Reviewerly/exHarmony huggingface dataset:
One-Round Authenticated Group Key Exchange from Isogenies

This paper proposes two one-round authenticated group key exchange protocols from newly employed cryptographic invariant maps (CIMs): one is secure in the quantum random oracle model and the other resists against maximum exposure where a non-trivial combination of secret keys is revealed. The security of the former (resp. latter) is proved under the n-way decisional (resp. n-way gap) Diffie–Hellman assumption on the CIMs in the quantum random (resp. random) oracle model. We instantiate the proposed protocols on the hard homogeneous spaces with limitation where the number of the user group is two. In particular, the protocols instantiated by using the CSIDH, commutative supersingular isogeny Diffie–Hellman, key exchange are currently more realistic than the general n-party CIM-based ones due to its realizability. Our two-party one-round protocols are secure against quantum adversaries.
Temperature-aware power consumption modeling in Hyperscale cloud data centers

Since the development of data centers, power management (i.e., assessment, consumption and monitoring) has been a great challenge among scientists and engineers. By emerging a new generation of data center in the form of Hyperscale cloud data centers (HCDC), this concern has become more devastating than ever. The huge physical scale and the high level of system utilization through large power compensating system are some of the main characteristics of today's HCDC. The lack of appropriate power assessment from available power estimation models, prevents professionals from designing an accurate HCDCcapacity planning. In particular, during steady state workload processing at the high utilization rate of HCDCtechnology, the inlet cold temperature level rises up and this trace can be effect to the computable power consumption. In this article, we show how the ambient temperature have significant negative impact on power consumption and for an accurate and proper estimation of the power consumption of HCDC, we propose a temperature-aware power consumption model. The model is evaluated by SimWare for various server types and configurations and results prove the effectiveness and accuracy of the proposed model. At the end, we show the proposed model can be applicable for both homogeneous and heterogeneous HCDCs.
Does religion affect cross-border acquisitions? Tales from developed and emerging economies

This article examines how religious distance and religious freedom influence cross-border acquisition (CBA) activity and firm's choice of control.Using a global sample of 75,151 CBAs across 45(57) acquirer (target) countries over a period of 34 years from 1980 to 2014, we find that the CBA volume is lower with firms preferring partial control when countries are religiously distant. We use three different constructs to examine the role of religious freedom. Our findings show that government favoritism and social regulations reduce the CBA volume whereas state regulations on religion drive CBA volume. In addition, firms opt for full control when countries exhibit higher government favoritism and lower state regulations on religion. We also demonstrate that internationalization decisions depend on the country of origin. Finally, we find that acquirers with a foothold in the target mitigate the information asymmetry associated with religious distance.
Scalable Dynamic Graph Summarization

Large-scale dynamic interaction graphs can be challenging to process and store, due to their size and the continuous change of communication patterns between nodes. In this work, we address the problem of summarizing large-scale dynamic graphs, while maintaining the evolution of their structure and interactions. Our approach is based on grouping the nodes of the graph in supernodes according to their connectivity and communication patterns. The resulting summary graph preserves the information about the evolution of the graph within a time window. We propose two online algorithms for summarizing this type of graphs. Our baseline algorithm kC based on clustering is fast but rather memory expensive. The second method we propose, named /LC, reduces the memory requirements by introducing an intermediate step that keeps statistics of the clustering of the previous rounds. Our algorithms are distributed by design, and we implement them over the Apache Spark framework, so as to address the problem of scalability for large-scale graphs and massive streams. We apply our methods to several dynamic graphs, and show that we can efficiently use the summary graphs to answer temporal and probabilistic graph queries.
A Virtual Reality Food Court to Study Meal Choices in Youth: Design and Assessment of Usability

Regular consumption of take-out and fast foods with sugary drinks is associated with poor quality diets and higher prevalence of obesity. Among the settings where such food is consumed is the food court typically found in shopping malls prominent in many countries.The objective of this research was to develop a virtual reality food court that could be used to test food environmental interventions, such as taxation, and ultimately to facilitate the selection of healthier food choices.Fourteen food courts in Sydney, Australia were selected to include those in the city center and suburbs of high and low socioeconomic status. Researchers visited the courts to collect information on number and type of food outlets, all menu items for sale, cost of foods and beverages and sales promotions. This information was used to assemble 14 food outlets typically found in food courts, and representative menus were compiled. The UNITY gaming platform was used to design a virtual reality food court that could be used with HTC VIVE goggles. Participants navigated the virtual reality food court using the head-mounted display, keyboard, and mouse and selected a lunch meal, including food and beverage. A validated questionnaire on presence within the virtual reality food court and system usability was completed at the end of the session. The constructs for presence included a sense of control, sensory fidelity, realism, distraction, and involvement. Questions were rated on a scale from 1 (worst) through 7 (best) for each of 28 questions giving a maximum total score of 196. The systems usability scale (SUS) that gives a final score out of 100 was also assessed.One hundred and sixty-two participants with a mean age of 22.5 (SD 3.1) years completed the survey. The mean score for total presence was 144 (SE 1.4) consisting of control: 62.1 (SE 0.8), realism: 17.5 (SE 0.2), involvement: 9.6 (SE 0.2), sensory fidelity: 34.9 (SE 0.4), and distraction: 24.0 (SE 0.3). The mean SUS was 69 (SE 1.1).Virtual reality shows promise as a tool to study food choice for test interventions to inform practice and policy.
Fractional-Order Spatial Steganography and Blind Steganalysis for Printed Matter: Anti-Counterfeiting for Product External Packing in Internet-of-Things

This paper advocates a novel conceptual formulation of the fractional-order spatial steganography (FSS) and blind steganalysis for printed matter, which can be efficiently employed in the anti-counterfeiting for product external packing in Internet-of-Things (IoT). Traditional digital steganography is not printable. Within the limits of our knowledge, until now, there are not a well-established steganography and a corresponding steganalysis for printed matter in IoT, which should receive desired attention. Fractional calculus has potentially received prominence in applications in the domain of image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity. Therefore, in an attempt to overcome the aforementioned technical limitation of traditional digital steganography, this paper has studied here, as an interesting theoretical problem, would it be possible to apply the capability of preserving the edges and textural details of fractional calculus to the achievement of the steganography and steganalysis for printed matter in IoT. Motivated by this inspiration, in this work, this paper introduces a novel conceptual formulation of an FSS and a fractional-order blind steganalysis (FBS) for printed matter. At first, according to the opponent process theory of color vision, to better achieve the imperceptibility of the hidden secret information, this paper uses both the self-similar complex textures in a neighborhood and the opponent channel of blue versus yellow to implement FSS for printed matter. Second, without requiring a priori knowledge regarding the characteristics of the original carrier image, hidden secret image, and steganography, an FBS, a fractional-order multimodal function optimization algorithm, is proposed. Finally, the efficient capability of hiding secret information of FSS and that of detecting secret information of FBS are analyzed in detail experimentally, respectively. These two important advantages lead to the superiority of the proposed approach for defending against statistics attack, rotation and distortion attack, cropping attack, scaling attack, noise attack, and color copy attack. The main contribution of this paper is the first preliminary attempt of a feasible achievement of a spatial steganography and a blind steganalysis for printed matter.
An Adaptive Decomposition Scheme for Wideband Signals of Power Systems Based on the Modified Robust Regression Smoothing and Chebyshev-II IIR Filter Bank

Distributed energy resources and new types of electrical loads are morphologically changing the signals of power grids. Grasping the wideband information of field data will play an important role in helping smart grids to operate in a safe manner. A wideband signal model composed of deterministic and stochastic components is proposed to enhance the wideband measurement. Based on the model, an adaptive decomposition scheme is designed for the offline analysis of wideband signals. In the scheme, a modified robust local regression smoothing method is presented to denoise the wideband signal. A totally data-dependent threshold is established to extract adaptively interharmonic components. Further, the Taylor-Fourier transform algorithm is used to estimate the parameters of deterministic components based on the subsignals extracted by the Chebyshev-II infinite impulse response filter bank. Finally, the stochastic components are extracted using the adaptive threshold based on the signal that the deterministic components are removed. The performances of the decomposition scheme under different conditions have been validated. In the end, the proposed decomposition scheme is carried out and verified with the field data of the 24-h current signals at one residence community.
Depth-Assisted Full Resolution Network for Single Image-Based View Synthesis

Research works in novel viewpoint synthesis are based mainly on multiview input images. In this paper, we focus on a more challenging and ill-posed problem that is to synthesize surrounding novel viewpoints from a single image. To achieve this goal, we design a full resolution network to extract fine-scale image features, which contributes to prevent blurry artifacts. We also involve a pretrained relative depth estimation network, thus three-dimensional information is utilized to infer the flow field between the input and the target image. Since the depth network is trained by depth order between any pair of objects, large-scale image features are also involved in our system. Finally, a synthesis layer is used to not only warp the observed pixels to the desired positions but also hallucinate the missing pixels from other recorded pixels. Experiments show that our technique successfully synthesizes reasonable novel viewpoints surrounding the input, while other state-of-the-art techniques fail.
Autoencoder for Semisupervised Multiple Emotion Detection of Conversation Transcripts

