Reviewerly/exHarmony huggingface dataset:
Tell Me More? Can AI Enhance User Experience for List Answers?

In this work we present a new AI-based UI for question answering systems when the answer is a list. We use AI to analyze list answers so that we can provide additional information in the UI to enhance user experience. We explore the list type, the introductory text, and the size of the list. We conduct a user-experience study with an indirect and direct evaluations to show the impact of this novel list presentation. Our initial findings show that users in the direct evaluation prefer the AI-based UI over the commonly used one.
Ten simple rules to complete successfully a computational MSc thesis

Completing an MSc or PhD thesis requires self-reflection, choosing the right project and supervisor, implementing feedback, and good time and research management skills. This article provides ten simple rules for successfully navigating through a Master's or PhD’s thesis project. It emphasizes the importance of defining personal learning goals before beginning an MSc project to ensure that the project aligns with the student's interests and objectives and the necessity of finding the right supervisor for a PhD project. The critical role of the thesis supervisor is also discussed, including the importance of choosing a compatible supervisor and considering their advice and suggestions. The article also provides suggestions for time and research management, as well as tips for drafting and writing the thesis report and sharing research outputs.
Harnessing Multi-Modal Data and Causal Inference for Robust Domain Generalization: A Comprehensive Approach

Domain generalization (DG) is a crucial problem in machine learning that aims to develop models that can generalize well to new, unseen data distributions. Existing general-purpose approaches to DG have struggled to outperform standard empirical risk minimization baselines consistently. This paper proposes a comprehensive approach to domain generalization by harnessing multi-modal data and causal inference to achieve robustness to distribution shifts. We discuss the underlying principles, methodologies, and techniques, drawing on a range of recent research in the field. Our proposal leverages domain-level meta-data, explores multiple modalities, incorporates known invariances and domain knowledge, and employs causal modeling. Empirical analysis of existing DG methods and theoretical investigations of the DG problem are also presented, along with potential solutions and future directions for research.
A Platform for Tracking Charity Donation Using Blockchain

Charity organizations are susceptible to the same kinds of fraud that harm businesses because donors lack confidence in how their money is being used. the requirement to provide a unified platform for monitoring donations that will monitor all data related to donations transactions and donors Lack of trust on the part of donors in how donations are used is a result of skepticism over how donations are used. Building a single platform for tracking donations that would manage all information regarding gift transactions is essential since charities are vulnerable to fraud that affects corporations, such as executive misuse of funds and embezzlement. Keywords—Transparent system, Smart contract, Blockchain, Charity, Ethereum, Digital charity.
Currency Detection and Recognition System Based on Deep Learning

-This paper suggests a method for recognizing currency through image analysis. The method relies on the three characteristics of color, height, and texture, which utilized for distinction. The method put forth in this document can be used to find the currencies of numerous nations. For the purposes of execution, only paper currencies from India are taken into account. This system, which employs the CNN (Convolution Neural Network), makes it simpler to check currency anywhere, at any moment. Every denomination of India was used in our testing, and the system was able to identify with 95% precision. To improve the technique's accuracy, a classification model is created using all of the previously stated factors. Features of paper money are crucial to this identification. Key Words: Currency recognition, color, CNN, Open CV, ResNet.
Category-based memory bank design for traffic surveillance in context R-CNN

Traffic surveillance cameras recognize a large number of objects. In this configuration, since vehicles appear in the same lanes, past footage is useful information. Context R-CNN has been proposed to store past videos in a memory bank and use them for recognition. In this paper, we propose a method to improve recognition performance by selecting objects to be stored in memory banks. We found that the proposed method can improve mAP by 0.37 points on average compared to the conventional method.
Cloud Based Password Wallet and Manager

Although various solutions have been suggested for password management systems, most methods require the support of external physical hardware infrastructure. Due to the increase in complexity and cost of set-up of supporting hardware requirements, scalability will always be an issue with such systems. In this paper, we present the design of a Cloud based password management system. The proposed method uses Cryptography for better encryption and is supported by a web-based architecture, for easily managing passwords and providing an indoor cloud based password manager system . Keyword: Cloud Computing Restful API Cyber Security Password Manager
An examination on eye-gaze input using a bubble cursor in AR

In recent years, research on augmented reality (AR) has been active, and eye gaze input has attracted attention as one of the input methods in AR. One problem with gaze input is that it is difficult to input when the target is small or crowded. In response to this problem, the bubble cursor facilitates eye gaze input by changing the size of the circular cursor according to the distance to the nearest target. In addition, by using the distance from the eyeball to the point where the lines of sight of both eyes intersect, called the convergence distance, it is believed that the user's gazing point can be accurately determined without relying on collision determination with virtual objects. In this study, we use the convergence distance to determine the gazing point in an AR environment and consider the bubble cursor centered on the gazing point.
BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.&#x0D;
CoRBS: A Dynamic Storytelling Algorithm using a Novel Contextualization Approach for Documents utilizing BERT Features

Abstract Storytelling is the process of connecting documents one after another representing the evolution of an event. Existing algorithms for storytelling connect events based on content overlaps between consecutive documents ignoring the role of the same term in different documents and the contemporary contexts (e.g., dynamic embeddings) of documents and terms. Due to the lack of role and contemporary contexts in the designs of the existing storytelling methods, the resultant stories frequently jump to documents with the keywords to form a chain but not a meaningful one. In this paper, we present a novel storytelling algorithm -- Contextual Role-Based Storytelling (CoRBS) -- that generates a chain of documents explaining the evolution of an event, addressing role and contemporary context issues of existing methods. CoRBS starts with a given document and moves forward temporally, stitching together role and context-driven documents to represent the evolution of the events that appear in the first document. We define the role of a term in a document as a distribution of similarities of the nearest neighbors of the term based on BERT-embeddings of all terms of that document. Contemporary contexts are incorporated as a mechanism to discover a coherent next document while the story progresses. Our experiments demonstrate that CoRBS generates more meaningful stories compared to other baseline storytelling techniques.
Applying the Flyweight Design Pattern to Android Application Development

This research aims to demonstrate how the Flyweight design pattern enhances programmers' ability to develop mobile applications while being aware of memory consumption flexibly. By following the systematic approach to software development, the two Android applications have been designed using a class diagram based on two paradigms: general object-oriented programming and the Flyweight design pattern. Both applications are developed in Java and installed on a physical Android phone. Memory usage is monitored by using the Android Profiler and using programming. The results show that both Android applications consume almost the same amount of memory, given all classes or package classes. Therefore, applying the Flyweight design pattern to mobile software development does not affect memory usage and follows a professional software design.
The influence of scene tilt on saccade directions is amplitude dependent

When exploring a visual scene, humans make more saccades in the horizontal direction than any other direction. While many have shown that the horizontal saccade bias rotates in response to scene tilt, it is unclear whether this effect depends on saccade amplitude. We addressed this question by examining the effect of image tilt on the saccade direction distributions recorded during freely viewing natural scenes. Participants (n = 20) viewed scenes tilted at −30°, 0°, and 30°. Saccade distributions during free viewing rotated by an angle of 12.1° ± 6.7° (t(19) = 8.04, p < 0.001) in the direction of the image tilt. When we partitioned the saccades according to their amplitude we found that small amplitude saccades occurred most in the horizontal direction while large amplitude saccades were more oriented to the scene tilt (p < 0.001). To further study the characteristics of small saccades and how they are affected by scene tilt, we looked at the effect of image tilt on small fixational saccades made while fixating a central target amidst a larger scene and found that fixational saccade distributions did not rotate with scene tilt (−0.3° ±1.7° degrees; t(19) = −0.8, p = 0.39). These results suggest a combined effect of two reference frames in saccade generation: one egocentric reference frame that dominates for small saccades, biases them horizontally, and may be common for different tasks, and another allocentric reference frame that biases larger saccades along the orientation of an image during free viewing.
A New Data Integrity Verification Scheme with Low Communication Cost

&lt;p&gt;The development of cloud storage is indispensable for today’s Internet information technology. The superiority of cloud storage is that the ownership of the storage medium and the ownership of the stored data are divided and held by two parties with respective interests - cloud server providers and data users. However, the transmission, storage and utilization of data constantly caused the violation of users&amp;rsquo; rights and interests. Data integrity as a countermeasure has been paid growing attention to by researchers. Traditional data integrity protection schemes are based on the cloud server-client architecture, which requires high computational overhead and communication. To address this scenario, this paper proposes an improved data integrity verification scheme that enables local users to perform rapid and efficient data integrity verification of data stored on cloud servers, reduces the volume of communication data during the verification process, and effectively prevents the loss or tampering of data stored in the cloud server.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An Architecture for Electronic Exchange of Official Document Based on Email and Blockchain

