Reviewerly/exHarmony huggingface dataset:
Rancang bangun Sistem Informasi Pelayanan Masyarakat Berbasis Web Pada Desa Sambeng Kulon Kabupaten Banyumas

Abstract: In the current era of globalization, information technology advancing rapidly. Developments in information technology have led to a new revolution in the form of a conventional shift working system into the digital era. This change has also changed the perspective of each person in doing various activities one of which is on the activities of government agencies. The number of managed data and the need for rapid delivery of information in the public service activities at the Village Office Sambeng Kulon, Banyumas District. Public services such as the pcopulation census should be carried out continuously, continuous, timely and accurate. Because every day people must have come to the village office for various purposes such as making a written request KTP, KK, cover letters moving, death certificates, birth certificates and a letter was not able to take a long time and every resident who apply for the required letter is not uncommon back to the house to complete the necessary requirements for at least information about the documents needed to make the required letter. It is of course not efficient. To solve the above problem, a computerized system should be applied over existing systems to ensure the accuracy of the data. With so all processes can be fully realized public services effectively and efficiently because it does not have to require a longer time. The system development method used in designing this information system is the systemwaterfall method.&#x0D; &#x0D; Keywords: Information Systems, Website Services System, Waterfall
Information-theoretic meaning of quantum information flow and its applications to amplitude amplification algorithms

The advantages of quantum information processing are in many cases obtained as consequences of quantum interactions, especially for computational tasks where two-qubit interactions are essential. In this work, we establish the framework of analyzing and quantifying loss or gain of information on a quantum system when the system interacts with its environment. We show that the information flow, the theoretical method of characterizing (non-)Markovianity of quantum dynamics, corresponds to the rate of the minimum uncertainty about the system given quantum side information. Thereafter, we analyze the information exchange among subsystems that are under the performance of quantum algorithms, in particular, the amplitude amplification algorithms where the computational process relies fully on quantum evolution. Different realizations of the algorithm are considered, such as (i) quantum circuits, (ii) analog computation, and (iii) adiabatic computation. It is shown that, in all the cases, our formalism provides insight into the process of amplifying the amplitude from the information flow or leakage on the subsystems.
MANAJEMEN PEMBINAAN KEDISIPLINAN SISWA: STUDI KASUS PADA ABSENSI FINGERPRINT BERBASIS SMS GATEWAY DI SMK NU KAPLONGAN INDRAMAYU

The purposes of this research are to know the management of students’ discipline development trough fingerprint attendance system based on SMS gateway in SMK NU Kaplongan, and to determine the effectiveness of the students’ discipline development management through fingerprint attendance system based on SMS gateway SMK NU Kaplongan, and to determine the implications of the students’ discipline development through fingerprint attendance based on SMS gateway about increasing students discipline in SMK NU Kaplongan. The method of research which is used is a qualitative method. The techniques of accumulation data are observation participant, in-depth interviews, documentation, and triangulation. The results of this research are firstly the discipline of student can be materialized trough applying the rules. Applying fingerprint attendance system through some steps, they are fingerprint identification and matched with student ID numbers and also their parent phone numbers. The students do it, and the data will be sent to the school computer, it will be processed and sent to the central server. Finally, the data will be sent to the head master, teachers, and their parents.
Strict experimental test of macroscopic realism in a light-matter-interfaced system

Macroscopic realism is a classical worldview that a macroscopic system is always determinately in one of the two or more macroscopically distinguishable states available to it and so is never in a superposition of these states. The question of whether there is a fundamental limitation on the possibility to observe quantum phenomena at the macroscopic scale remains unclear. Here we implement a strict and simple protocol to test macroscopic realism in a light-matter-interfaced system. We create a micro-macro entanglement with two macroscopically distinguishable solid-state components and rule out those theories which would deny coherent superpositions of up to 76 atomic excitations shared by ${10}^{10}$ ions in two separated solids. These results provide a general method to enhance the size of superposition states of atoms by utilizing quantum memory techniques and to push the envelope of macroscopicity at higher levels.
An exponential similarity measure for collaborative filtering

