Reviewerly/exHarmony huggingface dataset:
Identifying Multiple Peer Influences on Smart Contract Adoption in Blockchain User Network

Decentralized applications (DApps) are digital applications supported by blockchain and smart contract technology. They have been widely used in many fields. However, the adoption rate for such useful applications is much lower than the industry expected. Currently, there is no thorough study justifying the reason for such no rate. To fill this gap, we use peer influence to explain the diffusion of DApps, since it relies on the blockchain, a peer-to-peer network, as the carrier. We firstly explore whether and how peer influence affects individual adoption decisions of DApps. Our hypotheses are examined in the context of Ethereum, the largest smart contract platforms, using big data collected from the platform, consisting of over 490 million transactions from over 60 million distinct accounts. Our results suggest that the diffusion of DApps is positively affected by the adoption decisions of neighbors. Such findings are well supported by the theories in social network and telecommunication. Moreover, we also find that the effect size of peer influence is associated with network structure characteristics, such as network size, density, and decentralization. The results provide strong evidence of peer influence on the diffusion of DApps through various mechanisms, and can be used by the industry to design marketing strategies to increase the adoption rate.
Bengali Ethnicity Recognition and Gender Classification using CNN &amp; Transfer Learning

In this paper, we have demonstrated how to apply CNN (Convolutional Neural Network) structured model and transfer learning to identify the ethnicity of Bengali people and it's a systematic process of gender classification too. We also applied several models of transfer learning like VGG16, Mobilenet, Resnet50, etc. to find out which model is more convenient to get our desired accuracy. But problems arise because there are many Indian people who look like and get dressed up like Bengali since in India many Bengali dwell in when many of them speak Bangla as well! (people of Kolkata along with some other provinces). So, the Bengali people are not only found in Bangladesh but also elsewhere in the world. That's why our model is based on facial images along with the tradition of their costumes. We tried to build a sophisticated model using CNN and transfer learning for this purpose and we got some tremendous performances applying transfer learning.
Testing System for the Mechanical Properties of Small-Scale Specimens Based on 3D Microscopic Digital Image Correlation

The testing of the mechanical properties of materials on a small scale is difficult because of the small specimen size and the difficulty of measuring the full-field strain. To tackle this problem, a testing system for investigating the mechanical properties of small-scale specimens based on the three-dimensional (3D) microscopic digital image correlation (DIC) combined with a micro tensile machine is proposed. Firstly, the testing system is described in detail, including the design of the micro tensile machine and the 3D microscopic DIC method. Then, the effects of different shape functions on the matching accuracy obtained by the inverse compositional Gauss-Newton (IC-GN) algorithm are investigated and the numerical experiment results verify that the error due to under matched shape functions is far larger than that of overmatched shape functions. The reprojection error is shown to be smaller than before when employing the modified iteratively weighted radial alignment constraint method. Both displacement and uniaxial measurements were performed to demonstrate the 3D microscopic DIC method and the testing system built. The experimental results confirm that the testing system built can accurately measure the full-field strain and mechanical properties of small-scale specimens.
Better Together: Shading Cues and Multi-View Stereo for Reconstruction Depth Optimization

Passive reconstruction methods such as traditional multi-view stereo are capable to accurate reconstruction results. However, the depth calculation of multi-view stereo encounters significant difficulties, especially when the corresponding points have some degree of inaccuracy or unreliability. In this paper, we make use of geometric and shading cues information in the multi-view stereo approach to construct a robust energy function, which is also effective to optimize the depth information provided by multi-view stereo. This work improves the accuracy of point cloud depth. we evaluated our algorithm in the famous DTU datasets, and established our own dinosaur datasets. All the data is collected by mobile devices under natural light condition, while the reconstruction results are completed and accurate.
A Persuasive Technology for Managing Waste Disposal through Smart Trash Bin and Waste Disposal Tracker

