Reviewerly/exHarmony huggingface dataset:
Darknet Traffic Analysis: Investigating the Impact of Modified Tor Traffic on Onion Service Traffic Classification

Classifying network traffic is important for traffic shaping and monitoring. In the last two decades, with the emergence of privacy concerns, the importance of privacy-preserving technologies has risen. The Tor network, which provides anonymity to its users and supports anonymous services known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Onion Services</i> , is a popular way to achieve online anonymity. However, this anonymity (especially with Onion Services) is frequently misused, encouraging governments and law enforcement agencies to de-anonymise them. Therefore, in this paper, we try to identify the classifiability of Onion Service traffic, focusing on three main contributions. First, we try to identify Onion Service traffic from other Tor traffic. The techniques we have used can identify Onion Service traffic with >99% accuracy. However, there are several modifications that can be done to the Tor traffic to obfuscate its information leakage. In our second contribution, we evaluate how our techniques perform when such modifications have been done to the Tor traffic. Our experimental results show that these conditions make the Onion Service traffic less distinguishable (in some cases, the accuracy drops by more than 15%.) In our final contribution, we identify the most influential feature combinations for our classification problem and evaluate their impact.
Class-Incremental Learning Based on Anomaly Detection

In Class-Incremental Learning (CIL), the memoryless replay CIL algorithm trains the network model by using the closed training set of the current task, while using knowledge distillation or finetuning constraints to keep the old knowledge. Due to the closed set recognition method used in the training process, the training set of each task are independent of each other, and the knowledge learned by the network model cannot effectively deal with the classes that do not appear in the corresponding task training set, that is, the class samples of other tasks cannot be effectively rejected during testing, resulting in the misrecognition of the same test sample in multiple tasks. This is the main factor that the performance of the memoryless replay CIL is limited. In this paper, from the perspective of open set recognition, a CIL algorithm ADCIL based on anomaly detection mechanism is proposed to enhance the discrimination ability of the network model between tasks, so that each task can learn the knowledge that is effective in the open space only through its own training set, and the robustness of the algorithm is greatly improved. It effectively prevents the catastrophic forgetting phenomenon in CIL.
Artificial history? Inquiring ChatGPT on historiography

In this paper, I examine the historiographical capabilities of ChatGPT, a state-of-the-art artificial intelligence (AI) chatbot released on 30 November 2022, by OpenAI corporation. I begin by outlining a concise history of AI, highlighting the key innovations that have culminated in the development of advanced applications such as ChatGPT. Subsequently, I present an analysis of my interactions with the chatbot between February and May 2023, focusing on a long ‘interview’ conducted on the theory and methodology of historical science. My preliminary findings suggest that while ChatGPT holds significant potential for aiding historians in their research, the prospect of AI-driven history production remains distant and unlikely to materialize soon.
Detecting Outliers in Non-IID Data: A Systematic Literature Review

Outlier detection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and identically distributed (IID) assumption. This presents a challenge in real-world datasets where correlations, dependencies, and complex structures are common. In recent literature, several methods have been proposed to address this issue and each method has its own strengths and limitations, and the selection depends on the data characteristics and application requirements. However, there is a lack of a comprehensive categorization of these methods in the literature. This study addresses this gap by systematically reviewing methods for outlier detection in non-IID data published from 2015 to 2023. This study focuses on three major aspects; data characteristics, methods, and evaluation measures. In data characteristics, we discuss the differentiating properties of non-IID data. Then we review the recent methods proposed for outlier detection in non-IID data, covering their theoretical foundations and algorithmic approaches. Finally, we discuss the evaluation metrics proposed to measure the performance of these methods. Additionally, we present a taxonomy for organizing these methods and highlight the application domain of outlier detection in non-IID categorical data, outlier detection in federated learning, and outlier detection in attribute graphs. We provide a comprehensive overview of datasets used in the selected literature. Moreover, we discuss open challenges in outlier detection for non-IID to shed light on future research directions. By synthesizing the existing literature, this study contributes to advancing the understanding and development of outlier detection techniques in non-IID data settings.
ConvFormer: Tracking by Fusing Convolution and Transformer Features

