Reviewerly/exHarmony huggingface dataset:
Breast Cancer Prognosis Using a Machine Learning Approach

Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine.
Breaking LTE on Layer Two

Long Term Evolution (LTE) is the latest mobile communication standard and has a pivotal role in our information society: LTE combines performance goals with modern security mechanisms and serves casual use cases as well as critical infrastructure and public safety communications. Both scenarios are demanding towards a resilient and secure specification and implementation of LTE, as outages and open attack vectors potentially lead to severe risks. Previous work on LTE protocol security identified crucial attack vectors for both the physical (layer one) and network (layer three) layers. Data link layer (layer two) protocols, however, remain a blind spot in existing LTE security research. In this paper, we present a comprehensive layer two security analysis and identify three attack vectors. These attacks impair the confidentiality and/or privacy of LTE communication. More specifically, we first present a passive identity mapping attack that matches volatile radio identities to longer lasting network identities, enabling us to identify users within a cell and serving as a stepping stone for follow-up attacks. Second, we demonstrate how a passive attacker can abuse the resource allocation as a side channel to perform website fingerprinting that enables the attacker to learn the websites a user accessed. Finally, we present the A LTE R attack that exploits the fact that LTE user data is encrypted in counter mode (AES-CTR) but not integrity protected, which allows us to modify the message payload. As a proof-of-concept demonstration, we show how an active attacker can redirect DNS requests and then perform a DNS spoofing attack. As a result, the user is redirected to a malicious website. Our experimental analysis demonstrates the real-world applicability of all three attacks and emphasizes the threat of open attack vectors on LTE layer two protocols.
Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques

Internet of Things (IoT) is a domain wherein which the transfer of data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, the elliptic Galois cryptography protocol is introduced and discussed. In this protocol, a cryptography technique is used to encrypt confidential data that came from different medical sources. Next, a Matrix XOR encoding steganography technique is used to embed the encrypted data into a low complexity image. The proposed work also uses an optimization algorithm called Adaptive Firefly to optimize the selection of cover blocks within the image. Based on the results, various parameters are evaluated and compared with the existing techniques. Finally, the data that is hidden in the image is recovered and is then decrypted.
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation

Despite the great success of many matrix factorization based collaborative filtering approaches, there is still much space for improvement in recommender system field. One main obstacle is the cold-start and data sparseness problem, requiring better solutions. Recent studies have attempted to integrate review information into rating prediction. However, there are two main problems: (1) most of existing works utilize a static and independent method to extract the latent feature representation of user and item reviews ignoring the correlation between the latent features, which may fail to capture the preference of users comprehensively. (2) there is no effective framework that unifies ratings and reviews. Therefore, we propose a novel d ual a ttention m utual l earning between ratings and reviews for item recommendation, named DAML. Specifically, we utilize local and mutual attention of the convolutional neural network to jointly learn the features of reviews to enhance the interpretability of the proposed DAML model. Then the rating features and review features are integrated into a unified neural network model, and the higher-order nonlinear interaction of features are realized by the neural factorization machines to complete the final rating prediction. Experiments on the five real-world datasets show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction.
Nanophotonic media for artificial neural inference

We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Complex information is encoded in the wavefront of an input light. The medium transforms the wavefront to realize sophisticated computing tasks such as image recognition. At the output, the optical energy is concentrated in well-defined locations, which, for example, can be interpreted as the identity of the object in the image. These computing media can be as small as tens of wavelengths and offer ultra-high computing density. They exploit subwavelength scatterers to realize complex input/output mapping beyond the capabilities of traditional nanophotonic devices.
Few-Shot Learning With Localization in Realistic Settings

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new “meta-iNat” benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.
Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size

Objective of multiple object tracking (MOT) is to assign a unique track identity for all the objects of interest in a video, across the whole sequence.Tracking-by-detection is the most common approach used in addressing MOT problem.In this work, we propose a method to address MOT by defining a dissimilarity measure based on object motion, appearance, structure, and size.We calculate the appearance and structure-based dissimilarity measure by matching histograms following a grid architecture.Motion and size for each track are predicted using the information from track's history.These dissimilarity values are then used in the Hungarian algorithm, in the data association step for track identity assignment.In addition, we introduce a method to address any false detection in stable tracks.The proposed method runs in real time following an online approach.We evaluate our method in both MOT17 benchmark data-set for pedestrian tracking and KITTI benchmark data-set for vehicle tracking using the same system parameters to verify the robustness of the proposed method.The method can achieve state-of-the-art results in both benchmarks.
Black box fairness testing of machine learning models

Any given AI system cannot be accepted unless its trustworthiness is proven. An important characteristic of a trustworthy AI system is the absence of algorithmic bias. 'Individual discrimination' exists when a given individual different from another only in 'protected attributes' (e.g., age, gender, race, etc.) receives a different decision outcome from a given machine learning (ML) model as compared to the other individual. The current work addresses the problem of detecting the presence of individual discrimination in given ML models. Detection of individual discrimination is test-intensive in a black-box setting, which is not feasible for non-trivial systems. We propose a methodology for auto-generation of test inputs, for the task of detecting individual discrimination. Our approach combines two well-established techniques - symbolic execution and local explainability for effective test case generation. We empirically show that our approach to generate test cases is very effective as compared to the best-known benchmark systems that we examine.
Cost and makespan-aware workflow scheduling in hybrid clouds

