Reviewerly/exHarmony huggingface dataset:
BRUCE: Bundle Recommendation Using Contextualized item Embeddings

A bundle is a pre-defined set of items that are collected together. In many domains, bundling is one of the most important marketing strategies for item promotion, commonly used in e-commerce. Bundle recommendation resembles the item recommendation task, where bundles are the recommended unit, but it poses additional challenges; while item recommendation requires only user and item understanding, bundle recommendation also requires modeling the connections between the various items in a bundle. Transformers have driven the state-of-the-art methods for set and sequence modeling in various natural language processing and computer vision tasks, emphasizing the understanding that the neighbors of an element are of crucial importance. Under some required adjustments, we believe the same applies for items in bundles, and better capturing the relations of an item with other items in the bundle may lead to improved recommendations. To address that, we introduce BRUCE - a novel model for bundle recommendation, in which we adapt Transformers to represent data on users, items, and bundles. This allows exploiting the self-attention mechanism to model the following: latent relations between the items in a bundle; and users’ preferences toward each of the items in the bundle and toward the whole bundle. Moreover, we examine various architectures to integrate the items’ and the users’ information and provide insights on architecture selection based on data characteristics. Experiments conducted on three benchmark datasets show that the proposed approach contributes to the accuracy of the recommendation and substantially outperforms state-of-the-art methods
Hands on Explainable Recommender Systems with Knowledge Graphs

The goal of this tutorial is to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs. We will first introduce conceptual foundations, by surveying the state of the art and describing real-world examples of how knowledge graphs are being integrated into the recommendation pipeline, also for the purpose of providing explanations. This tutorial will continue with a systematic presentation of algorithmic solutions to model, integrate, train, and assess a recommender system with knowledge graphs, with particular attention to the explainability perspective. A practical part will then provide attendees with concrete implementations of recommender systems with knowledge graphs, leveraging open-source tools and public datasets; in this part, tutorial participants will be engaged in the design of explanations accompanying the recommendations and in articulating their impact. We conclude the tutorial by analyzing emerging open issues and future directions. Website: https://explainablerecsys.github.io/recsys2022/.
Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood

Frequent updates and model retraining are important in various application areas of recommender systems, e.g., news recommendation. Moreover, in such domains, we may not only face the problem of dealing with a constant stream of new data, but also with anonymous users, leading to the problem of streaming session-based recommendation (SSR). Such problem settings have attracted increased interest in recent years, and different deep learning architectures were proposed that support fast updates of the underlying prediction models when new data arrive. In a recent paper, a method based on Graph Neural Networks (GNN) was proposed as being superior than previous methods for the SSR problem. The baselines in the reported experiments included different machine learning models. However, several earlier studies have shown that often conceptually simpler methods, e.g., based on nearest neighbors, can be highly effective for session-based recommendation problems. In this work, we report a similar phenomenon for the streaming configuration. We first reproduce the results of the mentioned GNN method and then show that simpler methods are able to outperform this complex state-of-the-art neural method on two datasets. Overall, our work points to continued methodological issues in the academic community, e.g., in terms of the choice of baselines and reproducibility.1
Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation

Sequential recommendation has attracted a lot of attention from both academia and industry. Since item embeddings directly affect the recommendation results, their learning process is very important. However, most existing sequential models may introduce bias when updating the item embeddings. For example, in a sequence where all items are endorsed by a same celebrity, the co-occurrence of two items only indicates their similarity in terms of endorser, and is independent of the other aspects such as category and color. The existing models often update the entire item as a whole or update different aspects of the item without distinction, which fails to capture the contributions of different aspects to the co-occurrence pattern. To overcome the above limitations, we propose aspect re-distribution (ARD) to focus on updating the aspects that are important for co-occurrence. Specifically, we represent an item using several aspect embeddings with the same initial importance. We then re-calculate the importance of each aspect according to the other items in the sequence. Finally, we aggregate these aspect embeddings into a single aspect-aware embedding according to their importance. The aspect-aware embedding can be provided as input to a successor sequential model. Updates of the aspect-aware embedding are passed back to the aspect embeddings based on their importance. Therefore, different from the existing models, our method pays more attention to updating the important aspects. In our experiments, we choose self-attention networks as the successor model. The experimental results on four real-world datasets indicate that our method achieves very promising performance in comparison with seven state-of-the-art models.
Inductive biases in theory-based reinforcement learning

Understanding the inductive biases that allow humans to learn in complex environments has been an important goal of cognitive science. Yet, while we have discovered much about human biases in specific learning domains, much of this research has focused on simple tasks that lack the complexity of the real world. In contrast, video games involving agents and objects embedded in richly structured systems provide an experimentally tractable proxy for real-world complexity. Recent work has suggested that key aspects of human learning in domains like video games can be captured by model-based reinforcement learning (RL) with object-oriented relational models-what we term theory-based RL. Restricting the model class in this way provides an inductive bias that dramatically increases learning efficiency, but in this paper we show that humans employ a stronger set of biases in addition to syntactic constraints on the structure of theories. In particular, we catalog a set of semantic biases that constrain the content of theories. Building these semantic biases into a theory-based RL system produces more human-like learning in video game environments.
Digitizing historical balance sheet data: A practitioner’s guide

