Reviewerly/exHarmony huggingface dataset:
MKPM: Multi keyword-pair matching for natural language sentences

Abstract Sentence matching is widely used in various natural language tasks, such as natural language inference, paraphrase identification and question answering. For these tasks, we need to understand the logical and semantic relationship between two sentences. Most current methods use all information within a sentence to build a model and hence determine its relationship to another sentence. However, the information contained in some sentences may cause redundancy or introduce noise, impeding the performance of the model. Therefore, we propose a sentence matching method based on multi keyword-pair matching (MKPM), which uses keyword pairs in two sentences to represent the semantic relationship between them, avoiding the interference of redundancy and noise. Specifically, we first propose a sentence-pair-based attention mechanism sp-attention to select the most important word pair from the two sentences as a keyword pair, and then propose a Bi-task architecture to model the semantic information of these keyword pairs. The Bi-task architecture is as follows: 1. In order to understand the semantic relationship at the word level between two sentences, we design a word-pair task (WP-Task), which uses these keyword pairs to complete sentence matching independently. 2. We design a sentence-pair task (SP-Task) to understand the sentence level semantic relationship between the two sentences by sentence denoising. Through the integration of the two tasks, our model can understand sentences more accurately from the two granularities of word and sentence. Experimental results show that our model can achieve state-of-the-art performance in several tasks. Our source code is publicly available 1 .
An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved by the proposed MVKNN algorithm compared to the well-known machine learning algorithms (KNN, support vector machine, decision tree, and naive bayes) in the case of multi-view data. The results also show that our method outperforms the state-of-the-art multi-view learning methods in terms of accuracy.
Practice-Centered Education in Higher School

The development of practice-centered education is attributable to the needs of widely understood social practices, framed as a demand of the economic sphere in highly qualified personnel with certain skills and qualification profile. Today, the employers expect from educational organizations more than just graduates familiar with industry and having a good theoretical background, but the employees who are able to perform various tasks relating to production, design or organizational issues from the first days of entry on duty. Therefore, the educational process in the engineering university should be organized to ensure the conditions for students to perform certain types of work (professional tryouts) related to future professional activities. It is necessary to develop and build on practical skills and competences in line with the educational program. The student's professionally oriented activity is viewed as a type of educational and work activity aimed at the professional development of a person achieved though gaining experience in using means and methods of professional activity in the social environment. The basis of this educational process is practice-centered education. The article examines the existing models of practice-centered training (PCL) such as dual education, sandwich courses, social partnership between university and enterprise, learning through volunteering, project activity, analyzes their merits and possible risks.
21<sup>st</sup> Century Skills for Sustainable Education: Prediction Level of Teachers’ Information Literacy Skills on Their Digital Literacy Skills

Abstract The current digital environment offers a unique access opportunity to information, tools, and sources for teachers to educate and teach using digital. It is vital for teachers, who have important roles to raise future generations, to have information literacy and digital literacy skills. It is essential that teachers who educate individuals raised with a technology called the Z generation to have both, information literacy and digital literacy skills. Within this context, the present study aims to define the relationship between information literacy skills and digital literacy skills of teachers in the age of digitalization, in which the transformation has been experienced in the education sector. A cross-sectional survey was used within this study, which was prepared according to the quantitative research paradigm. The research includes random and disproportionate cluster sampling methods and focuses on teachers who worked in a preschool, or in primary, secondary and high schools in the Eastern and Southeast Anatolia Regions in the 2018–2019 academic year. Structural equation modeling was used to determine to what extent teachers’ information literacy skills predict their digital literacy skills. The relationship between the teachers’ information literacy and digital literacy skills was calculated using Pearson product-moment correlation coefficient, and the frequency, percentage, arithmetic mean, and standard deviation values of the data collected from teachers through scales were calculated. As a result of the analysis, one of the results is that the teachers’ information literacy skills affect their digital literacy skills positively.
Applications of Machine Learning in Digital Forensics

Digital forensics (DF) has become a substantial process to perform in depth investigations. But due to the digitalization, the potential Data volumes are increasing and hence it has become difficult to analyze them. Machine Learning (ML) is a panacea in this regard. It not only facilitates the analysis process but also yields accurate results. Therefore, with a focus on DF, this paper surveys a wide range of publications mentioning ML based techniques that can be used to ease the process of DF principally in the field of malware, network forensics, image/video forensics, and mobile/memory forensics. The results of the review show that ML is a fast and reliable procedure and needs to be explored more actively, particularly in DF field. The results are also used to develop a conceptual framework for a general procedure of ML based Digital Forensics.
Privacy-Preserving Representation Learning on Graphs

