Reviewerly/exHarmony huggingface dataset:
A Novel Method to Reduce Attributes from Reducts

A major challenge in present day data mining is the high dimension of datasets. Because of the high dimensionality of data, feature selection becomes a necessary pre-processing step to data mining, which removes superfluous features and enhances performance of classification. Thus, attribute reduction becomes an important pre-processing step for data mining. The concepts of rough set theory have been successfully used for attribute reduction. Reducts in rough set theory provide a minimal set of attributes preserving the knowledge. Removal of an attribute from a reduct creates information loss. The severity of information loss due to removal of an attribute depends on the significance of that attribute. In many cases, the variation in classification accuracy when an attribute that has negligible significance is removed from a reduct may not be significant. Removal of insignificant attributes is useful when dealing with high dimensional data sets. In this paper, a novel method is proposed to identify attributes with negligible significance which can be removed from a reduct. Experimental results for various datasets are presented. The results prove that the accuracy of classification do not vary much after the removal of the insignificant attributes from the reduct.
A Parallel Evolutionary Search for Shortest Vector Problem

The hardness assumption of approximate shortest vector problem (SVP) within the polynomial factor in polynomial time reduced to the security of many lattice-based cryptographic primitives, so solving this problem, breaks these primitives.In this paper, we investigate the suitability of combining the best techniques in general search/optimization, lattice theory and parallelization technologies for solving the SVP into a single algorithm.Our proposed algorithm repeats three steps in a loop: an evolutionary search (a parallelized Genetic Algorithm), brute-force of tiny full enumeration (in role of too much local searches with random start points over the lattice vectors) and a single main enumeration.The test results showed that our proposed algorithm is better than LLL reduction and may be worse than the BKZ variants (except some so small block sizes).The main drawback for these test results is the notsufficient tuning of various parameters for showing the potential strength of our contribution.Therefore, we count the entire main problems and weaknesses in our work for clearer and better results in further studies.Also it is proposed a pure model of Genetic Algorithm with more solid/stable design for SVP problem which can be inspired by future works.
A Face Recognition System Based on Principal Component Analysis-Wavelet and Support Vector Machines

Face recognition can represent a key requirement in various types of applications such as human-computer interface, monitoring systems, as well as personal identification. In this paper, design and implement of face recognition system are introduced. In this system, a combination of principal component analysis (PCA) and wavelet feature extraction algorithms with support vector machine (SVM) and K-nearest neighborhood classifier is used. PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used as a classifier to compare its results with the proposed system. For a more comprehensive comparison, two face image databases (Yale and ORL) are used to test the performance of the system. Finally, the experimental results show the efficiency and reliability of face recognition system, and the results demonstrate accuracy on two databases indicated that the results enhancement 5% using the SVM classifier with polynomial Kernel function compared to use feedforward neural network classifier.
"Doggy Dolly": Design Exploration of Customised Mobility Device for a Handicapped Dog

This paper introduces the research and process of assistive device design for a special 4-year-old dog who lost one of his front left legs due to a gun shot by a policeman on the street.This project involves diverse design activities that include the observation of the 4 dog's daily activities and gait (walking style), data collection and analysis, concept generation, prototyping, and product testing.The primary goal of this project is to design a customised mobility device which enables the dog to walk around with ease and comfort.Mobility, durability, stability, safety, ergonomics, and comfort were the main objectives to make this product successful, and they were tested during the entire design process to reach the goal.This paper illustrates how a design team from The Design School at Arizona State University in the U.S.A. helped the handicapped dog with an assistive device by sharing the challenges, successes and focused ergonomic research on the user experiences of a dog.
AN EMPIRICAL STUDY ON GUI-II INTERVIEW METHODS IN PARTICIPATORY DESIGN

Graphical user interface interaction interview (GUI-ii), is a recently purposed method in which designers can remotely co-design and review GUI prototypes by eliminating the need for physical presence of co-designers.However, there are some concerns regarding the accuracy of such remote interview methods, as users do not have any physical interaction with the designers during their interview.In this work, for the first time, we compare GUI-ii methods with the traditional face-to-face interview processes to study their effectiveness in various design phases.The result shows that GUI-ii method is most effective when used in Ozlab.
Application of self-evaluation and co-evaluation on learning processes

