Reviewerly/exHarmony huggingface dataset:
Exploring the Efforts of IAN-BGRU Justifications in Food Recommender System and its User Preferences

There has been a general increase in the frequency with which people discuss diet, eating, and cooking. In actuality, there are far more dishes to choose from and ingredient combinations to try, and chefs (and even IBM supercomputers) create new dishes all the time. Many people find it difficult to keep up with the ever-evolving landscape of accessible foods and eating habits, which contributes to the rising prevalence of chronic diseases like obesity and diabetes. Our recipe recommendation engine thus not only offers tasty but also nutritious options for the consumer. The proposed approach designed our software for mobile because the proposed system envisions it being utilized in many different rooms and environments, including kitchens. Data processing, feature selection, and model training are the first three stages of the suggested procedure. Normalization and encoding are two components of data processing. As a criterion for feature selection, they employ inconsistency. After features are selected, IAN-BGRU is used to train the models. The proposed technique outperforms IAN and BGRU, two of the most used alternatives.
Motion Planning of 2D Wheeled Mobile Robot using Reinforcement Learning

This paper discusses new approaches to planning the movement of a mobile wheeled robot based on the developed new features using the reinforcement learning method. In particular, the problem of “following the leader” is solved, as a subclass of traffic planning problems, using a special simplified 2D environment. The 2D environment has been improved with the addition of dynamic obstacles in order to bring the environment closer to the real conditions of the agent’s functioning and complicate the training configuration to solve the problem of following the leader. Approaches to the configuration of input features to the neural network model are formulated. Neural network models have been obtained for planning the movement of a robotic platform based on training with reinforcement to prevent collisions with dynamic obstacles. The evaluation of the quality of these models was carried out, the efficiency of the methods was demonstrated and shortcomings were identified.
Unmasking the Threat: Detecting Cloaking Attacks in Facial Recognition

Individuals who are concerned about their privacy may choose to safeguard their online photographs by adding imperceptible pixel-level disturbances. This technique, known as “cloaking” is a form of clean-label poisoning attack. Manipulated data is fed to a facial recognition model for training while maintaining the original labels, potentially misleading the model, and slowly poisoning the model’s understanding of a person’s facial features. Consequently, the performance of the model is diminished for certain identities. By retraining a model to be robust against these disturbances, it is possible to avoid these attacks. However, the expenses associated with retraining or replacing a model may be prohibitive, especially since there is no assurance that the model will be robust against future attacks. Instead, we offer a method for identifying cloaked pictures and excluding them from training pipelines. Our method uses img2vec with a ResNet-18 model that has been pretrained for rapid convergence. Our approach achieved an accuracy of 97% across a wide variety of perturbation budgets, and it is also possible to reuse elements of an existing model to decrease the need to construct yet another model.
Security and Efficiency in Vehicle Insurance: A Blockchain-Based Solution

This research study presents a blockchain-based approach for improving efficiency and transparency in the vehicle insurance industry. By leveraging the Ethereum blockchain and Solidity smart contracts, a smart contract named “VehicleInsurance” is introduced to automate policy management and claims processing. The smart contract incorporates enums and structs to represent coverage levels, claim statuses, and policy details. It enables policy creation, renewal, cancellation, claim registration, and processing. Modifiers ensure policy validity, mappings store policy and claim data for efficient retrieval. Events notify stakeholders about policy actions and claim events. The smart contract is implemented and tested in Ethereum REMIX IDE platform. The smart contract deployed on Ethereum network is tested rigorously by creating various test scenarios. Overall, this smart contract demonstrates the potential of blockchain technology in revolutionizing the insurance industry. Benefits include improved customer experience, streamlined operations, cost reduction, and better fraud detection. By eliminating the need for intermediaries and providing a transparent and immutable system, it enhances trust between policy holders and insurance providers.
Environmental, Social, and Corporate Governance Perspectives in Development of Quantum Vector Databases Based on Grover’s Algorithm

