Reviewerly/exHarmony huggingface dataset:
Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World

Adversarial examples that can fool deep neural network (DNN) models in computer vision present a growing threat. The current methods of launching adversarial attacks concentrate on attacking image classifiers by adding noise to digital inputs. The problem of attacking object detection models and adversarial attacks in physical world are rarely touched. Some prior works are proposed to launch physical adversarial attack against object detection models, but limited by certain aspects. In this paper, we propose a novel physical adversarial attack targeting object detection models. Instead of simply printing images, we manufacture real metal objects that could achieve the adversarial effect. In both indoor and outdoor experiments we show our physical adversarial objects can fool widely applied object detection models including SSD, YOLO and Faster R-CNN in various environments. We also test our attack in a variety of commercial platforms for object detection and demonstrate that our attack is still valid on these platforms. Consider the potential defense mechanisms our adversarial objects may encounter, we conduct a series of experiments to evaluate the effect of existing defense methods on our physical attack.
Singular which, mention-some, and variable scope uniqueness

Singular wh-questions carry a uniqueness presupposition. Dayal (1996) proposed that uniqueness is triggered by an answer operator (ANS), which occurs highest in the question LF, outside the question nucleus. We observe data which we take to show that uniqueness may be triggered at a low scope site, beneath operators which themselves are within the question nucleus. In response to these “low uniqueness” cases, we remove the uniqueness presupposition from ANS, and suggest re-localizing it to the wh itself, which can reconstruct into the question nucleus to take narrow scope. This paves the way for a weakening of ANS previously suggested in Fox (2013) to accommodate mention-some questions.
Coverage-Guided Fuzzing for Feedforward Neural Networks

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support multiple built-in configurations which are easy to extend. We evaluated DeepHunter on two popular datasets and the results demonstrate the effectiveness of DeepHunter in achieving coverage increase and detecting real defects. A video demonstration which showcases the main features of DeepHunter can be found at https://youtu.be/s5DfLErcgrc.
Undersampling Near Decision Boundary for Imbalance Problems

Undersampling the dataset to rebalance the class distribution is effective to handle class imbalance problems. However, randomly removing majority examples via a uniform distribution may lead to unnecessary information loss. This would result in performance deterioration of classifiers trained using this rebalanced dataset. On the other hand, examples have different sensitivities with respect to class imbalance. Higher sensitivity means that this example is more easily to be affected by class imbalance, which can be used to guide the selection of examples to rebalance the class distribution and to boost the classifier performance. Therefore, in this paper, we propose a novel undersampling method, the UnderSampling using Sensitivity (USS), based on sensitivity of each majority example. Examples with low sensitivities are noisy or safe examples while examples with high sensitivities are borderline examples. In USS, majority examples with higher sensitivities are more likely to be selected. Experiments on 20 datasets confirm the superiority of the USS against one baseline method and five resampling methods.
Efficient and scalable quantum walk algorithms via the quantum Fourier transform

Quantum walks (QWs) are of interest as examples of uniquely quantum behavior and are applicable in a variety of quantum search and simulation models. Implementing QWs on quantum devices is useful from both points of view. We describe a prototype one-dimensional discrete time QW algorithm that economizes resources required in its implementation. Our algorithm needs only a single shift (increment) operation. It also allows complete flexibility in choosing the shift circuit, a resource intensive part of QW implementations. We implement the shift using the quantum Fourier transform (QFT), yielding, to date, the most efficient and scalable, quadratic size, linear depth circuit for the basic QW. This is desirable for Noisy Intermediate-Scale Quantum (NISQ) devices, in which fewer computations implies faster execution and reduced effects of noise and decoherence. As the QFT diagonalizes unitary circulant matrices, we generalize the shift in the basic QW to introduce spatial convolutions in the QW. We demonstrate our basic QW algorithm using the QFT based shift by running it on publicly accessible IBM quantum computers.
Black‐Box and Public Traceability in Multi‐authority Attribute Based Encryption

