Reviewerly/exHarmony huggingface dataset:
From one to hundreds: multi-licensing in the JavaScript ecosystem

Open source licenses create a legal framework that plays a crucial role in the widespread adoption of open source projects. Without a license, any source code available on the internet could not be openly (re)distributed. Although recent studies provide evidence that most popular open source projects have a license, developers might lack confidence or expertise when they need to combine software licenses, leading to a mistaken project license unification. This license usage is challenged by the high degree of reuse that occurs in the heart of modern software development practices, in which third-party libraries and frameworks are easily and quickly integrated into a software codebase. This scenario creates what we call “multi-licensed” projects, which happens when one project has components that are licensed under more than one license. Although these components exist at the file-level, they naturally impact licensing decisions at the project-level. In this paper, we conducted a mix-method study to shed some light on these questions. We started by parsing 1,426,263 (source code and non-source code) files available on 1,552 JavaScript projects, looking for license information. Among these projects, we observed that 947 projects (61%) employ more than one license. On average, there are 4.7 licenses per studied project (max: 256). Among the reasons for multi-licensing is to incorporate the source code of third-party libraries into the project’s codebase. When doing so, we observed that 373 of the multi-licensed projects introduced at least one license incompatibility issue. We also surveyed with 83 maintainers of these projects aimed to cross-validate our findings. We observed that 63% of the surveyed maintainers are not aware of the multi-licensing implications. For those that are aware, they adopt multiple licenses mostly to conform with third-party libraries’ licenses.
Spatio-Temporal Deepfake Detection with Deep Neural Networks

Deepfakes generated by generative adversarial neural networks may threaten not only individuals but also pose a public threat. In this regard, detecting video content manipulations is an urgent task, and many researchers propose various methods to solve it. Nevertheless, the problem remains. In this paper, the existing approaches are evaluated, and a new method for detecting deepfakes in videos is proposed. Considering that deepfakes are inserted into the video frame by frame, when viewing it, even with the naked eye, fluctuations and temporal distortions are noticeable, which are not taken into account by many deepfake detection algorithms that use information from a single frame to search for forgeries out of context with neighboring frames. It is proposed to analyze information from a sequence of multiple consecutive frames to detect deepfakes in video content by processing the video using the sliding window approach, taking into account not only spatial intraframe dependencies but also interframe temporal dependencies. Experiments have shown the advantage and potential for further development of the proposed approach over simple intraframe recognition.
Text-linguistic analysis of performed language: revisiting and re-modeling Koch and Oesterreicher

Abstract The present contribution starts from the general observations (i) that the study of text varieties has commonly emphasized the mode (speech vs. writing) as an essential variable and (ii) that linguistic analyses increasingly consider performed language (i.e., fictional scripted material as represented in telecinematic language and lyrics, for instance) as an object worth studying in its own right. It is recognized (i) that assessing performed language in terms of the traditional spoken-written dichotomy fails due to a number of inherent properties of relevant text varieties and their circumstances of production and reception, and (ii) that schemes applied within conventional (bottom-up) register perspectives may not be fully adequate for a text-linguistic approach toward performed language either. Koch and Oesterreicher’s communicative model (KOM), which takes account of contextual factors as well as of specific linguistic strategies and establishes a continuum between the language of distance and the language of immediacy, as well as three attempts at modifying KOM are introduced. It is argued that both KOM (as a top-down model) and its modifications have weaknesses when dealing with performed text varieties. An enhanced communicative model, including the dimensions synchronicity and authenticity, is sketched out, which could serve both as a starting point and complement for empirical investigations of textual variation whenever performed language is included.
Improving the cognitive clarity of graph models of knowledge representation and decision-making using visualization

The article discusses the ways to improve the cognitive clarity of graph models for knowledge representation and decision making by applying visualization capabilities. Using fuzzy cognitive models as an example, it is shown that applying an approach based on visualization metaphors allows one to structure and partially formalize the task of increasing the cognitive clarity of models, breaking it down into separate easily interpreted stages, each of which contributes to providing cognitive clarity in general. The conclusion is made about the relevance of developing visualization metaphors to increase the cognitive clarity of graph models of other types.
Are We There Yet? Evaluating the Effectiveness of a Recurrent Neural Network-Based Stopping Algorithm for an Adaptive Assessment

Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations, RNN models can improve on the performance of these existing methods, in this work we take a different approach. Rather than directly comparing RNNs with other models, we are instead interested in the results when RNNs are combined with one of these existing models. In particular, we attempt to improve the performance of ALEKS (“A ssessment and LE arning in K nowledge S paces”), an adaptive learning and assessment system based on Knowledge Space Theory, through the use of RNN models. Using data from more than 1.4 million ALEKS assessments, we first build an RNN classifier that attempts to predict the final result of each assessment. After verifying the accuracy of these predictions, we develop our stopping algorithm, with the goal of improving the efficiency of the ALEKS assessment by reducing the total number of questions that are asked. Based on this stopping algorithm, we give a comprehensive analysis of the possible effects it would have on students. We show that the combination of an RNN with the ALEKS assessment can reduce the average assessment length by over 26%, while a high degree of accuracy is maintained.
Convexity and freezing sets in digital topology

