Reviewerly/exHarmony huggingface dataset:
A Noise Study of the PSW Signature Family: Patching DRS with Uniform Distribution †

At PKC 2008, Plantard et al. published a theoretical framework for a lattice-based signature scheme, namely Plantard–Susilo–Win (PSW). Recently, after ten years, a new signature scheme dubbed the Diagonal Reduction Signature (DRS) scheme was presented in the National Institute of Standards and Technology (NIST) PQC Standardization as a concrete instantiation of the initial work. Unfortunately, the initial submission was challenged by Yu and Ducas using the structure that is present on the secret key noise. In this paper, we are proposing a new method to generate random noise in the DRS scheme to eliminate the aforementioned attack, and all subsequent potential variants. This involves sampling vectors from the n-dimensional ball with uniform distribution. We also give insight on some underlying properties which affects both security and efficiency on the PSW type schemes and beyond, and hopefully increase the understanding on this family of lattices.
Metallogenic Characteristics of Some localities of gold mineralization Along Shear Zones of Elgaab, Dum el Tor and Sodri -Um Bader, (Sudan)

Study area is located in the eastern sector of Saharan Meta -Craton that includes the western part of Bayuda Terrane and extend southwest to Sodri -Um Badir shear zones.The sector is dominated by metasediments, metavolcanic -sedimentary series represented by chlorite -schist, quartzite, calcsilicate, and marble.Low -grade metamorphic arc -related volcano -sedimentary sequence occurs as thin narrow bands and subjected to tectono -thermal events of Pan -African orogeny during the Late -Proterozoic era (900 -550 Ma).All these rock sequences have been intruded by syn to Late Orogenic Granitoids and post -orogenic ring -shaped complex and felsic dykes.The intrusions are thought to be the source of hydrothermal fluids that caused the precipitation of gold.Similar characteristics of geological features and tectonic settings support the possibility of the extension of gold mineralization between shear zones.The comparison between the three gold mineralizations reveals variations in minerals assemblages, pathfinders and gold grade.The result of this study defines a new metallogenic province in the west of the River Nile, which occupies a very large area covered by Phanerozoic sediments.Numerous mineral deposits of economic potentiality have been targeted along the shear zones and related fractures and faults.Among those potential deposits, five of them have been chosen as targets for future gold exploration and evaluation.
Development of the GIS Software Technology in the Big Data Era

In the era of the big data, GIS faces the following challenges and opportunities: the efficient management of very large-scale data, including the data management system and architecture, the real-time processing and analysis of the stream data, and the efficient query and analysis of the historical data and patterns. Under the big data environment, GIS is in the environment of the large volume of the data, the rapid expansion of the data, the diverse data sources, the inaccurate data and the low value density of the data. This paper describes the salient features of the GIS data in the big data environment, discusses the challenges of the GIS technology in the data acquisition, storage, analysis and processing under the big data environment, and looks forward to the development trend of the storage de-structuring, the computing memory, the analysis de-modeling and the map holography in the face of these challenges.
Extracting Primitive Tasks from Procedural Videos using Machine Learning

Narrated instructions videos are the rich source of information. The problem of automatically extracting the procedures that are narrated in the video and to summarize the important steps to reduce the time and provide the information to the user is addressed. Natural Language Processing Technique is used for extracting the summarization of the narrated task in the given video. The input video dataset contains real world interactions between a human and machine to explain the procedure to complete a certain task. The given data set is analyzed, processed, and finally a list of important steps are extracted which will help in achieving the given task. Procedure identification or Procedure Extraction is useful for a variety of applications including intelligent tutoring systems, representation learning, transfer learning and video understanding.
Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm

Various nature-inspired algorithms are used for optimization problems. Recently, one of the nature-inspired algorithms became famous because of its optimality. In order to solve the problem of low accuracy, famous computational methods like machine learning used levy flight Bat algorithm for the problematic classification of an insulin DNA sequence of a healthy human, one variant of the insulin DNA sequence is used. The DNA sequence is collected from NCBI. Preprocessing alignment is performed in order to obtain the finest optimal DNA sequence with a greater number of matches between base pairs of DNA sequences. Further, binaries of the DNA sequence are made for the aim of machine readability. Six hybrid algorithms are used for the classification to check the performance of these proposed hybrid models. The performance of the proposed models is compared with the other algorithms like BatANN, BatBP, BatGDANN, and BatGDBP in term of MSE and accuracy. From the simulations results it is shown that the proposed LFBatANN and LFBatBP algorithms perform better compared to other hybrid models.
Automatic Modulation Parameter Detection In Practice

