Reviewerly/exHarmony huggingface dataset:
Keystroke Dynamics Anonymization System

Keystroke Dynamics enables the authentication or identification of users by analyzing their way of typing, e.g.when browsing the Internet. Most studies in the state of the art focus on increasing Keystroke Dynamics Sys-tems performances. In this paper, we address the problematic of avoiding the biometric capture of keystrokedynamics in order to protect users’ privacy. Authentication/identification, profiling can be considered as at-tacks we limit in this contribution. Experimental results obtained on significant datasets show the benefits ofthe proposed approaches.
Enforcing Equilibria in Multi-Agent Systems

We introduce and investigate Normative Synthesis: a new class of problems for the equilibrium verification that counters the absence of equilibria by purposely constraining multi-agent systems. We show that norms are powerful enough to ensure a positive answer to every instance of the equilibrium verification problem. Subsequently, we focus on two optimization versions, that aim at providing a solution in compliance with implementation costs. We show that the complexities of our procedures range between 2ExpTime and 3ExpTime, thus that the problems are no harder than the corresponding equilibrium verification ones.
Quantifying the morphosyntactic content of Brown Clusters

Manuel R. Ciosici, Leon Derczynski, Ira Assent. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Cyber-Training: Relations, Connections, Synergistic and Negative Reactions

The cyber-training service is designed as an Internet based platform using a new teaching methodology. The core of the methodology is management of working time organized in training sessions. Each training session consists of five stages: the building of the problems, the built-problems qualification, the voluntary solving of the built-problems, the evaluation of the solutions to the built-problems, and the individual valorization of session’s outcomes. Qualification of problems and evaluation of solutions are done by a quantitative vote of students’ option by means of two proposed indicators: the opportunity of the built-problems, and the usability of the offered solutions, respectively. The values of these indicators are the basis of individual scores for the authors of the contributions and for the activity carried out during the training session. Using www.cybertrainer.online, the members of the training session are functionally included in the cybernetic structure of the service, together with the training methodology. A detailed assessment is made of how cyber-training is an operational way of developing critical thinking. The cyber-training platform described in this paper provides an effective approach for stimulating and evaluating training in learning settings and develops experiences based on critical thinking.
A Step towards the Improvement in the Performance of Text Classification

The performance of text classification is highly related to the feature selection methods.Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features.The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network).This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation.The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance.The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.
Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Automatic detection of a designated building area (DBA) is a research hotspot in the field of target detection using remote sensing images. Target detection is urgently needed for tasks such as illegal building monitoring, dynamic land use monitoring, antiterrorism efforts, and military reconnaissance. The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes. To address the problems of the current detection methods, this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images. Our method was conducted in two main stages. (1) During keypoint generation, we proposed a screening method based on structural pattern descriptors. The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram (MLPH) feature; then, we used one-class support vector machine (OC-SVM) merely to screen those building attribute keypoints. (2) To match the screened keypoints, we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints. We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection. We tested the proposed method on building area sets of different types and at different time phases. The experimental results show that the proposed method is both highly accurate and computationally efficient.
Improving efficiency of remote data audit for cloud storage

The cloud storage service becomes a rising trend based on the cloud computing, which promotes the remote data integrity auditing a hot topic.Some research can audit the integrity and correctness of user data and solve the problem of user privacy leakage.However, these schemes cannot use fewer data blocks to achieve better auditing results.In this paper ,we figure out that the random sampling used in most auditing schemes is not well apply to the problem of cloud service provider (CSP) deleting the data that users rarely use, and we adopt the probability proportionate to size sampling (PPS) to handle such situation.A new scheme named improving audit efficiency of remote data for cloud storage is designed.The proposed scheme supports the public auditing with fewer data blocks and constrains the server's malicious behavior to extend the auditing cycle.Compared with the relevant schemes, the experimental results show that the proposed scheme is more effective.
Reduction of Noise of Cloud Medical Images Using Image Enhancement Technique

