Reviewerly/exHarmony huggingface dataset:
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

AbstractWe study here a fixed mini-batch gradient descent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple nonoverlapping partitions. Once the partitions are formed, they are then fixed throughout the rest of the algorithm. For convenience, we refer to the fixed partitions as fixed mini-batches. Then for each computation iteration, the gradients are sequentially calculated on each fixed mini-batch. Because the size of fixed mini-batches is typically much smaller than the whole sample size, it can be easily computed. This leads to much reduced computation cost for each computational iteration. It makes FMGD computationally efficient and practically more feasible. To demonstrate the theoretical properties of FMGD, we start with a linear regression model with a constant learning rate. We study its numerical convergence and statistical efficiency properties. We find that sufficiently small learning rates are necessarily required for both numerical convergence and statistical efficiency. Nevertheless, an extremely small learning rate might lead to painfully slow numerical convergence. To solve the problem, a diminishing learning rate scheduling strategy can be used. This leads to the FMGD estimator with faster numerical convergence and better statistical efficiency. Finally, the FMGD algorithms with random shuffling and a general loss function are also studied. Supplementary materials for this article are available online.KEYWORDS: Fixed mini-batchGradient descentLearning rate schedulingRandom shufflingStochastic gradient descent Supplementary MaterialsThe detailed proofs of all propositions and theorems can be found in the supplementary materials.AcknowledgmentsThe authors thank the Editor, Associate Editor, and two anonymous reviewers for their careful reading and constructive comments.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingThis work is supported by National Natural Science Foundation of China (No.72001205), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (21XNA026).
Information Security using GNU Privacy Guard

The CIA trinity of data system confidentiality, integrity, and availability is significantly reliant on communication networks in today's society. A major portion of the vital communication system infrastructure is dependent on huge, sophisticated software systems that may have defects and pose security threats. Data security has always been a primary issue, both online and offline. Several email, data, and communication systems are usually several years old and must be upgraded on a regular basis to keep possible hazards at bay. As a result, there is a pressing need to protect communication networks, internet data, and email systems from external threats. An overview of the GNU Privacy Guard (GNUPG) in the era of information security is presented in this paper. The paper also investigates the possibility and requirement of data transmission security. There is a description of various types of cryptography algorithms, mathematics, and science used in various asymmetric key algorithms. Furthermore, the GNUPG applications in several fields of keys and certificates are evaluated.
PreActo: Efficient Cross-Camera Object Tracking System in Video Analytics Edge Computing

Cross-camera real-time object tracking is one of the important, yet challenging applications of video analytics in edge computing environments. To provide accurate and efficient real-time tracking, a tracking target's future movements need to be predicted. Particularly, the destination camera and travel time of the target object are to be identified so that tracking duties can be handover-ed seamlessly. In this paper, we propose a collaborative cross-camera tracking system, called PreActo, with two key features: (1) ResNet-based trajectory learning to exploit the rich spatio-temporal information embedded within objects' moving patterns, which has not been utilized by the existing literature, and (2) collaboration between the edge server and the edge device for real-time trajectory prediction and tracking handover. To prove the validity of our proposed system, we evaluate PreActo on a video dataset leveraging real-world trajectories. Evaluation results show that the proposed system reduces up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7\times$</tex> the number of processed frames for handover, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times$</tex> lower latency while providing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.5\times$</tex> tracking precision improvement compared to the state-of-the-art.
SEEK: Detecting GPS Spoofing via a Sequential Dashcam-Based Vehicle Localization Framework

GPS spoofing is a great threat to the safety of transportation systems as well as other systems that rely on GPS for navigation. This paper proposes a novel computer vision based approach for GPS spoofing detection, termed SEquential dashcam-based vEhicle localization frameworK (SEEK). SEEK utilizes vehicle dashcam images to identify a vehicle's true location and detects possible GPS spoofing attacks through verifying if the reported GPS locations of the vehicle are correct. However, it is nontrivial to use dashcam images for vehicle localization due to multiple challenges caused by real-world driving, including the complicated lighting/weather conditions, season/timing variations of the images, large blockage ratio in the images, and varying driving speeds. SEEK features a unique design with novel schemes to address complicated lighting/weather conditions, transform images to align with season changes, reduce blockage, and adopt a sequential image matching scheme. The performance evaluation shows that SEEK significantly outperforms the previous GPS spoofing detection scheme, and achieves a detection accuracy of up to 94%.
A Survey on Data Integrity Attacks and DDoS Attacks in Cloud Computing

