Reviewerly/exHarmony huggingface dataset:
A Study of the Relationship Between a CS1 Student's Gender and Performance Versus Gauging Understanding and Study Tactics

Metacognitive monitoring is an individual's ability to assess their level of mastery. This skill is integral to learning because students decide what to study based on what they believe they do not understand. Therefore, how well can a CS1 student gauge their mastery? We had students demonstrate their metacognitive monitoring skills by predicting their scores for all three exams of a 15-week CS1 course. We collected data from two course offerings. Moreover, we had students predict both before and after each exam to understand the effect of seeing it and surveyed students on how they studied. We found that our study's students were reasonably accurate, but low performers were worse than high performers. However, high performers did not improve between their before and after predictions. We did not have sufficient evidence that students improve their predictions over time. Prediction accuracy did not have a gender effect. Finally, we found no difference in study tactics by gender and little difference between high and low performers. Overall, we found only some similarities to related work.
Combining BRIEF and AD for Edge-Preserved Dense Stereo Matching

Overlap between the patches from which descriptors of adjacent pixels are extracted results in edge fattening. Edge fattening can be addressed by adjusting aggregation techniques such as binary aggregation, that are proved successful in intensity based dense matching, for use with descriptors. However, binary aggregation applies masks that risk descriptiveness. Therefore, an intensity based metric such as AD (Absolute Difference) is proposed to supplement a masked binary descriptor such as BRIEF (Binary Robust Independent Elementary Features). The proposed matching cost function adaptively weights the contribution of each metric according to the patch’s content. This hybrid metric reduces the error by up to 2.86% without adding computational complexity by re-using pre-computed values. Middlebury stereo dataset and evaluation SDK (Software Development Kit) are used to evaluate the results.
Exploration on the Teaching System of Artificial Intelligence Practice Course

Artificial intelligence professional course is a new and targeted course in undergraduate colleges and universities in recent years. As an interdisciplinary and interdisciplinary specialty, the theoretical system of artificial intelligence is very complex. How to promote the construction of artificial intelligence practice courses in Colleges and universities and cultivate interdisciplinary and applied talents of artificial intelligence specialty has become a particularly important point. Taking Shenyang University as an example, combined with the experience of teaching practice, the practical teaching of artificial intelligence specialty this paper is analyzed in this paper. Combining the discipline characteristics of artificial intelligence with the characteristics of cooperative enterprises, this paper discusses the practical teaching settings of the course from two aspects of practical teaching system and practical teaching management implementation scheme, and puts forward how to implement reform measures for the talent training system, training mode and talent training objectives in engineering specialty, which is of great practical significance.
Recurrent Motion Neural Network for Low Resolution Drone Detection

Drones are becoming increasingly prevalent in everyday usage with many commercial applications in fields such as construction work and agricultural surveying. Despite their common commercial use, drones have been recently used with malicious intent, such as airline disruptions at Gatwick Airport. With the emerging issue of safety concerns for the public and other airspace users, detecting and monitoring active drones in an area is crucial. This paper introduces a recurrent convolutional neural network (CNN) specifically designed for drone detection. This CNN can detect drones from down-sampled images by exploiting the temporal information of drones in flight and outperforms a state-of-the-art conventional object detector. Due to the lightweight and low resolution nature of this network, it can be mounted on a small processor and run at near real-time speeds.
Taxonomy-Based Hybrid Recommendation System for Lifelong Learning to Improve Professional Skills

In light of the vast amount of information related to lifelong learning courses, this paper proposes a hybrid recommendation system to overcome information overload. This system is based on user taxonomy profiles and makes it possible to extract unstructured data from multiple sources. The use of taxonomy allows knowledge about both user profiles and course contents to be modeled, thus enhancing system performance. The recommender engine operates in four phases: the first step uses a collaborative filter to determine users' areas of work; another collaborative filter is then used on this result to determine the related skills of the profiles; a third content-based filter is applied to make a preliminary course shortlist; followed by a final recommendation that is refined by using heuristics. The proposed recommendation system was tested on 120 user profiles and an improvement in the quality of the recommendation was observed.
Koncokelas: Aplikasi Video Pembelajaran Berbasis Animasi sebagai Usaha Pemberdayaan Anak Putus Sekolah melalui Rumah Belajar

