Reviewerly/exHarmony huggingface dataset:
The Scenarios Perspective of Social Studies Pedagogy to Next Citizenship in the 22nd Century

During the previous century, the preparation for global citizenship has proceeded rapidly, in order to adapt to social, technological, and environmental disruption. In this process, basic knowledge and skills necessary for citizenship of the digital world are determined. Since the world has become stable due to such preparation, the challenges of predicting the next century (2100 – 2199), such as studies of the next citizenship, and the revision of social studies pedagogy to next citizenship with respect to future changes of the post-digital society, prove to be interesting topics. This article presents the scenarios based on the projected future view of the next citizenship in the next century, in addition to the perspective of the possibilities in terms of the preparation of the next citizenship to tackle future changes.
Polynomial anonymous dynamic distributed computing without a unique leader

Abstract Counting the number of nodes in Anonymous Dynamic Networks is enticing from an algorithmic perspective: an important computation in a restricted platform with promising applications. Starting with Michail, Chatzigiannakis, and Spirakis [1] , a flurry of papers sped up the running time guarantees from doubly-exponential to polynomial [2] , [3] . There is a common theme across all those works: a distinguished node is assumed to be present, because Counting cannot be solved deterministically without at least one. In the present work we study challenging questions that naturally follow: how to efficiently count with more than one distinguished node, or how to count without any distinguished node. More importantly, what is the minimal information needed about these distinguished nodes and what is the best we can aim for (count precision, stochastic guarantees, etc.) without any. We present negative and positive results to answer these questions. To the best of our knowledge, this is the first work that addresses them.
Bootstrap an End-to-End ASR System by Multilingual Training, Transfer Learning, Text-to-Text Mapping and Synthetic Audio

Bootstrapping speech recognition on limited data resources has been an area of active research for long. The recent transition to all-neural models and end-to-end (E2E) training brought along particular challenges as these models are known to be data hungry, but also came with opportunities around language-agnostic representations derived from multilingual data as well as shared word-piece output representations across languages that share script and roots. We investigate here the effectiveness of different strategies to bootstrap an RNN-Transducer (RNN-T) based automatic speech recognition (ASR) system in the low resource regime, while exploiting the abundant resources available in other languages as well as the synthetic audio from a text-to-speech (TTS) engine. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements, allowing us to bootstrap a model for a new language with a fraction of the data that would otherwise be needed. The best system achieved a 46% relative word error rate (WER) reduction compared to the monolingual baseline, among which 25% relative WER improvement is attributed to the post-ASR text-to-text mappings and the TTS synthetic data.
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem

We recently proposed DOVER-Lap, a method for combining overlap-aware speaker diarization system outputs. DOVER-Lap improved upon its predecessor DOVER by using a label mapping method based on globally-informed greedy search. In this paper, we analyze this label mapping in the framework of a maximum orthogonal graph partitioning problem, and present three inferences. First, we show that DOVER-Lap label mapping is exponential in the input size, which poses a challenge when combining a large number of hypotheses. We then revisit the DOVER label mapping algorithm and propose a modification which performs similar to DOVER-Lap while being computationally tractable. We also derive an approximation bound for the algorithm in terms of the maximum number of hypotheses speakers. Finally, we describe a randomized local search algorithm which provides a near-optimal $(1-\epsilon)$-approximate solution to the problem with high probability. We empirically demonstrate the effectiveness of our methods on the AMI meeting corpus. Our code is publicly available: this https URL.
Software tools for creating electronic educational resources in the resource-based learning process

The current stage of development of digital technologies in the educational process is characterized by the development of a new information and educational environment, one of the necessary components of which is electronic educational resources (EER). At the same time, there is a search for effective methods of developing didactic materials in digital form. Teachers of higher education institutions are actively implementing the possibilities of digital technologies in the educational process, creating a new digital didactic support for the study of disciplines. The problem of qualitative creation, development and effective use in the educational process of EER, which determines one of the areas of implementation of the concept of resource-based learning (RBL), is becoming relevant. The authors of the article conducted a profound analysis of one of the tools for creating EER for RBL – Dr.Explain software application. This paper considers user options and the most effective ways for designing electronic educational resources in Dr.Explain program. It appears that Dr.Explain is a software program aimed at generating software documentation (help-files), help systems, on-line user guides and manuals, technical documentation for software and technical systems. Dr.Explain proves itself to be a useful tool for creating CHM help files, ready-to-print documents, e-books, allowing the user to compile hypertext e-learning resources in two formats (PDF and HTML). Regarding the experience of using such EER, we would like to point out that it is advisable to use electronic study-guides in PDF format on tablets and smartphones, and that is to say what students do when downloading it from the Google platform or university official website.    E-guides in HTML format are easy to use by installing it on desktop computers in classrooms or on student laptops. In general, such EERs (both formats) are easy to install and view, and do not depend on operating system and software. All you need is a browser or a PDF viewer, which in its turn, is free and easy to install on modern computer devices of any kind.
Hybrid symmetric cryptography approach for secure communication in web application

