Reviewerly/exHarmony huggingface dataset:
Free Adversarial Training with Layerwise Heuristic Learning

Due to the existence of adversarial attacks, various applications that employ deep neural networks (DNNs) have been under threat. Adversarial training enhances robustness of DNN-based systems by augmenting training data with adversarial samples. Projected gradient descent adversarial training (PGD AT), one of the promising defense methods, can resist strong attacks. We propose “free” adversarial training with layerwise heuristic learning (LHFAT) to remedy these problems. To reduce heavy computation cost, we couple model parameter updating with projected gradient descent (PGD) adversarial example updating while retraining the same mini-batch of data, where we “free” and unburden extra updates. Learning rate reflects weight updating speed. Weight gradient indicates weight updating efficiency. If weights are frequently updated towards opposite directions in one training epoch, then there are redundant updates. For higher level of weight updating efficiency, we design a new learning scheme, layerwise heuristic learning, which accelerates training convergence by restraining redundant weight updating and boosting efficient weight updating of layers according to weight gradient information. We demonstrate that LHFAT yields better defense performance on CIFAR-10 with approximately 8% GPU training time of PGD AT and LHFAT is also validated on ImageNet. We have released the code for our proposed method LHFAT at https://github.com/anonymous530/LHFAT.
An Unsupervised Registration Method for Brain Images Based on Contour Guidance

For medical image registration with large deformation, the huge differences in shape, position and texture make this task challenging. At present, unsupervised registration methods usually use registration software or traditional registration methods for preprocessing, thus only focusing on nonlinear transformation. This paper designs an unsupervised affine registration network to achieve end-to-end registration. The algorithm consists of two parts: (1) Use the designed affine network to predict affine parameters. (2) Use the affine transformer to perform affine transformation on the image. To make the network converge faster, we design a parameter regularization loss for affine parameter constraint. We tested our method on two datasets, including a private dataset and a public dataset, BraTS2018. We use normalized cross-correlation (NCC), dice similarity coefficient (DSC) and mean square error (MSE) as evaluation metrics. Compared with some commonly used unsupervised methods, our method achieves the best results in these three evaluation metrics. Through qualitative analysis, our method can register large deformation image well.
A Conceptual Framework for Leveraging English language teachers’ Awareness of Digital Literacy

Many factors drive the education to adopt digital technology. Globalization, rapid advancement of technology, global pandemic and generation z go together to support quick transformation of traditional method to technology enhanced learning. This situation demands English language teachers to be digitally literate. Myriad studies have discussed the issue of digital literacy practices within EFL/ESL context. Yet, limited of the studies provide a conceptual framework guiding English language teachers to view the practices of digital literacy in a holistic way. Correspondingly, this study provides a conceptual framework that enhances English language teachers’ awareness of some core aspects of practicing digital literacy. The framework consists of five simultaneous components: knowing the notion of digital literacy, classifying digital tools, navigating purposes of using digital tools, applying digital literacy skills, and identifying possible challenges. Understanding the basic concept of digital literacy and its position among the grand theories of literacy provides a strong foundation that drives the English language teachers to always keep on the track of performing the practices of digital literacy. Classifying and navigating the purposes of utilizing digital tools assist the English language teachers to link and match the functions of digital tools to their teaching context. Subsequently, applying skills of digital literacy and identifying its possible challenges lead the English language teachers to perform a well-prepared management of digital contents. Essentially, the framework can guide the English language teachers on how to deal with digital literacy practices in real classroom contexts.
Analysis of Big Data Survey Results and Research on System Construction of Computer Specialty System

The comprehensive quality training system for college students is of great significance for cultivating high-quality talents of the times and social development, and achieving the "Two Centenary" goals. In order to improve the training quality of computer major, the comprehensive quality training system for computer major is investigated and studied. Based on the findings of the survey and research, the problems of the comprehensive quality of college students were analyzed, and the basic principles of the construction of comprehensive quality training system for college students were followed, and comprehensive quality training system for computer major was constructed, including scientific literacy, professional competence, humanities and art, physical and mental health, practice and innovation. The research results serve the teaching reform of the computer major, and help cultivate applied talents with the ability to solve practical problems, innovation ability, sustainable development ability and good professional quality.
Robust Ellipse Fitting with an Auxiliary Normal

