Reviewerly/exHarmony huggingface dataset:
Neuroscience Goes Virtual: How to Measure Consumers‘ Responses in Extended Realities

Abstract The metaverse and extended realities offer new avenues for studying consumer behavior. Consumer neuroscience tools are being utilized to understand interactions in these virtual spaces. The 3S framework (Stimuli, Sensors and Signals) provides a foundation for applying these tools. Stimuli can be manipulated to study brand choices, and sensors capture physiological responses such as heart rate and facial expressions. Different interface devices like monitors, smartphones and head-mounted displays offer varying immersion levels and sensor compatibility. Augmented reality requires minimal additional technology, while virtual reality allows detailed tracking of user interactions. Mixed reality combines both. Applications of extended realities in consumer research include pretesting products and designs, virtual try-on experiences and analyzing the impact of virtual presences. The available technology and ongoing advancements present exciting opportunities for understanding consumer behavior in virtual and augmented spaces.
On the computational modeling of English relative clauses

Abstract Even in this era of parameter-heavy statistical modeling requiring large training datasets, we believe explicit symbolic models of grammar have much to offer, especially when it comes to modeling complex syntactic phenomena using a minimal number of parameters. It is the goal of explanatory symbolic models to make explicit a minimal set of features that license phrase structure, and thus, they should be of interest to engineers seeking parameter-efficient language models. Relative clauses have been much studied and have a long history in linguistics. We contribute a feature-driven account of the formation of a variety of basic English relative clauses in the Minimalist Program framework that is precisely defined, descriptively adequate, and computationally feasible in the sense that we have not observed an exponential scaling with the number of heads in the Lexical Array. Following previous work, we assume an analysis involving a uT feature and uRel feature, possibly simultaneously valued. In this article, we show a detailed mechanical implementation of this analysis and describe the structures computed for that , which , and who/whom relatives for standard English.
APPLICATION OF THE GROWING TREES METHOD FOR OPTIMIZING PLANS OF MULTIFACTOR EXPERIMENTS

Nowadays, the high cost of production and resources is the problem in the world, because of this, the issue of optimizing production to reduce the use of resources becomes acute. The research of the experiment at the initial stage makes it possible to reduce resource costs due to detailed analysis. For this we identify steps that we can simplify, which saves resources during production or research. Most often, experiments are multifactorial and related to the search for optimal conditions, selection of the most rational equipment and high-quality raw materials. There is a need to increase the effectiveness of experimental research. These researches allow us to study objects in detail, which provides the ability to obtain more information and offers conditions for their optimization. In the process of researching objects, it is necessary to build their mathematical models, which allow us to determine a rational ratio of parameters. Experiment planning allows for calculating the most effective order of performing experiments and studying the influence of individual factors on optimization criteria. The use of experimental planning methods helps in obtaining the maximum amount of useful information with minimal cost and time spent. This article examines the growing tree method for cost optimization of multifactor experimental plans. To confirm its functionality and effectiveness, a comparative analysis is conducted with existing optimization methods. The method is inspired by the evolution of growing trees and includes the stages of planting and growth. An algorithm and software that implement this method have been developed. The software implementation of the algorithm is made with the help of the framework Angular. In the study of technological processes, the functionality and effectiveness of the method of growing trees for cost optimization of plans of multifactor experiments has been proven. It has been compared with the bacterial optimization method and the method based on the use of the Gray code. The object of research: the process of optimization of plans of multifactor experiments according to its cost. The subject of study: growing tree method for cost optimization of multifactor experimental plans and software implementing it.
A glimpse of the multiple applications of virtual reality

Abstract A growing number of clinical trials and laboratory studies have examined the role of immersive virtual reality (VR) in medical and nonmedical contexts, especially in pain management. By creating a sense of presence and immersion, in addition to an experience of embodiment and interactivity, VR has excellent potential for improving clinical outcomes. Evidence from laboratory studies and clinical trials supports VR capability to enhance experimental pain tolerance as well as reduce clinical acute pain, chronic pain, and anxiety. By applying the principles of embodiment, virtual rehabilitation programs can facilitate adaptive body movements in chronic pain patients. Applying VR to educational contexts, VR-enhanced training allows students and healthcare professionals to be trained in realistic contexts prior to interacting with patients and real-world circumstances. Future advances in VR-based technology, group counseling, monitoring, and educational training will result in more widespread applications of VR in medical and nonmedical environments.
Graph Neural Network for representation learning of lung cancer

