Reviewerly/exHarmony huggingface dataset:
An optical multiple-image authentication based on transport of intensity equation

An optical multiple-image authentication approach based on transport of intensity equation technique has been proposed. Initially, a phase-encoded plaintext is synthesized with significant blocks chosen from the multiple plain images by evaluating their spatial frequency coefficients, which is bonded with a random intensity mask generated with logistic map to constitute the complex amplitude. Then, the complex amplitude is encrypted to a real-valued ciphertext with noise-like distribution by using Fresnel diffraction. In the process of authentication, the phase information is firstly reconstructed by solving transport of intensity equation. The existence of a plain image can be identified by calculating the nonlinear correlation between it and its partial data only containing the extracted significant blocks from the phase information by aid of the corresponding binary mask. To our best knowledge, it is the first time to apply the transport of intensity equation technique to implement the optical multiple-image authentication. A set of numerical simulations are carried out to demonstrate the feasibility of the proposed approach.
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicroscopy images, still today have a low number of informative pixels which hampers their quality. Post-processing techniques, such as Super-Resolution (SR), are a potential solution to increase the quality of these images. SR techniques are often supervised, requiring aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to train a model. However, in our domain, the lack of HR images hinders the collection of such pairs and makes supervised training unsuitable. For this reason, we propose an unsupervised SR framework based on an adversarial deep neural network with a physically-inspired cycle consistency, designed to impose some acquisition properties on the super-resolved images. Our framework can exploit HR images, regardless of the domain where they are coming from, to transfer the quality of the HR images to the initial LR images. This property can be particularly useful in all situations where pairs of LR/HR are not available during the training. Our quantitative analysis, validated using a database of 238 endomicroscopy video sequences from 143 patients, shows the ability of the pipeline to produce convincing super-resolved images. A Mean Opinion Score (MOS) study also confirms this quantitative image quality assessment.
Improving embedding efficiency for digital steganography by exploiting similarities between secret and cover images

Digital steganography is becoming a common tool for protecting sensitive communications in various applications such as crime/terrorism prevention whereby law enforcing personals need to remotely compare facial images captured at the scene of crime with faces databases of known criminals/suspects; exchanging military maps or surveillance video in hostile environment/situations; privacy preserving in the healthcare systems when storing or exchanging patient’s medical images/records; and prevent bank customers’ accounts/records from being accessed illegally by unauthorized users. Existing digital steganography schemes for embedding secret images in cover image files tend not to exploit various redundancies in the secret image bit-stream to deal with the various conflicting requirements on embedding capacity, stego-image quality, and undetectibility. This paper is concerned with the development of innovative image procedures and data hiding schemes that exploit, as well as increase, similarities between secret image bit-stream and the cover image LSB plane. This will be achieved in two novel steps involving manipulating both the secret and the cover images, prior to embedding, to achieve higher 0:1 ratio in both the secret image bit-stream and the cover image LSB plane. The above two steps strategy has been exploited to use a bit-plane(s) mapping technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This paper will demonstrate that this strategy produces stego-images that have minimal distortion, high embedding efficiency, reasonably good stego-image quality and robustness against 3 well-known targeted steganalysis tools.
IoT Big Data Security and Privacy Versus Innovation

In this paper, we address the conflict in the collection, use, and management of Big Data at the intersection of security and privacy requirements and the demand of innovative uses of the data. This problem is exaggerated in the context of the Internet of Things (IoT). We propose a three-part decomposition of the design space, in order to clarify requirements and constraints. To reach this final analysis, we begin by clarifying the challenges in the design space: 1) there is a little agreement on what is meant by IoT, and in particular the security and privacy implications of different definitions; 2) we then consider the requirement and constraints on the Big Data that result from various IoT system designs; and 3) in parallel, we examine the intricacies of the demand for innovation from both the legal and economic perspectives. In this context, we then can decompose the set of drivers and objectives for security/privacy of data as well as innovation into: 1) the regulatory and social policy context; 2) economic and business context; and 3) technology and design context. By identifying these distinct objectives for the design of IoT Big Data management, we propose that more effective design and control is possible at the intersection of these forces, through an iterative process of review and redesign.
Emerging Blockchain Technology Solutions for Modern Healthcare Infrastructure

