Reviewerly/exHarmony huggingface dataset:
COLONIZERS IN THE NEIGHBORHOOD: A CONTENT ANALYSIS OF NEXTDOOR USERS’ “POSTRACIAL” SURVEILLANCE

Today, colonizing the neighborhood is more accessible to the everyday individual than ever before with the widespread accessibility and popularity of hyper-local digital platforms like Nextdoor. Our project investigates how individuals use these platforms to better understand how their colonial practices are reshaped and perpetuated in the era of the networked smart home. Through a content analysis of user-generated posts in West Philadelphia, we find that users rarely engage with explicitly racist language or topics. Rather than interpret this finding as an indication that Nextdoor users are not racist, or that the platform’s content moderation strategies prevent racism, we argue that users, instead, rely on postracial practices to simultaneously normalize and obscure anti-Blackness. Drawing from Mukherjee et al.’s (2019) concept of “postrace,” we find that users commonly engage in three practices. First, users increasingly shifted away from explicit racial identifiers to more general "race-neutral" goals of safety and community. Second, users shied away from using problematic but non-explicit racialized language (e.g., "thugs"), opting to embed their racializing opinions in broader “policy” discussions. Third, even with increased attention on the harms of surveillance technologies on people of color, discussions around monitoring remained relatively consistent. These themes reflect postracial racism as yet another iteration of colonization in the neighborhood, entrenching essentializing tactics, perpetuating carceral logics, and developing and perfecting surveillance through the site of Blackness.
Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement

As the demand for high quality and high resolution in real-time rendering grows, superresolution is on its way to becoming a necessary component in modern real-time rendering applications (e.g., video games). The superresolution technique allows graphic applications to save computational costs by rendering at a lower resolution and reconstructing a high-resolution result. Nvidia introduced DLSS to the market as the first superresolution application in 2020, and NSRR was published on Siggraph the same year. Each of these approaches has shown powerful capabilities and is well suited to the needs of the industrial sector. In this paper, the authors propose the optimization potential of superresolution algorithms by introducing feature enhancement and feature caching modules and attempt to improve the current algorithms.
DEVELOPMENT OF A FRAMEWORK AND A SOFTWARE TOOL FOR CONSTRUCTING AN AUTONOMOUS DISTRIBUTED SYSTEM

The purpose of this study is to propose a concept of framework for constructing an autonomous distributed system, and to evaluate effectiveness of proposed idea by developing software tools based on the concept. In the framework of this study, autonomous distributed system consists of a set of agents which is autonomous processing elements having identical data and identical goal of performance. Each agent preforms decision procedure to achieve its goal with exchanging messages and informations each other on computing environment in which several computers are connected by information link. To improve flexibility of an autonomous distributed system, the concept of transportation of agent that agent can be dynamically moved on hardware environment and the concept of dynamic extension of agent that function of agent can be dynamically extended by merging two agents are introduced in this study. And to realize the autonomous distributed system based on the concepts, software tools have been developed. These software tools are coded by C language and implemented into the computer system that several UNIX based EWS is connected by information link.
Managing Accounts and Security

As data becomes more and more important for the organization and with the sharp rise in cybercrime, data security is a major concern for every enterprise. Now Snowflake believes in continuous data protection (CDP) by providing a set of features that help protect data stored in Snowflake at every stage within the data life cycle. Another important aspect to understand is user and account management. Snowflake supports role-based access control, which forbids any privilege assignment directly to users. Instead, here privileges are assigned to roles that are assigned to users.
Formación entre pares. Experiencia digital sincrónica en tiempos de pandemia

Background: COVID-19 impacted all areas of our society, and we took advantage of new technologies such as telemedicine to deliver information. Peer education is another tool that can be used.
Why Stories Matter for Representation, Action, and Collectivization

In this essay, we reflect on our storytelling experiences and processes at FemLab and focus on what storytelling can achieve for a project that operates within a similar framework. We explore the role of stories as devices to document concepts, ideas, and experiences, and argue for its importance to build not just agency, but also present different imaginations and vocabularies of people’s lived realities. Through an examination of different project outputs, we reflect upon the method of storytelling, thinking of it as a process, and a mechanism through which these different perspectives are documented, while at the same time, reflecting on its meaning for us as storytellers and translators of stories.
Oblivious Transfer with Constant Computational Overhead

