Reviewerly/exHarmony huggingface dataset:
Prime Time News, and: What Use is Clairvoyance When the World Wants to Splay on My Palms?

Prime Time News, and: What Use is Clairvoyance When the World Wants to Splay on My Palms? Dure Ahmed (bio) Prime Time News The women who buy paper towels from a billionaire are sweating in the kitchen, washing dishes, making post-dinner chai while the men and I gawp at the TV. I'm not a woman, I want to announce but I don't want to sound self-absorbed and complicit. What I really want to do is go into the kitchen and radicalize everyone. The ocean is on fire, our homelands are drowning, two-day shipping is not a blessing my loves, people and backs and bladders and the Earth are breaking. Listen, if we roll out an ancient device to usher in a revolution no one will ever ask you to make chai, and serve it in small cups to each ungrateful, distracted hand. If we storm the castles of the oppressors, this time for the sick and dying, the ones shot by drones, your children's unfunded schools, your right to the doctor's visits you've hidden from your husbands, you'll also never again need to get your mons and labia waxed. There are so many of you, as you rally to see if you need your tubes tied and where to get the cheapest halal lamb. There are so many of you, drooling at the new Costco, the ailing aunt who has a kilo of gold. There are so many of you please wake up. There are so many of you, I need you to help me. [End Page 101] What Use is Clairvoyance When the World Wants to Splay on My Palms? After Kaveh Akbar ("Being in This World Makes Me Feel Like a Time Traveler") Nail salons and indoor dining re-open the technician yawns above my toes European Wax Center leaves a message on my phone We can do the bikini but sorry not your eyebrows, they are on your face I roar with laughter like I'll never die The prophet Joseph interpreted dreams for a cruel king which means that visions like poems are not very discerning about who they pursue so why did I live in fear? For years I wrapped my hair in opaque fabric and feared charcuterie boards ("sorry, it's the prosciutto") and bars even women but I still dreamt of flocks of crows like I were Cain and thought Will only crows be tender to me? Do signs in dreams leave you when you find other things? I drop my bag and frozen in the bounce of purple lights I watch it spill coins a birth-control bubble pack Polaroids eleven liquid lipstick bottles among a hundred dancing feet No one look at me except the beauty I'll drag home you, who will hair-chested, slightly boobed, nibble up my legs this chain around your neck with God's name will glint wet when your face emerges from between my thighs [End Page 102] Dure Ahmed Dure Ahmed is an immigrant, Muslim writer who lives in New Jersey. Their poems have appeared in Guernica, Black Warrior Review, and ANMLY among others. Dure is a 2023 Bread Loaf Contributor, and they have an MFA from the University of Arizona. Copyright © 2023 Pleiades and Pleiades Press
GRIFFIN: Enhancing the security of smart contracts

In the rapidly evolving landscape of decentralized systems, ensuring the integrity and trustworthiness of smart contracts is paramount for developers. This paper presents a comprehensive strategy for enhancing smart contract security by focusing on specific high-risk areas, including Integer Overflow, Dangerous Delegate Calls, Timestamp Dependency, Reentrancy Vulnerabilities, Race Conditions, and Sybil attacks. Despite the growing significance of smart contracts in blockchain ecosystems, a notable research gap exists in the development of specialized tools capable of providing real-time vulnerability detection and mitigation guidance. To bridge this gap, our research introduces the ‘GRIFFIN’ - Smart Contracts. Vulnerability Detector is a powerful tool that has been rigorously tested and validated. Our study has yielded significant results, demonstrating the efficacy of the GRIFFIN in proactively identifying and mitigating critical vulnerabilities within a diverse dataset of 12,000 real-world solidity smart contracts. The tool leverages state-of-the-art static analysis techniques and machine learning algorithms, achieving superior accuracy rates when compared to existing solutions. This heightened accuracy not only empowers developers but also boosts the overall robustness and dependability of smart contract ecosystems. The cornerstone of our research is the development and validation of a practical, user-centric solution. By providing actionable insights, code snippets, and real-time feedback to developers, GRIFFIN equips them with the knowledge and tools needed to address vulnerabilities swiftly and effectively. This innovative approach is not merely an academic endeavor but a significant stride towards cultivating resilient and dependable smart contract environments. It instills a culture of security-conscious development practices, ensuring that the smart contracts crucial to decentralized systems can operate with the highest level of trust and reliability. Index Terms— Smart Contracts; Integer overflow; Dangerous Delegate call; Timestamp Dependence; Reentrancy Attack; Race Condition; Sybil Attack; Static Analysis; Detection
Registers Used in Online Food Shops of Instagram: A Sociolinguistic Analysis

