Reviewerly/exHarmony huggingface dataset:
CLF-AIAD: A Contrastive Learning Framework for Acoustic Industrial Anomaly Detection

Acoustic Industrial Anomaly Detection (AIAD) has received a great deal of attention as a technique to discover faults or malicious activity, allowing for preventive measures to be more effectively targeted. The essence of AIAD is to learn the compact distribution of normal acoustic data and detect outliers as anomalies during testing. However, recent AIAD work does not capture the dependencies and dynamics of Acoustic Industrial Data (AID). To address this issue, we propose a novel Contrastive Learning Framework (CLF) for AIAD, known as CLF-AIAD. Our method introduces a multi-grained contrastive learning-based framework to extract robust normal AID representations. Specifically, we first employ a projection layer and a novel context-based contrast method to learn robust temporal vectors. Building upon this, we then introduce a sample-wise contrasting-based module to capture local invariant characteristics, improving the discriminative capabilities of the model. Finally, a transformation classifier is introduced to bolster the performance of the primary task under a self-supervised learning framework. Extensive experiments on two typical industrial datasets, MIMII and ToyADMOS, demonstrate that our proposed CLF-AIAD effectively detects various real-world defects and improves upon the state-of-the-art in unsupervised industrial anomaly detection.
Towards Better Evaluations of Class Activation Mapping and Interpretability of CNNs

As deep learning has been widely used in real life, there is an increasing demand for its transparency and its interpretability has received much attention from all walks of life. Current efforts in this field includes post-hoc visualization technique and intrinsically interpretable framework. However, there are still shortcomings in both of these techniques. In the post-hoc visualization techniques, the metric evaluating CAM method suffers from the ambiguity of evaluation object. We proposed a pair of quantitative evaluation metrics based on threshold cropping. The explanation maps were obtained by threshold cropping in order to minimize the variation of input images, thus making the evaluation object more focused on the CAM method itself. Experimental results show that these metrics can evaluate the accuracy and intensity of various CAM methods comprehensively, and get the conclusion that the gradient-based CAM methods are more stable, and Score-CAM is susceptible to model. Meanwhile, most of the existing intrinsically interpretable frameworks tend to enhance the interpretability of models by mapping their intermediate results to concepts that can be understood by humans. However, the intermediate results of the model often contain a variety of information that hinders its correspondence with single concept, which is manifested as a many-to-many relationship between filters and classes in convolutional classification networks. To address this situation, we proposed an interpretable training framework based on mutual information neural maximization to alleviate filter-class entanglement. MIS metric, classification confusion matrix and adversarial attack experiments all confirmed the validity of this method.
Accelerate Support Vector Clustering via Spectral Data Compression

This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key cluster properties of the original data sets based on a novel spectral data compression approach. Then, the resultant spectrally-compressed data sets are leveraged for the development of fast and high quality algorithm for support vector clustering. We conducted extensive experiments using real-world data sets and obtained very promising results. The proposed method allows us to achieve 100X and 115X speedups over the state of the art SVC method on the Pendigits and USPS data sets, respectively, while achieving even better clustering quality. To the best of our knowledge, this represents the first practical method for high-quality and fast SVC on large-scale real-world data sets.
Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeoff

The conventional recipe for Automatic Speech Recognition (ASR) models is to 1) train multiple checkpoints on a training set while relying on a validation set to prevent over fitting using early stopping and 2) average several last checkpoints or that of the lowest validation losses to obtain the final model. In this paper, we rethink and update the early stopping and checkpoint averaging from the perspective of the bias-variance tradeoff. Theoretically, the bias and variance represent the fitness and variability of a model and the tradeoff of them determines the overall generalization error. But, it's impractical to evaluate them precisely. As an alternative, we take the training loss and validation loss as proxies of bias and variance and guide the early stopping and checkpoint averaging using their tradeoff, namely an Approximated Bias-Variance Tradeoff (ApproBiVT). When evaluating with advanced ASR models, our recipe provides 2.5%–3.7% and 3.1%–4.6% CER reduction on the AISHELL-1 and AISHELL-2, respectively (The code and sampled unaugmented training sets used in this paper will be public available on GitHub).
Semiautomatic Design of Ontologies

