Reviewerly/exHarmony huggingface dataset:
Research on Construction of Supply Chain Financial Platform Based on Blockchain Technology

Supply chain finance can reduce the financing cost of enterprises, which is of great significance for promoting inclusive finance. However, there are many problems in the current supply chain finance, e.g., information asymmetry, inefficient financing, and high performance risk. In this paper, we propose a novel supply chain financial service platform based on blockchain technology. We first analyze the limitations of mainstream supply chain finance mode. Then, we introduce the proposed financial service platform in detail, including its components, operation process, and advantages. Toward the end, we discussed the future development trends of the platform. This paper is aimed at providing helpful guidance and reference for future research efforts.
Buffer Overflow Vulnerability Location in Binaries Based on Abnormal Execution

Buffer overflow is a very common and dangerous vulnerability. Now there are many methods and tools for this vulnerability, but it is difficult to locate it in binary code. Due to the lack of program semantics in binary programs, it is difficult to determine whether the memory access exceeds its boundary, so we can restore its memory layout to locate the vulnerability. This paper proposes a method: for the same program, input a group of successful execution and abnormal execution data respectively, and then recover the memory of the two kinds of execution. If the memory of successful execution recovery is taken as the buffer boundary, it is easier to judge the buffer overflow in the abnormal execution. In this paper, 20 programs with buffer overflow vulnerability are tested. The experimental results show that the proposed method is more effective than some static program analysis tools in terms of time cost and vulnerability location accuracy.
LEMON: Explainable Entity Matching

State-of-the-art entity matching (EM) methods are hard to interpret, and there is significant value in bringing explainable AI to EM. Unfortunately, most popular explainability methods do not work well out of the box for EM and need adaptation. In this paper, we identify three challenges of applying local post hoc feature attribution methods to entity matching: cross-record interaction effects, non-match explanations, and variation in sensitivity. We propose our novel model-agnostic and schema-flexible method LEMON that addresses all three challenges by (i) producing dual explanations to avoid cross-record interaction effects, (ii) introducing the novel concept of attribution potential to explain how two records could have matched, and (iii) automatically choosing explanation granularity to match the sensitivity of the matcher and record pair in question. Experiments on public datasets demonstrate that the proposed method is more faithful to the matcher and does a better job of helping users understand the decision boundary of the matcher than previous work. Furthermore, user studies show that the rate at which human subjects can construct counterfactual examples after seeing an explanation from our proposed method increases from 54&#x0025; to 64&#x0025; for matches and from 15&#x0025; to 49&#x0025; for non-matches compared to explanations from a standard adaptation of LIME.
Deep learning for wireless modulation classification based on discrete wavelet transform

Summary In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs).
High-dimensional linear discriminant analysis using nonparametric methods

The classification of high-dimensional data is a very important problem that has been studied for a long time. Many studies have proposed linear classifiers based on Fisher’s linear discriminant rule (LDA) which consists of estimating the unknown covariance matrix and the mean vector of each group. In particular, if the data dimension p is larger than the number of observations n (p>n), the sample covariance matrix cannot be a good estimator of the covariance matrix due to the well-known rank deficiency. To solve this problem, many studies proposed methods by modifying the LDA classifier through diagonalization or regularization of covariance matrix. In this paper, we categorize existing methods into three cases and discuss the shortcomings of each method. To compensate for these shortcomings, our baseline idea is that we consider estimation of the high dimensional mean vector and covariance matrix altogether while existing methods focus on shrinkage estimator of either mean vector or covariance matrix. We provide theoretical result that the proposed method is successful in both sparse and dense situations of the mean vector structures. In contrast, some existing methods work well only under specific situations. We also present numerical studies that our methods outperform existing methods through various simulation studies and real data examples such as electroencephalogy (EEG), gene expression microarray, and Spectro datasets.
A Design of Interactive Online English Translation System Based on Deep Learning

In order to overcome the low translation speed and accuracy of traditional online English translation system, this paper proposes a new interactive English translation system based on deep learning. Based on B/S architecture, a three-tier system architecture is designed. The hardware part of the system includes corpus preprocessing module, English morphology classification module and interactive online translation service module to improve the performance of the system. In the software part of the system, the two-way long-term and short-term memory network of deep learning technology is used to extract features of English translation data, so as to improve the accuracy of translation. The experimental results show that, compared with the existing translation system, the translation speed and accuracy of the designed system are effectively improved, so the practical application performance of the designed system is strong.
Design of English Mobile Learning Platform Based on GSM-R Wireless Network Communication System

Because the traditional English mobile learning platform has low student satisfaction and a long learning time, an English mobile learning platform based on the GSM-R wireless network communication system is designed. A GSM-R wireless network communication system is built through existing facilities, new facilities, and business-side facilities. Platform function modules are designed based on the communication system, including course resource management module, online examination management module, online Q&amp;A management module, platform management module, and user login. Based on this module, the platform software is designed according to the three steps of database design, platform encryption technology, and learning recommendation algorithm to create the English mobile learning platform. The simulation experiment results show that the application of the designed platform can improve student satisfaction and improve student learning efficiency.
RA-SIFA: Unsupervised domain adaptation multi-modality cardiac segmentation network combining parallel attention module and residual attention unit

Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation.In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU).First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second, the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner.The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively.The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.
Flip Learning: Erase to Segment

Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation. Unlike existing weakly-supervised approaches, in this study, we propose a novel and general WSS framework called Flip Learning, which only needs the box annotation. Specifically, the target in the label box will be erased gradually to flip the classification tag, and the erased region will be considered as the segmentation result finally. Our contribution is three-fold. First, our proposed approach erases superpixel level using a Multi-agent Reinforcement Learning framework to exploit the prior boundary knowledge and accelerate the learning process. Second, we design two rewards: classification score and intensity distribution reward, to avoid under- and over-segmentation, respectively. Third, we adopt a coarse-to-fine learning strategy to reduce the residual errors and improve the segmentation performance. Extensively validated on a large dataset, our proposed approach achieves competitive performance and shows great potential to narrow the gap between fully-supervised and weakly-supervised learning.
Progress of machine learning based automatic phoneme recognition and its prospect

A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes.The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. The ML based techniques do not follow the conventional method based on identification of acoustic properties. Rather, ML techniques build their own trained model (algorithm) using readily available data. They do so by finding out the hidden patterns in speech signals, and acquire predictive intelligence through learning. Therefore, ML techniques can be said to provide a more generalized model for phoneme classification. In this paper, we present a comprehensive survey of ML tools to build phoneme recognizers. We also highlight some applications of speech (especially phoneme) recognition which illustrate the current scope as well as future prospects of APR.
Can “micro VM” become the next generation computing platform?: Performance comparison between light weight Virtual Machine, container, and traditional Virtual Machine

Serverless computing - including “Function as a service (FaaS)”, gives a flexible computing model for users. Today, cloud providers use container to create isolated computing environment for FaaS users. However, containers share a same kernel for all instances run on top of that, which cannot guarantee an ABI-level security as virtual machine does. Therefore, a new kind of virtual machine with container-level low overhead, named as “micro VM” or “light weight virtual machine” comes. But using virtual machines means trade off. Comparing to the high performance and lightweight containers, virtual machines usually have unavoidable problems like I/O (input and output), and some existing problems of containers like the cold start latency may become more severe. But how much it takes and if it is deserving? This paper provides a comparison between traditional virtual machine, container, and the new light weight virtual machine (named micro VM) in terms of scalability and performance, aiming to determine whether the micro VM can be the suitable computing platform for FaaS.
Comparison of Deep-Neural-Network-Based Models for Estimating Distributed Representations of Compound Words

Word embeddings or word vectors have become fundamental in language processing techniques, especially deep learning approaches. Although many languages have compound words (e.g., "robot arm" and "maple leaf"), such words have not received much attention from researchers. Most research on compound word embeddings considered only two-word compounds; there has been little detailed analysis on the learning representations of arbitrary-length compound words. This paper discusses the necessity for learning-based approaches for estimating the distributed representations of compound words instead of a simple average of the representations of constituents. An evaluation of two downstream tasks confirms the effectiveness of compositional models in encoding useful information into vector spaces. The experimental results suggest that complex architectures such as long short-term memory, gated recurrent units, and transformers learn better representations for long entities, whereas simpler models such as recurrent neural networks are more applicable for downstream tasks where there are only short compounds (two or three words in length), as in the noun compound interpretation task.
VR Educational Game Design and Research Based on Multi-Modal Interaction from the Perspective of Embodied Cognition

The embodied cognition theory advocates the liberation and return of learners’ body in learning, and emphasizes learners’ experience learning; while VR educational games also attach importance to learners’ situational teaching, which combines cognition, body and environment to improve teaching effect. With the swift progress of the times, educational games have been widely used in each study section of education. Nowadays, educational games supported by virtual reality technology and other new technologies have been gradually used. Researchers in various fields have explored VR educational games to varying degrees from different perspectives, but researches on VR educational games from the perspectives of embodied cognition theory and multimodal interaction are still rare. Based on embodied cognition theory and multi-modal interaction technology, this paper gives a brief overview of the current situation of VR educational games in recent years, and uses the unity3D platform to try to construct the basic development and design framework of VR educational games. Summarizing the potential relationship of VR educational games based on multi-mode and the overall trend of future development.
DevRec: Multi-Relationship Embedded Software Developer Recommendation