Cryptography is a science that studies methods so that data or messages remain safe when sent, from the sender to the recipient without experiencing interference from unauthorized parties. The cryptographic system used is a combination of symmetrical cryptographic algorithms, and asymmetric cryptographic algorithms also called hybrid cryptosystems. This research work tries to explore the need of integration of different encryption algorithms and enhance the strength of encryption and complexity. This work observes that integrity check algorithm can also be used to generate key of asymmetric key cryptography and they can help to enhance power of confidentiality. In this paper, we present a new hybrid by using Blowfish, RC6 and RSA algorithms. Within this, the data is encrypted through symmetric algorithm using RC6 and blowfish algorithm and key is encrypted using an asymmetric algorithm called RSA. The complete solution was implemented using Java technology and it was evaluated based on computation time.
Human Security in a Datafying South Asia: Approaching Data Protection

In an increasing datafying world, protection of data created and generated as a result of everyday interactions assumes imperativeness. Last year, Europe adopted the General Data Protection Rules (GDPR), which has yet to be subject to substantial reviews to check for inconsistencies and possible blind-spots. Similarly, other national (like India and Brazil) and regional juridical bodies seek to work out frameworks that address data protection. This paper looks at some possible ways to think about Data Protection legislations and practices in South Asia. By alluding to ideas of data justice (Taylor, 2017; Dencik, Hintz & Cable, 2016) and underscoring the idea of ‘multiplicity’ of data regimes, this essay paper draws on the idea of human security (King & Murray, 2001) as central to thinking about data protection legalities. This is does, by placing the protection of the essence, proprietary and otherwise, of the human, at the centre of this legal exercise of formulating data protection legislations, to uphold data democracy.
Adversarial Semi-supervised Learning for Corporate Credit Ratings

Corporate credit rating is an analysis of credit risks withina corporation, which plays a vital role during the management of financial risk. Traditionally, the rating assessment process based on the historical profile of corporation is usually expensive and complicated, which often takes months. Therefore, most of the corporations, duetothelack in money and time, can’t get their own credit level. However, we believe that although these corporations haven’t their credit rating levels (unlabeled data), this big data contains useful knowledgeto improve credit system. In this work, its major challenge lies in how to effectively learn the knowledge from unlabeled data and help improve the performance of the credit rating system. Specifically, we consider the problem of adversarial semi-supervised learning (ASSL) for corporate credit rating which has been rarely researched before. A novel framework adversarial semi-supervised learning for corporate credit rating (ASSL4CCR) which includes two phases is proposed to address these problems. In the first phase, we train a normal rating system via a machine-learning algorithm to give unlabeled data pseudo rating level. Then in the second phase, adversarial semi-supervised learning is applied uniting labeled data and pseudo-labeleddatato build the final model. To demonstrate the effectiveness of the proposed ASSL4CCR, we conduct extensive experiments on the Chinese public-listed corporate rating dataset, which proves that ASSL4CCR outperforms the state-of-the-art methods consistently.
Tourism Companies Assessment via Social Media Using Sentiment Analysis

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.
Deep Quality evaluator guided by 3D Saliency for Stereoscopic Images

Fast track article for IS&amp;T International Symposium on Electronic Imaging 2021: Human Vision and Electronic Imaging 2021 proceedings.
Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.
Insights into few shot learning approaches for image scene classification

Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets (MiniSun; MiniPlaces) for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy.
A Category of Probability Spaces

We introduce a category Prob of probability spaces whose objects are all probability spaces and arrows are corresponding to measurable functions satisfying an absolutely continuous requirement. We can consider a Prob-arrow as an evolving direction of information with a way of its interpretation. We introduce a contravariant functor E from Prob to Set, the category of sets. The functor E provides conditional expectations along arrows in Prob, which are generalizations of the classical conditional expectations. For a Prob arrow f, we introduce two concepts f-measurability and f-independence and investigate their interaction with conditional expectations along f. We also show that the completion of probability spaces is naturally formulated as an endofunctor of Prob.
Developing an Online Test to Measure Writing and Speaking Skills Automatically

