Reviewerly/exHarmony huggingface dataset:
The Student's Error in Forming Nominal Clause in Complex Sentence

This  research  uses  descriptive  qualitative  method  that  aims  at  analyzing  the  errors  in  the formation of nominal clause in complex sentence conducted by students of the fourth semester of the English Language Study Program, Tadulako University. Data collection was taken through giving tests to 49 students. The test was given in the form of a written test by asking students to make nominal clause. The results showed that the most dominant type of error made by students in making nominal clauses is omission, which is to reduce parts of words such as suffix -s as plural marker on nouns or as present marker on verbs. In connection with the functions of nominal clause in sentence, the students have difficulty in making nominal clause that functions as complement and object of preposition.
Building a Winning Business Case for Standards Adoption

In a recent issue of Ieee Engineering Management Review, we explored the problem of building a winning business case and the elements that can differentiate an “average” business case from a “winning” one. For this second installment, we will show how these winning elements were applied in practice. To make it even more interesting, we will illustrate how this can be accomplished with arguably one of the most difficult cases to make: adoption of technical standards. Our stakeholder is a large power utility.
Training Digital Literation by Digital Book Format Electronic Publisher Themes of Climate for Junior High School Students

This electronic document is a "live" template and already This study aims to train the digital literacy of seventhgrade junior high school students with electronic books in electronic publisher format.The book used in this study has been validated previously included in the category of very feasible.The design used in this study was the one-group pretestposttest design.The instruments used in the study were observation sheets for the implementation of learning, student response questionnaires, and about the pretest and posttest.Data collection methods are observation, questionnaire, and pretest-posttest.The data analysis technique uses the Likert scale and N-Gain.The subjects of this study were 20 students at SMP Negeri 22 Surabaya.The results of the research on digital literacy abilities obtained by students based on the N-Gain score found 13 students experienced an increase in the high category and 7 students including the medium category.Student responses to the use of ebook books on average the results of students' responses to digital books by 97.5% like the use of ebooks
The Session Inventory Module within an Intelligent Tutoring System for Data Normalization

The knowledge acquisition of data normalization has been commonly perceived as a hurdle that is challenging students in the beginning database classes. As an effort to provide students with additional assistance while learning this topic, an intelligent tutoring system is proposed and implemented that can be used as a virtual private tutor to teach students in a one-on-one manner. This paper describes the strategically design and management of the tutorial sessions within this system. These sessions are designed and maintained according to their theme topics and difficulty levels so that the virtual tutor can dynamically select sessions based on the assessment of a student’s knowledge level and progress.
New Attack Potential Measurement Method to Kaizen Event for Web Application Security Vulnerabilities

With recognition of the importance of web application security, there is a need for research on an action program for measurement and improvement of web application security. Therefore, the main purpose of this study was to formulate a Kaizen program suitable for measurement and improvement of web application security vulnerabilities. An improvement working procedure is introduced to implement the Kaizen program. Further, an augmented attack potential measurement method is proposed to measure the effectiveness of the formulated Kaizen program. The proposed new attack potential measurement method is considered to be an umbrella under which several novel techniques and methods are included, such as OWASP’s web application security vulnerabilities assessment method, ISO/IEC 18045 attack potential ratings method and fuzzy evaluation method. The numerical results of an example are presented to show that the augmented attack potential measurement method is not only comparable but also distinguishable. It is more reasonable and effective than that of the traditional method for measuring web application security improvement. Finally, conclusions are made and suggestions for future work are proposed. To cite this document: Kuo-Sui Lin, "New Attack Potential Measurement Method to Kaizen Event for Web Application Security Vulnerabilities", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.89-112, 2019. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1536
Intelligent sensing, neural computing and applications

In this paper, an adaptive neural tracking control approach is presented for a class of non-affine nonlinear systems with finite-time output constraint. The non-affine form of the original system is converted to the affine form by means of the mean value theorem. To constraint the output tracking error within a predefined boundary in finite time, a modified performance function, i.e., finite-time performance function, is introduced. During the process of controller design, the dynamic surface control is employed to handle the ‘explosion of complexity’ problem occurred in the conventional backstepping method. According to the approximation of radial basis functions neural networks, an adaptive neural control scheme is developed which guarantees that the output tracking error is preserved within a specified prescribed performance and all the signals in closed-loop system are bounded by appropriately choosing the design parameters. The stability of the closed-loop system is proved by Lyapunov stability analysis and the simulation results are proposed to demonstrate the effectiveness of the developed control approach.
Histopathology color image processing in prostate carcinoma

