Reviewerly/exHarmony huggingface dataset:
Characteristics of Masonry Prepared with KM Soil as Fine Aggregate in Cement Mortar and Concrete Block

Manufactured Sand (M Sand) has become a viable alternative for the river sand in the construction industry as a potential fine aggregate. The M Sand poses environmental implications as it is produced by crushing natural resources like stones. The extensive use of M Sand may also lead to a reduction in availability due to the depletion of natural rocks in the days to come. Hence, this study attempts to assess the feasibility of utilizing the clayey soil containing Kaolinite – Montmorillonite (KM) as a predominant clay mineral, as a replacement to the Manufactured sand in the preparation of cement mortar and cement block that are used in masonry construction. In the case of concrete block masonry units, the M Sand is replaced by 25%, 50%, and 100% with KM Soil, and an attempt is also made to produce the mortars by completely replacing the M Sand with KM soil. Further, the properties of the concrete blocks and mortars prepared with KM soil are assessed and compared with the ones prepared with M Sand. Also, the performance of the stack bonded masonry prisms like compressive strength and bond strength in shear is assessed for the various masonry prism prepared with combinations of the Concrete block masonry units and mortars. The results indicate that the stack bonded masonry prism assemblies with KM soil both in cement mortar and concrete block have performed moderately well in compressive strength and shear bond strength in comparison with the one prepared with M Sand.
Research on Sports Participation in Social Integration of the Migrant Elderly from the Perspective of "Active Aging"

China has entered a rapid stage of population aging, the population structure of getting old before getting rich has brought many disadvantages to the social and economic development.Under the dual function of aging population and floating family, more and more elderly people join the floating army to support their children's career development and realize the maximization of family interests.However, the social integration of the floating elderly has a big problem, which seriously affects their physical and mental health.In order to improve the quality of life of the elderly, the country has formulated an "active aging" strategy.Sports participation can effectively improve individual human capital and social capital, so as to improve social integration, which is an important choice to implement the national strategy at present.
Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks

The massive size of modern neural networks has motivated substantial recent interest in neural network quantization. We introduce Stochastic Markov Gradient Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. The SMGD algorithm is designed for settings where memory is highly constrained during training. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
AN IMPROVED TERM WEIGHTING AND DOCUMENT RANKING METHOD USING RANDOM WALK MODEL FOR INFORMATION RETRIEVAL

Document representation is one of the most fundamental issues in information retrieval application. The graph-based ranking algorithms represent document as a graph. Once a document is represented as graph, the similarity of that document to a query can be calculated in various ways and the calculation provides ranking to documents. This paper introduces an improved random-walk method to rank a document by considering position of a term within a document and information gain of that term within the whole document set. The experiments on various collection sets show that our approach improves the recall and precision than other proposed methods.
Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

We present a differentially private extension of the block coordinate descent based on objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, the algorithm also offers a utility guarantee; a tolerance parameter indicates how much the differentially private regression may deviate from an analysis without differential privacy. The algorithm&amp;rsquo;s performance is compared with the standard block coordinate descent algorithm and the trade-off between utility and privacy is studied. The performance is studied using both artificial test data and the forest fires data set. We find that the algorithm is fast and able to generate practical predictions with single-digit privacy budgets, albeit with some accuracy loss.
Rich Specifications for Ethereum Smart Contract Verification

Smart contracts are programs that execute inside blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that do not arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specification constructs and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications based on resources and resource transfers, which allow expressing a contract's behavior in intuitive and concise ways and exclude typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in the Vyper language, and demonstrated its effectiveness in succinctly capturing and verifying strong correctness guarantees for real-world contracts.
Subject Review: Image Processing Techniques for Object Tracking in Video Surveillance

The article is devoted to real-time algorithms for detecting events described by four scenarios: movement in a forbidden direction, being in a sterile zone, leaving (stealing) an object, throwing an object. The main idea of the algorithms is the analysis of the trajectories of moving objects, for which two different approaches are proposed in the article.
The Use of Fly Swatter Game to Improve Students’ Vocabulary Mastery

This study attempts to investigate the effectiveness of the use of the fly swatter game in improving students’ vocabulary with specific reference to nouns, verbs, and adjectives. It involved one class of the seventh grade with 27 out of 87 students at State Junior High School Number 1 Kalaena, South Sulawesi Province, Indonesia. The method of the study was descriptive quantitative. Vocabulary test in the forms of pre-test and post-test was used to collect the research data. The number of questions for the pre-test and the post-test was 50 numbers. The researchers conducted the treatment, in this case, the use of the fly swatter game in teaching vocabulary, after giving the pre-test. After the treatment, the post-test was conducted. The data collected were analyzed by using Statistical Product and Service Solution (SPSS) program version 20. The finding of this study shows that there was a significant difference between the mean scores in the pre-test and in the post-test. The result shows that the mean score of the pre-test was 36,68 and in the post-test was 83,85. Furthermore, it also can be seen by comparing the result of the t-test where the p-value (0.00) is smaller than the significance level α (0.05) or 0.00&lt;0.05. It means that the Null Hypothesis (H0) is rejected, and the Alternative Hypothesis (H1) is accepted. Therefore, it can be concluded that the fly swatter game is effective in improving students’ vocabulary mastery.
Learning Robust Models Using The Principle of Independent Causal Mechanisms

Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, Peters et al. (2017)) can turn this weakness into an opportunity: one can take advantage of distribution shift between different environments during training in order to obtain more robust models. We propose a new gradient-based learning framework whose objective function is derived from the ICM principle. We show theoretically and experimentally that neural networks trained in this framework focus on relations remaining invariant across environments and ignore unstable ones. Moreover, we prove that the recovered stable relations correspond to the true causal mechanisms under certain conditions. In both regression and classification, the resulting models generalize well to unseen scenarios where traditionally trained models fail.
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency. For high speed driving scenarios, latency is a crucial parameter as it provides more time to react to dangerous situations. Typically a voxel or point-cloud based 3D convolution approach is utilized for this module. Firstly, they are inefficient on embedded platforms as they are not suitable for efficient parallelization. Secondly, they have a variable runtime due to level of sparsity of the scene which is against the determinism needed in a safety system. In this work, we aim to develop a very low latency algorithm with fixed runtime. We propose a novel semantic segmentation architecture as a single unified model for object center detection using key points, box predictions and orientation prediction using binned classification in a simpler Bird's Eye View (BEV) 2D representation. The proposed architecture can be trivially extended to include semantic segmentation classes like road without any additional computation. The proposed model has a latency of 4 ms on the embedded Nvidia Xavier platform. The model is 5X faster than other top accuracy models with a minimal accuracy degradation of 2% in Average Precision at IoU=0.5 on KITTI dataset.
Stop the Presses! Let’s Begin Conversion to the New Depository Library System

By 1997, the last working photo-offset printing press owned by the Government Printing Office will be dismantled for shipment to the Smithsonian Institute. The presses will be sold one by one over the years because less and less government information will be published in printed form. The specialized printing jobs still remaining will be contracted out.
Intercomparison of Estimates of Reference Evapotranspiration of Surat Region of Gujarat, India

Evapotranspiration is a vital parameter for irrigation planning and management, and for hydrological and climatological studies. This can be estimated by using Blaney-Criddle (BC), Thornthwaite (TW), Hargreaves (HG) and Penman-Monteith (PM) methods. Out of these methods, BC and TW are known as temperature based whereas HG is radiation based and PM is based on Food and Agriculture Organization (FAO) report on crop water requirements. This paper illustrates a study on comparison on estimates of reference evapotranspiration (ETo) obtained from temperature, radiation and FAO based methods for Surat region. The meteorological data such as air temperature, relative humidity and sunshine hours observed at Surat region for the period 2001 to 2016 is used. In addition, the wind speed and solar radiation data is downloaded from the International Water Management Institute website and used in estimation of ETo using PM. The performance of the methods used in estimation of ETo is evaluated through regression analysis by developing a linear regression between the observed and estimated values of ETo using BC, TW and HG, and FAO based PM. The results of linear regression analysis indicated that there is a good line of agreement between the observed and estimated ETo using PM with CC of 0.968 when compared with those values of BC, TW and HG. The study show that the PM is better suited amongst four methods applied in estimating the ETo for Surat region, Gujarat.
A Novel Biometric Identification Scheme Based on Zero-Knowledge Succinct Noninteractive Argument of Knowledge

Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>x</mi> </mrow> </mfenced> <mo>+</mo> <mi mathvariant="normal">log</mi> <mtext> </mtext> <mtext> </mtext> <mi>C</mi> </math> , respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.
NIST’s Post-Quantum Cryptography Standards Competition

Quantum computing is a completely new paradigm for computers. A quantum computer uses quantum properties such as superposition, which allows a qubit (a quantum bit) to be neither 0 nor 1, but something much more complicated. In theory, such a computer can solve problems too complex for conventional computers.
Attribute Feature Classification of English Grammar Entry Base Based on Support Vector Machine Classification Algorithm

An attribute feature classification method of English grammar vocabulary entry database based on support vector machine classification algorithm is proposed; this method takes news English as the research object and focuses on the classification of attributes and features of the English grammar lexicon database. First, the k-means algorithm is used to cluster the training set, and the one-to-many method is used to train two types of classifiers for the texts that cannot be correctly clustered in each class, that is, the classifiers of the corresponding categories are trained, and then the training set passed through a pair of the classifier generated by multiple SVMs is tested, and the samples that fall in the inseparable area are retrained by a one-to-one method, so as to achieve the purpose of balancing the training samples and reducing the inseparable area. The results show that, compared with the FDAGSVM algorithm, the proposed three multiclass classification algorithms have significantly improved classification speed and classification accuracy, and the macro average accuracy rates are 77.94%, 73.94%, and 72.36%, respectively. While ensuring the classification speed and classification accuracy of the single-label samples, the multiclass classification is realized, and it has high accuracy, recall rate, and value, which better solves the multiclass classification problem and expands the classification capability of the support vector machine. In addition, a comprehensive index based on the SVM classification algorithm is proposed to ensure the specialization of the attribute feature classification.
Paths to a Career in Copyright Leadership: When Metadata Met Copyright

