Reviewerly/exHarmony huggingface dataset:
Towards increased reliability and transparency in projects with manual linguistic coding

Manually coded data form the basis of many of our analyses in corpus linguistics. It is thus imperative that we work towards increased reliability and enhanced transparency in our coding practices, since failing to do so may ultimately lead us to draw erroneous conclusions about language. Using spoken data from a study on adverb usage for illustration, this methods paper discusses some strategies for identifying threats to the reliability of our coding and offers suggestions for how to mitigate these and ensure that our coding can be assessed and replicated. The paper also includes suggestions for best practices for manual linguistic coding and concludes with a discussion of the benefits of such practices. With this paper, we expand on the ongoing discussions in the field on issues of reliability and transparency as they relate to manual coding. We argue that while tests of inter-rater reliability offer a helpful starting point, further steps are needed to ensure increased reliability and transparency.
METAPHYSICAL TRINITY OF PHYSICS, MATHEMATICS AND PHILOSOPHY

The article, firstly, draws attention to the need to develop new foundations of fundamental physics, secondly, it is noted that this must be done within the framework of the relational paradigm, and thirdly, this should be done on the basis of the metaphysical principles underlying the three inextricably linked with each other sections of science (physics, mathematics and philosophy) and, fourthly, the necessary mathematical apparatus suitable for this purpose is the theory of binary systems of complex relations. It is shown that this apparatus implements key metaphysical principles.
Fast hashing to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">G</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> on pairing-friendly curves with the lack of twists

Pairing-friendly curves with the lack of twists, such as BW13-P310 and BW19-P286, have been receiving attention in pairing-based cryptographic protocols as they provide fast operation in the first pairing subgroup G1 at the 128-bit security level. However, they also incur a performance penalty for hashing to G2 simultaneously since G2 is totally defined over a full extension field. Furthermore, the previous methods for hashing to G2 focus on pairing-friendly curves admitting a twist, which can not be employed for our selected curves. In this paper, we propose a general method for hashing to G2 on curves with the lack of twists. More importantly, we further optimize the general algorithm on curves with non-trivial automorphisms, which is certainly suitable for BW13-P310 and BW19-P286. Theoretical estimations show that the latter would be more efficient than the former. For comparing the performance of the two proposed algorithms in detail, high speed software implementation over BW13-P310 is also provided on a 64-bit processor. Experimental results show that the general algorithm can be sped up by up to 88% if the computational cost of cofactor multiplication for G2 is only considered, while the improved method is up to 71% faster than the general one for the whole process.
Comparison between K and alpha sampling constants with the Pierre Gy’s factors for bauxite

Sampling is an important tool for gathering information about mineral deposits, helping to estimate the ore grade, and has great relevance at any stage of a mining project, to ensure reliable results and consequently, to project a better mining planning.There are several errors to which the sampling is exposed and among them we can mention the fundamental sampling error (FSE).This error cannot become zero as it is related to the constitutional heterogeneity of the ore, so it represents the minimum sampling error.This study applies Pierre Gy's theory of sampling (TOS) to estimate the values of K and α sampling constants at different deposits of bauxite in Brazil and compare them with the calculated factors, then calculate the relative standard deviation of the fundamental sampling error s(FSE) based on the results of the heterogeneity test (HT) in each deposit.The deposits are located in: Barro Alto -State of Goiás, Juruti -State of Pará, and Poços de Caldas -State of Minas Gerais.The weathering process is the main reason of the alteration of the physical and chemical factors diversifying bauxites in Brazil and worldwide.However, when sampling is carried out correctly, the Gy's formula becomes a powerful tool for improving mining processes as long as the factors are estimated correctly for each ore.
EVALUATION OF THE E-LEARNING SYSTEM OF PT. OTAK KANAN THROUGH BLACKBOX TESTING AND SYSTEM USABILITY SCALE (SUS)