Large language models have a big influence on spheres that require data analysis and information retrieval, one of those fields is Environmental, social, and corporate governance (ESG), a field that depends on analyzing and finding correlations in big amounts of varied data from multiple sources. This paper introduces a quantum computing approach using Grover’s algorithm to create a vector database that stores embeddings based on Controlled-S gates, with each embedding represented by a binary numerical value. The creation of these embeddings, is conducted by a classical computer, while the quantum computer handles the search process through the database. This method is very efficient in terms of qubits, enabling large amounts of data to be stored within a single quantum register. And the use of quantum computers has the potential to make querying this database quick and power-efficient. By making use of this approach, managers, investors and policymakers can make informed decisions based on a wider range of ESG data, ultimately promoting environmental sustainability, social responsibility, and good corporate governance
Resource Allocation Algorithms for Single, Cluster and Tired Virtual Machines

Data centers must provide sufficient resources for the smooth operation of applications hosted in them in conditions of variable workload. Resource management is a necessity for guarantee SLA (Service Level Agreements). This paper provides an overview of methods and algorithms for virtual machine allocation in cloud data centers based on the analysis of recent work. The advantages and disadvantages of existing approaches are considered, as well as the requirements that must be met by a universal method of hosting virtual machines that allows overcoming existing limitations. The cloud data center resource management process consists of the following steps: server monitoring, identification of overloaded/underloaded servers, virtual machine selection for migration, destination host selection. Two algorithms for virtual machine selection for migration and destination host selection are proposed. They are suitable for single, cluster and tired virtual machines. These algorithms allow to find an exact solution for the combined criterion of energy efficiency and SLA violation, which is on average 3.5 times better than the widely used FFD (First Fit Decreasing) and BFD (Best Fit Decreasing) heuristics.
Convergence Analysis of Accelerated Stochastic Gradient Descent Under the Growth Condition

We study the convergence of accelerated stochastic gradient descent (SGD) for strongly convex objectives under the growth condition, which states that the variance of stochastic gradient is bounded by a multiplicative part that grows with the full gradient and a constant additive part. Through the lens of the growth condition, we investigate four widely used accelerated methods: Nesterov’s accelerated method (NAM), robust momentum method (RMM), accelerated dual averaging method (DAM+), and implicit DAM+ (iDAM+). Although these methods are known to improve the convergence rate of SGD under the condition that the stochastic gradient has bounded variance, it is not well understood how their convergence rates are affected by the multiplicative noise. In this paper, we show that these methods all converge to a neighborhood of the optimum with accelerated convergence rates (compared with SGD), even under the growth condition. In particular, NAM, RMM, and iDAM+ enjoy acceleration only with a mild multiplicative noise, whereas DAM+ enjoys acceleration, even with a large multiplicative noise. Furthermore, we propose a generic tail-averaged scheme that allows the accelerated rates of DAM+ and iDAM+ to nearly attain the theoretical lower bound (up to a logarithmic factor in the variance term). We conduct numerical experiments to support our theoretical conclusions.
A Real Time Multiple Object Tracking in Videos using CNN Algorithm

Object tracking serves as a fundamental task in the field of computer vision and machine learning. This assignment involves accurately determining the positions of objects across consecutive frames in a video sequence. The objective of this undertaking is to develop a system for detecting and tracking objects using advanced computer vision techniques. The end result of this project is a functional tracking system that combines optical and modern infra-red technologies. This system finds applications in domains like unsupervised surveillance and is notably reliable, capable of functioning independently or seamlessly integrating into more extensive frameworks. The primary aim of this research work is to create an object tracking system using Python and OpenCV, with the intention of providing an efficient and easily accessible solution. The system allows for the tracking of objects from various sources, including uploaded videos and live webcam feeds. In real-world scenarios, such as monitoring traffic on highways, it is often necessary to track multiple objects simultaneously. By employing object tracking algorithms, this project facilitates the simultaneous tracking of numerous objects, such as cars on a highway, enabling the extraction of valuable traffic-related information
File Tracking System Using SAP Technology with Cloud Web Services

Institutional offerings of offers SAP courses in many modules and to varied students are conventional approach. But right now, the total admission process is organised but very time-consuming from the perspective of students as well as the college administration. To make this process more efficient and easier, the institute needed a solution according to the advancing technologies and methods. Right now, online administration processes are in a good position. Many institutes have converted their administration process to an online process. This proposed the idea of the SAP Portal for student convenience. A web portal proposed for the efficient management of SAP activities on a single point with ease in administration for college and dynamic handling provided to students as well. With the usage of latest technologies like ReactJS, Spring Boot, etc., college can convert the offline, time-consuming process to an online, time-saving process. The purpose of this project is to make a unique website totally fulfilling the needs of an administration portal and comprising all the aspects of SAP administration and processes.
Indoor Building Room Navigation Using Augmented Reality and SLAM Method (Case Study: Politeknik Negeri Malang)