As a result of the Japanese Ministry of Education's recent edict that students' written and spoken English should be assessed in university entrance exams, there is an urgent need for tools to help teachers and students prepare for these exams. Although some commercial tools already exist, they are generally expensive and inflexible. To address these shortcomings, a new open-source, online test for assessing English ability was developed. The test features the automatic grading not only of reading and listening, but also of speaking and writing. Thus, the general English ability of large numbers of students can be checked quickly online, making the test suitable for use in entrance exams and placements tests. It is based around the Moodle LMS and features several new plugins to automatically grade speaking and writing. This paper details plugin development, shows preliminary samples, and explains how test reliability will be verified by comparing students' scores with human-ratings and widely used tests such as IELTS, TOEIC, and CASEC.
Artificial Intelligence Systems as Inventors? A Position Statement of 7 September 2021 in View of the Evolving Case-Law Worldwide

On 30 July 2021 the Federal Court of Australia handed down a decision in which it accepted that an artificial intelligence (AI) system called DABUS can be deemed the inventor under Australian patent law. While the decision appears ground-breaking at first sight, it was mostly based on unverified assumptions regarding the technical capabilities of AI systems in general and DABUS in particular. Furthermore, the decision omits important questions regarding the consequences that may follow from attributing inventorship to an entity that lacks legal capacity without undertaking a comprehensive analysis that would justify such attribution. This Position Statement highlights the shortcomings of the decision and points to those factual and legal questions that need to be answered first before recognising AI systems as inventors. While it responds primarily to the decision of the Australian Federal Court, the presented arguments can be of relevance for any jurisdiction dealing with the question of whether an AI system can be deemed an inventor under patent law.
Sublabel-Accurate Multilabeling Meets Product Label Spaces

Functional lifting methods are a promising approach to determine optimal or near-optimal solutions to difficult nonconvex variational problems. Yet, they come with increased memory demands, limiting their practicability. To overcome this drawback, this paper presents a combination of two approaches designed to make liftings more scalable, namely product-space relaxations and sublabel-accurate discretizations. Our main contribution is a simple way to solve the resulting semi-infinite optimization problem with a sampling strategy. We show that despite its simplicity, our approach significantly outperforms baseline methods, in the sense that it finds solutions with lower energies given the same amount of memory. We demonstrate our empirical findings on the nonconvex optical flow and manifold-valued denoising problems.
Technology for Remote Caregiving: A Guide for Caregivers Providing Care at a Distance

Older adults with poor physical or cognitive function often require support from their caregivers. Proper care can improve function, safety, and quality of life. Many caregivers are unable to attend to the needs of their loved one in person because of time constraints or physical distance yet are still committed to overseeing their loved one's well-being and needs on a regular basis. This is true whether the person lives in their own home, in an assisted living facility, or in a nursing home. Mobile phones and personal computers can help caregivers communicate with their loved ones and provide care remotely even when in-person care is not possible.
Learning Unbiased Representations via Rényi Minimization

In recent years, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Differently from classical prediction retreatment algorithms, we focus on learning fair representations of the inputs. The challenge is to learn representations that capture most relevant information to predict the targeted output Y, while not containing any information about a sensitive attribute S. We leverage recent work which has been done to estimate the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation coefficient by learning deep neural network transformations and use it as a min-max game to penalize the intrinsic bias in a multi dimensional latent representation. Compared to other dependence measures, the HGR coefficient captures more information about the non-linear dependencies, making the algorithm more efficient in mitigating bias. After providing a theoretical analysis of the consistency of the estimator and its desirable properties for bias mitigation, we empirically study its impact at various levels of neural architectures. We show that acting at intermediate levels of neural architectures provides best expressiveness/generalization abilities for bias mitigation, and that using an HGR based loss is more efficient than more classical adversarial approaches from the literature.
Using Full-text Content of Academic Articles to Build a Methodology Taxonomy of Information Science in China

Research on the construction of traditional information science methodology taxonomy is mostly conducted manually. From the limited corpus, researchers have attempted to summarize some of the research methodology entities into several abstract levels (generally three levels); however, they have been unable to provide a more granular hierarchy. Moreover, updating the methodology taxonomy is traditionally a slow process. In this study, we collected full-text academic papers related to information science. First, we constructed a basic methodology taxonomy with three levels by manual annotation. Then, the word vectors of the research methodology entities were trained using the full-text data. Accordingly, the research methodology entities were clustered and the basic methodology taxonomy was expanded using the clustering results to obtain a methodology taxonomy with more levels. This study provides new concepts for constructing a methodology taxonomy of information science. The proposed methodology taxonomy is semi-automated; it is more detailed than conventional schemes and the speed of taxonomy renewal has been enhanced.