In everyday life and education, the internet and information systems play a key role in providing information services to the educational community. Learning has transformed into an online method known as e-learning, which utilizes technology as a means of education. PT. Otak Kanan is currently introducing E-Learning to support students and interns, and they are developing the E-learning Sigarda application. Software testing is an essential part of this process to ensure the quality and functionality of the application and to prevent errors during development. In the testing of the E-Learning Sigarda application, the Blackbox Testing method is used to examine the application's functionality, while the System Usability Scale method is employed to measure user satisfaction. The test results demonstrate the application's alignment with test case scenarios on both admin and user pages. Additionally, the system's E-Learning testing, using the System Usability Scale method with 15 respondents, yielded a score of 91, falling within the "Acceptable" category of the Acceptability range and receiving an "A" rating on the Grade Scale, along with a "Best Imaginable" evaluation in the Adjective rating.
Global Flows Security Policy for Technologically Isolated Networks and Critical Infrastructures

The article discusses the possible extension of security policies to control the information flows of critical information infrastructures. The route that goes through trusted transit technologically isolated networks is considered as a secure route in the network. The concept of the trust index of a technologically isolated network is introduced, both on the basis of possible external control actions (control actions of regulatory acts and administrative bodies, etc.) and on the basis of trust indices of connected networks through which information flow routes are being passed. It is proposed to extend the security policy for critical information infrastructures on the basis of secure routes.
Hydrus: Improving Personalized Quality of Experience in Short-form Video Services

Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency on the server side. Through an analysis of 15 million users, however, we find that for short-form video apps, user experience depends on both response latency and recommendation accuracy. This observation brings a dilemma to service providers since improving recommendation accuracy requires adopting complex strategies that demand heavy computation, which substantially increases response latency.
Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems

Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.
Improved Vector Quantization For Dense Retrieval with Contrastive Distillation

Recent work has identified that distillation can be used to create vector quantization based ANN indexes by learning the inverted file index and product quantization. The argued advantage of using a fixed teacher encoder for queries and documents is that the scores produced by the teacher can be used instead of the label judgements that are required when using traditional supervised learning, such as contrastive learning. However, current work only distills the teacher encoder outputs of dot products between quantized query embedddings and product quantized document embeddings. Our work combines the benefits of contrastive learning and distillation by using contrastive distillation whereby the teacher outputs contrastive scores that the student learns from. Our experimental results on MSMARCO passage retrieval and NQ open question answering datasets show that contrastive distillation improves over current state of the art for vector quantized dense retrieval.
Research On the Application of Blockchain Technology in Big Data Auditing

Abstract: Studying the combination of blockchain technology and big data auditing will help to improve the authenticity of audit data, the efficiency of the audit process, and the accuracy of audit results. This paper starts from the characteristics of blockchain technology and combines the analysis of big data, put forward the application concept of blockchain technology in big data audit, at the same time, combined with the traditional audit process, reconstruct the audit process under the blockchain technology, and provide new ideas for the development of audit work. Finally, combined with the current bottleneck of blockchain technology, the practical problems that should be understood in the application of blockchain technology are put forward to provide ideas for the combination of blockchain technology and auditing.
A Hierarchical Autonomous Exploration Algorithm for Large-scale and Complex Environments with Mobile Robot

In order to solve the problem of low exploration efficiency caused by repeatedly backtracking the frontiers in large and complex environments, a hierarchical autonomous exploration algorithm is proposed. Firstly, based on the robot's static model to judge the environment's traversability, which allows robots to explore complex 3D environments. Following that, a hybrid strategy is used to filter out frontiers within a local planning horizon, thus complete exploration of the local area is achieved by solving the traveling salesman problem (TSP). Finally, the sparse global topology map generates transfer paths between sub-areas, transferring the robot to another sub-area to resume exploration. Compared to the RRT autonomous exploration algorithm and the GBP autonomous exploration algorithm, the method in this work reduces the exploration path by more than 13.8% and the exploration time by more than 23.7%. The results show that the proposed algorithm significantly improves the autonomous exploration efficiency of mobile robots in large and complex environments.
Faster Dynamic Pruning via Reordering of Documents in Inverted Indexes

