Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Temporal Action Localization by Multi-Stage Fusion Network

Most temporal action localization methods are usually trained using video data-sets with frame-wise annotations which are expensive and time-consuming to acquire.To alleviate this problem, many weakly supervised temporal action localization methods which only leverage video-level annotations during training are proposed.In this paper, we first analyze three problems of weakly supervised temporal action localization, namely feature similarity, action completeness, and weak annotation.Based on these three problems, we propose a novel network called multi-stage fusion network, which decomposes the problems into three different modules within the network, namely feature, sub-action, and action modules.Specifically, for feature similarity, a Triplet Loss was introduced to ensure the action instances from the same class having similar feature sequences and expand the margin of the action instance from different classes in the feature module.For action completeness, each stage of the sub-action module can discover the different sub-actions.The complete action instances can be localized in the action module by fusing multiple sub-actions from the sub-action module.To alleviate weak annotation, we localize multiple action proposals from multi-stage outputs of the network in the action module and select the action proposals with higher confidence scores as predicted action instances.Extensive experiment results on data-sets Thumos'14 and ActivityNet1.2demonstrate that our method outperforms the state-of-the-art methods and the average mean Average Precision (mAP) on Thumos'14 is significantly improved from 40.9% to 43.3%.
Adaptive Shivers Sort: An Alternative Sorting Algorithm

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Adaptive Shivers Sort: An Alternative Sorting AlgorithmVincent JugéVincent Jugépp.1639 - 1654Chapter DOI:https://doi.org/10.1137/1.9781611975994.101PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We present a new sorting algorithm, called adaptive ShiversSort, that exploits the existence of monotonic runs for sorting efficiently partially sorted data. This algorithm is a variant of the well-known algorithm TimSort, which is the sorting algorithm used in standard libraries of programming languages such as Python or Java (for non-primitive types). More precisely, adaptive ShiversSort is a so-called k-aware merge-sort algorithm, a class that was introduced by Buss and Knop that captures “TimSort-like” algorithms. In this article, we prove that, although adaptive ShiversSort is simple to implement and differs only slightly from TimSort, its computational cost, in number of comparisons performed, is optimal within the class of natural merge-sort algorithms, up to a small additive linear term: this makes adaptive ShiversSort the first k-aware algorithm to benefit from this property, which is also a 33% improvement over TimSort's worst-case. This suggests that adaptive ShiversSort could be a strong contender for being used instead of TimSort. Then, we investigate the optimality of k-aware algorithms: we give lower and upper bounds on the best approximation factors of such algorithms, compared to optimal stable natural merge-sort algorithms. In particular, we design generalisations of adaptive ShiversSort whose computational costs are optimal up to arbitrarily small multiplicative factors. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Fast RT-LoG operator for scene text detection

This paper proposes a new real-time Laplacian of Gaussian (RT-LoG) operator for scene text detection. This method takes advantage of the Gaussian kernel distribution in the spatial/scale-space domains and kernel decomposition with the box filtering method. Two levels of optimization are given. The first level of optimization within the spatial domain is obtained by box mutualization. The second level of optimization within the spatial/scale-space domains is performed using a mixed method for box selection. The proposed RT-LoG operator is evaluated on the ICDAR2017 RRC-MLT dataset in terms of robustness and time processing. The results are compared with the state-of-the-art real-time operators for scene text detection. The proposed operator appears as the top performance with the best trade-off between robustness and time processing. The proposed operator can support approximately 30 frames per second (FPS) up to the Quad-HD resolution on a regular CPU architecture with a low-level latency. In addition, the proposed operator can support the full pipeline for scene text detection. Our system is competitive with the top accurate systems of the literature while processing with a difference of two orders of magnitude in term of processing resources.
High-Payload Data-Hiding Method for AMBTC Decompressed Images

Data hiding is the art of embedding data into a cover image without any perceptual distortion of the cover image. Moreover, data hiding is a very crucial research topic in information security because it can be used for various applications. In this study, we proposed a high-capacity data-hiding scheme for absolute moment block truncation coding (AMBTC) decompressed images. We statistically analyzed the composition of the secret data string and developed a unique encoding and decoding dictionary search for adjusting pixel values. The dictionary was used in the embedding and extraction stages. The dictionary provides high data-hiding capacity because the secret data was compressed using dictionary-based coding. The experimental results of this study reveal that the proposed scheme is better than the existing schemes, with respect to the data-hiding capacity and visual quality.
"Fundamental Education In University In Development Of Future Teachers` Professional Competences"