Learning with graphs has attracted significant attention recently. Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-related tasks such as node classification, link prediction, etc. However, we observe that these methods could leak serious private information. For instance, one can accurately infer the links (or node identity) in a graph from a node classifier (or link predictor) trained on the learnt node representations by existing methods. To address the issue, we propose a privacy-preserving representation learning framework on graphs from the mutual information perspective. Specifically, our framework includes a primary learning task and a privacy protection task, and we consider node classification and link prediction as the two tasks of interest. Our goal is to learn node representations such that they can be used to achieve high performance for the primary learning task, while obtaining performance for the privacy protection task close to random guessing. We formally formulate our goal via mutual information objectives. However, it is intractable to compute mutual information in practice. Then, we derive tractable variational bounds for the mutual information terms, where each bound can be parameterized via a neural network. Next, we train these parameterized neural networks to approximate the true mutual information and learn privacy-preserving node representations. We finally evaluate our framework on various graph datasets.
On the Transformer Growth for Progressive BERT Training

Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressivelystart from an inferior but low-cost model and gradually grow the model to increase the computational complexity.Our objective is to advance the understanding of Transformer growth and discover principles that guide progressive training.First, we find that similar to network architecture search, Transformer growth also favors compound scaling.Specifically, while existing methods only conduct network growth in a single dimension, we observe that it is beneficial to use compound growth operators and balance multiple dimensions (e.g., depth, width, and input length of the model).Moreover, we explore alternative growth operators in each dimension via controlled comparison to give operator selection practical guidance.In light of our analyses, the proposed method CompoundGrow speeds up BERT pretraining by 73.6% and 82.2% for the base and large models respectively, while achieving comparable performances 1 .
Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.
KnitKit

In this work, we introduce KnitKit , a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns.
Perturbative Quantum Simulation

Approximation based on perturbation theory is the foundation for most of the quantitative predictions of quantum mechanics, whether in quantum many-body physics, chemistry, quantum field theory, or other domains. Quantum computing provides an alternative to the perturbation paradigm, yet state-of-the-art quantum processors with tens of noisy qubits are of limited practical utility. Here, we introduce perturbative quantum simulation, which combines the complementary strengths of the two approaches, enabling the solution of large practical quantum problems using limited noisy intermediate-scale quantum hardware. The use of a quantum processor eliminates the need to identify a solvable unperturbed Hamiltonian, while the introduction of perturbative coupling permits the quantum processor to simulate systems larger than the available number of physical qubits. We present an explicit perturbative expansion that mimics the Dyson series expansion and involves only local unitary operations, and show its optimality over other expansions under certain conditions. We numerically benchmark the method for interacting bosons, fermions, and quantum spins in different topologies, and study different physical phenomena, such as information propagation, charge-spin separation, and magnetism, on systems of up to 48 qubits only using an 8+1 qubit quantum hardware. We demonstrate our scheme on the IBM quantum cloud, verifying its noise robustness and illustrating its potential for benchmarking large quantum processors with smaller ones.
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities, or domains.In pursuit of these goals, we introduce the RAD-DLE 1 benchmark 2 , a collection of corpora and tools for evaluating the performance of models across a diverse set of domains.By including tasks with limited training data, RADDLE is designed to favor and encourage models with a strong generalization ability.RADDLE also includes a diagnostic checklist that facilitates detailed robustness analysis in aspects such as language variations, speech errors, unseen entities, and out-of-domain utterances.We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain.Adversarial training is also proposed to improve model robustness against noisy inputs.Overall, existing models are less than satisfactory in robustness evaluation, which suggests opportunities for future improvement.
Efficient Stepping Algorithms and Implementations for Parallel Shortest Paths

The single-source shortest-path (SSSP) problem is a notoriously hard problem in the parallel context. In practice, the Δ-stepping algorithm of Meyer and Sanders has been widely adopted. However, Δ-stepping has no known worst-case bounds for general graphs, and the performance highly relies on the parameter Δ, which requires exhaustive tuning. The parallel SSSP algorithms with provable bounds, such as Radius-stepping, either have no implementations available or are much slower than Δ-stepping in practice.
Hidden symmetry in the biased Dicke model