Abstract The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.
GCMA: Generative Cross-Modal Transferable Adversarial Attacks from Images to Videos

Existing cross-domain transferable attacks mostly focus on exploring the adversarial transferability across homomodal domains, while the adversarial transferability across heteromodal domains, e.g., image domains to video domains, has received less attention. This paper investigates cross-modal transferable attacks from image domains to video domains with the generator-oriented approach, i.e., crafting adversarial perturbations for each frame of video clips with the perturbation generator trained in the ImageNet domain to attack target video models. To this end, we propose an effective Generative Cross-Modal Attacks (GCMA) framework to enhance adversarial transferability from image domains to video domains. To narrow the domain gap between image and video data, we first propose a random motion module that warps images with synthetic random optical flows. We then integrate the random motion module into the feature disruption loss to incorporate additional temporal cues in the training phase. Specifically, feature disruption loss minimizes the cosine similarity between intermediate features of warped benign and adversarial images. Furthermore, motivated by the positive correlation between transferability and temporal consistency of adversarial video clips, we also introduce a temporal consistency loss that maximizes the cosine similarity between intermediate features of warped adversarial images and adversarial counterparts of warped benign images. Finally, GCMA trains the perturbation generator by simultaneously optimizing feature disruption loss and temporal consistency loss. Extensive experiments demonstrate the effectiveness of our proposed method, achieving state-of-the-art performance on Kinetics-400 and UCF-101. Our code is available at https://github.com/kay-ck/GCMA.
On the Importance of Spatial Relations for Few-shot Action Recognition

Deep learning has achieved great success in video recognition, yet still struggles to recognize novel actions when faced with only a few examples. To tackle this challenge, few-shot action recognition methods have been proposed to transfer knowledge from a source dataset to a novel target dataset with only one or a few labeled videos. However, existing methods mainly focus on modeling the temporal relations between the query and support videos while ignoring the spatial relations. In this paper, we find that the spatial misalignment between objects also occurs in videos, notably more common than the temporal inconsistency. We are thus motivated to investigate the importance of spatial relations and propose a more accurate few-shot action recognition method that leverages both spatial and temporal information. Particularly, a novel Spatial Alignment Cross Transformer (SA-CT) which learns to re-adjust the spatial relations and incorporates the temporal information is contributed. Experiments reveal that, even without using any temporal information, the performance of SA-CT is comparable to temporal based methods on 3/4 benchmarks. To further incorporate the temporal information, we propose a simple yet effective Temporal Mixer module. The Temporal Mixer enhances the video representation and improves the performance of the full SA-CT model, achieving very competitive results. In this work, we also exploit large-scale pretrained models for few-shot action recognition, providing useful insights for this research direction.
Relation Triplet Construction for Cross-modal Text-to-Video Retrieval

Cross-modal text-to-video retrieval aims to find semantically related videos for a text query. Since video and text are distinct modalities, the major challenge comes from building the correspondence between two modalities, thus relevant samples could be matched. Inherently, the text contains multiple relatively complete semantic units and each one is composed of three primary components, i.e., subject, predicate and object (SVO triplet). Therefore, it requires similar modeling of video content -- objects and their relations, to correctly retrieve videos for texts. To model fine-grained visual relations, this paper proposes a Multi-Granularity Matching (MGM) framework that considers both fine-grained relation triplet matching and coarse-grained global semantic matching for text-to-video retrieval. Specifically, in the proposed framework, we represent videos as SVO triplet tracklets by extracting frame-level relation triplets followed by temporal relation association across frames. Moreover, we design a transformer-based Bi-directional Fusion Block (BFB) to express each SVO triplet with a highly unified representation. The constructed SVO triplet tracklets provide a reasonable way to model fine-grained video contents, fulfilling a better alignment between videos and texts. Extensive experiments conducted on three benchmark datasets, i.e., MSR-VTT, LSMDC and MSVD, demonstrate the effectiveness of our proposed method.
Generalizing Face Forgery Detection via Uncertainty Learning

