Reviewerly/exHarmony huggingface dataset:
Adapted Long Short-Term Memory (LSTM) for Concurrent\\ Human Activity Recognition

In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs.In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity.In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences.The proposed method is implemented for simultaneous human activity recognition (HAR) using heterogeneous sensor data in a smart home.HAR assists artificial intelligence in providing services to users according to their preferences.The sync-LSTM algorithm improves learning performance and sees its potential for real-world applications in complex HAR, such as concurrent activity, with higher accuracy and satisfactory computational complexity.The adapted algorithm for HAR is also applicable in the fields of ambient assistive living, healthcare, robotics, pervasive computing, and astronomy.Extensive experimental evaluation with publicly available datasets demonstrates the competitive recognition capabilities of our approach.The sync-LSTM algorithm improves learning performance and has the potential for real-life applications in complex HAR.For concurrent activity recognition, our proposed method shows an accuracy of more than 97%.
Communicative data-driven learning: a two-year pilot study

Abstract With the development of language corpora, linguists have been able to identify how often specific words, phrases, and expressions are used, and in which contexts. However, applications of corpora in the wider domain of language teaching have remained limited. This article presents an approach to utilizing corpora, combining principles from communicative language teaching and data-driven learning, and based on a two-year action-research-based trial of the approach with a monolingual class at a Japanese university. Beginning with learner training in their L1, students learned to analyse concordance lines and were given the opportunity to recontextualize and conceptualize any identified language patterns and features through realistic communicative activities. While challenges remain in relation to the effectiveness of learner training and the preparation of suitable concordance lines, students were found to have increased their fluency and accuracy, and to have made improvements to their speaking confidence and complexity.
Evolutionary computation and machine learning in cryptology

tutorial Open AccessEvolutionary computation and machine learning in cryptology Authors: Stjepan Picek TU Delft, The Netherlands TU Delft, The NetherlandsView Profile , Domagoj Jakobovic University of Zagreb, Croatia University of Zagreb, CroatiaView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 1089–1118https://doi.org/10.1145/3449726.3461420Published:08 July 2021Publication History 1citation179DownloadsMetricsTotal Citations1Total Downloads179Last 12 Months79Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
DIAGNOSIS OF LUNG CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK

Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. This study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the multiscale convolution. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The use of image processing techniques and identifying patterns in the detection of lung cancer from CT images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect lung cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents CT picture to several deep convolutional neural networks with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state of the art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches a diagnosis accuracy of [Formula: see text] using multiscale convolution technique, which reveals the efficiency of the proposed method.
Tokenomics

The tokenomics of Initial Coin Offerings is a new field of research. The wording Initial Coin Offering is only a few years old, the field of tokenomics is even younger. This chapter will discuss the parameters of the tokenomics of Initial Coin Offerings in a qualitative and quantitative way to gather knowledge about upcoming standards and the definition of current requirements. As more and more Initial Coin Offerings coming to the market, a fundamental view of tokenomics is required. This chapter identifies 13 important parameters of tokenomics. Each of them is examined by literature and the used sample data set. Further parameters are identified as well as further research objectives in the field of tokenomics.
Research on Modeling of E-banking Fraud Account Identification Based on Federated Learning

With the intelligence and groupization of the black market, the characteristics of various fraud methods have become more and more concealed, and cross-industry fraud has gradually become the norm. A single fraud runs through multiple links in social media and banks, and it is difficult for each institution to deal with it based on its own data. Since anti-fraud information involves a large amount of private data, considering that companies are paying more and more attention to the protection of data assets, and the global legal system for privacy data supervision is constantly improving, it is difficult to carry out anti-fraud collaboration among companies. On the premise of satisfying monitoring privacy protection, this paper uses vertical federated learning technology to combine financial and social characteristics to construct a federated learning model of social financial fraud accounts. Compared with the logistic regression (LR) model and the extreme gradient boosting decision tree (XGBoost) model that only use financial feature training, the federated learning model has an accuracy rate of 5.9%, a precision increase of 4.9%, and a recall rate of 5.5% compared with the local model, which verifies the effectiveness of the federated learning technology in improving the model effect.
Mobile Game-Based Learning

The massive spread of mobile computing is undeniable with the draw of mobile games reaching epic proportions. This popularity, along with the anytime, anywhere, and on-any-device characteristics of mobile computing has ignited mounting interest in the use of mobile games in educational contexts, as illustrated by a growing number of articles on this topic. This chapter offers a review of the research on mobile game-based learning (mGBL), citing mobile games that have been experimented with and/or used to explore learning. Although many of the studies to date have emphasized the location-based capabilities of mobile devices, the emerging research focused on mGBL is promising, showing that learning can take place through the use of these games. Future research, however, should look beyond individual devices and functionality, and place greater importance on pedagogy.
Anomalous Concentration of Uranium in Groundwater in Parts of Salem District, Central Tamil Nadu, India

