Reviewerly/exHarmony huggingface dataset:
ProLog2vec: Detecting Novices’ Difficulty in Programming Using Deep Learning

Encountering difficulties that emerge in programming learning are ubiquitous for novices. Those difficulties may reduce novices' motivation for learning, even lead them to drop out. Automatically detecting the difficulties has been proved as an effective way to solve this problem. A potential method for the detection is to identify programmers' behavioural patterns based on the sequences of programming events (e.g., editing, deleting, or debugging) when they experience programming difficulties. However, due to the diversity of these events, the plentiful difficulties generated from novices, and the naturally complicated behavioural patterns, accurately detecting the difficulties remains challenging. In this study, we propose a deep representation based on the programming events as the pre-treatment of a recurrent neural network with an attention mechanism to detect programming difficulty. This representation can be conducive to encode the complicated behavioural features (e.g., co-occurrence, sequences, and time intervals of the events) of the difficulty patterns for deep learning models. The results show that the accuracy of our method reaches 94%, which increases by 16% compared with the traditional machine learning model.
Data Encryption on Cloud Database Using Quantum Computing for Key Distribution

Data encryption is growing fast and has become famous when they merge the basic schemes into difficult schemes. Various data encryption techniques introduce the distinct methods like big data, networks, and web. The important hindrance forms for accessing are to give security and competency to reflex data encryption methods. The manuscript recommends estimating data encryption framework that merges with quantum cryptography and adds a fine-grained control access policy to our proposed method where the authorization valid for every single entry reaches our aim of optimizing the performance on sharing secret key for data encryption or data decryption. Finally, our proposed method gives secure data sharing with heterogeneous cloud service providers.
Perfect $L_p$ Sampling in a Data Stream

In this paper, we resolve the one-pass space complexity of perfect $L_p$ sampling for $p \in (0,2)$ in a stream. Given a stream of updates (insertions and deletions) to the coordinates of an underlying vector $f \in \mathbb{R}^n$, a perfect $L_p$ sampler must output an index $i$ with probability $|f_i|^p/\|f\|_p^p$ and is allowed to fail with some probability $\delta$. So far, for $p > 0$ no algorithm has been shown to solve the problem exactly using ${poly}( \log n)$-bits of space. In 2010, Monemizadeh and Woodruff introduced an approximate $L_p$ sampler which, given an approximation parameter $\nu$, outputs $i$ with probability $(1 \pm \nu)|f_i|^p /\|f\|_p^p$, using space polynomial in $\nu^{-1}$ and $\log(n)$. The space complexity was later reduced by Jowhari, Sağlam, and Tardos to roughly $O(\nu^{-p} \log^2 n \log \delta^{-1})$ for $p \in (0,2)$, which matches the general $p\geq 0$ lower bound of $\Omega(\log^2 n \log \delta^{-1})$ in terms of $n$ and $\delta$, but is loose in terms of $\nu$. Given these nearly tight bounds, it is perhaps surprising that no lower bound exists in terms of $\nu$---not even a bound of $\Omega(\nu^{-1})$ is known. In this paper, we explain this phenomenon by demonstrating the existence of an $O(\log^2 n \log \delta^{-1})$-bit perfect $L_p$ sampler for $p \in (0,2)$. This shows that $\nu$ need not factor into the space of an $L_p$ sampler, which closes the complexity of the problem for this range of $p$. For $p=2$, our bound is $O(\log^3 n \log \delta^{-1})$-bits, which matches the prior best known upper bound of $O(\nu^{-2}\log^3n \log \delta^{-1})$, but has no dependence on $\nu$. Note that there is still a $\log n$ gap between our upper bound and the lower bound for $p=2$, the ution of which we leave as an open problem. For $p<2$, our bound holds in the random oracle model, matching the lower bounds in that model. However, we show that our algorithm can be derandomized with only a $O((\log \log n)^2)$ blow-up in the space (and no blow-up for $p=2$). Our derandomization technique is quite general, and can be used to derandomize a large class of linear sketches, including the more accurate count-sketch variant of Minton and Price [Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, Philadelphia, 2014, pp. 669--686], resolving an open question in that paper. Finally, we show that a $(1\pm\epsilon)$ relative error estimate of the frequency $f_i$ of the sampled index $i$ can be obtained using an additional $O(\epsilon^{-p} \log n)$-bits of space for $p < 2$, and $O( \epsilon^{-2} \log^2 n)$ bits for $p=2$, which was possible before only by running the prior algorithms with $\nu = \epsilon$.
Robotic Wheelchair Control System for Multimodal Interfaces Based on a Symbolic Model of the World

