Reviewerly/exHarmony huggingface dataset:
Accurate calibration for crosstalk coefficient based on orthogonal color phase-shifting pattern

The crosstalk coefficient calibration of de-crosstalk in color fringe projection profilometry is an essential step for the high-accuracy measurement. In this paper, a novel approach for calibrating crosstalk matrix of color camera is proposed. The wrapped phase error model introduced by color crosstalk in orthogonal pattern is established. Compared with the existing calibration methods depending on calculating the modulation of the crosstalk channel, the crosstalk coefficients are obtained from phase error in our method. By projecting the designed color orthogonal phase-shifting fringe patterns onto a white plate, the phase-shifting fringe patterns in both horizontal and vertical directions can be separated from captured images. The coefficients between different channels are calibrated by fitting the error relationship between the wrapped phase containing crosstalk and the standard ones. Coefficient fitting simulations and experimental validations including shape measurement of a white plate and distance measurement of a step block are carried out to verify the effectiveness of the proposed method.
Natural Language Processing: Towards a Multi-Dimensional View of the Learning Process

As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students’ responses to a variety of assignments and tasks. While NLP is widely used to deliver students with formative feedback, it can also be used to provide educators with information about task difficulty, students’ individual differences, and student performance. In this chapter, we will first provide an overview of NLP, followed by a discussion of how NLP could be used to examine the learning process across a number of time points. Finally, we consider the future applications of NLP in the learning analytics domain.
AI VS. HUMAN: THE PUBLIC'S PERCEPTIONS OF THE DESIGN ABILITIES OF ARTIFICIAL INTELLIGENCE

Abstract With the increasing implementation of artificial intelligence (AI) in the design process, it is crucial to understand how users will accept AI-designed products. This work studies how the public currently perceives an AI's design capability as compared to a human designer's capability by conducting an online survey of 205 people via Amazon Mechanical Turk. The survey collects the respondents' perception on 16 specific bicycle design goals, demographic information, and self-reported level of design and AI/ML knowledge. Findings reveal that people think an AI would perform worse than a human designer on most design goals, particularly the goals that are user-dependent. This work also shows that the higher people's self-reported level of knowledge in design and the older they are, the more likely they are to think an AI's design capability would exceed a human designer's capability. The insights from this work add to the understanding of user acceptance of AI-designed products, as well as human designers' acceptance of AI input in human-AI teams.
Araştırma Makalesi Yazımında GPT-3 Yapay Zeka Dil Modeli Değerlendirmesi

Artificial intelligence (AI) has helped to obtain accurate, fast, robust results without any human errors.Hence, it has been used in various applications in our daily lives. The Turing test has been afundamental problem that AI systems aim to overcome. Recently developed various natural language problem (NLP) models have shown significant performances. AI language models, used intranslation, digital assistant, and sentiment analysis, have improved the quality of our lives. It canperform scans on thousands of documents in seconds and report them by establishing appropriatesentence structures. Generative pre-trained transformer (GPT)-3 is a popular model developedrecently has been used for many applications. Users of this model have obtained surprising results onvarious applications and shared them on various social media platforms. This study aims to evaluatethe performance of the GPT-3 model in writing an academic article. Hence, we chose the subject ofthe article as tools based on artificial intelligence in academic article writing. The organized querieson GPT-3 created the flow of this article. In this article, we have made an effort to highlight theadvantages and limitations of using GPT-3 for research paper writing. Authors feel that it can be usedas an adjunct tool while writing research papers.
HotGPT: How to Make Software Documentation More Useful with a Large Language Model?

It is well known that valuable information is contained in the natural language components of software systems, like comments and manual, and such information can be used to improve system performance and reliability. Past research has attempted to extract such information through task-specific machine learning models and tool chains. Here, we investigate a general, one-model-fit-all solution through a state-of-the-art large language model (e.g., the GPT series). Our investigation covers three representative tasks: extracting locking rules from comments, synthesizing exception predicates from comments, and identifying performance-related configurations; it reveals challenges and opportunities in applying large language models to system maintenance tasks.
The Influence of E-Marketing Mix Strategy on Organizational Performance: An Empirical Analysis of Jordanian Smes

Purpose: The purpose of this empirical research article is to investigate the association between e-marketing mix strategy (EMMS) and organizational performance (OP) in Jordanian SMEs. Theoretical framework: The study used a quantitative research methodology to explore the impact of EMMS factors on OP indicators, such as product development, pricing strategies, promotional activities, and online distribution channels. Design/methodology/approach: The study's data is gathered using a survey questionnaire issued to 250 Jordanian SMEs. The dependent variables include OP indicators such as financial performance, customer happiness, market share, and innovation, whereas the independent variables are EMMS factors. Control factors including business size, firm age, and industry type are also taken into account. To evaluate the hypothesized correlations, the obtained data is analyzed using the PLS-SEM (PLS 4.0) technique. Findings: The results of this study show that in the Jordanian context, all e-marketing techniques, including product development, price strategies, promotional activities, and online distribution channels, have a significant positive impact on OP metrics Research implications: The findings of this study demonstrate the value of e-marketing and the necessity of sound e-marketing tactics for fostering the expansion and competitiveness of SMEs in the digital era. Practical and social implications: The study underscores the significance of implementing e-marketing strategies for SMEs in Jordan, as they contribute to enhancing OP. Policymakers can utilize these findings to develop supportive measures for SMEs, while practitioners can gain insights into the value of e-marketing strategies in achieving growth and competitiveness. Originality/value: This study adds to the current literature by investigating the association between EMMS and OP in Jordanian SMEs. The study adds to the field's knowledge base by providing empirical evidence and insights on the impact of various e-marketing methods on OP measures.
DETECT: Feature extraction method for disease trajectory modeling in electronic health records.

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm DiseasE Trajectory fEature extraCTion (DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.
Analysis of the Feasibility of Immersive Virtualization in Technical Training

Goal: propose a method of training in regulatory standards using immersive virtualization, aiming at improving student knowledge retention compared to traditional online models.&#x0D; &#x0D; Theoretical Reference: use of literature through research on similar methods already applied in immersive training, dissertations, articles and national standards.&#x0D; &#x0D; Method: practical application of training in an immersive environment in two large industries located in the metropolitan region of Curitiba, PR, where the evaluated ones completed an evaluation questionnaire according to their degree of satisfaction, which is the main parameter used in this work to prove the efficiency of the method.&#x0D; &#x0D; Results and conclusion: the practical application of immersive training, compilation of data and analysis of results confirm the viability proposed in the method. As the country develops technologically, the method will have its applicability increasingly accessible and proven.&#x0D; &#x0D; Research implications: a method of technological training developed as an alternative to the current molds, will certainly bring great contributions to the academy, and, at the same time, with the reduction of machine downtime, displacements, logistical costs and agglomerations, this work will bring contributions to the industry.&#x0D; &#x0D; Originality/Value: with the advent of the covid-19 pandemic experienced in recent times, traditional methods of professional training needed to be quickly adapted, aiming to minimize the impact caused by the restriction of physical contact between people. The proposed method provides the possibility of maintaining professional training processes, offering society the possibility of being trained effectively.&#x0D;
Bao-Enclave: Virtualization-based Enclaves for Arm

General-purpose operating systems (GPOS), such as Linux, encompass several million lines of code.Statistically, a larger code base inevitably leads to a higher number of potential vulnerabilities and inherently a more vulnerable system.To minimize the impact of vulnerabilities in GPOS, it has become common to implement securitysensitive programs outside the domain of the GPOS, i.e., in a Trusted Execution Environment (TEE).Arm TrustZone is the de-facto technology for implementing TEEs in Arm devices.However, over the last decade, TEEs have been successfully attacked hundreds of times.Unfortunately, these attacks have been possible due to the presence of several architectural and implementation flaws in TrustZone-based TEEs.In this paper, we propose Bao-Enclave, a virtualization-based solution that enables OEMs to remove security functionality from the TEE and move them into normal world isolated environments, protected from potentially malicious OSes, in the form of lightweight virtual machines (VMs).We evaluate Bao-Enclave on real hardware platforms and find out that Bao-Enclave may improve the performance of security-sensitive workloads by up to 4.8x, while significantly simplifying the TEE software TCB.
Examination of eye movements during visual scanning of real-world images in Alzheimer's disease and amnestic mild cognitive impairment

Available evidence shows that Alzheimer's disease (AD) patients display inefficiencies in visual scanning of their surroundings, directing their attention towards non-relevant aspects of scenes. Not much is known about eye movements during visual scanning in individuals with amnestic mild cognitive impairment (aMCI). Notably, a consensus has yet to be reached on this issue. Furthermore, prior research has indicated the existence of visual attention deficits in individuals diagnosed with AD and aMCI. The objective of this study was to examine the visual scanning patterns of individuals diagnosed with AD, aMCI, and healthy controls (HC) when viewing various scenes. Thirty patients with AD, 32 aMCI, and 32 HC were included in the study. A set of 30 real-life scene images were presented to the participants in the visual scanning task. Participants' eye events were monitored using the EyeLink 1000 Plus in this task. The results indicate a significant reduction in total fixation duration, number of scanned areas, fixation counts, and saccade counts in AD as compared to those with aMCI and HC. The research did not reveal any significant statistical differences in eye parameters between the aMCI and HC groups. This study found abnormalities in visual scanning in AD compared to aMCI and HC. Visual scanning patterns of aMCI patients were not different from HC. Previous studies have specifically shown visual attention difficulties in AD and MCI. Our findings may be related to visual attention difficulties in AD. In addition, this study is the first to examine visual scanning behaviour with real-world images in AD and aMCI.
normflows: A PyTorch Package for Normalizing Flows

Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.
Two-frame advanced iterative self-tuning algorithm for accurate phase retrieval