The medical images are commonly available on cloud by researchers and doctors for better diagnosis and find new cures to diseases. However, due to blurriness and noises presented in such images, the intended purpose is not served. This paper presents stationary wavelet transform based two techniques i.e. Daubechies (DB) and HAAR wavelets for Gaussian noise removal from medical images. The computer simulations are carried out on a set of 20 medical images. The remarkable rise in entropy value of every image is noticed. The comparative analysis of MATLAB results suggest that DB is better than HAAR wavelet transform based method to improve the medical images and make them much more useful.
Declarative Syntax Definition for Modern Language Workbenches

Programming languages are one of the key components of computer science, allowing programmers to control, define, and change the behaviour of computer systems. However, programming languages require considerable effort to design, implement, and maintain. Fortunately, declarative approaches can be used to define programming languages facilitating their development and implementation. Commonly, the first step to define a programming language consists of specifying its syntax. Syntax definition formalisms are based on grammars, defining rules that specify the words that belong to a language and how these words must be structured to construct valid programs. Grammars are multipurpose, i.e., they provide an understandable source of documentation, and can also be used to derive language implementations. Language workbenches assist language engineers to develop and prototype programming languages by deriving syntactic services from a syntax definition formalism. Many challenging problems still exist when using declarative syntax definitions in a language workbench. To enable truly declarative syntax definitions, parsers and other tools must support grammars in their natural form, i.e., they must be able to handle ambiguous grammars. Parsers that support ambiguous grammars lack a clear semantics for disambiguation, restricting their parsing performance and the languages they can successfully implement. Complementary to parsing, editor services such as pretty-printing and code completion, often need to be implemented by hand, increasing the cost of maintaining and evolving a language. Our goal is to use declarative syntax definitions to effectively define the syntax of programming languages and generate efficient tools. To address the above problems, we propose a new semantics for disambiguating context-free grammars, particularly the subset of grammars that define expressions. We study how often these ambiguities occur in real programs, showing the need for efficient disambiguation. Finally, we implement this semantics, generating a parser that performs disambiguation with near-zero performance overhead. Moreover, we develop a technique to automatically derive parsers and pretty printers for layout-sensitive languages from the syntax definition. By enabling the declarative specification of layout-sensitive languages, we tackle important issues, including usability, performance, and tool support, which prevent the adoption of these languages in tools such as language workbenches. Finally, we propose a principled approach to derive syntactic code completion from the syntax definition. The current implementation of completion services is often ad-hoc, unsound, and incomplete. By using a principled approach, we are able to reason about soundness and completeness of code completion, opening up a path to richer editing services in language implementations.
On a side note

Collaborative work on large high-resolution displays is shown to have many advantages and is getting more affordable all the time. Using notes with summaries, ideas, and reminders on large displays can offer improvement on workflows that can benefit from spatial arrangement. In this paper, we focus on the Quick Notes application in the SAGE2 collaborative environment for large displays. Specifically, since visibility of content is diminished at the extreme sides, we want to know what is the connection, if there is one, between sitting position in relation to the large display and workflow.
Constructing an Adversary Solver for Equihash

Blockchain networks, especially cryptocurrencies, rely heavily on proof-of-work (PoW) systems, often as a basis to distribute rewards.These systems require solving specific puzzles, where Application Specific Integrated Circuits (ASICs) can be designed for performance or efficiency.Either way, ASICs surpass CPUs and GPUs by orders of magnitude, and may harm blockchain networks.Recently, Equihash is developed to resist ASIC solving with heavy memory usage.Although commercial ASIC solvers exist for its most popular parameter set, such solvers do not work under better ones, and are considered impossible under optimal parameters.In this paper, we inspect the ASIC resistance of Equihash by constructing a parameterindependent adversary solver design.We evaluate the product, and project at least 10x efficiency advantage for resourceful adversaries.We contribute to the security community in two ways: (1) by revealing the limitation of Equihash and raising awareness about its algorithmic factors, and (2) by demonstrating that security inspection is practical and useful on PoW systems, serving as a start point for future research and development.
Blockchain and the New Economics of Healthcare