Cloud computing has become a beneficial and profitable industry due to its main characteristics of pay-as-you-go and scalability. These are the traits that have attracted businesses to transfer their data and services to a cloud platform accessed through the Internet. However, internet based services and remote access to data have become a main concern in this new model. An unstable and insufficient security layer has created a penetrable wall to the assets and services of their customers. In other words, businesses are being targeted by a vast list of security attacks that primarily affect service availability and data integrity. In this survey paper, identification of security attacks affecting service availability and data integrity are established. Moreover, possible solution approaches are studied and analyzed for such attacks.
Zero-Effort Two-Factor Authentication Using Wi-Fi Radio Wave Transmission and Machine Learning

The proliferation of sensitive information being stored online highlights the pressing need for secure and efficient user authentication methods. To address this issue, this paper presents a novel zero-effort two-factor authentication (2FA) approach that combines the unique characteristics of a user's environment and Machine Learning (ML) to confirm their identity. Our proposed approach utilizes Wi-Fi radio wave transmission and ML algorithms to analyze beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi access points to determine the user's location. The aim is to provide a secure and efficient method of authentication without the need for additional hardware or software. A prototype was developed using Raspberry Pi devices and experiments were conducted to demonstrate the effectiveness and practicality of the proposed approach. Results showed that the proposed system can significantly enhance the security of sensitive information in various industries such as finance, healthcare, and retail. This study sheds light on the potential of Wi-Fi radio waves and RSSI values as a means of user authentication and the power of ML to identify patterns in wireless signals for security purposes. The proposed system holds great promise in revolutionizing the field of 2FA and user authentication, offering a new era of secure and seamless access to sensitive information.
Smartphone Context Event Sequence Prediction with POERMH and TKE-Rules Algorithms

Smartphone applications run in complex environments and are sensitive to context events, i.e., changes to screen orientation, location, battery, etc. Context events and sequences make reliability, accuracy, and testing of mobile applications costly and more application development susceptible to errors. Modern smartphones have continued to increase the number of context event possibilities compared to earlier versions. Multiple context events may occur within quick intervals and complicate an application's behavior. While apps are continuously released and updated, future devices will likely have more features with more context events that will lead to even greater challenges. In this work, we implemented a model for prediction of common context event sequences by using two sequence rule mining algorithms: POERMH: Partially-Ordered Episode Rule Mining with Head Support, and TKE-Rules: Top-K Episode mining, along with a sequence prediction algorithm to increase smartphone application testing efficiency. Indeed, testing frequently encountered scenarios often relates to user perceived reliability of an app. The experiment was significantly successful at predicting next sequence in sequence of events for individual events as compared to overall performance using both POERMH and TKE-Rules in terms of Recall, Precision and F-1 score.
Sentiment Analysis of Social Media Comments in Mauritius

Sentiment analysis is becoming increasing important with the rise in the amount of content on social media. However, sentiment analysis remains challenging for under-resourced languages such as Kreol Morisien (KM), the native language of Mauritius. In fact, it has been observed that in Mauritius, social media comments often consist of more than one language among English, French and Kreol Morisien. In this paper, we first create an annotated dataset of 1300 sentences and then outline a framework through which sentiment analysis can be performed on social media comments. We propose a KM sentiment analyzer using two algorithms namely Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB). Our results show that SVM outperforms MNB for sentiment analysis in Kreol Morisien, achieving an accuracy of 66.15% after pre-processing techniques stopwords removal and spell checking are applied. This paper highlights the need to develop further tools in order to enable natural language processing of Kreol Morisien.
Physical-layer security analysis of a quantum noise randomized cipher assisted by chaos masking

By applying intensity modulation, the quantum noise random cipher (QNRC) hides the signal amplitude in quantum noise to prevent eavesdropping. It is an essential topic for evaluating the secret performance of the QNRC system. In this paper, the physical-layer security of the QNRC based on intensity modulation (IM-QNRC) with chaos masking (CMA) is quantitatively analysed. The model of the transmission system is built. Additionally, the noise-mask-signal number (NMN) and the probability of correctly guessing data for the interceptor (PCG) are used as metrics to assess the security of the physical layer. The general expression of PCG for an eavesdropper is derived. A comprehensive evaluation and comparison of the security metrics versus several system parameters are carried out. Based on simulation, the mixing of chaos masking reduces the PCG of the QNRC system with suitable parameters by approximately 8 orders of magnitude. The QNRC system combined with CMA can realize higher anti-interception communication.
Task-Oriented Collaborative Graph Embedding Using Explicit High-Order Proximity for Recommendation

