Reviewerly/exHarmony huggingface dataset:
MDSE: Searching Multi-source Heterogeneous Material Data via Semantic Information Extraction

In this paper, we demonstrate MDSE, which provides effective information extraction and searching for multi-source heterogeneous materials data that are collected as XML documents. The major features of MDSE are: (1) We propose a transfer-learning-based approach to extract material information from non-textual material data, including images, videos, etc. (2) We present a heterogeneous-graph-based method to extract the semantic relationships among material data. (3) We build a search engine with both Google-like and tabular searching UIs to provide functional searching on integrated material data. After a brief introduction to the architecture and key technologies of MDSE, we present a case study to demonstrate the working process and the effectiveness of MDSE.
Numerical Analysis of Bio-signal Using Generative Adversarial Networks

In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI). In our previous study, measuring similarity (stationarity, fractality, and degree of determinism) of variations in the exchange rates to the pseudo-exchange rates generated by Generative Adversarial Networks (GANs), we compared Winner processes with the GANs. From the viewpoint of stationarity, the similarity of sequences in the pseudo exchange rates were higher than those generated by the Winner processes, and high scores in the similarity were resulted from both sequences in terms of degree of determinism. In this study, we have applied this AI system to numerical simulations of the stabilogram whose randomness is remarkably greater than that of the other bio-signal in accordance with the nonlinear analysis.
A Framework for Verifying the Fixity of Archived Web Resources

The number of public and private web archives has increased, and we implicitly trust content delivered by these archives. Fixity is checked to ensure that an archived resource has remained unaltered (i.e., fixed) since the time it was captured. Currently, end users do not have the ability to easily verify the fixity of content preserved in web archives. For instance, if a web page is archived in 1999 and replayed in 2019, how do we know that it has not been tampered with during those 20 years? In order for the users of web archives to verify that archived web resources have not been altered, they should have access to fixity information associated with these resources. However, most web archives do not allow accessing fixity information and, more importantly, even if fixity information is available, it is provided by the same archive delivering the resource, not by an independent archive or service. In this research, we present a framework for establishing and checking the fixity on the playback of archived resources, or mementos. The framework defines an archive-aware hashing function that consists of several guidelines for generating repeatable fixity information on the playback of mementos. These guidelines are results of our 14-month study for identifying and quantifying changes in replayed mementos over time that affect generating repeatable fixity information. Changes on the playback of mementos may be caused by JavaScript, transient errors, inconsistency in the availability of mementos over time, and archive-specific resources. Changes are also caused by transformations in the content of archived resources applied by web archives to appropriately replay these resources in a user’s browser. The study also shows that only 11.55% of mementos always produce the same fixity information after each replay, while about 16.06% of mementos always produce different fixity information after each replay. The remaining 72.39% of mementos produce multiple unique fixity information. We also find that mementos may disappear when web archives move to different domains or archives. In addition to defining multiple guidelines for generating fixity information, the framework introduces two approaches, Atomic and Block, that can be used to disseminate fixity information to web archives. The main difference between the two approaches is that, in the Atomic approach, the fixity information of each archived web page is stored in a separate file before being disseminated to several on-demand web archives, while in the Block approach, we batch together fixity information of multiple archived pages to a single binary-searchable file before being disseminated to archives. The framework defines the structure of URLs used to publish fixity information on the web and retrieve archived fixity information from web archives. Our framework does not require changes in the current web archiving infrastructure, and it is built based on well-known web archiving standards, such as the Memento…
Automated Surveillance Model for Video-Based Anomalous Activity Detection Using Deep Learning Architecture

Due to need of monitoring and safeguarding the activities of people and achieving global security, surveillance cameras are deployed in public places. It is important to find out interesting patterns such as suspicious behavior of entities (people, objects) from such videos. Automatically monitoring such videos for detection of anomalies without any assistance of human personnel is the need of the hour. Therefore, deep learning approach for modeling video-based anomalous activity detection has been put forth in this paper. The problem of anomaly detection has been handled as one-class classification problem. The proposed methodology involves auto-encoder-based two-dimensional convolutional neural network for feature learning, long short-term memory model for identifying temporal statistical correlation and radial basis function as activation function in fully connected network for one-class classification. Use of deep learning model for calculating the likelihood from multivariate Gaussian distribution has made our approach a novel one. The proposed methodology perfectly works for simulated data being generated for anomaly detection, and this justifies that it is the best suitable methodology for modeling video-based anomaly detection.
Productivity Evaluation Indicators Based on LEAN and their Application to Compare Agile and Waterfall Projects

