Reviewerly/exHarmony huggingface dataset:
Smart government policy implementation for smart city concept realization

The term "smart city" refers to a concept developed to manage urban processes. This system is connected to the information systems of local governments, schools, campuses, transit systems, hospitals, businesses, commerce, power generation, water supply networks, law enforcement, job openings, and other community services. As part of the smart city idea, the Semarang municipal government is committed to establishing an intelligent government. The significance of smart government services based on open data platforms is to promote integrated and transparent public services. As a result, this study will analyze the application of smart government policies in governance in Salatiga City to create a smart city. This study employs a qualitative method with a descriptive approach. According to the study's findings, smart government is a concept that continues the e-Government program. This new concept has been refined following technology and innovation to achieve better government performance, where public services are centralized and integrated into the smart government. To reach a smart city in Salatiga City, Smart Government must be based on three essential elements: support, capacity, and value.
WPFD: Active User-Side Detection of Evil Twins

The bothersome evil twin problem has an active user-side remedy in the form of the Wireless Packet Forwarding Detector (WPFD). The evil twin issue can lead to further security problems, including man-in-the-middle (MITM) attacks. Open public Wi-Fi connections have provided potential answers to this issue, although they often need more data that people either cannot get or are too pricey for regular users. The solution that we created does not require these standards. It allows users’ notebooks to be used to check for evil twins. We have succeeded in developing a user-side detection system that can successfully identify the presence of an evil twin. The packet forwarding behavior generated by the evil twin and the TCP/IP (Transmission Control Protocol/Internet Protocol) protocol are both used by the WPFD. It can identify evil twins without a hitch when we utilize accessible Wi-Fi settings in public spaces or IoT smart homes with unencrypted WLANs (Wireless Local Area Network). However, neither additional data nor a wireless network administrator’s assistance is needed. We compare our work to various publications on popular Rogue Access Points (APs) or IoT (Internet of Things) smart homes. The WPFD does not require any extra setup to install on the host of any end user. According to experimental findings, the WPFD true positive and true negative rates are 100% even when Received Signal Strength Index (RSSI) is 45%.
APPLICATION OF COMMON DATA ENVIRONMENT (CDE) AS A METHOD OF DESIGN REVIEW IN CONSTRUCTION PROJECT

The development of technology in construction projects produces a method known as Building Information Modelling (BIM). Common Data Environment (CDE) is one of the cloud technology adoption systems that can effectively convey various data information from several BIM softwares without having to download the softwares. Searching for accurate project information according to the latest version takes a long time if the document or information is not well structured. This study aims to analyze the application of CDE to the project in the design review and to determine the criteria that affect the success of the implementation of CDE in the design review and the biggest obstacles in the use of CDE. This research was conducted using a survey method which was distributed to representative respondents. The results showed that the implementation of CDE as a design review on the project was in accordance with CDE standards with an average respondent's level of achievement of 83.28%. The most influential criterion on the success of CDE implementation in the design review is data security. The biggest obstacles are in the form of lack of cooperation between CDE users, undisciplined use, and data that is not always updated.
Customizable visualization of quality metrics for object-oriented variability implementations

Many large-scale software systems intensively implement variability to reuse software and speed up development. Such mechanisms, however, bring additional complexity, which eventually leads to technical debt, threatening the software quality, and hampering maintenance and evolution. This is especially the case for variability-rich object-oriented (OO) systems that implement variability in a single codebase. They heavily rely on existing OO mechanisms to implement their variability, making them especially prone to variability debt at the code level. In this paper, we propose VariMetrics, an extension of a visualization relying on the city metaphor to reveal such zones of indebted OO variability implementations. VariMetrics extends the VariCity visualization and displays standard OO quality metrics, such as code duplication, code complexity, or test coverage, as additional visual properties on the buildings representing classes. Extended configuration options allow the user to choose and combine quality metrics, uncovering the critical zones of OO variability implementations. We evaluate VariMetrics both by reporting on the exposed quality-critical zones found on multiple large open-source projects, and by correcting the reported issues in such zones of one project, showing an improvement in quality.
A Fine-Grained Network Congestion Detection Based on Flow Watermarking

