Reviewerly/exHarmony huggingface dataset:
User preference Based Personalized Collaborative Filtering for Large Scale Recommender Systems

The Indian market for internet shopping has already been penetrated by E-Commerce. People are increasingly attracted to shop online, which has altered the market landscape. Amazon, Snap deal, Flipkart, and more companies offer online shopping sites that are gaining traction. As the number of online buyers and dealers grows, smart business approaches are required to deal with the massive amounts of data generated every day. Recommendation systems play a crucial role in sifting this data and giving consumers with relevant information. Various strategies for recommender systems have been used, such as Collaborative Filtering, Content-based, and Demographic, but these techniques have a number of flaws that make them ineffective. Among these collaborative filtering approach is widely popular because of its simplicity and accuracy. But still these systems can be improved in their accuracy when used for multiple domains. As a result, more distinguishing traits must be identified in order to optimize these procedures. This can be accomplished by combining the benefits of multiple strategies in a hybrid approach. This paper describes a hybrid approach that identifies the users with shared preferences (semantic users) and their dynamic interests with parallel training to improve recommendation efficiency. Results were compared with other state of art techniques and the accuracy of our system is found to be improved by 2% and seems to give promising results for multiple domains.
HÉR! An Exploration of Artistic Agency - Media Repository

Media repository published as part of Halla Steinunn Stefánsdóttir's doctoral thesis 'HÉR! An Exploration of Artistic Agency' (2023). See here: https://bit.ly/LUResearchPortal Abstract: This doctoral thesis is concerned with agency in the work of a performer, curator, and composer, and how these agencies are socio-culturally constructed. Grounded in creative practice as primary research methodology, the project builds on feedback loops between creation, analysis, and interpretation. The aim is to better understand the conventional norms that define the roles of composer and performer and, further, to explore more dynamic approaches to these agencies through the perspectives of a composer, performer, and curator active in the fields of contemporary music and sound art. The project responds to the following research questions: o How can ecological-enactive and post-phenomenological perspectives on musical practice within Western classical music challenge current understandings of the roles of performer and composer? o What artistic methods can be employed to provide a more robust understanding of the fluidity of these roles, to uncover their potential for artistic renewal in the creation and performance of contemporary music? The research was designed as a series of micro-laboratories to look at agency through artistic collaboration in situations set in the concert hall, the recording studio, the virtual domain, and ecological sound art. This resulted in electroacoustic compositions, multi-channel installations, and site-specific work, including performances within a sonic hologram and with artificial intelligence. The analytical approach was built on autoethnography, and audio and video analysis through stimulated recall. The findings outline how the technological, non-human, and human agencies of environments affect and shape processual work. The analysis highlights how co-relation between theory and practice may both serve to unpack the pre-conditioning of agency, while the artistic experimentation in the laboratories seeks to explore other agential relations. Here, the intentionality of the technologies used in the lab have unfolded new perspectives on the conventional roles of composer and performer, while enabling the development of more dynamic practices. Through a study of the author’s curatorial practice, the role of a curator is understood not as defined by prescribed methods, but rather as dependent on the negotiation of the many agencies at play in artistic practice. The method development of the project has implications for the design of artistic research through the model of the laboratory. Herein, the use of audio and video technologies, particularly their application within stimulated recall methods, are integrated parts of analysis and artistic creation. The artistic research laboratory is proposed as a framework through which the potential of artistic research in music—as a vehicle for the development of new practices in professional contexts and in teaching environments—is substantiated and facilitated.
APACHE WEB SERVER RESPONSE TIME OPTIMIZATION

This article explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytical methods, we show that MaxClients has a concave-upward effect on response time, and hence hill-climbing techniques can be used to determine the optimal value of MaxClients. We investigate two optimizers that use hill climbing—one based on Newton’s method and one based on fuzzy control. A third technique is a heuristic that exploits the relationship between bottleneck utilization and response time minimization. In all cases, online optimization reduces the response time by a factor of 10 or more compared to the static default value. The trade-offs between online schemes are as follows. Newton’s method is well known but does not provide consistent results for highly variable data such as response times. Fuzzy control is more reliable but converges slowly. The heuristic works well in our prototype system, but it can be difficult to generalize because it requires knowledge of resource bottlenecks and the ability to measure their use. This topic is relevant in the context of a rapidly growing number of web applications and high requirements for the performance and efficiency of web servers. This topic explores how to improve the speed and response time of the Apache web server using various techniques and settings, such as optimizing server settings, using caching, data compression, optimizing request routing, and more. The purpose of this research is to improve the performance and response speed of the Apache web server, which can be useful for developers and administrators of web applications and services. The speed and response time of web servers are critical factors in meeting user needs and achieving business goals for web applications and services. Apache is one of the most widely used web servers in the world, so optimizing the response time of the Apache server is an important task for many developers and administrators of web applications and services. This study will examine various approaches and techniques for optimizing the response time of the Apache web server, including configuring server parameters, using caching, data compression, optimizing request routing, and others. The results of the study can be useful for developers and administrators of web applications and services that deal with the use of the Apache web server. Optimizing Apache server response time can significantly improve the performance and efficiency of web applications and services, which in turn can lead to user satisfaction and business goals.
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model’s output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario.
<i>μ</i>Tesla-Based Authentication for Reliable and Secure Broadcast Communications in IoD Using Blockchain

The Internet of Drones (IoD) manages and coordinates communications between drones in Internet of Things (IoT) applications. Ensuring security and privacy in unmanned aerial vehicles (UAVs) networks, i.e., drones, is essential to protect data from cyber attacks. In this context, providing authentication is a major challenge due to the fact that drones are devices limited in power capabilities. The problem is aggravated by the dynamism of IoD networks due to the high mobility of drones, being sensitive to packet loss and handovers. Blockchain technology is attractive to address the problem of centralization of existing authentication protocols. In this article, we provide a decentralized, secure, and efficient authentication protocol, based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Tesla, that relies on Blockchain to manage drone authentication. We analyze the security and performance of the proposed solution. Simulation results show that the proposed solution outperforms several approaches in the literature, achieving an authentication delay of less than 250 ms with a low information exchange of 1024 bits for 128-bit security level while maintaining low computational requirements.
Controlling Pre-trained Language Models for Grade-Specific Text Simplification

Text simplification (TS) systems rewrite text to make it more readable while preserving its content. However, what makes a text easy to read depends on the intended readers. Recent work has shown that pre-trained language models can simplify text using a wealth of techniques to control output simplicity, ranging from specifying only the desired reading grade level, to directly specifying low-level edit operations. Yet it remains unclear how to set these control parameters in practice. Existing approaches set them at the corpus level, disregarding the complexity of individual inputs and considering only one level of output complexity. In this work, we conduct an empirical study to understand how different control mechanisms impact the adequacy and simplicity of text simplification systems. Based on these insights, we introduce a simple method that predicts the edit operations required for simplifying a text for a specific grade level on an instance-per-instance basis. This approach improves the quality of the simplified outputs over corpus-level search-based heuristics.
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems

Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains. Calc-X is suitable for teaching language models to offload computations to a symbolic system. We survey and unify several existing chain-of-thought datasets into a proposed format, resulting in a standard collection of over 300,000 samples requiring arithmetic reasoning. Finally, we use the new Calc-X collection to train open-source calculator-using models we call Calcformers and show that these models approximately double the accuracy of generating correct results compared to vanilla language model baselines. We make all Calc-X datasets, source code and Calcformers models publicly available.
MENINGKATKAN KEAHLIAN MEMAHAMI BACAAN DENGAN MENGGUNAKAN METODE PQ4R PESERTA DIDIK KELAS IV SEKOLAH DASAR

