Reviewerly/exHarmony huggingface dataset:
The role of affective touch in whole-body embodiment remains equivocal

Previous studies have highlighted that affective touch delivered at slow velocities (1–10 cm/s) enhances body-part embodiment during multisensory illusions, yet its role towards whole-body embodiment is less established. Across two experiments, we investigated the role of affective touch towards subjective embodiment of a whole mannequin body within the full body illusion, amongst healthy females. Participants perceived affective touch to be more pleasant than non-affective touch, but this did not enhance subjective embodiment within the illusion and no interaction between synchrony (Experiment 1), or congruency (Experiment 2), and the velocity of touch was observed. Finally, the perceived pleasantness of touch was not modulated by subthreshold eating disorder psychopathology, as measured by means of a self-report questionnaire. Therefore, the present findings suggest that enhancement of embodiment due to affective touch may be body-part specific, and not generalise to greater ownership towards a whole body.
Review of Blockchain Frameworks and Platforms

Development of blockchain technology and the spheres where they can be applied has led to the increasing demand for a necessary platform to meet all the requirements. To meet these requirements new frameworks and platforms have appeared. Tasks set for a certain blockchain greatly determine the choice of design and its implementation. For example presence or absence of tokens; whether it is documentary, private or public; for banking sphere or social need. All these criteria should be taken into account while creating a blockchain thus saving time and money. The article has reviewed and analyzed the existing solutions and what models they are appropriate for.
Evaluation of English intercultural communication ability based on machine learning and fuzzy mathematics

The process of international integration is accelerating continuously, which puts forward certain requirements for the current college students’ communicative ability and English ability. Therefore, it is necessary to further improve the students’ cross-cultural communicative ability in combination with English teaching. This paper combines machine learning and fuzzy mathematics methods to build an evaluation model of English cross-cultural communication ability. Moreover, based on the basic assumptions of college students’ oral communication ability evaluation, this paper builds a basic model for college students’ oral communication ability evaluation. In addition, through factor analysis and correlation analysis, this paper verifies the hypothesis of the student’s oral communication ability evaluation model and obtains an optimized university student’s oral communication ability evaluation model. After the model’s hypothesis testing and a series of statistical analysis, the evaluation system of college students’ oral communication ability is finally obtained. Finally, this article combines the investigation and analysis to test the performance of the model constructed in this article. The research results show that the capability evaluation model constructed in this paper has good performance.
How SASE is defining the future of network security

Secure networking is one of the fastest-moving fields in technology. As digital transformation activity continues to dominate the business agenda, there is an ever-growing demand for infrastructure solutions that will enable organisations to connect more locations at higher speeds – while keeping everything secure. There is an ever-growing demand for infrastructure solutions that will enable organisations to connect more locations at higher speeds – while keeping everything secure. A recent development is secure access service edge (SASE) – a collection of existing services that are integrated as a common set of functions. SASE's ability to deliver multiple integrated secure networking solutions through a single service has huge potential for any organisation that must secure high-value data across multiple locations and use-cases, says Michael Wood of Versa Networks.
Coherent control with user-defined passage

Abstract Stimulated Raman adiabatic passage (STIRAP) is a standard technique to combat experimental imperfections and can be used to realize robust quantum state control, which has many applications in physics, chemistry, and beyond. However, STIRAP is susceptible to decoherence since it requires long evolution time. To overcome this problem, stimulated Raman ‘user-defined’ passage (STIRUP) is proposed, which allows users to design the passages unlike the STIRAP but fast and robust against both decoherence and experimental imperfections. Here, we further develop a more general STIRUP method. Comparing with shortcut to adiabaticity and its’ variants, the generalized STIRUP is more simpler and compatible with more complex energy-level structure and many-body systems. Furthermore, the generalized STIRUP has many important applications such as geometric phase measurement, coherent population transfer, and quantum state preparation. Specifically, as examples, we show how to realize the high-fidelity quantum state transfer and entangled state generation in a robust way via STIRUP with the state-of-the-art experimental superconducting circuits.
Possibilities of conducting XSS-attacks and the development of countermeasures

