Reviewerly/exHarmony huggingface dataset:
Data Compression with Private Local Decodability

Classical compression schemes suggest that message symbols cannot be privately decoded; if a string X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> is encoded into a codeword C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nR</sup> at a non-trivial rate R, then the decoding of an individual symbol X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> reveals information about the rest of the symbols X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> \X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> .While this holds for virtually all lossless compression schemes, it is shown that this need not be the case. This paper proposes a lossless compression scheme for bit strings with the following properties. For any sufficiently small p > 0, it encodes each length-n bit string of Hamming weight at most np into a binary codeword of length $O\left( {np{{\log }^2}\frac{1}{p}} \right)$ such that the subset of compressed bits that need to be probed in order to decode a particular message bit reveals no additional information about the other message bits.
Randomized Decoding of Linearized Reed–Solomon Codes Beyond the Unique Decoding Radius

In this paper we address the problem of decoding linearized Reed–Solomon (LRS) codes beyond their unique decoding radius. We analyze the complexity in order to evaluate if the considered problem is of cryptographic relevance, i.e., can be used to design cryptosystems that are computationally hard to break. We show that our proposed algorithm improves over other generic algorithms that do not take into account the underlying code structure.
Experimental Comparison of Graph Edit Distance Computation Methods

Graph edit distance (GED) is a fundamental graph similarity metric. GED computation is NP-hard [10], and exact GED computation is only feasible for small graphs. Therefore, many methods of approximate GED computation have been proposed in the literature. In this paper, we select the five representative GED approximation methods and compare their performance on two real-world datasets. We observe that non-heuristic algorithms such as LSa [1] are fast and accurate in computing true GED for small graphs, and heuristic algorithms such as GENN [4] are very effective in computing the estimated path cost. This effort helps us pinpoint suitable algorithms for different applications.
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification

Cross-resolution person re-identification (CRReID) is a challenging and practical problem that involves matching low-resolution (LR) query identity images against high-resolution (HR) gallery images. Query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. State-of-the-art solutions for CRReID either learn a resolution-invariant representation or adopt a super-resolution (SR) module to recover the missing information from the LR query. In this paper, we propose an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric that is adaptive to the resolution of a query image. We realize this idea by learning resolution-adaptive representations for cross-resolution comparison. We propose two resolution-adaptive mechanisms to achieve this. The first mechanism encodes the resolution specifics into different subvectors in the penultimate layer of the deep neural network, creating a varying-length representation. To better extract resolution-dependent information, we further propose to learn resolution-adaptive masks for intermediate residual feature blocks. A novel progressive learning strategy is proposed to train those masks properly. These two mechanisms are combined to boost the performance of CRReID. Experimental results show that the proposed method outperforms existing approaches and achieves state-of-the-art performance on multiple CRReID benchmarks.
Multimode nonlinear integrated optics for quantum and machine learning-assisted signal processing

Integrated optics offer advanced platforms for the development of new classical and quantum telecom-based applications. In this work, we leverage on-chip photonic devices that support multimode interaction via four-wave mixing, to develop novel, highly efficient, and secure smart technologies for telecom signal processing.
Opacity Enforcement via Greedy Privately-and-Publicly Known Insertion Functions

This paper investigates the enforcement of (current-state) opacity, an important information-flow security property, via insertion functions. An insertion function is an obfuscation mechanism that inserts fictitious events to the outputs in order to confuse the outside observer (intruder) such that the secret of the system is not revealed. In some situations, the secret may be revealed when the insertion mechanism is (or becomes) publicly known. This leads to the problem of synthesizing private-and-public enforcing (PP-enforcing) insertion functions in the sense that opacity is still enforced even when the mechanism is discovered or published by the designer. Existing works that have investigated this synthesis problem are either only sound or have limited applicability as we show in this work. For this reason, and more importantly, to better solve the synthesis problem, a new approach is proposed upon an improved greedy criterion. We show that the proposed algorithm is both sound and complete, and can be used to completely solve the synthesis problem for the PP-enforcing insertion function. With slight modifications of our algorithm, infinite-step opacity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ K$</tex-math></inline-formula> -step opacity can also be enforced under publicly-known insertion mechanisms.
Supplemental Material: Orogenic cyclicity and episodic tectono-magmatic processes in the formation of the Paleozoic northern Yili magmatic arc, Central Asian Orogenic Belt

