Reviewerly/exHarmony huggingface dataset:
Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system

One of the main advantages of the traditional instructor/student interaction is the possibility of receiving immediate instructional feedback within the defined lecture periods. However, the transition into full online, and hybrid pedagogy has gradually replaced instructional feedback with peer feedback. While essential for knowledge transfer among learners, peer feedback largely depends on prior knowledge from learning engagements that may not fully be related to the particular course in question. What we have observed in our learning management system is lots of unnecessarily repeated errors which could easily be averted with the right instruction. To this end, we employed optical image recognition to extract structured query language-based keywords from lecture slides and exercise sheets. Using these keywords, we automatically rank the degree to which a lecture slide suitable matches an exercise task, and thus provide an automatic recommendation to students encountering errors in respective tasks during exercise task engagements. Overall, we achieved a precision of 0.707 for German lecture slides and 0.72 for English lecture slides, which validates our strategy of assisting students with automatic lecture slide recommendations during their online exercise engagements.
Classification based on Association Rules: Complexity and Interestingness Guided Algorithm

Classification based on association rules has proved able to get high precision and deal with data noise in real scenarios. However, very low support thresholds are used to get these results, generating a great number of rules. In this process, interesting and non-interesting itemsets are considered for subsequent rule generation. In this paper, we propose a new method to identify not only frequent but also interesting itemsets, including an interestingness measure when pruning candidate itemsets. With this approach, we reduce the number of association rules in the final classifier and increase precision. We combine it with an iterative approach, reducing the execution time and complexity of the final rule set. We have tested the proposal using a COVID19 patient database.
Electronic Health Records (EHR) Management in Hospitals using Blockchain Technology

The popularity of blockchain technology has grown rapidly since Bitcoin was launched in 2009. Blockchain has a tremendous impact and a huge scope. The objective of this paper is to examine the relevant literature regarding the implementation of blockchain platforms in the healthcare industry. It also aims to address electronic health record management issues using blockchain platforms. This is because of their extraordinary capabilities such as shared ledgers, cryptography, trust systems, and contracts that govern business operations. In the digital world, this technology platform facilitates trusted exchanges. A tool called Hyperledger is enterprise-grade distributed ledger technology that enables secure data management and integrated sharing between patients, payers, and providers. It solves the problem of electronic medical records (EHR) for secure data management and integrated sharing between the patient, payer, and provider. As a result, the work is aligned with existing literature on the implementation and monitoring of electronic health records in hospitals. A summary of the research work and findings is presented to evaluate recent blockchain technology platforms. In this work, blockchain technology is used in conjunction with the Hyperledger platform. This allows us to certify information and secure data exchange between health organizations and application vendors. In the design, the workflow tracks the medical record of a patient from the moment he visits a physician to the time he has a diagnosis and treatment plan. The validation of this work is carried out by testing the efficacy and feasibility of blockchain architecture for health-related services. Furthermore, to establish the feasibility of implementing Blockchain in healthcare, the ratio of advantages to disadvantages of Blockchain in healthcare is conducted. The EHR systems strategies were evaluated, and their performance factors were considered in their design and implementation.
Unmasking Fraud in Food Delivery Business: Harnessing Amazon Web Services’ Random Cut Forest for Efficient Detection

This paper presents the development and evaluation of a fraud detection model for identifying fraudulent cases and anomalous instances in a business system. The authors faced a dilemma between choosing a univariate model (Isolation Forest) or a multivariate model (Random Cut Forest) to identify fraudulent cases and ultimately decided on the multivariate model due to its ability to detect all types of anomalies and its safety for the system. The model was evaluated using a limited number of confirmed fraudulent cases and a cutoff score of 1.4 and was able to identify instances similar to the known cases of fraud. In addition to identifying confirmed cases of fraud, the model also identified anomalies that required further investigation, which was essential in building a more effective supervised model and improving the system’s accuracy in identifying future instances of fraud. The findings from the investigations were also used to refine the engine rules, strengthening the overall security and stability of the system. Although the abstract does not mention the specific context of a food delivery business or Amazon, the paper describes a fraud detection model that could be applied in various business systems. The paper contributes to the current state of knowledge by presenting a reliable and effective fraud detection model that improves the overall security and stability of a business system.
OCR quality: Key to enhanced Data Mining

