Reviewerly/exHarmony huggingface dataset:
Generic Traceable Proxy Re-encryption and Accountable Extension in Consensus Network

Proxy re-encryption provides a promising solution to share encrypted data in consensus network. When the data owner is going to share her encrypted data with some receiver, he will generate re-encryption key for this receiver and distribute the key among the consensus network nodes following some rules. By using the re-encryption key, the nodes can transform the ciphertexts for the receiver without learning anything about the underlying plaintexts. However, if malicious nodes and receivers collude, they can obtain the capability to decrypt all transformable ciphertexts of the data owner, especially for multi-nodes setting of consensus network. In order to address this problem, some “tracing mechanisms” are naturally required to identify misbehaving nodes and foster accountability when the re-encryption key is abused for distributing the decryption capability. In this paper, we propose a generic traceable proxy re-encryption construction from any proxy re-encryption scheme, with the twice size ciphertext as the underlying proxy re-encryption scheme. Then our construction can be instantiated properly to yield the first traceable proxy re-encryption with constant size ciphertext, which greatly reduces both the communication and storage costs in consensus network. Furthermore, we show how to generate an undeniable proof for node’s misbehavior and support accountability to any proxy re-encryption scheme. Our construction is the first traceable proxy re-encryption scheme with accountability, which is desirable in consensus network so that malicious node can be traced and cannot deny his leakage of re-encryption capabilities.
Subaperture stitching test for large aperture mild acylinders

Large aperture acylinders are typical optical elements in synchrotron radiation systems and laser shaping devices. The manufacture of such ultra-precision optical elements requires ultra-precision measurement of the surface figure. The use of computer-generated hologram (CGH) customized for the acylindrical surface can only achieve a “one-to-one” test. Limited by the aperture of CGH, it also cannot meet the requirements for large aperture acylinders. In order to realize flexible test of large aperture acylinders with mild aspherical departure, a method of surface figure reconstruction for subaperture measurements under near-null test is proposed. It is based on ray tracing and rigid body transformation. By using a general CGH for cylindrical surface, near-null test for various acylindrical surfaces can be realized. Subaperture stitching test can overcome the problem of the limited CGH aperture which cannot meet the requirement of large aperture acylinders. Finally an elliptical cylindrical mirror with an aperture of 240mm (sagittal) × 350mm (tangential) is measured. And the cross test is realized by using a customized CGH to verify the validity and precision of the proposed method.
Reasoning on $$\textit{DL-Lite}_\mathcal{R}$$ with Defeasibility in ASP

Reasoning on defeasible knowledge is a topic of interest in the area of description logics, as it is related to the need of representing exceptional instances in knowledge bases. In this direction, in our previous works we presented a framework for representing (contextualized) OWL RL knowledge bases with a notion of justified exceptions on defeasible axioms: reasoning in such framework is realized by a translation into ASP programs. The resulting reasoning process for OWL RL, however, introduces a complex encoding in order to capture reasoning on the negative information needed for reasoning on exceptions. In this paper, we apply the justified exception approach to knowledge bases in $$\textit{DL-Lite}_\mathcal{R}$$ , i.e. the language underlying OWL QL. We provide a definition for $$\textit{DL-Lite}_\mathcal{R}$$ knowledge bases with defeasible axioms and study their semantic and computational properties. The limited form of $$\textit{DL-Lite}_\mathcal{R}$$ axioms allows us to formulate a simpler encoding into ASP programs, where reasoning on negative information is managed by direct rules. The resulting materialization method gives rise to a complete reasoning procedure for instance checking in $$\textit{DL-Lite}_\mathcal{R}$$ with defeasible axioms.
Software Quality Improvement Practices in Continuous Integration

CI (Continuous Integration) is a development practice that provides an efficient and effective basis for the whole SDLC (Software Development Life Cycle) [1]. We intended to improve quality of the software by means of process automation throughout CI infrastructure. Process automation let the improvement team to quantify the data that was collected automatically. By means of the autonomous SDLC via CI practices, we realized the benefits of the CI based process automation from quality assurance perspective. This paper is prepared to share the results of those quality improvement practices on SPI Manifesto basis. Main objective of this study is to improve the quality of the software. DDE (Defect Detection Effectiveness) and DD (Defect Density) metrics are collected for this purpose. These metrics are collected to evaluate the benefits of process automation till the end of the CSCI (Computer Software Configuration Item) tests. Benefits of the improvement study is emphasized quantitatively to enlighten the planned future work on software quality improvement.
A two-phase approach for unexpected pattern mining

