Reviewerly/exHarmony huggingface dataset:
Transport analysis of infinitely deep neural network

We investigated the feature map inside deep neural networks (DNNs) by tracking the transport map. We are interested in the role of depth--why do DNNs perform better than shallow models?--and the interpretation of DNNs--what do intermediate layers do? Despite the rapid development in their application, DNNs remain analytically unexplained because the hidden layers are nested and the parameters are not faithful. Inspired by the integral representation of shallow NNs, which is the continuum limit of the width, or the hidden unit number, we developed the flow representation and transport analysis of DNNs. The flow representation is the continuum limit of the depth, or the hidden layer number, and it is specified by an ordinary differential equation (ODE) with a vector field. We interpret an ordinary DNN as a transport map or an Euler broken line approximation of the flow. Technically speaking, a dynamical system is a natural model for the nested feature maps. In addition, it opens a new way to the coordinate-free treatment of DNNs by avoiding the redundant parametrization of DNNs. Following Wasserstein geometry, we analyze a flow in three aspects: dynamical system, continuity equation, and Wasserstein gradient flow. A key finding is that we specified a series of transport maps of the denoising autoencoder (DAE), which is a cornerstone for the development of deep learning. Starting from the shallow DAE, this paper develops three topics: the transport map of the deep DAE, the equivalence between the stacked DAE and the composition of DAEs, and the development of the double continuum limit or the integral representation of the flow representation. As partial answers to the research questions, we found that deeper DAEs converge faster and the extracted features are better; in addition, a deep Gaussian DAE transports mass to decrease the Shannon entropy of the data distribution. We expect that further investigations on these questions lead to the development of an interpretable and principled alternatives to DNNs.
Identification and control of dynamical systems using different architectures of recurrent fuzzy system

Fuzzy logic based systems are very widely used for modeling and control of complex non-linear, plants. Fuzzy systems require the knowledge about the structure of the dynamic plant in order to achieve fruitful results. Recurrent Fuzzy systems (RFS) are a variation of fuzzy systems and have the ability to model and control dynamic plants without using the information about the structure of the plant. This paper presents identification and control of non-linear dynamical systems using two different architectures of recurrent fuzzy system (RFS). It highlights the importance of RFS over the conventional type-1 fuzzy based system. The objective of system identification as well as control has been achieved using both the architectures of RFS and the simulation results clearly show their efficiency. This paper also highlights yet another advantage of RFS over the conventional type-1 fuzzy systems which comes into light when dealing with higher order systems. The paper explains how the computational complexity can be greatly reduced by using RFS for higher order dynamical systems. A comparative analysis between the conventional type-1 fuzzy system and the two recurrent fuzzy systems has also been performed.
A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort

Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55–10.64) and for Volpara volumetric breast density, 1.10 (0.67–1.81). The AUC for the LDA trained classifier was 0.68 (0.64–0.73), compared to 0.52 (0.47–0.57) for Volpara volumetric breast density (p < 0.001). The regression analysis of OR values for the LDA classifier also showed a significant increase in slope (p < 0.02). Mammographic texture features derived from digital mammograms are important quantitative measures for breast cancer risk assessment based models. Parenchymal texture analysis has an important role for stratifying breast cancer risk in women, which can be implemented to routine breast cancer screening strategies.
Launching an Accessible Archive of Environmental Data

A new digital archive enables community use of terrestrial and subsurface ecosystem data sets.
Information Literacy and Instruction: Making a Place for Makerspaces in Information Literacy

In recent years, many libraries have experimented with the idea of creating makerspaces in their facilities. Library makerspaces provide patrons with opportunities to experiment not only with different technologies, but with different ways of thinking. In this column, Leah Mann explores the relationship between making and information literacy and describes how librarians can use makerspaces to create unique pedagogical opportunities to develop practical real-world skills, build problem-solving abilities, and enhance knowledge transfer. She explains how the maker movement can affect not only school libraries, but other types of libraries as well.—Editor
Complementarity in Requirements Tracing

