Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic-based Expert System for Effort Estimation in Scrum Projects

This article presents an expert system based on fuzzy logic for estimating effort in scrum projects. The scrum process splits the work of the development team into short periods called sprints. A wide range of factors significantly impacts the work estimation for each task in a sprint. These factors include the development team expertise, task complexity, task size, and working environment. Typically, these factors are expressed through the use of linguistic quantifiers. As a result, this article applies fuzzy logic concepts to the development of a fuzzy logic-based expert system capable of improving effort estimation in the scrum process. A MA TLAB simulation of the proposed system is used to test its behavior. The result shows that if team expertise is a beginner, task complexity is high, and if the task size is large, the story point value is complex. However, if a team is experienced, task complexity is easy, task size is small, and the work environment is best, then story point value is easy. The proposed system is tested on real data set of software projects and the results obtained are promising.
Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.
The effect of knowledge sharing on the relationship between empowerment, service innovative behavior and entrepreneurship

This study is aimed at exploring the effect of empowerment on creative work and entrepreneurship behavior. It is also aimed at identifying the mediating role of sharing knowledge between them. The relevant data were collected through a questionnaire from (9) Jordanian commercial banks, where (340) questionnaires were distributed to workers in the Jordanian commercial banks. In addition, a statistical analysis was used to show the relationship between the variables and draw the conclusions. The results of the study show there is an empowering effect on creative work behavior through knowledge sharing. Moreover, it also showed that there was an empowering effect on entrepreneurship through knowledge sharing. This study does, to a large extent, bridge the gaps within the literature and develops an understanding of how to empower employees in order to increase their creative behavior, make them entrepreneurs, and enhance the same through knowledge sharing. The results of this study indicate that there are certain important benefits for the workers in the banking industry which can be gained through facilitating more innovative behavior among their employees. The results also indicate that employee’s empowerment and enhancing knowledge sharing are essential to creative and entrepreneurial work behavior.
Facial Retouching and Alteration Detection

Abstract On the social media platforms, the filters for digital retouching and face beautification have become a common trend. With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN) leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.
Implementation of Item Response Theory (IRT) Rasch Model in Quality Analysis of Final Exam Tests in Mathematics

This study was conducted to analyze the test instrument used to measure the ability of students in the odd semester final exam in mathematics. Sampling using purposive sampling technique. These students consist of 67 people. The questions given are in the form of multiple choice questions totaling 40 items related to the odd semester final exam material. The data analysis technique used quantitative descriptive analysis. The Rasch model is used to obtain fit items. This analysis was carried out with the help of Winsteps 3.73 software. From the output of the Winsteps program, 35 items were obtained according to the Rasch model with an average value of Outfit MNSQ for persons and items of 1.09 and 1.09, respectively. While the Outfit ZSTD values for person and item are -0,1 and -0,2 respectively. Meanwhile, the instrument reliability expressed in Cronbach's alpha is 0.77.
Financial and Non-Financial Applications of Blockchain

There are hundreds of technologies today. Companies and brands continuously try to create and bring something innovative in the market to attract consumers to them in order to get a rise in market share. In the world where people have started getting used to hundreds of technologies, if asked about those which have affected them the most in last ten to twelve years, no one will miss mentioning blockchain. Blockchain has gained very much popularity after the introduction of bitcoin and ethereum in its environment. Blockchain mainly has two types of functionalities. One that involves transactions and the other which talks about contracts. This work highlights some of the very much talked about applications of this technology in the real world. The work also considers various factors and methods by which this technology can be introduced to the audience by suggesting ways in which blockchain can be introduced in the lives. Discussion on how this technology can affect human lives in the future is also an important part of this paper. Because blockchain has huge number of applications that the paper has tried to inculcate, it can be a technology of future which many scientists and industrialists have already started to believe. That is why this work finds a unique and all in one collection of applications and possibilities of Blockchain.
THE USE OF ONLINE LITERATURE CIRCLES TO ENHANCE THE STUDENTS’ CRITICAL READING SKILL