The service activities in Duria Village aim to improve the quality of individuals in the context of human resource development, form community cadres as agents of change, become a pillar of pride for the community and know the work process and how to implement community empowerment activities with Concloc activities. The implementation of activities is carried out using 4 (four) stages, namely: the initial stage of introducing and planting material, values and norms to school dropouts in Duria Village, the second stage is to develop the orientation of Koncokelas cadres, the third stage is a continuation of the stage the first and second stage, and the last stage, where at this stage the Koncokelas Cadres are ready to carry out field activities. The service that was carried out in Duria Village was carried out for 4 (four months). In this service activity, the target audience is school dropouts aged 10-15 years. The concept of service, namely Koncokelas: Application of Animation-Based Learning Videos as Efforts to Empower School Dropouts through Learning Houses. The result of this dedication is that the Classroom activities have succeeded in increasing the knowledge and skills of school dropouts as well as increasing their interest and motivation to learn.
Towards Practical Reuse of Custom Static Analysis Rules for Defect Localization

[Context] Several static analysis tools allow the development of custom rules for locating application-specific defects. Although this feature is powerful and commonly available, it is not well explored in practice. Custom static analysis rules can check design and policies that are shared between applications, allowing the reuse of rules. However, the benefits, scope, and concerns that software engineers should have on reusing custom static analysis rules are unknown. [Goal] In this preliminary study, we investigate the reuse of custom static analysis rules produced by applying Pattern-Driven Maintenance (PDM). PDM is a method to locate defect patterns in web applications that produces custom static analysis rules as output. [Method] We selected a set of rules produced by a previous usage of the PDM method and applied them to other three applications in two contexts, within the same company where the rules were produced, and in other companies. [Results] We successfully reused some rules in both scenarios with minor adjustments, finding new defects to be fixed. The reuse of rules could discard from 58-90% of source code locations found by a naive search for the defects, reducing verification effort. However, the reused rules need adjustments to improve precision for defect localization, as precision ranged from 40-75%. Finally, we identified factors that have an impact on reusing custom rules. [Conclusions] We put forward that reusing customized static analysis rules can be beneficial, in particular when similarities in the architecture and programming style are observed. However, adjustment of the rules might be needed to enable effective reuse. We shared our insights and methodology on how to reuse custom static analysis rules properly.
Face Recognition Based on CD-RBM and BM-ILM

Abstract In recent years, the application of neural networks in face recognition has improved its accuracy greatly. However, it still suffers from the disappearance of the network gradient and the training cost. In this paper, we propose a new hybrid method which combines the contrast divergence algorithm (CD) accelerated Restricted Boltzmann Machine (RBM) and the Integrated Learning Method (ILM) with the Boosting algorithm (BM). First, we use RBM to establish a minimum energy model of the sample distribution. Then we use CD to speed up the feature extraction of samples in RBM. And then we build a multi-classifier with heterogeneous samples for ILM by BM. Finally, we experience our proposed method on the ORL database and the AR database. The results show that, with a simpler neural network determined by CD-RBM and BM-ILM, the recognition accuracy is better than that without the proposed method.
The Influence of Mobile Operating Systems on User Security Behavior

Mobile security remains a concern for multiple stakeholders. Safe user behavior is crucial key to avoid and mitigate mobile threats. The research used a survey design to capture key constructs of mobile user threat avoidance behavior. Analysis revealed that there is no significant difference between the two key drivers of secure behavior, threat appraisal and coping appraisal, for Android and iOS users. However, statistically significant differences in avoidance motivation and avoidance behavior of users of the two operating systems were displayed. This indicates that existing threat avoidance models may be insufficient to comprehensively deal with factors that affect mobile user behavior. A newly introduced variable, perceived security, shows a difference in the perceptions of their level of protection among the users of the two operating systems, providing a new direction for research into mobile security.
Certainty Pooling for Multiple Instance Learning

Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings. We present a novel pooling operator called Certainty Pooling which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. We compare our proposed method with other pooling operators in controlled experiments with low evidence ratio bags based on MNIST, as well as on a real life histopathology dataset - Camelyon16. Our method outperforms other methods in both bag level and instance level prediction, especially when only small training sets are available. We discuss the rationale behind our approach and the reasons for its superiority for these types of datasets.
Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload

Although cloud computing ensures benefits like disaster recovery, remote access, large resource pools and increased collaboration, it comes with its own problems like managing and monitoring virtual machines during consolidation. Cloud service providers run multiple servers simultaneously to quench the requirements of the clients. Since these servers have static energy consumption, they consume a lot of energy against their utilization. They run multiple virtual machines in each server parallel, distributing the available resources like number of cores in CPU, memory, HDD storage, CPU execution time, etc., among multiple client applications, thereby ensuring complete usage of resources. Perpetual monitoring generates huge amounts of log data, and it becomes computationally expensive to perform analysis on the data. We propose an effective method by which we reduce the amount of data involved in analysis, thereby reducing computation and storage costs. In this paper, we have focused on its efficiency to cluster into two known clusters based on real traces and how accurate the results are with respect to these two clusters, namely fast and RND depending upon various characteristics including CPU cores, CPU capacity provisioned, CPU usage, memory provisioned and memory usage. We have compared the use of the proposed methodology autoencoders to choose the most pertinent information to group similar VMs and compared it with existing methodology PCA-based components using clustering techniques including k-means, hierarchical and a probabilistic approach Gaussian mixture model. Autoencoders-based components perform far better concerning PCA-based components in terms of predicting which cluster does the VM belongs to.
On Influence of Computer Multimedia Technology in Language Learning

Abstract In the 21st century, with the development of information technology, the popularization of multimedia technology and application of network technology, teaching methods and education technology have undergone great changes. How to fully understand the characteristics of multimedia technology, how to correctly use it to assist language teaching, thus effectively improve the efficiency of foreign language learning, is worth exploring. On the basis of defining the meaning of multimedia teaching, this paper analyzes in detail the advantages of applying multimedia technology in language teaching, thus creating good conditions for improving the efficiency of language classroom learning.
Application of Random Simulation Algorithm in Physical Education Teaching Evaluation

In the classical comprehensive evaluation theory, the information form of evaluation conclusion is usually absolute. In view of the absoluteness of judging the advantages and disadvantages of traditional physical education (PE) teaching evaluation and the inconsistency of multiple evaluation conclusions, this paper constructs an independent advantage evaluation method which highlights its own advantages, and puts forward a comprehensive evaluation mode of random simulation. In other words, by setting parameters, the traditional evaluation mode can be transformed into a random mode, and the possibility ranking conclusion of the comparison between the advantages and disadvantages of the schemes can be obtained. Because of the independence of the stochastic simulation solution method, this paper applies it to the evaluation model of PE, and constructs a new independent evaluation method to evaluate the advantages of the evaluation object by calculating the winning degree of each evaluation object. Finally, the random simulation comprehensive evaluation model was taken as the experimental group, and the traditional sports evaluation model as the control group. The results showed that each index of the experimental group was higher than that of the control group, and the comprehensive score was 0.83 higher than that of the control group. Thus, the random simulation evaluation model is an extension of the traditional evaluation model, providing a structural framework for various information forms and evaluators’ preferences, so that the evaluation process is no longer limited by single or limited data form and information structure, and can further enhance the practical application scope of comprehensive evaluation method.
3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints

This paper presents a novel edge-based 3D reconstruction method using a monocular camera. The edge information is known to be illumination-invariant and to include abundant structural information in a relatively small number of pixels. However, since edge line cannot explicitly determine the pixel-to-pixel correspondence as in the feature point approach, it is difficult to perform accurate matching of pixels in a scene with dense edge lines. In this study, edge-based 3D reconstruction using ICP (iterative closest point algorithm) with geometric constraints has been proposed. In our approach, quasi-rigid body assumption for the edge line deformation and smart search for the matching process are introduced for the improvement of matching robustness in scenes with dense edge lines. Experimental results show that the performance of our method for both motion parallax estimation and depth estimation is greatly improved compared with two recent edge-based methods.
Two-Loop System with Reference Model for Control of Spatial Movement of Cargo Underwater Vehicle

