Reviewerly/exHarmony huggingface dataset:
Performance Models of Data Parallel DAG Workflows for Large Scale Data Analytics

Abstract Directed Acyclic Graph (DAG) workflows are widely used for large-scale data analytics in cluster-based distributed computing systems. The performance model for a DAG on data-parallel frameworks (e.g., MapReduce) is a research challenge because the allocation of preemptable system resources among parallel jobs may dynamically vary during execution. This resource allocation variation during execution makes it difficult to accurately estimate the execution time. In this paper, we tackle this challenge by proposing a new cost model, called Bottleneck Oriented Estimation (BOE), to estimate the allocation of preemptable resources by identifying the bottleneck to accurately predict task execution time. For a DAG workflow, we propose a state-based approach to iteratively use the resource allocation property among stages to estimate the overall execution plan. Furthermore, to handle the skewness of various jobs, we refine the model with the order statistics theory to improve estimation accuracy. Extensive experiments were performed to validate these cost models with HiBench and TPC-H workloads. The BOE model outperforms the state-of-the-art models by a factor of five for task execution time estimation. For the refined skew-aware model, the average prediction error is under 3% when estimating the execution time of 51 hybrid analytics (HiBench) and query (TPC-H) DAG workflows.
File Hiding Web Application (Fhwa) Using Image Steganography

File hiding is a process of hiding any files in computer from being accessed by anyone.This process is necessarily used in order to prevent any snoopers from accessing private and confidential files.Today, various extant file hiding applications existed but there is unfriendly user issue arises where only certain files can be hidden.Some of the applications can hide text file only and some others can only hide image files.Thus, it is difficult for users to hide any file of their choice because they need to use more than one tools or applications that support the file type.This paper presents File Hiding Web Application (FHWA) which can be used to hide any type of files using image steganography as a security mechanism.This application is developed using Agile methodology and Visual Studio Code is used to build the system as the code editor.Programming languages like Tailwind CSS, Semantic UI and JavaScript including Node.js, ReactJS and Next.js are used for frontend and backend development while MongoDB is used to store the databases.Based on user acceptance testing, the result shows on average 98% of the respondents are able to use FHWA to hide any type of files and reveal them successfully.
The Development Of A Lock Folder Application With Graphical Password

Nowadays, data protection is an essential issue for all computer users.Users continuously need to ensure that the data stored in their computers are secured.It became a significant concern for all computer users because cyber-crimes are constantly increasing day by day.In some cases, the situation turns out to be worst when their personal or confidential files being exposed and breached by an unauthorized user.It is risky when users probably will not distinguish the individual who attempts to access their confidential data typically stored in a folder on their personal computer or office desktop.Therefore, providing an extra security measure and protection to the laptop or specifically to the folder is the best solution, thus may prevent the folder from being breached by hackers.This project focuses on developing a lock folder application that implements a graphical password as its authentication factor to protect a folder from being accessed by an unauthorized user.It implements graphical passwords to verify user identity.Graphical password is an alternative authentication method that can replace a conventional password pattern where images are used in its authentication scheme.It will also send a notification via email if someone tries to unlock the folder without valid authorization.It has also been tested with users to study the user response towards its usability and functionality.The result shows the application is fully functioning and most likely benefit the user as it is user friendly and effortless to use.
Investigating the Effects of Computer-Generated Contextual Landmarks on Short-Term Recall of E-Texts

E-texts have many advantages over their paper counterparts, especially when they are reflowable and available as open educational resources (OERs). Unfortunately, research suggests that e-texts are, on the whole, less memorable than p-texts, in part due to their relative lack of visual navigational landmarks that help to anchor recall. The Landmarks project team is, therefore, building an application that inserts computer-generated artificial imperfections – abstract or representational landmarks – into the display of e-texts, that remain consistently associated with text passages even when documents are reflowed or reformatted. We hypothesize that it may consequently be easier to recall the associated contents. The application is designed to provide the means to present modified open texts using a range of generated landmarks and variations on them, and to test recall of the content. In this initial pilot study, results of tests for readers receiving different landmarks will be compared, with the intent of identifying promising approaches to use for future studies.
Securing Academic Student File Using Aes Algorithm For Cloud Storage Web-Based System

