Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Quantum Random Walk Model in Recommender Systems

Graph Convolutional Networks (GCNs) are effective in providing more relevant items at higher rankings in recommender systems. However, in real-world scenarios, it is important to provide recommended items with diversity and novelty as well as relevance to each user's preference. Additionally, users often desire a wide range of recommendations not just based on their past search behaviors and histories. To enhance each user's satisfaction, it is important to develop a recommender system that provides much more relevant and diverse items. LightGCN can achieve this, which is a GCN-based recommender system that learns latent vectors of users and items using multiple layers of aggregation functions and an adjacency matrix. However, LightGCN often provides recommendations without diversity when the number of layers is insufficient. On the other hand, when the number is excessive, the accuracy declines, which is known as the over-smoothing problem. To overcome this, we propose a novel approach using a continuous-time quantum walk model derived from a quantum algorithm to reconstruct the user-item adjacency matrix of LightGCN, improving the relevance and diversity of recommendations.
Automatic Hint Generation

At times when answers to user questions are readily and easily available (at essentially zero cost), it is important for humans to maintain their knowledge and strong reasoning capabilities. We believe that in many cases providing hints rather than final answers should be sufficient and beneficial for users as it requires thinking and stimulates learning as well as remembering processes. We propose in this paper a novel task of automatic hint generation that supports users in finding the correct answers to their questions without the need of looking the answers up. As the first attempt towards this new task, we design and implement an approach that uses Wikipedia to automatically provide hints for any input question-answer pair. We then evaluate our approach with a user group of 10 persons and demonstrate that the generated hints help users successfully answer more questions than when provided with baseline hints.
Text Classification of Climate Change Tweets using Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation

The climate change discourse on social media happens rapidly with microblogging sites such as Twitter. On these types of sites, there is a divide of stances. Some people believe that climate change is man-made, and some people deny its existence. This study aimed to classify climate change tweets in the given labeled dataset with the created text classification model that used Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation. Additionally, domain-specific preprocessing methods for climate change tweets and adding features by appending the majority topic of a given tweet between each word are applied. This study has shown that the created text classification model improved the F1 score of the two undersampled classes by 1 % and 6 % respectively while still maintaining a good F1 score for the majority class. The text classification model overall increased both macro and weighted averages by 3 % and 1 % respectively.
YODA: Covert Communication Channel over Public DNS Resolvers

Enterprises are increasingly migrating to public domain name system (DNS) resolvers for reliability, cost optimizations, and, most importantly, improved security and user privacy. The integrated threat intelligence feeds at these resolvers enable easy identification and blocking of malicious exploits that use DNS queries. However, we observe that the shared local caches at these public DNS resolvers enable covert communication channels from otherwise secure enterprises accessible to any remote adversary, thus cautioning the migration to public DNS resolvers. We present YODA, a covert communication channel via public DNS resolvers that can exfiltrate sensitive information from a victim enterprise to a remote adversary. Unlike prior works, YODA overloads DNS queries for popular domains to transfer the data without revealing any identity of the adversary. Consequently, YODA cannot be blocked by domain name filtering. We demonstrate our attack on public DNS resolvers such as Google, Cloudflare, Quad9, OpenDNS, and LibreDNS. Our evaluations show that the adversary can achieve a bandwidth of 480bps with desktop devices.
A Novel Framework for Relating Quasi-Cyclic Codes and Quasi-Twisted Codes

In this paper, we aim to analyze the algebraic structure of repeated-root quasi-cyclic codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p^{k}{n}\ell$</tex> and index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell$</tex> over the finite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> is a positive integer, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q=p^{\alpha}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(n,p)=1$</tex> . For this purpose, a quasi-cyclic code over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}$</tex> is regarded as a linear code over an auxiliary ring. By introducing a ring isomorphism, we provide a one-to-one correspondence between this class of quasi-cyclic codes and nonrepeated-root <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-u)$</tex> . quasi-twisted codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n\ell$</tex> and index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell$</tex> over the chain ring <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}+u\mathbb{F}_{q}+\cdots+u^{p^{k}-1}\mathbb{F}_{q}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$u^{p^{k}}=0$</tex> . Our approach enables us to extend the results regarding non-repeated-root quasi-twisted codes over rings to repeated-root quasi-cyclic codes over finite fields. To illustrate the effectiveness of our method, we provide examples that demonstrate how it simplifies the structure of this class of codes.
JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.
A Study on AR Materials and Teaching Methods to Support the Clarification of Students’ Naive Conceptions of Electric Current and Transformation into Scientific Conceptions

