Reviewerly/exHarmony huggingface dataset:
Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization

In this paper, an inexact proximal-point penalty method is studied for constrained optimization problems, where the objective function is non-convex, and the constraint functions can also be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-based method. The computational complexity of the proposed method is analyzed separately for the cases of convex constraint and non-convex constraint. For both cases, the complexity results are established in terms of the number of proximal gradient steps needed to find an $\varepsilon$-stationary point. When the constraint functions are convex, we show a complexity result of $\tilde O(\varepsilon^{-5/2})$ to produce an $\varepsilon$-stationary point under the Slater's condition. When the constraint functions are non-convex, the complexity becomes $\tilde O(\varepsilon^{-3})$ if a non-singularity condition holds on constraints and otherwise $\tilde O(\varepsilon^{-4})$ if a feasible initial solution is available.
A High-Embedding Efficiency RDH in Encrypted Image Combining MSB Prediction and Matrix Encoding for Non-Volatile Memory-Based Cloud Service

With the rise of non-volatile memory used in cloud service, it becomes urgent to reduce the write cost so as to improve the data manipulation capability.This paper proposed a high embedding efficiency reversible data hiding scheme for encrypted images (RDH-EI).It embeds data at the locations of most significant bit (MSB) using matrix encoding, resulting in a high embedding efficiency.In order to restore the image completely at the receiver side, a highly accurate MSB prediction based on orthogonal projection technique is preprocessed on the original image and the prediction result is self-embedded to help recover the image.The experimental results verify that the proposed scheme outperforms the previous related schemes, it can achieve a higher embedding capacity of 0.412 bits per pixel at average, moreover, a significantly higher embedding efficiency of 3.43 bits per change is obtained.That means the proposed scheme can reduce the write cost by minimizing the modification in the case of the same payload and is especially suitable for applications used the non-volatile memory.Meanwhile, the process of data extraction is error free and the image can be recovered completely.
Exploring the Benefit and Sacrifice Factors of Virtual Reality Gameplay

Although Virtual reality (VR) entertainment is now relatively popular, the adoption of VR devices is still low. In this study, a framework based benefit and sacrifice factors was developed to understand players' intention to use VR devices to play games. Online questionnaire items were developed and published to collect the responses from university students in Taiwan. The feedback of 152 inexperienced players and 150 experienced players were collected. The eleven hypotheses were tested by using SmartPLS, a structural equation modeling (SEM) tool. The analysis results show that the influences of the benefit factors (flow, spatial presence, and relaxation) on the adoption intention in two groups are consistent. All of the positive influences are supported. Moreover, visual fatigue had the strongest negative effects on flow and intention. Players who are opener to new technology are more possible to adopt VR devices. The findings can provide insights to VR device developers to design their VR devices/contents and marketing strategies.
The Development of the Algorithm for Estimating the Spectral Correlation Function Based on Two-Dimensional Fast Fourier Transform

This chapter presents the algorithm for estimating spectral correlation function of a random process that is a valid bi-frequency description of the probabilistic properties of any wide-sense cyclostationary process and relates to its cyclic autocorrelation function via Fourier transform. The key point of the algorithm is that it is based on the two-dimensional discrete Fourier transform of the sample dyadic correlation function weighted by the two-dimensional windowing function, which is rectangular in the direction orthogonal to the current-time axis shape. The dedicated mathematical software libraries implementing fast Fourier transform, which is typically used for image processing, achieve higher performance in comparison with other algorithms involving spectra accumulation. The signal containing a pulse sequence with random amplitudes masked by the additive stationary white Gaussian noise is used in numerical simulation to provide an example of the spectral correlation function estimation procedure and obtain results demonstrating the effectiveness of the proposed algorithm.
Quantum estimation of acceleration and temperature in open quantum system