The design of ontologies is a time-consuming and resource-intensive endeavour. Rather than (manually) design the ontology first and then associate it with data, can we (semiautomatically) design the ontology from the data itself? This paper presents a novel approach to the semi-automated design of ontologies that incorporates axiom generation from data models, semantic parsing, and ontology learning from examples and counterexamples via search through an ontology repository.
Investigating Quality Attributes in Behavior-Driven Development Scenarios: An Evaluation Framework and an Experimental Supporting Tool

Behavior-Driven Development (BDD) refers to an agile development practice to express the fulfillment of a requirement often depicted in a user story. BDD is meant to facilitate the understanding of how to properly execute requirements among role-divergent stakeholders in a software project. In that way, the development team avoids an excessive focus on coding at the early requirements definition stage and can focus on truly capturing the features and behaviors that are expected by the end-users. In BDD, user-driven scenarios are written in structured natural language following a defined template. Notwithstanding, not much attention has been placed in the literature in terms of defining/studying the quality aspects of the written BDD scenarios; therefore, practitioners tend to use the technique in an ad-hoc manner. In this study, we explore the quality attributes assigned to a well-written BDD scenario. We refine an existing framework by establishing formal definitions for each of the scenarios’ attributes, study their applicability through real BDD scenarios, and link them to the quality attributes appointed to user stories. We then develop and present an experimental Computer-Aided Software Engineering (CASE) tool that helps practitioners assess the quality of the BDD scenarios through the automated evaluation of a set of conforming quality attributes namely Uniqueness, Essentiality, Integrity, and Singularity. We further validate the framework and the tool by collecting two expert opinions.
Evaluating ArchiMate for Modelling IoT Systems

The Internet of Things (IoT) is a disruptive technology that allows connecting physical objects with the digital world. This challenges organizations in adjusting their Enterprise Architecture (EA) for adopting the IoT technology to improve their operations and maximize their value. Considering the complexity of such changes, a suitable modelling language must be used for EA design and documentation. ArchiMate, being a de facto standard for EA modelling, has already been used for modelling IoT systems, however, it is not used in a consistent manner and different extensions have been proposed to address limitations identified in specific domains. Based on the most common practices reported in the literature, we propose and evaluate a set of guidelines for modelling IoT elements in ArchiMate. The guidelines are evaluated by performing qualitative research based on case studies. The evaluation shows that ArchiMate is suitable for modelling IoT systems, but only at the conceptual level. When technical details are required, extensions to the standard may be needed. In addition, domain-specific limitations such as the lack of showing time-based communication and redundancy in an industry context are identified.
A Domain-Specific e3value Extension for Analyzing Blockchain-Based Value Networks

Adopting blockchain technologies in organizations has multiple implications for business models. To make adoption successful, both the business as well as the technical perspectives must be carefully aligned. However, understanding the impact of the technological changes on business models is a challenge due to the technological complexity, the lack of knowledge in the organization, and regulatory requirements. Further, domain-specific modeling methods that inherently deal with blockchain concepts in business models are currently missing. To address this gap, we present an extension of the $${e}^{3} value$$ modeling method to depict blockchain-specific aspects in value networks, including the automatic inference of transparency based on blockchain usage and configuration. The extended modeling method was implemented on the ADOxx metamodeling platform and applied to three exemplary use cases for a first evaluation.
A Health Evaluation Algorithm for Edge Nodes Based on LSTM

The health state of edge layer nodes significantly affects the reliability of the calculation and the development of related applications. Edge layer nodes health assessment is adopted to forecast node state to arrange calculation and application reasonably. The existing multidimensional data evaluation algorithms have achieved good predictive performance. However, with a small scale of training data, those algorithms could easily encounter overfitting and poor robustness. Therefore, we propose an evaluation algorithm based on the multidimensional operation data of edge layer nodes in this study. In order to solve the problem above, we propose an improved Long Short Term Memory (LSTM) model to implement the evaluation. We add feature discretization and annealing processes to the model to reduce the risk of model overfitting. Compared with typical time series prediction models, the proposed LSTM model has stronger applicability and better accuracy in the evaluation of network delay of edge layer nodes in our experiment.
Embedding Entity and Relation for Knowledge Graph by Probability Directed Graph

