks. To verify that this effect is generic we use 36000 configurations of small (2–10 hidden layer) conventional neural networks in a non-linear classification task and select the best performing feed-forward nets. Then we show that feedback reduces total entropy in these networks always leading to performance increase. This method may, thus, supplement standard techniques (e.g. error backprop) adding a new quality to network learning.
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-Based Detection

Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences. They can also be used to generate fake reviews, which can then be used to attack online review systems and influence the buying decisions of online shoppers. To perform such attacks, it is necessary for experts to train a tailored LM for a specific topic. In this work, we show that a low-skilled threat model can be built just by combining publicly available LMs and show that the produced fake reviews can fool both humans and machines. In particular, we use the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96%) to filter out reviews with undesired sentiments. Because none of the words in the review are modified, fluent samples like the training data can be generated from the learned distribution. A subjective evaluation with 80 participants demonstrated that this simple method can produce reviews that are as fluent as those written by people. It also showed that the participants tended to distinguish fake reviews randomly. Three countermeasures, Grover, GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately detect fake review.
The digital competence of university students: a systematic literature review

Digital competence is a pressing need for students in the 21st century. The present study is a systematic review of the literature on university students’ digital abilities. The methodology consisted of selecting documents (n=126) from three different databases (Scopus, Web of Science and ERIC) via peer review. The documents were selected for inclusion following predefined criteria and then analysed using a qualitative analysis software (ATLAS.ti). Digital competence is made up of several elements (information skills, content creation, communication, ethical skills, problem solving, technical skills/use and strategic skills). We identified that the most frequently-used terms in the literature are digital literacy and digital competence. However, the terminology used varies among authors. We also found that most authors do not believe that young people actually have the digital abilities that they are assumed to have. In other words, students do not have a high level of digital competence. Educational institutions therefore need to help them to develop this competence, which is so necessary in the context of 21st century education.
Assessing the feasibility of blockchain technology in industries: evidence from Turkey

Purpose The objective of this study is to assess quantitatively how feasible blockchain is for various industries, such as logistics and supply chain, health, energy, finance, automotive, pharmaceutical and agriculture and food using a comprehensive list of indicators. Design/methodology/approach A decision aid was applied to the problem of identifying the feasibility of blockchain in Turkish industries. To this end, first, a set of indicators was identified. Then, the fuzzy AHP and fuzzy TOPSIS were utilized to assess the feasibility comparatively using the data gathered from a group of experts. Finally, a scenario analysis was conducted to ensure the consistency of our evaluation. Findings The findings of this study suggest that comparatively, logistics and supply chain , finance and health industries are the most feasible industries for blockchain. This study further suggests that blockchain is the least feasible for the automotive industry compared to the rest of the identified industries. Research limitations/implications It is cumbersome to find out the respondents who have sufficient knowledge of both blockchain and the identified industries. Even if we took the utmost care in identifying the right respondents, we limited our search to the biggest industrial hubs of Turkey. Practical implications The findings of this research may help various decision-makers employed in governments, conglomerates, software and consulting firms and national research institutions make more informed decisions and allocate their resources more effectively. Originality/value To this date, the current studies have solely investigated possible research opportunities in blockchain and demonstrated several blockchain applications in stand-alone cases. To the best of our knowledge, however, no single study exists that evaluates the feasibility of blockchain comparatively and holistically among a group of industries using various indicators.
Hong-Ou-Mandel interferometry on a biphoton beat note

Abstract Hong-Ou-Mandel interference, the fact that identical photons that arrive simultaneously on different input ports of a beam splitter bunch into a common output port, can be used to measure optical delays between different paths. It is generally assumed that great precision in the measurement requires that photons contain many frequencies, i.e., a large bandwidth. Here we challenge this “well-known” assumption and show that the use of two well-separated frequencies embedded in a quantum entangled state (discrete color entanglement) suffices to achieve great precision. We determine optimum working points using a Fisher Information analysis and demonstrate the experimental feasibility of this approach by detecting thermally-induced delays in an optical fiber. These results may significantly facilitate the use of quantum interference for quantum sensing, by avoiding some stringent conditions such as the requirement for large bandwidth signals.
Learning Visual Representations with Caption Annotations

Pretraining general-purpose visual features has become a crucial part of tackling many computer vision tasks. While one can learn such features on the extensively-annotated ImageNet dataset, recent approaches have looked at ways to allow for noisy, fewer, or even no annotations to perform such pretraining. Starting from the observation that captioned images are easily crawlable, we argue that this overlooked source of information can be exploited to supervise the training of visual representations. To do so, motivated by the recent progresses in language models, we introduce image-conditioned masked language modeling (ICMLM) – a proxy task to learn visual representations over image-caption pairs. ICMLM consists in predicting masked words in captions by relying on visual cues. To tackle this task, we propose hybrid models, with dedicated visual and textual encoders, and we show that the visual representations learned as a by-product of solving this task transfer well to a variety of target tasks. Our experiments confirm that image captions can be leveraged to inject global and localized semantic information into visual representations. Project website: https://europe.naverlabs.com/ICMLM .
Research Challenges and Security Threats to AI-Driven 5G Virtual Emotion Applications Using Autonomous Vehicles, Drones, and Smart Devices

Thanks to comprehensive potential with new access technology, massive bandwidths and ultrahigh reliability, it is highly expected that recent 5G network and beyond 5G network (B5G) will fundamentally influence various autonomous systems, applications and devices. Also, Artificial intelligence (Ai) is expanding its applicability to numerous existing and next generation systems and research branches. Conversely, Ai can be utilized to threaten 5G-enabled components as well as to attack Ai-based systems and machine learning algorithms. So, it is indispensable to investigate new Ai-based security threats and solutions in 5G-enabled smart cities. in this article, we introduce Ai-driven 5G integrated virtual emotion system, called 5G-i-VEmoSYS, which covers Ai-VEmoBAR, Ai-VEmoFLOW, and Ai-VEmoMAP as applications in 5G environments. First, we explain how to operate those Ai-driven promising applications in 5G-enabled components including autonomous vehicles, smart UAVs or drones. Also, we describe how to perform those applications in public areas and private areas with different perspectives. Then, we carefully deal with research challenges, security threats and issues according to the proposed 5G-i-VEmoSYS.
Intelligent financial fraud detection practices in post-pandemic era

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.
An Artificial Intelligence–based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload

Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.