The article describes the investigation process of the possibilities of XSS–attacks, and the development of counteraction means to these attacks. Researches were determined whether XSS–attack can be fulfilled successfully, and vulnerability detection methods can be applied; were developed the logical and structural diagrams of XSS–vulnerability detection program; were realized program implementation (software) of algorithms for detecting XSS–vulnerabilities on the Web – sites. The software implementation is Web extension for the Google Chrome browser. Main purpose of implementing this software is to confirm or deny the presence of XSS–vulnerabilities on the site, and to counteract the possible attack.
Satisfiability modulo theories and chiral heterotic string vacua with positive cosmological constant

We apply Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers in the context of finding chiral heterotic string models with positive cosmological constant from $\mathbb{Z}_2\times \mathbb{Z}_2$ orbifolds. The power of using SAT/SMT solvers to sift large parameter spaces quickly to decide satisfiability, both to declare and prove unsatisfiability and to declare satisfiability, are demonstrated in this setting. These models are partly chosen to be small enough to plot the performance against exhaustive search, which takes around 2 hours 20 minutes to comb through the parameter space. We show that making use of SMT based techniques with integer encoding is rather simple and effective, while a more careful Boolean SAT encoding provides a significant speed-up -- determining satisfiability or unsatisfiability has, in our experiments varied between 0.03 and 0.06 seconds, while determining all models (where models exist) took 19 seconds for a constraint system that allows for 2048 models and 8.4 seconds for a constraint system that admits 640 models. We thus gain several orders of magnitude in speed, and this advantage is set to grow with a growing parameter space. This holds the promise that the method scales well beyond the initial problem we have used it for in this paper.
True experimental reconstruction of quantum states and processes via convex optimization

We use a constrained convex optimization (CCO) method to experimentally characterize arbitrary quantum states and unknown quantum processes on a two-qubit NMR quantum information processor. Standard protocols for quantum state and quantum process tomography are based on linear inversion, which often result in an unphysical density matrix and hence an invalid process matrix. The CCO method, on the other hand, produces physically valid density matrices and process matrices, with significantly improved fidelity as compared to the standard methods. We use the CCO method to estimate the Kraus operators and characterize gates in the presence of errors due to decoherence. We then assume Markovian system dynamics and use a Lindblad master equation in conjunction with the CCO method, to completely characterize the noise processes present in the NMR system.
Geodesic complexity of motion planning

We introduce the geodesic complexity of a metric space, inspired by the topological complexity of a topological space. Both of them are numerical invariants, but, while the TC only depends on the homotopy type, the GC is an invariant under isometries. We show that in many cases they coincide but we also develop tools to distinguish the two in a range of examples. To this end, we study what we denote the total cut locus, which does not appear to have been explicitly considered in the literature. To the knowledge of the author, the GC is a new invariant of a metric space. Furthermore, just like the TC, the GC has potential applications to the field of robotics.
Short Paper: Coercion-Resistant Voting in Linear Time via Fully Homomorphic Encryption

We present an approach for performing the tallying work in the coercion-resistant JCJ voting protocol, introduced by Juels, Catalano, and Jakobsson, in linear time using fully homomorphic encryption (FHE). The suggested enhancement also paves the path towards making JCJ quantum-resistant, while leaving the underlying structure of JCJ intact. The exhaustive, comparison-based approach of JCJ using plaintext equivalence tests leads to a quadratic blow-up in the number of votes, which makes the tallying process rather impractical in realistic settings with a large number of voters. We show how the removal of invalid votes can be done in linear time via a solution based on recent advances in various FHE primitives such as hashing, zero-knowledge proofs of correct decryption, verifiable shuffles and threshold FHE. We conclude by touching upon some of the advantages and challenges of such an approach, followed by a discussion of further security and post-quantum considerations.
Narrative scientific data visualization in an immersive environment