Knowledge graph embedding (KGE) represents entities and relationship as low dimensional dense vectors in knowledge graphs (KGs), and to improve the computational efficiency of downstream tasks. This paper regards the semantic relations between the head and tail entities in the KGs as semantically probability transfers, and proposes a method based on the structured probability model for KGE. This method considers the relations as nodes and uses probability-directed graphs to model the KGs. The scoring function is defined as a probability distribution that represents the directed transitivity between the entities and relations. Finally, the function is used to infer the probability that the triples are true. Experimental results on several standard datasets show that this method achieves better results in complex relations and uneven distribution of triples.
Development of a <scp>VR</scp> Training System for Saito's Music Conducting Method

It is important for university students studying elementary music education to master basic conducting gestures. We have developed a VR training system for self‐study of Saito's music conducting method. The system uses a depth camera and magnetic motion capture system to measure hand movements, and an HMD to superimpose the teacher's hand on the trainee's own hand. With the system, trainee can self‐study while visually and three‐dimensionally checking the motion and timing of the teacher's and the trainee's own hand gestures. In this paper, we describe an evaluation experiment with our system. The results showed that the VR environment is effective for learning three‐dimensional movements, and that the addition of a visual line display function for trajectories, a function to display trajectories from different viewpoints, and a replay function improves the learning effect. Specifically, the results of a short training session of 5 min revealed that the developed system was effective in bringing 75% of the trainees closer to the teacher's gesture. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Universal Representations: A Unified Look at Multiple Task and Domain Learning

Abstract We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations , a single deep neural network. Learning multiple problems simultaneously involves minimizing a weighted sum of multiple loss functions with different magnitudes and characteristics and thus results in unbalanced state of one loss dominating the optimization and poor results compared to learning a separate model for each problem. To this end, we propose distilling knowledge of multiple task/domain-specific networks into a single deep neural network after aligning its representations with the task/domain-specific ones through small capacity adapters. We rigorously show that universal representations achieve state-of-the-art performances in learning of multiple dense prediction problems in NYU-v2 and Cityscapes, multiple image classification problems from diverse domains in Visual Decathlon Dataset and cross-domain few-shot learning in MetaDataset. Finally we also conduct multiple analysis through ablation and qualitative studies.
Technology Experience: Postsecondary Education Exploration by Non-native English-Speaking Immigrant Parents

The availability and advancement of technology continue to shape the American educational landscape. This makes the exploration of postsecondary education options accessible to all users seeking information about postsecondary education, including non-native English-speaking parents. Despite the acculturation issues faced by immigrants, technology has become an effective tool that enables them to navigate and get accustomed to their new milieu. Amidst the advancement and positive attitude to technology, there is sparse research on how the environment, behavior, and personal factors influence the use of technology by non-native English-Speaking immigrant parents to explore postsecondary education options for their children’s success. The goal of this phenomenological study was to identify how the environmental, behavioral, and personal factors influence the use of technology by non-native English-speaking immigrant parents in Iowa, United States to support their children toward postsecondary education. Three main themes emerged after careful analysis of the descriptive data from the semi-structured interviews and demographic survey. The findings show that the interdependent nature of the environmental, personal, and behavioral factors contributes to the perceived usefulness and ease of use which influence the attitude of immigrant parents to use technology to access educational information to support the attainment of postsecondary education for their children. The outcome of this study contributed to the development of a Technology-Mediated Community-Level model. This model allows postsecondary education institutions to collaborate with local organizations to structure educational information in a simplified format accessible to users in their respective native languages via technology.
Machine Learning for Gaze-Based Selection: Performance Assessment Without Explicit Labeling

