Reviewerly/exHarmony huggingface dataset:
Towards a Human-centered Tutorial Design for the Nautilus Cluster

In this paper, we describe our evaluation of the current Nautilus on-boarding and tutorials based on a small user study, as part of an effort towards a human-centered design (HCD) approach to documentation improvements for the NSF-funded Nautilus Cluster[9]. Often with academic computing systems, documentation is developed from the perspective of a skilled user without consideration to the barriers that exist to adoption for modestly skilled users, or users with no experience. This is the case for Nautilus. We have observed a gap between these emergent user groups and groups conversant in high performance and high-throughput computing in adoption of the platform. To mitigate these barriers, we must first understand where users struggle. This paper presents the results of a small user study using "quick and dirty" methodologies [13] and suggests next steps in redesigning the Nautilus on-boarding and tutorials as a means to improve learner outcomes and ultimately broaden adoption of the platform by new user communities.
Deep Learning Benchmark Studies on an Advanced AI Engineering Testbed from the Open Compass Project

We present the Open Compass project’s pilot deep learning benchmark results with various AI accelerators. Those accelerators are NVIDIA V-100 and A-100, AMD MI100, as well as emerging novel accelerators such as Cerebras CS-2 and Graphcore. We evaluate their performance on various deep learning training tasks. We then discuss key insights from our experiments and share experiences about evaluating and integrating those novel AI accelerators with our supercomputing systems.
Security Standards and use of Cyberinfrastructure Systems in Clinical Research

Many areas of biomedical research, including precision health research, inherently involve the use of IT systems properly secured to store and analyze protected health information (PHI). Managing cybersecurity policies in ways that allow such storage and analysis of PHI on computing systems is involved, but so doing can facilitate important research. In 2005, Indiana University established the ability to analyze PHI (HIPAA alignment) as the minimal security level for its High Performance Computing (HPC) systems and research storage systems. Starting in 2010, REDCap was also offered as a central service, providing capabilities for conducting surveys that collect PHI and storing those data in ways that align with HIPAA. Usage data shows that providing computational and data resources aligned with HIPAA has increased the use of advanced research cyberinfrastructure over time. We explain some of our tactics in providing secure environments for handling protected health information and offer suggestions that other cyberinfrastructure centers may find useful in setting strategies for serving medical researchers.
SimpleText Best of Labs in CLEF-2022: Simplify Text Generation with Prompt Engineering

This paper reports our approach to the SimpleText@CLEF-2022. For the task 1: what is in (or out)?, we designed a two-stage filtering scheme that utilizes the traditional keyword finding approach TF-IDF score to find the important documents in the first stage and the important sentences in the second stage. The result is comparable to manual run and ranked first in task 1. For the Task 3: Rewrite this!, our system adopts the T5 generation model to rewrite the original sentences. We fine-tuned the model to generate simplified sentence. The result ranked second in task 3. The simplified sentence generated by T5 model cannot fully express the meaning of the original sentence, in a following further experiments, we adopted the GPT3.5 and GPT4 models to generate simple text, and they give better results according to in our evaluation metrics based on readability and vocabulary simplicity.
Semi-supervised Learning Based Intelligent Fault Diagnosis Methods

In practical industrial application, it is usually difficult to obtain sufficient labelled data due to the high cost of manual marking labels, which makes it hard to apply the traditional supervised learning in fault diagnosis. Unsupervised learning is a machine learning approach where the algorithm learns patterns and structures from unlabeled data without explicit input-output pairs, however, the trained model is often hard to be generalized without providing supervised information. While supervised learning relies solely on labeled data for training and unsupervised learning works with unlabeled data, semi-supervised learning leverages the benefits of both by using a smaller amount of labeled data along with a larger amount of unlabeled data. Semi-supervised learning in fault diagnosis helps to address the challenge of imbalanced data, where the number of faulty instances is often significantly fewer than that of normal instances. By utilizing unlabeled data, the model’s ability will be improved to generalize to various fault scenarios, including previously unseen ones. In this Chapter, semi-supervised intelligent fault diagnosis methods, such as Kernel Principal Component Analysis (KPCA), fuzzy kernel clustering algorithms, Self-organizing Map (SOM) neural networks, and Relevance Vector Machines (RVM), are introduced. These methods are validated and have suceeded in incipient fault diagnosis of transmissions and bearings.
Attribute Repair for Threat Prevention

We propose a model-based procedure for preventing security threats using formal models. We encode system models and threats as satisfiability modulo theory (SMT) formulas. This model allows us to ask security questions as satisfiability queries. We formulate threat prevention as an optimization problem over the same formulas. The outcome of our threat prevention procedure is a suggestion of model attribute repair that eliminates threats. We implement our approach using the state-of-the-art Z3 SMT solver and interface it with the threat analysis tool THREATGET. We demonstrate the value of our procedure in two case studies from automotive and smart home domains.
SEGMENTAL ERRORS INDICATED BY FORMANTS: A PRAAT ANALYSIS

This study aims to find out the segmental error occurred by English students by concerning to their formants. This study used descriptive qualitative method. The subjects were a native speaker whose sound was taken from GStatic and two students whose background were Javanese and majoring English Education Study Program. The data were in the form sound and formants gotten from PRAAT software after input the sounds. The data were analyzed, described, and concluded. The result showed that the segmental error faced by the participants were /?/, /?/, /e/, /?/, and /?/. Participants’ formants tended to higher or lower than Native speaker’s formants.
The Impact of Training Data Shortfalls on Safety of AI-Based Clinical Decision Support Systems

Decision support systems with Artificial intelligence (AI) and specifically Machine Learning (ML) components present many challenges when assuring trust in operational performance, particularly in a safety-critical domain such as healthcare. During operation the Human in/on The Loop (HTL) may need assistance in determining when to trust the ML output and when to override it, particularly to prevent hazardous situations. In this paper, we consider how issues with training data shortfalls can cause varying safety performance in ML. We present a case study using an ML-based clinical decision support system for Type-2 diabetes related co-morbidity prediction (DCP). The DCP ML component is trained using real patient data, but the data was taken from a very large live database gathered over many years, and the records vary in distribution and completeness. Research developing similar clinical predictor systems describe different methods to compensate for training data shortfalls, but concentrate only on fixing the data to maximise the ML performance without considering a system safety perspective. This means the impact of the ML's varying performance is not fully understood at the system level. Further, methods such as data imputation can introduce a further risk of bias which is not addressed. This paper combines the use of ML data shortfall compensation measures with exploratory safety analysis to ensure all means of reducing risk are considered. We demonstrate that together these provide a richer picture allowing more effective identification and mitigation of risks from training data shortfalls.
Cem Mil Podcasts: A Spoken Portuguese Document Corpus for Multi-modal, Multi-lingual and Multi-dialect Information Access Research

In this paper we describe the Portuguese-language podcast dataset we have released for academic research purposes. We give an overview of how the data was sampled, descriptive statistics over the collection, as well as information about the distribution over Brazilian and Portuguese dialects. We give results from experiments on multi-lingual summarization, showing that summarizing podcast transcripts can be performed well by a system supporting both English and Portuguese. We also show experiments on Portuguese podcast genre classification using text metadata. Combining this collection with previously released English-language collection opens up the potential for multi-modal, multi-lingual and multi-dialect podcast information access research.
A Taxonomy of Software Defect Forms for Certification Tests in Aviation Industry

The analysis of real software defects data not only helps to identify problems in a project, company, or industry but also enables continuous improvement in technologies and processes. In the Chinese aviation industry, a rigorous software quality assurance procedure is implemented, consisting of following a national process standard, conducting internal tests, expert milestone reviews, and formal documentation of all activities and results. After these quality assurance activities are completed, software systems undergo certification tests conducted by independent third-party centers. Discovering patterns of defects found in certification tests has significant practical implications for the industry on continuous improvement. This paper presents a taxonomy of defect forms based on the real defects found in 10 software systems, using Grounded Theory. The defect forms contain detailed information on how the defects are formed, compared to existing defect types. We then applied and validated this taxonomy on 9 additional software projects, using five software certification engineers independent of this paper. The results demonstrate that the developed taxonomy can describe the forms of 98% of defects found in certification tests. We recommend this taxonomy for process improvement and defect prevention in the aviation industry.
Using Authorship Embeddings to Understand Writing Style in Social Media

With the escalation of misinformation and malicious behavior issues on social media platforms, traditional detection-based measures often fail to address the problem in time. The use of multiple accounts or the continuous creation of new accounts makes it difficult to re-detect the presence of a user who, for example, has disseminated false information. In this paper, we present a novel approach to understanding and characterizing authorship in social media using a model called $$PART_{SCL}$$ , an improvement of the previous PART model. $$PART_{SCL}$$ generates "authorship embeddings", numerical representations of an author's writing style, allowing for more accurate and earlier detection of malicious behavior. Our main contributions include the $$PART_{SCL}$$ model itself, a new pre-training approach for authorship attribution, and the application of our model on different datasets. These advances help bridge the gap between popular Natural Language Processing techniques such as Transformers and feature engineering, providing a robust tool for the ongoing fight against online misbehavior and misinformation.
A Cognitive Framework for Modeling Coincident Software Faults: An Experimental Study