The interpretation of tissue images is the first step performed by the pathologist before render an accurate diagnosis. Digital Pathology is challenging to implement in developing countries since, due to the acquisition process, these images usually become low illuminated and contrasted, which significantly affects their analysis. Transformation of these low-quality images would be needed to improve the pathologist diagnosis process. This article aims to show the results obtained from a test carried out by three pathologists on a series of classic color manipulation algorithms that improve the information (i.e., nuclear shape, stroma, and gland formation) contained in prostate images. For this study, images from prostatic tissue supplied by the Instituto Caldense de Patologia (ICP) were used. Seven biopsy samples were used, each of them captured with 10x, 20x, and 40x magnifications, forming a total of 50 images. Then, a subjective quality test was performed by three pathologists. This test consisted of grading a series of 42 classical image processing algorithms on a scale of 0 to 4. The value 0 represents the worst rating (the new image did not provide any information), and 4 represents the best rating (the original image highlights exciting diagnosis features and offers essential information). 52.27% of the transformations were classified as useful (scores between 3-4) by the pathologists (23 of 44 in total). The color transformations succeeded in highlighting the different structures such as the cytoplasm, stroma, and nucleus, improving the perception of contours and shapes, which will help to render an accurate diagnosis.
Improving The Progress Monitoring Task Of Agile Kanban Method: An Enhanced Theoretical Framework And Its Implication

Several studies claimed that software project management (SPM) failed in realizing software projects due to inadequate progress monitoring.Indeed, the successful implementation of software projects relies entirely on effective monitoring approaches. Recently, the adoption of Agile Kanbanmethod in SPM is continuously on the rise despite its shortcoming in its progress monitoring task. Therefore, this paper aims to enhance the theoretical framework for improving the progress monitoring task of Agile Kanban method and highlight its implication on SPM domain. To achieve this aim, an exploratory research design was employed to identify the problem, review the related literature, and to develop the enhanced framework. The findings confirm the highly need of improving the monitoring task of Agile Kanban method, which would assist project managers to effectively monitor the development process of software projects. Accordingly, the results of literature have been utilized to construct an enhanced theoretical framework, thus delivering software products on time within budget could be achieved. Practically, it is hoped that the framework can provide a guideline for developingmodels focus on improving the progress monitoring task.
Side-information-induced reweighted sparse subspace clustering

Subspace clustering segments a collection of data from a union of several subspaces into clusters with each cluster corresponding to one subspace. The geometric information of the dataset reflects its intrinsic structure and can be utilized to assist the segmentation. In this paper, we propose side-information-induced reweighted sparse subspace clustering (SRSSC) for high-dimensional data clustering. In our method, the geometric information of the high-dimensional data points in a target space is utilized to induce subspace clustering as side-information. We solve the method by iterating the reweighted $ l_1 $-norm minimization to obtain the self-representation coefficients of the data and segment the data using the spectral clustering framework. We compare the performance of our proposed algorithm with some state-of-the-art algorithms using synthetic data and three famous real datasets. Our proposed SRSSC algorithm is the simplest but the most effective. In the experiments, the results of these clustering algorithms verify the effectiveness of our proposed algorithm.
MoNA: Mobile Neural Architecture with Reconfigurable Parallel Dimensions

Emerging mobile deep neural networks (DNNs) are prevalent in resource-limited devices because of their shrunken parameter size and reduced computation. However, when deploying mobile DNNs on conventional DNN accelerators, there will be a performance loss due to the reduction of processing element (PE) utilization. Conventional accelerators are generally designed base on a fixed pattern of data reuse, but the data reuse opportunities of different layers in mobile DNNs are diverse. To process mobile DNNs with high performance, we propose an architecture called MoNA (Mobile Neural Architecture), which includes a flexible dataflow and a reconfigurable computing core. The dataflow efficiently supports reconfigurable processing parallelism to maximize the PE utilization of mobile DNNs. The computing core is a 3D PE array, which supports the dataflow and avoids high bandwidth requirement.
FORECASTING ACCEPTANCE OF NEW STUDENTS USING DOUBLE EXPONENTIAL SMOOTHING METHOD

Forecasting is the activity of estimating what will happen in the future. There are several methods that can be used in the forecasting process, including double exponential smoothing. In this research, forecasting is done to predict the number of new student admissions in a study program at a university by using the double exponential smoothing method. The results of the study are based on admission data for 10 years, the best forecasting value is obtained using the parameters α = 0.8 and β = 1, with a Mean Percentage Error (MPE) value of 0.1172.
The Latent Semantic Power of Labels: Improving Image Classification via Natural Language Semantic