A recommender or recommendation system is a subclass of information filtering systems that seeks to predict the "rating" or "preference" that a user would assign to an item. Although many collaborative filtering (CF) approaches based on neural matrix factorization (NMF) have been successful, significant scope for improvement in recommendation systems exists. The primary challenge in recommender systems is to extract high-quality user–item interaction information from sparse data. However, most studies have focused on additional review text or metadata instead of fully used high-order relationships between users and items. In this paper, we propose a novel model—Cross Neighborhood Attention Network (CNAN)—that solves this problem by designing high-order neighborhood selection and neighborhood attention networks to learn user–item interaction efficiently. Our CNAN performs rating prediction using an architecture considering only user–item interaction data. Furthermore, the proposed model uses only user–item interaction (from the user–item ratings matrix) information without additional information such as review text or metadata. We evaluated the effectiveness of the proposed model by performing experiments on five datasets with review text and three datasets with metadata. Consequently, the CNAN model demonstrated a performance improvement of up to 7.59% over the model using review text and up to 1.99% over the model using metadata. Experimental results show that CNAN achieves better recommendation performance through higher-order neighborhood information integration with neighborhood selection and attention. The results show that our model delivers higher prediction performance via efficient structural improvement without using additional information.
Collapse-revival and stability of magnon-superconducting qubit entanglement in a tripartite hybrid cavity system

In this paper we pay our attention to the generation and manipulation of bipartite entanglement in a hybrid cavity system which is comprised of cavity microwave photons, a magnon and a superconducting qubit. To achieve the purpose of paper, at first, the time-independent effective Hamiltonian of the system is derived with considering some assumptions, and then the system solution is obtained via solving the Lindblad master equation. At last, the bipartite magnon-superconducting qubit entanglement is quantitatively analyzed using negativity, as a suitable measure of bipartite entanglement. The numerical simulations obviously show that, by adjusting system parameters, collapse-revival behavior of the entanglement can be apparently observed, and by appropriate choice of thermal occupation photon number and cavity decay rate, quasi-stable, even stable with significant degrees of entanglement are accessible. Our results indicate that, such a hybrid cavity system considered here can be used for studying large-scale quantum phenomena which has a crucial role in quantum information science and technologies.
OC-SMOTE-NN: A Deep Learning-based Approach for Imbalanced Classification

We propose a deep learning-based over-sampling approach to overcome the class imbalance problem. Our model consists of three major components: 1) A variant of SMOTE algorithm with learnable parameters; 2) A deep-learning model as a discriminator; 3) A loss function with an orthogonality-based regularization term. Contrary to traditional over-sampling approaches with manual fine-tuning, our model is capable of performing adaptive over-sampling. The experimental results demonstrate that our model considerably outperforms the baselines with up to a 9% increase in F1-score or provides comparable performance.
Inductive Link Prediction Banking Fraud Detection System Using Homogeneous Graph-Based Machine Learning Model

Graph machine learning and fraud detection systems are growing and popular today. Fraud detection systems have been widely used as a tool to detect potentially fraudulent transactions. Fraud detection systems can be used to determine patterns of transactions that are suspected of being criminal transactions. Graph machine learning development can be implemented in anything that can be represented in graph form. The banking fraud detection system can be implemented in graph form by connecting customers who have made transactions with other customers or customer transactional activities. From the graph that has been formed, predictions will be made so that new transactions can be classified as fraudulent transactions or not by connecting these transactions with the graphs that have been made. The experimental results show that the graph-based fraud detection model produces better performance than the tree-based fraud detection model, but with a longer inference time.
Explainable Neural Network Recognition of Handwritten Characters

The prevalence of Artificial Intelligence (AI), in many aspects of life, has brought about an increasing need for explainability of AI solutions. Previous work has been posed to interpret the weights of Neural Networks and provide visual hints of explainability. This work introduces a partitioning approach, constructing an explainable architecture that recognizes handwritten characters. The accuracy of the architecture is improved by introducing unexplainable components and a metric to characterize explainability. Ambiguous and mislabeled samples in training data prove challenging. Techniques are applied to identify issues and prune the training set to improve explainability. Pruned samples from the training set are presented to the architecture in an attempt to resolve issues. Results and explainable rationale from pruned data is presented.
A Study of Text Summarization in the Medical Domain using BERT and its Variants