The question of when different programmers tend to commit the same errors is a critical issue for achieving fault diversity in fault tolerance. This problem is interdisciplinary and related to theories of human error in cognitive psychology. This paper proposes a psychological framework that combines Rasmussen's performance levels with cross-level errors, represented by post-completion error, to model situations in which different programmers are prone to making the same errors. To validate the framework, we conducted an experiment where 200 student programmers independently solved the same problem, with the same tool and language. The results indicate that programmers unlikely commit the same errors in skill-based performances, most likely make the same errors in rule-based performances. These findings suggest that natural independent development may be less effective in preventing common errors in functions involving rule-based performance and post-completion scenarios, whereas it could be effective in preventing common errors in skill-based and knowledge-based performances. The results provided new insights into the strategies for avoiding coincident faults in N-version programming, from a human factor perspective.
A Study on the Effect of the ARCS Model on Woodcut Teaching - Focused on Cultivating Learning Motivation in a Learner s Course -

연구목적은 학습만족도, 학습투입 등 학생 중심 인 고등교육의 질적 평가 지표는 대학생의 학습과 발전 영역에서 대학교육 과정을 탐구하는데 중요시 되고 있다(Luo, 2009). 목판 판화 학습과정에서 ARCS 모델을 적용하여 이러한 인수의 관계를 분석할 수 있으며, 교과과정 수업의 유효한 평가는 수업에 촉진작용을 일으킬 수 있다. 연구방법은 질적 연구법에서의 사례분석법을 적용하여 ARCS 모델 시행 후 대학 목판화 학습과정에 대한 관심도, 학습욕구, 학습신뢰, 학습만족도 현황을 연구하고, ARCS 모델의 실시는 수업 중 학습 동기의 발생, 동기의 전환 및 지속적 발전에 긍정적인 작용을 할 수 있다. 연구결과는 현재 미술대학의 미술교육에서 목판화 교사는 학생들의 감정과 심리에 대한 분석과 연구를 강화해야 하며, ARCS 모델을 채택할 수 있다면 교실 학습동기 확립을 체계적으로 안내하고 효과적인 교수법을 제시할 수 있다. 판화 교실 교수자는 수업시간 학생들의 정서 및 심리 분석과 연구를 강화하여 수업시간 동기 의식의 선도, 지속적 발전의 법칙을 파악하여 학생들의 사상인식을 과학적으로 유도하였다. ARCS 모델을 이용하여 수업시간 동기의 수립을 체계적으로 지도하며 목각 판화 교실 수업의 효율성만을 보장하였다. 교과목표 달성도가 보장되고, 학습자 중심의 교육이념이 구현되어 목판화 예술의 전통문화 계승에 중요한 역할을 할 수 있다.Objectives: As indicators of student-centered higher education quality assessment, professional satisfaction and engagement in learning have become key concepts in the exploration of the university education process in the field of university student learning and development(Luo, 2009). By using the ARCS model in the woodcut course, the relationship between these factors can be analyzed and can contribute to the evaluation of the effectiveness of the course. Methods: This study used qualitative analysis and case studies to study the current situation of learning concern, learning needs, learning confidence, and learning satisfaction in the university woodcut learning course after implementing the ARCS model and investigating the correlation between learning motivation and its development. Results: In the current art university education of fine arts, woodcut printing teachers should strengthen the analysis and research of students on their emotions and psychology. By using the ARCS motivation development model in the woodcut course, the relationship between these factors can be analyzed and can contribute to the evaluation of the effectiveness of the course. Implementing the ARCS model can mㅈake the establishment of classroom learning motivation provide systematic guidance. The concept of learner-centered education can be reflected, and the inherit nature of the art of woodcut plays an important role.
Assessing Augmented Reality Selection Techniques for Passengers in Moving Vehicles: A Real-World User Study

Nowadays, cars offer many possibilities to explore the world around you by providing location-based information displayed on a 2D-Map. However, this information is often only available to front-seat passengers while being restricted to in-car displays. To propose a more natural way of interacting with the environment, we implemented an augmented reality head-mounted display to overlay points of interest onto the real world. We aim to compare multiple selection techniques for digital objects located outside a moving car by investigating head gaze with dwell time, head gaze with hardware button, eye gaze with hardware button, and hand pointing with gesture confirmation. Our study was conducted in a moving car under real-world conditions (N=22), with significant results indicating that hand pointing usage led to slower and less precise content selection while eye gaze was preferred by participants and performed on par with the other techniques.
Manipulating the Orientation of Planar 2D Content in VR as an Implicit Visual Cue for Mitigating Passenger Motion Sickness

The use of Virtual Reality technology in vehicles is poised to bring about a new era of transport experiences, but its use can cause or enhance motion sickness. However, with careful design, VR devices have the potential to mitigate motion sickness and provide immersive experiences while travelling. We propose a novel implicit motion cue for manipulating a virtual display presented in Virtual and Extended Reality. Our design mitigates motion sickness by providing awareness of physical orientation changes through changes in the visual orientation of the virtual planar content. We performed two experiments on a rotating chair, testing mitigation properties of different levels of rotational gain applied to the display. Our results showed that the technique significantly reduced motion sickness, without negatively affecting task performance. Our findings show that we can subtly interleave motion cues in existing spatial content like planar displays and this can contribute to lessening motion sickness experienced.
What’s (Not) Tracking? Factors of Influence in Industrial Augmented Reality Tracking: A Use Case Study in an Automotive Environment

Augmented Reality (AR) is a key technology for digitization in enterprises. However, often there is a lack of stable tracking solutions when used inside manufacturing environments. Many different tracking technologies are available, yet, it can be difficult to choose the most appropriate tracking solution for different use cases with their varying conditions. In order to shed light on common tracking requirements and conditions for automotive AR use cases we conducted a use case study spanning 61 use cases within the complete product life-cycle of a large automotive manufacturer. By analyzing the gathered data we were able to note the frequency of different tracking requirements and conditions within automotive AR use cases. Based on these use cases we could also derive common factors of influence for AR tracking in the automotive industry, which show the various challenges automotive AR tracking is currently facing.
Identification of One-dimensional Directional Blur Amount for Sharpening of Blurred Concrete Structure Images Acquired by UAV

In order to perform reliable maintenance of infrastructures such as bridges, it is desirable to utilize robotic technologies such as UAV to support engineers. In this research, we aim to propose a method to identify one-dimensional directional blur amount for sharpening blurred images acquired by UAV. In order to sharpen one-dimensional blurred images of concrete structures, we focused on simple and uniform features such as sandy patterns peculiar to concrete surfaces and used a cepstrum of partial images of such features to obtain at least one candidate image of the amount of blur. In this paper, we apply our proposed "MD method" to blurred images of concrete bridges actually captured by UAV and to sharpen the degraded image with PSF-MB corresponding to the identified amount of blur.
When Sarcasm Hurts: Irony-Aware Models for Abusive Language Detection

Linguistic literature on irony discusses sarcasm as a form of irony characterized by its biting nature and the intention to mock a victim. This particular trait makes sarcasm apt to convey hate speech and not only humour. Previous works on abusive language stressed the need to address ironic language to lead the system to recognize correctly hate speech, especially in spontaneous texts, like tweets [13]. In this context, our main hypothesis is that information about the presence of sarcasm could help to improve the detection of hateful messages, especially when they are camouflaged as sarcastic. To corroborate this hypothesis: i) we perform analysis on HaSpeeDe20_ext, an Italian corpus of tweets about the integration of cultural minorities in Italy, ii) we carry out computational experiments injecting the knowledge of sarcasm in a system of hate speech detection, and iii) we adopt strategies of validation in terms of performance and significance of the obtained results. Results confirm our hypothesis and overcome the state of the art.
Development of Portfolio Related Scales for Early Childhood Teachers: A Validity and Reliability Study

Portfolio is one of the common forms of pedagogical documentation around the world. However, there is no suitable instrument to measure teachers’ portfolio practices in early childhood education. In this study, it was aimed to develop scales on portfolio practices and its possible predictors based upon extended Theory of Planned Behavior (TPB). Initially, content validity was ensured with expert opinions and cognitive interviews with early childhood teachers. After that pilot data were collected from 371 early childhood teachers and exploratory factor analysis (EFA) was conducted to explore the factor structure of the scales. Then, main study data were gathered from 605 early childhood teachers, and confirmatory factor analysis (CFA) was conducted to confirm the factor structure of the scales. Internal consistency coefficients were also calculated for the reliability analysis in both pilot study and main study data. As a result, findings confirmed the validity and reliability of the scales.
ATCA Multi-Node Management System Protocol Optimization

AbstractThe out-of-band management system defined by IPMI only supports IIC bus for inter-node communication, which has low communication rate and poor flexibility for multi-node layout, and it is not suitable for cluster management applications with large data volume of multiple nodes. This article adopts Ethernet instead of IIC to realize the structure layout of multi-node distributed cluster management system. The operation and maintenance information are trans-mitted by RMCP+, but the original RMCP + is rate limited and less stable in the transmission of large data volume in multi-cluster management system due to the use of AES-CBC encryption algorithm. This article optimizes the RMCP + communication process logic to reduce the redundancy in the system cascade data about transmission encryption and decryption calculation, and proposes the method of using parallel CTR for data block encryption instead of serial CBC data block encryption, which realizes the loose coupling of database decryption in data packets, in-creases the decryption rate of data cipher text at remote clients, educes the error transmission caused by tight coupling of data blocks, and improves the communication efficiency. The experimental environment of QEMU-based multi-node management system is built to verify that the communication method proposed in this article is suitable for the operation and maintenance management of distributed multi-node complex ATCA.Keywords: ATCA equipmentIPMIout-of-band management systemAES algorithm Disclosure StatementNo potential conflict of interest was reported by the author(s).