Reviewerly/exHarmony huggingface dataset:
An empirical study of blockchain system vulnerabilities: modules, types, and patterns

Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart contracts have been extensively investigated, security bugs of the underlying blockchain systems are much less explored. In this paper, we conduct an empirical study on blockchain's system vulnerabilities from four representative blockchains, Bitcoin, Ethereum, Monero, and Stellar. Specifically, we first design a systematic filtering process to effectively identify 1,037 vulnerabilities and their 2,317 patches from 34,245 issues/PRs (pull requests) and 85,164 commits on GitHub. We thus build the first blockchain vulnerability dataset. We then perform unique analyses of this dataset at three levels, including (i) file-level vulnerable module categorization by identifying and correlating module paths across projects, (ii) text-level vulnerability type clustering by natural language processing and similarity-based sentence clustering, and (iii) code-level vulnerability pattern analysis by generating and clustering code change signatures that capture both syntactic and semantic information of patch code fragments. Our analyses reveal three key findings: (i) some blockchain modules are more susceptible than the others; notably, each of the modules related to consensus, wallet, and networking has over 200 issues; (ii) about 70% of blockchain vulnerabilities are of traditional types, but we also identify four new types specific to blockchains; and (iii) we obtain 21 blockchain-specific vulnerability patterns that capture unique blockchain attributes and statuses, and demonstrate that they can be used to detect similar vulnerabilities in other popular blockchains, such as Dogecoin, Bitcoin SV, and Zcash.
Understanding the How and the Why

This paper presents the results of in-depth study of 14 teams' development processes during a three-week undergraduate course organized around a secure coding competition. Contest participants were expected to first build code to a specification---emphasizing correctness, performance, and security---and then to find vulnerabilities in other teams' code while fixing discovered vulnerabilities in their own code. Our study aimed to understand why developers introduce different vulnerabilities, the ways they evaluate programs for vulnerabilities, and why different vulnerabilities are (not) found and (not) fixed. We used iterative open coding to systematically analyze contest data including code, commit messages, and team design documents. Our results point to the importance of existing best practices for secure development, the use of security tools, and development team organization.
SemCluster: a semi-supervised clustering tool for crowdsourced test reports with deep image understanding

Due to the openness of crowdsourced testing, mobile app crowdsourced testing has been subject to duplicate reports. The previous research methods extract the textual features of the crowdsourced test reports, combine with shallow image analysis, and perform unsupervised clustering on the crowdsourced test reports to clarify the duplication of crowdsourced test reports and solve the problem. However, these methods ignore the semantic connection between textual descriptions and screenshots, making the clustering results unsatisfactory and the deduplication effect less accurate.
Sometimes you have to treat the symptoms: tackling model drift in an industrial clone-and-own software product line

Many industrial software product lines use a clone-and-own approach for reuse among software products. As a result, the different products in the product line may drift apart, which implies increased efforts for tasks such as change propagation, domain analysis, and quality assurance. While many solutions have been proposed in the literature, these are often difficult to apply in a real-world setting. We study this drift of products in a concrete large-scale industrial model-driven clone-and-own software product line in the railway domain at our industry partner. For this purpose, we conducted interviews and a survey, and we investigated the models in the model history of this project. We found that increased efforts are mainly caused by large model differences and increased communication efforts. We argue that, in the short-term, treating the symptoms (i.e., handling large model differences) can help to keep efforts for software product-line engineering acceptable — instead of employing sophisticated variability management. To treat the symptoms, we employ a solution based on semantic-lifting to simplify model differences. Using the interviews and the survey, we evaluate the feasibility of variability management approaches and the semantic-lifting approach in the context of this project.
Reading Ron Right:

This paper discusses the possibility of speculative toy fiction to portray a fictitious internet connected toy, namely a B-bot named “Ron”, as a tool to understand envisioned affordances of future IoToys. The paper positions speculative toy fiction as a tool for design play that allows the imagination to dream up scenarios, which do not yet manifest in the world as innovations, but for which creative fictions have already sketched possible directions. The IoToys is a developing area of technologically-enhanced and connected toys with multidimensional affordances making use of sensors, light, sound, and movement. Previous research demonstrates how current IoToys offer edutainment opportunities through their physical, functional, and fictional affordances. However, what remains scarcely researched, are the affective dimensions of use of connected playthings. By analyzing the transcript of dialogue of the film “Ron's Gone Wrong” (2021) and consulting earlier work on speculative fiction, the author seeks to illustrate the key affordances of the B-bot to synthesize design ideas for future IoToys. The paper suggests how Ron's envisioned affordances together with previous design guidelines provide a pathway to think and design affective features for IoToys of the future, which persuade players to form friendships with and through the play machines.
Underwater Target Detection Based On Modified YOLOv5

