Reviewerly/exHarmony huggingface dataset:
Code Search Method Based on Multimodal Representation

Developers tend to search and reuse code snippets from large-scale corpora while implementing some of the features that existed in development. This will improve the efficiency of development. Code search is to search for semantically relevant code snippets based on a given natural language query. In existing methods, the semantic similarity between code and query is quantified as their distance in the shared vector space. To improve the vector space and map the code vector and query vector into a shared vector space so that the semantically similar code-query pairs are close to each other, we propose a code search method with multimodal representations. It can better enhance the semantic relationship between code snippets and queries. Experiments on Java datasets show that the multimodal representation model MulCS improves the quality of code search. MulCS outperforms several existing advanced models in several performance metrics.
On Securing the Communication in IoT Infrastructure using Elliptic Curve Cryptography

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.
Visual-ISAM: A Visualization Method for Software Failure Analysis and Evaluation based on Knowledge Graph Utilizing Improved SALKU Model

Software faults constantly appear during software development and evolution. The information on various platforms for bug knowledge recording, such as Stack Overflow, is mostly stored in weak entity relational database missing linkable relationships, which results in negative impacts on knowledge reuse. To enrich the relationships between entities and construct a software fault knowledge graph, we improve the SALKU model by considering the direction of prediction results between a pair of knowledge units, and utilize it to predict the class of linkable knowledge units. Experiment results show the improved model increases the ratio of knowledge unit pairs with equivalent link prediction results from 90.2% to 100% based on the premise of ensuring precision, recall, and F1-score. Eventually, we visualize the data from Stack Overflow in the knowledge graph based on the extracted relationships.
RandomMix: An Effective Framework to Protect User Privacy Information on Ethereum

With the wide application of blockchain technology, the user privacy information involved in Ethereum is increasingly rich. But with the introduction of de-anonymization technologies, more and more attacks may lead to the disclosure of user privacy information. In this paper, as a user privacy protection approach, RandomMix is proposed based on account mixing and transaction value encryption. We conduct experiments on the real transactions on Ethereum and find out that RandomMix can resist account clustering attack, transaction fingerprint analysis attack and Sybil attack. Meanwhile, RandomMix consume less gas compared with other approaches.
Multi Pair Swap-Based Weather Derivative DeFi

For solar power producers, fluctuation in power generation due to changes in solar radiation are one of the major risks because they can lead to unstable income. To deal with this risk, solar power producers have been trading weather derivatives, financial instruments that generate income calculated based on solar radiation. Prior research has proposed one-to-one bilateral swap-based weather derivatives utilizing DeFi. However, in bilateral transactions, the parties need to agree on various terms and conditions of the swap in advance, which may lead to lost time due to repeated negotiations and adjustments, as well as the possibility of failed negotiations. In this paper, we extend bilateral transactions to N-to-N multi-pair swaps, which do not require negotiation before the swap transaction and are easier to participate in. We implement a prototype on the Ethereum test network and show that our proposed method can mitigate income loss risk due to weather fluctuations.
The Real-Time General Display and Control Platform Designing Method based on Software Product Line

Software product lines can significantly improve productivity, shorten the development cycle, improve product quality and user satisfaction. Real-time display control platform general using the method to realize software reuse of software product line, from the communications, navigation, radar display control projects in areas such as series software can be derived from the common needs and variable demand, design a display control framework and common software reusable component and reuse into the library, finally applied to the similar project in the form a series of software products. The successful implementation case of a radar display control project fully proves that the real-time universal display control platform based on a software product line has the characteristics of high reuse, high quality, and low cost.
Recommendation Algorithm for Graph Convolutional Networks based on Multi-Ralational Knowledge Graph

Classic recommendation technologies such as collaborative filtering have some challenging problems such as cold start. Because knowledge graph can enrich data information, in recent years, many scholars have applied it to recommendation systems to solve the above problems. However, Most of the methods only exploit relations and entities involved in the knowledge graph, and do not further explore the correlation information between the entities in the knowledge graph. In order to solve the above problems, recommendation algorithm for graph convolutional networks based on multi-relational knowledge graph (Multi-RKGCN) is proposed, which expands the relations and entities in knowledge graph through reflexive and self-circulating ways. At the time of aggregation, the tail entity and the corresponding relationship are combined and embedded by the knowledge graph embedding technology to enrich the semantics of the entity. Finally, the performance of AUC and F1 is verified on two publicly available datasets. The experimental results show that Multi-RKGCN method is better than KGCN method.
Knowledge Graph Question Answering based on Contrastive Learning and Feature Transformation

Traditional Knowledge Graph Question Answering(KGQA) usually focuses on entity recognition and relation detection. Common relation detection methods cannot detect new relations without corresponding word entries in the system, and the propagation of errors leads to the loss of some semantic similarity information. In this paper, we propose an end-to-end knowledge graph question-answering framework (TransCL). Latent knowledge is first mined from the knowledge base and augmented information is generated in the form of question-answer pairs. Positive features are then transformed into difficult positive features using a feature transformation method based on positive extrapolation. We use contrastive learning methods to aggregate vectors and retain the original information, capturing deep matching features between data samples by contrast. TransCL is more capable of fuzzy matching and dealing with unknown inputs. Experiments show that our method achieves an F1 score of 85.50% on the NLPCC-ICCPOL-2016 open domain QA dataset.
Detecting Security Vulnerabilities with Vulnerability Nets

Detecting code vulnerabilities is a crucial part in secure software development. Many static analysis tools have been proven useful in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some work incorporates the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents vulnerability nets, which are a special Petri net that integrates with data dependence graphs and control flow graphs. Specifically, the proposed approach is intended for detecting taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. In this paper, the construction and use of vulnerability nets are discussed in detail. Furthermore, we show the feasibility by presenting a case study in analyzing an example adapted from a real-world case.
Super-resolution of Retinal Fundus Images Using Generative Adversarial Networks

It is important to have high-resolution medical images for the early diagnosis of diseases. It became more challenging once the structure is very small as in retinal fundus images. A generative adversarial network (GAN) centered retinal fundus image super resolution system is proposed for the early detection of retinal diseases. The low resolution images are fed as the input to the system and the output obtained will be a high resolution image. The method is tested on standard databases like DRIVE, STARE, etc. The PSNR & SSIM values obtained in the suggested method outperform the state of art methods.
Hardy Weinberg Principle Automation : A Comparative study of Neural network and Fuzzy based accessibility model for the study of Evolution

Major diversity in population, heredity, and varied survival rates contribute to evolution in organism populations. A common technique to study evolution in population is to investigate the variation of the population's allele frequencies (genetic frequency) from one generation to the other. In plain terms this means that instead of focusing on two main parental species evolution should be studied by investigating the entire population. The most effective method is to analyze the variation in allele frequency in the first generation of the population and then predict the corresponding variation in the future filial generations. Hardy-Weinberg Equilibrium model equations in Ecology and Sustainability are used in general to predict a population's allele frequency in the future. As the population model becomes complicated these ecological equations become too difficult to realize and likewise the prediction of the population of the species for the future generation. For example, considering the re-fragmentation of forest landscape. This paper presents two unique methods that utilizes computational intelligence (CI) techniques to automate the Hardy-Weinberg principle by integrating Engineering and Ecology. The automation is done using two CI techniques, which are the Mamdani Fuzzy Inference System and Back Propagation Theory. The Mamdani FIS is tuned using MATLAB fuzzy inference toolbox. The back propagation training model is deployed using python which utilizes Keras and Tensorflow for predicting evolution. To prove the efficiency of the trained models a standard allele frequency database named “ALFRED” is used.
A Density Peak Clustering algorithm based on Adaptive K-nearest Neighbors with Evidential Strategy

A novel clustering method based on density peaks(DPC) was published on the journal Science. However, for DPC, it is a challenge to choose an appropriate cutoff distance dc, which can affect local density distribution of the datasets. Additionally, the assigning strategy in DPC may cause a domino effect: Many following points might be wrongly assigned once a point is assigned erroneously. To overcome these drawbacks, we introduce a density peak clustering method based on adaptive K-nearest neighbors with evidential assigning strategy (named as AKE-DPC). Firstly, we introduce an adaptive K-nearest neighbors strategy to find a proper K which can form a reasonable local density distribution and find cluster centers simultaneously; secondly, after the cluster centers are found, we introduce a strategy based on evidential rules to assign the remaining points. The advantages of AKE-DPC are obvious that negate the need for decision graph and input parameter such as p, a percentage which is used to computer the cutoff distance dc in DPC. Experiments on both synthetic and real-world datasets are conducted to compare our algorithm with both DPC and some DPC-KNN algorithms such as ADPC-KNN, FKNN-DPC and DPC-KNN. The clustering results demonstrate that our algorithm is not only effective but also outperforms other algorithms on most cases.
A Passive Optical Motion Capture Method towards Occlusion Conditions Based on Multi-vision System

When using passive optical motion capture technology to measure the 6 degree of freedom(6-DoF) motion of mobile robots, the target attached to it may be obscured by the obstacles in the environment. The occlusion will lead to the marker points missing and further affect the accuracy of the pose estimation. In this paper, a passive optical motion capture method based on feature reconstruction and state estimation is proposed. The feature reconstruction is conducted by the means of monocular EPnP algorithm to make use of the priori information and keep the dimensions of the observations constant. Then an adaptive unscented Kalman filter(UKF) is adopted to accomplish state estimation. Finally the information from different binocular systems is fused in a multi-vision system to further improve the robustness of the motion capture system. The measurement results of our system show that the average absolute position error is 7.892mm and the average absolute attitude error is 1.395° during the measurement of the 6-DoF motion under some severe occlusion conditions. The results demonstrate the effectiveness and augmentability of the proposed method.
Subspace learning based on label release and low-rank representation for small sample face recognition

Subspace learning is often used to solve face recognition problems, and has achieved good results in some scenes. However, the current methods based on subspace learning still have the following problems: As a kind of important information, label information is often ignored in the process of model building. Besides, many methods lack the theoretical basis for enlarging the difference between classes. To solve these problems, this paper proposes a subspace learning method based on Label Release and Low-rank Representation (LRLR), and applies this method to small sample face recognition. In LRLR, on the one hand, we use label information to build a label release model, and embed this model in the process of subspace learning, so that the learned mapping matrix can map samples to the new subspace to achieve the purpose of increasing the difference between classes. On the other hand, the nuclear norm and sparse norm are used to protect the intrinsic structure of the data. Experimental results show that the proposed LRLR is effective in face recognition.
End-to-End Entity Linking Combined with Bert-based Siamese and Interaction Network

The Entity Linking (EL) task is actually more complex than it seems. Not only are most of the given texts short and unlabeled with candidate mention information, but the entities to be predicted generally do not appear in the training set of the pre-trained model. This paper introduces an end-to-end Entity Linking model combined with Bert-based Siamese and Interaction Network, which can complete the EL task quickly and accurately given only short textual information without pre-specified mention boundaries at the time of input. The model is divided into two stages to achieve end-to-end entity linking. The first part is a pretrained BERT encoder-based Siamese network (BSN) architecture. BSN includes a text encoder and an entity encoder. These encoders are used to obtain the word embedding of the input text and the entity description token of the knowledge base respectively. We can also calculate the probability that a span can be used as a mention. In the candidate generation process, we first take the average value of the token word vector in a mention as the representation vector of the mention. We further obtain the similarity between the mention and the entity in the knowledge base and the span corresponding to an entity probability. During training, the loss of mention detection and candidate generation is counted as the loss of the first part. In the second part, we add an interaction network (BIN) based on pretrained BERT encoder (BIN). Concatenate the mention context and candidate entities, obtain the representation vector of the mention context-candidate entity through the encoder. Then add a linear layer to obtain the score and reorder the original candidate entities to achieve final disambiguation. Besides, a corresponding solution is proposed for the Speed-Accuracy Tradeoff problem. In experiments, the model achieves near-SOTA performance on the Zero-shot EL dataset and the WikilinksNED dataset. And it is shown that the stage of Mention Detection (MD) and Candidate Entity Generation (CEG) are crucial in the end-to-end entity linking process, and the two tasks can benefit from each other from joint training.
Automatic Labeling of SDN Controller Defect Text based on Neural Topic Model

Software defined networking controller defects may cause unexpected failures and reduce network reliability. The historical defect texts provide information on defecting symptoms and fixing strategies, but they are not sufficiently labeled. An approach based on a neural topic model is proposed to automatically assign phrase labels to the defect text. First, five types of information that can be extracted from the defect text are provided to guide the generation of candidate phrases. Second, contextualized embedding representations are extracted by the pre-training BERT Overflow model and input to the contextualized topic model to extract a more coherent topic distribution. Third, the accuracy of the unsupervised assignment of labels is improved by filtering candidate phrases through two-level label filtering. Finally, FastText is applied to train a multi-label classifier to assign labels to the new defect text. The experimental results demonstrate that the proposed approach can effectively assign interpretable labels to the defect text.
Similarity Analysis in Data Element Matching based on Word2vec

With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.
Human body gesture-controlled gaming application

Gaming has always held the requirement of peripheral devices such as keyboards, mouse, joystick, etc. But with computer technology evolving every single day, human-body gestures are found to be a potential replacement for the primary accessory of Human-Computer Interaction (HCI). In this paper, an idea of a human-body gesture controlled gaming desktop application has been proposed. With a simple web camera, body gestures have been detected and image processing techniques have been performed with the help of mediapipe - a prebuilt computer vision neural network, which enables a user to directly interact with the gaming console and with the use of OpenCV, a library designed to solve computer vision problems and which provides many useful programming functions used for features like face, edge, object detection, etc. So the input images can be processed and communicated with the gaming commands virtually. The activity of the said user is recorded and stored to MongoDB, an open source No-SQL document based database program. With this desktop application, a user can play any of the various games offered on the portal.
IRRT: An Automated Software Requirements Traceability Tool based on Information Retrieval Model

In the software development process, requirements traceability is a key part for ensuring the success of the entire project. It is very important to generate requirements traceability links, which can promote the software development and maintenance processes, such as software requirements integrity analysis, software evaluation, software testing, software validating, etc. However, the generation of the requirements traceability links is usually time-consuming and labor-intensive. In order to solve it, we designed and developed an automated software requirements traceability tool based on information retrieval (IR) model. The tool can not only automatically generate trace links but also evaluate trace links. It uses Vector Space Model (VSM) and the trace recommendation-based code class structure (TRCCS) links to generate trace links. We measure the performance in term of Precision, Recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{F}_{2}$</tex> are used to evaluate the trace links. The experimental results show that the tool can improve the efficiency of requirements traceability links generation and better support software development activities.
A Data-Efficient Method of Deep Reinforcement Learning for Chinese Chess

The computer game is the Drosophila in the field of artificial intelligence. Recently, a series of computer game systems., such as AlphaGo and AlphaGo Zero, defeating the world human champion of Go, has greatly refreshed people's understanding of the creativity of machine. This paper applies the deep reinforcement learning method to the computer Chinese Chess. We are committed to decrease the demand for computing resources heavily from multi-perspectives, such as data augmentation and using more intermediate results as labels. The experiment shows that the level of our program is increased rapidly.