Reviewerly/exHarmony huggingface dataset:
Temporal Alignment of Human Motion Data: A Geometric Point of View

Temporal alignment is an inherent task in most applications dealing with videos: action recognition, motion transfer, virtual trainers, rehabilitation, etc. In this paper we dive into the understanding of this task from a geometric point of view: in particular, we show that the basic properties that are expected from a temporal alignment procedure imply that the set of aligned motions to a template form a slice to a principal fiber bundle for the group of temporal reparameterizations. A temporal alignment procedure provides a reparameterization invariant projection onto this particular slice. This geometric presentation allows to elaborate a consistency check for testing the accuracy of any temporal alignment procedure. We give examples of alignment procedures from the literature applied to motions of tennis players. Most of them use dynamic programming to compute the best correspondence between two motions relative to a given cost function. This step is computationally expensive (of complexity $O(NM)$ where $N$ and $M$ are the numbers of frames). Moreover most methods use features that are invariant by translations and rotations in $\mathbb{R}^3$, whereas most actions are only invariant by translation along and rotation around the vertical axis, where the vertical axis is aligned with the gravitational field. The discarded information contained in the vertical direction is crucial for accurate synchronization of motions. We propose to incorporate keyframe correspondences into the dynamic programming algorithm based on coarse information extracted from the vertical variations, in our case from the elevation of the arm holding the racket. The temporal alignment procedures produced are not only more accurate, but also computationally more efficient.
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention

Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (search). Such models are limited in their application due to a common approach relying on trained target detectors for all possible objects, and the availability of human gaze data for their training (both not scalable). In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem. In contrast to existing methods using object detector modules, Gazeformer encodes the target using a natural language model, thus leveraging semantic similarities in scanpath prediction. We use a transformer-based encoder-decoder architecture because transformers are particularly useful for generating contextual representations. Gazeformer surpasses other models by a large margin on the ZeroGaze setting. It also outperforms existing target-detection models on standard gaze prediction for both target-present and target-absent search tasks. In addition to its improved performance, Gazeformer is more than five times faster than the state-of-the-art target-present visual search model.
Cascaded variational quantum eigensolver algorithm

We present a cascaded variational quantum eigensolver algorithm that only requires the execution of a set of quantum circuits once rather than at every iteration during the parameter optimization process, thereby increasing the computational throughput. This algorithm uses a quantum processing unit to probe the needed probability mass functions and a classical processing unit perform the remaining calculations, including the energy minimization. The ansatz form does not restrict the Fock space and provides full control over the trial state, including the implementation of symmetry and other physically motivated constraints.
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU

Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually work well in the hospitals they were trained for, they tend to be less reliable when applied at new hospitals. This makes it difficult to deploy them at scale. Using carefully harmonised intensive care data from four data sources across Europe and the US (totalling 334,812 stays), we systematically assessed the reliability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or explicitly optimising for generalisability during training improves model performance at new hospitals. We found that models achieved high AUROC for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, performance dropped at new hospitals, sometimes by as much as -0.200. Using more than one data source for training mitigated the performance drop, with multi-source models performing roughly on par with the best single-source model. This suggests that as data from more hospitals become available for training, model robustness is likely to increase, lower-bounding robustness with the performance of the most applicable data source in the training data. Dedicated methods promoting generalisability did not noticeably improve performance in our experiments.
Learning to Zoom and Unzoom

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well.
FACTORS AFFECTING INFORMATION TECHNOLOGY SKILLS OF STUDENTS SPECIALIZING ACCOUNTING - AUDITING

As we all know, the 4th industrial revolution (Industry 4.0) has brought many great applications to all areas of society. Therefore, it can be seen that mastering information technology (IT) skills will help improve work efficiency, reduce costs, increase global cooperation, thereby gaining competitive advantages (Mamahani, 2006). IT skills are not only important for worker, but also for students, this is one of the necessary skills. The objective of this study is to find out the current situation and evaluate the application of IT skills of students majoring in accounting and auditing at the university of economics in Hanoi in order to meet Industry 4.0, through a survey of 200 students and applying the method of calculating the mean value, the researchers has shown the influence of the factors. This is also the basis for the researchers to propose some recommendations to help students majoring in accounting - auditing improve and enhance their IT skills to create a convenient learning environment as well as open up job opportunities in the future.
Feature Separation and Recalibration for Adversarial Robustness

Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust activations for more robust feature maps through Separation and Recalibration. The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack. The Recalibration part then adjusts the non-robust activations to restore the potentially useful cues for model predictions. Extensive experiments verify the superiority of FSR compared to traditional deactivation techniques and demonstrate that it improves the robustness of existing adversarial training methods by up to 8.57% with small computational overhead. Codes are available at https://github.com/wkim97/FSR.
Improving the Performance of Sentiment Analysis by Ensemble Hybrid Learning Algorithm With NLP And Cascaded Feature Extraction

Sentiment analysis is a challenging problem in Natural Language Processing since every language has its own character within several difficulties such as ambiguity, synonymy, negative suffixes…etc. Since words with ambiguity can have different sentiment scores depending on the meaning they have in their corresponding context, we accomplished a study on Turkish language to determine whether the polarity scores of these polysemous words may differ according to their meaning. For a word with ambiguity, we first made a polarity calculation module to calculate its polarity score. This way, we calculated the polarity scores of 100 Turkish polysemous words. Then, since negation directly affects the correct meaning of the word in the sentiment analysis, a negation handler module is also implemented. After that, we prepared a sentiment polarity corpus which consists of 159,876 Turkish words including 100 Turkish polysemous words. Actually, the main purpose of this study is to detect sentiment polarity of Turkish texts by considering and building a specialized module for polysemous words. In short, we built a system for Turkish sentiment polarity detection task including these modules: 1) Pre-processing, 2) Polarity Calculation Module, 3) Negation Handling Module, 4) Feature Generation Module, and 5) Classification Module. According to our knowledge, this is the first study which includes all of these modules in one Turkish sentiment analysis task. Finally, we conducted this corpus using an ensemble hybrid regularized learning algorithm on two self-collected Twitter-datasets. Experimental results show that the suggested approach improves the classification performance on Turkish sentiment analysis task.
Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach

Clustering conceptually reveals all its interest when the dataset size considerably increases since there is the opportunity to discover tiny but possibly high value clusters which were out of reach with more modest sample sizes. However, clustering is practically faced to computer limits with such high data volume, since possibly requiring extremely high memory and computation resources. In addition, the classical subsampling strategy, often adopted to overcome these limitations, is expected to heavily failed for discovering clusters in the highly imbalanced cluster case. Our proposal first consists in drastically compressing the data volume by just preserving its bin-marginal values, thus discarding the bin-cross ones. Despite this extreme information loss, we then prove generic identifiability property for the diagonal mixture model and also introduce a specific EM-like algorithm associated to a composite likelihood approach. This latter is extremely more frugal than a regular but unfeasible EM algorithm expected to be used on our bin-marginal data, while preserving all consistency properties. Finally, numerical experiments highlight that this proposed method outperforms subsampling both in controlled simulations and in various real applications where imbalanced clusters may typically appear, such as image segmentation, hazardous asteroids recognition and fraud detection.
Improving Question Answering Performance Using Knowledge Distillation and Active Learning

Contemporary question answering (QA) systems, including transformer-based architectures, suffer from increasing computational and model complexity which render them inefficient for real-world applications with limited resources. Further, training or even fine-tuning such models requires a vast amount of labeled data which is often not available for the task at hand. In this manuscript, we conduct a comprehensive analysis of the mentioned challenges and introduce suitable countermeasures. We propose a novel knowledge distillation (KD) approach to reduce the parameter and model complexity of a pre-trained BERT system and utilize multiple active learning (AL) strategies for immense reduction in annotation efforts. In particular, we demonstrate that our model achieves the performance of a 6-layer TinyBERT and DistilBERT, whilst using only 2% of their total parameters. Finally, by the integration of our AL approaches into the BERT framework, we show that state-of-the-art results on the SQuAD dataset can be achieved when we only use 20% of the training data.
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN PERPUSTAKAAN MENGGUNAKAN QR CODE BERBASIS WEBSITE

The school library is one of the educational facilities supporting learning activities and has an important role in supporting the achievement of educational goals. At SMPN 1 Mandalawangi, the library plays a role in assisting students and teachers in obtaining subject matter references to support teaching and learning activities. In the library, activities such as visitor registration, book recording, book borrowing, returning transactions, and making reports at the library are still done manually, so it requires more time and effort, and mistakes often occur in finding the required data. The author designed a library management information system using a web-based QR Code to solve this problem so that activities can be carried out more quickly and easily. The Waterfall development method is used to build this web-based information system. The stages that are passed in this method are the Requirement, Design, Coding &amp; Testing, Integration &amp; Testing, and Operation and Maintenance stages. The information system is built using the JavaScript programming language, the ReactJS library, Cloud Firestore as the database, and UML as the system model. The final result of this design is to produce a library management information system using a web-based QR Code for the SMPN 1 Mandalawangi Library. It can be interpreted that the system that has been designed can facilitate library staff in the service process, data management, and making reports at the SMPN 1 Mandalawangi library.
USE OF GROUP WORK ACTIVITIES IN THE ENGLISH LANGUAGE PERFORMANCE OF JUNIOR HIGH SCHOOL STUDENTS

To exercise using the target language, users must engage in interactive activities that enable them to exchange ideas with one another. The study concentrated on the effective use of group work activities as a teaching strategy in English instruction. This study included two sections of Grade 8 junior high school students from Tagoloan, Misamis Oriental. Comparing the level of difference in pre- and post-test performance between students exposed to group work activities (the experimental group) and those who were not (the control group), a quasi-experimental design was used. Both groups received the same eight (8) learning competencies in a one-month period. In this study, descriptive statistics and inferential analysis, such as the Z-test, were used to determine whether a significant difference was found between the means of the two groups. The findings revealed that group work activities or cooperative learning as an intervention and learning approach for students were effective in improving learning, and the data demonstrating their positive attitudes toward its use in classes illustrated their interest in interaction among their classmates while engaging in language use. As a result, English teachers are encouraged to use cooperative learning to engage students while they authentically communicate in the language.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0059/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Research on software defect management and automatic repair technology

