Reviewerly/exHarmony huggingface dataset:
An EOG-Based Human–Machine Interface to Control a Smart Home Environment for Patients With Severe Spinal Cord Injuries

This paper presents an asyn-chronous electrooculography (EOG)-based human-machine interface (HMI) for smart home environmental control with the purpose of providing daily assistance for severe spinal cord injury (SCI) patients.The proposed HMI allows users to interact with a smart home environment through eye blinking. Specifically, several buttons, each corresponding to a control command, randomly flash on a graphical user interface. Each flash of the buttons functions as a visual cue for the user to blink. To issue a control command, the user can blink synchronously with the flashes of the corresponding button. Through detecting blinks based on the recorded EOG signal, the target button and its corresponding control command are determined. Seven SCI patients participated in an online experiment, during which the patients were required to control a smart home environment including household electrical appliances, an intelligent wheelchair, as well as a nursing bed via the proposed HMI.The average false operation ratio in the control state was 4.1%, whereas during the idle state, no false operations occurred.All SCI patients were able to control the smart home environment using the proposed EOG-based HMI with satisfactory performance in terms of the false operation ratio in both the control and the idle states.The proposed HMI offers a simple and effective approach for patients with severe SCIs to control a smart home environment. Therefore, it is promising to assist severe SCI patients in their daily lives.
Email fraud: The search for psychological predictors of susceptibility

Decisions that we make about email legitimacy can result in a pernicious threat to security of both individuals and organisations. Yet user response to phishing emails is far from uniform; some respond while others do not. What is the source of this diversity in decision-making? From a psychological perspective, we consider cognitive and situational influences that might explain why certain users are more susceptible than others. Alongside an email judgment task employed as a proxy for fraud susceptibility, 224 participants completed a range of cognitive tasks. In addition, we manipulated time pressure for email legitimacy judgments. We identify cognitive reflection and sensation seeking as significant, albeit modest, predictors of susceptibility. Further to this, participants asked to make quicker responses made more judgment errors. We conclude there are cognitive signatures that partially contribute to email fraud susceptibility, with implications for efforts to limit online security breaches and train secure behaviors.
Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems

Repeat consumption is a common scenario in daily life, such as repurchasing items and revisiting websites, and is a critical factor to be taken into consideration for recommender systems. Temporal dynamics play important roles in modeling repeat consumption. It is noteworthy that for items with distinct lifetimes, consuming tendency for the next one fluctuates differently with time. For example, users may repurchase milk weekly, but it is possible to repurchase mobile phone after a long period of time. Therefore, how to adaptively incorporate various temporal patterns of repeat consumption into a holistic recommendation model has been a new and important problem.
Reviewing and Benchmarking Parameter Control Methods in Differential Evolution

Many differential evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral component of a complex DE algorithm. Thus, the characteristics and performance of each method are poorly understood. We present an in-depth review of 24 PCMs for the scale factor and crossover rate in DE and a large-scale benchmarking study. We carefully extract the 24 PCMs from their original, complex algorithms and describe them according to a systematic manner. Our review facilitates the understanding of similarities and differences between existing, representative PCMs. The performance of DEs with the 24 PCMs and 16 variation operators is investigated on 24 black-box benchmark functions. Our benchmarking results reveal which methods exhibit high performance when embedded in a standardized framework under 16 different conditions, independent from their original, complex algorithms. We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.
Learning-Aided User Identification Using Smartphone Sensors for Smart Homes

Smart homes expects to improve the convenience, comfort, and energy efficiency of the residents by connecting and controlling various appliances. As the personal information and computing hub for smart homes, smartphones allow people to monitor and control their homes anytime and anywhere. Therefore, the security and privacy of smartphones and the stored data are crucial in smart homes. To protect smartphones from potential attacks, various built-in sensors can be utilized for user authentication/identification and access control to achieve enhanced security. In this paper, we propose a framework, smartphone sensor user identification (SSUI), in order to facilitate user identification based on the relationships between different types of sensor data and smartphone users. Specifically in SSUI, the time and frequency features are extracted and learned separately using convolution neural network (CNN). The CNN outputs are then processed using recurrent neural network, according to several time bins. Using both of our own dataset (collected from 17 participants) and a publicly available dataset (i.e., Heterogeneity Dataset for Human Activity Recognition), we demonstrate the effectiveness of the proposed SSUI framework, where we achieve an accuracy rate of over 91.45% in various scenarios.
High speed FPGA-based chaotic oscillator design

