Reviewerly/exHarmony huggingface dataset:
A Pure Visual Approach for Automatically Extracting and Aligning Structured Web Data

Database-driven websites and the amount of data stored in their databases are growing enormously. Web databases retrieve relevant information in response to users’ queries; the retrieved information is encoded in dynamically generated web pages as structured data records. Identifying and extracting retrieved data records is a fundamental task for many applications, such as competitive intelligence and comparison shopping. This task is challenging due to the complex underlying structure of such web pages and the existence of irrelevant information. Numerous approaches have been introduced to address this problem, but most of them are HTML-dependent solutions that may no longer be functional with the continuous development of HTML. Although a few vision-based techniques have been introduced, various issues exist that inhibit their performance. To overcome this, we propose a novel visual approach, i.e., programming-language-independent, for automatically extracting structured web data. The proposed approach makes full use of the natural human tendency of visual object perception and the Gestalt laws of grouping. The extraction system consists of two tasks: (1) data record extraction, where we apply three of the Gestalt laws (i.e., laws of continuity, proximity, and similarity), which are used to group the adjacently aligned visually similar data records on a web page; and (2) data item extraction and alignment, where we employ the Gestalt law of similarity, which is utilized to group the visually identical data items. Our experiments upon large-scale test sets show that the proposed system is highly effective and outperforms the two state-of-art vision-based approaches, ViDE and rExtractor. The experiments produce an average F1 score of 86.02%, which is approximately 55% and 36% better than that of ViDE and rExtractor for data record extraction, respectively; and an average F1 score of 86.19%, which is approximately 39% better than that of ViDE for data item extraction.
Multi-view predictive latent space learning

In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multi-view graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms.
An optimized unsupervised manifold learning algorithm for manycore architectures

Multimedia data, such as images and videos, has become very popular in people’s daily life as a result of the widespread use of mobile devices. The ever-increasing amount of such data, along with the necessity for real-time retrieval, has lead to the development of new methods that can process them in a timely fashion with acceptable accuracy. In this paper, we study the performance of ReckNN, an unsupervised manifold learning algorithm based on the reciprocal neighbourhood and the authority of ranked lists. Most of the related work in this field do not fully investigate optimization strategies, an aspect that is becoming more important with the high availability of manycore machines. In order to address that issue, we fully investigate optimization opportunities in this article and make the following three main contributions. Firstly, we develop an efficient and scalable method for storing and accessing the distances between objects (e.g., video or image) based on dictionaries. Secondly, we employ memoization to speed up the computation of authority scores, leading to a significant performance gain even on single-core architectures. Lastly, we devise and implement several parallelization strategies and show that they are scalable on a 72-core Intel machine. The experimental results with MPEG-7, Corel5k and MediaEval benchmarks show that the optimized ReckNN delivers both efficiency and scalability, highlighting the importance of the proposed optimizations for manycore machines.
A Method for Measuring the Constraint Complexity of Components in Automotive Embedded Software Systems

The rapid growth of software-based functionalities has made automotive Electronic Control Units (ECUs) significantly complex. Factors affecting the software complexity of components embedded in an ECU depend not only on their interface and interaction properties, but also on the structural constraints characterized by a component’s functional semantics and timing constraints described by AUTomotive Open System ARchitecture (AUTOSAR) languages. Traditional constraint complexity measures are not adequate for the components in embedded software systems as they do not yet sufficiently provide a measure of the complexity due to timing constraints which are important for quantifying the dynamic behavior of components at run-time. This paper presents a method for measuring the constraint complexity of components in automotive embedded software systems at the specification level. It first enables system designers to define non-deterministic constraints on the event chains associated with components using the AUTOSAR-based Modeling and Analysis of Real-Time and Embedded systems (MARTE)-UML and Timing Augmented Description Language (TADL). Then, system analysts use Unified Modeling Language (UML)-compliant Object Constraint Language (OCL) and its Real-time extension (RT-OCL) to specify the structural and timing constraints on events and event chains and estimate the constraint complexity of components using a measure we have developed. A preliminary version of the method was presented in M. Garg and R. Lai, Measuring the constraint complexity of automotive embedded software systems, in Proc. Int. Conf. Data and Software Engineering, 2014, pp. 1–6. To demonstrate the usefulness of our method, we have applied it to an automotive Anti-lock Brake System (ABS).
Subtypes in fuzzy type theory

