Reviewerly/exHarmony huggingface dataset:
Generating QUEST Representations for Narrative Plans Consisting of Failed Actions

Increasingly, research on narrative planning is expanding the expressive range of narrative generation systems, producing plot lines with structures like failed action, mistaken character belief and the integration of authorial and character-centered plans and intentions. Evaluation of these systems’ expressive capabilities is essential to determining their strengths. Some prior evaluative methods have measured the efficacy of narrative planners by characterizing a user’s experience during generated narratives. These approaches have focused on comparing the mental model a user forms during the experience of a narrative with the plan data structure that served as the basis of the narrative’s plot, but have not considered evaluating the user’s understanding of failed actions in narrative. To that end, we propose an algorithm to translate plans containing failed actions into a commonly used cognitive mode of narrative comprehension called QUEST. We then sketch how this translation will play a role in a planned evaluation of users’ experiences reading stories produced by narrative planning systems that generate stories with failed actions.
Research on image analysis technology for determining alkali active component content of alkali in low-grade metamorphic rock based on deep learning

To accurately compare the alkali activity of aggregates, this paper proposes applying microscopic imaging and computer image analysis technology to calculate active mineral content in lithofacies examination and analyze its correlation with the rock aggregate expansion rate. The results show that SiO2 is the main chemical component of metamorphic rocks, and its content is over 60%. Low-grade metamorphic rocks in Guizhou province are composed of sericite, feldspar, quartz, calcite, and other minerals. The active minerals are mainly microcrystal quartz and strain quartz, and their active interference color is different from other inactive minerals. Equipped with microscope imaging and computer image analysis technologies, computers can quantitatively and qualitatively estimate the active mineral content of rocks. Compared with the conventional lithofacies method, the results are more accurate, the acquisition parameters can be saved, and the analysis is faster. Furthermore, the calculation results of alkali active mineral content of rock correlate with the expansion rate measured by the rapid mortar bar method.
Virtual Reality and Its Influence in People Emotions

Nowadays, Virtual Reality, commonly known as VR, is becoming more and more popular and accessible for everyone. The main purpose of this paper is to explain ways in which virtual reality can influence people emotions. This experiment will be conducted by using a horror VR game where some users will explain their feelings while playing a demo. The goal is to understand how the distribution of the different elements in the screen, the variety of sounds, the music, and all in all, the aesthetic of the game, can influence some psychological aspects in order to comprehend the best method of creating an interesting and fascinating experience for future VR users. In addition, players will be asked to solve a problem while avoiding an enemy who will scare them if they get caught. This way, it will also be possible to study how to create difficult but fun challenges in videogames. Unity is the main software that will be used to create the experience while Oculus Quest 2 will be the platform where users will have the opportunity to test the game. Additional software like Audacity and Paint will be used to develop the environment
Why are you predicting this class?

Big data-driven learning models are created by training connectionist models. With the increase in computing power and memory size, these models are becoming practical solutions for predicting image classifications, driving trajectories and users' behaviors. Although these models can be shown to perform with high accuracy, this success measure is not enough to understand why the network predicts certain outputs for certain inputs. These networks behave as black boxes, able of processing very large amounts of data, without being transparent about their inner workings. This paper extends the architecture of a convolutional neural network and trains only the new connections to output an explanation for every prediction of the original classifier. The explanations are taken from a semantic language that is either computed or annotated from available data. Our work includes (1) defining and computing a language relevant to the classifier domain and semantically understandable by humans (2) computing the explanatory layer of the original network (3) training the extended architecture without changing the original given weights and (4) formatting the explanations in a user understandable manner. We applied our algorithmic solution to two existing classifiers in the automated driving domain. We showed successful results explaining predictive classifications of driving comfort and driving trajectories.
Combined Edge Detection (CED) for Similar Images Retrieval and Re-ranking

Edge detection techniques are an essential and fundamental problem in machine learning, computer vision, and content-based image retrieval system. A big challenge in content-based image retrieval is feature extraction due to the well-known semantic gap. Edge detection methods are one of the strong features for characterizing images. We aim to maximize the similarity images and promote more relevant re-ranking images. We propose a Combined Edge Detection (CED) framework for image retrieval re-ranking. In this study, the Combined Edge Detection (CED) framework for multi-edge detection techniques that are Roberts, Sobel, Prewitt, Canny, and LoG (Laplacian of Gaussian) provides a solution for efficient retrieval in similarity images. Hence, from the experimental results, it is proved that the Combined Edge Detection Model (CED) can significantly and efficiently improve the retrieved results and minimize user effort.
Digital Video Watermarking Based on Hybrid Algorithm and Attack Detection

