Reviewerly/exHarmony huggingface dataset:
Rapid detection of molybdenum ore grade based on visible-infrared spectroscopy and MTSVD-TGJO-ELM

The rapid determination of ore grade can improve the efficiency of beneficiation. The existing molybdenum ore grade determination methods lag behind the beneficiation work. Therefore, this paper proposes a method based on a combination of Visible-infrared spectroscopy and machine learning to rapidly determine molybdenum ore grade. Firstly, 128 molybdenum ores were collected as spectral test samples to obtain spectral data. Then 13 latent variables were extracted from the 973 spectral features using partial least square. The Durbin-Watson test and the runs test were used to detect the partial residual plots and augmented partial residual plots of LV1 and LV2 to determine the non-linear relationship between spectral signal and molybdenum content. Extreme Learning Machine (ELM) was used instead of linear modeling methods to model the grade of molybdenum ores because of the non-linear behavior of the spectral data. In this paper, the Golden Jackal Optimization of adaptive T-distribution was used to optimize the parameters of the ELM to solve the problem of unreasonable parameters. Aiming at solving ill-posed problems by ELM, this paper decomposes the ELM output matrix by using the improved truncated singular value decomposition. Finally, this paper proposes an extreme learning machine method based on a modified truncated singular value decomposition and a Golden Jackal Optimization of adaptive T-distribution (MTSVD-TGJO-ELM). Compared with other classical machine learning algorithms, MTSVD-TGJO-ELM has the highest accuracy. This provides a new method for rapid detection of ore grade in the mining process and facilitates accurate beneficiation of molybdenum ores to improve ore recovery rate.
Evaluating the Vulnerability of YOLOv5 to Adversarial Attacks for Enhanced Cybersecurity in MASS

The development of artificial intelligence (AI) technologies, such as machine learning algorithms, computer vision systems, and sensors, has allowed maritime autonomous surface ships (MASS) to navigate, detect and avoid obstacles, and make real-time decisions based on their environment. Despite the benefits of AI in MASS, its potential security threats must be considered. An adversarial attack is a security threat that involves manipulating the training data of a model to compromise its accuracy and reliability. This study focuses on security threats faced by a deep neural network-based object classification algorithm, particularly you only look once version 5 (YOLOv5), which is a model used for object classification. We performed transfer learning on YOLOv5 and tested various adversarial attack methods. We conducted experiments using four types of adversarial attack methods and parameter changes to determine the attacks that could be detrimental to YOLOv5. Through this study, we aim to raise awareness of the vulnerability of AI algorithms for object detection to adversarial attacks and emphasize the need for efforts to overcome them; these efforts can contribute to safe navigation in MASS.
NASA Science Mission Directorate Knowledge Graph Discovery

The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) data catalog is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.
Query-Driven Knowledge Graph Construction using Question Answering and Multimodal Fusion

Over recent years, large knowledge bases have been constructed to store massive knowledge graphs. However, these knowledge graphs are highly incomplete. To solve this problem, we propose a web-based question answering system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. To utilize unstructured information from the Web for knowledge graph construction, we design multimodal features and question templates to extract missing facts, which can achieve good quality with very few questions. The question answering system also employs structured information from knowledge bases, such as entity types and entity-to-entity relatedness, to help improve extraction quality. To improve system efficiency, we utilize a few query-driven techniques for web-based question answering to reduce the runtime and provide fast responses to user queries. Extensive experiments have been conducted to demonstrate the effectiveness and efficiency of our system.
Identifying Topic and Cause for Sarcasm: An Unsupervised Knowledge-enhanced Prompt Method

Sarcasm is usually emotional and topical. Mining the characteristics of sarcasm semantics in different emotional tendencies and topic expressions helps gain insight into the sarcasm cause. Most of the existing work detect sarcasm or topic label based on a supervised learning framework, which requires heavy data annotation work. To overcome the above challenges, inspired by the multi-task learning framework, this paper proposes an unsupervised knowledge-enhanced prompt method. This method uses the similarity interaction mechanism to mine the hidden relationship between the sarcasm cause and topic, which integrates external knowledge, such as syntax and emotion, into the prompting and generation process. Additionally, it identifies the sarcasm cause and topic simultaneously. Experimental results on a real-world dataset verify the effectiveness of the proposed model.
Improving the Exploration/Exploitation Trade-Off in Web Content Discovery

New web content is published constantly, and although protocols such as RSS can notify subscribers of new pages, they are not always implemented or actively maintained. A more reliable way to discover new content is to periodically re-crawl the target sites. Designing such “content discovery crawlers” has important applications, for example, in web search, digital assistants, business, humanitarian aid, and law enforcement. Existing approaches assume that each site of interest has a relatively small set of unknown “source pages” that, when refreshed, frequently provide hyperlinks to the majority of new content. The state of the art (SOTA) uses ideas from the multi-armed bandit literature to explore candidate sources while simultaneously exploiting known good sources. We observe, however, that the SOTA uses a sub-optimal algorithm for balancing exploration and exploitation. We trace this back to a mismatch between the space of actions that the SOTA algorithm models and the space of actions that the crawler must actually choose from. Our proposed approach, the Thompson crawler (named after the Thompson sampler that drives its refresh decisions), addresses this shortcoming by more faithfully modeling the action space. On a dataset of 4,070 source pages drawn from 53 news domains over a period of 7 weeks, we show that, on average, the Thompson crawler discovers 20% more new pages, finds pages 6 hours earlier, and uses 14 fewer refreshes per 100 pages discovered than the SOTA.
Forest Classroom: A Case Study of Educational Augmented Reality Design to Facilitate Classroom Engagement

