Reviewerly/exHarmony huggingface dataset:
Highly Optimized Montgomery Multiplier for SIKE Primes on FPGA

New primes were proposed for Supersingular Isogeny Key Encapsulation (SIKE) in NIST standardization process of Round 2 after further cryptanalysis research showed that the security levels of the initial primes chosen were overestimated [1], [2]. In this paper, we develop a highly optimized E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> Montgomery multiplication algorithm and architecture that further utilizes the special form of SIKE prime compared to previous implementations available in the literature. We then implement SIKE for all Round 2 NIST security levels (SIKEp434 for NIST security level 1, SIKEp503 for NIST security level 2, SIKEp610 for NIST security level 3, and SIKEp751 for NIST security level 5) on Xilinx Virtex 7 using the proposed multiplier. Our best implementation (NIST security level 1) runs 29% faster and occupies 30% less hardware resources in comparison to the leading counterpart available in the literature [3] and implementations for other security levels achieved similar improvement.
Analysis of crosstalk in NISQ devices and security implications in multi-programming regime

The noisy intermediate-scale quantum (NISQ) computers suffer from unwanted coupling across qubits referred to as crosstalk. Existing literature largely ignores the crosstalk effects which can introduce significant error in circuit optimization. In this work, we present a crosstalk modeling analysis framework for near-term quantum computers after extracting the error-rates experimentally. Our analysis reveals that crosstalk can be of the same order of gate error which is considered a dominant error in NISQ devices. We also propose adversarial fault injection using crosstalk in a multiprogramming environment where the victim and the adversary share the same quantum hardware. Our simulation and experimental results from IBM quantum computers demonstrated that the adversary can inject fault and launch a Denial-of-Service attack. Finally, we propose system- and device-level countermeasures.
Comparative Analysis of Software Development Life Cycle Models (SDLC)

Software Engineering encompasses a lot of processes of which writing of efficient codes for development of high quality and effective programs is one of them. Development of quality software is achieved through a well-articulated Software Development Life Cycle (SDLC) model. Delivery of software project within timeline and budget require thorough analysis from which these two pertinent questions must be answered such as what shall we do next and how long shall we continue to do it’? Therefore, the need for an appropriate SDLC model for a software project is of high significance. V-Model is applicable in project that requires a lot of time and are complex in nature. When the focus of the project is more of quality than fast delivery time, then V-Model is a choice of SDLC to be considered. Also, Waterfall model is desirable when quality control is of concern with short project duration. On the other hand, Spiral Model is desirable when organizational expectations are unpredictable concerning the software development process. Irrespective of the size of the project, delivery time and level of expertise involved, the organization of the project plays a significant role in the choice of SDLC model. SDLC model under consideration, must take into cognizance the level of risk, duration of the project and cost implication associated with every software project for successful completion. Comparative analysis of the three SDLC models studied was presented.
Tailoring Echo State Networks for Optimal Learning

As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing. A key feature of the ESN paradigm is its reservoir-a directed and weighted network of neurons that projects the input time series into a high-dimensional space where linear regression or classification can be applied. By analyzing the dynamics of the reservoir we show that the ensemble of eigenvalues of the network contributes to the ESN memory capacity. Moreover, we find that adding short loops to the reservoir network can tailor ESN for specific tasks and optimize learning. We validate our findings by applying ESN to forecast both synthetic and real benchmark time series. Our results provide a simple way to design task-specific ESN and offer deep insights for other recurrent neural networks.
FPGA implementation of a run-time configurable NTT-based polynomial multiplication hardware

