Reviewerly/exHarmony huggingface dataset:
CAMU: Cycle-Consistent Adversarial Mapping Model for User Alignment Across Social Networks

The user alignment problem that establishes a correspondence between users across networks is a fundamental issue in various social network analyses and applications. Since symbolic representations of users suffer from sparsity and noise when computing their cross-network similarities, the state-of-the-art methods embed users into the low-dimensional representation space, where their features are preserved and establish user correspondence based on the similarities of their low-dimensional embeddings. Many embedding-based methods try to align latent spaces of two networks by learning a mapping function before computing similarities. However, most of them learn the mapping function largely based on the limited labeled aligned user pairs and ignore the distribution discrepancy of user representations from different networks, which may lead to the overfitting problem and affect the performance. To address the above problems, we propose a cycle-consistent adversarial mapping model to establish user correspondence across social networks. The model learns mapping functions across the latent representation spaces, and the representation distribution discrepancy is addressed through the adversarial training between the mapping functions and the discriminators as well as the cycle-consistency training. Besides, the proposed model utilizes both labeled and unlabeled users in the training process, which may alleviate the overfitting problem and reduce the number of labeled users required. Results of extensive experiments demonstrate the effectiveness of the proposed model on user alignment on real social networks.
Research on Chinese multi-documents automatic summarizations method based on improved TextRank algorithm and seq2seq

In this paper a two-stage automatic summarization model is proposed, which combines traditional method with deep learning method. In the first stage, this paper uses improved TextRank algorithm which combines with sentence weight to extract key sentences from multiple documents. In the second stage, a summary sentence is generated from the key sentences sequence based on the Seq2seq model. The experiments on LCSTS and self-constructed corpus show that the scores of the model in this paper of Rouge are all improved with character level input, which shows the effectiveness of the proposed method of this paper.
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Abstract Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art models in grounded question answering often do not explicitly perform decomposition, leading to difficulties in generalization to out-of-distribution examples. In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. Our model induces latent trees, driven by end-to-end (the answer) supervision only. We show that this inductive bias towards tree structures dramatically improves systematic generalization to out-of- distribution examples, compared to strong baselines on an arithmetic expressions benchmark as well as on C losure, a dataset that focuses on systematic generalization for grounded question answering. On this challenging dataset, our model reaches an accuracy of 96.1%, significantly higher than prior models that almost perfectly solve the task on a random, in-distribution split.
Building and Evaluation of Cloud Storage and Datasets Services on AI and HPC Converged Infrastructure

AI Bridging Cloud Infrastructure (ABCI) is a world-leading open AI computing infrastructure, for accelerating R&D activities of artificial intelligence. In order to share and reuse AI software assets with ease, ABCI supports container-based application deployment and fine-grained resource allocation on top of the conventional HPC architecture, and provides tens of peta-bytes of high performance storage. One of the on-going major challenges in ABCI, however, is to more efficiently and flexibly exchange and share machine learning models and data related to AI, with other services deployed outside of ABCI in the real world. Our new services called as ABCI Cloud Storage and ABCI Public Datasets are designed for tackling the challenge and taking a role of "Data Harbor" of ABCI. The services allow users to store input and output data of jobs to be run on the ABCI compute nodes, and to share them with not only ABCI users but also non-ABCI users. This paper presents our design and integration of the services to conventional HPC architecture, as a case of ABCI, and reports performance evaluation of them. Based on our attempt and experience, the paper finally summarizes discussion about future direction of the S3 based front data/storage service of the AI and HPC converged system.
Nonlinear loose coupled non-negative matrix factorization for low-resolution image recognition

