Reviewerly/exHarmony huggingface dataset:
Edit blindness is not related to immersion and presence in Hollywood movies

The concept of presence describes participants’ feelings of “being there” – in a mediated setting such as a virtual environment or a Hollywood movie. Consistently, it has been reported that higher levels of immersion (i.e. providing richer information) are related to higher presence levels. Immersion and presence are measured, asking participants to report their state of mind. The present study links presence to edit blindness – a concept also known as being sensitive to the richness of information (filmic cut detection is impaired for films with a soundtrack compared to films without a soundtrack). We investigated whether edit blindness might be considered a behavioral correlate of presence or immersion. Participants watched short excerpts of Hollywood movies (with vs. without a soundtrack) in two experimental sessions; they were asked (1) to detect filmic cuts in one session and (2) to rate experienced immersion and presence after each clip in another session. While audiovisual films elicited a reduced cut detection performance, longer cut detection times, and higher presence ratings, the behavioral measures of edit blindness were related neither with immersion nor presence ratings. We thus conclude that the self-reported and the behavioral measures of media reception are not readily equivalent.
The LSTM-Based Automated Phishing Detection Driven Model for Detecting Multiple Attacks on Tor Hidden Services.

Abstract Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their personal digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, the dynamic and time-variant with new promising techniques such as Long-Short-term -Memory (LSTM), accurately detects the new forms of vulnerable attacks in phishing-based Tor hidden services.
An Intuitionistic Fuzzy and Contrast Limited Adaptive Histogram Equalization based Low-Light Color Image Enhancement Technique

Abstract This paper proposes a new intuitionistic fuzzy-based algorithm for color image enhancement. A unique aspect of the proposed method is determining the degree of hesitation using Yager’s intuition-istic fuzzy generator technique. Here a boosting parameter α is used to stimulus the intuitionistic fuzzy image for enhancement technique. In this proposed method, first normal fuzzification transforms the given image into a fuzzy image. Then it is again transferred to an intuition-istic fuzzy image. Finally, one can get the proposed enhanced image after applying contrast limited adaptive histogram equalization. The proposed method is compared with other existing methods like brightness preserving dynamic fuzzy histogram equalization, histogram equalization , contrast limited adaptive histogram equalization, histogram specification approach, dehazing algorithm, intuitionistic fuzzy algorithm, and interval-valued intuitionistic fuzzy algorithm. We got better results through performance analyses like entropy, structural similarity index, absolute mean brightness error, and contrast improvement index. The outcomes uncovered that the proposed technique beats other existing strategies on generally speaking execution measurements and visual quality. Mathematics Subject Classification (2020): 68U10 . 94D05
Image encryption via a 2-D Logistic-Baker map and fractional discrete Meixner moments

Abstract In this paper, we introduce an efficient and robust image encryption anddecryption scheme based on 2-D chaotic map and 2-D fractional discreteorthogonal moments. In the confusion stage, the compound LogisticBaker map is used to enhance the complexity and generate pseudorandom sequences. In order to resist a variety of attacks and increasesecurity, Fractional Discrete Meixner Moments (FrDMMs) are used in thediffusion stage. FrDMMs had been computed using the eigenvalues andthe corresponding multiplicity of the discrete Meixner polynomials. Thesecret key is created by combining the Logistic-Baker map and FrDMMs.The experimental results and the security analysis demonstrate that theproposed algorithm is robust against existing security attacks and hasa large key space, high key sensitivity, and a strong encryption effect.
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations

Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments prevent widespread application of DRL agents in real-world scenarios. Although advances have been made recently, most prior works assume sufficient online interaction on training environments, which can be costly in practical cases. To this end, we focus on an offline-training-online-adaptation setting, in which the agent first learns from offline experiences collected in environments with different dynamics and then performs online policy adaptation in environments with new dynamics. In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation. In offline training phase, the environment representation and policy representation are learned through contrastive learning and policy recovery, respectively. The representations are further refined by mutual information optimization to make them more decoupled and complete. With learned representations, a Policy-Dynamics Value Function (PDVF) [Raileanu et al., 2020] network is trained to approximate the values for different combinations of policies and environments from offline experiences. In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF. Our experiments show that PAnDR outperforms existing algorithms in several representative policy adaptation problems.
Evaluating Accuracy of Solar Ground Station System Data