Current mainstream single-object trackers adopt the Transformer as the backbone for target tracking. However, due to the Transformer’s limitations in local information acquisition and position encoding, we proposed a new tracking framework called ConvFormer to enhance the model’s performance. Our framework aims to improve the feature extraction ability by combining the local feature extraction ability of CNN with the global feature extraction ability of the Transformer. To achieve synchronous feature extraction and fusion of the template and search region, we propose Mix Net Module (MNM), which achieves both global and local feature extraction and fusion for the template and search regions. Based on MNM, we stacked MNM modules and added a location head to complete the construction of the ConvFormer framework. Moreover, we designed a post-processing module to reduce the impact of tracker mistracking and improve the model’s robustness against interference from similar objects. Our framework achieved state-of-the-art performance on six benchmarks, including OTB2015, VOT2018, GOT-10k, LaSOT, TrackingNet, and UAV123. Notably, on the TrackingNet dataset, our tracker outperformed OSTrack by 1.4% with 83.2% precision. Additionally, on the LaSOT dataset, our tracker surpassed OSTrack by 2.6% with 77.4% precision. Finally, we conducted numerous ablation experiments to validate the efficacy of our approach.
An Interval Optimization Algorithm With Embedded Point Evolutionary Strategy and Its Application to Bounded Error Modeling

Aiming at the problems of low efficiency and difficulty in constructing acceleration devices in traditional interval optimization algorithms (IOAs), this paper constructs a valid acceleration device based on a more concise point evolutionary strategy (ES), and then proposes a novel hybrid IOA (HIOA) with no requirement on the derivative of the objective function. The HIOA first divides the initial search area into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> equal parts, randomly selects multiple point individuals in each subinterval to represent their information, and performs the optimization with fewer iterations using ES for all point individuals to make them closer to the optima; then selects reliable subintervals containing more point individuals to split, and deletes unreliable subintervals without any point individuals; finally, provides a reliable upper bound to direct the pruning operation to further improve the search efficiency. Furthermore, the convergence property of the proposed algorithm is analyzed. Extensive numerical experiments on several typical test functions and the application to the bounded error parameter estimation demonstrate the superiority of HIOA by comparing it with the existing conventional algorithms, which confirms the effectiveness and applicability of the suggested algorithm.
Dialogue Situation Recognition in Everyday Conversation From Audio, Visual, and Linguistic Information

In recent years, such intelligent systems as dialogue systems have been applied to daily living. They will be able to function better for users by associating conversations with dialogue situations, including a dialogue’s location and the relationship between participants. However, since previous studies generally assumed that systems work under limited and specific dialogue situations, research has neglected the recognition of everyday dialogue situations. We propose a dialogue situation recognition method using Gated Recurrent Units (GRU) and Bidirectional Encoder Representations from Transformers (BERT) that fuse multimodal features. The target dialogue situations contain dialogue styles, places, activities, and the relations between participants. In our experiments, we used the Corpus of Everyday Japanese Conversation (CEJC), which records natural everyday conversations in various situations. Our models with multi-task learning obtained an average F1-score of 0.541 with multimodal features. The improvement of the BERT-based approach is 2.3 percentage points more than the GRU-based method. We also analyzed the relationship between the dialogue situation recognition performance and the size of the dataset. To the best of our knowledge, ours is the first study that tackles the understanding of dialogue scenes using audio, visual, and linguistic information.
Object-oriented basis of artificial intelligence methodologies

If O2 is for I, then what is O2 for AI? What is oxygen for humans is what is object-oriented thinking for artificial intelligent systems. Much the same way as humans need O2 knowingly or unknowingly, the first step in designing an AI system requires the application of object-oriented principles either explicitly or implicitly. The basis of the definition of state in AI is a description of the concept of interest as an object with properties. The idea of an object extends beyond typical noun forms that describe elements of the real world. The verb forms are included as well with -able suffixes such as runnable, serializable, and executable. In the software world, the first step in modeling a business requirement is the identification of objects of interest and defining their properties and interactions. For instance, in the case of web services, a service is an object; in the case of database systems, a table or a transaction is an object; or in the case of large-scale integration, electronic components are objects. The concept of an object extends beyond the software realm and into the mathematical world in an implicit form. Functional analysis is an old topic in mathematics where each function is an object indeed. The concept of space in mathematics relates closely to the possible value ranges of all attributes of an object. Mathematical operators are the same as methods of objects. It is day-to-day practical life in any modern operating system software dealing with process objects and applications such as Python scripts involving function objects. In this chapter, the application of object-oriented thinking to convert a business requirement to a machine learning (ML) formulation is presented with examples. The five steps of supervised ML formulation based on vector representations of input and output, mapping function, loss function, and data set are clarified. The scope and limitation of ML formulation as against general AI methodology are discussed to demystify popular myths. This chapter also reveals the secret behind the success of deep learning methodology as automatic differentiation involving function objects.
Open Data Evaluation Model in Brazilian Governmental Portals

