Reviewerly/exHarmony huggingface dataset:
Comparison between text compression algorithms in biological sequences

Inverted indexes are mainstream in Information Retrieval systems and many compression techniques have been proposed. The purpose of this paper is to explore the compression efficiency on a two-level inverted index tailored for n-gram indices. We use two compression techniques Optimal PForDelta and IPC. Both techniques are applied to a previous work of us, that has focused on developing a threshold to efficiently store subsequences inside a one or two level inverted index, based on their number of occurrences inside a biological sequence. We study the performance of these two compression algorithms over different fluctuations of the threshold. The compression ratio of the OptPFD is affected by the changes in the threshold and is also efficient as in text documents. Whereas, IPC has a different performance for each threshold and it is more stable, although it is much less efficient than in text documents.
Beta-Liouville Regression and Applications

A novel regression algorithm has been proposed, Beta-Liouville regression, to solve regression of compositional data, where the prediction is multi-dimensional and sums to unity. Applications include market-share data mining in relation to its score in Google-trends and smart building occupancy estimation. The study of Google-trends in relation to market-shares gives a good estimate of whether the company's investment in advertisements have yielded any results in improving their market-shares. Secondly, occupant behaviour in buildings gives useful insights on the required levels of air conditioning, lighting and even initiating help during emergency. Sensors to estimate the occupancy of a smart building include microphone, door/window positions, motion detection, power consumption. The Beta-Liouville regression algorithm is compared to ordinary least squares regression with compositional transformations and Dirichlet regression.
On the Antecedent Normal Form of Conditional Knowledge Bases

Desirable properties of a normal form for conditional knowledge are, for instance, simplicity, minimality, uniqueness, and the respecting of adequate equivalences. In this paper, we propose the notion of antecedentwise equivalence of knowledge bases. It identifies more knowledge bases as being equivalent and allows for a simpler and more compact normal form than previous proposals. We develop a set of transformation rules mapping every knowledge base into an equivalent knowledge base that is in antecedent normal form (ANF). Furthermore, we present an algorithm for systematically generating conditional knowledge bases in ANF over a given signature. The approach is complete in the sense that, taking renamings and equivalences into account, every consistent knowledge base is generated. Moreover, it is also minimal in the sense that no two knowledge bases are generated that are antecedentwise equivalent or that are isomorphic to antecedentwise equivalent knowledge bases.
Road obstacle detection

In recent years, there has been rapid development in the research area of the car's driving assistance. Deep learning was used to solve different problems, such as object detection, visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems. In this paper, we'll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, then we present an approach where we detect roadsides, then we seek objects located on the road area to prevent driver. As the state of the driver is very important information, we try to detect driver's drowsiness. We use a camera with an algorithm to calculate the eye aspect ratio. Finally, we evaluated the three modules of proposed system using our collected data-set.
Implementation of Type-2 Fuzzy Control of PMSM Position Drive with Flexible Coupling

Nowadays permanent magnet synchronous motor (PMSM) drives use in many industrial applications. There are some problems with tuning of control loops' controllers related with nonlinear mathematical model of motors, variable load torque and inertia uncertainty. Additional complexity is appeared when engine has flexible coupling between motor and plant in case it's necessary to provide accuracy of positioning or smoothness of movement. Fuzzy Expert Systems are one step of Artificial Intelligence, so they are suitable for such tasks. Especially for such tasks it is preferable to use interval type-2 fuzzy control which differs from type-1 fuzzy algorithm by blurring of the boundaries of the membership functions, therefore the system becomes robust in a larger range of internal and external distributions. Paper presents application of Type-2 Fuzzy Control for PMSM Position Drive with flexible coupling. Comparison of dynamics and accuracy in systems with different types of position controllers: PID, Type-2 Fuzzy is given. It's shown that system with Interval Type-2 Fuzzy position controller has the best robust among represented controllers.
Bitcoin: A Reader’s Guide (The Beauty of the Very Idea)