This study aimed to create Augmented Reality (AR) materials aimed at understanding the scientific conceptions of electric current, devise teaching methods using the created AR materials, and verify the effectiveness of the teaching methods through class-room practice. To achieve these objectives, this study created AR materials that superimpose electric current models on experimental apparatuses and worksheets using a tablet PC, and devised a teaching method that focuses on clarifying the students’ mis-conceptions. The results showed that the AR materials and teaching methods contributed to the understanding of the directional concept of electric current. They also contributed to understanding some of the concepts of continuity and conservation of current.
Extreme Gradient Boosting with Synthetic Minority Over Sampling Technique for an Improved Breast Cancer Prediction

Breast cancer is one major contributor to global mortality and the second-leading reason of cancer deaths in women worldwide. Early prediction of breast cancer plays a vital part in improving patient's survival outcome by examining tumors whether malignant or benign. In this paper, the researchers formulated a machine learning (ML) classifier based on an ensemble learning called extreme gradient boosting (XGBoost) algorithm in predicting a benign or malignant (cancerous) tumor. The researchers integrated the synthetic minority oversampling technique (SMOTE) to resolve the class imbalance problem found in the dataset. Data-set utilized in this study are clinical cases of patients from the University of Wisconsin Hospitals. Experimental results showed that the proposed approach yielded better performance as compared to methods used in previous literature's, with an accuracy of 98.87%, a kappa statistic of 0.9774, and an f - score of 0.9890. Further, feature importance analysis showed that, among all input features, ‘Bare Nuclei’ variable contributed the greatest predictive power in classifying a malignant or benign tumor. This result is consistent with previous literature's, which emphasizes that Bare nuclei are typically seen in benign tumors as compared to malignant tumors.
An Automatic Monitoring System for Sports Training Indicators Based on Deep Learning and Multiple Sensors

In recent years, with the rapid development of computer and network technology, people have become increasingly concerned about health. In order to ensure health and human health, more and more people have begun to pay attention to physical exercise. However, currently many fitness monitoring systems only collect and collect exercise data, without real-time monitoring of exercise. Moreover, traditional methods for monitoring sports training indicators lack real-time performance, and the accuracy of sports testing is also not high. This article first explores deep learning, then explains some of the changes it has brought to the field of automatic monitoring system design, and then explores the important achievements and breakthroughs that can be achieved by combining deep learning algorithms in the setting of automatic monitoring systems. Using the data survey method to investigate and statistically analyze the deep learning algorithms used in the design of automatic monitoring systems, it can be seen from the survey results that the automatic detection system using deep learning algorithms has better efficiency and accuracy than the old system. Therefore, the design of deep learning algorithms and multi-sensor sports training index automatic monitoring systems plays a crucial role.
BASB-SG: A Biohashing-based Authentication Scheme for Secure Blockchain-enabled Smart Grids

Smart grids offer several uses and advantages that make them a compelling technology for modern power systems. They provide improved reliability and efficiency and reduce cost and energy consumption while also integrating renewable energy sources. Authentication is crucial in smart grid security to ensure that only authorized parties can access the system's resources. However, most authentication mechanisms used in the past are computationally intensive and may not be appropriate for devices with limited resources. To address this challenge, we propose a secure smart grid authentication mechanism using blockchain technology and biohashing to verify smart meters to a service agency. Thus, enabling smart meters to access services such as electricity consumption reports and feedback in a secure manner. We perform an analysis and compare BASB-SG with other works in the same field to prove that BASB-SG guarantees better security while remaining lightweight which makes it ideal to use in real-world smart grids.
Application of Classification Techniques for Document Summarization Problem

