Reviewerly/exHarmony huggingface dataset:
Mobile Phone Price Class Prediction Using Different Classification Algorithms with Feature Selection and Parameter Optimization

Machine Learning (ML) algorithms are used in many fields such as finance, education, industry, medicine and e-commerce. ML algorithms show performance differences depending on the dataset and processing steps. Choosing the right algorithm, preprocessing and postprocessing methods has great importance to achieve good results. In this paper, Random Forest Classifier, Logistic Regression Classifier, Decision Tree Classifier, Linear Discriminant Analysis, K-Nearest Neighbor Classifier and SVC methods are compared to predict mobile phone price class. The "Mobile Price Classification" dataset which is taken from Kaggle.com is used to evaluate methods. Firstly, all values at the dataset are checked to verify that there are no missing values. After that, scaling is applied to dataset in order to obtain more relevant data for ML algorithms. Then, feature selection methods which reduce the computational cost by reducing the number of inputs are performed to get meaningful features. Finally, the parameters of classification algorithms are tuned to improve the system accuracy. According to obtained results, it is seen that ANOVA f-test feature selection method is more convenient for this dataset. It gives satisfying accuracy with a minimum number of features. It is also seen that the SVC classifier has the highest test accuracy compared to other models.
Embedding and extraction of knowledge in tree ensemble classifiers

Abstract The embedding and extraction of knowledge is a recent trend in machine learning applications, e.g., to supplement training datasets that are small. Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and defence, respectively. This paper studies the embedding and extraction of knowledge in tree ensemble classifiers, and focuses on knowledge expressible with a generic form of Boolean formulas, e.g., point-wise robustness and backdoor attacks. For the embedding, it is required to be preservative (the original performance of the classifier is preserved), verifiable (the knowledge can be attested), and stealthy (the embedding cannot be easily detected). To facilitate this, we propose two novel, and effective embedding algorithms, one of which is for black-box settings and the other for white-box settings. The embedding can be done in PTIME . Beyond the embedding, we develop an algorithm to extract the embedded knowledge, by reducing the problem to be solvable with an SMT (satisfiability modulo theories) solver. While this novel algorithm can successfully extract knowledge, the reduction leads to an NP computation. Therefore, if applying embedding as backdoor attacks and extraction as defence, our results suggest a complexity gap (P vs. NP) between the attack and defence when working with tree ensemble classifiers. We apply our algorithms to a diverse set of datasets to validate our conclusion extensively.
Sharding Techniques in the Era of Blockchain

Blockchain is a peer-to-peer ledger that records a growing list of transactions in a tamper-resistant manner using cryptographic hashes. Centralized points of vulnerability are eliminated in blockchain, and so it is considered secure by design under some reasonable assumptions, such as honest majority. But scalability remains a major limitation that can be improved by sharding. Full sharding is one of the approaches in achieving the high performance of blockchain systems. This paper proposes a locality-based full sharding protocol in permissioned blockchains. We introduce a simple and efficient cross-shard transaction handling protocol. A prototype is under development based on Hyperledger Fabric.
SRDN: A Unified Super-Resolution and Motion Deblurring Network for Space Image Restoration

Space target super-resolution (SR) is a domain-specific single image SR problem aiming to help distinguish the satellite and spacecrafts from numerous space debris. Compared to the other object SR problem, images for space target are always in low quality with varies of degradation condition, as a result of long distance and motion blur, which significantly reduces the manual classification reliability, especially for these small targets, e.g., satellite payloads. To address this challenge, we present an end-to-end SR and deblurring network (SRDN). Concretely, focusing on the low-resolution (LR) space target images with blind motion blur, we integrate the SR and deblur function together, improving the image quality by a unified generative adversarial network (GAN)-based framework. We implement a deblur module by using contrastive learning to extract degradation feature and add symmetrical downsampling and upsampling modules to the SR network in order to restore texture information, while shortcut connections are redesigned to maintain the global similarity. Extensive experiments on the public satellite dataset, BUAA-SID-share1.5, demonstrate that our network outperforms the state-of-the-art SR and deblur methods.
Promise $$\varSigma $$-Protocol: How to Construct Efficient Threshold ECDSA from Encryptions Based on Class Groups

Threshold Signatures allow n parties to share the ability of issuing digital signatures so that any coalition of size at least \(t+1\) can sign, whereas groups of t or fewer players cannot. The currently known class-group-based threshold ECDSA constructions are either inefficient (requiring parallel-repetition of the underlying zero knowledge proof with small challenge space) or requiring rather non-standard low order assumption. In this paper, we present efficient threshold ECDSA protocols from encryption schemes based on class groups with neither assuming the low order assumption nor parallel repeating the underlying zero knowledge proof, yielding a significant efficiency improvement in the key generation over previous constructions.
Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data

