Reviewerly/exHarmony huggingface dataset:
Monocular Vision Aided Depth Map from RGB Images to Estimate of Localization and Support to Navigation of Mobile Robots

Localization is one of the most challenging requirements needed for a mobile robots. Successful localization represents success in meeting the other principal requirements, such as perception and navigation. This article proposes a new approach for the localization of autonomous mobile robots using a Kinect sensor and the concept of Transfer Learning linked to Convolutional Neural Networks (CNNs). The images acquired from the sensor are applied in a mosaic form, which consists of the three color channels provided along with the depth information of the environment. Topological maps were used for indoor environment localization. The proposed computer vision system employed the Bayesian Classifier, k-Nearest Neighbor, Random Forest, Multi-layer Perceptron, and Support Vector Machine as the classifiers. The main focus of this work is the use of a unique configuration of RGB-D images transformed into a mosaic image, combined with the descriptive power of CNNs, in order to estimate the location of a mobile robot in an indoor environments. The results show that the proposed approach proved to be a convincing method for the tasks of localization and supporting the navigation of mobile robots; the results achieved 100% in Accuracy and in the F1-Score. The values obtained for the processing times were also suitable for a computer vision system, with 31.885ms, 0.040s, and 0.044s for the extraction, training, and classification stages, respectively. All these results were from the RGB-D mosaic images. The data demonstrated the relevance of the proposed work, providing a successful localization and, consequently, a successful navigation for mobile robots.
Informed Histogram-Based Watermarking

The existing works on histogram-based watermark embedding share the common notions of non-informed random host sample selection and uniform embedding, resulting in limited and yet similar performances. This letter develops two methods to improve imperceptibility and robustness of histogram-based embedding. We first propose a content-aware method which performs uniform embedding in ranked energy-significant regions of the host signal. We show that the embedded ternary watermark signal is more likely to be masked by the strong host signal component, thus improving imperceptibility without compromising robustness. Further, a non-uniform embedding method is proposed, which modifies host samples according to their relationships with the center of the target histogram bin. It ensures minimum host sample modifications without reducing the payload size. Meanwhile, watermark robustness could also be improved since it becomes the most difficult to move samples at bin centers into other bins via attacks. The proposed methods are supported by extensive experimental results.
Human skeleton mutual learning for person re-identification

Person re-identification refers to matching people across non-overlapping camera views on different locations and at different times. In the case of changes in perspective, light, background, veil, and person's clothing, traditional method cannot achieve person recognition effectively and reliably. In this paper, we propose a novel biometric metric learning method named Human Skeleton Mutual Learning person re-identification (HSMLP-Reid). The purpose of HSML person re-identification method (HSMLP-Reid) largely aims to use the new pedestrian local segmentation method proposed in this paper combined with the global skeleton information to solve the influence of background and local posture change. Firstly, bottom-up method is used to estimate the pedestrian posture and skeleton and the joint points of the pedestrians will be marked in this process. A new local segmentation method proposed in this paper named joint segmentation is used to locally segment pedestrians and perform local block matching. Furthermore, we learn the global skeleton information by defined joint distances from the pedestrian 2D skeleton estimation by the bottom-up method and use global skeleton information to global skeleton matching. Finally, we use local match and global skeleton match for mutual learning. Local match based on pedestrian nodes and global skeleton match based on pedestrian skeleton are based on biometrics. We learn the classification loss and metric learning loss to train model. Metric loss includes global skeletal distance and local block metric distance. Extensive experimental results on the large-scale Market1501, CUHK03 and CUHK-SYSU data sets demonstrate that the proposed method achieves consistently superior performance and outperforms most of the state-of-the-art methods.
Person re-identification with expanded neighborhoods distance re-ranking

In the person re-identification (re-ID) community, pedestrians often have great changes in appearance, and there are many similar persons, which incurs will degrades the accuracy. Re-ranking is an effective method to solve these problems, this paper proposes an expanded neighborhoods distance (END) to re-rank the re-ID results. We assume that if the two persons in different image are same, their initial ranking lists and two-level neighborhoods will be very similar when they are taken as the query. Our method follows the principle of similarity, and selects expanded neighborhoods in initial ranking list to calculate the END distance. Final distance is calculated as the combination of the END distance and Jaccard distance. Experiments on Market-1501, DukeMTMC-reID and CUHK03 datasets confirm the effectiveness of the novel re-ranking method in this article. Compare with re-ID baseline, the proposed method in this paper increases mAP by 14.2% on Market-1501 and Rank1 by 12.9% on DukeMTMC-reID.
Effects of an Integrated Learning Approach on Students' Outcomes in St. Petersburg Polytechnic University