In this paper we outline the economics of healthcare as a problem of coordinating data and examine how blockchain technology might be applied as new economic infrastructure to govern those data rights. We argue that blockchain as a technology of trust pushes the economic organisation of healthcare data away from large, centralised hierarchical organisation towards decentralised, emergent platform organisation. The fundamental problem in healthcare is the coordination and governance of information around decision making (e.g. patient records, licensing of professionals, medical trial data, supply chains). The new economics of healthcare emphasises how this information is governed (e.g. through firms, governments, markets, blockchains) and how the most effective governance changes through time as new technologies of trust are developed. We examine the potential of blockchain as new healthcare data infrastructure (including ensuring the integrity of pharmaceuticals and devices, medical records and data markets). Our view is that blockchain fundamentally shifts healthcare data property rights away from centralised third parties (e.g. hospitals, companies, governments) towards decentralised data property rights held by individual patients. The future platform-based healthcare ecosystem will act as the foundational institutional infrastructure for new competitive solutions to healthcare problems (powered up through other technologies such as the Internet of Things and Artificial Intelligence), helping to solve a growing healthcare productivity crisis.
Evaluating the effectiveness of image quality metrics in a light field scenario

In this contribution, an objective metric for quality evaluation of light field images is presented. The method is based on the exploitation of the depth information of a scene, that is captured with high accuracy by the light field imaging system. The depth map is estimated both from the original and impaired light field data. Then, a similarity measure is applied, and a mapping is performed to link the depth distortion with the perceived quality. Experimental test performed by comparing state-of-art metrics with the proposed one, demonstrate the effectiveness of the proposed metric.
Mining Query Plans for Finding Candidate Queries and Sub-Queries for Materialized Views in BI Systems Without Cube Generation

Materialized views are important for optimizing Business Intelligence (BI) systems when they are designed without data cubes. Selecting candidate queries from large number of queries for materialized views is a challenging task. Most of the work done in the past involves finding out frequent queries from the past workload and creating materialized views from such queries by either manually analyzing workload or using approximate string matching algorithms using query text. Most of the existing methods suggest complete queries but ignore query components such as sub queries for creation of materialized views. This paper presents a novel method to determine on which queries and query components materialized views can be created to optimize aggregate and join queries by mining database of query execution plans which are in the form of binary trees. The proposed algorithm showed significant improvement in terms of more number of optimized queries because it is using the execution plan tree of the query as a basis of selection of query to be optimized using materialized views rather than choosing query text which is used by traditional methods. For selecting a correct set of queries to be optimized using materialized views, the paper proposes efficient specialized frequent tree component mining algorithm with novel heuristics to prune search space. These frequent components are used to determine the possible set of candidate queries for creation of materialized views. Experimentation on standard, real and synthetic data sets, and also the theoretical basis, proved that the proposed method is able to optimize a large number of queries with less number of materialized views and showed a significant improvement in performance compared to traditional methods.
Internet Use in EFL Writing Classes

Aim. The present study seeks to investigate the effect of internet use on writing abilities of English as a Foreign Language (EFL) students studying in an Iranian university. The students of the study have been advised to use the Internet while searching for collocations and new words. They were also told to use grammar checkers while writing essays in English. After a two months’ period, their writing papers had been corrected by two raters. Compared to scores they obtained before the class equipped with new technologies, the students obtained higher scores, showing that the Internet can be used in language learning especially in writing classes where students have difficulties in using native-like words and structures. Methods. The participant of the present study were 28 Iranian university students studying ELT in an Iranian University in the capital Tehran. The students were divided into two classes consisting of 12 students in the experimental group and 16 students in the control group. The mean age of the students taking part in the study was 20. Additionally, according to a TOEFL iBT the level of the English proficiency of the students was 75. To put it other way, utilizing the test, the students were homogenized by the researchers of the study.&#x0D; Results. The results of the study showed that the use of the Internet in EFL classes can boost the writing abilities of EFL learners. In other words, the writing of the students will be improved in case the check different collocations and grammatical points in the Internet. Moreover, the use of grammar checker soft wares which can be found on the net can pave the way for EFL students to be better writers of English. Conclusions. To conclude, this study suggests that the use of the Internet can be beneficial to EFL learners while improving their writing abilities which can be seen by many scholars studying different skills as important.
SELF-EVALUATION METHODOLOGY OF PROGRAMMERS' COMPETITIVENESS IN THE CONTEXT OF LIFELONG EDUCATION AND PROFESSIONAL SELF-DEVELOPMENT

