Reviewerly/exHarmony huggingface dataset:
Using network visualizations to engage elementary students in locally relevant data literacy

Purpose This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data. Design/methodology/approach The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets. Findings Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them. Originality/value To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.
Authoritative DNS Server Discovery Method to Enhance DNS Privacy Preservation

Plaintext-based DNS domain name resolution poses significant privacy risks. Therefore, encrypting DNS communication across all pathways is essential for privacy preservation. The IETF has standardized DoT, DoH, and DoQ to achieve encryption between end terminals and DNS full-service resolvers. Currently, an Internet-Draft has been published for encrypted communication between DNS full-service resolvers and authoritative DNS servers. However, the probing policy on the Internet-Draft prioritizes compatibility and conducts plaintext communication until it discovers authoritative DNS servers that support encrypted communication. Therefore, the Internet-Draft's probing policy does not provide complete privacy preservation. In this paper, we propose a novel authoritative DNS server discovery approach that achieves privacy preservation while ensuring compatibility.
Job-Recommendation Website

Abstract: In today's competitive work world, it can be challenging to find the right employment opportunities that match an individual's talents and goals. To address this, we propose building a job recommendation website with HTML, CSS, JavaScript, Bootstrap, PHP, and SQL, with the purpose of linking job seekers with suitable employment openings. Personalized recommendation algorithms match users with job adverts based on their profiles, talents, and preferences. By delivering an intuitive user interface, effective job search tools, and data-driven insights, the website improves the whole job search experience, fostering meaningful interactions between job seekers and organizations.
Class-Incremental Learning for Generative Classifiers based on Class Enhancement

Abstract Incremental learning of new classes by neural network models would lead to catastrophic forgetting of old classes. To address this problem, this paper proposes a class-incremental learning method for generative classifiers based on class enhancement. First, in order to increase the gap between old and new classes, we train an independent conditional variational auto-encoder for each class that arrives at each stage and reserve the trained weights to record the information of that class. Second, for complex natural image datasets, we incorporate a feature extractor to transform pixel replay into feature replay, making the retained information more representative. Finally, we use importance sampling and the Bayesian criterion for classification to obtain reliable classification results. The experimental results on the MNIST and CIFAR-10 datasets show that the proposed method can improve the classification accuracy of images and effectively reduce the impact of catastrophic forgetting by using batch learning for class-incremental learning. Furthermore, for the CORe50 and OpenLORIS-Object datasets, the proposed method can well adapt to the changes of the real-time environment by using online learning for continuous target recognition, showing its robustness.
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information

We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets.
Can LLMs Patch Security Issues?

Large Language Models (LLMs) have shown impressive proficiency in code generation. Nonetheless, similar to human developers, these models might generate code that contains security vulnerabilities and flaws. Writing secure code remains a substantial challenge, as vulnerabilities often arise during interactions between programs and external systems or services, such as databases and operating systems. In this paper, we propose a novel approach, Feedback-Driven Solution Synthesis (FDSS), designed to explore the use of LLMs in receiving feedback from Bandit, which is a static code analysis tool, and then the LLMs generate potential solutions to resolve security vulnerabilities. Each solution, along with the vulnerable code, is then sent back to the LLM for code refinement. Our approach shows a significant improvement over the baseline and outperforms existing approaches. Furthermore, we introduce a new dataset, PythonSecurityEval, collected from real-world scenarios on Stack Overflow to evaluate the LLMs' ability to generate secure code. Code and data are available at \url{https://github.com/Kamel773/LLM-code-refine}
Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review

Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data mining (PPDM). PPDM technique or approach enables the extraction of actionable insight from enormous volume of data while safeguarding the privacy of individual information and benefiting the entire society Medical research has taken a new course as a result of data mining with healthcare data to detect diseases earlier and improve patient care. Data integration necessitates the sharing of sensitive patient information. However, substantial privacy issues are raised in connection with the storage and transmission of potentially sensitive information. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics. The review concluded that no single strategy outperforms all others. Hence, future research should focus on adequate techniques for privacy solutions in the age of massive medical data and the standardization of evaluation standards.
Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections

Recent developments in Large Language Models (LLMs) have manifested significant advancements. To facilitate safeguards against malicious exploitation, a body of research has concentrated on aligning LLMs with human preferences and inhibiting their generation of inappropriate content. Unfortunately, such alignments are often vulnerable: fine-tuning with a minimal amount of harmful data can easily unalign the target LLM. While being effective, such fine-tuning-based unalignment approaches also have their own limitations: (1) non-stealthiness, after fine-tuning, safety audits or red-teaming can easily expose the potential weaknesses of the unaligned models, thereby precluding their release/use. (2) non-persistence, the unaligned LLMs can be easily repaired through re-alignment, i.e., fine-tuning again with aligned data points. In this work, we show that it is possible to conduct stealthy and persistent unalignment on large language models via backdoor injections. We also provide a novel understanding on the relationship between the backdoor persistence and the activation pattern and further provide guidelines for potential trigger design. Through extensive experiments, we demonstrate that our proposed stealthy and persistent unalignment can successfully pass the safety evaluation while maintaining strong persistence against re-alignment defense.
X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation

In recent times, automatic text-to-3D content creation has made significant progress, driven by the development of pretrained 2D diffusion models. Existing text-to-3D methods typically optimize the 3D representation to ensure that the rendered image aligns well with the given text, as evaluated by the pretrained 2D diffusion model. Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects. Consequently, employing 2D diffusion models directly for optimizing 3D representations may lead to suboptimal outcomes. To address this issue, we present X-Dreamer, a novel approach for high-quality text-to-3D content creation that effectively bridges the gap between text-to-2D and text-to-3D synthesis. The key components of X-Dreamer are two innovative designs: Camera-Guided Low-Rank Adaptation (CG-LoRA) and Attention-Mask Alignment (AMA) Loss. CG-LoRA dynamically incorporates camera information into the pretrained diffusion models by employing camera-dependent generation for trainable parameters. This integration enhances the alignment between the generated 3D assets and the camera's perspective. AMA loss guides the attention map of the pretrained diffusion model using the binary mask of the 3D object, prioritizing the creation of the foreground object. This module ensures that the model focuses on generating accurate and detailed foreground objects. Extensive evaluations demonstrate the effectiveness of our proposed method compared to existing text-to-3D approaches. Our project webpage: https://xmu-xiaoma666.github.io/Projects/X-Dreamer/ .
Adiabatic-Passage-Based Parameter Setting for Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) exhibits significant potential for tackling combinatorial optimization problems. Despite its promise for near-term quantum devices, a major challenge in applying QAOA lies in the cost of circuit runs associated with parameter optimization. Existing methods for parameter setting generally incur at least a superlinear cost concerning the depth p of QAOA. In this study, we propose a novel adiabatic-passage-based parameter setting method that remarkably reduces the optimization cost, specifically when applied to the 3-SAT problem, to a sublinear level. Beginning with an analysis of the random model of the specific problem, this method applies a problem-dependent preprocessing on the problem Hamiltonian analytically, effectively segregating the magnitude of parameters from the scale of the problem. Consequently, a problem-independent initialization is achieved without incurring any optimization cost or pre-computation. Furthermore, the parameter space is adjusted based on the continuity of the optimal adiabatic passage, resulting in a reduction in the disparity of parameters between adjacent layers of QAOA. By leveraging this continuity, the cost to find quasi-optimal parameters is significantly reduced to a sublinear level.
Optimal Attack and Defense for Reinforcement Learning

To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external attacker has the power to manipulate the victim agent's interaction with the environment. We study the full class of online manipulation attacks, which include (i) state attacks, (ii) observation attacks (which are a generalization of perceived-state attacks), (iii) action attacks, and (iv) reward attacks. We show the attacker's problem of designing a stealthy attack that maximizes its own expected reward, which often corresponds to minimizing the victim's value, is captured by a Markov Decision Process (MDP) that we call a meta-MDP since it is not the true environment but a higher level environment induced by the attacked interaction. We show that the attacker can derive optimal attacks by planning in polynomial time or learning with polynomial sample complexity using standard RL techniques. We argue that the optimal defense policy for the victim can be computed as the solution to a stochastic Stackelberg game, which can be further simplified into a partially-observable turn-based stochastic game (POTBSG). Neither the attacker nor the victim would benefit from deviating from their respective optimal policies, thus such solutions are truly robust. Although the defense problem is NP-hard, we show that optimal Markovian defenses can be computed (learned) in polynomial time (sample complexity) in many scenarios.
Learning active tactile perception through belief-space control

Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration policies by developing a generative world model that is leveraged to 1) estimate the object's physical parameters using a differentiable Bayesian filtering algorithm and 2) develop an exploration policy using an information-gathering model predictive controller. We evaluate our method on three simulated tasks where the goal is to estimate a desired object property (mass, height or toppling height) through physical interaction. We find that our method is able to discover policies that efficiently gather information about the desired property in an intuitive manner. Finally, we validate our method on a real robot system for the height estimation task, where our method is able to successfully learn and execute an information-gathering policy from scratch.
Text Attribute Control via Closed-Loop Disentanglement

Changing an attribute of a text without changing the content usually requires to first disentangle the text into irrelevant attributes and content representations. After that, in the inference phase, the representation of one attribute is tuned to a different value, expecting that the corresponding attribute of the text can also be changed accordingly. The usual way of disentanglement is to add some constraints on the latent space of an encoder-decoder architecture, including adversarial-based constraints and mutual-information-based constraints. However, the previous semi-supervised processes of attribute change are usually not enough to guarantee the success of attribute change and content preservation. In this paper, we propose a novel approach to achieve a robust control of attributes while enhancing content preservation. In this approach, we use a semi-supervised contrastive learning method to encourage the disentanglement of attributes in latent spaces. Differently from previous works, we re-disentangle the reconstructed sentence and compare the re-disentangled latent space with the original latent space, which makes a closed-loop disentanglement process. This also helps content preservation. In addition, the contrastive learning method is also able to replace the role of minimizing mutual information and adversarial training in the disentanglement process, which alleviates the computation cost. We conducted experiments on three text datasets, including the Yelp Service review dataset, the Amazon Product review dataset, and the GoEmotions dataset. The experimental results show the effectiveness of our model.
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable Image Classification

Prototypical-part interpretable methods, e.g., ProtoPNet, enhance interpretability by connecting classification predictions to class-specific training prototypes, thereby offering an intuitive insight into their decision-making. Current methods rely on a discriminative classifier trained with point-based learning techniques that provide specific values for prototypes. Such prototypes have relatively low representation power due to their sparsity and potential redundancy, with each prototype containing no variability measure. In this paper, we present a new generative learning of prototype distributions, named Mixture of Gaussian-distributed Prototypes (MGProto), which are represented by Gaussian mixture models (GMM). Such an approach enables the learning of more powerful prototype representations since each learned prototype will own a measure of variability, which naturally reduces the sparsity given the spread of the distribution around each prototype, and we also integrate a prototype diversity objective function into the GMM optimisation to reduce redundancy. Incidentally, the generative nature of MGProto offers a new and effective way for detecting out-of-distribution samples. To improve the compactness of MGProto, we further propose to prune Gaussian-distributed prototypes with a low prior. Experiments on CUB-200-2011, Stanford Cars, Stanford Dogs, and Oxford-IIIT Pets datasets show that MGProto achieves state-of-the-art classification and OoD detection performances with encouraging interpretability results.
PyNeRF: Pyramidal Neural Radiance Fields

Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial grid representations. However, they do not explicitly reason about scale and so introduce aliasing artifacts when reconstructing scenes captured at different camera distances. Mip-NeRF and its extensions propose scale-aware renderers that project volumetric frustums rather than point samples but such approaches rely on positional encodings that are not readily compatible with grid methods. We propose a simple modification to grid-based models by training model heads at different spatial grid resolutions. At render time, we simply use coarser grids to render samples that cover larger volumes. Our method can be easily applied to existing accelerated NeRF methods and significantly improves rendering quality (reducing error rates by 20-90% across synthetic and unbounded real-world scenes) while incurring minimal performance overhead (as each model head is quick to evaluate). Compared to Mip-NeRF, we reduce error rates by 20% while training over 60x faster.
Unsupervised Keypoints from Pretrained Diffusion Models

Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised counterpart, making their practicability questionable. We leverage the emergent knowledge within text-to-image diffusion models, towards more robust unsupervised keypoints. Our core idea is to find text embeddings that would cause the generative model to consistently attend to compact regions in images (i.e. keypoints). To do so, we simply optimize the text embedding such that the cross-attention maps within the denoising network are localized as Gaussians with small standard deviations. We validate our performance on multiple datasets: the CelebA, CUB-200-2011, Tai-Chi-HD, DeepFashion, and Human3.6m datasets. We achieve significantly improved accuracy, sometimes even outperforming supervised ones, particularly for data that is non-aligned and less curated. Our code is publicly available and can be found through our project page: https://ubc-vision.github.io/StableKeypoints/
Student Activity Recognition in Classroom Environments using Transfer Learning

The recent advances in artificial intelligence and deep learning facilitate automation in various applications including home automation, smart surveillance systems, and healthcare among others. Human Activity Recognition is one of its emerging applications, which can be implemented in a classroom environment to enhance safety, efficiency, and overall educational quality. This paper proposes a system for detecting and recognizing the activities of students in a classroom environment. The dataset has been structured and recorded by the authors since a standard dataset for this task was not available at the time of this study. Transfer learning, a widely adopted method within the field of deep learning, has proven to be helpful in complex tasks like image and video processing. Pretrained models including VGG-16, ResNet-50, InceptionV3, and Xception are used for feature extraction and classification tasks. Xception achieved an accuracy of 93%, on the novel classroom dataset, outperforming the other three models in consideration. The system proposed in this study aims to introduce a safer and more productive learning environment for students and educators.
Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines

Rhetoric, both spoken and written, involves not only content but also style. One common stylistic tool is $\textit{parallelism}$: the juxtaposition of phrases which have the same sequence of linguistic ($\textit{e.g.}$, phonological, syntactic, semantic) features. Despite the ubiquity of parallelism, the field of natural language processing has seldom investigated it, missing a chance to better understand the nature of the structure, meaning, and intent that humans convey. To address this, we introduce the task of $\textit{rhetorical parallelism detection}$. We construct a formal definition of it; we provide one new Latin dataset and one adapted Chinese dataset for it; we establish a family of metrics to evaluate performance on it; and, lastly, we create baseline systems and novel sequence labeling schemes to capture it. On our strictest metric, we attain $F_{1}$ scores of $0.40$ and $0.43$ on our Latin and Chinese datasets, respectively.
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural network approach can be used to improve not only the uncertainty estimates but also the downstream performance on semi-supervised node-classification tasks. Moreover, we propose a new measure of uncertainty for contrastive learning, that is based on the disagreement in likelihood due to different positive samples.