Due to the complex underwater environment, it is not easy to achieve high accuracy of underwater target recognition. To solve this problem, this paper presents a modified YOLOv5s model to improve the accuracy of underwater target detection. Firstly, the number of bottlenecks in the first C3 module of current YOLOv5s is increased from original one to three. Secondly, the Coordinate Attention(CA) module is used to replace the bottleneck module to improve the attention level of the model to the target in the image. Finally, the SE module is embedded in the backbone network of the model to further strengthen the attention of the model, so that the model can concentrate the computing power on the target and ignore the unimportant parts of the image. In the neck network, we make changes to the fusion feature layer. The experimental results show that the improved YOLOv5s model has a 3.7% increase in mAP compared to the original model for the same underwater dataset.
Path Planning Based on Probabilistic Roadmap and Ant Colony Optimization

In order to handle the path planning problem in complex environments, a novel method combining probabilistic roadmap, ant colony optimization, and third order Bezier curve has been developed in this study. There are three steps in the suggested procedure. Using a probabilistic roadmap approach, a random map is first created based on the complexity of the surrounding area. This could be accomplished by selecting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{N}$</tex> nodes randomly in complex static environments, then establishing connections between these nodes according to specific criteria or conditions. The created roadmap offers a significant number of potential path segments that could link the start and final point. The second stage entails choosing a path within the already-built roadmap. The final path between the start point and the goal point will be searched by using ant colony optimization. Then, the third stage employs a Bezier curve to smooth out and shorten the obtained path. Several simulations in different environments demonstrate that the proposed approach guarantee to obtain a short, smooth, and safe path between the start point and the goal point.
Hybrid Tamil spell checker with combined character splitting

Summary Spell checker is the application, which helps in finding the spelling errors in a given text. Applications like word processors, mails, search engines, speech recognition and social media forums need these kinds of spell checking tools to increase the correctness of the system. Spell checking is completely implemented in languages such as English, French, and Chinese. But as far as Indian regional languages is concerned, very few works have been carried out, that too partially. Tamil is one such Indian regional language, which requires a fully implemented spell checking application as many people started using this language in social media platforms like Facebook and Twitter. Spelling errors fall on different categories in Tamil language, which involves Sandhi errors, Homophone errors (Mayangoli), and misspelt words error. To tackle all these errors, a new ensemble approach is proposed in this paper. The proposed approach consists of Levenshtein's edit distance algorithm, rule‐based algorithm, Soundex algorithm along with LSTM (Long Short Term Memory) model. We have used a special feature called combine character splitting of Tamil alphabets for feeding the LSTM model to improve the performance of the system. Proposed system produced an accuracy of 95.67%, which is approved by the Tamil scholar.
Fully adaptive recommendation paradigm: top-enhanced recommender distillation for intelligent education systems

Abstract Top-N recommendation has received great attention in assisting students in providing personalized learning guidance on the required subject/domain. Generally, existing approaches mainly aim to maximize the overall accuracy of the recommendation list while ignoring the accuracy of highly ranked recommended exercises, which seriously affects the students’ learning enthusiasm. Motivated by the Knowledge Distillation (KD) technique, we skillfully design a fully adaptive recommendation paradigm named Top-enhanced Recommender Distillation framework (TERD) to improve the recommendation effect of the top positions. Specifically, the proposed TERD transfers the knowledge of an arbitrary recommender (teacher network), and injects it into a well-designed student network. The prior knowledge provided by the teacher network, including student-exercise embeddings, and candidate exercise subsets, are further utilized to define the state and action space of the student network (i.e., DDQN). In addition, the student network introduces a well-designed state representation scheme and an effective individual ability tracing model to enhance the recommendation accuracy of top positions. The developed TERD follows a flexible model-agnostic paradigm that not only simplifies the action space of the student network, but also promotes the recommendation accuracy of the top position, thus enhancing the students’ motivation and engagement in e-learning environment. We implement our proposed approach on three well-established datasets and evaluate its Top-enhanced performance. The experimental evaluation on three publicly available datasets shows that our proposed TERD scheme effectively resolves the Top-enhanced recommendation issue.
Are machine programming systems using right source-code measures to select code repositories?

Machine programming (MP) is an emerging field at the intersection of deterministic and probabilistic computing, and it aims to assist software and hardware engineers, among other applications. Along with powerful compute resources, MP systems often rely on vast amount of open-source code to learn interesting properties about code and programming and solve problems in the areas of debugging, code recommendation, auto-completion, etc. Unfortunately, several of the existing MP systems either do not consider quality of code repositories or use atypical quality measures than those typically used in software engineering community to select them. As such, impact of quality of code repositories on the performance of these systems needs to be studied.
Internet of Vehicles System Based on Blockchain

The Internet of Vehicles is considered an adaptation to the current transportation system's severe position, thanks to advancements in wireless communication systems and intelligent mobility technologies. The autos in the Network of Vehicles system generate Vehicular Networks and constantly deliver additional functionality inspired by real driving data transmitted by the vehicles using wireless communication technology. Drivers may modify the distance between automobiles while planning their route depending on current traffic conditions, positively affecting the efficiency and safety of the transport network. Because of the unique capabilities of a Technology of Devices service platform, automobiles must transmit their position information often. Assailants can gather and analyze automobile transmit data to acquire personal information and perhaps even track the customer's vehicle path, creating new security challenges. This article offers a Blockchain-based confidentiality solution for such Vehicular networks. This same solution incorporates blockchain technology with the Website of Vehicles network to provide a secure and convenient two-way authentication mechanism procedure based on encryption, as well as removing the conventional Internet of Vehicles system's centralized dependency issue.
Information Security by Hiding Data in Binary Images based on Block-Diagonal Partition Pattern

Due to the shift from paper to electronic paradigm, much information is transferred in the form of binary images in multiple domains. This includes medical imaging, military communication, business transactions, and banking. During transmission, the confidentiality and security of information is compromised. Steganography and watermarking are the most popular means to serve this purpose. In both the techniques, secret data and a watermark are embedded respectively in cover images taking care that it is imperceptible. Hiding data and keeping changes caused by hiding unnoticeable is a significant challenge for binary images. This paper proposes a block diagonal partition pattern based method for hiding data in binary images. Image blocks of size 3X3 are considered to apply this method. Based on the number of black and white pixels in each diagonal partition, the potential blocks to carry data bits are identified. This is before and after embedding. The connectivity among pixels in each partition is also checked to declare block's suitability to carry data bit which takes care of distortion factor. The central pixel of a block chosen as a data bit carrier. For extracting the hidden data, the original image does not required as all blocks holding the data satisfy the embeddability criteria before and after hiding. The technique encrypts data twice to keep data more secure. The result shows that our method is secure and has good hiding capacity causing less distortion.
Online Storage, Retrieval &amp; Authentication of Healthcare Documents using Ethereum Blockchain

This paper discusses how blockchain (Ethereum) can be used to as a solution to improve or mitigate communication and storage issues of healthcare information. Smart hospital functionality of drug tracing has been achieved using the non-replicable feature offered by non-fungible tokens (NFTs) generated by ERC721. Every manufacturer along with the medicines allotted to the distributer are treated as unique entities which cannot be replicated. All entity details are recorded in SQL databases. Registration of a patient with the insurance claim system, assigns a unique ID using Secure Hash Algorithm 256 (SHA256) improving security.
Towards CIM-friendly and Energy-Efficient DNN Accelerator via Bit-level Sparsity

The rising popularity of deep neural network (DNN) algorithms calls for energy-efficient accelerators to enable DNNs run on edge devices. In this paper, we presented BitS-Net, a bit-level sparsity method that quantize the network to desirable numbers with more zeros in their bit representation. We demonstrated that BitS-Net can preserve the accuracy (67.73 %) with accuracy drop ¡1% compared to the original network. Moreover, it achieved up to 5x energy efficiency for ResNet-18 models on the ImageNet dataset compared to the baseline methods.
Path Planning of Unmanned Surface Vehicle Port Docking Based on Improved Double Deep Q-Network

