Reviewerly/exHarmony huggingface dataset:
Restaurant Recommender System Based On Sentiment Analysis

Sentiment analysis is used to design a recommender system many different sectors, including all the restaurant and food sectors. The majority of these recommenders, however, rely on fixed information like cuisine, price, and service quality. By determining the users’ dietary preferences and examining their choices, these systems produce customised recommendations. The usage of a scenario suggestion system is advised since it may identify people’s meal preferences based on their comments and propose restaurants that cater to those preferences. Food names are gathered and categorised using user comments to evaluate user sentiment. The constancy of the choices suggests a nearby restaurant that really is open for business. A data collection contains different user reviews. The platform’s precision, recalls, and f-measure are evaluated using three alternative scenarios top-1, top-3, and top-5. With such a 92.8% precision rate, the proposed technique is expected to provide users with exceptionally accurate recommendations. In proposed project we are including more features like the waiting time, main course, reachability, and deserts. We considered data on restaurants and over 2990 reviews on these restaurants as a dataset and we will be generating the data plot using LSTM and GRU algorithms.
Knowledge Based Perceptions of Chatbots in Healthcare

This order to gauge a happy life, it is essential to have a healthy body. However, it might be difficult to get a doctor’s appointment for any type of health condition. The objective is to use AI to build a health chatbot that can identify illnesses and provide basic information about them. before calling a physician. It is possible to reduce healthcare costs and improve access to medical information by using a medical chatbot. Computer programs known as chatbots interact with users orally. The chatbot stores the data to identify the sentence keywords, select a question, and respond to the query. Each sentence’s score is determined from the given input sentence, and other similar sentences will be found.
Research on Forward Design Process of Network Security Protection Design for Instrument and Control System in Nuclear Power Plant

Network security protection design for instrument and control (I&C) system in nuclear power plant is an important measure to ensure safe and stable operation of instrument and control system in nuclear power plant. Relevant national departments have put forward a number of regulatory requirements for network security design of instrument and control system, including the general principles of safety protection of power monitoring system proposed by Energy Bureau, Guidelines for grading of classified protection of cyber security by Ministry of Public Security and the technical policy requirements of network security of nuclear power plant proposed by China Nuclear and Radiation Safety Center. How to coordinate network security protection measures to meet multiple regulatory requirements is an important issue in the design of nuclear power plant network security protection. This paper introduces a forward design process of network security that coordinates and considers the above regulatory requirements, and analyzes and explains the design elements and reference standard for the design process, such as zoning and domain design, grading of classified protection, and critical digital asset classification protection.
Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis (CAD) models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods (Ⅰ. Parameter values are Gauss filter 𝜎=3, Laplacian filter 𝜎=0.6 and 𝛼=0.6; Ⅱ. Parameter values are Gauss filter 𝜎=1, Laplacian filter 𝜎=2 and 𝛼=2). In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.
Studi Eksperimen terhadap Model Pembelajaran PAI MI dengan Quantum Learning di SDN 1 Jati Datar

This study aims to find out (1) How is the application of the PAI learning model with Quantum Learning at SDN 1 Jatidatar. (2) How much student learning outcomes in PAI subjects withQuantum Learning at SDN 1 Jatidatar before applying the Quantum Learning learning model (3) How much student learning outcomes in PAI subjects withQuantum Learning at SDN 1 Jatidatar after applying the Quantum Learning learning model (3) How to increase student results in PAI subjects byQuantum Learning at SDN 1 Jatidatar. This research is experimental research using The One-Group pretest-posttest design involving the dependent variable, namely the learning outcomes of PAI, and the independent variable, namely the learning model. Quantum Learning. The population in this study were all fourth-grade students at SDN 1 Jatidatar in the odd semester of the 2018/2019 school year with a sample of 24 students. The research instrument used was the PAI learning achievement test. The results of the descriptive analysis show the average value of PAI learning outcomes for fourth-grade students before being taught using the learning modelQuantum Learning of 10.66 and a standard deviation of 2.74 and after being taught using a learning modelQuantum Learning of 15.54 and a standard deviation of 3.48 with an average Normalized Gain value of 0.32 in the medium category. From the results of the analysis, it can be concluded that the learning model Quantum Learning can improve PAI learning outcomes for class IV students at SDN 1 Jatidatar.
Modeling, evaluating, and orchestrating heterogeneous environmental leverages for large-scale data center management

Data centers are very energy-intensive facilities that can generate various environmental impacts. Numerous energy, power, and environmental leverages exist and can help cloud providers and data center managers to reduce some of these impacts. But dealing with such heterogeneous leverages can be a challenging task that requires some support from a dedicated framework. This article presents a new approach for modeling, evaluating, and orchestrating a large set of technological and logistical leverages. Our framework is based on leverages modeling and Gantt chart leverages mapping. First experimental results based on selected scenarios show the pertinence of the proposed approach in terms of management facilities and potential impacts reduction.
Combining Higher-Order Logic with Set Theory Formalizations

The Isabelle Higher-order Tarski-Grothendieck object logic includes in its foundations both higher-order logic and set theory, which allows importing the libraries of Isabelle/HOL and Isabelle/Mizar. The two libraries, however, define all the basic concepts independently, which means that the results in the two are disconnected. In this paper, we align significant parts of these two libraries, by defining isomorphisms between their concepts, including the real numbers and algebraic structures. The isomorphisms allow us to transport theorems between the foundations and use the results from the libraries simultaneously.
Quadratic covariance‐constrained filtering for linear and non‐linear systems with non‐Gaussian noises

Abstract This study considers a robust quadratic covariance‐constrained filtering problem for discrete time‐varying linear and non‐linear dynamic systems with non‐Gaussian noises. Non‐Gaussian noises are presumed to be unknown, bounded, and limited in a specified ellipsoidal set. In this approach, first, a general standard linear form of the filter is introduced for state estimation in linear dynamic systems. The filter gain is obtained by minimizing the upper bound of the estimation error's covariance matrix. The Lyapunov theory demonstrates the stability of this filter. Second, we extend the proposed filtering approach to non‐linear dynamic systems that are considered as a combination of linear and non‐linear terms. The Lipschitz‐like condition is assumed for the non‐linear part. A new filter structure is proposed in this case and the filter gain is obtained by the same idea to minimize the upper bound of the error's covariance matrix. Finally, four numerical examples are presented to signify the effectiveness and performance of the proposed filters for linear and non‐linear systems.
WOMT: Wasserstein Distribution Based Minimization of False Positives in Breast Tumor Classification Using Deep Learning

Women’s life suffered and killed by invasive cancerous tumors is the most frequently highlighted header in many newsletters since 2010. There can be many. By their nature, invasive tumors spread from tissue to tissue and abduct themselves to cause new tumors. In many of the human biological parts, CT scan has given an objective approach to the early and successful detection of cancerous tumors. However, there were cases where diagnosis with CT scan images failed, resulting in many false positives. In economically backward countries, these false positives raised the notifying concern of women who cannot afford multiple diagnostic tests. Due to changes in biological metabolism, the growth of breast fat in women to considerably abnormal size is the main cause of false positives. In many of the images under study, this huge thick breast fat layer led to the rise of misclassification rate with false positives. Rendering societal help requires a precise mechanism that can reduce the false positives at an initial diagnosis. The proposed method introduces a novel constraint-based algorithm to classify a mammogram image as cancerous, aiming to reduce false positives. The proposed deep learning algorithm WOMT is trained with Wasserstein Distribution constraints that are derived from the mass transfer of cancerous patches to non-cancerous patches. The experimental simulations with a deep learning model trained with these constraints resulted in reduced false positives.
Decoding Reinforcement Learning for newcomers

The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.
Comprehensive Readability Assessment of Scientific Learning Resources

Readability is the measure of how easier a piece of text is. Readability assessment plays a crucial role in facilitating content writers and proofreaders to receive guidance about how easy or difficult a piece of text is. In literature, classical readability, lexical measures, and deep learning based model have been proposed to assess the text readability. However, readability assessment using machine and deep learning is a data-intensive task, which requires a reasonable-sized dataset for accurate assessment. While several datasets, readability indices (RI) and assessment models have been proposed for military agencies manuals, health documents, and early educational materials, studies related to the readability assessment of computer science literature are limited. To address this gap, we have contributed Computer science (CS) literature dataset <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AGREE</b> , comprising 42,850 learning resources(LR). We assessed the readability of learning objects(LOs) pertaining to domains of Computer Science (CS), machine learning (ML), software engineering (SE), and natural language processing (NLP). LOs consists of research papers, lecture notes and Wikipedia content of topics list of learning repositories for CS, NLP, SE and ML in English Language. From the statistically significant sample of LOs two annotators manually annotated LO’s text difficulty and established gold standard. Text readability was computed using 14 readability Indices (RI) and 12 lexical measures (LM). RI were ensembled, and readability measures were used to train the model for readability assessment. The results indicate that the extra tree classifier performs well on the AGREE dataset, exhibiting high accuracy, F1 score, and efficiency. We observed that there is no consensus among readability measures for shorter texts, but as the length of the text increases, the accuracy improves. The AGREE and SELRD datasets, along with the associated readability measures, provide a novel contribution to the field. They can be used to train deep learning models for readability assessment, develop recommender systems, and assist in curriculum planning within the domain of Computer Science. In the future, we plan to scale AGREE by adding more LOs and adding multimedia LOs. In addition, we would explore the use of deep learning methods for improved readability assessment.
Composable Anonymous Proof-of-Location With User-Controlled Offline Access

A proof-of-location ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol$ </tex-math></inline-formula> ) is a digital credential issued to a user after proving their location to an issuer. The user can use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol$ </tex-math></inline-formula> at a later time to prove to a verifier that they have been present at a claimed location. A secure Proof-of-Location (POL) system requires that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol\text{s}$ </tex-math></inline-formula> be unforgeable and non-transferable to other users. POL systems can be used to provide fine-grained authentication and authorization and must ensure the privacy of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol$ </tex-math></inline-formula> owner against the issuer and the verifier while allowing efficient presentation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol\text{s}$ </tex-math></inline-formula> combined with other credentials when needed. Efficiency is in terms of communication overhead in user-verifier POL sessions, which has particular significance in high-volume <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol$ </tex-math></inline-formula> verification scenarios. We first propose a POL system that (i) is provably secure in a simulation-based framework, allowing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol$ </tex-math></inline-formula> to be securely used with other credentials, and (ii) provides anonymity against the issuer and the verifier. We then extend the system to allow <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pol\text{s}$ </tex-math></inline-formula> to be stored on a public distributed ledger system and selectively be presented to the verifiers by the user. This is the first POL system that satisfies the above properties. We implement POL algorithms on a mobile phone and present our experimental results showing the practicality of the system. Our proposed scheme is highly scalable compared to existing systems, reducing the user-verifier POL communication overhead by up to a factor of 94.
Merging the gap between physical and virtual realities: A pilot study on the role designed props play in creating a more immersive virtual experience

Virtual Reality (VR) technologies have gained momentum in various fields, including art and design, where it plays a significant role in modelling, prototyping, and testing. However, most VR headsets offer only visual and auditory inputs, leaving other senses unintrigued. This study has two objectives: First, to investigate whether a physical prop can create a more immersive experience. Second, to establish the relationship between the physical prop and memory retention of the virtual experience. Two VR rollercoaster conditions were created: one where participants simply sat on a chair, and another where they were secured in with a harness. Various qualitative and quantitative data collection methods were used, including physiological, observational, and self-reporting techniques. Biosensors were used to measure heart rate, breathing rate, skin conductance and brain activity, in addition to a survey. Early results confirm a positive correlation, with varying degrees, between the use of props in immersion and memory retention.
Expressive Authorization Policies using Computation Principals

In authorization logics, it is natural to treat computations as principals, since systems need to decide how much authority to give computations when they execute. But unlike other kinds of principals, the authority that we want to give to computations might be based on properties of the computation itself, such as whether the computation is differentially private, or whether the computation is memory safe. Existing authorization logics do not treat computation principals specially. Instead, they identify computation principals using a brittle hash-based naming scheme: minor changes to the code produce a distinct principal, even if the new computation is equivalent to the original one. Moreover, existing authorization logics typically treat computation principals as "black boxes," leaving any reasoning about the structure, semantics, or other properties of the computation out of the logic. We introduce Coal, a novel programming-language calculus that embeds an authorization logic in its type system via the Curry- Howard isomorphism. A key innovation of Coal is computation principals: computations that can be treated like other principals but also allow reasoning about the computation itself. Critically, Coal allows equivalent computations to be treated as equivalent principals, avoiding the brittleness of identity-based approaches to computation principals. Coal enables us to cleanly express fine-grained access control policies that are dependent on the structure and semantics of computations, such as expressing trust in all computations that are analyzed to be differentially private by any program analyzer that has been verified correct.
A Framework for Privacy-Preserving White-Box Anomaly Detection using a Lattice-Based Access Control

Privacy concerns are amongst the core issues that will constrain the adoption of distributed anomaly detection. Indeed, when outsourcing anomaly detection, i.e. with a party other than the data owner running the detection, confidential or private aspects of the observed data may need protection. Some privacy-enhancing function is usually employed. Because of the impact that this restriction causes in the creation of explainable alerts, finding mechanisms to balance the trade-off between privacy and usefulness has become increasingly important. Due to this motivation, in this paper, a privacy-preserving white-box anomaly detection framework is presented to facilitate matching the compatibility between service requirements and privacy restrictions of an user by using an access control based on a lattice of privacy protection levels. Our framework allows entities to verify these trade-offs by specifying required protection at the level of features. We evaluate the framework in a real-world scenario within the e-health setting. The results point out that it can generate interpretable alerts while protecting the confidentiality of the data.
Poster: Non-repudiable Secure Logging System for the Web

To resolve disputes between servicers providing web services and their users, non-repudiable evidence is crucial because it allows one party to dismiss the denial of facts or false allegations. We propose a logger that securely records web requests and responses in a Trusted Execution Environment (TEE) to generate non-repudiable evidence for web services, which we call LogNEWT: Logger for Non-rEpudiation of Web with TEE. LogNEWT solves security issues in deploying LibSEAL to practical web services, i.e., logger-bypassing, undefined user management, and complex logger verification. In addition, LogNEWT can be transparently deployed to the existing web services.