The solar radiation data is crucial for many solar applications.In this regard, this manuscript analyzed and compared the correctness of ground station data with data from the PVGIS.Empirical equations were adopted to build software.This software calculates the global, direct, diffuse, and rates of solar radiation data on two axes.The result of the analysis was employed to assess the compatibility, dependability, and confidence of these types of data.The comparison showed that vastly variations in some curves from dawn to sunset.In sum, every site exhibit variation in solar radiation station rates.These results could serve as a starting point for users of solar data when analyzing the expected uncertainty for a given data
RDLNN-based Image Forgery Detection and Forged Region Detection Using MOT

Image forgery detection TEMPhas become an emerging research area due to the increasing number of forged images circulating on the internet and other social media, which leads to legal and social issues.Image forgery detection includes the classification of an image as forged or authentic and as well as localizing the forgery wifin the image.In this paper, we propose a Regression Deep Learning Neural Network (RDLNN) based image forgery detection followed by Modified Otsu Thresholding (MOT) algorithm to detect the forged region.The proposed model comprises five steps that are preprocessing, image decomposition, feature extraction, classification and block matching.In the preprocessing step, the RGB images are converted to YCbCr color format.Then, the images are decomposed using the new Polar Dyadic Wavelet Transform (PDyWT), followed by the extraction of important features.The classification phase called RDLNN effectively classifies the normal image and the forged image.For localization of the forgery, the forged image is divided into a number of blocks, and then Genetic ThreeStep Search (GTSS) algorithm is exploited to identify the dissimilar blocks.To get the exact forged region in the image, the dissimilar blocks are analyzed by the Modified Otsu Thresholding (MOT) algorithm.The proposed algorithm is compared wif widely used image forgery detection algorithms.The results show that the proposed method improves the forgery detection accuracy and precision by at least 6.04% and 3.77%, respectively, as compared to the already existent techniques such as ANFIS, KNN, ANN, and SVM.Moreover, the training time of the proposed network is lower by at least 64.3 % than the above existing techniques
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models

Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain. This paper describes our NER system in the SemEval 2022 task11: MultiCoNER. We proposed an effective system to adaptively ensemble pre-trained language models by a Transformer layer. By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively. Experimental results show that our method achieves superior performances in Farsi and Dutch.
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing

Strategy video games challenge AI agents with their combinatorial search space caused by complex game elements. State abstraction is a popular technique that reduces the state space complexity. However, current state abstraction methods for games depend on domain knowledge, making their application to new games expensive. State abstraction methods that require no domain knowledge are studied extensively in the planning domain. However, no evidence shows they scale well with the complexity of strategy games. In this paper, we propose Elastic MCTS, an algorithm that uses state abstraction to play strategy games. In Elastic MCTS, the nodes of the tree are clustered dynamically, first grouped together progressively by state abstraction, and then separated when an iteration threshold is reached. The elastic changes benefit from efficient searching brought by state abstraction but avoid the negative influence of using state abstraction for the whole search. To evaluate our method, we make use of the general strategy games platform Stratega to generate scenarios of varying complexity. Results show that Elastic MCTS outperforms MCTS baselines with a large margin, while reducing the tree size by a factor of $10$. Code can be found at: https://github.com/egg-west/Stratega
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead

Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiation-induced misprediction probability can be so high to impede a safe deployment of DNNs models at scale, urging the need for efficient and effective hardening solutions. In this work, we propose to tackle the reliability issue both at training and model design time. First, we show that vanilla models are highly affected by transient faults, that can induce a performances drop up to 37%. Hence, we provide three zero-overhead solutions, based on DNN re-design and re-train, that can improve DNNs reliability to transient faults up to one order of magnitude. We complement our work with extensive ablation studies to quantify the gain in performances of each hardening component.
Data Trust and IoT