Ciphertext-policy Attribute-based encryption (CP-ABE) is a promising tool for implementing finegrained cryptographic access control. While the uniqueness of generating private keys brings extra security issues. The key escrow is inherent in CP-ABE systems because the trusted authority has the power to decrypt every ciphertext. The private keys are only associated with the attributes nor the user's identity. Some malicious users might be tempted to leak their decryption privileges for financial gain without the risk of being caught as the decryption privilege could be shared by multiple users who own the same set of attributes. We propose a new multiauthority CP-ABE with blackbox and public traceability, where the private keys are assigned by the cooperation between one central authority and multi-authorities. The performance and security analyses indicate that the proposed scheme is highly efficient and provably secure under the security model.
Hybrid Seismic-Electrical Data Acquisition Station Based on Cloud Technology and Green IoT

Traditional geophysical prospecting instruments cannot fulfill the requirements of deep energy prospecting.The instruments that measure single physical quantities, such as seismic and electrical instruments, have certain limitations.Moreover, the time period required for traditional instruments to collect, acquire, and process data is too long.To address these issues, a hybrid seismic-electrical data acquisition system based on cloud technology and green IoT was proposed and developed.A seismic analog acquisition circuit and an electrical analog acquisition circuit were designed, and the control module was designed and debugged.The system is equipped with a wireless module connected to a wireless-to-4G/5G module, which uploads the data collected by the hybrid seismic-electrical data acquisition station to the cloud platform.The background master control center completes the rapid processing of geophysical data using the robust storage and computing capabilities of the cloud.Meanwhile, it sends control commands to the cloud to control the acquisition system.This system has completed simultaneous prospecting of multiple physical quantities and achieved rapid monitoring through cloud technology.Finally, the system was used to perform fracture monitoring and a comparison of two mines in Daqing City, Heilongjiang Province.The monitoring results were satisfactory.Thus, the presented system can play a role in seismic-electrical prospecting, and can be applied to actual engineering endeavors quickly and reliably.
Scalable spectral ensemble clustering via building representative co-association matrix

Ensemble clustering via building co-association matrix and combining multiple basic partitions from the same dataset into the consensus one has been widely used in spectral clustering and subspace clustering. However, with the ever-increasing cost of calculating the co-association matrix, the conventional ensemble clustering algorithm is no longer fit for dealing with the large-scale datasets due to its less scalability and time-consuming. In this paper, we propose a scalable spectral ensemble clustering method via building a representative co-association matrix to improve the ensemble clustering problem. Our method mainly includes constructing a sparse matrix to select the representative points and building the co-association matrix, and a robust and denoising representation for the co-association matrix can be learned through a low-rank constraint in a unified optimization framework. The experiments verify the high efficiency and scalability but less time cost of our method compared with state-of-art clustering methods in the six real-world datasets, especially in the large-scale datasets.
Bringing Stories Alive: Generating Interactive Fiction Worlds

Interactive fictions—also called text-based games—are games in which a player interacts with a virtual world purely through textual natural language. In this work, we focus on procedurally generating interactive fiction worlds. Generating these worlds requires (a) referencing everyday and thematic commonsense priors in addition to (b) being semantically consistent, (c) interesting, (d) coherent throughout, all while (e) producing fluent natural language descriptions of places, people, and things. Using existing story plots from books as inspiration, we present a method that first extracts a partial knowledge graph encoding basic information regarding world structure such as locations and objects. This knowledge graph is then automatically completed utilizing thematic knowledge and used to guide a neural language generation model that fleshes out the rest of the world. We evaluate generated worlds with human-participant studies, comparing our technique against rule-based and human-made baselines
The Impacts of Flood and Drought on Food Security in Central Java

To achieve food security, water must be available at the right place, at the right time, in the right quantity, and be of the right quality. Water-related disasters will negatively affect agricultural areas and crop production, which can threaten food security. Nevertheless, flood and drought strike Central Java, one of the rice production centres in Indonesia, every year, and climate change has been worsening the condition because extreme events occur more frequently. This study reviews the impacts of flood and drought on paddy fields in Central Java from 2014 to 2018. A set of historical documents, including reports on flood and drought, rainfall records, and rice production, were collected from government institutions. Quantitative analysis was conducted using statistics and geographic information system tools. The results showed that the 2014 flood event reflected badly on 94,306 hectares (ha) paddy fields. Four severely affected regencies were Pati (25,460 ha), Demak (13,560 ha), Jepara (13,281 ha), and Kudus (12,203 ha). Meanwhile, drought in 2015 affected 82,324 ha paddy field. The areas severely damaged by drought were Blora (12,335 ha), Cilacap (11,503 ha), Grobogan (10,514 ha), and Pemalang (10,134 ha). Pearson’s correlation analysis results indicated that the correlation between annual rainfall and drought impact (r = −0.865, p = 0.058) is more significant than that between annual rainfall and drought impact (r = 0.794, p = 0.108). The stakeholders have adopted some strategies to minimise losses, such as establishing a 1,000 small water reservoirs program, preparing temporary pump irrigation, and providing agricultural insurance. Nevertheless, more efforts are still necessary to fight against food insecurity.
Digital education: from school for all to school for each

Problem and goal. The article is devoted to the development of digital education. The gap between generations and social groups exacerbates the contradictions between the education system and the demands of the information society, and the increase in the volume, accessibility, speed of acquisition and exchange of knowledge has changed the style of knowledge of modern man. The key competence was the ability to distinguish important from unimportant, reliable from unreliable. Students need to purposefully develop digital identity. All this gives rise to a set of problems facing the modern school. The purpose of the study described in the article is to identify promising ways to digitalize the school, build a model of digital school and identify a set of factors that are important for the transformation of the school for all in the school for everyone. Methodology. In the course of the study, based on the analysis of existing approaches to the development of digital technologies and digitalization of education, the ways of formation and development of a digital school are revealed. The structure of the core of digital education providing comparability of the knowledge, values, skills and competences mastered and shown in various types of activity of the person is defined. This is the value, activity and content basis for the formation of digital socio-cultural educational environment. This core is proposed to be considered as a basis for the development of concepts of subject areas, GEF and educational programs at all levels of education. Results. The interpretation of the term «digital education» is given, which is understood as educational activity, the key factors of which are digital data, processing, exchange and analysis of which allow the individual to achieve new results of education in a specific sociocultural situation. According to the model, the digital school provides not only training and education, but also effective integration of the individual into a high-tech rapidly changing complex environment. The educational ecosystem is an integrative socio-cultural environment, in the center of which is not a teacher of a traditional school, but a student, surrounded by peers and adults who help him realize his personal educational trajectory. Conclusion. The article outlines the prerequisites for a new era-the era of digital education, formulated the definition of the term «digital education», justified the relevance of the development and the structure of the core of digital education, describes the model of digital school, presents the structure of the educational ecosystem and the effects of its implementation for different stakeholders.
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in neural networks. By constraining all quantization levels as the sum of Powers-of-Two terms, APoT quantization enjoys high computational efficiency and a good match with the distribution of weights. A simple reparameterization of the clipping function is applied to generate a better-defined gradient for learning the clipping threshold. Moreover, weight normalization is presented to refine the distribution of weights to make the training more stable and consistent. Experimental results show that our proposed method outperforms state-of-the-art methods, and is even competitive with the full-precision models, demonstrating the effectiveness of our proposed APoT quantization. For example, our 4-bit quantized ResNet-50 on ImageNet achieves 76.6% top-1 accuracy without bells and whistles; meanwhile, our model reduces 22% computational cost compared with the uniformly quantized counterpart.
Design of English Vocabulary Mobile Apps Using Gamification: An Indonesian Case Study for Kindergarten