One of the important points within the evaluation is who performs it, long ago it was understood that the evaluation always came from an external instance to the evaluation, which was usually the teacher. Self-assessment is when the student is evaluated, while co-evaluation is when the group evaluates in a reciprocal way, encouraging participation, critical and constructive reflection, identifying achievements and shortcomings within the process of Learning. This article aimed to identify the application of self-assessment and co-evaluation in learning processes, where the research was of exploratory type, the scientific method was applied, synthetic analytical, gave as end result that teachers know the importance of these evaluative practices, because of situations of conformity, monotony, and fear of causing enmities with students do not use them.
Connecting the Dots

The idea of having a program which writes other programs based on specifications provided by humans was born very early in the history of computing. After going through several "winters" because of the results not living up to the high expectations, the idea is again popular and some commercial applications already exist, under the generic name of robotic process automation. It so happens that at the same time several other apparently unrelated fields to program synthesis, some of them also veterans of several "winters" (e.g. AI) are also enjoy high interest both in the scientific community and in business: AI, Machine Learning, Deep [Reinforced] Learning, Process Mining, Intention Mining, Cybersecurity.
Anglicized Words and Misspelled Cognates in Native Language Identification

In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2).In particular, we look at misspelled words that indicate lexical uncertainty on the part of the author, and separate them into three categories: misspelled cognates, "L2-ed" (in our case, anglicized) words, and all other spelling errors.We test the assumption that such errors contain clues about the native language of an essay's author through the task of native language identification.The results of the experiments show that the information brought by each of these categories is complementary.We also note that while the distribution of such features changes with the proficiency level of the writer, their contribution towards native language identification remains significant at all levels.
Monitoring and Controlling System of the Water Sports Security Based on the Passive Antenna

Motion process monitoring is the core component of motion state analysis system and motion safety assurance system. For water sports, the implementation of monitoring is directly related to the safety and security of sportsmen, which is more significant. Based on the passive antenna, a real-time monitoring system for underwater motion safety is designed in this paper. The system consists of data acquisition module, data analysis and processing module and background response and alarm module. Firstly, a number of passive antennas are installed on water, surface and underwater to monitor the real-time motion state of sportsmen. Then, the collected motion data were compared with the sample set of drowning state feature data. Assessment of the possibility of drowning among sportsmen is made. Once the warning signs of drowning are confirmed, the alarm system is activated. The position information of the sportsmen is transmitted to the nearest lifeguard, aim to carry out rapid and effective rescue and stop drowning incidents. The monitoring system uses passive antenna for data acquisition, which can effectively avoid power outage. The installation location of the equipment is not limited by the power supply, so it can achieve all-round monitoring coverage in the whole time and all fields. The drowning behavior is identified by the established drowning feature data sample set, which avoids the occurrence of invisible drowning events that are difficult to detect. By using the high-speed DSP, the data processing and alarm system’s timely response can be realized, which greatly shortens the rescue time, directly improves the survival probability of drowning and reduces the physiological damage. The design of the monitoring system will eventually help the lifeguards to complete the task of underwater movement safety.
THE DIFFICULTIES OF INDONESIAN STUDENTS IN LEARNING ENGLISH PLURAL FORM: CONTRASTIVE ANALYSIS

A comparative analysis is needed between the two languages or known as contrastive analysis to identify their structural similarities and differences so that the material and methods of teaching will be appropriate. Further than that, this paper is aimed to give a clear understanding about how Indonesian differs from English in the sense of its language features, especially in plural form. It is known that the structure of the plural between English and Indonesian is different although some similarities were found. Indonesian learners will find it difficult to learn plural words because in terms of word formation, English and Indonesian are very different. The formation of plural words in English is through the process of affixation, while in Indonesian, the formation of plural is through reduplication and addition of quantifier and number. It is necessary to give more explanations and exercises to learn the plural of English because with a lot of practice, it is hoped that Indonesian learners will start to get used to the formation of the plurals.
Изменения в системе образования в конце XIX: великие реформы Александра II и их значение для системы инженерного образования