In this paper, we introduce extension of the fuzzy type theory (FTT) by subtypes. This demand arises in situations when, for example, it is useful to distinguish rational and real numbers, where the set of rational numbers is a subset of the reals. In the original FTT, this causes a problem because we cannot assign rational numbers a special type. The solution is to introduce special types for both of them and declare that the former is a subtype of the latter. The resulting fuzzy type theory is denoted by sFTT. When constructing the frame for sFTT we must realize that, though all the considered functions are total, if we take them as functions between subsets then they become partial.
Up-to techniques for behavioural metrics via fibrations

Abstract Up-to techniques are a well-known method for enhancing coinductive proofs of behavioural equivalences. We introduce up-to techniques for behavioural metrics between systems modelled as coalgebras, and we provide abstract results to prove their soundness in a compositional way. In order to obtain a general framework, we need a systematic way to lift functors: we show that the Wasserstein lifting of a functor, introduced in a previous work, corresponds to a change of base in a fibrational sense. This observation enables us to reuse existing results about soundness of up-to techniques in a fibrational setting. We focus on the fibrations of predicates and relations valued in a quantale. To illustrate our approach, we provide an example on distances between regular languages.
Decoding the Perception of Sincerity in Dialogues

Identifying deception in online conversations is an important problem. A core component in interpreting deception is understanding how people perceive sincerity. Given that the larger proportion of human communication is non-verbal, decoding perceived sincerity is difficult in media where the only signal is written text. We introduce psycholinguistic features of perceived sincerity for two domains, dating and online games. Our results further the understanding of how language, deception, and perception connect.
Application of sequential Gaussian simulation and concentration-volume fractal model to delineate alterations in hypogene zone of miduk porphyry copper deposit, SE Iran

The paper presents a quantitative modeling for delineating alteration zones in hypogene zone of Miduk porphyry copper deposit (SE Iran) based on drill core data. The main goal of this study is applying Sequential Gaussian Simulation (SGS) and Concentration-Volume (C-V) fractal model based on Cu grades to separate different alteration zones. In this study, 10 realizations were generated using the SGS modeling and C-V log-log plots were created for them to determine Cu threshold values which delineate alteration zones. Correlation derived from log ratio matrix, which was used to compare between geological and C-V fractal results, shows that Cu values less than 0.575% from simulation number 9 (sim 9) has more overlapped voxels with phyllic alteration zone by overall accuracy (OA) of 0.75. Furthermore, the comparison revealed that sim 9 with Cu values between 0.575% and 0.933% and OA of 0.88 can determine propylitic alteration. Spatial correlation between the potassic alteration zone resulted from 3D geological modeling and high concentration zones for the C-V fractal model shows that the alteration zone has Cu values between 0.955% and 1.380% (sims 2, 3 and 4) with OA of 0.71. Finally, based on correlation between classes in the geological and the fractal models of hypogene zone, this research resulted that potassic alteration, which is the richest zone, has a strong overlap with sims 2, 3 and 4 while phyllic and propylitic alterations, with lower grades, have an appropriate overlap with sim 9.
Virtual Machine Extrospection: A Reverse Information Retrieval in Clouds

In a virtualized environment, it is not difficult to retrieve guest OS information from its hypervisor. However, it is very challenging to retrieve information in the reverse direction, i.e., retrieve the hypervisor information from within a guest OS, which remains an open problem and has not yet been comprehensively studied before. In this paper, we take the initiative and study this reverse information retrieval problem. In particular, we investigate how to determine the host OS kernel version from within a guest OS. We observe that modern commodity hypervisors introduce new features and bug fixes in almost every new release. Thus, by carefully analyzing the seven-year evolution of Linux KVM development (including 3,485 patches), we can identify 19 features and 20 bugs in the hypervisor detectable from within a guest OS. Building on our detection of these features and bugs, we present a novel framework called Hyperprobe that for the first time enables users in a guest OS to automatically detect the underlying host OS kernel version in a few minutes. We implement a prototype of Hyperprobe and evaluate its effectiveness in six real world clouds, including Google Compute Engine (a.k.a. Google Cloud), HP Helion Public Cloud, ElasticHosts, Joyent Cloud, CloudSigma, and VULTR, as well as in a controlled testbed environment, all yielding promising results.
Experimental Analysis of Subscribers’ Privacy Exposure by LTE Paging