The jargon of any given online store will be unlike that used in regular communication. In the context of online grocery stores, there are special words for describing certain occurrences. Many online grocery store registers are available, with the English language being the most common. In this regard, this study studies the linguistic forms and the significance of those registries employed by online food businesses and Instagram profiles. This study is a qualitative in nature quantitative investigation. This study aims to examine the English registers used on Facebook sites for restaurants and grocery stores with the goal of determining their forms and implications. The collected information consists of register phrases, phrases, and phrases used in food website shop articles, captions. Instagram food groups and shops, messages exchanged between buyers and sellers, and user-uploaded content from those stores all contribute to the data set. Finding responses, captions, and debates in the mail of food internet shops, reading them over and over again, signaling registers words, and recording discussion, registered words, and phrases are all steps in the data collection process. Larson's Hypothesis of Ambient Relevance and Halliday's Model of Register are applied to the data. The writer sorts the material into types, examines the language in its entirety, examines the situational context, finds the differences between ambient while lexical meanings, and finally draws an conclusion. Scholar discovers information structured according to the syntax (7 verb data, 5 noun data, 1 adjective datum, 12 compound word data, 6 shorthand data, 6 noun phrase data, 6 verb phrase data, and 3 sentence data). The author gathers information from the meaning of words and their context to create an argument.
Breast cancer early detection in TP53 SNP protein sequences based on a new Convolutional Neural Network model

INTRODUCTION: Breast cancer (BC) is the most commonly occurring cancer and the second leading cause for women’s disease death. The BC cases are associated with genital mutations which are inherited from older generations or acquired overtime. If the diagnosis is done at the first stage, effects associated with certain treatments can be limited, costs can be saved and the diagnostic time can be minimized. This can also help specialists target the best treatment to increase the rate of cures. Nevertheless, its discovery in patients is very challenging due to silent symptoms aside from the fact the routine screening is not recommended for women under 40 years old.OBJECTIVES: Several efforts are aimed at the BC early detection using machine and deep learning systems. The proposed algorithms use different data types to distinguish between cancerous and non-cancerous cases; as: mammography, ultrasound and MRI (magnetic resonance imaging) images. Then, different learning tools were applied on this data for the classification task. Despite the classification rates which exceed 90%, the major drawback of all these methods is that they are applicable only after the appearance of the cancerous tumors, which reduces the cure rates.METHODS: We propose a new technique for early breast cancer screening. For the data, we focus on cancerous and non-cancerous SNP (Single Nucleotide Polymorphism) protein sequences of the TP53 gene in chromosome 17. This gene is shown to be linked to different single amino acid mutations on which we will shed light here. The method we propose transforms SNP textual sequences into digital vectors via coding. Then, RGB scalogram images are generated using the continuous wavelet transform. A pretreatment of color coefficients is applied to scalograms aiming at creating four different databases. Finally, a CNN deep learning network is used for the binary classification of cancerous and non-cancerous images.RESULTS: During the validation process, we reached good performance with specificity of 97.84%, sensitivity of 96.45%, an overall accuracy of 95.29% and an equal run time of 12 minutes 3 seconds. These values ensure the efficiency of our method.To enhance more these results, we used the ORB feature detection technique. Consequently, the classification rates have been improved to reach 95.9% as accuracyCONCLUSION: Our method will allow significant savings time and lives by detecting the disease in patients whose genetic mutations are beginning to appear.
UPCYCLING LIMBAH PLASTIK DENGAN TEKNIK MERENDA (CROCHET) UNTUK PEMBUATAN UPHOLSTERY PADA PRODUK POUF

The daily use of plastic bags on a large scale has an impact on the increasing amount of plastic bag waste generated. Responding to these problems, the author initiated the utilization of plastic bag waste in the interior field, namely by upcycling plastic bag waste using crochet techniques for making upholstery on pouf products. That way in this design can be formulated a problem of how to process plastic bag waste with crochet techniques into upholstery on pouf products. The purpose of designing this work is to provide alternatives and variations in the processing of plastic bag waste using crochet techniques. This design uses the method of creation proposed by Palgunadi (2007), which includes three processes of planning and making products, namely the exploration process, extraction process, and termination point. The results obtained from this design are that plastic bag waste, especially HDPE types, can be made into plastic fibers because this plastic waste has a medium thickness, but is still strong and very much available. Furthermore, to process plastic waste into plastic fibers, steps are needed such as sorting, cleaning and then cutting plastic bag waste to produce plastic fibers that are ready to be crocheted to produce upholstery poufs that are in accordance with taste.
Insight into the Capabilities of Machine Learning Explainability Software Through the Evaluation of Two Prominent Open-Source Tools

