Reviewerly/exHarmony huggingface dataset:
Empirical Analysis of Data Sampling-Based Ensemble Methods in Software Defect Prediction

This research work investigates the deployment of data sampling and ensemble techniques in alleviating the class imbalance problem in software defect prediction (SDP). Specifically, the effect of data sampling techniques on the performance of ensemble methods is investigated. The experiments were conducted using software defect datasets from the NASA software archives. Five data sampling methods (over-sampling techniques (SMOTE, ADASYN, and ROS), and undersampling techniques (RUS and NearMiss) were combined with bagging and boosting ensemble methods based on Naïve Bayes (NB) and Decision Tree (DT) classifier. Predictive performances of developed models were assessed based on the area under the curve (AUC), and Matthew’s correlation coefficient (MCC) values. From the experimental findings, it was observed that the implementation of data sampling methods further enhanced the predictive performances of the experimented ensemble methods. Specifically, BoostedDT on the ROS-balanced datasets recorded the highest average AUC (0.995), and MCC (0.918) values respectively. Aside NearMiss method, which worked best with the Bagging ensemble method, other studied data sampling methods worked well with the Boosting ensemble technique. Also, some of the developed models particularly BoostedDT showed better prediction performance over existing SDP models. As a result, combining data sampling techniques with ensemble methods may not only improve SDP model prediction performance but also provide a plausible solution to the latent class imbalance issue in SDP processes.
Pre-Trained Models Based Receiver Design With Natural Redundancy for Chinese Characters

Pre-trained Models (PTMs) have reached the state-of-the-art (SOTA) on many Natural Language Processing (NLP) tasks and Computer Vision (CV) tasks, and are called the foundation models of artificial intelligence (AI) systems. In this letter, we introduce PTMs into communication receiver design, which is the first attempt to apply PTMs to the communications field. The powerful capability of PTMs to capture knowledge from massive data enables the PTMs-based receivers to utilize the natural redundancy (NR) that widely exists in the transmitted sources to obtain high performance gains. Intended for uncompressed Chinese text sources, we design a high-performance receiver based on the Bidirectional Encoder Representation from Transformers (BERT) model in the NLP field. We achieve an approximate maximum of posterior marginals (MPM) estimation by obtaining the conditional distribution of the input data from the pre-trained BERT model, and the performance of the approximate MPM estimation can be improved by fine-tuning the BERT model with self-supervised learning. Simulation results demonstrate that the performance of our designed PTMs-based receiver significantly outperforms the classical receiver.
Cloud Edge-Client Collaborative Trajectory Privacy Protection System and Technology

Location-based service (LBS), with its personalized, real time, and mobile features, is one of the more popular mobile applications (apps) in the world today. However, under the centralized LBS architecture, the high dimensional trajectory data collected from LBS users are directly exposed to the central server, which entails serious privacy risks. Although existing privacy protection technologies can protect the private user trajectories to a certain extent, they often ignore the reasonable data requirements of LBS providers (e.g., data required to maintain or improve services). Focusing on the weaknesses of centralized LBS and the shortcomings of existing solutions, this article proposes a cloud edge-client collaborative trajectory privacy protection system. The system migrates LBS from the cloud to the network edge and balances privacy and utility-including service utility and data utility-with anonymous authentication, dummy location, and privacy risk evaluation mechanisms. The theoretical analysis and simulation results show that the system can effectively protect trajectory privacy under the premise of high availability of services and data, which is a significant improvement over the current LBS system.
An Empirical Study on the Fairness of Pre-trained Word Embeddings

Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves.With widely distributed models, it is important to ensure that they do not exhibit undesired behaviour, such as biases against population groups.For this purpose, we carry out an empirical study on evaluating the bias of 15 publicly available, pre-trained word embeddings model based on three training algorithms (GloVe, word2vec, and fastText) with regard to four bias metrics (WEAT, SEMBIAS, DIRECT BIAS, and ECT).The choice of word embedding models and bias metrics is motivated by a literature survey over 37 publications which quantified bias on pre-trained word embeddings.Our results indicate that fastText is the least biased model (in 8 out of 12 cases) and small vector lengths lead to a higher bias.
Human-in-the-Loop Data Collection and Evaluation for Improving Mathematical Conversations

