Reviewerly/exHarmony huggingface dataset:
Quality Control of Cigarettes Packaging using Convolutional Neural Network

The tobacco industry is a source of incomes for our country. Currently, cigarette consumers in Indonesia are already very high. Quality is considered as an essential point for every industry to satisfy its customers. However, some problems often occur in the Industry. Including the decline in cigarettes quality. One of the reasons for the decline in the quality of cigarettes is the lack of selection of the sorting system on cigarette packaging. The lack of quality of the packaging system makes the quality of cigarettes decreased. In a tobacco company in East Java, the fetching of defective products is still done manually. Based on the problem, the author takes that study case as the background of this research. In this system, the packs of cigarettes will be detected and selected using image processing. On the image processing will be implemented as a method of convolution neural network (CNN) this algorithm has a function to detect texture from cigarette packaging. If there is non-conforming or defective cigarette packaging, the rejector will be active and discard the cigarette packaging and it will not enter into the next packing process. The result of training data from this study had a success rate of 96.06%. When using real-time data, the system is able to classify 148 data from 154 existing data correctly. The real-time system success rate is 94,59%.
Student Preference for Opting Biology and Computer Science at Secondary Level

This study entitled student preferences for opting Biology & computer Education at secondary level was a descriptive survey type study. All the students enrolled in Islamabad Model Colleges of Boys and Girls (IMCB, IMCG) at S. S. C level in district Islamabad were the population of this study. A systematic random sampling was selected from all the IMCB & IMCG (total eight) presently offering biology and computer education. Two sections of grade 9th & 10th were selected randomly. A systematic random sample of students was selected by taking the students whose roll number was multiple of two. The objective of the study was to explore the factor’s which effect student’s preference for biology & computer science, & to find out the difference among the boys & girls preference. The data was collected through a questionnaire. Main findings of the study were as follows:- students awareness of the future prospects of the subject there self-interest, & to some extent parents influence & family trend are the major deciding factor in students choice of the subjects. However the view that biology is a girl subject & computer is a boy subject do not prevail among students not at least among male students hence it is not a deciding factor. Factors such as lack of computer lab facilities, difficulty in mathematical & theoretical aspects of computer education made students to decide against opting for the subject. While the descriptive nature of biology, cramming the classifications & terms, handling of animals & plants, are some of the major factors affecting students to decide against the choice of biology. It is recommended that less stress should be given to cramming in biology & the subject should be made more interesting & creative to enhance student in the subject while more facilities should be provided particularly in female schools for computer education. Computer should studied as a compulsory tool of education, rather as an elective subject.
Multi-Hypothesis Approach for Efficient Human Detection

Detection of human beings in a complex background environment is a challenging task in computer vision. Most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. In this paper, we propose a new feature extraction technique that combines three types of visual information; shape, color, and texture, and is named as the Color space Phase features with Gradient and Texture (CPGT) algorithm. Gradient concept and the phase congruency in color domain are used to localize the shape features. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to extract the texture information of the image. Fusing of these complementary features yields to capture a broad range of the human appearance details that improves the detection performance. The proposed features are formed by computing the gradient magnitude and CSLBP values for each pixel in the image with respect to its neighborhood in addition to the phase congruency of the three-color channels. Only the maximum phase congruency magnitudes are selected from the corresponding color channels. The histogram of oriented phase and gradients as well as the histogram of CSLBP values for the local regions of the image are determined and concatenated to construct the proposed descriptor. Principal Component Analysis (PCA) is performed to reduce the dimensionality of the resultant features. Several experiments were conducted to evaluate the performance of the proposed descriptor. The experimental results show that the proposed approach yields promising performance and has lower error rates when compared to several state of the art feature extraction methodologies. We observed a miss rate of 2.23% in the INRIA dataset and 2.6% in the NICTA dataset.
Modelling and hardware implementation of quantization levels of digital cameras in DCT based image compression

