Reviewerly/exHarmony huggingface dataset:
Conditioning Customers' Product Reviews for Accurate Classification Performance

In recent years, people use Internet as a platform to express their own ideas and opinions about various subjects or products. The data from these sites serve as sources for sentiment analysis. On e-commerce websites, the costumer product review conventionally expresses sentiment that corresponds with the given star rating; however, this is not always true; there are reviews that express sentiments opposite to the given star rating, which can be labeled as outliers. This paper builds on previous work that finds outliers in product review datasets, scraped from Amazon.com, using a statistics-based outlier detection and correction method (SODCM). This work focuses on 3-star reviews specifically and studies the correct polarity assignment of 3-star reviews. It investigates the behavior of SODCM when 3-star reviews are classified as negative and positive respectively.
Rapid Knowledge Transfer Based on Gradient Response and Cross-Domain Consistency

With the rapid development of deep learning in the field of computer vision, data scarcity is a common and serious problem. Data generation is viable to solve this problem. GAN (Generative Adversarial Networks) uses internal discriminator (D) and generator (G) against each other to improve performance. MOGAN (Morphologic-structure-aware Generative Adversarial Network) is an unsupervised structure-aware controllable generative model for a single training sample. It can maintain the rationality of semantic information while ensuring the diversity of generated results. This paper proposes a knowledge transfer method driven by gradient response and cross-domain consistency. The method guides the training of new tasks by using the generative model completed by the original task training. We complete the rapid transfer from the existing model to the new model. Our method can achieve better results at the same time cost. We have demonstrated the effectiveness of this method through experiments.
Open the Black Box of Recurrent Neural Network by Decoding the Internal Dynamics

With the development of the neural network, the complexity of the model goes far beyond the imagination. The number of neurons in the network is growing, and the black box problem requires to be solved. Although technics can record the internal dynamics of hidden neurons, the high dimension and complexity of the data bring poor interpretability. This paper introduces Tensor Component Analysis (TCA) to obtain low-dimensional information from the internal dynamics of recurrent neural networks (RNN). The proposed method extracts three interrelated neural factors: neuron factors, temporal factors, and input factors, to decode the forward propagation. This paper designs a variety of experiments to analyze the activity of RNN, and low-dimensional factors are used to explain the model's decision. The experiment shows the broad applicability of the TCA, which can accurately find the functional clustering of neurons and predict most of the classification.
Evaluating NewsQA Dataset With ALBERT

Machine question answering tasks are widely in use these days. Many of the RNN (Recurrent Neural Networks) models including improved models like LSTMs (Long short-term memory), GRUs (Gated recurrent units) are being investigated to capture long term dependencies for this task. Overcoming the traditional deep learning architectures, latest transformer models outperformed the previous ones. This paper uses transformer model named ALBERT that is based on the architecture of BERT. A pre-trained ALBERT base and large models are used from the hugging face library that are fine-tuned on NewsQA dataset that is machine reading comprehension dataset. This dataset is converted into SQuAD (Stanford Question Answering Dataset) format which is later trained on our model. The model achieves state of the art results on the dataset.
Homogeneous-Heterogeneous Interaction Graph for Deep Learning-based Recommendation Systems

Graph convolutional neural network is a deep learning model for graph-structured data. It has become a popular approach for recommendation system research due to its powerful feature extraction and characterization learning capabilities. As for rating prediction in recommendation systems, most existing models based on graph convolutional networks use heterogeneous interaction information between users and items but lack sufficient use of homogeneous interaction information in the user and item graphs, thus it leads to the degradation of recommendation accuracy performance. For this purpose, this paper proposes some methods for constructing homogeneous interaction graph models that can be combined with heterogeneous interaction graphs to fully aggregate the node similarity and edge link information in the graph so that node embedding representations based on graph data can be better learned through graph convolutional networks. Experimental results based on several recommendation datasets show that the proposed homogeneous interaction graph can help the recommendation model to better mine the potential feature information and reduce the prediction error of ratings.
A Novel Block Transmission Model in Blockchain Networks

In a blockchain network, the instability of the block transmission process can affect the speed of block transmission. If blocks cannot be accepted by nodes and saved on a blockchain in time, which may lead to inconsistent blockchain ledgers stored by nodes, thus reducing the security of blockchain networks. However, when nodes transmit blocks, they often encounter problems of too large blocks and insufficient bandwidth, which results in slow block transmission speed and low efficiency. To solve the problems, it proposes a block transmission model, which encodes units into packets. Based on the model, the corresponding encoding and decoding processes are designed. The proposed method is compared with two state-of-the-art methods: Velocity and Kadcast. Experimental results show that the proposed method performs better than its peers in terms of block synchronization time, block transmission success ratio, and packet retransmission ratio.
Distant supervision for fine-grained biomedical relation extraction from Chinese EMRs

