Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Temporal Action Localization through Contrast based Evaluation Networks

Given only video-level action categorical labels during training, weakly-supervised temporal action localization (WS-TAL) learns to detect action instances and locates their temporal boundaries in untrimmed videos. Compared to its fully supervised counterpart, WS-TAL is more cost-effective in data labeling and thus favorable in practical applications. However, the coarse video-level supervision inevitably incurs ambiguities in action localization, especially in untrimmed videos containing multiple action instances. To overcome this challenge, we observe that significant temporal contrasts among video snippets, e.g., caused by temporal discontinuities and sudden changes, often occur around true action boundaries. This motivates us to introduce a Contrast-based Localization EvaluAtioN Network (CleanNet), whose core is a new temporal action proposal evaluator, which provides fine-grained pseudo supervision by leveraging the temporal contrasts among snippet-level classification predictions. As a result, the uncertainty in locating action instances can be resolved via evaluating their temporal contrast scores. Moreover, the new action localization module is an integral part of CleanNet which enables end-to-end training. This is in contrast to many existing WS-TAL methods where action localization is merely a post-processing step. Besides, we also explore the usage of temporal contrast on temporal action proposal (TAP) generation task, which we believe is the first attempt with the weak supervision setting. Experiments on the THUMOS14, ActivityNet v1.2 and v1.3 datasets validate the efficacy of our method against existing state-of-the-art WS-TAL algorithms.
Problems of students in following the online learning process in the covid-19 pandemic

Abstract The Covid-19 pandemic requires people to work remotely from home or Work from Home (WFH), including lecture activities. The goal of WFH is to break the chain of spread of the corona virus. Transfer of lecture activities from campus to home as an effort to maintain social distancing. Like it or not, students must learn more independently. Many students respond well to online learning when it is implemented. However, students actually experience difficulties in learning after the online lecture process is running which has an impact on decreasing the quality of their learning. This survey research aims to determine the obstacles faced by physics students in participating in the online learning process during the Covid-19 pandemic. The research sample of 331 respondents who have participated in the online learning process activities in the Covid-19 pandemic were taken randomly. The research instrument was a questionnaire sheet distributed on google form for 20 days. The results showed that students tended to choose the learning process on campus compared to studying online. The factor influencing online learning are the internet network, the readiness of students to receive and send information and the accessing the Learning Management System (LMS). The influence of these factors is found by students when they take courses in Disaster Management, Fundamental Physics, Mathematical Physics, Computational Physics, Algorithms and Computer Programming.
Multi-modal Contextual Graph Neural Network for Text Visual Question Answering

Text visual question answering (TextVQA) targets at answering the question related to texts appearing in the given images, posing more challenges than VQA by requiring a deeper recognition and understanding of various shapes of human-readable scene texts as well as their meanings in different contexts. Existing works on TextVQA suffer from two weaknesses: i) scene texts and non-textual objects are processed separately and independently without considering their mutual interactions during the question understanding and answering process, ii) scene texts are encoded only through word embeddings without taking the corresponding visual appearance features as well as their potential relationships with other non-textual objects in the images into account. To overcome the weakness of existing works, we propose a novel multi-modal contextual graph neural network (MCG) model for TextVQA. The proposed MCG model can capture the relationships between visual features of scene texts and non-textual objects in the given images as well as utilize richer sources of multi-modal features to improve the model performance. In particular, we encode the scene texts into richer features containing textual, visual and positional features, then model the visual relations between scene texts and non-textual objects through a contextual graph neural network. Our extensive experiments on real-world dataset demonstrate the advantages of the proposed MCG model over baseline approaches.
Common Evaluation Pitfalls in Touch-Based Authentication Systems

