Reviewerly/exHarmony huggingface dataset:
Improving Micro-video Recommendation by Controlling Position Bias

As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements.
Calibrate to Interpret

Trustworthy Machine learning (ML) is driving a large number of ML community works in order to improve ML acceptance and adoption. The main aspect of trustworthy ML are the followings: fairness, uncertainty, robustness, explainability and formal guaranties. Each of these individual domains gains the ML community interest, visible by the number of related publications. However few works tackle the interconnection between these fields. In this paper we show a first link between uncertainty and explainability, by studying the relation between calibration and interpretation. As the calibration of a given model changes the way it scores samples, and interpretation approaches often rely on these scores, it seems safe to assume that the confidence-calibration of a model interacts with our ability to interpret such model. In this paper, we show, in the context of networks trained on image classification tasks, to what extent interpretations are sensitive to confidence-calibration. It leads us to suggest a simple practice to improve the interpretation outcomes: Calibrate to Interpret.
User Requirement Analysis for a Real-Time NLP-Based Open Information Retrieval Meeting Assistant

Meetings are recurrent organizational tasks intended to drive progress in an interdisciplinary and collaborative manner. They are, however, prone to inefficiency due to factors such as differing knowledge among participants. The research goal of this paper is to design a recommendation-based meeting assistant that can improve the efficiency of meetings by helping to contextualize the information being discussed and reduce distractions for listeners. Following a Wizard-of-Oz setup, we gathered user feedback by thematically analyzing focus group discussions and identifying this kind of system’s key challenges and requirements. The findings point to shortcomings in contextualization and raise concerns about distracting listeners from the main content. Based on the findings, we have developed a set of design recommendations that address context, interactivity and personalization issues. These recommendations could be useful for developing a meeting assistant that is tailored to the needs of meeting participants, thereby helping to optimize the meeting experience.
Multilingual Detection of Check-Worthy Claims Using World Languages and Adapter Fusion

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.
Cross-View Image Synthesis From a Single Image With Progressive Parallel GAN

Cross-view image synthesis aims to synthesize a ground-view image covering the same geographic region for a given single aerial-view image (or vice versa). Existing approaches typically tackle this challenging task by relaxing the single-image constraint and using a ground-truth semantic map as additional input to aid synthesis. However, this is nearly infeasible in practice. In this paper, we investigate how to generate a detail-enriched and structurally accurate ground-level image from only a single aerial-level input image, in which there are no other prior knowledge except for the input image. Towards this goal, we propose a novel Progressive Parallel Generative Adversarial Network (PPGAN) that starts from generating low-resolution outputs and progressively produces ground images at higher resolutions as the network propagates forward. In this manner, our PPGAN decomposes the task into several manageable sub-tasks, which helps to generate detail-enriched and structurally accurate ground images. During progressive generation, the PPGAN employs a parallel generation paradigm that enables the generator to produce multi-resolution images in parallel, thereby avoiding excessive time cost on training. Furthermore, for effective information propagation across multi-resolution images, a feature fusion module (FFM) is devised to mitigate the domain gap between cross-level image features, which enables a balance of detail and structural information synthesis. Additionally, the proposed Channel-Space Attention Selection Module (CSASM) learns the mapping relationship between cross-view images in a larger scale space to enhance the quality of the output image. Quantitative and qualitative experiments demonstrate that, our method requires only one input image without the aid of additional inputs, but is capable of synthesizing detail-enriched and structurally accurate ground images and outperforms the existing state-of-the-art methods on two famous benchmarks.
Online Multipedestrian Tracking Based on Fused Detections of Millimeter Wave Radar and Vision

This article focuses on the challenge of pedestrian tracking using camera and millimeter wave (MMW) radar in autonomous driving. Pedestrian tracking using a single sensor has inherent limitations due to the lack of comprehensive dimensionality of tracking information. Meanwhile, the existing multisensor-based tracking algorithms suffer from limited tracking accuracy by applying the fusion of projected positions. To enhance the tracking accuracy and robustness, a multisensor-based tracking algorithm based on fused detection of MMW radar and vision is proposed, which improves the association of detection results from multiple heterogeneous sensors by utilizing newly designed back-projection and undirected graph, and finally improves the fusion detection by simultaneously utilizing a pedestrian’s appearance, and local and global location information. Field tests are conducted to produce dataset, and the performance evaluation results based on the self-produced dataset have verified the superiority of the proposed algorithm over the conventional single-sensor-based tracking algorithm and multisensor-based tracking algorithms.
Random Cycle Loss and Its Application to Voice Conversion

Speech disentanglement aims to decompose independent causal factors of speech signals into separate codes. Perfect disentanglement benefits to a broad range of speech processing tasks. This paper presents a simple but effective disentanglement approach based on cycle consistency loss and random factor substitution. This leads to a novel random cycle (RC) loss that enforces analysis-and-resynthesis consistency, a main principle of reductionism. We theoretically demonstrate that the proposed RC loss can achieve independent codes if well optimized, which in turn leads to superior disentanglement when combined with information bottleneck (IB). Extensive simulation experiments were conducted to understand the properties of the RC loss, and experimental results on voice conversion further demonstrate the practical merit of the proposal. Source code and audio samples can be found on the webpage http://rc.cslt.org.
Integrated Geometric Optimal Control of Spacecraft Attitude and Orbit Based on <i>SE</i>(3)

A model predictive convex programming (MPCP) on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</i> (3) parametrized by trigonometric series control is proposed in this paper, to solve the optimal control problem of spacecraft attitude orbit integration. Firstly, the geometric modeling of the spacecraft with six degrees of freedom for the attitude orbit integration is performed by the differential manifold <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</i> (3), which can effectively avoid the problems of ambiguity, receding winding, and singularity that occurs in the conventional methods for rigid body attitude description. Then, based on differential geometric theories, such as the variational principle, the left-invariant principle of Lie group, and the topology of Lie algebraic space, MPCP is applied to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</i> (3). It can solve a class of optimization problems with process constraints and control input constraints during spacecraft flight. Furthermore, a control framework of trigonometric series is constructed, which is seamlessly integrated into MPCP to achieve smoother trajectory optimization control. Finally, the practicality and effectiveness of the proposed method are verified by numerical simulation.
Blockchain-Based Route Selection With Allocation of Radio and Computing Resources for Connected Autonomous Vehicles

With the advent of connected and autonomous vehicles (CAVs), we observe a growing need for new resource allocation solutions in mobile networks. Currently, most of the resource allocation solutions for CAVs communication do not consider the driving routes of the cars. In this paper, we introduce joint vehicular route selection and radio and computing resource allocation for CAVs. The proposed approach is based on the graph search-based lexicographic A* algorithm that minimizes the ratio of failed tasks along the entire vehicular route considering the availability of both radio and computing resources. To manage the allocation of resources among multiple CAVs for each vehicular route, we develop a blockchain-based framework allowing resource reservation by means of nonfungible tokens (NFTs). Each NFT represents an exclusive right to the required amount of radio and computing resources for the given road segment and defined time interval. The effectiveness of the proposed approach is demonstrated by simulations showing that the proposed vehicular route selection algorithm reduces the ratio of tasks not completed before the deadline by up to 69% compared to the existing state-of-the-art algorithms.
Online Active Learning Framework for Data Stream Classification With Density-Peaks Recognition

This paper considers a practical scenario—data stream classification, and uses online active learning to assist the classifier. Most active learning methods are proposed based on label uncertainty, but the methods based on representativeness are faced with difficulties in stream scenarios. The sampling criteria based on representativeness are usually fixed, which makes it difficult for them to work effectively in dynamic sample spaces, especially for data streams with concept drift. This paper proposes a novel online active learning framework based on sample representativeness. The framework uses local nearest-neighbor information to measure the representativeness of unlabeled samples and divides the maximum influence space of the representative sample. We also develop an independent mechanism to identify and store short-term cluster fragments to ensure the integrity of information. The framework is deployed to the online environment and adapts to any incremental learner. Simulation experiments on multiple datasets and classifiers show that our algorithm can improve the performance of the primary classifier. Compared with other online active learning, the proposed method can achieve more stable accuracy and better anti-noise ability under fewer labeled samples.
Promoting the Cyber Wellness of Internet Users in Papua New Guinea