One of the great challenges related to the publication and consumption of data on the web is the standardization of informational structures. Many organizations publish their datasets privately, without regard to developed publishing standards and principles established by the web community to facilitate the sharing and leveraging of information.
Toward Intrinsic Adversarial Robustness Through Probabilistic Training

Modern deep neural networks have made numerous breakthroughs in real-world applications, yet they remain vulnerable to some imperceptible adversarial perturbations. These tailored perturbations can severely disrupt the inference of current deep learning-based methods and may induce potential security hazards to artificial intelligence applications. So far, adversarial training methods have achieved excellent robustness against various adversarial attacks by involving adversarial examples during the training stage. However, existing methods primarily rely on optimizing injective adversarial examples correspondingly generated from natural examples, ignoring potential adversaries in the adversarial domain. This optimization bias can induce the overfitting of the suboptimal decision boundary, which heavily jeopardizes adversarial robustness. To address this issue, we propose Adversarial Probabilistic Training (APT) to bridge the distribution gap between the natural and adversarial examples via modeling the latent adversarial distribution. Instead of tedious and costly adversary sampling to form the probabilistic domain, we estimate the adversarial distribution parameters in the feature level for efficiency. Moreover, we decouple the distribution alignment based on the adversarial probability model and the original adversarial example. We then devise a novel reweighting mechanism for the distribution alignment by considering the adversarial strength and the domain uncertainty. Extensive experiments demonstrate the superiority of our adversarial probabilistic training method against various types of adversarial attacks in different datasets and scenarios.
Bitcoin Shared Send Transactions Untangling in Numbers

Bitcoin uses an unspent transaction output (UTXO) model for coin circulation, which is similar to the banknotes. The transaction history is publicly available and allows to trace cryptocurrency flows. Different users merge transactions into a single bigger one to tangle flows. The merged transaction is called a shared send mixer (SSM). One can try to find the original subtransactions–solve an untangling problem. Based on the number of untanglings and their size, one extracts additional information about coin circulation. Theoretical analysis of the untangling problem is known from the literature. The paper aims to collect statistics of the SSM usage by transaction type for Bitcoin blockchain. We propose an algorithm to solve the problem, prove its correctness, and provide a source code. We applied the algorithm to the Bitcoin historical data. 15% transactions are SSM, and 90% of them allow unique untangling. The future work is an algorithm application to other UTXO systems and the results adaptation to an address grouping.
Smart contracts for creating transparent transactions to reduce corruption

Corruption is widely spread and not easy to avoid. Blockchain-based smart contract technology enables the opportunity to develop transactions in such a way that corruption should not be possible. In this paper, we develop and evaluate an arrangement based on blockchain-based smart contracts to avoid and reduce corruption. Smart contracts are used for buying and selling goods, in which the public must agree that the goods arrived and are used to contribute to the creation of societal value. Only then will the supplier be paid. Al transaction data is stored in a blockchain and opened to the public to create transparency. In this way, the price of the good and the sellers can be inspected to avoid price manipulation and nepotism. The smart contract avoids the likelihood that corruption will happen, and it can be spotted if it happens.
Towards a Mobile-First Cross-Border eID Framework

The eIDAS technical framework has been successfully enabling cross-border e-government processes for many years. When initially conceived, today’s user habits and the prevalence and ubiquity of smartphones was nothing but a glimmer on the horizon. As a consequence, the concepts, technologies chosen, and technical standards used to carry out cross-border authentication were designed and chosen with browser-based user flows in mind. In this context, the network of eIDAS nodes and the interfaces defined to integrate them with all kinds of different national eID systems has stood the test of time. At the same time, however, transitioning these workflows to a mobile setting presents various significant challenges: Instead of using a single application (a web browser) to orchestrate the interaction of eID systems, eIDAS nodes and e-government service frontends (mostly using SAML), users are accustomed to using distinct native apps for every service and for interacting with eID systems. This work discusses different concepts essential for transitioning from such browser-based user flows to native app-to-app communication and combines them into a coherent concept. It presents a framework, which maintains browser compatibility, while at the same time providing all the benefits of native mobile apps, taking currently deployed eIDAS-based cross-border authentication to the next level by making it mobile-first, all without requiring invasive changes to existing infrastructure. As will be shown, a slew of technical constraints to overcome makes this a lofty goal, especially considering the heterogeneity of national eID systems which must obviously integrate well with the proposed concept.
Item Attribute-Aware Contrastive Learning for Sequential Recommendation