Problem statement: Wheelchairs have a widespread usage both as a rehabilitation tool and as an assistive device in the daily lives of people with disabilities. However, some disabilities make it difficult to use traditional wheelchairs that are operated manually or with a joystick. This paper describes an intelligent control system for wheelchair automatization, which allows the user to give commands by different means—voice, eye movements, muscle tension. The object recognition system and logical processing of commands supports a wide variety of interfaces and commands. To achieve these goals, a semiotic model of the world is used. Purpose of research: development of a control system for a robotic wheelchair that supports multimodal interfaces and has a high level of automation that enables efficient operation for users with various disabilities. Results: The paper describes the developed architecture of the control system based on the semiotic model of the world, modules for the speech interface, gaze control, and the interface based on myosensors. The navigation system and the processing module for the semiotic world model ensure the safe execution of user commands, including movement, object recognition and processing of commands that contain references to known objects. The system supports interfacing with a manipulator, which is controlled using a linguistic model: a language for describing actions representing the admissible movements of the manipulator in the form of a formal grammar. The study tested the proposed system on a developed model of the robot and a detailed model of the room in a Gazebo environment, as well as on the corresponding software and hardware implementation of the robotic wheelchair. Practical significance: Automation and the use of models and methods of artificial intelligence in the development of wheelchairs allows us to make them more versatile, which increases the number of users for whom a particular model is suitable, reduces the strain on the operator of the wheelchair and expands the capabilities of the wheelchair. The proposed usage of the sign models achieves these goals by combining logical processing of commands as a means of handling multimodal interfaces and executing complex commands.
Reconstruction of Generative Adversarial Networks in Cross Modal Image Generation with Canonical Polyadic Decomposition

Generating pictures from text is an interesting, classic, and challenging task. Benefited from the development of generative adversarial networks (GAN), the generation quality of this task has been greatly improved. Many excellent cross modal GAN models have been put forward. These models add extensive layers and constraints to get impressive generation pictures. However, complexity and computation of existing cross modal GANs are too high to be deployed in mobile terminal. To solve this problem, this paper designs a compact cross modal GAN based on canonical polyadic decomposition. We replace an original convolution layer with three small convolution layers and use an autoencoder to stabilize and speed up training. The experimental results show that our model achieves 20% times of compression in both parameters and FLOPs without loss of quality on generated images.
Optimization of CNN Model With Hyper Parameter Tuning for Enhancing Sturdiness in Classification of Histopathological Images

The field of pathology has advanced so rapidly that it is now possible to produce whole slide images (WSI) from glass slides with digital scanners producing high-quality images. Image analysis algorithms applied to such digitized images facilitate automatic diagnostic tasks whilst assisting a medical expert. Successful detection of malignancy in histopathological images largely depends on the expertise of radiologists, though they sometimes disagree with their decisions. Computer-aided diagnosis provides a platform for a second opinion in diagnosis, which can improve the reliability of an expert's opinion. Deep learning provides promising results compared to the conventional approach that relies on manual extraction of features which is time-consuming and labor-intense. Due to the huge size, whole slide images are converted into patches and trained using a Convolutional Neural Network (CNN), a variant of the deep learning model for images. Experimental results show that the proposed native model achieved patch wise classification accuracy of 92.8% and area under ROC curve 0.97 which is close to the values while comparing with the existing pre-trained models.
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models.

In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.
An Optimized Quantitative Argumentation Debate Model for Fraud Detection in E-Commerce Transactions

Since the existing machine-learning-based approaches for fraud detection are incapable of providing explanations, we propose a fraud detection method based on quantitative argumentation, which is intrinsically interpretable. First, we construct an argumentative tree by combining human-level knowledge and the knowledge learned from data. Second, we extend the existing quantitative argumentation debates (QuAD) frameworks by adding correlation strength between arguments and exploit the particle swarm optimization algorithm (PSO) to identify the correlation strength between arguments. Third, the performance of the new method is investigated by an empirical study, using the data from Ant Financial, the Alibaba Group's financial services provider. The results show that the new method has better performance than the existing DF-QuAD algorithm and is competitive with other machine learning methods, including Xgboost, ANN, SVM, and LR.
Standing in Your Shoes: External Assessments for Personalized Recommender Systems

