Reviewerly/exHarmony huggingface dataset:
Recurrent Convolution Attention Model (RCAM) for Text Generation based on Title

Natural Language Generation (NLG) is one of the most important part in Natural Language Processing (NLP). Recently, generating text automatically with deep learning method has been improved a lot. While there are lots of defects in text generation such as the quality is not satisfied and the text of title is not clear. The paper used the recurrent convolution attention model with LSTM (Long Short-Term Memory) cells for text generation by giving a title. The result proved that it can generate sentence according with the title and make the text express more fluently. Moreover, it uses less time to train by contrast with the SeqGAN (Sequence Generative Adversarial Networks). At the same time, the result is better than other attention mechanism with LSTM models. Therefore, it has more significance for NLP research.
Graph-Based Semantic Learning, Representation and Growth from Text: A Systematic Review

The Vector Space Model (VSM), is the main technique to model the semantics from the text. However, the VSM model suffers from notable limitations. The main alternative model for VSM model is a graph-based model. This paper presents a systematic review on the graph-based processes of Semantic Learning, Representing and Growth (SLRG) from the text. Then it describes a new branch in graph-based SLRG modeling, inspired from the cognitive-semantics.
Completeness and expressiveness of pointer program verification by separation logic

Reynolds' separation logical system for pointer program verification is investigated. This paper proves its completeness theorem that states that every true asserted program is provable in the logical system. In order to prove the completeness, this paper shows the expressiveness theorem that states the weakest precondition of every program and every assertion can be expressed by some assertion. This paper also introduces an extension of the assertion language with inductive definitions and proves the soundness theorem, the expressiveness theorem, and the completeness theorem.
Making Interactive Learning Media Based on Augmented Reality on the Concept of Molecular Chirality

This research is motivated by the difficulty of students in representing submicroscopic concepts in the material of molecular chirality. Therefore, learning media are needed that can establish two-way communication in visualizing molecular chirality to be easily understood by students. The method used is design-based research (DBR) with a modified ADDIE model. The stages of the research include analysis of the concept of molecular chirality, design, and media creation. Based on the results of research and development, the conclusions are AR-based media on the concept of molecular chirality has contrasting coloring characteristics, interactive, and 3D object visualization so that students can develop student responses actively and student submicroscopic representation, based on validation tests on aspects of visual communication, software engineering, linguistic feasibility, cognitive learning, the substance of the concept of molecular chirality, and the feasibility of their use according to the purpose of making the product, obtained an average rcalculation of 0.8807 or feasible, while the average value of the feasibility of limited trials was obtained 90.12%. This shows that the presentation of interactive learning media based on AR on the concept of molecular chirality is suitable to be used as a supporting medium in learning.
A Semantic Platform for Developing Data-Intensive Mobile Apps

Mobile devices have become the primary method of communication and information access for people around the world. Currently, most mobile applications create or consume data stored in standalone databases. This means most of these applications exist in silos without exploiting the potential of being linked with data collected from other applications or external data on the Web. The Web community has advocated the use of Linked Data technologies to address this issue for data interoperability on Web-based applications. Although we have seen some success from Web content publishers in using or publishing Linked Data, there are few examples to be found on mobile platforms. Our platform, developed as an extension of MIT App Inventor, bridges this gap by enabling developers to build mobile applications that can take advantage of Linked Data. The platform includes components for querying remote SPARQL endpoints, generating and storing triples from input and context, as well as enable crowd-sourcing of Linked Data through cloud based messaging. We believe that our platform will reduce the burden on mobile developers working with Linked Data and open up many opportunities for building mobile applications that interact with and contribute to the Linked Data world.
Datamining for Fraud Detecting, State of the Art

Fraud detection is a rapidly developing field; several technologies have been used to prevent fraud such as data mining (DM). The use of data mining applications have shown their utility in different fields and have attracted increasing attention and popularity in the financial world. Data mining plays an important role in the field of fraud because it is often applied to extract and discover the truths hidden behind very large amounts of data. For this purpose, our contribution explores the applications of data mining techniques to fraud detection, and groups the various researches carried out in this field from 1966 to 2017. The result of this study will support and guide future research in this field.
Is Privacy possible without Anonymity?

Traditional approaches to privacy are usually based on top of anonymizing or pseudonymizing systems. For example, users who would like to protect their identity and/or hide their activities while browsing the web frequently use anonymizing systems (e.g., Tor) or services (e.g., VPNs and proxies). Although anonymizing systems are usually effective, recent revelations suggest that anonymization can be compromised and can be used to provide a false sense of security. In this paper we assume a world where anonymization is (practically) not possible. Imagine, for example, a community where the use of anonymizing systems is frowned upon or even forbidden. Is it possible for users to protect their privacy when they can not hide their identity?
&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${\mathbb{Z}_{2}\mathbb{Z}_{4}}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Additive Cyclic Codes: Kernel and Rank

A ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -additive code C ⊆ ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> × ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sup> is called cyclic if the set of coordinates can be partitioned into two subsets, the set of ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> coordinates and the set of ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> coordinates, such that any cyclic shift of the coordinates of both subsets leaves the code invariant. Let Φ(C) be the binary Gray map image of C. We study the rank and the dimension of the kernel of a ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -additive cyclic code C, that is, the dimensions of the binary linear codes (Φ(C)) and ker (Φ(C)). We give upper and lower bounds for these parameters. It is known that the codes (Φ(C)) and ker (Φ(C)) are binary images of ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -additive codes that we denote by R(C) and K(C), respectively. Moreover, we show that R(C) and K(C) are also cyclic and determine the generator polynomials of these codes in terms of the generator polynomials of the code C.
Trajectory Planning for Aerial Vehicles in the Area Coverage Problem with Nearby Obstacles

In this paper, we address the coverage path planning with curvature-constrained paths for a fixed-wing aerial vehicle. The studied problem is to provide a cost-efficient solution to cover a given area by the vehicle sensor from the specified altitude to provide a sufficient level of details in the captured snapshots of the area. In particular, we focus on scenarios where the area to be covered is surrounded by nearby obstacles such as trees or buildings, and the vehicle has to avoid collisions with the obstacles but maximizes the area coverage. We propose an extension of the existing coverage planning algorithm to determine a shortest collision-free path that is accompanied by Dubins Airplane model to satisfy the motion constraints of the vehicle. The reported results support the feasibility of the proposed approach to avoid nearby obstacles by optimal adjustments of the vehicle altitude while the requested complete coverage is satisfied. If such a solution is not found because of too close obstacles, a feasible solution maximizing the coverage is provided.
A fast X-corner detection method based on block-search strategy

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the four-step and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some X-corners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 × 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.
Machine Learning Enhances the Efficiency of Cognitive Screenings for Primary Care

Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol against one of the most extensively used, clinician-administered instruments-the Montreal Cognitive Assessment (MoCA).Two hundred six participants (mean age = 67.44, standard deviation [SD] = 11.63) completed the MoCA and the computerized test. Three machine learning algorithms (ie, Support Vector Machine, Random Forest, and Gradient Boosting Trees) were trained to classify participants according to the clinical cutoff score of the MoCA (ie, < 26) from participant performance on 25 features of the computerized test. Analysis employed Synthetic Minority Oversampling TEchnic to correct the sample for class imbalance.Gradient Boosting Trees achieved the highest performance (accuracy = 0.81, specificity = 0.88, sensitivity = 0.74, F1 score = 0.79, and area under the curve = 0.81). A subsequent K-means clustering of the prediction features yielded 3 categories that corresponded to the unimpaired (mean = 26.98, SD = 2.35), mildly impaired (mean = 23.58, SD = 3.19), and moderately impaired (mean = 17.24, SD = 4.23) ranges of MoCA score ( F = 222.36, P < .00). In addition, compared to the MoCA, the computerized test correlated more strongly with age in unimpaired participants (ie, MoCA ≥26, n = 165), suggesting greater sensitivity to age-related changes in cognitive functioning.Future studies should examine ways to improve the sensitivity of the computerized test by expanding the cognitive domains it measures without compromising its efficiency.
Strange Places

The inspiration for designing tangible, embedded, and embodied interaction can come from stranger places than needs assessments and observations. In conceptualizing home+, a pair of interactive furnishings enabling independent living, our design-research team found inspiration in modern painting and contemporary dance. Giorgio de Chirico's painting "Furniture in the Valley" (1928) captures the curious vitality and intermingling of furniture outside their normal confines that our team translated into this active pair of home+ robots and their intimate rapport. Two contemporary dance works, Andrea Miller's "(C)arbon" and Ohad Naharin's "STOP," explore the interrelationships of three bodies, coping with their circumstances, which inspired the interoperability and control strategies across the pair of robots and a human "in-the-loop." This paper aims to inspire other design researchers to seek, in strange places, inspirations for tangible, embedded, and embodied interaction.
A Novel Probabilistic Nuclei Segmentation Algorithm for H&amp;E Stained Histopathological Tissue Images

In this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&amp;amp;E stained histopathological tissue images. The proposed algorithm doesn’t require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels’ color value in the images with RGB color, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. The other contributions of the study are false positive removal algorithm and automatically determination of nuclei cluster for K-means. In order to compare and evaluate the performance of the proposed algorithm in terms of time and cost efficiency, K-Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations are revealed that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K-Means and Expert is 5%.
Identifying Feature Clones: An Industrial Case Study

During its software evolution, the original software system of our industrial partner was split into three variants. These have evolved over time, but retained a lot of common functionality. During strategical planning our industrial partner realized the need for consolidation of common code in a shared code base towards more efficient code maintenance and re-use. To support this agenda, a feature-clone identification approach was proposed, combining elements of feature location (to identify the relevant code in one system) and clone detection (to identify that common feature’s code across systems) techniques. In this work, this approach is used (via our prototype tool CoRA) to locate three features that were identified by the industrial partner for re-factoring, and is evaluated. The methodology, involving a system expert, was designed to evaluate the discrete parts of the approach in isolation: textual and static analyses of feature location, and clone detection. It was found that the approach can effectively identify features and their clones. The hybrid textual/static feature location part is effective even for a relative system novice, showing results comparable to more optimal system expert’s suggestions. Finally, more effective feature location increases the effectiveness of the clone detection part of the approach.11A preliminary version of this paper, explaining the motivation, approach and resultant tool was published in [1]. This paper extends that work with a discussion of the approach’s in-vivo empirical evaluation.
Blockchain Technology: Heart of Digital Financial Infrastructure for Managing Trust and Governance System

