Reviewerly/exHarmony huggingface dataset:
Automatic Mapping of Quranic Ontologies Using RML and Cellfie Plugin

The text of the Qur’an has been analysed, segmented and annotated by linguists and religious scholars, using a range of representations and formats, Quranic resources in different scopes and formats can be difficult to link due to their complexity. Qur’an segmentation and annotation can be represented in a heterogeneous structure (e.g., CSV, JSON, and XML). However, there is the lack of a standardised mapping formalisation for the data. For this reason, this study’s motivation is to link morphological segmentation tags and syntactic analyses, in Arabic and Buckwalter forms, to the Hakkoum ontology to enable further clarification of the Qur’an. For achieving this aim, the paper combines two mapping methods: the RDF (resources description framework) mapping language, which is an R2RML extension (the W3C level necessary when mapping relational databases into RDF), and Cellfie plugin, which is a part of the Protégé system. The proposed approach provides the possibility to automatically map and merge the heterogeneous data sources into an RDF data model. Also, the integrated ontology is evaluated by a SPARQL query using an Apache Jena Fuseki server. This experiment was conducted in all the Qur’an chapters and verses, containing all the words and segments of the entire Qur’an corpus.
Application of Immersive Media to Develop Model Making Skills of Industrial Design Learners

Unprecedented challenges during the COVID-19 pandemic led to the educational institutes close with uncertainty around the world. To continue the academic year most educational institutions have shifted to online educational platforms. Design institutes in India too adopted online classes using various online network platforms. Immersive media, such as Virtual Reality and Augmented Reality (VR and AR) has recently gained prominence in design education. Many researchers have highlighted that the VR/AR could help students improve their performance and conceptual understanding of a specific set of learning objectives. This study has helped them to identify the most common difficulty faced during online courses like model making course. VR and AR based instruction setup that allows the collaborative discussions between faculty and students are remotely discussed. The brainstorming process was used to generate ideas for improving the learning experience in the dexterity skill-based model making course through the use of immersive media. Heuristics analysis was also conducted to ensure the usability of the AR and VR based instructional interfaces. It was observed that the interactive AR based immersive instructions are better than the VR based instructions. This study can be continued further with design students to determine how effective it is to use visual clues in a model-making course using Immersive media.
High Efficiency Crypto-Watermarking System Based on Clifford-Multiwavelet for 3D Meshes Security

Since 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users and saved in distant multimedia databases. The novelty of our approach is that it uses a new Clifford-multiwavelet transform to insert copyright data in a multiresolution domain, allowing us to greatly expand the size of the watermark. After that, our method does two rounds of insertion, each applying a different type of Clifford-wavelet transform. Before being placed into the Clifford-multiwavelet coefficients, the watermark, which is a mixture of the mesh description, source mesh signature (produced using SHA512), and a logo encrypted using the RSA (Ronald Shamir Adleman) technique, is encoded using Turbo-code. Using the Least Significant Bit method steps, data embedding involves modulation and insertion processes. Finally, the watermarked mesh is reconstructed using the inverse Clifford-multiwavelet transform. Due to the utilization of a hybrid insertion domain, our technique has demonstrated a very high insertion rate while retaining mesh quality. The mesh is watermarked, and the extracted data is acquired in real-time. Our approach is also resistant to the most common types of attacks. Our findings reveal that the current approach improves on previous efforts.
The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality

Recommender Systems (RSs) research has become interested in assessing the system effect on the actual choices of their users: the distribution and the quality of the choices. By simulating users’ choices, influenced by RSs, it was shown that algorithmic biases, such as the tendency to recommend popular items, are transferred to the users’ choices. In this paper we conjecture that the effect of an RS on the quality and distribution of the users’ choices can also be influenced by the users’ tendency to prefer certain types of items, i.e., popular, recent, or highly rated items. To quantify this impact, we define alternative Choice Models (CMs) and we simulate their effect when users are exposed to recommendations. We find that RS biases can also be enforced by the CM, e.g., the tendency to concentrate the choices on a restricted number of items. Moreover, we discover that the quality of the choices can be jeopardised by a CM. We also find that for some RSs the impact of the CM is less prominent and their biases are not modified by the CM. This study show the importance of assessing algorithmic biases in conjunction with a proper model of users’ behaviour.
Spatiotemporal Activity Semantics Understanding Based on Foreground Object Segmentation: iCounter Scenario

