Reviewerly/exHarmony huggingface dataset:
Robust Single-Document Summarizations and a Semantic Measurement of Quality

The goal of this paper is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of extractive summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms not only meet the realtime requirements but also yield the best ROUGE scores on DUC-02 over all previously-known algorithms. We also evaluate our summarization methods over the SummBank dataset and other datasets to ensure that our methods are robust. Experiments show that summaries generated by our methods achieve higher or about the same ROUGE scores than extractive summaries generated by human evaluators. Moreover, we define a semantic measure based on word-embedding using Word Mover’s Distance to evaluate the quality of summaries without human-generated benchmarks. We show that for our algorithms, the orderings of the ROUGE scores and the scores under the new measure are highly comparable, suggesting that this new measure may serve as a viable alternative for measuring the quality of a summary.
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models

Mixture of exponential family models are among the most fundamental and widely used statistical models. Stochastic variational inference (SVI), the state-of-the-art algorithm for parameter estimation in such models is inherently serial. Moreover, it requires the parameters to fit in the memory of a single processor; this poses serious limitations on scalability when the number of parameters is in billions. In this paper, we present extreme stochastic variational inference (ESVI), a distributed, asynchronous and lock-free algorithm to perform variational inference for mixture models on massive real world datasets. ESVI overcomes the limitations of SVI by requiring that each processor only access a subset of the data and a subset of the parameters, thus providing data and model parallelism simultaneously. Our empirical study demonstrates that ESVI not only outperforms VI and SVI in wallclock-time, but also achieves a better quality solution. To further speed up computation and save memory when fitting large number of topics, we propose a variant ESVI-TOPK which maintains only the top-k important topics. Empirically, we found that using top 25% topics suffices to achieve the same accuracy as storing all the topics.
Teachers’ Strategies in responding students’ Minor Misbehavior (a Case Study in SMP Negeri 26 Samarinda)

This Study aimed to find out: (1) What students’ minor misbehavior appears in the classroom? (2) What strategies do teachers use to response the students’ minor misbehavior? This research used a qualitative study with case study design. The study involved the English teacher, 7th and 8th grade students on SMPN 26 Samarinda. Data were collected by observation class. To check the reliability of the data, triangulation was also used in this research. The researcher compared the accrued information with different relevant researchers and consulted the lectures implicated in the study. The findings showed that the common students' minor misbehavior that appeared in the class were: talking each other, clowning around, drumming, tapping a pen, sleeping, and daydreaming. Moreover, to responding the misbehavior, the teacher used a various strategy that were: ignoring, reminder in soft voice, calling on the student, public rule reminder, the look, facial expression, proximity and warning the consequence.
Software Development Process Ambidexterity and Project Performance: A Coordination Cost-Effectiveness View

Software development process ambidexterity (SDPA) is the ability to demonstrate both process alignment and process adaptability simultaneously. Realizing process ambidexterity has recently been suggested as an effective approach to improving the performance of software development (SD) projects. To understand the mechanisms underlying the effects of ambidexterity, we focus in this study on the mediating effects of coordination, one of the most important activity in SD projects. Specifically, we hypothesize a mediating effect of coordination costs and coordination effectiveness on the relationship between SDPA and project performance. We conducted a quantitative study involving 104 SD projects across 10 firms to test the model. The results strongly suggest that the positive relationship between SDPA and project performance is negatively mediated by coordination costs and positively mediated by coordination effectiveness. We validate our research model with a case study in an organization employing several hundred IT professionals and derive several practical implications on this basis.
Retraction: Early bearing fault diagnosis based on improved SFLA and ELM network

In this paper, an extreme learning machine (ELM) network based on an improved shuffled frog leaping algorithm (CCSFLA) is applied in early bearing fault diagnosis. ELM is a new type of single layer...
Resolving the Asymmetry of On-Exit versus On-Entry in Executable Models of Behaviour