English is essential for children because it is an international language that is al-ways used in various fields, both in the world of work, education, economics, and social. One method of learning for children is by playing while learning. Gamification in the field of education is an educational approach to motivate stu-dents to enjoy learning by applying the game design. The purpose of this study is to approach the learning of English, specifically for children in kindergarten schools so that children will be more interested and feel happy to continue learning. The gamification method uses a mobile phone-based application with video games. Gamification in question is an English language recognition game for children with games. The introduction of English includes three categories, namely numbers, pictures, and spelling. The result is a mobile-based application that can be used by children to play the introduction of English with the concept of gamification. With the existence of gamification, the conception of children who were initially afraid to learn English became brave and more confident. The contribution given through this paper is to motivate children's willingness to learn English through other learning methods, namely gamification. Make children feel comfortable and not afraid to learn new things, especially English.
3D Capsule Networks for Object Classification With Weight Pruning

The proliferation of 3D sensors, due to the increased demand for 3D data, induced the 3D computer vision research in the last decade, and 3D data processing has gained a lot of interest. As in many other applications in computer vision, deep learning-based methods were quickly applied to 3D data classification and have become the state-of-the-art in this area. More recently, capsule networks, which are novel neural structures, have been introduced to enhance the ability of neural networks to better capture the parts-relationship, which yields more accurate classification with less training data. Moreover, deploying deep machine learning models on mobile platforms requires the models to be optimized due to limited memory and computational constraints. In this work, we propose methods to boost the accuracies of a standard 3D CNN-based and a Capsule Network-based classifier, help the training to better generalize the data distribution with limited data, and optimize the models for resource-constrained environments, such as mobile platforms. We also show that the introduction of capsules to 3D object classification pipeline improves the classification performance with limited training data, while a specifically optimized weight pruning method keeps the model compact enough for mobile deployment. Our broad spectrum of experiments show that proposed methods improve the performance of the base model while significantly reducing the memory and computation requirements.
Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Based on a multi-class support vector machine, an urban real estate early warning model is constructed for the Beijing real estate market.The initial indicator system is established based on the historical development of Beijing's real estate market and the selection of real estate early warning indicators.Early warning index data for Beijing from 2000 to 2018 are selected, and the leading index is selected by a time difference correlation analysis as the warning index to be used for further implementation.The model is found to have a good early warning judgment performance, and demonstrates generalization ability.The model analyzes the real estate market from the aspects of land supply, credit scale, housing supply structure, restriction of speculation, and strengthening of transparency of real estate information.It predicts that the real estate market in Beijing will run smoothly in 2019.Based on the model's findings, the paper proposes policy recommendations to promote the healthy operation of China's real estate market.
Visual Trajectory Tracking of Wheeled Mobile Robots With Uncalibrated Camera Extrinsic Parameters

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach.
A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs

Software companies that offer web-based services instead of local installations can record the user's interactions with the system from a distance.This data can be analyzed and subsequently improved or extended.A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs.We present a technique for a next click recommender system.Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks.We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context.The technique was evaluated with a real-life data set from a financial software provider.We used a hidden Markov model (HMM) as the baseline.The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation.1 Note we use the term "clicks" for "click items".
The Intelligible Contract

This paper introduces a novel model of legal digital contracts automatically executable on Blockchain technologies.Legally enforceable and automatically executable digital contracts are receiving a renewed interest, mostly due to the increasing attention towards Blockchain technologies.However, current implementations of digital contracts are still far from being intelligible to humans, because of their differences with traditional written contracts.The main purpose of this paper is to provide a contribution in bridging the gap between traditional contracts and digital contracts towards the goal of making them intelligible and legal valid.Firstly, after highlighting the shortcomings in current technologies when used for legal digital contracts, the paper introduces a new generic specification for legal digital contracts, namely the Intelligible Contract.Secondly, we describe our implementation of Intelligible Contracts and, as a proof of feasibility, we model a simple scenario by means of our implementation.The paper concludes with some ideas for future research on Intelligible Contracts.