Benefiting from rich resources and virtualization technologies, hybrid cloud has emerged as a promising solution to processing large-scale scientific workflow applications for users in a pay-as-you-go manner. However, considering the complexity of resource configuration and deployment in hybrid clouds, existing workflow scheduling strategies designed for traditional distributed computing systems are limited and powerless. Therefore, for profit-driven infrastructure-as-a-service (IaaS) cloud providers, minimizing makespan and monetary cost of scheduling scientific workflows is an imperative concern. In this paper, we propose two efficient workflow scheduling approaches for hybrid clouds that both consider makespan and monetary cost. Specifically, we first propose a single-objective workflow scheduling optimization approach called DCOH (deadline-constrained cost optimization for hybrid clouds) for minimizing the monetary cost of scheduling workflows under deadline constraint. Based on DCOH, we further propose a multi-objective workflow scheduling optimization approach called MOH (multi-objective optimization for hybrid clouds) for optimizing makespan and monetary cost of scheduling workflows simultaneously. Extensive simulation experiments have been conducted to validate the effectiveness of DCOH and MOH. Simulation results show that our DCOH approach can reduce up to 100.0% monetary cost for users as compared to the competing algorithms under the same deadline constraint and our MOH approach can achieve better cost-makespan trade-off solutions as compared to the competing algorithms.
Learning to detect lymphocytes in immunohistochemistry with deep learning

The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3+ and CD8+ cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (κ=0.72), whereas the average pathologists agreement with reference standard was κ=0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org.
TechNet: Technology semantic network based on patent data

The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies.
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10–15% and 3–4% respectively, using benchmark Persian product and hotel reviews corpora.
On the Generalization of GAN Image Forensics

Recently GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to ensure the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic, which is also very challenging. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake images, unstable low level noise cues are destroyed, and the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.
Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment

The wide application of deep learning technique has raised new security concerns about the training data and test data. In this work, we investigate the model inversion problem under adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion model in the adversarial settings. First, we leverage the adversary's background knowledge to compose an auxiliary set to train the inversion model, which does not require access to the original training data. Second, we design a truncation-based technique to align the inversion model to enable effective inversion of the target model from partial predictions that the adversary obtains on victim user's data. We systematically evaluate our approach in various machine learning tasks and model architectures on multiple image datasets. We also confirm our results on Amazon Rekognition, a commercial prediction API that offers "machine learning as a service". We show that even with partial knowledge about the black-box model's training data, and with only partial prediction values, our inversion approach is still able to perform accurate inversion of the target model, and outperform previous approaches.
Adversarial Defense via Learning to Generate Diverse Attacks

With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains. Despite this success, however, it has been found that DNNs are surprisingly vulnerable to malicious attacks; adding a small, perceptually indistinguishable perturbations to the data can easily degrade classification performance. Adversarial training is an effective defense strategy to train a robust classifier. In this work, we propose to utilize the generator to learn how to create adversarial examples. Unlike the existing approaches that create a one-shot perturbation by a deterministic generator, we propose a recursive and stochastic generator that produces much stronger and diverse perturbations that comprehensively reveal the vulnerability of the target classifier. Our experiment results on MNIST and CIFAR-10 datasets show that the classifier adversarially trained with our method yields more robust performance over various white-box and black-box attacks.
Nonpeaked Discriminant Analysis for Data Representation

Of late, there are many studies on the robust discriminant analysis, which adopt L1-norm as the distance metric, but their results are not robust enough to gain universal acceptance. To overcome this problem, the authors of this article present a nonpeaked discriminant analysis (NPDA) technique, in which cutting L1-norm is adopted as the distance metric. As this kind of norm can better eliminate heavy outliers in learning models, the proposed algorithm is expected to be stronger in performing feature extraction tasks for data representation than the existing robust discriminant analysis techniques, which are based on the L1-norm distance metric. The authors also present a comprehensive analysis to show that cutting L1-norm distance can be computed equally well, using the difference between two special convex functions. Against this background, an efficient iterative algorithm is designed for the optimization of the proposed objective. Theoretical proofs on the convergence of the algorithm are also presented. Theoretical insights and effectiveness of the proposed method are validated by experimental tests on several real data sets.
Transparent SNARKs from DARK Compilers

We construct a new polynomial commitment scheme for univariate and multivariate polynomials over finite fields, with logarithmic size evaluation proofs and verification time, measured in the number of coefficients of the polynomial. The underlying technique is a Diophantine Argument of Knowledge (DARK), leveraging integer representations of polynomials and groups of unknown order. Security is shown from the strong RSA and the adaptive root assumptions. Moreover, the scheme does not require a trusted setup if instantiated with class groups. We apply this new cryptographic compiler to a restricted class of algebraic linear IOPs, which we call Polynomial IOPs, to obtain doubly-efficient public-coin interactive arguments of knowledge for any NP relation with succinct communication. With linear preprocessing, the online verifier’s work is logarithmic in the circuit complexity of the relation. There are many existing examples of Polynomial IOPs (PIOPs) dating back to the first PCP (BFLS, STOC’91). We present a generic compilation of any PIOP using our DARK polynomial commitment scheme. In particular, compiling the PIOP from PLONK (GWC, ePrint’19), an improvement on Sonic (MBKM, CCS’19), yields a public-coin interactive argument with quasi-linear preprocessing, quasi-linear (online) prover time, logarithmic communication, and logarithmic (online) verification time in the circuit size. Applying Fiat-Shamir results in a SNARK, which we call . Supersonic is also concretely efficient with 10 KB proofs and under 100 ms verification time for circuits with 1 million gates (estimated for 120-bit security). Most importantly, this SNARK is transparent: it does not require a trusted setup. We obtain zk-SNARKs by applying a hiding variant of our polynomial commitment scheme with zero-knowledge evaluations. Supersonic is the first complete zk-SNARK system that has both a practical prover time as well as asymptotically logarithmic proof size and verification time. The full version of the paper is available online [19].