Reviewerly/exHarmony huggingface dataset:
TEACHING READING BY USING WFR (WARMING-UP FOR READING) TO INCREASE READING SKILL

Abstract: This research is aimed at describing the implementation of teaching reading by using WFR, to describe the problems of using WFR in teaching reading and to know the students’ response of the implementation of WFR. The result of this study is intended to give contribution to the teaching and learning English. The research was done to the second year students of SMP N 5 Banjarbaru. As the sample, the researcher took 40 students of IIA class. The data were collected through observation, interview and test. After collecting the data and analyzing them and describing the result of them by action research procedure, the researcher described the result of the research. The WFR was conducted in four meetings. It is appropriate in teaching reading and helps the students increasing/developing their reading skill. There are some problems faced by the students in teaching learning process: in which most of students have difficulties in arranging the sentence correctly, the students have difficulties in translating the word from English into Indonesian and Indonesian into English, the students do not understand the instruction of the worksheet, and the students have difficulties in finding the main idea of the text. During the teaching learning process from the first meeting until fourth meeting the response of the students is good. They were enthusiastic in every meeting. Evidently, the result of the hypothesis testing showed that the t- observation is always greater than the t-table; in degree of freedom N - 1 = 39 and level of significance 0,05. It means that there is a significant difference of the students’ achievement before and after the implementation of WFR. The conclusion is the teaching reading using WFR to the second year students of SMP N 5 Banjarbaru yields a good result. WFR is appropriate method in increasing the students reading skills and helpful in teaching-learning process.
Graph exponentiation and neighborhood reconstruction

Any graph G admits a neighborhood multisetfor some other graph H.This note characterizes neighborhood reconstructible graphs as those graphs G that obey the exponential cancellation G K2 ∼ = H K2 =⇒ G ∼ = H.
Knowledge Refinement via Rule Selection

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.
The Beginning of Theory and Practice of New Media: Alan Kay’s and Ted Nelson’s Projects

The article elucidates the conceptual context of new media occurrence. Initially, the idea of a personal computer contained a concept of media, which was serving as a guideline in PC development. Having this considered, the research focuses on Alan Kay’s and Ted Nelson’s key ideas of (1) turning a computer into a personal meta-medium due to a user interface and (2) the idea of hypertext.Alan Kay considers media as a driving change for developing of thinking patterns and new competencies. While developing the concept of the user interface, Kay relies on the interpretation of Bruner’s theory of three types of learning. Alan Kay treats new media as meta-media in relation to traditional media because of their ability to simulate existing forms of media and to create the new ones.Ted Nelson has developed the concept of hypertext, which he understands as non-linear text that goes beyond the one-dimensional sequence of sentences. He considers a possibility of a hypertextual system that a) enables the simultaneous communication of all elements and simultaneous alternative displays of a text, b) allows working on documents collaboratively, c) let users track and save changes in documents, storing its versions, etc. According to Nelson’s plan, the implementation of hypertext systems will make computers more alike to the way people think.The article argues that the people who worked on the creation of new media technologies were also the first theoreticians of the new media. Furthermore, the new media combine both technological and humanitarian discourses. Thus, the theoretical background of the new media project allows concluding that they can naturally be considered as a subject of cultural studies. The article emphasizes that while studying the new media, one should avoid alienation from ‘the technical’ because it is technologies that determine their (new media) specificity.Article received 04.12.2017
Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.
New 2-designs over finite fields from derived and residual designs

Based on the existence of designs for the derived and residual parameters of admissible parameter sets of designs over finite fields we obtain a new infinite series of designs over finite fields for arbitrary prime powers $q$ with parameters $2\text{-}(8,4,\frac{(q^6-1)(q^3-1)}{(q^2-1)(q-1)};q)$ as well as designs with parameters $2\text{-}(10,4,85λ;2)$, $2\text{-}(10,5,765λ;2)$, $2\text{-}(11,5,6205λ;2)$, $2\text{-}(11,5,502605λ;2)$, and $2\text{-}(12,6,423181λ;2)$ for $λ = 7,12,19,21,22,24,31,36,42,43,48,49,55,60,63$.
Curvilinear tracing approach for recognition of Kannada sign language