A typical mining task is to retrieve all frequent patterns from a multi-dimensional dataset. Those patterns give us a basic idea of how the data look like and the hidden inherent regularities. However, this is only useful for an unfamiliar dataset, while for datasets that are analyzed periodically, “unexpected” patterns are more interesting (e.g., some customers decided to subscribe to long-term deposits despite the burden of housing loan). In this paper, we propose a new mining job, unexpected mining, which targets at retrieving frequent patterns that are not valid in a reference dataset, but are significant enough in a specific subgroup. Given a reference dataset, we step by step generate all unexpected patterns for all subgroups. We extend existing mining approaches to support the new mining job efficiently. In particular, our scheme consists of an offline process and an online process. Offline process generates candidate patterns and builds an index table. Online process can retrieve unexpected patterns from user-defined subgroups and a given support. Experiments on real datasets show that our approach can find interesting patterns and is very efficient compared to existing approaches.
Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection

Spam image detection is essential for protecting the security and privacy of Internet users and saving network resources. However, we observe a spam image detection system might be out of order due to adversarial perturbations, which can force a classification model to misclassify the input images. To defend against adversarial perturbations, previous researches disorganize the perturbations with fundamental image processing techniques, which shows limited success. Instead, we apply image restoration as a defense, which focuses on restoring the perturbed adversarial images to their original versions. The restoration is achieved by a lightweight preprocessing network, which takes the adversarial images as input and outputs their restored versions for classification. The further evaluation results demonstrate that our defense significantly improves the performance of classification models, requires little cost and outperforms other representative defenses.
Attention-based Visual-Audio Fusion for Video Caption Generation

Recently, most of the work of generating a text description from a video is based on an Encoder-Decoder framework. Firstly, in the encoder stage, different convolutional neural networks are using to extract features from audio and visual modalities respectively, and then the extracted features are input into the decoder stage, which will use the LSTM to generate the caption of video. Currently, there are two types of work concerns. One is whether video caption will be generated accurately if different multimodal fusion strategies are adopted. Another is whether video caption will be generated more accurately if attention mechanism is added. In this paper, we come up with a fusion framework which combines the two types of methods concerned above to produce a new model. In the encoder stage, two modalities of multimodal fusion, sharing weights and sharing memory are utilized respectively, which can make the two kinds of characteristics resonated to generated the final feature outputs. LSTM with attention mechanism are used in the decoder state to generate a description of video. Our fusion model combining the two methods is well validated on the dataset Microsoft Research Video to Text (MSR-VTT).
Why Are Some Societies More Entrepreneurial than Others? Evidence from 192 Countries over 2001-2018

Why do societies vary in their rates of entrepreneurship and organizational founding? Drawing on the largest available longitudinal sample comprising 192 countries over 2001-2018, I examine the evidence in relation to several explanations, including variation in the density of established organizations, national investment in research and development (R&D), technology transfer to new companies, the quality of science, technology, engineering and math (STEM) education, venture capital (VC) availability, and governmental support and policies for entrepreneurship. Contrary to prevailing theories, there is limited empirical support for these explanations. Rather, the evidence shows that the strongest predictors of cross-national variation in entrepreneurial activity were normative, with social norms being the most strongly associated with entrepreneurialism and rates of organizational founding. This study further examines the relationship between norms and societal culture and finds that more gender-egalitarian societies and societies that value and reward performance and endorse status privileges had on average higher rates of organizational founding, net of differences in national income and economic growth. The paper discusses the implications of these findings in relation to research on the social determinants of entrepreneurship and organizational founding.
Photon-Assisted Perfect Conductivity Between Arrays of Two-Level Atoms

Abstract We investigate interactions between two (parallel) arrays of two-level atoms (2LA) via photons through quantum electrodynamical interaction with one array (the source array) connected to a particle source, and we study the (photo-)resistivity of the other array (the measured array). The wave function of the interacted photon propagating in an array is a Bloch wave with a gap in its eigenvalue (the photonic dispersion). Due to interactions between arrayed 2LA and the dressed photonic field with non-linear dispersion, the conduction behaviors of the measured array can be very diversified according to the input energy of the particle source connected to the source array, and their relative positions. As a result, the resistivity of the measured array can be zero or negative, and can also be oscillatory with respect to the incoming energy of the particle source of the source array, and the separation between arrays.
InterCriteria Analysis of the Most Problematic Factors for Doing Business in the European Union, 2017–2018

