Reviewerly/exHarmony huggingface dataset:
Frequency of compromised passwords used by students and staff of Asia Pacific College: an analysis using NIST SP 800-63B and pwned passwords

The National Institute of Standards and Technology (NIST) released new guidelines in June of 2017 that recommended new standards for managing and accepting user passwords. Among the new guidelines is a requirement that verifiers should check if a user's supplied password is compromised – that is, already listed in previous breach corpuses. Using a corpus of 320 million breached passwords, the researchers conducted an experiment to gauge what percentage of the population of their home institution, Asia Pacific College, use compromised passwords. The study found that 16.72% overall – or 1 in 6 people – were using passwords that are part of the 320M breach corpus. This paper also provides a methodology that other institutions and companies can use to conduct the same analysis in order to gather data specific to their population that can guide the improvement of their password policies and related IT security services.
A Classification Method by Using Fuzzy Neural Network and Ensemble Learning

In this paper, a new multi-class classification technology combining fuzzy neural network and ensemble learning technology is proposed. Several fuzzy neural networks, including fuzzy TS neural network and fuzzy wavelet neural network, are considered as sub-classifiers. Restricted Boltzmann machines and gradient descent algorithm are used to train the parameters of fuzzy neural network. Then, adaptive boosting (AdaBoost) algorithm based on hypothesis margin and particle swarm optimization is provided to determine the weights of sub-classifiers. Numerical simulation results illustrate the validity of proposed method.
A Human-as-Sensors Approach to API Documentation Integration and Its Effects on Novice Programmers

In recent years, there has been a great interest in integrating crowdsourced API documents that are often dispersed across multiple places. Because of the complexity of natural language, however, automatically synthesized documents often fall short on quality and completeness compared to those authored by human experts. We develop a complementary “human-assensors” approach to document integration that generates API FAQs based on users' help-seeking behavior and history. We investigated the benefits and limitations of this approach in the context of programming education. This paper describes a prototype system called COFAQ and a controlled experiment with 18 novice programmers. The study confirms that the generated FAQs effectively fosters knowledge transfer between the programmers and significantly reduce the need for repeated search. It also discovers several difficulties novice programmers encountered when seeking API help as well as the strategies they used to seek and utilize API knowledge.
Automatic cropping of images under projective transformation

The paper considers the problem of images cropping obtained by projective transformation of source images. The problem is highly relevant to analysis of projective distorted images. We propose two cropping algorithms based on estimation of pixel stretching under the transformation. The algorithms use the ratio of pixel neighborhood areas and the ratio of their chord lengths. The methods comparison is conducted by estimation of cropped background relative areas. The experiment uses real dataset containing projective distorted images of the pages of Russian civil passports. The method based on chord lengths ratio shows better results on highly distorted images.
A System Integrated with C# Programing and Real-Time 3D Observation for Learning Virtual Reality Technology

With the rapid development of virtual reality technology (VR), application systems based on VR have been demonstrated in wide fields, which therefore make it attractive to many university students.The process of learning VR requires intensive programing and intuitive observation as well as strict understanding of basic concepts, significantly different to that of other courses.The current widely used framework of learning system is not suitable to the VR learning.In this paper, we present a learning platform based on Unity 3D for VR learning.This system combines knowledge learning, real-time interaction between virtual scene control and C# writing/compiling.Compared with other learning systems, this system can greatly promote the learning enthusiasm and efficiency for C# programming in 3D VR applications.
The monistic diversity of continuum informatics

Purpose The Information Age during the transition from the paper era to the digital one saw the fracturing and fragmenting of the information-based specialisations. More recently, professional norms for governance have been swept aside within new business models based on information based business applications. This paper aims to support an advance towards networked cohesion based on informatics, regenerating professionalism for the complex networked age. Design/methodology/approach New regulatory approaches will have to manage monistic diversity, connecting the deeper logic of continuum thinking in which information governance exists as part of a simple whole (the monistic component) with a recognition that the parts of information governance are much more complex than the whole (the expanding diversity). A continuum approach of this type involves studying things in motion as part of evolutionary processes. Findings The production of information is galloping ahead of its authoritative management, and this is at the heart of many of the failings of the post-truth information era. Informatics with its emphasis upon the joint operation of technologies, social processes and knowledge forming and its ability to be an umbrella term for many specialisations can be a cohering force. Practical implications The alignment of thought, action and ethical information governance across inter-connected practices for individuals, groups and organisations can be supported by the deeper logic and grounded experience of continuum thinking. Originality/value This paper will look to expand the array of sympathisers who wish to get more in touch with studying things in motion, including those trying to cope with the need to develop more adequate ways for managing nanosecond archiving processes.
Alignment by Composition