This study built an indoor navigation application using augmented reality and SLAM algorithm. Navigation is used to find a way to some place, especially in a building. Because in some cases, a regular sign cannot give clear information about the place in an indoor room. GPS can be used as a tool to locate a place, but based on the previous research, there was an accuracy problem in finding the position of the indoor building room. This study uses SLAM method to map the real location into a virtual 3D environment so the room information can be shown with augmented reality. The prototype of the application was tested on the building of Politeknik Negeri Malang. It used to find each indoor room of two buildings. From the testing conducted, the accuracy of indoor building room navigation finding is 90% and can be concluded that SLAM method can be the solution to the accuracy problem of GPS to navigate in indoor building rooms.
Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint Reduction in India

By boosting productivity, cutting waste, and raising yields, Artificial Intelligence (AI) has the potential to revolutionise Indian agriculture. It is crucial to consider the complete life cycle of AI systems to reduce the carbon footprint of AI in agriculture. Though the impact of artificial intelligence (AI) is always assumed to be positive in reducing carbon emissions, the forecasting analysis based on the exponential smoothing model and life cycle assessment (LCA) predicts that AI will decrease carbon emission in agriculture by 2030. To move forward, some policy recommendations include promoting energy-efficient AI hardware, adoption of renewable energy, optimizing AI algorithms for energy efficiency, supporting precision agriculture (PA), and embracing circular economy practices. The way to achieve sustainable agriculture with the combination of smart agriculture is through Precision farming, which has the potential to transform Indian agriculture, enhance food security and help farmers adapt to climate change while increasing efficiency. Data-driven decision in crop management can lessen climate change effects and reduce vulnerability to extreme weather events. Overall, lowering the carbon footprint of AI in agriculture would necessitate a combination of legislative initiatives that support energy-saving technologies, renewable energy, and environmentally friendly farming methods.
Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding

In the industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named video multimethod assessment fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain consistent quality while still achieving high compression performance. First of all, in order to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of rate-distortion optimization (RDO) mechanism is adaptively computed according to rate-QP and quality-QP models. The experimental results show that the proposed PVC solution has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and the relevant benchmark, notably with around 10% bitrate saving on average.
Revealing Interesting If-Then Rules

One of the challenges of data mining is revealing the interesting if-then rules in the mined dataset. We present two methods for the automated discovery of unexpected, and, thereby, interesting rules within the set of all the if-then rules previously revealed in the dataset. The first method calculates, for each rule, the probability that the rule exists accidentally. The lower this probability, the more unexpected the rule is. The second method calculates the conditional probability of the event described by the rule, given the relevant more basic rules. Once again, the lower this conditional probability, the more unexpected the rule is. These two methods are independent and can be combined.
User needs and design features of sanitation digital solutions in Kawempe division, Kampala Uganda: a user centered design approach

Despite improvements in access to water and sanitation services globally, a significant population in Sub Saharan Africa has limited access to improved sanitation facilities. Furthermore, there is lack of a centralized digital platform for data exchange among stakeholders for sanitation services planning and provision in Uganda. A user centered design (UCD) approach was used to develop and pilot a one stop sanitation digital solutions (SaniDigS) center in Kawempe division, Kampala, Uganda. This involved three phases (1) understanding the sanitation status of the community which involved interviewing 1,844 household heads, (2) specifying the user needs of the proposed digital solution through stakeholder meetings and (3) Co designing of the innovations with potential users through stakeholder workshops. The quantitative data was visualized through the SaniDigS platform dashboard. The stakeholder meeting transcripts and notes were thematically analyzed to identify the user needs. The community user needs included an innovation that ensures confidentiality, is low cost and user friendly without internet connection. The service provider needed a sanitation digital solution that can market their sanitation products while, policy makers and planners needed comprehensive, real time data collection and sharing for trend analysis and informed decision making. The codesigned features of the SaniDigS informed by the user needs included: The SaniDigS Champion Application, SaniDigS platform dashboard, SaniDigS mobile application and the call center. The community in Kawempe showed need for subsidized sanitation products and we recommend further research to evaluate the effects of SaniDigS on planning, coordination, and access to sanitation services.
An optimization approach to supervised principal component analysis

