Reviewerly/exHarmony huggingface dataset:
Random Polynomials in Legendre Symbol Sequences

Abstract It is important in cryptographic applications that the “key” used should be generated from a random seed. Thus, if the Legendre symbol sequence generated by a polynomial (as proposed by Hoffstein and Lieman) is used, that is <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="block"> <m:mrow> <m:mrow> <m:mo>{</m:mo> <m:mrow> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mfrac> <m:mrow> <m:mi>f</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mn>1</m:mn> <m:mo>)</m:mo> </m:mrow> </m:mrow> <m:mi>p</m:mi> </m:mfrac> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mfrac> <m:mrow> <m:mi>f</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mn>2</m:mn> <m:mo>)</m:mo> </m:mrow> </m:mrow> <m:mi>p</m:mi> </m:mfrac> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mfrac> <m:mrow> <m:mi>f</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mn>3</m:mn> <m:mo>)</m:mo> </m:mrow> </m:mrow> <m:mi>p</m:mi> </m:mfrac> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> <m:mo>⋯</m:mo> <m:mo>,</m:mo> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mfrac> <m:mrow> <m:mi>f</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mi>p</m:mi> <m:mo>)</m:mo> </m:mrow> </m:mrow> <m:mi>p</m:mi> </m:mfrac> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:mrow> <m:mo>}</m:mo> </m:mrow> <m:mo>,</m:mo> </m:mrow> </m:math> \left\{ {\left( {{{f\left( 1 \right)} \over p}} \right),\left( {{{f\left( 2 \right)} \over p}} \right),\left( {{{f\left( 3 \right)} \over p}} \right), \cdots ,\left( {{{f\left( p \right)} \over p}} \right)} \right\}, then it is important to choose the polynomial f “almost” at random. Goubin, Mauduit, and Sárközy presented some not very restrictive conditions on the polynomial f , but these conditions may not be satisfied if we choose a “truly” random polynomial. However, how can it be guaranteed that the pseudorandom measures of the sequence should be small for almost "random" polynomials? These semirandom polynomials will be constructed with as few modifications as necessary from a truly random polynomial.
Providing Reliable Services for Hardware Cryptography Accelerator in Virtualization

Hardware Cryptography Accelerator has been used as a cost saving solution to provide services for web servers which face high computational consumption from SSL/TLS processing in Cloud Computing. However, services provided by the hardware accelerator can’t be continuous during hardware maintenance phases in virtualized environment. To provide reliable(24*365) services in cloud, we introduce a “Crypto Fallback Architecture for Virtualization” that combines the hypervisor, VM(Virtual Machine) and hardware. Our solution has been deployed successfully on Intel® QuickAssist Technology (QAT) which are used widely by Microsoft, Facebook, Alibaba and so on. The evaluation shows that our solution could provide reliable services for nginx/openssl in the cloud.
Geofencing Implement For Self-Monitoring Wandering Behavior And Sharing Location In Realtime With Firebase

Geofencing is an advanced technique that collects geolocation data in order to test whether a path of travel has crossed into or out of a geographical area represented by a geometric shape. The Firebase Backend-as-a-Service (Baas) supports Android applications with data store and realtime share capabilities. These techniques are applied in location based services and areas of human activity recognition research, especially for problems in medicine like dementia/Alzheimer patients, HIV transmission prevention, and monitoring COVID-19 patients’ movements. In this paper, we present an Android application with a two-stage geofencing trigger used to initiate communication with the Firebase BaaS and trigger features that address wandering behavior in geriatric healthcare patients. The proposed tool uses the Android Location API, the Google Maps SDK and the Firebase BaaS. In order to integrate geofencing into our existing Firebase feature platform, this paper investigates use of the Android Location Library’s Geofence. Our toolchain and methods for analyzing geofence location data to address wandering behavior were preferred after review of related research and comparison with existing commercial solutions. Lastly, our geofence integration tool is developed, implemented and field-tested in order to calibrate user options for the primary geofence parameters. We also provide analysis and discussion of our geofencing optimizations in the context of geriatric healthcare.
Quantum-based Distributed Algorithms for Edge Node Placement and Workload Allocation

Edge computing is a promising technology that offers a superior user experience and enables various innovative Internet of Things applications. In this paper, we present a mixed-integer linear programming (MILP) model for optimal edge server placement and workload allocation, which is known to be NP-hard. To this end, we explore the possibility of addressing this computationally challenging problem using quantum computing. However, existing quantum solvers are limited to solving unconstrained binary programming problems. To overcome this obstacle, we propose a hybrid quantum-classical solution that decomposes the original problem into a quadratic unconstrained binary optimization (QUBO) problem and a linear program (LP) subproblem. The QUBO problem can be solved by a quantum solver, while the LP subproblem can be solved using traditional LP solvers. Our numerical experiments demonstrate the practicality of leveraging quantum supremacy to solve complex optimization problems in edge computing.
Job Recommendation Service for GPU Sharing in Kubernetes