Complementarity between activities reveals that doing any one of them increases the returns to doing the others. In other words, complementarity leads to the synergistic effect that the whole is greater than the sum of its parts. Identifying and exploiting complementarity can benefit many cybernetic activities, where human-machine interactions are inherent and dominant. One such activity is requirements tracing that helps stakeholders to track the status of their goals. Although various kinds of support for human analysts in requirements tracing have been proposed, little is known about the nature of complementarity when different tracing practices are involved. In this paper, we explore the role of complementarity by considering together the tagging-to-trace (T2T) and learning-to-trace (L2T) activities. We present a novel approach to examining which T2T and L2T practices enhance the qualities of each other. Our approach also uncovers the environmental factors which the complementarity is sensitive to. Applying our approach to the logs of 140 analyst-tracing units offers operational insights into the rigorous detection of complementarity and shows the importance of understanding the cybernetic conditions under which the requirements tracing practices may in fact be complementary.
A Comparative Study and Automated Detection of Illegal Weapon Procurement over Dark Web

Terrorist groups have reconnoitered smarter ways to use online discussion forums for their violent plans. They have been using their privately owned discussion forums for various illegal purposes. A comparative study of work done on various dark web forums of terroristic organizations is done in this paper. This paper proposes a novel approach to identify procurement of modern weapons over the social media forum by terrorist groups. We used data from four dark web forum websites named “Ansar Aljihad Network”, “IslamicAwakening”, “Gawaher”, and “IslamicNetwork”. Multiple experts independently annotated 313 randomly selected posts as procurement (YES) or non- procurement (NO) to label the forum threads. Mutual agreement between experts is computed to find the level of significance. Furthermore, we used machine learning classification techniques (MLCT) in order to classify labeled posts. To our knowledge, our procurement model presents a first of its kind model to automatically detect procurement of modern weapons over dark web. The work done presents application of social media analytics and text mining to counter terrorism.
Single Image-Based Scene Visibility Estimation

This paper proposes a novel method to utilize a single daylight image of the observed scene to estimate the extinction coefficient of the observed atmosphere and obtain the scene visibility. The proposed method consists of three steps. First, based on the theory of atmospheric physics, we estimate the extinction coefficient of the clear atmosphere in an observed scene. Second, a method combining the dark channel prior and the edge collapse-based transmission refinement is employed to calculate the ratio between the extinction coefficient of the observed atmosphere and that of the clear atmosphere. This ratio indicates the increased degree of the extinction coefficient due to the increment of the atmospheric turbidity. Finally, by multiplying the extinction coefficient of the clear atmosphere and the ratio, the extinction coefficient of the observed atmosphere is calculated and the scene visibility is obtained. The numerous experiments on the proposed method suggest that it performs well in measuring the scene visibility in various types of scenes without additional assistance (e.g., geometric calibration of the camera, road marking extraction, and ground truth data collection).
An Improved Rough Set Theory based Feature Selection Approach for Intrusion Detection in SCADA Systems

Despite the increasing awareness of cyber-attacks against Critical Infrastructure (CI), safeguarding the Supervisory Control and Data Acquisition (SCADA) systems remains inadequate. For this purpose, designing an efficient SCADA Intrusion Detection System (IDS) becomes a significant research topic of the researchers to counter cyber-attacks. Most of the existing works present several statistical and machine learning approaches to prevent the SCADA network from the cyber-attacks. Whereas, these approaches failed to concern the most common challenge, “Curse of dimensionality”. This scenario accentuates the necessity of an efficient feature selection algorithm in SCADA IDS where it identifies the relevant features and eliminates the redundant features without any loss of information. Hence, this paper proposes a novel filter-based feature selection approach for the identification of informative features based on Rough Set Theory and Hyper-clique based Binary Whale Optimization Algorithm (RST-HCBWoA). Experiments were carried out by Power system attack dataset and the performance of RST-HCBWoA was evaluated in terms of reduct size, precision, recall, classification accuracy, and time complexity.
A Feature Selection Method for Multi-Label Text Based on Feature Importance

