Reviewerly/exHarmony huggingface dataset:
Performance analysis of various training algorithms of deep learning based controller

Abstract Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10 −14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.
A Symmetry Histogram Publishing Method Based on Differential Privacy

The differential privacy histogram publishing method based on grouping cannot balance the grouping reconstruction error and Laplace noise error, resulting in insufficient histogram publishing accuracy. To address this problem, we propose a symmetric histogram publishing method DPHR (differential privacy histogram released). Firstly, the algorithm uses the exponential mechanism to sort the counting of the original histogram bucket globally to improve the grouping accuracy; secondly, we propose an optimal dynamic symmetric programming grouping algorithm based on the global minimum error, which uses the global minimum error as the error evaluation function based on the ordered histogram. This way, we can achieve a global grouping of the optimal error balance while balancing the reconstruction and Laplace errors. Experiments show that this method effectively reduces the cumulative error between the published histogram and the original histogram under long-range counting queries based on satisfying ε-differential privacy and improves the usability of the published histogram data.
A comparison of latent semantic analysis and correspondence analysis of document-term matrices

Abstract Latent semantic analysis (LSA) and correspondence analysis (CA) are two techniques that use a singular value decomposition for dimensionality reduction. LSA has been extensively used to obtain low-dimensional representations that capture relationships among documents and terms. In this article, we present a theoretical analysis and comparison of the two techniques in the context of document-term matrices. We show that CA has some attractive properties as compared to LSA, for instance that effects of margins, that is, sums of row elements and column elements, arising from differing document lengths and term frequencies are effectively eliminated so that the CA solution is optimally suited to focus on relationships among documents and terms. A unifying framework is proposed that includes both CA and LSA as special cases. We empirically compare CA to various LSA-based methods on text categorization in English and authorship attribution on historical Dutch texts and find that CA performs significantly better. We also apply CA to a long-standing question regarding the authorship of the Dutch national anthem Wilhelmus and provide further support that it can be attributed to the author Datheen, among several contenders.
Attention-based Stacked Bidirectional Long Short-term Memory Model for Word Sense Disambiguation

Word sense disambiguation is a basic task in Natural Language Processing which aims to identify the most appropriate sense of ambiguous words in different contexts by applying algorithm models. In this work, we propose a model that uses a stacked bidirectional Long Short-Term Memory neural network and attention mechanism to determine the sense of ambiguous words. First, the stacked bidirectional Long Short-Term Memory is employed for deep embedding-based representation of sentences containing ambiguous words. Then, we utilize the self-attention mechanism to highlight the contextual features of ambiguous words, and then construct the overall semantic representation of sentences. Finally, the sentence semantic representation is applied to the multilayer perception classifier to generate the appropriate category of the ambiguous word sense items. This model is tested on the Semeval-2007 task-17: English lexical samples dataset and using examples of ambiguous words sourced from Oxford, Cambridge, and Collins dictionaries as extra test datasets. The effectiveness of the proposed approach is demonstrated via comparison with existing word sense disambiguation approaches. Our experimental results show that the proposed model outperforms other word sense disambiguation methods in terms of the evaluation metrics (Average Accuracy, Micro F1-Score, Kappa, and Matthews Correlation Coefficient), and exhibits strong interpretability.
Causal embedding of user interest and conformity for long-tail session-based recommendations

Session-based recommendation is misleading by popularity bias and always favors short-head items with more popularity. This paper studies a new causal-based framework CauTailReS to increase the diversity of session recommendations. We first propose a new causal graph and then use the do-calculus in order to understand how popularity influences the process of making recommendations from the user's point of view. Popularity only misleads users temporarily, rather than in a long term and globally. Second, we believe that user clicks on popular products demonstrate their high quality and reputation. CauTailReS only eliminates 'bad' biases and retains 'good' effects through interest and consistent causal embedding mechanisms. To determine how similar various users are on various target items, CauTailReS also employs a re-ranking technique known as 'conformity-aware re-ranking'. To discover interactions based on what actual users want, CauTailReS also employs counterfactual reasoning. Extensive comparative experiments on four real world datasets have shown CauTailReS can well capture the true interests and consistency of users. As compared to the current state-of-the-art, CauTailReS enhances long-tail performance (APLT is increased by 8.14%) and recommendation accuracy (MRR is increased by 2.75%). This proves that introducing causal embeddings helps to reasonably enhance the diversity of recommendations.
VSBSIS: A verifiable SVD-based secret image sharing scheme for lossless and efficient reconstruction

