Reviewerly/exHarmony huggingface dataset:
Stare Towards Integrative Analysis with Minimized Data Wrangling Hassle

Analysis incorporating geoscience data from different data sources entails dealing with their immense variety and volume. Until now, combining, for example, two or more different swath datasets from spaceborne observations has been a tedious, laborious process, limiting the scalability of potentially impactful integrative analyses. With the technologies developed in the NASA-funded SpatioTemporal Adaptive Resolution Encoding (STARE) project, we have made strides towards enabling scalable integrative analysis of diverse, voluminous geoscience data. Using STARE as a consistent geo-spatiotemporal indexing scheme to unify different data sources according to spatiotemporal colocation, spatiotemporal data co-alignment can thus be maintained on distrib-uted/parallel/Cloud resources to minimize costly and often unnecessary data transfer and communication, and to drastically improve scalability.
Method for providing background traffic using IP random assigning in cyber range

This letter proposes a method for providing non-periodic background traffic applicable to cyber ranges. Background traffic is essential to cyber ranges, but it should not be easily distinguished from malicious traffic. The proposed method removes the repetition period of background traffic by using a random number and random shuffling. Details of concatenating traffic data using random numbers and random permutations are presented. Fisher's exact test was conducted to test whether the periodicity of the traffic changes by applying the proposed method, and the results are also illustrated.
Parallel modular multiplication using 512-bit advanced vector instructions

Abstract Applications such as public-key cryptography are critically reliant on the speed of modular multiplication for their performance. This paper introduces a new block-based variant of Montgomery multiplication, the Block Product Scanning (BPS) method, which is particularly efficient using new 512-bit advanced vector instructions (AVX-512) on modern Intel processor families. Our parallel-multiplication approach also allows for squaring and sub-quadratic Karatsuba enhancements. We demonstrate $$1.9\,\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.9</mml:mn> <mml:mspace /> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> improvement in decryption throughput in comparison with OpenSSL and $$1.5\,\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.5</mml:mn> <mml:mspace /> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> improvement in modular exponentiation throughput compared to GMP-6.1.2 on an Intel Xeon CPU. In addition, we show $$1.4\,\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.4</mml:mn> <mml:mspace /> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> improvement in decryption throughput in comparison with state-of-the-art vector implementations on many-core Knights Landing Xeon Phi hardware. Finally, we show how interleaving Chinese remainder theorem-based RSA calculations within our parallel BPS technique halves decryption latency while providing protection against fault-injection attacks.
Damage Identification Method of Reinforced Concrete Bridge Structure based on Modal Technology

In the actual identification of reinforced concrete bridge structure damage, the sensitivity of each mode shape to the structure damage location is different, resulting in the final damage identification method which can identify the number of damage indicators is too small. In view of this deficiency, a damage identification method of reinforced concrete bridge structure based on Modal technology is proposed. Based on the reinforced concrete bridge structure data, the modal technology is used to calculate the curvature of the load surface, and the multi-order modal curvature is selected to comprehensively locate the damage, construct the relationship between the damage degree and the bridge area, and combine the structural damage mechanical characteristics to form a damage identification process. Complete the design of damage identification method. Two traditional damage identification methods and the identification method designed in this paper are used to carry out experiments. The results show that the proposed method can identify more structural damage indicators.
Evaluating the Merits of Ranking in Structured Network Pruning

Pruning of channels in trained deep neural networks has been widely used to implement efficient DNNs that can be deployed on embedded/mobile devices. Majority of existing techniques employ criteria-based sorting of the channels to preserve salient channels during pruning as well as to automatically determine the pruned network architecture. However, recent studies on widely used DNNs, such as VGG-16, have shown that selecting and preserving salient channels using pruning criteria is not necessary since the plasticity of the network allows the accuracy to be recovered through fine-tuning. In this work, we further explore the value of the ranking criteria in pruning to show that if channels are removed gradually and iteratively, alternating with fine-tuning on the target dataset, ranking criteria are indeed not necessary to select redundant channels. Experimental results confirm that even a random selection of channels for pruning leads to similar performance (accuracy). In addition, we demonstrate that even a simple pruning technique that uniformly removes channels from all layers in the network, performs similar to existing ranking criteria-based approaches, while leading to lower inference time (GFLOPs). Our extensive evaluations include the context of embedded implementations of DNNs - specifically, on small networks such as SqueezeNet and at aggressive pruning percentages. We leverage these insights, to propose a GFLOPs-aware iterative pruning strategy that does not rely on any ranking criteria and yet can further lead to lower inference time by 15% without sacrificing accuracy.
Comparison Analysis of Extracting Frequent Itemsets Algorithms Using MapReduce

