Reviewerly/exHarmony huggingface dataset:
Pendampingan Penulisan Artikel Hasil Penelitian Pada Jurnal Ilmiah (Kunci Sukses Mahasiswa Menulis Di Jurnal Bereputasi Nasional)

This service is in the form of mentoring activities that aim to understand students in writing accredited national scientific articles that provide the key to student success in writing in nationally reputable journals). This service activity was conducted at the Institut Agama Islam Ma’arif NU (IAIMNU) Metro Lampung, Indonesia. This seminar activity consists of 4 actions: presentation of material by a servant, review of scientific articles of one student by a servant, question and answer, and evaluation of service. The evaluation results of this service activity indicate that the percentage of the score in filling out the questionnaire is 90% which is in the high category, which means that this mentoring activity is very successful in providing understanding to participants in writing scientific articles. This success is due to good cooperation between the Head of Study Program, presenters from the PKM element, and students participating in this mentoring activity. The service recommends that for students to write scientific articles, students must understand three knowledge: the stages of writing, writing components of scientific papers, and submitting scientific articles to journals.
medSafe- A Blockchain based Electronic Health Record Application

For a long time, the medical business was the only one that accepted new technologies first. As a result, they now accept cloud computing to store patient records in the cloud, which they refer to as an "Electronic Health Record." They went to the cloud for a variety of reasons, including the inability to retain a traditional base of servers within them. Cloud computing is more secure and responsive. Their security is sometimes compromised. Blockchain is currently an interesting research topic, and the benefits it provides have been used by a range of applications. Blockchain will substantially improve the healthcare sector if it is connected with its means because to its robust nature in privacy and data protection. It is also a network-based technology that is decentralized. Each node will contain a duplicate of the data. Furthermore, EHR systems require a system with strong data security, integrity, and management challenges. Blockchain is made up of blocks that contain data. With an address, each block in the blockchain points to the next block. To put it simply, if someone tampers with the data in the block, the hash of the block changes, and every block in the blockchain becomes invalid. The purpose of this study is to demonstrate how blockchain can be connected with HER systems.
A Reputation and Security Mechanism for Heterogeneous Vehicular Networks

Heterogeneous Vehicular Networks (HetVNETs) are an evolution of the traditional Vehicular Networks (VANETs) that integrate long-term and short-term communications technologies to deliver strategic services for roads and smart cities (for example, Intelligent Transport, Safety Notification, Multimedia Content, and others). Despite the recent evolution of HetVNETs, points related to security, data integrity and privacy are still open issues, since there is no standard to achieve trust among the network nodes. A promising approach to achieve trust is the usage of a reputation system, that monitors the behavior of network nodes to determine its trustworthiness. However, this de-termination is complex, because the uncertainty coming from the HetVNETs features, such as fast mobile nodes, self-organization, unstable communication links and frequently changing topology. Within this context, this paper presents a reputation system for HetVNETs, called TrustV, to perform a faster and reliable determination of network nodes trustworthiness based on game theory, as well as the long-term and short-term communication technologies of HetVNETs. The game theory approach allows TrustV to overcome the uncertain issues of HetVNETs, since the modeling of the cooperation as a game can identify selfish and malicious nodes during the time. The results suggest that the TrustV achieves a very high cooperation rate between vehicles at Het VNETs, even as it is capable to detect selfish and malicious nodes as well as black hole and grey hole attacks.
Neural Scene Decoration from a Single Photograph

Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty indoor space and a list of decorations with layout determined by user, we aim to synthesize a new image of the same space with desired furnishing and decorations. Neural scene decoration can be applied to create conceptual interior designs in a simple yet effective manner. Our attempt to this research problem is a novel scene generation architecture that transforms an empty scene and an object layout into a realistic furnished scene photograph. We demonstrate the performance of our proposed method by comparing it with conditional image synthesis baselines built upon prevailing image translation approaches both qualitatively and quantitatively. We conduct extensive experiments to further validate the plausibility and aesthetics of our generated scenes. Our implementation is available at https://github.com/hkust-vgd/neural_scene_decoration .
A Formal Facial Expression Recognition System Using Novel Scattered Image Classification by Comparing CNN over Alexnet