Previous articleNext article No AccessBitcoin: A Reader’s Guide (The Beauty of the Very Idea)Frances FergusonFrances Ferguson Search for more articles by this author Frances Ferguson is the Ann L. and Lawrence B. Buttenwieser Professor in the Department of English at the University of Chicago. Her most recent book is Pornography, the Theory: What Utilitarianism Did to Action (2005). She is also a coeditor of Critical Inquiry.PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Critical Inquiry Volume 46, Number 1Autumn 2019 Article DOIhttps://doi.org/10.1086/705302 Views: 1095Total views on this site Citations: 1Citations are reported from Crossref © 2019 by The University of Chicago. All rights reserved.PDF download Crossref reports the following articles citing this article:Jeffrey West Kirkwood From Work to Proof of Work: Meaning and Value after Blockchain, Critical Inquiry 48, no.22 (Dec 2021): 360–380.https://doi.org/10.1086/717303
Analysis of Flickr Images Using Feature Extraction Techniques

The rapid growth of digital technologies leads to the vast number of images are shared by users on social websites. Analyzing and identifying a particular image from a huge database is a challenging task. In this paper, the proposed method is based on color and texture. The proposed method gives 82.83% average performance for retrieving images from Flickr database. The color features are extracted using color moment and texture features using wavelet packet. These two features are combined by assigning weight to them. The combination of color moment and wavelet packet provides the best retrieval results as compared to every single one of the two features.
On Massive JSON Data Model and Schema

Abstract JSON (JavaScript Object Notation) is a lightweight semi-structured data format based on the data types of programming language JavaScript. It is a popular data exchange format over the World Wide Web and becomes a dominant standard format for sending API (Application Programming Interface) requests and responses in the past few years. Furthermore, JSON has also attracted attentions of database community research, especially in data intensive applications. JSON is not only can be integrated in traditional database systems, but also widely used in NoSQL database systems and graph database systems. Compared with XML, JSON document is a set of “key-value” pairs, in which the “value” itself can be a JSON document, which allows arbitrary levels of nesting, so it is more flexible to use and more difficult to process accordingly. JSON data model and schema describe the basic data structures and semantics of the underlying JSON data, so it is the fundamental and key aspects for JSON data format. JSON data model and schema are not only foundations for other data management technologies, such as data indexing, data querying, data searching, data mapping, data integrating, and data mining, but also has important theoretical significance and application prospects to provide theoretical basis and technical means for other related research, such as data integration, data conversion and other semi-structured and unstructured data queries. This paper analyzes the key problems of JSON data model and schema, including what data model should adopted by JSON and the specification and schema outline of JSON model.
MNAR Imputation with Distributed Healthcare Data

Missing data is a problem found in real-world datasets that has a considerable impact on the learning process of classifiers. Although extensive work has been done in this field, the MNAR mechanism still remains a challenge for the existing imputation methods, mainly because it is not related with any observed information. Focusing on healthcare contexts, MNAR is present in multiple scenarios such as clinical trials where the participants may be quitting the study for reasons related to the outcome that is being measured. This work proposes an approach that uses different sources of information from the same healthcare context to improve the imputation quality and classification performance for datasets with missing data under MNAR. The experiment was performed with several databases from the medical context and the results show that the use of multiple sources of data has a positive impact in the imputation error and classification performance.
Information Hiding in OOXML Format Data based on the Splitting of Text Elements

In this paper, a novel information hiding method is proposed to embed data in the Word documents that use OOXML format. The 2007 version and more recent versions of MS Word are all based on the OOXML format. The main document body of OOXML document consists of the text elements that correspond to the content of the document. It is found that, in the OOXML format, the printable text in an element can be “split” by separating the element into multiple elements. The digital code of the information determines if the adjacent characters in the text will be “split” or not, so as to achieve the purpose of information hiding. Since the format and all the other properties of the text in the OOXML format document is unchanged after the embedding, the embedded information is imperceptible. The code of the information is embedded circularly to enhance robustness. Experiments show that the proposed method can deal with many kinds of attacks, including “content”, “save as”, “copy” and part of “format”. The proposed method can be used in information security and copyright protection for OOXML format documents.
Dynamic Texture Classification Using Deterministic Partially Self-avoiding Walks on Networks

