Reviewerly/exHarmony huggingface dataset:
Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Attention capability is an essential component of human–robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot’s frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot’s view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot’s view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people’s attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM’s performance in people attention prioritization presents an approximately 20% improvement.
On the Relationship Between Resilient Boolean Functions and Linear Branch Number of S-Boxes

Differential branch number and linear branch number are critical for the security of symmetric ciphers. The recent trend in the designs like PRESENT block cipher, ASCON authenticated encryption shows that applying S-boxes that have nontrivial differential and linear branch number can significantly reduce the number of rounds. As we see in the literature that the class of $$4\times 4$$ S-boxes have been well-analysed, however, a little is known about the $$n \times n$$ S-boxes for $$n \ge 5$$ . For instance, the complete classification of $$5 \times 5$$ affine equivalent S-boxes is still unknown. Therefore, it is challenging to obtain “the best” S-boxes with dimension $$\ge $$ 5 that can be used in symmetric cipher designs. In this article, we present a novel approach to construct S-boxes that identifies classes of $$n \times n$$ S-boxes ( $$n = 5, 6$$ ) with differential branch number 3 and linear branch number 3, and ensures other cryptographic properties. To the best of our knowledge, we are the first to report $$6\times 6$$ S-boxes with linear branch number 3, differential branch number 3, and with other good cryptographic properties such as nonlinearity 24 and differential uniformity 4.
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To stabilize this method for contextual generation of categorical sequences, we estimate the gradient by evaluating a set of correlated Monte Carlo rollouts. Due to the correlation, the number of unique rollouts is random and adaptive to model uncertainty; those rollouts naturally become baselines for each other, and hence are combined to effectively reduce gradient variance. We also demonstrate the use of correlated MC rollouts for binary-tree softmax models which reduce the high generation cost in large vocabulary scenarios, by decomposing each categorical action into a sequence of binary actions. We evaluate our methods on both neural program synthesis and image captioning. The proposed methods yield lower gradient variance and consistent improvement over related baselines.
Real-Time Recognition of Daily Actions Based on 3D Joint Movements and Fisher Encoding

Recognition of daily actions is an essential part of Ambient Assisted Living (AAL) applications and still not fully solved. In this work, we propose a novel framework for the recognition of actions of daily living from depth-videos. The framework is based on low-level human pose movement descriptors extracted from 3D joint trajectories as well as differential values that encode speed and acceleration information. The joints are detected using a depth sensor. The low-level descriptors are then aggregated into discriminative high-level action representations by modeling prototype pose movements with Gaussian Mixtures and then using a Fisher encoding schema. The resulting Fisher vectors are suitable to train Linear SVM classifiers so as to recognize actions in pre-segmented video clips, alleviating the need for additional parameter search with non-linear kernels or neural network tuning. Experimental evaluation on two well-known RGB-D action datasets reveal that the proposed framework achieves close to state-of-the-art performance whilst maintaining high processing speeds.
Express Wavenet: A lower parameter optical neural network with random shift wavelet pattern

Compared with other optical diffractive neural networks, the express wavenet (ExWave) is an improved and efficient network which adopts much fewer parameters. For advantages of the ExWave, firstly, the wavelet-like pattern is used to modulate the phase of optical waves; secondly, the number of parameters can be reduced from O(n2) to O(n), for the input image with n2 pixels; thirdly, the accuracy can be still high with only 1% of total parameters of the famous D2 NN. Besides, in the modified national institute of standards and technology (MNIST) dataset, 93.5% accuracy can be achieved with only 2457 parameters, nevertheless, 250880 parameters are required to meet a similar accuracy for the standard optical network. Moreover, the random shift wavelets exhibit characteristics of optical network vividly, especially for the vanishing gradient phenomenon in the training process. We present the modified expressway structure for solving this problem. Afterward, simulation have been carried out to validate effects of both random shift wavelet and expressway structure. Finally, it can be shown that the optical diffractive network in this work utilizes much fewer parameters than other neural networks. The detailed source code can be available in the following link: https://github.com/closest-git/ONNet.
Proactive Preventive and Evidence-Based Artificial Intelligene Models: Future Healthcare