&lt;p&gt;We continue the study of freezing sets in digital topology, introduced in [4]. We show how to find a minimal freezing set for a "thick" convex disk X in the digital plane Z^2. We give examples showing the significance of the assumption that X is convex.&lt;/p&gt;
Student perceptions towards the usage of AR-supported STEMUP application in mobile courses development and its implementation into English learning

This study investigated the use of an augmented reality (AR) -supported application called STEMUP to develop mobile English courses on Android and iOS smartphones. It focused on vocabulary and listening and speaking skills, in an initial assessment of the effectiveness of implementing AR-based mobile English courses. The study was conducted with English major sophomores who took an elective course, Design and Practice of Mobile Learning, at a technology university in Taiwan. A preliminary curriculum was prepared for integrating STEMUP into English courses in which students would not only learn how to develop AR-based mobile English courses but also practise target English skills with two assigned English courses on STEMUP. The results from the student questionnaire indicated that the layout design of the student-generated AR-based mobile English courses complied with several of Mayer’s principles of multimedia learning and that the focus on learning with the mobile English courses met Chapelle’s seven suggested criteria for the development of multimedia computer-assisted language learning. The students of English as a foreign language responded that STEMUP was an easy application for developing AR-based mobile English courses and that learning with the assigned AR-based mobile English courses was interesting and motivational.
Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.
Image inpainting using frequency-domain priors

We present an image inpainting technique using frequency-domain information. Prior works on image inpainting predict the missing pixels by training neural networks using only the spatial-domain information. However, these methods still struggle to reconstruct high-frequency details for real complex scenes, leading to a discrepancy in color, boundary artifacts, distorted patterns, and blurry textures. To alleviate these problems, we investigate if it is possible to obtain better performance by training the networks using frequency-domain information (discrete Fourier transform) along with the spatial-domain information. To this end, we propose a frequency-based deconvolution module that enables the network to learn the global context while selectively reconstructing the high-frequency components. We evaluate our proposed method on the publicly available datasets: celebFaces attribute (CelebA) dataset, Paris streetview, and describable textures dataset and show that our method outperforms current state-of-the-art image inpainting techniques both qualitatively and quantitatively.
Deep Query Likelihood Model for Information Retrieval

The query likelihood model (QLM) for information retrieval has been thoroughly investigated and utilised. At the basis of this method is the representation of queries and documents as language models; then retrieval corresponds to evaluate the likelihood that the query could be generated by the document. Several approaches have arisen to compute such probability, including by maximum likelihood, smoothing and considering translation probabilities from related terms. In this paper, we consider estimating this likelihood using modern pre-trained deep language models, and in particular the text-to-text transfer transformer (T5) – giving rise to the QLM-T5. This approach is evaluated on the passage ranking task of the MS MARCO dataset; empirical results show that QLM-T5 significantly outperforms traditional QLM methods, as well as a recent ad-hoc methods that exploits T5 for this task.
Characterizing top ranked code examples in Google

Developers often look for code examples on the web to improve learning and accelerate development. Google indexes millions of pages with code examples: pages with better content are likely to be top ranked. In practice, many factors may influence the rank: page reputation, content quality, etc. Consequently, the most relevant information on the page, i.e., the code example, may be overshadowed by the search engine. Thus, a better understanding of how Google would rank code examples in isolation may provide the basis to detect its strengths and limitations on dealing with such content. In this paper, we assess how the Google search engine ranks code examples. We build a website with 1,000 examples and submit it to Google. After being fully indexed, we query and analyze the returned examples. We find that pages with multiple code examples are more likely to top ranked by Google. Overall, single code examples that are higher ranked are larger, however, they are not necessarily more readable and reusable. We predict top ranked examples with a good level of confidence, but generic factors have more importance than code quality ones. Based on our results, we provide insights for researchers and practitioners.
Periodicity of quantum walks defined by mixed paths and mixed cycles

In this paper, we determine periodicity of quantum walks defined by mixed paths and mixed cycles. By the spectral mapping theorem of quantum walks, consideration of periodicity is reduced to eigenvalue analysis of η-Hermitian adjacency matrices. First, we investigate coefficients of the characteristic polynomials of η-Hermitian adjacency matrices. We show that the characteristic polynomials of mixed trees and their underlying graphs are same. We also define n+1 types of mixed cycles and show that every mixed cycle is switching equivalent to one of them. We use these results to discuss periodicity. We show that the mixed paths are periodic for any η. In addition, we provide a necessary and sufficient condition for a mixed cycle to be periodic and determine their periods.
Wait, But Why?: Assessing Behavior Explanation Strategies for Real-Time Strategy Games

