Reviewerly/exHarmony huggingface dataset:
WaterHE-NeRF: Water-ray Tracing Neural Radiance Fields for Underwater Scene Reconstruction

Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. Addressing the limitations of existing underwater NeRF methods in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision, this study proposes WaterHE-NeRF. We develop a new water-ray tracing field by Retinex theory that precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF, through its illuminance attenuation mechanism, generates both degraded and clear multi-view images and optimizes image restoration by combining reconstruction loss with Wasserstein distance. Additionally, the use of histogram equalization (HE) as pseudo-GT enhances the network's accuracy in preserving original details and color distribution. Extensive experiments on real underwater datasets and synthetic datasets validate the effectiveness of WaterHE-NeRF. Our code will be made publicly available.
IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater

In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and detection methods have made notable progress, but their application in underwater conditions is limited due to the complexity and variability of these environments. Fine-tuning in-air models saves high overhead and has more optional reference work than building an underwater model from scratch. To address these issues, we design a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U. IA2U enables efficient application in underwater scenarios, thereby improving performance in Underwater IE and OD. IA2U integrates three types of underwater priors: the water type prior that characterizes the degree of image degradation, such as color and visibility; the degradation prior, focusing on differences in details and textures; and the sample prior, considering the environmental conditions at the time of capture and the characteristics of the photographed object. Utilizing a Transformer-like structure, IA2U employs these priors as query conditions and a joint task loss function to achieve hierarchical enhancement of task-level underwater image features, therefore considering the requirements of two different tasks, IE and OD. Experimental results show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks. The code will be made publicly available.
Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions

We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing graph statistics, including the count of subgraphs, under the edge local differential privacy, many suffer with high communication costs, making them less efficient for large graphs. Though data compression is a typical approach in differential privacy, its application in local differential privacy requires a form of compression that every node can reproduce. In our study, we introduce linear congruence hashing. With a sampling rate of $s$, our method can cut communication costs by a factor of $s^2$, albeit at the cost of increasing variance in the published graph statistic by a factor of $s$. The experimental results indicate that, when matched for communication costs, our method achieves a reduction in the $\ell_2$-error for triangle counts by up to 1000 times compared to the performance of leading algorithms.
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models

We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data. This problem primarily results in a decline in performance on the subsequent task. Inspired by the adaptive boosting method in traditional machine learning, we present an effective dynamic corrective self-distillation (DCS) approach to improve the fine-tuning of the PLMs. Our technique involves performing a self-distillation mechanism where, at each iteration, the student model actively adapts and corrects itself by dynamically adjusting the weights assigned to individual data points. This iterative self-correcting process significantly enhances the overall fine-tuning capability of PLMs, leading to improved performance and robustness. We conducted comprehensive evaluations using the GLUE benchmark demonstrating the efficacy of our method in enhancing the fine-tuning process for various PLMs across diverse downstream tasks.
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning

Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In this work, our focus is on addressing these issues in a distributed learning framework. We consider a new approach that simultaneously achieves communication efficiency and privacy protection by exploiting the privacy advantage offered by quantization. Specifically, we use a quantization scheme called \textbf{Gau}ssian \textbf{L}ayered \textbf{R}andomized \textbf{Q}uantization (Gau-LRQ) that compresses the raw model gradients using a layer multishift coupler. By adjusting the parameters of Gau-LRQ, we shape the quantization error to follow the expected Gaussian distribution, thus ensuring client-level differential privacy (CLDP). We demonstrate the effectiveness of our proposed Gau-LRQ in the distributed stochastic gradient descent (SGD) framework and theoretically quantify the trade-offs between communication, privacy, and convergence performance. We further improve the convergence performance by enabling dynamic private budget and quantization bit allocation. We achieve this by using an optimization formula that minimizes convergence error subject to the privacy budget constraint. We evaluate our approach on multiple datasets, including MNIST, CIFAR-10, and CIFAR-100, and show that our proposed method outperforms the baselines in terms of learning performance under various privacy constraints. Moreover, we observe that dynamic privacy allocation yields additional accuracy improvements for the models compared to the fixed scheme.
Patch-MI: Enhancing Model Inversion Attacks via Patch-Based Reconstruction