Gaze-based interaction typically requires certain actions to confirm selections, which often makes interaction less convenient. Recently, effective identification of the user’s intention to make a gaze-based selection was demonstrated by Isomoto et al. (2022) using machine learning applied to gaze behavior features. However, a certain bias could appear in that study since the participants were requested to report their intentions during the interaction experiment. Here, we applied several classification algorithms (linear discriminant analysis, RBF and linear support vector machines, and random forest) to gaze features characterizing selections made in a freely played gaze-controlled game, in which moves were made by sequences of gaze-based selections and their gaze-based confirmations, without separate reporting the correctness of the selection. Intention to select was successfully predicted by each of the classifiers using features collected before the selection.
Human Languages in HCI: Beyond User Interface Localization

This paper investigates the role of language in interaction design and computer-mediated communication. It starts by distinguishing between human languages and computer languages. Human language, as the primary code used in text and voice-based communication, is critical in the majority of communication processes. We also analyze two methods of computer-mediated communication. First, interaction design based on semiotic engineering is investigated; followed by user-to-user computer-mediated communication. This paper provides insights into the design of user interfaces, cross-cultural communication, and social media by discussing the complexities of language in human-computer interaction and computer-mediated communication. The findings highlight the significance of language as the main component of effective communication involving humans and computers in various contexts.
Evaluation

Suppose you are in charge of administering an enterprise database and are tasked with choosing an NLIDB system to provide a non-technical interface to the users of your database. You check the literature and find hundreds of research papers on the subject. You also check a few leaderboards and find tens of top-performing models. How do you decide on a system? What should you look for in choosing one from many choices? These are some of the questions that are answered in this chapter. This chapter covers the evaluation methodologies for NLIDBs with a focus on text-to-SQL. Datasets and benchmarks are presented and their statistics, in terms of supported query types, are compared. Two major evaluation methods, namely reference-based and human- centric evaluations, are discussed in more detail. Finally, other evaluation metrics are reviewed and some of the top-performing models and their characteristics are presented.
Balancing the Scales of Explainable and Transparent AI Agents within Human-Agent Teams

With the progressive nature of Human-Agent Teams becoming more and more useful for high-quality work output, there is a proportional need for bi-directional communication between teammates to increase efficient collaboration. This need is centered around the well-known issue of innate mistrust between humans and artificial intelligence, resulting in sub-optimal work. To combat this, computer scientists and humancomputer interaction researchers alike have presented and refined specific solutions to this issue through different methods of AI interpretability. These different methods include explicit AI explanations as well as implicit manipulations of the AI interface, otherwise known as AI transparency. Individually these solutions hold considerable merit in repairing the relationship of trust between teammates, but also have individual flaws. We posit that the combination of different interpretable mechanisms mitigates each other’s flaws and extenuates their strengths within human-agent teams.
Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM) algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights as the basis for information management and nearly real-time decision-making systems. According to experimental investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.
Interactivity

In this chapter, we discuss the top of interactivity, a key element of NLIDB impacting its effectiveness and usefulness. We introduce different dimensions of interactivity, present the corresponding techniques to support each dimension in detail, and discuss their benefits and corresponding challenges. We first focus on interactivity at query time and discuss methods for query disambiguation and query suggestions. We then switch focus to interactivity after the initial query and discuss automatic data insights and explanation techniques for different aspects of NLIDBs. Finally, we present how different interactive techniques discussed in the chapter may be combined together to provide conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language.
Human-Object Interaction Detection with Channel Aware Attention

Human-object interaction detection (HOI) is a fundamental task in computer vision, which requires locating instances and predicting their interactions. To tackle HOI, we attempt to capture the global context information in HOI scenes by explicitly encoding the global features using our novel channel aware attention mechanism. Our observation is that the context of an image, including people, objects and background plays important roles in HOI prediction. To leverage such information, we propose a channel aware attention, which applies global average pooling on the features to learn their channel-wise inter-dependency. Based on the channel aware attention, we develop a channel aware module and a channel aware encoder. Handling features in channel dimensions makes it convenient to encode the global features as well as to learn semantic features. Empirically, our model outstrips the strong baseline by 3.2 points on V-COCO and 0.79 points on HICO-DET respectively. The visual analysis demonstrates that our method is able to capture abundant interaction-related features by attending to relevant regions.