Extracting relevant information from such a large and unstructured text collection is a challenging mission that also takes a lot of time. And manually summarizing massive amounts of text is quite challenging for humans. Thus, Text Summarization (TS) and Classification are the techniques to deal with this kind of problem. Because the main goal is to create a condensed document with the original content, this might be seen as an optimization issue. In this work, the single document summarization problem is addressed through a comparison of a few classification approaches, including Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). The effectiveness of all these approaches is assessed using a variety of assessment measures, including Precision (P), Recall (R), and F-Score (F), to confirm summary relevance and non-redundancy across conventional and standard Document Understanding Conference (DUC) datasets.
Performance analysis of Ethereum smart contracts: A Study on Gas cost and block size impact

Blockchain technology has revolutionized the way transactions are conducted and verified in a decentralized manner. The performance analysis of Ethereum smart contract is crucial in understanding its limitations and potential for various applications. This study aimed to evaluate the gas cost of different sort algorithms and the impact of block size on the throughput of Ethereum network. The results showed that the gas cost of search algorithms such as quick sort and bubble sort varied significantly, with quick sort having a lower cost. Additionally, increasing the block size had a positive impact on the throughput of the Ethereum network, with a higher number of transactions processed per second. These findings provide valuable insights into the performance of Ethereum smart contracts and highlight the importance of considering gas cost and block size in the design and implementation of blockchain-based systems.
Knowledge Graph Service System Based on Data Fusion Technology

With the advent of the knowledge explosion and the era of big data, various disciplines are developing towards a more refined direction, and various disciplines are constantly intersecting and integrating. This requires a new way of knowledge organization, display, and management, namely the knowledge graph. It can connect knowledge between disciplines to form a knowledge centered network, achieving better integration and utilization of various resources. Therefore, this article conducted research on a knowledge graph service system based on data fusion technology. Firstly, knowledge graph, service system, and data fusion technology were explained in detail. Then, the superiority of data fusion technology for knowledge graph service system was analyzed through experiments. This article studied a knowledge graph service system based on data fusion technology, aiming to effectively manage, showcase, and utilize knowledge from different disciplines, and achieve better integration and utilization of various resources.
Intelligent Monitoring of Hydropower Signals Based on Knowledge Graph

Hydropower station monitoring system collects a large number of system running signals, and it is critical to intelligently monitor these signals and timely process them, which can ensure the safe and stable operation of hydropower stations. However, it is quite challenging to achieve intelligent monitoring efficiency. There are problems such as various device types, complicated signal information and difficult extraction of latent knowledge. In this paper, we propose a novel intelligent monitoring framework of hydropower signals based on knowledge graph. Specifically, we first collect amount of operational unstructured log text data from a real-world hydropower monitoring system, and then propose a hydropower signal knowledge graph (HSKG) construction method by combining the semantic parsing technology and expertise in the field of hydropower operation. We further propose a BERT-BiGRU-CRF model for automatic entity extraction of collected data. Finally, we develop an intelligent signal analysis model based on the constructed HSKG. The experimental results on real-world datasets evaluate the effectiveness and efficiency of our method for intelligent hydropower signal monitoring.
MalAder: Decision-Based Black-Box Attack Against API Sequence Based Malware Detectors