In order to solve the problems of large search space and low search efficiency in computer software defect automatic repair technology, a research method of computer software defect management and automatic repair technology was proposed. This paper points out the limitations and challenges of the current computer automatic repair technology by briefly describing the research on computer software defect management and computer automatic repair technology, analyzes three search spaces for automatic defect repair, and designs and implements the prototype tool GPSRepair in the existing technology. The experimental results on the Defects4j defect dataset show that GPSRepair has higher repair efficiency and accuracy than existing repair methods.
Impact of Social Media Influencers to promote Healthy Lifestyle behaviour: A Review from the Self-Determination Approach

Purpose: Malaysia as a rapidly developing country is now immersed in the current technology that makes people less active due to the use of modern equipment such as machines, remote controls, and push-button technology. Coronary heart disease, cancer, and stroke are examples of non-communicable diseases that are closely related to a sedentary lifestyle.&#x0D; Design/Methodology/Approach: The trend of social media influencers such as celebrities, fitness coaches, and health professionals as agents of healthy lifestyles showed positive impacts. The approach in this study will analyze the secondary existing literature on the impact of social media influencers on their followers to change their behavior through the content they create, sharing knowledge and skills to lead a healthy lifestyle.&#x0D; Findings: The conceptual framework was developed based on Self-Determination Theory (SDT) to predict an individual’s health behaviors and attraction to follow social media influencers in social media networking.&#x0D; Implications/Originality/Value: This study examined whether SDT concepts (basic psychological needs and&#x0D; autonomous motivation) were associated with engagement in healthy lifestyle behaviors among social media users
What to say to help one's partners in crime

This study explored how members of an illicit network navigate investigative interviews probing their crimes. We examined how perceived disclosure outcomes, namely, the projected costs and benefits, affect what members choose to reveal. We recruited N = 22 groups, maximum of six participants per group. Each group assumed the role of an illicit network and planned for possible interviews with investigators probing into the legitimacy of a business the network owns. All participants underwent an interview after the group planning stage. The results indicated that network members navigate the dilemma interviews bring by disclosing information they perceive would likely yield beneficial (or desirable) rather than costly (or undesirable) outcomes. Additionally, much of the participants’ sensitivity to potential costs and benefits was explained by the group of which they are a part: different networks likely respond to costs and benefits in unique ways. This work contributes to understanding how illicit networks manage information disclosure in investigative interviews.
Further training in the context of the digital transformation

Ongoing digitalisation has a range of different impacts on continuing training. This chapter shows how digitalisation can influence the demand for continuing education, as well as the content of continuing training courses and the way in which they are conducted. The Covid-19 pandemic has accelerated many developments in the labour market. In the area of continuing training, this is particularly evident in the way courses are run; there has been a huge push toward digital formats over the last two years. Continuing training is particularly important for those workers whose jobs are at risk from automation. However, these are also the groups that participate least in continuing education. Policymakers must take appropriate measures to ensure that workers whose jobs are particularly affected by digitalisation increase their participation in continuing education. In addition, the increased use of digital teaching methods and online courses must not exacerbate existing inequalities in participation in continuing education.
Computing Crowdsourcing using distributed, cloud-based systems

Computing has evolved from normal networked PCs and servers, and it now has number of different forms depending on the ways of computing, task distribution and other factors. With course of time, the computing tasks and their complexity also evolved, and it is increasing exponentially day by day. And it is becoming increasingly complex, specifically for individuals and small-scale organizations to perform these operations on the limited computing infrastructure they have.There are millions of computers with certain computing power connected to the internet, and very few of these computing systems are non-idle for 24 hours. There are times when these systems are idle or during normal operations, they are never utilized up to their highest potential performance.We are proposing establishing a system which can crowdsource heavy tasks which need to be performed by individuals or organizations using distributed computing over the internet. There are techniques which could be used for the same.
A Scholar between Versatility and Specialization. Review of the Book by Peter Burke “The Polymath: A Cultural History from Leonardo da Vinci to Susan Sontag”

The book by the famous British mediaevalist Peter Burke is perhaps the first detailed historical study of the phenomenon of versatile giftedness. The review notes the importance of the monograph. P. Burke has collected unique historical material about researchers of different centuries, who had wide interests and worked in interdisciplinary directions. It is shown that the information crises that have arisen in society throughout history were changing the practice of interaction between generalists and narrow scholars. An important observation about the convergence of the roles of polymaths and specialists in the course of scientific and technological development of society is made. The book is addressed to all those interested in the general history of science and the social history of the scientific community.