Reviewerly/exHarmony huggingface dataset:
Machine Translation to Sign Language Using Post-Translation Replacement Without Placeholders

Sign language is typically the first language for those who are born deaf or who lose their hearing in early childhood. To provide important information for these individuals, it is better to use sign language than to transcribe spoken languages. We have been developing a system that translates Japanese into Japanese Sign Language (JSL) and then generates computer graphics (CG) animation of JSL.In this paper, we propose a machine translation method for translating Japanese into JSL. The proposed method is based on an encoder-decoder model that utilizes a pre-trained model as the encoder, and the proper names in the translation result are revised using a dictionary by means of a post-translation replacement method without placeholders. Our experimental results demonstrate that using the pre-trained model as the encoder and performing the post-translation replacement of proper names both contributed to improving the translation quality.
Software Module Classification for Commercial Bug Reports

In this work, we curate and investigate a dataset named Turkish Software Report - Module Classification (TSRMC), consisting of commercial software bug reports of a company. Automated bug classification is required in large-scale software projects due to the vast amount of bugs. We analyze and report the statistical features and classification difficulty of the dataset. We use several methods from the text classification literature to assign each bug report of the TSRMC dataset a suitable software module. The utilized methods include traditional machine learning (ML) methods, such as support vector machine (SVM) and logistic regression; sequential deep learning (DL) models, such as gated recurrent unit (GRU) and convolutional neural networks (CNN); and Bidirectional Encoder Representations from Transformers (BERT)-based pre-trained language models (PLMs). Our work is one of the first efforts in automated bug report classification literature that focuses on commercial bugs and uses bilingual (Turkish and English) texts.
Improving Label-Deficient Keyword Spotting Through Self-Supervised Pretraining

Keyword Spotting (KWS) models are becoming increasingly integrated into various systems, e.g. voice assistants. To achieve satisfactory performance, these models typically rely on a large amount of labelled data, limiting their applications only to situations where such data is available. Self-supervised Learning (SSL) methods can mitigate such a reliance by leveraging readily-available unlabelled data. Most SSL methods for speech have primarily been studied for large models, whereas this is not ideal, as compact KWS models are generally required. This paper explores the effectiveness of SSL on small models for KWS and establishes that SSL can enhance the performance of small KWS models when labelled data is scarce. We pretrain three compact transformer-based KWS models using Data2Vec, and fine-tune them on a label-deficient setup of the Google Speech Commands data set. It is found that Data2Vec pretraining leads to a significant increase in accuracy, with label-deficient scenarios showing an improvement of 8.22% to 11.18% absolute accuracy.
Quality-of-Things Based Machine Learning for the MIoT Applications

Enhancing the Quality of Things (QoT) is urgently needed given the rapid evolution of the Multimedia Internet of Things (MIoT). One of the challenges with MIoT is Acceptable QoT. Achieving AQoT can optimize bandwidth and storage at a level that will satisfy MIoT application’s minimal requirements. Intelligent systems using Machine Learning (ML) techniques can improve the performance of MIoT applications by keeping the minimum requirements of the resources which are necessary to maintain AQoT. The aim of this study is to develop a MIoT system based on ML to provide high performance with an acceptable QoT. The Gaussian-Naive Bayes, Fine KNN, and AdaBoost ML algorithms were investigated against different video sequences with varying bitrates and network conditions. The results based on face recognition for a Ring Video Doorbell scenario showed that ML could be used on MIoT applications to achieve AQoT and significantly reduce bandwidth and storage usage.
ALL-IDB Patches: Whole Slide Imaging For Acute Lymphoblastic Leukemia Detection Using Deep Learning

The detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is being increasingly performed using Deep Learning models (DL) that analyze each blood sample to detect the presence of lymphoblasts, possible indicators of the disease. However, images included in current databases are either too large or already segmented. In this paper, we introduce ALL-IDB_Patches, a novel approach for processing Whole Slide Images (WSI) of ALL to take advantage of all the information available for ALL detection, by generating a larger number of samples and making the images usable by current DL models, without any pre-performed segmentation. To evaluate the attainable classification accuracy, we consider the OrthoALLNet, a Convolutional Neural Network (CNN) obtained by imposing an additional orthogonality constraint on the learned filters. The experimental results confirm the validity of our approach.
Sign Language Avatar Animation Search: An Ani2Ani Search Application

