Reviewerly/exHarmony huggingface dataset:
E-MONITORING PERFORMANCE LECTURER BASED ON CLOUD COMPUTING SYSTEM

Lecturers in study programs make a major contribution to the quality of study program accreditation, particularly in the upcoming IAPS 4.0 completeness. The lecturer base needs to be managed through a concrete, transparent, structured, comprehensive, and accountable system. Lack of data management in lecturers at the Mechanical Engineering Education / PTM Study Program so far is still paperless based so that it is less effective, efficient, having to copy documents, and tends only to collect data close to the accreditation of study programs. This research aims: (1) to produce e-monitoring products on the performance of lecturers based on a cloud computing system; (2) knowing the level of feasibility of e-monitoring products on the performance of lecturers based on cloud computing system at PTM Study Program. The research method uses Research and Development (R&D), which adopts the ADDIE (Analysis-Design-Development-Implementation-Evaluation) model. The research sample was 11 PTM lecturers. Validators of eligibility included the chairman of the LPM and the chair of the UST IT team. The instruments used included questionnaires and documentation. This research succeeded in producing a product in the form of a cloud computing system with a private cloud type with the Platform As A Service (PaaS) model. The data used as an assessment of lecturer performance include governance, HR performance, education performance, research performance, and service performance. The results of expert assessments obtained an average of 91.25%, which shows that this system is very feasible to use. While the evaluation results of the system implementation showed that 90% of PTTM lecturers were able to understand and skillfully operate the cloud system, thus this system is very feasible to be used as e-monitoring of lecturer performance that is accurate, accountable, objective, and safe to use.
Efficient Design for High Speed and Low Area Reverse Converter for Moduli Set $\{2^{n}, 2^{2n}-1,2^{2n}\{1\}$

Earlier, various reverse conversion technique has been adopted in literature for residue number system using Chinese remainder theorem (CRT), mixed radix conversion (MRC) and their variants. However, the complexities of those reverse converters are very high that directly affects the performance of the design. In this paper, a hardware efficient reverse converter using core function (CF) technique for the moduli set $\{2^{n}, 2^{2n}-1,2^{2n}+1\}$ is presented. The proposed design is compared with the literature that has same dynamic range. The approximate analysis is done using unit gate model and exact analysis is done using Cadence RTL Compiler for fair comparison. The results show that our proposed design has area-delay product (ADP) improvement about 51%, 46% and 42% for $n=4,8$ and 16 respectively, and power-delay product (PDP) improvement about 63%, 65% and 67% for $n=4,8$ and 16 respectively, when compared to recent design for same dynamic range.
FPGA-Based Implementation of Optical Flow for Object Detection

In this paper, we describe a FPGA implementation of the optical flow (OF) algorithm. LucasKanade (LK) approach has been chosen to meet the real time estimation of moving objects from a video sequence of a road scene. In our approach, to allow accurate calculation, we adopt a high level synthesis approach to increase the accuracy of the system and simplify the tasks. Hardware designs of the architectures are performed using Xilinx System Generator from Xilinx (XSG) and validated by a hardware co-simulation.
An overview of failure analysis expert system based on Bayesian networks

Nowadays, with the products and systems becomes more complex, it will be more difficulty to process the failure analysis in the dependability. Besides we can see the great progress in the field of machine learning, and the joint applications of machine learning and other fields are becoming more widespread. In this paper we propose a new function which combine the expert system and Bayesian networks to failure analysis. This method starts with data mining which will collect the clear, complete data to a data warehouse. Then it takes advantage of the Fault Tree and mapping it to Bayesian networks. At last we use the Bayesian networks and the statistical data to compute the probability of the failure in order to take analysis. We give an example to explain how this model works, and it will get a well performance because of it uses the data from failure cases and other related data.
SISTEM MANAJEMEN DAN REMINDER OTOMATIS JADWAL PEMBAYARAN TAGIHAN BERBASIS ANDROID PADA CV ARIYA DVC COMPUTER MALANG

