Reviewerly/exHarmony huggingface dataset:
Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.
Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.
Fine-Grained Hardness for Edit Distance to a Fixed Sequence

Nearly all quadratic lower bounds conditioned on the Strong Exponential Time Hypothesis (SETH) start by reducing k-SAT to the Orthogonal Vectors (OV) problem: Given two sets A,B of n binary vectors, decide if there is an orthogonal pair a ∈ A, b ∈ B. In this paper, we give an alternative reduction in which the set A does not depend on the input to k-SAT; thus, the quadratic lower bound for OV holds even if one of the sets is fixed in advance.Using the reductions in the literature from OV to other problems such as computing similarity measures on strings, we get hardness results of a stronger kind: there is a family of sequences {S_n}_{n = 1}^{∞}, |S_n| = n such that computing the Edit Distance between an input sequence X of length n and the (fixed) sequence S_n requires n^{2-o(1)} time under SETH.
ANALISIS PENERAPAN BUKU DINUL ISLAM UNTUK MENINGKATKAN KETERAMPILAN MEMBACA SISWI KELAS III PONDOK MODERN AL-BAROKAH NGANJUK

This research is motivated by the low reading ability of third grade students at Collage Modern Al-Barokah Nganjuk. This study aims to determine the application of the Islamic Religion book to improve the reading skills of third grade students at Collage Modern Al-Barokah Nganjuk. This type of research uses qualitative research. This research was conducted in class III Collage Modern Al-Barokah Nganjuk. The research subjects were 15 students in class III. This study uses descriptive qualitative data analysis techniques. The results showed an increase in the reading ability of third grade students at Collage Modern Al-Barokah Nganjuk.
A Hybrid Mod K-Means Clustering with Mod SVM Algorithm to Enhance the Cancer Prediction

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model “A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction” to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.
Navigating Through Creativity and Innovation Paths: Waze Navigation Application Case Study.

The primary subject matter of this case concerns entrepreneurship. Secondary issues examined include Mobile Navigation Applications. The case has a difficulty level of seven, appropriate for doctoral level. The case is designed to be taught in 2 class hours and is expected to require 3 hours of outside preparation by students.
Perception of Knowledge Management Practices Among HR Managers: An Analysis

Knowledge Management (KM) involves creating, sharing, using and managing the knowledge and information of an organization. This process is a multidisciplinary approach used to achieve the organizational objectives by best utilizing knowledge. KM as a modern concept helps the organizations and the people in various aspects; for instance, it enables innovation within the organization by making provision for free flow of ideas internally and externally. The concept is not popular among all the industries, therefore the present study is intended to analyze the awareness of KM practices of the HR managers in selected companies of Mysore city. For this purpose, data was collected using a questionnaire, and the collected data was analyzed through descriptive statistics and one-sample t-test. The study found that there is awareness of KM practices and a positive attitude among the HR managers of the selected companies of Mysore city.
A Methodology for Network Security Infrastructure according to the New Brazilian General Law for Personal Data Protection

The General Law on Protection of Personal Data -LGPD (Law No. 13,709/2018) is a new Brazilian law that deals with the management of personal data of third parties, carried out by people, companies, and institutions.This legal provision requires that these data be protected by all necessary technical means, imposing a set of sanctions on the Organization.Due to the lack of a reference methodology for implementing the protection requirements defined in the Law, this work proposes one that provides a basis for constructing the network architecture supported by the most common models and applying a security policy to computer network infrastructures.
Does the Adoption Process of Financial Technology in Africa Follow an Inverted U-Shaped Hypothesis? An Evaluation of Rogers Diffusion of Innovation Theory

As the global financial system evolves towards a technology-enabled financial solution-Fintech, its implication to the future of the financial system becomes a policy concern. This study investigates non-linear/inverted U-shaped Fintech adoption process among a panel of 32 African countries spanning from 2002–2018. The study argues that Fintech adoption in Africa will continue to rise and not follow the inverted U-shaped process if sustained through greater trade openness. The dynamic system GMM techniques found a strong evidence for an inverted U- shaped adoption process for the 32 African markets and 24 frontier African markets but violated among the emerging (N = 3) and fragile (N = 5) groups. The first lag of Fintech compatibility and the contemporaneous levels of relativeadvantage, complexity, trial-ability and observe-ability were its main determinants. The study concludes that Fintech will be replaced with new innovations in future irrespective of possible sustainability strategy. The need to strengthen African financial markets’ innovativeness to have a competitive edge on Fintech’s replacement is stressed.
Phase control of controlled-NOT gate using dressing field in atomic vapor

We experimentally demonstrate the phase control of the controlled-NOT using dressing field in a hot atomic ensemble. This is more convenient for modulating the controlled-NOT gate with various relative phases. Moreover, with the phase modulated by changing the angle between the probe field and the dressing field, the enhanced output signal can be switched to the suppressed state in this logic process. The study performed by such a setup not only possess orbital angular momentum characteristics but can also be extended to handle quantum information processing in atom vapor.
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension

This paper describes our system used in the SemEval-2021 Task 4 Reading Comprehension of Abstract Meaning, achieving 1st for subtask 1 and 2nd for subtask 2 on the leaderboard.We propose an ensemble of ELECTRAbased models with task-adaptive pretraining and a multi-head attention multiple-choice classifier on top of the pre-trained model.The main contributions of our system are 1) revealing the performance discrepancy of different transformer-based pretraining models on the downstream task, 2) presentation of an efficient method to generate large task-adaptive corpora for pretraining.We also investigated several pretraining strategies and contrastive learning objectives.Our system achieves a test accuracy of 95.11 and 94.89 on subtask 1 and subtask 2 respectively.
PROGRAM PENGABDIAN: SOSIALIASI BAHAYA KEBAKARAN MENGGUNAKAN PROTOTYPE ALAT BERBASIS IoT UNTUK WARGA HINTERLAND BATAM

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract.&lt;/em&gt;&lt;/strong&gt; &lt;em&gt;The dry season is starting to be felt in Batam and brings the threat of drought. This dry or hot season has an impact in the form of the threat of protected forest fires in the Rempang and Galang Island areas, Batam. Residents of Pasir Panjang at the end of last month a fire accident occurred in the residential area of the village. This has made residents wary of fires. The development of technology is currently increasing rapidly with the existence of the Internet of Thing (IOT) which is one of the solutions to problems in daily activities, including in handling fires. The purpose of this service is to try to make an application for fire detection in the residential area. The method of implementation is carried out in stages, needs analysis, training and mentoring programs, handing over of tools and evaluation of activities. The results of this service are in the form of a prototype IoT application product. Another result is from training in the form of socialization of fire hazard prevention by demonstrating a tool with a very good and enthusiastic response from the community based on an average rating scale above 4.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstrak.&lt;/strong&gt; Musim kemarau mulai terasa di Batam dan membawa ancaman kekeringan. Musim kemarau atau panas ini menimbulkan dampak berupa ancaman kebakaran hutan lindung di kawasan Pulau Rempang dan Galang, Batam. Warga Pasir Panjang akhir bulan lalu terjadi musibah kebakaran yang menimpa di kawasan perkampungan tempat tinggal. Hal ini membuat warga was-was terhadap bancana kebakaran. Perkembangan teknologi saat ini semakin pesat dengan adanya Internet of Thing (IOT) menjadi salah satu pemecahan masalah dalam aktifitas sehari-hari, tak terkecuali dalam penanganan kebakaran. Tujuan dari pengabdian ini adalah mencoba membuat suatu aplikasi untuk pendeteksi kebakaran di daerah perkampungan tempat tinggal. Metode pelaksanaan dilakukan secara bertahap analisa kebutuhan, program pelatihan dan pendampingan, penyerahan alat dan evaluasi kegiatan. Hasil pengabdian berupa produk prototype aplikasi IoT. Hasil lain yakni dari pelatihan berupa sosialisasi pencegahan bahaya kebakaran dengan memperagakan alat dengan respon masyarakat sangat baik dan antusias berdasarkan rata2 penilaian skala diatas 4.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="5ComdevIsiAbstrakIndo"&gt; &lt;/p&gt;&lt;div id="gtx-trans" style="position: absolute; left: 319px; top: 370px;"&gt; &lt;/div&gt;
Computational approach to social knowledge

Social and cognitive sciences have always faced the choice: either to meet the methodologi- cal standards given by successful natural sciences or to rely on their own. Talking about the conversion of knowledge into technology, the second way did not bring great success. The first way implies two alternative opportunities: reductionism or discovery of proprietary general laws. None of these chances have been realized with any satisfactory results, too. Methodological analysis shows that, to achieve significant progress in social sciences, what is missed there is not new facts or definitions but new conceptual schemes. The reason, as the author supposes, is the nomothetic approach being applied to systems with high degree of complexity and hierarchy. If we assume that social structures and processes are built upon cognitive psychological structures and processes, the former inherit the distributed computational architecture of the latter. The paper analyzes various conceptions of computations in order to determine their relevance to the task of building computational social science. The author offers a “generic” definition of computations as a process carried out by a computational system if the latter is understood as a mechanism of some representation. According to the author, the computationalization of social science implies “naturalization” of computations. This requires a theory that would explain the mechanism of growing complexity and hierarchy of natural (in particular, social) computational systems. As a method for constructing such a science, a kind of reverse engineering is proposed, which is recreation of a computational algorithmic scheme of social tissue by the determination and recombination of “social primitives” – elementary operations of social interaction.
Patient-Specific Readmission Prediction and Intervention for Health Care

Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.
H∞ Filter with Fuzzy Logic Estimation to Refrain Finite Escape Time