Internet of Things (IoT) devices have to be small and energy efficient so that resources for security mechanisms tend to be limited. Due to the lack of open source or license free standards, device manufacturers often use proprietary protocols. Software Defined Radios (SDR) provide a generic way to investigate wireless protocols because they operate on nearly arbitrary frequencies, but they output sine waves that have to be demodulated. This demodulation process slows down security investigations because it forces researchers to start on the physical layer while the real reverse-engineering is performed on the logical layer.
Parts Catalog Object Recognition Technology for Efficient Drawing Distribution Management

This study is an empirical study to efficiently manage the frequently changed drawings by detecting the object information of parts catalog by deep learning method that combines CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory). Through CNN, feature points for separating parts catalog letters or numbers and leader lines or parts elements are extracted, and using LSTM to recognize letters or numbers strings, the average recognition time reaches 0.24 seconds and object link recognition rate reaches 99.67%. It is very high performance for detecting the object information. The parts catalog object recognition system developed based on the results of this study was able to drastically reduce the mismatch of drawing design change information that can occur in the industrial design department and the parts sales department.
Current pole inner distribution method for measuring tower grounding resistance

Abstract Three electrode method is a common method in measuring tower grounding resistance. However, its wiring distance is too long to complete accurate measurement in some terrain narrow areas, which is also an application problem to be solved urgently in the field of tower grounding resistance measurement. The traditional three electrode method is to find the compensation point between the grounding pole and the current pole. After calculation, there are also compensation points near the current pole besides the grounding pole and the current pole by using the traditional three electrode method. Based on this principle, the current pole is arranged between the grounding pole and the voltage pole by the current pole inner distribution method, which can greatly shorten the distance of the distribution pole. Through simulation analysis and test verification, the pole inner distribution method can control the pole distribution distance around 50m on the basis of ensuring the accuracy of the measurement results. Compared with the three-pole method, the pole distribution distance is shortened to 60%.
I falsi epigrafici di Giuseppe Francesco Meyranesio

The fake inscriptions forged on paper by Giuseppe Francesco Meyranesio (1729-63) occupy large space in the chapter devoted to Ligurian and Piedmontese falsae within the fifth volume of the CIL , and include over 100 documents. Investigating these forgeries aims at establishing Meyranesio’s method of production. In particular, this research includes the identification of the models, from which he drew inspiration to elaborate his texts and some related aspects: the criteria adopted in the selection and use of archetypes; the printed works, which the forger consulted for this purpose and which were to be easily found in the libraries of the seminaries of the Savoy part of Piedmont. In fact, Meyranesio spent his existence as a priest between Pietraporzio and Sambuco, two mountain villages of the Valle Stura.
Naive Transfer Learning Approaches for Suspicious Event Prediction

In many real-world applications such as cyber security analysis, the training and the future data usually have different distributions. In such cases, it would be helpful if we could transfer the knowledge learned in one time period into a later time period. In this suspicious network event recognition competition, we train a gradient boosting machine to get a basic prediction model and then fine-tune it to fit the test dataset. We use heuristic methods to reduce the difference between the distributions of training and test data of each categorical feature, remove the features that lead to maximum mean discrepancy by a sequential backward search strategy to minimize the distance between domain distributions while preserving important statistical properties of the original data as possible, and adjust the prediction results according to the general statistical regularities extracted from the training set. Our solution gets a score slightly better than the baseline. The effectiveness of our approaches is also verified by experiment on the Microsoft malware dataset. We will provide more persuasive results by testing our methods on more datasets.
Generating Near and Far Analogies for Educational Applications: Progress and Challenges

Analogical reasoning, it has been argued, fundamentally underlies many cognitive processes and is an important marker of developmental cognition. This connection suggests that the clever use of analogical reasoning tasks can improve cognitive performance in specific ways, thus leading to clear educational applications, as recent psychological work has confirmed. However, currently there are no known methods to either solve or generate analogical word problems, at least to a degree of reliability that would be necessary before such educational applications are possible. To address these concerns we present work to both solve and generate analogy word problems: First, given an analogy word problem, our algorithm performs a parallel random walk through the semantic network ConceptNet to limit the number of choices that are then considered by a vector embedding. We achieve an improvement in accuracy beyond existing state-of-the-art. Second, we explore a method for automatically generating explainable n-step analogy word problems, and analyze the results.
On the RESTful Web Services for Managing Application Virtualization Environments

Today, application virtualization environments are used in many different fields. With the spread of application virtualization environments, solutions to manage these environments have become an important necessity today. Within the scope of this research, we propose a design of a RESTful web service for managing application virtualization environments. In this manuscript, we discuss the programming interface of the proposed service in detail. We describe our approach when designing “semantics” for the RESTful web service. We overview application usage scenarios in which our design has successfully been used. To show the usability of the proposed design, a prototype implementation is developed. The performance of the prototype implementation for different functionalities is evaluated. The evaluation results indicate that the proposed service can be utilized in the management of application virtualization environments.
Semi-supervised Topic Model for Sequential Data: A Genetic Algorithm Approach

Semi-supervised learning in topic models increase accuracy of topic predictions by introducing labeled data to guide the learning process. Inference algorithm in topic models is used for approximation of posteriori. This paper adapts the incremental naïve bayesian classification algorithm to sequentially analyse a set of test documents and classify them. To overcome the challenges of biased learning and inaccurate inferences the paper proposes a genetic algorithm approach to semi-supervised learning by introducing user's top hourly searched topic as labeled data. The learning process is continuous, labeled data is introduced into the population every hour while simultaneously removing the least fit based on the mean attribute to maintain initial population size. We successfully tested the functionality of the model with a small set of domain related papers. The results showed that genetic algorithm optimises the topic model through continuous learning by reducing the computation time complexity from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n).
Feature Preserving Regularized Savitzky–Golay Filter for Ultrasonic Images

The speckle noise being multiplicative in nature degrades the quality of ultrasonic images. This noise limits the contrast resolution, complicates image-based quantitative and visual measurements and diagnosis. Therefore in this work, a novel denoising and feature preserving regularized Savitzky–Golay filter is proposed. The proposed work combines smoothing attribute of Savitzky–Golay smoothing filter and feature preserving regularization characteristic of total generalized variation to denoise ultrasound images. The proposed filter is applied and tested on synthetic images, Field II kidney simulated images and real ultrasound images. The conventional filters, i.e., speckle reduction anisotropic diffusion, non-local means and Savitzky–Golay smoothing filter are also designed for comparative analysis. The quantitative and qualitative comparison of results is carried out on the basis of PSNR, MSE, SSIM and Canny’s edge detection. It is revealed that the proposed filter denoises ultrasound images and preserves features more effectively in comparison with conventional methods.
Ultrahigh accurate statistical interferometric technique utilizing uniformity of speckle phase in the study of plant physiology

Statistical interferometer technique (SIT) is a highly sensitive optical interferometer developed by us capable of measuring sub-nanometer displacements and when applied to plant growth studies revealed nanometric intrinsic fluctuations (NIF). NIF observed in minimum time scale of several tens of seconds is strongly influenced by the environmental conditions. Our earlier experiments with rice under ozone or heavy metal stress, such as cadmium even for a short duration of one hour decreased NIF. In contrast, having a micronutrient, such as zinc increased NIF. Therefore, presence of NIF is found to be a novel phenomenon characterizing plant condition that could appear only under sub-nanometric measurement. In this study, we report the effects of adding a plant growth hormone called auxin. Roots of rice seedlings were exposed to auxin solutions of different concentrations of 0, 1, and 4 &mu;M for 24 hours. Significant increment was seen in NIF for a concentration of 1 &mu;M while a significant reduction was seen in NIF for 4 &mu;M within an hour after immersion of the roots. Application of an inhibitor to auxin called TIBA also resulted in almost immediate reduction of NIF. Current results suggest for NIF affected by the enodgenous hormones to be related to growth, as the action of a growth-related endogenous hormone auxin is chemically inactivated. Thus, NIF not only could be applied to investigate and speedily assess the effects of environmental agents on plant elongation or shrinkage but also could be implicated as one of the possible mechanisms for the origin of NIF itself.
Application of Physical Education Teaching from the Perspective of Computer Multimedia