Multiplication of polynomials of large degrees is the predominant operation in lattice-based cryptosystems in terms of execution time. This motivates the study of its fast and efficient implementations in hardware. Also, applications such as those using homomorphic encryption need to operate with polynomials of different parameter sets. This calls for design of configurable hardware architectures that can support multiplication of polynomials of various degrees and coefficient sizes. In this work, we present the design and an FPGA implementation of a run-time configurable and highly parallelized NTT-based polynomial multiplication architecture, which proves to be effective as an accelerator for lattice-based cryptosystems. The proposed polynomial multiplier can also be used to perform Number Theoretic Transform (NTT) and Inverse NTT (INTT) operations. It supports 6 different parameter sets, which are used in lattice-based homomorphic encryption and/or post-quantum cryptosystems. We also present a hardware/software co-design framework, which provides high-speed communication between the CPU and the FPGA connected by PCIe standard interface provided by the RIFFA driver [1]. For proof of concept, the proposed polynomial multiplier is deployed in this framework to accelerate the decryption operation of Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme implemented in Simple Encrypted Arithmetic Library (SEAL), by the Cryptography Research Group at Microsoft Research [2]. In the proposed framework, polynomial multiplication operation in the decryption of the BFV scheme is offloaded to the accelerator in the FPGA via PCIe bus while the rest of operations in the decryption are executed in software running on an off-the-shelf desktop computer. The hardware part of the proposed framework targets Xilinx Virtex-7 FPGA device and the proposed framework achieves the speedup of almost 7 × in latency for the offloaded operations compared to their pure software implementations, excluding I/O overhead.
Hybrid scheduling for scientific workflows on hybrid clouds

Abstract Scientific workflows consist of many interdependent tasks dictated by their data dependencies. As these workflows are becoming resource-intensive in both data and computing, private clouds struggle to cope with their resource requirements. Private cloud’s limitations are claimed to be addressed by public clouds, however, the complete offloading of workflow execution to public clouds causes excessive data transfer. In this paper, we address the problem of scheduling scientific workflows on hybrid clouds aiming at cost reduction while improving execution time, with the consideration of public cloud costs, by minimizing data movements between private cloud and public cloud. To this end, we develop Hybrid Scheduling for Hybrid Clouds (HSHC) as a novel data-locality aware scheduling algorithm. HSHC adopts a hybrid approach consisting of static phase and dynamic phase. The former solves the problem of workflow scheduling, using an extended genetic algorithm, with static information of workflows and resources. The latter dynamically adapts the static schedule in response to changing execution conditions, such as locality of intermediate output data and performance degradation of tasks and resources. We evaluate HSHC with both random workflows and real-world scientific applications in execution time and cost. Experimental results compared with seven state-of-the-art algorithms demonstrate HSHC significantly reduces the cost by up to 40% and improves the execution time by up to 25%.
Job Recommendation based on Job Profile Clustering and Job Seeker Behavior

This article presents a recommender system that aims to help job seekers to find suitable jobs. First, job offers are collected from job search websites then they are prepared to extract meaningful attributes such as job titles and technical skills. Job offers with common features are grouped into clusters. As job seeker like one job belonging to a cluster, he will probably find other jobs in that cluster that he will like as well. A list of top n recommendations is suggested after matching data from job clusters and job seeker behavior, which consists on user interactions such as applications, likes and rating.
Compliance with bring your own device security policies in organizations: A systematic literature review

The omnipresence of Bring Your Own Device (BYOD) in organizations and employees' lives makes it a challenge to ensure adherence to security policy. In such situation, it is vital to understand the features of BYOD that impact the threat landscape, as well as identifying factors that contribute to security policy compliant behavior to create a secure environment in organizations. Though research on BYOD has progressed, the field still lacks a systematic literature analysis, as previous reviews have focused on traditional computing security policy compliance, leaving out the BYOD environment. A total of 21 articles published from 2012 to 2019 were reviewed systematically to answer a formulated set of research questions. The study reveals the unique features of the BYOD computing environment in organizations. This review provides an overview of the theories used to explain and predict security behaviors in this field and a detailed understanding of factors that influence BYOD security policy compliance behavior. Literature gaps for further research are proposed in this paper to present new insights in this field of research. Studies on BYOD security policy compliance remain scarce, which makes this review a novel contribution to human security behavioral studies.
Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.
An adaptive dimension level adjustment framework for differential evolution

