Reviewerly/exHarmony huggingface dataset:
Convolutional Recurrent Neural Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian Knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent Knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets.
Anachronism by Design: Understanding Young Adults’ Perceptions of Computer Iconography

Computer iconography in desktop operating systems and applications has evolved in style but, in many cases, not in substance for decades. For example, in many applications, a 3.5" floppy diskette icon still represents the “Save” function. But many of today's young adult computer users grew up without direct physical experience of floppy diskettes and many of the other objects that are represented by enduring legacy icons. In this article, we describe a multi-part study conducted to gain an understanding of young adults’ perceptions of computer iconography, and to possibly update that iconography based on young adults’ current mental models. To carry out this work, we gathered a set of 39 icons found on common desktop operating systems and applications and also recruited 30 young adults aged 18–22. In the first part of our study, an end-user elicitation study, we asked participants to propose sketches of icons they deemed most appropriate to trigger the functions associated with our selected icons. We elicited a total of 3,590 individual icon sketches and grouped these into a set of participant-generated icons. In the second part of our study, an end-user identification study, we showed participants the 39 icons from current operating systems and asked them to name the computing functions triggered when those icons were selected. We also asked them to identify the real-world objects, if any, those icons represented, and to tell us about their personal experiences with those objects. Finally, we conducted a second identification study with 60 new participants from Amazon's Mechanical Turk on the set of participant-generated icons we obtained from the first part of our study to see how recognizable our young adults’ sketched icons were. Our study results highlight 20 anachronistic icons currently found on desktop operating systems in need of redesign. Our results also show that with increased icon production, the chances for anachronism significantly decrease, supporting the “production principle” in elicitation studies. Furthermore, our results include an updated set of icons derived from our young adult participants. This work contributes an approach to using end-user elicitation to understand users, user interface design, and specifically, icon design.
Accurate Prediction of Fake Job Offers Using Machine Learning

The recent growth of online recruitment and candidate management system has established yet another media for fraudsters on the Internet. People who need job are getting being scammed by fake advertisements by fraudsters on popular websites. These fraudsters are enlarging their scams by motivating job seekers as full-time roles with basic minimum qualifications like B.Tech. and degree. Machine learning classification techniques are essential tools in extracting invisible knowledge from a huge dataset to increase the accuracy and efficiency in predictions. In this paper, we analysed fake job posting dataset and used machine learning techniques to classify job advertisements which are fraudulent or real. Various machine learning algorithms and performance metrics are used in this paper. The benefaction of this research is one encompassing of contextual capabilities space, which relieved absorbing improvements of accuracy, precision and recall.
Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.
The role of data and information security governance in protecting public sector data and information assets in national government in South Africa

Background: The deployment of information and communications technology (ICT) in the public sector, has been exposed to increasing security breaches and cyber-related crimes that have resulted in unauthorised access, theft, fraud and misuse of highly confidential, classified and sensitive public sector data and information (PSDI) assets. The government, as one of the biggest collectors and distributors of PSDI assets, needs to be constantly aware of the risks associated with the collection, classification, storage and dissemination of critical PSDI assets. The lack of sufficient data and information security measures could pose significant security risks that could impact on state security, thus causing national working relationships to be strained, which presents gaps and opportunities for external intruders to capitalise on the mistrust of the government to infiltrate further attacks on critical Information Technology (IT) infrastructure and systems. In order to mitigate and counteract critical and sensitive data and information-related crimes, the government must understand and analyse the importance of data and information security governance (DISG) and how it should be institutionalised through an integrated approach to improve and protect PSDI assets. Aim: The aim of this article is to analyse the institutionalisation of DISG measures government has implemented towards the protection of PSDI assets. Setting: The research setting is in three national government departments, namely the Department of Energy (DoE), the Department of Environmental Affairs (DEA) and the Department of Science and Technology (DST). This study investigates how the strategic combination of data governance (DG) and information security governance (ISG) practices and principles could be implemented and incorporated as one of the various approaches in public sector institutions to improve the DISG management functions of an organisation's overall data and information systems and processes. Methods: The research approach is qualitative, and the research methodology includes a multiple case study design. Data were collected through semi-structured interviews and was triangulated with literature review. Primary data was analysed using thematic analysis. Results: The research findings are presented according to the McKinsey 7S model, which served as the analytical framework in the study. The research findings indicate that the institutionalisation of DISG management practices and functions in the South African public sector context are very limited, and there is a dominant focus on IT and IT security. It was also identified that DISG policies, practices, and systems have been found to be lacking in public sector management and governance functions. Conclusion: The study concludes that there is currently a lack of sufficient DISG policies, management practices and systems, particularly in the national sphere of government.
Distribution inference from early-stage stationary data streams by transfer learning

