Reviewerly/exHarmony huggingface dataset:
The Price of Active Security in Cryptographic Protocols

We construct the first actively-secure Multi-Party Computation (MPC) protocols with an arbitrary number of parties in the dishonest majority setting, for an arbitrary field $$\mathbb {F}$$ with constant communication overhead over the "passive-GMW" protocol (Goldreich, Micali and Wigderson, STOC '87). Our protocols rely on passive implementations of Oblivious Transfer (OT) in the boolean setting and Oblivious Linear function Evaluation (OLE) in the arithmetic setting. Previously, such protocols were only known over sufficiently large fields (Genkin et al. STOC '14) or a constant number of parties (Ishai et al. CRYPTO '08). Conceptually, our protocols are obtained via a new compiler from a passively-secure protocol for a distributed multiplication functionality $$\mathcal{F}_{\scriptscriptstyle \mathrm {MULT}}$$ , to an actively-secure protocol for general functionalities. Roughly, $$\mathcal{F}_{\scriptscriptstyle \mathrm {MULT}}$$ is parameterized by a linear-secret sharing scheme $$\mathcal{S}$$ , where it takes $$\mathcal{S}$$ -shares of two secrets and returns $$\mathcal{S}$$ -shares of their product. We show that our compilation is concretely efficient for sufficiently large fields, resulting in an overhead of 2 when securely computing natural circuits. Our compiler has two additional benefits: (1) it can rely on any passive implementation of $$\mathcal{F}_{\scriptscriptstyle \mathrm {MULT}}$$ , which, besides the standard implementation based on OT (for boolean) and OLE (for arithmetic) allows us to rely on implementations based on threshold cryptosystems (Cramer et al. Eurocrypt '01); and (2) it can rely on weaker-than-passive (i.e., imperfect/leaky) implementations, which in some parameter regimes yield actively-secure protocols with overhead less than 2. Instantiating this compiler with an "honest-majority" implementations of $$\mathcal{F}_{\scriptscriptstyle \mathrm {MULT}}$$ , we obtain the first honest-majority protocol with optimal corruption threshold for boolean circuits with constant communication overhead over the best passive protocol (Damgård and Nielsen, CRYPTO '07).
A Study on the Issue of Blockchain’s Energy Consumption

Blockchain technology is one of the biggest innovative technology that has been developed and has potential usage in fields of education, business and industries. Since the creation of bitcoins, blockchain has emerged as a means for storing digital information without the intervention of any third parties. However, now, it is used for various other applications than just being a simple distributed ledger. With time, it has imposed a larger impact on different fields of economy and has gained popularity for its immutability. But, there are some issues faced by blockchain. One of such issues is the energy consumption. Blockchains are found to consume exorbitant amount of energy because of the algorithm followed for its creation. This paper explores the blockchain technology and the impacts of energy consumption due to the technology used.
Anomaly Detection for Industry Product Quality Inspection based on Gaussian Restricted Boltzmann Machine

In Industry 4.0, anomaly detection plays an important role in the process of product quality inspection, where the product data with high dimensions and highly imbalanced distribution give rise to some challenges. To handle these challenges, a novel anomaly detection method based on Gaussian Restricted Boltzmann Machine (GRBM) is proposed. To make it more tractable for training the model, the method performs distinct gradient compensations through integrating the free-energy function into the objective function in two stages of product quality inspection. Extensive experimental studies are respectively carried out on two real-world cases, i.e. wine quality and cigarette product testing. Three state-of art anomaly detection methods and two conventional GRBM methods are used for comparison analysis, and the results demonstrate that our proposed method provides effectiveness and superiority in product quality inspection.
Discrete Cosine Transform Space for Hiding Patient Information in the Medical Images

The following topics are dealt with: low-power electronics; medical image processing; data compression; field programmable gate arrays; integrated circuit design; integrated circuit modelling; nanoelectronics; organic field effect transistors; numerical analysis; zinc compounds.
Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction

• We propose an effective Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. • Our proposed WMEKL-NDC method achieves a highly competitive performance in three predictive targets containing the prediction of in-hospital, 30-day and 1-year mortality. • Our proposed WMEKL-NDC method is able to mine useful information from complex and diverse HF data and improve mortality prediction performance. • With the help of feature selection based on F-value, crucial clinical features are identified to assist the clinical researchers in designing clinical trials. Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by short-term (in-hospital) and long-term (6–12 month) mortality. Accurate prediction of HF mortality plays a critical role in evaluating early treatment effects. However, due to the lack of a simple and effective prediction model, mortality prediction of HF is difficult, resulting in a low rate of control. To handle this issue, we propose a Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. In our method, feature selection by calculating the F-value of each feature is first performed to identify the crucial clinical features. Then, different weights are assigned to each empirical kernel space according to the centered kernel alignment criterion. To make use of the discriminant information of samples, neighbor discriminant constraint is finally integrated into multiple empirical kernel learning framework. Extensive experiments were performed on a real clinical dataset containing 10, 198 in-patients records collected from Shanghai Shuguang Hospital in March 2009 and April 2016. Experimental results demonstrate that our proposed WMEKL-NDC method achieves a highly competitive performance for HF mortality prediction of in-hospital, 30-day and 1-year. Compared with the state-of-the-art multiple kernel learning and baseline algorithms, our proposed WMEKL-NDC is more accurate on mortality prediction Moreover, top 10 crucial clinical features are identified together with their meanings, which are very useful to assist clinicians in the treatment of HF disease.
On the (In)security of Kilian-Based SNARGs

