Reviewerly/exHarmony huggingface dataset:
A Joint Scene Text Recognition and Visual Appearance Model for Protest Issue Classification

Social movements have been a crucial means of political participation in democratic and non-democratic societies. In recent years, the Arab Spring in 2011 as a notable example, such movements aggressively used social media as a platform for organizing protesters. Visual images and texts spread around on the internet played vital roles in bonding and attracting citizens to the movements. Political scientists and sociologists attempted to exploit the vast amount of information on the internet to analyze social movements. Their use of artificial intelligence techniques, however, has been limited. In this paper, we newly introduce a joint scene-text recognition and visual appearance model. Specifically, our model employs a character-level scene text detection and an n-gram character embedding model. Our model can extract semantic information from scene texts including handwritten words on signs and placards, which past automated studies of social movements by social scientists have not yet utilized. By employing such semantic information, our model can, in contrast to past research, classify protest images into semantic categories such as types of protest issues (e.g., gender, race, green). We newly created the Protest Issue Image Dataset consisting of 2,859 images with five protest issue categories to evaluate our model. Our model scored F1 score and provided a significant boost in performance over the conventional visual model used by social scientists, which scored . Our model can be applied to various types of social movement studies, including recent research which examines how different types of protest issues relate to other political and social factors.
The planning of neurosurgical operations: methods and criteria for determining the safest tool trajectory from the skull surface to the tumour

Methods of surgical plan determination are the topic of this paper. Neurosurgical operations are especially dangerous because of the risk of damage important areas in the brain. Results of such damage can be patient's death or loss of health. The finding of the safest tool trajectory from skull surface to tumour is the goal of operation plan. This trajectory should allow access to tumour located in deep parts of the brain and avoid zones which damage is especially dangerous. Security criteria used to determine optimal trajectory of surgical tool tip are presented an compared in this paper. Stages of plan development and mathematical formulas are described. Simulation tests of proposed methods were performed on exemplary models and results are presented in this paper.
Autonomous robot control using hardware accelerated implementation of fuzzy interpolation

The FIVE (Fuzzy Interpolation in Vague Environment) is a possible method for behavior based control. Mobile robots can be also controlled by a behavior based controller. In addition to software implementations, FIVE also has a hardware implementation, which was implemented in FPGA (Field-Programmable Gate Array) using Verilog hardware description language.The hardware implemented behavior control using the FIVE method was demonstrated on a line follower robot application, which is presented in this paper.
Enhancing Agriculture Supply Chain Management Using Blockchain

The purpose of this proposed work is to design and introduce a distributed digital ledger in the Agriculture supply chain that can not only record but also govern transactions that occur between two parties in a permanent, efficient and a verifiable way. This system ensures the involvement of all stakeholders such as producers, particularly small farmers, in a connected network at institutional initiative that helps in the improvement of food safety and quality standard. A smart contract application is to be built that holds transaction records that are immutable, transparent and enhance traceability of the agriculture supply chain management system. This work targets to explore the means and methods of adaptation of blockchain smart contracts in the field of agriculture which focuses to design an agriculture-based supply chain prototype that would digitalize and smooth all relevant transactions with transparency, thereby verifying authenticity while enforcing the negotiation of a contract.
SecureVolt: Enhancing Deep Neural Networks Security via Undervolting

Deep neural networks (DNNs) are shown to be vulnerable to adversarial attacks; carefully crafted additive noise that undermines DNNs integrity. Previously proposed defenses against these attacks require substantial overheads, making it challenging to deploy these solutions in power and computational resource-constrained devices, such as embedded systems and the Edge. In this article, we explore the use of voltage over-scaling (VOS) as a lightweight and efficient defense against adversarial attacks. Specifically, we exploit the stochastic timing violations of VOS within computing elements to implement a moving-target defense for DNNs. Our experimental results demonstrate that VOS guarantees effective defense against different attack methods, does not require any software/hardware modifications, and offers a by-product reduction in power consumption. We propose a space exploration to identify a possible tradeoff between robustness, accuracy, and power gains. Furthermore, we observe the behavior of models’ epistemic uncertainty under variable undervolting aggressiveness. Our experiments show that model uncertainty analysis is coherent with the observation in our robustness/accuracy exploration.
Development of Pop-Up Books in Improving Reading Skills through an Inquiry Learning Model in Grade I Elementary School Students