For the UML, state charts are by far the most used modelling tools, both to communicate behaviour and to produce executable models.We investigate the inherent asymmetry of On-Entry and On-Exit Actions in UML Statecharts.We show first that the apparently simple and symmetric rules for handling the sequencing of On-Entry and On-Exit actions are hard to fully comprehend and apply effectively by software developers.Second, defining a semantics that results in executable models for applications such as reactive-systems and real-time systems is very delicate.Third, formal verification can be hampered because the semantics results in a combinatorial explosion of states.We evaluate the understandability of the semantics by taking out experiments with various tasks comprising sample UML Statechart and logic-labelled finite state machines (LLFSMs).Several experiments with software developers enable us to dissect how issues of understandability of state diagrams relate to nesting or event-driven vs logic-labelled.Since logic-labelled finite state machines achieve model composition through a subsumption architecture (suspend/restart/resume) we propose a specific alternative semantics for logic-labelled finite state machines that is suitable for robotic and embedded systems.
Development of Picture Book Media for Expressive Language Skills of Children With Autism

The expressive language skills of children with autism can be improved through teaching and learning. One of the factors supporting the success of teaching and learning process in improving the expressive language skills of children with autism is the selection of media which is in accordance with the characteristic of children with autism as target of learning. Children with autism are known to conceive information based on visual as their characteristic, so visual-based media will be more effectively in the learning process. This study uses the ADDIE (Analyze, Design, Development, Implementation, and Evaluation) development model that aims to develop visual-based media that can accommodate the expressive language skills of children with autism. The media developed is in form book-shaped media that contains a combination of writing and image illustration. This study produces a decent picture book media based on material conformity with curriculum, grammar used, media display, and communication theory used.
Application Focused on English Language Teaching for Children, with Speech Recognition and Synthesizing Capabilities

This project will present an application focused on the teaching of the English language to the children, this application being an important teaching tool, where the child can begin a cycle of learning a new language, something that will be very important in t heir training academic, and will serve in your professional future.In addition to showing how software is being developed and the resources used in it, this project is also concerned with presenting concepts such as: foreign language learning for children, voice recognition and synthesis, intelligent systems capable of recognizing and synthesizing the voice and the Java API Speech.To aid in English studies, the application makes use of illustrative images, themes, interactive questions, training mode, speech recognition and synthesis, which contributes to the development of writing and pronunciation in the language, mainly for making use of the resources of voice, which are the strongest point of this tool.
Peningkatan Kompetensi Microcontroller Siswa SMK

The increased interest in students in the field of Electronic Control is influenced by many factors, with family backgrounds, relationships, future expectations, development of science and technology and teachers. The implementation of the Community Service Program Based on Development of Foster Schools in Manba'ul Huda Dukuhseti, Pati Vocational School with the activities in the form of training and assistance in the use of electronic and computer software skills as additional knowledge in developing HR capabilities. Student Competency Improvement Training Related to Electronic and Computer Control Systems, namely programming and Arduino Uno application to create a led light control that can function according to a program made by each student. Through Arduino Uno training this makes science more for students in deepening microcontrollers. Keywords: electronic control, software, Arduino Uno
Effect of size-biased sampling on resource predictions from the three-part method for quantitative mineral resource assessment—A case study of the gold mines in the Timmins-Kirkland Lake area of the Abitibi greenstone belt, Canada:

First posted March 14, 2019 For additional information, contact: Director, Geology, Geophysics, and Geochemistry Science CenterU.S. Geological SurveyBox 25046, MS 964Denver, CO 80225 The three-part method for quantitative mineral resource assessment is used by the U.S. Geological Survey to predict, within a specified assessment area, the number of undiscovered mineral deposits and the quantity of mineral resources in those undiscovered deposits. The effects of size-biased sampling on such predictions are evaluated in a case study that involves gold mines from the Timmins-Kirkland Lake area of the Abitibi greenstone belt, Canada. The gold mines are divided, based upon the time of the assessment, into two groups: existing mines and future mines. The total produced gold for the existing mines are used to predict, with the three-part method, the total produced gold for the future mines. Then the predictions are compared to the known, total produced gold for the future mines. For comparisons using the mean, the predictions are 1.6 to 12 times too high, depending upon the time of the assessment and the probability density function characterizing the total produced gold in the existing mines. For comparisons using the median, the predictions are 1.3 to 10 times too high, depending upon the time of the assessment. The reason for these excessively high predictions is that the three-part method is based on the assumption that the total produced gold from the existing mines is representative of the total produced gold in the future mines; this assumption is inappropriate because of size-biased sampling. There is reason to be concerned that size-biased sampling adversely affected the resource predictions of previous U.S. Geological Survey assessments that were conducted with the three-part method.
Internet of Things based 5G Infrastructure for Securing Transportation Facilities in Smart Cities