Model inversion (MI) attacks aim to reveal sensitive information in training datasets by solely accessing model weights. Generative MI attacks, a prominent strand in this field, utilize auxiliary datasets to recreate target data attributes, restricting the images to remain photo-realistic, but their success often depends on the similarity between auxiliary and target datasets. If the distributions are dissimilar, existing MI attack attempts frequently fail, yielding unrealistic or target-unrelated results. In response to these challenges, we introduce a groundbreaking approach named Patch-MI, inspired by jigsaw puzzle assembly. To this end, we build upon a new probabilistic interpretation of MI attacks, employing a generative adversarial network (GAN)-like framework with a patch-based discriminator. This approach allows the synthesis of images that are similar to the target dataset distribution, even in cases of dissimilar auxiliary dataset distribution. Moreover, we artfully employ a random transformation block, a sophisticated maneuver that crafts generalized images, thus enhancing the efficacy of the target classifier. Our numerical and graphical findings demonstrate that Patch-MI surpasses existing generative MI methods in terms of accuracy, marking significant advancements while preserving comparable statistical dataset quality. For reproducibility of our results, we make our source code publicly available in https://github.com/jonggyujang0123/Patch-Attack.
Mask as Supervision: Leveraging Unified Mask Information for Unsupervised 3D Pose Estimation

Automatic estimation of 3D human pose from monocular RGB images is a challenging and unsolved problem in computer vision. In a supervised manner, approaches heavily rely on laborious annotations and present hampered generalization ability due to the limited diversity of 3D pose datasets. To address these challenges, we propose a unified framework that leverages mask as supervision for unsupervised 3D pose estimation. With general unsupervised segmentation algorithms, the proposed model employs skeleton and physique representations that exploit accurate pose information from coarse to fine. Compared with previous unsupervised approaches, we organize the human skeleton in a fully unsupervised way which enables the processing of annotation-free data and provides ready-to-use estimation results. Comprehensive experiments demonstrate our state-of-the-art pose estimation performance on Human3.6M and MPI-INF-3DHP datasets. Further experiments on in-the-wild datasets also illustrate the capability to access more data to boost our model. Code will be available at https://github.com/Charrrrrlie/Mask-as-Supervision.
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise granularity, categorizing noisy labels as clean, re-labeled, or ambiguous for robust optimization. Extensive experiments on three benchmark datasets demonstrate that BalanceMix outperforms existing state-of-the-art methods. We release the code at https://github.com/DISL-Lab/BalanceMix.
Multilingual large language models leak human stereotypes across language boundaries

Multilingual large language models have been increasingly popular for their proficiency in comprehending and generating text across various languages. Previous research has shown that the presence of stereotypes and biases in monolingual large language models can be attributed to the nature of their training data, which is collected from humans and reflects societal biases. Multilingual language models undergo the same training procedure as monolingual ones, albeit with training data sourced from various languages. This raises the question: do stereotypes present in one social context leak across languages within the model? In our work, we first define the term ``stereotype leakage'' and propose a framework for its measurement. With this framework, we investigate how stereotypical associations leak across four languages: English, Russian, Chinese, and Hindi. To quantify the stereotype leakage, we employ an approach from social psychology, measuring stereotypes via group-trait associations. We evaluate human stereotypes and stereotypical associations manifested in multilingual large language models such as mBERT, mT5, and ChatGPT. Our findings show a noticeable leakage of positive, negative, and non-polar associations across all languages. Notably, Hindi within multilingual models appears to be the most susceptible to influence from other languages, while Chinese is the least. Additionally, ChatGPT exhibits a better alignment with human scores than other models.
A Parallel Quantum Eigensolver for Quantum Machine Learning

Abstract Eigensolvers have a wide range of applications in machine learning. Quantum eigensolvers have been developed for achieving quantum speedup. Here, we propose a parallel quantum eigensolver (PQE) for solving a set of machine learning problems, which is based on quantummulti-resonant transitions that simultaneously trigger multiple energy transitions in the systems on demand. PQE has a polylogarithmic cost in problem size under certain circumstances and is hardware efﬁcient, such that it is implementable in near-term quantum computers. As a veriﬁcation, we utilize it to construct a collaborative ﬁltering quantum recommendation system (QRS) and implement an experiment of the movie recommendation tasks on a nuclear spin quantum processor. As a result, our recommendation system accurately suggests movies to the user that he/she might be interested in. We further demonstrate the applications of PQE in classiﬁcation and image completion. In the future, our work will shed light on more applications in quantum machine learning.&amp;#xD;
Adaptive Deep Inference Framework for Cloud-Edge Collaboration