As the society and the network technology develop, to apply the multimedia technology in teaching process has become more and more common. To a great extent, it has promoted the development of modern teaching mode and teaching reform. Compared with other courses, multimedia application technology in physical education starts late and its application is still immature. However, this does not affect the convenience and teaching progress of multimedia aided instruction technology in physical education teaching. Based on the author’s learning and practical experience, this work first analyzed the role of computer multimedia in physical education teaching, and then put forward the practice of computer multimedia in physical education teaching. Finally, some problems that need attention in the application of computer multimedia aided instruction are summarized.
A discrete mathematics approach for large scale improvement in classification training time

A scalable graphical method is presented for selecting datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. Lastly, the information graph is used for reducing a given training set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that the approach significantly speedup the training task when compared against that of state-of the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy.
The Conceptual Background of OPTIMIST’s AI Module

In our design of OPTIMIST we distill social science domain background knowledge about processes and data dependences to pre-set OPTMIST for our customers. So citizen data scientists with no statistical knowledge and only little training can quickly learn how to use their insights gained about their data with the analytics module to access and activate the automatic prediction module. This hybrid architecture uses both semantic technologies and machine learning and is called Aided Neural Networks liberating Citizen Data Scientist Actions (ANCA).
Pseudo-3D Display Based on Head Tracking for Viewpoint Image Generation

In this paper, we propose a pseudo 3D display system using head tracking. Our system tracks the user's face by using a camera commonly equipped at the top of its screen with an ordinary PC. Pseudo 3D display is performed by drawing 3D scenes that always correspond to the head position of the user. A series of experiments were conducted to evaluate the effectiveness of the proposed system.
An Adaptive Service Monitoring System in a Cloud Computing Environment

Cloud computing is a trending area of information technology (IT). In a cloud environment, the Cloud service provider (CSP) provides all the functionalities to the users or customers in terms of services. With the rapid development of cloud computing, the performance of any cloud environment relies on the quality of services (QoS) at the time of providing the services. A service level agreement (SLA) increases the confidence of the user or customer to use the cloud services in a cloud environment. There should be negotiation between the CSP and users to achieve a strong SLA. Many existing SLA models are already developed. However, these models do not concentrate to maintain the quality in a long-time duration period. To solve this issue, a novel SLA model has been proposed in this article by using Fuzzy logic. Both the theoretical and simulation results show the proficiency of the proposed scheme over the existing schemes in a cloud computing environment.
Serialized Keypoint Estimation Using Body Part Segmentation

Human pose estimation is a topic of interest in the field of computer vision. Once we precisely predict where the human body is, we can further use that information to perform high-level actions such as action recognition or behavior prediction. In this paper, we focus on finding keypoints of human along with body part segmentations that surround keypoints. After roughly finding body part segmentations, we hope to refine accurate keypoint from it. We used Stacked Hourglass model, which is often used in pose estimation problems, as the backbone and further attached model to predict body part segmentation. We also tested several networks to reduce unwanted side effect that occurs when using keypoints and body part segmentation together.
Visualization of Texture Expressions for Recipes by Using Reviews

Nowadays, there are lots of recipe sites on the net, work to inspire their users with visual and step-by-step guided cooking instructions. In these sites, recipe retrieval mechanisms are provided so that users can find recipes for dishes they want to cook by specifying their ingredients and nutrition facts or using keywords. In the circumstance as mentioned above, this paper focuses on the further question of the user: "What would be the result when the user cooks with this recipe?" A user's cooking review of the recipe says what happened when the reviewer tried the recipe. Not a few recipes have more than a thousand reviews. In this paper, we discuss recipe representation that extracts and aggregates texture terms from a large number of reviews of the recipe. This research aims to propose a visualization method that allows users to imagine the result of their cooking by following the recipe intuitively. Finally, we will show its visualization using a tag cloud.
Creating Cyber Deception Games

Cyber deception has typically been a tool used by attackers to mask reconnaissance activities and infiltrate networks while keeping hidden from watchful defenders. We believe the use of deception is a necessary component of network and system defense. This paper uses game theory to reason about a cyberattack scenario in which a deceiving defender uses lightweight decoys to hide and defend real hosts. In our model, a defender and an attacker play out a game with resources consisting of both real and decoy systems, a set of pre-determined actions for each player, and a method for defining and evaluating individual player strategies and payoffs. Our research provides a general framework for representing deception games using multiple game trees and an explicit representation of each individual player's knowledge of game structure and payoffs. We present a graphical representation of our multiple game tree model and a framework for representing and evaluating the strategy selection when an attacker is unaware of a subset of the defender's available strategies. Finally, we present several cyber deception scenarios using our framework.