Reviewerly/exHarmony huggingface dataset:
0EI2FA: Zero Effort Indoor Two Factor Authentication

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor is something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In conventional 2FA systems, a user is required to interact (e.g., typing a passcode) to implement the second layer of authentication, which is not very user-friendly. Nowadays, smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cell phone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in various ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster.Moreover, the broadcast messages can be received only within a range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In this paper, 0EI2FA is proposed, a zero-effort two-factor authentication (2FA) scheme based on something that is in the user’s environment (ambient access points). In our research, data from the broadcast messages are utilized to implement a 2FA scheme by determining whether two devices are proximate or not to ensure that they belong to the same user.
Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data

This paper introduces Taco-VC, a novel architecture for voice conversion based on Tacotron synthesizer, which is a sequence-to-sequence with attention model. The training of multi-speaker voice conversion systems requires a large number of resources, both in training and corpus size. Taco-VC is implemented using a single speaker Tacotron synthesizer based on Phonetic PosteriorGrams (PPGs) and a single speaker WaveNet vocoder conditioned on mel spectrograms. To enhance the converted speech quality, and to overcome over-smoothing, the outputs of Tacotron are passed through a novel speech-enhancement network, which is composed of a combination of the phoneme recognition and Tacotron networks. Our system is trained just with a single speaker corpus and adapts to new speakers using only a few minutes of training data. Using mid-size public datasets, our method outperforms the baseline in the VCC 2018 SPOKE non-parallel voice conversion task and achieves competitive results compared to multi-speaker networks trained on large private datasets.
Device Scheduling for Energy-Efficient Federated Learning over Wireless Network Based on TDMA Mode

With the help of edge computing, Federated Learning (FL) has become a promising machine learning model training paradigm. It enables users to train model on their devices without uploading private data to the cloud servers for centralized training, thus avoiding security risks. In FL, the edge server, served as the training organizer, schedules devices in its coverage to join in a model training activity. The devices receive model parameters from edge server and update this parameters based on its local dataset. However, this training paradigm may cause large energy consumption, which affects the endurance of the mobile devices. In order to optimize the energy consumption in the FL training, we formulate it as a nonlinear integer programming problem, and design a device scheduling strategy by adopting the classification method and hybrid branch-and-bound algorithm. Based on the information, such as CPU capacity and channel state of devices, a reasonable devices set is scheduled to participate in the training, which consumes lower energy with the training delay constraint obeyed. Simulation results show that our proposed strategy can reduce 43.3% of traversed nodes compared with the conventional best-first search strategy. When the number of total devices increases, it can also outperform the existing methods in terms of energy reduction rate by an average of 14.3% and 10.7% respectively.
Stratified opposition-based initialization for variable-length chromosome shortest path problem evolutionary algorithms

Initialization is the first and a major step in the implementation of evolutionary algorithms (EAs). Although there are many common general methods to initialize EAs such as the pseudo-random number generator (PRNG), there is no single method that can fit every problem. This study provides a new, flexible, diversity-aware, and easy-to-implement initialization method for a genetic algorithm for the shortest path problem. The proposed algorithm, called stratified opposition-based sampling (SOBS), considers phenotype and genotype diversity while striving to achieve the best fitness for the initialization population. SOBS does not depend on a specific type of sampling, because the main goal is to stratify the sampling space. SOBS aims at an initial population with higher fitness and diversity in the phenotype and genotype. To investigate the performance of SOBS, four network models were used to simulate real-world networks. Compared with the most frequently used initialization method, that is, PRNG, SOBS provides more accurate solutions, better running time with less memory usage, and an initial population with higher fitness. Statistical analysis showed that SOBS yields solutions with higher accuracy in 68–100% of the time. Although this study was focused on the genetic algorithm, it can be applied to other population-based EAs that solve the shortest path problem and use the same direct population representation such as particle swarm optimization (PSO).
A 0.7 pJ/bit, 1.5 Gbps Energy-Efficient Image-Based True Random Number Generator