Ellipse fitting is a critical part of various applications. Recovering the ellipse from noisy or incomplete arcs remains challenging. In this paper, we propose an optimization method for ellipse fitting with an auxiliary normal to improve the performance. Firstly, ellipse fitting using an unerring normal is derived according to the projection from a circle in the world frame to an ellipse in the image frame. It can recover an accurate one directly from a short arc without iterative steps. Then, owing to the measurement error, an ellipse can be fitted iteratively within a tolerance range centered on the measured faulty normal. Finally, the calculation of requisite fitting matrices is simplified to speedup optimization. Each matrix can be constructed directly from a general 6-by-6 ellipse fitting matrix. Experimental results show that our method performs better than 4 state-of-the-art methods with limited tolerance.
A Comparison of the Efficiency of Eye Movement Data Processing with the Usage of a Multi-model Platform

During eye-tracking experiments, a large amount of data is collected. Usually, it is stored in text files, or file formats meant for scientific data. Because these formats provide limited sets of mechanisms for more comprehensive data analysis, various database models were considered as an alternative for them in this study. They included a relational database defined in MS SQL Server and two commonly used NoSQL solutions – MongoDB and Cassandra. The data structures defined for each of them and their performance were compared based on a chosen set of operations. A special multi-database platform was developed, enabling access to all database models without knowing their mechanisms for manipulating data. The studies revealed that Cassandra is the best database to store eye-tracking data when adjusted to the data retrieving operations. MongoDB databases featured with similar performance for various query types, yet with worse execution time. The worst results were obtained for the relational database. Designed to avoid redundancy by assumption, it stores data in many tables, and many join operations are needed while reading data. This fact entails weaker operations efficiency.
Distributed Hierarchical Sentence Embeddings for Unsupervised Extractive Text Summarization

Unsupervised text summarization is a promising approach that avoids human efforts in generating reference summaries, which is particularly important for large-scale datasets. To improve its performance, we propose a hierarchical BERT [1] model that contains both word-level and sentence-level training processes to achieve semantic-rich sentence embeddings. We use the vanilla BERT as the word-level training, and redesign it for the sentence-level training with the new “Sentence Token Prediction” and “Local Shuffle Recovery” training tasks and suitable input format. We first train word-level model to get preliminary sentence embeddings, then we input them into the sentence-level model to further extract higher level and inter-sentence semantic information. After that, we obtain the context sensitive sentence embeddings and utilize them for the KMeans cluster algorithm to finally generate summaries by extracting sentences from the document. To accelerate the training of the BERT model, we adopt the PipeDream [2] model parallelism that distributes the model layers among multiple machines to conduct the training process in parallel. Finally, we show through experimental results that our proposed model outperforms most popular models and achieves a speedup of 2.7 in training time on 4 machines.
Efficient Example Mining for Anchor-Free Face Detection

How to select the most suitable samples for face detection model training is considered to be a core procedure to improve the performance of the model. including the provision of Gtboxes, the matching of Gtboxes and feature points, and the label assignment of feature points. In this paper, we propose a novel anchor-free face detector based on efficient example mining (EEM). Firstly, Introduction a density variable to measure the number of feature positions that can be matched for each Gtboxes of different scales. Secondly, we propose an adaptive multi-scale ERF Prior for probabilistic label assignment which is totally anchor-free. The proposed methods are implemented on Caffe. Extensive experiments on popular benchmarks WIDER FACE demonstrate that the performance of EEM training based on Mobilenet-v1 is close to the state-of-the-art face detectors.
No-Reference Image Quality Assessment via Broad Learning System

Deep Learning (DL) can be used to model the process of No Reference-Image Quality Assessment (NR-IQA), which has a great contribution to the field of image processing. Even though, a large number of super parameters make the computational complexity gradually increase. Surprisingly, Broad Learning System (BLS) can transform the deep structure of DL into a flat and visual network structure, which reduces the difficulty for practical applications. By applying BLS in NR-IQA, combining the structural and statistical features of the image to reflect the image quality, which expands the research of NR-IQA undoubtedly. In this paper, the mathematical relationship between the image and the score is modeled by BLS, the effectiveness of the proposed method is demonstrated in the numerical experiments.
Rhythm Modeling of Calligraphy Lines based on Font Shape Recognition

