Reviewerly/exHarmony huggingface dataset:
Provenance signatures and compositional transitions based on major element chemical composition of the stream sediment from the Psunj Mt., Croatia

Stream sediment is widely used as a medium in geochemical surveys. This study examined major element chemical composition of stream sediment produced by weathering in lithologically diverse terrain. Such sediment represents mixed sediment, therefore, the aim was to interpret its provenance and model the transitions within its major element composition. The particular interest was to link major element composition, mineral composition and the sediment source rocks. In order to interpret the sediment origin principal component analysis was employed and representative samples with highest and lowest scores were analysed for mineral composition. Scores also suggested four lithology-based groups of stream sediment samples. Transitions between groups were described with linear models for compositional data. Despite the ambiguity, links between major element composition, mineral composition and provenance were established. Besides sorting out sources of mixed sediment and minerals which control the transitions, this study contributes to general understanding of the Psunj Mt. geology, while the results can be used in geochemical surveys in areas of a similar geological setting.
Ghanaian Students’ Perceptions of Chinese Language Learning

Purpose: Chinese language proficiency presents myriads of opportunities. However, students’ perceptions influence their decisions to learn the language and since perceptions vary in different contexts, this study examined how Ghanaian students perceive Chinese language learning.&#x0D; Methodology: The qualitative approach, with the descriptive research design was used for the study. The target population was the University of Cape Coast students who were neither reading BA Chinese language programme nor taking free Chinese language and culture classes. Using the convenience sampling technique, twenty-two students who volunteered to partake in the study were selected. Data for the study were collected using an open-ended questionnaire. The data collected were analysed using the thematic content analysis based on the techniques of opening and axial coding.&#x0D; Findings: The results revealed that Ghanaian students generally had positive perceptions of Chinese language learning, and those with unfavourable perceptions are likely enroll onto a form of Chinese learning course if they become aware of the benefits associated with proficiency in Chinese language.&#x0D; Recommendations: The study recommend that outreach programmes should be instituted by Chinese language centres in Ghana to sensitise the public on the benefits of Chinese language learning. The study also recommend more research to be done on Chinese language.
Strong entanglement criteria for mixed states, based on uncertainty relations

Abstract We propose an entanglement criterion, specially designed for mixed states, based on uncertainty relation and the Wigner–Yanase skew information. The variances in this uncertainty relation do not involve any classical mixing uncertainty, and is therefore purely of quantum mechanical nature. We show that a large class of mixed entangled states can be characterized by our criterion. We demonstrate its utility for several generalized mixed entangled state including two-qubit and two-qutrit Werner states and it turns out, for the states discussed in this paper, to be stronger than any other known criterion in identifying the correct domain of relevant parameters for entanglement. The relevant uncertainty relation reduces to the Schrodinger–Robertson inequality for pure states.
Real-Time Oriented Ship Detection in Aerial Images for Embedded Platforms

Ships in aerial images are slender objects and can face arbitrary directions, thus are more effectively described by oriented bounding boxes (OBBs) rather than horizontal bounding boxes (HBBs) in the aerial detection task. Existing oriented object detectors usually use two-stage anchor-based architecture, making them hard to be deployed to embedded platforms with neural processing units (NPUs). In this work, we propose a lightweight one-stage anchor-free oriented ship detector that uses center keypoints and edge offset vectors to capture the ship OBBs. Also, the detector is designed to be NPU hardware-friendly and can be efficiently processed on embedded NPUs. Experiments results show that our proposed detector can achieve real-time speed on embedded NPUs while maintaining accuracy similar or superior to existing methods. When deployed to VeriSilicon VIP8000OI embedded NPU, the proposed detector can achieve 88.1% AP on the HRSC2016 dataset at 27 FPS.
Recommender Algorithms: From Matrix Factorization to Neural Network

With the rapid development of the Internet, the amount of global data shows an explosive growth, and the phenomenon of information overload has become more and more serious. How to obtain the information that users really care about has become one of the problems that people need to overcome. In this background, many recommendation algorithms have been widely used in all walks of life. This paper combs the background and future development trend of several recommendation algorithms.
Reliable Semi-Supervised Learning on Imbalanced Evolving Data Stream

Existing semi-supervised learning (SSL) algorithms often heavily depend on some assumptions (e.g., cluster assumption) and usually work on class-balanced static datasets. If the assumption(s) does (do) not hold, the biased prediction of unlabeled data may even hurt accuracy. This issue becomes more problematic in the context of streaming data due to the existence of concept drift. Therefore, it’s of great importance to enhance the reliability of the SSL algorithms on imbalanced concept-drifting data streams. In this paper, we propose a reliable and scalable SSL framework on imbalanced evolving data stream. Instead of relaxing different assumptions, we apply a novel sampling strategy and an additional balanced classifier to reduce the impact of imbalance and introduce the deep metric learning loss to enlarge the class margin to increase the degree of discrimination. We further maintain a small set of reliable micro-clusters dynamically in that embedding space and employ different strategies to update their reliabilities to maintain the most recent concept and cope with concept drifts. We conducted some experiments on real and synthetic stream datasets to evaluate the effectiveness of our proposed model.
A Multi-Information Enhanced Attention Network for Session-Based Recommendation

Session-based recommendations attempt to anticipate the subsequent item based on past interactions with anonymous users. Only a small subset of user interactions can be modeled in order to fully capture user preferences. Most of existing models have verified that attention mechanisms are useful for modeling user preferences. However, these attention-based models still have two limitations. First, these methods focus on pairwise item-item interactions. Categories and behaviors, which can provide side information to improve recommendation accuracy, are simply ignored. Second, irrelevant items (i.e., spurious user behaviors), which are not favorable for the final result, are difficult to identify and eliminate automatically. To solve the above difficulties, we propose a Multi-Information Enhanced Attention Network (MIEAN). Specifically, we capture attention to items in a session by combining behaviors with items and categories, respectively, and then design enhanced attention to decrease the effects of irrelevant items. We have conducted comprehensive experiments on two real and public datasets, JData and Tmall, and the experimental results show that the proposed model achieves state-of-the-art performance in the main test metrics and also prove that categories and behaviors are useful for modeling user preferences.
A Crosschain-Based Fair Exchange of Products For Poverty Alleviation

Through providing products or financial assistance, poverty alleviation is an effective way to reduce poverty. With properties of decentralization, traceability, and publicity, blockchain technology has been well studied to solve the problems of corruption, single point of failure, and complex transaction procedure in traditional centralized poverty alleviation platforms. However, most of poverty alleviation programs in the market are built on single blockchain, which contain only one kind of product information. It is difficult for products to be exchanged on different blockchains, which limits the further expansion of poverty alleviation. This paper proposes a universal and efficient cross-chain fair exchange scheme for poverty-alleviation products. Specifically, we employ an adaptor signature scheme to achieve fairness in the exchange. It ensures that when one party gets the exchanged poverty alleviation product, the other party will also get his corresponding product. In addition, we also adopt the verifiable time discrete logarithm algorithm to solve the problem that the lack of a special scripting language does not allow us to set "timeout" on the transaction and fall into a deadlock problem. Finally, we deploy the scheme on the local environment, public test chains of Ethereum, and conduct a series of experiments to verify its effectiveness and efficiency.
An Accelerator of Efficient DSP Based on FPGA

In recent years, neural networks have led to advanced applications in various fields of our daily life. Convolutional Neural Networks and Long Short-Term Memory Neural Networks have shown excellent performance in their own application fields. In this paper, we propose a hardware architecture to support the two models based on Field Programmable Gate Array with 8-bit fixed-point quantization input data. We optimize parallelism, data tiling and pipeline to improve computing resources efficiency and computing performance. The accelerator was implemented on Zynq UltraScale ZCU102 Field Programmable Gate Array and achieved 0.448 and 0.261 Giga Operations per second/digital signal processor respectively, which shows our design makes a contribution to high computing resource utilization.
Gesture Recognition Algorithm Based on WiFi and MMWR Fusion

The environment of gesture recognition is complex and changeable. The wireless fidelity signal can accurately be recognized in the non-line-of-sight environment and the millimeter wave radar signal has a strong anti-interference ability. This paper absorbs the advantages of two platforms and proposes a fusion neural network based on wireless fidelity and millimeter wave radar gesture recognition algorithm. The experimental results show that the algorithm can effectively improve the accuracy and robustness of the gesture recognition system. This paper’s contribution is using gate recurrent unit and attention mechanism to extract the depth features of channel state information data and compressed range-Doppler map respectively which are based on the data characteristics of signals collected by different platforms after signal processing. They improve the overall performance of the gesture recognition system.
RETR: End-To-End Referring Expression Comprehension with Transformers

