Reviewerly/exHarmony huggingface dataset:
Approximately optimal construction of parallel algorithm portfolios by evolutionary intelligence

<p indent="0mm">As a high-performance general-purpose form of parallel solvers, parallel algorithm portfolios (PAPs) have recently shown great performance for solving decision, counting, continuous, and discrete optimization problems. However, the manual construction of PAPs is laborious work, which typically requires substantial domain knowledge and human effort. To address this issue, this paper proposes AutoPAP, an evolutionary intelligence-based method for PAP construction. Overall, AutoPAP follows the (<italic>n</italic>+1) evolutionary framework, i.e., in each generation, <italic>n</italic> candidate algorithms are generated, and the best one among them is retained in the PAP. Considering that the algorithm configuration space is typically very large, this study designs a highly effective mutation operator to improve the practical performance of AutoPAP and further theoretically proves that AutoPAP can achieve (1−1/<italic>e</italic>) approximation. Finally, to validate the effectiveness of AutoPAP, this study uses it to build a PAP, namely, TSP_PAP, for traveling salesman problems (TSPs). The test results show that TSP_PAP significantly outperforms state-of-the-art TSP solvers EAX and LKH in terms of efficiency (runtime) and effectiveness (solution quality). On 128 TSP instances of sizes ranging from 1000 to 30000, compared with LKH and EAX, TSP_PAP can reduce the average runtime by at least 45.71% and lower the average deviation ratio by at least 87.50%, indicating the huge potential of AutoPAP in automatic algorithm design and evolution.
A Preliminary Data-driven Analysis of Common Errors Encountered by Novice SPARC Programmers

Answer Set Programming (ASP), a modern development of Logic Programming, enables a natural integration of Computing with STEM subjects. This integration addresses a widely acknowledged challenge in K-12 education, and early empirical results on ASP-based integration are promising. Although ASP is considered a simple language when compared with imperative programming languages, programming errors can still be a significant barrier for students. This is particularly true for K-12 students who are novice users of ASP. Categorizing errors and measuring their difficulty has yielded insights into imperative languages like Java. However, little is known about the types and difficulty of errors encountered by K-12 students using ASP. To address this, we collected high school student programs submitted during a 4-session seminar teaching an ASP language known as SPARC. From error messages in this dataset, we identify a collection of error classes, and measure how frequently each class occurs and how difficult it is to resolve.
Pragmatic and cognitive elements in literary machine translation

The development of neural machine translation (NMT) has made it possible to realistically test its application to literary texts traditionally reserved for human translation because of their inherent creativity. While recent research (Toral et al. 2020; Oliver González, Toral, and Guerberof Arenas 2019; among others) shows MT’s potential for literary translation, this chapter discusses linguistic issues from the point of view of a literary translator and analyses a section from a contemporary American novel translated into Italian with Google Translate, DeepL, and Microsoft’s Bing Translator. My focus is on how MT deals with those elements that contribute to the creativity and literariness of the text in order to assess the degree of usability of the outputs. The theoretical introduction reviews notions of creativity (Boden 2004; Veale 2015), pragmatics, and cognition (Vega Moreno 2007; Jakobsen 2017) in relation to the characteristics of machine-generated language and MT as highlighted by previous studies (Guerberof Arenas and Toral 2020; Popel 2020; among others). The practice-oriented section analyses the source text—an excerpt from Jackie Polzin’s Brood (2021)—focusing on pragmatic elements and cognitive demands on the reader/translator. Finally, the three machine-translated versions are discussed with particular attention to the latter aspects. To different degrees, the three outputs contain correct and fluent segments, but editing is frequently needed in terms of gender, number, prepositions, tense, lexical choices, information flow, and pragmatic adequacy. Overall, the findings show promising correctness of content and form, but also MT’s current inability to replace situated human experience, which reduces its usefulness for literary translators.
DIGITAL CULTURE AS AN INNOVATIVE COMPONENT OF A TEACHER'S PROFESSIONAL ACTIVITY