Current face forgery detection methods have made significant progress in achieving high intra-dataset accuracy by building a deterministic binary detector. However, deterministic networks cannot effectively capture noise and distribution shifts in the input, which makes them less robust and prone to poor generalization in real-world scenarios. To address this problem, in this paper, we propose an Uncertainty-Aware Learning (UAL) method for face forgery detection. Specifically, we extend the Transformer model in a probabilistic manner by modeling dependencies between patches as Gaussian random variables. Additionally, we introduce a Patch Selection Module that can efficiently and accurately identify discriminative regions with high-uncertainty information, which are further utilized for final classification. Furthermore, with the quantified uncertainty of the entire image, we design a novel Uncertainty-Aware One-Center Loss that enhances intra-class compactness for genuine faces only, thereby improving the inter-class separability in the embedding space. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, and the results verify that, our Uncertainty-Aware Learning method enjoys better robustness and generalization ability comparing against other state-of-the-art methods.
Performance Analysis and Improvement of Face Recognition Algorithms on TMS320C64x

Face recognition is an important part of today’s emerging biometrics and video surveillance markets. As face recognition algorithms move from research labs to real world products, power consumption and cost become critical issues, and DSP-based implementations become attractive. Our goal in this research project was to evaluate the CPU and memory requirements of face recognition algorithms on the TMS320C64x platform to determine the feasibility of implementing DSP-based face recognition systems.
Personal Information Protection in Government Data Openness Using Decision Tree Model

To address the issues of personal privacy and information security in the current digital age, this study first collects and analyzes relevant government data openness policies and personal information protection laws and regulations to understand the current policy and legal environment. Secondly, a complexity pruning decision tree model is constructed, which can identify and evaluate potential personal information protection risks in government data openness. Using the Singapore government open dataset, this decision tree model is applied for empirical analysis, and its accuracy and effectiveness are evaluated. The research results demonstrate that the complexity pruning decision tree model performs well in terms of accuracy, recall rate, F1 score, and the area under the ROC Curve (AUC). The model achieves an accuracy of 0.85 on the training and 0.8 on the test sets, indicating its high performance in personal information protection in government data openness.
PREPARATION OF THE FUTURE TEACHER FOR THE USE OF INTERNET TECHNOLOGIES IN PRACTICAL ACTIVITIES

The article highlights the main trends in the use of Internet technologies as an innovative direction in modern education, the basis for the formation of the information society, its globalization and informatization, which led to new requirements for professional activity and training of future specialists. The article substantiates the need for Internet resources and outlines the basic conditions for their use in practice; strengthening of the intellectual capabilities of education seekers in the information society, as well as individualization and improvement of the quality of education; organization of distance education; automation of the system of control and assessment of knowledge of education seekers; management of large volumes of information; formation of information competence of applicants; saving money and time, increasing the level of interest in training and professional growth of specialists. The solution of the set goal became possible with the help of such methods as observation, analysis of scientific sources on the raised problem, which made it possible to clarify the essence of the concept of “Internet technologies” and methods of compiling tasks with their help and solving them. The specifics of the use of Internet technologies, principles and means are characterized. Their effectiveness has been established: the use of Internet web resources provides access to a wide range of achievements of world science and research and development.The aim of the work was the theoretical substantiation of the peculiarities of training future specialists for professional activities by means of Internet technologies.It has been proven that the use of Internet technologies allows to positively influence the formation of the future specialist's personality, contributes to his information enrichment, development, ability to think critically, orient himself in the modern world. The main results of the work testify to the need to intensify approaches to the organization of the work of specialists with Internet technologies. In addition, the results of the work are drawing the attention of the community to the justification of the features of Internet technologies, which are determined by effective innovation in education, an effective means of training future specialists for professional activities.&#x0D; Keywords: information; Internet technologies; information educational environment; Internet environment; professional growth; Internet resources; students of higher education; specialists.
Implementing Discovery Learning Method in Teaching Reading Comprehension at Senior High School

This research aims at finding out: (1) whether EFL students’ reading comprehension improves with the Discovery Learning Method (DLM) in teaching reading or not, and (2) the students’ perceptions of the developing Reading Comprehension with the DLM. This research applied a pre-experimental with One Group Pre-test and Post-test design to assess the improvement of students’ Reading Comprehension and a questionnaire was used to find out the students’ perception of the DLM in English Reading Classroom. Thirty-six (36) students of XI Science 2 at SMAN 2 Baubau were selected as participants of the study using Cluster Random Sampling technique. The pre-test and post-test results were obtained from students’ reading tests and then analyzed by using IBM Statistic SPSS 25 Software. Questionnaire responses were classified into four (4) main indicators: the benefit, the interest, the activities, and the implementation. The study discovered: (1) the average result of the pre-test was 64.44 and in post-test was 76.91. It can be concluded that there is a significant effect of implementing DLM on students' Reading Comprehension; (2) the findings of students' perceptions based on the frequency, showed that average students agreed that DLM could improve their reading comprehension based on the four indicators: the benefit (63.9%), the interest (58.3%), the activities (50%), and the implementation (52.9%).
Optimal and near-optimal frequency-hopping sequences based on Gaussian period

&lt;abstract&gt;&lt;p&gt;Frequency-hopping sequences (FHSs) have a decisive influence on the whole frequency-hopping communication system. The Hamming correlation function plays an important role in evaluating the performance of FHSs. Constructing FHS sets that meet the theoretical bounds is crucial for the research and development of frequency-hopping communication systems. In this paper, three new classes of optimal FHSs based on trace functions are constructed. Two of them are optimal FHSs and the corresponding periodic Hamming autocorrelation value is calculated by using the known Gaussian period. It is shown that the new FHSs are optimal according to the Lempel-Greenberger bound. The third class of FHSs is the near-optimal FHSs.&lt;/p&gt;&lt;/abstract&gt;
Boosting Adversarial Training in Safety-Critical Systems Through Boundary Data Selection

AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve the models' robustness against them. Adversarial training is one approach to improve their robustness: it works by augmenting the training data with adversarial examples. This, unfortunately, comes with the cost of increased computational overhead and extended training times. In this work, we balance the need for additional adversarial data with the goal of minimizing the training costs by selecting the most ‘valuable’ data for adversarial training. In particular, we propose a robustness-oriented boundary data selection method, RAST-AT, which stands for robust and fast adversarial training. RAST-AT selects training data <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">near to the boundary</i> by considering adversarial perturbations. Our method improves the speed of model training on CIFAR-10 by 68.67%, and compared to other data selection methods, has 10% higher accuracy with 10% training data selected, and 7% higher robustness with 4% training data selected. Our method also significantly improves efficiency by at least 25% in adversarial training, with the same performance. Finally, we evaluate our method on a cobot system, generating adversarial patches as attacks, and adopting RAST-AT as the defense. We find that RAST-AT can defend against 60% of untargeted attacks and 20% of targeted attacks. Our work highlights the benefits of developing effective defenses against adversarial attacks to ensure the security and reliability of AI-powered safety-critical systems.
Demonstration of ChromoCloth: Re-Programmable Multi-Color Textures through Flexible and Portable Light Source

In this demo, we present ChromoCloth, a flexible and portable light source for reprogrammable multi-color texture on photochromic objects, whose color can be reprogrammed with external light sources. While prior work used external projectors to trigger the color change, ChromoCloth initiates the color change by covering the object. ChromoCloth consists of a textile substrate, 3D printed diffusive housing glued on top of the substrate and a flexible LED strip that is weaved through the housings.
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems Based on Large Language Models

Knowledge Base Question Answering (KBQA) aims to answer factoid questions based on knowledge bases. However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA. In this work, we focus on the CCKS2023 Competition of Question Answering with Knowledge Graph Inference for Unmanned Systems. Inspired by the recent success of large language models (LLMs) like ChatGPT and GPT-3 in many QA tasks, we propose a ChatGPT-based Cypher Query Language (CQL) generation framework to generate the most appropriate CQL based on the given NLQ. Our generative framework contains six parts: an auxiliary model predicting the syntax-related information of CQL based on the given NLQ, a proper noun matcher extracting proper nouns from the given NLQ, a demonstration example selector retrieving similar examples of the input sample, a prompt constructor designing the input template of ChatGPT, a ChatGPT-based generation model generating the CQL, and an ensemble model to obtain the final answers from diversified outputs. With our ChatGPT-based CQL generation framework, we achieved the second place in the CCKS 2023 Question Answering with Knowledge Graph Inference for Unmanned Systems competition, achieving an F1-score of 0.92676.