Anomalous concentration of Uranium in groundwater has been identified around Virakkal, Veerapanpalayam, Pakkanadu, Erithottam, Kammampatti, Kumarappalyam, Idappadiand Moolakkadu areas in parts of Salem district, Tamil Nadu. In this study, an area of 970 sq. km from Mettur in the north to Kumarapalayam in the south parallel to the Koratti shear zone has been examined for uranium content in groundwater. Systematic sampling of groundwater (n=78) from bore well, open well and hand pump have been collected. The samples were analysed for uranium concentration using LED Fluorimeter. Analysis resulted higher concentration of uranium along the southern extension of Koratti Shear Zone where the emplacement of younger granites, syenite and carbonatites has been reported. These lithounits might have played a vital role in the contribution of uranium to the groundwater. Based on the result, two areas have been identified such as North Block and South Block for detailed investigation.58 systematic samples from North block and 52 samples from South block have been collected and analysed. Uranium values assayed in these areas are many times higher than the prescribed limit of World Health organisation and Atomic Energy Regulatory Board of 30ppb and 60ppb respectively. In northern block, among 58 samples 18 samples were assayed higher than 30ppb in which the samples from Veerappanpalayam assayed upto 545.81 ppb. Apart from Veerappanpalayam, many areas such as Mosakumarapalayam (273.90), Arasiramani (250.49ppb), Vellarivalli (131.32ppb), Kodarapalayam (106.52ppb), Savuthanur (174.79ppb), Veerapanpalayam-2 (373.43ppb) and Kotamppalayam (100ppb) were assayed more than 100ppb. Similarly, in the southern block, 52 samples were assayed in which 15 samples were higher than the prescribed limit in which Kuppampatti analyzed 402.29ppb, Virakkal and Thannikuttampatti assayed more than 100ppb. Among the total samples (n=188), 28% of samples were falling under non-potable category. The Present study has spatially brought to light significant areas where groundwater enriched by uranium concentration. It is also provided indirect evidences for uranium exploration in the study area.
Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search

The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.
Dynamic Searchable Symmetric Encryption with Forward and Backward Privacy

Dynamic searchable symmetric encryption (DSSE) that enables a client to perform searches and updates on encrypted data has been intensively studied in cloud computing. Recently, forward privacy and backward privacy has engaged significant attention to protect DSSE from the leakage of updates. However, the research in this field almost focused on keyword-level updates. That is, the client needs to know the keywords of the documents in advance. In this paper, we proposed a document-level update scheme, DBP, which supports immediate deletion while guaranteeing forward privacy and backward privacy. Compared with existing forward and backward private DSSE schemes, our DBP scheme has the following merits: 1) Practicality. It achieves deletion based on document identifiers rather than document/keyword pairs; 2) Efficiency. It utilizes only lightweight primitives to realize backward privacy while supporting immediate deletion. Experimental evaluation on two real datasets demonstrates the practical efficiency of our scheme.
Socrative

Focusing on the use mobile devices to promote language learning, this chapter documents the use of the student response system Socrative in two German courses taking place at two different Portuguese higher education institutions. It is divided into three different sections. After establishing the project's theoretical framework, the authors analyse its implementation and present findings, based on users' perceptions and test results. Aiming at understanding the role mobile devices can play in promoting students' long-term content retention, this research achieved promising results, not only by confirming students' expectations towards mlearning, but also by suggesting that mobile initiatives can have a positive effect in motivating students and promoting engagement, change and innovation. Overall, students achieved positive results, with data providing educators with indicators and insights on learner's learning strategies and progress.
Fast graph centrality computation via sampling: a case study of influence maximisation over OSNs

Graph centrality computation, e.g., asking for the most important vertices in a graph, may incur a high time cost with the increasing size of graphs. To address this challenge, this paper presents a sampling-based framework to speed up the computation of graph centrality. As a use case, we study the problem of influence maximisation (IM), which asks for the k most influential nodes in a graph to trigger the largest influence spread, and present an IM-RWS algorithm. We experimentally compare IM-RWS with the state-of-the-art influence maximisation algorithms IMM and IM-RW, and the results show that our solution can bring a significant improvement in efficiency as well as a certain extent of improvement in empirical accuracy. In particular, our algorithm can solve the influence maximisation problem in graphs containing millions of nodes within tens of seconds with an even better performance result in terms of influence spread.
Short Text Classification Based on Cross-Connected GRU Kernel Mapping Support Vector Machine

