Reviewerly/exHarmony huggingface dataset:
Practical Inner Product Encryption with Constant Private Key

Inner product encryption, first introduced by Katz et al., is a type of predicate encryption in which a ciphertext and a private key correspond to an attribute vector and a predicate vector, respectively. Only if the attribute and predicate vectors satisfy the inner product predicate will the decryption in this scheme be correct. In addition, the ability to use inner product encryption as an underlying building block to construct other useful cryptographic primitives has been demonstrated in the context of anonymous identity-based encryption and hidden vector encryption. However, the computing cost and communication cost of performing inner product encryption are very high at present. To resolve this problem, we introduce an efficient inner product encryption approach in this work. Specifically, the size of the private key is only one G element and one Zp element, and decryption requires only one pairing computation. The formal security proof and implementation result are also demonstrated. Compared with other state-of-the-art schemes, our scheme is the most efficient in terms of the number of pairing computations for decryption and the private key length.
HDR (High Dynamic Range) Audio Wearable and Its Performance Visualization

We introduce a novel use of wearable technology to reconstruct and output high dynamic range (HDR) audio signals. The purpose of designing an end-to-end HDR audio wearable device is to create an audio device capable of replicating and even extending the dynamic range of human hearing. We also introduce a different method of visualizing audio by way of a rotary Sequential Wave Imprinting Machine (SWIM). The results show that real-time HDR audio processing and visualization are possible at the standard audio sampling rate of 44.1 kHz. Additionally, the quality of the recorded audio improved from the originally captured data after HDR processing. We further analyze the dependence of different audio gain combinations as well as the influence of the number of audio input channels on the quality of the HDR signal reconstruction in various acoustic environments.
Problems of the formation of digital competence in the modern educational space

Abstract The article examines the problems of digital competence as a phenomenon of modern life, especially aggravated in connection with the COVID-19 pandemic. Digital competence as the latest concept, skills necessary for the modern, connected with the scientific and technical implementation of new technologies in education, economy, politics and production. The development of digital literacy concepts relies on both traditional and diverse media. However, the main difficulty in its development is associated with the digital divide, overcoming which depends on many factors. In the educational environment, digital competence should take into account the age-related physiological, mental and psychological characteristics of schoolchildren, and in preparation for professional activity-support for professional digital competence. The work also provides the author’s version of the definition of the concept of "digital competence".
A Fall Risk Assessment Mechanism for Elderly People Through Muscle Fatigue Analysis on Data From Body Area Sensor Network

With the rapid growth and commercialization of wearable sensor technology, the research community foresees great potential of Body Area Sensor Network (BASN) applications in the healthcare industry, including elderly healthcare. We consider the issue of fall in elderly, which has severe consequences including death. Most of the related proposals in the literature are reactive. In this paper, we propose a proactive Fall Risk Assessment mechanism for elderly by performing Muscle Fatigue Analysis on data collected using BASN consisting of wearable sensors. We first discuss the in-house developed android application that connects with the wearable EMG sensors placed on the human body to collect data of four activities including walking, sitting and getting up from a chair, and ankle stretch (Dorsiflexion and planter flexion) in a controlled environment. Then we present our Fall Risk Assessment algorithm that computes the Fall Risk Assessment score based on various levels of muscle fatigue. We tested our algorithm using four features to determine muscle fatigue in two lower limb muscles i.e. Tibilias (TM) and Gastrocnemius (GM) muscles. The implementation results of various scenarios on Matlab are presented as the proof-of-concept. Our evaluation results of two lower limb muscles behavior in selected activities justify our claim that muscle fatigue in these two muscles could lead to falling in an elderly person.
Adaptation of the idea of concept drift to some behavioral biometrics: Preliminary studies