Aim:This study article's major goal is to classify facial expressions more accurately by utilizing Convolutional Neural Networks (CNNs) in contrast to AlexNet. Materials and Methods:The data of the facial expressions is taken from the FER2013 available on kaggle.The Convolutional neural network (CNN) is the algorithm most common method of visual analysis with best accuracy and Alexney is also an algorithm used here to compare the accuracy of novel image classification.Results: The Convolution neural network (CNN) produces 82% accuracy in predicting facial expressions on the dataset,whereas AlexNet produces 76% accuracy.Convolutional neural network (CNN) is better than AlexNet.The study groups differ from one another in a statistically significant way (p <0.05).Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to AlexNet for predicting facial expressions.
Resolving the Paradoxical Effect of Human-Like Typing Errors by Conversational Agents

Conversational agents (CA) interact with users via natural language in a turn-taking communication. They have been shown to trigger anthropomorphism in users, leading to the perception of humanness. However, this effect is influenced by various aspects, such as the CA having a name and its choice of words. In this context, the expected effect of typing errors is paradoxical. On the one hand, making typing errors is human-like and should, therefore, increase the perception of humanness. On the other hand, users understand that CAs are not human, and typing errors can be perceived as unexpected. To investigate the effect of typing errors on perceived humanness, we employ the dual processing theory of human cognition and propose a NeuroIS experiment to test our hypothesis. In this research in progress paper, we present the experimental design, expected outcome, and reflect on possible implications for CA research.
An Image Denoising Model Based on Nonlinear Partial Diferential Equation Using Deep Learning

In this paper, we present a deep neural network-based framework for solving nonlinear partial differential equations (PDEs) and applying in denoising image. A loss function that relies on form PDEs, initial and boundary condition (I/BC) residual was proposed. The proposed loss function is discretization-free and highly parallelizable. The network parameters are determined by using stochastic gradient descent algorithm. We demonstrated the performance of proposed method in solving nonlinear partial diferential equation and applying image denoising. The experimental results from this method were compared to the efficient PDE’s numerical method. We showed that the method attains significant improvements in term image denoising.
A Camera Movement Guidance Method based on Multi-Object Tracking

Multi-object tracking (MOT) attracts great attention in computer vision while playing an increasingly essential role in manufacturing and life. There is currently an urgent requirement for practical implementation of MOT algorithms to guide production in industry. Nevertheless, the algorithm guiding camera movement is prone to be naive in reality to some degree, which is not conducive to handle complex situations and may cause damage. In this paper, we propose a deep-learning-based MOT method to guide camera movement. In particular, jointly learnt detector and embedding model (JDE) is adopted to extract the features of live stream through data preprocessing and training, which detects the spacial and temporal information of objects, for instance, pedestrians. Moreover, we propose a cognitive-based network segmentation method which makes edge-cloud collaboration possible. Additionally, object location information provided by deep learning allows object clustering and weight allocation, followed by utilizing PID algorithm to guide camera motion. Our method is compared with conventional model through several metrics, especially Euclidean Average Distance, which indicates the effectiveness, reliability and robustness of our model.
TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING

Utilizing virtualization technology, a cloud computing service provides on-demand access to computer resources &amp; services through the internet. There are new ways to control the functioning of cloud resources since they are constructed in diverse places. More than one algorithm Possibly included in these plans. One of the most important components of a high-performing cloud computing system is the scheduling mechanism. Fault tolerance &amp; load balancing methods are also included in the scheduling system, not only task scheduling strategies. Fault handling Possibly accomplished via using scheduling systems. We analyze &amp; contrast a few different scheduling algorithms to see what they have going for them in terms of benefits &amp; drawbacks.
Evaluating Automated and Hybrid Neural Disambiguation for African Historical Named Entities