Despite the increasing importance of responding quickly to markets and customers, many organizations that are contracted to develop software are still unable to move from waterfall to agile development. Major reasons include not only the incompatibility of current labor laws with agile development but also the inability to determine the productivity (cost, duration) of agile development. This paper proposes indicators to evaluate and compare the productivity of projects by focusing on "value building" in LEAN. To promote agile development, NTT Laboratories have been running agile development trials using delegated agreements (similar to time and material contracts) since 2018. Using the proposed indicators, we compared the statistics of 20 agile trial projects and more than 200 waterfall development projects. Results revealed that agile development became superior to waterfall development in terms of delivery time and cost when its feature-used rate was 30% higher than that of waterfall development.
Ontology-Controlled Geometric Solver

The paper describes an ontology-controlled geometric solver. The solver functions as a part of an integrated problem-solving system with a natural language interface. It is implemented in a prototype version of the system for the “school geometry” subject area. We described the solver interaction with the system linguistic processor, ontology, and the graphic component. We provided examples demonstrating automated solving non-trivial geometric problems formulated in a natural language. We suggested using cognitive structures in the dialogue for an effective linguistic analysis and building up a correct drawing.
Predicting the Big-Five Personality Traits from Handwriting

This work is about how the personality can be derived from the image of handwriting which can be derived among any of the five big personality traits accompanied by the list of features or reflecting the nature of the handwriting image from seven features. We would be using handwriting images which decide the nature of the person based on big-five trait. In the feature list dataset, every features are mined from images to match images with the image dataset and creating a separate clean feature list. Training the data using feature list and tag list efficiently by iterating every image and generating list of 12 elements for every image. Data list is classified using various algorithms like support vector machine, K-nearest neighbor, and random forest to evaluate accuracy by displaying the features of the input essay along with five big traits.
Holistic lithography in the age of the Artificial Intelligence of Things

The convergence of 5G, artificial intelligence and billions of connected devices will start a new wave of innovation, bringing advanced computing power to massive amounts of data. This is the era of the Artificial Intelligence of Things. The key enabler continues to be affordable scaling, driven by advanced lithography, computational capabilities, fast metrology and inspection. In his keynote, ASML President and CTO Martin van den Brink will look at the key developments across ASML’s holistic product portfolio.
Multilingual Access and Services for Digital Collections

"Multilingual Access and Services for Digital Collections." Journal of Electronic Resources in Medical Libraries, 17(3-4), pp. 125–126
Optimization approaches to manage congestions for the phenomenon “Luci d’artista” in Salerno"

This paper focuses on the optimization of traffic flows in case of congestion phenomena due to the event “Luci D’Artista” in Salerno, Italy. The management of traffic deals with two different optimization techniques, that foresee, respectively, a decentralized approach and a genetic algorithm. A cost functional, that estimates the kinetic energy on a portion of the real network of Salerno, is maximized with respect to the distribution coefficients at nodes. The simulation results confirm the decongestion effects, that are also proved via the estimation of the time a car needs to cross fixed paths on the network object of study.
Recommendation Algorithm Based on Matrix SVD with Exponential Correction

Matrix decomposition is an important technology in collaborative filtering recommendation algorithm. Based on SVD, some improvements, e.g., RSVD algorithm with offset term and regular term, and SVD++ algorithm with explicit and implicit feedback, was proposed. Considering the influence of time factor on the recommendation process, we discussed a time factor to the algorithms, which were associated with the users, the projects and the interaction offset. Based on the different time decay characteristics of video and users, an algorithm with exponential correction called timeSVD++EXP is proposed. The results show that the proposed algorithm can improve the prediction accuracy of the model when root mean square error is used as the evaluation index. And the prediction accuracy of the model is further increased by considering the time factor and exponential correction.
Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification

Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including seasonal dummy/indicator variables or building separate models for each season. But these approaches have strong limitations in high-dimensional classification problems, or with complex seasonal patterns. This paper explores how to best handle seasonal drift in the specific context of news article categorization (or classification/tagging), where seasonal drift is overwhelmingly the main type of drift present in the data, and for which the data are high-dimensional. We introduce a novel classifier named Seasonal Averaged One-Dependence Estimators (SAODE), which extends the AODE classifier to handle seasonal drift by including time as a super parent. We assess our SAODE model using two large real-world text mining related datasets each comprising approximately a million records, against nine state-of-the-art stream and concept drift classification models, with and without seasonal indicators and with separate models built for each season. Across five different evaluation techniques, we show that our model consistently outperforms other methods by a large margin where the results are statistically significant.
Automatic stent and catheter marker detection in X-ray fluoroscopy using adaptive thresholding and classification

Abstract In this study, we propose a method for marker detection in X-ray fluoroscopy sequences based on adaptive thresholding and classification. Adaptive thresholding yields multiple marker candidates. To remove non-marker areas, 24 specific features are extracted from each extracted patch and four supervised classifiers are trained to differentiate non-marker areas from marker areas. Quantitative evaluation was carried out to assess different classifier performance by calculating accuracy, sensitivity, specificity and precision. SVM outperforms other classifiers based on the mean value for accuracy, specificity and precision with 81.56, 91.94 and 84.21%, respectively.
Blockchain Federation Enabled Trustable Internet of Things

The Internet of Things (IoT) provides an infrastructure enabling advanced functions based on interoperable information and communication technologies by interconnecting physical and virtual things. IoT has gained more and more attention from both academy and industry. However, the design, maintenance, and governance of IoT are facing challenges because of an enormous amount of heterogeneous devices involved. IoT requires strengthening its trustable features. The blockchains are immutable distributed ledger systems supporting trustable features including security, scalability, privacy, safety, and connectivity usually without a central authority. Due to the complexity of IoT system any current blockchain cannot satisfy all demands of heterogeneous devices, networks, and higher level functionalities. This paper proposes a novel reference architecture combines Blockchain Federation with IoT to make IoT capable of being trusted. The architecture enables trustable features by integrating blockchains with different IoT tiers and layers. Proof of concept system is implemented to demonstrate the correctness and feasibility of the architecture. Furthermore, the future directions of Blockchain Federation and IoT evolution are discussed.
See Through Occlusions: Detailed Human Shape Estimation From A Single Image With Occlusions

3D human body shape and pose reconstructing from a single RGB image is a challenging task in the field of computer vision and computer graphics. Since occlusions are prevalent in real application scenarios, it’s important to develop 3D human body reconstruction algorithms with occlusions. However, existing methods didn’t take this problem into account. In this paper, we present a novel depth estimation Neural Network, named Detailed Human Depth Network(DHDNet), which aims to reconstruct the detailed and completed depth map from a single RGB image contains occlusions of human body. Inspired by the previous works [1], [2], we propose an end-to-end method to obtain the fine detailed 3D human mesh. The proposed method follows a coarse-to-fine refinement scheme. Using the depth information generated from DHDNet, the coarse 3D mesh can recover detailed spatial structure, even the part behind occlusions. We also construct DepthHuman, a 2D in-the-wild human dataset containing over 18000 synthetic human depth maps and corresponding RGB images. Extensive experimental results demonstrate that our approach has significant improvement in 3D mesh reconstruction accuracy on the occluded parts.
Financial Risk Assessment and Pre-warning of Port Enterprises Based on Neural Network Model

Zhao, S., 2020. Financial risk assessment and pre-warning of port enterprises based on neural network model. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 243–246. Coconut Creek (Florida), ISSN 0749-0208.This paper designs a financial risk assessment model based on Principal Component Analysis (PCA) and RBF neural network in order to help port enterprises effectively identify and prevent from the financial risks. Here first refines assessment indicators at all levels by the factor analysis, thereby reducing the RBF neural network training data, streamlining the network structure. Then refined data from some commercial banks will be chosen for training with RBF neural network. In the end, The RBF-ANN system that has already trained is tested based on data from a few port enterprises. This paper provides new ideas and methodologies for the port enterprise to effective make financial risk assessment, watchdog and control.
A Context-Based Network For Referring Image Segmentation