In today’s world of computation, data compression takes vigorous part in most of the real–time and embedded system applications. Some of such end uses are essentially derived for specific task, however their realization are not described and delineated. To bridge this variance, an attempt is made to experimentally present an evidence behind usage of vector quantization levels of 50 in popular digital cameras, generating other vector quantization levels. This has been incorporated to reduce the trade–off between quality and size of reconstructed images by the use of vector quantization levels less than quantization level of 50. The prime objective of the work encompasses the generation of vector quantization levels from 10 to 75 for digital cameras in Discrete Cosine Transform (DCT) based image compression and analyzing the performance of proposed algorithm efficiency through parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), Bits per Pixel (bpp) and percentage Space Saving by applying it to standard test images. In this exploration, vector quantization level of four digital cameras namely Canon PowerShot A700 (Superfine), Fuji FinePix A700 (Fine), Nikon CoolPix P5000 (Fine) and Sony Digital Single Lens Reflex (DSLR) A700 (Fine) have been used. The experiment is also featured with the modeling and deploying of algorithm onto BeagleBone Black, Raspberry Pi low cost hardware devices along with presenting the comparative performance evaluation amongst cameras considered.
Collecting and Controlling Distributed Research Information by Linking to External Authority Data - A Case Study

With respect to the world wide web, scientific information has become distributed and often redundantly held on different server locations. The vision of a current research information system (CRIS) as an environment for constant monitoring and tracking of a researcher’s output has become vivid, but still fighting with issues like legacy information and institutional repository structures to be established yet. We therefore suggest to gather those scattered research information through identifying its authors by means of authority data already associated with them. We introduce author pages as a proof-of-concept application collecting research information not only from a local source such as an institutional repository, but also from other external bibliographic sources.
Predicting Hospital Length of Stay Using Neural Networks

Accurate prediction of hospital length of stay can provide benefits for hospital resource planning and quality-of-care. We describe the utilisation of neural networks for predicting the length of hospital stay for patients with various diagnoses based on selected administrative and clinical attributes. An all-condition neural network, that can be applied to all patients and not limited to a specific diagnosis, is trained to predict whether patient stay will be long or short in terms of the median length of stay as the cut-off between long and short, and predicted at the time the patient leaves the intensive care unit. In addition, neural networks are trained to predict whether patients of 14 specific common primary diagnoses will have a long or short stay, as defined as greater than or less than or equal to the median length of stay for that particular condition. Our dataset is drawn from the MIMIC III database. Our prediction accuracy is approximately 80% for the all-condition neural network and the neural networks for specific conditions generally demonstrated higher accuracy and all clearly out-performed any linear model.
On the performance of modern digital grammar checkers for native and second language learners

Abstract In this study we conducted an experiment aiming to compare the performance of language learners and digital grammar checkers at supplying gender marking in French. A set of test items exhibiting typical gender marking configurations was submitted to three grammar checkers for French (Antidote, Scribens and BonPatron). The outcomes were compared to those of native speakers and second language learners of French at a B1 level. The results revealed that only Antidote and Scribens outperformed both native speakers and second language learners of French in adjective-noun and fronted noun-past participle agreement constructions. An opposite pattern, however, appeared for clitic-past participle constructions for which native speakers outperformed Antidote and Scribens. We thus conclude that from the three grammar checkers under investigation, Antidote and Scribens might be effective to improve the native speakers’ and second language learners’ awareness of gender marking errors, but only in adjective-noun and fronted noun-past participle agreement constructions.
Four preconditions for solving MC4 machine consciousness

A machine is MC4 conscious if it has phenomenal experiences that are comparable to human conscious experiences. From an ethical point of view it is important to know whether we have created MC4 consciousness in a machine. MC4 consciousness research can also contribute to the development of general theories of human consciousness. This paper discusses four problems that have to be solved before we will be able to address MC4 machine consciousness in a systematic way: We need more clarity about the measurement of consciousness, we need better ways of describing the physical world and consciousness, and we need to reach agreement about the final form that a theory of consciousness should take. When these problems have been addressed we will be able to develop scientific theories of consciousness that can make accurate believable predictions about MC4 consciousness in machines.
Politeness, Intercultural Communication, E-Mails

In this chapter, the focus shifts from oral communication to written communication, considering the sometimes controversial issue of politeness in written intercultural communication. Firstly, a definition of politeness is discussed; secondly, some key issues related to the topic of intercultural communication are considered; and finally, intercultural e-mail communication is introduced. The aim is to understand how intercultural communication, mediated by the internet, can create compelling and sometime challenging conditions for additional language learning. For example, university students use e-mails a lot. Nonetheless, some issues, amongst which the level of formality when they address university staff, may vary, depending on their own national culture and on the training they receive on the use of this medium. As a result, intercultural clashes may occur. To facilitate foreign language teaching and communication, an instrument to assess politeness in intercultural e-mail communication in an intercultural context will be presented.
iBudaya.id: Indonesian Culture Recognition based on Software as a Service (SaaS)