Documents detailing South African history contain ambiguous names. These may be due to people having the same name or the same person being referred to by different names. Thus, when searching for information about a particular person, the name used may affect the results. This problem may be alleviated by using a Named Entity Disambiguation (NED) system to disambiguate names by linking them to a knowledge base. Hence, a multilingual language model-based NED system was developed to disambiguate people’s names within a historical South African context using documents from the 500 Year Archive (FHYA) written in English and isiZulu. The multilingual language model-based system improved on a probability-based baseline and achieved a micro F1-score of 0.726. However, the system performed worse on documents written in isiZulu compared to the English documents. Thus, the system was augmented with handcrafted rules, resulting in a small but significant improvement in F1-score.
Content Based Spam Classifying Algorithms in Email

The era of globalization has brought upon an era which we have never seen before. From a year, a month, a week, to a fraction of a second to send a message across the globe. The technological breakthrough of emails has simplified information deliveries and exchanges. Though an innovation means a new threat has also emerged in the form of spam emails. Spam emails are meant to target the vulnerability of the system in which anyone could send an email to someone if they knew their email address. These spam emails contain unwanted dangers that could prey on an unknowing victim. To combat this threat, spam classifying algorithms are developed, a method that could distinguish a spam email when it passes through. This paper will do a review on three content-based e-mail spam filtering, which are Support Vector Machines, Random Forest, and Multimodal Naïve Bayes. Comparing the effectiveness and usability of all three methods towards their capabilities and disadvantages were done. The experiment results in Support Vector Machines as the most efficient and the rest falling behind albeit by a fraction. Proving that even the smallest details matter in classifying algorithms.
The Effectiveness of Quiz Demonstration Practice Revision (QDPR) Model in Helping Students’ English Pronounciation

The study examined the effectiveness of Quiz Demonstration Practice Revision (QDPR) in helping students’ English pronunciation. There were two research questions in the study; 1) Is Quiz Demonstration Practice Revision (QDPR) model effective in helping students’ English pronunciation?, and 2) How Quiz Demonstration Practice Revision (QDPR) model is effective in helping students’ English pronunciation? To answer the research questions, the present researcher designed the study using an experimental quantitative research. The present researcher determined all of X grade students at MA. Tarbiyatul Muslimin Dasan Maalan as the population of the study and took 50 students as the sample of the study; 25 students as an experimental group and 25 students as a control group. In collecting the data of the study, the present researcher presented a test that as an instrument of the study. In analyzing the data, the present researcher calculated it using SPSS 16.0. The result of the study revealed the mean score in pre-test was 49.64 and the mean score was 73.68, while the control group, the mean score in pre-test was 71.60 and the mean score was 72.88. This means that Quiz Demonstration Practice Revision (QDPR) model is effective in helping students’ English pronunciation. Furthermore, the result of Paired Sample t-test in Sig. (two-tailed) is .000 which is lower than the significant level .005. It means that Quiz Demonstration Practice Revision (QDPR) model is significantly effective in helping students’ English pronunciation. Therefore, Quiz Demonstration Practice Revision (QDPR) model can be implemented in helping students’ English pronunciation especially senior high school students.
Implementation and Analyzing SURF Feature Detection and Extraction on WANG Images Using Custom Bag of Features Model

A novel technique of image classification using BOVW model also known as Bag of Visual Words is very popular for retrieval of images using features instead of text vocabulary. The entire process first involves feature detection of images by selecting key points or forming a Grid over images, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which SURF, a binary feature descriptor is employed. K-means clustering is then applied in order to quantize and make the bag of visual words. Every image, expressed as a histogram of visual words is fed to a supervised learning model, SVM for training. SVM is then tested for classification of images into respective classes. Matlab is used for implementation using bag class with Extractor fuction over 1000 image dataset WANG with 10 different categories.
ESL Students’ Learning Strategies for the Improvement of their Language Skills

Effective language strategies contribute to learners’ success in learning English. In relation to this, this study aims at investigating ESL students’ strategies in learning a language. Those strategies are metacognitive strategies, deep cognitive strategies, and surface cognitive strategies. 50 English department students participated in a survey that required them to reflect on their own learning strategies for the four language skills by answering 80 questions. The result of the study suggests that the metacognitive strategies are the most highly used to improve their language skills compared to the deep cognitive and surface cognitive strategies that are moderately used. The data also showed those strategies are widely used in written texts insteadof spoken texts.
Inner Product Encryption from Middle-Product Learning with Errors