Work in AI-based explanation systems has uncovered an interesting contradiction: people prefer and learn best from why explanations but expert esports commentators primarily answer what questions when explaining complex behavior in real-time strategy games. Three possible explanations for this contradiction are: 1.) broadcast audiences are well-informed and do not need why explanations; 2.) consuming why explanations in real-time is too cognitively demanding for audiences; or 3.) producing live why explanations is too difficult for commentators. We answer this open question by investigating the effects of explanation types and presentation modalities on audience recall and cognitive load in the context of an esports broadcast. We recruit 111 Dota 2 players and split them into three groups: the first group views a Dota 2 broadcast, the second group has the addition of an interactive map that provides what explanations, and the final group receives the interactive map with detailed why explanations. We find that participants who receive short interactive text prompts that provide what explanations outperform the no explanation group on a multiple-choice recall task. We also find that participants who receive detailed why explanations submit reports of cognitive load that are higher than the no explanation group. Our evidence supports the conclusion that informed audiences benefit from explanations but do not have the cognitive resources to process why answers in real-time. It also supports the conclusion that stacked explanation interventions across different modalities, like audio, interactivity, and text, can aid real-time comprehension when attention resources are limited. Together, our results indicate that interactive multimedia interfaces can be leveraged to quickly guide attention and provide low-cost explanations to improve intelligibility when time is too scarce for cognitively demanding why explanations.
Enhanced framework for ensemble effort estimation by using recursive‐based classification

Service-oriented software engineering is a software engineering methodology focussed on the development of software systems. The systematic application of technological and scientific knowledge depends on the methodology, experience, design for obtaining efficient implementation, testing and software documentation. Software effort estimation (SEE) plays an essential role in reusable service for ensembling the effort estimation of the software development. Effort estimation is the most efficient process applied in software engineering for the prediction of effort. SEE methods are utilised to achieve the effort, cost and human resources with the assistance of the dataset. It is hard to predict the cost, effort, size and schedule consistently through SEE and hence it causes damage to software enterprises. To overwhelm these limitations, an enhanced support vector regression algorithm is used that extracts the features and delivers the relevant features. This algorithm is used to standardise for main features and is related to the supervised learning algorithms. From this, the best features are extracted followed by the elimination of weakest features using the enhanced recursive elimination algorithm. From the selected features, an enhanced random forest classification is used to classify the results. The outcomes are executed to offer the best accuracy and thereby providing efficient prediction of effort estimation. Finally, the performance is measured with parameters such as Magnitude of Balanced Relative Error (MBRE), mean absolute residual, mean inverted balanced relative error, mean magnitude of error relative and mean magnitude of relative error. On comparing the existing methodologies, it is concluded that the proposed work offers better efficiency.
Recent advances in adversarial machine learning: status, challenges and perspectives

The recent advances in machine learning (ML) and Artificial Intelligence (AI) have resulted in widespread application of data-driven learning algorithms. Rapid growth of AI/ML and their penetration within a plethora of civilian and military applications, while successful, has also opened new vulnerabilities. It is now clear that ML algorithms for AI systems are viable targets for malicious attacks. Therefore, there is a pressing need for better understanding of adversarial attacks against ML models, in order to secure them against such malicious attacks. In this paper, we present a survey of adversarial machine learning and some associated countermeasures. We also present a taxonomy of ML/AI system attacks that follow the same properties and characteristics, allowing them to be linked with different defensive approaches. A taxonomy is given for both attack and defense, and attacks proposed in the literature are categorized according to our taxonomy.
Contracting, Pricing, and Data Collection Under the AI Flywheel Effect

This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider’s incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm’s decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider’s effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm’s data collection strategy. In particular, the firm can boost its profit by increasing the product’s capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm’s profit may actually decrease (i.e., more data are not necessarily better). This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4333 .
A Replication Study of Dense Passage Retriever

Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations. One recent work that has garnered much attention is the dense passage retriever (DPR) technique proposed by Karpukhin et al. (2020) for end-to-end open-domain question answering. We present a replication study of this work, starting with model checkpoints provided by the authors, but otherwise from an independent implementation in our group's Pyserini IR toolkit and PyGaggle neural text ranking library. Although our experimental results largely verify the claims of the original paper, we arrived at two important additional findings that contribute to a better understanding of DPR: First, it appears that the original authors under-report the effectiveness of the BM25 baseline and hence also dense--sparse hybrid retrieval results. Second, by incorporating evidence from the retriever and an improved answer span scoring technique, we are able to improve end-to-end question answering effectiveness using exactly the same models as in the original work.
Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

This paper introduces a new approach to enhance performance in performing logic programming in the Hopfield neural network by using agent-based modeling.Hopfield networks have been broadly utilized to solve problems of combinatorial optimization.However, this network yielded a satisfiability problem because the network has grown larger, and it is more complex.Therefore, an improved algorithm has been proposed to enhance the Hopfield network's capability by using the technique of fuzzy logic to provide more efficient energy relaxation and to avoid the local minimum solutions.Agent-based modeling has been introduced in this paper to conduct computer simulations, which aim at verifying and validating the introduced approach.By applying the technique of fuzzy Hopfield neural network clustering in the system, better quality solutions are produced, and the network is handled better despite the increasing complexity.Also, the solutions converged faster by the system.Accordingly, this technique of the fuzzy Hopfield neural network clustering in the system has produced better-quality solutions.