Foreground object segmentation that captures the spatial and temporal information of moving objects in video is the most fundamental task for activity understanding in many intelligent applications, such as smart stores.Recently, several methods are proposed for the detection and recognition of activity based on object segmentation.However, these methods are often inaccurate because they do not maintain the temporal associations of object segment consistency across time.In this work, we proposed a hierarchical approach for foreground object segmentation and activity semantics understanding from sequential video to preserve spatial and temporal connectivity in the frames.The proposed system consists of two main modules: (a) the concatenated deep learning network containing PSPNet and convolutional-GRU to segment the foreground of an object of interest; (b) the activity mining framework which incorporates three submodules (i) a RetinaNet-based frame classifier to detect and count objects of interest; (ii) a time-domain activity and event detection algorithm; (iii) an image-based item query engine to recognize the shopping items.To evaluate the proposed approach, we designed the smart checkout-box called iCounter to collect the shopping activities dataset named "NOL-41" which is used in extensive experiments.The results show that the accuracy of the foreground object segmentation is 90.6%, the accuracy of the frame classification is 93.4%, the accuracy of activity event detection is 98.4%, and the accuracy of item query is 94.3%.Finally, the overall accuracy of the shopping list is 95.2%.
Security Framework Based on Shannon’s Theory and Genetic Cryptography for Cloud Computing Framework

Many organisations and businesses are currently facing risks to their data management systems, and these security concerns provide a foundation for conducting research in this field. One of the most major difficulties facing the digital world, with the rise of distributed system technologies, is guaranteeing the security of sensitive and confidential data during transport and storage, which is recognised as one of the most essential concerns facing cloud computing. In the cloud computing storage environment, there are several techniques for strengthening data security. As a result, a variety of encryption techniques are utilised to offer security, integrity, and permitted access utilising a variety of ways, including DNA. They do, however, have some limits. In this research, we provide a novel form of cryptographic techniques that uses two levels of encryption to increase cloud computing security. The first layer, based on Shannon's theory of diffusion and confusion, divides the plaintext and key into equal sections using logical processes. The second layer is inspired by genetic structures for cryptographic reasons by reproducing natural processes such as genetic cryptography, transcription, and translation. It is founded on molecular biology's core principles. The outcomes of the experiment improved data security, which can be utilised to safeguard cloud computing applications. When compared to existing cloud computing solutions, the testing results of the recommended algorithm revealed a high level of security, as well as an apparent increase in cypher size and execution time.
CAE Based Image Compression for Energy Compaction

Image Compressions has been a significant theme for research for a long time. Top to bottom investigation was a significant forward leap in numerous PC supported applications, and its utilization in image compression was developing consistently. In this paper, a cutting edge power pressure framework dependent on image compression with conventional auto-encoder (CAE) makes high coding capacity. The primary contribution is to incorporate three angles: (1) Presenting CAE image compression by disintegrating into different examining activities; (2) in the CAE plan, factual examination in the region of force joining and are the principal errand to advance basic code discovery in planning organizations, which can go about a proportion of strength; (3) Depending on the coding benefit metric measurements, set the force sharing system, which adds to the propensity for losing work in the preparation stage cycle to help CAE increment coding benefit and kill pressure proficiency. Test outcomes show the BPG yield technique, as indicated by standard MS-SSIM measurements. Also, this method accomplishes more noteworthy effectiveness contrasted with existing adjustment techniques and give better coding execution contrasted with minute clog learning strategies at more exorbitant costs. The research this time is thus focused on developing a better standard for compression and also does this in the most energy efficient format. To conclude, in the end that a standard can be made above the best and show this at the end of the paper.
Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents

A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possible introduction of bugs. In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods for information extraction of legal and registration documents. We finetune models that jointly extract the information and generate the output already in a structured format. Post-processing steps are learned during training, thus eliminating the need for rule-based methods and simplifying the pipeline. Furthermore, we propose a novel method to align the output with the input text, thus facilitating system inspection and auditing. Our experiments on four real-world datasets show that the proposed method is an alternative to classical pipelines. The source code is available at https://github.com/neuralmind-ai/information-extraction-t5 .
Mixture and Latent Class Models

