Reviewerly/exHarmony huggingface dataset:
Conceptualising an Anti-Digital Forensics Kill Chain for Smart Homes

The widespread integration of Internet of Things (IoT) devices in households generates extensive digital footprints, notably within Smart Home ecosystems. These IoT devices, brimming with data about residents, inadvertently offer insights into human activities, potentially embodying even criminal acts, such as a murder. As technology advances, so does the concern for criminals seeking to exploit various techniques to conceal evidence and evade investigations. This paper delineates the application of Anti-Digital Forensics (ADF) in Smart Home scenarios and recognises its potential to disrupt (digital) investigations. It does so by elucidating the current challenges and gaps and by arguing, in response, the conceptualisation of an ADF Kill Chain tailored to Smart Home ecosystems. While seemingly arming criminals, the Kill Chain will allow a better understanding of the distinctive peculiarities of Anti-Digital Forensics in Smart Home scenario. This understanding is essential for fortifying the Digital Forensics process and, in turn, developing robust countermeasures against malicious activities.
Ecmas: Efficient Circuit Mapping and Scheduling for Surface Code

As the leading candidate of quantum error correction codes, surface code suffers from significant overhead, such as execution time. Reducing the circuit's execution time not only enhances its execution efficiency but also improves fidelity. However, finding the shortest execution time is NP-hard. In this work, we study the surface code mapping and scheduling problem. To reduce the execution time of a quantum circuit, we first introduce two novel metrics: Circuit Parallelism Degree and Chip Communication Capacity to quantitatively characterize quantum circuits and chips. Then, we propose a resource-adaptive mapping and scheduling method, named Ecmas, with customized initialization of chip resources for each circuit. Ecmas can dramatically reduce the execution time in both double defect and lattice surgery models. Furthermore, we provide an additional version Ecmas-ReSu for sufficient qubits, which is performance-guaranteed and more efficient. Extensive numerical tests on practical datasets show that Ecmas outperforms the state-of-the-art methods by reducing the execution time by 51.5% on average for double defect model. Ecmas can reach the optimal result in most benchmarks, reducing the execution time by up to 13.9% for lattice surgery model.
NoPose-NeuS: Jointly Optimizing Camera Poses with Neural Implicit Surfaces for Multi-view Reconstruction

Learning neural implicit surfaces from volume rendering has become popular for multi-view reconstruction. Neural surface reconstruction approaches can recover complex 3D geometry that are difficult for classical Multi-view Stereo (MVS) approaches, such as non-Lambertian surfaces and thin structures. However, one key assumption for these methods is knowing accurate camera parameters for the input multi-view images, which are not always available. In this paper, we present NoPose-NeuS, a neural implicit surface reconstruction method that extends NeuS to jointly optimize camera poses with the geometry and color networks. We encode the camera poses as a multi-layer perceptron (MLP) and introduce two additional losses, which are multi-view feature consistency and rendered depth losses, to constrain the learned geometry for better estimated camera poses and scene surfaces. Extensive experiments on the DTU dataset show that the proposed method can estimate relatively accurate camera poses, while maintaining a high surface reconstruction quality with 0.89 mean Chamfer distance.
A Hybrid Image Encryption Scheme based on Chaos and a DPA-Resistant Sbox

Image encryption is one of the most common and effective methods to secure digital images. Recently, Khalid M. Hosny presented an image encryption scheme based on 6D hyper chaotic mapping and Q-Fibonacci matrix, which, despite its remarkable theoretical and practical properties, has several weaknesses, including inaccuracy of black image encryption, inappropriate white image encryption (improper entropy parameters, correlation, chi-square test, histogram, UACI, and NPCR), weak keys, inappropriate key usage. In this paper, based on Khaled Hosny's design, a new effective design is presented that has improved encryption security and efficiency. In addition, in the proposed design, a secure key and a substitution box with a high degree of transparency order, which is resistant to DPA attacks, have been added. Also, a method to improve transferring chaos parameters is also proposed. The test results show the improvement of the resistance of the proposed design against the common attacks of image encryption schemes and improvement in bandwidth consumption. Also it has been shown that the proposed scheme has produced better results in terms of both security and efficiency compared to other similar new schemes.
Sample selection with noise rate estimation in noise learning of medical image analysis

Deep learning techniques have demonstrated remarkable success in the field of medical image analysis. However, the existence of label noise within data significantly hampers its performance. In this paper, we introduce a novel noise-robust learning method which integrates noise rate estimation into sample selection approaches for handling noisy datasets. We first estimate the noise rate of a dataset with Linear Regression based on the distribution of loss values. Then, potentially noisy samples are excluded based on this estimated noise rate, and sparse regularization is further employed to improve the robustness of our deep learning model. Our proposed method is evaluated on five benchmark medical image classification datasets, including two datasets featuring 3D medical images. Experiments show that our method outperforms other existing noise-robust learning methods, especially when noise rate is very big.
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms

In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. These data forms a stream which requires special handling. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy. This paper showcases two illustrative scenarios that highlight the potential of federated learning (FL) as a key to delivering efficient and privacy-preserving machine learning within IoT networks. We first give the mathematical foundations for key aggregation algorithms in federated learning, i.e., FedAvg and FedProx. Then, we conduct simulations, using Flower Framework, to show the \textit{efficiency} of these algorithms by training deep neural networks on common datasets and show a comparison between the accuracy and loss metrics of FedAvg and FedProx. Then, we present the results highlighting the trade-off between maintaining privacy versus accuracy via simulations - involving the implementation of the differential privacy (DP) method - in Pytorch and Opacus ML frameworks on common FL datasets and data distributions for both FedAvg and FedProx strategies.
Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories

The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care. Inspired by the flexibility and interpretability of graphs (networks), as well as the potency of sequence models like transformers and higher-order networks (HONs), we propose a method that identifies combinations of risk factors for a given outcome and accurately encodes these higher-order relationships in a graph. Using historical data from 913,475 type 2 diabetes (T2D) patients, we found that, compared to other approaches, the proposed networks encode significantly more information about the progression of T2D toward a variety of outcomes. We additionally demonstrate how structural information from the proposed graph can be used to augment the performance of transformer-based models on predictive tasks, especially when the data are noisy. By increasing the order, or memory, of the graph, we show how the proposed method illuminates key risk factors while successfully ignoring noisy elements, which facilitates analysis that is simultaneously accurate and interpretable.
Towards Consistent Language Models Using Declarative Constraints

Large language models have shown unprecedented abilities in generating linguistically coherent and syntactically correct natural language output. However, they often return incorrect and inconsistent answers to input questions. Due to the complexity and uninterpretability of the internally learned representations, it is challenging to modify language models such that they provide correct and consistent results. The data management community has developed various methods and tools for providing consistent answers over inconsistent datasets. In these methods, users specify the desired properties of data in a domain in the form of high-level declarative constraints. This approach has provided usable and scalable methods to delivering consistent information from inconsistent datasets. We aim to build upon this success and leverage these methods to modify language models such that they deliver consistent and accurate results. We investigate the challenges of using these ideas to obtain consistent and relevant answers from language models and report some preliminary empirical studies.
Limitation of independent and identical distribution to capture the hierarchy of nonlocality

Importance of considering independent and identically distributed (iid) scenario is well-established in quantum theory. From entanglement detection to entangled state transformation, this scenario is useful in many tasks. Nevertheless, here we focus on the limitation of iid scenario. For this purpose, we consider the notion of nonlocality associated with local state discrimination problems (LSDPs). Here, the term `nonlocality' denotes the difference between the local and the global abilities of discriminating quantum states. It is natural to consider LSDPs to study the aforesaid limitation because iid copies of quantum states are considered as resource in these problems. However, we first explore entanglement as resource in LSDPs where the states are pure. Then, we generalize our findings considering mixed states. In present LSDPs, our findings make the teleportation-based protocols sub-optimal. In fact, our results shed light on Schmidt rank as resource in LSDPs. Interestingly, we find instances where the states cannot be locally discriminated even if finite number of iid copies of the states are available. But local discrimination of these states can be done consuming quite less resource when we consider entanglement-assisted LSDPs. This allows us to compare the degrees of nonlocality associated with similar LSDPs within the same Hilbert space. Thus, we report a hierarchy of nonlocality which cannot be captured through iid scenario but it can be observed when entanglement is used as resource in certain LSDPs. Moreover, the difference in nonlocality may increase with increasing dimension. Finally, we show an application of our theory in secure locking of information and its resource-efficient extraction.
A Trust Region Approach for Few-Shot Sim-to-Real Reinforcement Learning

Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel in this environment but fail to provide reasonable performance in the target one. In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer. Addressing this challenge, we propose an innovative approach inspired by recent advancements in Imitation Learning and conservative RL algorithms. The proposed method introduces a penalty to regulate the trajectories generated by the source-trained policy. We evaluate our method across various environments representing diverse off-dynamics conditions, where access to the target environment is extremely limited. These experiments include high-dimensional systems relevant to real-world applications. Across most tested scenarios, our proposed method demonstrates performance improvements compared to existing baselines.
Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective

High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve these goals by minimizing the corresponding yet conflicting losses, such as the $\ell_1$ loss and the adversarial loss. Unfortunately, the commonly used gradient-based optimizers, such as Adam, are hard to balance these objectives due to the opposite gradient decent directions of the contradictory losses. In this paper, we formulate the perception-distortion trade-off in SR as a multi-objective optimization problem and develop a new optimizer by integrating the gradient-free evolutionary algorithm (EA) with gradient-based Adam, where EA and Adam focus on the divergence and convergence of the optimization directions respectively. As a result, a population of optimal models with different perception-distortion preferences is obtained. We then design a fusion network to merge these models into a single stronger one for an effective perception-distortion trade-off. Experiments demonstrate that with the same backbone network, the perception-distortion balanced SR model trained by our method can achieve better perceptual quality than its competitors while attaining better reconstruction fidelity. Codes and models can be found at https://github.com/csslc/EA-Adam.
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM

Search engines are crucial as they provide an efficient and easy way to access vast amounts of information on the internet for diverse information needs. User queries, even with a specific need, can differ significantly. Prior research has explored the resilience of ranking models against typical query variations like paraphrasing, misspellings, and order changes. Yet, these works overlook how diverse demographics uniquely formulate identical queries. For instance, older individuals tend to construct queries more naturally and in varied order compared to other groups. This demographic diversity necessitates enhancing the adaptability of ranking models to diverse query formulations. To this end, in this paper, we propose a framework that integrates a novel rewriting pipeline that rewrites queries from various demographic perspectives and a novel framework to enhance ranking robustness. To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness. Our extensive experimentation on both public and industrial datasets assesses the efficacy of our query rewriting approach and the enhanced accuracy and robustness of the ranking model. The findings highlight the sophistication and effectiveness of our proposed model.
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection

Recent remarkable success in the deep-learning industries has unprecedentedly increased the need for reliable model deployment. For example, the model should alert the user if the produced model outputs might not be reliable. Previous studies have proposed various methods to solve the Out-of-Distribution (OOD) detection problem, however, they generally require a burden of resources. In this work, we propose a novel and simple method, Multiple Input Mixup (MIM). Our method can help improve the OOD detection performance with only single epoch fine-tuning. Our method does not require training the model from scratch and can be attached to the classifier simply. Despite its simplicity, our MIM shows competitive performance. Our method can be suitable for various environments because our method only utilizes the In-Distribution (ID) samples to generate the synthesized OOD data. With extensive experiments with CIFAR10 and CIFAR100 benchmarks that have been largely adopted in out-of-distribution detection fields, we have demonstrated our MIM shows comprehensively superior performance compared to the SOTA method. Especially, our method does not need additional computation on the feature vectors compared to the previous studies. All source codes are publicly available at https://github.com/ndb796/MultipleInputMixup.
Implicit Subgoal Planning with Variational Autoencoders for Long-Horizon Sparse Reward Robotic Tasks

The challenges inherent to long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To alleviate these challenges, we introduce a novel algorithm, Variational Autoencoder-based Subgoal Inference (VAESI), to accomplish long-horizon tasks through a divide-and-conquer manner. VAESI consists of three components: a Variational Autoencoder (VAE)-based Subgoal Generator, a Hindsight Sampler, and a Value Selector. The VAE-based Subgoal Generator draws inspiration from the human capacity to infer subgoals and reason about the final goal in the context of these subgoals. It is composed of an explicit encoder model, engineered to generate subgoals, and an implicit decoder model, designed to enhance the quality of the generated subgoals by predicting the final goal. Additionally, the Hindsight Sampler selects valid subgoals from an offline dataset to enhance the feasibility of the generated subgoals. The Value Selector utilizes the value function in reinforcement learning to filter the optimal subgoals from subgoal candidates. To validate our method, we conduct several long-horizon tasks in both simulation and the real world, including one locomotion task and three manipulation tasks. The obtained quantitative and qualitative data indicate that our approach achieves promising performance compared to other baseline methods. These experimental results can be seen in the website \url{https://sites.google.com/view/vaesi/home}.
Inference of Dependency Knowledge Graph for Electronic Health Records

The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), which enables efficient feature selection, can enhance both statistical efficiency and interpretability. While various methods have emerged for constructing KGs, existing techniques often lack statistical certainty concerning the presence of links between entities, especially in scenarios where the utilization of patient-level EHR data is limited due to privacy concerns. In this paper, we propose the first inferential framework for deriving a sparse KG with statistical guarantee based on the dynamic log-linear topic model proposed by \cite{arora2016latent}. Within this model, the KG embeddings are estimated by performing singular value decomposition on the empirical pointwise mutual information matrix, offering a scalable solution. We then establish entrywise asymptotic normality for the KG low-rank estimator, enabling the recovery of sparse graph edges with controlled type I error. Our work uniquely addresses the under-explored domain of statistical inference about non-linear statistics under the low-rank temporal dependent models, a critical gap in existing research. We validate our approach through extensive simulation studies and then apply the method to real-world EHR data in constructing clinical KGs and generating clinical feature embeddings.
Report of the DOE/NSF Workshop on Correctness in Scientific Computing, June 2023, Orlando, FL

This report is a digest of the DOE/NSF Workshop on Correctness in Scientific Computing (CSC'23) held on June 17, 2023, as part of the Federated Computing Research Conference (FCRC) 2023. CSC was conceived by DOE and NSF to address the growing concerns about correctness among those who employ computational methods to perform large-scale scientific simulations. These concerns have escalated, given the complexity, scale, and heterogeneity of today's HPC software and hardware. If correctness is not proactively addressed, there is the risk of producing flawed science on top of unacceptable productivity losses faced by computational scientists and engineers. HPC systems are beginning to include data-driven methods, including machine learning and surrogate models, and their impact on overall HPC system correctness was also felt urgent to discuss. Stakeholders of correctness in this space were identified to belong to several sub-disciplines of computer science; from computer architecture researchers who design special-purpose hardware that offers high energy efficiencies; numerical algorithm designers who develop efficient computational schemes based on reduced precision as well as reduced data movement; all the way to researchers in programming language and formal methods who seek methodologies for correct compilation and verification. To include attendees with such a diverse set of backgrounds, CSC was held during the Federated Computing Research Conference (FCRC) 2023.
Instruction Fusion: Advancing Prompt Evolution through Hybridization

The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitations, impeding further enhancements in code generation tasks. This paper examines the constraints of existing prompt evolution techniques and introduces a novel approach, Instruction Fusion (IF). IF innovatively combines two distinct prompts through a hybridization process, thereby enhancing the evolution of training prompts for code LLMs. Our experimental results reveal that the proposed novel method effectively addresses the shortcomings of prior methods, significantly improving the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEval+, MBPP, MBPP+ and MultiPL-E, which underscore the effectiveness of Instruction Fusion in advancing the capabilities of LLMs in code generation.
Stochastic mean-shift clustering

In this paper we presented a stochastic version mean-shift clustering algorithm. In the stochastic version the data points "climb" to the modes of the distribution collectively, while in the deterministic mean-shift, each datum "climbs" individually, while all other data points remains in their original coordinates. Stochastic version of the mean-shift clustering is comparison with a standard (deterministic) mean-shift clustering on synthesized 2- and 3-dimensional data distributed between several Gaussian component. The comparison performed in terms of cluster purity and class data purity. It was found the the stochastic mean-shift clustering outperformed in most of the cases the deterministic mean-shift.