The Fiat-Shamir transform is an incredibly powerful technique that uses a suitable hash function to reduce the interaction of general public-coin protocols. Unfortunately, there are known counterexamples showing that this methodology may not be sound (no matter what concrete hash function is used). Still, these counterexamples are somewhat unsatisfying, as the underlying protocols were specifically tailored to make Fiat-Shamir fail. This raises the question of whether this transform is sound when applied to natural protocols. One of the most important protocols for which we would like to reduce interaction is Kilian's four-message argument system for all of $$\mathsf {NP}$$ , based on collision resistant hash functions ( $$\mathsf {CRHF}$$ ) and probabilistically checkable proofs ( $$\mathsf {PCP}$$ s). Indeed, an application of the Fiat-Shamir transform to Kilian's protocol is at the heart of both theoretical results (e.g., Micali's CS proofs) as well as leading practical approaches of highly efficient non-interactive proof-systems (e.g., $$\mathsf {SNARK}$$ s and $$\mathsf {STARK}$$ s). In this work, we show significant obstacles to establishing soundness of (what we refer to as) the "Fiat-Shamir-Kilian-Micali" ( $$\mathsf {FSKM}$$ ) protocol. More specifically: Put together, our results show that the soundness of $$\mathsf {FSKM}$$ must rely on some special structure of both the $$\mathsf {CRHF}$$ and $$\mathsf {PCP}$$ that underlie Kilian's protocol. We believe these negative results may cast light on how to securely instantiate the $$\mathsf {FSKM}$$ protocol by a synergistic choice of the $$\mathsf {PCP}$$ , $$\mathsf {CRHF}$$ , and Fiat-Shamir hash function.
Video super-resolution via pre-frame constrained and deep-feature enhanced sparse reconstruction

This paper presents a new video super-resolution (SR) method that can generate high-quality and temporally coherent high-resolution (HR) videos. Starting from the traditional sparse reconstruction framework that works well for image SR, we improve it significantly from the following aspects to obtain an effect video SR method. Firstly, to enhance the temporal coherence between adjacent HR frames, once a HR frame is estimated, we use it to guide the sparse reconstruction of the next low-resolution frame. Secondly, instead of using just hand-craft features, we further incorporate deep features generated by VGG16 into our sparse reconstruction based video SR method. Thirdly, we constantly update the dictionary, which is the core of the sparse reconstruction, by making use of the previously estimated HR frame. Finally, after the HR video is reconstructed, we use a joint bilateral filter to post-process it to remove artifacts and transfer image details. Experiments demonstrate that the proposed four strategies effectively improve our final results. In most of the experiments of this paper, our results are better than those produced by the latest deep learning based approaches.
Chatbots for online banking services

People are interacting increasingly with systems through voice assistants and chatbots sectors such as banking and financial are adopting them to create more responsive customer services. This project aimed to implement a web-based chatbot to assist with online banking, using tools that expose arti ficial intelligence methods such as natural language understanding. The chatbot allows users to view all their personal banking information from within the chatbot. The system was tested across a range of devices such as Google Home and Assistant on android devices to outline the key differences between the two modes of interaction, spoken and text dialog. These tests showed that chatbots can enhance accessibility, reaffirming that they are more than just a passing fad and have a viable use.
Construction Method and Performance Analysis of Chaotic S-Box Based on Fireworks Algorithm

How to choose a chaotic sequence to construct a chaotic S-box is a key factor to determine the performance of the S-box. In this paper, a construction method for chaotic S-box based on fireworks algorithm is proposed, in which the fireworks algorithm is used to search for the optimal solution in the solution space of the Lorenz chaotic system. The performances of S-box are tested through the nonlinearity, differential uniformity, strict avalanche criterion, output bits independence criterion and bijectivity, and compared with the existing classical S-boxes based on the Lorenz chaotic system. The results indicate that the S-box in this paper has more excellent cryptographic characteristics, which will provide a new idea for the efficient generation of chaotic S-box.
Evolutionary Game Theory based Multi-Objective Optimization for Control Allocation of Over-Actuated System