In the existing coupled mapping-based methods for low-resolution image recognition (LRIR), the potential relationship between the high- and low-resolution images conforming to the nature of the image characteristics is not considered and the mapping process has no strong actual physical meaning. This paper presents a novel nonlinear loose coupled non-negative matrix factorization (NLCNMF) algorithm to deal with LRIR. The target images can be seen as composed of different local features. The local features of high- and low-resolution images are at different resolution levels, but the way different resolution images are composed of corresponding local features should be similar. The kernel trick is used to achieve the nonlinear approach. The nonlinear mapped high- and low-resolution images are represented by corresponding non-negative local features. The representation coefficient vectors of the high- and low-resolution images belonging to the same target image pair are approximately equal. The proposed NLCNMF conforms to the principle of the nonlinear approach used by the human visual system in analyzing images. When the proposed method describe the common features of the images with different resolution, the idea of "part constitute the whole" is utilized, which has a strong interpretability. The nonlinear algorithm models based on polynomial and Gaussian kernel function are presented. The convergence of the proposed algorithm is proved theoretically. The proposed algorithm performs experiments on 5 image databases. Three different low-resolution (8 × 7, 10 × 10 and 12 × 12) images are involved in the experiment. The experimental results show that the proposed method outperforms the state-of-the-arts in LRIR task.
Real-time traffic quantization using a mini edge artificial intelligence platform

Traffic analysis is dependent on reliable and accurate datasets that quantify the vehicle composition, speed and traffic density over a long period of time. The utilisation of big data is required if equitable and efficient transportation networks are to be realised for smart, interconnected cities of the future. The rapid and widespread adoption of digital twins, IoT (Internet of Things), artificial intelligence and mini edge computing technologies serve as the catalyst to rapidly develop and deploy smart systems for real-time data acquisition of traffic in and around urban and metropolitan areas. This paper presents a proof of concept of a mini edge computing platform for real-time edge processing, which serves as a digital twin of a multi-lane freeway located in Pretoria, South Africa. Video data acquired from an Unmanned Aerial Vehicle (UAV) is processed using a neural network architecture designed for real-time object detection tracking of vehicles. The implementation successfully counted vehicles (cars and trucks) together with an estimation of the speed of each detected vehicle. These results compare favourably to the ground truth data with vehicle counting accuracies of 5% realised. Detection of sparse motorcycles and pedestrians were less than optimal. This proof of concept can be easily scaled and deployed over a wide geographic area. Integration of these cyber-physical assets can be incorporated into existing video monitoring systems or fused with optical sensors as a single data acquisition system.
Image Reflection Removal via Contextual Feature Fusion Pyramid and Task-Driven Regularization

In this paper, we propose a deep neural network for single image reflection removal. More specifically, we design a convolutional-grid module and take it as the building block of a feature fusion pyramid. The module leverages the combination effect of the grid topology to create a rich ensemble of receptive fields. Embedding the modules into a pyramidal architecture further expands the coverage of receptive fields. Another benefit of the pyramid is to fuse the multi-scale features learned by the modules locating at the ascending and descending pathways. The rich diversity of features helps the neural network analyze the contexts around overlapping objects at various spatial ranges and harvest the cues for layer separation. The proposed work also exploits useful semantic cues from the hyper-column descriptors generated by a pre-trained VGG-19 model to reduce the ambiguity of layer separation. In light of the low correlation between background and reflection layers, we design a channel-correlation based conditional discriminator to penalize residual reflection. The discriminator uses channel-wise attention to screen the features that can distinguish real background images from estimated ones. This paper also presents a task-driven regularization strategy. The high sensitivity of semantic segmentation to reflection is exploited for assessing the completeness of reflection removal. Training with this regularization strategy can boost the performance of both reflection removal and high-level task. The comparison against state-of-the-art algorithms on four public benchmark datasets demonstrates that this work exhibits superior performance in handling the complex reflections in wild scenarios. The proposed network architecture is also applicable to haze removal, which is another ill-posed layer separation problem, and has shown encouraging performance.
Area-Time Efficient Hardware Architecture for Signature Based on Ed448

In this brief, we proposed a highly-optimized FPGA-based implementation of the Ed448 digital signature algorithm. Despite significant progress in elliptic curve cryptography (ECC) implementations, Ed448 hardware architecture, to the best of our knowledge, has not been investigated in the literature. In this work, we demonstrate a high throughput while maintaining low resource architecture for Ed448 by employing a new combined algorithm for refined Karatsuba-based multiplier with precise scheduling. Furthermore, a compact distributed memory unit is developed to increase speed while keeping the area low. Our variable-base-point Ed448 architecture performs 327 signatures and 189 verifications per second at a notably higher security level of 224 bits, using not more than 6,617 Slices and 16 DSPs on a Xilinx Artix-7 FPGA. We also proposed possible countermeasures and extensions to Ed448 to counter the physical attacks.
The effects of social media news that users trusted and verified on anxiety level and disease control perception in COVID-19 Pandemic (eng)

