Reviewerly/exHarmony huggingface dataset:
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization

Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these problems are difficult or infeasible to obtain. The gradient-free (zeroth-order) method can address these problems because only the objective function values are required in the optimization. Recently, the first zeroth-order proximal stochastic algorithm was proposed to solve the nonconvex nonsmooth problems. However, its convergence rate is O(1/√T) for the nonconvex problems, which is significantly slower than the best convergence rate O(T1) of the zerothorder stochastic algorithm, where T is the iteration number. To fill this gap, in the paper, we propose a class of faster zeroth-order proximal stochastic methods with the variance reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and ZO-ProxSAGA, respectively. In theoretical analysis, we address the main challenge that an unbiased estimate of the true gradient does not hold in the zerothorder case, which was required in previous theoretical analysis of both SVRG and SAGA. Moreover, we prove that both ZO-ProxSVRG and ZO-ProxSAGA algorithms have O(T1) convergence rates. Finally, the experimental results verify that our algorithms have a faster convergence rate than the existing zeroth-order proximal stochastic algorithm.
Moving Target Tracking Based on Improved GMPHD Filter in Circular SAR System

A new moving target tracking method based on an improved Gaussian mixture probability hypothesis density (GMPHD) filter in a circular synthetic aperture radar (SAR) system is proposed. In order to obtain the temporal dynamic behavior of moving targets in a SAR scene, a multiple-target observation is first extracted from a sequence of clutter-suppressed subaperture circular SAR images. Then, a new multiple-target tracking model for the circular SAR system is established based on the dynamic behavior of moving targets. An improved labeled GMPHD filter with an adaptive target birth intensity is proposed to realize the moving target detection and tracking in a strong ground clutter environment, which is suitable for the circular SAR system. This method can not only provide accurate estimates for the number and states of moving targets but also can extract their trajectories. The effectiveness of the proposed method is verified through simulations and experimental Gotcha data.
The NLP4NLP Corpus (I): 50 Years of Publication, Collaboration and Citation in Speech and Language Processing

This paper introduces the NLP4NLP corpus, which contains articles published in 34 major conferences and journals in the field of speech and natural language processing over a period of 50 years (1965-2015), comprising 65,000 documents, gathering 50,000 authors, including 325,000 references and representing approximately 270 million words. Most of these publications are in English, some are in French, German or Russian. Some are open access, others have been provided by the publishers. In order to constitute and analyze this corpus several tools have been used or developed. Many of them use Natural Language Processing methods that have been published in the corpus, hence its name. The paper presents the corpus and some findings regarding its content (evolution over time of the number of articles and authors, collaborations between authors, citations between papers and authors), in the context of a global or comparative analysis between sources. Numerous manual corrections were necessary, which demonstrated the importance of establishing standards for uniquely identifying authors, articles or publications.
Indoor tracking trajectory data similarity analysis with a deep convolutional autoencoder

Building energy consumption is influenced not merely by the energy-saving performance of hardware but also by the occupants' indoor behaviours. Advances in indoor positioning technologies can generate large volumes of spatial trajectory data on the occupants, which can reveal the distribution of the occupants or be interpreted to reflect the occupants' behaviours. This calls for systematic research on new computing technologies to identify information from trajectory data rather than from visualizations or statistics. Due to the imperfections and complexities of trajectory data, few robust techniques are available for similarity comparisons, which are critical for further clustering and pattern mining. In this work, we propose a novel means of evaluating similarities in occupant trajectory data based on the use of a convolutional autoencoder (CAE). Trajectory data can be compared and their feature vectors extracted with the CAE in an unsupervised manner. We applied this approach to high-precision tracking data collected from an ultra-wide band (UWB) indoor positioning system (IPS) installed in an exhibition hall. The calculated results show that our approach offers great advantages in terms of its application, robustness, and flexibility.
Comparison of stemming algorithms on Indonesian text processing

Stemming is one of the stages performed on the process of extracting information from the text. Stemming is a process of converting words into their roots. There is an indication that the most accurate stemmer algorithm is not the only way to achieve the best performance in information retrieval (IR). In this study, seven Indonesian stemmer algorithms and an English stemmer algorithm are compared, they are Nazief, Arifin, Fadillah, Asian, Enhanched confix stripping (ECS), Arifiyanti and Porter. The data used are 2,734 tweets collected from the official twitter account of PLN. First, the aims are to analyze the correlation between stemmer accuracy and information retrieval performance in Indonesian text language. Second, is to identify the best algorithm for Indonesian text processing purpose. This research also proposed improved algorithm for stemming Indonesian text. The result shows that correlation found in the previous research does not occur for the Indonesian language. The result also shows that the proposed algorithm was the best for Indonesian text processing purpose with weighted scoring value of 0.648.
Efficient CCA2 Secure Flexible and Publicly-Verifiable Fine-Grained Access Control in Fog Computing