This chapter introduces mixture models and latent class models. After a motivating example, formal definitions of these models are presented in Sect. 2.2. In Sect. 2.3, several methods for maximum likelihood parameter estimation are outlined. In Sects. 2.4 and 2.5, parameter inference is discussed: Are parameters different from zero? Is there justification to constrain parameters to be equal to one another? What are the standard errors of parameter estimates? In Sect. 2.6, model selection is treated. A main question here is how many states (mixture components) a model should have and the various criteria that are used to decide this are defined and discussed. In Sect. 2.7, we then discuss how to model the effect of covariates on the prior probability of the states. Finally, in Sect. 2.8, we consider whether the parameters of mixture models are identifiable.
Eye Tracker Outcomes from Static, Mobile, Virtual Reality Eye Tracking Devices

Examining eye movement through the use of eye tracking technology provides us with copious information across a variety of human processes. The advent of static, mobile, and virtual reality (VR) based eye trackers provides flexibility in studying eye movement under an array of conditions. This chapter explores the use and application of these eye tracking devices; diving into the suitability of each with respect to study design, data collection, data quality, and data analysis. We will also identify key visual behavior measures that can be assessed using eye tracking methodology and what these measures tell us. Lastly, we will examine the use of different eye trackers in relevant case studies.
Law in the Service of Misinformation: How Anti-Vaccine Groups Use the Law to Help Spin a False Narrative

Social movements use legal tools in a variety of ways, including to create a narrative. That narrative can be used to support social agendas – or to mislead. In this article, we look at the anti-vaccine movement as a social movement using the law, and examine how one influential anti-vaccine organization, the Informed Consent Action Network, used its far-reaching platform to create false narratives around legal action. We document how, again and again, the anti-vaccine group misrepresented both the legal and the factual meanings of court decisions, settlements, and other legal actions to create a narrative that will galvanize its followers and influence newcomers. The group did it by filing lawsuits that were, essentially, a setting out of anti-vaccine arguments – even when the legal framework did not fit doing so – and misrepresenting the result. For example, although FOIA cannot be used to ask queries, the group wrote its FOIA lawsuits as if they were asking the agency to answer questions, rather than provide records – and then presented results as answers supporting the group’s narrative that, for example, vaccines cause autism when the answers did not, in fact, address that question. This article shows that legal tools can be used in the service of disinformation and misinformation, to create a misleading alternative reality.
AI Gives You a Good Beginning

A good beginning makes a good end. Apart from our lives, this proverb applies to marketing campaigns too. A good headline is essential for effective marketing communication, and a good email headline can significantly increase the conversion of your email marketing campaign. Therefore, generating good headlines is a critical function of any ad agency. However, given the multiplicity of brands and number of campaigns per brand that marketers need to handle, creating good headlines is a challenge. In this paper, we propose an AI system which automatically generates creative and high open rates email headlines. Our AI system provides marketers with a decision-aid tool for them to create great headlines to kick off a great email campaign.
Data

The discovery of the structure of DNA in the middle of the twentieth century represents a crucial landmark in the history of Life Sciences, and Genetics in particular. It was a decisive step for understanding life as a natural phenomenon at the molecular level, and developing a technology that enables us to intervene in any living being’s vital functions.
Hdsrnet: A Simple Segmentation-Free Method for Unconstrained Handwritten Digit String Recognition

Handwritten digit string recognition (HDSR) is one of the most challenging tasks in the area of off-line handwritten optical character recognition. The main challenge comes from the segmentation errors caused by several factors such as touching, breaking, complex background, and unknown length of string. In this paper, we propose a new segmentation-free HDSR system using a one-stage object detector based on a convolutional neural network (CNN). This is a simple, efficient, and lightweight algorithm. This work describes the following: (1) the problem of HDSR can be solved successfully by CNN-based object detectors which determine both locations and classifications for objects and it makes us completely free from the complicated segmentation; (2) the network for HDSR does not need to be too heavy and its depth can be determined suitably by the size of receptive field; (3) it is efficient to use single-scale and single-anchor method rather than multi-scale and multi-anchor one, in the detector for HDSR. The experiments conducted on the benchmark datasets (CVL, ORAND-CAR) show that the proposed method achieves excellent performance compared to other state-of-the-art methods.
VMs Migration Mechanism for Underloaded Machines in Green Cloud Computing

High cost of Cloud data centers’ energy consumption have inspired numerous researches to provide more efficient virtual machine (VM) management approaches. Migration of VM is one of the important VM management solutions. However, inefficient VMs migration can also increase energy consumption. Thus, it must be handled very cautiously and at least as possible. In this work, a VMs migration mechanism is proposed, particularly the VMs migration of the underloaded machines. The suggested mechanism is founded on Minimum and Maximum thresholds to not have underloaded and overloaded machines, and to have stable hosts. The objective is to reduce the energy use. Finally, we present experimentation results on the simulator: CloudSim, to show the advantages of the adopted solution.
Ant Colony Optimization Algorithms: Introductory Steps to Understanding