In this paper we present a novel strategy that utilizes concept drift to improve some biometric procedures. The proposed method can be applied whenever behavioral signals change and those changes need to be detected. From a security point of view, this is important because detection of and appropriate response to change should result in some alteration in the operation of the biometric system. As one example, this allows for the detection of legitimate and illegitimate users. Experiments performed on real biometric signals have demonstrated that the proposed techniques could be introduced into existing professional biometric systems based on behavioral features.
Survival Regression Models With Dependent Bayesian Nonparametric Priors

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. Supplementary materials for this article are available online.
Generating Out of Distribution Adversarial Attack Using Latent Space Poisoning

Traditional adversarial attacks rely upon the perturbations generated by gradients from the network which are generally safeguarded by gradient guided search to provide an adversarial counterpart to the network. In this paper, we propose a novel mechanism of generating adversarial examples where the actual image is not corrupted rather its latent space representation is utilized to tamper with the inherent structure of the image while maintaining the perceptual quality intact and to act as legitimate data samples. As opposed to gradient-based attacks, the latent space poisoning exploits the inclination of classifiers to model the independent and identical distribution of the training dataset and tricks it by producing out of distribution samples. We train a disentangled variational autoencoder (beta-VAE) to model the data in latent space and then we add noise perturbations using a class-conditioned distribution function to the latent space under the constraint that it is misclassified to the target label. Our empirical results on MNIST, SVHN, and CelebA dataset validate that the generated adversarial examples can easily fool robust l_0, l_2, l_inf norm classifiers designed using provably robust defense mechanisms.
Imitation Learning-Based Algorithm for Drone Cinematography System

Viewpoint selection for capturing human motion is an important task in autonomous aerial videography, animation, and virtual 3-D environments. Existing methods rely on heuristics for selecting the “best” viewpoint, which requires human effort to summarize and integrate viewpoint selection rules into a visual servo system to control a camera. In this work, we propose an integrated aerial filming system for autonomously capturing cinematic shots of action scenes on the basis of a set of demonstrations given for imitation. Our model, which is built on the basis of the deep deterministic policy gradient, takes a sequence of a subject’s skeleton and the camera pose as input and outputs the camera motion with an optimal viewpoint related to the subject. In addition, we design a spatial attention network to selectively focus on the discriminative joints of the skeleton within each frame. Given the demonstrations with human motions, our framework learns to predict the next best viewpoint by imitating the demonstrations for viewing the motion of the subject. Extensive experimental results in simulated and real outdoor environments demonstrate that our method can successfully mimic the viewpoint selection strategy and capture a more accurate viewpoint than state-of-the-art autonomous cinematography methods.
Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge

Distributed learning plays a key role in reducing the training time of modern deep neural networks with massive datasets. In this article, we consider a distributed learning problem where gradient computation is carried out over a number of computing devices at the wireless edge. We propose hierarchical broadcast coding, a provable coding-theoretic framework to speed up distributed learning at the wireless edge. Our contributions are threefold. First, motivated by the hierarchical nature of real-world edge computing systems, we propose a layered code which mitigates the effects of not only packet losses at the wireless computing nodes but also straggling access points (APs) or small base stations. Second, by strategically allocating data partitions to nodes in the overlapping areas between cells, our technique achieves the fundamental lower bound on computational load to combat stragglers. Finally, we take advantage of the broadcast nature of wireless networks by which wireless devices in the overlapping cell coverage broadcast to more than one AP. This further reduces the overall training time in the presence of straggling APs. Experimental results on Amazon EC2 confirm the advantage of the proposed methods in speeding up learning. Our design targets any gradient descent based learning algorithms, including linear/logistic regressions and deep learning.
Extreme ensemble of extreme learning machines