In this paper, a H∞ Filter with Fuzzy Logic (FHF) based on mobile robot localization and mapping is proposed to effectively prevent the Finite Escape Time (FET) issue in H∞ Filter (HF). The capabilities of HF to offer better solution than Kalman Filter (KF) is limited by the shortcomings of Finite Escape Time. FET define as a state that can go infinite in normal condition that lead to in accurate estimation result, with the present of FET, the estimation cannot be totally guarantee. Therefore, it is essential to guarantee no FET is observed in the state covariance on its update. A new FHF is developed for this matter by using fuzzy logic approach. The analysis is done by implementing the Fuzzy Logic technique with triangular membership. The simulation result show that Fuzzy Logic effectively avoid the FET from appear and simultaneously improve the estimation between mobile robot and the landmark.
A Cooperation of the Multileader Fruit Fly and Probabilistic Random Walk Strategies with Adaptive Normalization for Solving the Unconstrained Optimization Problems

A swarm-based nature-inspired optimization algorithm, namely, the fruit fly optimization algorithm (FOA), hasa simple structure and is easy to implement. However, FOA has a low success rate and a slow convergence, because FOA generates new positions around the best location, using a fixed search radius. Several improved FOAs have been proposed. However, their exploration ability is questionable. To make the search process smooth, transitioning from the exploration phase to the exploitation phase, this paper proposes a new FOA, constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). This involves two population types working together. CPFOAs performance is evaluated by 18 well-known standard benchmarks. The results showed that CPFOA outperforms both the original FOA and its variants, in terms of convergence speed and performance accuracy. The results show that CPFOA can achieve a very promising accuracy, when compared with the well-known competitive algorithms. CPFOA is applied to optimize twoapplications: classifying the real datasets with multilayer perceptron and extracting the parameters of a very compact T-S fuzzy system to model the Box and Jenkins gas furnace data set. CPFOA successfully find parameters with a very high quality, compared with the best known competitive algorithms.
Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning

<p indent=0mm>To achieve meaningful combination of low-level features and semantic information of salient regions or targets, and to obtain saliency detection results with more complete structure and clearer boundary, an algorithm of color image saliency detection via two-stream feature fusion and adversarial learning (SaTSAL) is proposed. Firstly, different levels of image features are extracted from bottom to top by means of a two-stream heterogeneous backbone network based on VGG-16 and Res2Net-50. Secondly, in each stream, different feature maps from the same level are fetched into one convolution tower module to enrich intra-level multi-scale information. Thirdly, a predicted saliency map is generated by top-down laterally fusing of cross-stream feature maps level by level, so as to effectively make full use of high-level semantic features and low-level image features. Finally, under the mainframe of conditional generative adversarial networks (CGAN), a higher structural similarity between detected results and salient objects can be strengthened by adversarial learning. By taking P-R curve, <italic>F</italic>-measure, mean absolute error and <italic>S</italic>-measure as evaluation indexes, comparative experiments performed on four public datasets including ECSSD, PASCAL- S, DUT-OMRON and DUTS-test show that SaTSAL algorithm is superior to most of other ten saliency detection methods based on deep learning.
Sentence-LDA and Deep Learning: A Combined Approach for Implicit Aspect Extraction and Sentiment Analysis

The increasing usage of E-commerce market like Ali Baba, Amazon and JD are increasing day by day, such platform offering customers to analyze their services and provide useful information of the service for future customers. Furthermore, companies utilize consumer information to improve their facilities for surviving and compete in the market. This importance refers to the study of user comments or opinions. A previous study in the field of opinion analysis has expected that a review does not contain different sentiments, although, this is not correct. In this research work, we analyze the reviews which contain multiple sentiments. Aspects based sentiments analysis (ABSA) is suggested to report this problem. ABSA aims to recognize implicit aspects in the text, and then conduct the text sentiment analysis. This method is proposed for solving the above issue, the topic models are used with the combination of deep learning and Bag-of-Words approach.
The new netiquette: Choosing civility in an age of online teaching and learning

The abrupt pivot to online teaching and learning during the Spring semester of 2020 because of worldwide stay-at-home directives left educators and their stakeholders with few guidelines to enact teaching and learning in online plat-forms. Network etiquette, coined as netiquette in 1995 by In-tel (Hambridge, 1995), established nascent directives during the Internet’s infancy, mostly regarding standards for email usage. After the National Science Foundation Network (NFS-NET) retired in April of 1995, the Internet transitioned to a commercially-owned backbone of networks that we know today (Shah & Kesan, 2007) with 4.5 billion active users worldwide (Statista, 2020). Educators and their stakeholders were thrown into an online environment as the sole platform for learning that lawmakers felt compelled to protect against cyberbullying with laws, school policies, and sanctions in 49 of the 50 United States (Hinduja & Patchin, 2018). This article addresses the need for a new netiquette and suggests guidelines for online teaching and learning based on Dr. P. M. Forni’s (2002) twenty-five rules of considerate conduct. © 2021, Association for the Advancement of Computing in Education. All rights reserved.