&lt;p&gt;Official documents are considered as a critical communications channel among agencies, organizations, and the public. With the rise of e-government, most official documents have been evolved from printed to electronic form in recent decades. However, the convenience of electronic documents also brings higher security risks. Through the integration of email and blockchain technology, this study proposes an architecture for the electronic exchange of official documents to bridge the gap between convenience and security. With the verification of the proposed system, the findings provide a valuable reference for the governance of agencies.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An Approach to Critical Success Factors in Designing Cloud-based Application Solutions

&lt;p&gt;Recently, the cloud computing is next Internet technology, many companies have designed a comprehensive cloud--based system to consider functional and nonfunctional requirements. Therefore, this paper conducts a two-stage experiments and analysis. In the first stage, we invite professionals to interview and fill out the questionnaire, and then conduct a reliability analysis through the SPSS tool to validate corrections in the questionnaire. This stage erases unnecessary influence factors to find out the CSFs. Secondly, they calculate the weight ratio and rank the sequence for each of the CSFs through analysis of the AHP for questionnaire. After performing above experiments, it obtains the most important CSFs for each facet: the facet of Input Editing is User Experience; the facet of Processing Feature is Computing Logic; the facet of Output Result is Data Accuracy; the facet of Security and Privacy is Data Privacy; the facet of System Architecture is Accessibility; the facet of System Performance is Reliability. In the final calculation of the overall weights of CSFs across all six facets, the top six ranked CSFs cover Data Accuracy, Reliability, Computing Logic, Robustness, Accessibility, and Data Privacy and Encryption. In the future, the proposed methodology can be applied to innovative Cloud-based applications.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Exploring Parental Attitude and Practices towards Story Book Reading

This study sought to examine current trends towards shared stories inurban Pakistan. It explored the experiences of the parent’s attitude towardsreading and children’s interest in reading. It also looked at the associationbetween parents’ qualifications on child reading and shared readingexperiences at home. A cross-sectional survey study design was employedfor this study with universal sampling employed to seek data from theparents during August and September 2021. This study found that childrenbenefits greatly when parents share stories with their children. It alsofound that generally parents who buy and read books themselvessupported children to become readers. The findings of this study provideinsights into building awareness programs for the parents to provide themwith hand on skills to initiate, sustain and promote story reading at home.
Basic principle of cuckoo optimization algorithm

This paper introduces the basic principle of cuckoo algorithm from cuckoo's habits. The cuckoo algorithm can be applied to more data analysis and processing, providing a basic principle for cuckoo algorithm data processing, and laying a solid foundation for the improvement of cuckoo algorithm.
The Mixed Reality Passthrough Window: Rethinking the Laptop Videoconferencing Experience

The growth in remote and hybrid work has resulted in an increased demand for collaborative, videoconferencing experiences that offer a more seamless and immersive transition between virtual and physical environments. The Mixed Reality Passthrough Window (MRPW) addresses this demand by introducing a new paradigm for the integration of augmented/mixed reality into laptop design. The design is characterized by two screens, situated back to back, with two mounted cameras, facing in opposite directions. This creates the effect of looking through a window, upon which virtual content can be augmented and overlaid. This configuration allows local users sitting around the laptop to more easily interact with remote users, who appear on both sides of the Mixed Reality Passthrough Window, giving the sense that all users are sharing the same space in the round. Additionally, these features create affordances for the outward facing screen to serve as a site for presentations (e.g. slide decks) and other sharable content.
TAMOR: Tier-Aware Multi-objective Recommendation for Ant Fortune Financial Marketing

