Reviewerly/exHarmony huggingface dataset:
Adversarial Prefetch: New Cross-Core Cache Side Channel Attacks

Modern x86 processors have many prefetch instructions that can be used by programmers to boost performance. However, these instructions may also cause security problems. In particular, we found that on Intel processors, there are two security flaws in the implementation of PREFETCHW, an instruction for accelerating future writes. First, this instruction can execute on data with read-only permission. Second, the execution time of this instruction leaks the current coherence state of the target data. Based on these two design issues, we build two cross-core private cache attacks that work with both inclusive and non-inclusive LLCs, named Prefetch+Reload and Prefetch+Prefetch. We demonstrate the significance of our attacks in different scenarios. First, in the covert channel case, Prefetch+Reload and Prefetch+Prefetch achieve 782 KB/s and 822 KB/s channel capacities, when using only one shared cache line between the sender and receiver, the largest-to-date single-line capacities for CPU cache covert channels. Further, in the side channel case, our attacks can monitor the access pattern of the victim on the same processor, with almost zero error rate. We show that they can be used to leak private information of real-world applications such as cryptographic keys. Finally, our attacks can be used in transient execution attacks in order to leak more secrets within the transient window than prior work. From the experimental results, our attacks allow leaking about 2 times as many secret bytes, compared to Flush+Reload, which is widely used in transient execution attacks.
Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations

The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of the world. In this paper, we evaluate and report the computational and predictive performance design trade-offs for four candidate deep learning models that can be deployed for rapid malaria case finding. The goal is to maximise malaria detection accuracy while reducing computing resource and energy consumption. Based on our experimental results using a blood smear malaria test data set, the quantised versions of Basic Convolutional Neural Network (B-CNN) and MobileNetV2 have better malaria detection performance (up to 99% recall), lower memory usage (2MB 8-bit quantised model) and shorter inference time (33-95 microseconds on mobile phones) than VGG-19 fine-tuned and quantised models. Hence, we have implemented MobileNetV2 in our mobile application as it has even a lower memory requirement than B-CNN. This work will help to counter the negative effects of COVID-19 on the previous successes towards global malaria elimination.
A-PixelHop: A Green, Robust and Explainable Fake-Image Detector

A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work. It has three advantages: 1) low computational complexity and a small model size, 2) high detection performance against a wide range of generative models, and 3) mathematical transparency. A-PixelHop is designed under the assumption that it is difficult to synthesize high-quality, high-frequency components in local regions. It contains four building modules: 1) selecting edge/texture blocks that contain significant high-frequency components, 2) applying multiple filter banks to them to obtain rich sets of spatial-spectral responses as features, 3) feeding features to multiple binary classifiers to obtain a set of soft decisions, 4) developing an effective ensemble scheme to fuse the soft decisions into the final decision. Experimental results show that A-PixelHop outperforms state-of-the-art methods in detecting CycleGAN-generated images. Furthermore, it can generalize well to unseen generative models and datasets.
Design of interactive augmented reality functions for robotic surgery and evaluation in dry‐lab lymphadenectomy

Augmented reality (AR) has been widely researched for use in healthcare. Prior AR for robot-assisted minimally invasive surgery has mainly focussed on superimposing preoperative three-dimensional (3D) images onto patient anatomy. This article presents alternative interactive AR tools for robotic surgery.We designed, built and evaluated four voice-controlled functions: viewing a live video of the operating room, viewing two-dimensional preoperative images, measuring 3D distances and warning about out-of-view instruments. This low-cost system was developed on a da Vinci Si, and it can be integrated into surgical robots equipped with a stereo camera and a stereo viewer.Eight experienced surgeons performed dry-lab lymphadenectomies and reported that the functions improved the procedure. They particularly appreciated the possibility of accessing the patient's medical records on demand, measuring distances intraoperatively and interacting with the functions using voice commands.The positive evaluations garnered by these alternative AR functions and interaction methods provide support for further exploration.
Automatic Modulation Classification in Cognitive-IoT Radios using Generalized Dynamic Bayesian Networks

This paper proposes a novel Automatic Modulation Classification (AMC) method for CR-IoT based on learning multiple Generalized Dynamic Bayesian Networks (GDBN) as representations of various signals under different modulation schemes. The CR-IoT performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best model that explains the received signal and recognize the modulation scheme accordingly. The simulated results based on a real dataset demonstrate that the proposed GDBN-based AMC method outperforms both Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) in terms of classification accuracy.
Performance Benchmarking and Comparison of NoSQL Databases: Redis vs MongoDB vs Cassandra Using YCSB Tool