This research presents multi-objective optimization for control allocation problem based on the Evolutionary Game Theory to solve distribution of redundant control input on the over actuated system in real-time. Optimizing the conflicting objectives, an evolutionary game theory based approach with replicator dynamics is used to find the optimal weighting using the weighted sum method. The main idea of this method is that the best strategy or dominant solution can be selected as a solution that survives among other non-dominant solutions. The Evolutionary Game Theory considers strategies as a player and investigates how these strategies can survive using replicator dynamics with payoff matrix. The numerical simulation results show the optimal weightings selected by Evolutionary Game and how the payoff has been changed in replicator dynamics.
On Independent Axes Estimation for Extended Target Tracking

The trend towards high resolution sensos in combination with a growing number of automotive applications where precise estimates of dense near-range objects are required, results in an enormous need for high performance algorithms for tracking extended targets. Conventionally, this is soved by an ellipse shape approximation of the object extent. In this paper a novel method to estimate the shape parameters of an ellipse using multiple measurements is proposed. By means of an Eigenvalue Decomposition of the measurement spread matrix, the half axis can be measured. A Gaussian model for the feature observations is derived. The performance and consistency is shown by means of Monte Carlo simulations in comparison to state-of-the-art methods in literature.
Computer-Assisted Diagnosis of Thyroid Cancer Using Medical Images: A Survey

Thyroid cancer is the common cancer which can be found mostly in women as compared to men around the world. Thyroid gland is a butterfly-shaped gland that is located around the voice box. Earlier, doctors used to evaluate thyroid cancer manually, but now they are using computer-aided diagnosis (CAD) system for automatic detection. As incidence rate of thyroid cancer is increasing day by day, therefore, a better technology is required for its earlier detection. There are different types of imaging modalities, such as magnetic resonance imaging (MRI), ultrasound (US), and computerized tomography (CT), which are utilized for early detection of diseases. This paper presents and discusses the major trends for an exhaustive overview of thyroid nodule detection, segmentation, classification, and feature extraction techniques. The approaches used in CAD are summarized with their advantages and disadvantages.
Efficient Noninteractive Certification of RSA Moduli and Beyond

In many applications, it is important to verify that an RSA public key (N, e) specifies a permutation over the entire space $$\mathbb {Z}_N$$ , in order to prevent attacks due to adversarially-generated public keys. We design and implement a simple and efficient noninteractive zero-knowledge protocol (in the random oracle model) for this task. Applications concerned about adversarial key generation can just append our proof to the RSA public key without any other modifications to existing code or cryptographic libraries. Users need only perform a one-time verification of the proof to ensure that raising to the power e is a permutation of the integers modulo N. For typical parameter settings, the proof consists of nine integers modulo N; generating the proof and verifying it both require about nine modular exponentiations. We extend our results beyond RSA keys and also provide efficient noninteractive zero-knowledge proofs for other properties of N, which can be used to certify that N is suitable for the Paillier cryptosystem, is a product of two primes, or is a Blum integer. As compared to the recent work of Auerbach and Poettering (PKC 2018), who provide two-message protocols for similar languages, our protocols are more efficient and do not require interaction, which enables a broader class of applications.
Ai Chatbots: Transforming the Digital World

There is no doubt that Artificial Intelligence is changing the world and the outlook of technology and is no longer considered as the future technology as we have already started experiencing it in our daily lives. Artificial Intelligence has been adopted continuously at a rapid scale by organizations and business for the automation of their process. Artificial intelligence has laid the foundation of the creation of intelligent agents that we today known as chat bots. Chat bots have brought a revolution in the business communication process as well as helped in attaining customer satisfaction at a large scale. This chapter describes about the history of chat bots along with their development platforms. For the better understanding we have created conversations with various available chat bot platforms and also tried to present the procedure of a Chatbot creation.
Optimizing Deep Belief Echo State Network with a Sensitivity Analysis Input Scaling Auto-Encoder algorithm

Echo State Network (ESN) is a specific class of recurrent neural networks, which displays very rich dynamics owing to its reservoir based hidden neurons. ESN has been viewed as a powerful approach to model real-valued time series processes. In order to integrate with deep learning theory, Deep Belief Echo State Network (DBESN) is employed to address the slow convergence in Deep Belief Network (DBN). In DBESN, the DBN part is employed for feature learning in an unsupervised fashion and the ESN part is utilized as a regression layer of DBN. However, the ESN input layer is still not working in an unsupervised status in DBESN. Moreover, ESN's input dimension increases dramatically because of the DBN layer in DBESN. Namely, the DBN layer in DBESN makes the ESN more difficult to construct the input scaling parameters. For purpose of constructing an optimal input weights matrix and input scaling parameters in the ESN layer of DBESN, a novel Sensitivity Analysis Input Scaling Auto-Encoder (SAIS-AE) algorithm is employed in this paper through an unsupervised pre-training process. Initially, the output weights matrix of ESN layer is pre-trained by total input data set. Then, the pre-trained output weights matrix is injected into the input weights matrix of the ESN layer to ensure the specificity of AE. Finally, the input scaling parameters of ESN layer are tuned based on a sensitivity analysis algorithm. Two multivariable sequence tasks and one univariate sequence benchmark are applied to demonstrate the advantage and superiority of SAIS-AE. Extensive experimental results show that our SAIS-AE-DBESN model can effectively improve the performance of DBESN.
What is the minimum training data size to reliably identify writers in medieval manuscripts?

