Reviewerly/exHarmony huggingface dataset:
On the relative expressiveness of higher-order session processes

By integrating constructs from the λ-calculus and the π-calculus, in higher-order process calculi exchanged values may contain processes. This paper studies the relative expressiveness of HOπ, the higher-order π-calculus in which communications are governed by session types. Our main discovery is that HO, a subcalculus of HOπ which lacks name-passing and recursion, can serve as a new core calculus for session-typed higher-order concurrency. We show that HO can encode HOπ fully abstractly (up to typed contextual equivalence) more precisely and efficiently than the first-order session π-calculus (π). Overall, under the discipline of session types, HOπ, HO, and π are equally expressive; however, we show that HOπ is more tightly related to HO than to π.
Control for smart systems: Challenges and trends in smart cities

There have been tremendous developments in theories and technologies in control for smart systems. In this paper we review applications to various systems that are crucial for the future of smart cities, for example enterprise and manufacturing systems, transportation systems, energy systems, and data centres. Beyond discussing the existing technological trends and the methodological approaches developed so far for managing and controlling such systems, we also provide visions on the future challenges for these systems in these various aspects.
Intelligent career planning via stochastic subsampling reinforcement learning

Career planning consists of a series of decisions that will significantly impact one's life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one's career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one's career life.
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning

Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table.Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change.We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations.Accordingly, we propose an equivariance learning framework, LATTICE ( ), which encodes tables with a structure-aware self-attention mechanism.This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective.Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells.Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab.Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformationbased data augmentation, have seen significant performance drops. 1
Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques.
Fifty Years of Prolog and Beyond

Abstract Both logic programming in general and Prolog in particular have a long and fascinating history, intermingled with that of many disciplines they inherited from or catalyzed. A large body of research has been gathered over the last 50 years, supported by many Prolog implementations. Many implementations are still actively developed, while new ones keep appearing. Often, the features added by different systems were motivated by the interdisciplinary needs of programmers and implementors, yielding systems that, while sharing the “classic” core language, in particular, the main aspects of the ISO-Prolog standard, also depart from each other in other aspects. This obviously poses challenges for code portability. The field has also inspired many related, but quite different languages that have created their own communities. This article aims at integrating and applying the main lessons learned in the process of evolution of Prolog. It is structured into three major parts. First, we overview the evolution of Prolog systems and the community approximately up to the ISO standard, considering both the main historic developments and the motivations behind several Prolog implementations, as well as other logic programming languages influenced by Prolog. Then, we discuss the Prolog implementations that are most active after the appearance of the standard: their visions, goals, commonalities, and incompatibilities. Finally, we perform a SWOT analysis in order to better identify the potential of Prolog and propose future directions along with which Prolog might continue to add useful features, interfaces, libraries, and tools, while at the same time improving compatibility between implementations.
The ethics of self-tracking. A comprehensive review of the literature

This paper presents a literature review on the ethics of self-tracking technologies which are utilized by users to monitor parameters related to their activity and bodily parameters. By examining a total of 65 works extracted through a systematic database search and backwards snowballing, the authors of this review discuss three categories of opportunities and ten categories of concerns currently associated with self-tracking. The former include empowerment and well-being, contribution to health goals, and solidarity. The latter are social harms, privacy and surveillance, ownership control and commodification of data, autonomy, data-facilitated harm, datafication and interpretability of data, negative impact on relation to self and others, shortcomings of design, negative impact on health perception, and regulation and enforcement of rules. The review concludes with a critical analysis of the existing literature and an overview of a future research agenda that could complement the current work on ethics of self-tracking.
A conceptual model for unifying variability in space and time: Rationale, validation, and illustrative applications

Abstract With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.
Exploring the potential of multispectral satellite images for estimating the contents of cadmium and lead in cropland: The effect of the dimidiate pixel model and random forest

Rapid and accurate predictions of heavy metal contents in farmland are of great significance to ensure the safety of agricultural products and maintain ecosystem balance. Combining multispectral images and chemometric modeling provides a feasible means of estimating cadmium (Cd) and lead (Pb) contents in farmland. We collected 640 samples from the surface soils of farmland in Xiangzhou District, Hubei Province, China. The Cd, Pb, soil organic matter (SOM), pH, and Fe of the soil samples were measured in a laboratory. In this study, the dimidiate pixel model was used to process remote sensing images. In particular, we used random forest (RF) to screen the best spectral indices for use as input variables. Partial least squares regression (PLSR), backward propagation neural network (BPNN), and RF were used to calibrate the spectral data with Cd and Pb contents, and the optimal model was used for the regional mapping of soil Cd and Pb contents. Additionally, we explored the potential of using spectral estimation mechanisms to estimate Cd and Pb contents. The mechanism for estimating Cd and Pb contents with multispectral images depended mainly on the covariance of Cd and Pb contents with that of SOM. For both Cd and Pb estimations, the double-date image estimation model performed better than the single-date image estimation model, and the unmixed image estimation model was more accurate than the original image estimation model. Overall, the estimation model using the best spectral indices as input variables performed better than the model using full-band data as input variables. The RF model outperformed the PLSR and BPNN models in all cases. Relatively satisfactory estimates of Cd and Pb contents in farmland of the study area (maximum R2val (determination coefficient of the validation set) = 0.60 for Cd and R2val = 0.63 for Pb) were obtained. Our results show that areas of farmland contaminated by Cd and Pb throughout the study area have increased and that contamination levels have worsened since 1990. In addition, the spatial patterns of Cd and Pb contents in farmland throughout the study area were analyzed and validated using field survey results. The results of the study provide a theoretical basis and methodological reference for the rapid prediction of Cd and Pb contents in regional farmland.
Transition imaging phase measuring deflectometry for high-precision measurement of optical surfaces

The phase measuring deflectometry is a powerful measuring method for optical freeform surfaces, but the surface under test (SUT) and the screen pattern cannot be in-focus simultaneously. The defocusing of the screen in the imaging system can cause serious convolution effect. Then the mismatching errors arising in the phase demodulation will reduce the measurement accuracy. To solve this problem, the transition imaging phase measuring deflectometry is proposed. An auxiliary focusing mirror is applied to image the screen pattern to the position of the SUT, so that the camera can clearly capture the images of the screen pattern and the SUT at the same time. The system design, the geometrical calibration, and the fast solving method of the gradient field are developed. Finally, the measurement accuracy of the proposed method is experimentally demonstrated and it can be improved by 5.8 times when measuring complex surfaces.
Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks.Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples.These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world.We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., inter-class loss distribution discrepancy and misleading predictions due to uncertainty.The first issue is that existing methods often perform class-agnostic noise modeling. However, loss distributions show a significant discrepancy among classes under class imbalance, and class-agnostic noise modeling can easily get confused with noisy samples and samples in minority classes.The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples. Inspired by our observations, we propose an Uncertainty-aware Label Correction framework(ULC) to handle label noise on imbalanced datasets. First, we perform epistemic uncertainty-aware class-specific noise modeling to identify trustworthy clean samples and refine/discard highly confident true/corrupted labels.Then, we introduce aleatoric uncertainty in the subsequent learning process to prevent noise accumulation in the label noise modeling process. We conduct experiments on several synthetic and real-world datasets. The results demonstrate the effectiveness of the proposed method, especially on imbalanced datasets.
Adversarial Graph Perturbations for Recommendations at Scale

Graph Neural Networks (GNNs) provide a class of powerful architectures that are effective for graph-based collaborative filtering. Nevertheless, GNNs are known to be vulnerable to adversarial perturbations. Adversarial training is a simple yet effective way to improve the robustness of neural models. For example, many prior studies inject adversarial perturbations into either node features or hidden layers of GNNs. However, perturbing graph structures has been far less studied in recommendations.
Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user's interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use.
Where Does the Performance Improvement Come From?

This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retrieval models. Due to the increase of multimodal data over the last decade, image-text retrieval has steadily become a major research direction in the field of information retrieval. Numerous researchers train and evaluate image-text retrieval algorithms using benchmark datasets such as MS-COCO and Flickr30k. Research in the past has mostly focused on performance, with multiple state-of-the-art methodologies being suggested in a variety of ways. According to their assertions, these techniques provide improved modality interactions and hence more precise multimodal representations. In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.
The Blockchain Technologies in Healthcare: Prospects, Obstacles, and Future Recommendations; Lessons Learned from Digitalization

For today's healthcare data management systems, fundamental problems are data transparency, traceability, immutability, audit, data provenance, flexible access, trust, privacy, and security. Blockchain is a promising technology that can help to optimise healthcare data management operations by increasing data efficiency and ensuring trust. The current study examined the usefulness of blockchain technology in healthcare and the barriers to adoption. Finally, we looked at the future of blockchain technology in healthcare. The current study reviewed 53 papers and utilised the PRISMA framework 2020 to include and exclude records from the Scopus database. The VOS Viewer software is used to categorize the published literature, and the prevalence of essential terms is often used to authenticate the main themes in classifications. The current study findings indicate that Blockchain offers a wide range of applications and functions in healthcare. By allowing the transfer of patient medical information, controlling the medication supply chain, and facilitating the safe transfer of patient medical records, blockchain technology assists healthcare researchers in discovering genetic codes. Because blockchain can support novel analytics, healthcare firms may notice changes in their data in real-time, allowing them to make swift choices without human involvement
Refactoring Test Smells With JUnit 5: Why Should Developers Keep Up-to-Date?

Test smells are symptoms in the test code that indicate possible design or implementation problems. Previous research demonstrated their harmfulness and the developers’ acknowledgment of test smells’ effects, prevention, and refactoring strategies. Test automation frameworks are constantly evolving, and the JUnit, one of the most used ones for Java projects, has its version 5 available since late 2017. However, we do not know the extent to which developers use the newly introduced features and whether such features indeed help refactor existing test code to remove test smells. This article conducts a mixed-method study investigation to minimize these knowledge gaps. Our study consists of three parts. First, we evaluate the usage of this framework and its features by analyzing the source code of 485 popular Java open-source projects on GitHub that use JUnit. We found that 15.9% of these projects use the JUnit 5 library. We also found that, from 17 new features detected in use, only 3 (i.e., 17.6%) are responsible for more than 70% of usages, limiting optimized propositions to test code creation and maintenance. Second, after identifying features in the JUnit 5 framework that could be considered to test smells removal and prevention, we use these features to propose novel refactorings. In particular, we present refactorings based on 7 introduced JUnit 5 features that help to remove 13 test smells, such as Assertion Roulette, Test Code Duplication, and Conditional Test Logic. Third, to evaluate our refactorings with the opinions of experienced developers, we (i) survey 212 developers for their preferences and comments about our refactorings, corroborating the benefits of our proposals and raising community feedback on JUnit 5 features, and (ii) we refactor actual test code from popular GitHub Java projects and submit 38 Pull Requests, reaching a 94% acceptance rate among respondents. As implications of our study, we alert the software testing community (i.e., practitioners and researchers) to the need to study the JUnit 5 features to effectively remove and prevent test smells. To better assist this process, we give directions on how test smells can be refactored using such features.