Collaboration efficiency is of paramount importance for software development. Finding suitable developers is critical and challenging due to the difficulty of capturing developers’ expertises, relevance as well as the sparsity of explicit developer-task interactions. To tackle this problem, existing developer recommendation approaches focus on modelling the developer’s expertise or interactions with tasks based on their historical information. However, such approaches often suffer from low performance because they ignore some useful information that might help improve recommendation performance, such as 1) developers’ collaboration relationship; 2) the interaction relationship between developer and task; and 3) the tasks’ similarity relationship. To leverage the above-mentioned relationships, this paper proposes DevRec, a novel multi-relationship embedded approach for software developer recommendation. We first formally define the multi-relationships and then learn the vector representations (aka. embeddings) of developers and tasks. Second, we explicitly encoded the multi-relationships into the embedding process. Third, to refine the embeddings of developers and tasks, we recursively propagate the embeddings from their high-order connectivity based on graph convolution network. Moreover, to reveal the importance of different relationships, we generate their attentive weights based on attention mechanism. Finally, to evaluate the performance of DevRec, we conduct extensive experiments on two real-world datasets, and to investigate the usefulness of DevRec in practice, we conduct a user study at a large software company. The results show that DevRec outperforms other five state-of-the-art approaches significantly.
Hybrid Maintainability Prediction using Soft Computing Techniques

Effective software maintenance is a crucial factor to measure that can be achieved with the help of software metrics. In this paper, authors derived a new approach for measuring the maintainability of software based on hybrid metrics that takes advantages of both i.e. static metrics and dynamic metrics in an object-oriented environment whereas, dynamic metrics capture the run time features of object-oriented languages i.e. run time polymorphism, dynamic binding etc. which is not covered by static metrics. To achieve this, the authors proposed a model based on static and hybrid metrics to measure maintainability factor by using soft computing techniques and it is found that the proposed neuro-fuzzy model was trained well and predict adequate results with MAE 0.003 and RMSE 0.009 based on hybrid metrics. Additionally, the proposed model was validated on two test datasets and it is concluded that the proposed model performed well, based on hybrid metrics.
A Holistic Approach for Locating Traffic Differentiation in the Internet

The worldwide debate over Network Neutrality (NN) has been raging on for nearly two decades.According to NN principles, all traffic in the Internet must be treated with impartiality.In particular, unfair Traffic Differentiation (TD) is not allowed.Several strategies have been proposed for detecting TD, but locating the source of TD is still an under-explored topic.In this work, we present a holistic approach for unifying TD detection solutions into a single framework with the purpose of locating the source of TD.We propose an algorithm for combining measurements from multiple vantage points, and a strategy for selecting good vantage points.Our proposals leverage Internet peering properties to infer the behavior of individual Autonomous Systems (ASes), without requiring knowledge of the exact routes traversed by measurement probes.To evaluate our proposals, we first ran several experiments to confirm that indeed Internet routes do present the required properties.Then, several simulations were performed to assess the efficiency of our proposals.Results show that our approach is capable of locating TD under several different conditions.Another finding is that issuing measurements from a few end-hosts of core Internet ASes achieves similar results than from a much larger number of end-hosts at the edge.
A Novel Technique for Fake Signature Detection Using Two-Tiered Transfer Learning

Fake signature refers to an unauthorized person imitating another person's signature. We propose a new method to counter said problem with the help of transfer learning. First, we use a convolution neural network architecture, namely VGG-16 as a feature extractor, and input the data into different classifiers, namely random forest, k-nearest neighbours, extra tree and support vector machine for classification. Finally, the output of the classifier is inputted into an artificial neural network for final prediction. Our experimental results prove that the proposed algorithm performs well and obtains 97.3% accuracy.
Ultra-short-term solar PV power forecasting based on cloud displacement vector using multi-channel satellite and NWP data

Satellite cloud image data plays an important role in the ultra-short-term photovoltaic (PV) power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate the cloud cluster's occlusion of the PV power plant for a period of time in the future, thereby forecasting the PV power. The satellite cloud image data provided by FY-4A Satellite has large and unequal sampling time intervals, which brings difficulties to cloud displacement vector calculation. This paper proposes a method for calculating the displacement vector of the cloud edge block based on the cosine similarity and the adaptive adjustment of the matching spatial scale. The parameter equation is used to establish the relationship between the relative position of the cloud cluster and the PV plant over time, and to predict the future position. On this basis, this paper proposes an ultra-short-term forecasting method of PV power based on cloud displacement vector. The performance of the proposed model is verified through case analysis.