Over the last years, considerable attention has been given to the privacy of individuals in wireless environments. Although significantly improved over the previous generations of mobile networks, LTE still exposes vulnerabilities that attackers can exploit. This might be the case of paging messages, wake-up notifications that target specific subscribers, and that are broadcasted in clear over the radio interface. If they are not properly implemented, paging messages can expose the identity of subscribers and furthermore provide information about their location. It is therefore important that mobile network operators comply with the recommendations and implement the appropriate mechanisms to mitigate attacks. In this paper, we verify by experiment that paging messages can be captured and decoded by using minimal technical skills and publicly available tools. Moreover, we present a general experimental method to test privacy exposure by LTE paging messages, and we conduct a case study on three different LTE mobile operators.
Distinctive-Attribute Extraction for Image Captioning

Image captioning has evolved with the progress of deep neural networks. However, generating qualitatively detailed and distinctive captions is still an open issue. In previous works, a caption involving semantic description can be generated by applying additional information into the RNNs. In this approach, we propose a distinctive-attribute extraction (DaE) method that extracts attributes which explicitly encourage RNNs to generate an accurate caption. We evaluate the proposed method with a challenge data and verify that this method improves the performance, describing images in more detail. The method can be plugged into various models to improve their performance.
Developing for non-human users: Reflecting on practical implications in the ubiquitous computing era

Advances in modern technology, such as the Internet of Things (IoT) and ubiquitous computing, open up new exciting opportunities for technology for animals. This is evidenced by the explosion of products and gadgets available for pets, digital enrichment for captive animals in zoos, sensor based smart farming, etc. At the same time, the emerging discipline of Animal-Computer Interaction (ACI) marks a new era in the design and development of animal technologies, promoting a more animal-centric approach, considering the needs of the animal in the development process. In this article, we reflect on the ways in which ideas of animal-centric development may impact the development of technology for animals in practice. We start by looking at the process of development for and with animals, and propose a development model facilitating the principles of Agility, Welfare of Animals, and eXperts' involvement (AWAX) within the development lifecycle. While promoting the animal-centric approach, it is important to acknowledge that an animal usually uses technology through humans and in a particular environment. We further extend the AWAX model to include considerations of the human in the loop and the environment, and discuss some practical implications of this view, including aspects such as security and privacy.
Generalized cyclotomic numbers and cyclic codes of prime power length over Z4

Generalized cyclotomic numbers of order [Formula: see text] with respect to an odd prime power are obtained. Hence, explicit expressions for primitive idempotents in the ring [Formula: see text] are obtained in two cases, when the multiplicative order of 2 modulo [Formula: see text] is [Formula: see text] and [Formula: see text], where [Formula: see text] is an odd prime. Orthogonality and self-duality of some [Formula: see text] cyclic codes are also discussed. Further, a method for obtaining cyclic self-dual/isodual codes of length [Formula: see text] over [Formula: see text] is given.
On the size of the smallest alphabet for Lyndon trees

We consider the problem of reverse-engineering the Lyndon tree, i.e., given a full binary ordered tree T with n leaves as input, we are to compute a string w of length n of which Lyndon tree is isomorphic to the input tree T. Hereby we call such a string a solution string. Although the problem is easily solvable in linear time for binary alphabets and unbounded-size alphabets, it is not known how to efficiently find the smallest alphabet size for a solution string. In this paper, we show several new observations concerning this problem. Namely, we show that: 1) For any positive integer n, there exists a full binary ordered tree T with n leaves, s.t. the smallest alphabet size of a solution string for T is ⌊n2⌋+1. 2) For any full binary ordered tree T with n leaves, there exists a solution string w over an alphabet of size at most ⌊n2⌋+1. 3) For any full binary ordered tree T, there exists a solution string w over an alphabet of size at most h+1, where h is the height of T. 4) For any complete binary ordered tree T with 2k leaves, there exists a solution string w over an alphabet of size at most 4.
Recent Neuro-Fuzzy Approaches for Feature Selection and Classification