In this paper, we use the method of the InterCriteria Analysis, based on the concepts of intuitionistic fuzzy sets and index matrices, to analyze a dataset extracted from the Global Competitiveness Index, concerning the most problematic factors for doing business in the European Union member states. The method is applied on the data of these 28 countries extracted from the Global Competitiveness Report 2017–2018.
Quality Control Framework of Big Data for Early Warning of Agricultural Meteorological Disasters

Agricultural meteorological disasters, including floods, droughts, dry hot winds, low temperature chills, typhoons, hail and continuous rain, can lead to significant reduction in agricultural output. Big data platform for early warning of agricultural meteorological disaster is the basis of business operation system for early warning of agricultural meteorological disasters, and the data quality is an important guarantee for success of the early warning. Quality control of big data for early warning of agricultural meteorological disaster involves names of data sets, metadata, data documents and content of data sets. The quality control for contents of data sets is divided into quality control of attribute data and that of spatial data, and quality control of spatial data is divided into quality control of vector data and that of raster data. Methods for data quality control are divided into fully automatic, semi-automatic and full manual control methods.
Evaluating Pointing Modes and Frames of Reference for Remotely Supporting an Augmented Reality User in a Collaborative (Virtual) Environment

With the availability of powerful and affordable Augmented Reality (AR) devices, scenarios have become popular in which people wearing AR devices are supported by remote experts. These experts often use 2D peripherals to access the video feed of the 3D head mounted device (HMD) and to augment it with verbal or digital information. This raises the question whether tools that work for these scenarios also work for remote consultations. We conducted a study to (re-)evaluate these tools in a furniture sales consultation context. We focused on the consultant side of these settings and explored how the use of different pointing methods and perspectives affect different situations during a consultation. For this, we developed and evaluated a prototype with ten furniture store workers. Initial results show that while most usability and task load scores were even, the participants reported clear favorites for certain settings. We use these results to derive design recommendations for similar future projects.
Attention-Based Improved BLSTM-CNN for Relation Classification

Relation Classification as a foundational task with regard to many other natural language processing (NLP) tasks, has caught many attentions in recent years. In this paper, we propose a novel network architecture called Attention-Based Improved Bidirectional Long Short-Term Memory and Convolutional Neural Network (AI-BLSTM-CNN) for this task. To be specific, we take improved BLSTM that makes the utmost of sequential context information and word information in order to obtain temporal features and high-level contextual representation. Besides, attention mechanism is applied to improved BLSTM making it focus on the segments of a sentence related to the relation automatically. Finally, we take advantage of CNN to capture the local important features for relation classification. The experimental results on SemEval-2010 Task 8 and KBP37 benchmark datasets show that AI-BLSTM-CNN achieves better performance than the majority of existing methods.
Batch Binary Weierstrass

Bitslicing is a programming technique that offers several attractive features, such as timing attack resistance, high amortized performance in batch computation, and architecture independence. On the symmetric crypto side, this technique sees wide real-world deployment, in particular for block ciphers with naturally parallel modes. However, the asymmetric side lags in application, seemingly due to the rigidity of the batch computation requirement. In this paper, we build on existing bitsliced binary field arithmetic results to develop a tool that optimizes performance of binary fields at any size on a given architecture. We then provide an ECC layer, with support for arbitrary binary curves. Finally, we integrate into our novel dynamic OpenSSL engine, transparently exposing the batch results to the OpenSSL library and linking applications to achieve significant performance and security gains for key pair generation, ECDSA signing, and (half of) ECDH across a wide range of curves, both standardized and non-standard.
How to tag non-standard language: Normalisation versus domain adaptation for Slovene historical and user-generated texts

