Reviewerly/exHarmony huggingface dataset:
Integrated Gbps quantum random number generator with real-time extraction based on homodyne detection

High-speed quantum random number generators are highly demanded in cryptographic systems, whose bottleneck lies in the ease of implementation and the efficiency of real-time post-processing. Based on homodyne detection of a coherent state, we realize a compact Gbps real-time quantum random number generator with a silica planar light circuit. We integrate all the optical and electrical modules into a stand-alone device, use standard low-cost components, and make the whole system simple and robust. Homodyne detection and real-time hashing extraction circuits are built into compact electronics with a final generation rate of 1 Gbps. Our demonstration shows an economic, compact, robust, and high-speed quantum random number generator available for immediate commercialization.
PINE: Optimizing Performance Isolation in Container Environments

With the development of virtualization technologies, containers are widely used to provide a light-weight isolated runtime environment.Compared with virtual machines, containers can achieve high-resource utilization and provide a more convenient way of sharing, but there are significant security challenges due to potential resource contention among services.When the regular services that share the storage system with the key services are over-used, a lot of resources may be preempted, which breakdowns the whole system and delays other services.This paper addressed the performance isolation in fierce resource competition situations, that is, when different services (latency-sensitive services and throughputfirst services) are deployed on a host machine using container technology.We proposed PINE, a performance isolation optimization solution in container environments, which can adaptively allocate the storage resource for each service according to their performance behaviors through dynamical resource management and I/O concurrency configuration.The experimental results show that PINE can effectively optimize the performance of the running services and achieve the optimal result in a relatively short time.
Rasch-based tailored goals for nutrition assistance systems

Choosing adequate goals plays is central to the success of a task. With this study, we investigate tailoring the goals of a nutrition assistance system to the user's abilities according to a Rasch scale. To that end, we evaluated two versions of a mobile system that offers dietary tracking, visual feedback, and personalized recipe recommendations. The original version targets optimal nutritional behavior and focuses on the six least optimal nutrients (N=51). The adapted version targets only improved nutritional behavior compared to the status quo and thus tailors the advice to the next six achievable nutrients according to a Rasch scale (N=47). Results of the two-week study indicate that the tailored advice leads to higher success for the focused nutrients, and is perceived to be more diverse and personalized, and thus more effective.
User performance effects with graphical icons and training for elderly novice users: A case study on automatic teller machines

This study investigated the effects with training and graphical icons on task performance for elderly novice users on automatic teller machines (ATMs). 124 elderly novice users who had no prior ATM experience participated in the training and test of ATM usage. Participants in the control group (n = 62) were training to use the traditional text-based ATM interface meanwhile those in the experimental group (n = 62) were training to use the alternative ATM interface with graphical icons. They were asked to learn how to perform three major tasks of ATMs. A test immediately following the training and a test one month after training was carried out. Task completion score was used for measuring user performance. The results showed although training could help elderly novice users succeed in learning to use ATMs, they still had a significant decline in user performance in the test one month later compared with the immediate test. Participants of the experimental group outperformed those of the control group in both the immediate test and one month test after training, especially in the latter. The finding suggested graphical icons could help to improve the learning and retention of ATM usage for elderly novice users, especially the latter, which would compensate for the drawback of training and reduce forgetting rate to some extent. Well-designed graphical icons could benefit elderly novice users much from learning phase to retention phase. The findings of this study can be applied to guide the design and development of ATMs or other public technology devices considering elderly novice users.
Noise Robust Speaker Recognition Based on Adaptive Frame Weighting in GMM for i-Vector Extraction

Even though speaker recognition has gained significant progress in recent years, its performance is known to be deteriorated severely with the existence of strong background noises. Inspired by a recently proposed clean-frame selection approach, this work investigates a relatively elegant weighting method when computing the Baum-Welch statistics of Gaussian mixture models (GMMs) in i-vector extraction. By introducing weighting parameters to the frames of enrollment/testing utterances, the optimization problem is redefined and solved. New updating rules are derived by incorporating weights to the computation of posterior probabilities, mean vectors, and covariance matrices of the GMM. The experiments conducted on the Speakers in the Wild (SITW) database show that the proposed algorithm has significantly improved the performance of i-vector-based speaker recognition systems in noisy environments. Compared with the GMM i-vector baseline, the equal error rate is reduced from 5.75 to 4.72 and the minimum value of cost function (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">det</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sup> ) is reduced from 0.4825 to 0.4505. Slight but significant superiority is also observed over the method with an additional feature enhancement frontend by using deep neural networks.
Evolutionary parallel extreme learning machines for the data classification problem