To improve the learning outcomes of students and adjust the professional competences of the future specialists to the current labor market demands we designed an integrated methodology for CLIL learners and implemented it in the course of International Business. The suggested methodology involves flipped classroom activities, online resource for self-directed study and project-based learning practices. Russian high schools are quite reluctant to student-centered approach to delivery of professional disciplines due to their traditional systems of education that are focused on the formation of theoretical knowledge as the basis of professional competences. To prove efficiency of the suggested methodology we designed and carried out the assessment of students' performance and achievements. The set of tests on English proficiency, knowledge obtained in the course of the discipline was conducted to identify the level of professional skills development. Course satisfaction was measured, results were analyzed and then the correlation with students' achievements was done. Positive outcomes of students' academic performance prove the necessity of refocusing professional disciplines at Russian high schools from teacher-centered approach to new learning and teaching practices.
A beetle antennae search algorithm based on Lévy flights and adaptive strategy

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Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)

Outlier detection is one of the main challenges in the pre-processing stage of data analyses. In this study, we suggest a new non-parametric outlier detection technique which is based on the frequency-domain and Fourier Transform definitions and call it as the frequency-domain based outlier detection (FOD). From simulation results under various distributions and real data applications, we observe that our proposal approach is capable of detecting quasi-periodic outliers in time series data more successfully compared with other commonly used methods like z-score, box-plot and also faster than some specialized methods Grubbs method and autonomous anomaly detection (AAD) method. Therefore, we consider that our proposal approach can be an alternative approach to find quasi-periodic outliers in time series data.
Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem

In imbalanced problems, the asymmetric number of samples in different classes brings great challenges to traditional classifiers, especially to the Nearest Neighbors (NN) classifiers. When NN-based classifier deals with imbalanced problems, the criterion of itself makes the classification result data-dependent, thus biasing towards the majority class. To overcome the drawback in NN-based classifiers, a meta heuristic NN-based algorithm named Gravitational Fixed Radius Nearest Neighbor classifier (GFRNN) is proposed to solve imbalanced problems by drawing on Newton’s law of universal gravitation. However, GFRNN still has three major problems including negligence of the distribution of samples, unreasonable calculation of data mass and improper distance metric. To this end, this paper proposes an Entropy and Gravitation based Dynamic Radius Nearest Neighbor algorithm (EGDRNN). Different from GFRNN, EGDRNN determines the radius in a dynamic and rapid way. EGDRNN uses entropy information to make samples at different locations have different importance. Finally, by utilizing a general Lp-norm to calculate the distance between two samples, the classification performance is greatly improved. The experimental result validates that the proposed EGDRNN not only achieves the highest classification accuracy but also takes the lowest time consuming among all comparison algorithms.
Doing vs. Being: A philosophy of design for artful VR

We present a practical philosophy for how to design artfully for virtual reality. Framing this design philosophy is the balance of doing vs. being as a central duality in all design for VR. The values underlying this way of thinking draw from a diverse set of interdisciplinary perspectives and from our own practice in computer music design. We develop a set of design principles, through which we critically analyse several popular musical VR experiences as well as some of our own creations. These include a tool for audio programming in VR, a set of design etudes on musical instruments and a multi-movement artistic work. Through these principles and case studies, we hope to provide lenses – both critical and practical – to inform the design of artful VR.
Accurate and Fast Classification of Foot Gestures for Virtual Locomotion

This work explores the use of foot gestures for locomotion in virtual environments. Foot gestures are represented as the distribution of plantar pressure and detected by three sparsely-located sensors on each insole. The Long Short-Term Memory model is chosen as the classifier to recognize the performer's foot gesture based on the captured signals of pressure information. The trained classifier directly takes the noisy and sparse input of sensor data, and handles seven categories of foot gestures (stand, walk forward/backward, run, jump, slide left and right) without manual definition of signal features for classifying these gestures. This classifier is capable of recognizing the foot gestures, even with the existence of large sensor-specific, inter-person and intra-person variations. Results show that an accuracy of ~80% can be achieved across different users with different shoe sizes and ~85% for users with the same shoe size. A novel method, Dual-Check Till Consensus, is proposed to reduce the latency of gesture recognition from 2 seconds to 0.5 seconds and increase the accuracy to over 97%. This method offers a promising solution to achieve lower latency and higher accuracy at a minor cost of computation workload. The characteristics of high accuracy and fast classification of our method could lead to wider applications of using foot patterns for human-computer interaction.
Walk, Stop, Count, and Swap: Decentralized Multi-Agent Path Finding With Theoretical Guarantees