The computational overhead of a cryptographic task is the asymptotic ratio between the computational cost of securely realizing the task and that of realizing the task with no security at all. Ishai, Kushilevitz, Ostrovsky, and Sahai (STOC 2008) showed that secure two-party computation of Boolean circuits can be realized with constant computational overhead, independent of the desired level of security, assuming the existence of an oblivious transfer (OT) protocol and a local pseudorandom generator (PRG). However, this only applies to the case of semi-honest parties. A central open question in the area is the possibility of a similar result for malicious parties. This question is open even for the simpler task of securely realizing many instances of a constant-size function, such as OT of bits. We settle the question in the affirmative for the case of OT, assuming: (1) a standard OT protocol, (2) a slightly stronger “correlation-robust" variant of a local PRG, and (3) a standard sparse variant of the Learning Parity with Noise (LPN) assumption. An optimized version of our construction requires fewer than 100 bit operations per party per bit-OT. For 128-bit security, this improves over the best previous protocols by 1–2 orders of magnitude. We achieve this by constructing a constant-overhead pseudorandom correlation generator (PCG) for the bit-OT correlation. Such a PCG generates N pseudorandom instances of bit-OT by locally expanding short, correlated seeds. As a result, we get an end-to-end protocol for generating N pseudorandom instances of bit-OT with o(N) communication, O(N) computation, and security that scales sub-exponentially with N. Finally, we present applications of our main result to realizing other secure computation tasks with constant computational overhead. These include protocols for general circuits with a relaxed notion of security against malicious parties, protocols for realizing N instances of natural constant-size functions, and reducing the main open question to a potentially simpler question about fault-tolerant computation.
FORECASTING RAINFALL WITH MACHINE LEARNING

Rainfall is one of the most destructive natural disasters and is extremely difficult to model. As a result, a numerical analysis model based on a hydraulic theory and a machine learning algorithm were combined to produce a classification-based real-time rainfall prediction for the ground truth model. Using a two- dimensional inundation model and the environmental protection agency's storm model, the rainfall dataset is built in advance for various rainfall scenarios. The rainfall depth data for each map grid are divided by year based on the average rainfall prediction based on ground truth. If the observed rainfall is entered, a model is built to predict the representative cumulative volume. Rainfall-accuracy based on the actual situation in the proposed ADA SVM, ADA NB, MLP, and J48 algorithm. Keywords: ADA SVM,ADA NB,MLP, AND J48 ALGORITHM
VEHICLE BREAKDOWN ASSISTANCE MANAGEMENT SYSTEM

Daily travel has become a common occurrence for everyone. We utilise many sorts of vehicles for transportation. A machine is not supposed to last forever, and due to regular use and testing under varied situations, it is bound to experience malfunctions of some form. Several malfunctions can be fixed on the spot by the user. When a vehicle breaks down on the road, many individuals have trouble finding help. These issues served as the impetus for the creation of this project, which would assist those in need when their vehicle breaks down on a road. The goal is to create a Django application that will enable users to access nearby resources and request assistance through the application. Keywords :malfunctions, Django application, assistance
Credit Scoring Model Construction Based On LinkedIn Social Media Data

In the credit acceptance process, the financial institutions analyze the borrowers' creditworthiness through their demographic data based on the 5C principle; character, capacity, conditions, capital, and collateral.However, the legacy credit scoring methods have drawbacks, including not having an excellent credit reputation as it is limited to the structural nature of demographic data.We construct a credit scoring model by combining the demographic element and adding two social media elements; content and network.The content considers creditworthiness by assessing borrowers' posts, which consist of opinions and conversations on social media.In comparison, the network considers borrowers' connectivity to their social community.The paper proposes a new credit scoring model better to represent the quality of borrowers' characteristics and behavior.The data is collected from LinkedIn, which is suitable to represent the professional network.The proposed model has been verified through expert judgment, including the credit providers, and has been simulated through a machine learning approach to automate credit acceptance decisions.
Applications of Internet of things in Increasing the Business Efficiency: An Empirical Study

The Internet of Things (IoT) has arisen as a potent technology that can empower enterprises to augment their efficacy by automating sundry procedures, optimizing the utilization of resources, and refining their capacity for decision-making. The aim of this investigation is to scrutinize the utilization of IoT in augmenting the efficacy of commercial endeavours while also determining the factors that impact the assimilation of IoT within organizations. An empirical study was conducted on different industries. The research suggests that the Internet of Things (IoT) possesses a substantial affirmative impact on business efficacy. This is due to the capability of organizations to accumulate data in real-time from diverse sources, examine the information, and make knowledgeable resolutions founded on the received insights. The study also divulged that the integration of IoT is influenced by multiple aspects, such as the perceived usefulness, perceived ease of use, and organizational preparedness for change. The findings of this study have important implications for businesses that are considering adopting IoT to increase their efficiency. By understanding the factors that affect the adoption of IoT, organizations can develop effective strategies for implementing this technology and reap the benefits of increased efficiency and productivity.
Effects of a Technology-Supported Decision, Reflection, and Interaction Approach on Nursing Students’ Learning Achievement and Self-Efficacy in Professional Training: A Pilot Study