An issue report documents the discussions around required changes in issue-tracking systems, while a commit contains the change itself in the version control systems. Recovering links between issues and commits can facilitate many software evolution tasks such as bug localization, defect prediction, software quality measurement, and software documentation. A previous study on over half a million issues from GitHub reports only about 42.2% of issues are manually linked by developers to their pertinent commits. Automating the linking of commit-issue pairs can contribute to the improvement of the said tasks. By far, current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results, sometimes to the point that imposes human supervision on the predicted links. The low performance gets even more severe when there is a lack of textual information in either commits or issues. Current approaches are also proven computationally expensive. We propose Hybrid-Linker, an enhanced approach that overcomes such limitations by exploiting two information channels; (1) a non-textual-based component that operates on non-textual, automatically recorded information of the commit-issue pairs to predict a link, and (2) a textual-based one which does the same using textual information of the commit-issue pairs. Then, combining the results from the two classifiers, Hybrid-Linker makes the final prediction. Thus, every time one component falls short in predicting a link, the other component fills the gap and improves the results. We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects. Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and 41.3%, regarding the F-measure. Moreover, the proposed approach exhibits extensive improvements in terms of performance as well. Finally, our source code and data are publicly available.
FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example

Abstract Background The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. Results Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. Conclusions We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.
Implications of Alternative Serverless Application Control Flow Methods

Function-as-a-Service or FaaS is a popular delivery model of serverless computing where developers upload code to be executed in the cloud as short running stateless functions. Using smaller functions to decompose processing of larger tasks or workflows introduces the question of how to instrument application control flow to orchestrate an overall task or workflow. In this paper, we examine implications of using different methods to orchestrate the control flow of a serverless data processing pipeline composed as a set of independent FaaS functions. We performed experiments on the AWS Lambda FaaS platform and compared how four different patterns of control flow impact the cost and performance of the pipeline. We investigate control flow using client orchestration, microservice controllers, event-based triggers, and state-machines. Overall, we found that asynchronous methods led to lower orchestration costs, and that event-based orchestration incurred a performance penalty.
Visual Sensing Human Motion Detection System for Interactive Music Teaching

The purpose is to study the interactive teaching mode of human action recognition technology in music and dance teaching under computer vision. The human action detection and recognition system based on a three-dimensional (3D) convolutional neural network (CNN) is established. Then, a human action recognition model based on the dual channel is proposed on the basis of CNN, and the visual attention mechanism using the interframe differential channel is introduced into the model. Through experiments, the performance of the system in the process of human dance image recognition based on the Kungliga Tekniska Högskolan (KTH) dataset is verified. The results show that the dual-channel 3D CNN human action recognition system can achieve high accuracy in the first few rounds of training after the frame difference channel is added, the error can be reduced quickly, and the convergence can start quickly; the recognition accuracy of the system on KTH dataset is 96.6%, which is higher than that of other methods; for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>3</mn> <mo>×</mo> <mn>3</mn> <mo>×</mo> <mn>3</mn> </math> basic convolution kernel, the best performance of the classification network can be obtained by pushing forward 0.0091 seconds in the calculation. Thereby, the dual-channel 3D CNN recognition system has good human action recognition accuracy in the dance interactive teaching mode of music teaching.
Graph Neural Collaborative Topic Model for Citation Recommendation

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.
Grammar-based cooperative learning for evolving collective behaviours in multi-agent systems

This paper presents a novel grammar-based evolutionary approach which allows autonomous emergence of heterogeneity in collective behaviours. The approach adopts a context-free grammar to describe the syntax of evolving rules, which facilitates an evolutionary algorithm to evolve rule structures without manual intervention. We propose modifications to the genome structure to address the requirements of heterogeneity, and two cooperative learning architectures based on team learning and cooperative coevolution. Experimental evaluations with four behaviours illustrate that both architectures are successful in evolving heterogeneous collective behaviours. Both heterogeneous architectures surpass a homogeneous model in performance for deriving a flocking macro behaviour, however the homogeneous model is superior for evolving micro behaviours such as cohesion and alignment. The results infer that by placing the entire set of agent rules and their syntax under evolutionary control, effective solutions to complex problems can be evolved when human knowledge and intuition becomes insufficient.
Accessible authentication: Assessing the applicability for users with disabilities