Health care is one of the most important agenda in every nation’s economic and social growth. Due to amalgamation in food habits and lifestyle, today’s health care is under tremendous burden because of an increase in chronic diseases. An era of health care is under development due to the fast and accurate diagnosis ability of artificial intelligence from the last 2 years. Due to AI, it is possible in the future to provide cost-effective models in health care at the reach of every patient. The basic motivation behind this paper is to insight some of the AI models of the health care, their future scope, and implementation problems. This study will surely help to identify the thought process behind each model and generate some new guidelines for implementation in the future. These models require certain modifications in terms of data handling, security, image enhancement, and reconstruction. Use of smartphone and wearable devices has increased rapidly, and hence we need to provide a unique solution to society in terms of the new techniques of artificial intelligence.
Universal DV-CV interaction mechanism for deterministic generation of entangled hybridity

We propose a general approach for deterministic generation of the entangled hybrid states consisting of continuous variable (CV) and discrete variable (DV) states. Peculiarity of the interaction of DV and CV states on the beam splitter (BS) is key for the birth of the entangled hybridity provided that some measurement event is registered in auxiliary mode. We show that the CV states forming the output entangled state consist exclusively of either even or odd Fock states. Various input CV states, including the Schrödinger cat state, are tested at the input of the BS, where they mix with delocalized single photons. We show the hybrid entanglement generation is observed at arbitrary values of the experimental parameters used. The degree of the hybrid entanglement is evaluated. Conditions for generating the maximally entangled hybrid states are established.
A Framework of Dimensionality Reduction Utilizing PCA for Neural Network Prediction

This paper proposes the utilization of Principal Component Analysis (PCA) to decrease high-dimensional information and to enhance the prescient execution of the Neural Network machine learning model. Tests are done on a high-dimensional dataset, in which the errand is to recognize an objective. The investigations demonstrate that the utilization of this PCA strategy can enhance the execution of machine learning in the arrangement of high-dimensional information. It is broadly utilized in a large portion of the example acknowledgment applications like face acknowledgment, picture pressure, and for discovering designs in high-dimensional information. The work in this paper includes highlight decreased utilization of PCA pursued by the order which is finished utilizing Neural Network calculation. The exact outcomes exhibit Neural Network order with PCA is a productive characterization for extensive datasets.
Alternative Access to Humusoft Magnetic Levitation System

The article deals with an alternative solution of communication for the educational model of magnetic levitation produced by Humusoft company. The new proposal is based on the use of Arduino Due microprocessor. The resulting solution is tested by means of a simple controller based on the inverse dynamics method.
A numerically stable algorithm for integrating Bayesian models using Markov melding

Abstract When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodels that have a common quantity. One challenge is that the prior for this quantity can be implicit, and its prior density must be estimated. We show that error in this density estimate makes the two-stage Markov chain Monte Carlo sampler employed by Markov melding unstable and unreliable. We propose a robust two-stage algorithm that estimates the required prior marginal self-density ratios using weighted samples, dramatically improving accuracy in the tails of the distribution. The stabilised version of the algorithm is pragmatic and provides reliable inference. We demonstrate our approach using an evidence synthesis for inferring HIV prevalence, and an evidence synthesis of A/H1N1 influenza.
Removing multi-frame Gaussian noise by combining patch-based filters with optical flow

Patch-based approaches such as 3D block matching and non-local Bayes are widely accepted filters for removing Gaussian noise from single-frame images. We propose three extensions for these filters when there exist multiple frames of the same scene. The first of them employs reference patches on every frame instead of a commonly used single-reference frame method, thus utilizing the complete available information. The remaining two techniques use a separable spatiotemporal filter to reduce interactions between dissimilar regions, hence mitigating artifacts. In order to deal with non-registered datasets, we combine all our extensions with robust optical flow computation. Two of our proposed multi-frame filters outperform existing extensions on most occasions by a significant margin while also being competitive with a state-of-the-art neural network-based technique. Moreover, one of these two strategies is the fastest among all due to its separable design.
Efficient two-qubit pulse sequences beyond CNOT