In professional training, it is important to provide students with opportunities to make judgments on practical cases. However, most training courses are conducted in a one-to-many teaching mode, and it is not easy to consider the needs of individual students. In this study, a technology-supported Decision, Reflection, and Interaction (DRI)-based professional training approach is proposed to cope with this problem for those courses aiming at fostering students’ competence in making correct judgments when facing real cases. To verify the effectiveness of the proposed method, an experiment was conducted. Two classes of 38 students from a nursing school were the participants. One class was an experimental group using the DRI-based professional training approach, and the other class was the control group using the conventional technology-assisted training approach. The experimental results showed that applying the proposed approach significantly improved the students’ learning achievement and self-efficacy more than the conventional technology-assisted approach. In addition, based on the interview results, the students generally believed that learning through the DRI-based professional training approach benefited them from several perspectives, including “increasing the value of activities”, “enhancing the planning and expensive capacity of conspicuous approaches”, “promoting decision-making”, “improving learning reflection”, and “providing students with personalized interaction”.
Android Based Application for Searching Indekos In Pasar Minggu Jakarta South With Location Based Service (LBS) Method

Often the availability of the number of boarding houses is not comparable to the number of boarding house seekers, so boarding seekers must find information more quickly in order to get a place to live according to what they want. Boarding house owners promote their boarding houses in a conventional way, namely by word of mouth, distributing brochures, or posting announcements in public places. Based on these problems, an Android-based boarding house search application is needed, an application system that can be a place for out-of-towners who feel confused in finding boarding houses that suit their wishes, and can help boarding owners promote their boarding houses online via a smartphone based on android. The research objective was to design a boarding house search application in Pasar Minggu, South Jakarta, the method used was the waterfall methodology, which is a system method that begins with the requirements analysis stage, then system design, implementation of application development, and users are carried out. This application system was developed using the Java programming language and utilizing Location Based Services and Google Maps. The results of the research resulted in a boarding house search application that makes it easier for users to find boarding houses according to their needs and also this application can provide information about boarding facilities and this application can also make it easier for boarding house owners to publish their boarding houses.
A Method for Complex Question-Answering over Knowledge Graph

Knowledge Graph Question-Answering (KGQA) has gained popularity as an effective approach for information retrieval systems. However, answering complex questions involving multiple topic entities and multi-hop relations presents a significant challenge for model training. Moreover, existing KGQA models face difficulties in extracting constraint information from complex questions, leading to reduced accuracy. To overcome these challenges, we propose a three-part pipelined framework comprising question decomposition, constraint extraction, and question reasoning. Our approach employs a novel question decomposition model that uses dual encoders and attention mechanisms to enhance question representation. We define temporal, spatial, and numerical constraint types and propose a constraint extraction model to mitigate the impact of constraint interference on downstream question reasoning. The question reasoning model uses beam search to reduce computational effort and enhance exploration, facilitating the identification of the optimal path. Experimental results on the ComplexWebQuestions dataset demonstrate the efficacy of our proposed model, achieving an F1 score of 72.0% and highlighting the effectiveness of our approach in decomposing complex questions into simple subsets and improving the accuracy of question reasoning.
Onscreen/OffscreenBy Constantine V. Nakassis (Ed.). Toronto: University of Toronto Press, 2023. Pp. xviii +382

Journal of Linguistic AnthropologyVolume 33, Issue 1 p. 105-108 BOOK REVIEW Onscreen/Offscreen By Constantine V. Nakassis (Ed.). Toronto: University of Toronto Press, 2023. Pp. xviii +382 Spencer C. Chen, Corresponding Author Spencer C. Chen [email protected] orcid.org/0000-0001-9217-1365 University of California, Los Angeles Email: [email protected]Search for more papers by this author Spencer C. Chen, Corresponding Author Spencer C. Chen [email protected] orcid.org/0000-0001-9217-1365 University of California, Los Angeles Email: [email protected]Search for more papers by this author First published: 20 April 2023 https://doi.org/10.1111/jola.12390Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume33, Issue1May 2023Pages 105-108 RelatedInformation
Our future in space: the physical and virtual opening-up of parliaments to publics

Parliaments are physical symbols of nationhood and democracy. Public access to these spaces is often strictly regulated, yet it remains highly influential to public experiences of parliament (and their engagement with it). Drawing on data collected for the Inter-Parliamentary Union’s 2022 Global Parliamentary Report, this article discusses ways in which parliamentary ‘space’ can be utilised to encourage public engagement. This encompasses the effective use of physical space, virtual reality and augmented reality for the purpose of public engagement. In doing so, we show the most important and effective strategy for (re)using, and opening up, parliamentary spaces: the complementary use of physical and virtual methods in not only bringing publics to parliament, but also bringing parliament to publics.
Fastesy Instant Delivery Application

Android app development can help a firm reach out to new customers, increase revenue, and generate a following of devoted clients. The project's goal is to create a single app that offers a variety of services to the user. It will partner with other retailers. For all products offered in the app, there won't be any involvement from a third party seller in the process. Customers will have direct access to the goods manufacturers. As a result, the end user will find the solution to be affordable. The main goal of this project is to provide the user with a straightforward yet effective solution that will enable them to quickly and effectively meet their demands with the best product the manufacturer has to offer.
The Regulatory Challenges of Digital Therapeutics

Digital Therapeutics remain in a legal grey area as a clear and unique definition is not provided by the European Union nor by EU Member State legislation. Although they are often considered as, or similar to, medical devices, this approach may be inadequate and misleading as it tends to mistake the device with the therapy that it delivers. The evolving and expanding sector of digital therapies mandates the adoption of new rules, hopefully at European Union level, to properly address the challenges of digital health technologies both from the legal and the regulatory perspective.
Use of AEE Technology for Feedback Generating on Written Text: A Revolution in Academic Writing Domain

AEE, a computer-generated grading system that delivers written comments on the quality of written texts, is the topic of this book. It is already possible to find both commercial and non-commercial AEE applications that feature a scoring engine that provides automatic scores based on techniques like artificial intelligence (AI), natural language processing (NLP), and latent semantic analysis. AEE scoring engines are taught using human ratings, and AEE is being used for summative assessments in tests such as the TOEFL and the Graduate Management Admissions Test, in combination with human ratings. In addition to spoken feedback, many AEE programmes also incorporate written feedback in the form of comments and corrections. Using written AEE feedback to give formative input in writing courses has grown in popularity in recent years, especially in American schools at the secondary and postsecondary levels. The book is a follow-up to Automated Essay Scoring: A Cross-Disciplinary Approach, which was published in 2003 and is the subject of this volume.
Tackling Credential Abuse Together

Despite long-ago predictions [1] that other user-authentication technologies would replace passwords, passwords remain pervasive and are likely to continue to be so [2]. This talk will describe our research on methods to tackle three key ingredients of account takeovers for password-protected accounts today: (i) site database breaches, which is the largest source of stolen passwords for internet sites; (ii) the tendency of users to reuse the same or similar passwords across sites; and (iii) credential stuffing, in which attackers submit breached credentials for one site in login attempts for the same accounts at another. A central theme of our research is that these factors are most effectively addressed by coordinating across sites, in contrast to today's practice of each site defending alone. We summarize algorithms to drive this coordination; the efficacy and security of our proposals; and the scalability of our designs through working implementations.
Current issues and problems of legal training of specialists in non-legal specialties

The main purpose of the article is to investigate the role and purpose of legal education in the training of specialists of other special, separate from legal persons; to develop methods of teaching legal disciplines to students of nonlegal professions, as well as to find out the need and influence of legal knowledge and other areas and branches. For a detailed study of this topic, the following methods were used: dialectical method, formal-legal method, logical method, method of induction and deduction, method of system analysis, logical method, and others. The results of the study – definition, and analysis of the main problems of legal training of future specialists of non-legal professions; the state of teaching legal disciplines to students of non-legal specialties was investigated and clarified; the existing and proposed new methods of teaching nonlegal students were analyzed; the impact of legal knowledge on other areas of activity was clarified. The practical significance of this article consists in the development of approaches and methods of teaching legal disciplines to nonlegal students for their development in their chosen profession. The scientific novelty of this article consists in the analysis of existing and developed new methods and techniques of teaching students of non-legal specialties. Conclusions: within this article, the author has made an attempt to develop and research methods and approaches for legal training of specialists of non-legal professions for future development and improvement of various spheres of activity, and also to find out how knowledge of the law can affect the formation of the student as a future professional in the chosen industry.