CV Ariya DVC Computer is a company engaged in the field of computer services and sales. Constraints on CV Ariya DVC Computer is the application which used for managing payments to suppliers on a monthly basis, has some weaknesses, such as page access speed, neatness of appearance, and the ease of current application usage on mobile devices, so that Android-based systems is needed, which can help the effectiveness and efficiency of performance in the company. The purpose of this study is the establishment of a management system and automatic payment schedule reminders using Android that can solve problems from previous web-based applications in terms of page access speed, appearance neatness, and ease of application usage. This study implements the system using Ionic framework, then uses the REST Web API as a communication medium with a MySQL-based database processing system. The results of implemented system is expected to help the productivity of CV Ariya DVC Computer Malang, which can be accessed anytime and anywhere, using Android smartphone, and also push notifications that will greatly help reduce late invoice payments. Keywords: Automatic Reminder System, Android, Ionic, REST
Successful Parenting in the Contemporary Era of Technology in Pakistan

Pakistan is one of the developing countries having low literacy rate in contemporary era of technology (Male: 72.5% and Female:51.8% but varies regionally), holding 180th position in the list of 221 big or small countries of the world. Media (social/electronic/print) has replaced parents, therefore, children learn and follow, as they find/listen/watch on it, parents have no more control over their children and consequently their teaching is no more effective. But in the present era of challenges/difficulties where parents find themselves hopeless/helpless, we find some parents have performed or are very successfully performing their duties. The objective of this research study was to discover the techniques used or parenting style followed by those successful parents. For the purpose, through snowball sampling procedure hundred (100) parents were selected. A questionnaire consisting close-ended questions was distributed among the participants and the purpose was to keep selected respondents on required track or provide them technical terms used in the world or to have fixed responses, while open-ended questions were posed to explore the techniques or parenting style adopted by them. The quantitative responses were analyzed using SPSS, while quantitative responses were sorted, labelled and quantified. The results show that parenting style or technique of using yardstick as per nature of a child is the reason behind successful parenting in the parent era.
Feature enhanced faster R-CNN for object detection

In recent years, deep convolutional neural networks (CNNs) have achieved great successes in object detection, however, feature extraction is still sensitive to scale variation. FPN is one of the majority strategies to deal with this problem. It uses a top-down pathway and lateral connection to combine high-level features with low-level features, and then generates robust features. However, in FPN, high-level features are still unable to capture the detail information, and this results in the inconsistent representations for the same objects with different scales. To solve this problem, we proposed a Feature Enhanced Module to get more robust features, which can help the networks to produce object localization with higher quality, i.e., without bells and whistles. The performance of the proposed method is shown by the experiments in which it achieves a 1.1 point AP50 gain and 2.3 point AP75 gain on the Pascal VOC dataset, comparing to the Faster RCNN with FPN.
Organisational Culture and Perception of Service Quality among Employees in the Insurance Industry in Nigeria

The achievement of any service organisation has to do with the service quality. Literaturehas continuously shown the importance of service quality and how pertinent it is to thesurvival of any organisation regardless of the industry. Further analysis of the literatureposits that organisational culture has significant impact on a wide range of events withinan organisation such as human resource and profitability amongst others. This study,therefore, examined the relationship between organisational culture and perceivedservice quality. Employing a cross-sectional research design and convenience samplingtechnique, the questionnaire was used to elicit information from 129 employees out of thestudy population in the insurance industry. Correlation and regression analysis wereused as the statistical tools for the study. The findings revealed a significant relationshipbetween bureaucratic culture dimension and perceived service quality (r=.22; p < .05), anegative significant relationship between innovative culture and perceived service quality(r= -.09; p < .05), while supportive culture dimension had a positive significantrelationship with perceived service quality (r=.49; p < .05). Furthermore, the dimensionsof organisational culture jointly predict perceived service quality. Based on the findings,the study recommended that management should endeavour to develop an effective andstrong organisational culture as it will help to improve the service quality within andoutside the organisation, while organisational culture should be based on the strength ofthe firm.
Histogram equalization and its variants on principal component analysis based face recognition system

