Reviewerly/exHarmony huggingface dataset:
Fine-Grained Chinese Named Entity Recognition Based on RoBERTa-WWM-BiLSTM-CRF Model

Named entity recognition (NER) is a basic technology of Natural Language Processing (NLP). It is mainly used to identify entities and entity types. Compared with traditional entity recognition, fine-grained entity recognition can provide more precise semantics. In order to improve the effect of fine-grained Chinese N ER, w e propose a model based on RoBERTa-WWM-BiLSTM-CRF and compare it with other high-quality models. The experimental results show that this model has better effect on the CLUENER2020 dataset of fine-grained Chinese NER.
Realtime Single-Shot Refinement Neural Network With Adaptive Receptive Field for 3D Object Detection From LiDAR Point Cloud

Object detection plays an important role in autonomous driving systems. LiDAR is widely used in autonomous driving vehicles and robots as a sensor for environmental perception. Recently, the development of computational power and deep learning technology makes it possible to classify and locate objects from LiDAR point cloud in a single end-to-end learnable network. However, objects are sparsely distributed in large point cloud field, and are always been partly scanned by LiDAR, which pose a challenge for accurate and rapid object positioning and classification from the raw point cloud. In this paper, we introduce a new single-shot refinement neural network for fast and accurate 3D object detection from the raw LiDAR point cloud. Firstly, we exploit self-attention mechanism in main object detection branch to enhance object feature representation. Secondly, we apply deformable convolution for learning adaptive receptive fields to fully capture the features of rotating and partially visible objects. Thirdly, an object refinement branch is introduced to produce a finer regression of objects upon the primary estimation from the main detection branch. All proposed modules have been proven to effectively improve the accuracy of object detection. Our method is evaluated on KITTI 3D detection benchmark and achieves state-of-the-art results while maintains real-time efficiency. Furthermore, real-time test in autonomous driving vehicle demonstrates that our method is robust to 16 channels LiDAR and can meet the demands of accuracy, efficiency, and visibility of object detection in various urban scenarios.
Remixing real and imaginary in art education with fully immersive virtual reality

This article explores digital material/ism by examining student teachers’ experiences, processes and products with fully immersive virtual reality (VR) as part of visual art education. The students created and painted a virtual world, given the name Gretan puutarha (‘Greta’s Garden’), using the Google application Tilt Brush. They also applied photogrammetry techniques to scan 3D objects from the real world in order to create 3D models for their VR world. Additionally, they imported 2D photographs and drawings along with applied animated effects to construct their VR world digitally, thereby remixing elements from real life and fantasy. The students were asked open-ended questions to find out how they created art virtually and the results were analysed using Burdea’s VR concepts of immersion, interaction and imagination. Digital material was created intersubjectively and intermedially while it was also remixed with real and imaginary. Various webs of meanings were created, both intertextual and rhizomatic in nature.
Few-Shot Relation Extraction Towards Special Interests

With the continuous development of natural language processing, Relation extraction (RE) has been intensively studied and well performed in extracting relations from unstructured texts in both English and modern Chinese. In this paper, we study to extract relations from a special type of text, that is, Chinese textual description of Han Dynasty Stone Reliefs (HanDSR). We aim to develop an efficient relation extractor for special interests with a small number of samples. The problem is challenging due to the large number of rare words in the texts and the mixed-use of modern and ancient Chinese in the same sentence without a domain corpus. To address these problems, we propose a relation extraction method based on dependency parsing and utilize the information of HanDSR on the basic parser. To exploit the representation of dependency trees, we design five dependency semantic path patterns(DSPPs) to extract relation triples of special interests. Besides, we build the HanDSR Treebank that includes 4190 sentences, 28124 dependency trees, following the annotation format of the Penn Chinese Treebank 8.0, which addresses the lack of domain-specific corpus and could be used in extract relations from such texts. Extensive experiments on HanDSR dataset demonstrate the accuracy and efficiency of our solution. The experimental results illustrate that our proposal significantly outperforms the rule-based relation extraction model in both effectiveness and efficiency.
Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure

The rise of deep learning technologies has quickly advanced many fields, including generative music systems. There exists a number of systems that allow for the generation of musically sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody generation model based on a hierarchical recurrent neural network. This model allows us to generate melodies with long-term structures based on given chord accompaniments. We also propose a novel and concise event-based representation to encode musical lead sheets while retaining the melodies' relative position within the bar with respect to the musical meter. With this new data representation, the proposed architecture is able to simultaneously model the rhythmic, as well as the pitch structures effectively. Melodies generated by the proposed model were extensively evaluated in quantitative experiments as well as a user study to ensure the musical quality and long-term structure of the output. We also compared the system with the state-of-the-art AttentionRNN [1]. The comparison shows that melodies generated by CM-HRNN contain more repeated patterns (i.e., higher compression ratio) and a lower tonal tension (i.e., more tonally concise). Results from our listening test indicate that CM-HRNN outperforms AttentionRNN in terms of long-term structure and overall rating.
Interview Bot for Improving Human Resource Management

This study aims to explore the feasibility of implementing a chatbot for an interview process. The development of chatbots evolved rapidly to efficiently collect information in numerous fields, including customer service, health care, and etc. However, there was limited discussion of how chatbots are used to conduct an interview process autonomously. A human-driven interview also has some major limitations, e.g., it may only be conducted on a small-scale and is susceptible to bias. Hence, this study provides the design of a chatbot to conduct an interview, as well as processing the interview result by using Artificial Intelligence (AI) or machine learning. We have identified the difference between the typical chatbot communication method and the interview bot. This finding can be an opportunity to make a new interview bot or improve the implementation of a chatbot. In the end, we also discuss the challenges and benefits of the development of an interview bot.
Neural symbolic reasoning with knowledge graphs: Knowledge extraction, relational reasoning, and inconsistency checking

Knowledge graphs (KGs) express relationships between entity pairs, and many real-life problems can be formulated as knowledge graph reasoning (KGR). Conventional approaches to KGR have achieved promising performance but still have some drawbacks. On the one hand, most KGR methods focus only on one phase of the KG lifecycle, such as KG completion or refinement, while ignoring reasoning over other stages, such as KG extraction. On the other hand, traditional KGR methods, broadly categorized as symbolic and neural, are unable to balance both scalability and interpretability. To resolve these two problems, we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle, including KG extraction, completion, and refinement, which correspond to three subtasks: knowledge extraction, relational reasoning, and inconsistency checking. In addition, we propose the implementation of KGR using a novel neural symbolic framework, with regard to both scalability and interpretability. Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.
Achieving privacy-preserving sensitive attributes for large universe based on private set intersection

Nowadays, an increasing amount of data has been sent to the cloud for analysis and storage, and data security in the cloud has been widely concerned. Among them, CP-ABE is regarded as one of the most promising technologies to protect outsourced data. However, in most CP-ABE schemes, attackers may obtain user privacy information from policy plaintext. More likely, partial policy hiding programme neither satisfies full policy hiding nor applies to the needs of the large universe. In this paper,we hide both attribute values and names in policy by using private set intersection (PSI). The CP-ABE programme not only supports a complete hiding policy, but also can calculate the authorization relationship and mapping relationship between user passwords and keys. We use a polynomial-based PSI and a recursive algorithm to calculate the authorization relationship. And with the help of the algorithm and the label vector, the mapping is determined under communication restrictions. Through the outsourcing index, we achieve an efficient hiding strategy and effectively reduce the user's computing overhead. Finally, theoretical analysis and experiments show that our model has better performance while effectively protecting sensitive attributes.
Short-wave infrared (SWIR) spectral and geochemical characteristics of hydrothermal alteration minerals in the Laowangou Au deposit: Implications for ore genesis and vectoring