We propose an unsupervised method to establish dense semantic correspondences between images depicting different instances of the same object category. We posit that alignment is compositional in nature and requires the detection of a similar visual concept between images. We realize this in a top-down fashion using objectness, saliency, and visual similarity cues to co-localize the regions of holistic foreground objects. Jointly maximizing visual similarity and bounding the geometric distortion induced by their configuration, the target foreground object is then composed by the subregions of the source foreground object. The resultant composition is used to form a dense motion field enabling the alignment. Experimental results on several benchmark datasets support the efficacy of the proposed method.
Research on the Smart Information System of Weihe River Basin

To avoid the problems of imperfect function, single content, insufficient application of new technology and low management level in the Weihe River Basin (WRB) information system, a smart WRB information integration system is established. The application of intelligent water conservancy business is developed to serve for decision-making. Firstly, the master plan of building smart WRB information integration system is put forward. Secondly, several information technologies including the Beidou technology, big data, cloud computing, three-dimension technology are used to build the overall framework of smart WRB information integration system. Lastly, based on the smart WRB system, massive information integration, intelligent forecasting and warning, smart response is exploited. The results demonstrate that the smart WRB information integration system is able to integrate the massive information, share information resources and improve the ability of user's quick response while presenting the three-dimension visualization.
Understanding of Virtual Reality with Visual Sensor Networks

With the development of computers and information technology, people are looking for new types of Human-Computer Interaction technologies to achieve a better experience. In recent years, virtual reality technology has become one of the major hotspots. Virtual reality technology provides a virtual scene for the users and allows them to generate virtual behaviors or control the elements in the virtual scene through limb movements. And the research on virtual scene modeling can enable users to obtain a more realistic experience. This article mainly discusses the virtual reality technology from the perspective of visual sensor networks, including the common scene modeling methods, the characteristics of visual sensor networks, and some related applications.
Rapid Technique to Eliminate Moving Shadows for Accurate Vehicle Detection

Elimination of moving shadows is an essential step to achieve accurate vehicle detection and localization in automated traffic surveillance systems that aim to detect vehicles on road scenes captured by surveillance cameras. However, this is still a challenging problem as existing pixel based methods miss parts of vehicles and region-based methods, while accurate, incur higher computations. In this paper, we propose a highly accurate yet low-complexity block-based moving shadow elimination technique, which can effectively deal with varying shadow conditions. A novel shadow elimination pipeline is proposed that employs computationally lean features to quickly classify distinct vehicles from shadows, and uses a more sophisticated interior edge feature only for classification of difficult scenarios. Extensive evaluations on freely available and self-collected datasets demonstrate that the proposed technique achieves higher accuracy than other state-of-the-art techniques in varying scenarios. Additionally, it also achieves over 20 times speedup on a low-cost embedded platform, Odroid XU-4, over a state-of-the-art technique that achieves comparable accuracy. Experimental results confirm the realtime capability of the proposed approach while achieving robustness to varying shadow scenarios.
A uniform and flexible model for three-dimensional measurement of line-structured light sensor

Line-structured light sensor is a three-dimensional measurement method which combines the advantage of high precision and speed, applicable in many fields. The mathematical model of line-structured light sensor is an essential foundation which determines measurement precision. Through the comparison of the three representative models which are deduced from different starting points and analysis of the model constraint conditions, we build a uniform and flexible model which shows the common characteristics and connections of the previous models and combines the flexibility and clear geometric meanings of the structured parameters with a correctable constraint condition. It is suitable to most conditions and structured parameters design of line-structured light sensor.
ICSD: Interactive Visual Support for Understanding Code Control Structure