Differential evolution (DE) has been recognized as one of the most popular evolutionary algorithms. There are numerous DE variants adopting multi-operators based cooperation strategy to improve their performance, but almost all of the adopted cooperation strategies are essentially implemented at the individual level or population level, and the implementation at the dimension level are scarce. In this paper, an adaptive dimension level adjustment (ADLA) framework is designed to relieve the premature convergence or stagnation problem faced by DE algorithm, which can be easily combined with diverse DE variants. When the current optimal individual cannot get improved for a given uninterrupted iterations, ADLA framework will be triggered to select some individuals at random according to specific rule and reinitialize portion of their dimensions from a dynamic search space that adjusted by a population level macroparameter and one individual level microparameter. Moreover, ADLA framework contains two reinitialization operators with different search characteristics, and the coordination between them is executed at the dimension level, which has potential advantages in balancing the global exploration ability and local exploitation ability. Extensive comparison experiments are carried out based on IEEE CEC 2014 test platform, two basic DE algorithms and six outstanding DE variants. The experimental results demonstrate that ADLA framework can memorably enhance the performance of every DE algorithm used for comparison.
Generating diagnostic report for medical image by high-middle-level visual information incorporation on double deep learning models

Background and objectives: Writing diagnostic reports for medical images is a heavy and tedious work. The automatic generation of medical image diagnostic reports can assist doctors to reduce their workload and improve diagnosis efficiency. It is of great significance to introduce image caption algorithm into medical image processing. Existing approaches attempt to generate medical image diagnostic reports using image caption algorithms but without taking the accuracy of pathological information in generated diagnostic reports into account. Methods: To solve the mentioned problem, we propose a Semantic Fusion Network (SFNet) including a lesion area detection model and a diagnostic generation model. The lesion area detection model can extract visual and pathological information from medical image, and the diagnostic report generation model can learn to fuse the two kinds of information to generate reports. Thus, the pathological information in the generated diagnostic reports can be more accurate. Results: Experimental results have verified the performance of our model (Accuracy increases 1.2% on the Ultrasound Image Dataset and 2.4% on the Open-i X-ray Image Dataset), compared with the model only using visual feature to generate diagnostic reports. Conclusions: This work utilizes computer algorithms to generate the more accurate diagnostic reports for medical images automatically, which expands the application of computer-aided diagnosis and promotes the implementation of deep learning in the medical image analysis field.
ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences.The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils.In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification.The dataset, named 'Archives of Digital Morphology' (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-yearold fishes.The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification.To assess the method performance and dataset representability, we performed extensive experiments.Compared with multiview convolutional neural networks (MVCNN) (91.54%),PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset.We also verified data preprocessing (92.36%) and feature extraction (97.10%).We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%.We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields.The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.
EmoSen: Generating Sentiment and Emotion Controlled Responses in a Multimodal Dialogue System

An essential skill for effective communication is the ability to express specific sentiment and emotion in a conversation. Any robust dialogue system should handle the combined effect of both sentiment and emotion while generating responses. This is expected to provide a better experience and concurrently increase users’ satisfaction. Previously, research on either emotion or sentiment controlled dialogue generation has shown great promise in developing the next generation conversational agents, but the simultaneous effect of both is still unexplored. The existing dialogue systems are majorly based on unimodal sources, predominantly the text, and thereby cannot utilize the information present in the other sources, such as video, audio, image, etc. In this article, we present at first a large scale benchmark Sentiment Emotion aware Multimodal Dialogue (SEMD) dataset for the task of sentiment and emotion controlled dialogue generation. The SEMD dataset consists of 55k conversations from 10 TV shows having text, audio, and video information. To utilize multimodal information, we propose multimodal attention based conditional variational autoencoder (M-CVAE) that outperforms several baselines. Quantitative and qualitative analyses show that multimodality, along with contextual information, plays an essential role in generating coherent and diverse responses for any given emotion and sentiment.
A supervised non-negative matrix factorization model for speech emotion recognition