One of the most important research topics in the field of palaeography is the identification of the different scribes who participated in the writing process of a medieval book. Using traditional palaeographic tools, a palaeographer spends a lot of time reading, measuring and comparing thousands of letters or graphic signs. The aim is to evaluate different characteristics, such as height or width of letters, distance between characters, angles of inclination, number and type of abbreviations etc., which allow a reliable identification of the scribes who contributed to the production of a given manuscript. Despite the growing scientific interest that has been observed in recent years in the use of computer techniques applied to palaeographic research, a general agreement has not yet been reached among researchers, either about the effectiveness of automatic analysis tools, or on the features that should be considered to perform such an analysis. However, in the context of a highly standardized school, the use of some basic page layout features can be very useful for automatically identifying the presence of different hands. In this context, the aim of our study is to verify whether it is possible to strongly reduce the amount of data a palaeographer must analyse manually, in an attempt to answer the following question: what is the minimum size of the training set that allows a classification system to identify the different scribal hands reliably? To this purpose, we have considered two well-known and highly efficient classification techniques, progressively varying the size of the training set and comparing the corresponding classification results. To improve the classification reliability, we have also introduced a multi-expert classification architecture, enabling an easy implementation of a reject option. The experimental results, performed on two large sets of digital images extracted from two entire 12th-century Bibles, show that using only a few pages of these bibles as a training set, it is possible to identify automatically the scribal hands in the remaining pages with great reliability.
Deep End-to-end 3D Person Detection from Camera and Lidar

We present a method for 3D person detection from camera images and lidar point clouds in automotive scenes.The method comprises a deep neural network which estimates the 3D location and extent of persons present in the scene.3D anchor proposals are refined in two stages: a region proposal network and a subsequent detection network.For both input modalities high-level feature representations are learned from raw sensor data instead of being manually designed.To that end, we use Voxel Feature Encoders [1] to obtain point cloud features instead of widely used projectionbased point cloud representations, thus allowing the network to learn to predict the location and extent of persons in an end-to-end manner.Experiments on the validation set of the KITTI 3D object detection benchmark [2] show that the proposed method outperforms state-of-the-art methods with an average precision (AP) of 47.06% on moderate difficulty.
Breast Cancer Histology Image Classification using Deep Learning

The breast cancer histology image classification task is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. A common challenge of the breast cancer histology image classification, also in other medical domain, is a lack of sufficient data. To address the limitations of the small amount of data, we applied additional patch extraction (APE) of whole-slide image (WSI) as dataset extension approach. Besides, to improve the classification accuracy, we proposed to use the test time augmentation (TTA) technique (with horizontal/vertical flipping, and ±90 degree rotation) upon the original convolution neural network (CNN) so that model is able to make a better decision on several breast cancer testing images instead of single prediction. Experiments was done by applying APE on ICIAR 2018 dataset to get the extended one (ICIAR-EXT) which is publicly available at https://github.com/canhnp/ICIAR-EXT. Moreover, when applying TTA technique with CNN on ICIAR-EXT at testing state, the accuracy has achieved a satisfactory result of 78% for 4-class breast cancer classification for 100 unlabeled ICIAR 2018 test set.
A Survey on Human Group Activity Recognition by Analysing Person Action from Video Sequences Using Machine Learning Techniques

There has been a tremendous advance in machine learning techniques especially for automatic group activity recognition (GAR) over the past few decades. This review article surveys the modern advancement made towards video-based group activity recognition technique. Various applications, including video surveillance systems, sports analytics and human behaviour for robotics characterization, require a group activity recognition system. Comprehensive reviews of machine learning (ML) techniques like hidden Markov models (HMMs), graphical method and support vector machines employed in GAR are being discussed. A comprehensive review on the latest progress in deep learning model has delivered important developments in GAR performance; those are also presented. The main purpose of this survey is to broadly categorize and analyse GAR according to handcrafted features based on machine learning model and learned features based on deep model. Various GAR models illustrated by considering activities of individual person, person-to-person interaction, person-to-group interaction and group interaction using temporal sequence information from video frames for recognition of group activity are discussed. The review facilitates in diverse applications, and the models described in different applications present specifically in surveillance, sport analytics, video summary, etc.