The selection of the right learning model and media is one of the teacher's efforts to achieve the success of the learning process. In this case, the selection of pop-up book media in learning and inquiry models involves more student activities than teacher activities. This study aims to develop pop-up book media to improve students' reading skills through inquiry learning. Pop-up books are more focused on the factor of presenting interesting media without being designed so that students can find a concept. The method used in this study was R&amp;D Borg and Gall. Data collection techniques use interviews, observations, questionnaires, scale tests. The small-scale test was conducted with 30 students of SD Negeri Sumurwuni grade I and large-scale was carried out on 140 students of State Elementary School located in cluster V of Argasunya sub-district, Harjamukti District, Cirebon City. The results of the development of pop-up book learning media are acceptable and able to improve students' reading skills through an inquiry learning model in grade I of Elementary School. Keywords: pop-up books, inquiry, reading ability
Quantum holography with single-photon states

The retrieval of the phase with single-photon states is a fundamentally and technically challenging endeavor. Here we report an experimental realization of hologram recordings with heralded single-photon illumination and continuous observation of photon statistics. Thereby, we demonstrate the basic principle of holography with single-photon states which cannot be described with the classical wave theory. Under conditions with illumination more than 200 times weaker than the noise of the detector, a hologram (interferogram) recorded with a heralded single-photon source revealed an object not visible with nonheralded illumination and slightly higher intensity. The dramatic improvement in retrieval of amplitude and phase information achieved with the heralded single-photon source can be explained by the strong suppression of noise due to the nonclassical temporal correlation between twin photons and the small coincidence time window. The method could be useful for recording and retrieving amplitude and phase information in the presence of strong noise, for covert imaging, and for imaging of photosensitive biological and material samples.
Mastering all genres: Exploring Ninth Grade Students' Text Comprehension Skills Through Google Forms

Understanding what one reads is a significant component of the reading journey. To effectively engage with different types of texts, students must possess strong reading comprehension skills. The goal of this study is to figure out how well kids can understand various forms of literature. Descriptive, recount, report, narrative, and procedural texts are examples of different types of texts. In this research, a quantitative method is utilized to perform data calculations. Reading assessments are incorporated as a tool in this process. The assessments consist of 25 questions and offer a selection of five text types: descriptive, recount, report, narrative, and procedure texts. The findings indicate that students have demonstrated a 50 percent proficiency in comprehending different text types, which suggests a relatively low level of overall comprehension. However, there is a positive trend observed in the children's understanding of descriptive language.
Impact of Internet and Smartphone usage among Children in Bangladesh

All living organ on earth required certain level of care and guidance in their development stages. Children has no exception to that. Appropriate parenting and child development goes hand in hand. Unfortunately, due to the impact of globalization, technological advancement, and hardship of life many parents are forced to share less concern and time for their children. The Internet and Smartphone, the two most handy inventions of science have slowly replaced and reduced total amount of interaction time and care giving moments of parents with their kids. Starting from a toddler to teenager majority of these group are often found occupied using Smartphone and surfing over the Internet instead of interacting with parents, playing with friends, and spending time with relatives. It is somewhat unfortunate but true that the Internet and Smartphone have become an inevitable part of life and living of people of all ages. This study explores current status of the Internet and adaptability rate of Smartphone across Bangladesh especially among children. Different data on Internet and Smartphone usage across the globe is also portrait here. This study explored whether it is healthy for children to spend best moments of their life over the Internet and Smartphone instead of enjoying all the adventures and astonishing moments of childhood and parental care. Through questionnaire survey author tried to explore respondent’s opinion on the impact of Internet and Smartphone usage among children in Bangladesh and how much essential it is for children to spend time over the Internet and Smartphone.
Evolutionary Computation and Tunneling at the Edge of Quantum Computing

tutorial Free Access Share on Evolutionary Computation and Tunneling at the Edge of Quantum Computing Author: Darrell Whitley Colorado State University, Fort Collins, USA Colorado State University, Fort Collins, USA https://orcid.org/0000-0002-2752-6534Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1503–1532https://doi.org/10.1145/3583133.3595063Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Concept of Integration of Blockchain and Artificial Intelligence