Chinese calligraphy is one of the treasures of the splendid culture of the motherland. With the development of science and technology and the improvement of people's quality of life, the state's investment in cultural undertakings and the public's pursuit of literature and art show a rising trend. In this paper, an improved algorithm based on Z-S fast parallel algorithm is proposed. The algorithm improves the problem of fracture and burr when extracting skeleton. The extracted skeleton image can well show the “shape” of calligraphy characters, and can be better used for the calculation of image skeleton similarity.
Accelerometer based Complex Nurse Care Activity Recognition using Machine Learning Approach

Researchers have been working for a long time to recognize human activities based on sensor-based data. Despite the ongoing advancements in this field, it remains challenging to recognize complex human activities for a specific domain. To bring attention to this issue, the “Third Nurse Care Activity Recognition Challenge” gathered accelerometer data from smartphones to estimate daily nurse care activities. The main challenge was handling the noisy and inconsistent dataset, which is a persistent issue in any real-life data. Also, each activity depends on both the subject and the receiver, making its recognition more complex. Our team, Team Alkaline, used high pass and low pass filters to reduce noise, adopting an overlapping windowing technique. Then we extracted features in multiple domains to derive the necessary information required for classifying the human activities more accurately. Later on, we used a feature selection method to select the most significant features. We applied Random Forest (RF) classifier for training and achieved 99.0% accuracy on the validation set.
Dialog Speech Sentiment Classification for Imbalanced Datasets

Speech is the most common way humans express their feelings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sentiments in different kinds of datasets is still a challenging task. In this paper, we use single and bi-modal analysis of short dialog utterances and gain insights on the main factors that aid in sentiment detection, particularly in the underrepresented classes, in datasets with and without inherent sentiment component. Furthermore, we propose an architecture which uses a learning rate scheduler and different monitoring criteria and provides state-of-the-art results for the SWITCHBOARD imbalanced sentiment dataset.
Εφαρμογές Επαυξημένης Πραγματικότητας στην Εκπαίδευση

The present dissertation researches the technology of Augmented Reality (AR) and explores the need for developing an Augmented Reality application generally for education and particularly for Greek school textbooks.Initially the research focuses on the Augmented Reality definitions, examines the available technologies and devices, as well as the various fields that this technology can be used in. Furthermore, an analysis is presented of the use of new technologies in education and specifically of the applicability of Augmented Reality. The pedagogical framework and learning theories that suit it are laid out and a study is provided of the benefits and the limitations that this technology has. All the available AR development platforms are analyzed subsequently and the field of educational AR applications is mapped. As a result the lack of this kind of apps for Greek textbooks is easily observed.The 3rd grade primary school history textbook and specifically the Cycladic Culture section, is used as a case study in order to develop an AR app. The primary purpose of this study is to provide students with extra information and improved experience during the tuition. All the objectives of the history lesson are defined and the use of new technologies in this section is explained in detail.The requirement analysis and development stages of the application follow. Through research among educators and students the advantages of the use of AR apps during the teaching process are evaluated and prove to be significant and the quality characteristics that this kind of apps must have are extracted through questionnaires. Based on the findings of the above research the application was developed in Unity 3D with Vuforia SDK and was presented to educators and students in primary school.The resulting application was evaluated by experts and end users. The findings show that the whole attempt was successful, contributed positively to the learning process and the users were very enthusiastic about it.Finally, conclusions were drawn regarding all the questions that were initially set by the present dissertation.
Temporal Based Instance-Level Fusion for Video Object Detection

Static image object detection is naively applied to videos in a frame-by-frame approach, and the prediction is often temporally inconsistent. Enhancement of every selected frame becomes a typical solution by aggregating temporal nearby frames features. But the solutions above just use features in pixel-level. In this work, we emphasize the importance of the temporal sequential relationship, which considers calibrating instance-level features on time line, and combining the pixel-level features with the calibrated instance-level features finally. In order to prevent mismatch in video boundary, pixel-level re-fusion is remained to amend above issue. Although the instance-level fusion gains more global motion cues for the pixel-level calibration’s sake of robustness to occlusion, using temporal information can further improve the robustness. Experiments on the large-scale ImageNet VID dataset state the improvement of the our work for object detection in videos.
Resilient Level Ancestor, Bottleneck, and Lowest Common Ancestor Queries in Dynamic Trees

Abstract We study the problem of designing a resilient data structure maintaining a tree under the Faulty-RAM model [Finocchi and Italiano, STOC’04] in which up to $$\delta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> memory words can be corrupted by an adversary. Our data structure stores a rooted dynamic tree that can be updated via the addition of new leaves, requires linear size, and supports resilient (weighted) level ancestor queries, lowest common ancestor queries, and bottleneck vertex queries in $$O(\delta )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>δ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> worst-case time per operation.
ViCA: Combining visual, social, and task-oriented conversational AI in a healthcare setting