In this study, autonomous Lü-Chen (2002) chaotic system has been implemented on FPGA using Heun numerical algorithm in VHDL 32-bit IQ-Math fixed-point number format for developing embedded chaos-based engineering applications. The core units like multiplier, adder and subtractor units, which are compatible with fixed-point number standart used in FPGA-based design, have been created by IP CORE Generator that developed for Xilinx. The designed system has been simulated and synthesized for the Virtex–6 FPGA chip. The chip statistics obtained from Place&Route processing of FPGA-based system and the phase portraits of the results received from the outputs have been presented. The results obtained from FPGA-based Lü-Chen chaotic oscillator have been compared with the computer-based results and the accuracy of the digital circuit-based design has been certified with the successful results. The maximum operating frequency of designed FPGA-based Lü-Chen chaotic oscillator is 464.688 MHz. The performed FPGA-based Lü-Chen chaotic oscillator presents the highest operating frequency among the others in the literature. Besides, the design provides better results than the other 3D FPGA-based chaotic oscillator structures with respect to FPGA resource utilization. In addition, MSE and RMSE accuracy analyzes were performed on the designed chaotic oscillator. The design provides the highest operating frequency among the others in the literature. In addition, it presents better results than the alternatives with respect to FPGA resource utilization. For this reason, this study demonstrates that hardware-based design of Lü-Chen chaotic system can be used in various chaos-based embedded system applications including cryptography, secure communication and random number generation.
DECODE: Deep Confidence Network for Robust Image Classification

Recent years have witnessed the success of deep convolutional neural networks for image classification and many related tasks. It should be pointed out that the existing training strategies assume that there is a clean dataset for model learning. In elaborately constructed benchmark datasets, deep network has yielded promising performance under the assumption. However, in real-world applications, it is burdensome and expensive to collect sufficient clean training samples. On the other hand, collecting noisy labeled samples is very economical and practical, especially with the rapidly increasing amount of visual data in the web. Unfortunately, the accuracy of current deep models may drop dramatically even with 5%-10% label noise. Therefore, enabling label noise resistant classification has become a crucial issue in the data driven deep learning approaches. In this paper, we propose a DEep COnfiDEnce network (DECODE) to address this issue. In particular, based on the distribution of mislabeled data, we adopt a confidence evaluation module that is able to determine the confidence that a sample is mislabeled. With the confidence, we further use a weighting strategy to assign different weights to different samples so that the model pays less attention to low confidence data, which is more likely to be noise. In this way, the deep model is more robust to label noise. DECODE is designed to be general, such that it can be easily combined with existing studies. We conduct extensive experiments on several datasets, and the results validate that DECODE can improve the accuracy of deep models trained with noisy data.
Construction of S-Box Based on Chaotic Map and Algebraic Structures

The Advanced Encryption Standard (AES) is widely used in different kinds of security applications. The substitution box (S-box) is the main component of many modern symmetric encryption ciphers that provides confusion between the secret key and ciphertext. The S-box component that is used in AES is fixed. If we construct this component dynamically, the encryption strength of AES would be greater than before. In this manuscript, we used chaotic logistic map, Mobius transformation and symmetric group S 256 to construct S-box for AES. The idea behind the proposed work is to make supplementary safe S-box. The presented S-box is analyzed for the following analyses: linear approximation probability (LP), nonlinearity (NL), differential approximation probability (DP), strict avalanche criterion (SAC), and bit independence criterion (BIC). The analyses show that the proposed technique is useful in generating high resistance S-box to known attacks.
Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage.We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5".The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.
Towards a Decentralized Process for Scientific Publication and Peer Review using Blockchain and IPFS

The current processes of scientific publication and peer review raise concerns around fairness, quality, performance, cost, and accuracy.The Open Access movement has been unable to fulfill all its promises, and a few middlemen publishers can still impose policies and concentrate profits.This paper, using emerging distributed technologies such as Blockchain and IPFS, proposes a decentralized publication system for open science.The proposed system would provide (1) a distributed reviewer reputation system, (2) an Open Access by-design infrastructure, and (3) transparent governance processes.A survey is used to evaluate the problems, proposed solutions and possible adoption resistances, while a working prototype serves as a proof-of-concept.Additionally, the paper discusses the implementation, in a distributed context, of different privacy settings for both open peer review and reputation systems, introducing a novel approach supporting both anonymous and accountable reviews.The paper concludes reviewing the open challenges of this ambitious proposal.
From Hype to Reality: A Taxonomy of Blockchain Applications