Feature representation plays a critical role in speech emotion recognition (SER). As a method of data dimensionality reduction, Non-negative Matrix Factorization (NMF) can obtain the low-dimensional representation of data by matrix decomposition, and make the data more distinguishable. In order to improve the recognition ability of NMF for SER, we conduct a potential study on NMF and propose a supervised NMF model, called joint discrimination ability and similarity constraint of NMF (DSNMF). This model incorporates the discriminative information and similarity information of samples into basic NMF as prior knowledge, so that the original data can be decomposed into more distinguished low-dimensional data. Specifically, on the one hand, the labels of the training set are used to improve the discriminative ability of the model; on the other hand, with the similarity of the training samples, the data of similar samples are more highly aggregated in the low-dimensional space. In addition, the convergence of DSNMF is proved theoretically and experimentally. Extensive experiments on EMODB and IEMOCAP corpora show that the proposed approach has a better classification effect on low-dimensional representation data than other NMF models.
Optimum azimuth and inclination angle of BIPV panel owing to different factors influencing the shadow of adjacent building

In the present numerical investigation, optimum azimuth and inclination angle of building integrated photovoltaic (BIPV) system are determined due to the adverse shadow effect caused by the adjacent buildings. The factors which influence this shadow are the relative height of the building with reference to BIPV panel, plan area/orientation of the building and horizontal distance of the building from the BIPV panel. HDKR (Hay, Davies, Klucher and Reindl) anisotropic model, encompassing a shadow analysis model, is employed to determine the solar irradiation values. This model involves both shading coefficient and sky-view factor for the accurate evaluation of irradiation values due to shadow caused by the adjacent building. To determine the shading coefficient, the projection of the adjacent building is superimposed over the sun path diagram and the sky view factor is calculated by integrating the sky-trapped curve plotted for any given inclination angle of the BIPV panel Results show that the BIPV panel with optimum azimuth and inclination angle generates significantly higher irradiation as compared to the south-faced BIPV panel having an inclination angle equal to the latitude of the studied location.
Optimized interesting region identification for video steganography using Fractional Grey Wolf Optimization along with multi-objective cost function

A novel approach is presented in this paper for hiding the data using Fractional Grey Wolf Optimization. In this work, the key frames are extracted using the Structural Similarity Index measurement (SSIM). Then, individual key frames are subject to the formation of regions with the help of grid lines. The optimization algorithm is used to select the optimal region based on multi-objective cost function, namely energy, coverage, intensity and kurtosis. Simultaneously, the secret information is pre-processed with encryption to strengthen the security of the proposed method. And, finally, on the key frames Lifting Wavelet Transform (LWT) is applied to accomplish the wavelet coefficient frame to hide the secret information. With the help of metrics such as Peak Signal to Noise Ratio (PSNR), Embedding Capacity and Normalized Correlation (NC) the system performance is measured. Moreover, the proposed method obtained a maximal PSNR of 75.141 dB, and embedding capacity of 70.8% respectively to provide more security and higher imperceptibility for a decent video quality.
<i>Ab initio</i> molecular dynamics on quantum computers

Ab initio molecular dynamics (AIMD) is a valuable technique for studying molecules and materials at finite temperatures where the nuclei evolve on potential energy surfaces obtained from accurate electronic structure calculations. In this work, we present an approach to running AIMD simulations on noisy intermediate-scale quantum (NISQ)-era quantum computers. The electronic energies are calculated on a quantum computer using the variational quantum eigensolver (VQE) method. Algorithms for computation of analytical gradients entirely on a quantum computer require quantum fault-tolerant hardware, which is beyond NISQ-era. Therefore, we compute the energy gradients numerically using finite differences, the Hellmann–Feynman theorem, and a correlated sampling technique. This method only requires additional classical calculations of electron integrals for each degree of freedom without any additional computations on a quantum computer beyond the initial VQE run. As a proof of concept, AIMD simulations are demonstrated for the H2 molecule on IBM quantum devices. In addition, we demonstrate the validity of the method for larger molecules using full configuration interaction wave functions. As quantum hardware and noise mitigation techniques continue to improve, the method can be utilized for studying larger molecular systems.