Recent developments in computer vision and conversational systems have provided the AI community with novel perspectives towards improving the cognitive capabilities of engaging socially assistive robots. We show how to develop conversational skills for a hospital receptionist robot that incorporates social conversation based on visual information as well as task-based dialog. Fusing the traditional modular conversational system architecture with recent developments in computer vision and scene graph research, our agent (called ‘ViCA’) supports both visual question answering and social conversational capabilities based on the visual scene. In particular, our agent can provide guidance to users by locating visible objects in the room and can engage in social dialog using visual prompts, such as the user’s clothing or possessions. We conduct a comprehensive online evaluation study with 21 participants, showcasing that the ViCA system is perceived as both helpful and entertaining.
Studying the Transferability of Non-Targeted Adversarial Attacks

There is no doubt that the use of machine learning is increasing every day. Its applications include self-driving cars, malware detection, recommendation systems and many other fields. Although the broad scope of this technology highlights the importance of its reliability, it has been shown that machine learning models can be vulnerable to adversarial attacks. In this paper, we study a property of these attacks called transferability across different architectures and models, measuring how these attacks transfer based on a specific number of parameters among three adversarial attacks: Fast Gradient Sign Method, Projected Gradient Descent and HopSkipJumpAttack.
Material change: transforming experience

Our relationship with materials, and the materials themselves, are constantly changing. Designers and manufacturers typically focus on the pristine object that entices the purchaser, with little consideration of how materials will change over days, years, or centuries of use, and how in turn this will influence the product’s life: from careful use to abuse and from reuse to disposal. Attitudes to material aging are highly variable, subjective, and shaped by myriad factors, which include both individual and societal influences. In this chapter a tentative framework for understanding the influence of material change on people’s experience of the material world which surrounds us is presented. The framework includes the consideration of the condition of the object, how its condition will change with use, and how this will influence people’s response to that object. Throughout the chapter diverse forms of material change that show the importance of time, context, and esthetics in influencing how material change is experienced are illustrated as case study examples. The chapter culminates with considerations on how material change may direct, inform, and create user experiences through shifting attitudes and behavior as an overt design strategy.
Monocular Visual Analysis for Electronic Line Calling of Tennis Games

Electronic Line Calling (ELC) is an auxiliary referee system used for tennis matches based on binocular vision technology. While ELC has been widely used, there are still many problems, such as complex installation and maintenance, high cost and etc. We propose a monocular vision technology based ELC method. The method has the following steps: (1) locate the tennis ball’s trajectory. We propose a multistage tennis ball positioning approach combining background subtraction and color area filtering. (2) Then We propose a bouncing point prediction method by minimizing the fitting loss of the uncertain point; (3) Finally, we find out whether the bouncing point of the ball is out of bounds or not according to the relative position between the bouncing point and the court side line in the two-dimensional image.We collected and marked 394 samples, we achieved 99.4% accuracy, and among the 11 samples with bouncing points near the line, we obtained 81.8% accuracy. The experimental results show that our method is feasible to judge if a ball is out of the court with monocular vision and significantly reduce complex installation and costs of ELC system with binocular vision.
Deployment of a change‐level software defect prediction solution into an industrial setting

Abstract Applying change‐level software defect prediction (SDP) in practice has several challenges regarding model validation techniques, data accuracy, and prediction performance consistency. A few studies report on these challenges in an industrial context. We share our experience in integrating an SDP into an industrial context. We investigate whether an “offline” SDP could reflect its “online” (real‐life) performance, and other deployment decisions: the model re‐training process and update period. We employ an online prediction strategy by considering the actual labels of training commits at the time of prediction and compare its performance against an offline prediction. We empirically assess the online SDP's performance with various lengths of the time gap between the train and test set and model update periods. Our online SDP's performance could successfully reach its offline performance. The time gap between the train and test commits, and model update period significantly impacts the online performance by 37% and 18% in terms of probability of detection (pd), respectively. We deploy the best SDP solution (73% pd) with an 8‐month time gap and a 3‐day update period. Contextual factors may determine the model performance in practice, its consistency, and trustworthiness. As future work, we plan to investigate the reasons for fluctuations in model performance over time.