Textual emotion detection is a challenge in computational linguistics and affective computing study as it involves the discovery of all associated emotions expressed within a given piece of text. It becomes an even more difficult problem when applied to conversation transcripts, as we need to model the spoken utterances between speakers, keeping in mind the context of the entire conversation. In this paper, we propose a semisupervised multilabel method of predicting emotions from conversation transcripts. The corpus contains conversational quotes extracted from movies. A small number of them are annotated, while the rest are used for unsupervised training. We use the word2vec word-embedding method to build an emotion lexicon from the corpus and to embed the utterances into vector representations. A deep-learning autoencoder is then used to discover the underlying structure of the unsupervised data. We fine-tune the learned model on labeled training data, and measure its performance on a test set. The experiment result suggests that the method is effective and is only slightly behind human annotators.
Communicating without shared reference frames

We generalize a quantum communication protocol introduced by Bartlett et al. [New J. Phys. 11, 063013 (2009)], in which two parties communicating do not share a classical reference frame, to the case where changes of their reference frames form a one-dimensional noncompact Lie group. Alice sends to Bob the state ${\ensuremath{\rho}}_{R}\ensuremath{\bigotimes}{\ensuremath{\rho}}_{S}$, where ${\ensuremath{\rho}}_{S}$ is the state of the system Alice wishes to communicate and ${\ensuremath{\rho}}_{R}$ is the state of an ancillary system serving as a token of her reference frame. Because Bob is ignorant of the relationship between his reference frame and Alice's, he will describe the state ${\ensuremath{\rho}}_{R}\ensuremath{\bigotimes}{\ensuremath{\rho}}_{S}$ as an average over all possible reference frames. Bob measures the reference token and applies a correction to the system Alice wished to communicate conditioned on the outcome of the measurement. The recovered state ${\ensuremath{\rho}}_{S}^{\ensuremath{'}}$ is decohered with respect to ${\ensuremath{\rho}}_{S}$, the amount of decoherence depending on the properties of the reference token ${\ensuremath{\rho}}_{R}$. We present an example of this protocol when Alice and Bob do not share a reference frame associated with the one-dimensional translation group and use the fidelity between ${\ensuremath{\rho}}_{S}$ and ${\ensuremath{\rho}}_{S}^{\ensuremath{'}}$ to quantify the success of the recovery operation.
A more efficient leveled strongly-unforgeable fully homomorphic signature scheme

Homomorphic signatures (HS) allow anyone to perform arbitrary efficient computation on signed data, whose output can be verified using the corresponding public verification key, without needing to have the underlying data. Existing HS schemes (e.g., Gorbunov et al., 2015; Fiore et al., 2016) are however inefficient in generating signatures, due to the fact that the signing algorithm can only generate one signature for an individual message at a time. In this work, we propose a new leveled fully homomorphic signature scheme, which only needs to call the signing algorithm twice for generating signatures for all messages in a dataset. This is a significant improvement in efficiency over the previous HS schemes that need to call the signing algorithm for polynomial times. We achieve this by using a vector encoding technique, which encodes all messages in a dataset into two matrices. Furthermore, we prove that our construction is secure against fully adaptive chosen-message attacks under the standard small integer solution (SIS) assumption.
Integrating an Attention Mechanism and Convolution Collaborative Filtering for Document Context-Aware Rating Prediction

Deep learning has become a recent, modern technique for big data processing, with promising results and large potential.For recommender systems, user and item information can be used as input vectors to perform prediction tasks.However, augmenting the number of layers to improve feature extraction will increase the computational complexity considerably and may not achieve the desired results.This paper proposes a method called attention convolution collaborative filtering (Att-ConvCF), which integrates an attention mechanism with a collaborative filtering model to improve the effectiveness of the feature extraction by reassigning the weights of feature vectors.Descriptive documents for the items are used to enrich the background information through a convolutional neural network.Finally, extensive experiments with realworld datasets were performed, and the results showed that Att-ConvCF could effectively extract the feature values of the data and significantly outperform the existing recommendation models.
An improved color image defogging algorithm using dark channel model and enhancing saturation