In this chapter most common knowledge of the Ant Colony Optimization Algorithm (ACO) is presented especially in water and environmental science. A brief introduction and literature review of the ACO and its application are demonstrated in detail. Then the process and the basic pseudo-code of ACO are introduced as well. Additionally, the Antlion Optimization (ALO) algorithm is represented as a single objective algorithm of the Ant family. Finally, a Multi-objective Antlion Optimization (MOALO) algorithm and its pseudo code are suggested to put forward the implementation of this algorithm.
The Island of Play: Reflections on How to Design Multiuser VR to Promote Social Interaction

This article consists of reflections and considerations concerning a virtual reality design case: The Island of Play. It is a multiuser virtual reality prototype aimed at maintaining and encouraging social relationships between long term hospitalized children and their friends. The motivation behind this design is the dire situation long term hospitalized children often find themselves in. They experience isolation and marginalization due to constraints from hospitalization. A consequence hereof is a limited access to social interaction as well as a reduced opportunity to play with friends from either home or school. The Island of Play was essentially designed to set up a virtual meeting place to stimulate socialization through play. This article sits at the intersection between game design theory and actual design impressions, with a particular focus on how real-world design interweaves with theoretical considerations. The argument that follows is structured over five sections: 1) First, we contemplate the design of the player’s character. 2) Second, we scrutinize the relationship between game objects and playful interactions. 3) Then we move on to consider the design of social experiences, 4) followed by the fourth section where we inspect the value of the magic circle as a design metric. 5) Finally, in the fifth section, we reflect on the importance of weighing the player’s sensation of purpose and skill against interacting with the application. Overall, this design case pivots around design issues and considerations involved in the development of play and game scenarios in a multiuser VR-application aimed at bolstering the social fabric between long term hospitalized children and their friends.
Consensus Algorithm for Bi-clustering Analysis

Bi-clustering is an unsupervised data mining technique, which involves concurrent clustering of rows and columns of a two-dimensional data matrix. It has been demonstrated that bi-clustering may allow accurate and comprehensive mining of information, important for many practical applications. Numerous algorithms for data bi-clustering were proposed in the literature, based on different approaches and leading, in general, to different outputs. In this paper we propose a consensus method for combining outputs of many bi-clustering algorithms for improved quality of predictions. The proposed algorithm includes two steps. The first step, “assignment”, leads to detecting groups of bi-clusters of high similarity and the second step, “trimming”, results in transforming a group of similar bi-clusters into one bi-cluster of high quality. We demonstrate, on the basis of both simulated and real datasets, that using our algorithm highly improves quality of bi-clustering. We also provide an easy to use software tool, which includes implementations of several bi-clustering algorithms and our consensus method.
Adaptive Instructional System for Complex Equipment Trainings in the Post-covid Era: Breaking the Ice of Time-Consuming Tasks

Complex equipment trainings frequently rely on in-person trainings to describe instrument parts and personalize explanations based on training objectives, prior knowledge, and cognitive abilities of trainees. In this study, we assess the main challenges of adapting intelligent instructional system by training centers with limited human and technological resources. We found that the preparation of the training material is the most time-consuming task. During the COVID-19 pandemic, many training centers were forced to remotely conduct their trainings, thus generating a massive amount of digital training content. Here, we explore this unique opportunity to recycle this training material to design an adaptive instructional system (AIS) for bioimaging training. In this paper, we discuss the functional features of AIS that facilitate autonomous training for trainees and instructors: progress bar, notification system, built-in teleconferencing, and chatting tools. To address a high level of customization of in-person trainings, we designed AIS trainings as module-based instructions that can be easily tailored to accommodate the objectives and needs of the trainees. We also demonstrate that modular design of the training material database accelerates allocation and preparation of training content for similar types of equipment. We set up a framework for implementing a recommendation system that would accommodate the training material to the trainee's experience. Our study shows that over the short or medium term, the potential of AIS solution for equipment trainings significantly outweighs the most time-consuming tasks like preparation of the training material.
Practice examples

Classification and regression algorithms discussed in previous chapters will be used to create predictive models using real-world data sets. Data sets are obtained from Gene Expression Omnibus. Models’ performances in making predictions are compared and discussed.