Reviewerly/exHarmony huggingface dataset:
An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Abstract We describe the first gradient methods on Riemannian manifolds to achieve accelerated rates in the non-convex case. Under Lipschitz assumptions on the Riemannian gradient and Hessian of the cost function, these methods find approximate first-order critical points faster than regular gradient descent. A randomized version also finds approximate second-order critical points. Both the algorithms and their analyses build extensively on existing work in the Euclidean case. The basic operation consists in running the Euclidean accelerated gradient descent method (appropriately safe-guarded against non-convexity) in the current tangent space, then moving back to the manifold and repeating. This requires lifting the cost function from the manifold to the tangent space, which can be done for example through the Riemannian exponential map. For this approach to succeed, the lifted cost function (called the pullback) must retain certain Lipschitz properties. As a contribution of independent interest, we prove precise claims to that effect, with explicit constants. Those claims are affected by the Riemannian curvature of the manifold, which in turn affects the worst-case complexity bounds for our optimization algorithms.
Implementing Electronic Voting System With Blockchain Technology

Elections and voting are the basic mechanisms of a democratic system. There have been various attempts to make modern elections more flexible by using digital technologies. Basic characteristics of free and fair elections are intractability, immutable, transparency and the privacy of the involved actors. This corresponds to a few of the many features of blockchain-like decentralized ownership, the immutability of chain, anonymity and distributed ledger. This work-in-progress paper attempts to do a comparative analysis of various blockchain technologies under development and propose a `Blockchain based Electronic Voting System' solution by weighing these technologies based on the need for the proposed solution. The main aim of this paper is to present a robust blockchain-based election mechanism that not only will be reliable but also flexible according to present needs.
Multifrequency Inductive Sensor System for Classification of Bimetallic Coins

The reliable classification of coin sets from different countries and counterfeit coins is an essential means of securing cash circulation. Especially bimetallic coins, such as 2-Euro coins, are often subjected to counterfeit by mixing them with coins of other countries or by imitations. In this article, a real-time embedded sensor system is proposed, based on inductance spectroscopy to characterize and identify bimetallic coins having similar geometric properties and looking similar at a first view. The system is based on inductance spectroscopy varying the penetration depth of the magnetic field in the bimetallic structure of the coin, which generally contains buried layers of other metals. The experimental evaluation shows that the bimetallic coins from different countries can be identified and classified by the use of the support vector machine, a machine learning algorithm. Experimental results showed that the system reached a classification accuracy up to 100% within a response rate of 36.62 ms per five bimetallic coins.
Wi-Fi-Dependent Consensus Mechanism for Constrained Devices Using Blockchain Technology

Consensus mechanisms are at the core of any Distributed Ledger Technology. They define how transactions are validated and verified, how the network resolves conflicts and protects against malicious intent, and how multiple devices on a distributed network with no central authority arrive at the same global view of the ledger. Experience with the consensus mechanisms employed in popular blockchain platforms have warranted years of research into more scalable and energy-efficient consensus mechanisms for a green society using constrained devices. Most of the “high-security” consensus mechanisms require the use of computational resources which are simply not available to devices with a constrained nature. Hence, the presence and activity of these devices do not contribute to the security of the ledger, irrespective of how numerous they are on the network. In this paper, we propose a novel consensus scheme, fully dependent on WiFi technology, suitable for constrained devices. Whilst in traditional Blockchain consensus mechanisms, constrained devices do not add to the security of the ledger, the security of our consensus mechanism increases with the number of connected devices.
Suppression techniques for privacy-preserving trajectory data publishing

In this paper, we study the problem of protecting privacy in trajectory datasets from adversaries who can exploit their partial knowledge to infer unknown locations. To efficiently solve this problem, we propose a tree-based indexing structure to store all trajectory data and develop pruning strategies. We provide two algorithms to find a safe counterpart of the original trajectory dataset by using the pruning strategies. Finally, our experimental results demonstrate the efficiency of the proposed algorithms.
Unifying Protocols for Conducting Systematic Scoping Reviews with Application to Immersive Learning Research