Blockchain is a secure, decentralised way to move and store data. Whereas AI is a process which makes use of learning through self and is founded on the examination and identification of patterns in vast volumes of data. It also evaluates and makes judgments based on the study of a lot of data using supervised or unsupervised learning. The foundation of this essay is a conceptual understanding of how blockchain and AI could complement one another or how blockchain might help AI. It indicates that both can eventually be combined for the advantage of both businesses and the people dealing with it.
Publish to Web

All of the methods for sharing Power BI files in the Power BI Service (such as dashboard sharing, workspaces, apps, and so on) need paid Power BI subscriptions for consuming reports. Users need to be either Power BI Pro or free accounts under the Power BI premium capacity. The Publish to Web option is the only way to share Power BI content through the service for free. With this method, you can share Power BI content with users who don’t have a Power BI account.
Too Legit To Quit: Discursive Strategies in Cryptocurrency’s Categorical Emergence

Category literature has described how members of an emergent category work together to form a collective identity in order to achieve legitimacy. However, the emergence of cryptocurrency challenges this model as it represents a category plagued by infighting among members since its inception that nevertheless has garnered widespread adoption by individuals, financial institutions, and even governments. In order to build theory from this theoretical anomaly, we take a discursive perspective on category emergence and explore power dynamics amongst category members. Drawing on an archival qualitative approach, we identify a novel set of subject positions that materialize with the category and become the voice of the narrative for either the category beacon (Bitcoin) or its challengers (XRP and Ethereum). While both the category beacon and challengers advance a Discourse of Improvement regarding the inefficiencies in current financial systems, they differ vociferously in how to define and structure the emergent category. The category beacon leverages its power to advance a Discourse of Protection in an attempt to mold the category discourse to its image and thereby exclude other members. In contrast, challengers use a Discourse of Inclusion to order to combat the category beacon’s narrative and expand the scope of the category’s boundaries. As a whole, we identify a battle over whether cryptocurrency should be established as a prototypical or goal-based category which reveals important insights into the emergence of a non-cooperative category.
Neural Style Transfer—Parameter Optimization Including Performance, Loss Function and Security

Neural Style Transfer (NST) is an emerging and known technique for convolutional neural networks by generating images of distinct artistic styles. Humans have championed the skill of creating extraordinary visual experiences by blending content and image style. It might be challenging to render an image’s semantic content in many visual styles. Perhaps a key constraint for earlier methods has been the absence of explicit representations of semantic information in images that make it possible to distinguish between the content and style of a picture. Image Steganography is the practice of concealing sensitive information by embedding the information in an audio, video, image or text file. This paper explores the use of Image Steganography on abstract images produced using Neural Style Transfer to conceal sensitive information.
Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.
The Problematicization of educational welfare policy: Focusing on WPR (What's the Problem Represented to be?)

Objectives This study examines the important educational essence and values that have been missed in the process of educational welfare developing into a policy. Through this, it was intended to derive implications for the development of educational welfare policies in the future.&#x0D; Methods In this study, Bacchi's WPR was used as an analysis framework. Therefore, educational welfare policy was regarded as a discourse, and the way “problems” and solutions were composed in educational welfare policy was divided into “change in the historical origin of problem and method of reproducing problem,” “phenomenon caused by problem,” and reinterpretation of reproduced problem.”&#x0D; Results In this study, it could be explained that the educational welfare policy, which has been reduced to a political means through the discourse of welfare populism, has been transformed into a “business” while excluding “students.” In addition, in the historical process of becoming a problem with educational welfare, school members also recognized educational welfare as an area of social welfare, not education. This has led to a focus on who will be in charge of the “who” business.&#x0D; Conclusions Based on the results derived from this study, it was suggested that educational consideration and the necessity of enacting related laws required in the process of converting educational welfare to ‘integrated support tailored to students’.
Detection of Situational Context From Minimal Sensor Modality of A Smartphone Using Machine Learning Algorithm