Automatically extract relations between medical entity pairs is fundamental in biomedical research. Since the annotated dataset is very expensive, distant supervision provides an efficient solution to reduce the cost of annotation by utilizing rough corpus labeled with semantic knowledge base. However, two same entities mentioned in different sentences may express different relations, it is difficult for the traditional distant supervision methods to distinguish these different relations. In this paper, we propose a new model for biomedical relation extraction in Chinese EMRs. First, the distant supervision is used for coarse-grained relation labeling. Then, the fine-grained relations are annotated initially by measuring the distance between the contextual information of the relation instance to the semantic profile of each candidate fine-grained relation category. Finally, the high confidence fine-grained relation instances are selected as initial training set for PCNN model, in addition, a bootstrap learning is introduced in the training process to enhance the performance of fine-grained relation extraction. Experiments conducted on a real-word dataset and the results show that our method outperforms all baseline systems.
Domain Adaptation Soft Sensing with Parameter Transferring

Soft sensor modeling often encounters a distribution discrepancy problem, when working conditions or environmental factors change. Such problem leads to an insufficient number of training data samples for an accuracy regression model construction. In addition, a soft sensor constructed for a specific mode is unlikely to obtain reliable prediction results for other modes. This paper presents a new transfer learning-based soft sensor model to handle the domain adaptation issue with a transferring parameter, which is suitable for multi-mode processes with limited target training samples. The difference between the source and target domains is considered as a parameterized maximum mean discrepancy regularization term in the objective function, based on which a trade-off between minimizing the two domains' difference and maximizing the prediction performance on the target domain's testing samples can be realized. Furthermore, an alternating optimization algorithm is formulated to optimize the transferring parameter along with the output weights. The proposed method is expected to fully leverages the limited target samples and the related source ones simultaneously to construct an adaptive target soft sensor. Comparative studies with several popular soft sensing approaches are conducted to demonstrate the effectiveness and advantages of our approach.
A Kernel Propagation-Based Graph Convolutional Network Imbalanced Node Classification Model on Graph Data

As an important research topic in graph learning, the performance of node classification has been improved with the development of some new methods, among which graph neural networks (GNNs) have achieved state-of-the-art node classification performance. However, the existing GNN-based methods mainly address the classification problem with balanced distribution of node samples. However, many real application scenarios of graph data usually have a highly skewed class distribution, i.e., the majority classes occupy most of the samples while the minority classes contain only a few samples. When the nodes exhibit an imbalanced class distribution, existing GNN-based methods favor the majority class and under-represent the minority class. Therefore, we propose a novel Kernel Propagation-based model for Imbalanced Node Classification in Graph Convolutional Networks (KINC-GCN). First, we introduce a kernel propagation method as a preprocessing step to exploit higher-order structural features. The node features are enhanced by concatenating the higher-order structural feature matrix with the node feature matrix. Node embeddings are obtained from the enhanced feature and adjacency matrices by a two-layer GCN, and then a self-optimizing cluster analysis and graph reconstruction module are introduced. The self-optimizing cluster analysis module performs cluster analysis on the node embeddings to enhance the representativeness of the node embeddings. The graph reconstruction module uses an inner product decoder to reconstruct the graph structure and minimize the differences between the reconstructed graph and the original graph. The effectiveness of KINC-GCN in node classification is demonstrated by experiments on three real-world imbalanced graph datasets.
Songketchain: A Framework to Protect Unique Cultural Product using Blockchain Technology

Songket market in Sarawak has been established since 1970, making it one of the longest existing cultural and handmade products. Songket preservation is essential to ensure the originality of the artwork in the community. An effort can be done by encouraging the community to report pirated pattern. However, the method available does not lessen the case of pirated pattern in the songket market. Songket in Sarawak are mainly copied by parties that want to make profit from creating copycat product of lesser quality and value but being sold as original merchandise. By creating a new system that can detect the ownership of the songket pattern, it will increase the value of the songket pattern. The proposed system is to assist the creator of the songket to preserve their songket pattern. The main objective of the project is to preserve the authenticity of the produced songket. This is achieved through the introduction of a framework for traceability and traceability of songket product and production process, by utilizing a blockchain solution. A generic framework has been proposed which will be implemented in the next stage of this research.
Challenges and Application of Blockchain in Healthcare Systems