Multi-label text classification refers to a text divided into multiple categories simultaneously, which corresponds to a text associated with multiple topics in the real world. The feature space generated by text data has the characteristics of high dimensionality and sparsity. Feature selection is an efficient technology that removes useless and redundant features, reduces the dimension of the feature space, and avoids dimension disaster. A feature selection method for multi-label text based on feature importance is proposed in this paper. Firstly, multi-label texts are transformed into single-label texts using the label assignment method. Secondly, the importance of each feature is calculated using the method based on Category Contribution (CC). Finally, features with higher importance are selected to construct the feature space. In the proposed method, the feature importance is calculated from the perspective of the category, which ensures the selected features have strong category discrimination ability. Specifically, the contributions of the features to each category from two aspects of inter-category and intra-category are calculated, then the importance of the features is obtained with the combination of them. The proposed method is tested on six public data sets and the experimental results are good, which demonstrates the effectiveness of the proposed method.
Weak randomness impacts the security of reference-frame-independent quantum key distribution

Perfect randomness is of great significance in various quantum key distribution (QKD) protocols. In this Letter, we investigate the effect of weak randomness on the state preparation in reference-frame-independent QKD (RFI-QKD), which may be implemented with imperfect random numbers or quantum-state encoding devices. In the scenario of weak randomness, the maximal amount of information the eavesdropper can acquire should be carefully evaluated. With practical experimental parameters, we demonstrate that even a small proportion of weak randomness will impact the security of RFI-QKD seriously. Furthermore, we briefly study the side effect of weak randomness on RFI measurement-device-independent QKD (RFI-MDI-QKD), and simulation results show that weak randomness damages the performance of RFI-MDI-QKD more critically than that of RFI-QKD.
Swarm debugging: The collective intelligence on interactive debugging

One of the most important tasks in software maintenance is debugging. To start an interactive debugging session, developers usually set breakpoints in an integrated development environment and navigate through different paths in their debuggers. We started our work by asking what debugging information is useful to share among developers and study two pieces of information: breakpoints (and their locations) and sessions (debugging paths). To answer our question, we introduce the Swarm Debugging concept to frame the sharing of debugging information, the Swarm Debugging Infrastructure (SDI) with which practitioners and researchers can collect and share data about developers’ interactive debugging sessions, and the Swarm Debugging Global View (GV) to display debugging paths. Using the SDI, we conducted a large study with professional developers to understand how developers set breakpoints. Using the GV, we also analyzed professional developers in two studies and collected data about their debugging sessions. Our observations and the answers to our research questions suggest that sharing and visualizing debugging data can support debugging activities.
Equiprobable unambiguous discrimination of quantum states by symmetric orthogonalisation

We show that the maximal probability of equiprobable unambiguous discrimination of a set of pure quantum states is given by the minimal eigenvalue of the Gram matrix of this set. We illustrate this result with several examples important for the protocols of quantum key distribution realized with weak coherent states of light.
NIST PQC: CODE-BASED CRYPTOSYSTEMS

The code-based schemes, which were submitted to the contest of post-quantum crypto algorithms NIST PQC, are studied in this work. The general characteristics of the algorithms are explored and basic properties and parameters are estimated. A comparative analysis of the electronic digital signature schemes, public-key cryptosystems and key encapsulation schemes are carried out according to the criteria of speed and length of the main cryptographic parameters.
CytoProcessor&lt;sup&gt;TM&lt;/sup&gt;: A New Cervical Cancer Screening System for Remote Diagnosis

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Current automated cervical cytology screening systems still heavily depend on manipulation of glass slides. We developed a new system called CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; (DATEXIM, Caen, France), which increases sensitivity and takes advantage of virtual slide technology to simplify the workflow and save worker time. We used an approach based on artificial intelligence to identify abnormal cells among the tens of thousands in a cervical preparation. &lt;b&gt;&lt;i&gt;Objectives:&lt;/i&gt;&lt;/b&gt; We set out to compare the diagnostic sensitivity and specificity of CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; and the ThinPrep Imaging System (HOLOGIC, Marlborough, MA, USA). &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; A representative population of 1,352 cases was selected from the routine workflow in a private laboratory. Diagnoses were established using the ThinPrep Imaging System and CytoProcessor&lt;sup&gt;TM&lt;/sup&gt;. All discordances were resolved by a consensus committee. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Compared to the ThinPrep Imaging System, CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; significantly improves diagnostic sensitivity without compromising specificity. The sensitivity of detection of “atypical squamous cells of undetermined significance (ASC-US) and more severe” and “low-grade squamous intraepithelial lesion and more severe” was significantly higher using CytoProcessor&lt;sup&gt;TM&lt;/sup&gt;. Considering that cases with a truth diagnosis of ASC-US or more severe required clinical follow-up, 1.5% of the cases (21/1,360) would have been missed if the CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; diagnosis had been used for clinical decision-making. In contrast, 4% of the cases (54/1,360) were missed when the ThinPrep Imaging System diagnosis was used for clinical decision-making. There were 2.6 times fewer false negatives using CytoProcessor&lt;sup&gt;TM&lt;/sup&gt;. The CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; workflow was 1.5 times faster in terms of worker time. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; is the first of a new generation of automated screening systems, demonstrating improved sensitivity and yielding significant gains in processing time. In addition, the fully digital nature of slide presentation in CytoProcessor&lt;sup&gt;TM&lt;/sup&gt; allows the remote diagnosis of Papanicolaou tests for the first time.
Assessing Cyberbiosecurity Vulnerabilities and Infrastructure Resilience