The progress of immersive learning research as a field requires a clear vision of its status, of the current knowledge being produced and of the open problems and gaps. Typical survey efforts however suffer from lack of systematization, providing a scattered perspective of the field. We have combined the literature on conducting systematic scoping reviews and applied it to the field, presenting the resulting protocol. It contributes a clarification on the sequence of steps and processes for delineating a gap, finding the evidence and depart from it to conduct literature reviews.
Securing Logs of a System - An IoTA Tangle Use Case

Outbreak of the Internet and technological dependence make the Cyber Technology Industry grow at a very fast pace. Cyber-security is the dire need of today which helps prevent or defend the systems from cyber-attacks. Distributed Ledger Technology like Blockchain with its various properties and advantages opens many opportunities for its use in the cyber-security domain and mitigating the associated risks. These technologies remove the need of any central trusted party or authority, which would facilitate recording and tracking of resources. This paper focuses on the use of Distributed Ledger Technology (DLT) in the field of Cyber-security. It focuses on the discussion of new distributed ledger technology IOTA, its features and working. This paper also focuses on one of the uses of the IOTA Tangle technology, which provides a solution for securing logs of a system. As the logs of a system are the most important evidence in the investigation phase of Cyber-Forensics, thus preserving the logs securely and maintaining their integrity is mandatory. Later in this paper, the use of IOTA is demonstrated for securing the logs of a system.
Nonlinear estimation based on conversion-sample optimization

For nonlinear estimation, the linear minimum mean square error (LMMSE) estimator using the original measurement is the best of all estimators linear in the measurement. Recently, it has been proven that an LMMSE estimator using a stacked measurement vector composed of the original measurement and its nonlinear conversion (transformation or function) outperforms the original LMMSE estimator. However, to find an optimal conversion for various nonlinear problems is very difficult, because it involves functional optimization. Recognizing that the conversion affects estimation performance only through conversion-dependent quantities needed by the augmented LMMSE-based estimator, this paper proposes an optimized conversion-sample filter (OCF), which optimizes all these quantities to optimize estimation performance. These quantities are much easier to obtain than the optimal conversion itself. Using deterministic sampling, obtaining these quantities amounts to determining the converted values at both the deterministic sample points and the measurement value. In the OCF, these values are obtained optimally and analytically by solving a constrained Rayleigh quotient problem. The proposed OCF has a simple and analytical form and can be generally applied to nonlinear estimation. Simulation results demonstrate the effectiveness of the proposed OCF compared with some popular nonlinear estimators, including several LMMSE-based filters, a recently-proposed uncorrelated conversion based filter, and a particle filter.
Active Perception Using Light Curtains for Autonomous Driving

Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient controllable sensor that measures depth at user-specified locations in the environment. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active perception. Given a neural network's uncertainty, we derive an optimization objective to place light curtains using the principle of maximizing information gain. Then, we develop a novel and efficient optimization algorithm to maximize this objective by encoding the physical constraints of the device into a constraint graph and optimizing with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy. Code and details can be found on the project webpage: http://siddancha.github.io/projects/active-perception-light-curtains.
Scientific Exploration of Hand Gesture Recognition to Text

Hand Gesture to Text Converter is emerging as a proliferated research area. The main aim is to bridge the communication gap between two such persons in order that the speech impaired person could convey the message easily and comfortably to a traditional one, who cannot understand the signing. This research paper shows the sign identity of human hand shape with four fingers and one thumb. The objective of this paper is to style a convenient system which is beneficial to negate any sort of barrier between a traditional person and a speech impaired person. The given gesture then dictates into the further text. The allowed system helps in performing dimensionality reduction. Nowadays researchers are paying more attention to form an outsized vocabulary for signing recognition systems.
Decentralised Blockchain Technology: Application in Banking Sector

With ever-evolving technologies, the banking systems can update from their traditional methodologies to a digital, immutable, distributed ledger that can be implemented via Blockchain. Blockchain Technology is a distributed peer to peer linked structure which can solve the problem of maintaining and recording transactions in a banking system. Blockchain provides properties like transparency, robustness, auditability, and security. This paper aims at giving these functionalities in a distributed banking system using blockchain, which will be at par with the current methodologies. It will also focus on the limitations while implementing blockchain and future scope.
EncELC: Hardening and Enriching Ethereum Light Clients with Trusted Enclaves