Abstract Extreme learning machine (ELM) has attracted attentions in pattern classification problems due to its preferences in low computations and high generalization. To overcome its drawbacks, caused by the randomness of input weights and biases, the ensemble of ELMs was proposed. The diversity of ELMs forming the ensemble was studied broadly in the literature, via using different activation functions and/or different number of hidden neurons. However, less attention was paid to aggregation mechanism in ensemble of ELMs. To speed up this aggregation process, we propose an ensemble framework for ELMs, called extreme ensemble of ELMs (EEoELMs) because of its extreme speed in ensemble process. In this framework, the input weights of each ELM are randomly pre‐assigned as usual. The ELMs make use of the same/distinct activation functions to increase the diversity of classifiers and so the generalization of ensemble. The output weights of each ELM are set using Moore–Penrose inverse method. However, the aggregation mechanism in EEoELM is novel. Instead of using majority/weighted voting on the prediction results of ELMs, their output neurons are combined in a new decision/ensemble layer. The output of this layer determines the ensemble output. As the weights of this decision layer are also computed using Moore–Penrose inverse method, the ensemble is extremely fast. Experimental results on synthetic and real‐world datasets indicate the acceptable classification performance of EEoELM in much less computational efforts.
Automated requirement sentences extraction from software requirement specification document

In the requirement reuse and natural language document-based Software Product Line (SPL) domain analysis, requirement sentences of the requirement document are the primary concern. Most studies conducted in this research area have document preprocessing stage in their methods that is a manual process to separate requirement sentences and non-requirement sentences from the document. This manual labor process might be tedious and error-prone since it will need much time and expert intervention to make this process completely done. In this paper, we present a method to automate requirement sentence extraction from the Software Requirement Specification (SRS) document by leveraging Natural Language Processing (NLP) approach and requirement boilerplate sentence patterns. Conducted experiments in this research show this method has such accuracy from 64% to 100% on precision value and recall value in the range of 64% to 89%.
Learning the boundary of inductive invariants

We study the complexity of invariant inference and its connections to exact concept learning. We define a condition on invariants and their geometry, called the fence condition, which permits applying theoretical results from exact concept learning to answer open problems in invariant inference theory. The condition requires the invariant's boundary---the states whose Hamming distance from the invariant is one---to be backwards reachable from the bad states in a small number of steps. Using this condition, we obtain the first polynomial complexity result for an interpolation-based invariant inference algorithm, efficiently inferring monotone DNF invariants with access to a SAT solver as an oracle. We further harness Bshouty's seminal result in concept learning to efficiently infer invariants of a larger syntactic class of invariants beyond monotone DNF. Lastly, we consider the robustness of inference under program transformations. We show that some simple transformations preserve the fence condition, and that it is sensitive to more complex transformations.
AwareME: Public Awareness through Game-Based Learning

It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: "AwareME" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The "AwareME" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of "AwareME" platform.
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning

Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data. Therefore, learning a joint distribution of seen-unseen classes and preserving the distinction between seen-unseen classes is crucial for GZSL methods. However, existing methods only learn the underlying distribution of seen data, although unseen class semantics are available in the GZSL problem setting. Most methods neglect retaining seen-unseen classes distinction and use the learned distribution to recognize seen and unseen data. Consequently, they do not perform well. In this work, we utilize the available unseen class semantics alongside seen class semantics and learn joint distribution through a strong visual-semantic coupling. We propose a bidirectional mapping coupled generative adversarial network (BMCoGAN) by extending the concept of the coupled generative adversarial network into a bidirectional mapping model. We further integrate a Wasserstein generative adversarial optimization to supervise the joint distribution learning. We design a loss optimization for retaining distinctive information of seen-unseen classes in the synthesized features and reducing bias towards seen classes, which pushes synthesized seen features towards real seen features and pulls synthesized unseen features away from real seen features. We evaluate BMCoGAN on benchmark datasets and demonstrate its superior performance against contemporary methods.
Voice Interaction for Augmented Reality Navigation Interfaces with Natural Language Understanding

Voice interaction with natural language understanding (NLU) has been extensively explored in desktop computers, handheld devices, and human-robot interaction. However, there is limited research into voice interaction with NLU in augmented reality (AR). There are benefits of using voice interaction in AR, such as high naturalness and being hands-free. In this project, we introduce VOARLA, an NLU-powered AR voice interface, which navigate courier driver delivery a package. A user study was completed to evaluate VOARLA against an AR voice interface without NLU to investigate the effectiveness of NLU in the navigation interface in AR. We evaluated from three aspects: accuracy, productivity, and commands learning curve. Results found that using NLU in AR increases the accuracy of the interface by 15%. However, higher accuracy did not correlate to an increase in productivity. Results suggest that NLU helped users remember the commands on the first run when they were unfamiliar with the system. This suggests that using NLU in an AR hands-free application can make the learning curve easier for new users.
A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, becomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing de-identified face images according to a single input. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing de-identified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.
Research Report: Building a Wide Reach Corpus for Secure Parser Development

Computer software that parses electronic files is often vulnerable to maliciously crafted input data. Rather than relying on developers to implement ad hoc defenses against such data, the Language-theoretic security (LangSec) philosophy offers formally correct and verifiable input handling throughout the software development lifecycle. Whether developing from a specification or deriving parsers from samples, LangSec parser developers require wide-reach corpora of their target file format in order to identify key edge cases or common deviations from the format's specification. In this research report, we provide the details of several methods we have used to gather approximately 30 million files, extract features and make these features amenable to search and use in analytics. Additionally, we provide documentation on opportunities and limitations of some popular open-source datasets and annotation tools that will benefit researchers which need to efficiently gather a large file corpus for the purposes of LangSec parser development.
Assessment of Cyber Security Implications of New Technology Integrations into Military Supply Chains

Military supply chains play a critical role in the acquisition and movement of goods for defence purposes. The disruption of these supply chain processes can have potentially devastating affects to the operational capability of military forces. The introduction and integration of new technologies into defence supply chains can serve to increase their effectiveness. However, the benefits posed by these technologies may be outweighed by significant consequences to the cyber security of the entire defence supply chain. Supply chains are complex Systems of Systems, and the introduction of an insecure technology into such a complex ecosystem may induce cascading system-wide failure, and have catastrophic consequences to military mission assurance. Subsequently, there is a need for an evaluative process to determine the extent to which a new technology will affect the cyber security of military supply chains. This work proposes a new model, the Military Supply Chain Cyber Implications Model (M-SCCIM), that serves to aid military decision makers in understanding the potential cyber security impact of introducing new technologies to supply chains. M-SCCIM is a multiphase model that enables understanding of cyber security and supply chain implications through the lenses of theoretical examinations, pilot applications and system wide implementations.
Ontology - based context resolution in internet of things enabled diagnostics

Internet of things (IoT)-generated data from industrial systems are often collected in non-actionable form, thus not directly aiding maintenance actions. Context information management is often seen as an enabler for interoperability and context-based service adaptation, acting as a mechanism for linking data with knowledge to adaptive data and services. Ontology-based approaches for semantic maintenance have been proposed in the past as a data and service mediation mechanism and are adopted here as the starting point employed to develop a context resolution service for industrial diagnostics. The underlying ontology of the context resolution mechanism is relevant to failure analysis of mechanical components. The terminology and relationship between concepts are structured on the basis of relevant standards with a reliability-oriented knowledge grounding. A reasoning mechanism is employed to deliver context resolution and the derived context can add a metadata layer on data or events generated by automated and human-driven means. The approach is applied on a gearbox test rig appropriate for emulating complex misalignment cases met in many manufacturing and aerospace applications.
Effective Elastic Scaling of Deep Learning Workloads

We examine the elastic scaling of Deep Learning (DL) jobs and propose a novel resource allocation strategy for DL training jobs, resulting in improved job run time performance as well as increased cluster utilization. We begin by analyzing DL workloads and exploit the fact that DL jobs can be run with a range of batch sizes without affecting their final accuracy. We formulate an optimization problem that explores a dynamic batch size allocation to individual DL jobs based on their scaling efficiency, when running on multiple nodes. We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs. We demonstrate empirically that our elastic scaling algorithm can complete up to as many jobs as compared to a strong baseline algorithm that also scales the number of GPUs but does not change the batch size, with average completion times up to faster.