In order to address the problem that numerical labels are difficult to optimize, one-hot encoding is introduced into image classification tasks, and has been widely used in current models based on CNNs. However, one-hot encoding neglects the textual semantics of class labels, which closely relate to image characteristics and contain latent connections between images. Inspired by distributional similarity based representations in Natural Language Processing society, we propose a framework by introducing Word2Vec into classic CNN models to improve image classification performance. By mining the latent semantic power of classes labels, word vector representations participate in the classification model instead of the traditional one-hot encoding. In the evaluation experiments implemented on data sets of CIFAR-10 and CIFAR-100, a series of representative CNNs have been tested as the feature extraction component for our framework. Experimental results show that the proposed method has revealed compelling ability to improve the classification accuracy.
A Survey Paper on Gender Classification using Deep Learning

With technological advancements many small to large, simple to complex activities are automated. Growth of Artificial Intelligent techniques has eased the way we would look to solve the real-world problems. One such area which has recently gained lot of attention is the biometric analytics like Face Recognition, Fingerprint, voice etc. It involves extracting features such as face expressions, gender, age etc. Gender information plays a vital role in areas such as human computer interaction, crime detection, gender preferences, facial biometrics for digital payments etc. This paper proposes gender recognition using facial images and fingerprints with different algorithm like Visual Geometry Group-VGGNet16?, Segmentation based Fractal Texture Analysis (SFTA) etc.
Research on the Application of Students' Answered Record Analyze Model and Question Automatic Classify Based on K-Means Clustering Algorithm

In the era of smart education, computer assisted instruction (CAI) has been a hot research topic in the field of smart education. In the current study, most of the questions answered by students are summarized and classified by labor. The teachers classify the students' scores by hand according to the students' answer records in the examination, and then propose individualized instructional programs for students of the same type in view of their deficiencies in the knowledge points corresponding to the exam. Due to the large workload and the influence of teachers' experience, it is difficult to guarantee the efficiency and quality of manually classifying students' answered records. In the paper, we propose a model named CMD based on K-means clustering algorithm in order to analysis students' answer record. The model can automatically classify the test questions according to correct or wrong, modified times and the difficulty level of the students' answers. The experimental result shows that the CMD model can effectively realize the automatic classification of test questions, which not only reduces the labor consumption of teachers, but also has high accuracy.
A Study on Impact of Social Media on Youth

The extensive use of social media in India has been on the rise among the new generation youths. In today’s world, use of social media has become an integral part of everyday life of human being. This paper throws a light on pattern of social media usage and its impact on youth. The new age social networking culture has been accepted and got an enthusiastic response. It is evident from the studies that social media has both positive and negative impact on youths.
Intelligent SOMA interactive gaming system

The traditional SOMA cube is a solid dissection puzzle and an educational toy. However, to complete assembly SOMA cube is a difficult challenge for most children and even adults. In this paper, we developed smart electronic intelligent SOMA (iSOMA) blocks that can self-detect each other’s position and contact surface. We also have developed an interactive gaming system based on virtual interactive technology. This interactive gaming system can quickly teach children how to quickly assemble iSOMA blocks and let them learn math and geometry problems through the game. This technology, in addition to being used in assembling puzzles, can also be used in the assembly of real objects in life.
Neutrosophic Cognitive Maps for Situation Analysis

There are various factors which lead to the criminal behaviour in humans. Prominent re-searchers monitoring the situation of crime in Nigeria cite poverty, unemployment, family-breakdown, bribing & corruption,lack of co-operation from public and negative perception of police to be the major causesbehind criminal behaviour.The factors likeunderemployment, inadequateequipment, NGOs are not taken into accountby the researchersbecause theseare considered to beindeterminate.To show how these indeterminate factors are actually related to crime in Nigeria we model the situation mathe-matically using FCMs and NCMs.The work also shows how efficient is the technique of Neutrosophic Cognitive Maps (NCM) against Fuzzy Cognitive Maps (FCM) to deal with the uncertainties and inde-terminacyinSituationAnalysis.The obtained results are interpreted which demonstrate the im-portance of indeterminate factors in analysing the situation of crime in Nigeria. This shows how inde-terminate factors when taken into consideration could enhance the accuracy and efficiency of mathe-matical models using the concept of Neutrosophic Cognitive Maps.
Reliability Aware Cost Optimization for Memory Constrained Cloud Workflows