The rapid growth of Ethereum blockchain has brought extremely heavy overhead for coin owners or developers to bootstrap and access transactions on Ethereum. To address this, light client is enabled, which only stores a small fraction of blockchain data and relies on bootstrapped full nodes for transaction retrievals. However, because the retrieval requests are outsourced, it raises several severe concerns about the integrity of returned results and the leakage of sensitive blockchain access histories, largely hindering the wider adoption of this important lightweight design. In addition to security issues, the continuously increasing blockchain storage also urges for more effective query functionalities for the Ethereum blockchain, so as to enable more flexible and precise transaction retrievals.In this paper, we propose EncELC, a new Ethereum light client design that enforces full-fledged protections for clients and enables rich queries over the Ethereum blockchain. EncELC leverages trusted hardware (e.g., Intel SGX) as a starting point for building efficient yet secure processing, and further crafts several crucial performance and security refinement designs to boost query efficiency and conceal leakages inside and outside SGX enclave. We implement a prototype of EncELC and test its performance in several real settings, and the results have confirmed the practicality of EncELC.
End-to-End Full Projector Compensation

Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usually solve the two parts separately and may suffer from suboptimal solutions. In this paper, we propose the first end-to-end differentiable solution, named CompenNeSt++, to solve the two problems jointly. First, we propose a novel geometric correction subnet, named WarpingNet, which is designed with a cascaded coarse-to-fine structure to learn the sampling grid directly from sampling images. Second, we propose a novel photometric compensation subnet, named CompenNeSt, which is designed with a siamese architecture to capture the photometric interactions between the projection surface and the projected images, and to use such information to compensate the geometrically corrected images. By concatenating WarpingNet with CompenNeSt, CompenNeSt++ accomplishes full projector compensation and is end-to-end trainable. Third, to improve practicability, we propose a novel synthetic data-based pre-training strategy to significantly reduce the number of training images and training time. Moreover, we construct the first setup-independent full compensation benchmark to facilitate future studies. In thorough experiments, our method shows clear advantages over prior art with promising compensation quality and meanwhile being practically convenient.
Data Augmentation for Human Keypoint Estimation Deep Learning based Sign Language Translation

Deep learning technology has developed constantly and is applied in many fields. In order to correctly apply deep learning techniques, sufficient learning must be preceded. Various conditions are necessary for sufficient learning. One of the most important conditions is training data. Collecting sufficient training data is fundamental, because if the training data are insufficient, deep learning will not be done properly. Many types of training data are collected, but not all of them. So, we may have to collect them directly. Collecting takes a lot of time and hard work. To reduce this effort, the data augmentation method is used to increase the training data. Data augmentation has some common methods, but often requires different methods for specific data. For example, in order to recognize sign language, video data processed with openpose are used. In this paper, we propose a new data augmentation method for sign language data used for learning translation, and we expect to improve the learning performance, according to the proposed method.
STOIC: Serverless Teleoperable Hybrid Cloud for Machine Learning Applications on Edge Device

Serverless computing is a promising new event-driven programming model that was designed by cloud vendors to expedite the development and deployment of scalable web services on cloud computing systems. Using the model, developers write applications that consist of simple, independent, stateless functions that the cloud invokes on-demand (i.e. elastically), in response to system-wide events (data arrival, messages, web requests, etc.). In this work, we present STOIC (Serverless TeleOperable HybrId Cloud), an application scheduling and deployment system that extends the serverless model in two ways. First, it uses the model in a distributed setting and schedules application functions across multiple cloud systems. Second, STOIC supports serverless function execution using hardware acceleration (e.g. GPU resources) when available from the underlying cloud system. We overview the design and implementation of STOIC and empirically evaluate it using real-world machine learning applications and multi-tier (e.g. edge-cloud) deployments. We find that STOIC's combined use of edge and cloud resources is able to outperform using either cloud in isolation for the applications and datasets that we consider.
Optical Flow Estimation and Denoising of Video Images Based on Deep Learning Models

This work was
MagPrint: Deep Learning Based User Fingerprinting Using Electromagnetic Signals