Quality of scanned documents vary, that is a fact. Another fact is that without high quality optical character recognition (OCR) it is difficult or impossible to achieve good usability or findability. This paper focuses on improving the OCR quality by performing different pre-OCR image manipulations and testing the performance of different OCR engines. The results clearly indicate that there are differences between the performance of the OCR engines, but the pre-OCR image manipulations did not significantly affect the results. Tests were conducted with mostly restricted content which came from the National Archives of Finland. The figures shown in this paper are based on tests where material from the Central Archives of Finnish Business Records was used.
An Efficient Hardware Design for Fast Implementation of HQC

Hamming Quasi-Cyclic (HQC), as a code-based Key Encapsulation Mechanism (KEM) algorithm, has been selected as one of the three code-based candidates in the fourth round of standardizing post-quantum cryptographic primitives. Efforts are needed for the efficient hardware implementation for HQC. However, in existing hardware designs for HQC, they cost too many clock cycles in the polynomial multiplication module and do not pay enough attention to the decoders used in the decryption. Therefore, this paper presents an improved hardware design for HQC. Through applying a low-latency polynomial multiplication design, every stage of our design saves amounts of clock cycles. Moreover, an efficient Reed-Solomon (RS) decoder based on the enhanced Parallel Inversionless Berlekamp-Massey (ePIBM) algorithm and a Reed-Muller (RM) decoder based on the Fast Hadamard Transform (FHT) algorithm are introduced to reduce the overhead in the decryption. A complete architecture is finally implemented on the Xilinx Artix 7 FPGA (xc7a200t-3). Experimental results show that the proposed design for the HQC-128 requires 25% less area-delay product (ADP) than the latest one in decryption. Furthermore, the proposed design can perform key generation in 0.11ms, encapsulation in 0.22ms, and decapsulation in 0.38ms, which significantly outperforms the state-of-the-art design.
Kalman Filter-Based Suspicious Object Tracking for Border Security and Surveillance Systems using Fixed Automotive Radar

With recent active research related to autonomous driving, object tracking technology using autonomous driving sensors such as LiDAR and radar has also undergone extensive development. Accordingly, attempts are being made to apply autonomous sensors not only on autonomous vehicles but also in various fields such as security and surveillance. However, since security and surveillance systems should be able to detect and track objects even under extreme environmental conditions such as snow, rain, and fog during the day or night, radar systems that meet the relevant requirements are essential. In South Korea, the distance of the Military Demarcation Line (MDL) is 250 km, and a considerable investment would be required to install more than 1,000 radars and PCs with built-in GPUs in all sections for a border security and surveillance system. Therefore, in this study, a Kalman filter-based object tracking system is explored rather than applying deep learning, which requires GPU processing. Additionally, most objects along the MDL are highly likely to be suspicious objects, so a radar sensor is most suitable because it provides coordinates, distance, and speed of movement without needlessly determining whether an object is an enemy or not. For accurate object detection and tracking performance, two motion models for the Kalman filter, a constant acceleration model (CAM) and a constant turn rate and acceleration model (CTRAM), are compared to identify a suitable model for each object movement state.
Robustness Assessment of Neural Network Architectures to Geometric Transformations: A Comparative Study with Data Augmentation

This study explores the robustness of different neural network architectures when exposed to various geometric transformations, including translation, rotation, and scaling. The objective is to evaluate the performance and resilience of these architectures in real-world scenarios where such transformations are commonly encountered. Extensive experiments are conducted using benchmark datasets, and a comparison is made between convolutional neural networks (CNNs), Residual Networks (RESNet), fully connected networks (FCNs), and transformer-based architectures. The study focuses on assessing the generalization capabilities and the efficiency of these architectures when data augmentation techniques are employed. The results aim to identify the architectures that exhibit superior performance and fast adaptation to transformed inputs.
Anomaly Location and Damage Recovery in Batch Data of Seasonal Time Series: A Case Study of User Power Data in A Province

Time series anomaly location and damage recovery has become the focus of researchers. Yet most of the existing sequence recovery algorithms have high requirements on the source data specifications, as long as poor compatibility. In this research, an anomaly detection framework was proposed, which could be compatible with corrupted data and intact data. This task was divided into four stages, which were sequence classification, rough detection of abnormal data, precise detection of abnormal data and correction of abnormal data. Finally, the power consumption data of more than 300,000 users in a province was applied as the research case for verification. The analysis results showed that the proposed framework had high generalization and compatibility for different types of time series, and could effectively recover the source data.
Recommender System in Social Networks using Fuzzy Logic

Today, the importance of recommendation systems (RSs) has led to extensive activities and research efforts to improve the accuracy of the recommendations in different applications of social networks. Because various uncertainties exist in computing similarity between users and movies, it is an important issue to achieve high recommendation accuracy. This paper proposes a fuzzy logic-based recommender system (FRS) using Pearson correlation in social networks (SNs). The proposed scheme measures the similarity between users and movies by utilizing users’ interests based on labels, friendship, and group memberships for accurate recommendations in social networks. This paper defines a novel item prediction scheme that makes use of both user-to-user and item-to-item similarities. Experimental results show the effectiveness of the proposed scheme in improving prediction accuracy.
Bridging the Capacity Gap for Online Knowledge Distillation

Online knowledge distillation (KD) has drawn increasing attention in recent years. However, little attention has been paid to the capacity gap that exists between the models in the online KD paradigm. In this work, we investigate the impact of the capacity gap and experimentally verified that a large capacity gap can have a detrimental effect on performance. Moreover, to address this issue, we propose Auxiliary Branch assisted Mutual Learning (ABML), which leverages auxiliary branches to mitigate performance deterioration caused by the capacity gap. Experimental results show that our ABML can significantly improve the performance of online KD even when a capacity gap is present. Our code is available at https://github.com/maorongwang/ABML.
Your Identity is Your Behavior - Continuous User Authentication based on Machine Learning and Touch Dynamics

The aim of this undergraduate research paper is to investigate the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Something that is starting to occur in the modern day is the use of mobile devices rising steadily, and in today's world, the number of mobile phone subscriptions has surpassed 6 billion [1]. What we will be looking into is the touch dynamics of users and how their interactions on a device can be utilized for authentication and security. The term touch dynamics refers to patterns from an individual, these patterns being completely unique, on a mobile device via the touch screen. These interactions could be swipes, taps, the amount of pressure on the screen, and several more. The use of this type of data expands well into continuous authentication for security. Continuous authentication refers to monitoring and verifying a user's identity on a device continuously, rather than just a login or a one-time security feature [2]. For this research to have enough data for algorithms to utilize, we collected touch data from 40 participants, since this is seen to be a good starting point [3],[4], using the LG V30+. The way this data was collected was through each participant playing 4 different mobile games, Minecraft, Slither.io, PUBG, and Diep.io for 10 minutes each game. Since these games are high-action games that utilize two-finger input, it allowed us to gather a lot more data from everyone. Once the data was collected and cleaned, the three algorithms stated before were then trained and tested, and their performance was evaluated based on their Accuracy, F1 Score, False Negative Rate, and False Positive Rate. The results from each of these models show that they were able to accurately classify unique users with an accuracy ranging from 75% to 95%, very similar to [5]. The SVC and XGBoost algorithms performed slightly behind the Neural Network, with the Neural Network achieving an accuracy of 90.04%. These results show us that touch dynamics can uniquely identify individuals based on patterns they make on a device. We also learn that the type of model that is decided for recognizing and learning these patterns matters a lot. Different algorithms have slightly different functionality which can affect the results in small ways.
Automatic Waste Classification System using Deep Leaning Techniques

Waste management refers to a system that starts with classifying different kinds of waste and gradually managing it from its inception to its final disposal. This type of study can bring about a positive change as it will help making the environment pollution free and reuse the waste as much as we can by classifying the recyclable stuff from the waste that are considered useless. This paper proposes a custom CNN model that classifies different types of waste materials accurately. Here, a large dataset named "Garbage Classification" was used and 8 different classes: battery, biological, cardboard, clothes, green-glass, paper, plastic, trash - have been detected. The images have been augmented in order to make all the classes equal in size which has resulted in a total of 16,000 images. Pre-trained CNN models such as VGGNet16, Resent50, MobileNetV2, InceptionV3 along with custom CNN models have been used and successfully achieved 87.57%, 94.34%, 96.99%, 95.71%,97.16% train accuracy and 89.38%, 94.34%, 96.81%, 94.47% and 97.58% validation accuracy respectively. Later on, the paper also evaluates the custom CNN model’s performance on an unseen test dataset via confusion matrix.
Summarizing Clustering Results using Sentence prototype based Language Models

Amount of experiment-related data in almost every domain has increased multifold. Hence, Understanding these novel datasets’ essential characteristics is vital to developing complex statistical models and testing various hypotheses. Exploratory data analysis using unsupervised techniques such as data clustering is becoming popular among researchers. Although several clustering algorithms exist, little research has been done to develop strategies to help understand the characteristics and physical implications of generated clusters. This paper proposes to use natural language to describe the results generated by a clustering algorithm linguistically for straightforward interpretation and understanding. We discuss salient characteristics of clusters that one looks for while interpreting clustering results and develop several linguistic prototype summaries for each of them. These summaries are then coherently combined to form linguistic summarization of clustering results. Numerical experiments on real-life IRIS dataset generate highly readable and easy-to-understand descriptions of the clusters generated through three popular clustering algorithms - k means, single linkage, and DBSCAN.
EduARt: Augmented Reality Based Learning Experience