The deployment of Deep Neural Networks (DNNs) requires significant computational and storage resources, which is challenging for resource-constrained end devices. To this end, collaborative deep inference is proposed, in which the DNN is divided into two parts and deployed on end devices and cloud respectively. How to find the optimal split point for collaborative deep inference in a changing network environment is a big challenge. It is also important to reduce decision latency for model partition to achieve adaptivity. To address these challenges, in this paper, we propose an Adaptive Partition (AP) framework to achieve adaptive deep inference through cloud-edge collaboration. Considering the inference performance in different network environments, we perform a weighted joint optimization of inference latency and quantization loss so that the focus of the optimization can change adaptively with the network. Then we propose a method to make fast decision on the selection of optimal split point. Extensive experimental evaluations have demonstrated the effectiveness of AP in improving the performance of adaptive collaborative deep inference. Compared with the state-of-the-art methods, AP reduces the decision latency and the total inference latency by up to 65.0x and 2.98x.
Comprehensive Evaluation of Encryption Algorithms: A Study of 22 Performance Tests

Encrypting private information is a critical step in preventing unauthorized access or reading. However, selecting a trustworthy encryption technique is crucial. While many encryption algorithms are produced yearly, only a few established methods exist to assess their performance. Some examples of such methods include GB/f 32915-2016 from SCA, SP 800-22 from NIST, and AIS 20 and AIS 31 from BSI. These methods only do fifteen tests, which may need more to determine how well the encryption scheme works. This study aims to propose a software programmer1 that can assess the efficacy of any encryption scheme by running its encrypted data through a series of twenty-two tests. To do this, the proposed software is built oo top d’ the Tinker framework based on the Python programming language. The proposed software is tested by evaluating the performance of five different encryption methods: AES, ARC4, RSA, Logistic Map, and SHA-512 with twenty tests. Featuring a user-friendly interface and effortless encryption algorithm evaluation, the proposed software can guide you in making the optimal choice to assess the performance of encryption algorithms.
Cogni Chat

Abstract: This project introduces an intelligent conversational chatbot leveraging advanced natural language processing and machine learning techniques. The chatbot excels in understanding user inputs, retaining context for personalized interactions, and offering support across diverse domains. With continuous learning capabilities, seamless integration with external systems, and a user-friendly feedback mechanism, the COGNICHAT aims to elevate the overall user experience in interactive systems.
Research on the Reform of University Computer Curriculum Based on OBE Concept--Taking Data Structure as an Example

The computer major in application-oriented undergraduate colleges focuses on cultivating students’ practical ability to solve complex engineering problems in the field of computer science. Data structure, as a core foundational course in computer science, plays an important role in connecting the past and the future in the curriculum system. This article adopts the OBE concept, student-centered, and results-oriented, and proposes an overall plan for the reform of mixed online and offline courses in data structure. It optimizes teaching content, constructs a multi-dimensional course assessment and evaluation system, and strives to achieve the training goals for students from three levels of knowledge, ability, and quality.
VIEW GRAPH CONSTRUCTION FOR LARGE-SCALE UAV IMAGES: AN EVALUATION OF STATE-OF-THE-ART METHODS

Abstract. Structure from Motion (SfM) is a 3D reconstruction framework that has achieved great success on large-scale Unmanned Aerial Vehicle (UAV) images. Due to the high time consumption of feature matching, a matching candidate subset is obtained by image retrieval to improve efficiency. Bag of Word (BoW) based image retrieval has been widely used in SfM systems, but the large number of local features and the high dimension of the BoW vector cause the retrieval method time-consuming. Vector of Locally Aggregated Descriptors (VLAD) and learning-based NetVLAD perform well in image retrieval, and these vector representation methods are evaluated in this study. After images are transformed into vectors, Nearest Neighbour (NN) searching methods like Brute-force and KD-Tree are used to find similar images. But as the number of images and the vector dimension increase, Approximate Nearest Neighbour (ANN) searching methods like Hierarchical Navigable Small World (HNSW) and Locality-Sensitive Hashing (LSH) are considered to replace NN searching to avoid efficiency degradation. These vector searching methods are also evaluated in this study. The test results demonstrate that the optimal method VLAD with HNSW can speed up about 100 times in finding matching candidate subset. A view graph that guides scene partition and sub-scene reconstruction in parallel SfM can be created by the optimal method. With this view graph construction method, the efficiency of SfM is significantly improved.
"Story Time!": Coping with Low-Motivated Students using Big Book