Principal Component Analysis (PCA) is an appearance-based feature extraction technique that is widely used in facial recognition system which suffers from illumination conditions, thus the challenge of knowing which illumination control methods to be used in facial recognition system based on PCA algorithm is very important. This paper compared the performance of Principal Component Analysis based face recognition using illumination normalization techniques: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The evaluation of the performance of the developed system was done comparatively in terms of false acceptance rate, false rejection rate and recognition rate. The best recognition rate of 83.67% was achieved in AHE for ORL, the effective false acceptance rate (FAR) of 20% was recorded in CLAHE with FERET database and also effective false recognition rate (FRR) of 10% was obtained in CLAHE for FERET database.Keywords: Histogram Equalization, Adaptive Histogram Equalization, Contrast Adaptive Limited Histogram Equalization, Principal Component Analysis
Visualization of Codes and Code Smells with 3D Mushroom Farm Metaphor

Code visualization presents a visual representation of the state of the code to support code comprehension activities. In addition to representing code elements (for example, methods, classes and packages) and relationships between them, previous studies of code visualization also represent metrics to indicate code smell. However, the metric value only shows vague symptoms in describing the code smell behind it. This study tries to visualize the code smell directly using detection strategies, which are formulation of one or more than one metric and its threshold value for each type of code smell. ‘Smelly’ elements are immediately shown in code visualization according to the predetermined detection strategies. 3D mushroom farm metaphor is a new metaphor proposed in this study for code visualization. Through code visualization in a familiar natural 3D environment, code properties are displayed in an attractive manner. The presented code properties are chosen by considering object-oriented paradigm key ideas. During this time, there has never been a fundamental reason as to why a specific property was chosen to be included in a code visualization metaphor. Therefore, this study questioned code properties visualized to participants of the evaluation: is the early comprehension of the code sufficient enough or should it be added/reduced. The evaluation results showed that the code smell that was immediately shown in the visualization could be captured by the user correctly. All evaluated participants managed to identify all the smelly elements. Broadly speaking, participants understood the purpose of visualization and agreed that code visualization with the 3D mushroom farm metaphor is intuitive and easy to use. All participants stated that they needed all code properties displayed in code visualization with the 3D mushroom farm metaphor, although there were participants who proposed several properties to be added to the visualization.
Time-Efficient Analysis of Complex Dependencies

The volumes of data that are collected and analyzed in all aspects of life rise steadily and offer numerous challenges and opportunities. When analyzing large volumes of data, two main considerations are the capability to detect relationships within the data and the time efficiency necessary when processing the data. Large data sets in particular often contain dependencies that are less obvious than linear or proportional relationships. Such complex dependencies could be quantified and analyzed with methods from the field of information theory. In this dissertation, we study time-efficient methods to detect, quantify and illustrate complex dependencies.
Semi-Supervised Ovulation Detection Based on Multiple Properties

Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.
Improved Reversible Data Hiding Schemes Based On Invariant Image Classification

Reversible data hiding is the technique that allows several confidential data to be hidden or embedded inside the cover image. The cover image with confidential data is called marked image. The data which is hidden cannot be visible to normal eyes. In reversible data hiding the appropriate receiver can extract the message which is hidden inside the marked image. In addition to that the receiver can restore the original cover image back without or with minimum distortion. Hidden data can be retrieved as required and the exact copy of the original image is retrieved. The higher the capacity the more confidential information can be embedded inside the image. When more confidential data are hidden and at data extraction process some distortions may occur in the original cover image. To achieve reversible data hiding with less distortion improved recursive code construction is used in this paper. Improved recursive code construction reaches the distortion bound by increasing the embedding capacity. To reduce the distortion and to achieve more reversibility reference image is obtained before embedding so that more data can be embedded with less distortion.
L’invisibilità dei diritti: riflessioni a proposito del ‘dovere di morire’

The following reflections summarize the current theme of human rights, the right-duty to die and its configurability from the point of view of the general theory of law and from the perspective of Christian bioethics. Where can freedom go? What is right and what is not? The claim of the new rights takes the form of an ever stronger demand for justice. The interruption of health care despite the demand for life has meant that death has lived and life has died. This new legal phenomenon has inevitable and paradoxical consequences: freedom and the right to die become a duty.
Review on Image Annotation Techniques and its Applications