Support vector machine (SVM) has achieved excellent results in short text classification. However, its performance is limited in the kernel function. This paper presents a short text classification method based on Cross-connected GRU Kernel Mapping Support Vector Machine (C-GRUKMSVM), to further improve the accuracy of short text classification. The method consists of a feature mapping module and a classification module. The feature mapping module first represents the text as a word vector using the glove method, and then explicitly maps the low-dimensional word vector to a high-dimensional space using a three-layer cross-connected GRU; the classification module uses a soft-margin support vector machine for classification. Experimental results on five publicly available short text datasets show that C-GRUKMSVM achieves better text classification performance than convolutional networks, support vector machines and Naïve Bayes. Additionally, different cross-connected methods, recurrent units and recurrent structures have an impact on the performance of C-GRUKMSVM.
Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individual's health.
Automatic Minimization of Execution Budgets of SPITS Programs in AWS

Cloud computing platforms offer a wide variety of computational resources with different performance specifications for different prices. In this work, we experiment how Spot instances and Availability Zones on the Amazon Web Services (AWS) could be utilized to reduce the processing budget. Not only that, but we propose instance selection algorithms in AWS to minimize the execution budget of programs implemented using the programming model Scalable Partially Idempotent Task System (SPITS). Our results show that the proposed method can identify and dynamically adjust the virtual machine types that offer the best price per performance ratio. Therefore, we conclude that our algorithms can minimize the budget given a long enough execution time, except in situations where the startup overhead caused the budget difference or in a short period execution.
Large-scale Autonomous Navigation and Path Planning of Lunar Rover via Deep Reinforcement Learning

Future lunar surface exploration needs to be carried out in areas with more complex terrain and richer scientific research value without prior information. Therefore, the patrol device needs to be able to autonomously detect terrain and obstacle avoidance when planning exploration tasks. However, Deep reinforcement learning has been successfully applied in various game-like environments, and it is a challenging task to apply deep reinforcement learning to the navigation and obstacle avoidance detection of the rover on lunar surface. We propose an autonomous decision-making method for lunar vehicle patrols based on deep reinforcement learning. The algorithm first uses lidar for simultaneous positioning and graphing. For the constructed static three-dimensional point cloud map of the lunar surface, it is described as a two-dimensional grid using octree-map projection The grid map is processed by the long-short time memory (LSTM) to process the two-dimensional grid obstacle information. Using dueling deep Q network (dueling DQN) with multi-step learning to train the planetary vehicle automatic path planning, and finally in the ROS static obstacle environment simulation verification experiment, the simulation results verify that the proposed method is effective and adaptable for the lunar rover in different terrain environments.
Artificial Intelligence and Blockchain for Future Cyber Security Application

AI (Artificial intelligence) application on Big Data had been developed fast. AI cyber security defense for the facing threats were required. Blockchain technology was invented in 2008 with BTC (Bit coin. This technology could be benefited alongside the custom of Blockchain, AI, Big Data and so on. There were a rapid progress in the advancement of Blockchain. This subject had recently become a discussion topic in the ICT (Information and Communications Technology) world. In this paper, AI security is discussed from the initial stage. Suggestion: In this paper, we discussed the impact of AI security from the initial stage and its impact and benefits to IT engineers, ICT students and CS (Computer Sciences) academic researchers, using a case study of medical records with personal recognizable identification privacy information that needs strict access control security. We considered its need for trustworthy cyber security, anti-fake, anti-alteration and transaction accounting transparency reputation to be applied to the NHS (National Health Service). Lastly, the paper provided some necessarily analysis. Blockchain technology had trustworthy cyber security, anti-fake, anti-alteration and transaction accounting transparency reputation to be considered to be applied to NHS (National Health Service). This short paper provided some analysis necessarily.
A Philosophical Analysis of AI and Racism

This paper addresses the problem of racism against Latinx and Black people in Artificial Intelligence (AI) and offers possible solutions. This ethical analysis is necessary because with a dramatic increase in the production of AI, the way we use it is critical in eliminating its current perpetuation of racism. I offer evidence of the current perpetuation of racism through AI by analyzing its use in banking and in law enforcement. I argue that the current way we produce and use AI needs to be seriously reconsidered and reinforce this argument with the use of Rawls’ theories of justice.
A Report on the Second Workshop on Emerging Software Engineering Education (WESEE 2019)

Software engineering is fast evolving to the needs of modern-day customers and the challenges to support the unstoppable technological innovations. A well-trained and highly-skilled workforce required to push this evolution forward hence becomes the key element for a successful future of software engineering. Inculcating the art and science of software engineering in the young and old demands the introduction of out-of-the-box teaching methods at all levels of education dissemination. With a unique theme of Software engineering teaching-learning in academia and industry, the second edition of the Workshop on Emerging Software Engineering Education (WESEE) aims to bring together software engineering educators from academia -- who understand how the education scenarios and research are changing the mechanisms of teaching software engineering related courses, and from industry -- who understand how software engineers are trained or how software development is coached. In this report, we present a pre-organization summary of the workshop to be held on February 14, 2019, at College of Engineering, Pune (India), co-located with the 12th Innovations in Software Engineering Conference (ISEC 2019).