Reviewerly/exHarmony huggingface dataset:
Adaptive Forgetting Curves for Spaced Repetition Language Learning

The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.
Technology-Needs Assessment Data Analysis using PCA for the Design of a WLAN-based Hybrid Classroom Response and Learning Management System Android Application

The influence of technology in the evolution of modern-day education grows significantly as more educational institutions attempt to improve the quality of education. This study aims to develop a hybrid of a classroom response system and learning management system web and android applications without the use of the internet. A technology-needs assessment (TNA) was administered on 213 high school students. The TNA questionnaire was comprised by 16 closed-ended questions, 15 of which required Likert scale-based responses and one required a yes/no response. The TNA data was analyzed with the help of principal component analysis (PCA). The results of the PCA became the basis for the configuration of a wireless local area network of devices connected to a central server through a wireless router dedicated for the implementation of the web and android applications designed for the needs identified by the study. The web and android applications were developed using a test-driven development (TDD) approach. The system configuration was based on the system capacity requirements to enable simultaneous wireless connection of up to 100 mobile devices. Two groups of high school students were identified by the school to be part of the field testing of the web/android app since these students were of significantly similar academic standing in their respective classes. The study group (n=7) used the web/android app in their statistics class and a control group (n=10) were subjected to the traditional classroom set-up. The field testing lasted for two weeks covering various teaching and learning activities including exams. The exam results were subjected to Analysis of Variance using two-sample F-test and t-test. The results show that at 5% significance level, a p-value of 0.0026 was obtained, which indicates that the means between the summative test results data sets are significantly different. The performance of the students in the study group is significantly different from the performance of the students in the control group in the summative test. As observed in the summative test mean scores of the groups, the study group showed significant improvement in their performance when compared to the control group.
Adversarial examples and where to find them

Adversarial robustness of trained models has attracted considerable attention over recent years, within and beyond the scientific community. This is not only because of a straight-forward desire to deploy reliable systems, but also because of how adversarial attacks challenge our beliefs about deep neural networks. Demanding more robust models seems to be the obvious solution -- however, this requires a rigorous understanding of how one should judge adversarial robustness as a property of a given model. In this work, we analyze where adversarial examples occur, in which ways they are peculiar, and how they are processed by robust models. We use robustness curves to show that $\ell_\infty$ threat models are surprisingly effective in improving robustness for other $\ell_p$ norms; we introduce perturbation cost trajectories to provide a broad perspective on how robust and non-robust networks perceive adversarial perturbations as opposed to random perturbations; and we explicitly examine the scale of certain common data sets, showing that robustness thresholds must be adapted to the data set they pertain to. This allows us to provide concrete recommendations for anyone looking to train a robust model or to estimate how much robustness they should require for their operation. The code for all our experiments is available at this http URL .
A Blockchain Based Solution for Securing Data of IoT Devices

In today’s time, the number of IoT devices are increasing rapidly. We everyday hear about Amazon echo, Google Mini, Smart watches etc. These devices collect confidential data of a person and as most of these systems follow centralized architecture approach, So most of the data on internet is basically managed by some central authority or organization. Though these organizations have strict policy regarding data misuse or changing of data without consent but one can’t overlook the fact that these organizations have the ability to do so. Blockchain helps solve such problem as it is not managed by single party if somebody tries to change data in his Blockchain then the hash of the particular block will no longer match and the particular Blockchain will become invalid and other Blockchain will still be intact and therefore users data won’t be compromised. But due to high resource requirement, it becomes problem to run complete Blockchain node on all IoT devices mainly on low power or memory devices. In this paper, we have developed decentralized architecture leveraged from Blockchain technology coupled with an alternative centralized cloud architecture which is a classic client/server architecture with an underlying Blockchain at back-end support with smart contract application written in Solidity language at Ethereum platform. We implement this framework and show how this architecture prevent data misuse by using functionality of Blockchain without requiring the all IoT devices to actually run a Blockchain node. For an end-user experience, it will appear to be same as a normal web app and from the developers perspective, the smart contract application will have similar software design to current web apps thus allowing an easy transition for both of centralized and decentralized methods while still retaining the trust of decentralization.
Review of Cybersecurity Audit Management and Execution Approaches