The transition from kindergarten to primary school involves preparing students for a more structured classroom-based learning environment, which is typically different from the play-based model in kindergartens. Building on the Forest Room concept, which connects restless and disengaged students to nature as a calming medium, this case study describes the design of a combined storybook and augmented reality application to provide a literacy primer that integrates this concept. The design case study is presented relative to three frameworks that review the support for educational content, motivation and engagement mechanisms, and features of the AR application. This serves to validate the design process relative to these criteria and identifies opportunities for enhancement, including opportunities for meaningful interaction. The resulting application demonstrates appropriate design strategies to support its target age group and focus. It provides a stimulating and flexible learning activity that can be readily integrated into the classroom and that supports the kindergarten transition to appropriate classroom behaviour by encouraging active engagement and collaboration, blending aspects of both outdoor and classroom-based activities.
What Image do You Need? A Two-stage Framework for Image Selection in E-commerce

In e-commerce, images are widely used to display more intuitive information about items. Image selection significantly affects the user’s click-through rate (CTR). Most existing work considers the CTR as the target to find an appropriate image. However, these methods are challenging to deploy online efficiently. Also, the selected images may not relate to the item but are profitable to CTR, resulting in the undesirable phenomenon of enticing users to click on the item. To address these issues, we propose a novel two-stage pipeline method with content-based recall model and CTR-based ranking model. The first is realized as a joint method based on the title-image matching model and multi-modal knowledge graph embedding learning model. The second is a CTR-based visually aware scoring model, incorporating the entity textual information and entity images. Experimental results show the effectiveness and efficiency of our method in offline evaluations. After a month of online A/B testing on a travel platform Fliggy, the relative improvement of our method is 5% with respect to seller selection on CTCVR in the searching scenario, and our method further improves pCTR from 3.48% of human pick to 3.53% in the recommendation scenario.
STRUM: Extractive Aspect-Based Contrastive Summarization

Comparative decisions, such as picking between two cars or deciding between two hiking trails, require the users to visit multiple webpages and contrast the choices along relevant aspects. Given the impressive capabilities of pre-trained large language models [4, 11], we ask whether they can help automate such analysis. We refer to this task as extractive aspect-based contrastive summarization which involves constructing a structured summary that compares the choices along relevant aspects. In this paper, we propose a novel method called STRUM for this task that can generalize across domains without requiring any human-written summaries or fixed aspect list as supervision. Given a set of relevant input webpages, STRUM solves this problem using two pre-trained T5-based [11] large language models: first one fine-tuned for aspect and value extraction [14], and second one fine-tuned for natural language inference [13]. We showcase the abilities of our method across different domains, identify shortcomings, and discuss questions that we believe will be critical in this new line of research.
Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability

This paper presents a qualitative analysis of the recoverability of various webpages on the live web, using their archived counterparts as a baseline. We used a heterogeneous dataset consisting of four web archive collections, each with varying degrees of content drift. We were able to recover a small number of webpages previously thought to have been lost and analyzed their content and evolution. Our analysis yielded three types of lost webpages: 1) those that are not recoverable (with three subtypes), 2) those that are fully recoverable, and 3) those that are partially recoverable. The analysis presented here attempts to establish clear definitions and boundaries between the different degrees of webpage recoverabilty. By using a few simple methods, web archivists could discover the new locations of web content that was previously deemed lost, and include them in future crawling efforts, and lead to more complete web archives with less content drift.
Don’t Trust, Verify: The Case of Slashing from a Popular Ethereum Explorer

Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in , a leading explorer serving Ethereum’s proof-of-stake (PoS) update. According to the explorer, we find that more than 75% of slashable Byzantine actions were not slashed. Since Ethereum relies on the “stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum’s security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer’s mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.
Creation and Analysis of a Corpus of Scam Emails Targeting Universities

Email-based scams pose a threat to the personally identifiable information and financial safety of all email users. Within a university environment, the risks are potentially greater: traditional students (i.e., within an age range typical of college students) often lack the experience and knowledge of older email users. By understanding the topics, temporal trends, and other patterns of scam emails targeting universities, these institutions can be better equipped to reduce this threat by improving their filtering methods and educating their users. While anecdotal evidence suggests common topics and trends in these scams, the empirical evidence is limited. Observing that large universities are uniquely positioned to gather and share information about email scams, we built a corpus of 5,155 English language scam emails scraped from information security websites of five large universities in the United States. We use Latent Dirichlet Allocation (LDA) topic modelling to assess the landscape and trends of scam emails sent to university addresses. We examine themes chronologically and observe that topics vary over time, indicating changes in scammer strategies. For example, scams targeting students with disabilities have steadily risen in popularity since they first appeared in 2015, while password scams experienced a boom in 2016 but have lessened in recent years. To encourage further research to mitigate the threat of email scams, we release this corpus for others to study.
Multi-Source Domain Adaptation via Latent Domain Reconstruction