We design efficient controlled-rotation gates with arbitrary angle acting on three-spin encoded qubits for exchange-only quantum computation. Two pulse sequence constructions are given. The first is motivated by an analytic derivation of the efficient Fong-Wandzura sequence for an exact cnot gate. This derivation, briefly reviewed here, is based on elevating short sequences of swap pulses to an entangling two-qubit gate. To go beyond cnot, we apply a similar elevation to a modified short sequence consisting of swaps and one pulse of arbitrary duration. This results in two-qubit sequences that carry out controlled-rotation gates of arbitrary angle. The second construction streamlines a class of arbitrary cphase gates established earlier. Both constructions are based on building two-qubit sequences out of subsequences with special properties that render each step of the construction analytically tractable.
Predicting the Spatial Distributions of Elements in Former Military Operation Area Using Linear and Nonlinear Methods Across the Stavnja Valley, Bosnia and Herzegovina

This study has the purpose of developing a realistic soil prediction maps of the spatial distribution of elements by evaluating and comparing different modelling techniques: Kriging, artificial neural network-multilayer perceptron (ANN-MLP) and multiple polynomial regressions (MPR). The Stavnja Valley was selected as a test area due to the following reasons: (1) intensive metal ore mining and metallurgical processing; (2) peculiar geomorphological natural features; (3) regular geological setting, and (4) the remaining minefields. Geospatial parameters from digital elevation models (DEM) are used as an input to advanced prediction modelling techniques: ANN-MLP and MPR. Soil measurements, land use data, and remote sensing are applied, developed, and finally incorporated into the models of spatial distribution in the form of 2D or 3D maps. In order to reconstruct the different processes that influenced the entire study area simultaneously, we used novel approaches to modelling. This comprehensive approach not only represents an innovation in contamination mapping, but developed prediction models also help in the reconstruction of main distribution pathways, assess the real size of the affected area, and improve the data interpretation.
Deep stacked sparse auto-encoder based on patches for image classification

Image classification is an area where deep learning and especially stacked Auto-encoders have really proven their strength. The contributions of this paper lie in the creation of a new classifier to remedy some classification problems. This new method of classification presents a combination of the most used techniques in Deep Learning (DL) and Sparse Coding (SC) in the field of classification. Proposed deep neural networks consist of three stacked Auto-encoders and a Softmax used as an outer layer for classification. The first Auto-encoder is created from a sparse representation of all images of the dataset. The sparse representation of all images represents the decoder part of the first Auto-encoder. Then the transpose of the matrix is applied to get the encoder part. Experiments performed on standard datasets such as ImageNet and the Coil-100 reveal the efficacy of this approach.
The Future of Open Science

The topic of this episode is the future of Open Science, and what it is like to be an outspoken critic of the current publishing system. Our guest is Jon Tennant, paleontologist, independent researcher and the founder of Open Science MOOC. The host of this episode is Erik Lieungh.&#x0D; This episode was first published 21 December 2018.
A two-dimensional quantum key distribution protocol based on polarization-phase encoding

In this paper, we introduce a new quantum key distribution protocol, which we refer to as polarization-phase (PoP) protocol. In this protocol, two degrees of photonic freedom of the same particle in a hybrid manner are used to encode the information in the format of two- or high-dimensional quantum states (qubits and qudits, respectively). Here, we only discuss the qubit version of the general PoP protocol (we refer to two-dimensional PoP (TD-PoP) protocol) as an interesting extension for the standard BB84 protocol. We investigate the performance of the TD-PoP protocol using infinite-key analysis against restricted individual attacks, i.e. the intercept-resend and photon number splitting attacks, in both ideal single-photon and Poisson (attenuated laser) sources. In addition, we demonstrate that this protocol, despite using two physical dimensions, is simple and fully has empirical implementation capability. Ability to extract two bits of information from each detection event, and increasing the sifting parameter, the secure key rate, and the likelihood of detection of an eavesdropper, are the advantages of the TD-PoP protocol compared to the standard polarization- or phase-encoded BB84 protocol.
A New Image Segmentation Method Based on Support Vector Machine

Image segmentation is a hot and difficult research topic in the fields of pattern recognition and computer vision. At present, the method based on support vector machine has been widely used in image segmentation, but the selection of training samples mostly depends on manual selection. This reduces the adaptability of image segmentation, but also affects the classification performance of support vector machine. A new image segmentation method based on support vector machine (SVM) is proposed, which combines the advantages of mean clustering algorithm to automatically obtain training samples, then extracts image color features and texture features respectively, and uses them as training samples of SVM. Finally, the trained classifier is used to segment the image. The experimental results show that the proposed algorithm is better than the traditional support vector machine-based algorithm.
Bidirectional Information Flow and the Roles of Privacy Masks in Cloud-Based Control