In this paper, we investigate common pitfalls affecting the evaluation of authentication systems based on touch dynamics. We consider different factors that lead to misrepresented performance, are incompatible with stated system and threat models or impede reproducibility and comparability with previous work. Specifically, we investigate the effects of (i) small sample sizes (both number of users and recording sessions), (ii) using different phone models in training data, (iii) selecting non-contiguous training data, (iv) inserting attacker samples in training data and (v) swipe aggregation. We perform a systematic review of 30 touch dynamics papers showing that all of them overlook at least one of these pitfalls. To quantify each pitfall's effect, we design a set of experiments and collect a new longitudinal dataset of touch dynamics from 470 users over 31 days comprised of 1,166,092 unique swipes. We make this dataset and our code available online. Our results show significant percentage-point changes in reported mean EER for several pitfalls: including attacker data (2.55%), non-contiguous training data (3.8%), phone model mixing (3.2%-5.8%). We show that, in a common evaluation setting, cumulative effects of these evaluation choices result in a combined difference of 8.9% EER. We also largely observe these effects across the entire ROC curve. Furthermore, we validate the pitfalls on four distinct classifiers - SVM, Random Forest, Neural Network, and kNN. Based on these insights, we propose a set of best practices that, if followed, will lead to more realistic and comparable reporting of results in the field.
The Thinkit System for Icassp2021 M2voc Challenge

In this paper, we introduce the low resource text-to-speech system from the ThinkIT team submitted to Multi-Speaker Multi-Style Voice Cloning Challenge (M2VoC). The challenge has two tasks: few-shot track1 provides 100 samples for each person and one-shot track2 offers 5 samples only. Each track contains two sub-tracks A and B. Instead of sub-track A, sub-track B can use extra public data besides the released data. But we participate in the sub-track A only. We choose the finetune as our backbone strategy. Our submitted systems include BERT based prosody boundary prediction module, FastSpeech based acoustic model to generate acoustic features from text input, and HIFIGAN based vocoder to generate waveform from acoustic features. Among them, acoustic models are susceptible to low resource speakers. To prevent over-fitting, we modified the acoustic model and split out validation set to assist the manual model selection. Evaluation results provided by the challenges organizers demonstrate the effectiveness of our system.
On a conjecture on APN permutations

The single trivariate representation proposed in [C. Beierle, C. Carlet, G. Leander, L. Perrin, A Further Study of Quadratic APN Permutations in Dimension Nine, arXiv: 2104.08008 ] of the two sporadic quadratic APN permutations in dimension 9 found by Beierle and Leander (2020) is further investigated. In particular, using tools from algebraic geometry over finite fields, we prove that such a family does not contain any other APN permutation for larger dimensions.
Digital twin in advanced training of engineering specialists

Abstract A review of scientific literature showed the relevance of the issue of assessing the training of an engineering specialist. Engineering includes a variety of works that relate to production issues. To assess the training of an engineering specialist, the digital twin of the enterprise is used. The digital twin of an enterprise includes all major pre-production, production and production support processes. A method for assessing the competencies received by an engineering specialist has been selected. The set of competencies is associated with the ongoing processes of the digital twin of an industrial enterprise. The normal mode of execution of the enterprise production processes is measured. The mode of operation of the processes was measured taking into account the new competencies of an engineering specialist. The study proposes a methodology for assessing the competence of an engineering specialist. The assessment of the effectiveness of the training passed by him.
Evidence for a Relationship Between Self-Avatar Fixations and Perceived Avatar Similarity within Low-Cost Virtual Reality Embodiment

In this study, we investigate potential relationships between avatar appearance and viewpoint within the context of low-cost motion tracking using Body and Mirror factors. Six experimental conditions were developed that combined Body and Mirror factors. In this between-groups design, participants were able to control their self-avatar by using HTC Vive controllers and trackers. Our results suggest a relationship between perceived similarity with the selfavatar and looking at the self-avatar.
A Systematic Review on Hadith Authentication and Classification Methods

Background : A hadith refers to sayings, actions, and characteristics of the Prophet Muhammad peace be upon him. The authenticity of hadiths is crucial, because they constitute the source of legislation for Muslims with the Holy Quran. Classifying hadiths into groups is a matter of importance as well, to make them easy to search and recognize. Objective : To report the results of a systematic review concerning hadith authentication and classification methods. Data sources : Original articles found in ACM, IEEE Xplore, ScienceDirect, Scopus, Web of Science, Springer Link, and Wiley Online Library. Study selection criteria : Only original articles written in English and dealing with hadith authentication and classification. Reviews, editorial, letters, grey literature, and restricted or incomplete articles are excluded. Data extraction : Two authors were assigned to extract data using a predefined data extraction form to answer research questions and assess studies quality. Results : A total of 27 studies were included in this review. There are 14 studies in authentication and 13 studies in classification. Most of the selected studies (17 of 27) were published in conferences, while the others (10 of 27) were published in scientific journals. Research in the area of hadith authentication and classification has received more attention in recent years (2016–2019). Conclusions : Hadith authentication methods are classified into machine learning, rule-based, and a hybrid of rule-based and machine learning and rule-based and statistical methods. Hadith classification methods are classified into machine learning and rule-based. All classification studies used Matn, while the majority of authentication studies used isnad. As a dataset source, Sahih Al-Bukhari was used by most studies. None of the used datasets is publicly available as a benchmark dataset, either in hadith authentication or classification. Recall and Precision are the most frequent evaluation metrics used by the selected studies.
A Low Computational Complexity JPDA Filter With Superposition

Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented. The concept of superposition is introduced and applied to the well-known Joint Probabilistic Data Association (JPDA) filter to derive the JPDA with superposition (JPDAS) filter. The JPDAS intensity function is evaluated to machine precision “for free” by computing the generating functional of the posterior process using complex arithmetic. A simulated example with eight targets is presented.
Multi-Bank On-Chip Memory Management Techniques for CNN Accelerators

Since off-chip DRAM access affects both performance and power consumption significantly, convolutional neural network (CNN) accelerators commonly aim to maximize data reuse in on-chip memory. By organizing the on-chip memory to multiple banks, we may hide off-chip DRAM access delay by prefetching data to unused banks during computation. When and where to prefetch data and how to reuse the feature map data between layers define the multi-bank on-chip memory management (MOMM) problem. In this paper, we propose compiler techniques to solve the MOMM problem with two different objectives: one is to minimize the off-chip memory access volume, and the other is to minimize the processing delay caused by unhidden DRAM accesses. By running CNN benchmarks on a cycle-level NPU simulator, we demonstrate the trade-off relation between two objectives. Compared with the baseline approach that does not reuse the feature map between layers, we could reduce the DRAM access volume and the processing delay up to 55.0 and 79.4 percent, respectively. Moreover, we extend the proposed techniques to consider layer fusion that aims to reuse feature maps between layers. Experiment results confirm the superiority of the proposed hybrid fusion technique to the per-layer processing technique and the pure fusion technique.
Remote services of national libraries

Many modern national libraries implement the model of a library open to any member of society. In conditions of rapid and pervasive development of digital technologies and the formation of the Internet information space, a website is an indispensable tool for ensuring the availability of the library, its information resources and services in the virtual space. The creation and use of websites to organize remote (virtual) services is one of the most significant activities of libraries. The article objective is to present and summarize the results of studying national library websites as a platform for remote user services. The Russian State Library researchers study websites of 24 national libraries in 22 countries as a part of investigating current state and prospects for development of the digitalization processes of public libraries. The researchers have identified main directions of remote services (informing users about the library, its resources and services; ensuring the availability of library information resources; improving the comfort of using the library), and the range of remote services, which includes above dozen remote library and information services, as well as nearly the same number of additional digital services that increase the comfort of users. They proposed promising directions to develop remote services (information about library resources and services, consulting and training on using library resources, personalization of services), and the introduction of digital technologies (multimedia and augmented reality technologies).
Human-AI Interactive and Continuous Sensemaking: A Case Study of Image Classification using Scribble Attention Maps

Advances in Artificial Intelligence (AI), especially the stunning achievements of Deep Learning (DL) in recent years, have shown AI/DL models possess remarkable understanding towards the logic reasoning behind the solved tasks. However, human understanding towards what knowledge is captured by deep neural networks is still elementary and this has a detrimental effect on human's trust in the decisions made by AI systems. Explainable AI (XAI) is a hot topic in both AI and HCI communities in order to open up the blackbox to elucidate the reasoning processes of AI algorithms in such a way that makes sense to humans. However, XAI is only half of human-AI interaction and research on the other half - human's feedback on AI explanations together with AI making sense of the feedback - is generally lacking. Human cognition is also a blackbox to AI and effective human-AI interaction requires unveiling both blackboxes to each other for mutual sensemaking. The main contribution of this paper is a conceptual framework for supporting effective human-AI interaction, referred to as interactive and continuous sensemaking (HAICS). We further implement this framework in an image classification application using deep Convolutional Neural Network (CNN) classifiers as a browser-based tool that displays network attention maps to the human for explainability and collects human's feedback in the form of scribble annotations overlaid onto the maps. Experimental results using a real-world dataset has shown significant improvement of classification accuracy (the AI performance) with the HAICS framework.
Sentiment analysis-based method for matching creative agri-product scheme demanders and suppliers: A case study from China

Transformation and upgrading are crucial for the development of agri-products, whereas an appropriate creative agri-product scheme is the key. How to match the demanders and suppliers of creative agri-product schemes based on the demand analysis of demanders and the expected implement effect analysis of creative schemes provided by suppliers is of great significance, while research concerning this issue is still relatively scarce, especially in how to organize and mine the demand information and expected implement effect information. The purpose of this paper is to develop a novel sentiment analysis-based method for matching creative agri-product scheme demanders and suppliers by taking a case study from China. The procedure of this method includes five parts: first, structured processing of demand documents of different demanders; second, formation of creative agri-product scheme demand matrix; third, formation of creative agri-product scheme supply matrix; fourth, measurement of matching degrees of creative agri-product scheme supply and demand; fifth, construction and solution of a multi-objective optimization model for the matching problem. To illustrate the use of the proposed method, an empirical analysis of generating suitable creative agri-product scheme demand and supply matching pair for nut agri-products in XS village is given. The obtained computational process and results clearly demonstrate how to generate the optimized matching pairs for creative agri-product scheme between demanders and suppliers.
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk. Specifically, we define the tree induced error (TIE) on a hierarchical semantic tree and extend it to its increasing function from the optimization perspective. The semantic similarity in each level of a tree is integrated with the information gain. We achieve promising results on several large scale image classification tasks with a semantic tree structure in a plug and play manner.
An algorithm for the automatic deglitching of X-ray absorption spectroscopy data

Analysis of X-ray absorption spectroscopy data often involves the removal of artifacts or glitches from the acquired signal, a process commonly known as deglitching . Glitches result either from specific orientations of monochromator crystals or from scattering by crystallites in the sample itself. Since the precise energy – or wavelength – location and the intensity of glitches in a spectrum cannot always be predicted, deglitching is often performed on a per spectrum basis by the analyst. Some routines have been proposed, but they are prone to arbitrary selection of spectral artifacts and are often inadequate for processing large data sets. Here, a statistically robust algorithm, implemented as a Python program, for the automatic detection and removal of glitches that can be applied to a large number of spectra, is presented. It uses a Savitzky–Golay filter to smooth spectra and the generalized extreme Studentized deviate test to identify outliers. Robust, repeatable, and selective removal of glitches is achieved using this algorithm.
Image Super-Resolution Using Multi-Scale Space Feature and Deformable Convolutional Network

Recent researches on image super-resolution (SR) have achieved great progressing with the great development of convolutional neural networks (CNNs). However, existing CNNs usually adopt fixed filter structures and the convolutions just rely on the local information contained in the fixed receptive field. Above phenomena prevent high-level convolution layers from encoding semantics over spatial locations and largely limits the learning capacity of CNNs. What's more, many methods simply used a single-size feature map and failed to consider the spatial information, thereby these results also are unsatisfactory. To address these problems, in this paper, a network with multi-scale space features and deformable convolutional (MulSSD) is presented to further improve the reconstruction accuracy. Specifically, a multi-scale space features compressed block containing the deformable convolutional layer is proposed, which can augment the spatial sampling locations and incorporate the multi-scale space compression features and adaptively adjust the sampling grid and receptive fields. In addition, the design of symmetrical combinations make the information can be smoothly propagated through multiple channels during the training, which effectively improves the training efficiency. Extensive experiments on benchmark datasets validate that the proposed method achieves outperforming quantitative and qualitative performance. And the experimental results also proved that our proposed MulSSD can reconstruct high-quality high-resolution (HR) images at a relatively fast speed and outperform other methods by a large margin.