The convergence of advances in biotechnology with laboratory automation, access to data, and computational biology has democratized biotechnology and accelerated the development of new therapeutics. However, increased access to biotechnology in the digital age has also introduced additional security concerns and ultimately, spawned the new discipline of cyberbiosecurity, which encompasses cybersecurity, cyber-physical security, and biosecurity considerations. With the emergence of this new discipline comes the need for a logical, repeatable, and shared approach for evaluating facility and system vulnerabilities to cyberbiosecurity threats. In this paper, we outline the foundation of an assessment framework for cyberbiosecurity, accounting for both security and resilience factors in the physical and cyber domains. This is a unique problem set, but despite the complexity of the cyberbiosecurity field in terms of operations and governance, previous experience developing and implementing physical and cyber assessments applicable to a wide spectrum of critical infrastructure sectors provides a validated point of departure for a cyberbiosecurity assessment framework. This approach proposes to integrate existing capabilities and proven methodologies from the infrastructure assessment realm (e.g., decision science, physical security, infrastructure resilience, cybersecurity) with new expertise and requirements in the cyberbiosecurity space (e.g., biotechnology, biomanufacturing, genomics) in order to forge a flexible and defensible approach to identifying and mitigating vulnerabilities. Determining where vulnerabilities reside within cyberbiosecurity business processes can help public and private sector partners create an assessment framework to identify mitigation options for consideration that are both economically and practically viable and ultimately, allow them to manage risk more effectively.
Customer Responses to Service Robots – Comparing Human-Robot Interaction with Human-Human Interaction

This paper investigates how service failures affect customers by comparing human-robot interactions with human-human interactions. More specifically, it compares customers’ satisfaction in a service robot interaction depending on a service failure with the customers’ satisfaction in a frontline service employee interaction. On a theoretical basis, extant literature on the uncanny valley paradigm proposed that service robots would create lower satisfaction than human frontline employees would.
Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier

Bagging and Voting are both types of ensemble learning, which is a type of machine learning where multiple classifiers are combined to get better classification results. This paper presents an experimental comparison of Bagging and Voting ensemble machine learning algorithms. The iris dataset which has 150 data instances and 5 attributes was used to conduct the experiment.  It was observed that bagging is a better ensemble learning algorithm than voting based on the experimental data used for classification. Key words : Bagging, Voting, Machine Learning, Classifier, Algorithm, dataset.
The Phase and Shift-Invariant Feature by Adaptive Independent Subspace Analysis for Cortical Complex Cells

It has previously been suggested that complex cells in primary visual cortex (V1) selectly respond to bars and edges at a particular location and orientation emerged from the natural image rather than random stimuli. In this paper, we proposed an adaptive independent subspace analysis (AISA) algorithm based on the maximum likelihood of super-gaussian distribution of the norm of the partially independent components involving difference to efficiently study the characteristics of complex cells. The multiple parameters are updated with different restrictions by contrast divergence (CD) method based on the super-Gaussian (sparse) distribution. In terms of the AISA results, the inside and outside subspace of energy correlation illustrate the properties of independence. Furthermore, AISA features, similar to the characteristics of complex cells in V1, have phase and shift-invariant, as well as frequency and orientation selectivity via the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function. Compared with the principal component analysis (PCA) filters and other ICA filters, AISA filters are analogous to Gabor filters for edge detection and human visual system via varying the frequency and orientation representation.