The main objective is to look into Text Summarization techniques and their application within the medical domain. The challenge to catalogue, standardize biomedical literature is its enormous size which currently constitutes more than 30 Mn citations and abstracts which are part of PubMed database. The need for research on biomedical text summarization is twofold. With the increasing in 3V's (velocity, variety and volume) of data (Medical text are usually accompanied by images), along with growing regulation, it becomes important to interpret and summarize data in a fast, secure, and error freeway. The medical domain has its own distinct language models, jargon, vocabulary, and abbreviations compared to other domains or general English models.
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI

A positive phrase or a sentence with an underlying negative motive is usually defined as sarcasm that is widely used in today's social media platforms such as Facebook, Twitter, Reddit, etc. In recent times active users in social media plat-forms are increasing dramatically which raises the need for an automated NLP-based system that can be utilized in various tasks such as determining market demand, sentiment analysis, threat detection, etc. However, since sarcasm usually implies the opposite meaning and its detection is frequently a challenging issue, data meaning extraction through an NLP-based model becomes more complicated. As a result, there has been a lot of study on sarcasm detection in English over the past several years, and there's been a noticeable improvement and yet sarcasm detection in the Bangla language's state remains the same. In this article, we present a BERT-based system that can achieve 99.60% while the utilized traditional machine learning algorithms are only capable of achieving 89.93%. Additionally, we have employed Local Interpretable Model-Agnostic Explanations that introduce explainability to our system. Moreover, we have utilized a newly collected bangla sarcasm dataset, BanglaSarc that was constructed specifically for the evaluation of this study. This dataset consists of fresh records of sarcastic and non-sarcastic comments, the majority of which are acquired from Facebook and YouTube comment sections.
A new method for localized necking detection in uniaxial tensile testing based on a multi-camera DIC system

Understanding and predicting the onset and behavior of localized necking is important for the design and optimization of structures and components. Capturing the onset of localized necking more accurately has been a challenge in experimental mechanics. In this paper, a new alternative method for detecting the onset of localized necking in uniaxial tensile tests is presented. This method is inspired by the fact that different behaviors are exhibited as localized necking starts at the different regions of the cross-sectional area. When localized necking begins to occur, the cross-sectional area in the center of the necking area decreases sharply, while the cross-sectional area at the boundary of the necking area no longer decreases. A multi-camera digital image correlation system consisting of four cameras, two placed at the front and two at the back of the specimen, has been developed to measure the cross-sectional area at different positions of the sample. The effectiveness and accuracy of the proposed method are verified through a series of tensile tests and are compared with different localized necking detection methods.
Introduction to the special issue emergence of speech and language from prediction error: error-driven language models

Last year, 2022, marked the 50th anniversary of the Rescorla-Wagner learning equations – a landmark in the development of learning theory. Originally based on animal learning, the equations and error-driven learning models as a whole were rapidly adopted and applied in several areas of human psychology. While language acquisition research initially took a different path, interest in the role of error-driven learning in language is growing. With the aim of strengthening this emerging research field, sparking discussion and cross-fertilisation of ideas across linguistics, psychology and cognate fields, this Special Issue presents nine papers that address the role of error-driven learning in language. The papers investigate a wide range of subfields of linguistics, and include two review articles, in addition to computational modelling and experimental research. The collection thus serves both as an introduction for those new to the subject and as an overview for those already working in the field.
Kdb-D2CFR: Solving Multiplayer imperfect-information games with knowledge distillation-based DeepCFR

Counterfactual regret minimization (CFR) is a popular method for finding approximate Nash equilibrium in imperfect-information games (IIG). However, CFR based methods for the IIG are either only designed for two-player IIGs or require much expert knowledge. In this paper, towards to solve the multiplayer IIG problem without utilizing much expert knowledge, we proposed a practical knowledge distillation based framework, which aims to transferring the knowledge from model of two-player IIG into the multiplayer one. By this framework, both of the training efficiency and performance is improved. To eliminate the requirement of expert knowledge, here we introduced a deep learning based CFR in the framework, by which the counterfactual value of CFR can be estimated in an end-to-end way without any expert knowledge and abstraction. We further propose kdb-DeepCFR and kdb-D2CFR based on DeepCFR and D2CFR respectively, which can effectively solve the strategy of multiplayer large-scale game problems. The extensive experiments conducted on 3-8 players poker games suggest that our method outperforms other baselines in the game performance.