Cloud infrastructures encourage the multi-tenancy of hardware resources. User-defined Machine Learning (ML) training jobs are offloaded to the cloud for efficient training. State-of-the-art resource schedulers do not preserve user privacy by accessing sensitive meta-data of the user-defined training workload. We present the design of a fine-grain, online, privacy-preserving job scheduler built on top of the Kubernetes platform in combination with Argo workflow. We categorize ML training workloads on standard benchmark architectures and datasets over sixty-six different features, cluster them based on exploratory data analysis, and perform inter - and intra-cluster task interference. We assume black-box access to the user-defined ML training jobs and refrain from accessing sensitive meta-data. We define three scheduler-level objectives to maximize gains from users’ and cloud providers’ perspectives. Our scheduler promotes multi-tenancy by intelligently selecting competitor jobs for concurrent execution in every pod while abiding by scheduler-level objectives.
Early Design Exploration of Space System Scenarios Using Assume-Guarantee Contracts

We present a compositional approach to modeling and analyzing space mission operation sequences with steps across multiple viewpoints. We consider different operation tasks such as communication, science observation, trajectory correction, and battery charging; and separate their interactions across discipline viewpoints. In each sequence step, these tasks are modeled as assume-guarantee contracts. They make assumptions on the initial state of a step, and if these assumptions are satisfied, they guarantee desirable properties of the state at the end of the step. These models are then used in Pacti, a tool for reasoning about contracts. We demonstrate a design methodology leveraging Pacti’s operations: contract composition for computing the contract for the end-to-end sequence of steps and contract merging for combining them across viewpoints. We also demonstrate applying Pacti’s optimization techniques to analyze the figures of merit of admissible sequences satisfying operational requirements for a CubeSat-sized spacecraft performing a small-body asteroid rendez-vous mission. We show that analyzing tens of thousands of combinations of sequences and operational requirements takes just over one minute, confirming the scalability of the approach. The methodology presented in this paper supports the early design phases, including requirement engineering and task modeling.
Predicting GameFi's Daily Market Direction Using Support Vector Machine

This research uses support vector machines to forecast the direction of the GameFi market on a daily basis. GameFi is a blockchain-based product that combines gaming and trading. The customer can profit from any in-game activity. Machine learning is a computational method that is able to forecast the future using historical data or attribute information such as open, high, low, close, volume, and market capitalization. Like many researchers who are working to develop accurate machine learning models that can forecast the market value of stocks, commodities like gold and gas, and cryptocurrencies, the main goal of this study is to use SVM to predict the daily direction of GameFi's price. The experimental result shows that SVM's prediction performance is best at 57.6%.
Comparison of CNN Models for Urban Garbage Image Classification in Thailand

The authors believe that for society to continue improving, it needs to have a better waste classification system. This paper presents a computer vision model trained with a novel dataset comprising images of waste collected from Thailand, aimed at improving computer vision-based waste classification. We compare several CNN models, including VGG16, DenseNet169, ResNet-101, MobileNetV2, and InceptionV3 by using the Trashnet dataset developed by Gary Thung and our novel dataset. Our findings demonstrate that our model trained with both the Trashnet dataset and our new dataset outperforms the model trained by either dataset alone. The authors also discuss how this novel dataset can be improved to provide better results.
A Comparison of Machine Learning and Neural Network Algorithms for an Automated Thai Essay Scoring

Thai students have relatively low scores on the reading literacy assessment conducted by PISA. Various studies reported that reading skills could be improved by writing. However, essay scoring is a time-consuming task. An automated essay scoring system can support both teachers and students by reducing the teachers' workload and providing predicted scores as feedback to students. A number of recent studies have focused on automated essay scoring dataset that contains only essays written in English. Little to no research has been done on the automated essay scoring system for the Thai language. The aim of this study is to develop a Thai essay scoring system using machine learning and deep learning models that have been reported to achieve good performance. We also try to improve the performance of our models by adding essay attribute features. The models that were used in this study are logistic regression, kNN, SVM, random forest, gradient boosting, XGBoost, LSTM (bag-of-words), LSTM (w2v), BERT-based, and LSTM+CNN (BERT embedding). The models were evaluated by six metrics, including accuracy, Quadratic weighted kappa, precision, recall, and F1-score along with 10-fold cross-validation. The experimental results show that XGBoost outperforms other models considering the majority of best metric scores in each set. For deep learning models with automatically extracted features from the text, the LSTM with word2vec features model yielded better performance than other deep learning models.
Recognizing Fall Risk Factors with Convolutional Neural Network