The evaluation of recommender systems relies on user preference data, which is difficult to acquire directly because of its subjective nature. Current recommender systems widely utilize users' historical interactions as implicit or explicit feedback, but such data usually suffers from various types of bias. Little work has been done on collecting and understanding user's personal preferences via third-party annotations. External assessments, that is, annotations made by assessors who are not the systems' users, have been widely used in information search scenarios. Is it possible to use external assessments to construct user preference labels? This paper presents the first attempt to incorporate external assessments into preference labeling and recommendation evaluation. The aim is to verify the possibility and reliability of external assessments for personalized recommender systems. We collect both users' real preferences and assessors' estimated preferences through a multi-role, multi-session user study. By investigating the inter-assessor agreement and user-assessor consistency, we demonstrate the reasonable stability and high accuracy of external preference assessments. Furthermore, we investigate the usage of external assessments in system evaluation. A higher degree of consistency with users' online feedback is observed, even better than traditional history-based online evaluation. Our findings show that external assessments can be used for assessing user preference labels and evaluating systems in personalized recommendation scenarios.
Method for Mining Frequent Item Sets Considering Average Utility

Data mining is used to determine helpful data and information from large data bases for decision making in different fields. In today’s scenario, mining high-utility itemsets (HUIs) has become a current investigation area. In traditional method HUIs are found on the basis individual utility of an item-set, which in turn is calculated as the totality of the utilities of individual items. But the problem is that the above way of finding HUIs does not consider length of item-set into consideration, while calculating HUIs. In real world situations, it is desirable to calculate average utility while considering both the length of item-sets and their utilities. This paper proposes an algorithm to find the high average-utility item-sets (HAUIs) seeing both parameters i.e. length of itemsets and their utilities. A numerical example is devised to explain the proposed approach. Experiments on real life databases shows that suggested model can proficiently discover the total set of HAUIs.
The Effect of DevOps Implementation on Teamwork Quality in Software Development

Background: The Agile method, which is claimed to reduce time needed for software development cycle has been widely used. It addresses communication gaps between customers and developers. Today, the DevOps has been extended as part of the Agile process to address communication gaps between developer’s team members. Despite the rising popularity, the effect of DevOps implementation on the teamwork quality in software development is still unknown.Objective: The objective of this research is to conduct a study on the impact of DevOps on teamwork quality. Two software houses, PT X and PT Y, are chosen as the case studies.Methods: This research uses quantitative methods to analyse research data using simple linear regression. The questionnaire technique is used to retrieve respondent data using 62 questions, consisting of 20 DevOps questions from 4 indicators and 42 teamwork quality questions from 6 indicators.Results: The results from various quality tests indicate that all instruments are valid and reliable while hypothesis tests showed that the DevOps implementation variable has an influence on the teamwork quality variable by 75.6%.Conclusion: It can be concluded that the implementation of the DevOps in software development has a positive correlation with the teamwork quality.
The Research on Single Page Application Front-end development Based on Vue

Abstract ‘Vuejs’ is a popular front-end framework, which uses MVVM (Model View View-Model) design pattern to support data-driven and component-based development. This paper discusses the basic working principle and development mode of Vue, and on this basis, uses SPA (Single Page Application) to design and implement a mobile mall front-end page based on Vue. This research fully demonstrates the advantages of front-end and back-end separation technology in Web development, and provides a powerful front-end support for full stack development.
Near real-time intrusion alert aggregation using concept-based learning

Intrusion detection systems generate a large number of streaming alerts. It can be overwhelming for analysts to quickly and effectively find related alerts stemmed from correlated attack actions. What if fast arriving alerts could be automatically processed with no prior knowledge to find related actions in near real-time? The Concept Learning for Intrusion Event Aggregation in Realtime (CLEAR) system aims to learn and update an evolving set of temporal 'concepts,' each consisting of aggregates of related alerts that exhibit similar statistical arrival patterns. With no training data, the system constructs the concepts in near real-time from statistically similar alert aggregates. Tracked concepts are then applied to incoming alerts for fast and high-fidelity aggregation. The concepts learned by CLEAR are significantly more unique and invariant when compared to those learned by alternative drift detection methods. Furthermore, it provides insights for how specific individual, or co-occuring, alerts arrive with distinct and consistent temporal patterns.
COVID-19 y el aprendizaje en línea en la educación superior – Un estudio de caso de alumnos de los Institutos Politécnicos de Portalegre y Beja