In an open quantum system, we study the evolution of a two-level atom as a detector which interacts with given environments. For a uniformly accelerated two-level atom coupled to a massless scalar field in the Minkowski vacuum, when it evolves for a certain time, we find that there exists a peak value for the quantum Fisher information (QFI) of acceleration, which indicates that the optimal precision of estimation is achieved when choosing an appropriate acceleration $a$. QFI has different behaviors for different initial state parameters $\ensuremath{\theta}$ of the atom, displaying periodicity. However, the periodicity fades away with the evolution of time, which means that the initial state cannot affect the later stable quantum state. Furthermore, adding a boundary, we observe that the peak value of QFI increases when the atom is close to the boundary, which shows that QFI is protected by the boundary. Here, QFI fluctuates, and there may exist two peak values with a certain moment, which expands the detection range of the acceleration. Therefore, we can enhance the estimation precision of acceleration by choosing an appropriate position and acceleration $a$. The periodicity of QFI with respect to the initial state parameter $\ensuremath{\theta}$ lasts a longer time than the previous unbounded case, which indicates that the initial state is protected by the boundary. Finally, for a thermal bath with a boundary, QFI of temperature has no more than one peak value with a certain moment, which is different from QFI of acceleration with a boundary. The periodicity also lasts a longer time than unbounded case, which shows the initial quantum state of the atom is protected by the boundary for two cases.
PGNet: Pipeline Guidance for Human Key-Point Detection

Human key-point detection is a challenging research field in computer vision. Convolutional neural models limit the number of parameters and mine the local structure, and have made great progress in significant target detection and key-point detection. However, the features extracted by shallow layers mainly contain a lack of semantic information, while the features extracted by deep layers contain rich semantic information but a lack of spatial information that results in information imbalance and feature extraction imbalance. With the complexity of the network structure and the increasing amount of computation, the balance between the time of communication and the time of calculation highlights the importance. Based on the improvement of hardware equipment, network operation time is greatly improved by optimizing the network structure and data operation methods. However, as the network structure becomes deeper and deeper, the communication consumption between networks also increases, and network computing capacity is optimized. In addition, communication overhead is also the focus of recent attention. We propose a novel network structure PGNet, which contains three parts: pipeline guidance strategy (PGS); Cross-Distance-IoU Loss (CIoU); and Cascaded Fusion Feature Model (CFFM).
Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark

At the dawn of the 10V or big data data era, there are a considerable number of sources such as smart phones, IoT devices, social media, smart city sensors, as well as the health care system, all of which constitute but a small portion of the data lakes feeding the entire big data ecosystem. This 10V data growth poses two primary challenges, namely storing and processing. Concerning the latter, new frameworks have been developed including distributed platforms such as the Hadoop ecosystem. Classification is a major machine learning task typically executed on distributed platforms and as a consequence many algorithmic techniques have been developed tailored for these platforms. This article extensively relies in two ways on classifiers implemented in MLlib, the main machine learning library for the Hadoop ecosystem. First, a vast number of classifiers is applied to two datasets, namely Higgs and PAMAP. Second, a two-step classification is ab ovo performed to the same datasets. Specifically, the singular value decomposition of the data matrix determines first a set of transformed attributes which in turn drive the classifiers of MLlib. The twofold purpose of the proposed architecture is to reduce complexity while maintaining a similar if not better level of the metrics of accuracy, recall, and F 1 . The intuition behind this approach stems from the engineering principle of breaking down complex problems to simpler and more manageable tasks. The experiments based on the same Spark cluster indicate that the proposed architecture outperforms the individual classifiers with respect to both complexity and the abovementioned metrics.
Improving out-of-distribution generalization via multi-task self-supervised pretraining

Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained using self-supervised learning are comparable to, or better than, supervised learning for domain generalization in computer vision. We introduce a new self-supervised pretext task of predicting responses to Gabor filter banks and demonstrate that multi-task learning of compatible pretext tasks improves domain generalization performance as compared to training individual tasks alone. Features learnt through self-supervision obtain better generalization to unseen domains when compared to their supervised counterpart when there is a larger domain shift between training and test distributions and even show better localization ability for objects of interest. Self-supervised feature representations can also be combined with other domain generalization methods to further boost performance.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware; (2) uneven or even long-tailed distribution of LiDAR points across space; and (3) an increasing number of extremely fine-grained semantic classes. In an attempt to jointly tackle all the aforementioned challenges, we propose a new LiDAR-specific, nearest-neighbor-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points across grid cells in a polar coordinate system, indirectly aligning a segmentation network's attention with the long-tailed distribution of the points along the radial axis. We find that our encoding scheme greatly increases the mIoU in three drastically different segmentation datasets of real urban LiDAR single scans while retaining near real-time throughput.
The Needs Analysis of Developing Mobile Learning Application for Cybergogical Teaching and Learning of Arabic Language Proficiency