Cloud storage is an innovative technology that provides storage services to users.Many universities or companies have recently started using cloud storage services and realised their importance.However, preserving data security control in cloud storage is an issue and a very challenging task to solve.To safeguard data stored in the cloud, this study presents a web-based system using encryption techniques to handle the security and privacy issue in cloud storage.File Encryption Cloud Storage (FECS) system is a web-based system that secures students' academic files in cloud storage using encryption techniques.The system can encrypt students' academic files in cloud storage to avoid unauthorised access and misused risk.The targeted users are students of Kolej University Poly-Tech MARA (KUPTM).Moreover, the JavaScript and PHP programming languages are employed to create this system in the Microsoft Visual Studio Code Integrated Development Environment (IDE).The MySQL database is used as the backend of the system.The Advanced Encryption Standard (AES) technique is employed in the system.Furthermore, the AES approach yielded a positive outcome in the system's development.The survey's results indicate that the system is valuable and helpful to KUPTM students.
Multi-Targeted Poisoning Attack in Deep Neural Networks

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks also have weaknesses, one of which is vulnerability to poisoning attacks. A poisoning attack reduces the accuracy of a model by training the model on malicious data. A number of studies have been conducted on such poisoning attacks. The existing type of poisoning attack causes misrecognition by one classifier. In certain situations, however, it is necessary for multiple models to misrecognize certain data as different specific classes. For example, if there are enemy autonomous vehicles A, B, and C, a poisoning attack could mislead A to turn to the left, B to stop, and C to turn to the right simply by using a traffic sign. In this paper, we propose a multi-targeted poisoning attack method that causes each of several models to misrecognize certain data as a different target class. This study used MNIST and CIFAR10 as datasets and Tensorflow as a machine learning library. The experimental results show that the proposed scheme has a 100% average attack success rate on MNIST and CIFAR10 when malicious data accounting for 5% of the training dataset have been used for training.
Intrinsic Representation Mining for Zero-Shot Slot Filling

Zero-shot slot filling is a domain adaptation approach to handle unseen slots in new domains without training instances. Previous studies implemented zero-shot slot filling by predicting both slot entities and slot types. Because of the lack of knowledge about new domains, the existing methods often fail to predict slot entities for new domains as well as cannot effectively predict unseen slot types even when slot entities are correctly identified. Moreover, for some seen slot types, those methods may suffer from the domain shift problem, because the unseen context in new domains may change the explanations of the slots. In this study, we propose intrinsic representations to alleviate the domain shift problems above. Specifically, we propose a multi-relation-based representation to capture both the general and specific characteristics of slot entities, and an ontology-based representation to provide complementary knowledge on the relationships between slots and values across domains, for handling both unseen slot types and unseen contexts. We constructed a two-step pipeline model using the proposed representations to solve the domain shift problem. Experimental results in terms of the F1 score on three large datasets—Snips, SGD, and MultiWOZ 2.3—showed that our model outperformed state-of-the-art baselines by 29.62, 10.38, and 3.89, respectively. The detailed analysis with the average slot F1 score showed that our model improved the prediction by 25.82 for unseen slot types and by 10.51 for seen slot types. The results demonstrated that the proposed intrinsic representations can effectively alleviate the domain shift problem for both unseen slot types and seen slot types with unseen contexts.
A Strengthened PAKE Protocol with Identity-Based Encryption

In [2], Choi et al. proposed an identity-based password-authenticated key exchange (iPAKE) protocol using the Boneh-Franklin IBE scheme, and its generic construction (UKAM-PiE) that was standardized in ISO/IEC 11770-4/AMD 1. In this paper, we show that the iPAKE and UKAM-PiE protocols are insecure against passive/active attacks by a malicious PKG (Private Key Generator) where the malicious PKG can find out all clients' passwords by just eavesdropping on the communications, and the PKG can share a session key with any client by impersonating the server. Then, we propose a strengthened PAKE (for short, SPAIBE) protocol with IBE, which prevents such a malicious PKG's passive/active attacks. Also, we formally prove the security of the SPAIBE protocol in the random oracle model and compare relevant PAKE protocols in terms of efficiency, number of passes, and security against a malicious PKG.
Practical Order-Revealing Encryption with Short Ciphertext

To support secure database management, a number of value-added encryption schemes have been studied including order-revealing encryption (ORE) schemes. One of outstanding features of ORE schemes is the efficiency of range queries in an encrypted form. Compared to existing encryption methods, ORE leads to an increase in the length of ciphertexts. To improve the efficiency of ORE schemes in terms of the length of ciphertext, a new ORE scheme with shorter ciphertext has been proposed by Kim. In this paper, we revisit Kim's ORE scheme and show that the length of ciphertexts is not as short as analyzed in their paper. We also introduce a simple modification reducing the memory requirement than existing ORE schemes.
Orthogonal Deep Feature Decomposition Network for Cross-Resolution Person Re-Identification