Referring Expression Comprehension (REC) is a basic and challenging task to identify the referred region given a language expression. However, existing two-stage or one-stage methods suffer from the region proposals, the limited range of visual context and the incomplete cross-modal alignment. To address these problems, we propose a simple yet effective one-stage model, termed REC TRansformer (RETR), which is trained end-to-end. Different from the manually designed multi-modal fusion, RETR adopts a transformer decoder with alternately stacked self-attention and cross-attention layers to capture the global visual context and establish the detailed visual-linguistic correspondence. Moreover, we utilize multiple learnable tokens to obtain diverse yet complementary region representations to give the accurate prediction. Extensive experiments are conducted on four datasets and RETR achieves the state-of-the-art performance.
Mitigating Both Covariate and Conditional Shift for Domain Generalization

Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domains contains the covariate shift and conditional shift, both of which the model must be able to handle for better generalizability. In this paper, a novel DG method is proposed to deal with the distribution shift via Visual Alignment and Uncertainty-guided belief Ensemble (VAUE). Specifically, for the covariate shift, a visual alignment module is designed to align the distribution of image style to a common empirical Gaussian distribution so that the covariate shift can be eliminated in the visual space. For the conditional shift, we adopt an uncertainty-guided belief ensemble strategy based on subjective logic and Dempster-Shafer theory. The conditional distribution given a test sample is estimated by the dynamic combination of that of source domains. Comprehensive experiments are conducted to demonstrate the superior performance of the proposed method on four widely used datasets, i.e., Office-Home, VLCS, TerraIncognita, and PACS.
Design and Implementation of Standardized Universal P2P Protocol Framework Based on Central Server

This paper proposes a standardized communication protocol that combines traditional C/S architecture and P2P architecture. It solves the exponential performance growth burden on the network, computing power and storage resources of servers in traditional networks. It solves the difficult problems of network communication, upgrading and operation under P2P network. The protocol is intended to enable Internet applications to have more choices and reduce development costs.
Graph Neural Networks with Multi-granularity Pooling

Graph Neural Networks (GNNs) are widely used in various tasks such as graph or node classification and achieved state-of-the-art results. However, current GNN models are typically using an inherently flat or single global pooling step to aggregate node features, which lack of semantic information. Here we propose MgPOOL that can generate multi-granularity representations of graphs and can be combine with multiple types of GNN models. MgPOOL can reduce the size of graph in an adaptive and learn a multi-granular cluster assignment for nodes at each layer, mapping the similar nodes into the same cluster, which then a coarse-grained input is constructed for the next layer. Here we combine several existing GNN models to demonstrate that multi-granularity node classification is possible. The experimental results are verified on several established graph classification benchmarks and achieving a new state-of-the-art on five common benchmark data sets. Furthermore, the method provides a better interpretability for deep GNN models.
A Survey of Deep Learning-based Hand Pose Estimation

Hand pose estimation is one of the representative tasks in computer vision. Solving the hand pose estimation problem is essential for various fields such as virtual reality, augmented reality, mixed reality, and human-computer interaction. Due to the significant development of deep learning techniques, the hand pose estimation task has reached significant performance on many hand pose estimation datasets. However, the hand pose estimation task still faces many challenges due to the lack of large-scale labeled data, severe occlusion, low hand resolution, and background clutter. To better understand the hand pose estimation task, this paper presents a comprehensive survey of outstanding papers over the last five years. The paper first introduces 19 common hand pose estimation datasets, then extensively discusses some of the mainstream approaches in hand pose estimation, including fully supervised, semi-supervised, weakly supervised, and self-supervised learning methods. Finally, we extensively discuss the future evolution trends of hand pose estimation.
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property

In the age of big data, the demand for hidden information mining in technological intellectual property (tech-IP) is increasing in discrete countries. Definitely, a considerable number of graph learning algorithms for technological intellectual property have been proposed. The goal is to model the technological intellectual property entities and their relationships through the graph structure and use the neural network algorithm to extract the hidden structure information in the graph. However, most of the existing graph learning algorithms merely focus on the information mining of binary relations in technological intellectual property, ignoring the higher-order information hidden in non-binary relations. Therefore, a hypergraph neural network model based on dual channel convolution is proposed. For the hypergraph constructed from technological intellectual property data, the hypergraph channel and the line expanded graph channel of the hypergraph are used to learn the hypergraph, and the attention mechanism is introduced to adaptively fuse the output representations of the two channels. The proposed model outperforms the existing approaches on a variety of datasets.
An enhanced anomalies detection method based on isolation forest and fuzzy set