Since the early spring of 2020, Portuguese higher education institutions, as well as other education institutions around the world, have been experiencing an unprecedented massive “migration” from traditional in‐class face‐to‐face education to online education. The massive changing models of teaching and learning brings out different perceptions from various parties. There are some advantages and disadvantages in this kind of learning model. The purpose of this study is to investigate the learners’ perception on online learning in a scenario of a COVID-19 pandemic. The subject of this research are students of two Portuguese Polytechnic Institutes (Portalegre and Beja) The instrument used is an online survey questionnaire, anonymous and voluntary, sent to undergraduate engineering students of the two institutions. This study not only report that online learning is good during COVID-19 pandemic, but also spotted the light on the availability of internet access and computers or cell phones. The main conclusions show that the impact was manly negative from the learners’ perception perspective, having mostly referred the workload and also the difficulty in clarifying doubts in time as major factors that had a very negative influence on their academic performance. Also, the social distancing from colleagues was mentioned as a negative aspect. Finally, most respondents expressed a clear preference for face-to-face education for the next academic year.
System technologies for the formation of a data control contour for the personal account of an applicant based on the 1C:Enterprise 8.3 platform

The article considers the actual information technologies of the organization of a component distributed system for the distance enrollment of applicants within the framework of the 1C:Enterprise 8.3 platform. The concept of a modified component architecture of interaction with a web contour is proposed, which implementing the module of dynamic designing of web contour interface, as well as the automated functionality of data management directly on the standard 1C software products. The system principles for the organization of the contour of the website “Personal account of an applicant” have been formulated, they allow to integrate the management of the web contour into the circulation software solutions both for higher education (1C:University PROF) and for secondary vocational education (1C:College PROF) in terms of mechanisms for the dynamic designing of interfaces and the contour of data processing of site users. The implemented contour of designing site interfaces allows you to dynamically change the components of the site’s web forms, as well as to define the details displayed in the web user interface. A description of the mechanisms of dynamic interaction of the interfaces of the “Personal account of an applicant” site with the dynamic data management contour within the framework of the 1C:Enterprise 8.3 platform is given. The information components of the interaction contour of the site interfaces have been determined. The elements of the site control contour and their purpose have been specified. A conceptual universal scheme for the development of a web based data moderating contour has been formulated, and technologies for interaction with an internal accounting system for automating an admission campaign have been determined.
RAINFALL VARIABILITY IN GUNUNGSEWU KARST AREA, JAVA ISLAND, INDONESIA

Karst area is highly susceptible to changes to climate parameters. One of the parameters is rainfall variability. In addition to shaping the condition of water resources, rainfall in the Gunung Sewu karst area determines the nature of crop and livestock of the agriculture sectors―the local population's main economic activities, warranting the significance of the rainfall variability studies. Rainfall variability in karst areas also affects disaster conditions such as drought and floods. However, due to insufficient meteorological data in quality and quantity, there has been no rainfall variability studies conducted in this locality. The research intended to analyze rainfall variability in the Gunung Sewu karst area in 1979‒2013 by utilizing rainfall predictions from satellite images that many scholars had tested in different locations and recognized as having good quality. In the analysis, mean monthly rainfall was calculated, and the trends of annual rainfall and average rainfall intensity, dry and rainy seasons, the number of rainy days, and the effect of ENSO (El Niño Southern Oscillation) on rainfall were analyzed. The research data were 35 years of daily rainfall records derived from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR). The analysis results showed that the mean rainfall, number of rainy days, and rainfall intensity had an increasing trend. Also, El Niño quantitatively influenced the rain in the Gunung Kidul karst area.
Enhancing transferability of adversarial examples via rotation‐invariant attacks

Deep neural networks are vulnerable to adversarial examples. However, existing attacks exhibit relatively low efficacy in generating transferable adversarial examples. Improved transferability to address this issue is proposed via a rotation-invariant attack method that maximizes the loss function w.r.t the random rotated image instead of the original input at each iteration, thus mitigating the high correlation between the adversarial examples and the source models and making the adversarial examples more transferable. Extensive experiments show that the proposed method can significantly improve the transferability of the adversarial examples with almost no extra computational cost and can be integrated into various methods. In addition, when this method is easily applied through a plug-in, the average attack success rate against six robustly trained models increases by 5.4% over the state-of-the-art baseline method, demonstrating its effectiveness and efficiency. The codes used are publicly available at https://github.com/YeXinD/Rotation-Invariant-Attack.