This research aims to find out whether using Online Literature Circles to enhance the student's critical reading skills is effective or not. This research belongs to quantitative research using a pre-experimental design. The population of this research is eleventh-grade students of SMK N 1 Puring in the academic year of 2020/2021 which consists of 382 students. The sample of this research is XI TKPI 1 consist of 34 students. The data were taken virtual from 17th May 2021 to 31st May 2021. The technique of the data collection is using a test. It was done by giving an online pre-test and online post-test. Meanwhile, the technique of data analysis used descriptive analysis and inferential analysis. The result of this research shows that the mean score of the pre-test is 49.79 which belongs to fairly sufficient category, and the mean score of the post-test is 70.79 which belongs to the good category. To see whether the hypothesis is accepted or rejected, the researchers use t-test through IBM SPSS 25. The result two-sided significant value is 0.000. Based on the 0.05 significant level, the computation shows the two-sided significance value is lower than 0.05 (0.000&lt;0.05). Therefore, it can be concluded that Online Literature Circles is effective to enhance the students' critical reading skills at the eleventh-grade students of SMK N 1 Puring in the academic year of 2020/2021.
A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels

Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effects caused by noisy labels for 3D medical image segmentation has not been fully investigated. In this paper, our purpose is to propose a novel hybrid robust-learning architecture to combat noisy labels for 3D medical image segmentation. Our method consists of three components. First, we focus on the noisy annotations of slices and propose a slice-level label-quality awareness method, which automatically generates label-quality scores for slices in a set. Second, we propose a shape-awareness regularization loss based on distance transform maps to introduce prior shape information and provide extra performance gains. Third, based on a re-weighting strategy, we propose an end-to-end hybrid robust-learning architecture to weaken the negative effects caused by noisy labels. Extensive experiments are performed on two representative datasets (i.e., liver segmentation and multi-organ segmentation). Our hybrid noise-robust architecture has shown competitive performance, compared to other methods. Ablation studies also demonstrate the effectiveness of slice-level label-quality awareness and a shape-awareness regularization loss for combating noisy labels.
Design and Implementation of True Parallelism Quad-Engine Cybersecurity Architecture on FPGA

Applications, such as Internet of Things, deal with huge amount of transmitted, processed and stored images that required a high computing capability.Therefore, there is a need a computing architecture that contribute in increasing the throughput by exploiting modern technologies in both spatial and temporal parallelisms.This paper conducts a parallel quadengine cybersecurity architecture with new configuration to increase the throughput.using DE1-SoC and Neek FPGA boards and HDL.In this architecture, each engine operates with 600MHz maximum frequency.Each image is divided into four parts of equal size and each part processed by single engine concurrently to achieve spatial parallelism.Internally, engine is handling image's part in temporal parallelism and deep pipelining abstraction applied in every engine by dividing it to sub modules to execute different tasks concurrently.All data processed in engines is encrypted via AES algorithm that implemented as a significant part of engine architecture.The obtained results increased the throughput by four times, with 153,600Mbps, that make this computing architecture efficient and suitable for fast applications such as IoT and cybersecurity level of processing.
Error Analysis of Machine Learning Predictions on Asthma Hospital Encounters

To guide the allocation of finite preventive care resources for asthma management, we recently developed the world’s most accurate machine learning model to predict which asthmatic patients will have poor outcomes of incurring asthma hospital encounters (AHEs) in the following 12 months, where an AHE is an emergency department visit or an inpatient stay for asthma. In this abstract, we present an error analysis of our model’s predictions. The patient cohort included all 14,644 adult asthmatic patients at the University of Washington Medicine (UWM) in 2018. We used the prescription of systemic corticosteroids in the following 12 months, encounters of any type for asthma exacerbations in the following 12 months, and AHEs between the following 13-24 months as surrogates of poor outcomes in the following 12 months. For each patient whom our model incorrectly predicted to experience ≥1 AHE in 2019, we used administrative and clinical data in the UWM enterprise data warehouse to assess whether the patient had any surrogate of a poor outcome. Our model incorrectly predicted that 1,310 adult asthmatic patients would experience AHEs in 2019. Among these patients, 316 were prescribed systemic corticosteroids in 2019, 126 had encounters for asthma exacerbations in 2019, and 18 experienced AHEs in 2020. In total, 358 patients (27.3%) had ≥1 surrogate of poor outcomes. A large percentage of adult asthmatic patients for whom our model’s predictions were false positives had surrogates of poor outcomes and were reasonable candidates for receiving preventive interventions.
Blister Package Classification Using ResNet-101 for Identification of Medication

This research aimed to fine-tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent.
BERT based Named Entity Recognition for Automated Hadith Narrator Identification

Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people.However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths.In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn.Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith.The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith.The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model.In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent.On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score.
Mitigation of Geometrical Attack in Digital Image Watermarking using Different Transform Based Functions