Sign languages are used as the main mode of communication for vocally disabled people; however, the diversity in sign symbol representation limits its usage to a particular region. There is a huge diversity in sign symbol representation from one country to another, one state to another. In India there are different sign languages observed for each state region. It is hence very difficult for one region individual to communicate to another using a significant symbol. This paper proposes a curvilinear tracing approach for shape representation and recognition of Kannada sign language to generate corresponding characters in Kannada language. To develop this approach, a dataset is created with all Swaragalu, Vyanjanagalu, and numbers in Kannada language. The dataset is formed by defining a vocabulary for different sign symbols used in common interfacing. In the representation of sign language for recognition, edge features of hand regions are considered to be an optimal feature representation of sign language.
Anytime Recursive Best-First Search for Bounding Marginal MAP

Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art solvers for this task are based on a hybrid best-first and depth-first search scheme that allows them to compute upper and lower bounds on the optimal solution value in an anytime fashion. These methods however are memory intensive schemes (via the best-first component) and do not have an efficient memory management mechanism. For this reason, they are often less effective in practice, especially on difficult problem instances with very large search spaces. In this paper, we introduce a new recursive best-first search based bounding scheme that operates efficiently within limited memory and computes anytime upper and lower bounds that improve over time. An empirical evaluation demonstrates the effectiveness of our proposed approach against current solvers.
Minimum Intervention Cover of a Causal Graph

Eliciting causal effects from interventions and observations is one of the central concerns of science, and increasingly, artificial intelligence. We provide an algorithm that, given a causal graph G, determines MIC(G), a minimum intervention cover of G, i.e., a minimum set of interventions that suffices for identifying every causal effect that is identifiable in a causal model characterized by G. We establish the completeness of do-calculus for computing MIC(G). MIC(G) effectively offers an efficient compilation of all of the information obtainable from all possible interventions in a causal model characterized by G. Minimum intervention cover finds applications in a variety of contexts including counterfactual inference, and generalizing causal effects across experimental settings. We analyze the computational complexity of minimum intervention cover and identify some special cases of practical interest in which MIC(G) can be computed in time that is polynomial in the size of G.
REFLEKSI DIRI TERHADAP HUBUNGAN MANUSIA DAN ALAM SEBAGAI SUMBER INSPIRASI PENCIPTAAN KARYA SENI RUPA EKSPERIMENTAL

&lt;p&gt;&lt;em&gt;Creation of experimental art with the title "Self Reflection Against Human and Natural Relations" in the background of the writer's anxiety about the environmental damage caused by humans. From the source of the idea, the writer creates experimental fine arts that will be used as an awareness media on how important the role of nature is for the survival of living thing on earth.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The results of this creation are experimental installation with elements of painting, audio, video and motion installation to point a space. The processing of this final project refers to the method of creation of Henri Cholis which refers to Hawkins, namely that in the process of create there are three stages: exploration, imvising and forming.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Keywords: self-reflection, experimental art, nature damage&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;
Improved Panoramic Representation via Bidirectional Recurrent View Aggregation for Three-Dimensional Model Retrieval

In a view-based three-dimensional (3-D) model retrieval task, extracting discriminative high-level features of models from projected images is considered as an effective approach. The challenge of view-based 3-D shape retrieval is that the shape information of each view is limited due to information deficiency in projection. Traditional methods in this direction mostly convert the model into a panoramic view, making it hard to recognize the original shape. To resolve this problem, we propose a novel deep neural network, recurrent panorama network (RePanoNet), which can learn to build panoramic representation from view sequences. A view sequence is rendered at a circle around the model to provide enough panoramic information. For each view sequence, we employ the bidirectional long short-term memory in RePanoNet to recognize spatial correlations between adjacent views to construct a panoramic feature. In our experiments on ModelNet and ShapeNet Core55, RePanoNet outperforms the methods in the state of the art, which demonstrates its effectiveness.
Implementasi Sistem Informasi Manajemen dalam Mendukung Pelayanan Administrasi di SMA Negeri 1 Barru

This study aimed to determine the implementation of system information management and administrative services at SMA Negeri 1 Barru. This research is a field research using qualitative methods and qualitative approaches. Data collection techniques used were interviews and observation. The results showed that the application of management information systems in supporting administrative services at SMA Negeri 1 Barru had been well implemented by utilizing all facilities and infrastructure facilities as a foothold in optimizing the application of system information management.
Deep learning based EVM correction for RF receiver of vector signal analyser

This Letter presents a novel deep learning approach for optimising the receiver performance with respect to the error vector magnitude (EVM) metric, which was verified and evaluated by applying it to a self-developed proprietary vector signal analyser (VSA). A four-layer neural network was built and trained to estimate and correct the systematic error of the VSA receiver by using a calibrated commercially available vector signal generator as the training source. Experimental results show that the EVM performance of the self-developed VSA is improved and approaches that of a state-of-the-art VSA.
Evolving Solutions to Community-Structured Satisfiability Formulas

We study the ability of a simple mutation-only evolutionary algorithm to solve propositional satisfiability formulas with inherent community structure. We show that the community structure translates to good fitness-distance correlation properties, which implies that the objective function provides a strong signal in the search space for evolutionary algorithms to locate a satisfying assignment efficiently. We prove that when the formula clusters into communities of size s ∈ ω(logn) ∩O(nε/(2ε+2)) for some constant 0
Refining Abstraction Heuristics during Real-Time Planning

In real-time planning, the planner must select the next action within a fixed time bound. Because a complete plan may not have been found, the selected action might not lead to a goal and the agent may need to return to its current state. To preserve completeness, real-time search methods incorporate learning, in which heuristic values are updated. Previous work in real-time search has used table-based heuristics, in which the values of states are updated individually. In this paper, we explore the use of abstraction-based heuristics. By refining the abstraction on-line, we can update the values of multiple states, including ones the agent has not yet generated. We test this idea empirically using Cartesian abstractions in the Fast Downward planner. Results on various benchmarks, including the sliding tile puzzle and several IPC domains, indicate that the approach can improve performance compared to traditional heuristic updating. This work brings abstraction refinement, a powerful technique from offline planning, into the real-time setting.
ANALISIS WACANA RUBRIK RESENSI PADA SURAT KABAR SOLOPOS EDISI JANUARI-DESEMBER 2017 SERTA RELEVANSINYA SEBAGAI BAHAN AJAR TEKS ULASAN DI SMP DAN SMA

&lt;em&gt;This research aimed at describing and explaining about (1) the discourse structure of review text on book review columns in Solopos Newspaper January-December 2017 edition; (2) the textual aspects of book review; (3) the contextual aspects of book review; (4) the relevance of book review columns in Solopos Newspaper January-December 2017 edition as the teaching materials of review text in Secondary Junior High Schools and in Secondary Senior High Schools. The type of this research is qualitative descriptive. The results of this research indicated that (1) the discourse structure of review text on book review columns included opening paragraph, contacting paragraph or content, and closing paragraph; (2) textual aspects supporting the completeness of book review were the topic of discourse, aspects of cohesion and coherence, and forms of words; (3) the contextual aspects on book review could be understood through four interpretive principles, namely: the principle of personal interpretation, the principle of local interpretation, the principle of temporal interpretation, and the principle of analogy; and (4) the results of this research also indicated that book review columns in Solopos Newspaper January-December 2017 edition could be used as the teaching materials of review text in Junior High Schools and Senior High Schools.&lt;/em&gt;
Compilation of malay criminological terms from online news

