Reviewerly/exHarmony huggingface dataset:
A Preference Learning Decoupling Framework for User Cold-Start Recommendation

The issue of user cold-start poses a long-standing challenge to recommendation systems, due to the scarce interactions of new users. Recently, meta-learning based studies treat each cold-start user as a user-specific few-shot task and then derive meta-knowledge about fast model adaptation across training users. However, existing solutions mostly do not clearly distinguish the concept of new users and the concept of novel preferences, leading to over-reliance on meta-learning based adaptability to novel patterns. In addition, we also argue that the existing meta-training task construction inherently suffers from the memorization overfitting issue, which inevitably hinders meta-generalization to new users. In response to the aforementioned issues, we propose a preference learning decoupling framework, which is enhanced with meta-augmentation (PDMA), for user cold-start recommendation. To rescue the meta-learning from unnecessary adaptation to common patterns, our framework decouples preference learning for a cold-start user into two complementary aspects: common preference transfer, and novel preference adaptation. To handle the memorization overfitting issue, we further propose to augment meta-training users by injecting attribute-based noises, to achieve mutually-exclusive tasks. Extensive experiments on benchmark datasets demonstrate that our framework achieves superior performance improvements against state-of-the-art methods. We also show that our proposed framework is effective in alleviating memorization overfitting.
Query-specific Variable Depth Pooling via Query Performance Prediction

Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the benchmark set. However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. Our hypothesis is that a lower depth for queries with a small number of relevant documents, and a higher depth for those with a larger number of relevant documents can potentially reduce the annotation effort without a significant change in IR effectiveness evaluation.We make use of standard query performance prediction (QPP) techniques to estimate the number of potentially relevant documents for each query, which is then used to determine the depth of the pool. Our experiments conducted on standard test collections demonstrate that this proposed method of employing query-specific variable depths is able to adequately reflect the relative effectiveness of IR systems with a substantially smaller annotation effort.
Priming and Actions: An Analysis in Conversational Search Systems

In order to accurately simulate users in conversational systems, it is essential to comprehend the factors that influence their behaviour. This is a critical challenge for the Information Retrieval (IR) field, as conventional methods are not well-suited for the interactive and unique sequential structure of conversational contexts. In this study, we employed the concept of Priming effects from the Psychology literature to identify core stimuli for each abstracted effect. We then examined these stimuli on various datasets to investigate their correlations with users' actions. Finally, we trained Logistic Regression (LR) models based on these stimuli to anticipate users' actions. Our findings offer a basis for creating more realistic user models and simulators, as we identified the subset of stimuli with strong relationships with users' actions. Additionally, we built a model that can predict users' actions.
Personalized Dynamic Recommender System for Investors

With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike representative areas of recommendation research such as e-commerce platforms where items' features are fixed, in investment scenarios financial instruments' features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. The proposed PDRSI considers two investor's personal features: dynamic preferences and historical interests, and two temporal environmental properties: recent discussions on the social media platform and the latest market information. The experimental results support the usefulness of the proposed PDRSI, and the ablation studies show the effect of each module. For reproduction, we follow Twitter's developer policy to share our dataset for future work.
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval

A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usually presumes that an entire matrix of test samples is available as DSL input. This work introduces a new postprocessing approach based on Sinkhorn transformations that outperforms DSL. Further, we propose a new postprocessing setting that does not require access to multiple test queries. We show that our approach can significantly improve the results of state of the art models such as CLIP4Clip, BLIP, X-CLIP, and DRL, thus achieving a new state-of-the-art on several standard text-video retrieval datasets both with access to the entire test set and in the single-query setting.
Accelerating variational quantum eigensolver convergence using parameter transfer