The advancement of technology has undoubtedly brought rapid changes and contributed to the rise to developments in many aspects of life, including in education.Consequently, the roles of educators and practitioners have undergone significant changes as teaching and learning tools, as well as educational materials become more diversified and learning takes place in a borderless world of shared resources.Exciting transformations in education that should be embraced and further explored, especially in language teaching and learning because language is an essential part of communication.Language has also become an important requirement for employment and a key success factor in life.This paper aims to explore the potential use of "Cybergogy" in T&L by emphasising the use of cybergogical tools in learning.To ensure the effective use of these tools, we have employed an appropriate instructional design.Sets of questionnaires on students' needs and perceptions were administrated to 196 Malaysian's university students studying the elementary Arabic language course.Descriptive quantitative analysis, through the use of IBM SPSS statistical software, was employed to analyse the data collected.The results show that there are positive responses over the adoption of technological tools for language learning as it increases students' motivation to learn Arabic.
Fast Image Index for Database Management Engines

Abstract Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.
Distributed peer review enhanced with natural language processing and machine learning

While ancient scientists often had patrons to fund their work, peer review of proposals for the allocation of resources is a foundation of modern science. A very common method is that proposals are evaluated by a small panel of experts (due to logistics and funding limitations) nominated by the grant-giving institutions. The expert panel process introduces several issues - most notably: 1) biases introduced in the selection of the panel. 2) experts have to read a very large number of proposals. Distributed Peer Review promises to alleviate several of the described problems by distributing the task of reviewing among the proposers. Each proposer is given a limited number of proposals to review and rank. We present the result of an experiment running a machine-learning enhanced distributed peer review process for allocation of telescope time at the European Southern Observatory. In this work, we show that the distributed peer review is statistically the same as a `traditional' panel, that our machine learning algorithm can predict expertise of reviewers with a high success rate, and we find that seniority and reviewer expertise have an influence on review quality. The general experience has been overwhelmingly praised from the participating community (using an anonymous feedback mechanism).
Nonlinear Unsharp Masking Algorithm

Image properties such as contrast, brightness, sharpness, and colorfulness exert a great influence on human perception of image quality. Thus, image enhancement has been assumed an important role in image analysis and computer vision. We propose an efficient algorithm for improving the image sharpness that automatically resolves the well-known out-of-range problem by the means of nonlinear transformation. Adding to that, an optional contrast enhancement step could be applied concurrently to further enhance the image. The proposed algorithm also reduces the halo effect by utilizing the modified hybrid median filter in an iterative manner. A comparative study against other benchmarking methods and experimental results validate the performance of the proposed method.
Robust Rank Constrained Sparse Learning: A Graph-Based Method for Clustering

Graph-based clustering is an advanced clustering techniuqe, which partitions the data according to an affinity graph. However, the graph quality affects the clustering results to a large extent, and it is difficult to construct a graph with high quality, especially for data with noises and outliers. To solve this problem, a robust rank constrained sparse learning method is proposed in this paper. The L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm objective function of sparse representation is introduced to learn the optimal graph with robustness. To preserve the data structure, the graph is searched within the neighborhood of the initial graph. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator and the final results is obtained without additional post-processing. Plenty of experiments on real-world data sets have proved the superiority and the robustness of the proposed approach.
Calling Attention to Passages for Biomedical Question Answering

Question answering can be described as retrieving relevant information for questions expressed in natural language, possibly also generating a natural language answer. This paper presents a pipeline for document and passage retrieval for biomedical question answering built around a new variant of the DeepRank network model in which the recursive layer is replaced by a self-attention layer combined with a weighting mechanism. This adaptation halves the total number of parameters and makes the network more suited for identifying the relevant passages in each document. The overall retrieval system was evaluated on the BioASQ tasks 6 and 7, achieving similar retrieval performance when compared to more complex network architectures.
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models