Access to the many benefits available from digital technology can often vary depending upon the capabilities and facilities of the individual who is attempting to engage with it. Many digital devices and services require us to be identified and so require some form of user authentication as part of the process. However, the authentication methods that are currently dominant can prove difficult to use and do not meet the needs of users with disabilities. As such, they can constitute an additional barrier that is not faced by other users. This paper presents an assessment of different forms of user authentication (including various forms of secret, token and biometric approaches) against a range of potential disability categories that may affect their suitability for related user communities. The study draws upon disability classification identified by the World Health Organization and analyzes how the usability and/or security of current authentication methods fail users with disabilities due to their non-inclusive design. The discussion is based upon a combination of literature review and examination of real-life examples, and identifies aspects of current implementations that can cause problems in different scenarios. The findings suggest that biometric approaches are likely to be more directly applicable without incurring additional overheads, although even here the potential usability impacts are more prominent for some forms of disability. It is further recognised that while some methods can be made more accessible via assistive technologies, the primary aim should be for authentication choices to be as inclusive as possible by default, rather than expecting or requiring that some user groups should face an additional challenge to accessibility.
Machine Learning to Combine Static Analysis Alerts with Software Metrics to Detect Security Vulnerabilities: An Empirical Study

Software developers can use diverse techniques and tools to reduce the number of vulnerabilities, but the effectiveness of existing solutions in real projects is questionable. For example, Static Analysis Tools (SATs) report potential vulnerabilities by analyzing code patterns, and Software Metrics (SMs) can be used to predict vulnerabilities based on high-level characteristics of the code. In theory, both approaches can be applied from the early stages of the development process, but it is well known that they fail to detect critical vulnerabilities and raise a large number of false alarms. This paper studies the hypothesis of using Machine Learning (ML) to combine alerts from SATs with SMs to predict vulnerabilities in a large software project (under development for many years). In practice, we use four ML algorithms, alerts from two SATs, and a large number of SMs to predict whether a source code file is vulnerable or not (binary classification) and to predict the vulnerability category (multiclass classification). Results show that one can achieve either high precision or high recall, but not both at the same time. To understand the reason, we analyze and compare snippets of source code, demonstrating that vulnerable and non-vulnerable files share similar characteristics, making it hard to distinguish vulnerable from non-vulnerable code based on SAT alerts and SMs.
Transferability Metrics for Selecting Source Model Ensembles

We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the target training set, yields the best performance on the target test set. Since fine-tuning all possible ensembles is computationally prohibitive, we aim at predicting performance on the target dataset using a computationally efficient transferability metric. We propose several new transferability metrics designed for this task and evaluate them in a challenging and realistic transfer learning setup for semantic segmentation: we create a large and diverse pool of source models by considering 17 source datasets covering a wide variety of image domain, two different architectures, and two pre-training schemes. Given this pool, we then automatically select a subset to form an ensemble performing well on a given target dataset. We compare the ensemble selected by our method to two baselines which select a single source model, either (1) from the same pool as our method; or (2) from a pool containing large source models, each with similar capacity as an ensemble. Averaged over 17 target datasets, we outperform these baselines by 6.0% and 2.5% relative mean IoU, respectively.
Smart Tourism and Artificial Intelligence: Paving the Way to the Post-COVID-19 Era

In these last years, we are witnessing the constant evolution of technologies such as artificial intelligence, machine learning, blockchain, IoT, and cloud computing. At the same time, before the COVID-19 crisis, the tourism sector steadily increased its growth yearly, raising to be one of the dominant sectors worldwide, generating a wide number of job openings in a plethora of industries. Gradually, the need to bridge technology with tourism, gave birth to the Smart Tourism concept, with an increasing number of researchers focusing on it. Artificial intelligence that has already been exploited in many fields could not leave the smart tourism sector unaffected. Actually, based on the realisation that AI could be a game-changer when it comes to the tourism industry, we aim at presenting how AI can be leveraged to bring smartness into tourism. We focus on approaches presented by both researchers and practitioners in the tourism industry, and we analyse the exploited technologies. Now more than ever, with the COVID-19 pandemic, the tourism industry needs to be transformed, destinations need to become "safe" and tourists need to be able to travel again. That being the case, we also attempt to recognise the already exploited AI approaches in this field, as well as ways of utilising technology to resume travel and reboot tourism worldwide safely.
Health Chatbot Using Natural Language Processing for Disease Prediction and Treatment

People who don't know about products or services provided by a company need a system that can provide answers to the questions that are usually asked. This system is called Frequently Asked Questions. This system is not effective and efficient in providing the information. In this paper, we propose a chatbot using Natural Language Processing to provide some information about health. Here, we use NLP to make a chatbot system that can understand and answer the questions that are asked by the user. The cosine similarity is used to find the similarities between the query words (the questions that are asked by the user) and the documents, then return the answers of the document with the highest similarity. Based on our development, our medical chatbot has successfully diagnose the user illness with approximately 87 percent accuracy.