&lt;p&gt;A Malay language corpus has been established by the Institute of Language and Literature (Dewan Bahasa dan Pustaka, DBP in Malaysia). Most of the past research on the Malay language corpus has focused on the description, lexicography and translation of the Malay language. However, in the existing literature, there is no list of Malay words that categorizes crime terminologies. This study aims to fill that linguistic gap. First, we aggregated the most frequently used crime terminology words from Malaysian online news sources. Five hundred crime-related words were compiled. No automatic machines were in the initial process, but they were subsequently used to verify the data. Four human coders were used to validate the data and ensure the originality of the semantic understanding of the Malay text. Finally, major crime terminologies were outlined from a set of keywords to serve as taggers in our solution. The ultimate goal of this study is to provide a corpus for forensic linguistics, police investigations, and general crime research. This study has established the first corpus of a criminological text in the Malay language.&lt;/p&gt;
Predicting Concrete and Abstract Entities in Modern Poetry

One dimension of modernist poetry is introducing entities in surprising contexts, such as wheelbarrow in Bob Dylan’s feel like falling in love with the first woman I meet/ putting her in a wheelbarrow. This paper considers the problem of teaching a neural language model to select poetic entities, based on local context windows. We do so by fine-tuning and evaluating language models on the poetry of American modernists, both on seen and unseen poets, and across a range of experimental designs. We also compare the performance of our poetic language model to human, professional poets. Our main finding is that, perhaps surprisingly, modernist poetry differs most from ordinary language when entities are concrete, like wheelbarrow, and while our fine-tuning strategy successfully adapts to poetic language in general, outperforming professional poets, the biggest error reduction is observed with concrete entities.
SMART: An Open Source Data Labeling Platform for Supervised Learning

SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of labeled data, and incorporates inter-rater reliability statistics to provide insight into label quality. SMART is designed to be platform agnostic and easily deployable to meet the needs of as many different research teams as possible. The project website contains links to the code repository and extensive user documentation.
Efficiently Reasoning with Interval Constraints in Forward Search Planning

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.
Human-Like Delicate Region Erasing Strategy for Weakly Supervised Detection

We explore a principle method to address the weakly supervised detection problem. Many deep learning methods solve weakly supervised detection by mining various object proposal or pooling strategies, which may cause redundancy and generate a coarse location. To overcome this limitation, we propose a novel human-like active searching strategy that recurrently ignores the background and discovers class-specific objects by erasing undesired pixels from the image. The proposed detector acts as an agent, providing guidance to erase unremarkable regions and eventually concentrating the attention on the foreground. The proposed agents, which are composed of a deep Q-network and are trained by the Q-learning algorithm, analyze the contents of the image features to infer the localization action according to the learned policy. To the best of our knowledge, this is the first attempt to apply reinforcement learning to address weakly supervised localization with only image-level labels. Consequently, the proposed method is validated on the PASCAL VOC 2007 and PASCAL VOC 2012 datasets. The experimental results show that the proposed method is capable of locating a single object within 5 steps and has great significance to the research on weakly supervised localization with a human-like mechanism.
Optimal information ratio of secret sharing schemes on Dutch windmill graphs

One of the basic problems in secret sharing is to determine the exact values of the information ratio of the access structures. This task is important from the practical point of view, since the security of any system degrades as the amount of secret information increases.A Dutch windmill graph consists of the edge-disjoint cycles such that all of them meet in one vertex. In this paper, we determine the exact information ratio of secret sharing schemes on the Dutch windmill graphs. Furthermore, we determine the exact ratio of some related graph families.
Performance analysis of time and cost efficient data pre-fetching in mobile cloud computing

Recently, many methods and algorithms have been proposed in pre-fetching area. However, pre-fetching integrated with workload scheduling approaches have not been investigated as much. Initially, this thesis reviews the principles of the existing pre-fetching strategies considering latency and cost factor as primary objectives. Later, it focuses on an integrated workload scheduling and pre-fetching model to enhance the performance of response time and minimize the cost. Furthermore, response time and cost problems are formulated and to overcome the total response time and cost problems a heuristic approach is proposed. Integrated model is tested for wide range of variables and, the effects of various parameters such as processing speed and pre-fetcher’s utilization are analysed and compared. Finally, based on the results integrated pre-fetching and workload scheduling model outperforms either of them, individually. Thus, this thesis can contribute to the the new solutions in this area.