The problem raised in this research is the high cultural crisis experienced by thepeople of Indonesia, especially the younger generation who prefer foreign culture rather thanthe local culture. This research seeks to change the perception of old fashioned local culture bycombining local culture with the development of information and communication technologywhich is now widely used by the society of all circles. The method used in this study is contentcrowdsourcing because it requires the help of outsiders to fill the content and data that will bemanaged later in large volumes. Therefore, it will use cloud computing, Software as a Service(SaaS). This research has successfully developed a portal that can accommodate cultural valuesand local wisdom in the form of multimedia content.
Development and testing of a smartphone adapter for image quality assurance and whole slide imaging

Background: When running large trials, histopathology services are used to assess the state of a tissue. However, in many clinics in low resource settings there are large variations in quality of such services, specifically in biopsy processing and histopathological interpretation/assessment of images. Quality assurance (QA) is needed, but it involves physically mailing slides to a remote clinic. A telemedicine solution can address this challenge. Methods: A novel smartphone adapter for microscopes was developed, consisting of a 3D printed attachment and software integration for the image capture. The attachment is used to couple the eyepiece of a low end microscope to a smartphone (Samsung J530). Image capture was controlled through the EVA System app. The entire system was characterized optically using standard calibration targets. Additionally, images captured on the attachment were compared to the standard method of shipping and scanning slides in a high end slice scanner at a remote clinic. Results: The resolution of the entire system (microscope + phone) with a 40X objective was &lt;1 &mu;m. The system is currently undergoing testing in Nigeria as part of a broader cervical cancer screening study.<sup>1</sup> Preliminary testing showed similar image quality between the smartphone-based system and high end scanner. Whole slide imaging requires stitching together images into a mosaic, made possible by a mobile application. Conclusion: The results here show that coupling a low end microscope to a smartphone yields similar results to a transporting slides to a high end microscope. Such an attachment can thus potentially provide a telemedicine solution to researchers in low resource settings.
An empirical investigation into code smells rectifications through ADA_BOOSTER

Object Oriented Programming has become one of the most established paradigms. It offers us features like encapsulation, polymorphism, inheritance etc. By using these features we are able to develop good software’s that are easy to understand. But when the size of the software goes on increasing it poses problems with respect to maintaining the source code. Also we come across the problems like code scattering and code tangling. So object oriented programming has some limitations and this is where Aspect Oriented Programming comes into play. Refactoring is one of the most important activities in software development. It is done to improve the design of the software, to make the software easier and better to understand and to help us in writing programs faster. After the software is refactored, it is important to note the behaviour of that software. Here, we propose new refactorings technique. The refactorings are identified. They are applied on the Systems. The ADA_BOOSTER technique to measure the metrics. By using refactoring we can change the existing software without affecting the behaviour of the software.
A Bayesian nonparametric model for upper record data

This paper proposes a nonparametric Bayesian approach using a Dirichlet process mixture model with Pareto kernels to estimate the density of observed upper record values and predict future upper record values. A reference distribution, G0, in the nonparametric Bayesian model is provided on the basis of an objective prior for unknown parameters of the Pareto distribution to avoid difficulties caused by finding values of hyperparameters in G0. For the posterior computation, two sampling algorithms are employed and compared. Finally, the approach provides the estimated distribution of observed upper record values and prediction of future upper record values in two real problems, average annual temperatures and carbon dioxide emissions.
ANALISIS BUTIR SOAL ULANGAN AKHIR SEMESTER MATA PELAJARAN BAHASA INDONESIA KELAS XI

