Reviewerly/exHarmony huggingface dataset:
SSK-DNN: Semantic and Sentiment Knowledge for Incremental Text Sentiment Classification

Sentiment analysis is a crucial task that extracts useful insights from online unstructured user-generated content for incremental sentiment classification. Traditional feature construction methods for text sentiment analysis have their own benefits in interpretability, computation, and lowering the high dimensionality of textual input. Although this enhances the classification accuracy of traditional machine learning classifiers, still they struggle with sparsity due to a lack of appropriate data representation strategies. In contrast, DNN architectures for text sentiment analysis have overcome the sparsity issue and obtained better results than traditional feature-based machine learning methods. Yet, they suffer from high-dimensional feature space due to noisy and irrelevant features from the unstructured user-generated review text. Moreover, state-of-the-art sentiment analysis methods can not fully exploit semantic and sentiment knowledge in order to extract meaningful relevant contextual sentiment features. Our goal is to mitigate such problems by employing standard DNN-based methods (BERT, BiLSTM with Attention Mechanism) and traditional feature construction methods with PCA to extract relevant sentiment features and reduce the dimensionality of feature space. We propose an effective way of joining the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment analysis. Additionally, we leverage incremental learning to adapt to new data effectively. Our main contributions are six folds (a) mining semantic and sentiment features in the review text for sentiment analysis, (b) creating dynamic word vector representations to enhance sentiment word embeddings, (c) filtering noisy and irrelevant features to reduce the high dimensional feature space, (d) analyzing the data’s inherent relationships and optimizing the weighting of key features for accurate sentiment classification, (e) integrating essential new features to support incremental learning, (f) practicality, Our model undergoes evaluation on different real-world benchmark datasets, showcasing its capacity to enhance the performance of sentiment classification in comparison to several existing methods.
Counterfactual Explanation for Regression via Disentanglement in Latent Space

Counterfactual Explanations (CEs) help address the question: How can the factors that influence the prediction of a predictive model be changed to achieve a more favorable outcome from a user’s perspective? Thus, they bear the potential to guide the user’s interaction with AI systems since they represent easy-to-understand explanations. To be applicable, CEs need to be realistic and actionable. In the literature, various methods have been proposed to generate CEs. However, the majority of research on CEs focuses on classification problems where questions like "What should I do to get my rejected loan approved?" are raised. In practice, answering questions like "What should I do to increase my salary?" are of a more regressive nature. In this paper, we introduce a novel method to generate CEs for a pre-trained regressor by first disentangling the label-relevant from the label-irrelevant dimensions in the latent space. CEs are then generated by combining the label-irrelevant dimensions and the predefined output. The intuition behind this approach is that the ideal counterfactual search should focus on the label-irrelevant characteristics of the input and suggest changes toward target-relevant characteristics. Searching in the latent space could help achieve this goal. We show that our method maintains the characteristics of the query sample during the counterfactual search. In various experiments, we demonstrate that the proposed method is competitive based on different quality measures on image and tabular datasets in regression problem settings. It efficiently returns results closer to the original data manifold compared to three state-of-the-art methods, which is essential for realistic high-dimensional machine learning applications. Our code will be made available as an open-source package upon the publication of this work.
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model

Representation learning to a High-Dimensional and Incomplete (HDI) matrix can be performed efficiently by an Alternating-direction-method-based Nonnegative Latent Factor (ANLF) model. However, it introduces multiple hyper-parameters into the learning process, which should be chosen with care to enable its superior performance. Its hyper-parameter adaptation is desired for enhancing its scalability. Targeting at this issue, this paper proposes an Adaptive Alternating-direction-method-based Nonnegative Latent Factor (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF) model, whose hyper-parameter adaptation is implemented following the principle of particle swarm optimization. Empirical studies on nonnegative HDI matrices generated by real applications indicate that A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF outperforms several state-of-the-art models in terms of computational efficiency, as well as maintains highly competitive estimation accuracy for an HDI matrix’s missing data.
Infinite forecast combinations based on Dirichlet process

Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single optimal forecasting model, this paper introduces a deep learning ensemble forecasting model based on the Dirichlet process. Initially, the learning rate is sampled with three basis distributions as hyperparameters to convert the infinite mixture into a finite one. All checkpoints are collected to establish a deep learning sub-model pool, and weight adjustment and diversity strategies are developed during the combination process. The main advantage of this method is its ability to generate the required base learners through a single training process, utilizing the decaying strategy to tackle the challenge posed by the stochastic nature of gradient descent in determining the optimal learning rate. To ensure the method’s generalizability and competitiveness, this paper conducts an empirical analysis using the weekly dataset from the M4 competition and explores sensitivity to the number of models to be combined. The results demonstrate that the ensemble model proposed offers substantial improvements in prediction accuracy and stability compared to a single benchmark model.
Annotating Panic in Social Media using Active Learning, Transformers and Domain Knowledge

Nowadays, researchers unanimously agree on the undeniable importance of mental health. However, the literature related to tracking mental disorders in textual content from social media platforms is heavily inclined towards specific problems. In particular, panic disorder/panic attacks are heavily understudied in the current literature and the relevant resources are missing. Therefore, in this work we focus on collecting an annotated dataset. To this end, in order to mitigate the annotation effort by selectively annotating unlabeled data, we propose an active-learning based approach with uncertainty sampling supported by contextualized (Transformer-based) representations, symptomatic and psychometric features and domain expertise. Our evaluation demonstrates the efficiency of the proposed approach both in terms of classification accuracy and predictions confidence. Our contribution to the research community is an annotated dataset of 13,036 tweets that distinguishes between personal panicking experiences such as panic attacks, other panic-related content and completely panic-unrelated content hoping that it will foster research on the topic.
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.
SGD with Partial Hessian for Deep Recommendation System Optimization

Due to the effectiveness of second-order algorithms in solving classical optimization problems, designing second-order optimizers to train deep neural networks (DNNs) has attracted much research interest in recent years. However, because of the very high dimension of intermediate features in DNNs, it is difficult to directly compute and store the Hessian matrix for network optimization. Most of the previous second-order methods approximate the Hessian information imprecisely, resulting in unstable performance. In this work, we propose a compound optimizer, which is a combination of a second-order optimizer with a precise partial Hessian matrix for updating channel-wise parameters and the first-order stochastic gradient descent (SGD) optimizer for updating the other parameters. We show that the associated Hessian matrices of channel-wise parameters are diagonal and can be extracted directly and precisely from Hessian-free methods. Our proposed method, namely SGD with Partial Hessian for Deep Recommendation System (SPH-DRS), inherits the advantages of both first-order and second-order optimizers. Compared with first-order optimizers, it adopts a certain amount of information from the Hessian matrix to assist optimization, while compared with the existing second-order optimizers, it keeps the good generalization performance of first-order optimizers. Experiments on synthetic and real recommendation system tasks demonstrate the effectiveness of our proposed optimizer SPH-DRS.
Combining Self-labeling with Selective Sampling

Access to labeled data is generally expensive, so semi-supervised methods are constantly popular. They enable the acquisition of large datasets without needing too many expert labels. This work combines self-labeling techniques with active learning in a selective sampling scenario by proposing how to build a classifier ensemble. When training the base classifiers, a decision is made on whether to request a new label or use the self-labeling based on evaluating the decision inconsistency of base classifiers. Additionally, a technique inspired by online bagging was used to ensure the ensemble’s diversity, whereby individual learning examples are presented to base classifiers at different intensities.The preliminary studies showed that naïve application of self-labeling could harm performance by introducing bias towards selected classes and consequently lead to skewed class distribution. Hence, we propose how to reduce this phenomenon. Experimental evaluation confirmed that the proposed method performs well compared to the known selective sampling methods.
FedNAT: Byzantine-robust Federated Learning through Activation-based Attention Transfer

Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fortresses. However, the presence of Byzantine attacks, such as data poisoning and backdoor attack, threatens the robustness of FL schemes. Currently, existing mainstream defense methods are susceptible to multiple adaptive attacks, some of which even violate the privacy principle of FL. Furthermore, these defense schemes become less robust when subjected to targeted poisoning attacks with highly non-IID data distributions. In this work, we propose FedNAT, a novel Byzantine-robust FL framework for whittling away these limitations mentioned above. Specifically, FedNAT first performs a privacy-respecting attention refinement on the activation layer outputs of the local uploads. Then, the server scores the local attentions by calculating their Wasserstein distances and clusters them through the k-median algorithm for global attention aggregation, thus rejecting poisoned local attentions for untargeted attacks. After this process, the global attention is transferred to local attention through the FedNAT loss function, which erases backdoors through the distillation concept. We conduct a comprehensive experimental evaluation to demonstrate that FedNAT significantly outperforms existing robust FL schemes in defending against Byzantine poisoning attacks under both IID and highly non-IID data proportions.
Optimizing Autonomous Path Tracking Using Population-Based Metaheuristic and Pure Pursuit Control

Autonomous driving encompasses a comprehensive system comprising perception, decision-making, planning, and movement control subsystems. Within this framework, path tracking stands as a pivotal task, involving the precise and secure guidance of a vehicle along a predefined trajectory. Traditional path tracking methodologies often hinge on rule-based or parameter-tuning approaches, which may lack adaptability in intricate and dynamic scenarios. In this study, we present an approach employing a population-based metaheuristic for optimizing autonomous path tracking via a pure pursuit controller, notable for its streamlined control. The experimental assessments serve to elucidate the efficacy of the used algorithm, albeit within the confines of a single illustrative case, accompanied by relevant performance metrics.
Exploiting Optimal Supports in Enhanced Multivariance Products Representation for Lossy Compression of Hyperspectral Images

Thanks to advancements in technology, the importance of computational methods used in tasks like storing and processing data is increasing as the data produced becomes more complex in both size and detail. Methods such as Tucker Decomposition, CANDECOMP/PARAFAC, Alternating Least Squares and their derivations, are widely used in the field to meet the requirements in numerous areas. These cases contain expressing high-dimensional data using lower-dimensional tensors, cleansing the data of errors that occur during data acquisition while also ensuring an efficient compression. This study proposes a new method that exploits the tensor structure of 3-dimensional data by calculating the lower-dimensional components via Enhanced Multivariance Products Representation and produces a superior approximation compared to well-known tensor decomposition methods. An iterative process is established to calculate the optimal support tensors and to determine the lower-dimensional components, which in further steps are employed to reconstruct the approximation.
Lightweight Photovoltaic Forecasting Method for Agricultural Microgrids

A lightweight forecasting method was developed for day-ahead (12 to 72-hours) and intra-day forecasting of photovoltaic (PV) array power output for use with energy management of agricultural microgrids. The proposed method allows the PV output to be forecast without requiring extensive computing resources or high-bandwidth communication channel to the PV site. The proposed day-ahead forecasting method combines historical PV output data with historical weather data to infer the relationship between weather and PV output by fitting a simple regression model. Based on the assumption that updated weather forecasts are in general not obtained throughout the day, the current-day PV output forecast was updated using time-series techniques. The proposed forecasting methods were applied to a dataset of rooftop PV output and compared to reference persistence forecasts. The method was demonstrated by using the forecasts in the optimized operation of an agricultural microgrid and simulating with actual PV output.
Gradient Maximization Regularization for Signed Adversarial Attacks

Deep learning-based computer vision models have proven their success in many fields. However, adversarial attacks show us that even the most decorated models have vulnerabilities. In this work, we investigate the difference between the effects of successful and unsuccessful adversarial attacks on the input by conducting a series of statistical tests, which leads us to find a positive correlation between success and the mean norm of the gradient map on the input that is computed by backpropagating some adversarial loss. In light of this finding, we craft a regularization method for the signed adversarial attacks, i.e. FGSM and PGD, and call the regularized algorithms GMFGSM (gradient maximized FGSM) and GM-PGD(gradient maximized PGD). We achieve considerable improvements in L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norms and fooling rates with the correct parameters compared to standard algorithms.
Analog Image Processing Using Gaussian Based Filtering for Motion Detection Applications