To improve sign language animation asset management, we developed a general animation search system supporting multiple input modalities. The system reads animations as pose sequences, embeds them into fixed-length representation vectors using engineered or learned features, and scores animation similarity using distance in the embedding space. We present an overview of the system, use-case scenarios, and feedback from deaf users.
FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training

Federated Learning (FL) provides an approach for performing the collaborative training of AI models without compromising data privacy. However, traditional implementations of FL require complex deployment strategies, making it challenging to perform training across multiple data centers. To address this issue, this article presents a platform designated as Federated Learning Agent (FLAg) which allows users to delegate their federated learning tasks to an automated process. FLAg features digital pathology databases as well as multiple pretrained models. The security of FLAg is ensured through its deployment on an HPC system and bastion host. While FLAg itself only offers FL training services, users may perform data annotation and inference through its integration with a proprietary ALOVAS AI pathology platform for a complete end-to-end process which includes annotation, FL training, and inference.
Querying A Sign Language Dictionary with Videos Using Dense Vector Search

To search for an unknown sign in a sign language dictionary, users typically indicate parameters of the query, e.g., hand shape and signing location. Recent advances in sign language recognition enable video-based sign language dictionary search. In such a system, users can record an unknown sign and retrieve a list of signs that look similar, preferably including the queried sign as one of the top results. We have realized such a system by interpreting it as a dense vector search task. First, we learn a mapping (embedding) from sign videos to a vector space. The dictionary can then be searched by looking for the vectors in this space that are closest to the vector corresponding to the query. We present a proof of concept on a subset of the Flemish Sign Language dictionary. Further research is required to scale up our method to the large vocabularies of entire dictionaries.
How Well Can Language Models Understand Politeness?

Politeness plays a key role in social communications. Previous work proposed an SVM-based computational method for predicting politeness using linguistic features on a corpus that contains Wikipedia and Stack Exchange requests data. To extend this prior work, we focus on evaluating the performance of state-of-the-art language models on politeness prediction using the same dataset. Two models are applied in this study. First, we fine-tune BERT on politeness data and then use the fine-tuned model for politeness prediction. Second, we use ChatGPT to predict politeness. The results show that both fine-tuned BERT and ChatGPT achieved better results than the state-of-the-art results on both Wikipedia and Stack Exchange data. Fine-tuned BERT outperforms zero shot ChatGPT, but ChatGPT can provide explanations for its prediction. Moreover, fine-tuned BERT outperforms human-level performance by 2.28% on Wikipedia corpus.
Evolution of Big Data Models from Hierarchical Models to Knowledge Graphs

For resilient computing in a sustainable cyber-physical world, it is important to well manage data. Hence, in this paper, we examine the evolution of big data models. These database models include hierarchical models, network models, relational models, semantic models like entity-relationship (ER) models, semi-structure models, graph models like knowledge graphs. We trace the evolution of these database models, the concept, theories and diversity of ideas. We compare and contrast some of these models on the basis of how the inherent challenges in one model resulted in the evolution of another model. In particular, we study how the dynamic nature of the society has affected the most notable and successful relational database model.
Explainability of AI-Driven Air Combat Agent

In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.
RL-KDA: A K-degree Anonymity Algorithm Based on Reinforcement Learning

K-degree anonymity is one of the main techniques for data privacy and has gained attention in academia, industry, and government. Many social network data publishing algorithms based on K-anonymity techniques have been proposed, but most studies focus on static social networks. Compared to static social networks, dynamic social networks suffer from problems such as higher information loss and lower data utility. To address the existing problem of dynamic social networks, we propose a K-degree anonymity dynamic data publishing algorithm based on reinforcement learning. The algorithm ends with two phases: anonymization sequence and graph modification. In the anonymous sequence phase, this paper combines the idea of reinforcement learning and the characteristics of dynamic data change to build a reinforcement learning model for anonymous sequences. In this way, an ideal anonymous sequence can be created. We also propose a new strategy for graph modification, which selects edges according to degree centrality to generate anonymous graphs. Finally, experiments on real datasets show the effectiveness of our algorithm.
Decentralizing Electric Vehicle Supply Chains: Value Proposition and System Design

