Reviewerly/exHarmony huggingface dataset:
Paper Co-citation Analysis Using Semantic Similarity Measures

Co-citation analysis can be exploited as a bibliometric technique used for mining information on the relationships between scientific papers. Proposed methods rely, however, on co-citation counting techniques that slightly take the semantic aspect into consideration. The present study proposes a new technique based on the measure of Semantic Similarity (SS) between the titles of co-cited papers. Several computational measures rely on knowledge resources to quantify the semantic similarity, such as the WordNet «is a» taxonomy. Our proposal analyzes the SS between the titles of co-cited papers using word-based SS measures. Two major analytical experiments are performed: the first includes the benchmarks designed for testing word-based SS measures; the second exploits the dataset DBLP (DBLP: Digital Bibliography & Library Project.) citation network. As a result, we found the SS measures behave the same as human judgement for the lexical similarity and can be consequently used for the automatic assessment of similarity between co-cited papers. The analysis of highly repeated co-citations demonstrates that the different SS measures display almost similar behaviours, with slight differences due to the distribution of the provided SS values. Furthermore, we note a low percentage of similar referred papers into the co-citations.
Camera Captured DIQA with Linearity and Monotonicity Constraints

Document image quality assessment (DIQA), which predicts the visual quality of the document images, can not only be applied to estimate document’s optical character recognition (OCR) performance prior to any actual recognition, but also provides immediate feedback on whether the documents meet the quality requirements for other high level document processing and analysis tasks. In this work, we present a deep neural network (DNN) to accomplish the DIQA task, where a Saimese based deep convolutional neural network (DCNN) is employed with customized losses to improve system’s capability of linearity and monotonicity to predict the quality of document images. Based on the proposed network along with the new losses, the obtained DCNN achieves the state-of-the-art quality assessment performance on the public datasets. The source codes and pre-trained models are available at https://gitlab.com/xujun.peng/DIQA-linearity-monotonicity .
Structures Discovering for Optimizing External Clustering Validation Metrics

Clustering is a set of unsupervised techniques for Machine Learning, that automatically recognizes the associations between samples from datasets. The unsupervised techniques use optimization methods to discover the unreviewed patterns within data. Hence, selecting the ideal clustering solution is a complex task and it directly depends on the problem at hand. Market segmentation, image comprehension, topic modeling, and social network analysis are some examples of data pattern recognition areas. Each one of these examples requires different clustering-geometry approaches. The choice of an appropriate clustering solution usually optimizes the number of clusters using internal validation measures, which indicates both the separability and distance relations between the clusters’ entities. Meanwhile, external indexes reflect the clustering efficiency and assess data distributions using real labels. This paper aims to recognize cluster structures by internal clustering validity indexes to improve the results of the external indexes. The work improves upon the state of the art methods. The experiments result in enhancements of 67% in the Adjusted Rand Index, 81% in the Normalized Mutual Information and 50% in the Clustering Accuracy for the total datasets.
A Restoration of Underwater Polarized Images Based on DCP

To address the problems of the details blurring and color distortion existing in the multi-angle underwater polarized images, the paper proposes a restoration algorithm of underwater polarized images based on DCP(dark channel prior), which combines the characteristics of underwater polarized images with the DCP. Firstly, the transmittance and the object reflected light with polarization characteristics of two Orthogonal polarized images are calculated by the improved DCP. Then, the polarization components existing in the object reflected lights are eliminated by the global polarization angle to obtain the original object radiance. Finally, the color correction is carried out by gray world algorithm to get the final restoration result. The proposed method is mainly designed to solve the problems of the limited utility of DCP in the deblurring of single underwater image and the noise caused by conventional polarized restoration methods. The experimental results show that the proposed method can make the details of images more obvious and significantly improve the clarity, contrast and the color distribution of underwater polarized images.
Proof of Policy (PoP): A New Attribute-Based Blockchain Consensus Protocol