The enormous social changes taking place in society have brought to the foreground the problem of the development of the creative potential of all society members and especially young people. The basis for the formation of a new generation of teachers, being able to solve the problem, is the changing content of higher pedagogical education, based on the principles of fundamentalization, humanization, differentiation, personal orientation of the entire educational process. This article is devoted to develop approaches to the study of General physics to strengthen the professional motivation of the future teacher, focusing on the use of special knowledge in the process of solving problems of development of the students personality. The main methods of research are: the study of the experience of the organization of fundamental professional training of teachers in the universities of the country, modeling, allowing considering the process of studying the course of General physics, as the basis for the formation of professional competencies. The article presents a model of formation of professional competences of teachers in the course of General physics. The model is aimed at the formation on the basis of fundamental knowledge in physics of a special methodological culture of the teacher – formed systemic, critical, reflective thinking, motivational, cognitive, operational and activity readiness for constant self-development and self-education.
Fragmentary Multi-Instance Classification

Multi-instance learning (MIL) has been extensively applied to various real tasks involving objects with bags of instances, such as in drugs and images. Previous studies on MIL assume that data are entirely complete. However, in many real tasks, the instance is fragmentary. In this article, we present probably the first study on multi-instance classification with fragmentary data. In our proposed framework, called fragmentary multi-instance classification (FIC), the fragmentary data are completed and the multi-instance classifier is learned jointly. To facilitate the integration between the completion and classifier learning, FIC establishes the weighting mechanism to measure the importance levels of different instances. To validate the compatibility of our framework, four typical MIL methods, including multi-instance support vector machine (MI-SVM), expectation maximization diverse density (EM-DD), citation- K nearest neighbors (Citation-KNNs), and MIL with discriminative bag mapping (MILDM), are embedded into the framework to obtain the corresponding FIC versions. As an illustration, an efficient solving algorithm is developed to address the problem for MI-SVM, together with the proof of convergence behavior. The experimental results on various types of real-world datasets demonstrate the effectiveness.
KCRS: A Blockchain-Based Key Compromise Resilient Signature System

Digital signatures are widely used to assure authenticity and integrity of messages (including blockchain transactions). This assurance is based on assumption that the private signing key is kept secret, which may be exposed or compromised without being detected in the real world. Many schemes have been proposed to mitigate this problem, but most schemes are not compatible with widely used digital signature standards and do not help detect private key exposures. In this paper, we propose a Key Compromise Resilient Signature (KCRS) system, which leverages blockchain to detect key compromises and mitigate the consequences. Our solution keeps a log of valid certificates and digital signatures that have been issued on the blockchain, which can deter the abuse of compromised private keys. Since the blockchain is an open system, KCRS also provides a privacy protection mechanism to prevent the public from learning the relationship between signatures. We present a theoretical framework for the security of the system and a provably-secure construction. We also implement a prototype of KCRS and conduct experiments to demonstrate its practicability.
Proposals for a Discourse Analysis Practice Integrated into Digital Humanities: Theoretical Issues, Practical Applications, and Methodological Consequences

In this article, I put forward a linguistic analysis model for analyzing meaning which is based on a methodology that falls within the wider framework of the digital humanities and is equipped with digital tools that meet the theoretical requirements stated. First, I propose a conception of the digital humanities which favors a close relationship between digital technology and the humanities. This general framework will justify the use of a number of models embodied in a dynamic conception of language. This dynamism will then be reflected in the choice of metrics and textual analysis tools (developed in the field of textometry, especially the Iramuteq software). The semantic functioning of linguistic units will be described by using these tools within the identified methodological framework and will help to better understand the processes of variations, whether temporal or generic, within vast discursive corpora. I propose a way of analyzing corpora with specific tools, confronting the humanities with computing/numerical technology.
IS WHATSAPP THE FUTURE OF WORKPLACE COMMUNICATION?: INVESTIGATING THE USE OF WHATSAPP IN DECISION- MAKING EPISODES

