Reviewerly/exHarmony huggingface dataset:
Enhanced Time Delay Neural Network Architectures for Sinhala Speech Recognition

Automatic Speech Recognition (ASR) has become a fast-growing research domain due to advancements in Machine Learning. In addition to the development of large training corpora, the introduction of novel architectures for ASR models has contributed to defining new boundaries for the performance of speech recognition systems. However, there is a significant difference in speech recognition accuracy between major world languages and low-resourced languages such as Sinhala, due to inadequate research. We have applied enhanced time-delay neural network architectures for acoustic modeling in Sinhala ASR, including the Multistream CNN architecture. Using the Kaldi ASR Toolkit, we have trained ASR models with a publicly available corpus of over 200 hours of speech data. The results show a remarkable improvement in the accuracy of Sinhala speech recognition as demonstrated by a reduction in the Word-Error-Rate (WER) to 25.12%.
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning

Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden temporal relation labels, which is lost by the discrete one-hot labels. To alleviate the loss of semantic information, we propose learning Temporal semantic information of the golden labels by Auxiliary Contrastive Learning (TempACL). Different from traditional contrastive learning methods, which further train the PreTrained Language Model (PTLM) with unsupervised settings before fine-tuning on target tasks, we design a supervised contrastive learning framework and make three improvements. Firstly, we design a new data augmentation method that generates augmentation data via matching templates established by us with golden labels. Secondly, we propose patient contrastive learning and design three patient strategies. Thirdly we design a label-aware contrastive learning loss function. Extensive experimental results show that our TempACL effectively adapts contrastive learning to supervised learning tasks which remain a challenge in practice. TempACL achieves new state-of-the-art results on TB-Dense and MATRES and outperforms the baseline model with up to 5.37% $$F_1$$ on TB-Dense and 1.81% $$F_1$$ on MATRES.
Investigations on &lt;i&gt;c&lt;/i&gt;-Bent&lt;sub&gt;4&lt;/sub&gt; Functions via the Unitary Transform and &lt;i&gt;c&lt;/i&gt;-Correlation Functions

In this paper, some properties of Boolean functions via the unitary transform and c-correlation functions are presented. Based on the unitary transform, we present two classes of secondary constructions for c-bent4 functions. Also, by using the c-correlation functions, a direct link between c-autocorrelation function and the unitary transform of Boolean functions is provided, and the relationship among c-crosscorrelation functions of arbitrary four Boolean functions can be obtained.
Reinforced Adaptation Network for Partial Domain Adaptation

Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature representations. By combining reinforcement learning and domain adaptation techniques, the proposed network alleviates negative transfer by automatically filtering out less relevant source data and promotes positive transfer by minimizing the distribution discrepancy across domains. Experiments on three benchmark datasets demonstrate that RAN consistently outperforms seventeen existing state-of-the-art methods by a large margin.
Fuzzy Models and Product's Quality Prediction in Converter Production

The paper observes the stages of fuzzy system development, designed for forecasting the defects of steel production, including analysis of the technological processes, forecasting problem statement, construction of fuzzy models and training algorithms. The results of experimental testing of the system on one of the metallurgical facilities are considered.
Towards Privacy-Preserving Computation on Gene: Construct Covid-19 Phylogenetic Tree using Homomorphic Encryption

Constructing a phylogenetic tree is an essential method of analyzing the evolution of the covid-19 virus. In the case of multiple entities holding different coronavirus genetic data, it is simple to aggregate all data into one entity and then calculate the phylogenetic tree. However, such a method is challenging to carry out. Genetic data is susceptible and has high economic value, and it is usually impossible to copy between different entities directly. Also, the direct sharing of genetic data can lead to data leaks or even legal problems. In this paper, we propose a homomorphic-encryption-based solution to tackle this problem, where two participants, A and B, both hold a part of covid-19 genetic data and compute the gene distance matrix calculation of the overall dataset without revealing the genetic data held by both parties. After the computation, participant A can decrypt the final distance matrix from the encrypted result and then use the plain-text result to construct the covid-19 phylogenetic tree. Experiment results show that the proposed method can process the genetic data accurately in a short time, and the phylogenetic tree generated by the proposed solution has no loss of accuracy compared to plain-text calculation. In terms of engineering optimization, we propose an optimized encryption method, which can further shorten the encryption time of the entire dataset without reducing the security level.
Financial Topic Modeling Based on the BERT-LDA Embedding

