Reviewerly/exHarmony huggingface dataset:
A Study of Grammar-Concept Understanding Problem for C Programming Learning

Currently, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">programming</i> is educated at the first programming course in universities across the world. However, a lot of students find difficulty due to formality in programming. To assist self-studies, we have developed <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">programming</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assistant</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPLAS</i> ) by extending works of JPLAS for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Java</i> . In this paper, we study the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grammar-concept</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">understanding</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">problem</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GUP</i> ) in CPLAS for the first-step study of novice students. A GUP instance has a source code and a set of questions on grammar concepts in the code, where the answer is a keyword. The correctness is marked through <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">string</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matching</i> . We select the keywords and the questions with the definitions for GUP. For evaluations, we generate 12 GUP instances and assign them to 75 undergraduate students in Japan and Myanmar. The results show that the proposal is effective in revealing the student understanding levels.
HackEd: A Pedagogical Analysis of Online Vulnerability Discovery Exercises

Hacking exercises are a common tool for security education, but there is limited investigation of how they teach security concepts and whether they follow pedagogical best practices. This paper enumerates the pedagogical practices of 31 popular online hacking exercises. Specifically, we derive a set of pedagogical dimensions from the general learning sciences and educational literature, tailored to hacking exercises, and review whether and how each exercise implements each pedagogical dimension. In addition, we interview the organizers of 15 exercises to understand challenges and tradeoffs that may occur when choosing whether and how to implement each dimension.We found hacking exercises generally were tailored to students’ prior security experience and support learning by limiting extraneous load and establishing helpful online communities. Conversely, few exercises explicitly provide overarching conceptual structure or direct support for metacognition to help students transfer learned knowledge to new contexts. Immediate and tailored feedback and secure development practice were also uncommon. Additionally, we observed a tradeoff between providing realistic challenges and burdening students with extraneous cognitive load, with benefits and drawbacks at any point on this axis. Based on our results, we make suggestions for exercise improvement and future work to support organizers.
The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems

Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment. Personalization systems typically build a user model, aiming to characterize the user at hand, and then use this model to personalize the interaction. Personalization and user modeling, however, are often intrinsically at odds with each other (a fact some times referred to as the personalization paradox). In this paper, we take a closer look at this personalization paradox, and identify two ways in which it might manifest: feedback loops and moving targets. To illustrate these issues, we report results in the domain of personalized exergames (videogames for physical exercise), and describe our early steps to address some of the issues arisen by the personalization paradox.
Semantics of Voids within Data: Ignorance-Aware Machine Learning

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.
Comparison of Combinations of Data Augmentation Methods and Transfer Learning Strategies in Image Classification Used in Convolution Deep Neural Networks

Several studies have already made a comparison of either Data Augmentation methods or Transfer learning strategies in Convolution Deep Neural Networks for Image Classification; however, comparison of combinations of Data Augmentation methods and Transfer learning strategies remains to be accomplished. Combination of Data Augmentation methods with the highest-performing results and Transfer learning strategy with the highest-performing results does not achieve top-performing results in total as well. We make the comparison of four Data Augmentation methods, the comparison of four Transfer learning strategies, used on five different image classification models and the comparison of all combinations of them. We use small dataset consists of 40 images for training and finetuning and accuracy as metric. Our research shows that the performance results of a model with combinations of methods and strategies cannot be expected from simple comparisons of parts of this combination.
Accelerating LSH-based Distributed Search with In-network Computation

Locality Sensitive Hashing (LSH) is widely adopted to index similar data in high-dimensional space for approximate nearest neighbor search. With the rapid increase of datasets, recent interests in LSH have moved to the implementation of distributed search systems with low response time and high throughput. However, as the scale of the concurrent queries and the volume of available data grow, large amounts of index messages still need to be transmitted to centralized servers for the candidate answer reducing and resorting. Hence, the network remains the bottleneck in distributed search systems.To address this gap, we turn our efforts to the network itself and propose NetSHa. NetSHa exploits the in-network computational capacity provided by programmable switches. Specially, NetSHa designs a sort-reduce approach to drop the potential poor candidate answers and aggregates the good candidate answers on programmable switches, while preserving the search quality. We implement NetSHa on Barefoot Tofino switches and evaluate it using 3 datasets (i.e., Random, Wiki and Image). The experimental results show that NetSHa reduces the packet volume by 10 times at most and improves the search efficiency by 3x at least, in comparison with typical LSH-based distributed search frameworks.
Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality

Social presence, the feeling of being there with a “real” person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR). The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. However, these PS models are time-consuming to build and are typically trained with limited data variability, which results in poor generalization and robustness. Major sources of variability that affects the accuracy of facial expression transfer algorithms include using different VR headsets (e.g., camera configuration, slop of the headset), facial appearance changes over time (e.g., beard, make-up), and environmental factors (e.g., lighting, backgrounds). This is a major drawback for the scalability of these models in VR. This paper makes progress in overcoming these limitations by proposing an end-to-end multi-identity architecture (MIA) trained with specialized augmentation strategies. MIA drives the shape component of the avatar from three cameras in the VR headset (two eyes, one mouth), in untrained subjects, using minimal personalized information (i.e., neutral 3D mesh shape). Similarly, if the PS texture decoder is available, MIA is able to drive the full avatar (shape + texture) robustly outperforming PS models in challenging scenarios. Our key contribution to improve robustness and generalization, is that our method implicitly decouples, in an unsupervised manner, the facial expression from nuisance factors (e.g., headset, environment, facial appearance). We demonstrate the superior performance and robustness of the proposed method versus state-of-the-art PS approaches in a variety of experiments.
Gaussian Based Non-linear Function Approximation for Reinforcement Learning

Abstract Reinforcement learning (RL) problems with continuous states and discrete actions (CSDA) can be found in classic examples such as Cart Pole and Puck World, as well as real world applications such as Market Making. Solutions to CSDA problems typically involve a function approximation (FA) of the mapping from states to actions and can be linear or nonlinear. Linear FAs such as tile-coding (Sutton and Barto in Reinforcement learning, 2nd ed, 2009) suffer from state information loss due to state discretization, whilst non-linear FAs such as DQN (Mnih et al. in Playing atari with deep reinforcement learning, https://arxiv.org/abs/1312.5602 , 2013) are practically infeasible in infinitely large state spaces due to their cubic time complexity ( $$O(n^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math> ). In this paper, we propose a novel, general solution to CSDA problems, called Gaussian distribution based non-linear function approximation (GBNLFA). Experimentation on three CSDA RL problems (Cart Pole, Puck World, Market Marking) demonstrates the superiority of GBNLFA over state-of-the-art FAs, namely tile-coding and DQN. In particular, GBNLFA resolves the state information loss problem with linear FAs and provides an asymptotically faster algorithm ( O ( n )) than linear FAs ( $$O(n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math> ) and neural network based nonlinear FAs ( $$O(n^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math> ).
Whale Watching in Inland Indonesia: Analyzing a Small, Remote, Internet-Based Community Cellular Network

While only generating a minuscule percentage of global traffic, largely lost in the noise of large-scale analyses, remote rural networks are the physical frontier of the Internet today. Through tight integration with a local operator’s infrastructure, we gather a unique dataset to characterize and report a year of interaction between finances, utilization, and performance of a novel, remote, data-only Community LTE Network in Bokondini, Indonesia. With visibility to drill down to individual users, we find use highly unbalanced and the network supported by only a handful of relatively heavy consumers. 45% of users are offline more days than online, and the median user consumes only 77 MB per day online and 36 MB per day on average, limiting consumption by frequently “topping up” in small amounts. Outside video and social media, messaging and IP calling provided by over-the-top services like Facebook Messenger, QQ, and WhatsApp comprise a relatively large percentage of traffic consistently across both heavy and light users. Our analysis shows that Internet-only Community Cellular Networks can be profitable despite most users spending less than $1 USD/day, and offers insights into the unique properties of these networks.
Construction of Ideological Education Evaluation System for College Students -- Based on BP Neural Network Model Algorithm

Abstract Because of the characteristics of Ideological Education, it cannot be expressed accurately by simple mathematical analysis like other disciplines. The emergence of artificial neural network can solve this problem, but there are not many researches about BPNN (BP neural network) in the evaluation system of Ideological Education in Colleges and universities. Therefore, this paper puts forward the construction of Ideological Education evaluation system for college students --- Based on BPNN model algorithm research. In this paper, the existing Ideological Education in Colleges and universities and the application of BPNN in the evaluation system of Ideological Education are deeply studied. It is considered that the existing evaluation system of Ideological Education has some technical problems such as large evaluation error. In view of this, according to the actual needs of Ideological Education evaluation in Colleges and universities, combined with the latest BPNN technology, this paper establishes a new evaluation system of Ideological Education in Colleges and Universities Based on BPNN. This paper optimizes the traditional BPNN algorithm, simplifies the calculation steps and improves the calculation accuracy of the model. In order to further verify the actual effect of the evaluation model in this paper, the relevant comparative experiments are carried out, and the simulation results are analyzed by comparing with the expert evaluation. The experimental results show that the error value of the simulation results of the evaluation model in this paper is kept below 1.5%, and the accurate value is within the acceptable range, which can meet most of the current Ideological Education evaluation needs.
Effective Machine Learning Approaches for Credit Card Fraud Detection