Person images captured by surveillance cameras in real scenes often have low resolution (LR), which suffers from severe degradation in recognition performance when matched with pre-stocked high-resolution (HR) images. There are existing methods which typically employ super-resolution (SR) techniques to address the resolution discrepancy problem in person re-identification (re-ID). However, SR techniques are intended to enhance the human eye visual fidelity of images without caring about the recovery of pedestrian identity information. To cope with this challenge, we propose an orthogonal depth feature decomposition network. And we decompose pedestrian features into resolution-related features and identity-related features who are orthogonal to each other, from which we design the identity-preserving loss and resolution-invariant loss to ensure the recovery of pedestrian identity information. When compared with the SOTA method, experiments on the MLR-CUHK03 and MLR-VIPeR datasets demonstrate the superiority of our method.
A Multi-Tree Approach to Mutable Order-Preserving Encoding

Order-preserving encryption using the hypergeomatric probability distribution leaks about the half bits of a plaintext and the distance between two arbitrary plaintexts. To solve these problems, Popa et al. proposed a mutable order-preserving encoding. This is a keyless encoding scheme that adopts an order-preserving index locating the corresponding ciphertext via tree-based data structures. Unfortunately, it has the following shortcomings. First, the frequency of the ciphertexts reveals that of the plaintexts. Second, the indices are highly correlated to the corresponding plaintexts. For these reasons, statistical cryptanalysis may identify the encrypted fields using public information. To overcome these limitations, we propose a multi-tree approach to the mutable order-preserving encoding. The cost of interactions increases by the increased number of trees, but the proposed scheme mitigates the distribution leakage of plaintexts and also reduces the problematic correlation to plaintexts.
Fully Dynamic Data Management in Cloud Storage Systems with Secure Proof of Retrievability

For reliable storage services, we need a way not only to monitor the state of stored data but also to recover the original data when some data loss is discovered. To solve the problem, a novel technique called HAIL has been proposed. Unfortunately, HAIL cannot support dynamic data which is changed according to users' modification queries. There are many applications where dynamic data are used. So, we need a way to support dynamic data in cloud services to use cloud storage system for various applications. In this paper, we propose a new technique that can support the use of dynamic data in cloud storage systems. For dynamic data update, we design a new data chunk generation strategy which guarantee efficient data insertion, deletion, and modification. Our technique requires O(1) operations for each data update when existing techniques require O(n) operations where n is the size of data.
Adversarial Example Detection Based on Improved GhostBusters

In various studies of attacks on autonomous vehicles (AVs), a phantom attack in which advanced driver assistance system (ADAS) misclassifies a fake object created by an adversary as a real object has been proposed. In this paper, we propose F-GhostBusters, which is an improved version of GhostBusters that detects phantom attacks. The proposed model uses a new feature, i.e, frequency of images. Experimental results show that F-GhostBusters not only improves the detection performance of GhostBusters but also can complement the accuracy against adversarial examples.
Discussions on historicism in education and transformation of educational models

Introduction. The paper examines modern historicist discussions in education. The purpose of the research is to determine the specifics of discussions on historicism in Education and show their influence on the transformation of educational models in higher education. Materials and Methods. The work implements integrated and interdisciplinary approaches, comparative methods, perspectivist approach, and dialectical method. The article reviews and analysis monographs and research papers published between 1990 and 2021 which address the issues of classical historicism, anti-historicism and new historicism in education. Results. The authors summarize the main directions of discussions in education devoted to the problems of historicism. It is pointed out that within the framework of these problematic discussions, the new historicism and intellectual history allowed the sciences to move away from linear models of history to contextual ones. The productive features of ‘new stories’ in education are highlighted: the possibility of discussing conflict interpretations in a dialogue, taking into account the multicultural mechanisms of the historical process, development of critical thinking and practical approaches. The main result of the rethinking of historicism in education is transition from the theoretical orientation to practice in the context of the current situation based on the analysis of the past. It allows to combine several sciences and disciplines in the educational process. Conclusions. The article concludes that problematic discussions on historicism in higher education are extremely important for updating educational strategies and for structural renovation of curricula. They lead to the contextual teaching, requiring its practical adjustment, as well as the development of interdisciplinary educational trajectories.
Blockchain Interoperability On Healthcare: A Systematic Review

