Reviewerly/exHarmony huggingface dataset:
ListMAP: Listwise learning to rank as maximum a posteriori estimation

Listwise learning to rank models, which optimize the ranking of a document list, are among the most widely adopted algorithms for finding and ranking relevant documents to user information needs. In this paper, we propose ListMAP, a new listwise learning to rank model with prior distribution that encodes the informativeness of training data and assigns different weights to training instances. The main intuition behind ListMAP is that documents in the training dataset do not have the same impact on training a ranking function. ListMAP formalizes the listwise loss function as a maximum a posteriori estimation problem in which the scoring function must be estimated such that the log probability of the predicted ranked list is maximized given a prior distribution on the labeled data. We provide a model for approximating the prior distribution parameters from a set of observation data. We implement the proposed learning to rank model using neural networks. We theoretically discuss and analyze the characteristics of the introduced model and empirically illustrate its performance on a number of benchmark datasets; namely MQ2007 and MQ2008 of the Letor 4.0 benchmark, Set 1 and Set 2 of the Yahoo! learning to rank challenge data set, and Microsoft 30k and Microsoft 10K datasets. We show that the proposed models are effective across different datasets in terms of information retrieval evaluation metrics NDCG and MRR at positions 1, 3, 5, 10, and 20.
Influence Mechanism of Implementation Intentions from the Perspective of Time and Space in Mobile Shopping

The emerging e-commerce is increasingly influenced by consumers’ sense of mobile shopping presence. Understanding correlation and match rules across spatial and temporal scene cues, as well as corresponding behavioral responses from mobile shopping, has become a new hot topic in the behavioral implementation intention theory. This study proposes a model incorporating time submissiveness, perceived visual-spatial crowding, psychological cost, and satisfaction to investigate the influencing mechanism and configuration paths of consumers’ mobile shopping implementation intention. To test the relations, we used a structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM model showed that both psychological cost and satisfaction exerted a positive impact on mobile shopping implementation intention. Time submissiveness and perceived visual-spatial crowding exerted a significant impact on mobile shopping implementation intention through the mediation of psychological cost and satisfaction. The fsQCA method examined the antecedents resulting in high and non-high implementation intention. This study better provides theoretical and practical inspiration for mobile consumers’ psychological characteristics and presence experience exploration.
A Random Forest approach to predict geology from geophysics in the Pontiac subprovince, Canada

Current “visual” integration approaches to map geology from geophysics are challenging, biased toward user assumptions, and time consuming. Applying a supervised machine learning Random Forest method to airborne geophysical data sets is an alternative solution to quickly generate interpretative maps, which can later be validated through targeted and therefore more efficient field campaigns. The approach is tested in the Malartic–Cadillac area of the Pontiac subprovince, Canada. Available airborne geophysical data sets include magnetic, frequency-domain electromagnetic, and radiometric data. Unlike the western part of the area, many studies have been done in the eastern part where the world-class Canadian Malartic gold deposit is located. The eastern part has abundant field observations and well-documented geology that are used as training data for Random Forest learning. A predicted map is built after preprocessing the data, gridding the area into a mesh of nodes and training using known geological knowledge. The results provide new information about the spatial distribution of lithological units, including diabase dikes, felsic-intermediate igneous rocks, mafic–ultramafic igneous rocks, and sedimentary rocks. Comparison between the predicted map and previous geological maps could suggest locations where future field mapping efforts should focus in the Malartic–Cadillac area.
Research on Spatial Agglomeration Characteristics of Aerospace Cultural and Creative Industries in Smart City under Multidata Fusion

Cultural and creative industries have the characteristics of spatial agglomeration. They not only show the situation of “local concentration and overall dispersion” as a whole but also have the function of self-strengthening development. In order to improve the service capacity of tTitan aerospace cultural and creative industry space, the park provides high availability and wide bandwidth physical outgoing lines, optimizes the private cloud computing capacity of the park, and improves the high availability performance of the network under the large business mode of the park. Finally, it is found that the increase of/in occupancy rate, GDP, and total service revenue of the park have been improved. In this paper, starting with the current situation of China’s population and the current situation of Fantian business combined with information technology, space aggregation was carried out to build an aviation business park by high-tech means, so that they can attract more talents with high knowledge and high R&amp;D ability to move in and drive the economic development at that time.
Temporal Exposure Reduction Protection for Persistent Memory