Random numbers cover a vast spectrum of applications. Hence generating it effectively with high performance is the need of the hour. This paper presents the novel design and implementation of high performance and energy-efficient true random number generator (TRNG) using images as a source. In the proposed work, the harvesting mechanism that comprises of hashing technique block (HTB) is used to reduce the intensity of pattern, an event counting circuit (ECC) is used for comparing the events, while a linear feedback shift register (LFSR), designed by considering a primitive polynomial function, is used to obtain the random numbers. The addition of 8 × 1 multiplexer (MUX) with meta-stable circuit feeder control lines had further increased the unpredictability in the proposed system. The implementation of the work has been done in the Xilinx Vivado simulation tool followed by the Cadence Virtuoso circuit simulation environment. The maximum speed of 1.5 Gbps with power dissipation of 1 mW and 0.7 pJ/bit energy efficiency with a layout area of 2218 μm2 has been achieved in this work. NIST 800.22 statistical test suite and uniformity test comprising of Kolmogorov–Smirnov and Chi-square test have also been performed for validation of generated random numbers. The obtained binary sequences have passed all tests successfully with calculated entropy up to 0.999999999. The autocorrelation factor (ACF) of the output bit streams has been obtained as approximately zero (∼0) within 96% confidence bounds of a Gaussian distribution (µ = 0, 3σ). The proposed design is thus suitable for true random number generation.
Optimizing FPGA-Based CNN Accelerator Using Differentiable Neural Architecture Search

Neural architecture search (NAS) aims to find the optimal neural network automatically for different scenarios. Among various NAS methods, the differentiable NAS (DNAS) approach has demonstrated its effectiveness in terms of searching cost and final accuracy. However, most of previous efforts focus on applying DNAS to GPU or CPU platforms, and its potential is less exploited on the FPGA. In this paper, we first propose a novel FPGA-based CNN accelerator. An accurate performance model of the proposed hardware design is also introduced. To improve accuracy as well as hardware performance, we then apply DNAS and encapsulate the proposed performance model into the objective function. Based on our FPGA design and NAS method, the experiments demonstrate that the network generated by NAS achieves nearly 95% accuracy on CIFAR-10, while decreasing latency by nearly 12 times compared with existing work.
Predicting Game Outcome in Multiplayer Online Battle Arena Games

Multiplayer online battle arena (MOBA) is currently one of most popular game genres in game artificial intelligence (AI). In this paper, we propose a method for predicting game outcome in MOBA games. Firstly, we extract features that contain the properties of the game outcome from game replay logs, taking into account game time. And then we implement a model for predicting the game outcome and interpret which features are important factors in the model. Experimental results show that the proposed method has high accuracy enough to predict the game outcome at a certain time point. It also analyzes the main play factors of game outcome for each game time zone.
WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II

Digital watermarking is a remarkable issue in the field of information security to avoid the misuse of images in multimedia networks. Although access to unauthorized persons can be prevented through cryptography, it cannot be simultaneously used for copyright protection or content authentication with the preservation of image integrity. Hence, this paper presents an optimized multipurpose blind watermarking in the Shearlet domain with the help of smart algorithms, including MLP and NSGA-II. In this method, four copies of the robust copyright logo are embedded in the approximate coefficients of Shearlet by using an effective quantization technique. Furthermore, an embedded random sequence as a semi-fragile authentication mark is effectively extracted from details by the neural network. Due to performing an effective optimization algorithm for selecting optimum embedding thresholds and also distinguishing the texture of blocks, the imperceptibility and robustness have been preserved. The experimental results reveal the superiority of scheme with regard to the quality of watermarked images and robustness against hybrid attacks over other state-of-the-art schemes. The average PSNR and SSIM of the dual watermarked images are 38 dB and 0.95, respectively; Besides, it can effectively extract the copyright logo and locates forgery regions under severe attacks with satisfactory accuracy.
Voice Controlled Wheelchair for Disabled Patients based on CNN and LSTM

Disabled patients with reduced mobility due to health problems such as disability, injury, paralysis, or other factors will experience difficulty in movement. They need tools that can help them, in which the most widely used is a wheelchair. The main objective of this research is to control wheelchair motion with voice commands. There are five commands for wheelchair control: forward, backward, right, left, and stop. Voice data is obtained from recording several subjects using Sound Recorder Pro and Sox Sound Exchange. The voice commands for wheelchair navigation were identified using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) combination embedded in Raspberry Pi 3. Voice data is first converted to spectrogram images before being fed into CNN using Mel-Frequency Cepstrum Coefficients (MFCC). This system can be controlled by simple voice commands given by the user. This method is proven to be useful in speech recognition with an accuracy level using CNN-LSTM above 97.80 %. Preliminary experimental results indicate that voice commands in wheelchairs using the CNN-LSTM can be recognized well.
A framework for incorporating virtual fitting into the costume design and production process