Healthcare Blockchains (HBC) are grooming nowadays due to the requirements of flexible Electronic Health Records (EHR) management, privacy needs, and controlled medical data access. Blockchain technology very much suits these requirements and support in various healthcare data access control methods. More focus on improved block generation time gives the expected blockchain performance. Block generation time can be significantly improved by the consensus algorithms used by the system. Traditional consensus algorithm of Proof of Work (PoW) and Proof of Stake (PoS) require heavy computational resources and hence by computational overheads. The proposed system introduces a new privacy-preserving HBC consensus protocol as a consequence of member attribute values and significantly reduced computational resources. The new attribute-based consensus algorithm for HBC called Proof of Policy (PoP) is constructed using Attribute-Based Ring Signature (ABRS). This signature agrees on the stake of attribute-based ring membership and can be considered as an extension of Proof of Stake (PoS). It is also found that the proposed PoP consensus algorithm requires less computational resources than popular PoW and PoS.
An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

Abstract An inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.
A New Adaptive Fuzzy Cognitive Map-Based Forecasting Model for Time Series

In a fuzzy cognitive map-based forecasting model, causal relationships (represented with a weight matrix) are constant. This may hinder the applicability of such a model. In this paper, we propose an adaptive fuzzy cognitive map-based forecasting model. Different from the existing models, the proposed model is made of a collection of fuzzy cognitive maps. Maps are constructed according to the clustering results of the so-called premises covering an entire time series. Subsequently, we use an optimization algorithm to train parameters of each fuzzy cognitive map individually. The proposed model construction procedure allows forming fuzzy cognitive maps that more flexible and, thus, suitable for forecasting of long time series. In experimental studies on synthetic time series and real time series, the proposed model performed very well in comparison with the original fuzzy cognitive map-based forecasting model and another two forecasting models.
Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

This paper proposes a study of existing environments used to enact data science pipelines applied to graphs. Data science pipelines are a new form of queries combining classic graph operations with artificial intelligence graph analytics operations. A pipeline defines a data flow consisting of tasks for querying, exploring and analysing graphs. Different environments and systems can be used for enacting pipelines. They range from graph NoSQL stores, programming languages extended with libraries providing graph processing and analytics functions, to full machine learning and artificial intelligence studios. The paper describes these environments and the design principles that they promote for enacting data science pipelines intended to query, process and explore data collections and particularly graphs.
Squeeze excitation densely connected residual convolutional networks for specific emitter identification based on measured signals

Abstract Specific emitter identification (SEI) refers to the process of distinguishing emitter individuals, which is important for electric support measure systems. Traditional SEI methods are based on hand-crafted features and have the problem of low accuracy. A novel convolutional neural network (CNN) approach, named squeeze excitation densely connected residual convolutional network (SEDCRN) approach, is therefore proposed. SEDCRN can directly recognize signals without complex preprocessing. It obtains the beneficial advantage of rediscovering new, more useful new features from low-level information and reducing feature redundancy by embedding the squeeze excitation module and residual connections into the densely connected convolutional structure. BesideIn addition, SEDCRN adopts center loss as an auxiliary loss function, which can further enhance the ability of feature learning and expression, especially for SEI tasks. The proposed approach is evaluated using a real automatic, dependent surveillance-broadcast signal dataset. Experiments show that SEDCRN outperforms traditional methods and other CNN-based methods in terms of accuracy and parameter efficiency.
FeatFlow: Learning geometric features for 3D motion estimation

3D motion estimation is an important prerequisite for the autonomous operation of vehicles and robots in dynamic environments. This work presents FeatFlow, a novel neural network architecture to estimate 3D motions from unstructured point clouds. Specifically, we learn deep geometric features to estimate the dense scene flow and the ego-motion of the platform. We build a scene flow estimation pipeline by an encoder-decoder architecture which comprises three novel modules: feature extractor, motion embedder, and flow decoder. By using a point-score layer to assign scores to the extracted features in a learning procedure, the feature extractor effectively extracts keypoints and features that are most significant for estimating the relative transformation between two consecutive point clouds. The whole model adaptively learns the required robust descriptors to represent a variety of point motions at the object or scene level. We evaluated our approach on synthetic data from FlyingThings3D, and real-world LiDAR scans from KITTI and Oxford RobotCar. Our network successfully generalizes to datasets with different patterns, outperforming various baselines and achieving state-of-the-art performance.
Investigation into the security and privacy of iOS VPN applications