Big Data is an ensemble of technologies founded on NoSQL databases that enable scalability of volumes, numbers, and data types. NoSQL databases assert that their performance is better than legacy relational database systems for higher workloads, particularly common in Big Data and Cloud Computing applications. There are several open-source and proprietary NoSQL models disposable on the market. It is hard to determine a suitable solution for a particular problem due to the large amount and variety of current solutions. The authors build on a comparative analysis of the performance of three commonly used solutions, Redis, MongoDB, and Cassandra, in elements relevant to query performance, based on inserts, updates, scans, and reads, by utilizing the YCSB tool to run six bespoke workloads. The goal is to furnish assistance and support to actors who are interested in Big Data and Cloud Computing in order to help them make decisions about which solutions to use in the future.
EdgeXAR: A 6-DoF Camera Multi-target Interaction Framework for MAR with User-friendly Latency Compensation

The computational capabilities of recent mobile devices enable the processing of natural features for Augmented Reality (AR), but the scalability is still limited by the devices' computation power and available resources. In this paper, we propose EdgeXAR, a mobile AR framework that utilizes the advantages of edge computing through task offloading to support flexible camera-based AR interaction. We propose a hybrid tracking system for mobile devices that provides lightweight tracking with 6 Degrees of Freedom and hides the offloading latency from users' perception. A practical, reliable and unreliable communication mechanism is used to achieve fast response and consistency of crucial information. We also propose a multi-object image retrieval pipeline that executes fast and accurate image recognition tasks on the cloud and edge servers. Extensive experiments are carried out to evaluate the performance of EdgeXAR by building mobile AR apps upon it. Regarding the Quality of Experience (QoE), the mobile AR apps powered by EdgeXAR framework run on average at the speed of 30 frames per second with precise tracking of only 1-2 pixel errors and accurate image recognition of at least 97% accuracy. As compared to Vuforia, one of the leading commercial AR frameworks, EdgeXAR transmits 87% less data while providing a stable 30FPS performance and reducing the offloading latency by 50 to 70% depending on the transmission medium. Our work facilitates the large-scale deployment of AR as the next generation of ubiquitous interfaces.
Service Recommendation for Composition Creation based on Collaborative Attention Convolutional Network

Service recommendation for composition creation is a widely applied technique, which expedites mashup development by reusing existing services. The core of service recommendations is to simultaneously understand user needs as well as the functions of available services. However, the descriptions provided by users and service providers may not always be accurate or up to date, which poses significant challenges to composition creating. To tackle this problem, in this paper we propose a deep learning-based service recommendation framework named coACN, short for Collaborative Attention Convolutional Network, which can effectively learn the bilateral information toward service recommendation. On the one hand, a domain-level attention module is constructed to refine user needs embeddings by drawing messages from related service domains. On the other hand, a graph convolutional network is established to excavate the service-composition graph and fuse structured information into service embeddings. For a service node in the graph, the information of its compositions as its first-order neighbor nodes is used to supplement the latest functions and features of the service; and the information of the services as its second-order neighbor nodes may bring collaborative relationships into the service. Extensive experiments on the real-world ProgrammableWeb dataset show the significant improvement of our proposed coACN framework over state-of-the-art methods.
Secure Aggregation is Insecure: Category Inference Attack on Federated Learning

Federated learning allows a large number of resource-constrained clients to train a globally-shared model together without sharing local data. These clients usually have only a few classes (categories) of data for training, where the data distribution is non-iid (not independent identically distributed). In this article, we put forward the concept of <i>category privacy</i> for the first time to indicate <i>which classes of data a client has</i> , which is an important but ignored privacy goal in the federated learning with non-iid data. Although secure aggregation protocols are designed for federated learning to protect the input privacy of clients, we perform the first systematic study on <i>category inference attack</i> and demonstrate that these protocols cannot fully protect category privacy. We design a differential selection strategy and two de-noising approaches to achieve the attack goal successfully. In our evaluation, we apply the attack to non-iid federated learning settings with various datasets. On MNIST, CIFAR-10, AG_news, and DBPedia dataset, our attack achieves <inline-formula><tex-math notation="LaTeX">$&gt;90\%$</tex-math></inline-formula> accuracy measured in F1-score in most cases. We further consider a possible detection method and propose two strategies to make the attack more inconspicuous.
BotSpot++: A Hierarchical Deep Ensemble Model for Bots Install Fraud Detection in Mobile Advertising

Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the most difficult to detect due to its ability to emulate normal users by implementing sophisticated behavioral patterns to evade from detection rules defined by human experts. Therefore, we proposed BotSpot 1 for bots install fraud detection previously. However, there are some drawbacks in BotSpot, such as the sparsity of the devices’ neighbors, weak interactive information of leaf nodes, and noisy labels. In this work, we propose BotSpot++ to improve these drawbacks: (1) for the sparsity of the devices’ neighbors, we propose to construct a super device node to enrich the graph structure and information flow utilizing domain knowledge and a clustering algorithm; (2) for the weak interactive information, we propose to incorporate a self-attention mechanism to enhance the interaction of various leaf nodes; and (3) for the noisy labels, we apply a label smoothing mechanism to alleviate it. Comprehensive experimental results show that BotSpot++ yields the best performance compared with six state-of-the-art baselines. Furthermore, we deploy our model to the advertising platform of Mobvista, 2 a leading global mobile advertising company. The online experiments also demonstrate the effectiveness of our proposed method.
Investigating the Evolution of Tree Boosting Models with Visual Analytics

Tree boosting models are widely adopted predictive models and have demonstrated superior performance than other conventional and even deep learning models, especially since the recent release of their parallel and distributed implementations, e.g., XGBoost, LightGMB, and CatBoost. Tree boosting uses a group of sequentially generated weak learners (i.e., decision trees), each learns from the mistakes of its predecessor, to push the model’s decision boundary towards the true boundary. As the number of trees keeps increasing over training, it is important to reveal how the newly-added trees change the predictions of individual data instances, and how the impacts of different data features evolve. To accomplish these goals, in this paper, we introduce a new design of the temporal confusion matrix, providing users with an effective interface to track data instances’ predictions across the tree boosting process. Also, we present an improved visualization to better illustrate and compare the impacts of individual data features (based on their SHAP values) across training iterations. Integrating these components with a tree structure visualization component, we propose a visual analytics system for tree boosting models. Through case studies with domain experts using real-world datasets, we validated the system’s effectiveness.
360° Image Reference-Based Super-Resolution Using Latitude-Aware Convolution Learned From Synthetic to Real

High-resolution (HR) 360° images offer great advantages wherever an omnidirectional view is necessary such as in autonomous robot systems and virtual reality (VR) applications. One or more 360° images in adjacent views can be utilized to significantly improve the resolution of a target 360° image. In this paper, we propose an efficient reference-based 360° image super-resolution (RefSR) technique to exploit a wide field of view (FoV) among adjacent 360° cameras. Effective exploitation of spatial correlation is critical to achieving high quality even though the distortion inherent in the equi-rectangular projection (ERP) is a nontrivial problem. Accordingly, we develop a long-range 360 disparity estimator (DE360) to overcome a large and distorted disparity, particularly near the poles. Latitude-aware convolution (LatConv) is designed to generate more robust features to circumvent the distortion and keep the image quality. We also develop synthetic 360° image datasets and introduce a synthetic-to-real learning scheme that transfers knowledge learned from synthetic 360° images to a deep neural network conducting super-resolution (SR) of camera-captured images. The proposed network can learn useful features in the ERP-domain using a sufficient number of synthetic samples. The network is then adapted to camera-captured images through the transfer layer with a limited number of real-world datasets.
An Efficient Approach of Product Recommendation System using NLP Technique

As we are moving toward an age of digital globalization and online shopping, there is an increasing need for an efficient and reliable system that can help the consumers and the visitors to find their suitable products. Currently, various websites display the searched product when a visitor comes to their website. What we need is a system, which can recommend the products which are like the searched products. This will help the consumer to find out another product in case the item is unavailable, or the searched product is not good enough, or when they would like to look through different similar products. A good recommendation system has been found out to be financially beneficial for the companies also. It is found out that consumer is 35% more likely to buy a product if the recommendation is good enough for consumers. This proposed approach to the problem of the product recommendation system is to make use of the Amazon Apparel database, which contains data of 180,000 apparels. We are going to use NLP technologies and CNN to help in predicting similar products. Title of the product is used as a major attribute during NLP analysis and recommendation. CNN used at last to create a feature vector from product images, and use this vector combined with all the other vectors, for prediction. We compare the distance between vectors of all products and recommend the products with least distance. VGG-16 architecture used to extract the features from the images.
Classification of Fraud Calls by Intent Analysis of Call Transcripts

While the rapid growth of technology makes life easier for consumers it has also brought new threats along with it. People can be duped to reveal sensitive data such as banking and credit card details, personally identifiable information, and passwords. One such technique is phishing through phone calls. The number of people who fall for such scams is an astounding figure. The conventional approach to detecting phishing call fraud depends on a blacklist of known fraud numbers. This creates a problem when new numbers or numbers which have not been encountered by the system before are used. To solve this, we propose a system that will classify a fraudulent call by analyzing the conversation between the potential victim and the caller. We used various machine learning techniques to perform intent analysis of call transcripts. We created two models and compared them. The models based on the Naive Bayes Algorithm and CNN have an accuracy of 94.57% and 97.21 % respectively.
Heterogeneous graph driven unsupervised domain adaptation of person re-identification

How to incrementally optimize a pre-trained classifier in an unlabeled target domain is a core challenging problem of domain adaptation (DA) for many visual tasks, such as Person Re-identification (re-ID). Most of the existing methods optimize the model based on pseudo labels or similarity of instance pairs, but ignoring the diverse manifold structures of unlabeled instances in the whole dataset. In this paper, we address the importance of such structural information in domain adaptation, and propose a Heterogeneous Graph driven Optimization scheme, namely H-GO, for structure based unsupervised learning. In particular, H-GO builds a heterogeneous graph of unlabeled images to consider the heterogeneous properties of images from various cameras with varied visual styles. A heterogeneous affinity propagation method is further applied to explore the graph based affinity between the instances which share similar manifold structures. Finally, a heterogeneous affinity learning procedure is taken to optimize the visual models by using the graph based affinity of instances. Comprehensive experiments are conducted on three large-scale re-ID datasets, and the results demonstrate the flexibility and the superior performance of H-GO than state-of-the-art unsupervised domain adaptation algorithms.
Secure Offloading of Intrusion Detection Systems from VMs with Intel SGX

Virtual machines (VMs) inside clouds need to be monitored using intrusion detection systems (IDS). Since host-based IDS can be easily disabled by intruders, IDS offloading with VM introspection (VMI) is used to securely run IDS outside a target VM. However, offloaded IDS can be still attacked because it runs on top of a vulnerable operating system (OS). Various systems have been proposed to protect offloaded IDS, but no systems provide an appropriate execution environment to IDS. This paper proposes SGmonitor for enabling the secure execution of IDS offloaded from VMs inside clouds using Intel SGX. SGmonitor executes IDS in SGX enclaves and preserves confidentiality and integrity. It provides secure VMI for memory and storage by using encryption and integrity checking. To make the development of offloaded IDS easier, it provides the in-kernel API to in-enclave IDS and enables transparent access to OS data in VMs. We have implemented SGmonitor in Xen with SGX support and showed that the overhead of in-enclave IDS was 31% in compensation for much stronger security.
A method to challenge symmetries in data with self-supervised learning

Symmetries are key properties of physical models and of experimental designs, but any proposed symmetry may or may not be realized in nature. In this paper, we introduce a practical and general method to test such suspected symmetries in data, with minimal external input. Self-supervision, which derives learning objectives from data without external labelling, is used to train models to predict 'which is real?' between real data and symmetrically transformed alternatives. If these models make successful predictions in independent tests, then they challenge the targeted symmetries. Crucially, our method handles filtered data, which often arise from inefficiencies or deliberate selections, and which could give the illusion of asymmetry if mistreated. We use examples to demonstrate how the method works and how the models' predictions can be interpreted. Code and data are available at https://zenodo.org/record/6861702.
Ultrasound Domain Adaptation Using Frequency Domain Analysis

A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data. Recently, Fourier Domain Adaptation (FDA) has been proposed in the field of computer vision to tackle the domain shift problem by replacing the magnitude of the low-frequency spectrum of a synthetic sample (source) with a real sample (target). This method is attractive in ultrasound imaging given that two important differences between synthetic and real ultrasound data are caused by unknown values of attenuation and speed of sound (SOS) in real tissues. Attenuation leads to slow variations in the amplitude of the B-mode image, and SOS mismatch creates aberration and subsequent blurring. As such, both domain shifts cause differences in the low-frequency components of the envelope data, which are replaced in the proposed method. We demonstrate that applying the FDA method to the synthetic data, simulated by Field II, obtains an 3.5% higher Dice similarity coefficient for a breast lesion segmentation task.