Widely used dynamic pruning algorithms (such as MaxScore, WAND and BMW) keep track of the k-th highest score (i.e., heap threshold) among the documents that are scored so far, to avoid scoring the documents that cannot get into the top-k result list. Obviously, the faster the heap threshold converges to its final value, the larger will be the number of skipped documents and hence, the efficiency gains of the pruning algorithms. In this paper, we tailor approaches that reorder the documents in the inverted index based on their access counts and ranks for previous queries. By storing such frequently retrieved documents at front of the postings lists, we aim to compute the heap threshold earlier during the query processing. Our approach yields substantial speedups (up to 1.33x) for all three dynamic pruning algorithms and outperforms two strong baselines that have been employed for document reordering in the literature.
Personalized Federated Relation Classification over Heterogeneous Texts

Relation classification detects the semantic relation between two annotated entities from a piece of text, which is a useful tool for structurization of knowledge. Recently, federated learning has been introduced to train relation classification models in decentralized settings. Current methods strive for a strong server model by decoupling the model training at server from direct access to texts at clients while taking advantage of them. Nevertheless, they overlook the fact that clients have heterogeneous texts (i.e., texts with diversely skewed distribution of relations), which renders existing methods less practical. In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. To further meet the challenges brought by heterogeneous texts, we present a novel framework, namely pf-RC, with several optimized designs. It features a knowledge aggregation method that exploits a relation-wise weighting mechanism, and a feature augmentation method that leverages prototypes to adaptively enhance the representations of instances of long-tail relations. We experimentally validate the superiority of pf-RC against competing baselines in various settings, and the results suggest that the tailored techniques mitigate the challenges.
Power Norm Based Lifelong Learning for Paraphrase Generations

Lifelong seq2seq language generation models are trained with multiple domains in a lifelong learning manner, with data from each domain being observed in an online fashion. It is a well-known problem that lifelong learning suffers from the catastrophic forgetting (CF). To handle this challenge, existing works have leveraged experience replay or dynamic architecture to consolidate the past knowledge, which however result in incremental memory space or high computational cost. In this work, we propose a novel framework name "power norm based lifelong learning" (PNLLL), which aims to remedy the catastrophic forgetting issues with a power normalization on NLP transformer models. Specifically, PNLLL leverages power norm to achieve a better balance between past experience rehearsal and new knowledge acquisition. These designs enable the knowledge adaptation onto new tasks while memorizing the experience of past tasks. Our experiments on paraphrase generation tasks show that PNLLL not only outperforms SOTA models by a considerable margin and but also largely alleviates forgetting.
Fairness for both Readers and Authors: Evaluating Summaries of User Generated Content

Summarization of textual content has many applications, ranging from summarizing long documents to recent efforts towards summarizing user generated text (e.g., tweets, Facebook or Reddit posts). Traditionally, the focus of summarization has been to generate summaries which can best satisfy the readers. In this work, we look at summarization of user-generated content as a two-sided problem where satisfaction of both readers and authors is crucial. Through three surveys, we show that for user-generated content, traditional evaluation approach of measuring similarity between reference summaries and algorithmic summaries cannot capture author satisfaction. We propose an author satisfaction-based evaluation metric CROSSEM which, we show empirically, can potentially complement the current evaluation paradigm. We further propose the idea of inequality in satisfaction, to account for individual fairness amongst readers and authors. To our knowledge, this is the first attempt towards developing a fair summary evaluation framework for user generated content, and is likely to spawn lot of future research in this space.
Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation

Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. These models, however, suffer from poor interpretability as they do not rely on explicit term matching. As a form of local per-query explanations, we introduce the notion of equivalent queries that are generated by maximizing the similarity between the NRM's results and the result set of a sparse retrieval system with the equivalent query. We then compare this approach with existing methods such as RM3-based query expansion and contrast differences in retrieval effectiveness and in the terms generated by each approach.
A Collision-free Path Planning Algorithm for Industrial Robots with Adaptive Search Mechanism