Credit card is a critical element in digital banking. As modern technology is evolving day by day, credit card fraud is increasing significantly. Many financial companies lose billions of dollars annually because of credit card fraud. Criminals constantly try to discover various rules to require illegal actions. As a result, fraud detection methods are crucial for non-banking and banking financial institutions for minimizing the losses. This paper mainly discusses the performance of random forest (RF), AdaBoost, CatBoost classifiers for classification of credit card fraudulent activities. In this paper, the most important features are also selected by applying these classifiers. Results show that among the classifiers considered, RF and CatBoost have the best performance obtaining 99.92% accuracy in detecting credit card frauds.
Orderista - AI-based Food Ordering Application

In today's world, with people hustling for their jobs all the time, a huge count of people don't have the time to actually prepare their lunch and here canteen plays a major role. But then again people are in a rush and don't have much time to spend in the canteen as well to place an order or wait until their order is ready. This project is based on an AI-based cross-platform application that focuses on automating all the major canteen functionalities. It enables the user to register online, browse and choose from an E-menu card followed by placing the order and receiving confirmation after successful payment. With the help of this app, students and faculties can order food beforehand and can receive it during the break to ensure that the time spent in ordering and collecting the order is as low as possible. The app also docks in AI, which will help the users to get personal recommendations and food items which are popular amongst others. The objective is to reduce the manual paperwork as the app digitalizes every factor and provides a graphical representation of daily sales and allows comparison for weekly, monthly and annual sales. The feedback system allows the admin to monitor the app and make changes based on the user feedback.
Improved real-time target detection algorithm for similar multiple targets in complex underwater environment based on YOLOv3

In this paper, we propose an improved real-time target detection algorithm for similar multiple targets in complex underwater environment based on YOLOv3, which is used for real-time detection of marine organisms (such as holothurians, echinus, scallop and starfish). In traditional methods, the average precision of the classifiers which need to be designed manually is low. In contrast, the target recognition system based on deep learning has great advantages in accuracy and generalization. However, due to their special shapes and appearances, marine organisms are not only difficult to be distinguished from each other, but also easy to be confused with the seabed background. Therefore, the existing target detection algorithms based on deep learning has not achieved good results in the recognition of underwater complex environment. In this regard, we processed the dataset - by data augmentation, image enhancement, sample equalization, which reduces the impact of the dataset on the training results and ensures the accuracy of recognition to some extent. We also made some improvements on yolov3, including adding the feature clear layers and pooling layers, so as to extract more effective features, and also avoid the over-fitting problem in the training process. The experimental results show that our method effectively improves the accuracy of target detection, and has almost no impact on the real-time performance of the system.
Improvement of student mathematics learning outcomes through Kahoot learning games application at elementary school

Abstract The purpose of this research is to improve the student mathematics learning outcomes through Kahoot learning games application at elementary school. This classroom action research adopted from Kemmis and Mc Taggart Model was conducted in three cycles for the fourth grade as many as 22 students at 02 Public Elementary school in Tomohon, North Sulawesi, in Indonesia. The result of this study shows there is an improvement of students learning outcomes from cycle one to cycle three reaching 64, 72, and 81 from the maximum score of 100. Percentage of teacher and student activities increased from cycle one to cycle three achieving 77, 80, and 86 %. It can be concluded that application of Kahoot learning games can improve the student mathematics learning outcomes at 02 Public Elementary school in Tomohon, North Sulawesi, in Indonesia.
vGaze: Implicit Saliency-Aware Calibration for Continuous Gaze Tracking on Mobile Devices

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, implicit saliency-aware calibration for continuous gaze tracking on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, and performs opportunistic calibration using only those "useful" frames. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm.
Adversarial Robust Model Compression using In-Train Pruning

Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model’s size and its robustness against attacks. Both need to be addressed even-handedly. We combine adversarial training and model pruning in a joint formulation of the fundamental learning objective during training. Unlike existing post-train pruning approaches, our method does not use heuristics and eliminates the need for a pre-trained model. This allows for a classifier which is robust against attacks and enables better compression of the model, reducing its computational effort. In comparison to prior work, our approach yields 6.21 pp higher accuracy for an 85 % reduction in parameters for ResNet20 on the CIFAR-10 dataset.
Analyzing Learning-Based Networked Systems with Formal Verification

As more applications of (deep) neural networks emerge in the computer networking domain, the correctness and predictability of a neural agent's behavior for corner case inputs are becoming crucial. Enabling the formal analysis of agents with nontrivial properties, we bridge between specifying intended high-level behavior and expressing low-level statements directly encoded into an efficient verification framework. Our results support that within minutes, one can establish the resilience of a neural network to adversarial attacks on its inputs, as well as formally prove properties that were previously relying on educated guesses. Finally, we also show how formal verification can help create an accurate visual representation of an agent behavior to perform visual inspection and improve its trustworthiness.