Digital video watermarking is the means of embedding digital rules into digital multimedia (audio, images, and video sequence). This paper presents blind digital video frame watermarking technique to achieve copyright protection. In digital watermarking (DW), a copyright image is embedded in the video frame secretly by using the proposed hybrid DCT and LWT algorithm along with Particle Swarm Optimization (PSO). The algorithm involves two divisions, watermark embedding (WE) and watermark extraction (WEXT). Subsequent to accounting the watermarked image to diverse attacks similar to adding noise, blurring, gamma correction (GC), cropping, rescaling, pixilation, rotation, contrast adjustment, histogram equalization, lossless compression, sharpening, etc., the formerly inserted watermark image is extracted and compared with existing approaches on the root of their NC, MSE and PSNR values. Experimental outcome illustrates that method is tough to different attacks. The results also demonstrate better results between imperceptibility and robustness.
Refinement of Bengali Obscene Words using sequence to sequence RNNs

In the recent past, a growing number of users have tolerated offensive behaviors or have witnessed objectionable activities through virtual platforms. Defamatory and abusive words are mostly used in the comment section on social networking sites that are more detrimental for children and teenagers. This type of offensive word can shape their thoughts in the wrong manners and influence their mental health. Our work targets to filter and replace the negative impression of defamatory words on children that are used on Facebook with the help of sequence to sequence RNNs along with LSTM. For doing this experiment, we have created a dataset by the comments and posts from Facebook. And also, done data pre-preprocessing, counting of vocabulary, counting of missing words, embedding words, finding the missing word, and so on. Our main focus was identifying the offensive word and filtering that word by the abstract text summarizer model and decreasing the training loss. After applying the model on the dataset, practically we have reached our target to decrease the training loss to 0.007 and are capable of making a filtered text from given input text.
Education network pirates, corporate control and the inverted panopticon: about the unfulfilled pandemic promise

There are two issues in the article. The first concerns the educational dimension of the pandemic in social spaces. The second issue is about the possibility that the pandemy opens up for academic education. I believe that the pandemic crisis could have provided a progressive lessons and an impetus for a pro-democratic change, both in society and in institutionalized education. However, this lesson has not been learnt, and the pandemic itself has been used to intensify authoritarian tendencies in the womb of the (post)neoliberal world.
Innovation in Preservice Teacher Education

This work presents the institutional policies and context of a teacher training institution which led to innovation in preservice teacher (PST) education. A drone-based learning activity was implemented to PSTs about the sustainability of transport infrastructure as an example of that innovation context. PSTs implemented cycles of engineering design to identify the best package to transport objects using a drone. Data collection instruments were students' work, field notes from class observations, audio and video records of the classes, questionnaires, and interviews. The PSTs engineering design performance was analyzed using the engineer notebook rubric. Their engineering education conceptions were analyzed using the framework for quality K-12 engineering education. Results show that drone-based learning proved to be a relevant approach for PSTs experiencing engineering design and construct adequate conceptions about engineering education. Furthermore, the institutional policies contributed to the experimentation of innovative technologies for educational purposes.
Statistical Methods in Recommender Systems

When we log on to a website or open an app, there is a good chance that statistical methods are working behind the scenes. They may be serving content that we are likely to engage with or showing products we may want to purchase. The common denominator for all of these algorithms is that they use past behaviors and known preferences to make statistical predictions of things that we would like. In this chapter, we will build a recommender system to suggest movies we might like.
Elaborative Investigation of Blockchain Technology in Intelligent Networks

The fifth generation (5G) network advancement focus is to help mixed upright applications by associating heterogeneous gadgets and machines with extreme upgrades regarding high quality of administration, extended organization limit, and improved framework throughput regardless of significant difficulties like decentralization, straightforwardness, dangers of information interoperability, network protection, and security weaknesses. The challenges and limitations of other intelligent 5G internet of networks (5G IoTs) are also to be met by using blockchain technology with the integration of cloud computing and edge computing technologies. In this chapter, the authors render an elaborated analytics of the empowering of blockchain technology in intelligent networks that include 5G networks and 5G-based IoT. The solutions for the spectrum management, data sharing, security, and privacy in 5G networks will also be analyzed. It is believed that the proposed chapter would definitely be useful for the researchers in the field of blockchain in intelligent networks.
Prospects for Artificial Intelligence Implementation to Design Personalized Customer Engagement Strategies

The rise of e-commerce and the explosion of digital shopper data with the advent of advanced Artificial Intelligence (AI) solutions are changing the landscape of retailing. Technology advancements with the arrival of AI will help in synthesizing and acting upon the consumer insights at every touch point in the data-driven digital economy. The research was designed with the objective to identify the impact created by artificial intelligence adoption in the retail industry on customer management strategies. This study touch upon personalized engagement marketing, agility in the supply chain (Robotics), and branding and customer management practices using artificial intelligence solutions. The research developed a conceptual model to identify branding and customer management implications in AI-powered retail. Primary research was conducted through an online survey among a selected tech-savvy target audience. Primary research was used to study perception about various factors affecting buying experience for shoppers in modern retail with the intervention of AI as a market driving tool. The study results provide relevant insights to retail marketers and supply chain partners on customer management practices by adopting AI solutions.
An Overview of Tools for Collecting Data on Software Development and Debugging Processes from Integrated Development Environments

Purpose. This paper presents the findings of a review of the literature published in the twenty-first century in order to identify and analyze the current state of tools that track developer interactions with integrated development environments, as well as to recommend future research directions based on the actual state. Methodology. By systematically searching in five digital libraries we conducted a systematic review of the literature on data collection tools from integrated development environments published in the twenty-first century. Fifty-five papers were selected as primary studies. Findings. 55 articles were analyzed and the findings show that using an integrated development environment to collect usage data provides more insight into developer activities than it was previously possible. Usage data allows us to analyze how developers spend their time. With usage data, you can learn more about how developers create mental models, investigate code, conduct mini-experiments through trial and error, and what can help everyone improve performance. The research community continues to be highly active in developing tools to track developer activity. The findings indicate that more research is needed in this area to better understand and measure programmer behavior. Originality. For the first time, systematization and analysis of tools for tracking programmer's behavior in an integrated development environment have been carried out. Practical value. Our study contributes to a better understanding of the current state of research on programmer behavior in integrated development environments. An analysis of the study can help define a research agenda as a starting point for the creation of a novel practical tool.
A Framework for Intuitionistic Grammar Logics

We generalize intuitionistic tense logics to the multi-modal case by placing grammar logics on an intuitionistic footing. We provide axiomatizations for a class of base intuitionistic grammar logics as well as provide axiomatizations for extensions with combinations of seriality axioms and what we call intuitionistic path axioms. We show that each axiomatization is sound and complete with completeness being shown via a typical canonical model construction.
Image Registration Via Marginal Distribution Adaptation

The distribution of feature vectors plays a critical role in image registration. In this letter, we propose a novel approach for remote sensing image registration based on marginal distribution adaptation. First, we map the feature vectors of reference and sensed images into a latent space. Transfer component analysis (TCA) is employed to compute the transformation matrix by minimizing the maximum mean discrepancy (MMD). Then, we match feature vectors in the latent space where their marginal distributions are similar, which can increase correct correspondences and enhance registration accuracy. Finally, we test the proposed algorithm on ten real image pairs. The effectiveness and efficiency of our approach are verified by experimental results.
Variant Translation Strategy of Dough Sculpture Art Based on Deep Neural Network Algorithm

With the development of globalization and information diversification, translation plays a pivotal role in the exchanges of countries around the world; many translation theories have also emerged from this and become increasingly mature. As an efficient tool, machine translation can realize the equivalent conversion between different languages while retaining the original semantics, which has important practical significance. This article aims to study the translation strategy of dough art translation based on deep neural network algorithms. Based on the analysis of the characteristics of dough art, the main content of the translation and the neural machine translation model, it aims at the inability of neural machine translation technology knowledge in the existing language translation system. It makes good use of the current situation and existing problems, and proposes a new neural machine translation system model with a large amount of speech sequence information. Based on the theoretical basis of fully integrating people’s attention and thinking mechanism gru two-way translation language model, the stanford parser is used to analyze the language and syntax, and after obtaining relevant data and information about the part of speech and the order of sentences in the language. The language and encoder components integrated into the language translation model in the form of two-way encoding, using vector splicing methods and forms to jointly construct a vector background. Experiments have proved that adding a word sequence of information can greatly improve the translation model.
Digital Transformation in Education in Knowledge Society

This article aims to trace the dimensions of the digital transformation that is taking place in modern knowledge societies to analyze the essence of digital transformation and the ways in which it becomes possible. A specific environment of digital transformation is education and in particular university education, which also changes under the influence of digitization. The most active digital transformation in university education was dictated by the Covid-19 pandemic, which the world community has experienced in the last year—2020. In practice, all university education is being restructured following the principles of digitization and carried out in an online environment. This article focuses on students’ reactions, their assessments, and views on ongoing online learning and its formatting. Methodology is based on qualitative methods, under the national project “Digital Media Literacy in the context of “Knowledge Society”: state and challenges”, № КП-06-H25/4, 2018, funded by National Science Fund—Bulgaria.
A Secure Distributed Hash-Based Encryption Mode of Operation Suited for Big Data Systems

Big Data systems are now present in almost all mature organizations and not just IT focused ones. From luxury hotels to health care organizations, data storage and processing is witnessing a huge technological improvement thanks to the extensive research that is being conducted to improve these two areas. However, there is a lack of adaptability when it comes to security and more importantly encryption, as traditional security solutions are still being used today without any changes that can adapt them to Big Data environments. In this article, we are interested in working on data at rest encryption in a big data environment. In particular we pay special attention to distributed storing as well as large volumes of data. We base our work of a very known encryption mode of operation called CBC which is heavily used for data at rest encryption, but suffers from the high cost of running sequential encryption over the entire plaintext. Our solution offers an alternative that guarantees the same properties as CBC, and even enhances some of them (namely the diffusion property), while offering the possibility for parallel encryption which makes it more efficient especially in distributed environments. These claims will be proved using a set of theoretical equations that will be detailed in the article.
Mobile Machine Vision for Railway Surveillance System using Deep Learning Algorithm

Trains have been a popular transportation in our daily life. However, there is no proper surveillance system for obstacle detection at the railway, leading to the happen of unwanted accidents. In order to overcome this issue, machine vision embedded with deep learning algorithm can be implemented. Obstacle detection can be achieved through vision-based object detection, where the object classification model computes the images similarity to its respective classes, classifying its potential as an obstacle. In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). The detection model used in this paper is Single-Shot multibox Detection (SSD) MobileNet detection model. This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. During simulation, the object recognition algorithm is able to detect and classify various objects into its respective classes. By applying past research approaches, the developed object detection model is able to analyze image as well as real-time video feed to identify multiple objects. Any object that has been detected at the Region of Interest (ROI) can be characterized as an obstacle.
Certificateless Proxy Authentication Scheme on Lattices Under the Background of Big Data

As a special digital signature, proxy signature is becoming more and more important in electronic authentication. Compared with the insecurity of proxy signatures based on the decomposition of large integers and the difficulty of discrete logarithms, lattice-based cryptography have higher security and computational efficiency. This paper uses the pre-image sampling algorithm and trapdoor generation algorithm to construct a certificateless proxy signature scheme on lattices, and puts the scheme into the background of big data to realize authentication. The safety of the problem is proved by using the difficulty of small integer solutions on the lattices. Compared with other existing proxy signature schemes, it has low computational complexity and higher security.
One Hidden Layer ANFIS Model for OOS Development Effort Estimation

In the present paper, applicability and capability of A.I techniques for effort estimation prediction has been investigated. It is seen that neuro fuzzy models are very robust, characterized by fast computation, capable of handling the distorted data. Due to the presence of data non-linearity, it is an efficient quantitative tool to predict effort estimation. The one hidden layer network has been developed named as OHLANFIS using MATLAB simulation environment.Here the initial parameters of the OHLANFIS are identified using the subtractive clustering method. Parameters of the Gaussian membership function are optimally determined using the hybrid learning algorithm. From the analysis it is seen that the Effort Estimation prediction model developed using OHLANFIS technique has been able to perform well over normal ANFIS Model.