Distributed ledger technologies are transforming existing business models and business relationships. In particular, blockchain allows non-trusting parties to manage a shared database in a decentralized way and improve the transparency, authenticity, and reliability of the exchanged data. Nonetheless, decentralized paradigms are not yet well established, resulting in only a fraction of blockchain-based applications being successful in the long term.In this paper, we present a blockchain-based solution for the electric vehicle supply chain that we designed in the context of the CONCORDIA project of the European Cybersecurity Competence Network. We describe the goals, the value proposition, the main design choices, and the architecture of our system. Moreover, we discuss the electric vehicle supply chain, analyzing the improvements and limitations introduced by our blockchain-based solution. We analyze our solution from the managerial and technical points of view through a lean business methodology for blockchain solutions. In particular, we developed an economic impact assessment to evaluate the potential costs and revenues of the application of blockchain technology in a supply chain context. Although the blockchain system is inspired by the supply chain of a multinational automotive company, it can be applied to any other multi-actor supply chain.
When graphs meet game theory: a scalable approach for robotic car racing

Autonomous vehicle racing is facing a growing interest both in industrial and academic settings spanning multiple disciplines. In this paper, we will explore how to create a robust, efficient, and reliable decision-making mechanism to decide, at every point in time, the trajectories that a vehicle should take to overtake its opponents and win the race. The proposed framework combines a graph-based path planner with a game-theoretic model to generate powerful racing strategies. We implemented the framework, and we carried out an experimental evaluation to show its effectiveness and evaluate the impact of the different parameters.
Anomaly Localization in Audio via Feature Pyramid Matching

Sound anomaly detection is a task that aims at identifying unusual or abnormal sounds within audio data. These sounds could be caused by different factors, such as background noise, equipment malfunctions, or unexpected events. Anomaly detection in sound is a well-studied topic, with a lot of research being done in the field. Anomaly localization refers to the process of identifying the specific location or region within a sample where an anomaly (or outlier) occurs. When applied to audio signals, anomaly localization can involve analyzing the spectral content of the sound to detect regions that deviate from the typical or expected pattern.In this study, we present a simple yet effective model based on the Student-Teacher Feature Pyramid Matching Method for locating anomalies in audio data. Utilizing the MIMII dataset by augmenting it with synthetic anomalies, we evaluate the method’s accuracy. Our results demonstrate that the proposed model can accurately locate artificially created anomalies within the spectrograms, both in terms of time and frequency. This approach offers a promising solution for identifying and determining the precise location of anomalies in various audio applications.
Semantically Enabled Content Convergence System for Large Scale RDF Big Data

The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.
Improved Deep Embedded K-Means Clustering with Implicit Orthogonal Space Transformation

The deep clustering algorithm can learn the latent embedded features of the data through the autoencoder, and cluster the data according to the similarity of the latent features. However, the feature information obtained by the autoencoder may not have a better value for the clustering algorithm and is not suitable for clustering, which greatly reduces the clustering effect. This paper proposes a deep K-means clustering algorithm with implicitly embedded space transformation to answer this question. We implicitly transform the latent feature space into a new type of space that is more friendly to the clustering task, which preserves space invariance. This implicit transformation is done through an orthogonal transformation matrix. The orthogonal transformation matrix is composed of the eigenvectors of the intra-class scattering matrix and the inter-class scattering matrix. In the new space, clusters can be better separated by cluster cohesion and inter-cluster difference. We alternately optimize feature acquisition and clustering to adjust the embedding space and disperse the embedding points, to enrich the clustering information in the latent feature space. Experimental results show that our proposed algorithm can produce better high-quality clusters than many current correlation clustering algorithms on the same experimental dataset.
Determining the Relative Importance of Features for Influencing Software Product Similarity Matching

As a software product line evolves a significant management challenge is comparing existing products to each other or planned products. The approach to product comparison will vary according to its purposes. One solution includes the representation of a configured product as a weighted binary string where 1 represents a feature’s presence, 0 represents its absence, and the weight represents the different levels of relative importance to the product that a feature is perceived to have. Relative importance values influence similarity matching so that the features considered important are the ones that primarily influence what is judged to be similar. A binary string similarity metric supports product comparison (a product similarity metric). For a product line that contains thousands of features the allocation of relative importance values is only practical when done automatically. This paper proposes a novel algorithm for automatically determining the relative importance of each feature. A feature tree can represent a product line in which a feature is a node in the tree and a relationship between features is an edge. A feature’s relative importance is calculated as a function of local and global tree structural measures. The local measures are the number of input and output nodes to which a feature is connected and the variability property of each of these nodes. The global measure is the distance of the feature from the root node. A mobile phone worked example illustrates the feasibility of the algorithm.
An Analysis of Grading Patterns in Undergraduate University Courses

University undergraduate course grades have several purposes: they provide feedback to the student and motivation to perform well; serve as admission criteria for entering a major; and are used as selection criteria for future employers and graduate programs. Accurate assignment of grades is therefore important and critical to ensure fairness. However, grades may also impact the student’s assessment of the instructor, which leads to a conflict of interest when such assessments are a component of employment, salary, or tenure decisions. This paper performs a detailed descriptive analysis of undergraduate grades collected over an eight year period from a major metropolitan university. Interesting grading patterns are identified and discussed, and the analysis suggests that grading policies vary substantially at the department, course, and instructor level. A connection is observed between course/department enrollment and average grades assigned. A particular focus of this study involves describing the grading behavior of instructors, with the goal of identifying instructors that assign grades that are statistically far above or below the norm. The analysis performed in this study can be applied to grade data from other universities using our publicly available Python-based analytics tool. The results of these analyses can be used to better understand existing grading policies, identify potential sources of grading inequities, and, when appropriate, take corrective action.
Machine Learning Based Method for Auditing Personnel Expenses in Public Expenditure

This study aims to investigate the use of text classification techniques to support the audit of municipal accounts by the Court of Accounts in the context of calculating Total Personnel Expenditure. The study contributes to the discovery of expenses incorrectly classified by municipal managers due to error or fraud, which would be left out of the calculation. It used data obtained from the Court of Accounts of the Municipalities of Goiás, Brazil – TCM. These data were labeled by human experts and then prepared to build expenditure classification models from their description. The TF-IDF algorithm was used for feature engineering, and the Support Vector Machines (SVM), Logistic Regression, and Multinomial Naïve Bayes were used for classification. The results showed that the proposed method is consistent, having reached an F-Score of 0.91 and 0.97 with the SVM algorithm in the binary and multiclass corpus, respectively. Even dealing with a highly unbalanced dataset in the multiclass approach, the performance can be considered very good. As an innovative study, this work seeks to insert, in the context of the analysis of public expenditure on personnel, the use of text mining techniques to solve problems that would require the effort of many human specialists. The authors understand that there is a practical contribution to the process of analysis of public expenditure on personnel carried out by the Court of Accounts.
Faulty Neural Networks

This study aims to investigate the response of the nervous system to injury through experiments using a neural network model trained with the MNIST dataset [1]. Multiple experiments are performed to examine the relationship between neural network damage and accuracy. How the damaged network can restore its functionality or accuracy with the aid of another neural network is also investigated. By analyzing these results, a better understanding of the nervous system’s ability to respond to injury and adapt to changes in neural networks can be gained.
Weakly-Supervised Self-Ensembling Vision Transformer for MRI Cardiac Segmentation

Deep learning techniques are crucial in medical image segmentation, but their effectiveness heavily relies on a vast amount of fully annotated data, which is costly in labour and time. To address this challenge, this paper introduced a framework for scribble-supervised learning with a self-ensembling approach. The transformation consistency scheme is further developed to boost performance. Inspired by the recent achievements of the Vision Transformer (ViT) in modeling long-range dependencies, and to enable a fair comparison with convolutional operations, we employ a U-shaped segmentation network composed of pure self-attention-based blocks. Our proposed scribble-supervised segmentation ViT is validated on a public benchmark dataset against classical methods with various evaluation metrics. The code, trained model, and preprocessed scribble-annotated sets are publicly available at https://github.com/ziyangwang007/CV-WSL-MIS.