Supervised dimensionality reduction has become an important theme in the last two decades. Despite the plethora of models and formulations, there is a lack of a simple model that aims to project the set of patterns into a space defined by the classes (or categories). We set up a model where each class is represented as a 1D subspace of the vector space formed by the features. Assuming the set of classes does not exceed the cardinality of the features, the model results in multi-class supervised learning in which the features of each class are projected into the class subspace. Class discrimination is guaranteed via the imposition of the orthogonality of the 1D class sub-spaces. The resulting optimization problem—formulated as the minimization of a sum of quadratic functions on a Stiefel manifold—while being non-convex (due to the constraints), has a structure for which we can identify when we have reached a global minimum. After formulating a version with standard inner products, we extend the formulation to a reproducing kernel Hilbert space and similarly to the kernel version. Comparisons with the multi-class Fisher discriminants and principal component analysis showcase the relative merits toward dimensionality reduction.
Distributed pseudospectral method for the trajectory optimization of an unmanned aerial vehicle based on a bilinear tangent

A new method for the trajectory optimization of an unmanned aerial vehicle (UAV) and a distributed pseudospectral method (DPM) based on a bilinear tangent are proposed. The problem of avoiding UAV collisions is investigated. The simulation results show that with the help of DPM it is possible to solve the problem of avoiding UAV obstacles. Compared with the pseudospectral method based on the penalty function, the computational efficiency can be increased by more than 5 times, which reduces the computational load. Keywords distributed pseudospectral method, bilinear tangent, trajectory optimization, avoidance strategy
ELV, ICT, IBMS, and Information Technology Works of the Hospital Building

Information and Communication Technologies (ICT) encompass services related to processing information (data, voice, video) and connecting devices. On the other hand, Extra Low Voltage (ELV) services focus on connectivity, security, safety, and automation. For instance, a nurse call system is employed to establish communication between nurses and patients. Extensive closed-circuit television (CCTV) solutions are essential for hospital security. An electronic private automatic branch exchange (EPABX) system enables easy two-way communication. Entertainment facilities, such as TV channels and videos, are provided for patients. Communication systems, such as intercoms and public address (PA) systems, are installed in hospitals. Integrated Building Management System (IBMS) automation controls aid in effectively and efficiently managing various electro-mechanical systems within the building. Creating an efficient IT or computer network is crucial in hospitals. This is necessary because the Hospital Information System (HIS), which encompasses the software used for operating the hospital, is employed across various locations and departments of the facility. The enclosed checklist provides the sequence of works related to ELV, ICT, IBMS, and IT, which shall be completed inside the building, aiding the site engineer to plan the start and end day of each activity along with the present status of that particular activity.
Comprehensive assessment methods are key to progress in deep learning

Bowers et al. eloquently describe issues with current deep neural network (DNN) models of vision, claiming that there are deficits both with the methods of assessment, and with the models themselves. I am in agreement with both these claims, but propose a different recipe to the one outlined in the target article for overcoming these issues.
The model-resistant richness of human visual experience

Abstract Current deep neural networks (DNNs) are far from being able to model the rich landscape of human visual experience. Beyond visual recognition, we explore the neural substrates of visual mental imagery and other visual experiences. Rather than shared visual representations, temporal dynamics and functional connectivity of the process are essential. Generative adversarial networks may drive future developments in simulating human visual experience.
A Post-Quantum Digital Signature Using Verkle Trees and Lattices

Research on quantum computers has advanced significantly in recent years. If humanity ever creates an effective quantum computer, many of the present public key cryptosystems can be compromised. These cryptosystems are currently found in many commercial products. We have devised solutions that seem to protect us from quantum attacks, but they are unsafe and inefficient for use in everyday life. In the paper, hash-based digital signature techniques are analyzed. A Merkle-tree-based digital signature is assessed. Using a Verkle tree and vector commitments, the paper explores novel ideas. The authors of this article present a unique technology for developing a post-quantum digital signature system using state-of-the-art Verkle tree technology. A Verkle tree, vector commitments, and vector commitments based on lattices for post-quantum features are used for this purpose. The concepts of post-quantum signature design utilizing a Verkle tree are also provided in the paper.