Topic modeling extracts useful potential topics that reflect market information from massive financial news and is widely used in data mining and economic research. Traditional topic modeling approaches such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) lack semantic information, and short texts have feature sparse problems. We develop a topic clustering model based on BERT-LDA joint embedding that takes both contextual semantics and thematic narrative into account. We cluster document embeddings with the HDBSCAN algorithm and utilize a class-based TF-IDF (c-TF-IDF) method to create topic representations. Empirical results show that the BERT-LDA model is competitive compared with traditional and single topic models. It generates coherent topic words that are dissimilar to each other.
Minimising the Number of Ranging Sensors Verifying Target Positioning Uncertainty

Indoor positioning applications are increasingly popular due to the availability of effective and low cost ranging sensors. Many solutions have been proposed recently using these type of sensors to estimate the coordinates (position) of a target within a given target uncertainty. In this paper, we consider the problem of deploying a large scale infrastructure that solves the positioning task with guaranteed estimation uncertainty while minimising the number of ranging sensors. To this end, we adopt a two steps procedure. In the first step, we identify a basic cell structure compounded of a small number of sensors deployed in symmetric configurations.Â In the second step, we use this basic cell as an elementary tile structure to be used in standard coverage algorithm minimising the portion of space left uncovered.
Smart Domain-Adapted Visual Saliency Perception in Advanced Driver Motion-Assisted System

Recently, estimation of the visual saliency map in car driving scenarios has received significant research interests. Visual saliency perception includes the processing of specific parts of the visual driving scene in which the subject (car driver) pays more attention (specifically the parts whose gaze is focused). This work makes further contributions to video saliency research with application on the sustainable assisted driver technologies. Ad-hoc Semantic Fully Convolutional Deep Network embedding Gradient-Reversal domain adaptation layer has been implemented to process the video frames captured by a commercial low frame-rate automotive-grade camera device hosted outside the vehicle. A parallel motion-magnified visual-to-physio drowsiness assessment of the car driver will complete the proposed full automotive solution. The collected experimental results confirmed the effectiveness of the proposed solution.
Comparing the Cost of IaaS and CaaS Services

Cloud computing environments allow businesses to deploy applications in a fast and scalable way. Infrastructure-as-a-service (IaaS) and container-as-a-service (CaaS) models can be adopted for the deployment of cloud-based applications. The current paper presents a specific near-real-world scenario of a cloud-based application, deployed by the two aforementioned cloud models. The deployment cost differs between the cloud models and relies on the number of utilized resources, which is driven by the user demand. Since the cost is a major importance factor that finally determines the adoption of cloud technology, it is challenging to estimate and examine the cost of each proposed approach. This research can help cloud computing professionals pick a model that meets their goals and budget. It's also useful for cloud cost analysis. The corresponding costs of the two deployments are estimated based on the pricing policies of major providers, Amazon, Google, and Microsoft.
LRGCN: Linear Residual Graph Convolution Network Recommendation System

With the vigorous development of the Internet and the continuous expansion of the scale of product information, people put forward higher requirements for filtering redundant information in product recommendation. Researchers use a bipartite graph to model the interaction between users and items so that Graph Convolutional Network (GCN), the most advanced graph representation model, can be widely and successfully applied in the recommendation system. However, GCN can cause the representation of nodes over smooth and the layers of most GCN models cannot be stacked deep to capture higher-order cooperative signals. In this work, we study the recommendation system by optimizing the over smoothing effect of GCN. Firstly, we remove the nonlinear part of GCN in the message passing process. Secondly, we introduce the residual network structure and propose the Linear Residual Graph Convolution Network (LRGCN) network model so that the number of stacked layers can be effectively increased while maintaining good performance. Finally, we optimize the negative sampling strategy to improve the performance of the recommendation system by 2.8%. Our proposed model is linear and achieves better results on three different real data sets than the baselines.
Combating Noisy Labels via Contrastive Learning with Challenging Pairs

The predictive performance of deep neural networks (DNNs) depends on the quality of labels, yet imperfect labels are ubiquitous in real-world datasets. Several recent methods training DNNs robust to label noise have been proposed mainly focusing on two techniques: label filtering by a model after standard warm-up training to discriminate noisy labels and pseudo-labeling to optimize the model with the divided dataset. The performance of these methods depends on the quality of warm-up training, while noisy labels affect the training to some extent. In this paper, we propose to solve the problem by warming-up the models without any labels. Specifically, we optimize the models by a specified contrastive learning way combining with adversarial training. By getting rid of the noisy labels, the method creates more challenging pairs by computing adversarial noises in an unsupervised manner. Thus, we can obtain a high-quality feature extractor through the proposed contrastive learning method with challenging pairs (CLCP). Extensive experimental results on CIFAR-10 and CIFAR-100 demonstrate that the proposed approach is compatible with the existing methods and can significantly outperform the state-of-the-art counterparts.
Anomaly Detection in Aerial Images VIA Semi-Supervised Adversarial Training

Most of the deep neural networks require a large amount of data for training to achieve good results. However, in practical anomaly detection applications, we usually only have few labeled anomalous samples that have various types. This study proposed an innovative hybrid architecture that aims to detect anomalies with a small number of labeled anomalous samples. The proposed method consists of two stages. First, the network learns the distribution of normal data with a generative adversarial network (GAN). Second, the discriminator of the network is combined with a classifier and the training process updates different network components depending on whether the labeled samples are normal or anomalies. From the experiments on CIFAR-10 and UC Merced datasets, we demonstrate that our method yields significant performance improvements then another GAN-based approach even when few labeled samples are provided.
YOLO-R Based Mask Recognition System with Winograd Convolution Acceleration Chip

In this paper, we proposed “YOLO-R based mask recognition system with Winograd convolution acceleration chip.” In order to prevent the spread of COVID-19, we proposed a mask recognition system based on YOLO-R that adopted the CSP backbone architecture and used prediction refinement to predict, optimize, and obtain high recognition results. In addition, we designed the Winograd convolution acceleration chip to accelerate the operation of the YOLO-R neural network, so that the system can achieve real-time computing.
Credit Card Fraud Legal Advisor Tool Using Machine Learning

In this era of digitalization, online transactions immensely increased the use of credit card and credit card frauds. Multiple verifications are not user-friendly and narrowed to certain fraud activities. However, automatic identification of fraud and real-time streaming consumes less time and computational resources. Identifying the hidden correlations in data with the help of machine-made devices used for prediction and developing trained models make the system robust in terms of security. This proposed system aims to detect fraudulent transactions on their card in duplicity or any such irrelevant procedures and provides a legal remedy. The primary objective of this system is to detect and identify suspicious and spurious events and report them to the analyst or individual allowing the genuine transactions to proceed automatically and conveniently. This system identifies and detects suspicious transactions over the Internet through machine learning algorithms and provides legal support against the predicted fraud that would help the legal team in big organizations to enhance and felicitate the flow of genuine transactions.
Investigating Bugs in AI-Infused Systems: Analysis and Proposed Taxonomy

Testing for critical AI systems is non-trivial as these systems are prone to a new breed of sophisticated software defects. The admissibility of these systems and their fundamental social acceptance is tightly coupled with assuring whether the potential hazards to humans, animals, and property posed by the prospect defects can be minimized and limited to an acceptable level. In this work, we address the problem of assurance for critical AI systems by firstly, analyzing the nature of defects that occur in AI -infused systems in general and how to combat these within a testing strategy. Secondly, developing a focused taxon-omy of prospect defects in critical AI systems. This taxonomy enables the development of the non-critical proxy (i.e., stand-in) equivalent by reproducing defects with similar characteristics.
NeurReview: A Neural Architecture Based Conformity Prediction of Peer Reviews

Peer review is at the heart of scholarly communications and the foundation of scientific evaluation. However, peer review’s effectiveness is continuously challenged due to biased and inconsistent peer reviews. Consequently, ensuring the quality of peer reviews is a time-critical problem. In this paper, we investigate the conformity between reviews and meta-reviews. To predict the review conformity and identify the effective features to distinguish the misaligned reviews, we propose NeurReview, which models the review process from the review structure and interactions with authors and other reviewers. Two evaluation datasets are constructed from the ICLR open reviews. The evaluation results verified the efficacy of our proposed model. In addition, we found that the divergence with other reviews and responses, the consistency of sentiment polarity with the recommendation score, etc., are beneficial features for identifying low-conformity reviews, which can assist meta-reviewers in making final decisions.
Non-literary communicative acts in literary style

Abstract Many literary texts prove a tendency to simulate features of diverse communicative acts, styles and genres that are primarily applied in non-literary spheres of communication (for instance journalism, administration, science). The aim of this paper is to suggest a possible theoretical framework for this phenomenon and to describe some ways of simulating non-literary styles in literary texts. The aforementioned simulating can be seen as a form of interdiscursivity that introduces external elements into literary texts. The functioning of it is conditioned by a process of decontextualization and recontextualization that is signalled by various paratextual and textual cues. Taking three Czech prosaic works as examples, the concrete use of this form of interdiscursivity is illustrated in the last part of the paper.
Fault Diagnosis of Motor based on Knowledge Graph and CNN

With the improvement of motor automation, the internal structure tends to be complex, and the approach of relying on traditional maintenance experience is too inefficient for fault diagnosis. The knowledge graph has natural semantic advantages, which provide the breakthrough for the relational representation of a large number of semantic knowledge information. Therefore, this paper designs the knowledge graph based on text data to complete the construction and storage of motor fault knowledge graphs. To address the problem that existing motor fault diagnosis methods rely on annotated data on a large scale in the auxiliary diagnosis process, and lack normative knowledge or expert experience. The motor fault diagnosis method (KG-CNN) based on the knowledge graph and convolutional neural network (CNN) is proposed. Semantic and knowledge enrichment is achieved using the completed structured data constructed, and the fault feature vectors from the motor fault description text are fed into the CNN along with the corresponding knowledge feature vectors. Finally, several groups of motor fault text data are used to verify the effectiveness of KG-CNN in fault diagnosis performance. The results show that KG-CNN can improve the accuracy of the knowledge graph in fault diagnosis.
Enhancing Cross-Modal Hash Retrieval with Multiple Similarity Matrices

In order to further improve the performance of cross-modal retrieval, a cross-modal hash retrieval method integrating multi-level similarity information is proposed. First, self-attention method is used to enhance the text features, and a new fusion feature is constructed based on the original features and hash features of different modalities. Then, based on these three features, three auxiliary similarity matrices are constructed, and the fourth auxiliary similarity matrix is constructed by a weighted combination method. Finally, these four different matrices are used to calculate the loss functions between different similarity matrices and between different modalities. Since the four matrices include different feature forms and different matrix construction methods, they can better express similarity information of different modalities and improve the retrieval performance. The experiments are conducted on three benchmark datasets of Wikipedia, MIRFlickr and NUS-WIDE. The results show that the mAP values at different code bits of proposed method is better than that of many state-of-the-art methods, which verifies the effectiveness and robustness of our method.
Human-Generated Web Data Disentanglement for Complex Event Processing

In social media, human-generated web data from real-world events have become exponentially complex due to the chaotic and spontaneous features of natural language. This may create an information overload for the information consumers, and in turn not easily digest a large amount of information in a limited time. To tackle this issue, we propose to use Complex Event Processing (CEP) and semantic web reasoners to disentangle the human-generated data and present users with only relevant and important data. However, one of the key obstacles is that the human-generated data can have no structured meaning sometimes even for the speaker, hindering the output of the CEP. Therefore, in order to adapt to the CEP inputs, we present two different techniques that allow for the discrimination and digestion of value of human-generated data. The first one relies on the Variable Sharing Property that was developed for relevance logics, while the second one is based on semantic equivalence and natural language processing. The results can be given to CEP for further semantic reasoning and generate digested information for users.
A Feature Fusion-Based Approach for Mammographic Mass Classification Using Deep Learning

Breast cancer seems to be the most deadly cancer for females globally. Mammographic masse detection is essential for the early diagnosis of breast cancer. The manual detection of breast masses using texture analysis from digital mammograms is hard because of its diverse patterns. Automatic detection of breast masses from mammograms with computer algorithms at early phases could help physicians to avoid unnecessary biopsies. In the current study, we investigated the limitations of texture analysis for mass detection by creating an MR taxon filter bank using Laplacian of Gaussian. We created a deep taxon feature fusion bank for the EfficinetNet DCNN to classify benign and malignant masses. The experimental results were examined on the public CBIS-DDSM and the local PINUM dataset. Our method yielded 0.96 and 0.98 accuracies, respectively. The results findings illustrated that our method significantly outperformed than the MobileNet, AlexNet, DenseNet, and previous studies.