Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction

Effectively predicting the size of information cascades is crucial for understanding the evolution of many social applications, such as influence maximization and fake news detection. Conventional methods face the challenge of data imbalance which, in turn, yields unsatisfactory prediction performance. To prevent the loss functions or metrics from being affected by extreme values and assure numerical stability, previous works reformulate the problem definitions or adopt other types of evaluation metrics. However, solving the regression prediction of information cascades from a long-tailed distribution perspective is under explored. In this paper, we propose a general decoupling prediction solution -- first extracting the representation, then fine-tuning the regressor, which combines the original prediction value and weighted bias generated by a sub-network (SUB) that we designed. Our experiments conducted on long-tailed benchmarks demonstrate that our method significantly improves the prediction accuracy over state-of-the-art methods and mitigates the long-tailed cascade prediction problem.
Recent advances in absolute quantification of peptides and proteins using LC-MS

Abstract Current bioanalytical methods for the quantification of peptides and proteins used in biomedical research and pharmaceutical R&D are predominantly based on immunoassay methodologies, such as enzyme-linked immunosorbent assays (ELISA). Although these antibody-based techniques are exceptionally sensitive, they suffer from issues, such as cross-reactivity, long development time, and high variability. Recently, liquid chromatography-mass spectrometry (LC-MS)-based quantification techniques for large molecules, including the bottom-up and top-down approaches, are gaining popularity due to their shorter development time, higher specificity, and ability to detect degradation products and post-translational modifications (PTMs). The multiple reaction monitoring (MRM)-based bottom-up LC-MS/MS approach, which targets enzymatic digestion-produced signature peptides as surrogates in a quantification assay, is a sophisticated method with good sensitivity, selectivity, and multiplex capacity, although it may miss critical information such as PTMs, sequence variants and isoforms, and truncation and degradation products. The LC-high resolution mass spectrometry (LC-HRMS)-based top-down approach quantifies intact target molecules, thus avoiding the shortcomings of the bottom-up approach. The current LC-HRMS approach is most suitable for peptides and proteins smaller than 10–15 kDa, though that may change with the development of more advanced mass spectrometers with higher resolving power and broader mass ranges. The drawbacks and bottlenecks of LC-MS-based large molecule quantification assays are low sensitivity, complicated sample preparation, and relatively low throughput. Technology advancement may address these challenges and expand their suitable applications. In this article, the recent advances and trends in this emerging field are reviewed, along with the basic workflows, analytical approaches, and other common issues.
REGIS: A Test Collection for Geoscientific Documents in Portuguese

Experimental validation is key to the development of Information Retrieval (IR) systems. The standard evaluation paradigm requires a test collection with documents, queries, and relevance judgments. Creating test collections requires significant human effort, mainly for providing relevance judgments. As a result, there are still many domains and languages that, to this day, lack a proper evaluation testbed. Portuguese is an example of a major world language that has been overlooked in terms of IR research -- the only test collection available is composed of news articles from 1994 and a hundred queries. With the aim of bridging this gap, in this paper, we developed REGIS (Retrieval Evaluation for Geoscientific Information Systems), a test collection for the geoscientific domain in Portuguese. REGIS contains 20K documents and 34 query topics along with relevance assessments. We describe the procedures for document collection, topic creation, and relevance assessment. In addition, we report on results of standard IR techniques on REGIS so that they can serve as a baseline for future research.
Estimating the Reliability of Health-related Search Results

Determining reliability of online data is a challenge that has recently received increasing attention. In particular, unreliable health-related content has become pervasive during the COVID-19 pandemic. The main objective of this Ph.D. thesis is to study how end-users judge the correctness and credibility of online content and provide them with a series of tools to assist them in assessing content reliability. To that end, we need to determine which sources of evidence may help to better assess the reliability of health-related online content, and how to combine them learning. Finally, I will also study which presentation aspects might help end-users to better assess reliability since previous research has proved that the format and layout of the information items, combined with user-based biases, influence their final assessments.
Retrieving Implicit Information for Stock Movement Prediction