As the number of elderly living alone is increasing every year, some seemingly common daily activities can potentially raise the risk of serious injuries and fatal accidents for these elderly. While falls can occur anywhere, they most often occur at home, this is especially true among the elderly. Without timely notification to medical personnel and assistance, the resulting injuries could be life-threatening. As falls are caused by many different risk factors, it is necessary to identify potential incidents and make needed changes accordingly in order to reduce the risk and prevent falls. Therefore, we propose a system using surveillance cameras to detect daily activities (e.g., bending down, sitting, standing, and walking) that potentially increase the risk of falling. Moreover, we recognize high risk factors of falls such as ones that involve using the phone while performing an activity, not paying attention to obstacles, and not holding the handrails while going upstairs or downstairs. Convolutional neural network is applied for activity classification in this work. This warning system is utilized for detecting risk factors of falls that commonly occur among the elderly, which could then be used to trigger a message and/or audible alert to designated persons such as a doctor, a caregiver, or family members for timely assistance and care.
An Efficiently Revocable Cloud-based Access Control Using Proxy Re-encryption and Blockchain

Existing cloud-based access control schemes employing ciphertext-policy attribute-based encryption (CP-ABE) generally achieve secure and fine-grained data sharing. However, most works have not taken the full support of both user and attribute revocation with the auditability of revocation transactions in their core contributions. In this paper, we proposed a blockchain-assisted cloud-based access control scheme composing a set of cryptographic protocols leveraging CP-ABE and blockchain technology to deliver an optimized user and attribute revocation cost which is capable of efficiently handling both users and attribute revocation with the auditable feature. Specifically, we devised cryptographic protocols to work with the blockchain and smart contracts to handle user authentication checking, cryptographic processes, and revocation management for data outsourced in the cloud. To evaluate the efficiency of our proposed scheme, we run experiments to show that the performance of ciphertext re-encryption and key update is efficient and implementable.
Improving Pre-Trained Models for Multi-Label Classification in Stack Overflow: A Comparison of Imbalanced Data Handling Methods

Tag classification is essential in Stack Overflow. Instead of combining through pages or replies of irrelevant information, users can easily and quickly pinpoint relevant posts and answers using tags. Since User-submitted posts can have multiple tags, classifying tags in Stack Overflow can be challenging. This results in an imbalance problem between labels in the whole labelset. Pretrained deep-learning models with small datasets can improve tag classification accuracy. Common multi-label resampling techniques with machine learning classifiers can also fix this issue. Still, few studies have explored which resampling technique can improve the performance of pre-trained deep models for predicting tags. To address this gap, we experimented to evaluate the effectiveness of ELECTRA, a powerful deep learning pre-trained model, with various multi-label resampling techniques in decreasing the imbalance that induces mislabeling in Stack Overflow's tagging posts. We compared seven resampling techniques, such as LP-ROS, ML-ROS, MLSMOTE, MLeNN, MLTL, ML-SOL, and REMEDIAL, to find the best method to mitigate the imbalance and improve tag prediction accuracy. Our results show that MLTL is the most effective selection to tackle the inequality in multi-label classification for our Stack Overflow data with deep learning scenarios. MLTL achieved 0.517, 0.804, 0.467, and 0.98 from the metrics Precision@l, Recall@5, F1-score@1, and AUC, respectively. Conversely, MLeNN gained only 0.323, 0.648, 0.277, and 0.95 from the same metrics.
Gender Classification of Social Network Text Using Natural Language Processing and Machine Learning Approaches

Gender is a crucial consideration in many fields of study. In the era of social networks, massive volumes of data are gathered and processed, enabling us to use this data for a variety of purposes. Our objective to build a gender classification model based on Thai text using natural language processing (NLP) and a machine learning approach. We collected the data on social media websites using web scraping. TF-IDF and n-gram were applied for feature extraction tasks. Logistic Regression, Naïve Bayes, and Random Forest have implemented classification models. Accuracy, precision, recall, and f1 score are used as evaluation metrics and demonstrate that the Logistic Regression model trained on the features derived from data received from texts longer than 200 words produces the best outcome. The dataset is available at https://github.com/supanat/gender-classification-thai-text.git.
Procedural Content Generation of Super Mario Levels Considering Natural Connection