This study proposes an Island Parallel Evolutionary Extreme Learning Machine algorithm (IPE-ELM) for the well-known data classification problem. The ELM is a fast and efficient machine learning technique with its single-hidden layer feed-forward neural network (SLFN). High prediction accuracy and learning speed of the ELM make it an elegant tool for the fitness calculation process of the evolutionary algorithms. The IPE-ELM algorithm combines the evolutionary genetic algorithms (for feature selection), ELM machine learning technique (for prediction accuracy calculation), parallel computation (for faster fitness evaluation), and parameter tuning (activation function selection and the number of hidden neurons) for the solution of this important problem. Each ELM that runs at a different processor selects one of four different activation functions (Sine, Cosine, Sigmoid and Hyperbolic Tangent) and uses a randomized number of hidden neurons to achieve higher prediction accuracy. The proposed algorithm provides high quality results with its (near)-linear scalability behavior. The IPE-ELM algorithm is compared with state-of-the-art data classification algorithms by using UCI benchmark datasets and significant improvements are reported in terms of prediction accuracy with reasonable execution times. The scalable IPE-ELM algorithm can be reported as the first island parallel evolutionary classification algorithm with its high prediction accuracy results that outperforms state-of-the-art algorithms in literature.
An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability.
Transferring Generalizable Motor Primitives From Simulation to Real World

Reinforcement learning provides robots with an autonomous learning framework where a skill can be learned by exploration. Exploration in real world is, however, inherently unsafe and time consuming, and causes wear and tear. To address these, learning policies in simulation and then transferring them to physical systems has been proposed. In this letter, we propose a novel sample-efficient transfer approach, which is agnostic to the dynamics of a simulated system and combines it with incremental learning. Instead of transferring a single control policy, we transfer a generalizable contextual policy generated in simulation using one or few samples from real world to a target global model, which can generate policies across parameterized real-world situations. We studied the generalization capability of the incremental transfer framework using MuJoCo physics engine and KUKA LBR 4+. Experiments with ball-in-a-cup and basketball tasks demonstrated that the target model improved the generalization capability beyond the direct use of the source model indicating the effectiveness of the proposed framework. Experiments also indicated that the transfer capability depends on the generalization capability of the corresponding source model, similarity between source and target environment, and number of samples used for transferring.
Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-$k$ Spatial–Textual Query

The convergence of technologies like Cloud computing, mobile, and smart phone technologies has led to the rapid development of location-based services (LBS) in smart cities. For flexibility and cost savings, there is a recent trend to migrate LBS to the Cloud, however it poses a serious threat to the user privacy. In this paper, we present a new privacy preserving top-k spatio-textual keyword (TkSK) query scheme, called privacy-preserving spatio-textual index (Pystin), which is performed over outsourced Cloud and can enable secure LBS in smart cities. In Pystin, a query user's accurate location is protected by the combination of Boneh-Goh-Nissim homomorphic encryption and hash bucket techniques, and the privacy of textual information are persevered by a one-way hash function. In addition, a quad-tree-based spatio-textual indexing is integrated into Pystin to further reduce the query latency. Detailed security analyzes show that the proposed Pystin scheme is indeed a privacy-preserving TkSK query scheme. Furthermore, extensive experiments are conducted, and results confirm the scalability, efficiency properties of our proposed Pystin scheme.
Unrestricted and compact certificateless aggregate signature scheme

A certificateless aggregate signature (CLAS) scheme employing a reduced signature size eliminates the complexity of certificate management in a traditional public key cryptosystem. A compact aggregate signature, where the size does not depend on the aggregate number, is desirable when the objective is to reduce storage cost and bandwidth. However, in conventional compact CLAS schemes, aggregation of signatures is restricted. The state information is meant to be used only once for security; it is generally used to restrict aggregation to signatures generated with the same state information. This paper proposes the first unrestricted and compact CLAS scheme where the signature size is constant and any combination of signatures can be aggregated. Aside from convenient storage and communication costs, our scheme is also equipped with a secure system against realistic adversaries. Moreover, we evaluate the performance of our CLAS scheme and demonstrate its effectiveness. Finally, this paper reveals that it is not possible to construct an unrestricted and compact CLAS scheme for a widely used structure with constant pairing computation.
Multisensory correlations—Not tactile expectations—Determine the sense of body ownership