&lt;p&gt;Waste management issue is a relevant topic that had been focused on many persuasive technologies. This issue is not only related to getting disposed of waste in proper places but also reducing waste disposal volume. This paper proposes a persuasive system that aims both goals by providing a smart trash bin and waste disposal tracker. The smart trash bin is an IoT-featured trash bin whereas waste disposal tracker is a mobile application which can record user daily waste disposal volume statistic. Based on the evaluation, our proposed system has achieved both goals. Additionally, it also solves several impediments such as incapability of moral background to encourage people to dispose waste on the trash bin, boring and monotonous waste disposal activity, and discouragement for disposing waste due to full trash bin. In order to attract people, our system includes attractive mechanisms such as playful experience, reflective experience, and gamification. Playful experience is applied to a smart trash bin, whereas the other two are applied to the waste disposal tracker.&lt;/p&gt;
Learning discriminative and meaningful samples for generalized zero shot classification

Generalized zero shot classification aims to recognize both seen and unseen samples in test sets, which has gained great attention. Recently, many works consider using generative adversarial network to generate unseen samples for solving generalized zero shot classification problem. In this paper, we study how to generate discriminative and meaningful samples. We propose a method to learn discriminative and meaningful samples for generalized zero shot classification tasks (LDMS) by generative adversarial network with the regularization of class consistency and semantic consistency. In order to make the generated samples discriminative, class consistency is used, such that the generated samples of the same classes are near and of different classes are far away. In order to make the generated samples meaningful, semantic consistency is used, such that the semantic representations of the generated samples are close to their class prototypes. It encodes the discriminative information and semantic information to the generator. In order to alleviate the bias problem, we select some confident unseen samples. We use the seen samples, the generated unseen samples and the selected confident unseen samples to train the final classifier. Extensive experiments on all datasets demonstrate that the proposed method can outperform state-of-the-art models on generalized zero shot classification tasks.
Preface to the special issue on Cross Reality (XR) and Immersive Learning Environments (ILE) in education

Today, there are many exciting advances occurring in Immersive Learning Environments (ILEs) and their innovative applications in education. Recent breakthroughs in technologies such as Virtual Real...
PENGEMBANGAN MEDIA PEMBELAJARAN PRAKTIKUM VIRTUAL BERBASIS ANDROID UNTUK MENINGKATKAN BERPIKIR KREATIF SISWA

This study aims to determine the feasibility and interest of the virtual application learning media based on android applications on the concept of invertebrate animal material. This research is a research and development (R&amp;D) that adopts the development model of Thiagrajan, S., et al. Using non-test data collection techniques in the form of expert / media expert questionnaire, material expert, and linguist expert and student response questionnaire. The results of the appraisal of androis-based virtual practicum learning media by experts / media experts were 71.7% with feasible criteria, the assessment of material experts was 91.5% with very decent criteria, 89.5% was assessed by linguists as very feasible criteria. Student response results by 77% in the interesting category. Based on the results of this study that the development of an Android-based virtual practicum learning media on the concept of invertebrate animals can be used as an alternative learning biology, especially preformance practicum activities, as well as increasing students' creative thinking. Keywords: virtual practicum, learning media, creative thinking, invertebrate animals.
Extending UML Use Case Diagrams to Represent Non-Interactive Functional Requirements

Background:The comprehensive representation of functional requirements is a crucial activity in the analysis phase of the software development life cycle.Representation of a complete set of functional requirements helps in tracing business goals effectively throughout the development life cycle.Use case modelling is one of the most widely-used methods to represent and document functional requirements of the system.Practitioners exploit use case modelling to represent interactive functional requirements of the system while overlooking some of the non-interactive functional requirements.The non-interactive functional requirements are the ones which are performed by the system without an initiation by the user, for instance, notifying something to the user or creating an internal backup.Aim: This paper addresses the representation of non-interactive requirements along with interactive ones (use cases) in one model.This paper calls such requirements 'operation cases' and proposes a new set of graphical and textual notations to represent them.Method: The proposed notations have been applied on a case study and have also been empirically evaluated to demonstrate the effectiveness of the new notations in capturing non-interactive functional requirements. Results and Conclusion:The results of the evaluation indicate that the representation of operation cases helps in documenting a complete set of functional requirements, which ultimately results in a comprehensive translation of requirements into design.
Application of deep learning technology to fringe projection 3D imaging