This article explores the experience of a librarian with a background in metadata and digital collections and the introduction of copyright into their career. It offers insight on how to handle copyright if it is added to your job description.
The Order of Success: A Slow Path to Copyright Librarianship

No faculty lines were open when I graduated with my MLS at the library where I was employed as a staff member, but I was able to take advantage of organizational restructuring to obtain a management position over interlibrary loan. My department head at the time was assigned with providing copyright assistance. I seized the opportunity to learn more while I searched for a position that required my degree. I was supported while pursuing continuing education in copyright. I was also given increasing responsibilities that I suspected were beyond my rank. My eagerness to develop a professional skill set had worked both for and against me.
Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks

Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic. Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. In this paper, we focus on investigating the effectiveness of different evasion attacks and see how resilient machine and deep learning algorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In most of our experimental results, deep learning shows better resilience against the adversarial samples in comparison to machine learning. Whereas, the impact of the attack varies depending on the type of attack.
Comparison of Handwritten Recognition Methods on Arabic and Latin Characters

In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 testings) were used. When the results of the experiment are examined, it is seen that successful results are obtained in the classification made with the MADBase dataset in some methods and in the classification made with the MNIST dataset in some methods. As a result, it can be stated that the handwriting character recognition success of a method cannot be measured only by the classification made on a dataset. Also, the digits written in the Arabic alphabet appear to be almost more recognizable than the digits written in the Latin alphabet.
Classification of Spam Emails through Hierarchical Clustering and Supervised Learning

Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on binary classification, spammers bypass them through new strategies, like word obfuscation or image-based spam. For the first time in literature, we propose to classify spam email in categories to improve the handle of already detected spam emails, instead of just using a binary model. First, we applied a hierarchical clustering algorithm to create SPEMC-$11$K (SPam EMail Classification), the first multi-class dataset, which contains three types of spam emails: Health and Technology, Personal Scams, and Sexual Content. Then, we used SPEMC-$11$K to evaluate the combination of TF-IDF and BOW encodings with Na\"ive Bayes, Decision Trees and SVM classifiers. Finally, we recommend for the task of multi-class spam classification the use of (i) TF-IDF combined with SVM for the best micro F1 score performance, $95.39\%$, and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an email in $2.13$ms.
On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks

In this paper, we explore the privacy problem of individuals in publishing data cubes using SUM queries, where a malicious user is expected to have an aggregate knowledge (e.g., average information) over the data ranges. We propose an efficient solution that maximizes the utility of SUM queries while mitigating inference attacks from aggregate knowledge. Our solution combines cube compression (i.e., suppression of data cells) and data perturbation. First, we give a formal statement for the privacy of aggregate knowledge based on data suppression. Next, we develop a Linear Programming (LP) model to determine the number of data cells to be removed and a heuristic method to effectively suppress data cells. To overcome the limitation of data suppression, we complement it with suitable data perturbation. Through empirical evaluation on benchmark data cubes, we show that our solution gives best performance in terms of utility and privacy.
Online Safety Assurance for Deep Reinforcement Learning

Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented system differs from its training environment, such systems often make badly informed decisions, leading to bad performance. We argue that safely deploying learning-driven systems requires being able to determine, in real time, whether system behavior is coherent, for the purpose of defaulting to a reasonable heuristic when this is not so. We term this the online safety assurance problem (OSAP). We present three approaches to quantifying decision uncertainty that differ in terms of the signal used to infer uncertainty. We illustrate the usefulness of online safety assurance in the context of the proposed deep reinforcement learning (RL) approach to video streaming. While deep RL for video streaming bests other approaches when the operational and training environments match, it is dominated by simple heuristics when the two differ. Our preliminary findings suggest that transitioning to a default policy when decision uncertainty is detected is key to enjoying the performance benefits afforded by leveraging ML without compromising on safety.
Approximation of discontinuous functions by Kantorovich exponential sampling series

The Kantorovich exponential sampling series at jump discontinuities of the bounded measurable signal f has been analysed. A representation lemma for the series is established and using this lemma certain approximation theorems for discontinuous signals are proved. The degree of approximation in terms of logarithmic modulus of smoothness for the series is studied. Further a linear prediction of signals based on past sample values has been obtained. Some numerical simulations are performed to validate the approximation of discontinuous signals f by the sampling series.