Abstract Part-of-speech (PoS) tagging of non-standard language with models developed for standard language is known to suffer from a significant decrease in accuracy. Two methods are typically used to improve it: word normalisation, which decreases the out-of-vocabulary rate of the PoS tagger, and domain adaptation where the tagger is made aware of the non-standard language variation, either through supervision via non-standard data being added to the tagger’s training set, or via distributional information calculated from raw texts. This paper investigates the two approaches, normalisation and domain adaptation, on carefully constructed data sets encompassing historical and user-generated Slovene texts, in particular focusing on the amount of labour necessary to produce the manually annotated data sets for each approach and comparing the resulting PoS accuracy. We give quantitative as well as qualitative analyses of the tagger performance in various settings, showing that on our data set closed and open class words exhibit significantly different behaviours, and that even small inconsistencies in the PoS tags in the data have an impact on the accuracy. We also show that to improve tagging accuracy, it is best to concentrate on obtaining manually annotated normalisation training data for short annotation campaigns, while manually producing in-domain training sets for PoS tagging is better when a more substantial annotation campaign can be undertaken. Finally, unsupervised adaptation via Brown clustering is similarly useful regardless of the size of the training data available, but improvements tend to be bigger when adaptation is performed via in-domain tagging data.
MineTime Insight: Visualizing Meeting Habits to Promote Informed Scheduling Decisions

Corporate meetings are a crucial part of business activities. While numerous academic papers investigated how to make the scheduling process of meetings faster or even automatic, little work has been done yet to facilitate the retrospective reasoning about how time is spent on meetings. Traditional calendar applications do not allow users to extract actionable statistics although it has been shown that reflection-oriented design can increase the users' understanding of their habits and can thereby encourage a shift towards better practices. In this paper, we present MineTime Insight, a tool made of multiple coordinated views for the exploration of personal calendar data, with the overarching goal of improving short and long-term scheduling decisions. Despite being focused on the working environment, our work builds upon recent results in the field of Personal Visual Analytics, as it targets users not necessarily expert in visualization and data analysis. We demonstrate the potential of MineTime Insight, when applied to the agenda of an executive manager. Finally, we discuss the results of an informal user study and a field study. Our results suggest that our visual representations are perceived as easy to understand and helpful towards a change in the scheduling habits.
Design, analysis and preliminary tests of a linear array CCD aerial camera for ground simulation

Demand for high quality images and simple structures is a trend in aerial camera lens design, however, aerial camera lens structures are complicated if there are complex optical systems. In this paper, to improve the imaging quality and reduce the weight of aerial camera lens, an aspherical surface is introduced to the optical system and an aerial camera is designed for ground simulation. As a result, the modulation transfer function (MTF) of the optical system is higher than 34% @ 91 lp/mm in all fields. Then, the modal and static properties of the developed aerial camera are verified by using finite element analysis, which satisfies the camera structural design requirements. The imaging test results indicate that the imaging performance satisfy design requirements of the aerial camera.
Digitalization as an Urgent Trend in the Development of the Social Sphere

The development of digital technologies (information and service «digital platforms») should be a key factor in improving the quality and availability of social services (health care, education, social protection and so on). The purpose of this article is to show the directions and problem aspects of the implementation of digital technologies into the social sphere at the present stage. The article analyzes the impact of digitalization on the economy of the Russian Federation and the Republic of Belarus. In the rating of Digital Evolution Index 2017, Russia is in a promising group of countries characterized by an increase in the overall level of «digitalization», passing to a group of leading countries, which allows to identify a number of prospective areas of the social sphere digitalization. According to the results of the study, they are: unified information services for socially unprotected citizens, for people with disabilities; «social assistants» in social services for aged and disabled citizens; technologies in the rehabilitation industry; automation of procurement of medicines; the development of various digital social services, for example, within the concept of «smart city» («smart» health care implies the introduction of telemedicine, the effective system of electronic services includes operations from «electronic registry» to getting the test results online, smart housing system implies the control and accounting of data and providing information to their users, smart «accessible environment» is created on the basis of geographic information systems); digitalization of employment and education processes.
Cooperative Mission Planning for Heterogeneous UAVs with the Improved Multi-objective Quantum-behaved Particle Swarm Optimization Algorithm

The cooperative operation of the unmanned aerial vehicle (UAV) is the trend of the application of UAVs. Mission planning is the application basis of UAV formation. The appropriate mission planning can fully utilize the technical potential and increase the application effectiveness. Mission planning should consider several criteria, like the completion time, expected benefit, energy consumption or equipment loss. Hence, it should be treated as a Multi-objective Optimization Problem. Aiming to mission characteristics of heterogeneous UAV formation in anti-radar operations, a mission planning model based on multi-objective optimization is proposed. The appropriate collaborative constraint between UAVs and mission execution is established through multi-layer coding and penalty function. The model is solved by an improved Multi-objective Quantum-Behaved Particle Swarm Optimization. The crossover and mutation of Genetic Algorithm are introduced to enhance the diversity of solutions. Finally, the proposed model and algorithm are verified by simulation experiments.