With the rapid development of the network, how to effectively reduce the dynamic delay and improve the performance of the network is an important and challenging problem. Specifically, network congestion is one of the key factors that hurt the network performance, so real-time detection of the network congestion is critical for recovering the network failure quickly. Current research in congestion detection mainly faces the problems of occupying extra bandwidth, decreasing the ratio of the effective payload of the packet, increasing the burden of the switches, etc. In this paper, we apply flow watermarking to network congestion detection and propose a fine-grained network congestion detection method based on flow watermarking. We also combine it with the eBPF (extended Berkeley Packet Filter) to improve the performance of congestion detection. Theoretical analysis and experimental results show that the changes in network status can be reflected in real-time and accurately in the watermark decoding information. The network congestion detection based on flow watermarking can detect network status on a small time scale and realize low-overhead and easily deployed congestion detection.
Cyber Security; Etiology and Importance

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats; malware, ransom-ware, social engineering, phishing etc. To prevent cyber-attacks one can use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, rganisations needed a strong base there are 5 types of cyber securities; Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time US is highly based on computers and on different software so it is really important for US to be more conscious about the security as they get many threats almost everyday for hacking their data and accounts.Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded on 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks so they try their best to take every precaution to prevent the hacking with double security and password manager tools.
A Simple Survey of Pre-trained Language Models

Pre-trained Language Models (PTLM) have remarkable and successful performance in solving lots of NLP tasks nowadays. And previous researchers have created many SOTA models and these models are included in many long surveys(Qiu et al., 2020). So, we would like to conduct a simple and short survey on this topic to help researchers understand the sketch of PTLM more quickly and comprehensively. In this short survey, we would provide a simple but comprehensive review of techniques, benchmarks, and methodologies in PTLM. And we would also introduce the applications evaluation of PTLM in this simple survey.
Enhanced Sparse Detection for End-to-End Object Detection

In this paper, we propose an enhanced end-to-end object detector based on Sparse R-CNN (EnSparse R-CNN), which aims at backbone, neck and head of object detector. For the backbone network, we constructure an aggregate residual network (ARNet) with the aggregate connection, which can achieve global features to improve the ability of feature extraction by extracting the salient feature from the feature maps. For the neck network, we build an attention context feature pyramid network (ACFPN) to fuse the global features with the context features, which can integrate multi-layer features and attention features to make full use of global context features. For the detection head, we design a gradually refined detection head (ReHead) to detect objects, which adopts the sparse detection with the refined bounding box regression to improve the confidence of the regression. The experimental results on COCO dataset show that EnSparse R-CNN outperforms Sparse R-CNN by 2.5 % and the anchor-free one-stage detector AutoAssign by 1.1%.
Age Prediction from Sclera Images using Deep Learning

Automatic age classification has drawn the interest of many scholars in the fields of machine learning and deep learning. In this study, we looked at the problem of estimating age groups using different biometric modalities of human beings. We looked at the problem of determining age groups in humans using various biometric modalities. Specifically, we focused on the use of transfer learning for sclera age group classification. 2000 Sclera images were collected from 250 individuals of various ages, and Otsu thresholding was used to segment the images using morphological processes. Experiment was conducted to determine how accurately the age group of a person can be classified from sclera images using pretrained CNN architectures. The segmented images were trained and tested on four different pre-trained models (VGG16, ResNet50, MobileNetV2, EffcientNet-B1), which were compared based on different performance metrics in which ResNet-50 was shown to outperform the others, resulting in an accuracy, precision, recall and F1-score of 95% while VGG-16, EffcientNetB1, and MobileNetV2 had 94%, 93%, and 91%, respectively. The findings from the study showed that there is an aging template in the sclera that can be utilized to classify age.
Sentiment Analysis on Amazon Product Reviews

As everyone is aware of the fact that in this technological era, we all are familiar with the online shopping and the feedbacks associated with them. Since the reviews, ratings and feedback are crucial part of any business to keep track of what are views of the customers on the product. The reviews are the only way to keep up with the customer’s needs, advices and corrections that needs to be in the product. For a single type of product which a company sells, includes millions of reviews associated with it and a proper channel is needed to be employed to work with it so as to filter out the information what a customer thinks about the product or how the product is performing in the market. In this paper, authors compared the Ensemble Classifier with other algorithms like logistic regression, SVM, Naïve Bayes, Decision Tree and Multinomial and found out that Ensemble Classifier outperformed the mentioned machine learning algorithms.
Noise-tolerant object detection and ranging using quantum correlations

Abstract Imaging, detection and ranging of objects in the presence of significant background noise is a fundamental challenge in optical sensing. Overcoming the limitations imposed in conventional methods, quantum light sources show higher resistance against noise in a time-correlation-based quantum illumination. Here, we introduce the advantage of using not only time correlations but also polarization correlations in photon pairs in the detection of an object that is embedded in a noisy background. In this direction, a time- and polarization-correlated photon pair source using the spontaneous parametric down-conversion process is exploited. We found that the joint measurement of correlated pairs allows distinguishing the signal from the noise photons and that leads to an improved signal-to-noise ratio. Our comparative study revealed that using polarization correlations in addition to time correlations provides improved noise rejection. Furthermore, we show that polarization correlation allows undoing the detector limitation where high background often leads to detector saturation.
ALIGNING BIM AND GIS - CITYGML AND PLU TO ACHIEVE COMPLIANCE CHECKING MODEL

Compliance checking for building models, cities and territories involves formalizing a set of model schemas and constraints.In this context, we aim at, on the one hand, conceiving a multi-scale model combining BIM and GIS information using semantic technologies (federated approach) and, on the other hand, at transforming urban regulations rules into SPARQL queries to allow querying and checking the level of compliance of the considered building.In this article we are introducing urban rules, define how they are connected to GIS and BIM.We give first present previous work related to transforming natural language rules into SPARQL queries, then we define directions to be addressed by our future work.
Boosting Convolutional Neural Networks Using a Bidirectional Fast Gated Recurrent Unit for Text Categorization

This paper proposes a hybrid text classification model that combines 1D CNN with a single Bidirectional Fast GRU (BiFaGRU) termed as CNN-BiFaGRU. Single convolution layer captures features through a kernel applying 128 filters which are slide over these embeds to find convolutions and drop entire 1D feature maps by using Spatial Dropout, combined vectors using Max-Pooling layer. Then, the Bidirectional CUDNNGRU block to extract temporal features, results of this layer is normalize by the Batch Normalization layer and transmitted to the Fully Connected Layer. The output layer produces the final classification results. Precision/loss score was used as the main criterion on five different datasets (WebKb, R8, R52, AG-News, and 20 NG) to assess the performance of the proposed model. The results indicate that the precision score of the classifier on WebKb, R8, and R52 data sets significantly improved from 90% up to 97% compared to the best result achieved by other methods such as LSTM and Bi-LSTM. Thus, the proposed model shows higher precision and lower loss scores than other methods.
Chosen-Plaintext Attack on Energy-Efficient Hardware Implementation of GIFT-COFB

Electronic devices are connected now more than ever with the prevalence of the Internet of Things. The ever-increasing communication between these lightweight devices presses for the need to embed a cryptographic mechanism to ensure the confidentiality and authentication of data. Lightweight cryptography nicely supports the need for encryption mechanisms on IoT devices with limited memory, storage, and computing capability. This paper focuses on breaking the hardware implementation of GIFT-COFB, one of NIST's Lightweight Cryptography finalists. The 2-round partial unrolled design of GIFT-COFB is shown to be the most energy-efficient among all other <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> -round partial unrolling and fully unrolled settings [1]. In this paper, we propose a chosen-plaintext attack to retrieve the master key <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> effectively and demonstrate the feasibility of our proposed attack on the 2-round partial unrolled GIFT-COFB. Our efficient attack can derive the secret key by exploiting the nonlinearity of the Sboxes with a worst-case complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(2^{4})$</tex> .
Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) &amp;amp; Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session &amp;amp; competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.
Towards Boosting Requirements Engineering of a Health Monitoring App by Analysing Similar Apps: A Vision Paper