Image annotation is a most useful technique for social media websites, most of the applications like Facebook, Instagram, Flicker uses automatic annotation technique. These applications allow a user to define free image tags, contributing significantly to the development of the retrieval and organization of web images. Searching image by tags is an important part of an image viewed by a user on social websites. There are many kinds of applications which use image annotation and one of them is Flicker. Flicker application allows user to annotate images with the set of descriptors such as tags, labels, keywords however in that application one of the drawback of duplication of the images with same tags, and which is not reliable for the diversity of Flicker application. The main challenge is to remove duplication of image in order to provide diversity of the images. One of the goal is to re-rank the images by their visual information, semantic information and social result for the particular image count. Typically, each user provides multiple images, so the task is to order the images by a ranking process. User who has a major contribution to the image which gives high rank on the basis of view count and the re-rank the image by user classified image set on the basis of feature extraction of that image by using Canny Edge Detection algorithm for the colour feature, texture feature and detection of edges of a particular image, which is useful for removing the duplication of images.
Predicting unknown mineral localities based on mineral associations

Mindat (mindat.org), the Mineral Evolution Database (RRUFF.info/Evolution), and the Global Earth Mineral Inventory are some of the well-known datasets in the field of mineralogy, which contain data about almost all known localities on Earth where minerals have been found. The increase in the amount and accuracy of mineral data and the improvements in technological resources make it possible to explore and answer large, outstanding scientific questions, such as, understanding the mineral assemblages on Earth and how they compare to assemblages and localities on other planets.. In this contribution, we present an affinity analysis method to: 1) Predict the previously unknown occurrence of a mineral of interest. 2) Predict the location of a geologic environment or deposit based on a mineral assemblage.Affinity Analysis, or Market Basket Analysis, is a machine learning method that uses mined association rules to find interesting patterns in the data. The strength of the rules is identified using some measures of interestingness, such as ‘lift’. For example, when the occurrence of a mineral predicted with high confidence at a given locality is unexpected (low support), the rule used for such a prediction is considered ‘very interesting’. Successful implementation of this methodology will greatly aid the mineral discovery process.
Design of a Mobile Application Integrated to an LMS for the Optimization of the Assistance Process in a University Environment

Historically taking attendance in classes is a process considered fundamental for the transmission of knowledge in a University environment, so in most courses the attendance list is a natural occurrence, but it takes time both in the classroom and at the time to register the attendance afterwards, and in addition to all this, is a process that turns out not to be so transparent for the student. Therefore, this article aims to aid teachers to carry out the lifting of class lists effectively, systematized and integrated to the LMS of the Tecnologico de Costa Rica (TEC) by means of a mobile application. For the design of this tool, we seek to optimize the time it takes the teacher to carry out this process, through workshops based on User Experience (UX) Strategies and usability tests, carried out at various stages of the development process to ensure that the tool complies with the established requirements, from the focus of the user's experience and its needs. One of the main results its 92.5% of the tasks to evaluate the proposal was achieved by teachers, and they talked the intuitive and easy to use is this proposal user interface, thanks to the methodology.
A method of adaptive learning rate tracking for embedded device based correlation surface evaluation

The Accuracy of correlation filtering trackers have got great improvement because of using high dimension features, but its real-time performance became worsen. And we often have the meet of running tracker on embedding device, in this case, we need less calculation. It is all known that the model updating strategy is also important for tracking performance. The fixed learning rate model updating strategy is difficult to deal with the situation that the object changes rapidly or slowly. For the problem, a new correlation surface quality evaluation metric is proposed in this paper. Meanwhile, we consider the occlusion of the object, and propose the occlusion judgment algorithm. Finally, the learning rate of model is updated adaptively according to the change speed of the object and whether the object is occluded. We further conduct experiment on the OTB50 dataset. Experimental results show that the correlation tracker with gray feature can improve the tracking accuracy by about 3% compared with MOSSE tracker, after adopting the learning rate adaptive strategy proposed in this paper and maintain high speed on embedding device.