Reviewerly/exHarmony huggingface dataset:
Echo state property and memory in octopus-inspired soft robotic arm

Soft body generates diverse dynamics that can be used as a computational resource based on the framework of physical reservoir computing. In this study, we set up an experimental platform for a soft continuum arm to extract the skeletal dynamics from its arm motion and to systematically investigate the performance of computational capability. Our focus is to clarify how the length of the soft arm and the environmental condition affect overall information processing capability. First, we collected the video data of the soft arm with different lengths in the air, tap- or sea-water conditions and tracked the arm's centerline motion. Next, we assigned a short-term memory task to the soft arm system and examined how the length of the arm affects these task performances. In addition to examining the memory capacity, we examined the local memory capacity along the arm to quantify the heterogeneity of the capacity. Finally, we examined the echo state property of the soft arm in different environmental conditions by quantifying the differences of two arm responses to the common input sequence. We found that the memory capacity was higher in water than air, but no difference was observed in sea- and tap-water conditions. The local memory capacity showed that the capacity of the tip region was especially low in air. In this condition, the responses of the tip region also did not synchronize as response to the common input. These results suggest that there is weak echo state property in the tip region of the soft arm: the states of the tip are not purely the function of the historical input. Our results demonstrate one of the concise examples on how the physical parameter in body and environment affects the information processing capability of a system.
MFPSE: Multi-user Forward Private Searchable Encryption with dynamic authorization in cloud computing

Dynamic Searchable Symmetric Encryption (DSSE) allows users to outsource data with ciphertext format to untrusted servers and supports the operations of data adding and deleting, which is adopted in common usage by government and business. Recently, the primitive of Forward Privacy in DSSE has drawn great public interest owing to its beneficial feature, which is that it can guard against the newly uploaded files from linking to previous search tokens. However, most of existing Forward Private Searchable Encryption (FPSE) schemes focus on single-user environment, which means that only the users themselves can utilize the data, greatly limits the wide application in cloud computing. To our knowledge, it is difficult to migrate the FPSE schemes to multi-user network. First, to realize forward privacy, the data owner should share the entire key group to the legitimate users, which means the users have the privilege of tampering or deleting data rather than query only; secondly, some FPSE schemes use the special structures and cannot be directly developed to multi-user network. Inspired by this, we present a scheme of multi-user forward private searchable encryption with dynamic authorization. The proposed scheme is based on a new structure of Multi-user State Chain and involves dynamic keyword-oriented authorization management. We prove our scheme can meet the secure characteristics, then conduct the performance evaluation and experiments. The results demonstrate that compared with the existing solutions, our scheme is superior in efficiency and practicability.
BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising

Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability approaching but not 100%. Also, the process of detecting a click fraud is executed by a single publisher, which makes a chance for the publisher to obtain illegal income by deceiving advertisers and media sites. Besides, the identity privacy of consumers is also exposed because the schemes deal with the plain text of consumers’ real identity. Therefore, in this paper, a blockchain-based click fraud detection and prevention scheme (BCFDPS) for online advertising is proposed to deal with the above problems. Specifically, the BCFDPS mainly introduces bilinear pairing to implicitly verify whether a consumer’s real digital identity is contained in a click message to significantly avoid click fraud and employs a consortium blockchain to ensure the transparency of the detection and prevention process. In our scheme, the clicks by machines or fraud ones by a human can be accurately detected and prevented by media sites, publishers, and advertisers. Furthermore, ciphertext-policy attribute-based encryption is adopted to protect the identity privacy of consumers. The implementation and evaluation results show that compared with the existing click fraud detection and prevention schemes based on machine learning and statistical analysis, BCFDPS achieves detection of each fraudulent click with a probability of 100% and consumes lower computation cost; furthermore, BCFDPS adds functions of consumers’ privacy protection and click fraud detection and prevention, compared to the existing blockchain-based online advertising scheme, by introducing limited communication cost ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>4,984</mn> </math> bytes) at lower storage cost.
Plausible sealing for gradual parametricity

Graduality and parametricity have proven to be extremely challenging notions to bring together. Intuitively, enforcing parametricity gradually requires possibly sealing values in order to detect violations of uniform behavior. Toro et al. (2019) argue that the two notions are incompatible in the context of System F, where sealing is transparently driven by potentially imprecise type information, while New et al. (2020) reconcile both properties at the cost of abandoning the syntax of System F and requiring user-provided sealing annotations that are not subject to graduality guarantees. Furthermore, all current proposals rely on a global form of dynamic sealing in order to enforce parametric behavior at runtime, which weakens parametric reasoning and breaks equivalences in the static language. Based on the observation that the tension between graduality and parametricity comes from the early commitment to seal values based on type information, we propose plausible sealing as a new intermediate language mechanism that allows postponing such decisions to runtime. We propose an intermediate language for gradual parametricity, Funky, which supports plausible sealing in a simplified setting where polymorphism is restricted to instantiations with base and variable types. We prove that Funky satisfies both parametricity and graduality, mechanizing key lemmas in Agda. Additionally, we avoid global dynamic sealing and instead propose a novel lexically-scoped form of sealing realized using a representation of evidence inspired by the category of spans. As a consequence, Funky satisfies a standard formulation of parametricity that does not break System F equivalences. In order to show the practicality of plausible sealing, we describe a translation from Funk, a source language without explicit sealing, to Funky, that takes care of inserting plausible sealing forms. We establish graduality of Funk, subject to a restriction on type applications, and explain the source-level parametric reasoning it supports. Finally, we provide an interactive prototype along with illustrative examples both novel and from the literature.
Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network