Fringe projection(structured light) 3D imaging is a widely used 3D imaging method. In recent years, the integrated three-dimensional sensor has developed rapidly, especially the three-dimensional sensor based on the principle of structured light has gradually become an essential sensor unit for high-end smart phones. However, with the increasing requirements from applications, people have higher and higher requirements on the efficiency, accuracy, stability and other aspects for the fringe projection technique. At the same time, the rapid development of deep learning technology has opened a new door for the development of optical imaging technology, and from this door we notice that with the introduction of the concept of artificial intelligence, the development of fringe projection technology is also experiencing a new breakthrough. In this paper, the basic theory of fringe projection 3D imaging was introduced. Then, by using the deep learning technology, the fringe projection technology based on the physical model can become a technology driven by and in this case, it showed the potential to surpass the traditional algorithm. Finally, the challenges and future research directions in this field from the aspects of neural network model, training data, training methods and so on were discussed.
RETRACTED: The Solution of Network Intellectual Property Infringement under the Background of Big Data

Abstract At present, big data applications have entered a new era. Compared with the network era, data storage and data transmission are the main content. The big data era is mainly based on data mining and data application. In the context of big data, intellectual property protection faces more severe challenges. To this end, this article proposes research on the solution of intellectual property rights infringement in the context of big data. This paper uses a combination of research methods such as literature sub-research and content analysis to conduct research. This paper finds that to resolve the infringement of online intellectual property rights in the context of big data, it is necessary to improve the system, strengthen network management, and form a social environment conducive to the operation of the intellectual property system. Support ideas, clarify measures, and strengthen law enforcement. In this era of online rampage, only by strengthening online supervision, establishing correct concepts, encouraging originality, and resisting piracy, can the origin of online intellectual property infringement be resolved.
Security Enumerations for Cyber-Physical Systems

Enumerations constitute a pivotal element of Cyber Threat Intelligence (CTI). References to enumerated artifacts support a universal understanding and integrate threat information. While traditional IT systems and vulnerabilities are covered by security enumerations, this does not apply to Cyber-Physical Systems (CPS). In particular, complexity and interdependencies of components within these systems demand for an extension of current enumerations. Taking on a CPS security management perspective this work identifies deficiencies within the Common Platform Enumeration (CPE) and the Common Vulnerabilities and Exposures (CVE) enumeration. Models for CPS are thus proposed to cover comprehensiveness and usability. A prototype is used to evaluate the feasibility by demonstrating key features of security enumerations for CPS.
Person Independent Recognition of Head Gestures from Parametrised and Raw Signals Recorded from Inertial Measurement Unit

Numerous applications of human–machine interfaces, e.g., dedicated to persons with disabilities, require contactless handling of devices or systems. The purpose of this research is to develop a hands-free head-gesture-controlled interface that can support persons with disabilities to communicate with other people and devices, e.g., the paralyzed to signal messages or the visually impaired to handle travel aids. The hardware of the interface consists of a small stereovision rig with a built-in inertial measurement unit (IMU). The device is to be positioned on a user’s forehead. Two approaches to recognize head movements were considered. In the first approach, for various time window sizes of the signals recorded from a three-axis accelerometer and a three-axis gyroscope, statistical parameters were calculated such as: average, minimum and maximum amplitude, standard deviation, kurtosis, correlation coefficient, and signal energy. For the second approach, the focus was put onto direct analysis of signal samples recorded from the IMU. In both approaches, the accuracies of 16 different data classifiers for distinguishing the head movements: pitch, roll, yaw, and immobility were evaluated. The recordings of head gestures were collected from 65 individuals. The best results for the testing data were obtained for the non-parametric approach, i.e., direct classification of unprocessed samples of IMU signals for Support Vector Machine (SVM) classifier (95% correct recognitions). Slightly worse results, in this approach, were obtained for the random forests classifier (93%). The achieved high recognition rates of the head gestures suggest that a person with physical or sensory disability can efficiently communicate with other people or manage applications using simple head gesture sequences.
Implementasi Metode Luther untuk Pengembangan Media Pengenalan Tata Surya Berbasis Virtual Reality