&lt;p&gt;Analytical and data filtering methods. Figures S1-S3 and Tables S1-S11.&lt;/p&gt;
Social media computing: tools and deployment platform for smart contract

With the development of the Internet of Things, virtual reality, artificial intelligence, and blockchain technologies, the world is currently experiencing a rapid digital transition. In order to give their customers an immersive experience, social media platforms are also developing through incorporating the internet of things and virtual reality. Machine learning (ML) tools employ the data that users generate while using these technologies to make decisions. These choices support geotagging photographs, proposing music to users, boosting photobook authoring through extensive multimedia analysis, and analyzing user preferences in games and brands. The users could be put in danger as a result of this data sharing. We will talk about how blockchain technology can be utilized in social media computing. We will also talk about the decentralized social network&apos;s blockchain-based framework. We will talk about the hands-on tools used for developing blockchain-based social media platforms towards the conclusion.
A sentiment analysis approach for travel-related Chinese online review content

Using technology for sentiment analysis in the travel industry can extract valuable insights from customer reviews. It can assist businesses in gaining a deeper understanding of their consumers’ emotional tendencies and enhance their services’ caliber. However, travel-related online reviews are rife with colloquialisms, sparse feature dimensions, metaphors, and sarcasm. As a result, traditional semantic representations of word vectors are inaccurate, and single neural network models do not take into account multiple associative features. To address the above issues, we introduce a dual-channel algorithm that integrates convolutional neural networks (CNN) and bi-directional long and short-term memory (BiLSTM) with an attention mechanism (DC-CBLA). First, the model utilizes the pre-trained BERT, a transformer-based model, to extract a dynamic vector representation for each word that corresponds to the current contextual representation. This process enhances the accuracy of the vector semantic representation. Then, BiLSTM is used to capture the global contextual sequence features of the travel text, while CNN is used to capture the richer local semantic information. A hybrid feature network combining CNN and BiLSTM can improve the model’s representation ability. Additionally, the BiLSTM output is feature-weighted using the attention mechanism to enhance the learning of its fundamental features and lessen the influence of noise features on the outcomes. Finally, the Softmax function is used to classify the dual-channel fused features. We conducted an experimental evaluation of two data sets: tourist attractions and tourist hotels. The accuracy of the DC-CBLA model is 95.23% and 89.46%, and that of the F1-score is 97.05% and 93.86%, respectively. The experimental results demonstrate that our proposed DC-CBLA model outperforms other baseline models.
Comparable and Parallel Corpora for Machine Translation

With the advent of Neural Machine Translation (NMT), a breakthrough has been achieved with regard to translation quality when compared to previous approaches such as rule-based, example-based, and statistical machine translation (MT). NMT systems tend to be considered as black boxes and it is not easy to predict their behavior.
Research on course recommendation technology based on behavior log of online education platform

To address the user cold start problem of behavior log recommendation algorithm, Focor course recommendation algorithm based on course selection probability calculation is proposed. Focor uses genetic algorithm to filter the optimal feature subset from the feature set composed of course selection dataset, and uses LightGBM to train a binary classification model on the optimal feature subset for whether to select a course or not, and makes course recommendation based on the course selection probability output from the model. The course recommendation is based on the probability of course selection output from the model. The experimental results show that the proposed algorithm, Focor course recommendation algorithm, is compared with LightGBM, XGBoost, decision tree, random forest, logistic regression and other algorithms on real data sets for experiments and performance evaluation, and achieves better performance in terms of F1 scores.
EXPLORING THE VIABILITY OF SERVICE ROBOTS IN PERFORMING HUMAN AESTHETIC LABOUR IN THE HOSPITALITY INDUSTRY

This literature review seeks to investigate the value of human aesthetic labour in light of the growing deployment of robotic service workers. As automation continues to advance and replace human workers in a variety of industries, including service industries such as retail, hospitality, and entertainment, the role, value and the significance of aesthetics and the human touch grows. Due to the contemporary nature of the topic, researchers combed databases like Scopus, Web of Science, and Google Scholar for relevant articles using terms like "human aesthetic labour," "robotic service workers," and "service industry." This review will investigate the extent to which the rise of robotic service workers has altered the perception and value of human aesthetic labour. The findings of this research review will contribute to the ongoing discussion on the future of work in the service industry and provide businesses with insights on the significance of preserving human aesthetic labour and its impact on customer and guest experience as well as business performance.
Student’s Perception on The Use of Code-Crossing in The Online Classroom Interaction (Zoom Cloud Meetings)