The management and execution of good cybersecurity audits help organizations to strengthen their cybersecurity operations by detecting and mitigating cyberattacks and threats from attacking and destroying organizational assets based on the result of audit findings. However, the objectives of this audit cannot be fully achieved because of the various problems prevalent in audit management and execution. This paper aims to review issues that motivate the research of cybersecurity audit and the approaches to manage and execute it. Previous works in this area of interest are discussed by classifying and analyzing the current approaches. Models, scopes, strengths and limitations are studied and the most effective and accurate synthesis that can improve the quality of cybersecurity audits is highlighted. Various approaches to manage and execute cybersecurity audit with their objectives and advantages are classified. From this literature review, it is clear that cybersecurity audits can be improved by addressing the highlighted issues and create a well-rounded approach to improve the management and execution of auditing. Timeliness, accuracy and measurement in regards of cybersecurity auditing are not included in this review and shall be discussed in future study as they have significant impact on the audits.
Choppy: Cut Transformer For Ranked List Truncation

Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the problem of determining how many results to return, i.e. how to optimally truncate the ranked result list, has received less attention despite being of critical importance in a range of applications. Such truncation is a balancing act between the overall relevance, or usefulness of the results, with the user cost of processing more results. In this work, we propose Choppy, an assumption-free model based on the widely successful Transformer architecture, to the ranked list truncation problem. Needing nothing more than the relevance scores of the results, the model uses a powerful multi-head attention mechanism to directly optimize any user-defined IR metric. We show Choppy improves upon recent state-of-the-art methods.
Zoë, Sonic Relationality and Posthuman Urban Sound Art

Posthuman theory questions traditional concepts of the human subject by challenging each of us to adopt a planetary consciousness.It achieves this by breaking down dualisms that separate body and mind, and society from nature.It instead preferences a monist understanding of the real as a tireless "self-organising force of living matter".This force is described as the vitality of matter, or zoë.Sonic practices have much to offer this emerging consciousness, in particular the concept of sonic relationality, which considers how listening interconnects bodies within a vibratory field of soundings.Three sonic interventions, realised thorough a recursive process of creative practice research, illustrate the connection between posthuman notions of zoë and emergent theories of sonic relationality.They are: Noise Transformation, which sought to reveal the aesthetic potential of traffic noise; Fielding, which explores the potential of sound art to inform new approaches to urban greening programs; and, Touchstone, which collapsed a sonic community into a performative digital sculpture marked by the Earth's rotation.Each of these interventions reveals methods by which sound art installation practices and soundscape design are able to create new relations between bodies, by augmenting the vitality inherent to everyday materialities.
Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. We also investigate the effect of latency constraints, and uncover interesting characteristics of the predictor over different Signal to Noise Ratio (SNR) ranges. The obtained insights open the door for implementing a deep-learning-based strategy that is scalable to large heterogeneous networks, generalizable to diverse wireless environments, and suitable for predicting frame error instances and rates within a congested shared spectrum.
Covolutional Multinomial Logistic Regression for Face Recognition

the system of machine learning in term of supervised learning at present. It is popular for building learning systems for recognition. Or classify objects within the image. This paper therefore presents methods and systems for enhancing efficiency in order to select face recognition with multinomial logistic regression (MLR). Which will use methods of filtering with information before bringing it into the learning process. Which starts by Gaussian Filtering that convolute with the image for smoothing image. Then reduce the pixel's amount and manage data that causes max pooling to be chosen in this paper. Next, it was used to create a model to find the weight of the face image data. And last, test the performance of the work to see the error test with weight that was calculated from MRL. The experimental in the paper was tested the creation of data on the face database AT&T, which is the most widely used face database.
Great Paper! But Can I Repeat it? Re-Executability Assessment of the Recent Autism Literature

The objectives of scientific literature is to report ‘claims’ derived from the findings of an experimental assessment. The ability of a paper to convey the necessary details for its claim(s) to be first, verified, and then second, generalized, is a critical need for the published literature.
On existence of primitive normal elements of rational form over finite fields of even characteristic