This paper discusses how to successfully digitize large-scale historical micro-data by augmenting optical character recognition (OCR) engines with pre- and post-processing methods. Although OCR software has improved dramatically in recent years due to improvements in machine learning, off-the-shelf OCR applications still present high error rates which limit their applications for accurate extraction of structured information. Complementing OCR with additional methods can however dramatically increase its success rate, making it a powerful and cost-efficient tool for economic historians. This paper showcases these methods and explains why they are useful. We apply them against two large balance sheet datasets and introduce quipucamayoc, a Python package containing these methods in a unified framework.
PSO + FL = PAASO: particle swarm optimization + federated learning = privacy-aware agent swarm optimization

Abstract In this paper, we present an unified framework that encompasses both particle swarm optimization (PSO) and federated learning (FL). This unified framework shows that we can understand both PSO and FL in terms of a function to be optimized by a set of agents but in which agents have different privacy requirements. PSO is the most relaxed case, and FL considers slightly stronger constraints. Even stronger privacy requirements can be considered which will lead to still stronger privacy-preserving solutions. Differentially private solutions as well as local differential privacy/reidentification privacy for agents opinions are the additional privacy models to be considered. In this paper, we discuss this framework and the different privacy-related alternatives. We present experiments that show how the additional privacy requirements degrade the results of the system. To that end, we consider optimization problems compatible with both PSO and FL.
Modelling patient trajectories using multimodal information

Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices (e.g. earlier disease diagnosis).We propose a solution to model patient trajectories that combines different types of information (e.g. clinical text, standard codes) and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism.The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available Medical Information Mart for Intensive Care (MIMIC)-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions.Herein, we explored DL-based techniques to model patient trajectories and propose two flexible architectures that explore patient admissions on an individual and sequence basis. The combination of clinical text with other types of information led to positive results, which can be further improved by including a fine-tuned version of clinicalBERT in the architectures. The proposed solution can be publicly accessed at https://github.com/bioinformatics-ua/PatientTM.
Software and hardware co-design and implementation of intelligent optimization algorithms

In order to improve the operating efficiency of the algorithm, some intelligent optimization algorithms are considered to be implemented on hardware. However, the existing design scheme has the problem of poor versatility. Therefore, this paper proposes a general software–hardware co-design scheme of intelligent optimization algorithms. In the design scheme, the initialization module and fitness module of the algorithm are deployed on the Advanced RISC Machines (ARM) for execution to increase the flexibility of the program. The update module of the algorithm is deployed on the Field Programmable Gate Array (FPGA) for execution to realize the hardware acceleration. The data between ARM and FPGA is transferred through Advanced eXtensible Interface (AXI) bus. In this paper, the PSO, BA, WOA, GWO, CMAES and EO algorithms are implemented with the proposed design scheme. And the six algorithms are tested on thirteen benchmark functions of different types. The experimental results prove the feasibility of the design scheme. In addition, by comparing with software and other implementation methods in execution time, resource occupancy and convergence, the effectiveness and superiority of the proposed scheme are proved.
A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification

Self-labeled techniques are semi-supervised classification models that overcome the shortage of labeled samples via an iterative process. Most relevant proposals are inspired by boosting schemes to iteratively enlarge labeled data, but these methods are constrained by the number and distribution of the initial labeled data. Up to the present, the only exceptions which can solve the above problem are SEG-SSC, k-means-SSC and LC-SSC. However, SEG-SSC relies on too many parameters. Besides, it is hard to improve the distribution of the initial labeled data when the initial labeled set can not roughly represent the distribution of the original data. k-means-SSC and LC-SSC fail to significantly improve the number of the initial labeled data by a limited number of representative points. To address the above issues, this paper proposes a framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification (LCSEG-SSC). First, a new method for finding local cores on labeled and unlabeled data is proposed to improve the distribution of the initial labeled data. Second, STOPF or active labeling is used to predict found local cores. Third, a new example generation technique is proposed to create synthetic labeled samples, intending to improve the number of the initial labeled data. After that, any self-labeled with boosting schemes can be executed on the improved labeled data effectively. Intensive experiments prove that LCSEG-SSC outperforms state-of-the-art methods, especially in a relatively low ratio of labeled data.
Did cryptocurrencies exhibit log‐periodic power law signature during the second wave of COVID‐19?

