Reviewerly/exHarmony huggingface dataset:
MS-GDA: Improving Heterogeneous Recipe Representation via Multinomial Sampling Graph Data Augmentation

We study the problem of classifying different cooking styles, based on the recipe. The difficulty is that the same food ingredients, seasoning, and the very similar instructions result in different flavors, with different cooking styles. Existing methods have limitation: they mainly focus on homogeneous data (e.g., instruction or image), ignoring heterogeneous data (e.g., flavor compound or ingredient), which certainly hurts the classification performance. This is because collecting enough available heterogeneous data of recipe is a non-trivial task. In this paper, we present a new heterogeneous data augmentation method to improve classification performance. Specifically, we first construct a heterogeneous recipe graph network to represent heterogeneous data, which includes four main-stream types of heterogeneous data: ingredient, flavor compound, image, and instruction. Then, we draw a sequence of augmented graphs for Semi-Supervised learning through multinomial sampling. The probability distribution of sampling depends on the Cosine distance between the nodes of graph. In this way, we name our approach as Multinomial Sampling Graph Data Augmentation (MS-GDA). Extensive experiments demonstrate that MS-GDA significantly outperforms SOTA baselines on cuisine classification and region prediction with the recipe benchmark dataset. Code is available at https://github.com/LiangzheChen/MS-GDA.
ProtoMGAE: Prototype-aware Masked Graph Auto-Encoder for Graph Representation Learning

Graph self-supervised representation learning has gained considerable attention and demonstrated remarkable efficacy in extracting meaningful representations from graphs, particularly in the absence of labeled data. Two representative methods in this domain are graph auto-encoding and graph contrastive learning. However, the former methods primarily focus on global structures, potentially overlooking some fine-grained information during reconstruction. The latter methods emphasize node similarity across correlated views in the embedding space, potentially neglecting the inherent global graph information in the original input space. Moreover, handling incomplete graphs in real-world scenarios, where original features are unavailable for certain nodes, poses challenges for both types of methods. To alleviate these limitations, we integrate masked graph auto-encoding and prototype-aware graph contrastive learning into a unified model to learn node representations in graphs. In our method, we begin by masking a portion of node features and utilize a specific decoding strategy to reconstruct the masked information. This process facilitates the recovery of graphs from a global or macro level and enables handling incomplete graphs easily. Moreover, we treat the masked graph and the original one as a pair of contrasting views, enforcing the alignment and uniformity between their corresponding node representations at a local or micro level. Lastly, to capture cluster structures from a meso level and learn more discriminative representations, we introduce a prototype-aware clustering consistency loss that is jointly optimized with the above two complementary objectives. Extensive experiments conducted on several datasets demonstrate that the proposed method achieves significantly better or competitive performance on downstream tasks, especially for graph clustering, compared with the state-of-the-art methods, showcasing its superiority in enhancing graph representation learning.
CAT: Continual Adapter Tuning for aspect sentiment classification

Humans can continually acquire, improve, and transfer knowledge throughout their lifespan so that they can accurately identify sentiment polarities of the data attributed to different domains. However, the continual learning of incrementally available aspect sentiment classification (ASC) tasks from different domains with non-stationary data distributions remains a long-standing challenge for learning-based models due to the catastrophic forgetting problem, which is the tendency to completely and abruptly forget previously learned knowledge upon learning new knowledge. In this work, we present Continual Adapter Tuning (CAT), a parameter-efficient framework that not only avoids catastrophic forgetting but also enables knowledge transfer from learned ASC tasks to new ASC tasks. To avoid catastrophic forgetting, we only learn and store a task-specific adapter for each ASC task while freezing the backbone pre-trained model. To promote new task learning, we propose a continual adapter initialization technique to transfer knowledge from preceding tasks. Besides, we also develop a novel label-aware contrastive learning to simultaneously learn the features of input samples and the parameters of classifiers in the same space so that we can efficiently classify a sample with the help of label semantics. To eliminate the need for task IDs in testing, we propose a simple yet efficient majority sentiment polarity voting strategy to obtain final sentiment polarities according to the polarities predicted by all reasoning paths in the adapter architecture. Experimental results show the high effectiveness of our CAT by achieving new state-of-the-art performance.
Implementation of the digital functional settlement at a federal public university: problems and proposed solutions

The aim of this article is to present a proposal for improving the implementation of the Digital Functional Record in a federal public university.In pursuit of this objective, the article seeks to outline the implementation phases, the structure it will take, the professionals involved, the regulations governing the digital functional record, and the challenges faced, while also suggesting some solutions. The applied methodology is qualitative and descriptive, relying on bibliographic and documentary analysis. It is concluded that the study introduces the digital functional record in a federal public university, detailing its stages, the challenges encountered, and proposing improvements. If these proposed improvements were put into practice and became ingrained in the institution's culture, it is believed that they would bring greater benefits, preventing numerous challenges during implementation.
Reliability of Partitioning Metric Space Data

