Reviewerly/exHarmony huggingface dataset:
Predicting suitability of finger marks using machine learning techniques and examiner annotations

Previous research has established the variability of examiners in reaching suitability determinations for friction ridge comparisons. Attempts to create predictive models to assist in this determination have been made, but have been largely confined to fully automated processes that focus on suitability for AFIS entry. This work develops, optimizes, and validates a hybrid predictive model that utilizes both examiner-observed variables and automated measures of quality and rarity to arrive at suitability classifications along four scales that have been proposed in our previous research: Value, Complexity, AFIS, and Difficulty. We show that a model based only on automatically extracted quality or selectivity measures does not perform as well as when used in conjunction with a limited set of user inputs. The model is then based on a limited set of input from the users while taking advantage of automatic measures with a view to limit the user encoding effort while maintaining accuracy. The developed model is able to make predictions at up to 83.13% accuracy when using full study data and maintains similar levels of accuracy in an external validation study. The model achieved accuracy at a similar level to that of examiners asked to make the same suitability determinations across all scales. The model can easily be introduced into an operational laboratory with very little additional operational burden to provide guidance on suitability, complexity, AFIS, and quality assurance decisions; to assist in designing testing and training exercises of progressive difficulty; to describe the difficulty of a mark in testimony; and to provide a consensus-based opinion in laboratories where a second opinion is desired but the laboratory lacks sufficient personnel to form a consensus panel.
DimLift: Interactive Hierarchical Data Exploration Through Dimensional Bundling

The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.
Tell as You Imagine: Sentence Imageability-Aware Image Captioning

Image captioning as a multimedia task is advancing in terms of performance in generating captions for general purposes. However, it remains difficult to tailor generated captions to different applications. In this paper, we propose a sentence imageability-aware image captioning method to generate captions tailoring to various applications. Sentence imageability describes how easily the caption can be mentally imagined. This concept is applied to the captioning model to obtain a better understanding of the perception of a generated caption. First, we extend an existing image caption dataset by augmenting its captions’ diversity. Then, a sentence imageability score for each augmented caption is calculated. A modified image captioning model is trained using this extended dataset to generate captions tailoring to a specified imageability score. Experiments showed promising results in generating imageability-aware captions. Especially, results from a subjective experiment showed that the perception of the generated captions correlates with the specified score.
Priority Order-Based Key Distribution in QKD-Secured Optical Networks

In quantum key distribution (QKD)-secured optical networks, blocking increases with the number of QKD lightpath requests as well as with the number of times modification required to improve the security level of QKD lightpath requests. The blocking is severe problem in such networks because of the limited number of resources (wavelengths and time-slots) in each fiber link. In this paper, we propose a priority order-based routing wavelength and time-slot allocation (POB-RWTA) scheme to reduce the blocking of QKD lightpath requests. In the proposed POB-RWTA scheme, the QKD lightpath requests are served according to their priority order. The priority order of each QKD lightpath request is based on the security level. The blocking of high priority lightpath requests (HPLRs) and total incoming QKD lightpath requests due to limited resources can be reduced using the above criterion of priority order. The performance analysis of the proposed POB-RWTA scheme is made in terms of the success probability of QKD lightpath requests considering different wavelengths for QSCh and the probability of secret key update failure. We compared the proposed POB-RWTA scheme with a non-priority order-based routing wavelength and time-slot allocation (NPOB-RWTA) scheme. Simulations performed on NSFNET topology show that by using the POB-RWTA scheme, the success probability of HPLRs and total incoming QKD lightpath requests are significantly improved as compared to NPOB-RWTA.
Using dynamical quantization to perform split attempts in online tree regressors

A central aspect of online decision trees is evaluating the incoming data and performing model growth. For such, trees much deal with different kinds of input features. Numerical features are no exception, and they pose additional challenges compared to other kinds of features, as there is no trivial strategy to choose the best point to make a split decision. Regression tasks are even more challenging because both the features and the target are continuous. Typical online solutions evaluate and store all the points monitored between split attempts, which goes against the constraints posed in real-time applications. In this paper, we introduce the Quantization Observer (QO), a simple yet effective hashing-based algorithm to monitor and evaluate split candidates in numerical features for online tree regressors. QO can be easily integrated into incremental decision trees, such as Hoeffding Trees, and it has a monitoring cost of O(1) per instance and a sub-linear cost to evaluate split candidates. Previous solutions had a O(logn) cost per insertion (in the best case) and a linear cost to evaluate split candidates. Our extensive experimental setup highlights QO’s effectiveness in providing accurate split point suggestions while spending much less memory and processing time than its competitors.
THEORY THROUGH ARGUMENT: APPLYING ARGUMENT MAPPING TO FACILITATE THEORY BUILDING

This paper proposes an extension of Fletcher and Huff's (1990) argument mapping technique, which focuses on an examination of individual arguments, to depict the broader argumentative terrain of a theoretical discourse. To illustrate the utility of argument mapping as a literature review technique, we demonstrate an application of this approach to the domain of Technology Frames of Reference (TFR) research. We then compare the insights derived from this approach to those derived from a more traditional literature review of this domain. Findings of this comparison suggest that an argument-centred approach could contribute the following new insights into a theoretical domain: (1) provide a means of weighting contributions to a theoretical discourse by depicting these contributions in terms of levels of argumentation; (2) identify new "types" of argument-centred gaps in the literature; (3) offer a structural and progressive view of theory development in a particular domain that could be used to could be used to link and compare insights derived from competing theoretical lenses; and (4) propose a closer linkage between theory testing and theory building by focusing the testing on "arguable" aspects of a theoretical claim.
Science, social networks, and collaboration: an analysis of publications in fisheries science from 1990 to 2018

Abstract Understanding the nature of collaboration underpinning scientific work in fisheries is critical to building the social foundations for effective scientific progress in addressing complex socio-ecological systems. We examine the nature of interdisciplinary work in fisheries through an analysis of nearly three decades of peer-reviewed papers authored by researchers affiliated with NOAA Fisheries’ Northeast Fisheries Science Center in the United States. Using social network analysis, and approaches novel to scientometrics such as grounded theory building, we map and analyse the relationships between authors of different disciplinary backgrounds, visualize these changing networks over time, and evaluate the nature of collaboration with a particular emphasis given to the integration of the social sciences. Our analysis suggests that areas of research such as ecosystem-based management and climate change have helped create synergies between the natural and social sciences, pointing to the importance of organizational changes promoting multiple perspectives, the institutionalization of integrated approaches, and openness to diverse understandings.
Restore from Restored: Video Restoration with Pseudo Clean Video

In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neural network (FCN) as the baseline, we can improve video denoising performance without accurate optical flow estimation and registration steps, in contrast to many conventional video restoration methods, due to the translation equivariant property of the FCN. Specifically, the proposed method can take advantage of plentiful similar patches existing across multiple consecutive frames (i.e., patch-recurrence); these patches can boost the performance of the baseline network by a large margin. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames. In our experiments, we apply the proposed method to state-of-the-art denoisers and show that our fine-tuned networks achieve a considerable improvement in denoising performance.
An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems

Current vulnerability scoring mechanisms in complex cyber-physical systems (CPSs) face challenges induced by the proliferation of both component versions and recurring scoring-mechanism versions. Different data-repository sources like National Vulnerability Database (NVD), vendor websites as well as third party security tool analysers (e.g. ICS CERT and VulDB) may provide conflicting severity scores. We propose a machine-learning pipeline mechanism to compute vulnerability severity scores automatically. This method also discovers score correlations from established sources to infer and enhance the severity consistency of reported vulnerabilities. To evaluate our approach, we show through a CPS-based case study how our proposed scoring system automatically synthesises accurate scores for some vulnerability instances, to support remediation decision-making processes. In this case study, we also analyse the characteristics of CPS vulnerability instances.
The Random Neural Network as a Bonding Model for Software Vulnerability Prediction

Software vulnerability prediction is an important and active area of research where new methods are needed to build accurate and efficient tools that can identify security issues. Thus we propose an approach based on mixed features that combines text mining features and the features generated using a Static Code Analyzer. We use a Random Neural Network as a bonding model that combines the text analysis that is carried out on software using a Convolutional Neural Network, and the outputs of Static Code Analysis. The proposed approach was evaluated on commonly used datasets and led to 97% training accuracy, and 93%–94% testing accuracy, with a 1% reduction in false positives with respect to previously published results on similar data sets.
GCF-Net: Gated Clip Fusion Network for Video Action Recognition

In recent years, most of the accuracy gains for video action recognition have come from the newly designed CNN architectures (e.g., 3D-CNNs). These models are trained by applying a deep CNN on single clip of fixed temporal length. Since each video segment are processed by the 3D-CNN module separately, the corresponding clip descriptor is local and the inter-clip relationships are inherently implicit. Common method that directly averages the clip-level outputs as a video-level prediction is prone to fail due to the lack of mechanism that can extract and integrate relevant information to represent the video. In this paper, we introduce the Gated Clip Fusion Network (GCF-Net) that can greatly boost the existing video action classifiers with the cost of a tiny computation overhead. The GCF-Net explicitly models the inter-dependencies between video clips to strengthen the receptive field of local clip descriptors. Furthermore, the importance of each clip to an action event is calculated and a relevant subset of clips is selected accordingly for a video-level analysis. On a large benchmark dataset (Kinetics-600), the proposed GCF-Net elevates the accuracy of existing action classifiers by 11.49% (based on central clip) and 3.67% (based on densely sampled clips) respectively.
Performance Improvement of a Parsimonious Learning Machine Using Metaheuristic Approaches

Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to their structural simplicity. However, these parsimonious algorithms depend upon predefined thresholds to adjust their structures in terms of adding or deleting rules. Besides, another adjustable parameter of PALM is the fuzziness in membership grades. The best set of such hyper parameters is determined by experts' knowledge or by optimization techniques such as greedy algorithms. To mitigate such experts' dependency or usage of computationally expensive greedy algorithms, in this work, a meta heuristic-based optimization technique, called the multimethod-based optimization technique (MOT), is utilized to develop an advanced PALM. The performance has been compared with some popular optimization techniques, namely, the greedy search, local search, genetic algorithm (GA), and particle swarm optimization (PSO). The proposed parsimonious learning algorithm with MOT outperforms the others in most cases. It validates the multioperator-based optimization technique's advantages over the single operator-based variants in selecting the best feasible hyperparameters for the autonomous learning algorithm by maintaining a compact architecture.
3DCNN: Three-Layers Deep Convolutional Neural Network Architecture for Breast Cancer Detection using Clinical Image Data

The breast cancer is a critical female disease and its proper identification is very essential for better cure and recovery. The diagnosis of BC is a critical issue for clinical specialists and scholars. Different researchers proposed breast cancer diagnosis methods using deep learning techniques. However, these proposed methods not diagnosis breast cancer accurately. In order to tackle the issue of accurate detection of breast cancer we proposed a 3-layers CNN architecture for accurate detection of breast cancer. The proposed model has been trained and tested on Breast histology images data set. The cross validation method Hold out has been applied for best model selection and hyper parameters tuning. Furthermore, different model evaluation metrics have been used for model performance evaluation. The experimental results demonstrated that propped method is more suitable for breast cancer and it would be incorporated in health care successfully.
Research on distributed new energy spot trading method based on blockchain Technology

With a large number of distributed new energy, electric vehicles, distributed energy storage, intelligent electrical equipment and other access to the power grid, new energy stakeholders are increasingly diversified and complex. The existing economic dispatching model of power grid has many problems, such as large error of renewable energy and load forecasting, low information transparency, high system security risk, high transaction cost, etc. As a new distributed database technology, blockchain technology has the characteristics of decentralization, transparency, credibility and traceability. It has a good fit with the power grid and can provide a distributed, safe and transparent information system for power grid dispatching and power trading. Therefore, a distributed new energy spot trading method based on weak centralized management is proposed, that is, using blockchain technology to store power trading information and settle accounts in the form of smart contracts.
Driver's Gaze Zone Estimation Method: A Four-channel Convolutional Neural Network Model

Driver's gaze has become an important indicator to analysis driving state. By estimating the gaze zone of drivers, we can further judge their fatigue state and even predict their driving intention in the next step. In this paper, we propose a four-channel gaze estimation model based on Convolutional Neural Network (CNN), which is used to estimate the gaze zones of the driver. In the proposed method, the images of the right eye, the left eye, the face, and the head are used as the input data of the multi-channel CNN. Then, the features of different channels are fused to estimate the gaze zone. Finally, we compared our method with several existing methods, and the experimental results show that the accuracy of our method is 96%.
Least auxiliary loss-functions with impact growth adaptation (Laliga) for convolutional neural networks

Model selection is a challenge, and a popular Convolutional Neural Networks (CNN) usually takes extra-need parameters. It causes overfitting in real applications. Besides, the extracted hidden features would be lost when the number of convolution layers increases. We use the least auxiliary loss-functions to solve both of these problems. To this end, an optimization problem is stated to select a set of layers with the highest contributions in the training process. Also, an impact growth adaptation procedure adjusts the weights of losses. The constructed Least Auxiliary Loss-functions with Impact Growth Adaptation (Laliga) is a professional forum to select the best settings of auxiliary loss functions for CNNs training. Laliga memorizes the hidden features carefully and better represents the space by using non-redundant and more relevant features. Also, it uses singular value decomposition to regularize the weights. The theoretical results show that Laliga decreases overfitting substantially. Although this algorithm is useful for all CNN models, its results are auspicious for Visual Geometry Group (VGG) networks. The testing accuracies of Laliga for different VGG models on MNIST, CIFAR-10, and CIFAR-100 datasets are 99.7%, 92.3%, and 73.4%, indicating Laliga overcomes many regularization methods in the dropout family. Besides, on more complicated datasets Caltech-101 and Caltech-256, its accuracies raise than 66.1% and 33.2%, which are better than dropout and close to Adaptive Spectral Regularization (ASR) results, although Laliga converges rapidly than ASR. Finally, we analyze the results of Laliga in a transportation case study.
TalkSQL: A Tool for the Synthesis of SQL Queries from Verbal Specifications

Recent advances in the field of Natural Language Processing (NLP) have led to many robust user interfaces (UIs) designed as intelligent tutoring systems (ITS) that help students learn, query and access data in relational databases. Such tools are generally referred to as Natural Language Interfaces to Databases (NLIDBs). Many of these UIs rely on voice or typewritten for further processing. Research has shown that typewritten remains the preferred input method used by database UIs designers for querying relational databases due to its flexibility. Still, there is a dearth of tools that require voice-based inputs for querying relational databases. Despite the scarcity of these tools, many of them fail to provide a comprehensive feedback to a user. In this paper, we introduce a voice-based query system named TalkSQL that takes voice inputs from a user, converts these words into SQL queries and returns a feedback to the user. Automatic feedback generation is of immense importance. To achieve this, we have used regular expressions, a representation of regular languages for the recognition of the Create, Read, Update, Delete (CRUD) operations in SQL and automatically generate a feedback using pre-defined templates. A survey on 53 participants showed that 91.2% agreed that they were able to understand the CRUD command using TalkSQL. The expected contributions are in two-fold: this work may assist a special (e.g. visually impaired) learner to understand SQL queries, and show that a voice-based interface can assist users in understanding SQL queries.
Intelligent Vehicle Path Planning Based on Q-Learning Algorithm with Consideration of Smoothness

Q-learning usually carries out global path planning in grid environment, which is difficult to satisfy the requirements of vehicle dynamics in practice. In this paper, a local path planning for intelligent vehicle based on Q-learning algorithm is proposed. Firstly, the vehicle-road model is established. The information of lane boundary and lane center line is obtained by interpolation method, and the position relationship between main vehicle and environment vehicle is determined. Secondly, the variables that can reflect the driving state of the vehicle and the relationship with the surrounding vehicle position are determined to describe the vehicle states. According to the actual driving situation and the mechanical structure of the vehicle, the action that the vehicle can take is determined. Then, the reward and punishment function is designed through the optimization objectives: driving in the lane, no collision with the surrounding vehicles and so on. Finally, the vehicle path discrete sequence is obtained after training, it is necessary to smooth the path to ensure that the vehicle can track the planned path well. In this paper, the cubic spline interpolation is used to process the discrete path sequence into smooth continuous path. In order to verify the effectiveness of the planning method, the path tracking simulation is carried out on MATLAB/CarSim software. The results show that the path solved by this method can satisfy the requirements of vehicle dynamics.