Classification is the task of assigning objects to one of several predefined categories. However, developing a classification system is mostly hampered by the size of data. With the increase in the dimension of data, the chance of irrelevant, redundant, and noisy features or attributes also increases. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy, and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and classification with better insight by representing knowledge in symbolic forms. The neuro-fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied to standard datasets to demonstrate their applicability and performance.
On the uniqueness of some girth eight algebraically defined graphs, Part II

Let F be a field. For a polynomial f ∈ F [ x , y ] and positive integers k and m , we define a bipartite graph Γ F ( x k y m , f ) with vertex partition P ∪ L , where P and L are two copies of F 3 , and ( p 1 , p 2 , p 3 ) ∈ P is adjacent to [ l 1 , l 2 , l 3 ] ∈ L if and only if p 2 + l 2 = p 1 k l 1 m and p 3 + l 3 = f ( p 1 , l 1 ) It is known that Γ F ( x y , x y 2 ) has no cycles of length less than eight. The main result of this paper is that Γ F ( x y , x y 2 ) is the only graph Γ F ( x k y m , f ) with this property when F is an algebraically closed field of characteristic zero; i.e. over such a field F , every graph Γ F ( x k y m , f ) with no cycles of length less than eight is isomorphic to Γ F ( x y , x y 2 ) . We also prove related uniqueness results over infinite families of finite fields.
Data Visualization in R

Data visualization helps the users to understand the relationships and associations between information. Visualization helps in minimizing the errors generated during decision making. Different visualization methods have been developed to unlock the valuable insight. These methods have been developed on the supposition that the information to be present is free from ambiguity. This chapter provides an overview of data visualization techniques in R programming. Various methods have been discussed with supported explanation and examples which in turn helps the reader to create their own visualization method. Later, four different case studies are presented to understand the importance and use of data visualization in real-world problems.
Decoding Chinese User Generated Categories for Fine-Grained Knowledge Harvesting

User Generated Categories (UGCs) are short but informative phrases that reflect how people describe and organize entities. UGCs express semantic relations among entities implicitly hence serve as a rich data source for knowledge harvesting. However, most UGC relation extraction methods focus on English and heavily rely on lexical and syntactic patterns. Applying them directly to Chinese UGCs poses significant challenges because Chinese is an analytic language with flexible language expressions. In this paper, we aim at harvesting fine-grained relations from Chinese UGCs automatically. Based on neural networks and negative sampling, we introduce two word embedding projection models to identify is-a relations. The accuracy of prediction results is improved via a collective refinement algorithm and a hypernym expansion method. We further propose a graph clique mining algorithm to harvest non-taxonomic relations from UGCs, together with their textual patterns. Two experiments are conducted to validate our approach based on Chinese Wikipedia. The first experiment verifies the is-a relation extraction approach achieves high accuracy, outperforming state-of-the-art methods. The second experiment shows that the proposed method can harvest non-taxonomic relations of large quantity and high accuracy, with minimal human intervention.
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise. We derive instance-dependent minimax rates for the sample complexity of this problem, and show how its dependence in time is related to the spectral properties of the Markov chain. We show that thereexists a wide statistical window, in terms of sample complexity for hypothesis testing between different pairs of initial distributions. We illustrate these results in several concrete examples.
State of the art on stylized fabrication

Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as stylized fabrication methods. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion, or to devise a particular interaction with the fabricated model. In this course, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research.
Local decodability of the Burrows-Wheeler transform

The Burrows-Wheeler Transform (BWT) is among the most influential discoveries in text compression and DNA storage. It is a reversible preprocessing step that rearranges an n-letter string into runs of identical characters (by exploiting context regularities), resulting in highly compressible strings, and is the basis of the <pre>bzip</pre> compression program. Alas, the decoding process of BWT is inherently sequential and requires Ω(n) time even to retrieve a single character.