Reviewerly/exHarmony huggingface dataset:
PolyScope: Multi-Policy Access Control Analysis to Triage Android Scoped Storage

Android's filesystem access control is its foundation for system integrity. It combines mandatory (e.g., SELinux) and discretionary (e.g., Unix permissions) access control with other specialized access controls (e.g., Android permissions), aiming to protect Android/OEM services from third-party applications. However, OEMs often introduce vulnerabilities when they add market-differentiating features because they fail to correctly reconfigure this complex combination of policies. In this paper, we present the POLYSCOPE tool, which triages the combination of Android filesystem access control policies to find the authorized operations that may be exploited by adversaries to escalate their privileges, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attack operations</i> . Critically, POLYSCOPE accounts for how adversaries may modify permissions for themselves and/or their victims to uncover latent attack operations. We demonstrate the effectiveness of POLYSCOPE by assessing the impact of the recently introduced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scoped Storage</i> defense for Android, showing that extending POLYSCOPE to analyze a new policy can be done independently if the new policy only restricts permissions, which is the case for Scoped Storage. We apply POLYSCOPE to three Google and five OEM Android releases, finding that Scoped Storage reduces the number of attack operations possible on external storage resources by over 50%. However, we also find two previously unknown vulnerabilities because OEMs only adopt Scoped Storage partially, limiting its benefit. Thus, we show how to use POLYSCOPE to assess an ideal scenario where all apps are compliant to Scoped Storage, which can reduce the number of untrusted parties that can access attack operations by over 65% on OEM systems. As a result, we find that POLYSCOPE can help Android OEMs triage complex access control policies to identify the specific attack operations worthy of further examination.
Deep-Based Film Grain Removal and Synthesis

In this paper, deep learning-based techniques for film grain removal and synthesis that can be applied in video coding are proposed. Film grain is inherent in analog film content because of the physical process of capturing images and video on film. It can also be present in digital content where it is purposely added to reflect the era of analog film and to evoke certain emotions in the viewer or enhance the perceived quality. In the context of video coding, the random nature of film grain makes it both difficult to preserve and very expensive to compress. To better preserve it while compressing the content efficiently, film grain is removed and modeled before video encoding and then restored after video decoding. In this paper, a film grain removal model based on an encoder-decoder architecture and a film grain synthesis model based on a conditional generative adversarial network (cGAN) are proposed. Both models are trained on a large dataset of pairs of clean (grain-free) and grainy images. Quantitative and qualitative evaluations of the developed solutions were conducted and showed that the proposed film grain removal model is effective in filtering film grain at different intensity levels using two configurations: 1) a non-blind configuration where the film grain level of the grainy input is known and provided as input, 2) a blind configuration where the film grain level is unknown. As for the film grain synthesis task, the experimental results show that the proposed model is able to reproduce realistic film grain with a controllable intensity level specified as input.
AFF-NET: An Adaptive Feature Fusion Network For Liver Vessel Segmentation From CT Images

Accurate liver vessel segmentation from CT images is essential in computer aided diagnosis and surgery. However, due to the complex structures of liver vessels, it is difficult to extract small vessels and edge vessels from the images. Therefore, we propose an adaptive feature fusion network (AFF-Net) to accurately segment vessels from liver CT images. The AFF-Net contains three novel components: 1) An adaptive feature connection (AFC) module is designed to suppress image background noise to accurately extract small vessels; 2) An enhanced auxiliary (EA) module is proposed to fully utilize the topological information of vessels to improve the segmentation integrity; 3) A global information supervision (GIS) module is introduced to extract liver edge features to improve edge vessel segmentation accuracy. Experiments on public datasets show that our method achieves the Dice score of 0.72 and the sensitivity score of 0.73, showing much higher accuracy than related methods.
ViGU: Vision GNN U-Net for fast MRI