The illegal act of digital multimedia data loss the value of information and integrity. The loss of information and integrity born the process of piracy of digital data. The piracy of digital data loss the brand value of documents and products. For the prevention of piracy used the digital watermarking technique. The digital watermarking techniques provide copyright protection and increase the value of brands — Watermarking techniques used in various fields such as image, video, audio, and text. The process of watermarking techniques proceeds in two manners spatial domain and frequency domain. The processing of frequency based watermarking techniques is roughest and fast processing of watermarking. Now in the current scenario, various transform function is used for the embedding process of watermarking techniques. In this approach present the studied of digital watermarking techniques based-on different transform function such as DCT, DWT, FFT, and many more transform function. The transform function based watermarking techniques faced a problem of geometrical attack. The Geometrical attack deforms the watermark and gets information. The prevention of watermarking techniques against the Geometrical attacks is a big challenge for the researcher in the field of digital watermarking.
Sales Forecasting using RNN

In today’s world, big malls and marts are in need of advanced prediction of sales forecasting for the future demand of the products. This leads the manufacturer to produce sufficient product without excess production and to avoid such loss, we need to predict the future demand of a product using Recurrent Neural Network. Long Short Term Memory (LSTM) model deals with the most important past behaviors and understands whether or not those behaviors are important features in making future predictions. Thus, we can reduce the wastage of the product and an increase in profit. In addition, the sales team can communicate with the manufacturing unit in case of insufficient product. This leads to avoiding excess quantity preparation from the production unit. Sales prediction and forecasting is always a best practice for company development.
Neural Network Training as an Optimal Control Problem : — An Augmented Lagrangian Approach —

Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this work we propose an alternative approach by viewing the training task as a nonlinear optimal control problem. Under this lens, backpropagation amounts to the sequential approach (single shooting) to optimal control, where the states variables have been eliminated. It is well known that single shooting may lead to ill conditioning, and for this reason the simultaneous approach (multiple shooting) is typically preferred. Motivated by this hypothesis, an augmented Lagrangian algorithm is developed that only requires an approximate solution to the Lagrangian subproblems up to a user-defined accuracy. By applying this framework to the training of neural networks, it is shown that the inner Lagrangian subproblems are amenable to be solved using Gauss-Newton iterations. To fully exploit the structure of neural networks, the resulting linear least squares problems are addressed by employing an approach based on forward dynamic programming. Finally, the effectiveness of our method is showcased on regression datasets.
Adaptive Pure-Pursuit Controller Based on Particle Swarm Optimization (PSO-Pure-Pursuit)

One of the most crucial tasks of autonomous vehicles (AVs) is the Path tracking module. It defines the ability of the vehicle to pursuit the generated free collision path. Furthermore, this task has to be done maintaining the stability of the vehicle's heading to ensure the passengers' comfort during the trip. Geometrical controllers are known and easy to be applied in robotics and autonomous driving. It is considered a type of lateral controller that neglects dynamic forces on the vehicles and assumes that no-slip condition holds at the wheels. A geometrical bicycle model defines the error and generates the optimal steering command that achieves path tracking. The main idea of the original pure pursuit (PP) is that a reference point is selected on the path with a look-ahead distance from the vehicle, and the optimal steering angle can be calculated, as it will be the angle needed to intersect with this point. The point continues moving forward as the vehicle turns to pursue the path to follow the generated curve, reducing the steering angle and slowly bringing the vehicle towards the path. The paper in hand uses particle swarm optimization (PSO) to calculate the optimal look-ahead distance each time step based on a prediction model, which is propagated to get the future states of the vehicle. The proposed method showed an advantage over both the original pure pursuit controller and the Basic Stanley (BS) controller in terms of the RMS of the lateral error. The performance is being measured using several simulations on the Car-sim simulator. A wide range of velocities was used to test the vehicle's behavior while carrying out the double lane change maneuver.
EvalNE: A framework for network embedding evaluation

In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks. Link prediction is one of the most popular choices for evaluating the quality of network embeddings. However, the complexity of this task requires a carefully designed evaluation pipeline in order to provide consistent, reproducible and comparable results. EvalNE simplifies this process by providing automation and abstraction of tasks such as hyper-parameter tuning and model validation, edge sampling and negative edge sampling, computation of edge embeddings from node embeddings, and evaluation metrics. The toolbox allows for the evaluation of any off-the-shelf embedding method without the need to write extra code. Moreover, it can also be used for evaluating any other link prediction method, and integrates several link prediction heuristics as baselines.
Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.