The symmetry operators generating the hidden $\mathbb{Z}_2$ symmetry of the asymmetric quantum Rabi model (AQRM) at bias $\epsilon \in \frac{1}{2}\mathbb{Z}$ have recently been constructed by V. V. Mangazeev et al. [J. Phys. A: Math. Theor. 54 12LT01 (2021)]. We start with this result to determine symmetry operators for the $N$-qubit generalisation of the AQRM, also known as the biased Dicke model, at special biases. We also prove for general $N$ that the symmetry operators, which commute with the Hamiltonian of the biased Dicke model, generate a $\mathbb{Z}_2$ symmetry.
Language Embeddings for Typology and Cross-lingual Transfer Learning

Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data.We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data.We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference 1 .
Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning

With the development of science and technology, inspection robots have attracted more and more attention, and research on the automatic reading of pointer instruments through inspection robots has become particularly valuable. Aiming at the problems of uneven illumination, complex dial background and damping fluid interference of the collected instrument images, this paper proposes a dial gauge reading algorithm based on coordinate positioning. First, the multi-scale retinex with color restoration (MSRCR) is applied to improve the uneven illumination of the image. Second, a circle detection algorithm based on the arc-support line segment is proposed to detect the disc to obtain the coordinate of the center and radius of the circle. Then, a pointerless template is used to obtain the pointer, and the concentric circle algorithm is applied to locate the refined pointer. Finally, the automatic reading is calculated using the relative position of the pointer and the zero scale. The experimental results prove that the proposed algorithm can accurately locate the center of the circle and the pointer and obtain readings automatically.
User Behaviour Analysis of Volumetric Video in Augmented Reality

Augmented reality (AR) is getting popular, and among other content creation techniques, volumetric video allows to bring dynamic real world content as captured by cameras into such applications. To develop efficient algorithms for compression and transmission of the volumetric media, it is important to understand how users will consume this new form of dynamic 3D content. In this paper, we analyse the user behaviour for the volumetric video consumption in AR. In particular, we study the distribution of users' viewpoints, relative locations, and average distances from the content. For this purpose, we built an Android AR application using the volumetric video and conducted a user study remotely. The results show that users spent most of their time looking at the frontal part of the volumetric video, and this indicates the importance of the face in visual attention. The collected user behaviour data are made public to support further research.
TransSum: Translating Aspect and Sentiment Embeddings for Self-Supervised Opinion Summarization

In this paper, we propose a novel selfsupervised opinion summarization framework TransSum, which models opinion summaries as translations operating on the low-dimensional aspect and sentiment embedding spaces.Specifically, we propose two contrastive objectives to learn the crucial aspect and sentiment embeddings of reviews, by taking advantage of the intra-and inter-group invariances that have not been considered in previous studies.Furthermore, these embeddings can be used to reduce opinion redundancy and construct highly relevant reviews-summary pairs to train a supervised multi-input opinion summarization model.Experimental results on three different domains show that TransSum outperforms several strong baselines in generating informative, relevant and low-redundant summaries, unveiling the effectiveness of our approach.
A Survey on Neural Speech Synthesis

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning communities and has broad applications in the industry. As the development of deep learning and artificial intelligence, neural network-based TTS has significantly improved the quality of synthesized speech in recent years. In this paper, we conduct a comprehensive survey on neural TTS, aiming to provide a good understanding of current research and future trends. We focus on the key components in neural TTS, including text analysis, acoustic models and vocoders, and several advanced topics, including fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS, etc. We further summarize resources related to TTS (e.g., datasets, opensource implementations) and discuss future research directions. This survey can serve both academic researchers and industry practitioners working on TTS.
Deep Learning Based On Different Methods For Text Summary: A Survey

Abstract—in today's rapidly growing information age, text summary has become a critical and important instrument for help understanding text information. it is really hard for human beings to physically summarize huge textual documents also there is an abundance of text content available online. text summarization is an active research field that works on compressing large pieces of text into smaller texts that preserve relevant information. text summary classified as extractive or abstractive. methods of extractive summarization working by deciding important text sentences and choosing them as a summary. that method based only on sentences from the source text. methods of abstractive summarization aim to paraphrase important information in a new form like that of humans. text summary can be achieved using different deep learning techniques, such as: fuzzy logic, Convolutional Neural (CNN), transformers, neural network, reinforcement learning, etc. in the past three years, the research trend in text summarization has also undergone a slight change, where new trends have appeared that are trends that lead to enhancement, how to improve the efficiency of text summarization to obtain high accuracy. we have made several attempts in this paper to discuss the various techniques used on the basis of deep learning for text summary in these years and observe the new trends in the field of deep learning.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources.Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model.To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.CHMM enhances the classic hidden Markov model with the contextual representation power of pretrained language models.Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations.We further refine CHMM with an alternate-training approach (CHMM-ALT).It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return.Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.