The analysis uses SPSS Version 22 and Anates program version 4. Results study, (1) quality of multiple choice questions is very difficult with 12 questions, difficult to number 25 items, the number is 6, and easy to 7. There are 3 questions, and 2 items are easy, (2) the quality of multiple choice questions is based on the distinguishing power, there are 32 items, the questions have enough differentiating power of 16 points, and items that have a negative differentiation are 2 questions; the description of the distinguishing power is quite 3 questions, and discriminating power is 2 questions, (3) quality of multiple choice questions is based on validity; valid 31 questions, 19 invalid questions. The description problem, classified as valid 5 questions, (4) quality of multiple choice questions based on reliability, has a high reliability coefficient of 0.898, while the problem of unreliable descriptions with a coefficient of 0.093
Using an Intelligent Tutoring System to Teach Red Black Trees

There are many tutoring systems that are being used to teach basic concepts in grade schools and high schools. There are few that address the issues involved in teaching complex skills and concepts such as are taught in CS2. We designed, developed and implemented an intelligent tutoring system (ITS) to help teach the concepts underlying red black trees. Red black trees are a balanced tree structure that are created and maintained with fairly complex insertion and deletion algorithms. The ITS helped the students improve their understanding of red black trees and also changed the instructors view of the causes of the students' difficulties. This paper describes effects on student learning, the lessons we learned from using the ITS and how it affected the way in which teach red black trees in our class. We have used the ITS for four years in our data structures class and it has benefited us in many ways.
A logical framework for view updating in indefinite databases

Abstract This paper introduces and studies a declarative framework for updating views over indefinite databases. An indefinite database is a database with null values that are represented, following the standard database approach, by a single null constant. Typically a database is represented by a single set of facts $D$ that model what is known to be true. This paper proposes a model of an indefinite extensional database that is more expressive with respect to the closed-world assumption (CWA) adapted for the setting of indefinite databases (Libkin, 1995, A semantics-based approach to design of query languages for partial information. In Semantics in Databases, vol. 1358 of LNCS, pp. 170–208. Springer; der Meyden, 1998, Logical approaches to incomplete information: a survey. In Logics for Databases and Information Systems, pp. 307–356. Kluwer Academic Publishers, Norwell, MA, USA). More specifically, in our model, databases are determined by two sets of facts: $D$ representing, as usual, all facts that are true in the database, and $E$ that is meant to represent exceptions to the ‘unknown range’, i.e. facts that cannot be unknown. Intuitively, unless they are explicitly implied to be true by the first set, the facts specified as exceptions are assumed false. The semantics is given by means of a two-level CWA tailored to the case of indefinite information. The paper characterizes the semantics of indefinite databases in terms of their possible worlds that are obtained by instantiating occurrences of null values by concrete constants and defines several classes of database repairs that realize view-update requests. Most notable is the class of constrained repairs. Constrained repairs change the database ‘minimally’and avoid making arbitrary commitments. They narrow down the space of alternative ways to fulfill the view-update request to those that are grounded, in a certain strong sense, in the database, the view and the view-update request.
Digitalized Evaluation of Welder Skill by using Cyclogram Characteristics

This paper proposes a new evaluation method for welder skill in Gas Metal Arc Welding (GMAW) process in term of studying the natural hand-movement that affect the signal processing. Weld quality of GMAW generally depends on welder skill to maintain the uniform of hand movement. Therefore, the welder skill is considered as the critical point to maintain the weld quality. Hence, welding current and voltage signal could be an alternative way for monitoring and assessing the skill of welder based on the signal variation of the welding process. This research defines in two stages, first is the physical-simulation using robot welding Fanuc Arc Mate 100iB and monitoring the signal using Cyclogram technique. Second is comparing the Cyclogram characteristic of robot welding and manual welder. By using the data acquired, the characteristic of Cyclogram was analyzed by varying Torch angle change (W1) and Torch-height change (W2) to investigate the signal processing. Furthermore, the data of current and voltage were generated as a quantitative method to determine the size of Cyclogram. The results show that the method capable of differentiating the two beginner welders compare to the robot welding performance based area of Cyclogram characteristic. Finally, the Cyclogram could be a novel tool for monitoring and evaluating the welder skill with high sensitivity to detect hand motion.
A novel method for 3D measurement of RFID multi-tag network using a machine vision system

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks.In this paper, a novel method for 3D coordinate measurement of RFID multitag networks is proposed.A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles.The iterative threshold segmentation and the morphological filtering method are used to process the images.The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag.After that, the 3D coordinate of each tag is obtained.Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance.In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.