Reviewerly/exHarmony huggingface dataset:
Maximising robustness and diversity for improving the deep neural network safety

This article proposes a novel yet efficient defence method against adversarial attack(er)s aimed to improve the safety of deep neural networks. Removing the adversarial noise by refining adversarial samples as a defence strategy is widely investigated in previous works. Such methods are simply broken if an attacker has access to both main and refiner networks. To cope with this weakness, the authors propose to refine the input samples relying on a set of encoder–decoders, which are trained in such a way to reconstruct the samples on completely different feature spaces. To this end, the authors learn several encoder–decoder networks and force their latent spaces to have a maximum diversion. In this way, if attacker gets access to one of the refiner networks, other ones can play as a defence network. The evaluation of the proposed method confirms its performance against adversarial samples.
Why Section 230 Is Better Than the First Amendment

47 U.S.C. § 230 (“Section 230”) immunizes Internet services from liability for third-party content. This immunity acts as a crucial legal foundation for the modern Internet. However, growing skepticism about the Internet has placed the immunity in regulators’ sights. If the First Amendment mirrors Section 230’s speech protections, narrowing Section 230 would be inconsequential. This Essay explains why that’s not the case. Section 230 provides defendants with more substantive and procedural benefits than the First Amendment does. Because the First Amendment does not backfill these benefits, reductions to Section 230’s scope pose serious risks to Internet speech.
Unique Quantitative Analysis of Tsunami Waves using Statistical Software: A Case Study of The Major Recorded Hawaii Incidents

Research Article Open Access Unique Quantitative Analysis of Tsunami Waves using Statistical Software: A Case Study of The Major Recorded Hawaii Incidents Mostafa Essam Eissa1,* and Engy Refaat Rashed2 1Microbiology and Immunology Department (Independent Researcher & Candidate), Faculty of Pharmacy, Cairo University, Cairo, Egypt 2Drug Radiation Research Department, National Centre for Radiation Research and Technology, Atomic Energy Authority, PO Box 29, Nasr City, Cairo, P.O.11787, Cairo, Egypt. Adv. Mater. Proc., 2021, 6 (1), 21010419 DOI: 10.5185/amp.2021.010419 Publication Date (Web):23 January 2021 Copyright © IAAM-VBRI Press Full Article
Specific Learning Disorder among Primary School Children of Sarai Alamgir

Abstract Objective: To examine the frequency of specific learning disorder (SLD) among primary school children of Sarai Alamgir, Pakistan. Methods: The current cross-sectional study was conducted from January to July 2018 in Sarai Alamgir. Multistage stratified sampling technique was used to select the sample from target population (N=914) of school children studying in 3rd and 4th class of six primary schoolsof Sarai Alamgir, Pakistan.Sample of n=837 children was selected through the Taro Yamane formula. Structured Clinical Diagnostic Interviews based on DSM-V (APA, 2013) diagnostic criteria of specific learning disorder were used to collect data. Children were approached in their classes. Screening tool was applied on all selected children to screen out the vulnerable cases. After screening the vulnerable cases, diagnostic criteria of different specific learning disorders were applied on all vulnerable cases. The frequencies and percentages were analyzed to see the frequency of learning disorder among children. Results:Findings indicate that 174 (20.7%) children were vulnerable to specific learning disorders out of sample of 837 children. Total 13 (7.5%) children were diagnosed as suffering from different specific learning disorders from vulnerable participants. From vulnerable cases, 1(7.7%) childfulfilled the diagnostic criteria of reading impairment, 3 (23.1%) were diagnosed with mathematics impairment, 4 (30.8%) with multiple impairments in mathematics and writing, 1 (7.7%) with multiple impairments in mathematics and reading, 1 (7.7%) with multiple impairments in reading and writing, and 3 (23.1%) children were diagnosed as suffering from multiple impairments in mathematics, reading and writing. Continuous...
Identification of nonlinear dynamic system using machine learning techniques

Identification of nonlinear systems finds extensive applications in control design and stability analysis. To identify complex nonlinear systems, the neural network has drawn the attention of many researchers due to its broad application area. In this paper, an improved identification method based on robust regularised exponentially extended random vector functional link network (RERVFLN) has been proposed for nonlinear system identification. The input is extended using trigonometric expansion which increases the accuracy of the algorithm. To verify the accuracy of the proposed model, some benchmark Monte Carlo simulations are carried out through simulation study and the obtained results are compared with some established techniques such as original RVFLN, ELM, and LMS. Prediction accuracy of the proposed method RERVFLN is higher than the normal RVFLN for different nonlinear systems which is clear from the performance evaluation section.
A method for improving quality of DSPI measurement