Data streams are prevalent in current manufacturing and service systems where real-time data arrive progressively. A quick distribution inference from such data streams at their early stages is extremely useful for prompt decision making in many industrial applications. For example, a quality monitoring scheme can be quickly started if the process data distribution is available and the optimal inventory level can be determined early once the customer demand distribution is estimated. To this end, this article proposes a novel online recursive distribution inference method for stationary data streams that can respond as soon as the streaming data are generated and update as regularly as the data accumulate. A major challenge is that the data size might be too small to produce an accurate estimation at the early stage of data streams. To solve this, we resort to an instance-based transfer learning approach which integrates a sufficient amount of auxiliary data from similar processes or products to aid the distribution inference in our target task. Particularly, the auxiliary data are reweighted automatically by a density ratio fitting model with a prior-belief-guided regularization term to alleviate data scarcity. Our proposed distribution inference method also possesses an efficient online algorithm with recursive formulas to update upon every incoming data point. Extensive numerical simulations and real case studies verify the advantages of the proposed method.
A novel optimal route planning algorithm for searching on the sea

ABSTRACT Emergency search and rescue on the sea is an important part of national emergency response for marine perils. Optimal route planning for maritime search and rescue is the key capability to reduce the searching time, improve the rescue efficiency, as well as guarantee the rescue target’s safety of life and property. The main scope of the searching route planning is to optimise the searching time and voyage within the constraints of missing search rate and duplicate search rate. This paper proposes an optimal algorithm for searching routes of large amphibious aircraft corresponding to its flight characteristics and sea rescue capability. This algorithm transforms the search route planning problem into a discrete programming problem and applies the route traceback indexes to satisfy the duplicate search rate and missing search rate.
From Semantic to Spatial Awareness: Vehicle Reidentification With Multiple Attention Mechanisms

The rapid development and popularization of video surveillance highlight the critical and challenging problem, vehicle reidentification, which suffers from the limited interinstance discrepancy between different vehicle identities and large intrainstance differences of the same vehicle. In this article, we propose a novel multilevel attention network to hierarchically learn an efficient feature embedding for vehicle re-ID. Three kinds of attention are designed in the network: hard local-level attention to localize vehicle salient parts, soft pixel-level attention to refine attended pixels both globally and locally, and spatial attention to enhance the encoder’s spatial awareness of salient regions within the windscreen area. Multigrain features are subsequently learned from semantic awareness to spatial awareness, guaranteeing the intraclass compactness and interclass separability for vehicle re-ID. Extensive experiments validate the effectiveness of each attention component and demonstrate that our approach outperforms the state-of-the-art re-ID methods on two challenging datasets: VehicleID and Vehicle-1 M.
Elasecutor: Elastic Executor Scheduling in Data Analytics Systems

Modern data analytics systems use long-running executors to run an application's entire DAG. Executors exhibit salient time-varying resource requirements. Yet, existing schedulers simply reserve resources for executors statically, and use the peak resource demand to guide executor placement. This leads to low utilization and poor application performance. We present Elasecutor, a novel executor scheduler for data analytics systems. Elasecutor dynamically allocates and explicitly sizes resources to executors over time according to the predicted time/varying resource demands. Rather than placing executors using their peak demand, Elasecutor strategically assigns them to machines based on a concept called dominant remaining resource to minimize resource fragmentation. Elasecutor further adaptively reprovisions resources in order to tolerate inaccurate demand prediction and reschedules tasks to deal with inadequate reprovisioning resources on one machine. Testbed evaluation on a 35-node cluster with our Spark-based prototype implementation shows that Elasecutor reduces makespan by more than 36% on average, and improves cluster utilization by up to 55% compared to existing work.
TULIP: A Toolbox for Linear Discriminant Analysis with Penalties

Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is carefully tailored for low storage and high computation efficiency. Moreover, our package is the first R package for many of these methods, providing great convenience to researchers in this area.
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In this paper, we propose a new solution for an old problem in robotics related to the calibration of the hand, i.e., the robot’s end-effector, and the eye, i.e., a stereo vision camera. The problem is formulated as a point set matching problem and a nonlinear estimator on manifold SO(3)×R3 used for obtaining the solution. The main advantage of the proposed approach is that it allows to decouple the error associated with the rotational estimation on Lie group SO(3) from the error of the translational estimation on R3, which subsequently allows to tune the learning rates for the rotation and translation estimation, separately. This will result in significant increase in the convergence speed of the proposed approach. To show the advantages of our approach, we compare the results with those obtained from other conventional hand–eye calibration solutions as well as those based on point set matching. The experimental results will demonstrate that our proposed hand–eye calibration approach outperforms other approaches in terms of accuracy and computational speed.
Security Assessment Process of IT-components for Cloud Infrastructure