Background and Purpose: Meetings, emails, phone calls and mobile telephone texts are often quoted as the primary methods of communication in the workplace settings. However, the rise of mobile messaging service has been superseding the use of these modes of communication. Hence, it is not surprising that messaging applications like Whatsapp has slowly replaced the transmission of messages in most workplace interaction. WA, the prominent global mobile messenger application (statistica.com) does not only allow users to send personal messages to others rapidly but also makes it possible for them to communicate in groups where the users can communicate interactively and achieve tasks swiftly.&#x0D; &#x0D; Methodology: Drawing on WA group messages and audio recordings of meetings of team meetings, this work analyses the impact of WA on internal team communication by focusing on the processes of decision-making displayed by the members of a group of academics at an institution of higher education in Malaysia.&#x0D; &#x0D; Findings: The analysis shows that the application functions as a useful platform for the team to negotiate solutions, make and revisit decisions that were previously decided in another interactional context.&#x0D; &#x0D; Contributions: This paper concludes that the use of WA constitutes a daily reality in the investigated workplace, illuminate the intertextuality of DM processes across these contexts, and that it is reflective of its main purposes: to make quick decisions, give confirmation or approval, and provide information regarding the issues raised. The study also addresses the crucial question: is WA the future of workplace communication and will it become the preferred medium for decision- making?&#x0D; &#x0D; Keywords: Decision-making, higher education, Malaysia, WhatsApp, workplace discourse.&#x0D; &#x0D; Cite as: Mohd Omar, N. A, Azmi, N. J., &amp; Ahmad Sani, N. (2020). Is Whatsapp the future of workplace communication?: Investigating the use of Whatsapp in decision-making episodes. Journal of Nusantara Studies, 5(1), 414-442. http://dx.doi.org/10.24200/jonus.vol5iss1pp414-442
AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning.

Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the problem-specific features directly from the input images. The success of deep learning models solicits architecture engineering rather than hand-engineering the features. However, designing state-of-the-art CNN for a given task remains a non-trivial and challenging task, especially when training data size is less. To address this phenomena, transfer learning has been used as a popularly adopted technique. While transferring the learned knowledge from one task to another, fine-tuning with the target-dependent Fully Connected (FC) layers generally produces better results over the target task. In this paper, the proposed AutoFCL model attempts to learn the structure of FC layers of a CNN automatically using Bayesian optimization. To evaluate the performance of the proposed AutoFCL, we utilize five pre-trained CNN models such as VGG-16, ResNet, DenseNet, MobileNet, and NASNetMobile. The experiments are conducted on three benchmark datasets, namely CalTech-101, Oxford-102 Flowers, and UC Merced Land Use datasets. Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research. The proposed AutoFCL method outperforms the existing methods over CalTech-101 and Oxford-102 Flowers datasets by achieving the accuracy of 94.38% and 98.89%, respectively. However, our method achieves comparable performance on the UC Merced Land Use dataset with 96.83% accuracy. The source codes of this research are available at this https URL.
USING SOCIAL MEDIA AS A LEARNING MEDIA OF FOREIGN LANGUAGE STUDENTS IN HIGHER EDUCATION

ABSTRACT&#x0D; Foreign language is an important component of someone in facing the era of Industry 4.0. The development of Information and Communication Technology (ICT) will have an impact on the creation of a learning media innovation that will facilitate users in the learning process in higher education. Likewise in foreign language learning. Online-based learning has been designed in such a way as to increase learning knowledge and motivation. This study uses a qualitative approach with the survey method. Data and Sources of data in this study are students of foreign language study programs at universities in Indonesia. The results of the study show that students generally always use smartphones and use social media in all their activities. Students use a lot of social media in exploring vocabulary and increasing vocabulary mastery, learning as well as building networks for native speakers (Muttersprachler), and making social media a means of learning by accessing foreign-language songs and interesting media in student foreign language learning. Thus social media can be one of the means of education for students which ends in improving foreign language skills.&#x0D; Keywords: Social Media, Learning Media, Learning Foreign Languages, Higher Education
Implementing a Central Bank Issued Digital Currency with Economic Implications Considerations

A central bank-issued digital currency (CBDC) could solve the volatility of a privately-issued cryptocurrency as well as keep intact its potential benefits. This research intended to analyze the possibilities for implementing a CBDC in a viable format that is also Shariah-compliant which may have the capacity to tackle issues plaguing the current financial system. We discuss possible scenarios and the resulting impact and consequences of CBDC implementation, through a deep examination of the benefits, opportunities, costs and issues of several conceptual formats. Methodologically, we used qualitative, comparative and analytical assessments on the critical impact on crucial levers like dilution to monetary policy, and the stability of the financial system. Finally, we found that the best format was a non-interest bearing CBDC for the interbank settlement and wholesale payment systems which would have the least disruption to the economy but strongest monetary policy transmission.
Service Quality of Border Region Higher Education Libraries