Frequent itemset mining (FIM) is among widely known and essential data analytics techniques, to discover and extract frequently co-occurring items. However, due to the massive information available online, it is difficult to extract valuable data with the help of FIM algorithms. Traditional FIM suffers from scalability, memory and computation issues. Onto this context, MapReduce framework can be used which can handle those issues along with algorithmic parallelization. In this paper, algorithms have been explored and compared majorly on two approaches for the reduction of the computation cost. The first approach for mining frequent itemset is candidate generation and data pruning; it is adopted by Apriori, equivalence class transformation and Sequence-Growth algorithm. The second approach for mining is through pattern growth, and the algorithm under this is FP growth. The parameters taken into comparative analysis are search strategy and load balancing.
PLRC*: A piecewise linear regression complex for approximating optimal robot motion

Discrete graphs are commonly used to approximately represent configuration spaces used in robot motion planning. This paper explores a representation in which the costs of crossing local regions of the configuration space are represented using piecewise linear regression (PLR). We explore a few simple motion planning problems, and show that for these problems, the memory required to store the representation compares favorably to that required for standard discrete vertex-and-edge models, while preserving the quality of paths returned from searches.
Automated composition and execution of web-based simulation systems through knowledge designing and reasoning

Modeling and simulation (M&S) techniques can be used to improve cooperation among participants by clarifying the various design aspects of a devised system. Whenever an M&S system with specific functions is required, redesigning it from scratch is time and resource intensive. Thus, an approach to reuse or reconfigure existing systems with functions similar to the required ones is a potential alternative to overcome such problems. This paper proposes a method to build composite systems providing specific M&S functions through the web. The method proposed herein offers an integrated process that includes web-based reuse of existing systems, analysis of the requirements of a composite system, derivation of the logical execution order of element systems, and interconnection of the input–output interfaces between linked constituent systems. This approach provides the required tools by dynamically reconfiguring the element systems at the requested locations. The experimental results demonstrate that the proposed procedure can provide automatic generation of the required M&S system (e.g., missile-interception simulation system) and deliver the system to the target location of the cooperative work through the web.
Detection of Breast Cancer Using Machine Learning Algorithms

Breast cancer represents one of the dangerous diseases that causes a high number of deaths every year. The dataset containing the features present in the CSV format is used to identify whether the digitalized image is benign or malignant. The machine learning models such as Linear Regression, Decision Tree, Radom Forest are trained with the training dataset and used to classify. The accuracy of these classifiers is compared to get the best model. This will help the doctors to give proper treatment at the initial stage and save their lives.
Improved Bernoulli Sampling for Discrete Gaussian Distributions over the Integers

Discrete Gaussian sampling is one of the fundamental mathematical tools for lattice-based cryptography. In this paper, we revisit the Bernoulli(-type) sampling for centered discrete Gaussian distributions over the integers, which was proposed by Ducas et al. in 2013. Combining the idea of Karney’s algorithm for sampling from the Bernoulli distribution Be−1/2, we present an improved Bernoulli sampling algorithm. It does not require the use of floating-point arithmetic to generate a precomputed table, as the original Bernoulli sampling algorithm did. It only needs a fixed look-up table of very small size (e.g., 128 bits) that stores the binary expansion of ln2. We also propose a noncentered version of Bernoulli sampling algorithm for discrete Gaussian distributions with varying centers over the integers. It requires no floating-point arithmetic and can support centers of precision up to 52 bits. The experimental results show that our proposed algorithms have a significant improvement in the sampling efficiency as compared to other rejection algorithms.
On approximations of EASY%Ro solutions to estimate maximum temperature from vitrinite reflectance