The fifth generation (5G) is going to dominate in the smart cities where 5G provides all the facilities with maximum security and safety.Regarding transportation services, unnecessary accidents rates are increasing with cyber attacks and threats.All security issues for 5G based infrastructure will be facing many challenges such as secure transportation services which is one of the 2030 initiatives in many countries.Despite many security solutions, developing energy-efficient cryptographic algorithms are recommended to secure the future transport systems which not only improve the security but also reduce the cost.The main aim of this strategic research is to develop a secure transportation system using efficient security solutions which not only reduce the exorbitant accident rates but also increase safety system that enhances the livability of smart cities. Employing secure multi-level IoT and 5G based infrastructure used within the transportation systems will be an efficient method.In this system, appropriate applied cryptographic algorithms will be employed to improve transportation services.According to the research idea of this project, the proposed model of the future transportation system will be delivered with better security solutions.In expected results, the dynamic security solutions will be considered.They are vital requirements to minimize accidents and secure smart cities.This research will be leading us to implement an effective security solution for future transport systems.Therefore, each passenger who is driving or using driverless vehicles will be protected from the evolving attacks within the smart cities.
For Quantum and Reversible Computing, Intervals Are More Appropriate Than General Sets, And Fuzzy Numbers Than General Fuzzy Sets

Need for faster and faster computing necessitates going down to quantum level -which means involving quantum computing.One of the important features of quantum computing is that it is reversible.Reversibility is also important as a way to decrease processor heating and thus, enable us to place more computing units in the same volume.In this paper, we argue that from this viewpoint, interval uncertainty is more appropriate than the more general set uncertaintyand, similarly, that fuzzy numbers (for which all alpha-cuts are intervals) are more appropriate than more general fuzzy sets.We also explain why intervals (and fuzzy numbers) are indeed ubiquitous in applications.
Aplikasi E-Task Berbasis Student Center Learning Pada Matakuliah Manajemen Proyek Sistem Informasi

At present, there are still many colleges that use Teacher Center Learning (TCL) based learning models. And this has resulted in the low interest of students in following the learning process. The result of the above problem is the decline in student learning outcomes. This study aims to develop a learning media based on Student Center Learning (SCL), by using an E-Task application that can improve students' results and independence in Information Systems Project Management subjects. Where in this case the student will use the E-Task application in working on assignments given by Lecturers independently or in groups, so that in terms of increasing student independence. This research uses the basic concept of system development life cycle (SDLC) system development which is described by the UML system design tool. The designed system flow is illustrated by global design, namely Use Case Diagram. The final result of this research is an E-Task application that is designed attractively so that students are expected to be able to independently carry out their duties and give their assignments to Lecturers anywhere and anytime. With this application students are able to actively work together, so that assignments given to students can be completed quickly, and this application can improve student learning outcomes.
THE EFFECTS OF READING METHOD ON THE COMPREHENSION PERFORMANCE OF THIRD SEMESTER STUDENTS OF BALIKPAPAN FOREIGN LANGUAGE ACADEMY

In this study the researcher investigated the relative effects of different reading methods on the comprehension performance of Balikpapan Foreign Language Academy EFL Third Semester students. The scores of participants who read three comparable passages in three ways (oral, silent and subvocalizing) were compared. Results revealed a significant difference between oral reading and subvocalization, and between oral reading and silent reading. Oral reading had the greatest effect on comprehension performance among the three reading methods examined. All groups reported that oral reading was the most preferred reading method with the majority of respondents feeling the style best supported comprehension. Feedback suggested that oral reading was preferred specifically because it helps in memorizing words and texts, concentration, and practicing and pronouncing words for real world encounters. It is recommended that second language lecturers and students use all available reading methods in order to identify which method best serves their study objectives.
Visual Tracking using Spatial-Temporal Regularized Support Correlation Filters

