Reviewerly/exHarmony huggingface dataset:
Embedding a Robust, Secure and Invisible Watermark for Copyright Protection-Transmitter

The drastic development in the field of Multimedia and Internet, the security and authenticity issues of digital videos isof foremost concern. On the Internet, digital videos are abundantly shared among multiple users of variouslocations.Large amount of digital videos is being communicated between a sender and a receiver, expectingguaranteed authenticity that the applications demand. The digital modern technology allows illegal double dealing ofvideos of digital format, copyrights of it becomes an imperative issue. Hence, a robust secure and invisiblewatermarking technology for copy right protection using fusion of visual and thermal images is need to be developed.The process in which undetectable data is fused with a cover (thermal) image is called Watermarking. This techniqueof watermarking implemented in video file helps in protecting the same from illegal usage or distribution. In this paper,the Lifting Wavelet Transformation Technique (LWT) and Singular Value Decomposition (SVD)has been implementedto embed the watermark. The encrypted watermark is embedded in an image into the region of high frequency subband coefficients. The LWT and SVD is mixed to increase the security, robustness and indistinguishable of the videothat has undergone the enhancement process.
katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

This paper describes systems submitted to Se-mEval 2021 Task 1: Lexical Complexity Prediction (LCP).We compare a linear and a nonlinear regression models trained to work for both tracks of the task.We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously.This approach proved to be the most beneficial for multiword expression targets.We also demonstrate that some hand-crafted features differ in their importance for the target types.
State of the Art and Future Challenges of the Portrayal of Facial Nonmanual Signals by Signing Avatar

Researchers have been developing avatars to portray sign languages as a necessary component of automatic translation systems between signed and spoken languages. Although sign language avatar technology has improved significantly in recent years, there are still open questions as to how best portray the linguistic and paralinguistic information that occurs on a signer's face. Three interdisciplinary themes influence the current state of the art. The first, linguistic discovery, defines the facial activity that an avatar must carry out. The second, Computer Generated Imagery (CGI), supplies the tools and technology required to build avatars, and which determines the fidelity of an avatar's appearance. In contrast, the third theme, Sign Language Representation Systems, determines the fidelity of timing of facial co-occurrences. This paper discusses the current state of the art and demonstrates how these themes contribute to the overall goal of creating avatars that can produce legible signed utterances that are acceptable to viewers.
TreeBERT: A Tree-Based Pre-Trained Model for Programming Language

Source code can be parsed into the abstract syntax tree (AST) based on defined syntax rules. However, in pre-training, little work has considered the incorporation of tree structure into the learning process. In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks. To utilize tree structure, TreeBERT represents the AST corresponding to the code as a set of composition paths and introduces node position embedding. The model is trained by tree masked language modeling (TMLM) and node order prediction (NOP) with a hybrid objective. TMLM uses a novel masking strategy designed according to the tree's characteristics to help the model understand the AST and infer the missing semantics of the AST. With NOP, TreeBERT further extracts the syntactical structure by learning the order constraints of nodes in AST. We pre-trained TreeBERT on datasets covering multiple programming languages. On code summarization and code documentation tasks, TreeBERT outperforms other pre-trained models and state-of-the-art models designed for these tasks. Furthermore, TreeBERT performs well when transferred to the pre-trained unseen programming language.
Strategic reasoning with a bounded number of resources: The quest for tractability

The resource-bounded alternating-time temporal logic RB±ATL combines strategic reasoning with reasoning about resources. Its model-checking problem is known to be 2exptime-complete (the same as its proper extension RB±ATL⁎) and fragments have been identified to lower the complexity. In this work, we consider the variant RB±ATL+ that allows for Boolean combinations of path formulae starting with single temporal operators, but restricted to a single resource, providing an interesting trade-off between temporal expressivity and resource analysis. We show that the model-checking problem for RB±ATL+ restricted to a single agent and a single resource is Δ2P-complete, hence the same as for the standard branching-time temporal logic CTL+. In this case reasoning about resources comes at no extra computational cost. When a fixed finite set of linear-time temporal operators is considered, the model-checking problem drops to ptime, which includes the special case of RB±ATL restricted to a single agent and a single resource. Furthermore, we show that, with an arbitrary number of agents and a fixed number of resources, the model-checking problem for RB±ATL+ can be solved in exptime using a sophisticated Turing reduction to the parity game problem for alternating vector addition systems with states (AVASS).
Educational journalism in Ukraine (results of content analysis)

