Reviewerly/exHarmony huggingface dataset:
Utilizing Informative Missingness for Early Prediction of Sepsis

Aims: Physicians have to routinely make crucial decisions about patients' health in the ICU.Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted.In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data.Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods.To further improve the model, we used masking vectors to represent the missingness of features in the dataset.Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model's performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before t optimal , achieved a Utility Score of 0.337 on the full test set.We identified as "CTL-Team" in the challenge and were officially ranked 5 th on the basis of this score.
On the hidden treasure of dialog in video question answering

High-level understanding of stories in video such as movies and TV shows from raw data is extremely challenging. Modern video question answering (VideoQA) systems often use additional human-made sources like plot synopses, scripts, video descriptions or knowledge bases. In this work, we present a new approach to understand the whole story without such external sources. The secret lies in the dialog: unlike any prior work, we treat dialog as a noisy source to be converted into text description via dialog summarization, much like recent methods treat video. The input of each modality is encoded by transformers independently, and a simple fusion method combines all modalities, using soft temporal attention for localization over long inputs. Our model outperforms the state of the art on the KnowIT VQA dataset by a large margin, without using question-specific human annotation or human-made plot summaries. It even outperforms human evaluators who have never watched any whole episode before. Code is available at https://engindeniz.github.io/dialogsummary-videoqa
Job Scheduling in Cloud Environment Using Lion Algorithm

Cloud computing is an emerging technology in the field of service oriented computing and software engineering.Security is a critical issue in cloud environment as it possess huge amount of data.In this circumstance, scheduling has become a challengeable mechanism and to utilize the resources in a secure manner we proposed a new metaheuristic algorithm called Lion algorithm (LA).In this paper, the scheduling problems are submitted to solve by eleven algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Optimisation with Differential Evolution (DEopt), Firefly Algorithm (FA), LA, Artificial Bee Colony (ABC), Glowworm Swarm Optimization (GSO), Bacterial Foraging Optimization (BFO), Gravitational Search Algorithm (GSA), Ant Lion Optimizer (ALO) and Cuckoo Search (CS) algorithm.Then the performances of the entire algorithms are compared with LA in terms of its cost function.The final result of convergence analysis and statistical analysis reported that LA has better convergence property and it possess better statistical metric values such as best, worst, mean, median and standard deviation than all the ten algorithms.
Blockchains Technology Analysis: Applications, Current Trends and Future Directions—An Overview

One of the most promising technical trends that have taken the world by storm is the blockchain technology which is the underlying mechanism of the bitcoin. It is a distributed, decentralized and innovative technology that is predicted to bring a huge change in the world. This technology is analogous to the Internet with built-in robustness. Although it is currently one of the most researched areas in the domain of information technology, it has a set of its own limitations. This paper discusses the blockchain technology from scratch, starting off with the general introduction followed by presentation of a comprehensive insight into blockchain applications, the current research trends, limitations and potential future directions. The paper concludes that blockchain networks are likely to change the outlook, perspectives and utilization techniques of the information technology industry in the future by decentralizing the World Wide Web and making the traditional business defunct. Moreover, the amalgamation of blockchain technology with IoT and data science is going to open new horizons of research and development in tech world.
Education in the Era of the Fourth Industrial Revolution: Development Vector, Prospects and Challenges for Russia

Modern civilisation has entered the era of the fourth industrial revolution, characterised by digital, Internet and cyber-expansion, virtualisation, mobile technologies, robotisation, global changes in energy, nano- and biotechnologies. It entails significant changes in all spheres of human activity. There is a mass need for entirely new professions. Scientific and technological progress gives the society not only broad prospects but also brings new challenges and threats. There is a tight (not always fair) competition between the leading countries of the world and transnational corporations for domination in entering the sixth technological order, to lead in digital technologies and artificial intelligence. At this stage, educational systems should provide revolutionary changes based on the latest scientific achievements. One more dangerous threat is that the achievements of modern science and high technologies are not always used for the benefit of humanity, that is, large-scale cyber-attacks, hybrid wars, public consciousness manipulation. Form this point, the formation of a single global educational space, taking into account the humanistic needs of the society, seems really important. The fourth industrial revolution leads to personality changes and not always in a positive direction. This is especially true of the generation “Y” or “network generation”, consuming “intellectual fast food” and easily falling under the influence of others and becoming a victim of manipulation. It also includes the problem of virtual reality, which influences the person so profoundly that he/she falls out of the real world. Obviously, education must also undergo a systemic transformation, based on the characteristics of the modern information society and the globalising world that has entered the postmodern and mass media era. The issues mentioned above are deeply and critically analysed and discussed in this study both from the global and BRICS (precisely Russia) countries perspective. The authors eventually suggest some ways to solve them.
Multi-label software bug categorisation based on fuzzy similarity