This paper presents a new approach to dynamic texture classification based on deterministic partially self-avoiding (DPS) walks on complex networks (or graphs). In this approach, for each pixel is assigned a vertex and two vertices are connected according to a given distance. In order to analyze appearance and motion, we propose two graph modeling: a spatial graph and a temporal graph. The DPS walks are agents that can obtain rich characteristics of the environment in which they were performed. Thus, the DPS walks are performed in the two modeled graphs (spatial and temporal) and the feature vector is obtained by calculating the statistical measures from the trajectories of the DPS walks. The results in two well-known databases have demonstrated the effectiveness of the proposed approach using a small feature vector. The proposed approach also improved the performance when compared to the previous DPS walks based method and the graph-based method.
Analysis of the convolutional neural network architectures in image classification problems

The work aims to construct effective methods for image classification. For this purpose, we analyze neural network convolutional architectures, which understand as the number of network layers, elements in the input and output layers, the type of activation functions, and the connections between neurons. We studied the application of various configurations of convolutional networks for solving image classification problems. Numerical experiments on BOSPHORUS database were conducted; we described the results in this work. A neural network architecture has been developed based on the analysis of convolutional neural networks, which for the data set under consideration, provides the most accurate classification. A new method combines the advantages of using RGB images and depth maps as input data is proposed for processing the output of a convolutional network.
Statistical quadrature evolution by inference for multicarrier continuous-variable quantum key distribution

We define the statistical quadrature evolution (QE) method for multicarrier continuous-variable quantum key distribution (CVQKD). A multicarrier CVQKD protocol uses Gaussian subcarrier quantum continuous variables for information transmission. The QE scheme utilizes the theory of mathematical statistics and statistical information processing. The QE model is based on the Gaussian quadrature inference (GQI) framework to provide a minimal error estimate of the CV state quadratures. The QE method evaluates a unique and stable estimation of the non-observable continuous input from the measurement results and through the statistical inference method yielded from the GQI framework. We prove that the QE method minimizes the overall expected error by an estimator function and provides a viable, easily implementable, and computationally efficient way to maximize the extractable information from the observed data. The QE framework can be established in an arbitrary CVQKD protocol and measurement setting and is implementable by standard low-complexity functions, which is particularly convenient for experimental CVQKD.
A Novel Competitive Particle Swarm Optimization Algorithm Based on Levy Flight

The particle swarm optimization is a classical optimization algorithm (PSO) that has been applied to various fields. It implements simple but efficient evolution operators to search for optimums in parameter space. However, it could not reach a good balance between global search and local search when it comes to multimodal’ problem. Levy flight is a kind of stochastic process with scale invariant, turned out to accord to animals’ behavior. This paper utilizes the levy flight operator to enhance the PSO’s global search ability during the iteration, which is based on the hierarchy structure of swarms. Therefore, a novel competitive particle swarm optimization based on levy flight (CLFPSO) is proposed. A number of benchmarks has been tested on the CLPSO with other five typical PSO algorithms. The experimental results show that the proposed algorithm could reach more outstanding and accurate consequences compared with other algorithms.
Quantifying Information Flow in Interactive Systems

We consider the problem of quantifying information flow in interactive systems, modelled as finite-state transducers in the style of Goguen and Meseguer. Our main result is that if the system is deterministic then the information flow is either logarithmic or linear, and there is a polynomial-time algorithm to distinguish the two cases and compute the rate of logarithmic flow. To achieve this we first extend the theory of information leakage through channels to the case of interactive systems, and establish a number of results which greatly simplify computation. We then show that for deterministic systems the information flow corresponds to the growth rate of antichains inside a certain regular language, a property called the width of the language. In a companion work we have shown that there is a dichotomy between polynomial and exponential antichain growth, and a polynomial time algorithm to distinguish the two cases and to compute the order of polynomial growth. We observe that these two cases correspond to logarithmic and linear information flow respectively. Finally, we formulate several attractive open problems, covering the cases of probabilistic systems, systems with more than two users and nondeterministic systems where the nondeterminism is assumed to be innocent rather than demonic.
Selection of Pattern Neurons for a Probabilistic Neural Network by Means of Clustering and Nearest Neighbor Techniques