The API call sequence based malware detectors have proven to be promising, especially when incorporated with deep neural networks (DNNs). Several adversarial attack methods are proposed to fool these detectors by introducing undetectable perturbations into normal samples. However, in real-world scenarios, the malware detector provides only the predicted label for a given sample, without exposing its network architecture or output probability, making it challenging for adversarial attacks under the decision-based black-box. Existing work in this area typically relies on random-based methods that suffer high costs and low attack success rates. To address these limitations, we propose a novel decision-based black-box attack against API sequence based malware detectors, called MalAder. Our approach aims to improve the attack success rate as well as query efficiency through a directional perturbation algorithm. First, it utilizes attention-based API ranking to assess the importance of API calls in the context of different API sequences. This assessment guides the insertion position for perturbation. Then, the perturbation is carried out using benign distance perturbing, which gradually shortens the semantic distance from adversarial API sequences to a set of benign samples. Finally, our algorithm iteratively generates adversarial malware samples by performing perturbations. In addition, we have implemented MalAder and evaluated its performance against two classic malware detectors. The results show that MalAder outperforms state-of-the-art decision-based black-box adversarial attacks, proving its effectiveness.
Don't Knock! Rowhammer at the Backdoor of DNN Models

State-of-the-art deep neural networks (DNNs) have been proven to be vulnerable to adversarial manipulation and backdoor attacks. Backdoored models deviate from expected behavior on inputs with predefined triggers while retaining performance on clean data. Recent works focus on software simulation of backdoor injection during the inference phase by modifying network weights, which we find often unrealistic in practice due to restrictions in hardware. In contrast, in this work for the first time, we present an end-to-end backdoor injection attack realized on actual hardware on a classifier model using Rowhammer as the fault injection method. To this end, we first investigate the viability of backdoor injection attacks in real-life deployments of DNNs on hardware and address such practical issues in hardware implementation from a novel optimization perspective. We are motivated by the fact that vulnerable memory locations are very rare, device-specific, and sparsely distributed. Consequently, we propose a novel network training algorithm based on constrained optimization to achieve a realistic backdoor injection attack in hardware. By modifying parameters uniformly across the convolutional and fully-connected layers as well as optimizing the trigger pattern together, we achieve state-of-the-art attack performance with fewer bit flips. For instance, our method on a hardware-deployed ResNet-20 model trained on CIFAR-10 achieves over 89% test accuracy and 92% attack success rate by flipping only 10 out of 2.2 million bits.
Analysis of Public Opinion on Public Transportation in Bandung and Jakarta in Twitter using Indonesian Bidirectional Encoder Representations from Transformer

Transportation has been one of the main challenges for people living in urban areas, especially in big cities. Handling transportation problems traditionally is no longer considered suitable due to the increasingly large and complex data, which calls for an intelligent transportation system. One source of data that can be used to is social media (Twitter), in which the development of user-generated content can improve the management of existing transportation systems. In this study, IndoBERT, as a state-of-the-art model in natural language processing tasks, is used to perform sentiment analysis on Indonesian tweets about public transportation to have a better understanding of tweet context. Experimental results show that IndoBERT performs better than traditional machine learning algorithm, with the best combination of hyperparameter tuning results in accuracy of 94.8% which generalizes best to the dataset.
Applying a Recurrent Neural Network to the Behaviour of an Autonomous Agent

This manuscript focuses on the application of deep recurrent learning techniques to build rational behaviour of an autonomous agent. The purpose of this study is to compare the encoder-decoder learning approach and classical Recurrent Neural Networks. The main hypothesis is that the encoder-decoder deep learning method is applicable in the area of sequential games and demonstrates better performance than classical Markov Deterministic Process learning. Thus, intelligent agents with a consistent approach can find better solutions to problems. in addition, in this manuscript, additional emphasis is placed on the training process in a dynamic sequential game environment.
Image Caption Generation using CNN-GRU Approach

There has been a rise of interest in the topic of picture caption creation in recent years, with the goal of automatically generating human language descriptions of photographs. The approach put forward in this study blends information extracted from a picture using a pre-trained convolutional neural network(CNN) with a gated recurrent unit (GRU) to produce captions. The suggested model is assessed on flickr8k and flickr30k datasets and gets impressive outcomes. The GRU’s incorporation enables modelling of long-term word relationships in the output captions. Overall, by effectively collecting contextual information and interactions between items in the image, this method seeks to advance existing image captioning methodologies. Hence, this method helps to create advanced models to give the appropriate and accurate results in caption generation.