We consider a cloud-based control architecture for a linear plant with Gaussian process noise, where the state of the plant contains a client’s sensitive information. We assume that the cloud tries to estimate the state while executing a designated control algorithm. The mutual information between the client’s actual state and the cloud’s estimate is adopted as a measure of privacy loss. We discuss the necessity of uplink and downlink privacy masks. After observing that privacy is not necessarily a monotone function of the noise levels of privacy masks, we discuss the joint design procedure for uplink and downlink privacy masks. Finally, the trade-off between privacy and control performance is explored.
Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problems in the context of decoding. An important development in this regard was due to Varsamopoulos et at. who proposed a two-step decoder using neural networks. Subsequent work of Maskara et at. used the same concept for decoding for various noise models. We propose a similar two-step neural decoder using inverse parity-check matrix for topological color codes. We show that it outperforms the state-of-the-art performance of non-neural decoders for independent Pauli errors noise model on a 2D hexagonal color code. Our final decoder achieves a threshold of 10%. Our result is comparable to the recent work on neural decoder for quantum error correction by Maskara et at. It appears that our decoder has advantages with respect to training cost and complexity of the network for higher distances when compared to that of Maskara et at.
CoRank: Simultaneously Ranking Publication Venues and Researchers

Many academic and administrative decisions rely on evaluation of publication venues and individual researchers, such as for the purpose of hiring, promotion, and grant distribution. Thus it is important to compare and rank publication venues and researchers in an objective and authoritative manner. While many endeavors exist, in this paper we propose a novel algorithm CoRank based on venue-researcher interaction that computes reputation scores and rankings for publication values and researchers simultaneously. We observe that good researchers publish many papers in good venues, good publication venues feature many good researchers, and venue scores and researcher scores can be defined in terms of each other. CoRank is designed to break this circular definition in an iterative manner to compute reputation scores and rankings for both venues and researchers. We implement CoRank and perform experiments on real DBLP dataset to demonstrate its promise.
More on the open string pair production

Motivated by the recent work [1] by one of the present authors, we here report that there exist two new systems, namely, D3/(F, D1) and D3/(D3, (F, D1)), either of which can give rise to a much larger sizable open string pair production rate at a condition much relaxed. Here the D3 is taken as our own (1 + 3)-dimensional world while the non-threshold bound state (F, D1) or (D3, (F, D1)) is placed parallel nearby in the directions transverse to both our D3 world and the non-threshold bound state considered.
A Memetic Algorithm Based on Breakout Local Search for the Generalized Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is one of the most popular Combinatorial Optimization Problem. It is well solicited for the large variety of applications that it can solve, but also for its difficulty to find optimal solutions. One of the variants of the TSP is the Generalized TSP (GTSP), where the TSP is considered as a special case which makes the GTSP harder to solve. We propose in this paper a new memetic algorithm based on the well-known Breakout Local Search (BLS) metaheuristic to provide good solutions for GTSP instances. Our approach is competitive compared to other recent memetic algorithms proposed for the GTSP and gives at the same time some improvements to BLS to reduce its runtime.
Enhancement of quantum correlations using correlation injection scheme in a cascaded four-wave mixing processes

Quantum correlations and entanglement shared among multiple quantum beams are important for both fundamental science and the development of quantum technologies. The enhancement for them is necessary and important to implement the specific quantum tasks and goals. Here, we report a correlation injection scheme (CIS) which is an effective method to enhance the quantum correlations and entanglement in the symmetrical cascaded four-wave mixing processes, and the properties of quantum correlations and entanglement can be characterized by the values of the degree of intensity-difference squeezing (DS) and the smallest symplectic eigenvalues, respectively. Our results show that the CIS can enhance the quantum correlations and entanglement under certain conditions, while for other conditions it can only decrease the values of the DS and the smallest symplectic eigenvalues to the level of standard quantum limit, respectively. We believe that our scheme is experimentally accessible and will contribute to a deeper understanding of the manipulations of the quantum correlations and entanglement in various quantum networks.