Blockchain is a decentralized digital ledger that challenges existing business models and theories by shifting the trust from institutions towards algorithms.However, the number of successfully developed blockchain-based systems remains low.This points towards a research gap between blockchain applications and technical blockchain characteristics.We answer the research question: What application areas fit blockchains with what technical characteristics?We develop a taxonomy, which comprises six blockchain application areas that are classified across eight technical dimensions.We demonstrate the utility of the taxonomy on ninetynine blockchain-based systems.We contribute to the scientific literature by delimiting blockchain application areas, identifying new technical dimensions, and linking application and technical knowledge on blockchain to guide development of blockchain-based systems.For practitioners, we present an overview of current blockchain-based systems.
Low-Power Computer Vision: Status, Challenges, and Opportunities

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions, and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries, and energy efficiency is critical. This paper serves the following two main purposes. First, examine the state of the art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This paper summarizes the 2018 winners’ solutions. Second, suggest directions for research as well as opportunities for low-power computer vision.
Requet

As video traffic dominates the Internet, it is important for operators to detect video Quality of Experience (QoE) in order to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a Machine Learning (ML) algorithm to predict QoE metrics, specifically, buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12X, 1.53X, and 3.14X, respectively.
Quantifying entanglement of formation for two-mode Gaussian states: Analytical expressions for upper and lower bounds and numerical estimation of its exact value

Entanglement of formation quantifies the entanglement of a state in terms of the entropy of entanglement of the least entangled pure state needed to prepare it. An analytical expression for this measure exists only for special cases, and finding a closed formula for an arbitrary state remains an open problem. In this work we focus on two-mode Gaussian states, and we derive narrow upper and lower bounds for the measure that get tight for several special cases. Further, we show that the problem of calculating the actual value of the entanglement of formation for arbitrary two-mode Gaussian states reduces to a trivial single parameter optimization process, and we provide an efficient algorithm for the numerical calculation of the measure.
High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays

Optical neural networks (ONNs) have become competitive candidates for the next generation of high-performance neural network accelerators because of their low power consumption and high-speed nature. Beyond fully-connected neural networks demonstrated in pioneer works, optical computing hardwares can also conduct convolutional neural networks (CNNs) by hardware reusing. Following this concept, we propose an optical convolution unit (OCU) architecture. By reusing the OCU architecture with different inputs and weights, convolutions with arbitrary input sizes can be done. A proof-of-concept experiment is carried out by cascaded acousto-optical modulator arrays. When the neural network parameters are ex-situ trained, the OCU conducts convolutions with SDR up to 28.22 dBc and performs well on inferences of typical CNN tasks. Furthermore, we conduct in situ training and get higher SDR at 36.27 dBc, verifying the OCU could be further refined by in situ training. Besides the effectiveness and high accuracy, the simplified OCU architecture served as a building block could be easily duplicated and integrated to future chip-scale optical CNNs.
High-resolution few-pattern method for 3D optical measurement

Accurately and quickly obtaining the three-dimensional (3D) shape information of objects has become increasingly important in various scientific fields. However, simultaneously achieving the high-resolution and high-speed 3D shape measurement of unknown objects remains challenging in practice. In this Letter, we propose a novel variant shifting-phase method for 3D optical measurement. Based on a digital fringe projection system, the method performs a point-wise high-resolution measurement of objects by projecting only four intensity-coded patterns. We can retrieve the wrapped phases and their corresponding fringe orders simultaneously from these four patterns, and do not require any pre-acquired information of the object. The experimental results successfully demonstrate the effectiveness of the easy-to-operate method.
XONN: XNOR-based oblivious deep neural network inference

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN (pronounced /z∧n/), a novel end-to-end framework based on Yao's Garbled Circuits (GC) protocol, that provides a paradigm shift in the conceptual and practical realization of oblivious inference. In XONN, the costly matrixmultiplication operations of the deep learning model are replaced with XNOR operations that are essentially free in GC. We further provide a novel algorithm that customizes the neural network such that the runtime of the GC protocol is minimized without sacrificing the inference accuracy.We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. We further provide a compiler to translate the model description from high-level Python (i.e., Keras) to that of XONN. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7×, MiniONN (ACM CCS'17) by 93×, and SecureML (IEEE S&P'17) by 37×. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis. The datasets include breast cancer, diabetes, liver disease, and Malaria.
An efficient and scalable privacy preserving algorithm for big data and data streams

A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.