The application of blockchain technology has been expanding at a rapid rate over the past few years, and it has made its way into almost every facet of the information and communications technology (ICT) sector. Blockchain has the potential to be deployed in a wide range of industries and domains, including logistics, supply chain management, Internet of Things (IoTs), and Healthcare. According to IBM, 72 % of healthcare leaders believe that the most significant impact of blockchain within the health domain. A wide comprehensive critical assessment of the most current research on blockchain-based healthcare applications is not addressed. This article is to provide the reader with a technical foundation in a variety of blockchain-based healthcare applications, with a particular emphasis on the most recent developments and accomplishments in this field. We give an examination of contemporary blockchain technologies that have been implemented in the healthcare industry i.e Medical Supply chain management, Electronic Medical Record (EMR), Clinical Data Sharing, Health Insurance Claims, Pharmaceutical industry, Remote Patient Monitoring (RPM) to examine the strengths and challenges of these application.
A Novel Machine Learning Method for Delayed Labels

Most research on machine learning relies on the availability of ground truth labels immediately after prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. Considering that there is a large amount of unlabeled data in delayed labels, supervised model cannot utilize unlabeled data. Therefore, most of the research on delayed labels begins to train semi-supervised models in delayed labels. However, most research on delayed labels ignores that the labels of unlabeled data will arrive after several periods in delayed labels. Neither supervised nor semi-supervised models can solve the problem in delayed labels effectively. Besides, there remains a problem of concept drift due to the long period of data. In this paper, we propose an incremental learning model that can adapt to delayed labels. First, we should detect whether the concept drift takes place. Then we use knowledge distillation to update supervised and semi-supervised models while retaining the corresponding knowledge of past labeled data. Finally, we combine the supervised and semi-supervised models to make predictions. Finally, we apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labels.
Efficiently Counting Triangles for Hypergraph Streams by Reservoir-Based Sampling

Hypergraph streams provide an efficient model to express and preserve complex connections in various applications. Triangles in a hypergraph can be formed by vertices and hyperedges. The specific counts of triangles are important to analyze various applications. Due to the huge costs of counting triangles based on the whole datasets, a sampling-and-estimating framework has low overhead while obtaining a relatively accurate result. However, existing sampling algorithms focus on pairwise graph streams, they estimate the counts of triangles formed by vertices with large estimation errors and can not be applied to count triangles formed by hyperedges. Therefore, this paper first proposes a sampling-and-estimating framework that produces hyperedge samples using a reservoir with static capacity to estimate the total counts of triangles by inferring the probabilities of forming the triangles respectively. Furthermore, to improve the estimation accuracy, this paper proposes another sampling-and-estimating framework to produce samples in the form of hyperedge pairs which can be used to compute the probabilities of the formations of triangles more accurately and then estimate the total triangle counts with smaller estimation variances. The extensive experiments based on real-world datasets confirm the efficiency and accuracy of our proposed frameworks for counting triangles in different types of hypergraphs at a small sampling ratio.
Adversarial Learning of Object-Aware Activation Map for Weakly-Supervised Semantic Segmentation

Recent years have witnessed impressive advances in the area of weakly-supervised semantic segmentation (WSSS). However, most of existing approaches are based on class activation maps (CAMs), which suffer from the under-segmentation problem (i.e., objects of interest are segmented partially). Although a number of literature works have been proposed to tackle this under-segmentation problem, we argue that these solutions built on CAMs may not be optimal for the WSSS task. Instead, in this paper we propose a network based on the object-aware activation map (OAM). The proposed network, termed OAM-Net, consists of four loss functions (foreground loss, background loss, average pixel and consistency loss) which ensure exactness, completeness, compactness and consistency of segmented objects via adversarial training. Compared to conventional CAM-based methods, our OAM-Net overcomes the under-segmentation drawback and significantly improves segmentation accuracy with negligible computational cost. A thorough comparison between OAM-Net and CAM-based approaches is carried out on the PASCAL VOC2012 dataset, and experimental results show that our network outperforms state-of-the-art approaches by a large margin. The code will be available soon.
EfShard: Toward Efficient State Sharding Blockchain via Flexible and Timely State Allocation