Abstract. This research aims to explore the phenomena of code-crossing in the teaching of English as a foreign language. It cannot be denied that in the classroom interaction, there are many aspects influencing the success of the communication between the lecturers/teachers and the students. One of the interesting aspects is the influence of social status differences which lead to the occurrence of code-crossing in the class. For that purpose, the study in this paper is directed to explore the perspectives on the use of code-crossing in the class, focusing on the online classroom interactions via Zoom Cloud Meetings. This research applied a qualitative research design taking one English class and their students at Universitas Negeri Makassar as the subject. The data of this paper were collected by employing a semi-structured interview by focusing on the students taken purposively among 40 students of the English class. The interview was also conducted through the online communication (WhatsApp chats). The data were analyzed descriptively by using qualitative model analysis (Miles, et al 2014). The result of the research shows that from the student’s point of view, most students did code-crossing in the online classes because they wanted to respect and speak formally to the older people (lecturers and seniors). In addition, for some students interviewed by the researchers, they thought that the use of code-crossing could show politeness. Findings from this study are worthy of reading for English language teaching practitioners in their effort to create effective classroom interaction both in the face-to-face interaction and online interaction. Keywords: Code-Crossing; Language And Power; Social Status; Students’ Perception; Online Classes
Object Recognition with Layer Slicing of Point Cloud

Object recognition is a significant research area within computer vision. While traditional approaches have primarily focused on 2D image-based methods, these approaches often lack range information necessary for accurate surface recognition. To address this limitation, this paper proposes a point cloud-based object recognition approach. The method involves slicing the point cloud of an object into multiple point images, which are then merged into a single composite image for recognition. The classification task is performed using a convolutional neural network. In our simulation utilizing the KITTI dataset, objects are classified into three distinct categories: vehicles, pedestrians, and street clutter. The experimental results demonstrate the effectiveness of the proposed method, with impressive accuracy rates achieved. Specifically, the accuracy for vehicle recognition reaches 93.75%, while pedestrian recognition achieves 82.35%. These findings highlight the strong performance of the proposed approach in object recognition tasks, validating its potential for practical applications.
Other Applications of Comparable Corpora

This section concerns applications of comparable corpora beyond pure machine translation. It has been argued [1, 2] that downstream applications such as cross-lingual document classification, information retrieval or natural language inference, apart from proving the practical utility of NLP methods
Multi-Timescale Actor-Critic Learning for Computing Resource Management With Semi-Markov Renewal Process Mobility

This paper studies artificial intelligence (AI) aided communication and computing resource allocation in a vehicular network that supports blockchain-enabled video streaming. Our study aims to improve the operating efficiency and to maximize the transcoding rewards for blockchain based vehicular networks. Our resource allocation policy considers the vehicular mobility, which is modelled with a highly-realistic Semi-Markov renewal process, as well as the real-time video service delay constraints. We propose a multi-timescale actor-critic-reinforcement learning framework to tackle these grand challenges. We also develop a prediction model for the vehicular mobility by using analysis and classical machine learning, which alleviates the heavy signaling and computation overheads due to the vehicular movement. A mobility-aware reward estimation for the large timescale model is then proposed to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the developed theoretical findings in this paper and the significant performance gains due to our proposed multi-timescale framework.
Image-to-Image Translation With Disentangled Latent Vectors for Face Editing

We propose an image-to-image translation framework for facial attribute editing with disentangled interpretable latent directions. Facial attribute editing task faces the challenges of targeted attribute editing with controllable strength and disentanglement in the representations of attributes to preserve the other attributes during edits. For this goal, inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others. We train these directions with orthogonality constraints and disentanglement losses. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections. We extensively compare with previous image translation algorithms and editing with pretrained GAN works. Our extensive experiments show that our method significantly improves over the state-of-the-arts.
PENGARUH MODEL PEMBELAJARAN TWO STAY TWO STRAY (TSTS) TERHADAP HASIL BELAJAR SISWA MATA PELAJARAN IPS KELAS IV SD NEGERI KADU SEMPUR