The article describes theoretical approaches to understanding educational journalism, characterizes the concepts of educational journalism in the Ukrainian and global media space, and builds a model of influence of external factors on the coverage of education issues in the mass media outlets. The tasks of the research are: 1) to study theoretical developments and approaches to the functioning of educational journalism in the media environment; 2) to trace the genre and thematic features of the analyzed educational media outlets (on the example of “Pedpresa”, “Osvita.ua”, “New Ukrainian School”, “Osvitoria”). The research methodology is content analysis of specialized media on educational topics. These media have a regular infill of multigenre and multithematic content of educational topics, actively use tools to create a multimedia content, have a high traffic in the segment of online media educational profile (according to the service statistics “Similar Web”). It was found among the analyzed content of the four online media of educational specialization (3,352 publications) that by the genre structure notes prevail (2,714; 80.9%), there is a lack of reportage (56; 1.7%) and interviews (130; 3.9%). The low number of analytical genres is especially noticeable with regard to the topics of preschool, inclusive and vocational education. A content analysis in Group 1 (2,366 materials, 70.6% of the total sample in the two groups) showed that the largest number of materials during the analyzed period concerned the topics of primary and secondary education (1,131 materials, 47.8%), and the smallest number concerned vocational education (131, 5.5%). Analysis of materials in the Group 2 (29.4% of the materials considered in both groups) showed that the topic of primary education prevails (401 materials, 40.7%), the share of materials relating to distance education is significant (16.7% in “NUS” and 16.9% in “Osvitoria”). Most of the materials of the media “PedPresa” (83%), as well as a significant part of “Osvita.ua” (63%) are borrowed or supplemented by the material from the sites of the MESU, UCEQA or other sources. These results determine the practical value of the study.
Anomaly Detection In Compressed Video

In this paper, an anomaly detection approach has been developed on video compressed in H.265 format. In order to detect anomalies, the motion vectors in the compressed video and the region information of the motion vectors were used. This information was provided as input to the autoencoder model, which is an unsupervised artificial neural network method, and thus the model was trained. The trained model was tested on video data containing anomalies. As output, during the streaming of any video, it is provided to draw a regularity score graph and display the anomaly regions by color. In this paper, we propose an autoencoder based method for anomaly detection in compressed video instead of the original uncompressed video.
How Can I Tell if My Algorithm Was Reasonable?

Self-learning algorithms are gradually dominating more and more aspects of our lives. They do so by performing tasks and reaching decisions that were once reserved exclusively for human beings. And not only that—in certain contexts, their decision-making performance is shown to be superior to that of humans. However, as superior as they may be, self-learning algorithms (also referred to as artificial intelligence (AI) systems, “smart robots,” or “autonomous machines”) can still cause damage. When determining the liability of a human tortfeasor causing damage, the applicable legal framework is generally that of negligence. To be found negligent, the tortfeasor must have acted in a manner not compliant with the standard of “the reasonable person.” Given the growing similarity of self-learning algorithms to humans in the nature of decisions they make and the type of damages they may cause (for example, a human driver and a driverless vehicle causing similar car accidents), several scholars have proposed the development of a “reasonable algorithm” standard, to be applied to self-learning systems. To date, however, academia has not attempted to address the practical question of how such a standard might be applied to algorithms, and what the content of analysis ought to be in order to achieve the goals behind tort law of promoting safety and victims’ compensation on the one hand, and achieving the right balance between these goals and encouraging the development of beneficial technologies on the other. This Article analyzes the “reasonableness” standard used in tort law in the context of the unique qualities, weaknesses, and strengths that algorithms possess comparatively to human actors and also examines whether the reasonableness standard is at all compatible with self-learning algorithms. Concluding that it generally is, the Article’s main contribution is its proposal of a concrete “reasonable algorithm” standard that could be practically applied by decisionmakers. This standard accounts for the differences between human and algorithmic decision-making. The “reasonable algorithm” standard also allows the application of the reasonableness standard to algorithms in a manner that promotes the aims of tort law while avoiding a dampening effect on the development and usage of new, beneficial technologies.
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary

This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team.We submit two multilingual Neural Machine Translation (NMT) systems (Indic-to-English and English-to-Indic).We romanize all Indic data and create subword vocabulary which is shared between all Indic languages.We use back-translation approach to generate synthetic data which is appended to parallel corpus and used to train our models.The models are evaluated using BLEU, RIBES and AMFM scores with Indicto-English model achieving 40.08 BLEU for Hindi-English pair and English-to-Indic model achieving 34.48 BLEU for English-Hindi pair.However, we observe that the shared romanized subword vocabulary is not helping English-to-Indic model at the time of generation, leading it to produce poor quality translations for Tamil, Telugu and Malayalam to English pairs with BLEU score of 8.51, 6.25 and 3.79 respectively.
The effectiveness of using visualization tools and forms in distance learning

The purpose of the article is to comprehensively consider the concept of visualization, which is one of the main factors of effective learning of educational material, as well as to analyze the possibilities of using visualization in distance learning. The article shows the relevance of visualization of distance education, describes visualization technologies as a tool for improving the effectiveness of distance learning, defines the essence of visualization in education, finds out the possibilities of using visualization to ensure the professional training of future specialists in the course of distance learning of academic disciplines, as well as considers the visualization tools used in students’ distance learning. Based on an empirical study of using visualization in the course of distance learning of the "Pedagogical Skills" course, the formedness of the cognitive and operational components of students' educational activities was evaluated. This allowed concluding that using contemporary visualization technologies in the educational process creates prerequisites for improving the quality and effectiveness of training, acts as a key factor in improving the effectiveness of the educational process. Visualization allows solving a set of pedagogical problems, such as making the complicated educational material of various disciplines understandable, accessible for awareness, without reducing the level of its scientific character, creating conditions for informal assimilation of the training content by learners, contributing acquisition of deep, systematic knowledge, and so on.
Research on Improved Seismic Instrumentation System for Nuclear Power Plants

According to the requirements of nuclear safety regulations, nuclear power plants must be equipped with seismic instrumentation systems, which are mainly used for monitoring alarm and automatic shutdown alarm during an earthquake. Both the second and third generation NPPs adopt Peak Ground Acceleration (PGA). However, among the seismic acceleration characteristics, isolated and prominent single high frequency acceleration peaks have no decisive influence on the seismic response. Especially when the earthquake monitoring alarm is at 1 out of 7, it is likely to cause a false alarm or false shutdown. In addition, it usually takes one month or more for the NPPs to restart after the shutdown. In this paper, an improved seismic instrumentation system based on the existing system is proposed. For high intensity areas, three components resultant acceleration is used to judge the 2 out of 4 logic of the automatic seismic trip system(ASTS). For low intensity areas, the seismic failure level is evaluated quickly by using three components resultant acceleration, seismic instrument intensity, cumulative absolute velocity, floor response spectrum and other multi-parameters, avoiding unnecessary and long-term shutdown inspection.
Impact of Organizational Learning and Transformational Leadership on Knowledge Sharing: An Empirical Study in Banking Sector of Pakistan

The basic objective of the empirical study is to identify the influence of organizational learning (OL) and transformational leadership (TL) on knowledge sharing (KS) in banking sector of Pakistan. The above said sector is selected as population of the current research. By using the simple random sampling technique, different branches of private banks and public banks are selected as a sample. 150 questionnaires were distributed to top managers and middle managers. 110 questionnaires were filled by employees and used for analysis. The overall response rate was 73.33%. Different statistical techniques i.e. Pearson’s correlation analysis, multiple regression analysis and reliability analysis are applied on collected data. The results of Pearson’s correlation analysis shows that there is positive relationship between organizational learning (OL), transformational leadership (TL) knowledge sharing (KS). Moreover, regression analysis’s results describe that organizational learning is solid forecaster of knowledge sharing as compare to transformational leadership. From the managerial point of view, the results give clear hint to Pakistan’s banking sector must understand the significance of organizational learning, transformational leadership for the purpose of knowledge sharing. In future researches, data may be collected to other sectors like textile industry, education zone and telecom industry etc. for more simplifying the outcomes and researches may also conducted on some other variables.
Use of Quizizz Education Game Assessment Media Towards Students' Motivation in Online Learning