This action research aims to provide solutions to the problem of low reading comprehension levels among fourth-grade students at SD Negeri Manuk Ponorogo. The researched solution is the use of the PQ4R method (Preview, Question, Read, Reflect, Recite, Review) to improve students' reading comprehension skills. This research collaborated with teachers in adopting the Hopkins Research Model, which consisted of two action cycles. The research subjects were five female and three male students in the fourth grade of SD Negeri Manuk. Data collection techniques for analysis in this research used both test and non-test methods. Meanwhile, the data analysis techniques used by the researchers were quantitative and qualitative descriptive analysis techniques. After conducting this research using the PQ4R method, it was found that the subject students' understanding of a reading had significantly increased. The pre-action initial test results showed an average score of 63.75, gradually increasing to an average score of 69.60 in cycle I, and an average score of 74.60 in cycle II. Furthermore, the percentage of students who were able to pass the threshold score of 70 also showed a significant improvement. In the initial pre-action, only 25% of students passed the threshold score, while in cycle I, it was 63%, and in cycle II, 88% of students had passed the threshold score.&#x0D;
Gorilla: Large Language Model Connected with Massive APIs

Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of-the-art LLMs such as GPT-4, largely due to their inability to generate accurate input arguments and their tendency to hallucinate the wrong usage of an API call. We release Gorilla, a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls. When combined with a document retriever, Gorilla demonstrates a strong capability to adapt to test-time document changes, enabling flexible user updates or version changes. It also substantially mitigates the issue of hallucination, commonly encountered when prompting LLMs directly. To evaluate the model's ability, we introduce APIBench, a comprehensive dataset consisting of HuggingFace, TorchHub, and TensorHub APIs. The successful integration of the retrieval system with Gorilla demonstrates the potential for LLMs to use tools more accurately, keep up with frequently updated documentation, and consequently increase the reliability and applicability of their outputs. Gorilla's code, model, data, and demo are available at https://gorilla.cs.berkeley.edu
Bipartite entanglement detection by local generalized measurements

Entanglement detection by local measurements, which can possibly be performed by far distant observers, are of particular interest for applications in quantum key distribution and quantum communication. In this paper sufficient conditions for arbitrary dimensional bipartite entanglement detection based on correlation matrices and joint probability distributions of such local measurements are investigated. In particular, their dependence on the nature of the local measurements is explored for typical bipartite quantum states and for measurements involving local orthonormal hermitian operators bases (LOOs) or generalized measurements based on informationally complete positive operator valued measures of the recently introduced $(N,M)$-type ($(N,M)$-POVMs) \cite{NMPOVM}. It is shown that symmetry properties of $(N,M)$-POVMs imply that sufficient conditions for bipartite entanglement detection exhibit peculiar scaling properties relating different equally efficient local entanglement detection scenarios. For correlation-matrix based bipartite local entanglement detection, for example, this has the consequence that LOOs and all informationally complete $(N,M)$-POVMs are equally powerful. With the help of a hit-and-run Monte-Carlo algorithm the effectiveness of local entanglement detection of typical bipartite quantum states is explored numerically. For this purpose Euclidean volume ratios between locally detectable entangled states and all bipartite quantum states are determined.
Toward Rhetorically Infused Methods for Relational Network Modeling: The Visualization of Agency in Seismic Risk Visuals

ABSTRACTABSTRACTThis article presents a pilot study in agentive modeling, a mixed-methods approach for visualizing networked models of agency. The study assesses technical and public seismic risk visuals from the websites of key organizations concerned with seismic activity. Preliminary findings indicate the need for visuals that stage more complex networks in order to create greater opportunities for engagement and danger-reducing action.KEYWORDS: Agencydata visualizationnetwork modelingrisk communicationearthquakes Disclosure statementNo potential conflict of interest was reported by the author.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/10572252.2023.2216729.Additional informationNotes on contributorsDanielle DeVastoDanielle DeVasto is an assistant professor in the Department of Writing at Grand Valley State University. Her research interests reside at the intersections of visual rhetoric, science communication, and uncertainty. Her work has been published in Communication Design Quarterly, Community Literacy Journal, Present Tense, and Social Epistemology.
A Survey of QUIC-Based Network Traffic Identification