Vitrinite reflectance Ro is commonly used as a paleotemperature proxy for rocks. Among various models used to reconstruct paleothermal histories, EASY%Ro, a set of ordinary differential equations to describe the maturation of vitrinite, has been widely used since 1990. Approximations of EASY%Ro solutions employing specific paleothermal histories have been used in the field of structural geology to estimate maximum paleotemperatures Tmax. However, comparisons of those approximations with results obtained with EASY%Ro have not been reported in scientific literatures, and the errors on the approximations and the limitations of their use to determine Tmax remain undocumented. Here, we performed such comparisons and found that the use of those approximations can lead to considerable deviations from the results obtained with EASY%Ro. We then derived new approximations of EASY%Ro solutions for four representative paleothermal histories that provided estimates of Tmax within 3 °C of those obtained with EASY%Ro. We also describe the Jacobian of EASY%Ro, which can be used to propagate errors from Ro to Tmax. We expect that the four approximations provided herein will be useful, although we recommend using EASY%Ro itself if greater precision or particular paleothermal histories other than those assumed in this study are required.
Predictive Medicine Using Interpretable Recurrent Neural Networks

Deep learning has been revolutionizing multiple aspects of our daily lives, thanks to its state-of-the-art results. However, the complexity of its models and its associated difficulty to interpret its results has prevented it from being widely adopted in healthcare systems. This represents a missed opportunity, specially considering the growing volumes of Electronic Health Record (EHR) data, as hospitals and clinics increasingly collect information in digital databases. While there are studies addressing artificial neural networks applied to this type of data, the interpretability component tends to be approached lightly or even disregarded. Here we demonstrate the superior capability of recurrent neural network based models, outperforming multiple baselines with an average of 0.94 test AUC, when predicting the use of non-invasive ventilation by Amyotrophic Lateral Sclerosis (ALS) patients, while also presenting a comprehensive explainability solution. In order to interpret these complex, recurrent algorithms, the robust SHAP package was adapted, as well as a new instance importance score was defined, to highlight the effect of feature values and time series samples in the output, respectively. These concepts were then combined in a dashboard, which serves as a proof of concept in terms of a AI-enhanced detailed analysis tool for medical staff.
Oxidation Detection on PCB Using Image Processing

A Manual visual inspection system in the manufacturing company resulted in decreased inspection performance due to the human error factor. Defects that naturally occurring in the mainboard are oxidation and it cannot be repaired. This study aims to improve the quality of inspection in identifying defects, especially the mainboard which is exposed to oxidation in the area of IC (Integrated Circuit) components. The system uses a digital microscope camera to capture the image of the IC component and using the Microsoft Visual Studio application as the interface. Template matching and the NCC (Normalized Cross-Correlation) algorithm as a method for detecting oxidation. NCC is a calculation of the correlation function between two imageries as a comparison value between the template image and the test object image. The experiment was carried out twice on 10 unoxidized PCB (Printed Circuit Board) samples and 10 oxidation-affected PCB samples. The percentage of successful detection of IC components in this study reached 100% with the accuracy or thoroughness value of 90%. The light intensity greatly affects the percentage outcome due to the inconstant light intensity resulting in a percentage value change from testing.
An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation.
Avoiding Geographic Regions in Tor

In this note, the further improvements to prior work in the area of geographical avoidance within the Tor network have been conducted, that aims to improve the security and performance of such systems. First, we propose a new approach to a prior method where the round-trip time from client to entry is directly measured rather than estimated. Secondly, we introduce a new system where we are able to extrapolate data collected from a comparatively small number of Tor circuits and apply it to other unmeasured connections. A dynamic threshold has been identified to partially compensate for inaccuracy and shifts up or down depending on the length of the circuit. The experimental results quantitively validate the effectiveness and efficiency of the new proposed system. The testbed produced in this work may prove useful to future research in the areas of traffic analysis, the Tor network, and geographical avoidance on the Internet.
Spatio-Temporal Change Detection with Granger Causality Based Cluster Sequence Mining

We proposed a method to extract causal relations of clusters from multi-dimensional event sequence data, along with another method to detect the changes of the extracted relations over time. The proposed Granger Cluster Sequence Mining (GCSM) algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extends the Cluster Sequence Mining algorithm, which utilized a statistical inference technique to identify occurrence relation, with a causality based on Granger causality. We also proposed a statistical model to infer the changes over time of each extracted causal relation. With experiments using synthetic data and semi-real data, we confirmed that the algorithm works correctly, and able to extract the embedded causal relations with high F-score and high accuracy of change points.
Nature-Inspired Metaheuristics for High-Dimensional Data Clustering