ABSTACT&#x0D; This study aims to determine the effect of the two stay two stray (TSTS) learning model on the learning outcomes of grade IV social studies subjects at SDN Kadu Sempur. This research is quantitative research using quasi-experimental research methods with pretest-postest. The population subjects in this study were grade IV students at SD Negeri Kadu Sempur by taking samples of two classes as many as 32 class IV B students as a control class and 32 grade IV A students as an experimental class. So it can be concluded "There is an influence of the two stay two stray learning model on student learning outcomes in grade IV social studies subjects of SDN Kadu Sampur". The results of the hypothesis testing study using paired sample t-test, for testing the posttest hypothesis from the results of the t-test obtained Tcalculate = 5.01. And it can be known the value of ttable 1.66. Based on the hypothesis that has been formulated earlier, it can be known that the calculated value of ttable &gt; then 5.01 &gt; 1.66. Thus H1 is accepted and H0 is rejected.&#x0D; Keywords: two stay two stray learning model, student learning outcome
The Gap Between UX Literacy and UX Practices in Agile-UX Settings: A Case Study

Integration of agile and user experience (UX) remains a challenge despite being a major research interest for both agile software development (ASD) and UX stakeholders. Typically, ASD stakeholders’ primary focus is delivering working software, whereas UX stakeholders focus on designing systems that meet user needs. These differences lead to friction between developers and designers. In this paper, we focus on ASD stakeholders working in an agile-UX setting and explore the gap between their UX literacy and UX practices. We adopted a case study approach involving ASD stakeholders from two organisations working in agile-UX settings. We studied both organisations for over a year, starting at the end of 2021. Specifically, we compared data about their UX literacy collected by questionnaire and semi-structured interview, to data about their UX practices collected by observation. We administered the questionnaire and conducted the semi-structured interviews twice, in rounds six months apart. We used participant observation in projects with which we were involved as UX researchers and designers. Our findings show that ASD stakeholders’ UX practices do not match their yet acceptable level of UX literacy. For example, ASD stakeholders still engage in premature development activities, although they understand the problems associated with late design changes. We encourage UX practitioners and researchers to conduct UX maturity assessments and address identified disparities, as we believe this will reduce the friction between different stakeholder groups and facilitate the integration of ASD and UX.
Tecnologías predominantes en la infraestructura educativa en pandemia y pos pandemia

The COVID-19 pandemic has had a significant impact on all aspects of daily life, and the education sector is no exception.Faced with the need to maintain continuity of learning during times of lockdown and social distancing, educational institutions have turned to various technologies to adapt to this new reality.As a result, communication network infrastructure has become the backbone of universities and other educational institutions, making it a crucial technology for addressing the saturation that manifests in transmission media.That is why the technologies that have predominated in educational infrastructure during the pandemic and post-pandemic focus on providing greater robustness and security to the network by expanding traditional services, such as the Internet, virtual classrooms, and virtual libraries.At the same time, they are implementing cutting-edge technologies, such as education and research networks based on SDN, which offer increased security, as well as digital preservation services aimed at safeguarding the information created by their teachers, researchers, administrators, and students.
La tecnología como herramienta en los procesos educativos

Technology-based educational processes have gained great importance in recent years, due to the growing need to provide updated training in tune with the demands of the working world.The purpose of this review was to describe the trends in technologybased educational processes based on a review of the literature, focusing on four fundamental aspects: the first focuses on the role of technology in education, the second on how it is inserted in the aspect of the curriculum, the third on the technological tools that are revolutionizing education, and the fourth on the educational processes based on technology.A systematic review of the existing literature was carried out.Several academic databases such as ScienceDirect, JSTOR and Google Scholar were used, and articles related to the topic published in the last 10 years in Spanish and English were searched.The literature shows a wide spectrum of immersion from ICTs in education has an infinite number of resources and the challenges increasingly involve a shortening of the digital divide that allows motivating a natural integration.Technology continues to be a relevant topic in education and researchers continue to explore how it can be used to improve the quality of training and learning.