The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtained by a simulation study for a given partition under the homogeneity null hypothesis to draw a conclusion using standard statistical procedures. The proposed concept is illustrated using three examples representing different types of objects under study. Some general considerations are given regarding the nature of the SP distribution under the null hypothesis and its dependence on the number of divisions and the amount of data within them. A detailed modus operandi (working method) for analyzing a metric data partition is also offered.
SSS: Towards Autonomous Drone Delivery to Your Door Over House-Aware Semantics

In this work, we present our attempt to tackle the last-hundred-feet problem for autonomous drone delivery. We take a computer-vision-based approach to progressively landing towards a convenient and safe drop-off point at all times (here, at the front/garage door). Specifically, we develop structural semantic segmentation (SSS), a new technique that leverages a single-family house structure to streamline and enhance semantic segmentation in the drop-to-door problem context. We implement SSS into an Android app; Our preliminary evaluation in a residential zone shows SSS is promising to make autonomous drop-to-door in real-time, with no need to wait for slow visual processing. Video demo is available at Youtube [5]. App is released at Github [6].
BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.
Exploiting Fine-Grained Redundancy in Set-Centric Graph Pattern Mining

Graph Pattern Mining (GPM) applications are memory intensive as they require a tremendous amount of edge checks. In recent years, the "set-centric" abstraction has gained attention for its powerful expressive abilities. By leveraging relational algebra, they optimized algorithms with methods like matching orders, early termination, automorphism-breaking, and result reuse to reduce redundancy. However, these approaches primarily address coarse-grained redundancy from exactly the same set formulas, neglecting that the data graph's inherent locality may lead to fine-grained duplicated edge checks. In fact, even unrelated set operations may check the same pair of vertices. This paper introduces the set union operation to the set-centric abstraction to fuse duplicated edge checks into one. It maintains the expressive power of relational algebra and previous optimizations while effectively avoids fine-grained redundancy in GPM tasks. Compared to state-of-the-art methods, our method achieves significant speedup on a V100 GPU cluster, demonstrating up to 305 × faster performance than the state-of-the-art GPM system G2Miner.
Two-frame wavefront reconstruction method with nonlinear optimization for Ronchi lateral shearing interferometry

Ronchi lateral shearing interferometry is widely applied in wavefront measurement of advanced lithography tools. In Ronchi lateral shearing interferometry, higher-order diffraction beam interference is a common challenge, currently mitigated by introducing more phase shifts, resulting in extended measurement times and added errors. In this study, we propose a nonlinear optimization approach that combines a 2-frame phase-shifting algorithm to suppress the influence of high diffraction orders, reducing the required number of phase shifts by more than 75%. We formulate a numerical model that aligns with the interferometry, construct a highly robust error function, and enable high-precision wavefront reconstruction by optimizing Zernike coefficients. The accuracy and robustness of the proposed method are verified through simulations and experiments.
Introduction

The introduction presents the community and ideas at the heart of this anthropological study. When virtual reality reemerged in the mid-2010s, it was praised for its ability to elicit empathy and aid social justice causes. This idea of VR as an empathy machine germinated in Los Angeles, where an active and enthusiastic VR community took shape. While the VR community is heterogeneous and global, in LA there was a persistent conversation about not only VR’s potential to be a good technology but also the necessity of VR being a diverse and inclusive industry. Against the rise of Trumpism, the growing techlash, and the outrages that spurred #MeToo, the VR community in LA imagined the technology and the industry as a salve for these wounds. Several fantasies reinforced this outlook, and the introduction enumerates the fantasy of place, the fantasy of being, and the fantasy of representation as key analytic framings.
A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT.
Tetris: Accelerating Sparse Convolution by Exploiting Memory Reuse on GPU

Convolutional neural networks (CNNs) have achieved remarkable success in various application fields. Although model compression techniques mitigate the ever-increasing resource demands of large CNN models, the compressed models usually exhibit irregular memory access and unstructured sparsity, which are difficult for dominant operators such as sparse convolution to achieve expected performance speedup on popular inference platforms such as GPU. In this paper, we propose Tetris, an efficient sparse convolution approach optimized for GPU. Tetris first fully exploits the input reuse opportunity of sparse convolution to reduce the memory accesses to global memory. It then adopts a stride packed filter (SPF) format and a bank-sensing reorganization scheme to eliminate the irregular memory accesses caused by unstructured sparsity. It also leverages a filter group reorder technique to address load imbalance among threads, and a parameter tuning method to determine the optimal parameters of the sparse convolution implementation. The experiment results show that Tetris outperforms dense/sparse convolution libraries and cutting-edge implementations with promising performance speedup.
Visual-and-Language Multimodal Fusion for Sweeping Robot Navigation Based on CNN and GRU