This study aims to analyze the development of student learning motivation in the online learning process during covid-19 by using a quizizz assessment media. This research is motivated by low student learning motivation, marked by the reduced participation of students in online learning. This assessment was carried out at 243 Cicabe Public Elementary School during distance learning, with a sample of grade VI students. This research is based on the idea that student learning motivation is influenced by many factors, one of which is the use of appropriate assessment media, including quizizz educational game assessment media. This research was conducted using qualitative research with technical analysis with the stages of data reduction, data display, conclusion, and verification. Data collection was carried out using questionnaires and interviews. The questionnaire was made in the form of google form while the interviews were conducted using zoom. The results of the study concluded that the use of quizizz assessment media can develop student learning motivation. Students feel enthusiastic about participating in online learning.
Sluggish State-Based Neural Networks Provide State-of-the-art Forecasts of Covid-19 Cases

At the time of writing, the Covid-19 pandemic is continuing to spread across the globe with more than 135 million confirmed cases and 2.9 million deaths across nearly 200 countries. The impact on global economies has been significant. For example, the Office for National Statistics reported that the UK's unemployment level increased to 5% and the headline GDP declined by 9.9%, which is more than twice the fall in 2009 due to the financial crisis. It is therefore paramount for governments and policymakers to understand the spread of the disease, patient mortality rates and the impact of their interventions on these two factors. A number of researchers have subsequently applied various state-of-the-art forecasting models, such as long short-term memory models (LSTMs), to the problem of forecasting future numbers of Covid-19 cases (confirmed, deaths) with varying levels of success. In this paper, we present a model from the simple recurrent network class, The Multi-recurrent network (MRN), for predicting the future trend of Covid-19 confirmed and deaths cases in the United States. The MRN is a simple yet powerful alternative to LSTMs, which utilises a unique sluggish state-based memory mechanism. To test this mechanism, we first applied the MRN to predicting monthly Covid-19 cases between Feb 2020 to July 2020, which includes the first peak of the pandemic. The MRN is then applied to predicting cases on a weekly basis from late Feb 2020 to late Dec 2020 which includes two peaks. Our results show that the MRN is able to provide superior predictions to the LSTM with significantly fewer adjustable parameters. We attribute this performance to its robust sluggish state memory, lower model complexity and open up the case for simpler alternative models to the LSTM.
STUDENT FRIENDLY CHATBOT- ANDREA

A chatbot is an artificial intelligence (AI) software that can simulate a conversation with a user in natural language through messaging applications, websites, mobile apps or through the telephone. A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. However, from a technological point of view, a chatbot only represents the natural evolution of a Question Answering system leveraging Natural Language Processing.
E-Money Tab As An Attendance Tracker For Elementary School Students

This study aims to describe: 1) the process of using e-money tab, 2) the advantages and disadvantages of using the e-money tab, 3) the impacts of using the e-money tab. This study was conducted as a qualitative study facilitating a phenomenological research design. Data was collected through interviews, live observation and analysis of documents. Study subjects included the principal, the IT team and students of 1 Muhammadiyah Elementary School, Ketelan Surakarta (Elementary School of Muhammadiyah 1 Ketelan Surakarta) and was conducted on location. Results of this study showed that: a) the use of e-money tabs as an attendance tracker can only be accessed at school and requires a strong internet network, b) an advantage to the use of e-money tab as an attendance tracker is being able to track attendance of students with ease, allowing teachers to ensure discipline and parents to monitor the arrival and departure time of their children to and from school. On the other hand, a disadvantage to this system is the instability of the internet network especially when multiple students are using the e-money tab at the same time, causing error in the server, c) the e-money tab has impacted the process of attendance tracking, in which it has maintained and improved discipline of students.
A protoype-based approach to object evolution.

We investigate, in the context of functional prototype-based languages, a calculus of objects which might extend themselves upon receiving a message, a capability referred to by Cardelli as a self-inflicted operation.We present a sound type system for this calculus which guarantees that evaluating a well-typed expression will never yield a message-not-found runtime error.The resulting calculus is an attempt towards the definition of a language combining the safety advantage of static type checking with the flexibility found in dynamically typed languages.
VCX Version 2020 - Further development of a transparent and objective evaluation scheme for mobile phone cameras

VCX or Valued Camera eXperience is a nonprofit organization dedicated to the objective and transparent evaluation of mobile phone cameras. The members continuously work on the development of a test scheme that can provide an objective score for the camera performance. Every device is tested for a variety of image quality factors while these typically based on existing standards. Tests include texture loss, resolution, low light performance, shooting time lag, image stabilisation performance and more, all for a variety of different capture conditions. This paper presents that latest development with the newly released version 2020 and the process behind it.