The utilization of machine learning, deep learning, data analytics, and natural language processing technologies are becoming ubiquitous, and soon they will be part of diverse applications in real-world business scenarios, such as bank lending, stock market decision-making, and traffic monitoring. As machine learning continues to influence our daily lives, it becomes crucial for data scientists to comprehend the reasoning behind their models' predictions. This article aims to assess and analyze the effectiveness of two open-source tools, Alibi and Explainer Dashboard, in providing explainability for different algorithms and their outcomes. By doing this, we provide a sample framework for comparison and insights into the capabilities of explainability software. In this study, two distinct datasets were utilized, one pertaining to vehicle information, specifically cars, and the other concerning breast cancer diagnostic data. Upon evaluating the XAI tools, it can be concluded that there is room for improvement in open-source explainability solutions. To facilitate a comprehensive evaluation, a benchmark chart was compiled, focusing on three key metrics: satisfaction, trust, and effectiveness. Multiple questions related to these metrics were rated on a scale of 1 to 5. The ratings for each tool were aggregated, and their mean values were calculated to determine an overall score for each explainability tool.
Smart Care Effective Online Medical System

Health care is one of the fastest growing industry in Pakistan. Life is too busy in this era. In this busy life there is lot of problems in getting medical services. Internet and online websites, mobile applications have changed our way of thinking, shopping. As we know that almost everything is going to be online. That’s why we decided to make a system which provide comfort to the patients and users. The main objectives of this research is to achieve Online Medical store and Online Doctor’s appointment. In online medical store medicines are available online it can be order by mobile application or website, then the seller will be delivered it as soon as possible according to the respective location of user? In online Doctor’s appointment a user or a patient can take appointment from doctor. In this way the patient can’t face the problem of long waiting queues. A database contains doctor details, patient details and appointment details.
LEARNING WHILE RELAXING: PHYSICAL STUDENTS’ EXPERIENCES OF LEARNING ENGLISH VOCABULARY BY SONGS

The objective of this study was to investigate the perspectives of students on the utilization of songs as a medium for acquiring English vocabulary. The primary approach employed for data collecting was conducting WhatsApp chats. The study encompassed a sample size of 10 participants who exhibited a range of linguistic backgrounds and possessed differing degrees of competency in the English language. Thematic analysis was employed to assess the data generated from interviews done through WhatsApp chat. The results of this study indicate that students hold a favorable impression of the utilization of songs as a pedagogical method for enhancing their acquisition of English vocabulary. The participants had a positive reception towards the learning process, indicating an enhanced motivation to acquire knowledge and a heightened level of engagement with the English language when incorporating music. The participants additionally said that songs facilitated the development of crucial listening skills, the acquisition of novel terminology, and enhanced their ability to retain information. Furthermore, the research revealed that the utilization of songs facilitated rapid advancement among students, hence enhancing their self-assurance in the acquisition of English vocabulary. Moreover, the aforementioned findings underscore the potential advantages of employing technology for data collection purposes, as well as the significance of taking into account the viewpoints of students when formulating efficacious pedagogical approaches.
Factors that contribute to integrity of academicians in Malaysian public university: A case study

Low integrity is a challenge for any organization. However, most organizations emphasize integrity without explaining what is required of an individual with high integrity. Exhibiting high integrity is necessary for academics; yet, the level of academic integrity remains unclear. Therefore, the purpose of this study is to examine the integrity level of academicians in a Malaysian public university. This paper shares the findings on the level of integrity of academics based on a questionnaire completed by 213 academicians. Data were collected by survey questionnaire and was analyzed using descriptive and inferential statistics. An overall mean score of 9.45 from a possible 10.0 indicated a high level of integrity among academics. The self-evaluation results by academics also demonstrated that they have attained integrity at a high level for their generic task, teaching and learning, research and publications and service for community with a mean score between 9.36 and 9.49. The value with the highest mean score was for “service to community”, whereas the lowest was for “research and publication”. These findings show that the university has successfully instilled values of integrity among academicians. Nevertheless, the university must continue to enhance academic integrity by exploring religiosity. Using Google Scholar, a literature search identified an Islam-based academic integrity model to explain the quantitative findings. Finally, a mixed method approach and involving all universities in Malaysia are recommended to further the findings of this study.
Cultural Awareness as a Main Approach to Teaching a Course of Web Customization for Effective Cross-Cultural Communication

Website customization is an important part of cross-cultural communication. Its goal is to make an existing website culturally, linguistically, graphically, technically, and rhetorically adapted (customized) to the target audience so that they can read and use it for a practical purpose as if it were originally created for their own use. Web customization places two tasks on cross-cultural communicators: to have a good understanding of the culture of the target country and a good command of its language conventions. They are the guarantee for making a source website well customized and accepted by the target audience. This article explores important cultural elements involved through secondary research or text analysis and worked out guidelines for teaching a course on Website Customization for effective cross-cultural communication. Important findings include that the effectiveness or success of customized websites is directly influenced by the impacts of cultural understanding and awareness. These impacts infiltrate every aspect of communication. The significance of the research is that it points out the aspects where problems easily pop up in web customization and has worked out guidelines for solving these problems, which is a contribution to successful cross-cultural communication.
An E-Coupon Service Based on Blockchain