Previous studies on the financial news focus mainly on the news articles explicitly mentioning the target financial instruments, and may suffer from data sparsity. As taking into consideration other related news, e.g., sector-related news, is a crucial part of real-world decision-making, we explore the use of news without explicit target mentions to enrich the information for the prediction model. We develop a neural network framework that jointly learns with a news selection mechanism to extract implicit information from the chaotic daily news pool. Our proposed model, called the news distilling network (NDN), takes advantage of neural representation learning and collaborative filtering to capture the relationship between stocks and news. With NDN, we learn latent stock and news representations to facilitate similarity measurements, and apply a gating mechanism to prevent noisy news representations from flowing to a higher level encoding stage, which encodes the selected news representation of each day. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework and show improvements over previous techniques.
Notes on the Music: A social data infrastructure for music annotation

Beside transmitting musical meaning from composer to reader, symbolic music notation affords the dynamic addition of layers of information by annotation. This allows music scores to serve as rudimentary communication frameworks. Music encodings bring these affordances into the digital realm; though annotations may be represented as digital pen-strokes upon a score image, they must be captured using machine-interpretable semantics to fully benefit from this transformation. This is challenging, as annotators’ requirements are heterogeneous, varying both across different types of user (e.g., musician, scholar) and within these groups, depending on the specific use-case. A hypothetical all-encompassing tool catering to every conceivable annotation type, even if it were possible to build, would vastly complicate user interaction. This additional complexity would significantly increase cognitive load and impair usability, particularly in dynamic real-time usage contexts, e.g., live annotation during music rehearsal or performance. To address this challenge, we present a social data infrastructure that facilitates the creation of use-case specific annotation toolkits. Its components include a selectable-score module that supports customisable click-and-drag selection of score elements (e.g., notes, measures, directives); the Web Annotations data model, extended to support the creation of custom, Web-addressable annotation types supporting the specification and (re-)use of annotation palettes; and the Music Encoding and Linked Data (MELD) Javascript client library, used to build interfaces that map annotation types to rendering and interaction handlers. We have extended MELD to support the Solid platform for social Linked Data, allowing annotations to be privately stored in user-controlled Personal Online Datastores (Pods), or selectively shared or published. To demonstrate the feasibility of our proposed approach, we present annotation interfaces employing the outlined infrastructure in three distinct use-cases: scholarly communication; music rehearsal; and rating during music listening.
Moving Visual Stimuli on Smart Glasses Affects the Performance of Subsequent Tasks

There is a rich body of literature on the effects of visual information on the viewer’s psychology and behavior. However, most of the existing studies ignore such effects after viewing. With the advent of smart glasses, it has become possible to view visual information all the time. In this study, we investigate whether the visual information presented on smart glasses affects the subsequent work efficiency in cycling situation. In the experiment, the participants cycling in a virtual city were presented with a running avatar (3.00 steps/s), a fast-running avatar (5.25 steps/s), or no avatars, through simulated smart glasses in a virtual environment. They then solved a dot-clicking task on a desktop monitor. It was found that the participants tended to solve the task most quickly after viewing a running avatar.
WWW3E8: 259,000 Relevance Labels for Studying the Effect of Document Presentation Order for Relevance Assessors

In IR evaluation based on depth-k pooling, there are several strategies to order the pooled documents for relevance assessors. Among them, the simplest approach is to completely randomise the order "so assessors cannot tell if a document was highly ranked by some system or how many systems (or which systems) retrieved the document. " An approach that is in sharp contrast to the above is the prioritisation approach taken by NTCIRPOOL, a tool widely used at NTCIR. NTCIRPOOL sorts the pooled documents by "pseudorelevance," a statistic that reflects the popularity of each document within the depth-k pools. Although these two strategies have coexisted for over two decades, the IR research community has yet to reach a consensus as to what advantages each of these two strategies actually offer. To help researchers directly address this question using their favourite methods of analysis, we have released a large-scale data set called WWW3E8. It comprises eight independent sets of qrels for the 160 English topics of the NTCIR-15 WWW-3 task: four qrels files constructed using the randomisation approach, and another four constructed using the prioritisation approach of NTCIRPOOL. Each qrels file covers 32,375 topic-document pairs; hence, WWW3E8 contains a total of 259,000 relevance labels. Moreover, the data set contains the raw English subtask run files from the WWW-3 task, the randomised and prioritised pool files, and topic-by-run score matrices of the official measures used in the task. Hence, researchers interested in the above research question regarding document ordering can utilise WWW3E8 as a common ground to directly compare the two strategies.
Category-aware Collaborative Sequential Recommendation