Two-shot random phase-shifting interferometry has been actively investigated and improved owing to more demanding requirements in investigating dynamic phenomena or sensing some transient events. Given the background intensity and phase step knowledge, a reconstruction algorithm can be used to recover a complex-valued signal from intensity-only measurements. Most algorithms assume the background intensity and modulation amplitude in a frame of interferogram are constant variables; however, in realistic systems, they vary laterally and axially across the field of view. The model can be used but often appears less precise. In this work, we propose an approach that leverages the iterative method to cope with spatially varying background intensity and modulation amplitude. Our approach, termed the two-frame advanced iterative self-tuning algorithm, uses the spatial mean algorithm paired with an iterative procedure to search the phase step from two captured fringe patterns. It represents a novel approach to reliable and practical two-step phase-shifting interferometry without pre-filtering due to the cancelation of simultaneously obtaining the phase shifts and measured phase through iterative operation in the spatial–temporal domain. Experimental results obtained using the proposed method indicate that it is a simple and robust solution for phase extraction from a two-frame unknown phase shift fringe pattern with spatially varying background intensity and modulation amplitude.
Automatic labeling of Parkinson’s Disease gait videos with weak supervision

Motor dysfunction in Parkinson’s Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients’ gait videos. The proposed method accepts videos of patients and classifies their gait scores as normal (Gait score in MDS-UPDRS = 0) or PD (MDS-UPDRS ≥ 1). Unlike previous work, the proposed method does not require a priori MDS-UPDRS ratings for training, utilizing only domain-specific knowledge obtained from neurologists. We propose several labeling functions that classify patients’ gait and use a generative model to learn the accuracy of each labeling function in a self-supervised manner. Since results depended upon the estimated values of the patients’ 3D poses, and existing pre-trained 3D pose estimators did not yield accurate results, we propose a weakly-supervised 3D human pose estimation method for fine-tuning pre-trained models in a clinical setting. Using leave-one-out evaluations, the proposed method obtains an accuracy of 89% on a dataset of 29 PD subjects – a significant improvement compared to previous work by 7%–10% depending upon the dataset. The method obtained state-of-the-art results on the Human3.6M dataset. Our results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.
The realness of fakes: Primary evidence of the effect of deepfake personas on user perceptions in a design task

Deepfakes, realistic portrayals of people that do not exist, have garnered interest in research and industry. Yet, the contributions of deepfake technology to human-computer interaction remain unclear. One possible value of deepfake technology is to create more immersive user personas. To test this premise, we use a commercial-grade service to generate three deepfake personas (DFs). We also create counterparts of the same persona in two traditional modalities: classic and narrative personas. We then investigate how persona modality affects the perceptions and task performance of the persona user. Our findings show that the DFs were perceived as less empathetic, credible, complete, clear, and immersive than other modalities. Participants also indicated less willingness to use the DFs and less sense of control, but there were no differences in task performance. We also found a strong correlation between the uncanny valley effect and other user perceptions, implying that the tested deepfake technology might lack maturity for personas, negatively affecting user experience. Designers might also be accustomed to using traditional persona profiles. Further research is needed to investigate the potential and downsides of DFs.
LADA-Trans-NER: Adaptive Efficient Transformer for Chinese Named Entity Recognition Using Lexicon-Attention and Data-Augmentation

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the semantic relationship before and after the sentence after integrating lexical information. Therefore, the regularity of word length information has not been fully explored in various word-character fusion methods. In this work, we propose a Lexicon-Attention and Data-Augmentation (LADA) method for Chinese NER. We discuss the challenges of using existing methods in incorporating word information for NER and show how our proposed methods could be leveraged to overcome those challenges. LADA is based on a Transformer Encoder that utilizes lexicon to construct a directed graph and fuses word information through updating the optimal edge of the graph. Specially, we introduce the advanced data augmentation method to obtain the optimal representation for the NER task. Experimental results show that the augmentation done using LADA can considerably boost the performance of our NER system and achieve significantly better results than previous state-of-the-art methods and variant models in the literature on four publicly available NER datasets, namely Resume, MSRA, Weibo, and OntoNotes v4. We also observe better generalization and application to a real-world setting from LADA on multi-source complex entities.
Safe Interval Path Planning with Kinodynamic Constraints

Safe Interval Path Planning (SIPP) is a powerful algorithm for solving a single-agent pathfinding problem where the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment. The original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously. However, this assumption often does not hold in practice, e.g. a mobile robot moving at a cruising speed cannot stop immediately but rather requires gradual deceleration to a full stop that takes time. In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case, the original SIPP is incomplete. To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration. In the experimental evaluation, we show that the key property of the original SIPP still holds for the modified version: it performs much fewer expansions compared to A* and, as a result, is notably faster.
Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering

In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. Instead of manually tuning optimal anchor with massive hyper-parameters, we propose to optimize the contribution weights of a group of pre-defined anchor numbers to avoid extra time expenditure among views. Most importantly, we propose a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion. Then, an efficient linear optimization algorithm is proposed to solve the resultant problem. Comprehensive experimental results demonstrate the effectiveness and efficiency of our proposed framework. The source code is available at https://github.com/Jeaninezpp/FDAGF.