The nature and quality of classroom instruction is highly correlated to teachers' ability to rehearse effective teaching strategies. Utilizing research-based teaching practices increases teacher effectiveness, confidence, and retention along with improving student achievements. High-fidelity, AI-based simulated classroom systems enable teachers to rehearse and get feedback on specific pedagogical skills. One primary challenge is that current conversational agents (CA) can have task-oriented conversations, however more varied dialogue-oriented conversations such as that between a teacher and student for a domain-specific task (like a mathematical scenario) can be difficult to model. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. The system relies on advances in deep-learning uncertainty quantification and natural language processing while acknowledging the limitations of CAs for specific pedagogical needs.
An Adaptive Biometric Authentication System for Online Learning Environments Across Multiple Devices

Online learning environments have become a crucial means to provide flexible and personalised pedagogical material, and a major driving cause is due to the COVID-19 pandemic. This has rapidly forced the migration and implementation of online education strategies across the world. Online learning environments have a requirement for high trust and confidence in establishing a student's identity and the authenticity of their work, and this need to lessen academic malpractices due to increased online delivery and assure the quality in education has accelerated. In addition to this, due to the ubiquity of mobile devices such as smartphones, tablets and laptops, students use a variety of devices to access online learning environments. Therefore, authentication systems for online learning environments should operate effectively on those devices to authenticate and invigilate online students. Confidence in authentication systems is also crucial to detect cheating and plagiarism for online education as strong authorisation and protection mechanisms for sensitive information and services are bypassed if authentication confidence is low. In this paper, we examine issues of existing authentication solutions for online learning environments and propose a design for an adaptive biometric authentication system for online learning environments that will automatically detect and adapt to changes in the operating environment. Multi-modal biometrics are applied in the proposed system which will dynamically select combinations of biometrics depending on a user's authenticating device. The adaptation strategy updates two thresholds (decision and adaptation) as well as the user's biometric template they are using the authentication system.
Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging

Online messaging is dynamic, influential, and highly contextual, and a single post may contain contrasting sentiments towards multiple entities, such as dehumanizing one actor while empathizing with another in the same message.These complexities are important to capture for understanding the systematic abuse voiced within an online community, or for determining whether individuals are advocating for abuse, opposing abuse, or simply reporting abuse.In this work, we describe a formulation of directed social regard (DSR) as a problem of multi-entity aspect-based sentiment analysis (ME-ABSA), which models the degree of intensity of multiple sentiments that are associated with entities described by a text document.Our DSR schema is informed by Bandura's psychosocial theory of moral disengagement and by recent work in ABSA.We present a dataset of over 2,900 posts and sentences, comprising over 24,000 entities annotated for DSR over nine psychosocial dimensions by three annotators.We present a novel transformer-based ME-ABSA model for DSR, achieving favorable preliminary results on this dataset.
FPI: Failure Point Isolation in Large-scale Conversational Assistants

Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation.An error in one of these modules can cascade through the system.Given the large traffic volumes in these assistants, it is infeasible to manually analyze the data, identify requests with processing errors and isolate the source of error.We present a machine learning system to address this challenge.First, we embed the incoming request and context, such as system response and subsequent turns, using pretrained transformer models.Then, we combine these embeddings with encodings of additional metadata features (such as confidence scores from different modules in the online system) using a "mixing-encoder" to output the failure point predictions.Our system obtains 92.2% of human performance on this task while scaling to analyze the entire traffic in 8 different languages of a large-scale conversational assistant.We present detailed ablation studies analyzing the impact of different modeling choices.
Protecting Student Data in ML Pipelines: An Overview of Privacy-Preserving ML

The rise of Artificial Intelligence in Education opens up new possibilities for analysis of student data. However, the protection of private data in these applications is a major challenge. According to data regulations, the application designer is responsible for technical and organizational measures to ensure privacy. This paper aims to guide developers of educational platforms to make informed decisions about their use of privacy-preserving ML and, therefore, protect their student data.
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets

Previous work on multi-task learning in Natural Language Processing (NLP) often incorporated carefully selected tasks as well as carefully tuning of architectures to share information across tasks.Recently, it has shown that for autoregressive language models, a multitask second pre-training step on a wide variety of NLP tasks leads to a set of parameters that more easily adapt for other NLP tasks.In this paper, we examine whether a similar setup can be used in autoencoder language models using a restricted set of semantically oriented NLP tasks, namely all SemEval 2022 tasks that are annotated at the word, sentence or paragraph level.We first evaluate a multi-task model trained on all SemEval 2022 tasks that contain annotation on the word, sentence or paragraph level (7 tasks, 11 sub-tasks), and then evaluate whether re-finetuning the resulting model for each task specificially leads to further improvements.Our results show that our monotask baseline, our multi-task model and our refinetuned multi-task model each outperform the other models for a subset of the tasks.Overall, huge gains can be observed by doing multi-task learning: for three tasks we observe an error reduction of more than 40%. 1
Design of Personalized Catering Recommendation System based on Spark Platform

With the continuous improvement of the quality of people&#x0027;s daily life, people&#x0027;s requirements for food and travel are gradually increasing, each person at different times and places have unique preferences. However, for lacking such a phenomenon dining areas recommendation system for society, this paper aims to design a personalized restaurant recommendation system based on Spark Platform. Based on Hadoop platform, the system processes massive data in a fully distributed cluster based on spark, and realizes data storage by distributed file system and mysql. The design based on spark platform provides a basis for improving the traditional recommendation system.
Using SVM and KNN to Evaluate Performance Based on Video Plagiarism Detectors and Descriptors for Global Features

The detection of video piracy has improved and emerged as a popular issue in the field of digital video copyright protection because a sequence of videos often comprises a huge amount of data. The major difficulty in achieving efficient and simple video copy detection is to identify compressed and exclusionary video characteristics. To do this, we describe a video copy detection strategy that created the properties for a spatial-temporal domain. The first step is to separate each video sequence into the individual video frame, and then extract the boundaries of each video frame by using PCA SIFT and Hessian- Laplace. Next, for each video frame, we have to implement SVM and KNN features in the spatial and temporal domains to measure their performance matrices in the feature extraction. Finally, the global features found in the Video copy detection are accomplished uniquely and efficiently. Experiments arranged a commonly used VCDB 2014 video dataset, showing that result. The proposed approach is based on various copy detection algorithms and shows various features in terms of both accuracy and efficiency.
Design and User Experience of a Hybrid Mixed Reality Installation that Promotes Tinian Marble Crafts Heritage

Hybrid physical-digital installations in museums are interactive systems or exhibits that seamlessly combine physical (tangible) artifacts with virtual environments. In the museum, hybrid installations offer direct, hands-on experiences to visitors and thus may enhance their interest and engagement. Moreover, the entanglement of tangible and virtual interfaces may reinforce learning about the respective heritage. This article reports on the design, implementation, and evaluation of an interactive installation that promotes the heritage of Tinian marble crafts that is included in the representative list of intangible heritage by UNESCO and resides in the Museum of Marble Crafts in Tinos, Greece. The installation puts the museum visitor in the role of a crane operator in a virtual reconstruction of a historic quarry and requires them to operate the crane and move marble volumes with safety in cooperation with non-playable characters (NPCs). Our design approach aimed to engage visitors in a playful learning experience, incorporating aspects of the two museum's exhibits (crane, quarry). The empirical evaluation took place at the lab and the museum with promising results on UX and learning. In addition, we identify and discuss several prospects and drawbacks of designing playful tangible interfaces for other practitioners to consider in similar applications.
A Lightweight and Accurate RNN in Wearable Embedded Systems for Human Activity Recognition

Human activity recognition (HAR) is an important technology for a wide range of applications including elderly people monitoring, ambient assisted living, sport and fitness activities. The aim of this paper is to address the HAR task directly on a wearable device, implementing a recurrent neural network (RNN) on a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal we first develop a lightweight RNN on the Human Activity Recognition Using Smartphones dataset in order to accurately detect human activity and then we port the RNN to the embedded device Cloud-JAM L4, based on an STM32 microcontroller. Experimental results show that this HAR RNN-based detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 90.50% with a very low memory cost (40.883 KB) and inference time (67.131 ms), allowing the design of a wearable embedded system for human activity recognition.
A Post-quantum Zero-Knowledge Proof System Using Quantum Information Theory