Support Correlation Filters (SCFs) have recently shown great potentials in real-time visual tracking. However, most of existing SCF trackers learn appearance models using the information of current frame, and completely neglect inter-frame information. Besides, they still suffer from unwanted boundary effects. In this paper, we proposed a novel Spatial-Temporal Regularized Support Correlation Filter (STRSCF) model, which introduces the spatial weight and temporal regularization term into SCF model. In order to improve the tracking performances, we extend STRSCF to multi-dimensional feature space. In addition, an effective optimization algorithm is developed to solve our STRSCF model in closed form solution. The experimental results on OTB-13 demonstrate that the STRSCF tracker performs superiorly against several state-of-the-art trackers in terms of accuracy and speed.
A shell and kernel descriptor based joint deep learning model for predicting breast lesion malignancy

Predicting lesion malignancy accurately and reliably in digital breast tomosynthesis is critically important for breast cancer screening. Tumor shape and interactive effect between the tumor and surrounding normal tissue are two of the most important indicators in radiologists’ reading. On the other hand, the density and texture of region within the tumor also play an important role in malignancy classification. Inspired by the above observations, shell and kernel descriptors were proposed in this work for breast lesion malignancy prediction, in which the shell descriptor is used for describing the tumor shape and surrounding normal tissue while the kernel descriptor is used to describe the internal tumor region. A joint deep learning model based on the AlexNet was designed to learn and fuse features from shell and kernel. Additionally, to obtain more reliable predictive results, a multi-objective optimization algorithm and a reliable classifier fusion strategy were used to train the predictive model and optimally combine outputs from both shell and kernel descriptors. In this study, 278 malignant and 685 benign cases were used through 2-fold cross validation. Compared with the single descriptor based models using either shell or kernel, the experimental results demonstrated that the combined shell and kernel descriptors can capture the most important features and the corresponding predictive model achieved the best performance as well.
THE PHENOMENON OF PROVINCIALITY IN THE CREATIVE ACTIVITY OF V. MAKANIN

The article is aimed at revealing the creative activity of the famous Russian writer V. Makanin in the context of provinciality problem.This phenomenon is defined as the property of personality and his existential self-identification in the modern society.It was found out that the ambivalence of attitude of Makanin heroes (as provincial and metropolitan) not only exposes their deep internal conflict but also becomes a peculiar indicator of their evolution.It is the phenomenon of provinciality clearly apparent in the works of the writer that predetermined his artistic discoveries and the possibility of forecasting the future of the country.
Spatial Error Concealment by Jointing Gauss Bayes Model and SVD for High Efficiency Video Coding

This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduces estimation error. A new recovery order based on confidence is established to resolve the error propagation problem. Compared to other state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations. It also has significantly lower complexity.
Learning Natural Programs from a Few Examples in Real-Time

Programming by examples (PBE) is a rapidly growing subfield of AI, that aims to synthesize user-intended programs using input-output examples from the task. As users can provide only a few I/O examples, capturing user-intent accurately and ranking user-intended programs over other programs is challenging even in the simplest of the domains. Commercially deployed PBE systems often require years of engineering effort and domain expertise to devise ranking heuristics for real-time synthesis of accurate programs. But such heuristics may not cater to new domains, or even to a different segment of users from the same domain. In this work, we develop a novel, real-time, ML-based program ranking algorithm that enables synthesis of natural, user-intended, personalized programs. We make two key technical contributions: 1) a new technique to embed programs in a vector space making them amenable to ML-formulations, 2) a novel formulation that interleaves program search with ranking, enabling real-time synthesis of accurate user-intended programs. We implement our solution in the state-of-the-art PROSE framework. The proposed approach learns the intended program with just {\em one} I/O example in a variety of real-world string/date/number manipulation tasks, and outperforms state-of-the-art neural synthesis methods along multiple metrics.
Analysis of the extraction accuracy of the corner point of the camera using polarization imaging

In view of the traditional method of corner extraction, the main idea is to improve the corner extraction algorithm and ignore the imaging process of the calibration image, in this paper, an optimal polarization angle image corner extraction algorithm based on linear polarization feedback is introduced in the process of camera calibration, this method is mainly aimed at the problem of high-light regions which are difficult to detect and eliminate in the multi-position calibration image of space under natural light. The method firstly adopts the linear feedback of Stokes variable through a CCD camera with polarizing plates and obtains the corner image of the optimal angle of the checkerboard lattice in different positions in space, then we use the sub-pixel level detection algorithm and GAUSS's fitting method to precisely locate the corner points in the image and to solve the sub-pixel coordinates of the image corner, at last, the two-dimensional pixel coordinates of the corner points in each checkerboard image are extracted.