Abstract Motivation Narrative visualization for scientific data explorations can help users better understands the domain knowledge, because narrative visualizations often present a sequence of facts and observations linked together by a unifying theme or argument. Narrative visualization in immersive environments can provide users with an intuitive experience to interactively explore the scientific data, because immersive environments provide a brand new strategy for interactive scientific data visualization and exploration. However, it is challenging to develop narrative scientific visualization in immersive environments. In this article, we propose an immersive narrative visualization tool to create and customize scientific data explorations for ordinary users with little knowledge about programming on scientific visualization. They are allowed to define point of interests (POIs) conveniently by the handler of an immersive device. Results Automatic exploration animations with narrative annotations can be generated by the gradual transitions between consecutive POI pairs. Besides, interactive slicing can be also controlled by device handler. Evaluations including user study and case study are designed and conducted to show the usability and effectiveness of the proposed tool. Availability and implementation Related information can be accessed at: https://dabigtou.github.io/richenliu/ Supplementary information Supplementary data (video) are available at Bioinformatics online.
MöbiusE: Knowledge Graph Embedding on Möbius Ring

In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring. The proposition of such a strategy is inspired by the classic TorusE, in which the addition of two arbitrary elements is subject to a modulus operation. In this sense, TorusE naturally guarantees the critical boundedness of embedding vectors in KGE. However, the nonlinear property of addition operation on Torus ring is uniquely derived by the modulus operation, which in some extent restricts the expressiveness of TorusE. As a further generalization of TorusE, MöbiusE also uses modulus operation to preserve the closeness of addition on it, but the coordinates on Möbius ring interacts with each other in the following way: any vector attaches to the surface of a Mobius ring becomes its opposite one if it moves along its parametric trace by a cycle. Hence, MöbiusE assumes much more nonlinear representativeness than that of TorusE, and in turn it generates much more precise embedding results. In our experiments, MöbiusE outperforms TorusE and other classic embedding strategies in several key indicators.
Iterative Refinement for Real-Time Multi-Robot Path Planning

We study the iterative refinement of path planning for multiple robots, known as multi-agent pathfinding (MAPF). Given a graph, agents, their initial locations, and destinations, a solution of MAPF is a set of paths without collisions. Iterative refinement for MAPF is desirable for three reasons: 1) optimization is intractable, 2) sub-optimal solutions can be obtained instantly, and 3) it is anytime planning, desired in online scenarios where time for deliberation is limited. Despite the high demand, this is under-explored in MAPF because finding good neighborhoods has been unclear so far. Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1) select a subset of agents, 2) use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged. Since the optimal solvers are used on small instances of the problem, this scheme yields efficient-enough solutions rapidly while providing high scalability. We also present reasonable candidates on how to select a subset of agents. Evaluations in various scenarios show that the proposal is promising; the convergence is fast, scalable, and with reasonable quality.
Transfer Extreme Learning Machine with Output Weight Alignment

Extreme Learning Machine (ELM) as a fast and efficient neural network model in pattern recognition and machine learning will decline when the labeled training sample is insufficient. Transfer learning helps the target task to learn a reliable model by using plentiful labeled samples from the different but relevant domain. In this paper, we propose a supervised Extreme Learning Machine with knowledge transferability, called Transfer Extreme Learning Machine with Output Weight Alignment (TELM-OWA). Firstly, it reduces the distribution difference between domains by aligning the output weight matrix of the ELM trained by the labeled samples from the source and target domains. Secondly, the approximation between the interdomain ELM output weight matrix is added to the objective function to further realize the cross-domain transfer of knowledge. Thirdly, we consider the objective function as the least square problem and transform it into a standard ELM model to be efficiently solved. Finally, the effectiveness of the proposed algorithm is verified by classification experiments on 16 sets of image datasets and 6 sets of text datasets, and the result demonstrates the competitive performance of our method with respect to other ELM models and transfer learning approach.
Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.
Unsupervised Trademark Retrieval Method Based on Attention Mechanism

Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and a lightweight attention mechanism is introduced to allocate a more reasonable learning weight to key features. With an unsupervised way, this proposed method can obtain good feature representation of trademarks and improve the performance of trademark retrieval. Extensive comparative experiments on the METU trademark dataset are conducted. The experimental results show that the proposed method is significantly better than traditional trademark retrieval methods and most existing supervised learning methods. The proposed method obtained a smaller value of NAR (Normalized Average Rank) at 0.051, which verifies the effectiveness of the proposed method in trademark retrieval.
Quantum one-time tables for unconditionally secure qubit-commitment

The commodity-based cryptography is an alternative approach to realize conventionally impossible cryptographic primitives such as unconditionally secure bit-commitment by consuming pre-established correlation between distrustful participants. A unit of such classical correlation is known as the one-time table (OTT). In this paper, we introduce a new example besides quantum key distribution in which quantum correlation is useful for cryptography. We propose a scheme for unconditionally secure qubit-commitment, a quantum cryptographic primitive forbidden by the recently proven no-masking theorem in the standard model, based on the consumption of the quantum generalization of the OTT, the bipartite quantum state we named<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">quantum one-time tables</mml:mtext></mml:mrow></mml:math>(QOTT). The construction of the QOTT is based on the newly analyzed internal structure of quantum masker and the quantum secret sharing schemes. Our qubit-commitment scheme is shown to be universally composable. We propose to measure the randomness cost of preparing a (Q)OTT in terms of its entropy, and show that the QOTT with superdense coding can increase the security level with half the cost of OTTs for unconditionally secure bit-commitment. The QOTT exemplifies an operational setting where neither maximally classically correlated state nor maximally entangled state, but rather a well-structured partially entangled mixed state is more valuable resource.
Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real low-surface brightness galaxies and artefacts. Employing the Dark Energy Survey (DES) and machine learning techniques, Tanoglidis et al. (2020) have shown how this task can be automatically performed by computers. Here, we build upon their pioneering work and further separate the detected low-surface brightness galaxies into spirals, dwarf ellipticals, and dwarf irregular galaxies. For this purpose, we have manually classified 5567 detections from multi-band images from DES and searched for a neural network architecture capable of this task. Employing a hyperparameter search, we find a family of convolutional neural networks achieving similar results as with the manual classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for dwarf irregulars. For dwarf irregulars - due to their diversity in morphology - the task is difficult for humans and machines alike. Our simple architecture shows that machine learning can reduce the workload of astronomers studying large data sets by orders of magnitudes, as will be available in the near future with missions such as Euclid.
Real‐time multi‐window stereo matching algorithm with fuzzy logic

Stereo matching obtains a depth map called a disparity map that indicates or shows the positions of the objects in a scene. To estimate a disparity map, the most popular trend consists of comparing two images (left-right) from two different points from the same scene. Unfortunately, small window sizes are suitable to preserve the edges, while large window sizes are required in homogeneous areas. To solve this problem, in this article, a novel real-time stereo matching algorithm embedded in an FPGA is proposed. The approach consists of estimating disparity maps with different window sizes by using the sum of absolute differences (SAD) as a local correlation metric. Once the disparity maps are obtained, the left-right consistency for each window size is computed. At the end of this stage, the centre pixel deviation is estimated through a 5 × 5 window and the Sobel gradient is extracted from the left image. Finally, both parameters are processed by a Fuzzy Inference System (FIS), which combines the calculated disparities and generates a final disparity map. An architecture embedded in FPGA is established and hardware acceleration strategies are discussed. Experimental results demonstrated that this algorithmic formulation provides promising results compared with the current state of the art.