Reviewerly/exHarmony huggingface dataset:
COINN: Crypto/ML Codesign for Oblivious Inference via Neural Networks

We introduce COINN - an efficient, accurate, and scalable framework for oblivious deep neural network (DNN) inference in the two-party setting. In our system, DNN inference is performed without revealing the client's private inputs to the server or revealing server's proprietary DNN weights to the client. To speedup the secure inference while maintaining a high accuracy, we make three interlinked innovations in the plaintext and ciphertext domains: (i) we develop a new domain-specific low-bit quantization scheme tailored for high-efficiency ciphertext computation, (ii) we construct novel techniques for increasing data re-use in secure matrix multiplication allowing us to gain significant performance boosts through factored operations, and (iii) we propose customized cryptographic protocols that complement our optimized DNNs in the ciphertext domain. By co-optimization of the aforesaid components, COINN brings an unprecedented level of efficiency to the setting of oblivious DNN inference, achieving an end-to-end runtime speedup of 4.7×14.4× over the state-of-the-art. We demonstrate the scalability of our proposed methods by optimizing complex DNNs with over 100 layers and performing oblivious inference in the Billion-operation regime for the challenging ImageNet dataset. Our framework is available at https://github.com/ACESLabUCSD/COINN.git.
End-to-end neural event coreference resolution

Conventional event coreference systems commonly use a pipeline architecture and rely heavily on handcrafted features, which often causes error propagation problems and leads to poor generalization ability. In this paper, we propose a neural network-based end-to-end event coreference architecture (E3C) that can jointly model event detection and event coreference resolution tasks and learn to extract features from raw text automatically. Furthermore, because event mentions are highly diversified and event coreference is intricately governed by long-distance and semantically-dependent decisions, a type-enhanced event coreference mechanism is further proposed in our E3C neural network. Experiments show that our method achieves a new state-of-the-art performance on both standard datasets.
Prostate Cancer Prognosis Using Multi-Layer Perceptron and Class Balancing Techniques

Prostate malignancy is one of the most common malignancies. Early prediction of a cancer diagnosis can upsurge the endurance rate of cancer patients. The advancement of cancer research is boosted with the advent of artificial intelligence. Researchers have developed programmes to aid in cancer detection and prognosis due to the availability of open-source healthcare statistics. Machine Learning (ML) algorithms play a vital role in the field of cancer prognosis. The current study highlights the applications of neural networks to predict prostate cancer. We have accessed prostate cancer records from a publically accessible data repository (Kaggle). Current research work stresses the applications of neural learning approach for cancer prognosis and attaining more accurate prediction outcomes. The study also stresses on the impact of different balancing techniques on imbalanced data. The proposed method enhanced the accurateness from 72% on the imbalanced data to 97% on the oversampled dataset. This study aims to determine whether an artificial neural network (multilayer perceptron, MLP) can accurately predict the diagnosis of prostate cancer. In addition, the experimental results confirm the necessity of data balancing techniques in classification.
Lithological mapping enhancement by integrating Sentinel 2 and gamma-ray data utilizing support vector machine: A case study from Egypt

Hybrid data fusion mostly gives a better diagnosis to lithological units compared to single-source mapping techniques. Rock unit discrimination depends mainly on variations in the concentrations of chemical elements. Remote sensing datasets reflect these variations as different spectral reflectances, while gamma-ray spectrometric measurements enable recording the varied concentrations of K, Th, and U in these rock units. Accordingly, in this study, we use Support-Vector Machine (SVM) learning algorithm to classify combined high spectral resolution Sentinel 2 data with K, Th, and U content of the rocks to better differentiate a lithologically complex area in Egypt. SVM classifier has been trained and tested on a reference map (built from FCCs, principal and independent component analysis of remote sensing images, as well as previous geological maps) to allocate 13 lithological targets. K, Th, U, and total count maps are interpolated using the inverse distance weighted (IDW) method, cubically resampled, and fused with Sentinel 2 data. We concluded that incorporating any single chemical concentration in the allocation gives better results than using remote sensing data solely and raised the Overall Accuracy by 4.14%, 5.11%, and 6.83% by adding U, K, and Th, respectively. Moreover, blending the total count band (K + Th + U) with Sentinel 2 data outstandingly boosts the classification accuracy by 7.77 %. We performed field reconnaissance to verify the classification results. The study demonstrates the effectiveness of integrating Sentinel 2 data with airborne geophysical spectrometric data, and the proposed approach may prove a more precise and sophisticated lithological map.
Multi-Level Authentication Scheme for Improving Privacy and Security of Data in Decentralized Cloud Server