Due to the increasing number of constituting jobs and input data size, the execution of modern complex workflow-based applications on cloud requires a large number of virtual machines (VMs), which makes the cost a great concern. Under the constraints of VM processing and storage capabilities and communication bandwidths between VMs, how to quickly figure out a cost-optimal resource provisioning and scheduling solution for a given cloud workflow is becoming a challenge. The things become even worse when taking the infrastructure-related failures with transient characteristics into account. To address this problem, this paper proposes a soft error aware VM selection and task scheduling approach that can achieve near-optimal the lowest possible cost. Under the reliability and completion time constraints by tenants, our approach can figure out a set of VMs with specific CPU and memory configurations and generate a cost-optimal schedule by allocating tasks to appropriate VMs. Comprehensive experimental results on well-known scientific workflow benchmarks show that compared with state-of-the-art methods, our approach can achieve up to 66% cost reduction while satisfying both reliability and completion time constraints.
A Target Tracking Algorithm Based on Multi-Feature Fusion

In order to improve the tracking accuracy of fast discriminative scale space tracking (fDSST) algorithm when the target is seriously occluded and rotated, this paper proposes a target tracking algorithm based on multi-feature fusion. In this algorithm, the fusion features are used to obtain more feature information of the target, and a high confidence strategy is introduced to reduce the probability of model drift when the target is obscured. OTB video sequence is used to test the algorithm, and compared with the other two tracking algorithms. The experimental results show that the algorithm proposed in this paper performs well when the target is seriously obscured and rotated.
BARIUM AND LEAD LEVELS IN SITES FOR DISPOSAL OF OIL WELL WASTE

ABSTRACT Indiscriminate disposal of waste from the oil industry has led to contamination of soils and water sources and toxic effects in the biota. Thus, this study aimed to assess barium (Ba) and lead (Pb) spatial variability in disposal sites for oil well drilling and prospecting wastes. The soil in the study area is classified as Red Latosol, and the area is located in Santa Maria do Oeste, Paraná State (Brazil). Barium and Pb spatial variabilities were determined using a 76-point sampling grid. The levels of metals were determined in soil samples collected at each sampling point, at the depth ranges of 0.0-0.3, 0.3-0.6, 0.6-0.9 and 0.9-1.2 m. Data were mapped using geostatistical interpolators for spatial dependence determination, modeling and validation of semivariograms, and respective interpolation. Thirty Ba and Pb high concentration samples were selected for leaching and solubilization assays. As semivariogram analysis showed spatial dependence, mapping by ordinary kriging was performed for both metals. The high levels found for both metals arose from well drilling activities, as no direct relationship was found between such levels with geology and genesis of the local rock. The concentrations of the metals were higher than the research values considering the Agricultural / AP-Max scenario, therefore, the study area was characterized as class 4 (contaminated). Although these metals have low solubility, i.e. low contamination risk to subsurface waters, they may pose contamination risks to water bodies by means of soil runoff.
Automated Extraction of Mutual Independence Patterns Using Bayesian Comparison of Partition Models

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic assumption of large sample size. As such, these methods have a very restricted scope of application. In this article, we propose to change the investigation of mutual independence from a hypothesis-driven task that can only be applied in very specific cases to a blind and automated search within patterns of mutual independence. To this end, we treat the issue as one of model comparison that we solve in a Bayesian framework. We show the relationship between such an approach and existing methods in the case of multivariate normal distributions as well as cross-classified multinomial distributions. We propose a general Markov chain Monte Carlo (MCMC) algorithm to numerically approximate the posterior distribution on the space of all patterns of mutual independence. The relevance of the method is demonstrated on synthetic data as well as two real datasets, showing the unique insight provided by this approach.
Genre preferences in Reading Comprehension materials in first level English book of Senior High School

This study aimed to know genre preferences in the reading comprehension material in first level English book of senior high school. This study, which is employed a qualitative-narrative technique with no statistical analysis, described some material for reading comprehension material, such as descriptive text, narrative text, and recount text. This study revealed that the reading comprehension materials in first level English book of senior high school was presented into ten reading comprehension materials. Such as: self-introduction, expression of compliment, plans and intention, descriptive text I and II, announcement, past and present prefect tense, recount text, narrative text, and song. It also revealed that genre preferences in the reading comprehension materials in first level English book senior high school was presented into three genre preferences: descriptive text, recount text, and narrative text. DOI:10.26905/enjourme.v4i2.3708