Procedural content generation (PCG) is one of the major research topics in the game research field. The objective of PCG is to automatically generate reusable game content, such as levels, items, or landscapes, that satisfy specific criteria such asp layability, believability, or difficulty. To the best of our knowledge, this paper i s the first to propose a level generation approach for Super Mario Bros. (Super Mario) that considers natural connection. In this study, a Super Mario level consists of several sublevels called patterns. Without natural connectivity of patterns, the level may be unplayable or not entertaining. We employ Conditional Generative Adversarial Networks (CGAN) to generate naturally connected patterns with a Matching mechanism to repair the defective outputs obtained from CGAN. The experimental results indicate that the levels generated with the proposed CGAN and Matching technique significantly improve natural connectivity compared to randomly generated patterns. The findings c an help improve the PCG's capability to generate content for better players' enjoyment.
A Web Platform for Measuring Frog Size and Counting via Contour Detection and Image Segmentation

Object counting in aquaculture is an important task, and has been widely applied to aquatic populations. The problem of too many frogs in a pond can lead to competition for resources and reduced growth and reproduction rates for the frogs. To address this issue, we propose using image processing techniques to help farmers to count the number of frogs present in the pond. Farmers used manual counting, or counting by hand. But it is slow and hard to count. Our results showed that using image processing was an effective method for accurately estimating the number of frogs present in the pond. The research objectives were 1) count the number of frogs seen through the frog's eyes and 2) measure the size of the frogs. The farmer divides the frog's size into two sizes. The first size is smaller (1-2.5 cm) and the second size is larger (2.5-5 cm), Both sizes would affect the density and mortality rate of frogs in the aquaculture pond. 3) allow us to monitor changes in the frog population over time. 4) Use text-to-speech technology to assist elders in hearing the number of frogs. This approach could be useful for managing frog density in ponds and ensuring the health and sustainability of the ecosystem.
Machine Learning Techniques for Water Quality Classification of Thailand's Rivers

Water is necessary for human consumption. To ensure that water is safe, a monitoring system for water quality is required. One part of the system is to be able to predict the water quality class. Using data collected from the Pollution Control Department of Thailand from 2009 to 2021, we compared four machine learning approaches for classifying water quality classes in four main rivers in Thailand: the Ping, Wang, Yom, and Nan rivers. Random Forest, Extreme Gradient Boosting (XGBoost), Logistic Regression, and Support Vector Machine were used in this study. Moreover, synthetic minority oversampling technique (SMOTE) and Random oversampling, two strategies for dealing with imbalanced data, were also used to improve classification F1 score. This study found that XGBoost with SMOTE achieved the highest score, and BOD was the most important feature in classifying water quality.
Text Classification Using Improving Term Frequency with Gaussian and Multilayer Perceptron Techniques

This paper proposes combining the term frequency with the Gaussian technique and the multilayer perceptron technique for text sentiment analysis using two datasets: tweet customer sentiments serving U.S. airlines and Amazon product sentiment reviews. The bag of words, term frequency-inverse document frequency, term frequency-inverse corpus document frequency, term frequency-inverse gravity moment, and term frequency with Gaussian are investigated in this study. The experimental results show that improving term frequency with Gaussian for term weighting in text sentiment analysis from tweets of customer sentiments serving U.S. airlines, with a higher F-score than previous techniques and an 8% increase in F-score from the original term frequency with Gaussian technique. Also, the term frequency with Gaussian technique was enhanced for term weighting in text sentiment analysis using Amazon product review scraping, with a higher F-score than the previous techniques and had a 7% increase in F-score compared to the original term frequency with Gaussian technique.
Steganography with Adaptable Separate Encrypted Code of Hidden Confidential Information

Steganography conceals information within another medium to avoid detection, often by hiding a message within an image, audio, or video file. It enables the secure transmission of information without attracting unwanted attention. This paper discusses a proposed algorithm with a new technique that hides the secret message inside the YCbCr and HSV image channels. In this proposed method, the information is converted or encrypted into ASCII and hidden inside the image channels. In this study, The HSV and YCbCr color spaces are used due to their dominant characteristics to hide information, and the extraction or decryption process achieved 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy. The results demonstrate that the proposed encryption and decryption method is successful. By using a blank image, our data can be concealed without raising suspicion or risking image corruption. To verify the integrity of the decrypted file, a character check was performed.
Contextualized vs. Static Word Embeddings for Word-based Analysis of Opposing Opinions

Word embeddings are useful for studying public opinions by summarizing opinions about a concept by finding the nearest neighbors in the word embedding space. Static word embeddings such as word2vec are powerful for handling large amounts of text, while contextualized word embeddings from transformer-based models yield better embeddings by some evaluation metrics. In this study, we explore the differences between static and contextualized embeddings for word-based analysis of opposing opinions. We find that pre-training is necessary for static embeddings when the corpus is small, but contextualized embeddings are superior. When the focus corpus is large, static embeddings reflect related concepts, while contextualized embeddings often show synonyms or cohypernyms. Static embeddings trained only on the focus corpus capture opposing opinions better than contextualized embeddings.