In recent days cloud domain gains a lot of user attention in order to store and access the data from remote locations connected through the internet. As it is generally known that all the sensitive data come from remote locations will be stored in the centralized storage medium and then try to access the data from that centralized storage space controlled by the cloud server. It is facing a problem like there is no security for the data in terms of user authorization and data authentication from the centralized storage location. Hence, it is required to migrate for a new storage procedure like Decentralized storage of cloud data in which the systems that do not rely on a central authority, so that the collusion resistance can be avoided by maintaining a global identifier. Here, the term de-centralized access means granting multi authorities to control the access for providing more security for the sensitive data. The proposed research study attempts to develop a new scheme by adding a global identifier like Attribute Authority (AA) for providing access keys for the data users who wish to access the sensitive information from the cloud server. The proposed research work attempts to incorporate the composite order bilinear groups scheme for providing access facility for the data users and provide more security for the sensitive data. By conducting various experiments on the proposed model, the obtained result clearly tells that the proposed system is very efficient to access the data in a de-centralized manner by using a global identifier.
Immersive Virtual Reality Physical Education Instructional Patterns on the Foundation of Vision Sensor

The creation of visual settings using avatar technology is the initial implementation of a voxel in the reporting context. This article is aimed at demonstrating the important role of a virtual reality immersive physical education model in current physical education and analyzing the immersive virtual reality physical education model. Relying on the mature VR virtual reality technology to establish a virtual simulation experiment platform, the application value in the education field is also reflected in the saving of experimental teaching costs. A complete set of VR teaching courseware also satisfies the functions of teaching training and assessment and is used repeatedly to maximize the value of utilization. This research mainly introduces the content of the optimization of the teaching method of the course combined with virtual reality technology. In order to make the data more convincing, reference literature and data in recent years have been referred to around immersive teaching. The first part is the discussion of immersive teaching, which includes the research of virtual immersive classroom teaching. The second part is a separate analysis of virtual reality technology. The third part is the practical exercise based on the first two parts; that is, from the learning effect and attitude of the student as the main body, the theoretical basis of the two parts on visual sensors and immersive virtual reality physical education teaching is transformed into real practice teaching. In the experiment part, in order to demonstrate the effectiveness and support of immersive virtual teaching, on the one hand, we started with the teaching of teachers, and on the other hand, we carried out investigations from the aspects of students’ learning. The desktop virtual environment teaching method is compared with the existing classroom teaching methods. The image processing and image analysis of the virtual reality technology are combined with the image gray level of the three-dimensional image to analyze the multitask algorithm of the vision sensor. The feasibility of immersive virtual teaching was verified. Research data shows that the 10 students participating in the experiment gave a score of 7.9 for the target attitude of immersive virtual reality physical education. Students’ interest in learning will increase, and efficiency will also be greatly improved. VR can not only provide students with a new learning experience but also be used to strengthen teachers’ teaching skills. Because VR can simulate a real teaching environment, teachers can use this set of tools to try new course materials and improve classroom management capabilities.
Generalized framework for the design of adaptive fractional-order masks for image denoising

This paper proposes a generalized fractional differential (GFD) mask which incorporates various fractional-order kernels such as power-law kernel, exponentional kernel, and Mittag-Leffler kernel, corresponding to Riemann-Liouville (RL)/Caputo, Caputo-Fabrizio (CF), and Atangana-Baleanu (AB) fractional-order differentials, respectively. Furthermore, a generalized fractional integral (GFI) mask incorporating RL and AB fractional integrals is put forward. The proposed generalized fractional masks provide a more flexible and appropriate tool for image enhancement and image denoising applications, and hence, avoids the construction of different masks for each kernel separately. Additionally, a generalized fractional integral and fractional differential based adaptive algorithm for image denoising (GFIFD-AA) is proposed. The proposed GFIFD-AA incorporates a novel noise detection method (NDM) for the detection of salt and pepper (SP) noise in images, which exhibits a profound advantage in avoiding misclassification of original pixels as noisy pixels when original image itself has some pixels with intensity values 0 or 255. The detected noisy pixels are processed by utilizing the proposed GFI-based adaptive mask (GFI-AM). The noise-free pixels are further updated by utilizing the proposed GFD-based adaptive mask (GFD-AM) so as to enhance the details of the image. Several standard and medical images of different characteristics are examined to evaluate the performance of the proposed approach on images affected by SP noise at various noise densities (i.e., 10%-95%). Furthermore, the performance of the proposed GFI mask is also tested for images affected by Gaussian noise and is compared against conventional fractional-order masks. The simulation results based upon several quantitative parameters validate the effectiveness of the proposed method against conventional methods.
Quantum information technology and innovation: a brief history, current state and future perspectives for business and management