In Papua New Guinea, there are concerns that the increase in internet accessibility has led to a rise in criminal activities in cyberspace. This paper identifies activities that constitute crimes in the cyberspace of Papua New Guinea. The main prongs of facilitating cyber wellness of Internet users are discussed to inform policy-making in Papua New Guinea. This paper provides guidance to policymakers, public health specialists, education and counselling psychologists, school teachers, educational researchers, and non-governmental organisations on cosmopolitan strategies for promoting cyber wellness in Papua New Guinea and the Pacific Islands in general. Keywords: Cyber wellness, Cyber health, Mental health, Papua New Guinea, Cybersecurity, Cybercrimes, Cyberspace.
On Galois groups of linearized polynomials related to the special linear group of prime degree

Let F be any field of prime characteristic p and let q be a power of p. We assume that F contains the finite field of order q. A q-polynomial L over F is an element of the polynomial ring F[x] with the property that all exponents are a power of q. We assume that the coefficient of the x term of L is nonzero. We investigate the Galois group G of L over F, under the assumption that L(x)/x is irreducible in F[x]. It is well known that if L has q-degree n, its roots are an n-dimensional vector space over the field of order q and G acts linearly on this space. Our main theorem is the following. We consider a monic q-polynomial L whose q-degree is a prime, r, say, with L(x)/x irreducible over F. Assuming that the coefficient of the x term of L is (−1)r, we show that the Galois group of L over F must be the special linear group SL(r,q) of degree r over the field of order q when q>2. We also prove a projective version for the projective special linear group PSL(r,q), and we present the analysis when q=2.
Incremental label propagation for data sets with imbalanced labels

Label Propagation (LP) is a popular graph-based semi-supervised learning algorithm. However, the imbalanced label distribution of labeled data affects the performance of LP, which makes it prefer to allocate more points for the class with majority labels. To get rid of this deficiency, we propose an Incremental Label Propagation (ILP) framework. The ILP framework includes an incremental balance strategy and a multiple results integration method to reduce the uncertainty of balancing labeled data. The incremental balance strategy can gradually add trusted pseudo labels to balance the label distribution so as to reduce the uncertainty caused by pseudo labels. Then, the integration method evaluates the quality of the balanced propagation results obtained from the incremental balance strategy and combines them with different weights to generate a robust outcome. The experimental results show that the proposed framework can combine with different LP methods and effectively solve the performance degradation of the LP algorithm caused by imbalanced labels.
A single test pair does not a method validation make: A response to Kirchhübel et al. (2023)

Field research within latent print comparison has remained sparse in the context of an otherwise growing body of literature examining the discipline. Studies examining how ACE-V procedures are implemented within active crime laboratories are especially lacking in light of research suggesting significant variability in examiner practices despite standardized ACE-V procedures. To date, no studies have examined a potentially important aspect of the Analysis phase: digital image editing. We provide information on the prevalence and types of latent print image editing within one laboratory (i.e., Houston Forensic Science Center), examine the potential effect of image editing on objective print quality and clarity (i.e., LQMetrics scores), and explore potential examiner differences in editing effectiveness. Results indicate that most latent prints are edited in some manner, and that image editing improves the quality and clarity of print images as defined by an objective quality metric, although examiners varied in their ability to improve the clarity of print images. Findings suggest that formal guidance or documentation of standard editing procedures would likely improve the reliability of examiner conclusions early in the latent print comparison process.
What is data stewardship? Towards a comprehensive understanding

Data stewardship is a term that is understood in heterogenous ways. In recent organisational developments and efforts to build infrastructures and hire professional staff for research data management in various scientific fields in Europe, data stewardship is understood as mainly aiming at optimising data management in line with the FAIR principles (findability, accessibility, interoperability, reusability) forpurposes of reuse in the interests of the scientific community and the public. In addition, especially in the health and biomedical sciences some understandings of data stewardship mainly focus on the responsibility to respect the informational rights of data subjects. Following on from these different understandings and from recent developments to include ever more stakeholders in data stewardship, we propose a comprehensive understanding of data stewardship. According to this comprehensive understanding, data stewardship includes responsibilities towards all pertinent stakeholders and to equally consider and respect their legitimate rights and interests in order to build and maintain an efficient, trusted and fair data ecosystem. We also point out some of the practical challenges implied in such a comprehensive understanding.
Design of a Learning Path Recommendation System Based on a Knowledge Graph

Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally represent subject knowledge. The system uses the node centrality and node weight to expand the knowledge graph system, which can better express the structural relationship among knowledge. It applies the particle swarm fusion algorithm of multiple rounds of iterative simulated annealing to achieve the recommendation of learning paths. Furthermore, the system feeds back the students' learning situation to the teachers. Teachers check and fill in the gaps according to the performance of the learners in the teaching activities. Aiming at the weak links of students' knowledge points, the particle swarm intelligence algorithm is used to recommend learning paths and learning resources to fill in the gaps in a targeted manner.
The future scribe: Learning to write the world

This study takes its point of departure in academic scholarship that points to how programming – reading and writing code – is the literacy of the future, in other words, non-specialized competence that should be acquired in education in parity with traditional reading and writing skills. The goal is to shed light on how programming can be orchestrated in education to break with the outworn dichotomy between the ‘two cultures’ that C. P. Snow formulated as a gap between, on the one hand, natural sciences, mathematics, and technology, and, on the other hand, the humanities, and social sciences. A discursive analysis of Swedish policy documents and curricula forms the empirical ground for discussing how reading and writing code are introduced, taught, and learnt within Swedish compulsory school. The results show that Swedish curricula are framing programming as specialized knowledge within technology and mathematics, rather than allowing it to be a dimension of several subjects, such as the humanities and social sciences. These findings are discussed in the light of recent studies in education that have explored interrelations between coding and reading and writing texts. The discussion leads up to suggestions for implementing reading and writing code as digital literacy in education.
Cultural Mapping Tools and Co-Design Process: A Content Analysis to Layering Perspectives on the Creative Production of Space

This study draws on the lessons learned during a summer school on cultural mapping (CM), which linked these methodologies and the co-design process to the city’s re-application to the UNESCO creative city designation. The event implemented artistic and participatory approaches, such as experimental initiatives that focused on the involvement of artists—the main creative producers of space—to foster participatory governance processes and reflect on the validity of the UNESCO label and its inherent monoculture emphasis. A content analysis of the course was done to provide a conceptual analysis of the theory and practice of cultural mapping that is generally under-theorized. By situating the course’s exercises in the theoretical triad of Lefebvre’s social production of space, the article indicates a possible structure for multi-perspective layering toward the participatory practices on the creative production of space. The results show that there is still no consistent way of presenting CM methodologies and processes, and for that CM remains not fully integrated into the planning and development practices of places. The study suggests further investigation on the links between cultural mapping and design science for co-design process crafting, and cultural mapping tool selection according to the different stages of multi-stakeholder work on articulating people-place meanings.
On the elusivity of dynamic optimisation problems

The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a better option than adaptation, although it is generally ignored in empirical studies. In this paper, we suggest the elusivity formulation to (i) quantify the preference for restart over adaptation for algorithms running on dynamic problems, and (ii) evaluate the advantage and behaviour of adaptation. Informally, we state that a dynamic problem is elusive to an algorithm if restart is more effective than adapting to changes. After reviewing existing formalisms for dynamic optimisation, the elusivity concept is mathematically defined and applied to two published empirical studies to evaluate its utility. Conducted experiments show that replicated works include elusive problems, where restart is better than (or equal to) adaptation, and demonstrate that some empirical research effort is being devoted to evaluating adaptive algorithms in circumstances where there is no advantage. Hence, we recommend how and when elusivity analysis can be gainfully included in empirical studies in the field of dynamic optimisation.