E-coupons are used often, as e-commerce becomes increasingly popular because of its mobility and ease of use. The majority of e-coupon providers manage e-coupon data on a single and centralized server, which makes them susceptible to security problems. To address this issue, a novel system is proposed to provide e-coupon service with enhanced security by employing blockchain technology and the HMAC Digital Signature Algorithm. Additionally, we improve the blockchain performance (in terms of speed and security) with a coupon recommendation contract and a malicious block avoiding contract. The experimental findings demonstrate that, when compared to an existing e-coupon service, stronger security is provided by our proposed service.
Mathematical Model for Improving Cloud Load Balancing Using Scheduling Algorithms

Cloud computing is an environment that provides processing and executing requests in the form of tasks on the Virtual Machines (VMs). The resource scheduling and load-balancing mechanisms of the cloud are important to keep an easy flow of task processing and execution at any instance of time, irrespective of the task size. Therefore, it becomes crucial to study the behavior of resource scheduling algorithms with respect to the load-balancing mechanism. The main objective of this research paper is to process and execute different-sized tasks in the WorkflowSim environment on the cloud VMs by using the resource scheduling algorithms Max–Min (MX–MN), Minimum Completion Time (MCT), and Min–Min (MN–MN) under different scenarios with the purpose to study the behavior of these resource scheduling algorithms with respect to the load-balancing mechanism. A mathematical model is also proposed to calculate the amount of load balanced by a specific VM. The mathematical model of linear regression is used to provide an empirical analysis and differentiate how the resource scheduling algorithms behaved under these various scenarios. From the experiment, results obtained, and empirical analysis, it can be said that the performance of the MX–MN is best, followed by the performances of MN–MN and MCT, respectively. The machine learning (ML) method of reinforcement learning (RL) is also proposed at the end to enhance the resource scheduling and load-balancing processes of the cloud.
The Motivation of Chinese Trisyllables and Their Generation Mechanisms

This paper discusses the motives for the generation of Chinese trisyllables and their generative mechanisms, and analyzes the generative laws of trisyllabic rhyme scheme 2+1 forms and 1+2 forms on this basis. The emergence of Chinese trisyllables is the result of the joint action of the need for semantic expression and the principle of linguistic economy. Due to the bisyllabification of Chinese language, the expression form of the unit above the word usually exceed two syllables, and the superiority of three-syllable words lies in the fact that they have richer semantics than two-syllable words and are more economical than four-syllable words. There are two ways to generate three-syllable words: one is to generate three-syllable words directly; one is to generate three-syllable words by compressing four-syllable words, and the principle of compression is that the ones that carry more information are not easy to be compressed, and the ones that carry less information are easier to be compressed. In the generation of three-syllable modifier-head structures, the two-syllable nouns at the back are prioritized to be compressed, so there are far more 2+1 forms than 1+2 forms; while in the generation of three-syllable predicate-object structures, the two-syllable verbs at the front are prioritized to be compressed, so there are far more 1+2 forms than 2+1 forms.
Perseverance of the Audio Data using RNN Implied Matrix Segmentation based Lossless Encoder

Lossless audio encoding is a crucial technique by reducing the size of an audio file while maintaining privacy and perseverance of the audio data. In the present paper, a lossless audio encoder has been developed using a matrix segmentation method based on recurrent neural networks and the RLE methodology. Experimental data are shown with statistical metrics to assess the quality and efficiency of the current method in comparison to other existing algorithms.
Malicious Transaction URL Detection Using Logistic Regression

The onset of COVID-19 has shifted people to the virtual world more to avoid social interaction. In fact, the trading systems have shifted to E-Commerce platforms which include virtual transactions as well. Besides being a boon, virtual transactions have also brought in several cybercrimes. Several fraudulent sites are created for distracting the users from authentic sites and lead them to the unsafe ones where users lose their personal data or even their wealth at worst cases. This paper is to illustrate a machine learning solution for putting a check on these kinds of activities. Different Python tools have been used to preprocess the dataset that contains different URLs which is classified into safe unsafe categories depending on the presence of SSL certificate or encryption of the URL which is further fed into a model that classifies URLs into safe and unsafe categories using logistic regression. Other than earlier approaches, our proposed model not only checks simple URLs but also check whether URLs are safe for transaction.