The process of designing and making costumes for the performing arts is highly collaborative, involving costume designers, directors, costume makers, and performers. Communication across the costume design and production process requires that these participants interact with both 2D and 3D representations of costumes in the forms of design drawings, muslin drapes, and prototypes. This qualitative investigation examined integrating three dimensional (3D) apparel simulation into costume design and production to enhance communication. Data were collected in two concurrent phases, (a) interviews with experts (n = 7) and (b) virtual fittings with costume designers (n = 5) and makers (n = 5). A preliminary framework was developed through analysis of expert interview responses. Then, insights from the virtual fittings were integrated to establish the final Costume Design and Production Process Framework (CDPPF). Results clarify opportunities for simulation and virtual fitting in the costume development process and highlight the importance of training and simulation accuracy to successful implementation.
Confronting Sparseness and High Dimensionality in Short Text Clustering via Feature Vector Projections

Short text clustering is a popular problem that focuses on the unsupervised grouping of similar short text documents, or entitled entities. Since the short texts are currently being utilized in a vast number of applications, the problem in question has been rendered increasingly significant in the past few years. The high cluster homogeneity and completeness are two among the most important goals of all data clustering algorithms. However, in the context of short texts, their fulfilment is particularly difficult, because this type of data is typically represented by sparse vectors that collectively comprise a very high dimensional space. In this article we introduce VEPHC, a two-stage clustering algorithm designed to confront the sparseness and high dimensionality traits of short texts. During the first stage (or else, the VEP part), the initial feature vectors are projected onto a lower dimensional space by constructing and scoring variable-sized combinations of features (that is, terms). In the second stage (or else, the HC part), VEPHC improves the homogeneity and completeness of the generated clusters through split and merge operations that are based on the similarities of all inter-cluster elements. The experimental evaluation of VEPHC on two real-world datasets demonstrates its superior performance over numerous state-of-the-art clustering algorithms in terms of F1 scores and Normalized Mutual Information.
An FPGA Based Heterogeneous Accelerator for Single Shot MultiBox Detector (SSD)

Single Shot MultiBox Detector (SSD) is a commonly used neural networks for real-time object detection, which has high requirements for inference speed. In this paper, an accelerator on a heterogeneous CPU-FPGA platform is proposed. An optimal graph partition method is also presented. By doing optimizations on both software and hardware sides, considerable performance improvement has been observed. Compared with CPU, experiment results demonstrate that the proposed accelerator achieves 4.5x acceleration performance for SSD_Mobilenet_V2. For SSD_Inception_V2 model, our accelerator achieves 1.42x that of Nvidia Titan X GPU.
Software Development Life Cycle early phases and quality metrics: A Systematic Literature Review

Abstract Most of the currently used software development metrics are concentrated on the latter stages like development and testing. However, early revealing of errors during the SDLC(Software Development Life Cycle) tremendously affects the efficiency of the team work by spending more time on prevention and less on correction in later stages. Furthermore, reworking in later stages increase the cost of quality, lead to extra waste of time of the development team. The objective of this review is to examine the classification of the existing SDLC(Software Development Life Cycle) early phases and define the set of software process quality metrics. Based on the SRL research protocol, we selected the most relevant studies from overall 200 publications by the use of search keywords and inclusion/exclusion criteria for quality assessment of primary studies. This systematic literature review yields the correlation of cost, time and software product quality with the SDLC stages.
A Fully Automatic Content Adaptive Inverse Tone Mapping Operator With Improved Color Accuracy