People IoT surroundings have become valuable information sources that can positively impact individuals and society. A user IoT data can be used for different purposes. For instance, research and improvement of public services. However, individuals lack the governance power to share their IoT data. Data trust is a concept that brings opportunities to address data sharing in IoT. This research reviews the idea of data trust. Then, we review IoT and its unique characteristics that implement data trust a challenge. We further discuss blockchain technology and how it can be used to enable data trust in IoT. Finally, we introduce a blockchain-based solution for data trust in IoT.
DEEP LEARNING IN NEURAL NETWORK

In this paper, we will discuss Deep Learning (DL) concepts.Deep learning has become an extremely active research area in machine learning and pattern recognition society.It has gained huge success in the field of speech recognition, computer vision, and language processing.This paper will contain the fundamental concepts of deep learning along with a list of the current and future applications.
Digital Skills in Africa

The European Union’s (EU) Comprehensive Strategy with Africa, adopted in 2020, prioritises digital skills in three of its five thematic areas. Besides the EU, other key stakeholders, including the World Bank and United Nations (UN), have also expressed interest in and initiated programmes to support digital skills in Africa. Despite these formal declarations, we argue in this chapter that the conditions do not yet exist for a meaningful cooperation between the EU and Africa in this area. This is due to (1) lack of conceptual clarity and agreed consensus on what is meant by digital skills; (2) the need to develop the ecosystem for digital skills training, including provision of digital devices, connectivity, and appropriate digital contents in education establishments; and (3) the need to teach digital skills along with other cognitive, social, and emotional skills in education and training programmes (formal and non-formal). Nonetheless, in light of the progressive penetration of digital technologies, the projected demand for digital skills could act as an enabler to drive EU–Africa collaboration in this area. This collaboration could be focused on developing policy frameworks for digital skills, investing in the digital skills ecosystem, and prioritising broad-based digital skills training to drive productivity improvements and job creation.
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search

Image semantic segmentation technology is one of the key technologies for intelligent systems to understand natural scenes. As one of the important research directions in the field of visual intelligence, this technology has broad application scenarios in the fields of mobile robots, drones, smart driving, and smart security. However, in the actual application of mobile robots, problems such as inaccurate segmentation semantic label prediction and loss of edge information of segmented objects and background may occur. This paper proposes an improved structure of a semantic segmentation network based on a deep learning network that combines self-attention neural network and neural network architecture search methods. First, a neural network search method NAS (Neural Architecture Search) is used to find a semantic segmentation network with multiple resolution branches. In the search process, combine the self-attention network structure module to adjust the searched neural network structure, and then combine the semantic segmentation network searched by different branches to form a fast semantic segmentation network structure, and input the picture into the network structure to get the final forecast result. The experimental results on the Cityscapes dataset show that the accuracy of the algorithm is 69.8%, and the segmentation speed is 48/s. It achieves a good balance between real-time and accuracy, can optimize edge segmentation, and has a better performance in complex scenes. Good robustness is suitable for practical application.
OPPORTUNITIES FOR INTERNATIONAL STUDENTS TO RECEIVE NON-FORMAL ART EDUCATION AT POLTAVA V. G. KOROLENKO NATIONAL PEDAGOGICAL UNIVERSITY

The article highlights the opportunities to provide international students with non-formal art education in the artistic groups at one of the best pedagogical universities in Ukraine, i.e., Poltava V. G. Korolenko National Pedagogical University. It is emphasized that the university has more than a century of traditions in training pedagogical and non-pedagogical staff and experience in teaching international students. It is pointed up that the university’s scientific and pedagogical staff is a driving force in the progressive development of pedagogical science and practice in Ukraine and provides quality educational services for more than 110 programs. It is noted that Poltava V. G. Korolenko National Pedagogical University has created a social and cultural environment, which traditionally converges different types of educational activities, in particular, formal and non-formal art. The expediency of combining these two educational activity types while studying at the university is substantiated. It allows international students to master the components of selected educational programs and successfully undergo sociocultural adaptation to the learning environment and Ukrainian society. It is highlighted that non-formal art education at Poltava V. G. Korolenko National Pedagogical University is provided to students in artistic and creative groups. These groups’ artistic level is highly recognized within Ukraine and abroad. It is emphasized that non-formal education is each student’s voluntary choice regarding the field of art, a particular group, and compositions to perform, etc. It is determined that international students’ artistic and educational activities, in particular, musical, choreographic, visual, literary, theatrical, etc., in artistic and creative groups, meet their needs to adapt to new conditions. It is a motivating factor for learning about Ukrainian culture. It activates the creative potential of each individual, has a positive effect on the harmonization of psycho-emotional and physical conditions, as well as contributes to the generalization of one’s own life experience, and helps to correlate it with the system of values that have historically developed in the Ukrainian state and modern youth environment, etc.
MODERN SPIRITUAL AND MORAL ASPECTS OF EDUCATION IN PEDAGOGICAL INSTITUTIONS OF HIGHER EDUCATION

The purpose of the article: to reveal the essence of the concept of spiritual and moral education in pedagogical institutions of higher education. To determine the directions of spiritual and moral education of students of higher education institutions. Create a model of spiritual and moral growth of the individual.
Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation

Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation. Meanwhile, it is highly desirable to avoid the high annotation cost related to the target dataset and protect the source dataset privacy. Therefore, we propose an effective source-free unsupervised domain adaptation method for cross-modality abdominal multi-organ segmentation without source dataset access. The proposed framework comprises two stages. In the first stage, the feature map statistics-guided model adaptation combined with entropy minimization is developed to help the top segmentation network reliably segment the target images. The pseudo-labels output from the top segmentation network are used to guide the style compensation network to generate source-like images. The pseudo-labels output from the middle segmentation network is used to supervise the learning progress of the desired model (bottom segmentation network). In the second stage, the circular learning and pixel-adaptive mask refinement are used to further improve the desired model performance. With this approach, we achieved satisfactory abdominal multi-organ segmentation performance, outperforming the existing state-of-the-art domain adaptation methods. The proposed approach can be easily extended to situations in which target annotation data exist. With only one labeled MR volume, the performance can be levelled with that of supervised learning. Furthermore, the proposed approach is proven to be effective for source-free unsupervised domain adaptation in reverse direction.
A Formal Composition of Multi-Agent Organization based on Category Theory

The application of organizational multi-agent systems (MAS) provides the possibility of solving complex distributed problems such as, task grouping mechanisms, supply chain management, and air traffic control. The composition of MAS organizational models can be considered as an effective solution to group different organizational multi-agent systems into a single organizational multi-agent system. The main objective of this paper is to provide a MAS organizational model based on the composition of two organizational models, Agent Group Role (AGR), and Yet Another Multi Agent Model (YAMAM), with the aim of providing a new MAS model combining the concepts of the composed organizational models. Category theory represents the mathematical formalism for studying and modeling different organizations in a categorical way. This paper is mainly based on the idea of modeling the multi-agent organization AGR and YAMAM in a categorical way in order to obtain formal semantic models describing these organizations of MAS, then compose them using also the theory of categories which represents a very sophisticated mathematical toolbox based on composition.
Study of Consistency and Performance Trade-Off in Cassandra

Cassandra is a distributed database with great scalability and performance that can manage massive amounts of data that is not structured. The experiments performed as a part of this paper analyses the Cassandra database by investigating the trade-off between data consistency andperformance. The primary objective is to track the performance for different consistency settings. The setup includes a replicated cluster deployed using VMWare. The paper shows how difference consistency settings affect Cassandra's performance under varying workloads. The results measure values for latency and throughput. Based on the results, regression formula for consistency setting is identified such that delays are minimized, performance is maximized and strong data consistency is guaranteed. One of our primary results is that by coordinating consistency settings for both read and write requests, it is possible to minimize Cassandra delays while still ensuring high data consistency.
Classifying Celeste as NP Complete

We analyze the computational complexity of the video game "CELESTE" and prove that solving a generalized level in it is NP-Complete. Further, we also show how, upon introducing a small change in the game mechanics (adding a new game entity), we can make it PSPACE-complete.