Augmented reality (AR) is being used in multiple experiments to help learners get motivated, improve their learning abilities, and get connected with concepts. It has also helped teachers as a pedagogical educational training mechanism and tool for assessment. The AR applications can also be extended to slow learners or absent students. Data structures is a crucial subject at the undergraduate level as well as for graduate studies since it forms the basis for many real-world applications in the industry. If students are unable to understand the basic concepts, they lose interest. Our app, EduARt, has been developed using AR for data structures. This paper elaborates on the design, development, and testing of a markerless AR-based tool, EduARt. Our application has been developed using the Unity Platform and the Vuforia SDK. To analyze the usefulness of our app, we collected feedback from undergraduate students. The feedback says that almost 71% of students had faced issues understanding data structures during their course, and 96% of them felt that such an app would help them.
Area&amp;power optimized modulo (2<sup>n</sup> ± 2<sup>p</sup> ± 1) multiplier

Modulo multiplication is a key operation determining the efficiency of the target hardware implemented using Residue Number System (RNS). For arbitrary modulo it is usually done via regular multiplication block followed by a modulo reduction unit with the primary target goal of reducing the complexity and the size of the reduction step. There are also known so-called special moduli allowing more efficient multiplication and/or reduction than generic schema for arbitrary modulo. In this paper we propose an optimized architecture for 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ±2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> ±1 modulo multipliers, where 2 ≤ p ≤ n/2+1. Proposed design can achieve an average hardware area savings of about 5.5% with an average delay improvements of about 11.1% for m = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> ±1 comparing with the best known so far implementations. It also could be used for regular reduction operation with up to 2n + 1-bit width input numbers.
ZodiacMSM: A Heterogeneous, Multi-node and Scalable Multi-Scalar Multiplication System for Zero Knowledge Proof Acceleration

In cryptography, Zero-knowledge proof (ZKP) is a protocol that enables a party to demonstrate the accuracy of a given statement without revealing any additional information beyond the validity of the statement itself. This protocol ensures both the confidentiality and integrity of the computation, and has therefore become widely utilized in various industries for privacy-preserving, including online age verification and electronic voting. The most computationally demanding component of most Zero Knowledge Proof systems is the Multi-Scalar Multiplication (MSM) module. To address this issue and achieve low power and cost efficient MSM for different applications, a novel heterogeneous MSM architecture, ZodiacMSM, is developed. The architecture utilizes the Pippenger algorithm and a multi-chip design with memory access partitioning, which enables scalable performance for polynomials of various sizes in different ZKP applications. The performance of this architecture increases almost linearly with the number of nodes. ZodiacMSM supports multiple elliptic curves through reconfigurable fully-pipelined point-addition to achieve lower latency and minimize scheduler effort. When evaluated in 28nm, ZodiacMSM is able to achieve 2.3x speedup on MSM with single node and 32x speedup on 16 nodes compared to previous peer-reviewed state-of-the-art ASIC results [8].
KREATION: Kotlin Framework for Self-Adaptive IoHT Applications

In recent years, we have observed an increased volume of proposals for Internet of Health Things (IoHT) applications in the literature. Developing this type of application takes work, as it involves a series of challenges, many of them related to the constant change in the context of these applications, such as challenges related to energy consumption, the type of network communication, and its latency. In this sense, the development of self-adaptive systems is an alternative that has been explored for these applications. Self-adaptive systems can adapt to changes in context automatically, changing their behavior. However, developing self-adaptive systems is not trivial since, unlike other systems, they must be designed to evolve and adapt their behavior at runtime continuously. In this sense, we propose a Kotlin framework for developing self-adaptive IoHT applications for Android mobile devices called KREATION. This framework was developed using the Model-View-Control architecture and incorporates the MAPE-K adaptation loop into its internal logic. It also presents mechanisms to help collect data from Android smartphone sensors and data from the Google Fit API, which allows you to obtain data collected from other Android devices, such as smart bands and smartwatches. We evaluated the framework using it to develop two proofs of concept demonstrating that it is functional and can help the development of self-adaptive IoHT applications.