Sequential recommendation aims to predict users’ next interaction items based on their historical interaction sequences, however, the problem of sparse user behavior and ineffective use of item attribute information makes it difficult to learn high-quality representations of user preferences. To address this problem, inspired by recent advances in contrastive learning techniques, we propose a novel Item Attribute-aware Contrastive Learning framework for Sequential Recommendation, named IACL4SR, which differs from previous contrastive learning-based sequential recommendation approaches by incorporating item attribute information in user behavior sequences to build an augmented view of users’ fine-grained preferences for item attributes, thereby capture the association between user and item attributes in the sequence transformation model. Specifically, we devise a dual-strategy relay data augmentation method to model user sequences and item attribute sequences, while we modify the fusion method of item attribute embedding in the self-attentive mechanism to obtain more accurate user representations. Finally, we jointly train and optimized the master sequential recommendation task and auxiliary contrastive learning task. Extensive experiments on three widely used real datasets show that IACL4SR achieves more advanced recommendation performance than existing baseline methods.
A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites

Phishing is a type of online scam where the attacker tries to trick you into giving away your personal information, such as passwords or credit card details, by posing as a trustworthy entity like a bank, email provider, or social media site. These attacks have been around for a long time and unfortunately, they continue to be a common threat. In this paper, we propose a boosting based multi layer stacked ensemble learning model that uses hybrid feature selection technique to select the relevant features for the classification. The dataset with selected features are sent to various classifiers at different layers where the predictions of lower layers are fed as input to the upper layers for the phishing detection. From the experimental analysis, it is observed that the proposed model achieved an accuracy ranging from 96.16 to 98.95% without feature selection across different datasets and also achieved an accuracy ranging from 96.18 to 98.80% with feature selection. The proposed model is compared with baseline models and it has outperformed the existng models with a significant difference.
A Microcontroller-Based Platform for Cognitive Tracking of Sensorimotor Training

A new generation of fitness trackers is pervasively invading different aspects of our life, taking profit from wireless technology, embedded sensors, and increasingly accurate AI-based data analysis. The most crucial aspects concerning the design of these systems include energy efficiency and accuracy. In this paper, we propose a system relying on two microcontroller-based sensor nodes to track the physical activity during sensorimotor training, a type of exercise that challenges the user’s balance skill, which has been proven to be very effective in improving performance, preventing injuries and recovering from them. One of the sensor nodes is integrated into a custom wobble-board and the second is wearable by the user. The nodes are adaptable to be set in different operating modes, depending on the use case needs, enabling different steps of near-sensor pre-processing. The most power-efficient operating mode executes a CNN-based analysis directly on the microcontroller, to recognize physical exercises. The algorithm provides an accuracy of respectively 99.4% and 97.6% on the two nodes. In-place execution of the CNN saves up to 65% power consumption with respect to the transmission of raw data for on-cloud analysis.
A Reduced-Memory Multi-layer Perceptron with Systematic Network Weights Generated and Trained Through Distribution Hyper-parameters

In this paper, we propose an alternate way to represent and train the weights of a Multi-Layer Perceptron. The individual weights of all connections from a node are represented by a statistical distribution for which the hyper-parameters are trained. Although the proposed distribution-based neural network was difficult to train using standard backpropagation and gradient descent algorithms, we successfully used a genetic algorithm-based training strategy on experimental datasets. On a number of binary classification datasets in the public domain, contemporary state-of-the-art algorithms were used for comparative benchmarking of the model efficiency. Distribution-based neural network was ranked two out of the six training methods in final accuracy (behind backpropagation) but achieved substantial memory and time complexity gains of 20 and more than 100 folds, respectively. We believe that the model offers a promising alternative training method for large networks with small performance trade-offs.