The author analyzes the key factors that determine the need for reform of the education system in the 1860s. Reforms are characterized by economic pragmatism and political liberalism. The author identifies their main directions: they are liberalization of the management system, codification of academic freedoms; reduction of class, national and political barriers to education; unification of the organization of the educational process throughout the Russian Empire; centralization of the management of the system of educational institutions of all types; the design of the Russian didactic-methodical system of training engineers (Russian method). The emphasis is made on the fact that one of the main conditions for the effectiveness of reforms carried out from above was the cooperation of the state and civil society institutions.
PENGEMBANGAN PROTOTYPE SISTEM MONITORING KETINGGIAN AIR UNTUK PERINGATAN DINI

Indonesia is a country with high rainfall at the rainy season; it can last four months within a year. With the increasing of development in the urban areas, causing the shortage of water absorption area. Many people disposing trash in the water flow is also a factor in the occurrence of floods. Flooding may cause the loss of property; it can also cause loss of life. It takes the presence of monitoring and warning system in order to avoid the occurrence of casualties. And minimize material losses incurred as a result of flooding By using prototype method A water quality monitoring system was created with arduino uno R3 and HCSR04 sensors. System Monitoring of water levels is made to be easily accessible anytime and anywhere. System w arnings are also made to be able to deliver warnings quickly, and have territory w ide coverage. By using this system can be monitored t hrough the web service. So this system can help the user, avoid or can suppress the loss Arising from the flood that occurred. Keywords — The flood monitoring system, Flood warning system, Arduino Uno R3
Evaluation of the Utility of Using Classification Algorithms when Designing New Polymer Composites

Polymer composites are the materials that can be successfully used in the places where high mechanical strength and chemical resistance as well as low absorbability are required.These unique features of polymer composites are obtained mainly due to a suitably selected binder, i.e. a synthetic resin.At the same time, this component accounts for the high production costs of these materials.Partial substitution of the resin with glycolisates obtained using poly(ethylene terephthalate) waste (PET), helps reduce the price of polymeric mortars, while maintaining favourable physicomechanical properties.This modification method also has a beneficial effect on the environment, as it allows the utilisation of a very common waste, which is difficult to dispose of.The article concerns three types of resin mortars, i.e. epoxy, polyester and polyester with the addition of colloidal silica, modified with PET glycolisate.On the basis of the obtained data set and database knowledge mining techniques, such as discriminant analysis and decision trees, it was shown to what extent the type of resin and the presence of an added modifier differentiate the mortar properties.The results obtained with both methods were compared.It was confirmed that these techniques are effective both in the classification and prediction of the type (selection) of mortar in the process of designing new composites.
Heterogenesis in Musical Rewriting and (Re)Performance

In the music of the Twentieth Century, Luciano Berio perhaps is one of the most concerned composers with musical rewriting, intercrossing several traditions, from either European music or traditional cultures. In a short text, Jürg Stenzl indicates the three forms of rewriting that Luciano Berio employs in his compositions: (1) the simple repetition of an original model; (2) the use of the original as an open field for experimentations and (3) the rewriting which exceed the limits of identity and anamorphosis of the original sonority until an unrecognizable situation. Musical writing is, therefore, crossed by a large set of music traditions, which can be used according to a “molécularization” process, closely related to the “allagmatic” concept developed by Gilbert Simondon. In this article we discuss the conceptual aspects of rewriting giving some examples from the practices in composition and performance.
Compositional Specification in Rewriting Logic

Abstract Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples.
RLE Edit Distance in Near Optimal Time.

We show that the edit distance between two run-length encoded strings of compressed lengths $m$ and $n$ respectively, can be computed in $\mathcal{O}(mn\log(mn))$ time. This improves the previous record by a factor of $\mathcal{O}(n/\log(mn))$. The running time of our algorithm is within subpolynomial factors of being optimal, subject to the standard SETH-hardness assumption. This effectively closes a line of algorithmic research first started in 1993.
A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task

This paper describes our submission to the WMT 2019 Chinese-English (zh-en) news translation shared task.Our systems are based on RNN architectures with pre-trained embeddings which utilize character and subcharacter information.We compare models with these different granularity levels using different evaluating metics.We find that a finer granularity embeddings can help the model according to character level evaluation and that the pre-trained embeddings can also be beneficial for model performance marginally when the training data is limited.
Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search