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical to many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developed MagPrint, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting. MagPrint has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation using a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users, MagPrint achieves 94.3% accuracy in classifying users from these traces, which represents an 10.9% improvement over the state-of-the-art classification method.
Rethinking educational theory and practice in times of visual media: Learning as image-concept integration

We propose a new relational direction in higher education that acknowledges external and internal images as integrated in thinking and learning. We expand educational theory and practice that commonly rely on discrete conceptual developments that exclude images. Our argument epistemologically relies on certain semiotic views that consider the role of iconic signs and iconicity (meaning making by the virtue of similarity) as significant in relation to knowledge and learning. The analogical and imaginative work required to discover similarity between external pictures and any educational concept is a form of iconic mind work that opens a space for transformational thinking and creative solutions. The novelty of our argument lies in the schematic philosophy of mind by C.S. Peirce, and specifically, an adaptation of Peirce’s triadic sign diagram for learning, exemplified via an ‘inquiry graphic’ sign and reflective method. This outlook seeks connections in diversity, arguing for an education that acknowledges and teaches intrinsic human relatedness to other humans, beings and things in the environment, thus challenging any notions of anthropocentrism and superiority. It calls for a reaction in international higher education to acknowledge the iconicity of concepts and thinking and embrace integrated image-concept signs for deep and critical teaching-learning, towards a modest educational contribution to counteracting social and environmental crises.
Machine Learning in Action: Securing IAM API by Risk Authentication Decision Engine

In this work we show the implementation of practical machine learning (ML) techniques into a DevOps ecosystem. Our system Risk Authentication/Assessment Decision Engine (RADE) is a ML model that uses digital identity's attributes such as IP address, browser user agent, and user activity to estimate risk level of each single authentication attempt. The model is part of a risk evaluation application programming interface (API). We have developed a risk classifier model for identifying user risk level and validate our system on a sample of login data attributes.
On Lattice-Based Interactive Protocols: An Approach with Less or No Aborts

A canonical identification (CID) scheme is a 3-move protocol consisting of a commitment, challenge, and response. It constitutes the core design of many cryptographic constructions such as zero-knowledge proof systems and various types of signature schemes. Unlike number-theoretic constructions, CID in the lattice setting usually forces provers to abort and repeat the whole authentication process once the distribution of the computed response does not follow a target distribution independent from the secret key. This concept has been realized by means of rejection sampling, which makes sure that the secrets involved in a protocol are concealed after a certain number of repetitions. This however has a negative impact on the efficiency of interactive protocols because it leads to a number of communication rounds that is multiplicative in the number of aborting participants (or rejection sampling procedures). In this work we show how the CID scheme underlying many lattice-based protocols can be designed with smaller number of aborts or even without aborts. Our new technique exploits (unbalanced) binary hash trees and thus significantly reduces the communication complexity. We show how to apply this new method within interactive zero-knowledge proofs. We also present BLAZE $$^{+}$$ : a further application of our technique to the recently proposed lattice-based blind signature scheme BLAZE (FC’20). We show that BLAZE $$^{+}$$ has an improved performance and communication complexity compared to BLAZE while preserving the size of keys and signatures.
MFALNet: A Multiscale Feature Aggregation Lightweight Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation labels each pixel in high-resolution remote sensing (HRRS) images with a category. To tackle with the large size and complexity of HRRS images, this letter presents a novel multiscale feature aggregation lightweight network (MFALNet) for semantic segmentation. Unlike standard convolution, asymmetric depth-wise separable convolution residual (ADCR) unit is used to reduce the parameter size of the network and makes the optimized structure deeper but lightweight and less complex. The proposed network is an encoder–decoder structure, where multiscale feature aggregation is implemented in both the encoder and the decoder. The spatial self-attention block helps to capture long-range contextual information, and the gated convolution modules are further used for refining features when aggerating high- and low-level feature maps in the decoder. The proposed MFALNet has evaluated on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2-D semantic labeling contest open benchmark data set, and the experimental results prove that the scheme can obtain a better tradeoff between segmentation accuracy and computational efficiency compared with the state-of-the-art semantic segmentation models.