Named entity recognition systems perform well on standard datasets comprising English news. But given the paucity of data, it is difficult to draw conclusions about the robustness of systems with respect to recognizing a diverse set of entities. We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities. We create entity-switched datasets, in which named entities in the original texts are replaced by plausible named entities of the same type but of different national origin. We find that state-of-the-art systems' performance vary widely even in-domain: In the same context, entities from certain origins are more reliably recognized than entities from elsewhere. Systems perform best on American and Indian entities, and worst on Vietnamese and Indonesian entities. This auditing approach can facilitate the development of more robust named entity recognition systems, and will allow research in this area to consider fairness criteria that have received heightened attention in other predictive technology work.
You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews

Coordination level matching is a ranking method originally proposed to rank documents given Boolean queries that is now several decades old. Rank fusion is a relatively recent method for combining runs from multiple systems into a single ranking, and has been shown to significantly improve the ranking. This paper presents a novel extension to coordination level matching, by applying rank fusion to each sub-clause of a Boolean query. We show that, for the tasks of systematic review screening prioritisation and stopping estimation, our method significantly outperforms the state-of-the-art learning to rank and bag-of-words-based systems for this domain. Our fully automatic, unsupervised method has (i) the potential for significant real-world cost savings (ii) does not rely on any intervention from the user, and (iii) is significantly better at ranking documents given only a Boolean query in the context of systematic reviews when compared to other approaches.
A Hybrid Evolutionary Computation Approach to Inducing Transfer Classifiers for Domain Adaptation

Domain adaptation utilizes learned knowledge from an existing domain (source domain) to improve the classification performance of another related, but not identical, domain (target domain). Most existing domain adaptation methods first perform domain alignment, then apply standard classification algorithms. Transfer classifier induction is an emerging domain adaptation approach that incorporates the domain alignment into the process of building an adaptive classifier instead of using a standard classifier. Although transfer classifier induction approaches have achieved promising performance, they are mainly gradient-based approaches which can be trapped at local optima. In this article, we propose a transfer classifier induction algorithm based on evolutionary computation to address the above limitation. Specifically, a novel representation of the transfer classifier is proposed which has much lower dimensionality than the standard representation in existing transfer classifier induction approaches. We also propose a hybrid process to optimize two essential objectives in domain adaptation: 1) the manifold consistency and 2) the domain difference. Particularly, the manifold consistency is used in the main fitness function of the evolutionary search to preserve the intrinsic manifold structure of the data. The domain difference is reduced via a gradient-based local search applied to the top individuals generated by the evolutionary search. The experimental results show that the proposed algorithm can achieve better performance than seven state-of-the-art traditional domain adaptation algorithms and four state-of-the-art deep domain adaptation algorithms.
Public Key Cryptography

Abstract Public Key Cryptography is a software model that presents the process of encoding information, in this case the documents contained in a file. The encoding process begins by determining the master file or document that is ready to be encoded, then by applying the combination method, shifting and implementing the pass phrase to the contents of the document, the encoded document will be formed by Encryption function. The contents of the encoded document are then returned back with the pass phrase implementation method, shifting and combinations to form the master document again by decryption function.
EMTST: Engineering the Meta-theory of Session Types

Abstract Session types provide a principled programming discipline for structured interactions. They represent a wide spectrum of type-systems for concurrency. Their type safety is thus extremely important. EMTST is a tool to aid in representing and validating theorems about session types in the Coq proof assistant. On paper, these proofs are often tricky, and error prone. In proof assistants, they are typically long and difficult to prove. In this work, we propose a library that helps validate the theory of session types calculi in proof assistants. As a case study, we study two of the most used binary session types systems: we show the impossibility of representing the first system in $$\alpha $$ -equivalent representations, and we prove type preservation for the revisited system. We develop our tool in the Coq proof assistant, using locally nameless for binders and small scale reflection to simplify the handling of linear typing environments.