Deep learning models have been widely applied for fast MRI. The majority of existing deep learning models, e.g., convolutional neural networks, work on data with Euclidean or regular grids structures. However, high-dimensional features extracted from MR data could be encapsulated in non-Euclidean manifolds. This disparity between the go-to assumption of existing models and data requirements limits the flexibility to capture irregular anatomical features in MR data. In this work, we introduce a novel Vision GNN type network for fast MRI called Vision GNN U-Net (ViGU). More precisely, the pixel array is first embedded into patches and then converted into a graph. Secondly, a U-shape network is developed using several graph blocks in symmetrical encoder and decoder paths. Moreover, we show that the proposed ViGU can also benefit from Generative Adversarial Networks yielding to its variant ViGU-GAN. We demonstrate, through numerical and visual experiments, that the proposed ViGU and GAN variant outperform existing CNN and GAN-based methods. Moreover, we show that the proposed network readily competes with approaches based on Transformers while requiring a fraction of the computational cost. More importantly, the graph structure of the network reveals how the network extracts features from MR images, providing intuitive explainability. The code is publicly available at https://github.com/ayanglab/ViGU.
Entangling quantum memories via heralded photonic Bell measurement

Analytical modeling of a quantum communication link is based on a photonic swap in the middle architecture. The effect of network and hardware nonidealities on the distributed entangled state is analyzed with the density operator formalism and with implications on the choice of distillation circuits of heralded states.
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention

Most event extraction methods have traditionally relied on an annotated set of event types. However, creating event ontologies and annotating supervised training data are expensive and time-consuming. Previous work has proposed semi-supervised approaches which leverage seen (annotated) types to learn how to automatically discover new event types. State-of-the-art methods, both semi-supervised or fully unsupervised, use a form of reconstruction loss on specific tokens in a context. In contrast, we present a novel approach to semi-supervised new event type induction using a masked contrastive loss, which learns similarities between event mentions by enforcing an attention mechanism over the data minibatch. We further disentangle the discovered clusters by approximating the underlying manifolds in the data, which allows us to achieve an adjusted rand index score of 48.85%. Building on these clustering results, we extend our approach to two new tasks: predicting the type name of the discovered clusters and linking them to FrameNet frames.
Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction

Information extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization aims to abstract the most salient information into a generated text summary – thus potentially encountering the technical limitations of current text generation methods (e.g., hallucination). To mitigate this risk, this work uses structured IE graphs to enhance the abstractive summarization task. Specifically, we focus on improving Multi-Document Summarization (MDS) performance by using cross-document IE output, incorporating two novel components: (1) the use of auxiliary entity and event recognition systems to focus the summary generation model; (2) incorporating an alignment loss between IE nodes and their text spans to reduce inconsistencies between the IE graphs and text representations. Operationally, both the IE nodes and corresponding text spans are projected into the same embedding space and pairwise distance is minimized. Experimental results on multiple MDS benchmarks show that summaries generated by our model are more factually consistent with the source documents than baseline models while maintaining the same level of abstractiveness.
The Implementation of Improved Advanced Encryption Standard and Least Significant Bit for Securing Messages in Images

Security and confidentiality are essential aspects needed in the process of exchanging messages. Maintaining the message's security and confidentiality can be done by encrypting the message with cryptographic techniques to protect the message. However, the encrypted message (ciphertext) usually raises suspicion from eavesdroppers so that they try to unravel the contents of the message or damage the contents of the message or prevent the message from reaching the intended recipient. To avoid eavesdroppers' suspicion of encrypted messages, after encryption, the ciphertext can then be inserted into an image using steganography techniques so that eavesdroppers do not know whether there is a secret message in an image. In this research, the authors use the Improved AES-128 (Advanced Encryption Standard) Algorithm to encrypt messages and LSB (Least Significant Bit) Algorithm to insert ciphertext into an image. Where the AES Algorithm Improvement is made by adding a sending and receiving applications ID to modify the Key Schedule process, with modifications to the Key Schedule process, messages can only be read on the original recipient's cellphone by entering the correct key. The results of this study show that eavesdroppers do not easily know the existence of the ciphertext, besides that even if the eavesdroppers get the ciphertext and know the encryption key, the message remains unreadable on their cellphone because the ID of the application sending or receiving the message has changed.
Three-Dimensional Flight Corridor: An Occupancy Checking Process for Unmanned Aerial Vehicle Motion Planning inside Confined Spaces

To deploy Unmanned Aerial Vehicles (UAVs) inside heterogeneous GPS-denied confined (potentially unknown) spaces, such as those encountered in mining and Urban Search and Rescue (USAR), requires the enhancement of numerous technologies. Of special interest is for UAVs to identify collision-freeSafe Flight Corridors (SFC+) within highly cluttered convex- and non-convex-shaped environments, which requires UAVs to perform advanced flight maneuvers while exploiting their flying capabilities. Within this paper, a novel auxiliary occupancy checking process that augments traditional 3D flight corridor generation is proposed. The 3D flight corridor is established as a topological structure based on a hand-crafted path either derived from a computer-generated environment or provided by the human operator, which captures humans’ preferences and desired flight intentions for the given space. This corridor is formulated as a series of interconnected overlapping convex polyhedra bounded by the perceived environmental geometries, which facilitates the generation of suitable 3D flight paths/trajectories that avoid local minima within the corridor boundaries. An occupancy check algorithm is employed to reduce the search space needed to identify 3D obstacle-free spaces in which their constructed polyhedron geometries are replaced with alternate convex polyhedra. To assess the feasibility and efficiency of the proposed SFC+ methodology, a comparative study is conducted against the Star-Convex Method (SCM), a prominent algorithm in the field. The results reveal the superiority of the proposed SFC+ methodology in terms of its computational efficiency and reduced search space for UAV maneuvering solutions. Various challenging confined-environment scenarios, each with different obstacle densities (confined scenarios), are utilized to verify the obtained outcomes.
Distributed quantum sensing with squeezed-vacuum light in a configurable array of Mach-Zehnder interferometers

We study an entangled distributed quantum sensing scheme based on an array of $d$ Mach-Zehnder interferometers (MZIs) for the estimation of relative phase shifts. The scheme uses $d$ coherent states and a single squeezed-vacuum state that is distributed among the MZIs by a quantum circuit (QC). The protocol can be optimized analytically: it overcomes the shot-noise limit and reaches the Heisenberg limit with respect to the average total number of probe particles, ${\overline{n}}_{T}$, for the estimation of arbitrary linear combinations of the $d$ phases. We compare the entangled strategy with a separable one that uses $d$ coherent and $d$ squeezed-vacuum states and the same ${\overline{n}}_{T}$. The entangled strategy benefits for a substantial reduction of resource overhead and can achieve a maximum gain equal to $d$ when using the same total squeezed-light intensity as the separable strategy. Interestingly, the entangled strategy using a single squeezed-vacuum state can reach the same sensitivity as the separable strategy that uses $d$ copies of the same state. Finally, given a random choices of the QC, we identify the optimal linear combination of the phases that can be estimated with maximum sensitivity. Our scheme paves the ways for a variety of applications in distributed quantum sensing with photonic and atomic interferometers.
DiffMix: Diffusion Model-Based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

Nuclei segmentation and classification is an important process in pathological image analysis. Deep learning-based approaches contribute significantly to the enhanced accuracy of this task. However, these approaches suffer from an imbalanced nuclei data composition, which results in lower classification performance for rare nuclei class. In this study, we proposed a realistic data synthesis method using a diffusion model. We generated two types of virtual patches to enlarge the training data distribution, which balanced the nuclei class variance and increased the chance of investigating various nuclei. Subsequently, we used a semantic label-conditioned diffusion model to generate realistic and high-quality image samples. We demonstrated the efficacy of our method based on experimental results on two imbalanced nuclei datasets, improving the state-of-the-art networks. The experimental results suggest that the proposed method improves the classification performance of the rare type nuclei classification, while showing superior segmentation and classification performance in imbalanced pathology nuclei datasets.
Perceptual Tone Mapping Model for High Dynamic Range Imaging

One of the key challenges in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Traditional tone mapping operators (TMOs) compress the luminance of HDR images without considering the surround and display conditions emanating into suboptimal results. Current research addresses this challenge by incorporating perceptual color appearance attributes. In this work, we propose a TMO (TMO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> ) that leverages CIECAM16 perceptual attributes, i.e., brightness, colorfulness, and hue. TMO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> accounts for the effects of both the surround and the display conditions to achieve more optimal colorfulness reproduction. The perceptual brightness is compressed, and the perceptual color scales, i.e., colorfulness and hue are derived from HDR images by employing CIECAM16 color adaptation equations. A psychophysical experiment was conducted to automate the brightness compression parameter. The model employs fully automatic and adaptive approach, obviating the requirement for manual parameter selection. TMO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> was evaluated in terms of contrast, colorfulness and overall image quality. The objective and subjective evaluation methods revealed that the proposed model outperformed the state-of-the-art TMOs.
Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors

Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone.
Improving the Detection and Positioning of Camouflaged Objects in YOLOv8

Camouflaged objects can be perfectly hidden in the surrounding environment by designing their texture and color. Existing object detection models have high false-negative rates and inaccurate localization for camouflaged objects. To resolve this, we improved the YOLOv8 algorithm based on feature enhancement. In the feature extraction stage, an edge enhancement module was built to enhance the edge feature. In the feature fusion stage, multiple asymmetric convolution branches were introduced to obtain larger receptive fields and achieve multi-scale feature fusion. In the post-processing stage, the existing non-maximum suppression algorithm was improved to address the issue of missed detection caused by overlapping boxes. Additionally, a shape-enhanced data augmentation method was designed to enhance the model’s shape perception of camouflaged objects. Experimental evaluations were carried out on camouflaged object datasets, including COD and CAMO, which are publicly accessible. The improved method exhibits enhancements in detection performance by 8.3% and 9.1%, respectively, compared to the YOLOv8 model.
Neighborhood contrastive representation learning for attributed graph clustering

Attributed graph clustering is a fundamental task in graph learning field. Because of the high-dimensional node features and the complex non-Euclidean graph structure, it is challenging for attributed graph clustering methods to exploit graph information. Recent studies on graph contrastive learning (GCL) have achieved promising results. However, existing GCL-based methods neither consider a clustering-friendly node representation nor a clustering-oriented loss function, resulting in inferior performance. To this end, we propose NCAGC, a neighborhood contrastive representation learning method for attributed graph clustering task. Specifically, NCAGC constrains the representation learning of similar nodes by a neighborhood contrast module to ensure the compactness in the latent space, thus facilitating the clustering task. Meanwhile, a contrastive self-expression module is present for learning a discriminative self-expression coefficient matrix, which is crucial for the subsequent subspace clustering. Moreover, the two designed modules are trained and optimized jointly, which benefits the node representation learning and clustering to achieve mutual refinement. Extensive experimental results on four attributed graph datasets demonstrate the superiority of NCAGC compared with 16 state-of-the-art methods, which surpasses the sub-optimal method on Cora dataset by 2.1%, 4.3%, and 3.7% in terms of ACC, NMI, and ARI, respectively. Our code and dataset is available at https://github.com/wangtong627/NCAGC-NeuroCom.
Decomposition of matrix product states into shallow quantum circuits

Abstract Tensor networks (TNs) are a family of computational methods built on graph-structured factorizations of large tensors, which have long represented state-of-the-art methods for the approximate simulation of complex quantum systems on classical computers. The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed TN algorithms to scale to even larger quantum simulation problems, and to be employed more broadly for solving machine learning tasks. The ‘quantum-inspired’ nature of TNs permits them to be mapped to parametrized quantum circuits (PQCs), a fact which has inspired recent proposals for enhancing the performance of TN algorithms using near-term quantum devices, as well as enabling joint quantum–classical training frameworks that benefit from the distinct strengths of TN and PQC models. However, the success of any such methods depends on efficient and accurate methods for approximating TN states using realistic quantum circuits, which remains an unresolved question. This work compares a range of novel and previously-developed algorithmic protocols for decomposing matrix product states (MPS) of arbitrary bond dimension into low-depth quantum circuits consisting of stacked linear layers of two-qubit unitaries. These protocols are formed from different combinations of a preexisting analytical decomposition method together with constrained optimization of circuit unitaries, with initialization by the former method helping to avoid poor-quality local minima in the latter optimization process. While all of these protocols have efficient classical runtimes, our experimental results reveal one particular protocol employing sequential growth and optimization of the quantum circuit to outperform all others, with even greater benefits in the setting of limited computational resources. Given these promising results, we expect our proposed decomposition protocol to form a useful ingredient within any joint application of TNs and PQCs, further unlocking the rich and complementary benefits of classical and quantum computation.