Due to the increasing number of recommendations for people to use Virtual Private Networks (VPNs) to protect their privacy, more application developers are creating VPN applications and publishing them on the Apple App Store and Google Play Store. In this 'gold rush', applications are being developed quickly and, in turn, not being developed with security in mind.
Cyber Resilience using State Estimation Updates Based on Cyber Attack Matrix Classification

Cyber-physical systems (CPS) maintain operation, reliability, and safety performance using state estimation and control methods. Internet connectivity and Internet of Things (IoT) devices are integrated with CPS, such as in smart grids. This integration of Operational Technology (OT) and Information Technology (IT) brings with it challenges for state estimation and exposure to cyber-threats. This research establishes a state estimation baseline, details the integration of IT, evaluates the vulnerabilities, and develops an approach for detecting and responding to cyber-attack data injections. Where other approaches focus on integration of IT cyber-controls, this research focuses on development of classification tools using data currently available in state estimation methods to quantitatively determine the presence of cyber-attack data. The tools may increase computational requirements but provide methods which can be integrated with existing state estimation methods and provide for future research in state estimation based cyber-attack incident response. A robust cyber-resilient CPS includes the ability to detect and classify a cyber-attack, determine the true system state, and respond to the cyber-attack. The purpose of this paper is to establish a means for a cyber aware state estimator given the existence of sub-erroneous outlier detection, cyber-attack data weighting, cyber-attack data classification, and state estimation cyber detection.
Research on maintenance guiding system based on augmented reality

In the process of repair, maintenance and use of the current equipment, there are problems such as complicated structure and lack of visual guidance, but the equipment maintenance guiding system based on augmented reality technology provides a good solution. Based on the research results of augmented reality equipment maintenance technology at home and abroad, this paper proposed a C/S architecture maintenance induction system framework, by using Microsoft Hololens glasses as the hardware platform and using augmented reality, artificial intelligence recognition, spatial positioning technology, designed and discussed a remote control tower maintenance and induction system. The example proves that the augmented reality maintenance induction system has a good application effect in complex equipment maintenance and training.
Web Log Anomaly Detection Based on Isolated Forest Algorithm

In order to improve the detection rate and time efficiency of the web log based anomaly detection method, a web log anomaly detection method based on the isolated forest algorithm is proposed. By analyzing the web log anomalies, an anomaly detection framework based on web logs was constructed, the web features for anomaly detection are extracted, a mathematical model is built for the web log and the feature values are calculated, and an anomaly detection is performed using the isolated forest. Compared with other methods through experiments, the results show that the method has greatly improved detection accuracy and detection efficiency.
Data journals: types of peer review, review criteria, and editorial committee members’ positions

Purpose: This study analyzed the peer review systems, criteria, and editorial committee structures of data journals, aiming to determine the current state of data peer review and to offer suggestions.Methods: We analyzed peer review systems and criteria for peer review in nine data journals indexed by Web of Science, as well as the positions of the editorial committee members of the journals. Each data journal’s website was initially surveyed, and the editors-in-chief were queried via email about any information not found on the websites. The peer review criteria of the journals were analyzed in terms of data quality, metadata quality, and general quality.Results: Seven of the nine data journals adopted single-blind and open review peer review methods. The remaining two implemented modified models, such as interactive and community review. In the peer review criteria, there was a shared emphasis on the appropriateness of data production methodology and detailed descriptions. The editorial committees of the journals tended to have subject editors or subject advisory boards, while a few journals included positions with the responsibility of evaluating the technical quality of data.Conclusion: Creating a community of subject experts and securing various editorial positions for peer review are necessary for data journals to achieve data quality assurance and to promote reuse. New practices will emerge in terms of data peer review models, criteria, and editorial positions, and further research needs to be conducted.
A Practical Learning-based Approach for Viewer Scheduling in the Crowdsourced Live Streaming