Background:&#x0D; Reading the picture book provides students with numerous positive effects, especially young students, such as attracting students' interest, boosting creativity, providing a pleasant, relaxed vibe, and creating fun and meaningful learning. This research seeks to investigate whether the reading comprehension of low-motivated students who are taught with the big book or with the daily storybook varies significantly.&#x0D; Methodology: &#x0D; This study used quasi-experiments, and each group received treatment eight times. The participants were 44 students recruited to become the sample of this study, consisting of experiment and control groups.&#x0D; Findings: &#x0D; This study revealed that students with low motivation who taught with Big Book as a platform are better than students with low motivation taught on traditional media. Several factors have made a big book an instrument that has a more significant impact on students' perception of reading than conventional media. Secondly, students engaged more in-class reading than in conventional media. Thirdly, the book sparked the interest of students and inspired them to learn more. Fourthly, the Big Book helped students create the meaning of the text and appreciate the text more thoroughly.&#x0D; Conclusion: &#x0D; English oral books such as Big-Book are more effective than traditional content in the second year. This study and published data support this conclusion that using large books as a tool for second years' reading understanding affects students' understanding of lectures significantly. The statistical study has found that the total student readership in the test group is higher than the average student score in the control group.&#x0D; Keywords: reading comprehension; low motivation; pupils; picture book.
Agents

This paper examines the syntactic operations taking place in ASL that encode the additionof an agent argument to intransitive motion predicates. Previous studies had showna structural correlation between, on the one hand, handling classifiers and agents incontinuous contact with the undergoer and, on the other hand, body part classifiersand agents in non-continuous contact with the undergoer. Here, a syntactic operationis identified, head movement of π-GO to ν-AG, whose presence yields the continuouscontactinterpretation, while its absence yields the non-continuous contact interpretation.We also find that the body part classifier correlation with non-continuous contactinterpretation constitutes a frequency effect and not a grammar effect: though infrequent,structures with body part classifiers can undergo π-GO to ν-AG, which results, aspredicted, in a continuous interpretation. Thus, this works illustrates the differencesbetween frequency and grammar: grammar provides the range of potential structuresexisting in the language, while frequency reflects either personal or context choice orpreference.
Improving Students' Writing Skills in Procedure text through Teams Games Tournament (TGT) and Picture Series

This research aims to enhance students' skills in writing procedure texts using the Teams Games Tournament with Pictures Series. The subjects of this study were all 32 students in class IX D at SMPN 12 Pontianak. The writers employed Classroom Action Research conducted over two cycles, in which each cycle consisted of two meetings. The data were collected using observation checklists, field notes, and tests. Based on the findings of this study, it can be concluded that the use of Teams Games Tournament and Pictures Series were able to improve students' writing skills. This can be seen from the increasement of the students’ mean scores from 76.3 in Cycle 1 to 84.9 in Cycle 2. In addition to score improvement, student engagement and enthusiasm during the learning process also significantly increased. Therefore, the researcher recommends the teachers to utilize both of these strategies in order to enhance students' writing skills.
A Study on the Influence Factors of Animated Emoticon on Users’ Positive Emotions and Continuous Use Intention

The rapid growth of social media has provided a new way for people to communicate in a non-face-to-face way. In non-face-to-face communication, the emotions of both parties are the main factor in maintaining the communication and enhancing each other’s emotions. By actively engaging each other’s emotions during the dialogue process, the communication can become more enjoyable and thus alleviate the negative state of each other. This paper takes animated emoticons as the object of study and uses the characteristics of emoticons as the theoretical basis. It investigates whether recipients induce positive emotions after receiving animated emoticons and the effect of recipients’ acquisition of positive emotions on the intention to use them consistently. The research methodology was based on questionnaire data from 272 people, and the model and hypotheses were tested using SPSS 21.0 and AMOS 24.0 software to generalise and extract four influencing factors based on the characteristics of emoji. It was found that the simplicity, entertainment, aesthetics and usefulness of the animated emoticon could induce positive emotions in the recipients after receiving them, while positively influencing the intention to continue using the animated emoticon.