ve doğruladığı sosyal medya haberlerinin Pandemisinde kaygı düzeyi ve hastalık kontrol algısı üzerindeki etkileri SUMMARY Objective: Since the beginning of the pandemic, social media users have been bombarded with news about the number of cases and deaths, virus origin, vaccine development process, symptoms, transmission ways, prevention and treatment methods of COVID-19.This study was planned considering that the type of news sources and verification of the news was considerable and these affect the anxiety level and COVID-19 disease control perception.It was considered that reliance on different types of social media accounts (official or unofficial), verifying social media posts with other news sources, frequency of exposure to negative COVID-19 social media contents may have effects on this state.The aim of this study was to reveal the effects of these three variables on anxiety level and COVID-19 disease control perception of participants.Method: The sample consisted of 1516 adults.Participants filled out instruments for sociodemographic data, verification/trust on posts of official/unofficial social media accounts, state anxiety, COVID-19 disease control perception, frequency of exposure to positive/negative COVID-19 social media contents.Results: It was detected that as trust in COVID-19 posts of official social media accounts increased, anxiety level decreased and COVID-19 disease control perception increased.It was observed that as exposure to negative COVID-19 social media contents increased, anxiety level increased and COVID-19 disease control perception decreased.There was no significant correlation between anxiety level and verifying the accuracy of social media posts with other news sources.Discussion: While physical contact accelerates the spread of COVID-19 disease, it was thought that digital contact could be a tool that could decelerate individual and mass anxiety if used appropriately.It is obvious that posts that are reliable and verified will play a key role in controlling anxiety level and disease perception.
CoviReader: Using IOTA and QR Code Technology to Control Epidemic Diseases across the US

Despite all scientific advancements, the spread of infectious diseases continue to pose a significant challenge to the health and prosperity of our society. Therefore, the prevention and control of infectious diseases that have the potential of becoming global threats is a central priority of public health. However, the silent emergence and uncontrolled global spread of the novel virus called SARS-Cov-2 has proven that neither governments nor healthcare systems are prepared to respond to a pandemic. As an example, the coming of the coronavirus (COVID-19) has not only disrupted economies and collapsed healthcare systems, but also filled hospitals and separated people from their families and work across the globe. While the outbreak originated in China, the pandemic's epicenter moved to the US, which has more cases than any country in the world (New York accounted for nearly more than one-third of the country's coronavirus infections). As the US continues to adapt its response to the evolving situation of the virus, the nation's coronavirus pandemic has highlighted many practical uses for blockchain-based applications. Motivated by this, we present a solution for fighting the coronavirus outbreak - and future pandemic outbreaks - in the US using IOTA and QR code technology. In this paper, we propose CoviReader: a decentralized healthcare management system that shares user's data anonymously. The system is built on the IOTA tangle platform and provides users detailed, unchangeable, and easy access to their personal information. CoviReader handles authentication, confidentiality, transparency and data sharing (with crucial considerations when revising confidential information). Users interact with IOTA's tangle via a mobile application which holds a FIREBASE database. The purpose of this short paper is to demonstrate a working prototype by which we assess and analyze strategies for mitigating the consequences of the COVID-19 health crisis in the US.
Underwater Image Quality: Enhancement and Evaluation

The underwater optical images are commonly captured by camera but with different statistical features to natural images. Due to the refraction and scattering of light in different water types, the colors and shapes of objects can be twisted that are incapable of providing acceptable visual qualities. Thus, it is imperative to develop algorithms to enhance underwater images. Besides, the quality evaluation of underwater images is also exploited as a criteria of underwater image enhancement. In the past decade, the related issues have attracted considerable attention. This paper presents a comprehensive review of the related techniques and their most recent achievements. In particular, we observe a significant trend of applying deep learning in underwater image processing in a small volume of data. We hope our review could benefit both the beginners and the experts of this area for discovering appealing and challenging research topics.
Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.
Oculomotor anticipation reveals a multitude of learning processes underlying the serial reaction time task