A novel approach to motion detection is presented in our study, utilizing a 3 × 3 Gaussian image filtering technique. Inspiration is drawn from the human visual system, particularly the human retina, and a straightforward Complementary Metal Oxide-Semiconductor (CMOS) circuit design is employed. Gaussian image filtering and noise mitigation are achieved through convolution and masking operations. The challenge lies in translating these mathematical operations into hardware using basic electronic components like current mirror and adder circuits. Additionally, enhancements are made to the 2-D pixel sensor array, optimizing the commonly used pixel circuitry.Our imaging technique conducts preliminary image processing before analog-to-digital conversion, supporting subsequent digital image processing stages in camera systems. We introduce an 8-MOSFET filtering cell array for retinomorphic analog image filtering. These cells collaborate to filter image signals received from the pixel circuit array. Our analysis is based on two moving scenes with a resolution of 150 × 150 pixels, and we compare the results with theoretical outcomes from digital image processing techniques. We utilize CMOS 0.18μm technology parameters for our proposed analog circuit structure.
Cost and Energy Optimization in Ground Mounted Solar Energy Power Plant Projects

With the increasing need for energy, all humanity is searching for new natural energy sources. The amount of fossil energy sources is decreasing worldwide, which is a threat to human health. Because of that reason, natural and renewable energy sources will alleviate this problem. Solar energy is a case in point for that problem. Before integrating solar energy sources into the national grid structure, their electrical projects should be designed carefully. Different schemes and layouts can be considered in that regard. This study explores the cost and energy optimization of solar energy projects during the design process. And the result shows that the savings from the optimized engineering design are approximately 200,000 USD. Along with the highest savings amount, the levelized cost of energy is also decreasing with a good engineering design up to 0.287 TRY.
Determining the Degree of Preference for Various Aspects of E-Voting System in Higher Education Institutions

The method being discussed offers the opportunity to determine the degree of preference (DP) for different aspects of an electronic voting system (EVS) in higher education institution (HEI). This method analyzes the data collected to assess how voters respond to various aspects of the EVS in HEI and election authorities can employ this method to enhance the delivery of electronic voting services, ensuring their effectiveness and alignment with voters' needs and preferences. The additional ability of the method to predict preferences will be useful for planning future updates and improvements to the EVS, as well as for making a more accurate and lasting decision regarding electoral procedures. The analysis proves valuable in identifying the most favored and effective EVS features and how they can be leveraged to enhance the voting process. Furthermore, it supports informed decision-making for election authorities. The considered method is a valuable tool for enhancing the EVS, aligning it with user requirements, and offering insights for resource optimization and anticipation of future needs.
Uniquely Identifying Device Fingerprint via Deep Learning over Ethernet Communication Signals

The widespread adoption of electronic devices has made imitation attacks a significant security concern, especially in the context of Ethernet communication. In this paper, a deep learning model is trained by using data from both received and transmitted Ethernet communication signals of devices to extract unique fingerprints and employs this model to provide system security. To accomplish this, we initially conducted a theoretical analysis of the LAN9514 Ethernet integrated circuit, followed by the acquisition of Ethernet communication signals from two Raspberry Pi 3 devices with identical models and software through an Analog-Digital Converter (ADC). These signals were then processed and labeled on CUDA cores, resulting in the creation of a dataset from analog signals. When this dataset was trained using the LSTM algorithm, the system exhibited a %98 accuracy rate.
SWOT Analysis of E-Voting System in Higher Education Institutions

Electronic voting (EV) systems have become integral to modern democratic processes, including those within higher education institutions (HEIs). This study employs a comprehensive SWOT analysis to evaluate the existing EV system in HEI. The analysis elucidates the system's strengths, which encompass its robust security measures, user training initiatives, and innovative features, ensuring a transparent and accessible electoral process. Concurrently, it identifies weaknesses, such as dynamic cybersecurity challenges and potential technological limitations, requiring vigilant monitoring, training, and infrastructure upgrades. The analysis uncovers opportunities for system enhancement. Addressing the identified threats, including cybersecurity vulnerabilities and user resistance, is crucial for ensuring the system's resilience and widespread acceptance. This SWOT analysis serves as a valuable tool for evaluating the EV system in HEIs. It lays the foundation for a strategic plan to enhance the system, improving its security, efficiency, and accessibility. Ultimately, this research contributes to the advancement of transparent and secure electoral processes in HEIs.