Can the mere expectation of a sensory event being about to occur on an artificial limb be sufficient to elicit an illusory sense of ownership over said limb? This issue is currently under debate and studies using two different paradigms have presented conflicting results. Here, we employed the two relevant paradigms, namely, the magnetic touch illusion and the "tactile expectation" version of the rubber hand illusion, to clarify the role of tactile expectations in the process of attributing ownership to limbs. The illusory senses of ownership and 'magnetic touch' were quantified using questionnaires, threat-evoked skin conductance responses and a combination of motion tracking synchronized with real-time subjective ratings and skin conductance. The results showed that the magnetic touch illusion was dependent on concurrent visual and tactile stimulation and that visually induced tactile expectations alone were insufficient. Moreover, in this study, tactile expectations were not associated with the rubber hand illusion, neither in terms of subjective ratings nor skin conductance changes. Together, these findings contradict the notion that the brain uses predictions of upcoming sensory events to determine whether or not a limb belongs to the self, and, instead, emphasize the importance of correlated multisensory information.
Differential privacy preservation in regression analysis based on relevance

With the development of data release and data mining, protecting the sensitive information in the data from being leaked has attracted quite a few attentions in the information security field. Differential privacy is an excellent paradigm for providing the preservation against the adversary that attempts to infer the sensitive information of individuals. However, the existing works show that the accuracy of the differentially private regression model is less than satisfactory since the amount of noise added in is uncertainty. In this paper, we present a novel framework PrivR, a differentially private regression analysis model based on relevance, which transforms the objective function into the form of polynomial and perturbs the polynomial coefficients according to the magnitude of relevance between the input features and the model output. Specifically, we add less noise to the coefficients of the polynomial representation of the objective function that involve strongly relevant features, and vice-versa. Experiments on Adult dataset and Banking dataset demonstrate that PrivR not only prevents the leakage of data privacy effectively but also retains the utility of the model.
A Comparative Analysis by KNN, SVM &amp; ELM Classification to Detect Sickle Cell Anemia

Red blood cell abnormalities involve erythrocytes that supply oxygen to all body tissues. Sometimes the formation and role of erythrocytes are hindered. Sickle cell anemia (SCA) is one kind of red blood cell disease. People carrying sickle cell anemia are increasing day by day. Sickle cell anemia shortens life expectancy. But life expectancy can be extended by diagnosing it an early stage. To identify the existence of sickle cells, an image processing procedure is developed. Blood samples are collected in the form of image format. The conversion of gray image, noise filtering and enhancement of image is done in image pre-processing. Fuzzy C means clustering is applied to determine the normal and sickle cells. Morphological operations are also applied to images. The geometrical and statistical features are used for extraction. Lastly, k nearest neighbor (knn), support vector machine (svm) & extreme learning machine (elm) classifiers are implemented to test images. Comparisons among the classifiers with reliable results are presented by this system.
Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features

Modulation recognition plays a key role in emerging spectrum applications including spectrum enforcement, resource allocation, privacy and security. While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i)a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter's bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity.This paper bridges the gap between real-world spectrum sensing and the growing body of methods for modulation recognition designed under simplifying assumptions. We propose to use local features, besides global statistics, extracted from raw IQ data, which collectively enable a robust framework for modulation recognition that outperforms baselines from the state-of-the-art. Specifically, we exploit the discriminative power of local patterns from consecutive IQ samples extracted based on a Fisher Kernel framework that captures non-linearity in the underlying data. With these domain-informed features, we employ lightweight linear support vector machine classification for modulation detection. Our framework is robust to noise, partial transmitter scans and data biases without utilizing prior knowledge of the underlying transmitter technology. The recognition accuracy of our approach consistently outperforms baselines in both simulated and real-world traces. We demonstrate up to a 98% accuracy and a 30% improvement over several counterparts from the literature with partial scans in a USRP testbed.
Non-Markovian dynamics of a system of two-level atoms coupled to a structured environment