Due to the variety of sensors and portability of mobile phones, an increasing amount of mobile apps are released for the purpose of health monitoring. To design a new health monitoring app, a conventional approach is to define a goal model with the intervention of stakeholders. As there are a large number of apps in this domain, many of them have similar features, which can be exploited to reduce the time consumption of requirements elicitation, improve their quality and help the requirements prioritization. In this paper, we propose a novel requirements engineering approach by analysing similar apps. In this approach, we identify similar apps and analyze their descriptions, user reviews, Android APK and source code for the construction and enrichment of a Domain Feature Model. This model will be used as a support for the requirements engineers in different phases of requirements engineering.
Digital pathology incorporation in CPT codes: a gateway for expansion into clinical practice?

Digital pathology is a field that is penetrating clinical practice and medicine with increasing power. That is why recently, for the first time in history, practices from this discipline have been incorporated into Current Procedural Terminology (CPT) codes. This fact has a direct impact on computational pathology and could be the capstone to promote the immersion of algorithms in clinical diagnosis.
Pengaruh Reward dan Punishment Terhadap Motivasi Belajar Siswa Kelas IV Sekolah Dasar

Reward and punishment are very important in motivating students because through reward and punishment students will become more confident and responsible with the tasks given. Reward and punishment are two contradictory words, however, the two things are interrelated. The purpose of this study was to determine how the influence of reward and punishment on the learning motivation of fourth-grade elementary school students. This type of research is quantitative. This study uses a random sampling technique, the research subjects are all fourth-grade students for the 2021/2022 academic year with a total student population of 92 students. The results of data processing shows that the independent variables (reward (X1) and punishment (X2)) simultaneously have a significant effect on the dependent variable of learning motivation (Y) this is indicated by the calculated F value of 44.722 which is greater than F table 2.69, meaning this causes Hi to be accepted.
A privacy-preserving dialogue system based on argumentation

Dialogue systems are a class of increasingly popular AI-based solutions to support timely and interactive communication with users in many domains. Due to the apparent possibility of users disclosing their sensitive data when interacting with such systems, ensuring that the systems follow the relevant laws, regulations, and ethical principles should be of primary concern. In this context, we discuss the main open points regarding these aspects and propose an approach grounded on a computational argumentation framework. Our approach ensures that user data are managed according to data minimization, purpose limitation, and integrity. Moreover, it is endowed with the capability of providing motivations for the system responses to offer transparency and explainability. We illustrate the architecture using as a case study a COVID-19 vaccine information system, discuss its theoretical properties, and evaluate it empirically.
Application of Augmented Reality in Teaching and Learning in Engineering Programs

Augmented reality is one of the new emerging technologies in the world, since its appearance was considered for many years its application in education as a tool in the student's learning process. Taking these aspects into account, the present research article was developed, which is based on the design proposal using augmented reality in the learning process at a university in northern Lima. In which the analysis, architecture and development of a prototype was showed in detail. The result of the article was obtained by means of surveys, in the first survey it was demonstrated that the augmented reality is an unknown technology for several of the students and the feasible use in the distance education as a support in the classes, in the same way the second survey demonstrated a high interest on the part of the students at the moment of using this technology in its learning of university formation.
BlockTrainHK: An online learning game for experiencing blockchain concepts

Although blockchain is regarded as a promising and revolutionary technology, not everyone, particular non-technical students, can effectively understand and appraise its mechanism. To spread knowledge of blockchain technology, an online educational game for learning blockchain concepts named ‘BlockTrainHK’ is designed and developed from the React Native framework. Users from multi-disciplinary backgrounds can experience the block mining process, where the whole journey of building the blockchain and other advanced concepts, including immutability, Merkle tree, and zero knowledge proof, can be revealed, such that technical barriers of learning blockchain can be overcome.