Reviewerly/exHarmony huggingface dataset:
Rpadrino: An R package to access and use <scp>PADRINO</scp>, an open access database of Integral Projection Models

Abstract Discrete time structured population projection models are an important tool for studying population dynamics. Within this field, integral projection models (IPMs) have become a popular method for studying populations structured by continuously distributed traits (e.g. height, weight). Databases of discrete time, discrete state structured population models, for example DATLife (life tables) and COMPADRE &amp; COMADRE (matrix population models), have made quantitative syntheses straightforward to implement. These efforts allow researchers to address questions in both basic and applied ecology and evolutionary biology. Since their introduction in 2000, over 300 works containing IPMs have been published, offering opportunities for ecological synthesis too. We describe a novel framework to quickly reconstruct these models for subsequent analyses using Rpadrino R package, which serves as an interface to PADRINO, a new database of IPMs. We introduce an R package, Rpadrino, which enables users to download, subset, reconstruct, and extend published IPMs. Rpadrino makes use of recently created software, ipmr, to provide an engine to reconstruct a wide array of IPMs from their symbolic representations and conduct subsequent analyses. Rpadrino and ipmr are extensively documented to help users learn their usage. Rpadrino currently enables users to reconstruct 280 IPMs from 40 publications that describe the demography of 14 animal and 26 plant species. All of these IPMs are tested to ensure they reproduce published estimates. Rpadrino provides an interface to augment PADRINO with external data and modify parameter values, creating a platform to extend models beyond their original purpose while retaining full reproducibility. PADRINO and Rpadrino provide a toolbox for asking new questions and conducting syntheses with peer‐reviewed published IPMs. Rpadrino provides a user‐friendly interface so researchers do not need to worry about the database structure or syntax, and can focus on their research questions and analyses. Additionally, Rpadrino is thoroughly documented and provides numerous examples of how to perform analyses which are not included in the package's functionality.
Source Camera Device Identification from Videos

Abstract Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the VISION data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of $$72.75 \pm 1.1 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>72.75</mml:mn> <mml:mo>±</mml:mo> <mml:mn>1.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of $$71.75\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>71.75</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the VISION and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs.
Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.
Analysis of Abnormal Flight and Controllers Data Based on DBSCAN Method

In order to quickly and timely analyze the airborne and controllers data of domestic ARJ21 (Advanced Regional Jet for 21st Century) aircraft and find out the existing flight problems and potential safety hazards, this paper proposes a DBSCAN (density-based spatial clustering of applications with noise) clustering analysis method for aircraft airborne and controllers data outlier detection to evaluate and monitor the flight status. The flight QAR (quick access recorder) data stored in the aircraft rapid data recorder are input to the DBSCAN clustering analysis algorithm to detect the flight parameters that are different from the normal range. Compared with the traditional airborne data analysis method, this method can realize the real-time analysis and prediction of data, improve the efficiency of data analysis, and find the potential security risks according to the analysis results and deal with them in time. In this paper, 1,102 ARJ21 aircraft operation data are used to test. The results show that the DBSCAN clustering anomaly data detection method based on density is fast and accurate in detecting the continuous parameters recorded in the flight process, and the display results are easy to analyze, which can predict the potential safety problems in time. The outliers detected by this method can provide support for the controller to detect the outliers and related flight risks in daily flights.
Continual Learning for Visual Search with Backward Consistent Feature Embedding

In visual search, the gallery set could be incrementally growing and added to the database in practice. However, existing methods rely on the model trained on the entire dataset, ignoring the continual updating of the model. Besides, as the model updates, the new model must re-extract features for the entire gallery set to maintain compatible feature space, imposing a high computational cost for a large gallery set. To address the issues of long-term visual search, we introduce a continual learning (CL) approach that can handle the incrementally growing gallery set with backward embedding consistency. We enforce the losses of inter-session data coherence, neighbor-session model coherence, and intra-session discrimination to conduct a continual learner. In addition to the disjoint setup, our CL solution also tackles the situation of increasingly adding new classes for the blurry boundary without assuming all categories known in the beginning and during model update. To our knowledge, this is the first CL method both tackling the issue of backward-consistent feature embedding and allowing novel classes to occur in the new sessions. Extensive experiments on various benchmarks show the efficacy of our approach under a wide range of setups <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/ivclab/CVS.
Enabling Decentralized and Auditable Access Control for IoT through Blockchain and Smart Contracts

The increase in the interconnection of physical devices and the emergence of the 5 G paradigm foster the generation and distribution of massive amounts of data. The complexity associated with the management of these data requires a suitable access control approach that empowers citizens to control how their data are shared, so potential privacy issues can be mitigated. While well-known access control models are widely used in web and cloud scenarios, the IoT ecosystem needs to address the requirements of lightness, decentralization, and scalability to control the access to data generated by a huge number of heterogeneous devices. This work proposes CapBlock, a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts. CapBlock is intended to manage the access to information in federated IoT environments where data need to be managed through access control policies defined by different data providers. The feasibility of CapBlock has been successfully evaluated in the scope of the EU research project IoTCrawler, which aims at building a secure search engine for IoT data in large-scale scenarios.
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts

Recent learned cardinality estimation (CE) models are vulnerable when query predicates or the underlying datasets drift from what the models were trained upon. We propose a system Warper that accelerates model adaptation to drifts; Warper generates additional queries when limited examples are available from the new workload and carefully picks which queries to use to update the CE model. We show that Warper can be used to adapt different CE models including ones that support queries over single tables and join expressions. Experiments with different drifts suggest that Warper has a small computational cost and adapts much faster compared to state-of-the-art solutions. We also show that faster model adaptation improves query performance by shortening the period for which imperfect query plans are picked by a query optimizer due to incorrect cardinality estimates.
The Fluctuating Effect of Thinking on Language Performance: New Evidence for the Island Ridge Curve

Thinking skills play a critical role in determining language performance. Recent advancement in cognitive diagnostic modelling (CDM) provides a powerful tool for obtaining fine-grained information regarding these thinking skills during reading. Studies are scant, however, exploring the relations between thinking skills and language performance, not to mention studies examining the variation of this association with language proficiency. The current study explored this variation through the lens of the Island Ridge Curve (IRC). Drawing on an English reading test data by 2,285 students, we identified five thinking skills using CDM. Next, we followed guidelines of IRC and put students into four language proficiency groups to examine the relations of each skill identified through reading tasks to language performance across groups. Results of multi-group path analysis showed the effect of each skill identified through reading test fluctuated in the pattern of the IRC. The potential of IRC for examining the moderation of language proficiency on language factors is discussed.
The IBM natural conversation framework: a new paradigm for conversational UX design

KEYWORDS: Conversational user interfacesconversational UX designConversation Analysischatbotsvoice assistants
Cloud-Native Transactions and Analytics in SingleStore

The last decade has seen a remarkable rise in specialized database systems. Systems for transaction processing, data warehousing, time series analysis, full-text search, data lakes, in-memory caching, document storage, queuing, graph processing, and geo-replicated operational workloads are now available to developers. A belief has taken hold that a single general-purpose database is not capable of running varied workloads at a reasonable cost with strong performance, at the level of scale and concurrency people demand today. There is value in specialization, but the complexity and cost of using multiple specialized systems in a single application environment is becoming apparent. This realization is driving developers and IT decision makers to seek databases capable of powering a broader set of use cases when looking to adopt a new database. Hybrid transaction and analytical (HTAP) databases have been developed to try to tame some of this chaos.
Secure E-Health Record Sharing using Blockchain: A Comparative Analysis Study

Blockchain is a digital ledger where the data entries are recorded in a decentralized fashion that cannot be altered. This is likely to prosper in a variety of industries, including healthcare. According to a survey, 70% of healthcare professionals believe that blockchain has the largest influence on clinical report management, regulating documents, and creating a framework for sharing electronic health records (EHR) in the healthcare industry. Even though blockchain technology is having real potential for improving health information systems, the rise of this technology has also led to innovative proposals and applications. This review mainly focuses on EHR data exchange between the patient and the hospital entities like doctors, record verifiers, insurance companies, pharmaceuticals, etc. The proposed research study has also discussed about the techniques used in these models to counter the problems present in the traditionally used server-client model like a data breach, Unauthorized access, centralized data management, etc., and improvisation of the blockchain-based models. Further, a comparative analysis has been provided on various blockchain-based systems for hosting a secure EHR sharing.
Deep-learning based framework for sentiment analysis in Urdu language

In recent times, Sentiment analysis has become a significant means for framing a successful business and can be very helpful in predicting customer trends to help organizations in their decision-making process. Though many software applications are available in the market for text analysis, one of the major limitations of such applications is that they are developed for rich languages like English, German, Spanish, Arabic, etc. and less popular languages like Urdu, Hindi, Roman Urdu are neglected due to lack of availability of resources. Therefore, this research project will provide an implementation of sentiment analysis in the Urdu language. Firstly, preprocessing is performed and a small-scale manual dictionary of 830 Urdu stem words is introduced for stemming. Then a deep learning-based framework of LSTM is used for Urdu sentiment analysis. Experimental results show high classification accuracy of 86.03% and 0.89 F1 Score with the use of LSTM that captures sequence information effectively to analyze sentiments than the conventional supervised machine learning approaches.
An enhanced digital forensic investigation framework for XSS attack

Cyber crimes and frauds are growing at a rapid rate and the law practitioners require proper methodology to collect the digital evidences. When the criminals use digital platform in order to commit the crime, forensic examiners apply practical frameworks and methods to collect digital evidences. The digital forensics method entails analyzing network traffic in order to detect and investigate intrusions. For cyber crime investigation, various models with merits and demerits have been offered. This study examines the characteristics of existing digital forensic investigation models and analyses some of the challenges surrounding digital forensic research.
Why? Why not? When? Visual Explanations of Agent Behaviour in Reinforcement Learning

Reinforcement learning (RL) is used in many domains, including autonomous driving, robotics, stock trading, and video games. Unfortunately, the black box nature of RL agents, combined with legal and ethical considerations, makes it increasingly important that humans (including those are who not experts in RL) understand the reasoning behind the actions taken by an RL agent, particularly in safety-critical domains. To help address this challenge, we introduce PolicyExplainer, a visual analytics interface which lets the user directly query an autonomous agent. PolicyExplainer visualizes the states, policy, and expected future rewards for an agent, and supports asking and answering questions such as: “Why take this action? Why not take this other action? When is this action taken?” PolicyExplainer is designed based upon a domain analysis with RL researchers, and is evaluated via qualitative and quantitative assessments on a trio of domains: taxi navigation, a stack bot domain, and drug recommendation for HIV patients. We find that PolicyExplainer's visual approach promotes trust and understanding of agent decisions better than a state-of-the-art text-based explanation approach. Interviews with domain practitioners provide further validation for PolicyExplainer as applied to safety-critical domains. Our results help demonstrate how visualization-based approaches can be leveraged to decode the behavior of autonomous RL agents, particularly for RL non-experts.
Investigating the relevance of Arabic text classification datasets based on supervised learning

Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset (SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification. In this investigation, well-known and accurate learning models are used, including naive Bayes, random forest, K-nearest neighbor, support vector machines, and logistic regression models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performance of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the support vector machine model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time, with the accuracy of 82%.
Peace and Prosperity for the Digital Age? The Colonial Political Economy of European AI Governance

 We are not short of alarming accounts of the global power asymmetries and detrimental environmental, social, and political effects fostered and amplified by the production, design, and use of artificial intelligence technologies (AITs).From "surveillance capitalism" [66], via the "black box society" [50], "automated inequality" [22], "algorithms of oppression" [47] to "extractive politics" [17]; from the "Californian ideology" [4] of "Big Tech" in Silicon Valley to the world of start-ups and specialist public sector contractors, like Palantir and Clearview, scholars highlight a wild west of disruptive technological innovation that has gone largely untamed.The whole globe is embroiled in its production and effects while the costs of "externalities" are paid by others: through large-scale environmental degradation from rare metal mining [17], intensive carbon consumption [19], underpaid and underemployed click workers [3], [58], [61], violations of privacy and data protection [5], [48], and the amplification of societal biases in automated decision-making (a useful collection in a 2021 special issue of Fordham Law Review).The European Union (EU) Commission's recent proposal for a regulation of artificial intelligence (AI Act) [27] appears to signal a change in approach.Despite worldwide and growing concern for the need to regulate AITs, efforts remain
WCC-JC: A Web-Crawled Corpus for Japanese-Chinese Neural Machine Translation

Currently, there are only a limited number of Japanese-Chinese bilingual corpora of a sufficient amount that can be used as training data for neural machine translation (NMT). In particular, there are few corpora that include spoken language such as daily conversation. In this research, we attempt to construct a Japanese-Chinese bilingual corpus of a certain scale by crawling the subtitle data of movies and TV series from the websites. We calculated the BLEU scores of the constructed WCC-JC (Web Crawled Corpus—Japanese and Chinese) and the other compared corpora. We also manually evaluated the translation results using the translation model trained on the WCC-JC to confirm the quality and effectiveness.
Deriving Semantics-Aware Fuzzers from Web API Schemas

We present Schemathesis, a tool for finding semantic errors and crashes in OpenAPI or GraphQL web APIs through property-based testing. Our evaluation, thirty independent runs of eight tools against sixteen containerized open-source web services, shows that Schemathesis wildly outperforms all previous tools.It is the only tool to find defects in four targets, finds 1.4× to 4.5× more unique defects than the respectively second-best tool for each remaining target, and is the only tool to handle more than two-thirds of our target services without a fatal internal error.Our full preprint [5] goes into considerably more detail.
How Artificial Intelligence Enhances Human Learning Abilities: Opportunities in the Fight Against COVID-19

This paper widens the focus on how artificial intelligence (AI) can foster the learning abilities of human actors, adopting a wider view with respect to a strict focus on tasks and activities. The interaction between AI and human learning has not been investigated in service research. Placing its theoretical roots in work by Huang and Rust [Huang MH, Rust RT (2021) Engaged to a robot? The role of AI in service. J. Service Res. 24(1):30–41.] in service research and on Bloom’s revised taxonomy in education studies [Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives (Longman, London).], this study offers an integrative framework for the ways AI enhances human learning abilities. Some cases in the context of COVID-19 offer insightful illustrations of the framework.