This paper attempts to discuss how the block chain technology forms the core means of logging data transactions with date and time stamped, verified via decentralization in the accounting system of any company. This digital platform leads to data transparency, automated review and eliminating errors at core level through integrated system of distributed ledger technology (DLT). A diagrammatic model of Digital Financial Infrastructure is presented which showcases the effectiveness of block chain technology in an accounting system. It streamlines the financial reporting and auditing process via digital auditing for managing trust of stakeholders and thereof improving the governance system by increased chances of uncovering fraudulent activity of any organization. A SWOT analysis of block chain technology in an accounting system is presented and discussed, which kindle some of the recent studies of block chain technology in accounting system. Further, this paper sums up, how this digitalization can hit the boundaries of the countryâ€™s economic archetype and contributes to the shift in economic paradigms.
Distributed Data Clustering by Neural Network Algorithms

The paper describes an analysis of the possibilities of applying Kohonen self-organizing maps for distributed data. We consider two strategies of distributed data analysis: with intermediate synchronization of results and with synchronization after full or partial construction of maps. Each strategy is considered taking into account both horizontal and vertical data distribution. We provide recommendations for the choice of a strategy depending on the type of data distribution. The obtained conclusions are confirmed by the results of an experiment.
Bimodal person identification using voice data and face images

The paper considers bimodal person identification problem by analyzing the speaker’s face and voice. Two speaker identification algorithms are developed and compared. The idea of the first algorithm consists of extracting features from the speech signal in the form of mel frequency cepstral coefficients and, with this basis, forming a speaker model using Gaussian mixtures. Second approach is based on the use of a universal background model obtained from the records of a large number of speakers. For face identification, a neural network with 13 convolutional layers was used. For the learning and testing, the databases of speech signals and face images of 100 people were formed. The final bimodal identification system shows the high level of accuracy identification of more than 95%. The results of this experiment demonstrated the possibility of applying the proposed algorithms to the person identification problem in real-life systems.
Method for spherical form error evaluation using cuckoo search algorithm

To obtain the accurate evaluation of minimum zone sphericity, this paper investigates a method in Cartesian coordinates using cuckoo search (CS) algorithm. In this method, an appropriate space is set as the search zone according to the solution obtained which is based on least square criteria. The aim of search is to find the best candidate position infinitely approximating the ideal reference center of minimum zone sphere. In order to improve the search efficiency, two essential parameters in CS, namely the control coefficient α of step size and the probability p<sub>a</sub> of discovering an invasive cuckoo's egg are set to 0.618 (value of golden ratio) and 0.05 (common value of statistical significance), respectively. The updating of the candidate points is carried out by Levy flights and biased/selective random walk mechanisms. Levy flights mechanism can ensure the real global optimum is not missed, biased/selective random walk mechanism guarantee the diversity of search direction and adaptability of search step size. During the updating, the new solution can be kept when it is better than the old one. In each search iteration, the position which corresponds to the smallest sphericity is regarded as the present optimum solution. When the iteration terminal condition is satisfied, the optimum position and corresponding sphericity are output as evaluation results. The validness of the proposed CS algorithm was tested by an application example, the results indicate that the proposed method has the advantage of excellent convergence and high efficiency, which is suitable for the hith-precision evaluation of minimum zone sphericity efficiently.
Stochastic synthesis of recursive functions made easy with bananas, lenses, envelopes and barbed wire

Stochastic synthesis of recursive functions has historically proved difficult, not least due to issues of non-termination and the often ad hoc methods for addressing this. This article presents a general method of implicit recursion which operates via an automatically-derivable decomposition of datatype structure by cases, thereby ensuring well-foundedness. The method is applied to recursive functions of long-standing interest and the results outperform recent work which combines two leading approaches and employs ‘human in the loop’ to define the recursion structure. We show that stochastic synthesis with the proposed method on benchmark functions is effective even with random search, motivating a need for more difficult recursive benchmarks in future.
Counter Terrorism on Online Social Networks Using Web Mining Techniques

Online Social Networks (OSN) like Twitter, Facebook, LinkedIn, Myspace, YouTube and Digg are getting very popular nowadays. They have become a part of everybody’s life as people use to share content like their opinions, feelings with other people. OSNs have also created some serious threats as some people steal personal information of OSN users, similarly some terrorists groups use it as a weapon to achieve certain goals like spread terror among innocent people, brainwashing and recruitment. Therefore, it is the need of the hour to counter such groups. Web mining can be employed to detect terrorism related activities on online social networks. In this paper some major web mining techniques have been discussed which can be helpful to identify such people and terrorism may be countered from OSN. Each technique is discussed thoroughly, and effectiveness along with its pros and cons are also presented. Hence, a number of future research directions are presented which can be undertaken to conduct and improve research in this area.