Nowadays, the attention has increased significantly on the IT-components security, including the assessment of multiple types of objects using cloud infrastructure. The article discusses the opinions of Russian and foreign experts on a wide range of issues of IT-security as stand-alone IT-components and holistic cloud services. The article proposes a practical approach based on the “Hybrid” methodology using formal assessment procedures based on two cardinalities of criteria: the assessment of management systems compliance degree (based on ISO/IEC 27001 series) and the assessment of functional safety requirements (based on IEC 61508 series and ISO/IEC 15408 series). The proposed method allows to obtain the traceability results of IT-security risk assessment of cloud IT-components in the given constraints. Sufficient and correctly implemented countermeasures minimize the risk (residual risk) for significant assets. These results can be applied to the processes of an independent assessment, including critical infrastructure, with the required accuracy of calculation.
Digitally-mediated parent–baby touch and the formation of subjectivities

This article examines how the use of emergent smart baby monitors re-mediates parent–baby touch, notions of connection, parental sensing and the interpretation of babies’ bodies, and contributes to the formation of subjectivities. Domestic baby monitors are a mid 20th-century phenomenon which normalizes parental anxieties. While baby monitoring is not new, the ‘next generation’ of wearable bio-sensing baby monitors offers a different relationship to the body via the physiological tracking of babies, and the sending of information or alerts to parents’ via connected mobile apps. These devices have been associated with creating unnecessary parental anxiety and the digital ‘replacement’ of parental touch, although little research exists on their use in the context of parent–infant interaction or touch. The authors present a qualitative case study of one such technology, Owlet, to explore how parents experienced, understood and negotiated the discourses of parent–infant touch that circulate around and through Owlet, with particular attention to the relationship between visual and tactile resources. The study focuses on both its multimodal design and take-up by parents through analysis of interviews with the Owlet designer, Owlet as a product, focus groups with parents and families’ home experiences of Owlet. Data is analysed through a tri-part lens, which first combines multimodal social semiotic and sensory ethnographic approaches, and then the analytical concept of governmentality. The findings are discussed in relation to four analytical themes: (1) creating a desire for digitally mediated touch; (2) spatiality of digitally mediated connection; (3) formulating the ‘right kind’ of touch; and (4) reconfiguring ‘knowing touch’. The authors discuss multimodal discourses pertinent to the shaping of parent–baby touch practices including: rationality and efficiency; individualism, autonomy and freedom; and self-improvement and empowerment. They conclude that the discourses that coalesce around Owlet contribute to the reconfiguration of parent–baby touch and the formation of neoliberal subjectivities.
Joint State and Dynamics Estimation With High-Gain Observers and Gaussian Process Models

With the rising complexity of dynamical systems generating ever more data, learning dynamics models appears as a promising alternative to physics-based modeling.However, the data available from physical platforms may be noisy and not cover all state variables.Hence, it is necessary to jointly perform state and dynamics estimation.In this paper, we propose interconnecting a high-gain observer and a dynamics learning framework, specifically a Gaussian process state-space model.The observer provides state estimates, which serve as the data for training the dynamics model.The updated model, in turn, is used to improve the observer.Joint convergence of the observer and the dynamics model is proved for high enough gain, up to the measurement and process perturbations.Simultaneous dynamics learning and state estimation are demonstrated on simulations of a mass-spring-mass system.
Model Compression for Domain Adaptation through Causal Effect Estimation

Abstract Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led to various attempts of compressing such models, but existing methods have not considered the differences in the predictive power of various model components or in the generalizability of the compressed models. To understand the connection between model compression and out-of-distribution generalization, we define the task of compressing language representation models such that they perform best in a domain adaptation setting. We choose to address this problem from a causal perspective, attempting to estimate the average treatment effect (ATE) of a model component, such as a single layer, on the model’s predictions. Our proposed ATE-guided Model Compression scheme (AMoC), generates many model candidates, differing by the model components that were removed. Then, we select the best candidate through a stepwise regression model that utilizes the ATE to predict the expected performance on the target domain. AMoC outperforms strong baselines on dozens of domain pairs across three text classification and sequence tagging tasks.1
Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal

Drones are applied for normal subjects likewise as forces exercise. Consultations with drones are liable to deal with and compromise since they're broadly used for self-directed conduct. Still, it is of great consequence to take on an automatic pilot framework which is vigorous to potential digital assault. Right now, we tend to propose an individual's implicit intent recognition model dependent on a multi-modular data that is a blend of the eye movement data and the EEG signal acquired from some eye-locators and EEG scanners separately. The eye movement will be used to extricate some highlights like fixation length and fixation count relating to the visual stimuli, and similarly, we will examine the graph signals observed on part synchronicity technique and consolidating this, we will also train a few classifiers such as the SVM classifier, Naïve Bayesian and Gaussian Mixture Model that might effectively recognize an individual's implicit intention into 2 characterized classes - navigational and informational intention, which will ultimately be used for training a drone. Also, we will be displaying a biometric framework to scramble letters between a drone and an electronic ground station which can be achieved by creating a key from the EEG signal of a user. Then, at the endpoint, once the correspondence with a drone is assaulted a security system facilitates it to a sheltered `home' area.