Reviewerly/exHarmony huggingface dataset:
Learning Visual Words for Weakly-Supervised Semantic Segmentation

Current weakly-supervised semantic segmentation (WSSS) methods with image-level labels mainly adopt class activation maps (CAM) to generate the initial pseudo labels. However, CAM usually only identifies the most discriminative object extents, which is attributed to the fact that the network doesn't need to discover the integral object to recognize image-level labels. In this work, to tackle this problem, we proposed to simultaneously learn the image-level labels and local visual word labels. Specifically, in each forward propagation, the feature maps of the input image will be encoded to visual words with a learnable codebook. By enforcing the network to classify the encoded fine-grained visual words, the generated CAM could cover more semantic regions. Besides, we also proposed a hybrid spatial pyramid pooling module that could preserve local maximum and global average values of feature maps, so that more object details and less background were considered. Based on the proposed methods, we conducted experiments on the PASCAL VOC 2012 dataset. Our proposed method achieved 67.2% mIoU on the val set and 67.3% mIoU on the test set, which outperformed recent state-of-the-art methods.
On Guaranteed Optimal Robust Explanations for NLP Models

We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left-out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. We show how our method can be configured with different perturbation sets in the embedded space and used to detect bias in predictions by enforcing include/exclude constraints on biased terms, as well as to enhance existing heuristic-based NLP explanation frameworks such as Anchors. We evaluate our framework on three widely used sentiment analysis tasks and texts of up to 100 words from SST, Twitter and IMDB datasets, demonstrating the effectiveness of the derived explanations.
Fostering University Students’ Digital Skills: De Jure vs De Facto

The article discusses the problem of within-the-education-process fostering digital skills among university students of various training programs. The specific skills (relevant to a particular sphere) are differentiated from the general ones (digital skills formed beyond the IT industry). The authors suggest that all training programs contain a «pillow» of general digital skills, which can be fixed both de jure – in the methodological and regulatory bases of the educational process, and de facto – in teaching practices. The study identifies a framework for general and specific digital skills. To test the hypotheses, two groups of data are analyzed: the normative and methodological bases of educational programs (fostering skills de jure) and students’ questionnaire answers on how the practices of developing and using digital skills in their studies are integrated (fostering skills de facto). The research is carried out in seven programs with obviously wide differences – from medicine to information technology and philology. The research has shown that digital skills are de jure represented in all programs, but normative consolidation is more typical for general skills, not for specific digital ones. There is identified a gap between de jure and de facto fostering digital skills, and this gap is various for different programs. The research results can be used in universities when designing strategies to form digital skills or when modifying such skills-related methodological bases of training programs.
Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.
Autonomous Drifting Using Reinforcement Learning

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.
Proposed Model for Context Topic Identification of English and Hindi News Article Through LDA Approach with NLP Technique

According to the survey, India has the world's second-largest newspaper market, with more than 100 K newspaper outlets, approx 240 million circulation, and 1300 million subscribers or readers. The topic modeling work is increasing day by day, and researchers have published multiple topic modeling papers and have implemented them in different areas like software engineering, political science and medical, etc. LDA topic modeling is used in this research because it has been introduced successfully for topic modeling and classification and it measures the probability of a text-dependent on the bag-of-words scheme without considering the word series. LDA is a common topic modeling algorithm with excellent implementation in the Gensim Python package. However, the challenge is how to extract good quality topics that are simple, separated, and meaningful. The purpose of this research deals with finding the main topics of the same category news articles which are in two different languages (Hindi and English) and then classifying these different language news topics with similarity measurement. In this research, the corpus is constructed with bigram. To achieve the research goal, we have to first build a headline and link extractor that scrap the top news from Google News feeds for both English and Hindi languages (Google News collects news stories that have appeared on different news website which is already accessible in 35 languages over the last 30 days) and then analyses which two news headlines are similar.
Facial Image Augmentation from Sparse Line Features Using Small Training Data

