Reviewerly/exHarmony huggingface dataset:
Abstractive text summarization and new large-scale datasets for agglutinative languages Turkish and Hungarian

Due to the exponential growth in the number of documents on the Web, accessing the salient information relevant to a user need is gaining importance, which increases the popularity of text summarization. Recent progress in deep learning shifted the research in text summarization from extractive methods towards more abstractive approaches. The research and the available resources remain mostly limited to the English language, which prevents progress in other languages. There is need in low-resourced languages for gathering large-scale resources suitable for such tasks. In this study, we release two large-scale datasets (TR-News and HU-News) that can serve as benchmarks in the abstractive summarization task for Turkish and Hungarian. The datasets are primarily compiled for text summarization, but are also suitable for other tasks such as topic classification, title generation, and key phrase extraction. Morphology is important for these agglutinative languages since meaning is carried mostly within the morphemes of the words. We utilize these morphological properties for tokenization to retain the semantic information and reduce the vocabulary sparsity introduced by the agglutinative nature of these languages. Using the datasets compiled, we propose linguistically-oriented tokenization methods (SeperateSuffix and CombinedSuffix) and evaluate them on the state-of-the-art abstractive summarization models. The SeperateSuffix method achieves the highest ROUGE-1 score on the TR-News dataset and provides promising results on the HU-News dataset. In another experiment, we show that the multilingual cased BERT model outperforms monolingual BERT models for both languages and reaches the highest ROUGE-1 score on the HU-News dataset. Lastly, we provide qualitative analysis of the generated summaries on the TR-News dataset.
Physics models in the course "The basics of computer simulation"

A review of the methodological literature on computer modeling shows the existence of different approaches to its teaching in secondary and higher education. It is a common approach in which the construction of models is carried out using the apparatus of higher mathematics, which is possessed mainly by senior students. This leads to the transfer of the course “Methods of mathematical modeling” for 7-8, and sometimes 9-10 semesters, which reduces its role in shaping the worldview of the future specialist, which takes place in high school and junior high school. This state of affairs forced us to create a propaedeutic course “The basics of computer simulation”, which was developed by the joint efforts of the Department of Informatics and Applied Mathematics of Kryvyi Rih State Pedagogical University and the Department of Informatics of the Kryvyi Rih Tsentralno-Miska gymnasium. The methodological support of the course is a textbook designed for high school students and junior high school students.
E-Learning Platform for Hearing Impaired Students

With the Spread of global pandemic Covid-19, the traditional education was transformed to online from traditional learning drastically. Hence the use of e-Learning platforms was increased. But this idea has issues with certain communities of people around the world. The hearing-impaired people have many issues with eLearning platforms because of their deficiency in hearing sound. Therefore, through this paper authors are introducing a learning platform for hearing impaired communities to aid in learning effectively. The proposed platform uses sign language to facilitate communication among students and tutors while providing sign language learning materials, practicing opportunities and Q&A sessions. The system has a low light enhancement module to enhance the videos uploaded by the tutor, module to convert the uploaded videos to American Sign Language and it also converts the questions asked via sign language to text.
Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts' assessment. Specifically, we improved and validated its scores' performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.
Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT

In the next few years, both the number of IoT devices and the performance of quantum computers will increase. Both technologies pose a challenge to our current crypto-strategies. Therefore, post-quantum n-to-n communication encryption is a crucial field of research. Here, the development of new schemes and the analysis, and comparison of existing schemes is necessary. However, current work only investigates the performance of post-quantum schemes only for 1-to-1 communication. Therefore, in this paper, we analyze existing post-quantum schemes concerning n-to-n communication and compare them with pre-quantum schemes. Our results show that the pre-quantum schemes perform better regarding computation times than the post-quantum schemes, but the differences are sometimes only marginal. However, these marginal differences in computation times lead to the lower energy efficiency of the post-quantum schemes. In terms of features, there is no difference between both scheme classes. We show that the post-quantum schemes require unicast, whereas some pre-quantum schemes also support broadcast. Deciding whether to use pre- or post-quantum schemes for n-to-n encryption in IoT use cases depends on (i) whether energy efficiency is essential – e.g., in case of limited power supply – and (ii) whether unicast or broadcast is available.
Authentic Intelligent Machine for Scaling Driven Discovery: A Case for Chiral Quantum Dots