With a growing trend in medicine towards individualized, patient-centric care, traditional health information technology limits progress. With high administrative costs and the lack of universal data access, contemporary electronic medical records serve more the institution rather than the patient. Blockchain technology, as presently described, was initially developed for use in financial markets, serving as a decentralized, distributed ledger of transactions. However, certain inherent characteristics of this technology suit it for use in the healthcare sector. Potential applications of the blockchain in medicine include interoperable health data access, data storage and security, value-based payment mechanisms, medical supply chain efficiency, amongst others. While the technology remains in nascent stages, it is essential that members of the healthcare community understand the fundamental concepts behind blockchain, and recognize its potential impact on the future of medical care.
The Observed Effects of Utility-Scale Photovoltaics on Near-Surface Air Temperature and Energy Balance

Abstract Utility-scale solar power plants are a rapidly growing component of the renewable energy sector. While most agree that solar power can decrease greenhouse gas emissions, the effects of photovoltaic (PV) systems on surface energy exchanges and near-surface meteorology are not well understood. This study presents data from two eddy covariance observational towers, placed within and adjacent to a utility-scale PV array in southern Arizona. The observational period (October 2017–July 2018) includes the full range of annual temperature variation. Average daily maximum 1.5-m air temperature at the PV array was 1.3°C warmer than the reference (i.e., non-PV) site, whereas no significant difference in 1.5-m nocturnal air temperature was observed. PV modules captured the majority of solar radiation and were the primary energetically active surface during the day. Despite the removal of energy by electricity production, the modules increased daytime net radiation Q * available for partitioning by reducing surface albedo. The PV modules shift surface energy balance partitioning away from upward longwave radiation and heat storage and toward sensible heat flux Q H because of their low emissivity, low heat capacity, and increased surface area and roughness, which facilitates more efficient Q H from the surface. The PV modules significantly reduce ground heat flux Q G storage and nocturnal release, as the soil beneath the modules is well shaded. Our work demonstrates the importance of targeted observational campaigns to inform process-based understanding associated with PV systems. It further establishes a basis for observationally based PV energy balance models that may be used to examine climatic effects due to large-scale deployment.
Fractional-order 4D hyperchaotic memristive system and application in color image encryption

In this paper, some properties of the fractional-order four-dimensional (4D) hyperchaotic memristive system are analyzed by the phase diagram, Lyapunov exponent spectrum and bifurcation diagram according to the Adomian decomposition method. Based on the chaotic system, a color image encryption scheme is proposed through combining the DNA sequence operation. The algorithm simulation results and security feature analysis show that the encryption scheme has good encryption effect and high safety performance, which provides an experimental basis and theoretical guidance for the safe transmission of image information.
A Survey on Graph Processing Accelerators: Challenges and Opportunities

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond what those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerators. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation, and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware configurations. We finally present and discuss several challenges in details, and further explore the opportunities for the future research.
Enhanced Elephant Herding Optimization for Global Optimization

The elephant herding optimization (EHO) algorithm is a relatively novel population-based optimization technique, which mimics herding behavior and can be modeled into two operators: clan updating operators and separating operators.Also, in the literature, EHO has received a great deal of attention from researchers since it was proposed applied to many application fields for its advantages of excellent global optimization ability and ease of implementation.However, there is still an insufficiency in the EHO algorithm regarding its lack of exploitation, which leads to slow convergence.In this paper, we propose three enhanced versions of EHO based on the γ value termed EEHO15, EEHO20, and EEHO25 to overcome the problems of fast unjustified convergence toward the origin of the basic EHO.The exploration/exploitation abilities of the EEHO algorithms are achieved by the updating of the two operators (clan and separation operator).To tackle this drawback, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms in general.Moreover, we utilize CEC'17 test suite benchmark functions to test the performance of the proposed three versions of EEHO against EHO, particle swarm optimization (PSO), bird swarm algorithm (BSA), and ant lion optimizer (ALO) algorithms.Eventually, the experimental results revealed that the proposed EEHO algorithms extremely obtained better results compared with other competitive algorithms.
Blockchain based smart energy trading platform using smart contract