Abstract One impediment to the useful application of variational quantum algorithms in quantum chemistry is slow convergence with large numbers of classical optimization parameters. In this work, we evaluate a quantum computational warm-start approach for potential energy surface calculations. Our approach, which is inspired by conventional computational methods, is evaluated using simulations of the variational quantum eigensolver. Significant speedup is demonstrated relative to calculations that rely on a Hartree–Fock initial state, both for ideal and sampled simulations. The general approach of transferring parameters between similar problems is promising for accelerating current and near-term quantum chemistry calculations on quantum hardware, and is likely applicable beyond the tested algorithm and use case.
Parallel Strong Connectivity Based on Faster Reachability (Abstract)

In this paper, we propose a parallel strongly connected components (SCC) implementation that is efficient on a wide range of graphs. Our speedup comes from two novel techniques: vertical granularity control (VGC) and parallel hash bag.
Empirical Challenge for NC Theory (Abstract)

Horn-satisfiability or Horn-SAT is the problem of deciding whether a satisfying assignment exists for a Horn formula, a conjunction of clauses each with at most one positive literal (also known as Horn clauses). It is a well-known P-complete problem, which implies that unless P = NC, it is a hard problem to parallelize. In this paper, we empirically show that, under a known simple random model for generating the Horn formula, the ratio of hard-to-parallelize instances (closer to the worst-case behavior) is infinitesimally small. We show that the depth of a parallel algorithm for Horn-SAT is polylogarithmic on average, for almost all instances, while keeping the work linear. This challenges theoreticians and programmers to look beyond worst-case analysis and come up with practical algorithms coupled with respective performance guarantees.
Cultivating Gratitude with a Chatbot

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Quaternionic Convolutional Neural Networks with Trainable Bessel Activation Functions

Abstract Quaternionic convolutional neural networks (QCNN) possess the ability to capture both external dependencies between neighboring features and internal latent dependencies within features of an input vector. In this study, we employ QCNN with activation functions based on Bessel-type functions with trainable parameters, for performing classification tasks. Our experimental results demonstrate that this activation function outperforms the traditional ReLU activation function. Throughout our simulations, we explore various network architectures. The use of activation functions with trainable parameters offers several advantages, including enhanced flexibility, adaptability, improved learning, customized model behavior, and automatic feature extraction.
High-Precision Calibration of a Monocular-Vision-Guided Handheld Line-Structured-Light Measurement System

Due to the advantages of simple construction, easy application and good environmental suitability, handheld structured-light measurement systems have broad application prospects in 3D measurements. Here, a monocular-vision-guided line-structured-light measurement system is developed, and the posture of the handheld device can be obtained via a specifically designed target attached to it. No more marker points need to be adhered onto the object under inspection. The key for the system calibration is to obtain the coordinate transformation matrix from the sensor to the featured target coordinate system. The mathematical model of the system is first established. Then, an improved multi-view calibration method is proposed, where a selection process for the image pairs is conducted for accuracy improvement. With this method, the maximum relative error of the measured stair heights can be reduced from 0.48% to 0.16%. The measurement results for the specific parts further verified the effectiveness of the proposed system and the calibration method.
Homemade Educational Videos for Learning General Chemistry Experiments: A Pedagogical Alternative for Remote Teaching

The COVID-19 pandemic has displayed new challenges for chemistry instructors and students worldwide. An essential part of learning chemistry involves interactions in a laboratory ambiance. However, nowadays, it is necessary to consider alternatives to teaching experimental chemistry in an online environment. As a course project proposal, the students produced homemade videos to investigate a specific topic and find alternative ways to transfer their knowledge through innovative pedagogical and communicational tools. This work was assigned to first-year undergraduate students in a Chemistry I course who could not access an on-campus laboratory. This activity was performed in eight lab sections, considering seven different lab assignments, with 56 homemade educational videos. Each course had an instructor who guided the students and gave their feedback to improve the video quality. The final video output was subjected to an evaluation process consisting of three steps: the instructors’ evaluation, pedagogical analysis, and communicational review. Making educational videos at home could be an excellent alternative to boost the transfer of chemistry knowledge through didactic and audiovisual resources that promote learning within the university, from home, and in remote environments.
Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction

Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.
Would You Hold My Hand? Exploring External Observers’ Perception of Artificial Hands

Recent technological advances have enabled the development of sophisticated prosthetic hands, which can help their users to compensate lost motor functions. While research and development has mostly addressed the functional requirements and needs of users of these prostheses, their broader societal perception (e.g., by external observers not affected by limb loss themselves) has not yet been thoroughly explored. To fill this gap, we investigated how the physical design of artificial hands influences the perception by external observers. First, we conducted an online study (n = 42) to explore the emotional response of observers toward three different types of artificial hands. Then, we conducted a lab study (n = 14) to examine the influence of design factors and depth of interaction on perceived trust and usability. Our findings indicate that some design factors directly impact the trust individuals place in the system’s capabilities. Furthermore, engaging in deeper physical interactions leads to a more profound understanding of the underlying technology. Thus, our study shows the crucial role of the design features and interaction in shaping the emotions around, trust in, and perceived usability of artificial hands. These factors ultimately impact the overall perception of prosthetic systems and, hence, the acceptance of these technologies in society.
Harmony and Normalisation in Bilateral Logic

In a recent paper del Valle-Inclan and Schlöder argue that bilateral calculi call for their own notion of proof-theoretic harmony, distinct from the usual (or ‘unilateral’) ones. They then put forward a specifically bilateral criterion of harmony, and present a harmonious bilateral calculus for classical logic. In this paper, I show how del Valle-Inclan and Schlöder’s criterion of harmony suggests a notion of normal form for bilateral systems, and prove normalisation for two (harmonious) bilateral calculi for classical logic, HB1 and HB2. The resulting normal derivations have the usual desirable features, like the separation and subformula properties. HB1-normal form turns out to be strictly stronger that the notion of normal form proposed by Nils Kürbis, and HB2-normal form is neither stronger nor weaker than a similar proposal by Marcello D’Agostino, Dov Gabbay, and Sanjay Modgyl.
Eta-Reduction in Type-Theory of Acyclic Recursion

We investigate the applicability of the classic eta-conversion in the type-theory of acyclic algorithms. While denotationally valid, classic eta-conversion is not algorithmically valid in the type theory of algorithms, with the exception of few limited cases. The paper shows how the restricted, algorithmic eta-rule can recover algorithmic eta-conversion in the reduction calculi of type-theory of algorithms.
Structural Rules in Natural Deduction with Alternatives

Natural deduction with alternatives extends Gentzen–Prawitz-style natural deduction with a single structural addition: negatively signed assumptions, called alternatives. It is a mildly bilateralist, single-conclusion natural deduction proof system in which the connective rules are unmodi_ed from the usual Prawitz introduction and elimination rules — the extension is purely structural. This framework is general: it can be used for (1) classical logic, (2) relevant logic without distribution, (3) affine logic, and (4) linear logic, keeping the connective rules fixed, and varying purely structural rules. The key result of this paper is that the two principles that introduce kinds of irrelevance to natural deduction proofs: (a) the rule of explosion (from a contradiction, anything follows); and (b) the structural rule of vacuous discharge; are shown to be two sides of a single coin, in the same way that they correspond to the structural rule of weakening in the sequent calculus. The paper also includes a discussion of assumption classes, and how they can play a role in treating additive connectives in substructural natural deduction.
The Design of a Virtual Prototyping System for Authoring Interactive Virtual Reality Environments From Real-World Scans