In this paper, a novel path planning scheme based on improved Deep Q-Network (DQN) algorithm for Unmanned Surface Vehicle (USV) docking in complex port environment is proposed. First, the port map is pre-processed for edge expansion, and the level of expansion is set according to the complexity of the port environment and the strength of wind. Next, with the view of solving the problem of slow convergence speed and instability of traditional DQN algorithm, an improved Double DQN (DDQN) algorithm with prioritized experience replay and hyperparameter adaptive control is proposed. In order to strike a balance between exploration and exploitation, a dynamic ε scheme is devised to reduce network stabilization time. Finally, the B-spline technique is used to smoothly interpolate the obtained path coordinate points to obtain a viable USV path for port docking. Simulation results demonstrate the effectiveness and efficiency of the proposed scheme.
Comparative Analysis using K - Nearest Neighbour with Artificial Neural Network to Improve Accuracy for Predicting Road Accidents

The purpose of this study is to use machine learning approaches to improve the accuracy of modern road accident prediction systems like the K-Nearest Neighbour Algorithm and Artificial Neural Networks Algorithm. Materials and techniques used include the K-Nearest Neighbour technique and the Artificial Neural Networks algorithm with sample size N=10, iterated 20 times in parallel to test the accuracy of forecasting road accidents. p0.05 indicates the significance of the K-Nearest Neighbour method. When comparing the results of the two algorithms, it is discovered that the K-Nearest Neighbour approach (81.22%) outperforms the Artificial Neural Networks algorithm (69.22%) in terms of accuracy in forecasting road accidents.
How AI is automating writing: The rise of robot writers

The rise of robot writers could have a major impact on marketing and communication. AI is capable of writing marketing content, thanks to the advances in natural language generation (NLG). The software not only mimics human speech, but it can also use data and language to narrate events in real time.
Multimodal Sentiment Analysis using Prediction-based Word Embeddings

In the digital era, information in large amounts may be discovered in the form of reviews and writings of customers. People create reviews using only text or emojis that express the customer’s thoughts or opinions. In most instances, user comments reflect their gut feelings about a product. A significant amount of value is extracted from these remarks by using sentiment analysis. The classification is one of the most pressing challenges in sentiment analysis which is our main agenda in this work. The accuracy is used as a performance evaluator and gives weightage to sentiments given in the form of emojis. A multimodal classification model is utilized here by considering both text and emojis for the final output. The comparati ve study is also done on the word embedding methods of the Word2Vec model. According to the findings of the experiments, the proposed system outperforms the sentiment analysis models, which considers only text for evaluation.
Abstractive Summarizer using Bi-LSTM

Abstractive Summarization (AS) of texts is the task of abstracting crucial information from the source. This paper presents an approach for text summarization in abstractive form with deep learning techniques. This paper develops a model that produces more precise and coherent summaries without redundancy problems. An efficient summarizer should provide the context from the input text in a brief manner. Thus, the output of the summarizer is abstracted information and is presented as a summary to the user. The dataset CNN Daily Mail is often used for multi -sentence summarizing techniques, and the AS models are usually used under an immense deep learning technique termed as seq-to-seq model. In the summarization part, the encoder-decoder model is typically applied. The most often used metric for evaluating the quality of summarization is identified: Recall - Oriented Understudy for Gisting Evaluation (ROUGE). The proposed summarizer performs better in terms of ROUGE.
Design &amp; Implementation of Product Recommendation Solution using Sentiment Analysis

E-Commerce portals and online selling websites are becoming popular day by day. This paper presents a product recommender model using the natural language processing of the reviews and feedbacks of the customers to enhance the quality of recommendation module. The descriptive data mining can be used to find the most accurate recommendations based on their preferences and behaviors. Various studies on product recommendation for e-commerce portals are being conducted to improve the selection in the quickest time frame, and it has been found that the majority of recommender system only works on selection frequency and user rating. The user's past buying history, as well as the opinions of other users on a product, can aid the development of trust in an online shopping website. In this dimension, an approach is being used and implemented on a variety of relevant projects to investigate the gap area in the traditional system and potential solutions to close it. This research work considers unstructured dataset as data input and perform data cleaning followed by stop word removal and lemmatization. Afterwards Sentiwordnet and ambiguity word net library has been used to estimate two different sentiment score for same sentence to prepare a hybrid sentimental score based on natural meaning and probability of ambiguous word arrangement. This work also integrates FP Intersect clustering algorithm to improvise searching queries after product recommendation. Proposed solution has been implemented using java technology and hadoop ecosystem to provide a big data infrastructure and consider Amazon dataset for experimental analysis. The complete solution was estimated on basis of computation time and also performed for two different dataset to evaluate the consistency of proposed solution. A significant improvement has been observed in multi node cluster solution in compare to single node cluster setup irrespective of enhancement in data size.