Reviewerly/exHarmony huggingface dataset:
Security Amplification of IoT: Blockchain

In today’s world, the IoT is based on federal system which has immense security distress. The federal network systems are effortless to be hacked and get access to the resources, which are major concern for the security with IOT that has hindered its large-scale deployment. Drawback of present centralized IoT network can be prevail over through the most secure distributed model of the blockchain technology. This chapter discusses about the security challenges in IoT and how it could be dealt using distributed ledger technique (DLT) of blockchain. With increasing interest in IoT and blockchain, the opportunity to create more trustworthy IoT devices has increased. The distributed concept of blockchain can leverage the IoT system by providing security threats such as cyber-threats, distributed denial of service (DDoS) attack, device spoofing, and reliable data sharing. Blockchain being a distributed database is robust against any tamper as it allows storage of data at every node which are transparent and immutable; this property of blockchain can be implemented in IoT network to make it stronger against any kind of threat as it can provide a more secure online environment.
COA-Secure Obfuscation and Applications

We put forth a new paradigm for program obfuscation, where obfuscated programs are endowed with proofs of “well formedness.” In addition to asserting existence of an underlying plaintext program with an attested structure, these proofs also prevent mauling attacks, whereby an adversary surreptitiously creates an obfuscated program based on secrets which are embedded in other obfuscated programs. We call this new guarantee Chosen Obfuscation Attacks (COA) security. We show how to enhance a large class of obfuscation mechanisms to be COA-secure, assuming subexponentially secure IO for circuits and subexponentially secure one-way functions. To demonstrate the power of the new notion, we also use it to realize:
Investigating the Effects of Screen Size and Orientation on the Usability of Touch Gestures-Based User Interfaces

The objective of this study was to investigate whether the usability of the four major touchscreen gestures (tap, drag, zoom in and zoom out) would be different for two screen sizes (smartphone (small size) vs. tablet (large size)), for two different orientations of the mobile device (portrait vs. landscape), for two age groups (younger adults vs. older adults) and for two gender groups (females vs. males). The study included forty participants. They were divided into two age groups (younger adults aged 23 to 46 years and older adults aged 47 to 66 years); with 10 females and 10 males in each age group. The participants completed tasks covering the four gestures with the two screen sizes in both orientations. They used an app that was designed for the purpose of this experiment. Task completion time, number of errors and satisfaction were used to measure usability. Results revealed that screen size, device orientation and user' age have significant effect on users' number of errors. Smartphone has less number of errors than tablet. Portrait orientation has less number of errors than landscape orientation. Younger adults have less number of errors than older adults. Additionally, the participants feel more satisfied when using mobile devices in the portrait orientation.
Unsupervised Anomaly Localization Using Locally Adaptive Query-Dependent Scores

We propose a framework of reducing falsely-high anomaly scores on the unsupervised anomaly localization method. In recent years, the performance of unsupervised anomaly detection has been improved significantly by using pretrained deep features. A pretrained feature extractor can make appropriate descriptions for unsupervised anomaly detection, with extractors using embedding-based methods outperforming conventional deep-learning-based extractors. However, the intra-class variance of anomaly-free patterns has not been well considered yet by the existing methods while it is important to prevent misdetection and achieve more accurate localizations. In this paper, a framework is proposed that addresses this problem by leveraging the subtraction of deep pretrained features among anomaly-free images. The anomaly scores at the locations with higher intra-class variances are reduced through a novel and simple framework to tackle with misdetection. The experimental results using the standard industrial dataset show the outperformances of the proposed method especially for the overdetection reductions.
A Novel Method for Function Smoothness in Neural Networks

Existing methods for function smoothness in neural networks have limitations. These methods can make training sensitive to their hyperparameters, or their smoothness constraints can limit model capacity. These methods can impose too much smoothness, even in areas without data, or they can impose non-meaningful smoothness constraints. The way these methods measure smoothness can also be computationally hard. One of the main methods for function smoothness, Lipschitz continuity, does not even imply differentiability theoretically, let alone continuous differentiability, that is smoothness. In this paper, we propose a method based on the theoretical definition of the derivative to ensure that the derivative of the parametrized function should tend toward its theoretical value for the given neural network parameters in the vicinity of training samples. The method changes the classifier and its training minimally and has no added hyperparameters. The proposed method is shown to achieve a smoother function in the vicinity of both training and testing samples for all tested datasets, as measured with decreased values of the Frobenius norm of the Jacobian with respect to inputs. Due to the correlation between function smoothness and generalization, the method makes classifiers generalize better and achieve higher accuracy than default classifiers for Restricted ImageNet, CIFAR10 and MNIST. Due to the correlation between function smoothness and adversarial robustness, the proposed method makes classifiers with high-capacity architecture more robust to adversarial samples generated with the PGD attack compared to default classifiers for the Restricted ImageNet, CIFAR10, Fashion-MNIST and MNIST datasets.
Privacy and Customer’s Education: NLP for Information Resources Suggestions and Expert Finder Systems

Privacy is one of the key issues for citizen's everyday online activities, with the United Nations defining it as "a human right in the digital age". Despite the introduction of data privacy regulations almost everywhere around the globe, the biggest barrier to effectiveness is the customer's capacity to map the privacy statement received with the regulation in force and understand their terms. This study advocates the creation of a convenient and cost-efficient question-answering service for answering customers' queries on data privacy. It proposes a dual step approach, allowing consumers to ask support to a conversational agent boosted by a smart knowledge base, attempting to answer the question using the most appropriate legal document. Being the self-help approach insufficient, our system enacts a second step suggesting a ranked list of legal experts for focused advice. To achieve our objective, we need large enough and specialised dataset and we plan to apply state-of-the-art Natural Language Processing (NLP) techniques in the field of open domain question answering. This paper describes the initial steps and some early results we achieved in this direction and the next steps we propose to develop a one-stop solution for consumers privacy needs.
Design and Implementation of Student Integrity Information Management System Based on Blockchain Technology

With the continuous expansion of the scale of the school, the number of students has increased dramatically, and the amount of information about students has doubled. Facing the huge amount of information, we need to have a student information management system to improve the efficiency of student management. Based on the blockchain technology, this paper designs the student’s information management system. Through this system, we can achieve the standardized management of information, scientific statistics and rapid query, so as to reduce the workload of management.
Multi-Agent Reinforcement Learning

On this planet, in our societies, millions of people live and work together. Each individual has their own individual set of goals and performs their actions accordingly. Some of these goals are shared. When we want to achieve shared goals, we organize ourselves in teams, groups, companies, organizations, and societies.
CASIA-onDo: A New Database for Online Handwritten Document Analysis

In this paper we introduce an online handwritten document database (CASIA-onDo), serving as a standard database for the development and evaluation of methods in the field of online handwritten document layout analysis. It consists of 2,012 documents including a total of 841,159 online strokes. The database, covering Chinese and English languages, was produced by 200 writers. Six types of contents occur in the documents, namely text, formulas, diagrams, tables, figures, and lists. The distribution of different types is close to the actual situation. Benefiting from detailed annotations, CASIA-onDo can support different tasks of layout analysis under online or offline settings. Firstly, based on the semantic level annotation, it can be used for many classification tasks such as text/non-text classification, table/non-table classification, multi-class stroke classification and so on. Secondly, based on the instance level annotation, it can be used for segmentation tasks such as text line separation and formula segmentation. Thirdly, based on the various writing styles, it can be used for handwriting recognition and writer clustering tasks. In addition, we perform preliminary experiments to provide a benchmark on this database with a state-of-the-art method. More techniques can be evaluated on this challenging database in the future.
Survey on Enabling Encrypted Cloud Emails with Data Encryption using advanced Encryption Techniques

Cloud computing offers a flexible and convenient means of exchanging data, with several advantages for both society and individuals. With the widespread usage of cloud emails and frequent reports of large-scale email leakage occurrences, the security property known as forward secrecy has become desirable and necessary for both users and cloud email service providers to increase the security of cloud email systems. Typical techniques of attaining forward secrecy, such as Diffie-Hellman key exchange and forward-secure public-key encryption, have not been extensively authorised and used because they fail to meet the security and practicality requirements of email systems at the same time. We introduce a new cryptographic primitive called forward-secure puncturable identity-based encryption (fs-PIBE) in this paper to capture forward secrecy of encrypted cloud email systems without sacrificing practicability. It allows an email user to perform fine-grained decryption capacity revocation. In the standard model, we design a framework for encrypted cloud email systems and instantiate it with a concrete fs-PIBE structure that has constant ciphertext size and proved security. We enhance the proposed fs-PIBE scheme to provide end-to-end encryption and outsourced decryption, respectively, to improve the security and efficiency of the presented framework.
A Study on Evaluating the Performance of Robot Motion Using Gradient Generalized Artificial Potential Fields with Obstacles

Motion planning (MP) is actually a specialized version with more general Artificial Intelligence (AI) planning problem. The goal of a general purpose planning problem is to come up with a sequence of actions that accomplish a given goal. There are two approaches that utilize random samples. One the Probabilistic Road Map algorithm that sought the construct of a road map. Second, the rapidly exploring random tree procedure, which constructs every evolving trees to explore the free space and forge paths between the start and the goal. Both algorithms have the pleasing property that they work quite well in practice, even on high dimensional configuration spaces. Finally, the behavior of the generalized potential fields with obstacles are discussed. It helps to steer the robot through configuration space by considering the gradient of this artificial potential field. A strength of these potential field methods is that they are relatively simple to implement, and they can often be carried out directly based on sensory input. The finding of the present research is to describe the challenges being faced in the area of motion planning of robot and controlling its speed using gradient in generalized artificial potential fields with obstacles.
Attention Mechanism Based Image Segmentation and its Applications in Intelligent Diagnosis for Bladder Cancer

Background: Artificial intelligence aided tumor segmentation is of much assistance for physicians to observe the potential malignant tissues and has been applied for various kinds of medical images. However, little research has been conducted for the cystoscopic image segmentation problem.Methods: By utilizing the cystoscopic images from Peking University Third Hospital within 2017-2022, we presented an attention mechanism based cystoscopic images segmentation (ACS) model in this paper. Our model is featured by the following points: (1) A mixed attention module including both the channel and spatial attention modules are integrated in our model for the encoder-decoder path, which helps to exploit the global information of the tumor area more effectively. (2) A guidance and fusion attention module is introduced in the skip connection part, facilitating the integration of the high-level semantic features with low-level fine-grained features and the discarding of irrelevant features. (3) An inception attention module is added to enhance the feature expression in the scale of pixel level, so as to better discriminate multi-scale targets.Results: The proposed ACS model showed an obviously better tumor segmentation performance than the compared models, with Dice of 82.7% and MIoU of 69% achieved.Conclusions: The proposed ACS model can achieve significantly better diagnostic performance than the previous bladder tumor segmentation method based on U-Net. Our ACS model is expected to be a useful support tool to assist for the intelligent segmentation of tumors under cystoscopy.
Priority-Based Replication Management for Hadoop Distributed File System

Hadoop distributed file system provides a fault-tolerant and reliable way of distributed storing data. First data is divided into blocks, and then, each block is assigned a data node by the name node. As the cluster consists of commodity hardware to offer fault-tolerant nature, replication of blocks is done. In latest version of Hadoop, default block size is 128 MB. Data is put to cluster by user. Data is divided into blocks and placed on data node. After successful placement of data block, acknowledgment is sent to the master. In this way, master forms metadata. This metadata will be used when user wishes to access the data again. To give flaw open-minded nature, Hadoop repeats each square of record of course, and three copies are framed. First duplicate is set at the data node geologically nearest to the client. This is done to decrease the entrance cost. Then, at that point data node having unique square reproduces it to another data node, and this data node will again imitate the square bringing about three reproductions. In this work to further develop planning calculation, we have altered information replication approach moreover. We have planned a need-based replication plot in which second and third imitations are framed dependent on needs. The subsequent copy is placed and a data node having high need and third at a datanode having low need and having adequate accessible space.
Bridging the Gap Between Usability and Security: Cultural Adaptation of a Graphical User Authentication

