Reviewerly/exHarmony huggingface dataset:
Revocable Public Key Encryption with Equality Test without Pairing in Cloud Storage

Abstract Public key encryption with equality test (PKEET) plays an important role in the cloud storage. It allows a third party to test whether two ciphertexts contain the same message without decryption. Since the third party is not fully trusted, it is sometimes necessary to prevent the third party from testing the ciphertexts all the time. To this end, we propose the notion of revocable public key encryption with equality test (R-PKEET). We give the construction of R-PKEET in the random oracle model, which employs time key to update trapdoor and partial ciphertexts. In details, our scheme enables a user to revoke the third party’s test right by sending a time key to the cloud server, which is responsible for updating partial ciphertexts using the time key. Compared with related works, our scheme achieves both lightweight revocation and lower computational complexity by using Shamir’s secret sharing and Lagrange interpolating polynomial.
Change detection and update of 3D sparse map by merging geometry and appearance

Large-scale 3D sparse maps generated by Structure-from-Motion (SfM) from images play an important role in many applications, including visual localization, augmented reality, etc. In these scenarios, the timeliness of the map i.e., detecting changes in the map and performing partial updates, is crucial. To solve this problem, in this paper we propose a novel method for 3D sparse map change detection and updating, to maintain the SfM map continuously over time. The core idea of this paper is to simultaneously detect the appearance and geometry changes of 3D map points, so as to find regions with significant changes, and update these regions locally without changing most of the stable map regions. In the proposed method, a local 3D map containing changing areas is computed from newly captured images by SfM and aligned to the old map according to the locations of new images in the new local map and their registration in the old map. Next, the overlapping map is partitioned into regular grids and the appearance uncertainty and geometry uncertainty are measured on each grid cell individually. Then the grid cells are labeled as changed or unchanged using Markov Random Field optimization by taking both cell uncertainty and consistency of adjacent cells into consideration. Finally, the visible old images of the point cloud in the changed cells are replaced with the corresponding visible new images, and the old map is updated by a local Bundle Adjustment. Experimental results on 3D maps reconstructed by aerial and ground images demonstrate the effectiveness and robustness of the proposed method.
Book Recommendation System using Collaborative and Content Based Filtering with Web Scraping

A recommender system, or a recommendation system, is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Recommender systems are used in a variety of areas, with commonly recognized example staking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.
Cryptocurrency Price Prediction using Machine Learning

Crypto currency has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Crypto currency price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a Machine Learning (ML), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Crypto currency price prediction. Crypto currency is one of the most popular and valuable crypto currency in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Crypto currency price prediction with different machine learning prediction algorithms. For the research: relevant features are taken from the dataset having strong correlation with Crypto currency prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the crypto currency price for the upcoming 5 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this paper, the work culminates with future improvements. In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable. Connecting Django server to create front end using HTML, CSS, Java Script
QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation

A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their accuracy. The proposed QCBA method was initially developed to postprocess quantitative attributes in models generated by the Classification based on associations (CBA) algorithm, but it can also be applied to the results of other rule learning approaches. We demonstrate the effectiveness on the postprocessing of models generated by five association rule classification algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners (FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller size and the overall best predictive performance for FOIL2+QCBA compared to all seven baselines. Postoptimised CBA models have a better predictive performance compared to the state-of-the-art rule learner CORELS in this benchmark. The article contains an ablation study for the individual postprocessing steps and a scalability analysis on the KDD'99 Anomaly detection dataset.
A Note on Comparing the Bifactor and Second-Order Factor Models: Is the Bayesian Information Criterion a Routinely Dependable Index for Model Selection?

This note demonstrates that the widely used Bayesian Information Criterion (BIC) need not be generally viewed as a routinely dependable index for model selection when the bifactor and second-order factor models are examined as rival means for data description and explanation. To this end, we use an empirically relevant setting with multidimensional measuring instrument components, where the bifactor model is found consistently inferior to the second-order model in terms of the BIC even though the data on a large number of replications at different sample sizes were generated following the bifactor model. We therefore caution researchers that routine reliance on the BIC for the purpose of discriminating between these two widely used models may not always lead to correct decisions with respect to model choice.
Extracting Pseudocode from Digital Block Diagram in Technical Documents

The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain modalities have already received, say per the textual information, there are several aspects that get disproportional attention throughout the bibliography. An instance of such a modality is the block diagrams found in the technical document, as they can provide crucial information about the functionality and the technical details of both their related parts and the technical document as a whole. This paper deals with the automatic understanding of block diagrams and the extraction of pseudocode associated with the functionality of a given diagram. In particular we present a complete methodology for the formal modelling of digital block diagrams and their elements, then we develop a generative framework and three novel annotated datasets on diagrams classification and captioning. After mapping the initial problem to a block diagram description task, we present several original predictive setups derived from image segmentation, analyze the inference capabilities, and we offer illustrative examples justifying our approach.
Hand Gestures Recognition For Virtual Mouse and Keyboard :A Novel Human- Computer Interaction Approach