Professional activity in the digital environment is an integral component of modern life in Industry 4.0. The success of the formation of digital competence of an individual in modern realities is largely determined by the level of digital culture of a teacher himself as a translator of the values of the digital society. In this regard, the problem of the research can be formulated as follows: what are the theoretical approaches to substantiating the category of "digital culture" in modern pedagogical discourse? The purpose of the research is to reveal the essence of the concept of "digital culture" as an actual pedagogical category. The research methods used are analysis, synthesis, differentiation, description. The results of the study are based on the analysis and synthesis of derivatives of the definition of "digital culture". Theoretical approaches to the content of the concept of "digital culture" as an innovative component of the teacher's professional activity have been presented; the concept of "digital culture" has been clarified; the basic components of a teacher's digital culture have been proposed. The key conclusions are the following: derivatives of the definition of "digital culture" are "digital literacy", "digital competence", ICT culture; The digital culture of a teacher is understood as the formation of the necessary skills for successful functioning in mixed reality, including the skills of safe and effective work with content, communication with other users in the electronic educational environment, search and consumption of information, continuous improvement, motivational and value attitude to the digital service, providing ergonomic functioning in the new digital educational space; the basic components of a teacher's digital culture are: digital literacy, ICT competencies, motivational-value attitude.
High-Dimensional Quantum Key Distribution with Scrambled Time-Bin Encoding

We propose and analyze a high-dimensional cryptographic protocol relying on a coherent-one-way quantum key distribution system. We demonstrate it over a 40km fiber link and observe a twofold enhancement in the secure key rate.
Generation of polarization entanglement via the quantum Zeno effect

The quantum Zeno effect reveals that continuous observation of a quantum system can significantly alter its evolution. Here, we present a method for establishing polarization entanglement between two initially unentangled photons in coupled waveguides via the quantum Zeno effect. We support our analytical investigation with numerical simulations of the underlying Schrodinger equation describing the system. Further, we extend our technique to three coupled waveguides in a planar configuration and determine the parameters required to generate three-qubit W-states. In contrast to existing schemes based on a vacuum and single-photon encoding, the polarization encoding in our approach is compatible with quantum information protocols that remove photon loss through post-selection. Our findings offer a powerful quantum state engineering approach for photonic quantum information technologies.
Construction and Effect Analysis of College Students’ Physical Education Teaching Mode Based on Data Mining Algorithm

With the wide application of computer technology, communication technology, network technology, and multimedia technology in modern education, teaching methods tend to be diversified and scientific. Based on the DM (data mining) algorithm, this paper implements a sports achievement management system. Through this system, the efficiency of inputting and counting students’ achievements can be improved, and teachers can be freed from the complicated achievement management. Aiming at the problem that SVM (Support Vector Machine) is slow in training large sample sets in DM classification, a DM classification algorithm based on improved SVM, PSO_SVM, is proposed, which applies the density of adjacent samples to the design of membership function to reduce the influence of noise points on classification. The results show that the training time of this algorithm is increased by 54.26 s and 55.69 s compared with SVM and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means, respectively, and the accuracy is increased by 35.62%. The results obtained by the DM algorithm will be helpful for teachers to diagnose teaching problems and construct PET (physical education teaching) model for college students with characteristics.
Crafting an audience: UX writing, user stylization, and the symbolic violence of little texts

To date, little attention has been paid to how producers of digital media complicate notions of participation and audience in digital media. Taking the work of user experience (UX) writers as a case study, I offer an analytic framework for approaching the conceptual challenges that come with this. The empirical focus of my analysis is an emblematic example of UX writers’ work: the ubiquitous microcopy (i.e. user interface texts) produced for cookie consent notices. Orienting to Jones’s (2020b) work on digital and algorithmic pragmatics, I demonstrate how these “little texts” act in ways which are both agentful and influential. More than a matter of implicit audience design (Bell, 1984), UX writers actively use the affordances of software interfaces for inventing, stylizing, and crafting an audience. It is through this strategic stylizing of users that UX writers produce what Bakhtin (1986) calls a superaddressee. By positioning users in particular ways – and in particular moments – these little, seemingly inconsequential texts of digital media can thus effectively exercise a form of symbolic violence.
A Hardware/Software Co-Design Vision for Deep Learning at the Edge

The growing popularity of edgeAI requires novel solutions to support the deployment of compute-intense algorithms in embedded devices. In this article, we advocate for a holistic approach, where application-level transformations are jointly conceived with dedicated hardware platforms. We embody such a stance in a strategy that employs ensemble-based algorithmic transformations to increase robustness and accuracy in convolutional neural networks, enabling the aggressive quantization of weights and activations. Opportunities offered by algorithmic optimizations are then harnessed in domain-specific hardware solutions, such as the use of multiple ultra-low-power processing cores, the provision of shared acceleration resources, the presence of independently power-managed memory banks, and voltage scaling to ultra-low levels, greatly reducing (up to 60% in our experiments) energy requirements. Furthermore, we show that aggressive quantization schemes can be leveraged to perform efficient computations directly in memory banks, adopting in-memory computing solutions. We showcase that the combination of parallel in-memory execution and aggressive quantization leads to more than 70% energy and latency gains compared to baseline implementations.
An End-to-End Named Entity Recognition Platform for Vietnamese Real Estate Advertisement Posts and Analytical Applications

The volume and complexity of publicly available real estate data have been snowballing. As a result, information extraction and processing have become increasingly challenging and essential for many PropTech (Property Technology) companies worldwide. The challenges are even more pronounced with languages other than English, such as Vietnamese, where few studies in this field have taken place. This paper presents an end-to-end framework for automatically collecting real estate advertisement posts from different data sources, extracting useful information, and storing computed data into proper data warehouses and data marts for the Vietnamese advertisement posts in real estate. After that, one can serve aggregated data for other descriptive and predictive analytics. We combine two models for constructing the most appropriate extraction step: Noise Filtering and Named Entity Recognition (NER). These models can help process initial input data and extract all helpful information. The experiment results show that using PhoBERT<sub><i>large</i></sub> can achieve the best performance compared to other approaches. Furthermore, we can obtain the corresponding F1 scores of the Noise filtering module and the NER module as 0.8697 and 0.8996, respectively. Finally, we utilize Superset for implementing analytic dashboards to visualize the predicted results and serve for further analysis and management processes.
On How to Push Efficient Medical Semantic Segmentation to the Edge: the SENECA approach

Semantic segmentation is the process of assigning each input image pixel a value representing a class, and it enables the clustering of pixels into object instances. It is a highly employed computer vision task in various fields such as autonomous driving and medical image analysis. In particular, in medical practice, semantic segmentation identifies different regions of interest within an image, like different organs or anomalies such as tumors. Fully Convolutional Networks (FCNs) have been employed to solve semantic segmentation in different fields and found their way in the medical one. In this context, the low contrast among semantically different areas, the constraint related to energy consumption, and computation resource availability increase the complexity and limit their adoption in daily practice. Based on these considerations, we propose SENECA to bring medical semantic segmentation to the edge with high energy efficiency and low segmentation time while preserving the accuracy. We reached a throughput of 335.4 ± 0.34 frames per second on the FPGA, 4.65× better than its GPU counterpart, with a global dice score of 93.04% ± 0.07 and an improvement in terms of energy efficiency with respect to the GPU of 12.7×.
A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple res-olutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple met-ric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/Oand memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues; correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.
Essentials of Parallel Graph Analytics

We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulk-synchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.
Sınıflar Arası Kenar Payını Genişletmek İçin Yeni Bir Örnek Seçim Algoritması

As discarding superfluous instances in data sets shortens the learning process, it also increases learning performance because of eliminating noisy data. Instance selection methods are commonly utilized to undertake the abovementioned tasks. In this paper, we propose a new supervised instance selection algorithm called Border Instances Reduction using Classes Handily (BIRCH). BIRCH considers k-nearest neighbors of each instance and selects instances that have neighbors from the only same class, namely, but not having neighbors from the different classes. It has been compared with one traditional and four state-of-the-art instance selection algorithms by using fifteen data sets from various domains. The empirical results show BIRCH well delivers the trade-off between accuracy rate and reduction rate by tuning the number of neighbors. Furthermore, the proposed method guarantees to yield a high classification accuracy. The source code of the proposed algorithm can be found in https://github.com/fatihaydin1/BIRCH.
A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal

The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it. Thus, increasing the robustness of the stego-system by compressing the steganocoded audio signal subject to the preservation of the integrity of text information, taking into account the features of the psychophysiological model of sound perception, is the main objective of this scientific research. The task of this scientific research is effectively solved using a multilevel discrete wavelet transform using adaptive block normalization of text information with subsequent recursive embedding in the low-frequency component of the audio signal and further scalar product of the obtained coefficients with the Daubechies wavelet filters. The results of the obtained experimental studies confirm the hypothesis, namely that it is proposed to use recursive embedding in the low-frequency component (approximating wavelet coefficients) followed by their scalar product with wavelet filters at each level of the wavelet decomposition, which will increase the average power of hidden data. It should be noted that upon analyzing the existing method, which is based on embedding text information in the high-frequency component (detailed wavelet coefficients), at the last level of the wavelet decomposition, we obtained the limit CR = 6, and in the developed, CR = 20, with full integrity of the text information in both cases. Therefore, the resistance of the stego-system is increased by 3.3 times to deliberate or passive compression of the audio signal in order to distort the embedded text information.
Fast and Robust Visual Tracking with Few-Iteration Meta-Learning

Visual object tracking has been a major research topic in the field of computer vision for many years. Object tracking aims to identify and localize objects of interest in subsequent frames, given the bounding box of the first frame. In addition, the object-tracking algorithms are also required to have robustness and real-time performance. These requirements create some unique challenges, which can easily become overfitting if given a very small training dataset of objects during offline training. On the other hand, if there are too many iterations in the model-optimization process during offline training or in the model-update process during online tracking, it will cause the problem of poor real-time performance. We address these problems by introducing a meta-learning method based on fast optimization. Our proposed tracking architecture mainly contains two parts, one is the base learner and the other is the meta learner. The base learner is primarily a target and background classifier, in addition, there is an object bounding box prediction regression network. The primary goal of a meta learner based on the transformer is to learn the representations used by the classifier. The accuracy of our proposed algorithm on OTB2015 and LaSOT is 0.930 and 0.688, respectively. Moreover, it performs well on VOT2018 and GOT-10k datasets. Combined with the comparative experiments on real-time performance, our algorithm is fast and robust.
Educational needs of the general secondary education institution heads as an indicator of the development of professional competence

The article provides an analytical review of modern studies of the educational needs of heads (leaders) of educational institutions, in particular, institutions of general secondary education, in the world and in Ukraine.It is substantiated that educational needs can serve as a kind of indicator of the development of professional competence of an educational
Building an Arabic Dialectal Diagnostic Dataset for Healthcare

Accurate diagnosis of patient conditions becomes challenging for medical practitioners in urban metropolitan cities.A variety of languages and spoken dialects impedes the diagnosis achieved through the exploratory journey a medical practitioner and patient go through.Natural language processing has been used in well-known applications, such as Google Translate, as a solution to reduce language barriers.Languages typically encountered in these applications provide the most commonly known, used or standardized dialect.The Arabic language can benefit from the common dialect, which is available in such applications.However, given the diversity of dialects in Arabic in the healthcare domain, there is a risk associated with incorrect interpretation of a dialect, which can impact the diagnosis or treatment of patients.Arabic language dialect corpuses published in recent research work can be applied to rule-based natural language applications.Our study aims to develop an approach to support medical practitioners by ensuring that the diagnosis is not impeded based on the misinterpretation of patient responses.Our initial approach reported in this work adopts the methods used by practitioners in the diagnosis carried out within the scope of the Emirati and Egyptian Arabic dialects.In this paper, we develop and provide a public Arabic Dialect Dataset (ADD), which is a corpus of audio samples related to healthcare.In order to train machine learning models, the dataset development is designed with multi-class labelling.Our work indicates that there is a clear risk of bias in datasets, which may come about when a large number of classes do not have enough training samples.Our crowd sourcing solution presented in this work may be an approach to overcome the sourcing of audio samples.Models trained with this dataset may be used to support the diagnosis made by medical practitioners.