Secret image sharing is a useful technique in various domains of image processing, like data hiding, image security, and visual authentication. It maintains the secrecy of an image by distributing it among many participants, known as shares. This article introduces a (n,n) singular value decomposition-based grayscale and color image-sharing techniques with verifiable and lossless reconstruction. A verifiable secret image sharing determines whether the reconstructed image is the original secret. The addition of all n shares is sufficient to reveal the secret image with no loss of information. Encryption before sharing and decryption after the addition of shares have been applied to resist information leakage of the reconstructed image by adding less than n shares. The verifiability, correctness, performance evaluation, and experimental security analysis of the proposed VSBSIS scheme have established its efficiency. Comparison with other contemporary polynomial-based secret image-sharing schemes demonstrates the efficient reconstruction of the proposed scheme.
MKBQA: Question Answering over Knowledge Graph Based on Semantic Analysis and Priority Marking Method

In the field of question answering-based knowledge graphs, due to the complexity of the construction of knowledge graphs, a domain-specific knowledge graph often cannot contain some common-sense knowledge, which makes it impossible to answer questions that involve common-sense and domain knowledge at the same time. Therefore, this study proposes a knowledge graph-based question answering method in the computer science domain, which facilitates obtaining complete answers in this domain. In order to solve the problem of natural language problems being difficult to match with structured knowledge, a series of logic rules are first designed to convert natural language into triples of the question. Then, a semantic query expansion strategy based on WordNet is proposed and a priority marking algorithm is proposed to mark the order of triples of the question. Finally, when a question triple corresponds to multiple triples in the knowledge graph, it can be solved by the proposed SimCSE-based similarity method. The designed logic rules can deal with each type of question in a targeted manner according to the different question words and can effectively transform the question text into question triples. In addition, the proposed priority marking algorithm can effectively mark the order in the triple of the question. MKBQA can answer not only computer science-related questions but also extended open domain questions. In practical applications, answering a domain question often cannot rely solely on one knowledge graph. It is necessary to combine domain knowledge and common-sense knowledge. The MKBQA method provides a new idea and can be easily migrated from the field of computer science to other fields. Experiment results on real-world data sets show that, as compared to baselines, our method achieves significant improvements to question answering and can combine common-sense and domain-specific knowledge graphs to give a more complete answer.
Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

Earthquake Early Warning (EEW) is an alert system, based on seismic wave propagation theory, to reduce human casualties. EEW systems mainly utilize technologies through both network-based and on-site methods. The network-based method estimates the hypocenter and magnitude of an earthquake using data from multiple seismic stations, while the on-site method predicts the intensity measures from a single seismic station. Therefore, the on-site method reduces the lead time compared to the network-based method but is less accurate. To increase the accuracy of on-site EEW, our system was designed with a hybrid method, which included machine learning algorithms. At this time, machine learning was used to increase the accuracy of the initial P-wave identification rate. Additionally, a new approach using a nearby seismic station, called the 1+ α method, was proposed to reduce false alarms. In this study, an on-site EEW trial operation was performed to evaluate its performance. The warning cases for small and large events were reviewed and the possibility of stable alert decisions was confirmed.
Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform

In recent years, digital image watermarking has gained a significant amount of popularity and developed into a crucial and essential tool for copyright protection, security, and the identification of multimedia content. Despite its high computational complexity, singular value decomposition (SVD) is an extensively utilized transformation in digital image watermarking. This research presents a robust and blind image watermarking scheme that directly alters the image pixels in the spatial domain to incorporate the watermark by quantizing the block-wise invariant maximum singular value. Using a distribution rule, pixels from the cover image are redistributed to obtain a new image that is divided into square and non-overlapping blocks to obtain invariant maximum singular values by using the matrix 2-norm in the spatial domain without performing an SVD transform. This modifies the pixels of the cover image such that the outcome is equivalent to the difference between the maximum singular values of the corresponding blocks in covers and watermarked images. The strengths of the proposed approach are highlighted by a comparison of experimental results with the most recent and comparable watermarking approaches.
Designing User Interfaces for Illiterate and Semi-Literate Users: A Systematic Review and Future Research Agenda