The efficiency of the software depends on timely detection of bugs. For better quality and low-cost development bug fixing time should be minimised. Categorisation of software bugs helps to understand the root cause of software bugs and to improve triaging. As the software development approach is modular and multi-skilled, it is possible that one software bug can affect multiple modules, and multiple developers can fix newly reported bugs. Hence, a multi-label categorisation of software bugs is needed. Fuzzy similarity techniques can be helpful in understanding the belongingness of software bugs in multiple categories. In this paper a multi-label fuzzy similarity based categorisation technique is presented for effective categorisation of software bugs. Fuzzy similarity between a pair of bugs is computed and, based on a user defined threshold value, the bugs are categorised. Experiments are performed on software bug data sets, and the performance of the proposed classifier is evaluated.
A Method to Estimate Position Relationship between Pedestrian and Crosswalk Based on YOLCAT++

Pedestrian is one of the most important participants in city transportation, at the same time, because of the property of pedestrian, they are the most vulnerable groups in the transportation. Therefore, the regulation for transportation participants should not ignore pedestrian. Recently, with the development of technology, traffic management becomes more and more intelligent. Video surveillance auxiliary equipments are used as a kind of assistive equipment on more and more streets. With the development of neural network, it is used more and more to deal with image classification and recognition problems. There are many excellent algorithms such as RCNN, YOLO, etc., which have been applied in real life to improve efficiency. The application of neural network on video surveillance can significantly improve pedestrian and small motor vehicle traffic violation inspection efficiency. This paper describes a method of using YOCALT++ model to identify pedestrians and zebra crossings and estimate the positional relationship between them.
Structural-Hierarchical Didactic Model of E-Learning

This article describes the original Structural-Hierarchical Didactic (SHD) model of e-learning. The model is based on a four-level methodological knowledge base. The first level includes a set of electronic educational encyclopedias in various fields of knowledge. The second level includes e-learning courses. The model supports the structuring of e-learning course on didactic components (vertical structuring) and levels of detail (horizontal structuring). The third level includes a set of syllabuses. The fourth level includes a set of a federal state educational standard of higher education (FSES HE). A distinctive feature of the SHD model is the division of educational objects into didactic components. This will allow, firstly, to automatically verify the didactic completeness of the electronic training course. Secondly, to include parts of one course in another without losing the didactic structure. Thirdly, to select a separate didactic layer from the electronic training course and, based on it, automatically generate specialized training materials: lecture notes, task collections, exam tests, etc. The basic operations of the SHD model are described, based on which analysis algorithms are proposed syllabuses and e-learning courses. In conclusion, a brief summary of the results and directions for further research is given.
Effective user preference mining-based personalised movie recommendation system

One of the primary issues of many websites is the suggestion of multiple choices to the users at the same time, which makes the task more complex and time consuming to find the right product. Web mining and recommendation system based on user behaviour helps users by providing essential information without asking explicitly. Several movie recommendation systems are available to suggest movies, but often they do not do that effectively. To achieve enhanced effectiveness and efficiency, user's movie ratings were retrieved, cleaned, formatted and grouped into proper, meaningful session and data profile was developed. In this paper, we have developed a new ontology for clear and better understanding of the movie domain. The user data consisting of movie ratings is used to recommend movies for the users. For the classification of users, we use adaptive K-nearest neighbour (AKNN) approach and post classification process; movies are recommended to the active target user. The obtained results of the proposed recommendation approach are compared with existing baseline methods, and the results prove that the presented approach to be proficient.
Robust, imperceptible and blind video steganography using RGB secret, maximum likelihood estimation and Fibonacci encryption