Fog is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. It is difficult to lock in and track off criminals clarity in the high-definition surveillance hazy image and driverless. In our research, we develop dark channel model, re-refined transmission map by transferring coefficient. Compared with other defogging methods based on single color image prior, our method is a effectiveness of the proposed defogging algorithm, enhanced the saturation of defogging image and a higher quality depth map can be obtained of fog removal. In this way, a clear hazy image is obtained while maintaining the fog removing quality. Moreover, a good quality defogging image can be validated of fog removal by PSNR and MSE.
An Efficient User-Centric Web Service Composition Based on Harmony Particle Swarm Optimization

Generally, the composition is the process of combining services to fulfill complex tasks based on their functional and non-functional values such as quality of services (QoS) and context attributes. However, to produce a composition with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach centered evolutionary algorithm called the harmony particle swarm optimization (HPSO) algorithm that leads to an efficient composition with better performance and execution time. The authors' proposed method is a new hybrid version of the harmony search and the particle swarm optimization. The HPSO is designed to generate the best web service composition in a discrete search space. Furthermore, the method includes two filtering processes called Skyline operator and local consistency reinforcement techniques. These methods filter the search space and keep only the most representative candidate services. Results show the effectiveness and the accuracy of the proposed approach.
Financial dependence and growth: The role of input-output linkages

We widen the understanding of the finance-growth nexus by accounting for the indirect effect of financial development through input-output (IO) linkages in determining the growth of industries across countries. If financial development is expected to promote disproportionately more the growth of industrial sectors that are more in need of external finance, it also favours more the industries that are linked by IO relations to more financially dependent industries. We explore this new channel in a sample of countries at different development stages over the period 1995–2007. Our results highlight that financial development, besides easing the growth of industries highly dependent on external finance, also fosters the growth of industries strongly linked to highly financially dependent upstream industries. Moreover, the indirect effect - propagated through IO linkages - of finance has a higher and non-negligible role compared to the direct effect and its omission leads to a biased and underestimated perception of the role of finance for industries’ growth.
Improving the performance of keyword spotting system for children's speech through prosody modification

Searching for words of interest from a speech sequence is referred to as keyword spotting (KWS). A myriad of techniques have been proposed over the years for effectively spotting keywords from adults' speech. However, not much work has been reported on KWS for children's speech. The speech data for adult and child speakers differs significantly due to physiological differences between the two groups of speakers. Consequently, the performance of a KWS system trained on adults' speech degrades severely when used by children due to the acoustic mismatch. In this paper, we present our efforts towards improving the performance of keyword spotting systems for children's speech under limited data scenario. In this regard, we have explored prosody modification in order to reduce the acoustic mismatch resulting from the differences in pitch and speaking-rate. The prosody modification technique explored in this paper is the one based on glottal closure instant (GCI) events. The approach based on zero-frequency filtering (ZFF) is used to compute the GCI locations. Further, we have presented two different ways for effectively applying prosody modification. In the first case, prosody modification is applied to the children's speech test set prior to the decoding step in order to improve the recognition performance. Alternatively, we have also applied prosody modification to the training data from adult speakers. The original as well as the prosody modified adults' speech data are then augmented together before learning the statistical parameters of the KWS system. The experimental evaluations presented in this paper show that, significantly improved performances for children's speech are obtained by both of the aforementioned approaches of applying prosody modification. Prosody-modification-based data augmentation helps in improving the performance with respect to adults' speech as well.
Low-Latency Double Point Multiplication Architecture Using Differential Addition Chain Over &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$GF(2^m)$ &lt;/tex-math&gt; &lt;/inline-formula&gt;

During the past decade, elliptic curve cryptography (ECC) has been widely deployed in different scenarios as the main asymmetric cryptosystem due to its smaller key length and relatively higher speed compared with other asymmetric cryptosystems. The most critical operation in ECC computation is point multiplication. In some popular applications such as signature verification schemes, the double point multiplication can be exploited. In this paper, we propose an algorithm and its corresponding architecture to speed up the double point multiplication using a modified binary differential addition chain. The proposed method is highly parallelizable and has been implemented on Virtex-4, Virtex-5, and Virtex-7 over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ), GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">283</sup> ), respectively. Experimental results using hardware implementation on Virtex-4 indicate that the proposed architecture achieves 63% and 16% improvements compared with the previous double point multiplication implementation in terms of required time and efficiency over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), respectively. Additionally, the proposed architecture shows time reduction compared with twice the execution time of the best previous single point multiplication by 39% while achieving 258% higher efficiency. The proposed architecture has also been implemented on ASIC, and the results show that the proposed work improves time and energy consumption compared with the previous work.