Reviewerly/exHarmony huggingface dataset:
CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs

Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.
The Development of Scientific Activity in Russian Universities

The aim of this article was to develop scientific activity in Russian universities. Using the survey method, a sample of quantitative indicators of research output for the years 2011&ndash;2019 was formed. The respondents were comprised of 934 lecturers from 13 universities in Russia with different work experience, academic degrees, and indicators of scientific activity. The main components method was used to study the structure of scientific activity productivity indicators. Using the method of additive convolution, a five-factor integral model of the structure of scientific activity of a teacher was formed to evaluate the individual and collective productivity of scientific activity. The factor values and the integral indicator of scientific activity output were calculated, showing that the individual growth rates of the young lecturers&rsquo; output exceeded the growth rates of the older lecturers. Destructive factors relating to the scientific activity output of the lecturers and researchers in Russian universities (divided into two groups of young scientists and senior scientists) were determined and systematized based on the level of dominant influence. The features of the influence of the factor of emotional burnout on the scientific productivity of university teachers were revealed.
Sources of Cryptocurrency Value Systems: The Case of Bitcoin

Cryptocurrencies are a hotly debated topic because it is not clear why they should be valued as they are. Bitcoin, by far the most prominent cryptocurrency, currently trades around $9,500 USD. "Why is Bitcoin valuable?" is a question often heard but seldom answered well. It is not the legal tender of any nation, nor does it represent anything of physical or intrinsic value. Some people attribute its value to its scarcity, others to its anonymity, and others to its immutability, all of which are created and managed by a computer algorithm. Current theories fail to explain Bitcoin's value, or it is not immediately apparent how they might explain it. We are therefore motivated to investigate the sources of cryptocurrency value through the emergent value systems of the Bitcoin community. We use concourse theory and Q-methodology to discover five types of Bitcoin value systems that are complementary and coexisting facets of a collective whole, each type being its own internally consistent "theory" of value, and therefore our typology avoids simplistic generalizations about cryptocurrencies and the motivations behind those involved. We named the types: Fintech, Libertarians, Purists, Average Joe, and Gentrifier. Of interest, four of the five types we identified appear not to value Bitcoin for its monetary or market value, despite what is usually assumed. Instead, Bitcoin is associated with its potential as an alternative currency that may be used to exchange value, to mitigate various forms of risk, or as a force for social and cultural change. These five types may also underlie broader digital innovation processes and provide a basis from which to understand them.
Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval

Multi-modal neuroimage retrieval has greatly facilitated the efficiency and accuracy of decision making in clinical practice by providing physicians with previous cases (with visually similar neuroimages) and corresponding treatment records. However, existing methods for image retrieval usually fail when applied directly to multi-modal neuroimage databases, since neuroimages generally have smaller inter-class variation and larger inter-modal discrepancy compared to natural images. To this end, we propose a deep Bayesian hash learning framework, called CenterHash, which can map multi-modal data into a shared Hamming space and learn discriminative hash codes from imbalanced multi-modal neuroimages. The key idea to tackle the small inter-class variation and large inter-modal discrepancy is to learn a common center representation for similar neuroimages from different modalities and encourage hash codes to be explicitly close to their corresponding center representations. Specifically, we measure the similarity between hash codes and their corresponding center representations and treat it as a center prior in the proposed Bayesian learning framework. A weighted contrastive likelihood loss function is also developed to facilitate hash learning from imbalanced neuroimage pairs. Comprehensive empirical evidence shows that our method can generate effective hash codes and yield state-of-the-art performance in cross-modal retrieval on three multi-modal neuroimage datasets.
Research on UAV Balance Control Based on Expert-fuzzy Adaptive PID

By analyzing the structure and principle of UAV, a balance control algorithm combining fuzzy adaptive PID control and expert PID control is proposed. The algorithm can realize the switching of control mode between the expert PID control and the fuzzy adaptive PID after judging the parameters of the UAV in real time. In terms of hardware, the preprocessing process design of UAV balance control is divided into two relatively independent parts, which are realized through FPGA technology to ensure the integrity of the system and realize modularization. Experiments show that the algorithm combines the advantages of fuzzy PID control and expert PID controller, and improves the balance control effect of UAV.
Topic categorization of Tamil News Articles using PreTrained Word2Vec Embeddings with Convolutional Neural Network

Almost all the problems in NLP are solved using various techniques from machine learning to Deep Learning. Still, there is mystery in language localization. NLP problems are unclear for languages other than English. The problems may be named as Entity Extraction, OCR or classification and prediction in sequence modelling. The amount of people using local language (Tamil, Telegu, Hindi etc) in the social media is increasing, so it is important to automate the process of classifying those contents. Here, the aim is to classify the Tamil news articles to its related topics (Sports, Cinema, Politics). In the existing work they have approached traditional machine learning methods with TFIDF of words as features. In this work we have compared the existing TFIDF feature learning along with Pre-Trained embeddings given to Convolutional Neural Networks (CNN). We found that CNN with pretrained embeddings gave better F1 score compare to TFIDF feature learned with Support Vector Machine (SVM), Naive Bayes (NB) algorithm.
Machine Learning Algorithm to Identify Eye Movement Metrics using Raw Eye Tracking Data

Eye-tracking studies in software engineering are becoming more prevalent and also in the areas like medical, gaming and commercial fields. Researchers may use the same metrics but it is majorly used to give a different name for same field that cause the difficulties in comparing studies, so in this work, a model is developed to reduce the existing challenges. Many existing algorithms are available to apply on eye tracking data but machine learning is one of the best algorithms, for example random forest is one the machine learning algorithms, which helps to hold the test set. In the eye movement metrics, the dataset will be divided into two sets they are: test set and training set. This paper reports on the eye-tracking metries using raw eye-tracking data. The proposed research work has used random forest, decision tree, KNN and SVM for experimentation in order to understand the dataset. The objective of this study is two-fold. First, the identification of various eye movement metrics events and Second, Apply visualization technique. It can be applied in medical field. Here first we will identify the accuracy, recall, precision and f-measure between KNN classifier and SVM, then identifying the eye movement metrics using machine learning algorithm. We give in this research a brief description of the eye movement metrics and which machine algorithm would give the best result, with its applications.
Network Traffic Analysis based IoT Device Identification

Device identification is the process of identifying a device on Internet without using its assigned network or other credentials. The sharp rise of usage in Internet of Things (IoT) devices has imposed new challenges in device identification due to a wide variety of devices, protocols and control interfaces. In a network, conventional IoT devices identify each other by utilizing IP or MAC addresses, which are prone to spoofing. Moreover, IoT devices are low power devices with minimal embedded security solution. To mitigate the issue in IoT devices, fingerprint (DFP) for device identification can be used. DFP identifies a device by using implicit identifiers, such as network traffic (or packets), radio signal, which a device used for its communication over the network. These identifiers are closely related to the device hardware and software features. In this paper, we exploit TCP/IP packet header features to create a device fingerprint utilizing device originated network packets. We present a set of three metrics which separate some features from a packet which contribute actively for device identification. To evaluate our approach, we used publicly accessible two datasets. We observed the accuracy of device genre classification 99.37% and 83.35% of accuracy in the identification of an individual device from IoT Sentinel dataset. However, using UNSW dataset device type identification accuracy reached up to 97.78%.
A Comprehensive Survey on Grey Wolf Optimization

: Grey wolf optimizer is a recently developed metaheuristic algorithm that mimics the hunting and social behaviour. It has been applied in most of the engineering design problems. Grey wolf optimizer and its variants have been effectively used to solve the real-life applications. For some complex problems, grey wolf optimizer has been hybridized with other metaheuristics. This paper summarizes the overview of grey wolf optimizer and its variants. The pros and cons of these variants have been discussed. The application of grey wolf optimizer have also been discussed with future research directions. This paper will encourage the researchers to use this algorithm for their real-life problems.
Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks

While most deep learning architectures are built on convolution, alternative foundations such as morphology are being explored for purposes such as interpretability and its connection to the analysis and processing of geometric structures. The morphological hit-or-miss operation has the advantage that it considers both foreground information and background information when evaluating the target shape in an image. In this article, we identify limitations in the existing hit-or-miss neural definitions and formulate an optimization problem to learn the transform relative to deeper architectures. To this end, we model the semantically important condition that the intersection of the hit and miss structuring elements (SEs) should be empty and present a way to express Don't Care (DNC), which is important for denoting regions of an SE that are not relevant to detecting a target pattern. Our analysis shows that convolution, in fact, acts like a hit-to-miss transform through semantic interpretation of its filter differences. On these premises, we introduce an extension that outperforms conventional convolution on benchmark data. Quantitative experiments are provided on synthetic and benchmark data, showing that the direct encoding hit-or-miss transform provides better interpretability on learned shapes consistent with objects, whereas our morphologically inspired generalized convolution yields higher classification accuracy. Finally, qualitative hit and miss filter visualizations are provided relative to single morphological layer.
Handwritten Digit Recognition System Based on Convolutional Neural Network

Image recognition is widely used in the field of computer vision today. As a kind of image recognition, digit recognition is widely used. Today, the online recognition technology in digit recognition is relatively mature while the offline recognition technology is not. This paper mainly introduces an offline recognition system for handwritten digits based on convolutional neural networks. The system uses the MINST dataset as a training sample and pre-processes the picture with the Opencv toolkit. Then it uses LeNet-5 in the convolutional neural network to extract the handwritten digit image features, repeatedly convolution pooling, and pull the result into a one-dimensional vector. And finally find the highest probability point to determine the result to achieve handwritten digit recognition with the Softmax regression model. The application of this system can greatly reduce labor costs and improve work efficiency, which is of great significance in many fields.
Scale-Residual Learning Network for Scene Text Detection

Detecting incidentally captured text in the wild remains an open problem due to challenging factors including unconstrained scenarios and large scale variation. In this paper, we establish a large-scale scene text detection dataset (LS-Text), containing 36, 000 images and 270, 783 text instances with various scales and complex scenarios, to promote the research of text detection. We propose a Scale-residual Learning Network (SLN) to deal with the scale variation problem in a progressive optimization manner. Specifically, we integrate both learnable feature concatenation and feature up-sampling operator. It can effectively eliminate the residuals between the outputs of SLN and ground-truth text instances by processing both the Feature Fusion Residuals (FFR) and the Scale Transformation Residuals (STR), simultaneously. By stacking multi-scale feature maps in a deep-to-shallow manner, SLN continuously optimizes feature representation by accumulating strong semantic information and rich texture details in a scale-residual learning way. Extensive experimental results on five challenging datasets demonstrate the state-of-the-art performance of the proposed SLN model, and the challenging aspects related to real-world scenarios of the proposed LS-Text dataset. Both the source code of SLN and the LS-Text dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SLN-Text-Detection</uri> .
Variable-Length Chromosome for Optimizing the Structure of Recurrent Neural Network

Designing a structure of Recurrent Neural Network (RNN) can be performed either manually or automatically using a Neural Architecture Search (NAS). Automatically approach is more preferred because manually approach to designing an RNN structure can be a time consuming and error-prone process, whereas NAS uses optimization algorithms such as Evolutionary Algorithm (EA) to find the optimal structure. As one of the NAS methods, a Neuro Evolution of Augmenting Topology (NEAT) searches a neural network structure constructively by adding a new neuron through mutation, which is time-consuming for the RNN structures with large neurons. Genetic Algorithm (GA) is one of the EA that is commonly used to solve optimization problems. Fix length chromosome representation dominates GA filed, this representation suitable for fix length solution. Finding the optimal structure of RNN, the number of nodes and its connections cannot be determined. In this research, a Variable-Length Chromosome Genetic Algorithm (VLC-GA) is exploited to represent RNN structure with a different number of nodes. VLC-GA able to evolve the structure of RNN constructively and destructively. Therefore, the processing time for a larger structure can be reduced. Evaluation for a language modeling task shows that it is capable of reaching a lower perplexity than NAS with Reinforcement Learning (RL).
Convolutional neural net face recognition works in non-human-like ways

Convolutional neural networks (CNNs) give the state-of-the-art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising 'errors'. We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face-matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to appear a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face-matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.
Robust line segment matching via reweighted random walks on the homography graph

This paper presents a novel method for matching line segments between stereo images. Given the fundamental matrix, the local homography can be over determined with pairwise line segment candidates. We exploit this constraint to initialize the candidate and construct the novel homography graph. Because the constraint between the node is based on the epipolar geometry, the homography graph is invariant to the local projective transformation. We employ the reweighted random walk on the graph to rank the candidate, then, we propose the constrained-greedy algorithm to obtain the reliable match. To the best of our knowledge, this is the first study to embed the epipolar geometry into the graph matching theory for the line segment matching. When evaluated on the 32 image patches, our method outperformed the state of the art methods, especially in the scenes of the wide baseline, steep viewpoint changes and dense line segments. The proposed algorithm is available at https://github.com/weidong-whu/line-match-RRW.
Flexible Bayesian Modelling for Nonlinear Image Registration

We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software. The idea is to perform inference in a probabilistic graphical model that accounts for variability in both shape and appearance. The resulting framework is general and entirely unsupervised. The model is evaluated at inter-subject registration of 3D human brain scans. Here, the main modeling assumption is that individual anatomies can be generated by deforming a latent 'average' brain. The method is agnostic to imaging modality and can be applied with no prior processing. We evaluate the algorithm using freely available, manually labelled datasets. In this validation we achieve state-of-the-art results, within reasonable runtimes, against previous state-of-the-art widely used, inter-subject registration algorithms. On the unprocessed dataset, the increase in overlap score is over 17%. These results demonstrate the benefits of using informative computational anatomy frameworks for nonlinear registration.
Bounding Maps for Universal Lesion Detection

(ULD) in computed tomography plays an essential role in computer-aided diagnosis systems. Many detection approaches achieve excellent results for ULD using possible bounding boxes (or anchors) as proposals. However, empirical evidence shows that using anchor-based proposals leads to a high false-positive (FP) rate. In this paper, we propose a box-to-map method to represent a bounding box with three soft continuous maps with bounds in x-, y- and xy-directions. The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate. In the \(1^{st}\) stage of the region proposal network, we replace the sharp binary ground-truth label of anchors with the corresponding xy-direction BM hence the positive anchors are now graded. In the \(2^{nd}\) stage, we add a branch that takes our continuous BMs in x- and y-directions for extra supervision of detailed locations. Our method, when embedded into three state-of-the-art two-stage anchor-based detection methods, brings a free detection accuracy improvement (e.g., a 1.68% to 3.85% boost of sensitivity at 4 FPs) without extra inference time.
A Dynamical Simplified Swarm Optimization Algorithm for the Multiobjective Annual Crop Planning Problem Conserving Groundwater for Sustainability

In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.