Today's computer vision technology is so good that a machine can recognize its owner by using a simple picture-processing programme.. People use this vision in many parts of daily life in the current technological age, including face recognition, color detection, autonomous vehicles, etc. The One of the marvels of Human-Computer Interaction (HCI) technology is the mouse. Since a wireless mouse or Bluetooth mouse still need a battery for power and a dongle to connect it to the PC, they are not entirely device-free at this time. This study suggests an interactive computer system that converts hand motions into a virtual keyboard and mouse using computer vision. To carry out the task, we can use an internal camera or an external camera. To operate the mouse and keyboard, we can use a built-in camera or an external camera. The computer's camera will scan the image of various hand gestures made by a person, and in accordance with the motions, the mouse pointer will move and even carry out various functions using various gestures. Other motions can also be used to access the keyboard's features. It will work as a virtual mouse and keyboard in the absence of any wiring or other hardware. The project's webcam is the only piece of hardware, and Python is used for all development on the PyCharm platform. Modern machine learning and computer vision techniques are used in this research to recognize hand gestures, and they operate without the need for any additional hardware.
PortEd: The Port that Connects You to the Sea of Knowledge

Electronic Learning is termed as a Network that enabled the transfer of Skills &amp; Knowledge, and the Delivery of Education is made to a large number of recipients at the same or different times. The masses of us are normally friendly with conventional teaching and learning where the teacher is teaching 30 to 40 students at a time and no one knows whether students are getting or not what is explained. So this kind of Study is averted more adequate than online Study. “portEd” is the platform using which a user can grasp all the knowledge related to academics, and programming through the free online courses as well as different features at their own pace and can experience proper supervision of Learning.
Decreasing Proof Size of BLS Scheme

Abstract Bootle et al. in CRYPTO 2019 proposed a zero knowledge proof for an $\mathrm{ISIS}_{m,n,q,\beta }$ instance $A\vec{s} = \vec{u} \bmod q$ with $\|\vec{s}\|_{\infty }\leq \beta $ (BLS scheme). It was implemented by transforming the instance into the form $A^{\prime }\vec{s}^{\prime } =\vec{u}\bmod q$, where the coefficients of $\vec{s}^{\prime}$ are in $\{0,1,2\}$, and proved the latter in an exact way. With the concrete parameters $m=1024,n=2048,\beta =1,q\approx 2^{32}$, their proof is of length 384.03KB. In this paper, we decrease the proof size of BLS scheme by two techniques. The first one takes effect on some special parameters. For these parameters, using the binary basic set instead of the ternary one results in a shorter proof. The second one deals with the repetition of the lower half in BLS scheme. Observing that what the lower half proves is of form $\mathbf{B}\vec{\mathbf{r}}=\vec{\mathbf{t}}$ with a short vector $\vec{\mathbf{r}}$ of polynomials, a variant of parallel repetition can be used to shorten the proof size. Combining these two techniques together, the proof size of the above-mentioned instance can be reduced to 220.01KB, only 57.3$\%$ of BLS scheme.
Decentralized Payment System by using Blockchain

Existing interbank payment systems are unable to offer cost-effective cross-border transactions because the correspondent banking process involves a large number of intermediaries operating in various time zones. They also experience lengthy transaction delays and a lack of transparency. These problems can be resolved by creating an auditable coin on a permissioned blockchain, which allows a set of administrators to control the network. Using an auditable cryptocurrency that supports unspent transaction output-based transactions in a consortium blockchain network, we suggest a low-cost, seamless cross-border payment system in this research. Participating nations perform the power-efficient proof of authority consensus method on the blockchain with equal privileges. Dynamic decentralized identifiers (DIDs), as opposed to traditional cryptocurrencies, are utilized as transacting addresses to enable self-manageable authentication.
FAITS

Faulkner Advisory for IT Studies (FAITS) is a Web-based resource containing industry reports that cover a wide range of information technology (IT) topics written for an academic audience. Available reports include products, standards, company profiles, and marketplace reports. The site is organized to allow browsing by topic or report type as well as searching.Reports are plainly written and utilize references to trade publications and news sources. They are routinely updated, and currency appears to be one of FAITS's strengths. Although it lags behind in expected user interface conventions, FAITS provides useful and timely reporting that students can use to inform scholarship and faculty to drive curriculum choices.
Exploring Grade Two Teachers’ Interpretation of New Curriculum Pedagogical Concepts in the Teaching of Writing