Sequence learning is the cognitive faculty enabling everyday skill acquisition. In the lab, it is typically measured in speed of response to sequential stimuli, whereby faster responses are taken to indicate improved anticipation. However, response speed is an indirect measure of anticipation, that can provide only limited information on underlying processes. As a result, little is known about what is learned during sequence learning, and how that unfolds over time. In this work, eye movements that occurred before targets appeared on screen in an ocular serial reaction time (O-SRT) task provided an online indication of where participants anticipated upcoming targets. When analyzed in the context of the stimuli preceding them, oculomotor anticipations revealed several simultaneous learning processes. These processes influenced each other, as learning the task grammar facilitated acquisition of the target sequence. However, they were dissociable, as the grammar was similarly learned whether a repeating sequence inhabited the task or not. Individual differences were found in how the different learning processes progressed, allowing for similar performance to be produced for different latent reasons. This study provides new insights into the processes subserving sequence learning, and a new method for high-resolution study of it.
High-precision quantum transmittometry of DNA and methylene-blue using a frequency-entangled twin-photon beam in type-I SPDC

Using the coincidence-count (CC) measurement of the generated frequency-entangled twin-photon beam (TWB) via the process of type-I spontaneous parametric-down conversion (SPDC) in BBO nonlinear crystal (NLC), we have precisely measured the transmittance of very diluted rabbit- and human-DNA, methylene-blue (MB) as a disinfectant, and a thin-film multilayer at near IR wavelength 810nm with an accuracy in order of %0.01 due to the quantum correlation, while the accuracy of classical-like measurement, single-count (SC), is in order of %0.1 in our setup. Moreover, using quantum measurement of the transmittance, the different types of DNA with the same concentration, and also very diluted (in order of pg/ μ l) different concentrations of DNA and MB solutions, are distinguished and detected with high-reliability. Interestingly, in the case of human-DNA samples in contrast to our classical-like measurement, we could precisely detect and distinguish two very diluted concentrations 0.01ng/ μl and 0.1ng/ μl with high reliability while the commercial standard spectrometer device of our DNA-manufacturer could neither detect nor distinguish them. Surprisingly, measurement on the thin-film multilayer illustrates that the introduced method in this work might be performed to cancer/brain tissues or stem cells for cancer therapy and may hopefully open a pave and platform for non-invasive quantum diagnosis in the future.
Email Embeddings for Phishing Detection

The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning algorithms have demonstrated the possibility of building classifiers with high accuracy. The existing research studies treat phishing and genuine emails through general indicators and thus it is not exactly clear what phishing features are contributing to variations of the classifiers. In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues are captured or disregarded by email embeddings, i.e., vectorizations. We then fed machine learning classifiers with the carefully crafted emails to find out about the performance of email embeddings developed. Our results show that using these indicators, email embeddings techniques is effective for classifying emails as phishing or legitimate.
Introducing the concept of cybersecurity footprint

Purpose This paper aims to introduce the concept of cybersecurity footprint. Design/methodology/approach Characteristics of cybersecurity footprint are presented based on documented cases, and the domino effect of cybersecurity is illustrated. Organizational and individual cybersecurity footprints are outlined. Active and passive – digital vs cybersecurity footprints are then reviewed. Taxonomy of aware/unaware vs active/passive cybersecurity footprints are presented, followed by brief discussion of the implications for future research. Findings The concept of cybersecurity footprint is defined, and the evidence from prior cyber incidents is shown to emphasize the concept. Smaller organizations may have a large cybersecurity footprint, whereas larger organizations may have smaller one. Cyberattacks are focusing on the individuals or small organizations that are in the supply chain of larger organizations causing the domino effect. Practical implications Implications of cybersecurity footprint to individuals, organizations, societies and governments are discussed. The authors present organizations with ways to lower cybersecurity footprint along with recommendations for future research. Social implications Cybersecurity has a significant social implication worldwide, as the world is becoming cyber dependent. With the authors’ introduction of the cybersecurity footprint concept and call to further understand how organizations can measure and reduce it, the authors envision it as another perspective of assessing cyber risk and further help mitigate future cyber incidents. Originality/value This paper extends the existing information and computer security body of knowledge on the concept of cybersecurity footprint with illustrated cases.