The Laowangou gold deposit is a newly discovered Au deposit in the North Qinling orogenic belt. Based on megascopic and microscopic textural relationships and mineral assemblages, the hydrothermal alteration and mineralization of the Laowangou Au deposit have been divided into porphyry-type (including potassic, chlorite-epidote and mica-quartz alteration stages) and overprinting mineralization periods. Hypogene Au mineralization mainly occurred as muscovite + quartz + Au-bearing pyrite veins during the overprinting mineralization period, and developed in the fault and fracture zone. SWIR analyses indicate that alteration minerals in the Laowangou Au deposit are chlorite (including Mg chlorite, Fe chlorite, and Fe-Mg chlorite), epidote and white micas with less dolomite, brucite and calcite. Two types of white mica are recognized at Laowangou. Type A white mica partially replaces plagioclase and K-feldspar in the Tangzang quartz diorite, representing the mica-quartz stage, whereas Type B white mica coexists with Au-bearing pyrite, quartz, calcite, and mainly developed in the fracture zone, representing the overprinting mineralization period. Compared with metamorphic minerals, Laowangou porphyry-related chlorite shows lower Al, Ti, and As, but higher Sr concentrations; porphyry-related epidote has higher Ti and Bi, but lower As concentrations. Type B white mica has lower Fe and Mg contents, but larger particle sizes and illite crystallinity (IC) values than Type A white mica, similar to geochemical characteristics of metamorphic white mica. These evidences indicate the Laowangou Au mineralization is different from typical magmatic-hydrothermal Au deposits, and the ore-forming fluid related to Au mineralization may have derived from the depth along faults. Higher wavelength of the Al-OH absorption (>2210 nm) and IC values (>1.2) of white mica can be used as effective vector toward high grade Au mineralization (>1 g/t) in the Laowangou Au deposit.
Hybrid optical-electronic neural network with pseudoinverse learning for classification inference

Constructing optical neural networks as analog artificial neural networks becomes a research highlight of implementing machine learning in a physical system. However, the training of an optical neural network with backpropagation error correction requires a complicated and less-efficient computational process. Here, we introduce a hybrid optical-electronic neural network to produce efficient artificial learning of handwritten digits without the backpropagation process. In the proposal, one can illuminate an input image with incident light and map the input image to a feature vector according to the transmitted light intensity. One can then encode the feature vector as an array of light and use an optical matrix multiplication unit to multiply the feature vector by a learning weight matrix. This learning weight matrix is realized by the spatial light modulator, which is constructed from the pseudoinverse learning method. The output light intensity through the optical multiplication unit represents the recognition result of the input image. The proposed neural network without backpropagation achieves sufficient accuracy of handwritten digits classification, exposing the advantages of training speed acceleration and energy efficiency improvement.
Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">next music recommendation and next new music recommendation</i> , to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users’ contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users’ preferences. To this end, this paper proposes a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ttentive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> emporal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rocess ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> ) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATPP</small> outperforms state-of-the-art baselines in both next and next new music recommendation tasks.
Edgeconomics: Price Competition and Selfish Computation Offloading in Multi-Server Edge Computing Networks

As edge computing provides crucial support for delay-sensitive and computation-intensive applications, many business entities deploy their own edge servers to compete for users, which forms multi-server edge computing networks. However, no prior work studies the competition among heterogeneous edge servers and how the competition affects users’ selfish computation offloading behaviors in such a network from an economic perspective. In this paper, we model the interactions between edge servers and users as a two-stage game. In Stage I, edge servers with heterogeneous marginal costs set their service prices to compete for users, and in Stage II, each user selfishly offloads its task to one of the edge servers or the remote cloud. Analyzing the equilibrium of the two-stage game is challenging due to edge servers’ heterogeneity and the congestion effect caused by resource sharing among users. We first prove that in Stage II, users’ selfish computation offloading game is a potential game and admits a unique Nash equilibrium (NE), for which we derive the explicit expression. We then analyze edge servers’ price competition game in Stage I and characterize the conditions for the uniqueness of the NE. We show that at equilibrium, users only choose low-priced edge servers, and hence edge servers with low marginal costs can win the price competition, which reflects the improvement of economic efficiency in competitive markets. Moreover, it is surprising that the equilibrium prices do not monotonically increase with the task execution delay. This is because a long execution delay gives a chance to edge servers with high marginal costs to win the competition, which results in more fierce competition among edge servers.
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm

We consider the problem to learn a step-size policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. This is a limited computational memory quasi-Newton method widely used for deterministic unconstrained optimization. However, L-BFGS is currently avoided in large-scale problems for requiring step sizes to be provided at each iteration. Current methodologies for the step size selection for L-BFGS use heuristic tuning of design parameters and massive re-evaluations of the objective function and gradient to find appropriate step-lengths. We propose a neural network architecture with local information of the current iterate as the input. The step-length policy is learned from data of similar optimization problems, avoids additional evaluations of the objective function, and guarantees that the output step remains inside a pre-defined interval. The corresponding training procedure is formulated as a stochastic optimization problem using the backpropagation through time algorithm. The performance of the proposed method is evaluated on the training of image classifiers for the MNIST database for handwritten digits and for CIFAR-10. The results show that the proposed algorithm outperforms heuristically tuned optimizers such as ADAM, RMSprop, L-BFGS with a backtracking line search, and L-BFGS with a constant step size. The numerical results also show that a learned policy can be used as a warm-start to train new policies for different problems after a few additional training steps, highlighting its potential use in multiple large-scale optimization problems.
Deep correlation mining for multi-task image clustering

Multi-task clustering (MTC) aims to enhance the performance of each individual task by leveraging the correlation information among them. Existing MTC algorithms usually first extract the feature representations of each task and then learn the relationships among multiple tasks for clustering. However, the multi-task correlations are not embedded into the feature learning in existing MTC. In addition, many real applications, such as image clustering, always perform visual feature extraction and clustering assignment separately, which often results in local optimal clustering resolutions. In this study, an end-to-end MTC framework, named Deep correlation mining for Multi-Task image Clustering (DMTC), is proposed to explore multi-task correlations and conduct image clustering simultaneously. Specifically, DMTC consists of two sub-networks: a between-task network (B-net) and a within-task network (W-net), which learn the correlations among multiple tasks and the relationships in each individual task, respectively, based on a deep convolutional network. To optimize B-net, an optimization procedure is proposed as follows: (1) DMTC builds a pseudo-graph to discover similar samples among tasks and obtain the positive pairs of possible related tasks. (2) A discriminator is designed to calculate the mutual information between the deep and shallow representations of related tasks, which can estimate the relatedness between each pair of related tasks. After that, the trained parameters in B-net are transferred to the within-task networks (W-net) as their initialized parameters, in which the above optimization procedure is performed again to obtain the final cluster partition by end-to-end training. Experimental results on NUS-Wide, Caltech-256, Cifar-100 and Pascal VOC demonstrate that our proposed DMTC method1compares favorably to the state-of-the-art methods.
Consequence-Aware Sequential Counterfactual Generation

Counterfactuals have become a popular technique nowadays for interacting with black-box machine learning models and understanding how to change a particular instance to obtain a desired outcome from the model. However, most existing approaches assume instant materialization of these changes, ignoring that they may require effort and a specific order of application. Recently, methods have been proposed that also consider the order in which actions are applied, leading to the so-called sequential counterfactual generation problem.In this work, we propose a model-agnostic method for sequential counterfactual generation. We formulate the task as a multi-objective optimization problem and present a genetic algorithm approach to find optimal sequences of actions leading to the counterfactuals. Our cost model considers not only the direct effect of an action, but also its consequences. Experimental results show that compared to state-of-the-art, our approach generates less costly solutions, is more efficient and provides the user with a diverse set of solutions to choose from.