At present, the economy has entered the new normal. In the environment of economic slowdown, agricultural operation and investment risk management is of great significance in investment activities. In order to avoid the related risks of the agricultural operation and investment, improve the stability of agricultural operation and investment risk assessment, and ensure the effect of the agricultural operation and investment risk assessment, an agricultural operation and investment risk assessment method based on genetic neural network is proposed. This method fully considers the global optimization capability of the genetic algorithm and optimizes the weights and thresholds of the BP neural network. According to the construction principle of the index system, from the perspective of systemic risk and nonsystematic risk, a risk assessment index system for agricultural business investment has been established. Based on the establishment of risk evaluation index system, using the genetic neural network method, the agricultural operation investment risk evaluation model is established to realize the agricultural operation investment risk evaluation. The experimental results show that the proposed method has a good effect on the risk assessment of the agricultural operation and investment and can effectively improve the stability of the risk assessment of agricultural operation and investment.
Standardization procedure to provide a unified multi-method elemental compositional dataset, application to ferruginous colouring matters from Namibia

Curation of archaeological materials often leads to carrying out multi-analytical methodologies that combine non-invasive and invasive elemental analyses. Such materials are often analysed with different techniques. It results in the production of complementary but apparently non-compatible compositional datasets that cannot be easily compared. In the present paper, we propose to compare results acquired on geological ferruginous colouring matters from Namibia with analytical techniques (X-Ray Fluorescence spectrometry (XRF), Proton-Induced-X-ray Emission spectrometry (PIXE), Inductively Coupled Plasma coupled to Optical Emission spectrometry (ICP-OES) and Inductively Coupled Plasma coupled to Mass Spectrometry (ICP-MS)). We aim to provide a unified elemental dataset about these ferruginous colouring matters, usually referred to as “ochre” in archaeology. Analysed geological samples come from three distinct tectonostratigraphic zones of Namibia surveyed in the frame of rock art research. When compared directly, three of the four datasets obtained from these measurements appear as non-compatible because of the inter-equipment variability. However, through a simple standardization procedure, we demonstrate that it is possible to unify these datasets. This procedure minimizes the inter-equipment variability, making the inter-zones of provenance predominant and allowing distinctions of the samples according to their sole origin. Beyond shedding new light on the possibility to compare different elemental analytical techniques, this procedure paves the way for complementary multimodal studies of ferruginous colouring matters.
Image tamper detection and correction using Merkle tree and remainder value differencing

This article proposes an image tamper detection and correction technique. It uses the principle of differencing and addresses the fall off boundary problem (FOBP). It operates on 2 × 2 non-overlapped blocks. From each pixel of a block the decimal value for 4 most significant bits (MSBs), is called as quotient, and the decimal value for 4 least significant bits (LSBs) is called as remainder. The 4 watermark bits (WBs) are computed from 4 quotients as the root of Merkle tree. These 4 WBs are XORed with the 4 bits generated from logistic map sequence to generate the 4 recovery bits (RBs). The 4 WBs and 4 RBs are stored in 4 remainders by LSB alteration and remainder value differencing (RVD). While extracting the watermark, the tampered blocks can be identified and a correction logic can be applied to get the true value of the pixels in the block. The peak signal-to-noise ratio (PSNR) value is 42.02 dB and structural similarity (SSIM) index value is 0.9765. The tampered blocks can be accurately identified and corrected.
Influence-Aware Successive Point-of-Interest Recommendation

Abstract In recent years, with the rapid development of mobile applications, user check-in histories have been increasing. Successive point-of-interest (POI) recommendation has gained growing attention. Existing successive point-of-interest recommendation methods learn long- and short-term user preferences through historical check-in sequences to provide more personalized services. However, due to sparse data and complicated temporal patterns, the application of such technique is still limited by two challenges: 1) difficulty meeting user travel needs in time; 2) difficulty capturing users complicated behavior patterns. To address this problem, we propose a new Inf luence- A ware successive POI recommendation M odel (InfAM), which can learn the influence of POIs in a short-term sequence fragment for next point-of-interest recommendation. To capture periodic patterns of user movements, InfAM takes a user’s check-in data within a day as an input sequence to address the current travel needs of the user. In addition, based on multihead attention mechanism and user embedding, InfAM focuses on the influence of POIs in short-term sequences and general user preferences in these sequences. Therefore, InfAM integrates three specific dependencies, which can fully learn the dynamic interaction between short-term preferences: the influence of POIs in short-term sequence fragments (POI-poi), user preferences (POI-user), and the periodicity of check-ins (POI-time). Evaluation results on real-world datasets show that InfAM achieves state-of-the-art recommendation performance.
Motivation Effect of Animated Pedagogical Agent’s Personality and Feedback Strategy Types on Learning in Virtual Training Environment