The energy market is entering the transitional period, and various types of energy markets such as solar energy will be formed beyond oil and gas. Correspondingly, energy prosumers that individuals and institutions produce and trade surplus electricity will become more widespread. Using the block chain, it guarantees the immutability and transparency of energy transactions, generates ERC20 tokens based on smart contracts, and transactions are automatically executed without third party intervention and can be extended to various transaction conditions. In the transaction, the energy is transferred using the Energy Storage System(ESS) which the seller and the buyer belong, and payment is made by transferring the token through a transaction. Based on this information, this paper suggests proposes a peer-to-peer (P2P) system that can freely trade the produced energy.
Doing more with less: meta-reasoning and meta-learning in humans and machines

Artificial intelligence systems use an increasing amount of computation and data to solve very specific problems. By contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. We identify two abilities that we see as crucial to this kind of general intelligence: meta-reasoning (deciding how to allocate computational resources) and meta-learning (modeling the learning environment to make better use of limited data). We summarize the relevant AI literature and relate the resulting ideas to recent work in psychology.
Heterogeneous Isolated Execution for Commodity GPUs

Traditional CPUs and cloud systems based on them have embraced the hardware-based trusted execution environments to securely isolate computation from malicious OS or hardware attacks. However, GPUs and their cloud deployments have yet to include such support for hardware-based trusted computing. As large amounts of sensitive data are offloaded to GPU acceleration in cloud environments, ensuring the security of the data is a current and pressing need. As deployed today, the outsourced GPU model is vulnerable to attacks from compromised privileged software. To support isolated remote execution on GPUs even under vulnerable operating systems, this paper proposes a novel hardware and software architecture, called HIX (Heterogeneous Isolated eXecution). HIX does not require modifications to the GPU architecture to offer protections: Instead, it offers security by modifying the I/O interconnect between the CPU and GPU, and by refactoring the GPU device driver to work from within the CPU trusted environment. A result of the architectural choices behind HIX is that the concept can be applied to other offload accelerators besides GPUs. This work implements the proposed HIX architecture on an emulated machine with KVM and QEMU. Experimental results from the emulated security support with a real GPU show that the performance overhead for security is curtailed to 26% on average for the Rodinia benchmark, while providing secure isolated GPU computing.
Adaptive guidance and integrated navigation with reinforcement meta-learning

This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt in real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four challenging environments with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a RL meta-learning optimized policy to implement a guidance law using observations consisting of only Doppler radar altimeter readings in a Mars landing environment, and LIDAR altimeter readings in an asteroid landing environment thus integrating guidance and navigation.
A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics

A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network width and training data size are considered. In the over-parametrized regime, it is shown that gradient descent dynamics can achieve zero training loss exponentially fast regardless of the quality of the labels. In addition, it is proved that throughout the training process the functions represented by the neural network model are uniformly close to that of a kernel method. For general values of the network width and training data size, sharp estimates of the generalization error is established for target functions in the appropriate reproducing kernel Hilbert space.
Efficient and privacy preserving access control scheme for fog-enabled IoT

The fog-to-things paradigm is introduced to mitigate the heavy burden on the edge of cloud-based network due to the centralized processing and storing of the massive volume of IoT data. Fog-enabled IoT architectures ensure small latency and enough computing resource that enables real time devices and applications. However, there still exist security and privacy challenges on data access control for fog-enabled IoT. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in cloud-fog computing systems. In this paper, we propose an efficient and privacy preserving outsourced multi-authority access control scheme, named PPO-MACS. All attributes of users are transformed to be anonymous and authenticable to realize privacy preserving. And the verifiable outsourced decryption is introduced to reduce computation overheads on the end user side. Meanwhile, an efficient user revocation method is proposed. Security and performance analysis show that our scheme is secure and highly efficient.
Nodes in the Bitcoin Network: Comparative Measurement Study and Survey

Bitcoin is a decentralized digital currency that has gained significant attention and growth in recent years. Unlike traditional currencies, Bitcoin does not rely on a centralized authority to control the supply, distribution, and verification of the validity of transactions. Instead, Bitcoin relies on a peer-to-peer (P2P) network of volunteers to distribute pending transactions and confirmed blocks, to verify transactions, and to collectively implement a replicated ledger that everyone agrees on. This P2P network is at the heart of Bitcoin and many other blockchain technologies. In this paper, we present a comparative measurement study of nodes in the Bitcoin network. We measure and analyze how many the so-called “volunteers” are in the Bitcoin P2P network by scanning the live Bitcoin network for 37 days in 2018 and compare them with the data reported by prior work in 2013~2016. This paper is motivated by the fact that Bitcoin has experienced explosive growth in terms of a number of users, transactions, value, and interest over a recent couple of years. Our investigation includes the IP addresses of Bitcoin nodes, size of the network, power law in the geographic distribution, protocol, and client versions, and network latencies and shows how today's network is different from early days. In addition, based on the observations made from the measurement study, we propose a simple distance-based peer selection rule for improved connectivity and faster data propagation. The evaluation results show that our proposed lightweight and backward-compatible peer selection rule has the potential to reduce data dissemination latency.
Effects of virtual presence and learning outcome using low-end virtual reality systems

Low-cost technology is essential to integrate Virtual Reality (VR) into educative institutions around the world. However, low-cost technology usually refers to low-end technology, which may compromise the level of immersion of the VR system. This study evaluates whether low-end and high-end VR systems achieve a comparable learning outcome regardless their immersion level. We also analyze the relationship between virtual presence and the learning outcome arising from a VR educational experience. An evaluation with 42 participants was conducted. We measured learning outcome and virtual presence under three different configurations, namely: a desktop computer, a low-end VR system, and a high-end VR system. The impact of simulator sickness was also analyzed. Results revealed a lower learning outcome in the less immersive configuration (i.e. desktop) and a similar learning outcome in both low-end and high-end VR systems. Even though low-end VR systems are less immersive and produce a lower level of virtual presence than high-end VR systems, the results support the use of low-end VR systems for educative applications.
The causal relationship between financial development and economic growth in Africa

Previous empirical studies on the causal relationship between financial development and economic growth are not instructive given their failure to unearth the causality trend across the different time periods. Using a more recently developed and robust indicator of financial development, we revisit the causal relationship between financial development and economic growth within the framework of a frequency-domain spectral causality technique which allows the causality to vary across time. Using data from 47 African countries over the period 1980–2016, our findings largely suggest that, even though there is some evidence of demand-following, supply-leading and feedback hypotheses, for most part, we find strong support of neutrality hypothesis. Thus, financial development and economic growth at most frequency levels evolve independently. We infer that caution must be exercised in making general conclusions about the causal nexus between financial development and economic growth.
Development of an Enhanced Threshold-Based Fall Detection System Using Smartphones With Built-In Accelerometers

Falls are a primary accident for elderly people living independently. Obviously, timely and accurate fall detection is critical to reduce the injuries and avoid the loss of life. In order to improve existing smartphone-based fall detection systems, this paper investigates the features of triaxial acceleration values acquired from built-in accelerometers of a smartphone, identifies crucial thresholds of the falls and non-falls, and then proposes an enhanced threshold-based fall detection approach, which could not only distinguish fall events from the most of daily activities (including walking, running, and sitting down), but also support four directions (forward, backward, left lateral, and right lateral) of the falls. In addition, once a falling accident is identified, the user position would be instantaneously transmitted to an emergency center in order to have timely medical assistance. As a consequence, experimental results show the feasibility of our enhanced approach with accuracy and detection rates of 99.38% and 96%, respectively, while a set of 650 test activities including 11 different kinds of daily activities are performed.