In this paper, we propose Deep Holographic Networks (DHN) to learn similarity metrics of videos for multi-label video search. DHN introduces a holographic composition layer to explicitly encode similarity metrics at intermediate layer of the network, instead of conventional deep metric learning approaches driven by ranking losses. The holographic composition layer is parameter-free and enables less memory footprint compared with state-of-the-art. Towards multi-label video search at large scale, we present a new video benchmark built upon the YouTube-8M dataset. Extensive evaluations on this dataset demonstrate that DHN performs better than traditional deep metric learning approaches as well as other compositional networks.
A Portrait About Writing Explanatory Skill of Fifth-Grade Elementary School

Indonesia has implemented the 2013 curriculum as a text-based learning framework. The text is used as a defender of science. The logical consequence, language skills are an absolute requirement for students so that they can achieve learning goals. On the other hand, writing is the highest skill in the language. Therefore, the study was aimed to describe the explanatory writing skills of fifth-grade elementary students. This research is a descriptive qualitative study using a sample of fifth-grade students in public elementary schools in the city of Surakarta, Central Java, Indonesia. Data collection techniques using documents and interviews. Source triangulation is used as data validity. The results showed that the greatest difficulty students had in writing explanations was writing cohesive and coherent sentences and paragraphs. They are only able to write their ideas without regard to syntactic rules. These writing skills involve students' metacognitive skills which also reflect other language skills such as listening and reading. This research output will be useful for academics and practitioners in determining the right learning approach to improve the success of text-based learning or the 2013 curriculum.
Region-based seam optimization for image stitching

Parallax disposition is a challenging task in image stitching. Thus far, the most effective method for parallax processing is the seam-driven method. However, its ability to handle complex textures is poor. To apply the seam-driven method to more scenes, we have incorporated the idea of spatially varying warps. Before finding the optimal seam, we try to register the overlapping area in as projective a manner as possible. Meanwhile, we set some constraints for the registered image to preserve the original appearance as much as possible. Among the constraints, we introduce local features to further improve the stitched image. After registration, we use the maximum flow method to obtain the optimal seam and introduce a series of seam evaluation systems. On one hand, these systems evaluate whether the resulting seam is misaligned, and on the other hand, they provide feedback to the previous registration, increasing the weights of the feature points near the dislocation point to fine-tune the seam. Comparisons with existing image stitching algorithms demonstrate the effectiveness and universality of our approach.
Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method

In this paper we prove large and moderate deviations principles for the recursive kernel estimators of a distribution function defined by the stochastic approximation algorithm.We show that the estimator constructed using the stepsize which minimize the Mean Integrated Squared Error (MISE) of the class of the recursive estimators defined by Mokkadem et al.gives the same pointwise large deviations principle (LDP) and moderate deviations principle (MDP) as the Nadaraya kernel distribution estimator.
Decentralisation is Coming: The Future of Blockchain

Advocates of blockchain believe that distributed ledger technologies can provide us with a technological infrastructure to challenge the concentrated power of tech giants such as Amazon, Facebook and Google, and create a more equitable, sustainable and decentralized world.This paper considers these claims and concludes that they are preferable to defending the status quo or arguing that a solution might be found in more and better regulations.Nevertheless, the future remains highly uncertain and we are currently living in a rapidly evolving "space" between two competing realities: a centralized old-world reality and a fast-emerging, but, as yet, incomplete, decentralized reality.We remain optimistic that decentralization is coming but identify powerful competing forces seeking to preserve the status quo.As such, we must encourage more organizations -business, government, investors, charities -to experiment with distributed ledger technologies and to participate actively in the digital transformation.We need more experimentation to address the current shortcomings of decentralisation and to ensure the early arrival of mainstream applications of a technology that has the potential to solve some of the most pressing global challenges of a digital age.
Toward the Evaluation of Case Base Maintenance Policies Under the Belief Function Theory

The life cycle of Case-Based Reasoning (CBR) systems implies the maintenance of their knowledge containers for reasons of efficiency and competence. However, two main issues occur. First, knowledge within such systems is full of uncertainty and imprecision since they involve real-world experiences. Second, it is not obvious to choose from the wealth of maintenance policies, available in the literature, the most adequate one to preserve the competence towards problems’ solving. In fact, this competence is so difficult to be actually estimated due to the diversity of influencing factors within CBR systems. For that reasons, we propose, in this work, an entire evaluating process that allows to assess Case Base Maintenance (CBM) policies using information coming from both a statistical measure and a competence model under the belief function theory.