Let $q$ be an even prime power and $m\geq2$ an integer. By $\mathbb{F}_q$, we denote the finite field of order $q$ and by $\mathbb{F}_{q^m}$, its extension degree $m$. In this paper we investigate the existence of primitive normal pair ($\alpha, \, f(\alpha))$, where $f(x)= \frac{ax^2+bx+c}{dx+e} \in \mathbb{F}_{q^m}(x)$ with $a\neq 0, dx+e\neq 0$ in $\Fm$ and establish some sufficient conditions to show that nearly all fields of even characteristic possess such elements. We conclude the paper by providing an explicit small list of genuine exceptional pairs $(q,m)$.
Stepwise Transfer of Domain Knowledge for Computer-Aided Diagnosis in Pathology Using Deep Neural Networks

Deep learning using deep convolutional neural networks (DCNNs) has demonstrated unprecedented power in image classification. Subsequently, computer-aided diagnosis (CAD) for pathology imaging has been largely facilitated by DCNN approaches. However, because DCNNs require massive amounts of labeled data, the lack of availability of such pathology image data as well as the high cost of labeling new data is currently a major problem. To avoid expensive data labeling efforts, transfer learning is a concept intended to overcome training data shortages by passing knowledge from the source domain to the target domain. However, weak relevance between the source and target domains generally leads to less effective transfers. Therefore, following the natural step-by-step process by which humans learn and make inferences, a stepwise fine-tuning scheme is proposed by introducing intermediate domains to bridge the source and target domains. The DCNNs are expected to acquire general object classification knowledge from a source domain dataset such as ImageNet and pathology-related knowledge from intermediate domain data, which serve as fundamental and specific knowledge, respectively, for the final benign/malignant classification task. To realize this, we introduce several ways to provide pathology-related knowledge by generating an intermediate dataset classified into various corresponding pathology features. In experiments, the proposed scheme has demonstrated good performance on several well-known deep neural networks.
Semantic Artificial Neural Networks

Neural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networks based on the conceptualisation and semantics of the application domain is proposed. The proposed approach, called Semantic Artificial Neural Networks, allows dealing with the problem of interpretability and also the definition of the structure of neural networks. In addition, the resulting neural networks are sparser and have fewer parameters than typical neural networks, while achieving high performance.
Soft Computing Approach to Determine Students’ Level of Comprehension Using a Mamdani Fuzzy System

Comprehension is a measure of one’s understanding a given piece of information, however extensive or summarized, complex or simple. Self-evaluation of one’s level of understanding is a difficult task, with imprecise boundaries and vague ranges. Typically, educational institutions use examinations to evaluate a student’s understanding of a subject or topic, assigning a mark (i.e., test score) or a grade to the student. Nevertheless, this approach calls for an evaluation prior to an estimation of the extent of student’s understanding. In contrast, this treatise proposes a method to self-evaluate one’s level of understanding, before an assessment, with an intent to highlight the deficient areas in one’s grasp of the concepts that need to be improved, before taking an examination. Using a simple questionnaire, to obtain inputs from the student, and a Mamdani fuzzy inference system (FIS) to process the inputs, the proposed model determines the level of understanding of a student, based on a three-level comprehension guide.
Stream-Monitoring Automata

Over the past nearly twenty years, numerous formal models of enforcement and runtime monitors have been investigated. This paper takes the lessons learned from earlier models and proffers a new general model of runtime enforcement that is more suitable for modeling security mechanisms that operate over infinite event streams. The new model, called Stream-Monitoring Automata (SMAs), enables the constraints and analyses of interest in previous models to be encoded, and overcomes several shortcomings of existing models with respect to expressiveness. SMAs capture the practical abilities of mechanisms to monitor infinite event streams, execute even in the absence of event inputs, enforce non-safety policies, and operate an enforcement model in which extraneous constraints such as transparency and uncontrollable events may be specified as meta-policies.
Knowledge Distillation for End-to-End Person Search.