The personality and feedback of an animated pedagogical agent (APA) are vital social-emotional features that render the agent perceptually believable. The effect of them on learning in virtual training remains to be examined. In this paper, an explanation model was proposed to clarify the underlying mechanism of how these two features affect learners. Two studies were conducted to investigate the model. Study 1 reexamined the effect of APA’s personality type and feedback strategy on flow experience and performance, revealing significant effects of feedback strategy on flow and performance, as well as a marginal significant effect of personality type on performance. To explore the mechanism behind these effects, a theoretical model was proposed by distinguishing between intrinsic and extrinsic motivation effect. Study 2 tested the model and round that the APA’s personality type significantly influences factors in the path of extrinsic motivation effect rather than those in the path of intrinsic motivation effect. By contrast, feedback strategy significantly affected factors in the path of intrinsic motivation effect. The proposed model was supported by these results; further distinguishing the two motivation effects is necessary to understand the respective effects of an APA’s personality and feedback features on learning experiences and outcomes.
Concept splatters: Exploration of latent spaces based on human interpretable concepts

Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present “concept splatters” as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion.
Inverse Hamiltonian design by automatic differentiation

An ultimate goal of materials science is to deliver materials with desired properties at will. In the theoretical study, a standard approach consists of constructing a Hamiltonian based on phenomenology or first principles, calculating physical observables, and improving the Hamiltonian through feedback. However, there is also an approach that bypasses such a cumbersome procedure, namely, to obtain an appropriate Hamiltonian directly from the desired properties. Solving the inverse problem has the potential to reach qualitatively different principles, but most research to date has been limited to quantitative determination of parameters within known models. Here, we present a general framework that enables the inverse design of Hamiltonians by optimizing numerous parameters using automatic differentiation. By applying it to the quantum anomalous Hall effect (AHE), we show that our framework can not only rediscover the Haldane model but also automatically generate a new Hamiltonian that exhibits a six-times larger AHE. In addition, the application to the photovoltaic effect (PVE) gives an optimal Hamiltonian for electrons moving on a noncoplanar spin texture, which can generate $\sim 900$~A/m$^2$ under solar radiation. This framework would accelerate materials exploration by establishing unprecedented models and principles beyond human intuition and empirical rules.
PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.
Nonclassical photon statistics and photon squeezing in the dissipative mixed quantum Rabi model

Abstract Nonclassical two-photon statistics and photon squeezing are considered as representative features of the nonclassicality of light. In this work we investigate two-photon correlation function and quadrature photon squeezing in the dissipative mixed quantum Rabi model (QRM), which includes both the one-photon and two-photon qubit–resonator interactions. The quantum dressed master equation combined with squeezed-coherent states is applied to obtain the steady state. Based on the zero-time delay two-photon correlation function, it is found that with the increase of the two-photon qubit–resonator interaction strength the photon antibunching behavior is monotonically suppressed, whereas the photon bunching signature persists. One additional giant photon bunching feature is unraveled at deep-strong two-photon coupling, which mainly stems from efficient successive transition trajectories. The finite-time delay two-photon correlation function asymptotically approaches the unit by raising the delayed time. Moreover, the steady-state quadrature photon squeezing becomes significant at strong two-photon coupling, which may become perfect in the zero temperature limit.
PneuBots: Modular Inflatables for Playful Exploration of Soft Robotics

PneuBots is a modular soft robotics construction kit consisting of seven types of self-foldable segments with high tensile strength, three types of pneumatic connectors and splitters, and a custom-designed box. The kit enables various assemblies to be made with the modular pieces, allowing creators to explore the world of soft robotics in playful ways. When combined with a FlowIO device, PneuBots allows seamless programmability of the different assemblies, enabling artists, designers, engineers, and makers to create dynamic, shape-changing, and interactive works that can be used for education, storytelling, dynamic art, or expression of affect and gratitude. In this paper, we present our current progress towards developing a soft robotics starter kit that is affordable, modular, programmable, and adaptable to users’ creative visions. Finally, we discuss some of our preliminary results with users who had the opportunity to try using PneuBots.
Concepts for creating augmented reality based technical documentations for the maintenance of machine tools

Abstract The maintenance process of machine tools becomes more complex and difficult due to its increasing complexity and individuality. Augmented reality (AR) can support the maintenance engineer by visualizing the process with individual working steps and further information in a user-friendly manner. In order to an efficient deployment, a short and automated creation process of the AR based documentation is required including the integration of existing systems and data such as the technical documentation, CAD models and PDM data. This avoids a complete rewriting and manual creation of the AR based documentation in addition to the standard paper-based version. Therefore, various concepts for creating AR based technical documentations for the maintenance of machine tools are presented and verified including an analysis of the required documentation data, systems and processes.