In path planning for industrial robots, collision avoidance is a critical constraint. This paper studies the problem of collision-free motion planning for a spraying robot, i.e., obtaining a series of joint configurations along a given spraying waypoint that satisfies the collision constraints and satisfy the kinematics of the robot. Aiming at this problem, a collision-free motion optimization algorithm based on the adaptive search is proposed in this paper. First, a boundary volume hierarchy (BVH) of the robot and environment is quickly generated based on the GPU parallel architecture. Second, the redundancy of spray incidence angles is used to search for feasible spray incidence angle directions in the redundant space and update the robot's BVHs for collision detection. In addition, during the optimization iterations, the size of the search space is adaptively reduced according to the number of collision failures in the previous iterations to avoid falling into a local optimum. Finally, the effectiveness of the proposed algorithm is verified by simulation on a six-degrees-of-freedom robot performing a spraying task.
Robotic motion planning with obstacle avoidance based on hierarchical deep reinforcement learning

When the task environment becomes complex, deep reinforcement learning (DRL) is easy to encounter the problems of gradient disappearance or explosion. To solve this problem, this paper proposes a hierarchical DRL framework consisting of task and action layers. The task layer learns interpretable representations of tasks and decision processes, and drives the action layer. The action layer learns to collaboratively accomplish complex tasks in different roles. The DRL algorithm based on this framework is tested on a redundant degree of freedom robot in obstacle avoidance motion planning tasks, and comparative experimental results prove the effectiveness and feasibility of the proposed method.
Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS)

To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is desired to group users with similar characteristics. In this paper, we propose a scalable K-anonymous cohort building algorithm called consecutive consistent weighted sampling (CCWS). The proposed method combines the spirit of the (p-powered) consistent weighted sampling (CWS) and hierarchical clustering, so that the K-anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of >70M users and ads campaigns demonstrate that CCWS achieves substantial improvements over several hashing-based methods including sign random projections (SignRP), minwise hashing (MinHash), as well as the vanilla CWS.
NC2T: Novel Curriculum Learning Approaches for Cross-Prompt Trait Scoring

Automated essay scoring (AES) is a crucial research area with potential applications in education and beyond. However, recent studies have primarily focused on AES models that evaluate essays within a specific domain or using a holistic score, leaving a gap in research and resources for more generalized models capable of assessing essays with detailed items from multiple perspectives. As evaluating and scoring essays based on complex traits is costly and time-consuming, datasets for such AES evaluations are limited. To address these issues, we developed a cross-prompt trait scoring AES model and proposed a suitable curriculum learning (CL) design. By devising difficulty scores and introducing the key curriculum method, we demonstrated its effectiveness compared to existing CL strategies in natural language understanding tasks.
How Civil are Comments on TikTok's Educational Videos? Insights for Learning at Scale

Social networking sites present opportunities for informal learning at scale. Researchers have explored the use of TikTok, a site for sharing short-formed videos, for informal and formal learning. As we consider the learning potential of sites like TikTok, it is equally important to investigate features that may discourage learning. Our work examines the pervasiveness of incivility, or offensive language targeting individuals and ideas, in videos categorized with educational hashtags on TikTok. We collect a corpus of 346,626 TikTok comments, and find a small percentage (1.5%) with uncivil content that might turn someone away from the discussion. To understand participation patterns, we compare comment threads in videos with the highest average incivility to the rest of the corpus. These comment threads generally have earlier appearance of uncivil content, lower participation from video creators, and less unique participation from different users. Findings have implications for content moderation in online discussions, to deprioritize uncivil content and encourage facilitation from content creators.
Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making

Algorithm aversion can be described as the tendency of human decision-makers to discount algorithmic recommendations more heavily than similar recommendations made by humans. It has been a phenomenon observed to be most acutely exhibited by domain experts. In our work, we focus on expert administrators in higher education making course credit equivalency decisions that affect the academic planning and potential degree progress of millions of prospective transfer students. Using human-centered design, we construct an AI-based platform for recommending matches to courses on a student's transcript to courses offered at another institution. We conduct a 2 x 2, between-subject experiment to investigate potential aversion mitigation techniques by manipulating the presence of outliers and allowing users to provide feedback to the algorithm. Our findings indicate that intentional, human-centered design and careful presentation of algorithm-based recommendations can help improve Human-AI interaction and productivity with implications for various domains of expertise.