The Nature-Inspired (NI) algorithms are able to find the solution of optimization problems (OP) faster than classical algorithms. Often, they are applied for OP with a reasonable number of parameters. The purpose of this research is to evaluate the capability of NI algorithms to solve high-dimensional OP. For this evaluation, a clustering problem was chosen. The Particle Swarm Optimization (PSO), Cuckoo Search (CSA) and Black Hole (BHA) algorithms were adapted by modifying the new individual's initialization sequence. The three algorithms were chosen based on the fact that PSO and CSA are among the most performing, and BHA is considered to be a simplified version of PSO. The results obtained by applying the usual and the modified versions of the three NI algorithms are compared and the performances are significantly better in the second case.
An attack tree template based on feature diagram hierarchy

Attack trees (ATs) are a popular model-based formalism to perform a security risk assessment. The benefits of using AT are numerous: graphical top-down representation of multi-stage attack scenarios, several analysis frameworks, and many supporting tools. The current practice of constructing an attack tree for a given system is using the rules-of-thumb. Though this process is flexible, in the absence of a template, it is non-standardized. Hence it is tedious and may result in contention between the stakeholders due to individual idiosyncrasies. To address these limitations, in this paper, we develop an AT template. We meticulously design the template by performing a literature survey of the industry-size ATs and extract the meta-categories used to build them. The AT template is then structured into layers by the systematic question-answering methodology of Potts et al. Each successive layer in our template is a refinement of the previous layer, adding more details. We link the AT template to standard threat databases. Thus, our template guides the practitioner on narrowing to the appropriate attack vectors. An important question here is how to keep the AT template flexible, given the diversity of context and system variables. To address the question, we use a feature diagram to represent the AT categories. We used the AT template to gain practical experience over a hypothetical case study of smart meters (not part of the paper). Based on our experience, we suggest future research directions.
Web Service Composition Sequence Learning Based on Neural Language Networks

Web service composition can provide value-added services and can satisfy consumers with complex functionality. In Web service composition, the common method is to collect suitable services from a large Web service repository and assemble them based on some rules or knowledge. However, the procedures require heavy data processing and manual work, Web service composition is still a complex task in automatic service composition. And these service composition sequences are a kind of abstract data. So, it is significant to find a method that we can extract some useful information or knowledge from these abstract sequences and apply them in Web service composition. In this study, we propose Web service composition sequence learning to construct a circular framework in Web service discovery. This method aims to learn the invocation relevance between services from the service composition sequences and use them to contribute to other nodes in the framework. Then, we adopt neural language networks to perform Web service composition sequence learning. The experimental results show that our models can learn Web service composition well and that the representative vectors generated by the pretrained model can be utilized to implement related service discovery.
Vocal Tract Length Estimation Using Accumulated Means of Formants and Its Effects on Speaker-Normalization

Differences in vocal tract lengths (VTLs) in individual speakers cause variations in acoustic features of phonemes. In this paper, a simple method to estimate speaker-specific VTLs and to quantitatively evaluate some speaker-normalization effects of the VTLs is proposed. We employed accumulated means of formant trajectories to estimate the VTLs of speakers ranging from children to adults. For the formant estimation, the inverse-filter control (IFC) system was used. In the system, the decision of analysis order, which means number of formants to be estimated, is automated. Moreover, to evaluate the speaker-normalization effect of VTLs, we proposed the data reduction method, which can reasonably find dense areas of ellipses from distributions in the formant space. Using these ellipse areas, we evaluated the three normalization effects of VTLs: normalization by the mean of all VTLs as the standard, by speaker-categorical means of VTLs, and by individual VTLs. The area reduced from the standard area of the original data by 39.5% and 46.6% in the case of the categorical means and individual VTLs, respectively. As a result, our proposed method was used to provide a “normalized vowel map (NVM)” that visualizes universal vowel-distributions as a core image of linguistic information. Finally, we compared the estimated VTLs with those by another method based on magnetic resonance imaging (MRI) data, using the proposed methods.