Data collection is expensive in many research fields. Data augmentation from a very small dataset, such as synthesising realistic images from limited or incomplete information available from a small number of sample images, is still an enormous challenge using deep convolutional neural networks that traditionally require a large number of training data to achieve reasonable performance. For the purpose of manipulating the synthetic results with diversity, line features, which can be easily obtained through computer vision, hand-drawn lines, or customer-designed sketches, can be utilized to provide extra details to effectively augment a small training dataset for many applications. In this paper, a novel conditional generative adversarial network (GAN) framework for synthesising photorealistic facial images using small training data and limited line features is proposed, where sparse line features are expected to simulate abstract and incomplete hand-drawn sketches for introducing diversity in the augmented facial images. The proposed GAN framework can automatically recover the lost information caused by incomplete input features, which has been proved to efficiently reduce unexpected distortions but enhance data diversity with controllable sparse line features. Experimental results have demonstrated that the proposed method with a very small dataset, 50 training images only, can generate images of higher quality than the traditional translation methods and preserve essential details to synthesise diverse but realistic facial images. Compared to the state-of-the-art methods, the proposed GAN framework can generate more photorealistic facial images using controllable sparse line features in terms of higher FID and KID scores as well as preference evaluation by human perception.
Compositional optimizations for CertiCoq

Compositional compiler verification is a difficult problem that focuses on separate compilation of program components with possibly different verified compilers. Logical relations are widely used in proving correctness of program transformations in higher-order languages; however, they do not scale to compositional verification of multi-pass compilers due to their lack of transitivity. The only known technique to apply to compositional verification of multi-pass compilers for higher-order languages is parametric inter-language simulations (PILS), which is however significantly more complicated than traditional proof techniques for compiler correctness. In this paper, we present a novel verification framework for lightweight compositional compiler correctness . We demonstrate that by imposing the additional restriction that program components are compiled by pipelines that go through the same sequence of intermediate representations , logical relation proofs can be transitively composed in order to derive an end-to-end compositional specification for multi-pass compiler pipelines. Unlike traditional logical-relation frameworks, our framework supports divergence preservation—even when transformations reduce the number of program steps. We achieve this by parameterizing our logical relations with a pair of relational invariants . We apply this technique to verify a multi-pass, optimizing middle-end pipeline for CertiCoq, a compiler from Gallina (Coq’s specification language) to C. The pipeline optimizes and closure-converts an untyped functional intermediate language (ANF or CPS) to a subset of that language without nested functions, which can be easily code-generated to low-level languages. Notably, our pipeline performs more complex closure-allocation optimizations than the state of the art in verified compilation. Using our novel verification framework, we prove an end-to-end theorem for our pipeline that covers both termination and divergence and applies to whole-program and separate compilation, even when different modules are compiled with different optimizations. Our results are mechanized in the Coq proof assistant.
On the practice of semantic versioning for Ansible galaxy roles: An empirical study and a change classification model

Ansible, a popular Infrastructure-as-Code platform, provides reusable collections of tasks called roles.Roles are often contributed by third parties, and like general-purpose libraries, they evolve.Therefore, new releases of roles need to be tagged with version numbers, for which Ansible recommends adhering to the semantic versioning format.However, roles significantly differ from generalpurpose libraries, and it is not yet known what constitutes a breaking change or the addition of a feature to a role.Consequently, this can cause confusion for clients of a role and new role contributors.To alleviate this issue, we perform an empirical study on semantic versioning in Ansible roles to uncover the types of changes that trigger certain types of version bumps.Our dataset consists of over 81 000 version increments spanning upwards of 8 500 Ansible roles.We design a novel structural model for these roles, and implement a domain-specific structural change extraction algorithm to calculate structural difference metrics.Afterwards, we quantitatively investigate the state of semantic versioning in Ansible roles and identify the most commonly changed elements.Then, using the structural difference metrics, we train a Random Forest classifier to predict applicable version bumps for Ansible role releases.Finally, we confirm our empirical findings with a developer survey.Our observations show that although most Ansible role developers follow the semantic versioning format, it appears that they do not always consistently follow the same rules when selecting the version bump to apply.Moreover, we find that the distinction between patch and minor increments is often unclear.Therefore, we use the gained insights to formulate a number of guidelines to apply semantic versioning on Ansible roles.These guidelines can be used by role developers to ensure a clear interpretation of the version increments.
Soft Skills Development with University Students at English Lessons

The influence of game-based teaching at English classes with university students for soft skills development has been shown. The purpose of the article is to test experimentally the effectiveness of game-based teaching at English classes with university students for the soft skills development. The theoretical (analysis and synthesis), empirical (questioning, observation, discussion), experimental (pedagogical experiment) methods have been used. The pedagogical experiment helped to evaluate the obtained data. The article results are the following. The concept “soft skills” was established as well as the synonyms “skills for employment”, “people skills”, “non-professional skills”, “extra-professional skills”, “key skills”, “skills for social progress”, “skills of the 21st century”. The eight soft skills that can be developed with university students through educational games at English classes were determined. They were critical thinking, creativity, team management, emotional intelligence, people interaction, conflict management, flexible consciousness and stress resistance. The features of the identified soft skills were described. The influence of game activities to different soft skills was revealed. Different game activities to develop soft skills were shown at English lessons. The effectiveness of game-based teaching to soft skills development was analyzed. The conclusion is the effectiveness of soft skills development based on game-based teaching in English was proved.
Intelligent textiles: designing a gesture-controlled illuminated textile based on computer vision

Artificial intelligence (AI) offers the potential for the development of e-textiles that give wearers a smart and intuitive experience. An emerging challenge in intelligent materials design is hand gesture recognition textiles. Most current research focuses on number gesture recognition via smart gloves, so there is a gap in research that studies contact-less number gesture recognition textiles via computer vision. Meanwhile, there is lack of exploration on the integration of illuminating function and number gesture recognition textiles to improve interactivity by real-time visualizing detection results. In this research, a novel interactive illuminating textile with a touch-less number gesture recognition function has been designed and fabricated by using an open-source AI model. It is used in sync with a polymeric optical fiber textile with illuminative features. The textile is color-changing, controlled by the system's mid-air interactive number gesture recognition capability and has a woven stripe pattern and a double-layer weave structure with open pockets to facilitate integration of the system's components. Also described here is a novel design process that permits textile design and intelligent technology to integrate seamlessly and in synchronization, so that design in effect mediates continuously between the physical textile and the intangible technology. Moreover, this design method serves as a reference for the integration of open-source intelligent hardware and software into e-textiles for enhancement of the intuitive function and value via economy of labor.
XQC at the Lifelog Search Challenge 2021: Interactive Learning on a Mobile Device

In a society dominated by mobile phones and still increasing media collections, Interactive Learning is slowly becoming the favored paradigm for managing these collections. Still, however, no scaling Interactive Learning system exists on a mobile phone. In this paper, we present XQC, an Interactive Learning platform with a user interface that fits most modern smartphones, and scales to large media collections.
Identification of Social-Media Platform of Videos through the Use of Shared Features

Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities, it has become essential to try new methods to verify the origin of videos from these platforms. However, collecting datasets large enough to train neural networks for this task has become difficult because of the privacy regulations that have been enacted in recent years. To mitigate this limitation, in this work we propose two different solutions based on transfer learning and multitask learning to determine whether a video has been uploaded from or downloaded to a specific social platform through the use of shared features with images trained on the same task. By transferring features from the shallowest to the deepest levels of the network from the image task to videos, we measure the amount of information shared between these two tasks. Then, we introduce a model based on multitask learning, which learns from both tasks simultaneously. The promising experimental results show, in particular, the effectiveness of the multitask approach. According to our knowledge, this is the first work that addresses the problem of social media platform identification of videos through the use of shared features.
Image Denoising Inspired by Quantum Many-Body physics

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising.In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements.This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics.The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics.The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising.The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well.Finally, the results show that our method achieves comparable or slightly better results than existing approaches.
An Augmented Reality Learning Toolkit for Fostering Spatial Ability in Mathematics Lesson: Design and Development

Previous research claimed that integration of augmented reality on educational settings helps to improve academic achievement of students in collaborative learning environments, as well as to improve their retention and ability to translate this within other environments. Since augmented reality is still considered relatively novel technology in educational fields, there is an inherent need for research-based guides to design effective and feasible augmented reality tools for school-based learning. The main aim of this study was therefore to design and develop an augmented reality learning toolkit to foster spatial ability in middle school students using mobile devices. The study was conducted in two parts, as preliminary research and a prototyping phase. The findings guided the characteristics for designing an augmented reality learning toolkit with a set of spatial tasks aimed at middle school students. In light of the results, it can be inferred that the students were able to use this designed toolkit to perform their spatial ability through given spatial tasks since the students encountered no technical difficulties with the final toolkit prototype, and that they were able to use the toolkit assuredly. In conclusion, the study showed that augmented reality seemed helpful in enhancing the usage of mobile devices, not just for the reading of books, communication or playing games, but also as a support mechanism for the learning of mathematics. Thus, the augmented reality toolkit developed in this study presents a new way for students and/or teachers to use mobile devices in the learning and teaching of mathematics.
Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.