For multi-agent path finding (MAPF) problems, finding the optimal solution has been shown to be NP-Complete. Here we present WSCaS (Walk, Stop, Count, and Swap), a decentralized multi-agent path-finding algorithm that can provide theoretical completeness and optimality guarantees. That is, WSCaS is able to deliver a worst case O(1)-approximate distance-optimal solution to MAPF instances on square grids without narrow passages. Moreover, the algorithm`s cost is independent of the swarm's size with respect to computation complexity, memory complexity, as well as communication complexity, therefore the algorithm can scale well with the number of agents in practice. The algorithm is executed on 1024 simulated agents as well as 100 physical robots, the results show that the WSCaS is robust to real-world non-idealitys.
Phase‐Modulated Interferometry, Spectroscopy, and Refractometry using Entangled Photon Pairs

Abstract The authors demonstrate a form of two‐photon‐counting interferometry by measuring the coincidence counts between single‐photon‐counting detectors at an output port of a Mach–Zehnder Interferometer (MZI) following injection of broad‐band time‐frequency‐entangled photon pairs (EPP) generated from collinear spontaneous parametric down conversion into a single input port. Spectroscopy and refractometry are performed on a sample inserted in one internal path of the MZI by scanning the other path in length, which acquires phase and amplitude information about the sample's linear response. Phase modulation and lock‐in detection are introduced to increase detection signal‐to‐noise ratio and implement a “down‐sampling” technique for scanning the interferometer delay, which reduces the sampling requirements needed to reproduce fully the temporal interference pattern. The phase‐modulation technique also allows the contributions of various quantum‐state pathways leading to the final detection outcomes to be extracted individually. Feynman diagrams frequently used in the context of molecular spectroscopy are used to describe the interferences resulting from the coherence properties of time‐frequency EPPs passing through the MZI. These results are an important step toward the implementation of a proposed method for molecular spectroscopy—quantum‐light‐enhanced 2D spectroscopy.
Combined weighted multi-objective optimizer for instance reduction in two-class imbalanced data problem

Instance reduction from class-balanced data has been investigated in much research. However, there is a lack of studies on class-imbalanced data. Learning from imbalanced data lately has attracted a lot of attention due to the practical applications. In the case of two-class imbalanced data, the instances from one class, majority class, are more numerous than the instances from the other class, which is a minority class. The present paper aims to introduce a new instance reduction method that preserves between-class distributions in the balanced data and handles minority class instance reduction in two-class imbalanced data, efficiently. The proposed method solves the instance reduction issue from an unconstrained multi-objective optimization problem aspect. Accordingly, a new combined weighted optimizer is designed. By employing the chaotic krill herd evolutionary algorithm, both the minority and majority class spaces with the accelerated convergence are explored. Through this method, the original data set is purged of those instances that decrease accuracy, and Gmean. The performance has been evaluated on both imbalanced and balanced data sets collected from the UCI repository by the 10-fold cross-validation method. Evaluations show that the proposed method outperforms state-of-the-art methods in terms of classification accuracy, Gmean, reduction rates, and computational time.
Active Learning with Deep Pre-trained Models for Sequence Tagging of Clinical and Biomedical Texts

Active learning is a technique that helps to minimize the annotation budget required for the creation of a labeled dataset while maximizing the performance of a model trained on this dataset. It has been shown that active learning can be successfully applied to sequence tagging tasks of text processing in conjunction with deep learning models even when a limited amount of labeled data is available. Recent advances in transfer learning methods for natural language processing based on deep pre-trained models such as ELMo and BERT offer a much better ability to generalize on small annotated datasets compared to their shallow counterparts. The combination of deep pre-trained models and active learning leads to a powerful approach to dealing with annotation scarcity. In this work, we investigate the potential of this approach on clinical and biomedical data. The experimental evaluation shows that the combination of active learning and deep pre-trained models outperforms the standard methods of active learning. We also suggest a modification to a standard uncertainty sampling strategy and empirically show that it could be beneficial for annotation of very skewed datasets. Finally, we propose an annotation tool empowered with active learning and deep pre-trained models that could be used for entity annotation directly from Jupyter IDE.
Learning and Reasoning on Graph for Recommendation

Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. Previous models largely follow a general supervised learning paradigm - treating each interaction as a separate data instance and building a supervised learning model upon the information isolated island. Such paradigm, however, overlook relations among data instances, hence easily resulting in suboptimal performance especially for sparse scenarios. Moreover, due to the black-box nature, most models hardly exhibit the reasons behind a prediction, making the recommendation process opaque to understand.
Design and Implementation of a Low-Latency Modular Multiplication Algorithm

Modular multiplication is one of the most compute-intensive arithmetic operations. Most public-key cryptosystems utilize modular multiplication of integers of various lengths, depending on security requirements. Efficient algorithms and implementations are required to realize a practical public-key cryptosystem. Different parameters, such as area, power and time, can be optimized for different implementation requirements. Low latency was not as important as high throughput requirement for modular multiplication implementations before. However, with recent work on Verifiable Delay Functions (VDFs), a necessity for the lowest possible latency for modular multiplication implementations emerged. VDFs are designed to take a prescribed time to realize the underlying computation that can be publicly verified. VDF constructions utilize inherently sequential arithmetic operations. Efficient VDF constructions have been proposed recently, based on time-lock puzzles constructed by Rivest, Shamir, and Wagner. An exponentiation operation in an RSA group needs to be realized for these VDF constructions. In this paper, a modular multiplication algorithm suitable for low-latency circuit implementations is proposed and an FPGA-optimized variant of this algorithm is presented.
VCLPKES: Verifiable Certificateless Public Key Searchable Encryption Scheme for Industrial Internet of Things

Nowadays the data of the industrial Internet of Things (IIOT) have been stored in cloud servers.The security and privacy of stored data have been hot research topics.The technique of public key searchable encryption (PKSE) may contribute to protect the privacy of industrial data.It is extremely significant how to use PKSE to encrypted data and retrieve the encrypted data without revealing users' private information.Meanwhile, most of the existing PKSE schemes do not consider the identity verification of the data owner who may upload bad ciphertext if he is malicious.In this paper, we firstly analyze the security of a certificateless searchable encryption scheme in the IIOT environment (Ma et al. scheme), and propose a feasible attack to demonstrate that their scheme is not secure.Through this attack, Type I adversary A I can forge the trapdoor value for all keywords.Then we proposed a verifiable certificateless public key searchable encryption (VCLPKSE) scheme.The scheme not only overcomes the security issue of Ma et al. scheme, but also offers the authentications of data owners and data users.Via the authentication mechanism, data owners could not repudiate the fact they uploaded the ciphertext.Finally, we proved that the VCLPKSE scheme satisfies the ciphertext indistinguishability, trapdoor indistinguishability and unforgeability in the random oracle model.Meanwhile, we also do the simulation experiment to demonstrate the scheme's efficiency.
Improved generalized labeled multi-Bernoulli filter for non-ellipsoidal extended targets or group targets tracking based on random sub-matrices

For non-ellipsoidal extended targets and group targets tracking, using an ellipsoid to approximate the target extension may not be accurate enough because of lacking shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Besides, in practical tracking scenes, the knowledge of detection probability is of critical importance, which, however, is prior unknown due to the fluctuation of target parameters. Thus based on the aforementioned random sub-matrices model, an improved generalized labeled multi-Bernoulli (GLMB) filter is presented for the tracking of non-ellipsoidal extended targets or group targets in the case of unknown detection probability. In addition, a cheaper Gibbs sampling based joint prediction and updating implementation of the proposed GLMB filter is given, within which the extended targets or group targets are modeled using Beta gamma Gaussian inverse Wishart (BGGIW) distributions, achieving the estimation to detection probability, measurement rate, and extension state of each sub-object and the kinematic state with the positions of all sub-objects and unified kinematics for each extended target or target group. The simulation results demonstrate the effectiveness of the proposed filter.
Institutions and financial development in ECOWAS

Earlier studies on institutions and financial sector development are less informative given their failure to examine how the different forms of institutions influence countries’ level of domestic financial development. In this study, we re-examine the impact of institutions on financial development in the ECOWAS regional bloc over the period 1995–2015. Employing the Augmented Mean Group (AMG) estimator, we show that overall institutions do not have a significant impact on financial development. However, after disaggregating the level of institutions into market-creating, market-legitimizing, market-stabilizing, and market-regulating, we find that only the effect of market-legitimizing institutions significantly enhances financial sector development. Thus, the impact of institutions on financial development is sensitive to the form of institutions. We discuss key implications for policy.