In recent decades, the importance of protecting computer systems and networks from information disclosure (relevant to information technology and cybersecurity fields) has risen to the utmost importance. With wide applications in subjects such as voting registration, insurance, credit card information, personal identity security, and as of recently crypto-based blockchains, the field is becoming increasingly significant. Due to the perpetual and expanding reliance on computer systems, the way that we handle and send our data is vital. Improper methods of establishing privacy for secure data transmission can compromise substantial amounts of user data, making the development of high-level privacy-preserving mechanisms impervious to tampering of immense importance. For example, the existence of most cryptographic systems is threatened by the development of quantum computing, and therefore, the development of making post-quantum/quantum-resistant cryptographic systems is in great demand. In this research, unlike most current existing systems, we propose a classical to quantum mapping channel for zero-knowledge that will not be negatively affected by the existence of quantum technologies.
Image processing methods decision mechanism for surveillance applications with UAVs

Purpose The use of unmanned aerial vehicles (UAVs) has significantly increased in the past decade and nowadays is being used for various purposes such as image processing, cargo transport, archaeology, agriculture, manufacturing, health care, surveillance and inspections. For this reason, using the appropriate image processing method for the intended use of UAVs increases the study’s success. This study aims to determine the most suitable one among the innovative methods that constitute the image processing system for a UAV to be used for surveillance purposes. Design/methodology/approach Analytical hierarchy process has been used in the solution of the decision problem to be handled in three stages, namely, platform, architecture and method. The most suitable alternative and the effect weights of these criteria results were determined at each stage. Findings As a result of this study, Jetson TX2 was determined as the most suitable embedded platform, ResNet is the optimum architecture and Faster R-convolutional neural networks was the best method in the image processing layer for a system that will provide surveillance with image processing method using UAV. Practical implications In UAV designs, where multiple hardware and software choices and system combinations exist, multi-criteria decision-making (MCDM) approaches can be used as a system decision mechanism. Originality/value The novelty of this work comes from the application of MCDM methods that are used as a multi-layered decision mechanism in UAV design.
Automatic Synthesis of Cognitive Model for Revealing Economic Sectors’ Needs in Digital Technologies

The paper addresses the automation of cognitive modelling for decision-making support in revealing economic sectors needs in digital technologies. It requires to take into consideration many factors, some of which have non-formalizable character. Some aspects do not have retrospective statistic history and are latent. Cognitive modelling is one of the artificial intelligence (AI) methods for describing such situations. It considers factors of enriching AI models by cognitive semantics, which experts help to create. The actual practice has shown that the process of model building can be costly and long term. An analysis of relevant big data (BD) can help to automate the process of cognitive modelling. There are two directions of automation: a) verification of cognitive models and b) synthesizing ones. The author's convergent approach based on the inverse problem-solving method and the genetic algorithm for decision-making on the cognitive model was applied. It helps to make the process of cognitive modelling more purposeful and stable. An experimental test and actual practice application of the approach have shown a high level of the models' verifications accuracy (about 93%) but a low level of accuracy of models synthesizing (about 34%). One of the reasons for such a low result lies in the fact that representatives of economic sectors and the digital industry often use different terminologies.
Transferability of Quantum Adversarial Machine Learning

Quantum adversarial machine learning lies at the intersection of quantum computing and adversarial machine learning. As the attainment of quantum supremacy demonstrates, quantum computers have already outpaced classical computers in certain domains (Arute et al. in Nature 574:505–510, 2019 [3]). The study of quantum computation is becoming increasingly relevant in today’s world. A field in which quantum computing may be applied is adversarial machine learning. A step toward better understanding quantum computing applied to adversarial machine learning has been taken recently by Lu et al. (Phys Rev Res 2:1–18, 2020 [13]), who have shown that gradient-based adversarial attacks can be transferred from classical to quantum neural networks. Inspired by Lu et al. (Phys Rev Res 2:1–18, 2020 [13]), we investigate the existence of the transferability of adversarial examples between different neural networks and the implications of that transferability. We find that, when the fast gradient sign attacks, as described by Goodfellow et al. (Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 [9]), is applied to a quantum neural network, the adversarially perturbed images produced with that method have transferability between quantum neural networks and from quantum to classical neural networks. In other words, adversarial images produced to deceive a quantum neural network can also deceive other quantum and classical neural networks. The results demonstrate that there exists transferability of adversarial examples in quantum machine learning. This transferability suggests a similarity in the decision boundaries of the different models, which may be an important subject of future study in quantum machine learning theory.