The architecture of the probabilistic neural network (PNN) depends on the input data cardinality. This is the effect of the arrangement of neurons in the network's pattern layer. Thus, in order to cope with large data classification tasks, PNN's structure must be minimized. In this work, new algorithm for the PNN's architecture simplification is proposed. The approach is realized in two phases. First, a k-means data clustering is performed and initial PNN's pattern neurons are appropriately selected using obtained centers. Second, based on the nearest neighbor to the determined centers, final data records are chosen and used to activate the pattern neurons. The algorithm is applied to the classification tasks of four repository data sets. PNN is trained by means of a conjugate gradient procedure with a Gaussian kernel function utilized for neurons' activation. The performance of original and reduced network is compared using a 10-fold cross validation method. The outcomes are also collated with state-of-the-art results.
Crow Search Algorithm for Continuous Optimization Tasks

The paper provides an insight into the novel meta-heuristic of the Crow Search Algorithm (CSA) as used for continuous optimization tasks. The presented procedure is inspired by the social behaviour of crows. Based on established CEC 2017 benchmark tasks instances, the paper concentrates on performed experimental parameter studies and on a comparison with the existing Particle Swarm Optimization strategy. Building on the test experiences, sets of internal parameters have been formulated which constitute recommendations for other numerical calculations. Finally, some concluding remarks on possible algorithm extensions are given.
A GRU Model for Aspect Level Sentiment Analysis

Abstract Sentiment analysis is a basic task of natural language processing, while aspect level sentiment analysis is an important topic in sentiment analysis. In the same sentence, different words have different influence on the sentiment polarity of aspect, so the key to solve the problem is how to build a relation model between the aspect and the words in the sentence. In this paper, by using two recurrent networks, we built a model for sentence and introduced attention mechanism to fuse aspect information, so as to achieve a better effect. An experiment on public dataset show that the proposed algorithm obtain a better result without carrying out complex feature engineering.
Computer Shogi Tournaments and Techniques

Shogi is a major chess variant in Japan. This article presents two shogi competitions: the World Computer Shogi Championship, the largest annual competition held by the Computer Shogi Association, and The Flood gate, which is a complementary internet competition held voluntarily by researchers. Notable technical contributions made by participants and useful resources for prospective developers are also presented. The techniques and therefore playing strength are still improving, partly because both competitions have attracted active participants, even after the playing strength of computer programs has apparently surpassed that of human players.
Efficient choice enumeration for narrative world design

An open challenge for AI in digital games is narrative experience management, the problem of automatically directing virtual characters in an interactive story to produce specific narrative effects for human participants. One important aspect of interactive narrative quality is participant choice, which provides the central distinction between interactive and linear storytelling. Most experience management work identifies storytelling patterns an agent should value or guides participants through high-value story trajectories. However, the types and quality of choices available in an interactive story are influenced not only by a model of narrative and specific story character actions, but also the world design that affords situations and actions. In this paper, we present a method for generating all unique, accessible choice combinations for a story world as a first step towards experience-driven interactive narrative world design. We benchmark the approach against several baselines and discuss its use as a tool for human and automated world designers.
Self-accounting in architecture-based self-adaptation

This paper proposes a work-in-progress approach regarding qualities of the managing layer in architecture-based self-adaptation. In particular, we establish the notion of self-accounting as self-* property and we present an inductive method, based on the structure of the MAPE pattern of the adaptation layer, to evaluate the cost of the adaptation logic in terms of latency time and availability of the managing system. We also show how the MSL (MAPE Specification Language), a language for modeling the adaptation layer in terms of MAPE patterns, has been extended to annotate MAPE components with values for these quality properties, so allowing the computation of the cost function and endowing an adaptation layer with a value for its self-accounting property.
Semantics-Aware Virtual Machine Image Management in IaaS Clouds

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management require dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Nevertheless, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages) with either restricted possibility to identify and extract reusable functionalities or with higher VMI publish and retrieval overheads. In this paper, we design, implement and evaluate Expelliarmus, a novel VMI management system that helps to minimize storage, publish and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it makes use of VMIs modelled as semantic graphs to expedite the similarity computation between multiple VMIs. Second, Expelliarmus provides a semantic aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, Expelliarmus can also assemble VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on the real test-bed. Experimental results show that our semantic-centric approach is able to optimize repository size by 2.2 - 16 times compared to state-of-the-art systems (e.g. IBM's Mirage and Hemera) with significant VMI publish and slight retrieval performance improvement.