Reviewerly/exHarmony huggingface dataset:
The Istella22 Dataset

Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.
A Literature Review on Mobile Augmented Reality in Education

Technology can change the way students learn, become a powerful motivation, and trigger the learning process. This article discusses mobile augmented reality technology in education. Mobile Augmented Reality (MAR) is more effective in providing students with a positive, effective, and powerful learning experience. The researcher looked for Google Scholar and Scopus articles from the year 2015–2020. This advanced technology can help people communicate with each other’s senses and experience applications, thereby stimulating consumers’ interest through natural interaction. Researchers have been studying ways to incorporate enhanced environments into education so that students can participate in real-world environments. Related topics of the study are also mainly discussed: the past reviewers of systematic reviews, augmented reality education, Mobile Augmented Reality software development kits, Augmented Reality technology, user interfaces in Mobile Augmented Reality, and the future of Augmented Reality in education. Mobile augmented reality has aroused increasing interest in academia and industry. The real world and virtual information work together to create new educational opportunities. The research results indicate that augmented reality has various potentials and advantages and can be added to the field of education.
Anomaly-Aware Adaptation Approach for Self-Adaptive Cyber-Physical System of Systems Using Reinforcement Learning

A cyber-physical system of systems (CPSoS) is a system composed of multiple constituent systems that interact with both physical and cyber environments. Self-adaptivity is essential for CPSoS because it works on both cyber and physical uncertainties in various environments. Main obstacles to achieving self-adaptive CPSoS are time constraints and system anomalies. An adaptation should be processed within a certain period and it should consider anomalies caused by system changes due to mechanical faults, cyber-attacks, or emergent behaviors. However, since existing adaptation approaches cannot fully handle both aspects, this paper proposes an advanced approach, A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> , for a self-adaptive system that can handle known anomalies in runtime. This approach learns the known anomalies before runtime and mitigates their impact when they are detected. We evaluated the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> approach for virtual and physical CPSoS and showed that A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> was more efficient than other approaches.
Tightly coupled distributed Kalman filter under non-Gaussian noises

Distributed Kalman filter (DKF) is an effective method to solve the problem of state estimation in multi-sensor systems. However, the accuracy of conventional DKF may be influenced by the non-Gaussian noises and the accumulated errors in local Kalman filters (LKFs). To encounter the above challenges, a tightly coupled DKF with covariance intersection is proposed. In our method, using the computation outputs of LKFs, a Gaussian Mixture Model is formulated to address the influence of non-Gaussian noises. In order to reduce the cumulative errors from LKFs, covariance intersection fusion is utilized. Furthermore, an index-Huber function is designed to reduce the impact of large covariance generated by the LKFs. Several simulation and real-world experiments are conducted to show the effectiveness of our methods. The method we proposed outperforms three other DKF algorithms in the metrics of RMSE and cumulative error. In addition, a real-world multi-sensor state estimation experiment is conducted on a hexapod robot.
On Extractive Summarization for Profile-centric Neural Expert Search in Academia

Identifying academic experts is crucial for the progress of science, enabling researchers to connect, form networks, and collaborate on the most pressing research problems. A key challenge for ranking experts in response to a query is how to infer their expertise from the publications they coauthored. Profile-centric approaches represent candidate experts by concatenating all their publications into a text-based profile. Despite offering a complete picture of each candidate's scientific output, such lengthy profiles make it inefficient to leverage state-of-the-art neural architectures for inferring expertise. To overcome this limitation, we investigate the suitability of extractive summarization as a mechanism to reduce candidate profiles for semantic encoding using Transformers. Our thorough experiments with a representative academic search test collection demonstrate the benefits of encoding summarized profiles for an improved expertise inference.
WisRule: First cognitive algorithm of wise association rule mining

This article proposes a new algorithm for a newly emerging domain wisdom mining that claims to extract wisdom from data. Association rule mining is one of the dominant data mining techniques based on which a new algorithm called WisRule is proposed that generates both positive and negative association rules. These rules can be used for decision-making with less influence from a specialist. The existing algorithms of association rule extraction are based on the frequency of an itemset, which was introduced into the Apriori algorithm for the first time. In these algorithms, those itemsets are converted to the rules of the form Antecedent ⇒ Consequent that qualify the threshold of support, confidence and similar other measures. WisRule is proposed as an extension to the CBPNARM algorithm. WisRule produces both positive and negative association rules based on their frequency evaluated in a certain context (C), utility (U), time (T) and location (L). Rules that are valid in a given context, have high utility and are valid across multiple time intervals and locations become part of the final ruleset. The evaluation of a rule in these four dimensions is claimed as mining wisdom from the given data that is currently used as a hypothetical basis for a domain expert’s decision. WisRule is compared with the Apriori, PNARM and CBPNARM algorithms in terms of precision, recall, number of rules, average confidence, F-measure and execution time.
Construction and Application of Music Teaching Resources Based on Recurrent Neural Network

Along with the rapid development of informational techniques, educational techniques are becoming increasingly important in the field of education, encouraging the reform and innovation of traditional educational concepts and teaching methods. Various music teaching aids have also become widely available. However, there is a scarcity of assistive software that is truly appropriate for music classroom instruction. A neural network is an artificial model that models and connects neurons, which are the basic units of the animal or human brain, to stimulate the nervous system’s learning, association, memory, and pattern recognition functions. We propose a recurrent neural network (RNN)-based music teaching resource construction, method, and application in this paper, focusing on the needs of music distance learning, analysing the user role needs of teachers, students, and system administrators involved in teaching and learning, and designing a mobile teaching platform in line with music teaching based on the characteristics of music teaching. The experimental results show that the system’s accuracy increases steadily with the number of iterations, and the accuracy fluctuation is stable, with an average accuracy of 72 percent and good system stability. As a result, research into this topic has significant implications for improving the quality and efficiency of music education in China, reforming the current school music education model, developing social music education, and realising music education for all and lifelong music education.
Model of Markov-Based Piano Note Recognition Algorithm and Piano Teaching Model Construction

Piano note recognition is a process that converts music audio files into digital music files automatically, which is critical for piano assistant training and automatic recording of musical pieces. The Merle spectral coefficients, for example, have been used to implement the majority of the existing examples. The piano is one of the most popular forms of student education in today's world. Piano teachers should be aware of the implications. We can only truly adapt piano teaching to the educational purposes of higher education institutions if we implement a systematic, progressive, practical, and innovative philosophy of piano teaching. The Markov model is a statistical model that is widely used in speech signal processing. This thesis develops a set of mathematical models for piano speech recognition based on the Markov model, learns them systematically and scientifically, and achieves a better teaching effect. It is demonstrated that the Markov method detects the corresponding endpoints with an accuracy of 72.83 percent, which is 16.42 percent better than the a priori method. In terms of amplitude and phase, the Markov model shows a significant improvement. The findings of this study can be used to improve piano playing techniques taught to students in accordance with their favourite popular music, depending on the theme.
Forest-based Deep Recommender

With the development of deep learning techniques, deep recommendation models also achieve remarkable improvements in terms of recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods also suffer from low efficiency of recommendation. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. However, such models have two shortcomings. First, the max-heap assumption in the hierarchical tree, in which the preference for a parent node should be the maximum between the preferences for its children, is difficult to satisfy in their binary classification objectives. Second, the learned index only includes a single tree, which is different from the widely-used multiple trees index, providing an opportunity to improve the accuracy of recommendation.
Meta conditional variational auto-encoder for domain generalization

Domain generalization has recently generated increasing attention in machine learning in that it tackles the challenging out-of-distribution problem. The huge domain shift from source domains to target domains induces great uncertainty in making predictions on the target domains to which no data is accessible during learning. In this paper, we propose meta conditional variational auto-encoder (Meta-CVAE), a new meta probabilistic latent variable framework for domain generalization. The Meta-CVAE can better model the uncertainty across domains by inheriting the strong ability of probabilistic modeling from VAE. By leveraging the meta-learning framework to mimic the generalization from source to target domains during learning, our Meta-CVAE learns to acquire the capability of generalization by episodically transferring knowledge across domains. Meta-CVAE is optimized with a variational objective based on a newly derived evidence lower bound under the meta-learning setting. To further enhance prediction performance, we develop the Wasserstein Meta-CVAE by imposing a Wasserstein distance based discriminative constraint on the latent representations, which essentially separate different classes in the semantic space. Extensive experiments on diverse benchmarks demonstrate that our methods outperforms previous approaches consistently, and comprehensive ablation studies further validate its effectiveness on domain generalization.
Hybrid teaching-learning with comprehensive learning capability for electromagnetic device design problems

Many optimal design problems in the engineering field are nonlinear, multivariate, mixed integer, multimodal, and constrained. Meta-heuristic approaches have been widely used to solve these complex problems, but most of them are often sensitive to the settings of tuning parameters for different optimization problems, and suffer from premature convergence during the evolution process. This article proposes a novel hybrid teaching-learning-based optimization (HTLBO) algorithm to tackle this problem. A comprehensive teaching-learning mechanism with no adjustable parameters is introduced to improve the global optimal solution while in the meantime maintaining the solution diversity. The performance of the proposed HTLBO is tested on nine unconstrained benchmark functions and two nonlinear constrained benchmark functions with integer variables. Then the algorithm is applied to solve two significant electromagnetic design problems, that is, optimal brushless direct-current (BLDC) motor design and electromagnetic actuator geometric construction design. Simulation results on both the benchmark functions and practical engineering design problems confirm the efficiency and robustness of the proposed algorithm.
Masked multi-center angular margin loss for language recognition

Abstract Language recognition based on embedding aims to maximize inter-class variance and minimize intra-class variance. Previous researches are limited to the training constraint of a single centroid, which cannot accurately describe the overall geometric characteristics of the embedding space. In this paper, we propose a novel masked multi-center angular margin (MMAM) loss method from the perspective of multiple centroids, resulting in a better overall performance. Specifically, numerous global centers are used to jointly approximate entities of each class. To capture the local neighbor relationship effectively, a small number of centers are adapted to construct the similarity relationship between these centers and each entity. Furthermore, we use a new reverse label propagation algorithm to adjust neighbor relations according to the ground truth labels to learn a discriminative metric space in the classification process. Finally, an additive angular margin is added, which understands more discriminative language embeddings by simultaneously enhancing intra-class compactness and inter-class discrepancy. Experiments are conducted on the APSIPA 2017 Oriental Language Recognition (AP17-OLR) corpus. We compare the proposed MMAM method with seven state-of-the-art baselines and verify that our method has 26.2% and 31.3% relative improvements in the equal error rate (EER) and C avg respectively in the full-length test (“full-length” means the average duration of the utterances is longer than 5 s). Also, there are 31.2% and 29.3% relative improvements in the 3-s test and 14% and 14.8% relative improvements in the 1-s test.
ACORDAR

Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.
Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation

Group recommendations are a specific case of recommender systems (RS), where instead of recommending for each individual independently, shared recommendations are produced for groups of users. Usually, group recommendation techniques (i.e., group aggregators) are built on top of common "single-user" RS and the resulting group recommendation should reflect both the overall utility of the recommendation as well as fairness among the utilities of individual group members.
A New Sequential Prediction Framework with Spatial-temporal Embedding

Sequential prediction is one of the key components in recommendation. In online e-commerce recommendation system, user behavior consists of the sequential visiting logs and item behavior contains the interacted user list in order. Most of the existing state-of-the-art sequential prediction methods only consider the user behavior while ignoring the item behavior. In addition, we find that user behavior varies greatly at different time, and most existing models fail to characterize the rich temporal information. To address the above problems, we propose a transformer-based spatial-temporal recommendation framework (STEM). In the STEM framework, we first utilize attention mechanisms to model user behavior and item behavior, and then exploit spatial and temporal information through a transformer-based model. The STEM framework, as a plug-in, is able to be incorporated into many neural network-based sequential recommendation methods to improve performance. We conduct extensive experiments on three real-world Amazon datasets. The results demonstrate the effectiveness of our proposed framework.
Expanded Lattice Embeddings for Spoken Document Retrieval on Informal Meetings

In this paper, we evaluate different alternatives to process richer forms of Automatic Speech Recognition (ASR) output based on lattice expansion algorithms for Spoken Document Retrieval (SDR). Typically, SDR systems employ ASR transcripts to index and retrieve relevant documents. However, ASR errors negatively affect the retrieval performance. Multiple alternative hypotheses can also be used to augment the input to document retrieval to compensate for the erroneous one-best hypothesis. In Weighted Finite State Transducer-based ASR systems, using the n-best output (i.e. the top "n'' scoring hypotheses) for the retrieval task is common, since they can easily be fed to a traditional Information Retrieval (IR) pipeline. However, the n-best hypotheses are terribly redundant, and do not sufficiently encapsulate the richness of the ASR output, which is represented as an acyclic directed graph called the lattice. In particular, we utilize the lattice's constrained minimum path cover to generate a minimum set of hypotheses that serve as input to the reranking phase of IR. The novelty of our proposed approach is the incorporation of the lattice as an input for neural reranking by considering a set of hypotheses that represents every arc in the lattice. The obtained hypotheses are encoded through sentence embeddings using BERT-based models, namely SBERT and RoBERTa, and the final ranking of the retrieved segments is obtained with a max-pooling operation over the computed scores among the input query and the hypotheses set. We present our evaluation on the publicly available AMI meeting corpus. Our results indicate that the proposed use of hypotheses from the expanded lattice improves the SDR performance significantly over the n-best ASR output.
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate

Cross-Lingual Summarization (CLS) is a task that extracts important information from a source document and summarizes it into a summary in another language. It is a challenging task that requires a system to understand, summarize, and translate at the same time, making it highly related to Monolingual Summarization (MS) and Machine Translation (MT). In practice, the training resources for Machine Translation are far more than that for cross-lingual and monolingual summarization. Thus incorporating the Machine Translation corpus into CLS would be beneficial for its performance. However, the present work only leverages a simple multi-task framework to bring Machine Translation in, lacking deeper exploration.
Improving Fairness and Transparency for Artists in Music Recommender Systems

Streaming services have become one of today's main sources of music consumption, with music recommender systems (MRS) as important components. The MRS' choices strongly influence what users consume, and vice versa. Therefore, there is a growing interest in ensuring the fairness of these choices for all stakeholders involved. Firstly, for users, unfairness might result in some users receiving lower-quality recommendations in terms of accuracy and coverage. Secondly, item provider (i.e. artist) unfairness might result in some artists receiving less exposure, and therefore less revenue. However, it is challenging to improve fairness without a decrease in, for instance, overall recommendation quality or user satisfaction. Additional complications arise when balancing possibly domain-specific objectives for multiple stakeholders at once. While fairness research exists from both the user and artist perspective in the music domain, there is a lack of research directly consulting artists---with Ferraro et al. (2021) as an exception.
The Journey: An AR Gamified Mobile Application for Promoting Physical Activity in Young Adults

Physical activity is important to improve an individual's overall well-being. Digital interventions as they use Virtual Reality (VR) and Augmented Reality (AR), have shown success in promoting physical activity (PA) in people of all ages. This work discusses the design of an AR gamified mobile application prototype for promoting physical activity in young adults. The application (app), “The Journey” aims to promote PA in young adults’ users while they explore various touristic sites and also acquire virtual assets. This is achievable at a low cost to users by using smartphones-based AR app to tour any location of interest from the comfort of their home or outdoor. Each user's step count tracked via mobile device is used to help them navigate the location of interest. The findings from our evaluation of 29 people show that The Journey has the potential to motivate people to improve their PA both indoors and outdoors.