In essence, the blockchain is a digital ledger with centralized, public, and decentralized records of executed transactions.One of the many features of blockchain technology is the interoperability capability that permit data to be exchanged between multiple systems and be considered simultaneously by multiple systems.Interoperability features in blockchain have the potential to significantly enhance outcomes in healthcare.In healthcare, blockchain researchers continue to explore how to bring innovation to this technology and how to make it interoperable.The purpose of this study is to present a systematic literature review (SLR) of research on healthcare blockchain interoperability.The researcher used the Preferred Reporting Items for Systematic Reviews guidelines, along with a systematic mapping study process, in which search protocols were used to identify, extract, and analyses all relevant publications across two scientific databases.The study highlights recent advances in the development of healthcare blockchain based on interoperability, their limitations, and the areas that require further research.Consequently, further research is needed to characterize and evaluate Blockchain interoperability in the healthcare sector.
A Process In Developing A Quality Model For Educational Web Application

Educational web application has become a popular platform among academicians in delivering daily routine task over the last decade.To ensure the application meets user satisfaction is not a straightforward process.A questionnaire has been used in determining the characteristics needed in web-based integrated student assessment or in short, WBISA application.Rasch Measurement Model (RMM) has also been applied in constructing the quality model of WBISA application.In the beginning, there is a need to identify and remove item misfit, person misfit which affect the model development in achieving the initial model.Furthermore, a category response from the respondents needs to be analysed to identify the pattern of responses to determine whether they follow the rules applied in the Principal Component Analysis (PCA).Seven misfit items and ten misfit persons have been removed from the final analysis which results in good and acceptable PCA value as suggested by Rasch Model.The Person reliability is 0.96 (Excellent) and item reliability is 0.88 (Good).The model error shows a value of 0.25 which is considered as very good.The PCA values increased from 34.3% to 41.9%.Using Rasch, a model with eight constructs consisting of Usability, Reliability, Efficiency, Functionality, Supportability, Availability, Security, and Integrity has been established.
Self-supervised Learning Method for Behavior Prediction during Dialogue Based on Temporal Consistency

Developing a communication agent that can mutually interact with a human has been expected. To realize the agent, real-time situation recognition and motion generation are necessary. The human-human interaction data is utilized to develop the recognition and the generation model. However, a cost of giving a certain label to the data is expensive, i.e., the number of labeled data becomes small. To cope with the small dataset problem, one of the approaches is to obtain the pre-trained weight by self-supervised learning. In this research, we propose estimating the amount of time-shift by “lag operation” as a task for self-supervised learning. The observed data is not isolated during the interaction between two people, and using both observed information from two people makes an estimation model reduce the uncertainty of situation detection. By exploiting these properties of interaction data, the time index of data of one person is shifted, i.e., the entrainment of two data is broken. This operation is called a “lag operation”, and estimating the amount of time-shift is defined as the pre-training task. We apply this pre-training to the prediction experiment that estimates near-future laughing during a conversation. The result shows the accuracy of the laughing prediction is improved by 1.3 points, and the lag operation is an effect for predicting the change of interaction situation.
Universal Lower Bound for Learning Causal DAGs with Atomic Interventions

A well-studied challenge that arises in the structure learning problem of causal directed acyclic graphs (DAG) is that using observational data, one can only learn the graph up to a "Markov equivalence class" (MEC). The remaining undirected edges have to be oriented using interventions, which can be very expensive to perform in applications. Thus, the problem of minimizing the number of interventions needed to fully orient the MEC has received a lot of recent attention, and is also the focus of this work. Our first result is a new universal lower bound on the number of single-node interventions that any algorithm (whether active or passive) would need to perform in order to orient a given MEC. Our second result shows that this bound is, in fact, within a factor of two of the size of the smallest set of single-node interventions that can orient the MEC. Our lower bound is provably better than previously known lower bounds. Further, using simulations on synthetic graphs and by giving examples of special graph families, we show that our bound is often significantly better. To prove our lower bound, we develop the notion of clique-block shared-parents (CBSP) orderings, which are topological orderings of DAGs without v-structures and satisfy certain special properties. We also use the techniques developed here to extend our results to the setting of multi-node interventions.