In this paper, we propose two exponential similarity measures for collaborative filtering in recommender systems. The proposed similarity measures are used to estimate the distance between two users or items. Furthermore, an algorithm is proposed to use the distance obtained from similarity measures introduced in this paper to evaluate empty rating elements in the rating matrix and compare the results of these similarity measures with our exponential similarity measures. Therefore, we deduce that the algorithm using the exponential similarity measures works better than the algorithm using Manhattan, Euclidean, Jaccard, Cosine or Pearson similarity measures when taking K-Most-Similar-Users to evaluate the similarity in order to find ratings for the user that we want to predict ratings for.
Mass Personalization of Deep Learning

We discuss training techniques, objectives and metrics toward mass personalization of deep learning models. In machine learning, personalization refers to the fact that every trained model should be targeted towards an individual by optimizing one or several performance metrics and often obeying additional constraints. We investigate three methods for personalized training of neural networks. They constitute three forms of curriculum learning. The methods are partially inspired by the shaping concept from psychology. Interestingly, we discover that extensive exposure to a limited set of training data in terms of class diversity \emph{early} in the training can lead to an irreversible reduction of the capability of a network to learn from more diverse training data. This is in close alignment with existing theories in human development. In contrast, training on a small data set covering all classes \emph{early} in the training can lead to better performance.
The Impacts of Social Capital on Private Enterprise Investment: An Empirical Study in East China Coastal Cities

Ji, H., and Zhang, R., 2019. The impacts of social capital on private enterprise investment: an empirical study in east china coastal cities. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 930–934. Coconut Creek (Florida), ISSN 0749-0208.Using the data of private listed companies in east China coastal cities from 2009 to 2014, this paper empirically tested the impact of social capital on investment of private enterprises in east coastal areas within the context of economic transformation. The results show that political connections, financial relations, and commercial association relations have significant impacts on private enterprise investment in east China coastal cities. However, with the increasingly fair competition market environment and the anti-corruption activities, it finds that the influence of hierarchical social capital (such as political relations) on private enterprise investment in east China coastal cities is gradually decreasing, while market based social capital (such as business association relations) still maintains a significant positive impact on private enterprise investment in east China coastal cities.
BLOCKCHAIN TECHNOLOGY IN COUNTERING CYBER TERRORISM

In the paper, modern blockchain technologies are called a revolutionary phenomenon, equal in importance to the ingenious invention of the 20th century — the Internet. Originally developed for Bitcoin digital currency and launching the network of the same name, the blockchain technology created a platform for the new type of the Internet, influenced the decentralization of the Network according to the distributed registry principle, and began to be used in various types and combinations for various purposes, including cybersecurity. The paper argues that the use of blockchain technology to ensure cybersecurity is infinite due to such unique properties as reliability, accessibility, high adaptability, economic efficiency, profitability. The use of blockchain technologies to combat cybercrime, including cyberterrorism, may extend to control over financial services, transportation or any other industry. However, the growth of criminal activity using the blockchain technology will also be enhanced if the law enforcement agencies are not technologically competent, can detect these developing centers, determine their actions and destroy plans at a faster pace.
Reactive Middleware for Effective Requirement Change Management of Cloud-Based Global Software Development

Requirement change management (RCM) for global software development (GSD), facilitated by the cloud platform, faces communication, coordination and control issues especially when there is no effective information and knowledge-sharing mechanisms. This paper describes a reasonably effective requirement change management approach for cloud-based GSD. Objective: In this regard, we contribute a Reactive Middleware which facilitates a set of guidelines defined to manage change and traceability. Methods: This Reactive Middleware provides services for user management, requirement management, change management, and traceability of cloud-based GSD projects. We present (1) a process model for change management and traceability (CM-T) for cloud-based GSD, and then (2) detail our management approach for system engineering processes as part of the presented GSD guidelines. Results: To ensure that the defined CM-T process model complies with the CMMI Level 2 (Baseline) Capability, the process model is validated using an expert panel review process where a total average, 85.58% of the experts support the maturity of the process model. Also, we demonstrate the continual tight linkage of stakeholders’ requirements and system engineering processes towards change management and traceability, with an Airlock Control System case study.
Incorporating Weights into a Quantum-Logic-Based Query Language