According to the International Telecommunication Union, currently 51% of the world's population has internet access, out of which over 80% prefers video sharing. Henceforth for secure communication, secret data hiding in video can be a viable option. Here a blind, key-based video steganography has been proposed which can hide RGB image within a colour video. The novelty of this approach lies in the security imposed by various factors including scene change detection by maximum likelihood estimation; pre-processing of secret image by Fibonacci encryption and discrete wavelet transform based keyed embedding technique, which can hide colour images without any perceivable differences and does not require cover video frame during extraction. Quality of the method has been analysed by peak signal to noise ratio, structural similarity index, normalised absolute error and correlation coefficient; robustness has been tested against random cropping, rotating, resizing and histogram equalization; comparison with the literature demonstrates its superiority.
Ecommerce Product Rating System Based on Senti-Lexicon Analysis

E-commerce is one of the popular systems for buying and selling the products. In comment section of products that they have purchased, customer express their opinion based on the quality of product, the attitude of vendor, the delivery of product etc. This information acts as a reference for the new customers, whether they have bought the product or not. To evaluate the users’ comments, sentiment analysis is played important roles where this approach not only focuses on the product itself but also the features of product itself. In this work, We have calculated the score /rating of user’s sentiment for Amazon products i.e. Mobile phone; by taking the comments from the review section of product which is implied by some words or phrases, are very significant and meaningful to express users’ opinion. This approach performs sentiment analysis using lexicon based approach with the help of Natural Language Toolkit (NLTK) and compare the result with the Amazon’s own product rating. The experimental results prove the effectiveness of the approach.
Collaborative Filtering

Collaborative filtering (CF) is a process to filter information or patterns with collaboration among multiple agents and resources. The main idea of CF is to effectively extract useful information from the overwhelming amount of collected data. This article discusses the perception of CF techniques and explains how to utilize CF in a recommender system (RS). RS provides recommendations to an active user based on items that other similar users prefer. CF makes automatic predictions of a user’s interests by utilizing stored data of various users, which makes it a key method for RS.
Multi-writer multi-reader conjunctive keyword searchable encryption

We explore the area of searchable encryption aiming to identify the schemes supporting multiple data owner (writers) and multiple data users (readers). Especially, we observe multi-writer multi-reader (MWMR) searchable encryption schemes focusing on multi-keyword search. However, such MWMR schemes offer a centralised token generation approach whereby an enterprise trusted authority (ETA) issues a search token to each reader in system, and thus introduce two serious issues, viz. leakage of keywords to ETA and O(q · R) communication overhead for R readers and q queries per reader. In this paper, we alleviate these issues by proposing an MWMR scheme with a decentralised token generation approach. With such an approach, a registered data reader constructs a search token without interacting with ETA and thus provides an efficient token generation with keyword privacy from ETA. Additionally, we incorporate a more expressive especially, conjunctive keyword search with the scheme. With formal security analysis, we prove that the scheme effectively stands against chosen keyword attack performed by inside or outside attacker. With theoretical and empirical analysis, we justify the effectiveness of the proposed scheme.
An efficient non-iterative method for computing the centroid of an interval type-2 fuzzy set

Interval type-2 fuzzy logic systems (IT2FLSs) have better abilities to cope with uncertainties in many applications. One major drawback of IT2FLSs is the high computational cost of the iterative Karnik-Mendel (KM) algorithms in type-reduction (TR). From the practical point of view, this prevents using IT2FLS in real-world applications. To address this issue, a novel non-iterative method called Moradi-Zirkohi-Lin (MZL) TR method is proposed for computing the centroid of an IT2FLS. This makes the practical implementation of the IT2FLSs simpler. Comparative simulation results show that the proposed method outperforms the KM TR method in terms of computational burden. Besides, closer results, in terms of accuracy, to the KM TR method among the existing non-iterative TR methods are also achieved by the proposed TR method.
Zero Trust