Code comprehension is a mental process in any maintenance activity. It becomes decisive in large methods. Such methods are burdened with programming constructs as lines of code (LOC) correlate with McCabe's cyclomatic complexity (MCC). This makes it hard to capture their code, as they span many pages even in large screens, and as a result hinders grasping their structural properties that might be key for maintenance. Visualization can assist in comprehending complex systems. It has been shown that control structure diagrams (CSDs) could be useful to better understand and discover structural properties, such as code regularity, of such large methods. IDEs and development tools have been moving from desktops to the Web so as to benefit from this ubiquitous environment that provides instant collaboration and easier integration.This paper presents ICSD, an interactive Web-based tool that implements CSD for Java methods. In particular it visualizes their control structure and nesting. The interactivity of the tool enables the developer to examine the underlying code of specific parts in the diagram. Using ICSD the developer can compare between different regions in the diagram that seem to have any kind of commonality or other interesting relation. ICSD easily conveys structural characteristics of the code, especially very large code, to the programmer and helps him to better understand and refactor.To demonstrate the usage and usefulness of ICSD, very large functions from real softwares are presented and the different features of ICSD are shown.
A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence Modeling

Background: Due to the increasing growth of social websites, a lot of user-generated data is available these days in the form of customer reviews, opinions, and comments. Objective: Sentiment analysis includes analyzing the user reviews and finding the overall opinions from the reviews in terms of positive, negative and neutral categories. Sentiment analysis techniques can be used to assign a piece of text a single value that represents opinion expressed in that text. Sentiment analysis using lexicon approaches is already studied. Methods: A new approach to sentiment analysis using deep neural networks techniques is proposed. Deep neural networks using Sequence to sequence model is studied in this paper. The main objective of this paper is to identify the sequence of relationships among the words in the reviews. Customer reviews are taken from Amazon and sentiment analysis is done using the word embedding method. Results: The results obtained by the proposed method are compared with the baseline algorithms such as Naïve, and logistic regression. Conclusion: Confusion Matrix along with receiver operating characteristics and area under the curve is analyzed. The accuracy of the proposed methodology is compared with other algorithms.
Precise measurement of large roll angle using digital speckle pattern interferometry

Recently, a non-cooperative method of roll angle measurement using digital speckle pattern interferometry (DSPI) is introduced. Mechanism was deduced and the mathematic model of roll angle measurement was established. The novel method also enjoys some other advantages, such as high-accuracy, stand-off, non-contact, and full-field measurement. However, this method is only used for precise measurement of very small roll angle which is up to a few milliradians due to the limitation of DSPI’s measuring range. In this article, a means of range enlargement for DSPI roll angle measurement is introduced. With this means, large roll angle can be divided into a number of small angles which are measured in sequence. Therefore, the large angle is then determined by calculating the sum of these small angles. The proposed method of roll angle is characterized by high-resolution and large-range measurement.
Design and VLSI Implementation of CSD based DA Architecture for 5/3 DWT

In the past two decades, wavelet method has emerged as a powerful signal and data analysis tool for scientist and engineers. It is often part of a low-power system for real time application and needs efficient hardware implementation. In this paper, we have designed and VLSI (Very Large-Scale In-tegration) implemented a lossless 5/3 Discrete Wavelet Transform (DWT) using the Canonical Signed Digit (CSD) based Distributed Arithmetic (DA) architecture. The combination of CSD and DA exploits prior knowledge of the filter co-efficients and uses the least number of adders and shift registers, to achieve hardware-efficient implementation. The filter-based implementation exploits data path diagram of CSD represented coefficients and selectively finds sum of products to give minimum realization. The proposed CSD based DA architecture is modeled using HDL (Hardware Description Language) and implemented on Altera DE-1 Cyclon-II FPGA (Field Programmable Gate Array). A comparison with filter-based architecture of 5/3 DWT reveals a saving up to 50% in hardware. The designed solution is hardware efficient and uses only 7 adders to provide low-power architecture for high-speed real-time applications.
Homotopy type-theoretic interpretations of constructive set theories

Abstract We introduce a family of ( k , h )-interpretations for 2 ≤ k ≤ ∞ and 1 ≤ h ≤ ∞ of constructive set theory into type theory, in which sets and formulas are interpreted as types of homotopy level k and h , respectively. Depending on the values of the parameters k and h , we are able to interpret different theories, like Aczel’s CZF and Myhill’s CST. We also define a proposition-as-hproposition interpretation in the context of logic-enriched type theories. The rest of the paper is devoted to characterising and analysing the interpretations considered. The formulas valid in the prop-as-hprop interpretation are characterised in terms of the axiom of unique choice. We also analyse the interpretations of CST into homotopy type theory, providing a comparative analysis with Aczel’s interpretation. This is done by formulating in a logic-enriched type theory the key principles used in the proofs of the two interpretations. Finally, we characterise a class of sentences valid in the ( k , ∞)-interpretations in terms of the ΠΣ axiom of choice.
A Soft Real-Time Stream Reasoning Service for the Internet of Things

Most Internet of Things applications have their algorithms driven by elementary events or basic sensor data. While this may be sufficient for some applications, it precludes the possibility of describing and detecting semantic relationships among these events/data and to infer new application-relevant facts out of these relations, which could give an IoT application means of reasoning about the ambient and its context. But since many IoT applications also require timely response times, such semantic reasoning has to happen nearly in real-time, and hence reasoning has to be done over data/event streams, a.k.a Stream Reasoning. This work presents a semantic model and an IoT middleware service for data stream reasoning using a combination of Complex Event Processing (CEP) and continuous queries over streams of RDF semantic data. In our approach we use CEP in the IoT Edge devices to derive time-annotated RDF data from the basic events, and C-SPARQL for processing online queries over a domain ontology, aiming to check if some indirect fact or property about the system or the environment can be deduced. In order to showcase and test the capacity of nearly real-time inference over data streams of our middleware service, we designed and implemented a scenario of fire warning in a hypothetical multi-store building with temperature and humidity sensors.
Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement

Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without global constraints or limited local representation of global appearance. To solve these problems, we propose a new multi-layer progressive face alignment method that combines global matches for a whole face with local refinement for a given region, where the errors caused by local optima are restricted by globally-matched appearance, and the local misalignments in the global method are avoided by supplementing the representation of local details. Our method consists of the following processes: Firstly, an input image is encoded as a multi-mode Local Binary Pattern (LBP) image to regress the face shape parameters. Secondly, the local multi-mode histogram of oriented gradient (HOG) features is applied to update each landmark position. Thirdly, the above two alignment shapes are weighted as the final result. The contributions of this paper are as follows: (1) Shape initialization by applying an affine transformation to the mean shape. (2) Face representation by integrating multi-mode information in a whole face or a face region. (3) Face alignment by combining handcrafted features with convolutional neural networks (CNN). Extensive experiments on public datasets show that our method demonstrates improved performance in real environments in comparison to some state-of-the-art methods which apply single scale features or single CNN networks. Applying our method to the challenging HELEN dataset, the samples with fewer than 8 mean errors reach 81.1%.
Memory Warps for Long-Term Online Video Representations and Anticipation

We propose a novel memory-based online video representation that is efficient, accurate and predictive. This is in contrast to prior works that often rely on computationally heavy 3D convolutions, ignore motion when aligning features over time, or operate in an off-line mode to utilize future frames. In particular, our memory (i) holds the feature representation, (ii) is spatially warped over time to compensate for observer and scene motions, (iii) can carry long-term information, and (iv) enables predicting feature representations in future frames. By exploring a variant that operates at multiple temporal scales, we efficiently learn across even longer time horizons. We apply our online framework to object detection in videos, obtaining a large 2.3 times speed-up and losing only 0.9% mAP on ImageNet-VID dataset, compared to prior works that even use future frames. Finally, we demonstrate the predictive property of our representation in two novel detection setups, where features are propagated over time to (i) significantly enhance a real-time detector by more than 10% mAP in a multi-threaded online setup and to (ii) anticipate objects in future frames.
Intelligent recommendation algorithm for social networks based on gradient particle swarm optimization

Aiming to improve the intelligent recommendation ability of users in social networks, an intelligent recommendation algorithm for social networks based on gradient particle swarm optimization (GPSO) is proposed. Under the limited sample training, the user association relationship model of social network is constructed, and the hybrid kernel function and the global kernel function are constructed to extract the correlation amount feature of the social network recommendation information. The hybrid particle swarm optimization algorithm is used for adaptive learning of social network recommendation, Logistic chaotic mapping is used to control the convergence of recommendation process, and the potential characteristics of network users and the universality and ergodicity of social features are analyzed. The particle swarm optimization and adaptive optimization of social network recommendation are implemented by using gradient algorithm to realize swarm intelligence recommendation of social network user information. The simulation results show that the proposed algorithm is more accurate and convergent, and it avoids falling into the local optimal solution in the process of intelligent recommendation of social network. The intelligence and global stability of social network recommendation are improved.