High Dynamic Range (HDR) technology offers a higher visual quality compared to its Standard Dynamic Range (SDR) counterpart, as it tries to imitate the way our eyes perceive brightness and color information. Converting SDR content to HDR format - using inverse Tone Mapping Operators (iTMOs) - to take advantage of the superior visual quality offered by HDR displays, is an attractive proposition to SDR content owners and real-time broadcasters. In this paper, we propose a novel content adaptive iTMO that works in the perceptual domain to model the sensitivity of the human eye to brightness changes in different areas of a scene. To preserve the overall visual impression, our proposed iTMO utilizes an entropy-based brightness segmentation, which also makes our method content adaptive. In addition, we propose a novel perception-based color adjustment method that can maintain the color accuracy between input SDR and generated HDR frames. By performing the color adjustment in the perceptual domain, our iTMO prevents hue shifts and generates HDR colors that closely follow their SDR counterparts. Our subjective evaluations indicate that our proposed method outperforms other state-of-the-art methods by an average of 81% in terms of visual quality, and 76% in terms of how closely the HDR colors match their SDR counterparts. In addition to subjective evaluations, we also performed objective evaluations using the HDR-VDP 2.2 and PU-SSIM metrics and concluded that, on average, our proposed iTMO outperforms the state-of-the-art methods in terms of these two metrics.
Fall detection system via smart phone and send people location

Due to the falling birth rate and industrial societies, the number of people over 65 is increasing and in 2050 almost 16% of the population will be elder. Falling in the elderly is the second leading cause of severe injury, leading to death. The aim of this study is to provide a high-speed method for diagnosing and treating the elderly. We have proposed a method to use the smartphone and the acceleration signal to detection falling. The proposed system has been able to detect a fall using the Smartphone sensor and report the location of the person. The results show that with the mean, skewness, and kurtosis features and support vector machine classifier, the accuracy of fall detection is 96.33%. The important point is the calculation time, which is less than 1 second.
Password Similarity Using Probabilistic Data Structures

Passwords should be easy to remember, yet expiration policies mandate their frequent change. Caught in the crossfire between these conflicting requirements, users often adopt creative methods to perform slight variations over time. While easily fooling the most basic checks for similarity, these schemes lead to a substantial decrease in actual security, because leaked passwords, albeit expired, can be effectively exploited as seeds for crackers. This work describes an approach based on Bloom Filters to detect password similarity, which can be used to discourage password reuse habits. The proposed scheme intrinsically obfuscates the stored passwords to protect them in case of database leaks, and can be tuned to be resistant to common cryptanalytic techniques, making it suitable for usage on exposed systems.
Open-Set Interference Signal Recognition Using Boundary Samples: A Hybrid Approach

The applications of neural network based classifier to the real world tasks have to deal with the problem of open-set recognition (OSR), where during the testing phase there may appear unknown classes not seen in the training phase. This paper presents a novel method for OSR problem and its potential application on wireless interference signal recognition. The proposed method uses more precise and stable boundary samples to imitate unknown classes. To be sepecific, the proposed method modifies and combines the methods of intra-class splitting (ICS) and adversarial samples generation to construct precise boundary samples. Numerical experiments are conducted with three commonly used image datasets and one wireless interference signal dataset collected at 2.4GHz ISM band by LimeSDR, and the analytical results demonstrate the effectiveness of the proposed OSR method.
Hierarchical Lossy Bilevel Image Compression Based on Cutset Sampling

We consider lossy compression of a broad class of bilevel images that satisfy the smoothness criterion, namely, images in which the black and white regions are separated by smooth or piecewise smooth boundaries, and especially lossy compression of complex bilevel images in this class. We propose a new hierarchical compression approach that extends the previously proposed fixed-grid lossy cutset coding (LCC) technique by adapting the grid size to local image detail. LCC was claimed to have the best rate-distortion performance of any lossy compression technique in the given image class, but cannot take advantage of detail variations across an image. The key advantages of the hierarchical LCC (HLCC) is that, by adapting to local detail, it provides constant quality controlled by a single parameter (distortion threshold), independent of image content, and better overall visual quality and rate-distortion performance, over a wider range of bitrates. We also introduce several other enhancements of LCC that improve reconstruction accuracy and perceptual quality. These include the use of multiple connection bits that provide structural information by specifying which black (or white) runs on the boundary of a block must be connected, a boundary presmoothing step, stricter connectivity constraints, and more elaborate probability estimation for arithmetic coding. We also propose a progressive variation that refines the image reconstruction as more bits are transmitted, with very small additional overhead. Experimental results with a wide variety of, and especially complex, bilevel images in the given class confirm that the proposed techniques provide substantially better visual quality and rate-distortion performance than existing lossy bilevel compression techniques, at bitrates lower than lossless compression with the JBIG or JBIG2 standards.
Deep Learning for Constellation-based Modulation Classification under Multipath Fading Channels

Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification.