Reviewerly/exHarmony huggingface dataset:
On block accelerations of quantile randomized Kaczmarz for corrupted systems of linear equations

Abstract With the growth of large data as well as large-scale learning tasks, the need for efficient and robust linear system solvers is greater than ever. The randomized Kaczmarz method (RK) and similar stochastic iterative methods have received considerable recent attention due to their efficient implementation and memory footprint. These methods can tolerate streaming data, accessing only part of the data at a time, and can also approximate the least squares solution even if the system is affected by noise. However, when data is instead affected by large (possibly adversarial) corruptions, these methods fail to converge, as corrupted data points draw iterates far from the true solution. A recently proposed solution to this is the quantileRK method, which avoids harmful corrupted data by exploring the space carefully as the method iterates. The exploration component requires the computation of quantiles of large samples from the system and is computationally much heavier than the subsequent iteration update. In this paper, we propose an approach that better uses the information obtained during exploration by incorporating an averaged version of the block Kaczmarz method. This significantly speeds up convergence, while still allowing for a constant fraction of the equations to be arbitrarily corrupted. We provide theoretical convergence guarantees as well as experimental supporting evidence. We also demonstrate that the classical projection-based block Kaczmarz method cannot be robust to sparse adversarial corruptions, but rather the blocking has to be carried out by averaging one-dimensional projections.
Experimental witnessing for entangled states with limited local measurements

We experimentally demonstrate a method for detection of entanglement via construction of entanglement witnesses from a limited fixed set of local measurements ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi mathvariant="script">M</mml:mi> </mml:mrow> </mml:math> ). Such a method does not require a priori knowledge about the form of the entanglement witnesses. It is suitable for a scenario where a full state tomography is not available, but the only resource is a limited set of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mi mathvariant="script">M</mml:mi> </mml:mrow> </mml:math> . We demonstrate the method on pure two-qubit entangled states and mixed two-qubit entangled states, which emerge from photonic implementation of controllable quantum noisy channels. The states we select are motivated by realistic experimental conditions, and we confirm it works well for both cases. Furthermore, possible generalizations to higher-dimensional bipartite systems have been considered, which can potentially detect both decomposable and indecomposable entanglement witnesses. Our experimental results show perfect validity of the method, which indicates that even a limited set of local measurements can be used for quick entanglement detection and further provide a practical test bed for experiments with entanglement witnesses.
The Effect of Grammatical Understanding on the Ability of Writing Thesis Students

The purpose of this research is to decrypt and analyze whether grammatical understanding affects the writing ability of Semarang University’s students thesis and how it affects. This type of research is quantitative research with the type of research is inference used to test hypotheses to determine the influence of grammatical understanding on the ability to write thesis. The sample of this study was students who were preparing the thesis of four courses that were taken randomly. Sampling techniques using probability sampling. Data collection techniques are carried out by direct observation using structured questionnaires. Quantitative data of questionnaire results tested with F-test. While qualitative data from thesis text analysis is analyzed descriptively. The result is that between right and wrong in 10 criteria of grammatically, that is 57.43% true and 42.57% wrong. Based on the F-test results obtained results that the regression coefficient of 0.672 with a significance value of 0.000 &lt; 0.05. This means that the effectiveness of sentences has a positive and significant influence on grammatical understanding in writing thesis. This influence supports the analysis of thesis documents with the findings of 636 sentences from the thesis author. Of the 636 sentences found 429 sentences were grammatical and 207 sentences were not grammatical. The ungrammatically of the sentence is caused by several things, namely sentences without a subject, without predicates, without predicates and predicates, double-substitutes, double predicates, objects preceded by forejectives, substitutes and double-predicates, repeating subjects, without objects, not using the conjunction ”bahwa”, and inaccuracies in the use of hyphens ”then”.
Personalized Keyword Spotting through Multi-task Learning

Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting user-agnostic pre-defined keywords. However, in practice, most user interactions come from target users enrolled in the device which motivates to construct personalized keyword spotting. We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user Only KWS (TO-KWS). To solve the tasks, we propose personalized keyword spotting through multi-task learning (PK-MTL) that consists of multi-task learning and task-adaptation. First, we introduce applying multi-task learning on keyword spotting and speaker verification to leverage user information to the keyword spotting system. Next, we design task-specific scoring functions to adapt to the personalized KWS tasks thoroughly. We evaluate our framework on conventional and personalized scenarios, and the results show that PK-MTL can dramatically reduce the false alarm rate, especially in various practical scenarios.
A PIMS Development Kit for New Personal Data Platforms

The web ecosystem is based on a market where stakeholders collect and sell personal data, but nowadays users expect stronger guarantees of transparency and privacy. With the PIMCity personal information management system (PIMS) development kit, we provide an open-source development kit for building PIMSs to foster the development of open and user-centric data markets.
Transformer-GAN architecture for anomaly detection in multivariate time series

Anomaly detection based on multivariate time series correlation data collected in real time during the process is one of the key aspects of preventing industrial process accidents and ensuring system safety. However, industrial multivariate time series anomaly detection still faces two major challenges: (1) the complex nonlinear correlation characteristics among time series data variables lack an effective representation method, and (2) the complex correlation among time series with highly unbalanced normal/abnormal distribution needs to be deeply explored. In this paper, we propose a Transformer generative adversarial networks (GAN)-based multivariate time series anomaly detection method (TGAN-MTSAD). TGAN-MTSAD employs transformer neural networks as the base model of GAN and introduces a graph attention layer to automatically learn complex dependencies among time series multivariate variables. It applies a patch trick to enable the model to effectively capture anomaly details within a time window. An anomaly score calculation method is proposed based on a combination of reconstruction and discrimination errors. An extensive performance validation and comparative experimental analysis of the proposed method were carried out using three real-world datasets. The results show that TGAN-MTSAD can effectively detect in-process timing anomalies, outperforming the baseline method in most cases, and has good interpretability for complex industrial anomaly detection.
Design and Study of the Psychometric Properties of a Professors’ Expectations of Virtual University Education Questionary

&lt;p style="text-align:justify"&gt;This work describes the design and validation of a questionnaire to assess the expectations of higher education professors regarding virtual education (CEDVES). The sample included 546 professors, 299 men (54.66%) and 247 women (45.23%), from different scientific disciplines of a university in Chile. The final version consisted of 38 items answered using a five-point Likert scale. Nine factors were identified from the exploratory factor analysis. This configuration accounts for 75% of the variance. The structure of the instrument was studied using confirmatory factor analysis. It was found that nine factors produced a good fit, derived from a hierarchical solution in which all these factors depend on a factor of second general order. Each of the scales, like the general factor, present good indicators of reliability. The analysis indicates that this questionnaire has adequate validation and could be broadly used in higher education.&lt;/p&gt;
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition

Self-supervised learning representations (SSLR) have resulted in robust features for downstream tasks in many fields. Recently, several SSLRs have shown promising results on automatic speech recognition (ASR) benchmark corpora. However, previous studies have only shown performance for solitary SSLRs as an input feature for ASR models. In this study, we propose to investigate the effectiveness of diverse SSLR combinations using various fusion methods within end-to-end (E2E) ASR models. In addition, we will show there are correlations between these extracted SSLRs. As such, we further propose a feature refinement loss for decorrelation to efficiently combine the set of input features. For evaluation, we show that the proposed 'FeaRLESS learning features' perform better than systems without the proposed feature refinement loss for both the WSJ and Fearless Steps Challenge (FSC) corpora.
An Investigation of the Use of Cohesive Devices in ESL Students’ Essay Writing

Essays are commonly used to project one's thoughts, awareness, and ideas in order to make them apparent to others.Despite its pervasiveness, many ESL (English as a Second Language) students view essay writing as a difficult task, especially at tertiary level where students are required to produce matured and sophisticated written texts.This study investigated the use of cohesive devices in ESL students' essays and highlighted the problems that these students face in writing essays.An action research approach using a quantitative and qualitative analysis of cohesive markers was used to carry out this research.The subjects of this study were 100 diploma students who had enrolled in the English for Academic Purposes (EAP) course.One hundred essay scripts written in the essay writing section of the final examination were analysed using Halliday and Hasan's (1976) framework of cohesion.Findings indicated that reference markers have the highest frequency, whereas substitutions were the least common cohesive device.The analysis also revealed that some cohesive devices were overused or incorrectly used by ESL learners, with conjunctions being the most common.The choice of cohesive devices was found to be influenced by the language proficiency of students because most of the students seemed to be unsure about the significance of using cohesive devices in their essays.Even though some students employed a sufficient number of cohesive devices in their essays, they were not aware of the functions conveyed by these resources.Given this, they require explicit instruction and direct exposure to the communicative meanings of cohesive devices as well as being familiarised with the ways to apply them in order to produce a cohesive piece of writing.
CSR Reporting and Blockchain Technology

Blockchain technology is a public ledger that stores data in a chain of blocks which can radically improve the quality of our records from “records that might be trustworthy” to “records that trust is absolute”. This chapter explores one area that blockchain technology can radically transform but has not yet received significant attention. We evaluate the suitability of applying blockchain technology for corporate social responsibility (CSR) reporting. We demonstrate that blockchain technology is suitable in the context of CSR reporting since there is a strong need for an immutable common database shared among various stakeholders with potential trust issues. We also argue that blockchain technology does not completely eliminate existing trusted third parties such as governments, international organizations that provide CSR reporting standards, major CSR reporting assurance companies and major CSR infomediaries. In particular, blockchain technology can be used as a platform that integrates all traditional trusted third parties, transforms their functions, and reduces their drawbacks for advancing CSR reporting. We also demonstrate that a permissionless public blockchain would be the most suitable structure.
Perceptions of university students about virtual reality as a didactic resource: a pre-experimental study with a control and experimental group

This article presents the results of a study of the impact of incorporating Second Life on the didactics of a university degree subject as well as on student satisfaction, administering a survey to two groups of students enrolled in the same subject through a 24-item questionnaire scored on a 5-point Likert scale for data collection. Subjects comprised 75 students who used the traditional model and 75 students who used a virtual reality model. To verify the hypotheses, the data analysis focused on the descriptive statistics through a comparison of the mean scores for the dependent variables of each of the groups, and with the verification of parametric assumptions, statistical inference was applied to determine the significance of the variances through Student’s t-test. The results show that students perceive specific advantages linked to the design of VR activities in virtual reality. Hence, the potential of virtual reality for improving the teaching process is confirmed. This study leads to the conclusion that students at Universidad Don Bosco greatly value the teaching learning process mediated by virtual reality.
Wireless Gyro-mouse for Text Input on a Virtual Keyboard

In this paper, we propose a gyroscopic pointing device that allows a user to type text by using a specialized virtual keyboard. We compare different typing methods that use the dwell time of the pointer as a character selection method. The proposed solution can be used in different applications, such as: computer gaming, virtual reality, remote control and to facilitate communication with people affected by certain disabilities and can easily accommodate more types of users. The performances of different methods have been analyzed by using the speed of typing measured in words per minute, the task success rate, as well as the feedback from the user regarding comfort and perceived ease-of-use.
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models

In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional approaches require extra parameters of the same size as the model for optimization, and it is difficult to apply these approaches to end-to-end ASR models because they have a huge amount of parameters. To solve this problem, we first investigate which parts of end-to-end ASR models contribute to high accuracy in the target domain while preventing catastrophic forgetting. We conduct experiments on incremental domain adaptation from the LibriSpeech dataset to the AMI meeting corpus with two popular end-to-end ASR models and found that adapting only the linear layers of their encoders can prevent catastrophic forgetting. Then, on the basis of this finding, we develop an element-wise parameter selection focused on specific layers to further reduce the number of fine-tuning parameters. Experimental results show that our approach consistently prevents catastrophic forgetting compared to parameter selection from the whole model.
CNN Classification of Computed Tomography Images for Pancreatic Tumor Detection

The five-year survival rate for pancreatic cancer (PC) is the lowest of any cancer kind, and it is the fourth greatest cause of cancer-related death, with a growing death rate. When it comes to cancer invasion, the most significant risk factors are: smoking; alcohol usage; diabetes; and prior pancreatitis. By using this method, we will be able to detect our PC, which is equipped with picture handling technology. Researchers used CT images as input in this study and preprocessed them to remove any noise in the images that had been learned using an adaptive Weiner filter. Preprocessing is followed by the use of a region grow ideal to segment the noise-free image. Scale Invariant Feature Transform (SIFT) is utilized once more to extract the tumor limits and principal component analysis (PCA) is used to enhance the retrieved structures to improve the types of pancreatic CT images. In order to activate the picture parameters, a convolutional neural network (CNN) classifier is used. In order to categorize an image as nonpancreatic cancer or pancreatic cancer, the test data were compared to the training data and the classified image was compared. MATLAB then initiates the entire process, and the most recent performance estimation approach is utilized, resulting in outstanding accuracy.
Teachers’ Competencies for Inclusive Teaching: Relation to Their Professional Development and Personality

It is of utmost importance for quality and contemporary education process to study the significant determinants of teachers' competencies for inclusive teaching, such as teachers' professional development and their personalities.In order to organize suitable and quality teaching and learning process for diverse learners, teachers need to board range of competencies, positive attitudes towards inclusive education, training preparation and professional development regarding their various personalities.Therefore, the aim of this study was to explore the relationship between teachers' competencies for inclusive teaching, professional development and personality traits, and which predictors of inclusive teaching skills are significant.The national representative sample of 1195 Croatian teachers (F = 975; M = 198; Age = 41.96) from first to eight grade were included.The Scales to measure self-perceived teachers' competencies for individualized instruction in inclusive classrooms and professional development as well as The Ten Item Personality Inventory were used.The results reflect a positive relationship between teachers' competencies for inclusive teaching and professional competencies and personality traits, and those with their demographic data such as age, working experience and working position.The hierarchical regression analysis revealed that the greatest predictors for inclusive teaching competencies are professional competencies, while openness to experience and work position have minor significance.The findings have discussed within the frame of their significant implications for enhancing the initial and continuing training and support of teachers as the key strategies for the realization of an inclusive and right-based education system.
Towards Event-level Causal Relation Identification

Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.
Over-the-Air Computation for Vertical Federated Learning

Vertical federated learning (FL) is a critical tech-nology to support distributed artificial intelligence (AI) services in futuristic 6G systems, since it enables efficient and secure collaborative machine learning from a number of heteroge-neous devices in Internet of Things. In order to improve communication efficiency in vertical FL, we propose an over-the-air computation (AirComp) assisted vertical FL approach to achieve fast global aggregation. We theoretically establish the convergence analysis of the approach and thus propose to minimize the mean-squared error (MSE) of AirComp to reduce the optimality gap. So as to tackle the intractable non-convex problem, we propose an algorithm based on superiorization of bounded perturbation with convergence guarantee. Numerical experiments demonstrate that our proposed algorithm achieves low AirComp MSE in short running time, thereby improving the learning performance of vertical FL.
Learning Precoding for Semantic Communications

When knowing the goal of transmission, resources can be used more efficiently in semantic communication systems, where only the information necessary for accomplishing the goal needs to be transmitted. Existing works for semantic commu-nications do not investigate resource allocation. In this paper, we consider a multi-antenna-multi-subcarrier system for trans-mitting images to multiple users, by taking a goal of classifying the images as an example. We propose a semantic information-aware precoding policy to mitigate multi-user interference based on deep learning, where the modulated symbols of the users are input into a graph neural network together with estimated channel matrix for learning the policy. To emphasize the impact of harnessing semantic information on precoding, we apply two convolutional neural networks to learn the mapping from the image of each user to modulated symbols and the mapping from the received symbols of each user to a representation of the image, respectively. A fully-connected neural network is followed for image classification. After training these neural networks jointly, the learned precoding policy operates in a water-filling manner, which allocates more power for transmitting stronger symbols, where the important information for classification is carried. Simulation results show that the learned precoding policy is superior to existing precoding policies in reducing the bandwidth for transmission required to achieve an expected classification accuracy when the signal-to-noise ratio is low, channel estimation error is high, and the number of users is large,