Early detection and continuous monitoring can help reduce the complexity of treatment and recovery. For this purpose, many modern technologies are being used like smart wearable devices to make the diagnosis of different types of human diseases and automated tutoring systems. There is a vast improvement in the sector of human healthcare and education delivery using artificial intelligence (AI). For these AI algorithms, there can be high error rates if situational contexts are ignored. Currently, there is no automated approach to detect situational context. In this work, we propose a novel approach to automatically detect situational context with a smartphone context detection app using AI from minimal sensor modality. We begin the process by converting a few sensor data from the smartphone app to a multitude of axes, then determine situational context from these axes by using a machine learning algorithm. At first, we evaluated <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means algorithm performance on the converted data and grouped them into different clusters according to the contexts. However, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means algorithm has many challenges that negatively affect its clustering performance. For this reason, to automatically detect the situational contexts more accurately we have performed different machine learning (ML) algorithms to differentiate their characteristic parameters and attributes. To train and test ML models, 145 features were extracted from the dataset. In our case, we have used a dataset with 53,679 distinct values to evaluate the performance of different algorithms in detecting five situational contexts of the users. Experimental result shows that the accuracy of the Support Vector Machine, Random Forest, Artificial Neural Network, and Decision Tree Classifiers are 95%, 99%, 97%, and 98% respectively. The most effective classifier overall is Random Forest. This preliminary work shows the feasibility of detecting situational context automatically from a few sensor data collected from the smartphone app by converting the sensor data to multiple axes and applying a machine learning algorithm.
6 Explanations and user control in recommender systems

Adaptive, personalized recommendations have become a common feature of today’s web and mobile app user interfaces. In most of modern applications, however, the underlying recommender systems are black boxes for the users, and no detailed information is provided about why certain items were selected for recommendation. Users also often have very limited means to influence (e. g., correct) the provided suggestions and to apply information filters. This can potentially lead to a limited acceptance of the recommendation system. In this chapter, we review explanations and feedback mechanisms as a means of building trustworthy recommender and advice-giving systems that put their users in control of the personalization process, and outline existing challenges in the area.
Identifikasi Jumlah Manusia Dalam Kerumunan Menggunakan Convolutional Neural Network

Public space is a place that generally used by the community in order to meet their needs and where crowds are usually formed. In a crowd, the number of people can be the first indicator in an anomaly in where the more number of people exists in a crowd, the more supervision is needed on the crowd to prevent chaos or other things that are not desirable in public spaces. The need for a crowd-counting is certainly needed to facilitate supervision and also the awareness of people in crowds. This research meant to develop a system that can identifies a crowd based on the number of people that exist in the crowd and also give the number of people as an output. The system applied the Convolutional Neural Network (CNN) algorithm. The CNN model is trained using a labeled crowd dataset with a total of 4372 crowd photos. The CNN works as a regression model that will count the number of people from the feature extracted from the image. The evalution shows the Mean Absolute Error value achieved is 55.1176 in the test data.
Learning and teaching through inquiry with geospatial technologies: a systematic review

The development of digital skills among pupils at the lowest levels is already a common part of education. The implementation of geospatial technologies (GST) is one of the ways to build and deepen these skills in primary and secondary schools. The use of these technologies is possible through an inquiry approach. This paper aims to identify the knowledge gaps on the issue of linking GST and inquiry in primary and secondary school learning and teaching. This article presents findings from 36 English-language empirical research studies published up to 2020 in the Web of Science and Scopus databases. The systematic review focuses mainly on the methods used and the results of the studies. The analysis of the studies shows that the methods used in them are strongly heterogeneous and qualitative and quantitative methods have similar representation. The results of the studies indicate that in this type of learning and teaching, prior teacher preparation is necessary, and students develop digital competencies and knowledge related to GST and the topic of the learning task that is addressed using these technologies. This systematic review presents recommendations and pitfalls for learning and teaching through inquiry with GST, but also recommendations for future research.