Referring image segmentation is an important task aiming at segmenting out the object referred by a natural language expression. Current works usually employ the methods of concatenating the visual and linguistic features. They underestimate the importance of language-to-vision and object-to-object relationships when the natural language expression has multiple entities. Therefore, we propose a new network named Context-Based Network(CBN) to improve the accuracy of locating the correct referent. The CBN is composed of two modules: Intra Relation Selection(Intra-RS) and Inter Relation Selection(Inter-RS). The Intra-RS can capture object-to-object relationships in an embedding visual and linguistic feature space and the Inter-RS uses the multi-scale linguistic features as a guide to match the most similar region from the image feature maps. Besides, we apply spatial pyramid pooling to get global information to solve the limited receptive field problem. Experimental results on four public datasets showed that CBN achieved comparable performance to the other state-of-art methods.
Terrorist Attacks for Fake Exposure Notifications in Contact Tracing Systems

In this work we show that an adversary can attack the integrity of contact tracing systems based on Google-Apple Exposure Notifications (GAEN) by leveraging blockchain technology. We show that through smart contracts there can be an on-line market where infected individuals interested in monetizing their status can upload to the servers of the GAEN-based systems some keys (i.e., TEKs) chosen by a non-infected adversary. In particular, the infected individual can anonymously and digitally trade the upload of TEKs without a mediator and without running risks of being cheated. This vulnerability can therefore be exploited to generate large-scale fake exposure notifications of at-risk contacts with serious consequences (e.g., jeopardizing parts of the health system, affecting results of elections, imposing the closure of schools, hotels or factories).As main contribution, we design a smart contract with two collateral deposits that works, in general, on GAEN-based systems. We then also suggest the design of a more sophisticated smart contract, using DECO, that could be used to attack in a different way GAEN-based systems (i.e., this second smart contract can succeed even in case GAEN systems are repaired making ineffective the first smart contract).Our work shows how to realize with GAEN-based systems (in particular with Immuni and SwissCovid), the terrorist attack to decentralized contact tracing systems envisioned by Vaudenay.
Fast Convolutional Neural Networks with Fine-Grained FFTs

The Convolutional Neural Networks (CNNs) architecture is one of the most widely used deep learning tools. The execution time of CNNs is dominated by the time spent on the convolution steps. Most CNNs implementations adopt an approach that lowers the convolution into a matrix-based operation through the im2col (image to column) process. The transformed convolution then can be easily parallelized with highly efficient BLAS libraries. The contribution of this paper is that we observe significant but intricately patterned data redundancy in this matrix representation of convolution. This redundancy has not been exploited before to improve the performance of CNNs. In this paper, we analyze the origin of the redundancy generated by the im2col process, and reveal a new data pattern to more mathematically concisely describe the matrix representation for convolution. Based on this redundancy-minimized matrix representation, we implement a FFT-based convolution with finer FFT granularity. It achieves on average 23% and maximum 50% speedup over the regular FFT convolution, and on average 93% and maximum 286% speedup over the Im2col+GEMM method from NVIDIA's cuDNN library, one of the most widely used CNNs libraries. Moreover, by replacing existing methods with our new convolution method in a popular deep-learning programming framework Caffe, we observe on average 74% speedup for multiple synthetic CNNs in closer-to-real-world application scenarios and 25% speedup for a variant of the VGG network.
Improving Dense Pixelwise Prediction of Epithelial Density Using Unsupervised Data Augmentation for Consistency Regularization

Although the amount of medical data keeps increasing, data annotations are scarce and often very difficult to obtain. It is even harder for the case that involves multi-modal imaging or data such as radiology-pathology correlation. In this regard, semi-supervised learning has the potential to leverage unlabeled data for improved medical image analysis. Herein, we propose a semi-supervised learning framework for the dense pixelwise prediction in multi-parametric magnetic resonance imaging (mpMRI). The proposed method predicts the epithelial density in mpMRI per-voxel basis. The ground truth annotations are only obtainable from the corresponding pathology images, which are often unavailable for mpMRI. Introducing unsupervised data augmentation and supervised training signal annealing strategies during training, the proposed method utilizes both labeled and unlabeled mpMRI in an efficient and effective manner. The experimental results demonstrate that the proposed framework is effective in improving the stability and accuracy of the density prediction. The proposed framework achieves the mean absolute error of 6.493, compared to 7.353 by the supervised learning counterpart, outperforming other competing methods. The results suggest that the semi-supervised learning framework could aid in resolving the scarcity of medical data and annotations, in particular for radiology-pathology correlation.