Authentication tasks are becoming more important, since these tasks are performed daily by millions of users across diverse cultures that share distinct characteristics and behaviors. Alphanumerical password authentication is often criticized because of its memorability issues. Thus, graphical passwords, which require users to select images as their password, have been proposed to increase memorability and ease the user authentication process. However, graphical passwords are globalized without considering that people from distinct cultures understand and interact with technologies differently. Culturally familiar graphical passwords allow the user to select pictures that relate to his cultural background. Such passwords exhibit a higher level of memorability, and leverage humans' cognitive strengths. This paper reports the finding of an investigation of the effect of cultural elements on graphical password usability. A pilot study was conducted with thirty-four participants to examine the usability impact of culture on a customized graphical user authentication application. Two groups of participants were part of the study, namely Cameroonian and American users. Participants were asked to create a graphical password with the option of selecting cultural related images as opposed to non-related cultural images. Data gathered was used to measure the correlation between cultural related images and memorability and user preferences. The results showed that cultural elements are well considered as factors that influence password choices and the memorability of users during the registration and the authentication process. Findings underpin the necessity to consider cultural differences in the design of graphical password authentications, to enhance usability features and strengthened security.
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems

In data-driven modeling of spatiotemporal phenomena careful consideration is needed in capturing the dynamics of the high wavenumbers. This problem becomes especially challenging when the system of interest exhibits shocks or chaotic dynamics. We present a data-driven modeling method that accurately captures shocks and chaotic dynamics by proposing a new architecture, stabilized neural ordinary differential equation (ODE). In our proposed architecture, we learn the right-hand-side (RHS) of an ODE by adding the outputs of two NN together where one learns a linear term and the other a nonlinear term. Specifically, we implement this by training a sparse linear convolutional NN to learn the linear term and a dense fully-connected nonlinear NN to learn the nonlinear term. This contrasts with the standard neural ODE which involves training a single NN for the RHS. We apply this setup to the viscous Burgers equation, which exhibits shocked behavior, and show stabilized neural ODEs provide better short-time tracking, prediction of the energy spectrum, and robustness to noisy initial conditions than standard neural ODEs. We also apply this method to chaotic trajectories of the Kuramoto-Sivashinsky equation. In this case, stabilized neural ODEs keep long-time trajectories on the attractor, and are highly robust to noisy initial conditions, while standard neural ODEs fail at achieving either of these results. We conclude by demonstrating how stabilizing neural ODEs provide a natural extension for use in reduced-order modeling by projecting the dynamics onto the eigenvectors of the learned linear term.
Recipe Recommendation System Using Content-Based Filtering

In this paper, we offer a cooking recipe recommendation system that runs as an interactive web application on a user's smartphone. An innovative method is utilized to determine the best-suggested dish based on the use of some available ingredients. The system's goal is to make it easier for those who cook to make decisions about their meals. The system uses a recommendation approach in which the user's ingredients are used as input, an analysis process is carried out with the help of the data set obtained, and the right dishes or recipes are suggested to the user. The algorithm will look at a few attributes to see how close the ingredients picked by the user are to the items in the dataset, and then recommend a recipe. The concept of content-based filtering is used to suggest recipes to users based on the ingredients that they provide[3]. The algorithm will look at a few attributes to see how close the ingredients picked by the user are to the items in the dataset, and then recommend a recipe
Quantum-Classical Solution Methods for Binary Compressive Sensing Problems

Compressive sensing is a signal processing technique used to acquire and reconstruct sparse signals using significantly fewer measurement samples. Compressive sensing requires finding the most sparse solution to an underdetermined linear system, which is an NP-hard problem and as a consequence in practise is only solved approximately. In our work we restrict ourselves to the compressive sensing problem for the case of binary signals. For that case we have defined an equivalent formulation in terms of a quadratic binary optimisation (QUBO) problem, which we solve using classical and (hybrid-)quantum computing solving techniques based on quantum annealing. Phase transition diagrams show that this approach significantly improves the number of problem types that can be successfully reconstructed when compared to a more conventional $$\mathcal {L}_1$$ optimisation method. A challenge that remain is how to select optimal penalty parameters in the QUBO formulation as was shown can heavily impact the quality of the solution.
Spatio-Temporal Dynamic Clustering Modeling for Solar Irradiance Resource Assessment

Nowadays, with the development of international policies and agreements to promote the integration of renewables, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such resource variability are generally scarce. Under this framework, this paper proposes a methodology focused on characterizing and clustering the spatio--temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows are subsequently evaluated by using various sources of information: satellite-based data combined with on-ground stations including additional meteorological and atmospheric parameters. The Spanish territory is selected as case study, considering 22-year period data (1999-2020) and 1,936,917 observations. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. This work gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution in Spain, providing a suitable methodology to help the current European sustainable energy transition.