Abstract Many studies have associated cryptocurrencies with bubbles, especially during stressed market conditions such as the recent outbreak of the second wave of COVID‐19. Although the majority of studies have focused on Bitcoin, we investigate the predictability of bubble formation in the cryptocurrency market by using the log‐periodic power law and we uncover some important stylized facts of this market. Our sample consists of data for a selection of 15 cryptocurrencies for the period between 1 January 2021 and 1 September 2021 which coincides with the second wave of COVID‐19. We analyse 86 speculative bubbles, and we find that the cryptocurrency market has three times higher drawdown over equities during stressed market conditions.
Study on Personalized Recommendation Algorithm of Online Educational Resources Based on Knowledge Association

In order to overcome the problems of low accuracy, low recommendation efficiency, and low user satisfaction of educational resources recommendation algorithm, this paper proposes a personalized recommendation algorithm for online educational resources based on knowledge association. Firstly, online education resources are collected according to association rules. Secondly, firefly algorithm is used to classify online education resources. Then, the vector space function is constructed to filter the classified online education resources. Finally, the correlation between knowledge points is calculated by knowledge association theory, and the knowledge with the highest user interest is selected as the target recommendation resource to realize the personalized recommendation of online education resources. The resource recommendation accuracy of this method can reach 97%, the recommendation time is less than 5.0 s, and users are more satisfied with it, indicating that its recommendation effect is good.
Progress in optical adhesive and lamination process of touch screen in 3C products

Abstract Compared with the traditional keyboard and mouse input method, touch screen technique has high convenience for users. With the development of technology, touch screen is widely application in various electronic products because of their high transmittance, clear images, and convenient operation. This review mainly introduces the optical clear adhesive (OCA), liquid optical clear adhesive (LOCA), and the most commonly used LOCA ultra violet (UV)‐curable adhesives. Preparation and performance of UV‐curable adhesives are deeply explored. The structure of touch screen, such as one glass solution (OGS), on‐cell and in‐cell is discussed. Moreover, lamination process includes full lamination and non‐full lamination is also discussed. This review introduces the alignment technique comprise mechanical alignment technique and non‐mechanical alignment technique (such as machine vision) and laminating equipment. The recent development and existing problems with the touch screen lamination process are discussed. Furthermore, this review describes the latest developments in the adhesive of touch screen lamination process and predicts possible future development trends, which could provide references for subsequent research work.
Research on Path Planning in 3D Complex Environments Based on Improved Ant Colony Algorithm

Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment information, which can record the flight environment and avoid the inefficiency of planning. Secondly, a multi-objective function of distance and angle is established to improve the efficiency and safety of the path. Then, a target-guided heuristic function is proposed, and an anti-deadlock mechanism is introduced to improve the efficiency of the ant colony algorithm. Next, the node pheromone update rules are improved to further improve the efficiency of the algorithm. Finally, experiments prove the effectiveness of the improved algorithm, TGACO, and its efficiency in complex environments has obvious advantages. In the 20×20×20 environment, compared with the ant colony algorithm (ACO), the improved algorithm (TGACO) in this paper improves the path length, total turning angle, and running time by 17.8%, 78.4%, and 95.3%, respectively.
OneButtonPIN: A Single Button Authentication Method for Blind or Low Vision Users to Improve Accessibility and Prevent Eavesdropping

A Personal Identification Number (PIN) is a widely adopted authentication method used by smartphones, ATMs, etc. PINs offer strong security and can be reset when compromised (unlike biometric authentication). However, PINs can be inaccessible for blind or low vision (BLV) users due to screen readers voicing PINs to bystanders or potential shoulder surfing attack risks---bystanders could watch the PIN being entered without the user noticing. To address this, we present OneButtonPIN, an interface to improve PIN entry accessibility and security for BLV users. Here, a single on-screen button, when pressed and held, triggers a haptic vibration sequence. A digit is entered by counting the vibrations and releasing the button. We explored introducing random timings to the vibration sequence to increase security. A week-long evaluation with 9 BLV participants and a security study with 10 sighted participants acting as shoulder surfers demonstrated OneButtonPIN's usability and resilience against eavesdropping.
Imprecise but Fun

Novel input methods for game design often excite users, especially if they extend the way one interacts with the system. Electromyography (EMG) has the inherent potential to provide an intuitive - yet challenging - input channel for interactive systems. While this difficulty in control often limits the scope of applications for EMG in most systems, we argue that these qualities are especially relevant for games and playful interaction. The inherently challenging qualities of EMG input make the modality a prime candidate for designing body-centric playful experiences. Yet, we still need to understand its limitations to create engaging rather than frustrating experiences for users. In this work, we investigate EMG's potential to support playful interaction through exploratory studies, deriving feasible game interactions based on EMG's technical constraints, and study their application in game design. Based on our findings, we highlight design implications and pitfalls to avoid when creating EMG-based entertainment systems.
Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization

Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.