This paper aims to discover teachers’ interpretation of the pedagogical concepts informing the underlying philosophical framework of the curriculum implemented in Indonesia when the study on which this paper is based was conducted. The curriculum was Kurikulum Tingkat Satuan Pendidikan (KTSP), meaning school-based curriculum. The teachers’ understandings of the pedagogical concepts were examined using the setting of writing lesson teaching in grade 2. The study used a qualitative method that involved 10 grade two teachers from 10 elementary schools in Makassar, Indonesia. Data collection techniques were classroom observations, informal discussion at the end of each observation, and interviews. Although the KTSP committed to distance from a traditional view of teaching and learning, the study found that the teachers’ interpretations of the pedagogical concepts of the KTSP in the teaching of writing appeared to echo this view. The study also found that the teachers interpreted the pedagogical concepts through competencies of writing lesson that grade two students should achieve. Although other forms of curricula have replaced the KTSP, the findings from this study could inform the government and other relevant decision-makers about teachers’ difficulties in understanding change that will have an implication on policy and practice regarding the support given to teachers in implementing existing and future curricula in Indonesia and different contexts.
A Trainable Open-Source Machine Learning Accelerometer Activity Recognition Toolbox: Deep Learning Approach

Background The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source. Objective To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors. Methods We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data. Results Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model. Conclusions HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.
Relation-aware Graph Contrastive Learning

Over the past few years, graph contrastive learning (GCL) has gained great success in processing unlabeled graph-structured data, but most of the existing GCL methods are based on instance discrimination task which typically learns representations by minimizing the distance between two versions of the same instance. However, different from images, which are assumed to be independently and identically distributed, graphs present relational information among data instances, in which each instance is related to others by links. Furthermore, the relations are heterogeneous in many cases. The instance discrimination task cannot make full use of the relational information inherent in the graph-structured data. To solve the above-mentioned problems, this paper proposes a relation-aware graph contrastive learning method, called RGCL. Aiming to capture the most important heterogeneous relations in the graph, RGCL explicitly models the edges, and then pulls semantically similar pairs of edges together and pushes dissimilar ones apart with contrastive regularization. By exploiting the full potential of the relationship among nodes, RGCL overcomes the limitations of previous GCL methods based on instance discrimination. The experimental results demonstrate that the proposed method outperforms a series of graph contrastive learning frameworks on widely used benchmarks, which justifies the effectiveness of our work.
Bring Environments to People – A Case Study of Virtual Tours in Accessibility Assessment for People with Limited Mobility

Digitization techniques of environments make it possible to remotely render the experience of visiting these environments for people with limited mobility, creating new opportunities and challenges. Examples of such experiences center around the increasingly popular term "Virtual Tours" which allows users to access the digital replicas of physical environments. In virtual tours, a wheelchair user can "tour" historical sites for an educational experience, view Airbnb rooms and workplaces to plan their physical visits, and even inspect an aircraft to check out the legroom for making better decisions and preparations for their visits. In this research, we conducted the first case study with users with limited mobility on their uses of virtual tours to assess the accessibility of physical environments. The study results established benchmarks and uncovered benefits and caveats, creating a foothold for further studies as well as an outlook for features of future virtual tour systems to facilitate remote accessibility assessment.
Authoring Web-accessible Mathematical Diagrams

extended-abstract Share on Authoring Web-accessible Mathematical Diagrams Authors: David Austin Department of Mathematics, Grand Valley State University, USA Department of Mathematics, Grand Valley State University, USA 0000-0002-8474-0246View Profile , Volker Sorge School of Computer Science, University of Birmingham, UK and School of Information Technology, Indian Institute of Technology, India School of Computer Science, University of Birmingham, UK and School of Information Technology, Indian Institute of Technology, India 0000-0003-2796-1488View Profile Authors Info & Claims W4A '23: Proceedings of the 20th International Web for All ConferenceApril 2023Pages 175–177https://doi.org/10.1145/3587281.3588146Published:30 April 2023Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A More Accessible Web with Natural Language Interface

The past decade has witnessed the rapid growth and evolution of the Web. Today, people can perform a multitude of tasks through the use of a single browser. Despite the immense power and capabilities of the Web, its increasing complexity and the overwhelming amount of information pose significant challenges for users to easily access the information they need and achieve their goals. Especially for those who are less technologically proficient or have disabilities. In this work, we propose to tackle this issue by building a general natural language interface for the Web, which enables users to express their needs in natural language and have the system carry out the arduous actions. We examine the key components crucial for building the natural language interface. On top of that, we present our ongoing efforts in curating a new benchmark dataset covering a diverse range of websites and tasks, and establishing baselines to demonstrate the feasibility of building such a system.
Ability + Motivation: Understanding Factors that Influence People with Cognitive Disabilities in Regularly Practicing Daily Activities

People with cognitive disabilities may experience challenges in consistently performing daily activities because they skip steps, struggle to track progress, or lack the motivation to complete them. These challenges are often along a range; people need assistive devices customized to their specific needs. However, existing assistive technologies, like prompting systems, lack the capabilities to customize support for diverse needs. With the advent of smart home devices, there are opportunities to design prompting systems that support diverse accessibility and motivational needs, thereby supporting the regular practice of daily activities. To understand design factors for such devices, we interviewed adults with cognitive disabilities, parents, and caregivers. Our participants described their needs for future prompting systems, including structuring tasks, supporting motivation, and introducing community support. This paper presents insights and design suggestions for context-aware assistive technologies that could help people with cognitive disabilities regularly perform everyday activities.