Fog computing enables computation, storage, applications, and network services between the Internet of Things and the cloud servers by extending the Cloud Computing paradigm to the edge of the network. When protecting information security in Fog computing, advanced security with low latency, wide-spread geographical distribution support, and high flexibility should be taken in to considertion first, because of its huge number of nodes. In this paper, we propose a new cryptographic primitive, named CCA2 secure publicly-verifiable revocable large-universe multi-authority attribute-based encryption (CCA2-PV-R-LU-MA-ABE), to achieve flexible fine-grained access control in Fog computing. In this primitive, end nodes in fogs generate private keys from multiple authorities that might be differentiated by their geographical locations or functions, and their attributes can be denoted by any strings in the large universe, which meets diverse needs in practical Fog applications. In addition, the accessibility of nodes can be revoked efficiently even by resource-limited devices. To ensure the validity of ciphertext, this primitive supports public verification and only valid ciphertext can be stored or transmitted. Based on the primitive and the feature of Fog computing, we construct a concrete CCA2-PV-R-LU-MA-ABE scheme. We define the security model of this primitive, which is much more secure than the CPA-secure scheme. Finally, we compare the efficiency of the proposed concrete scheme with that of the existing CPA-secure scheme by both theoretical and experimental analysis, and the results show that the extra consumption of efficiency to improving CPA to CCA2 is considerably low. The proposed scheme is highly secure, flexible, and efficient enough to be deployed in practical Fog computing.
Leggett-Garg tests of macrorealism for dynamical cat states evolving in a nonlinear medium

We show violations of Leggett-Garg inequalities to be possible for single-mode cat states evolving dynamically in the presence of a nonlinear quantum interaction arising from, for instance, a Kerr medium. In order to prove the results, we derive a generalized version of the Leggett-Garg inequality involving different cat states at different times. The violations demonstrate failure of the premise of macrorealism as defined by Leggett and Garg, provided extra assumptions associated with experimental tests are valid. With the additional assumption of stationarity, violations of the Leggett-Garg inequality are predicted for the multicomponent cat states observed in the Bose-Einstein condensate and superconducting circuit experiments of Greiner et al. [Nature (London) 419, 51 (2002)] and Kirchmair et al. [Nature (London) 495, 205 (2013)]. The violations demonstrate a mesoscopic quantum coherence, by negating that the system can be in a classical mixture of mesoscopically distinct coherent states. Higher orders of nonlinearity are also studied and shown to give strong violation of Leggett-Garg inequalities. Loopholes for testing macrorealism are discussed.
Developing base domain ontology from a reference collection to aid information retrieval

Information Retrieval (IR) is a common technique used to manage a growing technical document collection. Owing to the complexity of technical documents, the direct application of IR often leads to unsatisfactory results. Therefore, many semantic approaches, such as ontology, are applied to enhance IR performance. However, ontology development is often a labor-intensive process and the availability of ontologies significantly influences the applicability of IR. Consequently, many efforts are dedicated to the automation of the ontology development process for reducing the human labors. In addition, reference collections, which are developed as the golden standards to evaluate IR performance in many IR research, can be regarded as an available resource for ontology development. To ease domain ontology development for supporting IR, this research proposes a semi-automated approach to develop a base domain ontology from a reference collection. This research also validates the base ontology on an Earthquake Engineering reference collection, called the NCREE (National Center for Research on Earthquake Engineering) collection. The results reveal that the human workload of the proposed approach is affordable. Furthermore, the base domain ontology can help achieve a satisfactory IR performance.
Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
Deep Learning Class Discrimination Based on Prior Probability for Human Activity Recognition

Convolutional neural network (CNN) depicts the transformation of the input image through a series of convolutions and other non-linear phases for recognition and classification purposes.This method has gained popularity for its significant contributions to computer vision applications and the improvement of the state-of-the-art.With CNNs, the softmax loss is used as the traditional loss function.This loss function allows deep features of distinct classes to be separated and promote the effective training of deep neural networks.An improvement on CNNs' discriminative power for face recognition was recently reported, where softmax and center loss were jointly used as supervisory a loss signal.In this paper, it is shown that such a supervisory loss function is not optimal in human activity recognition, and hence a new likelihood regularization term aimed at improving the feature discriminative power of the CNN models.This regularization term is modeled from Bayesian distribution for the posterior estimation of class probability density.The regularization term is shown to improve different class discrimination, and it is capable of maximizing the distance between different classes and minimizing distances within the same class in human activity recognition.The results obtained on the KTH and Weizmann datasets were encouraging.
Unsupervised graph-based rank aggregation for improved retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions.
A Hierarchical Deep Fusion Framework for Egocentric Activity Recognition Using a Wearable Hybrid Sensor System