By definition, a zero trust security model advocates the creation of zones and segmentation to control sensitive IT resources. This also entails the deployment of technology to monitor and manage data between zones and, more importantly, authentication within a zone(s), whether by users, applications, or other resources. In addition, the model redefines the architecture of a trusted network inside a defined perimeter. This can be on-premise or in the cloud. This is relevant today since technologies and processes like the cloud, DevOps, edge computing, and IoT have either blurred, or dissolved altogether, the idea of a traditional perimeter. Therefore, the concept of a trust zone is important to manage any resources operating and communicating together.
Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of Helminth eggs and its application online

Conventional analytical techniques for evaluating Helminth eggs are based on different steps to concentrate them in a pellet for direct observation and quantification under a light microscope, which can generate under-counts or over-counts and be time consuming. To enhance this process, a new approach via automatic identification was implemented in which various image processing detectors were developed and incorporated into a Helminth Egg Automatic Detector (HEAD) system. This allowed the identification and quantification of pathogenic eggs of global medical importance. More than 2.6 billion people are currently affected and infected, and this results in approximately 80,000 child deaths each year. As a result, since 1980 the World Health Organization (WHO) has implemented guidelines, regulations and criteria for the control of the health risk. After the initial release of the analytical technique, two improvements were developed in the detector: first, a texture verification process that reduced the number of false positive results; and second, the establishment of the optimal thresholds for each species. In addition, the software was made available on a free platform. After performing an internal statistical verification of the system, testing with internationally recognized parasitology laboratories was carried out, Subsequently, the HEAD System is capable of identifying and quantifying different species of Helminth eggs in different environmental samples: wastewater, sludge, biosolids, excreta and soil, with in-service sensitivity and specificity values for the open library for machine learning TensorFlow (TF) model of 96.82% and 97.96% respectively. The current iteration uses AutoML Vision (a computer platform for the automatization of machine learning models, making it easier to train, optimize and export results to cloud applications or devices). It represents a useful and cheap tool that could be utilized by environmental monitoring facilities and laboratories around the world.•The HEAD Software will significantly reduce the costs associated with the detection and quantification of helminth eggs to a high level of accuracy.•It represents a tool, not only for microbiologists and researchers, but also for various agencies involved in sanitation, such as environmental regulation agencies, which currently require highly trained technicians.•The simplicity of the device contributes to the control the contamination of water, soil, and crops, even in poor and isolated communities.
‘Cultural Activities’ in Foreign Language Teaching: A Multimodal Perspective on Italian Language Textbooks

This article focuses on the cultural content present in the cultural sections of selected Italian-as-a-foreign-language (FL) textbooks used at the University of Warsaw to teach Italian courses. To explore such content, we apply both a multimodal approach based on visual social semiotics (Kress and van Leeuwen 2006) and a framework of cultural paradigms, via the use of which culture is usually introduced in foreign language education. We also conceptualise the notion of cultural activity as a basic research unit with the primary goal of organising a textbook’s cultural content. While exploring the identified cultural activities, we observed that the dominant cultural paradigm is the national one. The explicit focus is put on Italian artistic heritage, geography, and literature, whereas contemporary social problems are less frequently presented, often without references to different points of view. In the discussion, certain pedagogical and content solutions for teaching culture are indicated.
COVID-19 and education: insights into the impact of E-learning on accounting education: evidence from Jordan

Purpose This study aims to reveal the impact of e-learning on accounting education amid COVID-19 in Jordanian universities from the viewpoint of faculty members. Design/methodology/approach The questionnaire is used as a study instrument distributed to faculty members at the Accounting Departments of the Jordanian public and private universities. Due to the COVID-19 pandemic, Google Forms has been used to distribute 117 questionnaires that have been analyzed via the partial least squares-smart program. Findings The results show a negative impact of learning difficulties on accounting education, a negative impact on the personal skills of faculty members on the digitization of accounting education, no impact on the personal skills of faculty members, a positive impact on training courses, the use of technology on accounting education and the digitization of accounting education, as well as a positive impact on the skills of faculty members on the digitization of accounting education. Research limitations/implications These results are significant in determining the adequacy of online learning for accounting education at the Accounting Departments of Jordanian public and private universities from the viewpoint of faculty members amid COVID-19. Originality/value This study highlights the impact of the e-learning system imposed amid COVID-19 on accounting education, teaching efficiency and the future of accounting education.