Most existing anomaly detection models are model-based approaches to generate the pattern of normal instances, then mark instances that do not conform to the normal pattern as anomalies. This kind of algorithms are often complex. Isolation Forest, on the contrary, isolates anomalies rather than generating patterns of normal instances, it has linear time complexity and low memory requirements. However, the process of iForest sampling to construct Isolated Trees is random, and the algorithm preformance is not stable. In this paper, an enhanced anomalies detection method based on iForest and fuzzy set is proposed (short for, FForest). In the sampling process, the subset of possible anomalies is obtained by calculating the quartile points in advance instead of random sampling. At the same time, the fuzzy membership degree is used to measure the anomalyscore, so as to enhance the interpretable of the algorithm. The experiments with seven real-world data sets demonstrate our method outperforms four baseline methods.
GUARD: Multigranularity-based Unsupervised Anomaly Detection Algorithm for Multivariate Time Series

Unsupervised anomaly detection on multivariate time series (MTS) is a valuable problem in practice because it significantly reduces labeling costs. Even though most classical methods begin to consider inter-series dependencies, the hierarchical relationships between and within-series data are often ignored. To overcome these limitations, in this paper, we propose a multiGranUlArity based unsupeRvised anomaly Detection (GUARD) algorithm that captures the hierarchical relationships in three granularities and merges these dependencies in the following anomaly detection task. Specifically, the proposed algorithm first captures the internal dependencies within the subsequence and learns a hidden representation based on a trained autoencoder. Then, a hierarchical mechanism is designed to capture the local and global dependencies between the given subsequence and the whole set and learn their hidden representation. The experimental results show that the proposed algorithm can effectively and stably exploit the potential relationship of MTS and detect anomalies more accurately than the basic approaches.
Face Image Smart Capture System Using Embedded Device

At present, as an important means of identity authentication, face image recognition has been widely used in many fields. However, during obtaining a face image, it is often necessary for the person being captured to adjust his positions of face or a staff help to manually adjust the cameras position repeatedly. With this, the face images captured may meet the authentication requirements. For solving these problems, an intelligent face image capture system based on embedded device is designed. The hardware mainly consists of a control module with STM32F103C8T6 as CPU, and a two-degree-of-freedom pan tilt zoom camera (PTZ) with two steering gears and a digital camera. The two-scale fitting inverse combination active appearance model (AAM) algorithm is used for face image detection and location. Then the steering gear is controlled to adjust the camera’s direction according to the relationship between the center of the face image and the center of the camera’s field of view (FOV) to complete the face image capture automatically. Experiments show that the designed system meets the authentication requirements and has the good real-time performance and stability.
Robust Automated Essay Scoring by Using Attentive Capsule

The computer-assisted essay scoring systems can provide accurate scores in a short time, relieving the stress of teachers assessing student essays, which has attracted widespread attention from academia and industry fields. Considering the existing advanced NLP techniques, we propose a robust end-to-end AES model by leveraging the capsule network framework with multiple losses. The proposed model has testified with baseline models on the public Automated student Assessment Prize (ASAP) dataset. The results demonstrate that the proposed model achieved the stateof-the-art result of the ASAP dataset. The experiment results in the ASAP corpus proved that the proposed model can further improve the accuracy of the PLM model for the AES task.
Recallable Question Answering-Based Re-Ranking Considering Semantic Region for Cross-Modal Retrieval

Question answering (QA)-based re-ranking methods for cross-modal retrieval have been recently proposed to further narrow down similar candidate images. The conventional QA-based re-ranking methods provide questions to users by analyzing candidate images, and the initial retrieval results are re-ranked based on the user's feedback. Contrary to these developments, only focusing on performance improvement makes it difficult to efficiently elicit the user's retrieval intention. To realize more useful QA-based re-ranking, considering the user interaction for eliciting the user's retrieval intention is required. In this paper, we propose a QA-based re-ranking method with considering two important factors for eliciting the user's retrieval intention: query-image relevance and recallability. Considering the query-image relevance enables to only focus on the candidate images related to the provided query text, while, focusing on the recallability enables users to easily answer the provided question. With these procedures, our method can efficiently and effectively elicit the user's retrieval intention. Experimental results using Microsoft Common Objects in Context and computationally constructed dataset including similar candidate images show that our method can improve the performance of the cross-modal retrieval methods and the QA-based re-ranking methods.