We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identification jointly. These approaches for joint optimization show their largest drop in performance due to a sub-optimal detector. We propose two distinct approaches for extra supervision of end-to-end person search methods in a teacher-student setting. The first is adopted from state-of-the-art knowledge distillation in object detection. We employ this to supervise the detector of our person search model at various levels using a specialized detector. The second approach is new, simple and yet considerably more effective. This distills knowledge from a teacher re-identification technique via a pre-computed look-up table of ID features. It relaxes the learning of identification features and allows the student to focus on the detection task. This procedure not only helps fixing the sub-optimal detector training in the joint optimization and simultaneously improving the person search, but also closes the performance gap between the teacher and the student for model compression in this case. Overall, we demonstrate significant improvements for two recent state-of-the-art methods using our proposed knowledge distillation approach on two benchmark datasets. Moreover, on the model compression task our approach brings the performance of smaller models on par with the larger models.
Estimating g-Leakage via Machine Learning

This paper considers the problem of estimating the information leakage of a system in the black-box scenario. It is assumed that the system's internals are unknown to the learner, or anyway too complicated to analyze, and the only available information are pairs of input-output data samples, possibly obtained by submitting queries to the system or provided by a third party. Previous research has mainly focused on counting the frequencies to estimate the input-output conditional probabilities (referred to as frequentist approach), however this method is not accurate when the domain of possible outputs is large. To overcome this difficulty, the estimation of the Bayes error of the ideal classifier was recently investigated using Machine Learning (ML) models and it has been shown to be more accurate thanks to the ability of those models to learn the input-output correspondence. However, the Bayes vulnerability is only suitable to describe one-try attacks. A more general and flexible measure of leakage is the g-vulnerability, which encompasses several different types of adversaries, with different goals and capabilities. In this paper, we propose a novel approach to perform black-box estimation of the g-vulnerability using ML. A feature of our approach is that it does not require to estimate the conditional probabilities, and that it is suitable for a large class of ML algorithms. First, we formally show the learnability for all data distributions. Then, we evaluate the performance via various experiments using k-Nearest Neighbors and Neural Networks. Our results outperform the frequentist approach when the observables domain is large.
Proposed Authentication Protocol for IoT using Blockchain and Fog Nodes

The IoT offers enormous opportunities and also brings some challenges.Authentication considered one of the main challenges introduced by IoT.IoT devices are not able to protect themselves due to there limited processing and storage capabilities.Researchers proposed authentication algorithms with either a lack of scalability or vulnerable to cyberattacks.In this paper, we propose a decentralized token-based authentication based on fog computing and blockchain.The protocol provides a secure authentication protocol using access token, ECC cryptography, and also blockchain as decentralized identity storage.The blockchain uses cryptographic identifiers, records immutability, and provenance, which allows the implementation of a decentralized authentication protocol.These features ensure a light and secure identity management system.We evaluate this protocol communication between controller, gateways, and devices using AVISPA/ HLPSL, and results obtained from AVISPA simulation shows that our protocol is safe based on secrecy and strong authentication criteria.The paper uses four test cases to test the Ethereum smart contract implementation to ensure the system functions properly.
Smart Sprint Methodology for Financial Management System Development

Modern software development projects leverage the agile methodologies to deliver reliable and stable artifacts early. The project plan generation revolves around the complexity of use cases, technology stack, testbeds, and validations. In addition, the project plan under agile development strategies must help the sprint planner and developers to reduce iterations required to develop and deploy the product. However, the agile programming practices tend to translate into legacy waterfall model if the sprint planner is a novice on the project, technology stack and development plans leading to falling late in the pipeline. Hence, it is essential to draft a sprint plan in a futuristic and smart manner to reduce the risk of failure. This is possible, if the primitive code modules are raised to off-the-shelf projects. In this article, we propose two knowledge-driven plans to complement the agile strategy that avoids the necessity to freeze the fledgling user requirements, utilize the smart prototypes while generating sprints. We also propose a new capacity building strategy through cross-functional expertise and is demonstrated on reengineering of Financial Management Systems (FMS) maintained by Government of Andhra Pradesh.