Multi-Source Domain Adaptation (MSDA) is widely used in various machine learning scenarios for domain shifts between labeled source domains and unlabeled target domains. Conventional MSDA methods are built on a strong hypothesis that data samples from the same source belong to the same domain with the same latent distribution. However, in practice sources and their latent domains are not necessarily one-to-one correspondence. To tackle this problem, a novel Multi-source Reconstructed Domain Adaptation (MRDA) framework for MSDA is proposed. We use an Expectation-Maximization (EM) mechanism that iteratively reconstructs the source domains to recover the latent domains and performs domain adaptation on the reconstructed domains. Specifically, in the E-step, we cluster the samples from multiple sources into different latent domains, and a soft assignment strategy is proposed to avoid cluster imbalance. In the M-step, we freeze the latent domains clustered in the E-step and optimize the objective function for domain adaptation, and a global-specific feature extractor is used to capture both domain-invariant and domain-specific features. Extensive experiments demonstrate that our approach can reconstruct source domains and perform domain adaptation on the reconstructed domains effectively, thus significantly outperforming state-of-the-art (SOTA) baselines (e.g., 1% to 3.1% absolute improvement in AUC).
Knowledge Graph-Enhanced Neural Query Rewriting

The main task of an e-commerce search engine is to semantically match the user query to the product inventory and retrieve the most relevant items that match the user’s intent. This task is not trivial as often there can be a mismatch between the user’s intent and the product inventory for various reasons, the most prevalent being: (i) the buyers and sellers use different vocabularies, which leads to a mismatch; (ii) the inventory doesn’t contain products that match the user’s intent. To build a successful e-commerce platform it is of paramount importance to be able to address both of these challenges. To do so, query rewriting approaches are used, which try to bridge the semantic gap between the user’s intent and the available product inventory. Such approaches use a combination of query token dropping, replacement and expansion. In this work we introduce a novel Knowledge Graph-enhanced neural query rewriting in the e-commerce domain. We use a relationship-rich product Knowledge Graph to infuse auxiliary knowledge in a transformer-based query rewriting deep neural network. Experiments on two tasks, query pruning and complete query rewriting, show that our proposed approach significantly outperforms a baseline BERT-based query rewriting solution.
Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.
Efficient SOM’s Application to Seismic Fumarolic Tremor for the Detection of Anomalous Hydrothermal Activity in Campi Flegrei Volcano (Italy)

In recent decades, the Campi Flegrei caldera (Italy) showed unrest characterized by increases in seismicity, ground uplift, and hydrothermal activity. Currently, the seismic and hydrothermal phenomena are mostly concentrated in the Solfatara–Pisciarelli area, which presents a wide fumarolic field and mud emissions. The main fumarole in Pisciarelli is associated with a boiling mud pool. Recently, episodes of a sudden increase in hydrothermal activity and expansion of mud and gas emissions occurred in this area. During these episodes, which occurred in December 2018 and September 2020, Short Duration Events (SDEs), related to the intensity of mud pool boiling, were recorded in the fumarolic seismic tremor. We applied a Self-Organizing Map (SOM) neural network to recognize the occurrence of SDEs in the fumarolic tremor of Campi Flegrei, which provides important information on the state of activity of the hydrothermal system and about the possible phreatic activity. Our method, based on an ad hoc feature extraction procedure, effectively clustered the seismic signals containing SDEs and separated them from those representing the normal fumarolic tremor. This result is useful for improving the monitoring of the Solfatara–Pisciarelli hydrothermal area which is a high-risk zone in Campi Flegrei.
The Age of Snippet Programming: Toward Understanding Developer Communities in Stack Overflow and Reddit

Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. This work investigates the developers’ behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users’ posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these Q&A platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.
qEndpoint: A Wikidata SPARQL endpoint on commodity hardware

In this work, we demonstrate how to setup a Wikidata SPARQL endpoint on commodity hardware resources. We achieve this by using a novel triple store called qEndpoint, which uses a read-only partition based on HDT and a write partition based on RDF4J. We show that qEndpoint can index and query the entire Wikidata dump (currently 17 billion triples) on a machine with 600GB SSD, 10 cores and 10GB of RAM, while keeping the query performance comparable with other SPARQL endpoints indexing Wikidata.
Knowledge Distillation on Cross-Modal Adversarial Reprogramming for Data-Limited Attribute Inference

Social media generates a rich source of text data with intrinsic user attributes (e.g., age, gender), where different parties benefit from disclosing them. Attribute inference can be cast as a text classification problem, which, however, suffers from labeled data scarcity. To address this challenge, we propose a data-limited learning model to distill knowledge on adversarial reprogramming of a visual transformer (ViT) for attribute inferences. Not only does this novel cross-modal model transfers the powerful learning capability from ViT, but also leverages unlabeled texts to reduce the demand on labeled data. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on data-limited attribute inferences.