Traditional database query languages are based on set theory and crisp first order logic. However, many applications require imprecise conditions which return result objects associated with a degree of fulfillment. For example, a research paper should be reviewed by giving a degree of fulfillment for originality and relevance. Very often, imprecise conditions of a query are of different importance. Thus, a query language should allow the user to give conditions different weights. This paper proposes a weighting approach which is realized by means of conjunction, disjunction, and negation. Thus, our weighting is completely embedded within a logic. As a result, logical rules are preserved and can be used for query reformulation and optimization. As underlying logic-based query language, we use the CQQL query language. Furthermore we demonstrate that our weighting approach is applicable to further logic-based query languages.
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs

Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterance-denotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial "abstract program" and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WIKITABLEQUESTIONS and WIKISQL datasets. When compared to a standard attention baseline, we observe that the proposed structured-alignment mechanism is highly beneficial.
PENGEMBANGAN SISTEM MEMBACA AL-QUR’AN DENGAN METODE MULTIMEDIA DEVELOPMENT LIFE CYCLE

Al-Qur'an is the Holy Book which is the main and first source in Islamic teachings. It is important for us as Muslims to learn about how to read the Qur'an properly. This research uses two method approach namely Multimedia System Development method and learning method used is to use Tartil method. By using this two-method approach it is hoped that it will be more interesting for students to read and be able to accelerate how to read the Qur'an properly. The results of this research are in the form of an android-based application that can help to learn and read the Al-Qur’an properly. This application is tested using the Black Box method that shows all functions are running properly 100% according to what is expected.. The results show 85% progressed after using the application and only 15% did not progress.
Temporal Multiagent Plan Execution: Explaining What Happened

The paper addresses the problem of explaining failures that happened during the execution of Temporal Multiagent Plans (TMAPs), namely MAPs that contain both logic and temporal constraints about the action conditions and effects. We focus particularly on computing explanations that help the user figure out how failures in the execution of one or more actions propagated to later actions. To this end, we define a model that enriches knowledge about the nominal execution of the actions with knowledge about (faulty) execution modes. We present an algorithm for computing diagnoses of TMAPs execution failures, where each diagnosis identifies the actions that executed in a faulty mode, and those that failed instead because of the propagation of other failures. Diagnoses are then integrated with temporal explanations, that detail what happened during the plan execution by specifying temporal relations between the relevant events.
MR-MOSLO: VM Consolidation Using Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload Detection

Virtual Machine (VM)consolidation is a crucial process in improving the utilization of the resource in cloud computing services.As the cloud data centers consume high electrical power,the operational costs and carbon dioxide releases increases.The inefficient usage of the resources is the main reason for these problems and VM consolidation is a viable solution.VM consolidation includes host overload/under-load detection,VM selection and VM placement processes.Most existing host overload/under-load detection approaches of VM consolidation uses CPU utilization only for the determining host load.In this paper,three resources namely CPU utilization,memory utilization and bandwidth utilization are used for host overload detection and an adaptive regression based model called Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization(MR-MOSLO) is proposed.This model is based on combining the benefits of adaptive threshold based and regression based host overload detection algorithms.This approach of combining these features provide more advantages for threshold setting in dynamic environments with accurate prediction of host overloading.For this purpose, initially,Multiple Regression (MR)algorithm is used which relay on CPU utilization,memory utilization and bandwidth utilization for estimation of the host load conditions.Then a Multi-Objective Seven-Spot Ladybird Optimization(MOSLO)algorithm is introduced to select the upper and lower threshold limits for host utilization.Based on these algorithms,the host overload/under-load is detected with high accuracy and less power consumption.The simulations are conducted in CloudSim tool and the empirical results shows that the proposed MR-MOSLO algorithm detects the host overload efficiently with reasonably similar energy and SLA values while comparatively lesser SLATAH,PDM,SLAV and ESV values than most of the existing methods.
A probabilistic challenge for object detection

To safely operate in the real world, robots need to evaluate how confident they are about what they see. A new competition challenges computer vision algorithms to not just detect and localize objects, but also report how certain they are.
Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks

With the development of generative models, image synthesis conditioned on the specific variable becomes an important research theme gradually. This paper presents a novel spectral normalization based Hybrid Attentional Generative Adversarial Networks (HAGAN) for text to image synthesis. The hybrid attentional mechanism is composed of text-image cross-modal attention and self-attention of image sub regions. Cross-modal attention mechanism contributes to synthesize more fine-grained and text-related image by introducing word-level semantic information in generative model. The self-attention solves the long distance reliance of image local-region features when generate image. With spectral normalization, the training of GANs become more stable than traditional GANs, which conduces to avoid model collapse and gradient vanishing or explosion. We conduct experiments on widely used Oxford-102 flower dataset and CUB bird dataset to validate our proposed method. During quantitative and non-quantitative experimental comparison, the results indicate that the proposed method achieves the best performance on Inception score (IS), Fréchet Inception Distance (FID) and visual effect.
Technical Challenges, Performance Metrics and Advancements in Face Recognition System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Detection Method of Homograph Internationalized Domain Names with OCR

Currently, many attacks are targeting legitimate domain names.In homograph attacks, attackers exploit human visual misrecognition, thereby leading users to visit different (fake) sites.These attacks involve the generation of new domain names that appear similar to an existing legitimate domain name by replacing several characters in the legitimate name with others that are visually similar.Specifically, internationalized domain names (IDNs), which may contain non-ASCII characters, can be used to generate/register many similar IDNs (homograph IDNs) for their application as phishing sites.A conventional method of detecting such homograph IDNs uses a predefined mapping between ASCII and similar non-ASCII characters.However, this approach has two major limitations: (1) it cannot detect homograph IDNs comprising characters that are not defined in the mapping and (2) the mapping must be manually updated.Herein, we propose a new method for detecting homograph IDNs using optical character recognition (OCR).By focusing on the idea that homograph IDNs are visually similar to legitimate domain names, we leverage OCR techniques to recognize such similarities automatically.Further, we compare our approach with a conventional method in evaluations employing 3.19 million real (registered) and 10,000 malicious IDNs.Results reveal that our method can automatically detect homograph IDNs that cannot be detected when using the conventional approach.
MOSS: End-to-End Dialog System Framework with Modular Supervision

A major bottleneck in training end-to-end task-oriented dialog system is the lack of data. To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning, and natural language generation. With only 60% of the training data, MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms state-of-the-art models on CamRest676. Moreover, introducing modular supervision has even bigger benefits when the dialog task has a more complex dialog state and action space. With only 40% of the training data, MOSS-all outperforms the state-of-the-art model on a complex laptop network troubleshooting dataset, LaptopNetwork, that we introduced. LaptopNetwork consists of conversations between real customers and customer service agents in Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision from different dialog modules at both the framework level and model level. Therefore, MOSS is extremely flexible to update in a real-world deployment.
Sistem Monitoring Lulusan Perguruan Tinggi Dalam Memasuki Dunia Kerja Menggunakan Tracer Study

This research is titled Monitoring System of Higher Education Graduates in Entering the World of Work Using Tracer Study. The problem raised in this study is that the BKK (Bursa Kerja Khusus) STMIK Widya Cipta Dharma Samarinda get information about the performance of STMIK graduate students Widya Cipta Dharma, analyze graduate data to obtain the relevance of the competency of STMIK graduates Widya Cipta Dharma Samarinda, change the old system into a system that new. The purpose of this research is to provide information and reports on the results of tracer study data collection from alumni of STMIK Widya Cipta Dharma Samarinda. Data collection methods used are literature study, field studies. The system development method used is the waterfall method. Informatic System of Alumnus Record Tracer Study Based Website of STMIK Widya Cipta Dharma Samarinda is a computer-based system that was built with the aim of processing the alumnus search to find out the description of the profile, performance, and distribution of work location of the alumnus. Informatic System of Alumnus Record Tracer Study Based Website of STMIK Widya Cipta Dharma Samarinda is expected to help assist the BKK (Bursa Kerja Khusus) in recording alumnus and record the track record of alumnus scattered in serveral areas.