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its wide applicability in medical care, smart homes, and security monitoring. In this study, we developed and implemented a deep-learning-based hierarchical fusion framework for the recognition of egocentric activities of daily living (ADLs) in a wearable hybrid sensor system comprising motion sensors and cameras. Long short-term memory (LSTM) and a convolutional neural network are used to perform egocentric ADL recognition based on motion sensor data and photo streaming in different layers, respectively. The motion sensor data are used solely for activity classification according to motion state, while the photo stream is used for further specific activity recognition in the motion state groups. Thus, both motion sensor data and photo stream work in their most suitable classification mode to significantly reduce the negative influence of sensor differences on the fusion results. Experimental results show that the proposed method not only is more accurate than the existing direct fusion method (by up to 6%) but also avoids the time-consuming computation of optical flow in the existing method, which makes the proposed algorithm less complex and more suitable for practical application.
A Continuous Coordinate System for the Plane by Triangular Symmetry

The concept of the grid is broadly used in digital geometry and other fields of computer science. It consists of discrete points with integer coordinates. Coordinate systems are essential for making grids easy to use. Up to now, for the triangular grid, only discrete coordinate systems have been investigated. These have limited capabilities for some image-processing applications, including transformations like rotations or interpolation. In this paper, we introduce the continuous triangular coordinate system as an extension of the discrete triangular and hexagonal coordinate systems. The new system addresses each point of the plane with a coordinate triplet. Conversion between the Cartesian coordinate system and the new system is described. The sum of three coordinate values lies in the closed interval [−1, 1], which gives many other vital properties of this coordinate system.
How Intention Informed Recommendations Modulate Choices: A Field Study of Spoken Word Content

People's content choices are ideally driven by their intentions, aspirations, and plans. However, in reality, choices may be modulated by recommendation systems which are typically trained to promote popular items and to reinforce users' historical behavior. As a result, the utility and user experience of content consumption can be affected implicitly and undesirably. To study this problem, we conducted a 2 × 2 randomized controlled field experiment (105 urban college students) to compare the effects of intention informed recommendations with classical intention agnostic systems. The study was conducted in the context of spoken word web content (podcasts) which is often consumed through subscription sites or apps. We modified a commercial podcast app to include (1) a recommender that takes into account users' stated intentions at onboarding, and (2) a Collaborative Filtering (CF) recommender during daily use. Our study suggests that: (1) intention-aware recommendations can significantly raise users' interactions (subscriptions and listening) with channels and episodes related to intended topics by over 24%, even if such a recommender is only used during onboarding, and (2) the CF-based recommender doubles users' explorations on episodes from not-subscribed channels and improves satisfaction for users onboarded with the intention-aware recommender.
Detecting Java Code Clones Based on Bytecode Sequence Alignment

When the source code is copied and pasted or modified, there will be a lot of identical or similar code snippets in the software system, which are called code clones.Because code clones are believed to result in undesirable maintainability of software, numerous approaches and techniques have been proposed for code clone detection.However, most of them are based on the source code, while only a few employ the bytecode to detect code clones.In this paper, we introduce an approach based on Java bytecode, which mainly contains the steps of bytecode sequence alignment and similarity score comparison.In particular, we apply the Smith-Waterman algorithm to align bytecode sequences for precise matching.Moreover, we separately consider the similarities between instruction sequences and method call sequences, thus improving its effectiveness in detecting code clones.We conducted an extensive experiment on five open-source software to evaluate the proposed approach.The results show that our approach outperforms other state-of-the-art techniques.
Variational Session-based Recommendation Using Normalizing Flows

We present a novel generative Session-Based Recommendation (SBR) framework, called VAriational SEssion-based Recommendation (VASER) - a non-linear probabilistic methodology allowing Bayesian inference for flexible parameter estimation of sequential recommendations. Instead of directly applying extended Variational AutoEncoders (VAE) to SBR, the proposed method introduces normalizing flows to estimate the probabilistic posterior, which is more effective than the agnostic presumed prior approximation used in existing deep generative recommendation approaches. VASER explores soft attention mechanism to upweight the important clicks in a session. We empirically demonstrate that the proposed model significantly outperforms several state-of-the-art baselines, including the recently-proposed RNN/VAE-based approaches on real-world datasets.
Mobile phones: Impacts, challenges, and predictions

Abstract The mobile phone is stimulating one of the most important technological revolutions in human history. This article first presents impacts, challenges, and predictions of mobile phone use. It first indicates that the impact of the mobile phone on society has been predominantly positive while the mobile phone has certain negative attributes. It then discusses multiple ways to overcome mobile technology challenges (e.g., new radio technologies and specialized devices optimized for medical, educational, or "Internet of things" applications). The authors predict that, in the two or three more generations, mobile phones use will have exciting advances to achieve the full benefits, especially in the area of healthcare, education, industry, daily life, learning, and collaborations, which will be more effective, productive, and creative.