Contemporary social sciences, including pedagogy and psychology, carry out researches in the field of competitiveness. The more competitive each member of the society is, the more competitive is the society as such. The aim of research was: experimentally approbate the developed methodology for evaluating the competitiveness of programmers. Research methods were: survey as pedagogical experiment, projective method for data obtaining; Wilcoxon test for data processing. The results of the pedagogical experiment testify that: during the pedagogical experiment, research participants changed their competitiveness self-assessment. Therefore it is very important to know self-evaluation indicators of professional development because it significant impacts both: programmers notions about professional self-development and competitiveness as result of this development, changing competitiveness self-assessment as well. During the experiment, the participants of the research gained new experience of reflection, reflecting on their professional development, including professionalism, career, in past, present and future, and this reflection experience can serve as a basis for programmer's professional development and adequate competitiveness self-assessment. The experimentally approbated self-assessment methodology of professional development and competitiveness is valid and can be used in further research.
Notice of Retraction Dynamic clustering of data with modified K-prototype algorithm

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.-----------------------------------------------------------------------Clustering of mixed numerical and categorical data has become a challenge now a days. A number of algorithms dealing with mixed data have been proposed. Speed and simplicity are the two major features that have made the K-Prototype algorithm a famous partition based clustering algorithm. This algorithm has a constraint of providing the value of K initially and sometimes predicting the optimum number of clusters in advance becomes practically impossible. In this paper, a new algorithm based on the K-Prototype algorithm for clustering mixed data with advanced features for automatic generation of appropriate number of clusters is presented.
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning

The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting with dictionary learning to recover the continuous phase map. The method is validated both using numerically simulated data with strong additive white Gaussian noise and phase dislocations; and experimental data from fringe projection profilometry. Comparisons with other phase inpainting method referred to as PULSI+INTERP, show the suitability of the proposed method for phase restoration even in extremely noisy phases. The error given by the proposed method on the highest level of noise (RMSE=0.0269 Rad) remains the smallest compared to the error given by PULSI+INTERP for noise-free data (RMSE=0.0332 Rad).
On Coupling FCA and MDL in Pattern Mining

Pattern Mining is a well-studied field in Data Mining and Machine Learning. The modern methods are based on dynamically updating models, among which MDL-based ones ensure high-quality pattern sets. Formal concepts also characterize patterns in a condensed form. In this paper we study MDL-based algorithm called Krimp in FCA settings and propose a modified version that benefits from FCA and relies on probabilistic assumptions that underlie MDL. We provide an experimental proof that the proposed approach improves quality of pattern sets generated by Krimp.
An Improved Image Thinning Algorithm and Its Application in Chinese Character Image Refining

Chinese character image thinning helps to reduce redundant information and maintain the main structure information of the original Chinese character. It has important applications in the field of character recognition. However, the current image thinning algorithm will cause new distortion in thinning, thus increasing the interference and difficulty in recognition. And there are still some problems such as slow thinning speed. In view of the above problems, this paper proposes a fast thinning method for Chinese characters. Based on the pixel-by-pixel thinning (OPTA) algorithm, the algorithm reconstructs 14 neighborhood elimination templates and retention templates, and solves the distortion and incompleteness of thinning. In addition, the algorithm also improves the recognition order to improve the speed, it give priority to the right adjacent pixels of the target point P and the down adjacent pixels. Experiments on Chinese character images show that the algorithm can refine the ridges completely and the speed is faster than other algorithms.