Online marketing recommendation is crucially important for user growth of mobile applications. However, there are currently three common challenges in designing such an efficient recommendation system. First, on the user side, users can be stratified into different layers which have distinctive user characteristics and marketing objectives. Second, on the item side, items from heterogeneous business scenarios need to be mixed together for ranking. Third, there are often multiple marketing objectives, which are even internally related to each other. In this paper, we address the above challenges by proposing a joint training system Tier-Aware Multi-Objective Recommendation (TAMOR). The TAMOR system leverages all tiers of data to train a unified model, while the representation learned by the model for users and items are aware of data tiers. Besides, in order to better deal with the multi-objective prediction problem, the user bias learning is designed to learn user preferences, which are then used to assist learning for user-specific tasks. TAMOR has been deployed for financial marketing of Ant Fortune, which brings a 10.67% boost for the number of daily new high-holding users.
Image Data Augmentation and Convolutional Feature Map Visualizations in Computer Vision Applications

Deep neural networks (DNNs) perform exceptionally well in many vision tasks, including image classification, annotation, and object recognition. However, these networks are like a black box, and high-quality training datasets are required for deep learning models to achieve high performance. Due to the high cost of collecting a vast number of data samples, data augmentation techniques have been employed in many vision applications. Data augmentation aims to increase the dataset size without collecting new data while introducing variability. One of the means of augmenting the image data is by employing image transformations such as flipping, clipping, or rotation. Activation maps, also known as feature maps, illustrate how the filters are applied to the input image. The objective of visualizing a feature map for an input image is to comprehend what input features are captured in the feature maps. In this paper, we apply various transformations on images and investigate their effect on the multiple convolutional layers (at low, middle, and high levels) by employing intermediate feature map visualizations. We use the famous deep learning-based pre-trained network, VGG-16. Finally, we compare the visualization results of the image transformations at multiple levels and analyze their differences to evaluate the validity of these networks.
EEG-to-EEG: Scalp-to-Intracranial EEG Translation using a Combination of Variational Autoencoder and Generative Adversarial Networks

&lt;p&gt;A generative adversarial network (GAN) enables mapping a data sample from one domain to another one. It has extensively been employed in image-to-image and text-toimage translation. We propose an EEG-to-EEG translation model based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. Scalp EEG (sEEG) is plagued by noise and suffers from low resolution. On the other hand, intracranial EEG (iEEG) recordings enjoy high resolution. Here, we consider the task of mapping the sEEG to iEEG to enhance the sEEG resolution. &lt;br&gt; &lt;/p&gt;
PURIFYING ADVERSARIAL IMAGES USING ADVERSARIAL AUTOENCODERWITH CONDITIONAL NORMALIZING FLOWS

&lt;p&gt;We present a target-agnostic adversarial autoencoder with conditional normalizing flows specifically designed to, given any unlabeled image dataset, purify adversarial samples into clean images, i.e., remove adversarial noise from the images while preserving their visual quality. In our model interpretation, samples are processed by manifold projection in which the encoder brings the sample back into a posterior data distribution in latent space so that the sample is less likely to be irregular to the learned representation of any target classifier. Normalizing flows conditioned on top of our hybrid network structure and walk-back training are used to deal with common drawbacks of generative model and autoencoder-based approaches: not only the trade-off between compression loss and over-fitting on training data but also the structural model dependency on dataset classes and labels. Experiments demonstrated that our proposed model is preferable to existing target-agnostic adversarial defense methods particularly for large and unlabeled image datasets.&lt;/p&gt;
Peacemaking in Iraqi Educational Institutions After 2003 The (Concept and Application)

The subject of peace is considered one of the important and vital subjects because of its positive impacts on various levels particularly the political, religious, social and cultural ones.Starting from this point; we can say that peace making is a responsibility of all the institutions of the state.And as far as the educational; institutions are of the most important institutions, therefore they must be involved and concerned in peace making in order to be able to spread that culture and enhance it to become a daily behavior in dealing with the others.From this point comes the importance of the study to focus the light on peacemaking in the Iraqi educational institutions as a complete system starts in kindergarten and ends the various collegiate levels in addition to their moral and educational tasks that they are to achieve and affect positively in promoting the contemporary societies.The current study is divided into two sections.The first deals with the concept of peace and its pivots through two sub-sections, the first one tackles the concept of peace and its importance and the second tackles peace making bases represented by toleration, conversation, accepting the other's culture, and human rights.The second talks about.The second is devoted to study the mechanisms of peacemaking in the educational institutions, it falls into two sub-sections, the first one tackles governmental policy through focusing on the governmental support for the educational institutions and the improvement of the quality of curricula.The second subsection, tackles promoting the educational reality through the role of the educational and teaching staff and the students activities in the educational institutions.