The purpose of virtual reality is to enable a motor and cognitive sensor activity ofsomeone in the artificial world created digitally to become imaginary, symbolic orsimulate certain aspects in the real world [1]. This technology is applied to the mediaintroduction of the solar system using the Luther method. The Luther Method consistsof 6 stages, namely Concept, Design, Material Collecting, Assembly, Testing, andDistribution. Luther method has advantages compared to other methods because thereare stages of material collecting which is an important stage in the development ofmultimedia and this Luther method can be done in parallel or can go back to theprevious stage [2]. At the Assembly stage the implementation uses the Unity Engineand Google VR SDK for Unity, the result is a virtual reality application that can displaythe solar system with 3-dimensional objects and an explanation is available on eachobject. While testing the blackbox on a variety of Android devices with differentspecifications. From the results of the application of the Luther method, it is verystructured and can run well in the development of multimedia applications, while theresults of testing, this Android-based virtual reality application cannot run on devicesthat do not have Gyroscope sensors and can run on devices with a minimumspecification of 1GB RAM will but the rendering process on 3D objects is slow.
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.
Diana's World: A Situated Multimodal Interactive Agent

State of the art unimodal dialogue agents lack some core aspects of peer-to-peer communication—the nonverbal and visual cues that are a fundamental aspect of human interaction. To facilitate true peer-to-peer communication with a computer, we present Diana, a situated multimodal agent who exists in a mixed-reality environment with a human interlocutor, is situation- and context-aware, and responds to the human's language, gesture, and affect to complete collaborative tasks.
KIT’s IWSLT 2020 SLT Translation System

This paper describes KIT's submissions to the IWSLT2020 Speech Translation evaluation campaign.We first participate in the simultaneous translation task, in which our simultaneous models are Transformer-based and can be efficiently trained to obtain low latency with minimized compromise in quality.On the offline speech translation task, we applied our new Speech Transformer architecture to endto-end speech translation.The obtained model can provide translation quality which is competitive to a complicated cascade.The latter still has the upper hand, thanks to the ability to transparently access to the transcription, and resegment the inputs to avoid fragmentation.
TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.
Leakage-Resilient Key Exchange and Two-Seed Extractors

Can Alice and Bob agree on a uniformly random secret key without having any truly secret randomness to begin with? Here we consider a setting where Eve can get partial leakage on the internal state of both Alice and Bob individually before the protocol starts. They then run a protocol using their states without any additional randomness and need to agree on a shared key that looks uniform to Eve, even after observing the leakage and the protocol transcript. We focus on non-interactive (one round) key exchange (NIKE), where Alice and Bob send one message each without waiting for one another. We first consider this problem in the symmetric-key setting, where the states of Alice and Bob include a shared secret as well as individual uniform randomness. However, since Eve gets leakage on these states, Alice and Bob need to perform privacy amplification to derive a fresh secret key from them. Prior solutions require Alice and Bob to sample fresh uniform randomness during the protocol, while in our setting all of their randomness was already part of their individual states a priori and was therefore subject to leakage. We show an information-theoretic solution to this problem using a novel primitive that we call a two-seed extractor, which we in turn construct by drawing a connection to communication-complexity lower-bounds in the number-on-forehead (NOF) model. We then turn to studying this problem in the public-key setting, where the states of Alice and Bob consist of independent uniform randomness. Unfortunately, we give a black-box separation showing that leakage-resilient NIKE in this setting cannot be proven secure via a black-box reduction under any game-based assumption when the leakage is super-logarithmic. This includes virtually all assumptions used in cryptography, and even very strong assumptions such as indistinguishability obfuscation ( $$\textit{i}\mathcal {O}$$ ). Nevertheless, we also provide positive results that get around the above separation: