Reviewerly/exHarmony huggingface dataset:
LIMES: A Framework for Link Discovery on the Semantic Web

Abstract The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the Limes framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https://github.com/dice-group/LIMES together with a user manual and a developer manual.
When Function Signature Recovery Meets Compiler Optimization

Matching indirect function callees and callers using function signatures recovered from binary executables (number of arguments and argument types) has been proposed to construct a more fine-grained control-flow graph (CFG) to help control-flow integrity (CFI) enforcement. However, various compiler optimizations may violate calling conventions and result in unmatched function signatures. In this paper, we present eight scenarios in which compiler optimizations impact function signature recovery, and report experimental results with 1,344 real-world applications of various optimization levels. Most interestingly, our experiments show that compiler optimizations have both positive and negative impacts on function signature recovery, e.g., its elimination of redundant instructions at callers makes counting of the number of arguments more accurate, while it hurts argument type matching as the compiler chooses the most efficient (but potentially different) types at callees and callers. To better deal with these compiler optimizations, we propose a set of improved policies and report our more accurate CFG models constructed from the 1,344 applications. We additionally compare our results recovered from binary executables with those extracted from program source and reveal scenarios where compiler optimization makes the task of accurate function signature recovery undecidable.
Journey to the Center of the Cookie Ecosystem: Unraveling Actors' Roles and Relationships

Web pages have been steadily increasing in complexity over time, including code snippets from several distinct origins and organizations. While this may be a known phenomenon, its implications on the panorama of cookie tracking received little attention until now. Our study focuses on filling this gap, through the analysis of crawl results that are both large-scale and fine-grained, encompassing the whole set of events that lead to the creation and sharing of around 138 million cookies from crawling more than 6 million webpages. Our analysis lets us paint a highly detailed picture of the cookie ecosystem, discovering an intricate network of connections between players that reciprocally exchange information and include each other&#x0027;s content in web pages whose owners may not even be aware. We discover that, in most webpages, tracking cookies are set and shared by organizations at the end of complex chains that involve several middlemen. We also study the impact of cookie ghostwriting, i.e., a common practice where an entity creates cookies in the name of another party, or the webpage. We attribute and define a set of roles in the cookie ecosystem, related to cookie creation and sharing. We see that organizations can and do follow different patterns, including behaviors that previous studies could not uncover: for example, many cookie ghostwriters send cookies they create to themselves, which makes them able to perform cross-site tracking even for users that deleted third-party cookies in their browsers. While some organizations concentrate the flow of information on themselves, others behave as dispatchers, allowing other organizations to perform tracking on the pages that include their content.
A Projector-Based Augmented Reality Navigation System for Computer-Assisted Surgery

In the medical field, guidance to follow the surgical plan is crucial. Image overlay projection is a solution to link the surgical plan with the patient. It realizes augmented reality (AR) by projecting computer-generated image on the surface of the target through a projector, which can visualize additional information to the scene. By overlaying anatomical information or surgical plans on the surgery area, projection helps to enhance the surgeon’s understanding of the anatomical structure, and intuitively visualizes the surgical target and key structures of the operation, and avoid the surgeon’s sight diversion between monitor and patient. However, it still remains a challenge to project the surgical navigation information on the target precisely and efficiently. In this study, we propose a projector-based surgical navigation system. Through the gray code-based calibration method, the projector can be calibrated with a camera and then be integrated with an optical spatial locator, so that the navigation information of the operation can be accurately projected onto the target area. We validated the projection accuracy of the system through back projection, with average projection error of 3.37 pixels in x direction and 1.51 pixels in y direction, and model projection with an average position error of 1.03 ± 0.43 mm, and carried out puncture experiments using the system with correct rate of 99%, and qualitatively analyzed the system’s performance through the questionnaire. The results demonstrate the efficacy of our proposed AR system.
In-hospital resource utilization prediction from electronic medical records with deep learning

Effective healthcare resource allocation is critical for intelligent medical systems, and accurate in-hospital resource utilization prediction from medical records is a prerequisite. Existing methods for this task usually rely on manual feature engineering which needs massive domain knowledge, and do not exploit the textual information in electronic medical records, e.g., diagnosis and operation texts. In this paper, we propose a deep in-hospital resource utilization prediction approach to jointly estimate the in-hospital costs and length of stays from patients’ admission records via multi-task learning. Our approach can exploit the heterogeneous information in records, such as patient features, diagnosis/operation texts, and the diagnosis/operation IDs, via a multi-view learning framework, where Transformers are used to learn the representations of words, diagnoses and operations. In addition, we design a diagnosis–operation attention network to capture the relations between diagnoses and operations. Besides, since different words, diagnoses and operations have different importance for cost estimation, we incorporate a hierarchical attention network to select important words, diagnoses and operations for learning informative record representations. Extensive experiments on a real-world medical dataset validate the effectiveness of our approach.
Invisible Probe: Timing Attacks with PCIe Congestion Side-channel

PCIe (Peripheral Component Interconnect express) protocol is the de facto protocol to bridge CPU and peripheral devices like GPU, NIC, and SSD drive. There is an increasing demand to install more peripheral devices on a single machine, but the PCIe interfaces offered by Intel CPUs are fixed. To resolve such contention, PCIe switch, PCH (Platform Controller Hub), or virtualization cards are installed on the machine to allow multiple devices to share a PCIe interface. Congestion happens when the collective PCIe traffic from the devices overwhelm the PCIe link capacity, and transmission delay is then introduced.In this work, we found the PCIe delay not only harms device performance but also leaks sensitive information about a user who uses the machine. In particular, as user’s activities might trigger data movement over PCIe (e.g., between CPU and GPU), by measuring PCIe congestion, an adversary accessing another device can infer the victim’s secret indirectly. Therefore, the delay resulted from I/O congestion can be exploited as a side-channel. We demonstrate the threat from PCIe congestion through 2 attack scenarios and 4 victim settings. Specifically, an attacker can learn the workload of a GPU in a remote server by probing a RDMA NIC that shares the same PCIe switch and measuring the delays. Based on the measurement, the attacker is able to know the keystroke timings of the victim, what webpage is rendered on the GPU, and what machine-learning model is running on the GPU. Besides, when the victim is using a low-speed device, e.g., an Ethernet NIC, an attacker controlling an NVMe SSD can launch a similar attack when they share a PCH or virtualization card. The evaluation result shows our attack can achieve high accuracy (e.g., 96.31% accuracy in inferring webpage visited by a victim).
Toward text psychology analysis using social spider optimization algorithm

A bstract Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.
Reinforcement Recommendation with User Multi-aspect Preference

Formulating recommender system with reinforcement learning (RL) frameworks has attracted increasing attention from both academic and industry communities. While many promising results have been achieved, existing models mostly simulate the environment reward with a unified value, which may hinder the understanding of users’ complex preferences and limit the model performance. In this paper, we consider how to model user multi-aspect preferences in the context of RL-based recommender system. More specifically, we base our model on the framework of deterministic policy gradient (DPG), which is effective in dealing with large action spaces. A major challenge for modeling user multi-aspect preferences lies in the fact that they may contradict with each other. To solve this problem, we introduce Pareto optimization into the DPG framework. We assign each aspect with a tailored critic, and all the critics share the same actor. The Pareto optimization is realized by a gradient-based method, which can be easily integrated into the actor and critic learning process. Based on the designed model, we theoretically analyze its gradient bias in the optimization process, and we design a weight-reuse mechanism to lower the upper bound of this bias, which is shown to be effective for improving the model performance. We conduct extensive experiments based on three real-world datasets to demonstrate our model’s superiorities.
Classification of Osteosarcoma Tumor from Histological Image Using Sequential RCNN

Osteosarcoma is an osseous tumor that occurs in the metaphyseal area around the knee accounts for roughly 20% of bone cancers mostly affects patients younger than 20 years. Early diagnosis of osteosarcoma cancer can pave the way for an unlimited choice of therapy opportunities. Moreover, pathological estimation of necrosis and tumor cells determines the future intensity of chemotherapy radiation to apply to patient. The biopsy confirms the diagnosis and divulges the grade of the tumor, necrotic, and non-tumor cells. Due to a lack of radiologists in third world countries like Bangladesh, it is extremely difficult to diagnose cancer in the early stage. Moreover, to identify the chemotherapy effect during the chemotherapy period, multiple radiologists are required which is quite expensive for most cancer hospitals. In this paper, a Sequential Recurrent Convolutional Neural Network (RCNN) model consisting of CNN and bidirectional Gated Recurrent Units (GRU) is proposed, which performs exceptionally well with small numbers of histopathological osteosarcoma Haematoxylin and Eosin (H & E) stained images despite having the over-fitting problem, heterogeneity, intra-class variation, inter-class similarity, crowded context, the irregular shape of the nucleus and noisy data. Performance of the is compared with that of AlexNet, ResNet50, VGG16, LeNet and SVM models with the histopathological image dataset on osteosarcoma.
Mobile phone component object detection algorithm based on improved SSD

Aiming at the problem of low accuracy of small object detection and poor system robustness in the detection of mobile phone component image targets, an improved SSD algorithm is proposed to detect mobile phone component image targets. This article proposes an algorithm, CSP-SSD(Cross Stage Partial SSD). By improving the network structure, using gradient flow information, using deconvolution and using multi-scale transformation, the traditional SSD algorithm is improved, so that it can better adapt to the detection task of mobile phone components. The test results show that when the image size is 300*300, the mAP of the algorithm reaches 78.9%. The proposed method has the characteristics of high detection accuracy for small objects.
Multi-task learning with deformable convolution

Multi-task learning aims to tackle various tasks with branched feature sharing architectures. Considering its diversity and complexity, discriminative feature representations need to be extracted for each individual task. Fixed geometric structures as a limitation of convolutional neural networks (CNNs) in building models, is also exists and poses a severe challenge in multi-task learning since the geometric variations will augment when we deal with multiple tasks. In this paper, we go beyond these limitations and propose a novel multi-task network by introducing the deformable convolution. Our design, the Deformable Multi-Task Network (DMTN), starts with a single shared network for constructing a shared feature pool. Then, we present task-specific deformable modules to extract discriminative features to be tailored for each task from the shared feature pool. The task-specific deformable modules utilize two new parts, deformable part and alignment part, to extract more discriminative task-specific features while greatly enhancing the transformation modeling capability. Experiments conducted on various multi-task learning types demonstrate the effectiveness of the proposed method. On multiple classification tasks, semantic segmentation and depth estimation tasks, our DMTN exceeds state-of-the-art approaches against strong baselines.
Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect Level

Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven’t been fully exploited. Moreover, it lacks fine-grained studies at the aspect level. To address the above issues, we define two concepts of user maturity and item popularity, to better explore the dynamic changes for users and items. We strive to exploit fine-grained information at the aspect level and the evolution of users and items, for dynamic sentiment prediction. First, we analyze three real datasets from both the overall level and the aspect level, to discover the dynamic changes (i.e., gradual changes and sudden changes) in user aspect preferences and item aspect properties. Next, we propose a novel model of Aspect-based Sentiment Dynamic Prediction (ASDP), to dynamically capture and exploit the change patterns with uniform time intervals. We further propose the improved model ASDP+ with a bin segmentation algorithm to set the time intervals non-uniformly based on the sudden changes. Experimental results on three real-world datasets show that our work leads to significant improvements.
Securing Medical Images using Encryption and LSB Steganography

Medical imaging plays a vital role in the healthcare industry. Due to the advancements in the healthcare industry medical images are being transferred between geographically spread regions. As the medical images are transmitted through public networks several security challenges may arise such as authentication, integrity, and confidentiality. In this paper, the research focus is on preserving the confidentiality and integrity of the medical images. In general, integrity and confidentiality can be achieved by encryption (a piece of cryptography). To incorporate one more layer of security, steganography is applied to the medical image. In this paper, a one-time pad encryption algorithm is used to encrypt the medical image, then the encrypted image is implanted into a cover image to give a stego image making the system more resilient to the attacker. Further in this paper, the above said method is being implemented using MATLAB and the experimental results are compared with hiding the medical image using LSB steganography without encryption (one layer of security). Various formats of medical images have been taken into consideration such as DICOM, TIFF, BMP, and JPEG. Results show that the combination of encryption and steganography performs better than applying only steganography, concerning MSE and PSNR values.
TLS 1.3 in Practice:How TLS 1.3 Contributes to the Internet

Transport Layer Security (TLS) has become the norm for secure communication over the Internet. In August 2018, TLS 1.3, the latest version of TLS, was approved, providing improved security and performance of the previous TLS version. In this paper, we take a closer look at TLS 1.3 deployments in practice regarding adoption rate, security, performance, and implementation by applying temporal, spatial, and platform-based approaches on 687M connections.
xLightFM: Extremely Memory-Efficient Factorization Machine

The factorization-based models have achieved great success in online advertisements and recommender systems due to the capability of efficiently modeling combinational features. These models encode feature interactions by the vector product between feature embedding. Despite the improvement of generalization, the memory consumption of these models grows significantly, because they usually take hundreds to thousands of large categorical features as input. Several existing works try to reduce the memory footprint by hashing, randomized embedding composition, and dimensionality search, but they suffer from either substantial performance degradation or limited memory compression. To this end, in this paper, we propose an extremely memory-efficient Factorization Machine (xLightFM), where each category embedding is composited with latent vectors selected from codebooks. Based on the characteristics of each categorical feature, we further propose to adapt the codebook size with the neural architecture search techniques for compositing the embedding of each categorical feature. This further pushes the limits of memory compression while incurring negligible degradation or even some improvements in prediction performance. We extensively evaluate the proposed algorithm with two real-world datasets. The results demonstrate that xLightFM can outperform the state-of-the-art lightweight factorization-based methods in terms of both prediction quality and memory footprint, and achieve more than 18x and 27x memory compression compared to the vanilla FM on these two datasets, respectively.
Multi-scale wavelet network algorithm for pediatric echocardiographic segmentation via hierarchical feature guided fusion

The automatic segmentation of critical anatomical structures in pediatric echocardiography is the essential steps for early diagnosis and treatment of congenital heart disease. However, current segmentation algorithms rarely extract the information based on effective feature enhancement algorithms. Simultaneously, the algorithms are susceptible to image quality and the lack of detail information. To solve this, we propose a multi-scale wavelet network (MS-Net) combined with a bidirectional feature fusion (BFF-Net) and a wavelet-Unet (W-Unet) for end-to-end pediatric echocardiographic segmentation. In MS-Net, the entire network uses the discrete wavelet transform (DWT) instead of the sampling operation to reduce the impact of image noise while avoiding information loss. The algorithm enhances the edge information by designing the edge attention module (EAM) in the BFF-Net branch and fuses the context and detail information via the bidirectional feature fusion. Secondly, this algorithm uses W-Unet to obtain the detail features of high-resolution images by network depth reduction and propagation method, which supplements the features extracted by the BFF-Net branch. Finally, the hierarchical features of BFF-Net and W-Unet are fused and updated by guided filtering (GF) to obtain the final segmentation prediction. Using 127 pediatric echocardiographic cases of self-selection as the experimental dataset, the left atrium and left ventricle of the echocardiogram were segmented. The Dice coefficient values of 0.9532 and 0.9155, the pixel accuracy of 0.9914, and the specificity values of 0.9975 and 0.9984 were obtained. It was thus verifying its potential and effectiveness as a clinical auxiliary tool.