With the QUIC (Quick UDP Internet Connection) protocol recognized by the Internet Engineering Task Force as the core protocol of HTTP/3, network traffic based on the QUIC protocol (also known as “QUIC traffic”) will become one of the primary traffics on the Internet. Network administrators use QUIC traffic identification as the foundation for network management. Numerous studies on QUIC traffic identification and application are already underway with the goal of assisting network operators, and pertinent results are beginning to emerge. We evaluate the topic of QUIC traffic identification to help future researchers rapidly grasp the research frontier of QUIC traffic identification and to summarize the present research and understand the obstacles in the field.
Analyzing Tamil News Tweets in the Context of Topic Identification

This paper introduces a Tamil News dataset extracted from Tamil tweets. First, a keyword-based distant supervision technique is followed to label the topic of the tweets, and an extensive corpus analysis is provided. Next, a binary classification approach is used to identify the topic of a given tweet. The paper then presents results from multiple traditional baseline machine learning models. Finally, the limitations of the work are discussed.
Improving White Blood Cell Morphology Education Through Standardized Questions

Medical laboratory scientists routinely evaluate blood smears in the clinical laboratory. A high level of skill in white blood cell morphology is required to do the task quickly and accurately. Educational approaches to teaching white blood cell morphology consist of showing examples of cells to students and allowing them to practice using trial and error to hone their skills. Student progress with this traditional approach is often quite slow. In order to expedite the learning process, we created a series of questions that can guide morphological identification; these questions focus on the shape and description of the nucleus, cytoplasm, granules and cell overall. We hypothesized that white blood cells can be accurately and uniformly identified using the criteria we’ve outlined. We confirmed the accuracy of this approach to white blood cell identification using a random forest classifier algorithm on a data set of 3600 cells that we had previously categorized. Our approach has the potential to standardize white blood cell morphology training in undergraduate medical laboratory scientist programs, and could help improve the consistency of manual differential results between hospital laboratories. This work is funded by a Teaching Development Grant from the Brigham Young University College of Life Sciences This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
Digital image denoising by partial differential equation based on P-M model and its fuzzy evaluation method system

Aiming at the problems of storage, batch migration and centralized processing of visual digital images of infrared imaging products, this paper takes digital image noise reduction as the main research object and starts with the concept of image partial differential equation processing. Based on the development history, advantages, practicability and operability of digital image processing by partial differential equation, it is concluded that digital image processing technology based on P-M model method is more suitable for modern image processing, and also broadens and improves the basic algorithm of digital image processing in the past. On this basis, the image quality is evaluated by using the fuzzy comprehensive evaluation theory based on analytic hierarchy process. The results show that the optimized processing system can screen the advantages and disadvantages of visual digital images of infrared imaging products and provide technical support.
Introduction

Rapid developments of machine learning technologies such as deep learning and federated learning have nowadays affected everyone of us. On one hand, a large variety of machine learning models are used in all kinds of applications including finance, healthcare, public transportation, etc., reforming our lives in a unprecedentedly profound manner. On the other hand, the wide applicability of these machine learning models calls for appropriate managements of these models to ensure their use comply with legislation rules and ethics concerning privacy, fairness, and well-being, etc.
Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought

Language instructions and demonstrations are two natural ways for users to teach robots personalized tasks. Recent progress in Large Language Models (LLMs) has shown impressive performance in translating language instructions into code for robotic tasks. However, translating demonstrations into task code continues to be a challenge due to the length and complexity of both demonstrations and code, making learning a direct mapping intractable. This paper presents Demo2Code, a novel framework that generates robot task code from demonstrations via an extended chain-of-thought and defines a common latent specification to connect the two. Our framework employs a robust two-stage process: (1) a recursive summarization technique that condenses demonstrations into concise specifications, and (2) a code synthesis approach that expands each function recursively from the generated specifications. We conduct extensive evaluation on various robot task benchmarks, including a novel game benchmark Robotouille, designed to simulate diverse cooking tasks in a kitchen environment. The project's website is available at https://portal-cornell.github.io/demo2code/