Currently, autonomous underwater vehicles (AUV) are increasingly used to perform tasks related to the maintenance of underwater communications and various underwater production complexes, as well as performing underwater technological operations. To effectively perform these operations, AUV must have high-quality control systems that will ensure their accurate movement both along long spatial trajectories formed during their movement to the objects of work, and when performing complex maneuvers near underwater infrastructure objects. At the same time, the main difficulty that arises in the process of synthesis of AUV control systems is the significant non-linearity of the dynamic models of these control objects, the presence of interactions between their degrees of freedom, as well as the uncertainty and variability of their parameters. In this paper, we propose a method for synthesizing the spatial motion control system of the AUV, which allows us to take into account these negative effects. This system contains two loops. The first loop includes a combined system containing a nonlinear controller to achieve the desired dynamic characteristics of the AUV, when its parameters are equal to the nominal values, and a controller with self-tuning according to the reference model, which provides compensation for an unknown or variable part of the parameters. In this case, the parameters of the controller with the reference model are selected to reduce the possible amplitude of the discontinuous signal for controlling the AUV velocity. The second loop is a non-linear position controller that allows to take into account the dynamic properties of the velocity control loop and the kinematic properties of the AUV. The advantage of the proposed control system in comparison with traditional ones based on PID controllers is a higher control accuracy when moving along complex spatial trajectories, regardless of changes in the AUV parameters. The simulation results confirmed the high efficiency of the synthesized two-loop control system.
Academic mobility or the Russian style networking?

Academic mobility facilitates interactions of different scientific schools and collectives, influences formation of academic relations and indirectly affects positions of universities in academic rankings. Mobility helps establish networks of professional contacts, and that might have a positive impact on the level of research, allowing efficient academic collaboration, access to results of different studies and collected data. Mobility is an important issue not only for universities, but also for researchers because their collaboration with the colleagues and participation in joint projects characterize them for other members of the academic community, and that increases the value of academic networking. However, the way of evolution of the institution of networking raises various questions about the objectivity of the recruiting process and advantages that some candidates get over their rivals. The purpose of this article is identification and analysis of those factors that force the institution of mobility in the Russian academic reality to work against general social interests and the interests of universities.
PrivAG: Analyzing Attributed Graph Data with Local Differential Privacy

Attributed graph data is powerful to describe relational information in various areas, such as social links through numerous web services and citation/reference relations in the collaboration network. Taking advantage of attributed graph data, service providers can model complex systems and capture diversified interactions to achieve better business performance. However, privacy concern is a huge obstacle to collect and analyze user's attributed graph data. Existing studies on protecting private graph data mainly focus on edge local differential privacy(LDP), which might be insufficient in some highly sensitive scenarios. In this paper, we present a novel privacy notion that is stronger than edge LDP, and investigate approaches to analyze attributed graphs under this notion. To neutralize the effect of excessively introduced noise, we propose PrivAG, a privacy-preserving framework that protects attributed graph data in the local setting while providing representative graph statistics. The effectiveness and efficiency of PrivAG framework is validated through extensive experiments.
Redrawing-resampling rejection controlled sequential importance sampling

Monte Carlo computation has been widely applied in the field of dynamic systems. This paper focuses on the general framework in the implementation of sequential importance sampling by combining redrawing, resampling and rejection control simultaneously. The proposed algorithm is named as Redrawing Resampling Rejection Controlled Sequential Importance Sampling (RR-RC-SIS). It can reduce sampling computation and meanwhile maintain the diversity of random samples. Theoretical basis is given to prove that RR-RC-SIS has advantages in comparison with Rejection Controlled Sequential Importance Sampling. It also has practical value as illustrated in numerical simulation on blind deconvolution problem in digital communications.