Quantum information technology is rapidly commercialising today. It requires urgent attention to the strategic management of this technology by business leaders and policy makers.
A spatial reconnaissance survey for gold exploration in a schist belt

Geological data integration and spatial analysis for structural elucidation are more assertive approaches for reconnaissance scale mineral exploration. In this study, several methods involving Fry analysis, distance correlation analysis, prediction area plots as well as knowledge driven predictive models including TOPSIS, ARAS and MOORA were systematically employed for unravelling the spatial geological attributes related to gold mineralisation. Additionally, statistical validation of knowledge driven predictive models were implemented using the Receiver Operating Characteristic/Area Under Curve analysis (ROC/AUC). The evidence from Fry and distance correlation analysis suggests that gold occurrence within parts of the Malumfashi schist belt of Nigeria is defined by a strong spatial association with the ENE-WSW as well as the NNE-SSW trending structures. The prediction area plot also revealed a robust spatial correlation between mineral occurrence and spatial data related to geological structures. The application of knowledge driven predictive models suggest a high favourability for gold occurrence within the southern, central, and north-eastern parts of the study location, while statistical validation using the ROC/AUC curves suggest a high prediction accuracy greater than 70% for all models. The geospatial analysis for mineral exploration within the Malumfashi area has unveiled an invaluable geological criterion for gold targeting with a considerable level of certainty.
Verification of Image-based Neural Network Controllers Using Generative Models

Neural networks are often used to process information from image-based sensors to produce control actions. While they are effective for this task, the complex nature of neural networks makes their output difficult to verify and predict, limiting their use in safety-critical systems. For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closedloop performance of neural network controllers. However, these techniques do not scale to the high-dimensional and complicated input space of image-based neural network controllers. In this work, we propose a method to address these challenges by training a generative adversarial network (GAN) to map states to plausible input images. By concatenating the generator network with the control network, we obtain a network with a lowdimensional input space. This insight allows us to use existing closed-loop verification tools to obtain formal guarantees on the performance of image-based controllers. We apply our approach to provide safety guarantees for an image-based neural network controller for an autonomous aircraft taxi problem. We guarantee that the controller will keep the aircraft on the runway and guide the aircraft towards the center of the runway. The guarantees we provide are with respect to the set of input images modeled by our generator network, so we provide a recall metric to evaluate how well the generator captures the space of plausible images.
Poster: FLATEE: Federated Learning Across Trusted Execution Environments

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).
Molecular excited state calculations with adaptive wavefunctions on a quantum eigensolver emulation: reducing circuit depth and separating spin states

Ab initio electronic excited state calculations are necessary for the quantitative study of photochemical reactions, but their accurate computation on classical computers is plagued by prohibitive resource scaling. The Variational Quantum Deflation (VQD) is an extension of the quantum-classical Variational Quantum Eigensolver (VQE) algorithm for calculating electronic excited state energies, and has the potential to address some of these scaling challenges using quantum computers. However, quantum computers available in the near term can only support a limited number of quantum circuit operations, so reducing the quantum computational cost in VQD methods is critical to their realisation. In this work, we investigate the use of adaptive quantum circuit growth (ADAPT-VQE) in excited state VQD calculations, a strategy that has been successful previously in reducing the resources required for ground state energy VQE calculations. We also invoke spin restrictions to separate the recovery of eigenstates with different spin symmetry to reduce the number of calculations and accumulation of errors in computing excited states. We created a quantum eigensolver emulation package - Quantum Eigensolver Building on Achievements of Both quantum computing and quantum chemistry (QEBAB) - for testing the proposed adaptive procedure against two existing VQD methods that use fixed-length quantum circuits: UCCGSD-VQD and k-UpCCGSD-VQD. For a lithium hydride test case we found that the spin-restricted adaptive growth variant of VQD uses the most compact circuits out of the tested methods by far, consistently recovers adequate electron correlation energy for different nuclear geometries and eigenstates while isolating the singlet and triplet manifold. This work is a further step towards developing techniques which improve the efficiency of hybrid quantum algorithms for excited state quantum chemistry, opening up the possibility of exploiting real quantum computers for electronic excited state calculations sooner than previously anticipated.
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent

This paper is concerned with minimizing the average of n cost functions over a network in which agents may communicate and exchange information with each other. We consider the setting where only noisy gradient information is available. To solve the problem, we study the distributed stochastic gradient descent (DSGD) method and perform a non-asymptotic convergence analysis. For strongly convex and smooth objective functions, in expectation, DSGD asymptotically achieves the optimal network independent convergence rate compared to centralized stochastic gradient descent (SGD). Our main contribution is to characterize the transient time needed for DSGD to approach the asymptotic convergence rate. Moreover, we construct a "hard" optimization problem that proves the sharpness of the obtained result. Numerical experiments demonstrate the tightness of the theoretical results.
All you need are a few pixels: semantic segmentation with PixelPick

A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training. In this work, we show that in order to achieve a good level of segmentation performance, all you need are a few well-chosen pixel labels.We make the following contributions: (i) We investigate the semantic segmentation setting in which labels are supplied only at sparse pixel locations, and show that deep neural networks can use a handful of such labels to good effect; (ii) We demonstrate how to exploit this phenomenon within an active learning framework, termed PixelPick, to radically reduce labelling cost, and propose an efficient "mouse-free" annotation strategy to implement our approach; (iii) We conduct extensive experiments to study the influence of annotation diversity under a fixed budget, model pretraining, model capacity and the sampling mechanism for picking pixels in this low annotation regime; (iv) We provide comparisons to the existing state of the art in semantic segmentation with active learning, and demonstrate comparable performance with up to two orders of magnitude fewer pixel annotations on the CamVid, Cityscapes and Pascal VOC 2012 benchmarks; (v) Finally, we evaluate the efficiency of our annotation pipeline and its sensitivity to annotator error to demonstrate its practicality.
A Software-Defined-Networking-Enabled Approach for Edge-Cloud Computing in the Internet of Things

The proliferation of smart devices has led to a huge amount of data streaming in the Internet of Things (IoT). However, the resource-limited devices cannot satisfy the demands of computing-in-tensive but delay-sensitive applications. The data delivery among devices may be tampered with by malicious users. These pose new challenges to provide secure and intelligent services in IoT. Blockchain and reinforcement learning (RL) are promising techniques for establishing a secure environment and intelligent resource management. In this article, we introduce a novel software defined networking (SDN)-enabled architecture for edge-cloud orchestrated computing to support secure and intelligent services in IoT. We first introduce the SDN-enabled architecture by integrating cloud computing, edge computing, and IoT networks. Then we provide several applications of SDN-enabled architecture in edge-cloud orchestrated computing. Next, we propose the blockchain and RL envisioned solutions to implement secure and intelligent services in IoT. Moreover, a case study of blockchain- and RL-enabled secure and intelligent computing offloading is presented to validate its effectiveness. We finally provide our conclusion and discuss several promising research directions.
Arbitrary entangled state transfer via a topological qubit chain

Quantum state transfer is one of the basic tasks in quantum information processing. We here propose a theoretical approach to realize arbitrary entangled state transfer through a qubit chain, which is a class of extended Su-Schrieffer-Heeger models and accommodates multiple topological edge states separated from the bulk states. We show that an arbitrary entangled state, from $2$-qubit to $\mathcal{N}$-qubit, can be encoded in the corresponding edge states, and then adiabatically transferred from one end to the other of the chain. The dynamical phase differences resulting from the time evolutions of different edge states can be eliminated by properly choosing evolution time. Our approach is robust against both the qubit-qubit coupling disorder and the evolution time disorder. For the concreteness of discussions, we assume that such a chain is constructed by an experimentally feasible superconducting qubit system, meanwhile, our proposal can also be applied to other systems.