The long-living nature and byte-addressability of persistent memory (PM) amplifies the importance of strong memory protections. This paper develops temporal exposure reduction protection (TERP) as a framework for enforcing memory safety. Aiming to minimize the time when a PM region is accessible, TERP offers a complementary dimension of memory protection. The paper gives a formal definition of TERP, explores the semantics space of TERP constructs, and the relations with security and composability in both sequential and parallel executions. It proposes programming system and architecture solutions for the key challenges for the adoption of TERP, which draws on novel supports in both compilers and hardware to efficiently meet the exposure time target. Experiments validate the efficacy of the proposed support of TERP, in both efficiency and exposure time minimization.
Augmenting Social Bot Detection with Crowd-Generated Labels

Social media platforms are facing increasing numbers of cyber-adversaries seeking to manipulate online discourse by using social bots to help automate and scale their attacks. Likewise, some social media users have developed capabilities to identify social bot activity at varying degrees of confidence. We exploit this user intelligence to augment traditional bot detection systems. Furthermore, not all crowd-generated labels are of equal value or credibility. Some individuals are quite adept at identifying social bot activity, whereas others may become merely suspicious but remain uncertain. We design a system inspired by speech act theory to evaluate which crowd-generated labels are most credible for augmenting bot detection system efficacy.
Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 2: Application of self-organizing maps and artificial neural networks for exploration targeting

This paper is part of a two-publication series reporting target-scale prospectivity modeling results for gold enrichment in the Rajapalot project area. The study area is located in the Northern Fennoscandian Shield in Finland and consists of high-grade epigenetic-hydrothermal Au-Co prospects. The first publication of this series described the implementation of knowledge-driven- and hybrid- expert systems. In this second paper we demonstrate the application of machine learning methods such as unsupervised self-organizing maps (SOM) and K-means clustering and supervised artificial neural networks (ANNs). The results from SOM allowed the mapping of deposit-related geological patterns in the evidential layers. K-means clustering of the SOM results identified data clusters favorable for gold enrichment. Quantitative prospectivity values were computed using an ANN model trained on the SOM codebook vectors. This improved the resolution of the prospective-exploration areas mapped within the geospatial domains of the clusters, and reduced the exploration search areas. The ANN result has a significant area under curve value of 0.869 in the receiver operating characteristic plots, confirming the predictive ability of the model. Results identify new areas for exploration, as well as help prioritise drilling targets around the currently less explored prospects. In this second paper we therefore conclude that (1) SOM is an effective method for mapping and visualization of patterns (in the evidential layers) related to mineral deposits at the target scale, and (2) supervised classification of the SOM results, such as using an ANN, can help define new target areas for drilling around the existing prospects. Finally, this two-publication series collectively demonstrates that advanced machine-learning based pattern recognition and prospectivity modeling routines can be used for exploration targeting at camp- and mine- scales. The knowledge-driven methods are efficient at modeling mineralization processes and the data-driven methods are effective at identifying geological attributes related to mineral deposits and occurrences.
The interdisciplinarity of research data: How widely is shared research data reused in the STEM fields?

The aim of this study was to examine the interdisciplinarity of research data in the science, technology, engineering, and mathematics (STEM) fields. The findings revealed that interdisciplinarity was not distributed evenly across journals serving the STEM fields. Based on the diversity of the references as measured by the Gini coefficient index, the mathematical sciences showed the greatest inequality, followed by astronomy/physics, the earth sciences, the biological sciences, and technology. Based on the number of Essential Science Indicator (ESI) fields, the biological sciences showed the greatest variety, followed by the earth sciences, technology, the mathematical sciences, chemistry, and computing, while engineering showed no variety. Lastly, based on the Leydesdorff interdisciplinarity formula outcomes, the earth sciences showed the greatest diversity, but earth sciences articles were cited in articles in fewer fields than biological sciences articles. This study contributes to the study of interdisciplinary data citation for data sharing and reuse in STEM fields with respect to the measurement of the balance, variety, and diversity of research data.
What exactly is it that technical services does?: Promoting and advocating for technical services work to the academic community

The article suggests ways of advocating Technical Services to library users and the wider academic community. It starts with the reasons behind advocacy and then focuses on modern tools, such as social media, Canva, and word clouds. Finally, it provides a list of further reading for those interested in advocating without using visual aids.
Application of Data Resource Allocation in Economic Management Information System

With the development of the digital economy and the sharp increase in economic information, a large amount of data needs to be processed. Therefore, the demand for an economic management information system is also increasing, but how to realize the allocation of data resources in the system has become an urgent problem to be solved. This study conducts research on the above problems, first analyzes the research results of domestic and foreign scholars, finds out the content suitable for this research, and then introduces the structure of the economic management information system and the method of data resource allocation in detail. Finally, by comparing and testing the algorithm of data resource allocation and other allocation algorithms, it is concluded that the allocation algorithm used in this study is more effective than the research algorithm of previous scholars, thus proving the feasibility of this research. The test results show that the number of protocol function modules allocated by the GAA algorithm is 20% to 30% less than that of the RAA algorithm. So, it can be concluded that compared with the RAA algorithm, the GAA algorithm saves more network resources.
PenQuest Reloaded: A Digital Cyber Defense Game for Technical Education

Today&#x2019;s IT and OT infrastructure is threatened by a plethora of cyber-attacks conducted by actors with different motivations and means. Furthermore, the complexity of these exposed systems as well as the adversaries&#x2019; sophisticated technical arsenal makes it increasingly difficult to plan and implement an organization&#x2019;s defense. Understanding the link between specific attacks and effective mitigating measures is particularly challenging &#x2013; as is understanding the underlying information security concepts. To support the training of current, and more importantly, nascent security engineers, we propose PenQuest, a digital attack and defense game where an attacker attempts to compromise an abstracted IT infrastructure and the defender works to prevent or mitigate the threat. The game is based on MITRE ATT&#x0026;CK, D3FEND, and the NIST SP 800-53 security standard and incorporates a multitude of concepts such as cyber kill chains, attack vectors, network segmentation, and more. PenQuest is built to support security education and risk assessment and was evaluated with a class of engineering students as well as independent security experts. Initial results show a significant increase in knowledge retention and attest to the game&#x2019;s feasibility for educational use.
A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

Abstract Traditional path planning methods, such as sampling-based and iterative approaches, allow for optimal path’s computation in complex environments. Nonetheless, environment exploration is subject to rules which can be obtained by domain experts and could be used for improving the search. The present work aims at integrating inductive techniques that generate path candidates with deductive techniques that choose the preferred ones. In particular, an inductive learning model is trained with expert demonstrations and with rules translated into a reward function, while logic programming is used to choose the starting point according to some domain expert’s suggestions. We discuss, as use case, 3-D path planning for neurosurgical steerable needles. Results show that the proposed method computes optimal paths in terms of obstacle clearance and kinematic constraints compliance, and is able to outperform state-of-the-art approaches in terms of safety distance-from-obstacles respect, smoothness, and computational time.
Combination of Uniform Interpolants via Beth Definability

Abstract Uniform interpolants were largely studied in non-classical propositional logics since the nineties, and their connection to model completeness was pointed out in the literature. A successive parallel research line inside the automated reasoning community investigated uniform quantifier-free interpolants (sometimes referred to as “covers”) in first-order theories. In this paper, we investigate cover transfer to theory combinations in the disjoint signatures case. We prove that, for convex theories, cover algorithms can be transferred to theory combinations under the same hypothesis needed to transfer quantifier-free interpolation (i.e., the equality interpolating property, aka strong amalgamation property). The key feature of our algorithm relies on the extensive usage of the Beth definability property for primitive fragments to convert implicitly defined variables into their explicitly defining terms. In the non-convex case, we show by a counterexample that covers may not exist in the combined theories, even in case combined quantifier-free interpolants do exist. However, we exhibit a cover transfer algorithm operating also in the non-convex case for special kinds of theory combinations; these combinations (called ‘tame combinations’) concern multi-sorted theories arising in many model-checking applications (in particular, the ones oriented to verification of data-aware processes).
RaDE+: A semantic rank-based graph embedding algorithm

Due to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-dimensional vector representations for graphs, preserving their original properties such as topological characteristics, affinity, and shared neighborhood between nodes. Based on the vectorial representations, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an effective and efficient approach that considers rank-based graphs and representative nodes selection for learning a low-dimensional vector. We also present RaDE+, a variant that considers multiple representative nodes for more robust representations. The proposed approach was evaluated on 8 network datasets, including social, co-reference, textual, and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods.
Epistemic Rhetoric in Virtual Reality Interactive Factual Narratives