The scaling laws have long been used as evidence of science where many fundamental physics laws emerge. As emerging nanomaterials, quantum dots are also sensitive to scaling because of their strong size effect. In this work, we developed the chiral dielectric theory based on the exciton absorption mechanism to explain the increment of the dielectric constant from chirality via its dimensionality. To help researchers discover and develop scaling relevant theories, the Authentic Intelligent Machine (AIM) protocol was developed to generate and interpret experimental data in an analytical and scaling-oriented manner. We show how the AIM protocol interprets spectra such as transient absorption data of chiral quantum dots with theories, where discrepancies concerning the dielectric constant were discovered. Examples for applying the AIM protocol on other spectra, such as absorption spectra and photoluminescence spectra, are also given.
Fog-Cloud Enabled Internet of Things Using Extended Classifier System (XCS)

The digital services accessed through IoT devices require automated and intelligent computing algorithms that learn and adapt to the changing environment. The objective of this chapter is to address the technique that combines the capability to learn and evolve solutions for large-scale dynamic systems. The proposed chapter deals with the extended classifier system (XCS) which is an amalgamation of reinforcement learning (RL) and genetic algorithms (GA). While RL learns the model-free problem environment, the nature-inspired GA evolves better decision-making rules and improves the existing ones. The motive is to provide intelligent computation for fog-cloud-based IoT systems through XCS. The chapter reveals how the XCS algorithm estimates the optimal number of IoT workload that is to be processed in fog, the remaining of which is transferred to the cloud. The optimal number of workload estimated by the XCS algorithm balances the energy cost and delay in the fog-cloud-based resource allocation (RA) system.
Online Scheduling Algorithm for Heterogeneous Distributed Machine Learning Jobs

Distributed machine learning (ML) has played a key role in today's proliferation of AI services. A typical model of distributed ML is to partition training datasets over multiple worker nodes to update model parameters in parallel, adopting a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parameter server</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AllReduce</i> architecture. ML training jobs are typically resource elastic, completed using various time lengths with different resource configurations. A fundamental problem in a distributed ML cluster is how to explore the demand elasticity of ML jobs and schedule them with different resource configurations, such that the utilization of resources is maximized and average job completion time is minimized. To address it, we propose an online scheduling algorithm to decide the execution time window, the number and the type of concurrent workers and parameter servers for each job upon its arrival, with a goal of minimizing the weighted average completion time. Our online algorithm consists of (i) an online scheduling framework that groups unprocessed ML training jobs into a batch iteratively, and (ii) a batch scheduling algorithm that configures each ML job to maximize the total weight of scheduled jobs in the current iteration. Our online algorithm guarantees a good parameterized competitive ratio with polynomial time complexity. Extensive evaluations using real-world data demonstrate that it outperforms state-of-the-art schedulers in today's AI cloud systems.
Gym-µRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft II. However, existing approaches to tackle full games have high computational costs, usually requiring the use of thousands of GPUs and CPUs for weeks. This paper has two main contributions to address this issue: 1) We introduce Gym-JLRTS (pronounced “gym-micro-RTS”) as a fast-to-run RL environment for full-game RTS research and 2) we present a collection of techniques to scale DRL to play full-game µRTS as well as ablation studies to demonstrate their empirical importance. Our best-trained bot can defeat every µRTS bot we tested from the past µRTS competitions when working in a single-map setting, resulting in a state-of-the-art DRL agent while only taking about 60 hours of training using a single machine (one GPU, three vCPU. 16GB RAM).
Blockchain-Based Dashcam Video Management Method for Data Sharing and Integrity in V2V Network

A dashboard camera (Dashcam) is attached to the front or rear of a vehicle to record images and video. As it is effective in crime prevention and accident management and it can also be used as learning data for Traffic Accident Detection technology for Autonomous Vehicles the use of Dashcam is increasing. However, the video data is stored on a memory card or a cloud server, so there is a high possibility of data loss and forgery. Although it is possible to prevent forgery and falsification using distributed storage based on blockchain technology, the authenticity and privacy of the image data stored in the blockchain cannot be guaranteed. In this study, to solve this problem, we propose a multi-signature-based access control method by grouping and storing video data of multiple vehicles based on GPS (Global Positioning System) data. To ensure the privacy of the video data stored in the blockchain, only users uploading video belonging to the relevant GPS can access nearby Dashcam videos. Through these experimental results, it show that Experimental results proposed method can maintain low latency in large-scale request environment to the privacy and reach data management efficiently in a distributed file system. According to our experiments, the dashcam video data distributed storage latency was fast enough, with an average of 25 ms for uploads and less than 15 ms for downloads. The signature generation time is 10ms, and the verification time required for access control is also less than 100 ms, which is the same even if the number of nodes increases, so scalability is not affected. The blockchain transaction processing took about 2 seconds, but it does not affect the V2V network. Also, client registration time does not affect performance.
On Allocation of Systematic Blocks in Coded Distributed Computing

Coded distributed computing is used to mitigate the adverse effect of slow workers on the computation time in distributed computing systems. However, using error-correction codes results in encoding and decoding delays. In this work, we consider a systematic maximum-distance separable (MDS) coded matrix-vector multiplication problem with multi-message communication (MMC), where the master assigns multiple sub-tasks to each worker. In this setup, we show that the received systematic outputs can be used to reduce the decoding time by implementing a proper decoding algorithm. To further reduce the decoding time, we use the MMC property that sub-tasks are executed sequentially to propose an allocation of the systematic sub-tasks that significantly increases the number of received systematic outputs. Our results further demonstrate that the reduction in the decoding time is even more significant in applications that require only a partial recovery. In these applications, it suffices to complete a certain percentage of the computation, and using our approach, we show that decoding may be completely avoided.
A Comparative Analysis of Sentiment Classification Based on Deep and Traditional Ensemble Machine Learning Models

The era of the internet has transformed the way people share their thoughts and viewpoints. It is now achieved mostly through blog entries, product review blogs, social networks, and so on. We get immersive media through online networks, where users notify and affect others through the internet. In this research, positive and negative sentiments are used to do the document-level sentiment analysis using deep and traditional ensemble models. In this study, we attempt to evaluate the performances of recent deep learning ensemble models and traditional ensemble models for obtaining the highest accuracy for binary sentiment classification. Three traditional ensemble models (i.e., Voting Ensemble, Bagging Ensemble, and Boosting Ensemble) and three deep learning ensemble layout models (i.e., 7 Layer Convolutional Neural Network (7-L CNN) + Gated Recurrent Unit (GRU), 7-L CNN + GRU + Globe Embedding, and 7-L CNN + Long Short-Term memory (LSTM) + Attention Layer) have been applied in two different datasets to perform the sentiment classification. The deep learning ensemble models perform better than the traditional ensemble models in most cases. In both of the datasets, the deep learning ensemble models namely 7-L CNN + GRU + Globe and 7-L CNN + LSTM + Attention Layer achieve the highest accuracy by securing 94.19% and 96.37% respectively for the product and restaurant review dataset.
Authorship attribution using author profiling classifiers

Abstract Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.
PS-Net: Progressive Selection Network for Salient Object Detection

Low-level features contain abundant details and high-level features have rich semantic information. Integrating multi-scale features in an appropriate way is significant for salient object detection. However, direct concatenation or addition taken by most methods ignores the distinctions of contribution among multi-scale features. Besides, most salient object detection models fail to dynamically adjust receptive fields to fit objects of various sizes. To tackle these problems, we propose a Progressive Selection Network (PS-Net). Specifically, PS-Net dynamically extracts high-level features and encourages high-level features to guide low-level features to suppress the background response of the original features. We proposed a salient model PS-Net that selects features progressively at multiply levels. First, we propose a Pyramid Feature Dynamic Extraction module to dynamically select appropriate receptive fields to extract high-level features by Feature Dynamic Extraction modules step by step. Besides, a Self-Interaction Attention module is designed to extract detailed information for low-level features. Finally, we design a Scale Aware Fusion module to fuse these multiple features for adequate exploitation of high-level features to refine low-level features gradually. Compared with 19 start-of-the-art methods on 6 public benchmark datasets, the proposed method achieves remarkable performance in both quantitative and qualitative evaluation. We performed a lot of ablation studies, and more discussions to demonstrate the effectiveness and superiority of our proposed method. In this paper, we propose a PS-Net for effective salient object detection. Extensive experiments on 6 datasets validate that the proposed model outperforms 19 state-of-the-art methods under different evaluation metrics.
Analysis of Symmetric and Asymmetric Key Algorithms

Our world is making way towards complete digitalization, with more and more of data going to Internet. Thus, security of this data has become one of our utmost responsibilities. In 2018, 30 million cyber-attacks occurred all over the world costing $3.86 million on an average. With the statistics getting worse every year, it is very important to invest more in securing the data and more importantly design modern cryptographic techniques that are capable of mitigating modern adversaries. In this paper, we study different cryptographic algorithms that were designed after 1970 and are being used till date, and which were adopted and trusted to secure the data across the globe. Comparisons between different algorithms are being done on basis of flexibility, resistance against common attacks, execution speed, memory utilization, applications and the conclusion on best-suited encryption/cryptographic algorithms for securing our data is drawn. The study and comparisons of various algorithms have shown that asymmetric key encryption is far more secure than symmetric key encryption because of the application of two keys in place of one key and the algebraic complexity of their algorithms. Comparative analysis between symmetric key algorithms concluded that AES is the most versatile, secure and efficient algorithm. Comparative analysis between asymmetric key algorithms reveals that elliptical curve cryptography (ECC) is very secure due to its algebraic structure dependency on finite fields and elliptic curves.
AOGAN: A generative adversarial network for screen space ambient occlusion

Abstract Ambient occlusion (AO) is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces. Recently, a number of learning-based AO approaches have been proposed, which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed. However, most such methods have high error for complex scenes or tend to ignore details. We propose an end-to-end generative adversarial network for the production of realistic AO, and explore the importance of perceptual loss in the generative model to AO accuracy. An attention mechanism is also described to improve the accuracy of details, whose effectiveness is demonstrated on a wide variety of scenes.
Sustainability, Trust, and Blockchain Applications in Islamic Finance and Circular Economy: Best Practices and Fintech Prospects

Since the adoption of the SDGs in 2015, it has been a 5-year journey of trial-and-error experimentations all over the world to come up with innovative solutions beyond business-as-usual and get the job done. In this paper, we assess blockchain-backed solutions beyond the hype. While the technology has a promising potential to trigger disruptive innovations to fulfill the SDGs, it is not mature yet with many gaps in terms of approaches and tools to develop blockchain use cases, monitor and evaluate blockchain experiments, mitigate associated risks and ethical considerations while managing changes within organizations leading blockchain-powered platforms. It is only by filing these gaps that blockchain can deliver its promises and may be effectively used as an SDG accelerator. Islamic finance can play a key role in shaping the transition towards a more circular economy. One promising way of doing so, is by scaling-up the use of blockchain-enabled solutions in the practices of circular economy and Islamic finance. As the technology is still getting mature, more innovative and applied research is needed to capitalize on the lessons learned within various geographies and across a wide range of economic, social, and environmental spectrums.