Abstract Domain users (DUs) with a knowledge base in specialized fields are frequently excluded from authoring virtual reality (VR)-based applications in corresponding fields. This is largely due to the requirement of VR programming expertise needed to author these applications. To address this concern, we developed VRFromX, a system workflow design to make the virtual content creation process accessible to DUs irrespective of their programming skills and experience. VRFromX provides an in situ process of content creation in VR that (a) allows users to select regions of interest in scanned point clouds or sketch in mid-air using a brush tool to retrieve virtual models and (b) then attach behavioral properties to those objects. Using a welding use case, we performed a usability evaluation of VRFromX with 20 DUs from which 12 were novices in VR programming. Study results indicated positive user ratings for the system features with no significant differences across users with or without VR programming expertise. Based on the qualitative feedback, we also implemented two other use cases to demonstrate potential applications. We envision that the solution can facilitate the adoption of the immersive technology to create meaningful virtual environments.
Exceptional designs in some extended quadratic residue codes

Abstract In the present paper, we give proofs of the existence of a 3‐design in the extended ternary quadratic residue code of length 14 and the extended quaternary quadratic residue code of length 18.
LGANet: Local and global attention are both you need for action recognition

Abstract Due to redundancy in the spatiotemporal neighborhood and the global dependency between video frames, video recognition remains a challenge. Some prior works have been mainly driven by 3D convolutional neural networks (CNNs) or 2D CNNs with a well‐designed module for temporal information. However, convolution‐based networks lack the capability to capture the global dependency due to the limited receptive field. Alternatively, transformer for video recognition is proposed to build long‐range dependency between frame patches. Nevertheless, most transformer‐based networks have significant computational costs because attention is calculated among all the tokens. Based on these observations, we propose an efficient network which we dub LGANet. Unlike conventional CNNs and transformers for video recognition, the LGANet can tackle both spatiotemporal redundancy and dependency by learning local and global token affinity in shallow and deep layers, respectively . Specifically, local attention is implemented in the shallow layers to reduce parameters and eliminate redundancy. In the deep layers, spatial‐wise and channel‐wise self‐attention are embedded to realize the global dependency of high‐level features. Moreover, several key designs are made in the multi‐head self‐attention (MSA) and feed‐forward network (FFN). Extensive experiments are conducted on the popular video benchmarks, such as Kinetics‐400, Something‐Something V1&amp;V2. Without any bells and whistles, the LGANet achieves state‐of‐the‐art performance. The code will be released soon.
Deep Learning-based Human Height Estimation from a Stereo Vision System

This paper presents a deep learning-based human height estimation approach using a stereo vision system, which is part of a smart receptionist framework. The proposal consists of a smart screen with an integrated webcam and an additional webcam. The workflow of this approach initially acquires and processes the images in real-time, using deep neural networks detects a human in the images, aligns the images from the two cameras, then obtains the depth of the camera, and finally computes the height of the person under study. The proposed solution is evaluated by different people to check the effectiveness of the system. Obtained results show MAE below 1.0 cm in the computed heights, it is also compared with other techniques of the state-of-the-art.
Balanced Pedestrian Attribute Recognition for Improved Attribute-based Person Retrieval

Attribute-based person retrieval is a crucial task in surveillance systems since it enables the search for an individual if no image of the suspect is available. Searches can be based on soft biometric characteristics, understood as semantic attributes people typically use to describe each other. Examples are gender, age, or a description of the person's clothing and accessories. One possible solution is Pedestrian Attribute Recognition (PAR). Resulting attribute predictions are compared to the query attributes with the model's predictions. However, related literature indicates approaches tend to either perform well regarding the recognition of single attributes or achieve superior retrieval results at the expense of an increased number of false positives. This work aims to improve single-attribute recognition and attribute-based retrieval simultaneously by applying spatial projection and normalization modules. Spatial projection extracts features for different receptive fields to account for global as well as local attributes. Our PARNorm module normalizes attribute logits attribute-and instance-wise to enhance label-based PAR metrics without hurting instance-based performance. Furthermore, we propose a novel evaluation metric for attribute-based person retrieval that considers the degree of match between attribute queries and gallery images instead of using binary relevance labels. This allows for more realistic estimates of retrieval accuracy, which correlate strongly with the visual impression of the rankings. Our model outperforms the current state-of-the-art in generalization on the UPAR dataset and its challenge version.