Scheduling viewers effectively among different Content Delivery Network (CDN) providers is challenging owing to the extreme diversity in the crowdsourced live streaming (CLS) scenarios. Abundant algorithms have been proposed in recent years, which, however, suffer from a critical limitation: Due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we put forward LTS (Learn to Schedule), a novel scheduling algorithm that can adapt to the dynamics from both viewer traffics and CDN performance. In detail, we first propose LTS-RL, an approach that schedules CLS viewers based on deep reinforcement learning (DRL). Since LTS-RL is trained in an end-to-end way, it can automatically learn scheduling algorithms without any pre-programmed models or assumptions about the environment dynamics. At the same time, to practically deploy LTS-RL, we then use the decision tree and imitation learning to convert LTS-RL into a more light-weighted and interpretable model, which is denoted as Fast-LTS. After the extensive evaluation of the real data from a leading CLS platform in China, we demonstrate that our proposed model (both LTS-RL and Fast-LTS) can improve the average quality of experience (QoE) over state-of-the-art approaches by 8.71--15.63%. At the same time, we also demonstrate that Fast-LTS can faithfully convert the complicated LTS-RL with slight performance degradation (&lt; 2%), while significantly reducing the decision time (×7--10).
Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room

Data centers (DCs) are becoming increasingly important in recent years, and highly efficient and reliable operation and management of DCs is now required. The generated heat density of the rack and information and communication technology (ICT) equipment is predicted to get higher in the future, so it is crucial to maintain the appropriate temperature environment in the server room where high heat is generated in order to ensure continuous service. It is especially important to predict changes of rack intake temperature in the server room when the computer room air conditioner (CRAC) is shut down, which can cause a rapid rise in temperature. However, it is quite difficult to predict the rack temperature accurately, which in turn makes it difficult to determine the impact on service in advance. In this research, we propose a model that predicts the rack intake temperature after the CRAC is shut down. Specifically, we use machine learning to construct a gradient boosting decision tree model with data from the CRAC, ICT equipment, and rack intake temperature. Experimental results demonstrate that the proposed method has a very high prediction accuracy: the coefficient of determination was 0.90 and the root mean square error (RMSE) was 0.54. Our model makes it possible to evaluate the impact on service and determine if action to maintain the temperature environment is required. We also clarify the effect of explanatory variables and training data of the machine learning on the model accuracy.
A Novel Robotic System for Painting with Eyes

In this paper we present the preliminary analysis and results of a novel robotic system that allows a user to paint with eyes, using an eye tracker device and a collaborative robot. Eye tracking is a sensing technology that allows a computer to detect where a person is looking (point of gaze). This technology is used to control the motion of a collaborative robot that paints on a canvas based on the detected input. Results show the feasibility of the robotic system and its possible future application as a tool for artistic and creative painting by people with disabilities who cannot use their hands, or that have lost the control of all or part of their muscles.
Enabling Android NNAPI Flow for TVM Runtime

With machine learning on the rise, mobile platforms are striving to offer inference acceleration on edge devices so that related applications can achieve satisfiable performance. With this background, this work aims at interfacing inference on Android with TVM, an inference-focusing compiler for machine learning, and NNAPI, the official neural network API provided by Android. This work presents a flow to integrate NNAPI into TVM-generated inference model with a partition algorithm to determine which parts of the model should be computed on NNAPI and which should not. Conducted experiments show that properly partitioned models can achieve significant speedup using NNAPI when compared to pure TVM-generated CPU inference. In addition, our enable flow potentially benefits both frameworks by allowing them to leverage each other in AI model deployments.
Considerations for Comparing Video Game AI Agents with Humans

Video games are sometimes used as environments to evaluate AI agents’ ability to develop and execute complex action sequences to maximize a defined reward. However, humans cannot match the fine precision of the timed actions of AI agents; in games such as StarCraft, build orders take the place of chess opening gambits. However, unlike strategy games, such as chess and Go, video games also rely heavily on sensorimotor precision. If the “finding” was merely that AI agents have superhuman reaction times and precision, none would be surprised. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans. Here, I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video game play provided by AI agents.