Sequential recommendation is the task of predicting the next items for users based on their interaction history. Modeling the dependence of the next action on the past actions accurately is crucial to this problem. Moreover, sequential recommendation often faces serious sparsity of item-to-item transitions in a user's action sequence, which limits the practical utility of such solutions. To tackle these challenges, we propose a Category-aware Collaborative Sequential Recommender. Our preliminary statistical tests demonstrate that the in-category item-to-item transitions are often much stronger indicators of the next items than the general item-to-item transitions observed in the original sequence. Our method makes use of item category in two ways. First, the recommender utilizes item category to organize a user's own actions to enhance dependency modeling based on her own past actions. It utilizes self-attention to capture in-category transition patterns, and determines which of the in-category transition patterns to consider based on the categories of recent actions. Second, the recommender utilizes the item category to retrieve users with similar in-category preferences to enhance collaborative learning across users, and thus conquer sparsity. It utilizes attention to incorporate in-category transition patterns from the retrieved users for the target user. Extensive experiments on two large datasets prove the effectiveness of our solution against an extensive list of state-of-the-art sequential recommendation models.
What are the most used guitar positions?

The playing of stringed instruments can be notated with symbolic tablatures, indicating for each note, the string, and the position on the fretboard at which it has to be played. We propose an encoding for tablatures in which the hand position on the fretboard is encoded to ease computational analysis and generation of guitar music. We analyze a corpus of 1022 guitar tablatures in standard tuning that were accurately transcribed in the corpus MySongBook, spanning metal, rock, pop, jazz/blues, and baroque/classical/romantic repertoires. We show how some of the proposed encodings reveal relevant statistics and some invariance properties on such guitar music. We show study unusual guitarist gestures around the G/B string pair due to its interval of major third, compared to the perfect fourth between other adjacent pairs of strings. We study the corpus across genres and textures (rhythm and lead parts) and how same relative chord positions are used along the fretboard. We distribute sets of 1000 most frequent vectors in different encodings for these five music genres.
2nd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2021)

Information retrieval plays a crucial role in the patent domain. With the success of deep learning (DL) in other domains, patent practitioners and researchers are increasingly developing DL-based approaches to support experts in the patenting process or to automate processes for patent analysis. AI-enhanced information retrieval systems can improve patent search but also require lots of annotated data. When working with patent data, particular challenges arise that call for adaption and novel approaches of general IR and AI methods. with this workshop series we want to establish a two-way communication channel between industry and academia from relevant fields in information retrieval, such as natural language processing (NLP), text and data mining (TDM), and semantic technologies (ST), in order to explore and transfer new knowledge, methods and technologies for the benefit of industrial applications as well as support interdisciplinary research in applied sciences forthe intellectual property (IP) and neighbouring domains.
Learning to Rank for Mathematical Formula Retrieval

In Mathematical Information Retrieval (MIR), formulae can be used in a query to match other similar formulae in documents. However, due to the structural complexity of formulae, specialized processing is needed for formula matching. Formulae may be represented by their appearance in Symbol Layout Trees (SLTs) or by their syntax in Operator Trees (OPTs). Previous approaches for formula retrieval used one or both of these representations and used unification to improve search results for inexact matches (e.g., allowing different variable names to match). On these representations, models for matching full expressions (trees), subexpressions, and paths have been used. Recently embedding models were used to represent formulae as vectors. In this paper, the effectiveness of retrieval models and formula representations are studied to identify their relative strengths and weaknesses. Then, a learning to rank model is proposed, using SVM-rank over similarity scores from different formula retrieval models as features. Experiments on the ARQMath formula retrieval task results show that the proposed learning to rank model is effective, producing new state-of-the-art results.
Seer-Dock: A General-Purpose Dockerized Scholarly Document Collection and Management Framework

The harvesting, management, and analysis of thematic document collections is a major challenge in a wide variety of applications. While the criteria for compiling such collections are individual, the entire process is largely standardized. Therefore, it is not efficient to build new systems over and over again to take over these tasks. In this work, we introduce Seer-Dock, a novel and easy-to deploy general-purpose dockerized framework to build a scholarly document harvesting and management system. It is based on CiteSeerX, the most widely used scholarly search engine. Seer-Dock uses docker containers for all components and thus enables its users to rapidly deploy a full-fledged document collection and management system on any operating system platform and tailor it to the specific needs of an application domain. Moreover, it is easy to scale, orchestrate, maintain, and recover. In this resource paper, we introduce the architecture of Seer-Dock and its components. Like its kernel CiteSeerX, Seer-Dock is available under an Apache 2 open source license.
Dual Unbiased Recommender Learning for Implicit Feedback

Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback, which heavily affects their sub-optimal performance gains. This paper proposes a dual recommender learning framework that simultaneously eliminates the bias of clicked and unclicked data. Specifically, the proposed loss function adopts two propensity weighting to effectively estimate the true positive and negative preferences from clicked and unclicked data. We also prove that the proposed loss function converges to the ideal loss function for both clicked and unclicked data. Because of the model-agnostic property, it can be applied to any existing unbiased learning models. Experimental results show that the proposed method outperforms state-of-the-art unbiased models up to 5.54-24.56% for MAP@1 on three datasets.
QuARk: A GUI for Quality-Aware Ranking of Arguments

With the Web augmenting every day and computers increasingly getting more powerful, research in the field of computational argumentation becomes more and more important. One of its research branches is argument retrieval, which aims at finding and presenting users the best arguments for their queries. Several systems already exist for this purpose, all having the same goal but reaching it in different ways. In line with existing work, an argument consists of a claim supported or attacked by a premise. Now that argument retrieval has become a separate task in the CLEF lab Touché, displaying the ranking is becoming increasingly important. In this paper we present QuARk, a GUI that allows users to retrieve arguments from a focused debate collection for their queries. Since we strictly distinguished between frontend and backend and kept the communication between them simple, QuARk can be extended to integrate various argument retrieval systems, assuming some modifications are made. In order to demonstrate the GUI, we show the integration of a complex retrieval algorithm that we also presented in the CLEF lab Touché. Our retrieval process consists of two parts. In the first step, it finds the most similar claims to the query. Therefore, the user can select between different standard IR similarity methods. The second step ranks the premises directly related to the claims. Therefore, the user can choose to rank the arguments either by quantitative, qualitative, or a combined measure.
Attitudes of Indigenous Minority Leaders toward Political Events in Their Trans-State National Group: Between Identity, Conflict and Values

Abstract How do leaders of indigenous minorities perceive political events among their trans-state national group? What motivates their attitudes toward these events? This article, drawing on a survey and elite interviews, examines the attitudes of Palestinian leaders in Israel, as a case study of an indigenous minority, toward the events (the Arab Spring) in their trans-state national group. The findings suggest that attitudes of Palestinian leaders in Israel toward the Arab Spring are determined by three main factors: their national or religious collective identity rather than their civic identity (national level), their values (international level), and the backdrop of the Arab-Israeli conflict (local level). The implications are as follows: an absence of change in Palestinians’ traditional alienation toward Israel; membership in the Arab world has somewhat replaced the framework of the Israeli-Palestinian conflict as a basis for positions toward regional issues; Palestinian leaders in Israel are divided regarding their values, fears and hopes for change; and that the interaction between the Israeli and international sphere determines their attitudes as an indigenous people. The intermixing of these elements shows the flexible (sometimes secondary) role of collective identity and the rise in democratic values rather than traditional issues.
An Improved CRISPR/Cas9 System for Genome Editing in Populus by Using Mannopine Synthase (MAS) Promoter

Gene editing technology in woody plants has great potential for understanding gene function, and altering traits affecting economically and ecologically important traits. Gene editing applications in woody species require a high genome editing efficiency due to the difficulty during transformation and complexities resulting from gene redundancy. In this study, we used poplar 84K (Populus alba × P. glandulosa), which is a model hybrid for studying wood formation and growth. We developed a new CRISPR/Cas9 system to edit multiple genes simultaneously. Using this system, we successfully knocked out multiple targets of the PHYTOENE DESATURASE 8 in poplar. We found the mutation rate of our CRISPR/Cas9 system is higher (67.5%) than existing reports in woody trees. We further improved the mutation rate up to 75% at editing sites through the usage of the mannopine synthase (MAS) promoter to drive Cas9. The MAS-CRISPR/Cas9 is an improved genome-editing tool for woody plants with a higher efficiency and a higher mutation rate than currently available technologies.