In this work, taking $N$ noninteracting two-level atoms coupled to an anisotropic three-dimensional photonic crystal into account, we study exact non-Markovian dynamics of the atoms with the photonic crystal as an environment. We find that there exists a threshold number ${N}_{\mathrm{CR}}$ for the identical atoms, beyond which the system environment has a bound state regardless of how weak the system-environment coupling is. This means all identical atoms do not dissipate to the ground state. Further, when the whole system has no bound state, we find that the amplitude of collective excitation approaches a limit value $2/3$ as the number of atoms approaches to infinity. For nonidentical atoms with different transition frequencies, there exist $N$ bound states at most that lead to $N$-wave mixing in the system dynamics. The present prediction is possible to observe in the recent circuit QED experiment, and the results suggest that coherence in a many-body system can be maintained in bound states by manipulating the environment.
Analysing determinants of small and medium‐sized enterprise resilience using fuzzy cognitive mapping

Abstract Resilience is an increasingly important concept in current socioeconomic landscapes. Due to the competitive global context in which we live, this trend is particularly noticeable in small and medium‐sized enterprises (SMEs). Due to their limited resources and capabilities, these firms are exposed to unfavourable scenarios that can threaten their stability, viability, and development (i.e., their resilience). Thus, identifying the determinants of SME resilience and their respective cause‐and‐effect relationships is a vital endeavour because these companies' resilience involves a wide variety of conceptual or theoretical issues that need to be understood more fully. This study proposes the creation of a fuzzy cognitive map as a novel way to analyse the determinants of SME resilience. The fuzzy cognitive map was developed based on the insights obtained during two intensive group meetings with a panel of SME managers and entrepreneurs. The resulting well‐informed, process‐oriented framework contains the determinants that need to be considered in this research context. The static and dynamic analyses carried out produced an improved understanding of the cause‐and‐effect relationships between determinants of SME resilience, which facilitates better strategic planning by these firms' managers. The practical implications, advantages, and limitations of the proposed approach are also discussed.
Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

Designing face recognition systems that are capable of matching face images obtained in the thermal spectrum with those obtained in the visible spectrum is a challenging problem. In this work, we propose the use of semantic-guided generative adversarial network (SG-GAN) to automatically synthesize visible face images from their thermal counterparts. Specifically, semantic labels, extracted by a face parsing network, are used to compute a semantic loss function to regularize the adversarial network during training. These semantic cues denote high-level facial component information associated with each pixel. Further, an identity extraction network is leveraged to generate multi-scale features to compute an identity loss function. To achieve photo-realistic results, a perceptual loss function is introduced during network training to ensure that the synthesized visible face is perceptually similar to the target visible face image. We extensively evaluate the benefits of individual loss functions, and combine them effectively to learn the mapping from thermal to visible face images. Experiments involving two multispectral face datasets show that the proposed method achieves promising results in both face synthesis and cross-spectral face matching.
Augmented, Mixed, and Virtual Reality Applications in Cause-Related Marketing (CRM)

This chapter deals with emerging augmented, mixed, and virtual reality platforms and their applications in cause-related marketing (CRM) campaigns. This chapter provides definitions and examples of augmented, mixed, and virtual realities and explains their importance CRM professionals. Compared with traditional marketing platforms, reality-creating technologies are characterized with their capabilities to interact with marketing contents through their geolocation specificity, mobility, and synchronization of virtuality and reality. These technological characteristics have made reality-creating technologies very promising for many cause-related marketing (CRM) campaigns. This chapter surveys current discussions in the existing literature and ends with three cause-related marketing (CRM) campaigns. The study concludes with an overview of emerging issues, future directions, and professional best practice recommendations.
Assets on the blockchain: An empirical study of Tokenomics

Digital tokens linked to financial and economic ventures may have multiple functions and uses. In this work, we examine the relationship between various token functions and the market price of the corresponding token. We consider 86 venture related blockchain tokens, and develop the analysis through a stepwise testing of four hypotheses using panel ordinary least squares with cluster-robust standard errors. We find that token functions are statistically significant in relation to token prices. In the absence of an established legal framework, we argue that our results complements recent regulatory actions identifying tokens to be investment contracts in a common venture.