The quality of a qualified library makes the library as one of the centers of information for the general public. This study aims to determine how well the quality of library services, to find out how high the interest in reading students, and to determine the effect of the quality of college library services on strengthening the reading interest of students in the Library at the University of Timor Library UPT. This study uses a qualitative descriptive approach with a sample of 100 students. Data collection was carried out through observation, interview, documentation and questionnaire techniques. The data that has been collected is processed using descriptive statistical analysis techniques and inferential statistics. The results showed that the quality of library services at the University of Timor in the border area was in the quite good category with an average of 33.48. Student reading interest is in quite a good category with a mean value of 20, 56. The level of relationship or correlation between the quality of library services at the University of Timor in the border region to the interest in reading students at the University Library of Timor is in the strong category correlation with intervals of 0.60-0.799 means that the higher the quality of library services at the University of Timor in the border region, the higher the interest in reading students in the Library.
Design of password encryption model based on AES algorithm

Aiming at the demand for information system password encryption protection, this paper proposed a new set of password storage and transmission encryption model. In the process of password storage encryption, this paper built two keys included main key and working key, main key is responsible for the working key encryption, working key is responsible for the password encryption and updated automatically at regular intervals. In the process of password transmission encryption using AES algorithm, this paper improved the AES password transmission encryption process, adopted the method of password adding random number as a key to encrypted password. On this basis, this paper introduced RSA transmission encryption process, and compared AES with RSA in the process of transmission encryption. Experiments show that advanced AES process is faster than the RSA process and system has higher practicability and security.
On the Security of Sponge-type Authenticated Encryption Modes

The sponge duplex is a popular mode of operation for constructing authenticated encryption schemes. In fact, one can assess the popularity of this mode from the fact that around 25 out of the 56 round 1 submissions to the ongoing NIST lightweight cryptography (LwC) standardization process are based on this mode. Among these, 14 sponge-type constructions are selected for the second round consisting of 32 submissions. In this paper, we generalize the duplexing interface of the duplex mode, which we call Transform-then-Permute. It encompasses Beetle as well as a new sponge-type mode SpoC (both are round 2 submissions to NIST LwC). We show a tight security bound for Transform-then-Permute based on b-bit permutation, which reduces to finding an exact estimation of the expected number of multi-chains (defined in this paper). As a corollary of our general result, authenticated encryption advantage of Beetle and SpoC is about T(D+r2r)/2b where T, D and r denotes the number of offline queries (related to time complexity of the attack), number of construction queries (related to data complexity) and rate of the construction (related to efficiency). Previously the same bound has been proved for Beetle under the limitation that T &lt;&lt; min{2r, 2b/2} (that compels to choose larger permutation with higher rate). In the context of NIST LwC requirement, SpoC based on 192-bit permutation achieves the desired security with 64-bit rate, which is not achieved by either duplex or Beetle (as per the previous analysis).
An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

In this research, an improved differential evolution algorithm with a restart technique (DE-R) is designed for solutions of systems of nonlinear equations which often occurs in solving complex computational problems involving variables of nonlinear models. DE-R adds a new strategy for mutation operation and a restart technique to prevent premature convergence and stagnation during the evolutionary search to the basic DE algorithm. The proposed method is evaluated on various real world and synthetic problems and compared with the recently developed methods in the literature. Experiment results show that DE-R can successfully solve all the test problems with fast convergence speed and give high quality solutions. It also outperforms the compared methods.
VSB-DVM: An End-to-End Bayesian Nonparametric Generalization of Deep Variational Mixture Model

Mixture of factor analyzers is a fundamental model in unsupervised learning, which is particularly useful for high dimensional data. Recent efforts on deep auto-encoding mixture models made a fruitful progress in clustering. However, in most cases, their performance depends highly on the results of pre-training. Moreover, they tend to ignore the prior information when making clustering assignment, leading to a less strict inference and consequently limiting the performance. In this paper, we propose an end-to-end Bayesian nonparametric generalization of deep mixture model with a Variational Auto-Encoder (VAE) framework. Specifically, we develop a novel model called VSB-DVM exploiting the Variational Stick-Breaking Process to design a Deep Variational Mixture Model. Distinct from the existing deep auto-encoding mixture models, this novel unsupervised deep generative model can learn low-dimensional representations and clustering simultaneously without pre-training. Importantly, a strict inference is proposed using weights of stick-breaking process in a variational way. Furthermore, able to capture the richer statistical structure of the data, VSB-DVM can also generate highly realistic samples for any specified cluster. A series of experiments are carried out, both qualitatively and quantitatively, on benchmark clustering and generation tasks. Comparative results show that the proposed model is able to generate diverse and high-quality samples of data, and also achieves encouraging clustering results outperforming the state-of-the-art algorithms on four real-world datasets.