The turn to Interactive Digital Narratives to understand complexity offers a new model for creating, developing, and maintaining knowledge. At the same time, storytellers have turned their attention to Virtual Reality (VR). The confluence of these trends draws attention to how non-fiction practitioners can use the technical and aesthetic affordances of VR to create knowledge about complex subjects through the IDN form. This article explores the epistemic rhetorical nature of using narrative discourse in VR to create knowledge about a non-fiction subject. The IDN community has not addressed this rhetorical aspect in their proposed epistemological process. Clarifying the epistemic rhetorical aspect inherent in producing knowledge on complex subjects through IDN provides insights into practitioners’ persuasive and political design and development choices. These intentional choices, in turn, impact the kind of knowledge produced. This rhetorical approach to knowledge production can be grounded in a Neo-sophist epistemic tradition wherein aesthetic choices are used rhetorically. I will present and discuss the Sophist rhetorical tactics of antithesis, the rhetoric of the possible; enargeia, the rhetoric of vivid details; kairos, the rhetoric of opportune timing; and mêtis, the rhetoric of the body. Their implementation by practitioners, how these aesthetic choices rhetorically create knowledge in the System-Process-Product model is presented. The article clarifies these rhetorical processes and choices and analyzes the 2021 Tribeca Film Festival’s Best Immersive Narrative, The Changing Same: An American Pilgrimage: Episode 1 . This VR factual IDN allows interactors to experience historical moments of racial injustice in the United States. The production team was interviewed about how they used the technical and aesthetic qualities of VR and IDN rhetorically to produce knowledge about the complex and violent history of racial injustice in the United States. Their responses indicate their active use of epistemic rhetorical tactics that capitalize on the technical and aesthetic affordances of VR and IDN to create knowledge.
Robust Decentralized Proof of Location for Blockchain Energy Applications Using Game Theory and Random Selection

To combat the problem of illegal access to a service, several location proof strategies have been proposed in the literature. In blockchain-based decentralized applications, transactions can be issued by IoT nodes or other automated smart devices. Key pair encryption and private key signing have been defined mainly for human identification in blockchain applications, where users are personally and responsibly concerned about the confidentiality of their private key. These methods are not suitable for computing nodes whose private key is implemented in the software they run. Ensuring that transactions are issued by a legitimate sender with the proper credentials is a bigger concern in applications with financial stakes. This is the case with blockchain energy trading platforms, where prosumers are credited with tokens in exchange for their contributions of energy. The tokens are issued by smart meter nodes installed at fixed locations to monitor the energy inputs and outputs of a given prosumer and claim energy tokens on its behalf from a defined smart contract in exchange for the energy it feeds into the grid. To this end, we have developed a decentralized Proof-of-Location (PoL) system tailored to blockchain applications for energy trading. It ensures that automated transactions are issued by the right nodes by using smart contract-based random selection and a game-theoretic scenario suitable for blockchain energy trading.
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems

Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence paradigm where agents interact with their own environments and cooperatively learn a consensus policy while preserving privacy, has recently shown potential advantages and gained popularity. However, transient faults are increasing in the hardware system with continuous technology node scaling and can pose threats to FRL systems. Meanwhile, conventional redundancy-based protection methods are challenging to deploy on resource-constrained edge applications. In this paper, we experimentally evaluate the fault tolerance of FRL navigation systems at various scales with respect to fault models, fault locations, learning algorithms, layer types, communication intervals, and data types at both training and inference stages. We further propose two cost-effective fault detection and recovery techniques that can achieve up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.3\times$</tex> improvement in resilience with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;2.7\%$</tex> overhead in FRL systems.
Modelling online debates with argumentation theory

It is important to study and understand Internet debates because they often have consequences in the offline world, for better or worse. We show that argumentation theory, a branch of AI concerned with the resolution of disagreements, provides a powerful toolbox with which we can represent and reason about such debates. After summarising the relevant ideas of argumentation theory, we overview three recent contributions from the authors and their collaborators: (1) on how to automatically identify reply polarity (agreement or disagreement) between arguments submitted in online debates, (2) on locating where the justified arguments are likely to be and how that depends on the "degree of antagonism" of the debate, and (3) on how to present the arguments made in debates such that a reader would get as many of the justified arguments as possible without having to read the entire debate. We hope that this will lead to further work that applies argumentation theory to model and analyse online debates.