Using a systematic literature review, this review examines existing studies on the design, usability, user experience (UX), and adoption of computer applications designed for illiterate and semi-literate users. A total of 45 articles were reviewed from an initial set of 527 articles following an inclusion-exclusion criterion. As outcomes, in addition to synthesizing the present research activities, this review revealed a total of 16 design considerations or guidelines that may improve the usability, user experience, and adoption of software systems to illiterate and semi-literate people. The revealed design considerations were also investigated concerning the country context and application platform. Finally, a set of good rules categorized into five perspectives (uses of text, interaction pattern, uses of pictography, uses of audio-video, and info-architecture) were revealed to design and evaluate software systems to make them more usable to the illiterate and semi-literate users. The review also reveals future research opportunities that include: exploring the design considerations with respect to context-dependent applications; investigating the design requirements for public IT (e.g., vending machines and kiosks) systems; adopting the semiotic concept in UI design; developing a new usability evaluation framework; and exploring the effects of multi-platform and cross-country app design. Thus, the implication of this study is to discover the research gaps and provide future research directions aiming to design applications for illiterate and semi-literate users with enhanced usability and UX.
Increasing depth of neural networks for life-long learning

We propose a novel method for continual learning based on the increasing depth of neural networks. This work explores whether extending neural network depth may be beneficial in a life-long learning setting. We propose a novel approach based on adding new layers on top of existing ones to enable the forward transfer of knowledge and adapting previously learned representations. We employ a method of determining the most similar tasks for selecting the best location in our network to add new nodes with trainable parameters. This approach allows for creating a tree-like model, where each node is a set of neural network parameters dedicated to a specific task. The Progressive Neural Network concept inspires the proposed method. Therefore, it benefits from dynamic changes in network structure. However, Progressive Neural Network allocates a lot of memory for the whole network structure during the learning process. The proposed method alleviates this by adding only part of a network for a new task and utilizing a subset of previously trained weights. At the same time, we may retain the benefit of PNN, such as no forgetting guaranteed by design, without needing a memory buffer. Experiments on Split CIFAR and Split Tiny ImageNet show that the proposed algorithm is on par with other continual learning methods. In a more challenging setup with a single computer vision dataset as a separate task, our method outperforms Experience Replay. It is compatible with commonly used computer vision architectures and does not require a custom network structure. As an adaptation to changing data distribution is made by expanding the architecture, there is no need to utilize a rehearsal buffer. For this reason, our method could be used for sensitive applications where data privacy must be considered.
Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.
An Efficient Strategy for Catastrophic Forgetting Reduction in Incremental Learning

Deep neural networks (DNNs) have made outstanding achievements in a wide variety of domains. For deep learning tasks, large enough datasets are required for training efficient DNN models. However, big datasets are not always available, and they are costly to build. Therefore, balanced solutions for DNN model efficiency and training data size have caught the attention of researchers recently. Transfer learning techniques are the most common for this. In transfer learning, a DNN model is pre-trained on a large enough dataset and then applied to a new task with modest data. This fine-tuning process yields another challenge, named catastrophic forgetting. However, it can be reduced using a reasonable strategy for data argumentation in incremental learning. In this paper, we propose an efficient solution for the random selection of samples from the old task to be incrementally stored for learning a sequence of new tasks. In addition, a loss combination strategy is also proposed for optimizing incremental learning. The proposed solutions are evaluated on standard datasets with two scenarios of incremental fine-tuning: (1) New Class (NC) dataset; (2) New Class and new Instance (NCI) dataset. The experimental results show that our proposed solution achieves outstanding results compared with other SOTA rehearsal methods, as well as traditional fine-tuning solutions, ranging from 1% to 16% in recognition accuracy.
Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

With the increase in cyber threats in recent years, there have been more forms of demand for network security protection measures. Network traffic classification technology is used to adapt to the dynamic threat environment. However, network traffic has a natural unbalanced class distribution problem, and the single model leads to the low accuracy and high false-positive rate of the traditional detection model. Given the above two problems, this paper proposes a new dataset balancing method named SD sampling based on the SMOTE algorithm. Different from the SMOTE algorithm, this method divides the sample into two types that are easy and difficult to classify and only balances the difficult-to-classify sample, which not only overcomes the SMOTE’s overgeneralization but also combines the idea of oversampling and undersampling. In addition, a two-layer structure combined with XGBoost and the random forest is proposed for multiclassification of anomalous traffic, since using a hierarchical structure can better classify minority abnormal traffic. This paper conducts experiments on the CICIDS2017 dataset. The results show that the classification accuracy of the proposed model is more than 99.70% and that the false-positive rate is less than 0.34%, indicating that the proposed model is better than traditional models.
Semi-Supervised Model for Aspect Sentiment Detection

Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model.
Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users' comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.