Digital speckle pattern interferometry (DSPI) has a wide variety of useful applications but suffers from speckle noise that cannot be easily removed using conventional filtering. We study a method by which the contrast of speckle interference fringes may be enhanced, thus improving the measurement quality of DSPI. The results demonstrate both improved the fringe contrast and an improvement in the quality of the speckle fringes by reduction of the speckle noise is. When combined with a phase-stepping algorithm, the quality of the DSPI measurement is significantly improved. Finally, the measurement results of deformation using contrast enhancement also show improvement when compared with conventional filtering.
The Roles of Cataloging vs. Non-Cataloging Librarians and Staff in Non-MARC Metadata-Related Workflows: A Survey of Academic Libraries in the United States

A survey was conducted during May and early June of 2019, examining how non-MARC descriptive metadata workflows for digital projects are organized in academic libraries. It examined: (1) the extent to which cataloging librarians have transitioned to "metadata librarians"; (2) the extent to which non-MARC metadata activities are distributed within cataloging units and shared with cataloging paraprofessionals; (3) the extent to which such tasks are shared with library personnel in units outside of cataloging departments; and (4) the degree of satisfaction with metadata workflows. The present study also attempts to explore issues of concern to catalogers regarding such workflows.
Bridging the Public Services and Technical Services Divide: Hosting a Librarian Sabbatical

This concurrent session shared Albertsons Library’s experience with hosting an Access Services librarian’s sabbatical in Acquisitions and Collections. Original goals of the sabbatical were for the librarian to understand the framework for how the work of acquisitions is structured and executed; and understand the e-resource lifecycle to bring back critical skills to inform better service desk training. Though Access Services maintains the physical collection, and helps patrons navigate the digital collections through information and reference desks, staff in the area traditionally were not exposed to e-resource workflows, and the mechanics of how items are purchased, tracked and categorized. During the five-month long sabbatical, the librarian learned about the responsibilities of the unit and sub-groups that included ordering, receiving, serials, gifts, resource sharing, copy cataloging, budgeting, licensing, and electronic resources. This session discussed the value and benefits of using a sabbatical period to expose traditional public services staff to technical services, to deepen knowledge, and to support the institution.
Penerapan model role playing untuk meningkatkan pengetahuan konsep bhineka tunggal ika pada kelas V sekolah dasar

&lt;p&gt;Abstract. This study aims to find out how are the learning outcomes of students before and after the application of the role playing method in Setono No.95 State Elementary School Surakarta academic year 2019/2020. The method used in this research is a descriptive qualitative method, using a Classroom Action Research approach. The research subjects of the research that will be carried out are me as a researcher, teacher, and fifth grade students of SD Negeri Sutono No.95 Surakarta Academic Year 2019/2020. The data needed in this study includes primary data and secondary data. Based on the results of the study it can be concluded that the application of role playing models in fifth grade students of SD Negeri Setono No.95 in Surakarta 2019/2020 Academic Year has succeeded in increasing the understanding of the concept of Unity in Diversity. This is demonstrated by the presentation of the comprehension of Unity in Diversity in the initial conditions, cycle I and cycle II that continues.and provide the benefits of a very good contribution to student activities namely activity,curiosity,and courage.&lt;/p&gt;
Augmented Reality and Virtual Reality: New Tools for Architectural Visualization and Design

- This paper focuses on the use of augmented reality (AR) and virtual reality (VR) technologies aiming at innovating architectural design and architectural visualization through some reference projects and two pilot researches. The reference projects focus on the feasibilities in using AR and VR techniques in the architecture field, that generate the pilot researches based on testing them in both architecture design and architectural visualization. The first research presents the use of augmented reality in a three-dimensional model and illustrates how the technique works in the design process. To figure out a viable system in using VR technique, the second research experiments the VR technique in a properly said testing project. Thereby, after analyzing the potential of some existing applications of AR and VR technologies, and the use of these techniques in the experimental research, this paper aims to provide some guidelines and suggestions for their use in architectural design, architectural education, and other related fields.
ANT COLONY OPTIMIZATION BASED OPTIMAL STEGANOGRAPHY TECHNIQUE FOR SECRET IMAGE SHARING SCHEME

Data hiding earlier to transmission remains as an essential process for reducing the security issues in the cloud based environment. Image encryption and steganography techniques verify the safety of secret data during the transmission over the Internet. This paper presents a new multiple secret share creation (SSC) with ant colony optimization (ACO) based image steganography (SSC-ACO) technique to achieve security over image transmission. Initially, SSC algorithm is applied to generate a set of different shares for the applied image. Then, the ACO algorithm based image steganography technique is employed to generate the stego images for the applied cover and share images. The utilization of image steganography technique comprises a set of shares into the cover image to secure the details of the individual shares. The experimental validation of the projected model is tested using diabetic retinopathy (DR) images and the results are examined interms of peak signal to noise ratio (PSNR). The obtained PSNR values ensured the effective performance of the presented model on all the employed test images.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS

Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates. Unfortunately, existing defenses against these attacks are insufficient and mitigating both attacks at the same time is highly challenging, because while defeating backdoor attacks requires the analysis of model updates, protection against inference attacks prohibits access to the model updates to avoid information leakage. In this work, we introduce BAFFLE, a novel in-depth defense for FL that tackles this challenge. To mitigate backdoor attacks, it applies a multilayered defense by using a Model Filtering layer to detect and reject malicious model updates and a Poison Elimination layer to eliminate any effect of a remaining undetected weak manipulation. To impede inference attacks, we build private BAFFLE that securely evaluates the BAFFLE algorithm under encryption using sophisticated secure computation techniques. We extensively evaluate BAFFLE against state-of-the-art backdoor attacks on several datasets and applications, including image classification, word prediction, and IoT intrusion. We show that BAFFLE can entirely remove backdoors with a negligible effect on accuracy and that private BAFFLE is practical.
Betting on Bitcoin: Does Gambling Volume on the Blockchain Explain Bitcoin Price Changes?

The wallets with the largest volume of transactions on the Bitcoin blockchain are gaming applications. We use the historical gaming transaction data of Bitcoin holders, extracted directly from the blockchain, to measure risk-loving (lottery) appetite. Changes in the volume spent on the most lottery-like gambling wallet explain a significant portion (32%) of variation in the returns to the BTC price in the first half of our sample, but this effect disappears post-2016. This date coincides with a significant decrease in the proportion of Bitcoin transactions involved in gambling and aligns with other work that finds a decrease in the proportion of Bitcoin transactions linked to illegal activities and an increase in buy and hold investors around the same date.
Developing English materials for English for tourism

The potential of tourism industry increases every year. The tourism sector is believed to be able to create centers of economic growth of the country. One of the institutions that play a role in tourism industry is English Education Department, Universitas Ahmad Dahlan. This department cooperates with several tourism partners to train the students about tourism. There are two subjects about tourism named English for Tourism and Practicum on Tourism. The problem emerged from the students and materials. The students of English for Tourism lack of vocabulary and speaking. They need specific materials (textbook) for learning English about tourism. The materials of English for Tourism should be developed. Therefore, the objectives of the study are (1) developing English materials for English for Tourism; (2) finding out the possible English developed materials for English for Tourism. This study is research and development. The steps are identifying potentials and problems, collecting the data, product design, product validation, product revision and product trial. The data were obtained from an instrument of which validation was from an expert of material, instrument expert. The validated instrument was used for subject trial assessment. The subject of this research was the students of English for Tourism. Simple random sampling was used in this study. In composing the materials, the needs from the previous research by Effendi and Khafidhoh (2018) were one to consider in addition to the existing syllabus of English for Tourism in English Education Department. The product of this research is English developed materials (textbook) for English for Tourism. The total score of expert material’s validation is 82%, it shows that the developed materials are excellent. The mean of subject trial assessment is 82.94%, it shows that the product is excellent. The mean of the score validation and subject trial assessment is 82.47%, it is an excellent product.
On sums of Fibonacci numbers with few binary digits

In this paper we completely solve the Diophantine equation $F_n+F_m=2^{a_1}+2^{a_2}+2^{a_3}+2^{a_4}+2^{a_5}$, where $F_k$ denotes the $k$-th Fibonacci number. In addition to complex linear forms in logarithms and the Baker-Davenport reduction method, we use $p$-adic versions of both tools.
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies

Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, and end-to-end trainable. We further provide a statistical interpretation of our algorithm as a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. By deriving an approximate inference algorithm based on Small Variance Asymptotics, we obtain a natural extension that automatically learns the optimal number of anchors instead of having to tune it as a hyperparameter. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters (up to 40x compression) as compared to existing compression baselines.
Approximate Explanations for Classification of Histopathology Patches

An approximation method for faster generation of explanations in medical imaging classifications is presented. Previous results in literature show that generating detailed explanations with LIME, especially when fine tuning parameters, is very computationally and time demanding. This is true both for manual and automatic parameter tuning. The alternative here presented can decrease computation times by several orders of magnitude, while still identifying the most relevant regions in images. The approximated explanations are compared to previous results in literature and medical expert segmentations for a dataset of histopathology images used in a binary classification task. The classifications of a convolutional neural network trained on this dataset are explained by means of heatmap visualizations. The results show that it seems to be possible to achieve much faster computation times by trading off finer detail in the explanations. This could give more options for users of artificial intelligence black box systems in the context of medical imaging tasks, in regards to generating insight or auditing decision systems.
Detection of Porcine Huddling Behaviour Based on Improved Multi-view SSD

The huddling behaviour of pigs is closely related to disease prevention and comfort degree. In order to detect the huddling behaviour of pigs in complex breeding environment, a detection method for huddling behaviour based on improved multi-view Signal Shot MultiBox Detector (SSD) is proposed in this paper. Firstly, in order to solve the deficiency of SSD for small object detection and close-contact object detection, the improved multi-view SSD is developed to detect pigs in complex breeding environment. Meanwhile, MobileNet_v2, focal loss function and network parameters transfer are applied in this paper to further improve the detection accuracy and speed of SSD. Secondly, the dispersion of pigs is proposed to signify the aggregation degree of pigs. Finally, the threshold of dispersion is set to detect the huddling behaviour of pigs. The experimental results show that the huddling behaviour could be detected with an accuracy of 94.5%, a precision of 94.9%, a recall of 94.0%. The results indicate that this method can be used to detect huddling behaviour of pigs.
Accelerometer based classification of elbow flexion and extension exercises

In this study, we propose a human activity recognition (HAR) system for monitoring the adherence of the patients to the elbow flexion and extension physiotherapy exercise routines in their daily environments. The proposed solution utilizes single wrist-worn accelerometer data with a one-class classification (OCC) approach where training data are collected for the target class only and data that represent all other classes are produced artificially from the target activity data. We analyzed the system with four different classifiers; KNN (k-nearest-neighbors), SVM (Support Vector Machines), logistic regression and Naive Bayes classifiers. The results reveal that SVM achieves a median success rate of 100% and 83,3% in detection of target and other classes respectively in intra-subject evaluation as well as reaching a median success rate of 90% and 100% in inter-subject case on a per-subject basis, emerging as the best performing classifier among the ones we study. Our one-class approach is also observed to outperform one-class SVM of LIBSVM, which severely suffers from parameter selection issue.
SeqMed: Recommending Medication Combination with Sequence Generative Adversarial Nets

Nowadays, the algorithmic advances in deep learning cause revolutionizing changes in the health-care domain. Many complex health-care problems get suitable solutions with deep learning-based methods, especially for Medicine Combination Prediction (MCP) to patients with complex health conditions. However, existing works either ignore the inter-relationships among medicines or fail to depict these relationships in an integrated and robust deep learning framework. To solve the problems above, in this paper, we propose SeqMed, a sequence generation model for predicting medicine combination. With the power of Generative Adversarial Nets (GAN), SeqMed can learn an expressive representation from medical records for the certain patient, and give accurate medicine recommendations for this patient. Experiments on real-world electronic medical record (EMR) dataset, MIMIC-III, show that SeqMed outperforms previous methods with a great leap. Meanwhile, SeqMed can stably converge. As a result, SeqMed achieves an improvement of 5.81% and 6.49% on the metrics of Jaccard and f1-score compared with the recent state-of-the-art method for MCP.
On the Convergence of Deep Networks with Sample Quadratic Overparameterization.

Deep neural networks' remarkable ability to correctly fit training data when optimized by gradient-based algorithms is yet to be fully understood. Recent theoretical results explain the convergence for ReLU networks that are wider than those used in practice by orders of magnitude. In this work, we take a step towards closing the gap between theory and practice by significantly improving the known theoretical bounds on both the network width and the convergence time. We show that convergence to a global minimum is guaranteed for networks with widths quadratic in the sample size and linear in their depth at a time logarithmic in both. Our analysis and convergence bounds are derived via the construction of a surrogate network with fixed activation patterns that can be transformed at any time to an equivalent ReLU network of a reasonable size. This construction can be viewed as a novel technique to accelerate training, while its tight finite-width equivalence to Neural Tangent Kernel (NTK) suggests it can be utilized to study generalization as well.