Effectively fusing information between the visual and language modalities remains a significant challenge. To achieve deep integration of natural language and visual information, this research introduces a multimodal fusion neural network model, which combines visual information (RGB images and depth maps) with language information (natural language navigation instructions). Firstly, the authors used faster R-CNN and ResNet50 to extract image features and attention mechanism to further extract effective information. Secondly, GRU model is used to extract language features. Finally, another GRU model is used to fuse the visual- language features, and then the history information is retained to give the next action instruction to the robot. Experimental results demonstrate that the proposed method effectively addresses the localization and decision-making challenges for robotic vacuum cleaners.
Improving the performance of weak supervision searches using transfer and meta-learning

A bstract Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we seek to create neural networks that can learn from less experimental signal by using transfer and meta-learning. The general idea is to first train a neural network on simulations, thereby learning concepts that can be reused or becoming a more efficient learner. The neural network would then be trained on experimental data and should require less signal because of its previous training. We find that transfer and meta-learning can substantially improve the performance of weak supervision searches.
Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.
Virtual reality and tourism destinations marketing: can it transform travel? evaluating the impact of immersive experiences on travel intentions

This study investigates the relationship between the immersive VR experience, destination cognitive image, destination affective image, and tourists’ travel intention. The sample of this research is 167 visitors of three tourist attractions of Isfahan Province in Iran, through 360-degree virtual reality videos. In order to collect research data, an online survey and convenience sampling methods were used. PLS-SEM was utilised to assess both the measurement and structural models. The findings indicated that among factors of external technical environment stimuli, media richness has a stronger effect on the formation of the destination's cognitive image than the affective image. Also, perceived interactivity compared to media richness has a significant impact on the formation of the destination's affective image. In addition, vision consistency as an internal technical environment stimulus has a positive effect on the formation of the cognitive and affective image of the destination. Finally, the affective image of the destination resulting from the immersive VR experience has a stronger effect on travel intention than the cognitive image.
The future of court's procurators with the advent of artificial intelligence technologies

The use of artificial intelligence, as well as the digital transformation and the incorporation of advanced technologies in the field of the justice administration is a fact that has left no one indifferent. The progress of artificial intelligence-based technologies, which are allowing the automation of a multitude of tasks that are currently performed by different operators and legal professionals, will cause that many of these services and routine tasks will be developed by machines, with the consequent loss of prominence of these professional groups. In this context, what is the future for the legal professionals who represent the citizen in Courts? It seems difficult to imagine the need for the figure of Court's Procurator, especially when many other operators and legal professionals could be replaced by intelligent machines. For this reason, this paper will try to analyze the impact that these new technologies will have on the functions that these professionals perform daily on behalf of the parties before the courts. El uso de la inteligencia artificial, así como la transformación digital y la incorporación de tecnologías avanzadas en el ámbito de la Administración de Justicia es un hecho que no ha dejado indiferente a nadie. El avance de las tecnologías basadas en la inteligencia artificial, que están permitiendo la automatización de multitud de tareas que actualmente son realizadas por diferentes operadores y profesionales jurídicos, provocará que muchos de estos servicios y tareas rutinarias sean desarrollados por máquinas, con la consiguiente pérdida de protagonismo de estos colectivos profesionales. En este contexto, ¿cuál es el futuro de los profesionales del Derecho que representan al ciudadano ante los Tribunales? Parece difícil imaginar la necesidad de la figura del Procurador de los Tribunales, máxime cuando muchos otros operadores y profesionales del Derecho podrían ser sustituidos por máquinas inteligentes. Por ello, este trabajo tratará de analizar el impacto que estas nuevas tecnologías tendrán en las funciones que estos profesionales desempeñan diariamente en representación de las partes ante los tribunales.
A Lightweight Multi-Branch Context Network for Unsupervised Underwater Image Restoration

Underwater images commonly experience degradation caused by light absorption and scattering in water. Developing lightweight and efficient neural networks to restore degraded images is challenging because of the difficulty in obtaining high-quality paired images and the delicate trade-off between model performance and computational demands. To provide a lightweight and efficient solution for restoring images in terms of color, structure, texture details, etc., enabling the underwater image restoration task to be applied in real-world scenes, we propose an unsupervised lightweight multi-branch context network. Specifically, we design two lightweight multi-branch context subnetworks that enable multiple receptive field feature extraction and long-range dependency modeling to estimate scene radiance and transmission maps. Gaussian blur is adopted to approximate the global background light on the twice-downsampled degraded image. We design a comprehensive loss function that incorporates multiple components, including self-supervised consistency loss and reconstruction loss, to train the network using degraded images in an unsupervised learning manner. Experiments on several underwater image datasets demonstrate that our approach realizes good performance with very few model parameters (0.12 M), and is even comparable to state-of-the-art methods (up to 149 M) in color correction and contrast restoration.