State sharding is a promising approach to address the scalability issue in the blockchain system. However, the previous sharding schemes using the fixed data partitioning mechanism bring high proportion of costly cross-shard transactions and cannot effectively handle the workload imbalance that occurs in practice, which slows down the performance. To address these issues, we propose EFSHARD, an efficient state sharding blockchain system that enables allocating states flexibly and timely across shards according to recent transactions. Firstly, we propose a hierarchical state partition to enable flexible mapping of states to shards. Second, we design a new state transfer protocol to efficiently migrate states across shards while guaranteeing consistency and liveness. Then, we provide a greedy-based state allocation algorithm to decide when and how to migrate states. The allocation mechanism groups highly correlated state data into the same shard to reduce the proportion of cross-shard transactions and distributes state data to shards with relatively low load to balance workload, thus improving the performance. In the end, we conduct extensive experiments to evaluate EFSHARD and the results demonstrate that EFSHARD outperforms state-of-the-art approaches in terms of transaction throughput, confirmation latency, workload balance, and queue size of transaction pool.
Measurement-based interleaved randomised benchmarking using IBM processors

Quantum computers have the potential to outperform classical computers in a range of computational tasks, such as prime factorisation and unstructured searching. However, real-world quantum computers are subject to noise. Quantifying noise is of vital importance, since it is often the dominant factor preventing the successful realisation of advanced quantum computations. Here we propose and demonstrate an interleaved randomised benchmarking protocol for measurement-based quantum computers that can be used to estimate the fidelity of any single-qubit measurement-based gate. We tested the protocol on IBM superconducting quantum processors by estimating the fidelity of the Hadamard and T gates - a universal single-qubit gate set. Measurements were performed on entangled cluster states of up to 31 qubits. Our estimated gate fidelities show good agreement with those calculated from quantum process tomography. By artificially increasing noise, we were able to show that our protocol detects large noise variations in different implementations of a gate.
Digital Transformation in Education_Self-study System “Cyber3s”

At the end of the twentieth century, revolutionary shifts in Information Technology changed the way in which old, practically approved knowledge could be assimilated, identified, and effectively solved. Digital transformations are one of its demonstrations. An integral part of digital learning is the intelligent computer systems of knowledge acquisition, control and assessment. The computer system for knowledge acquisition, control and assessment developed by us is a combination of Cyber 3 web applications: Cyber 3S (Kiber3S.tsu.ge), Cyber 3SE, Cyber 3T, Cyber 3E, Cyber 3A. An effective way of using Cyber3 is achieved by the combined method—creative processes are mastered in the traditional way, and knowledge of the technique, problem-solving techniques and technology is mastered, identified and evaluated by a computer system. The purpose of “Cyber 3S” is to master the subjects provided in the curriculum independently, remotely in self-learning mode. Here the subject is taught by selecting the topics and certain tasks and tests by students. In Cyber3S it is possible to present a task/test with text, image and video. An integral part of the knowledge base of Cyber 3S is so-called “Reminder” as an assistance dealing with the task/test in the form of text or graphics or video or any combination of them, also, present the thematic assistance in the form of Pdf file as well as recommend advice when giving a wrong answer to a test. Cyber 3SE provides self-monitoring of issues mastered by Cyber 3S. It is a trial rehearsal for the exam. In Cyber 3SE as opposed to Cyber 3S: First—The self-examination trajectory changes according to the results of solving the assessment criteria selected by the student, in particular, if the assessment criterion is satisfied as a result of solving the given difficulty, the system automatically shifts the student to the next level of difficulty. Second—it is impossible for a student to stop self-examination at any time and solve problems according to the principle of difficulties as desired. Third—the teacher envisages obtaining a self-control protocol for conducting the debriefing. In Cyber3S, the student can: display the protocol describing the teaching process in the Excel environment on his/her computer, the test provides 3 correct answers out of 7 possible answers, the number of answers to the task is not limited. Answers can be represented by a number, a sentence written in natural language.
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Recent work by Huang, Lewis, Morales, Reiner, and Stanton suggests that the regular elliptic elements of GL n 𝔽 q are somehow analogous to the n-cycles of the symmetric group. In 1981, Stanley enumerated the factorizations of permutations into products of n-cycles. We study the analogous problem in GL n 𝔽 q of enumerating factorizations into products of regular elliptic elements. More precisely, we define a notion of cycle type for GL n 𝔽 q and seek to enumerate the tuples of a fixed number of regular elliptic elements whose product has a given cycle type. In some cases, we provide explicit formulas. Our main tool is a standard character-theoretic technique due to Frobenius, which we make use of by finding simplified formulas for the necessary character values. For every case in which we are not able to compute an explicit formula, we at least determine the asymptotic behavior. We conclude with some results about the polynomiality of our enumerative formulas and some open problems.