Inner product encryption (IPE) is an essential area of research for functional encryption systems. It has extensive applications in emerging fields such as cloud computing, where it can improve users’ access control and fine-grained queries. Lattice-based inner product encryption has the advantages of resistance to quantum algorithm attack and relatively simple encryption algorithm, so it has a good application prospect. At present, the provable security of lattice-based public key cryptography algorithms is mostly based on Learning With Errors (LWE) and Polynomial Learning With Errors (PLWE) problems. However, the majority of encryption schemes based on the LWE problem have the issue of excessively large public keys and ciphertexts size. Although the encryption scheme based on the PLWE problem reduces the size even further, its hardness is limited by polynomials, and the security guarantee is diminished. Compared with LWE and PLWE problem, Middle-Product Learning With Errors (MP-LWE) problems loosen the restrictions on polynomials, and make full use of the polynomial characteristics to achieve a better balance between security and efficiency. Therefore, in this paper, we present the first MP-LWE based inner product encryption schemes with Indistinguishability against Selective Chosen Plaintext Attack secure (Sel-IND-CPA-secure), while keeping the algorithm structure simple and efficient.
GUM: A Guided Undersampling Method to Preprocess Imbalanced Datasets for Classification

In imbalanced datasets, where the majority class has significantly more instances than the minority class, conventional classification methods exhibit poor minority-class detection performance because they tend to classify most instances as majority instances. To address this problem, this paper presents a general-purpose imbalanced-data preprocessing method that combines two instance-selecting techniques to obtain a clean and balanced set of training instances. The first technique, ensemble outlier filtering, removes outlier instances from both majority and minority classes. The second technique, normalized-cut sampling, samples the majority class aiming to preserve its distribution across the majority region. Our proposed data preprocessing method uses these two techniques and can be combined with any general classification methodology on the sub-sampled data to construct a classification model. Computational results show the proposed method outperforms several widely used imbalanced-data classification methods.
Excommunication: Transforming $$\pi $$-Calculus Specifications to Remove Internal Communication

In this paper, we present a new automatic transformation algorithm called excommunication that transforms $$\pi $$ -calculus processes to remove parallelism, and hence internal communication. We prove that the transformation is correct and that it always terminates for any specification in which the named processes are in a particular syntactic form we call serial form. We argue that this transformation facilitates the proving of properties of mobile processes, and demonstrate this by showing how it can be used to simplify a leakage analysis.
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture search (NAS) methods have omitted resource utilization, preventing DNNs to take full advantage of the target inference platforms. Modeling resource utilization efficiently and accurately is challenging, especially for widely-used array-based inference accelerators such as Google TPU. In this work, we propose a novel hardware-aware NAS framework that does not only optimize for task accuracy and inference latency, but also for resource utilization. We also propose and validate a new computational model for resource utilization in inference accelerators. By using the proposed NAS framework and the proposed resource utilization model, we achieve $$2.8-4\times $$ speedup for DNN inference compared to prior hardware-aware NAS methods while attaining similar or improved accuracy in image classification on CIFAR-10 and Imagenet-100 datasets. (Source code is available at https://github.com/yuezuegu/UBoostNAS ).
Cybersecurity of Critical Infrastructures

The chapter gives an overview of different viewpoints and experiences about the real impact of cyber threats to critical infrastructure services delivery.
A Machine Learning Method for Customer Sentiment Analysis on Social Media

Customer Data analysis is a significant part of different ventures utilizing figuring applications, for example, E-business and online shopping. Enormous information is utilized for advancing items which gives better availability among retailers and customers. These days, individuals consistently utilize online advancements to think about the best shops for purchasing better items. This shopping experience furthermore, assessment of the customer’s shop can be seen by the client experience shared across online media stages. Another client while looking through a shop needs data about manufacturing date and manufacturing price, offers, quality, and ideas which must be given by the past client experience. The MRP and MRD are as of now accessible on the item cover or mark. A few methodologies have been utilized for anticipating the item subtleties however not giving precise data. This paper is persuaded toward applying Machine Learning algorithms for picking up, breaking down, and arranging the item data and the shop data dependent on the client experience. The accuracy of the proposed method is 99.4%.
AN APPLICATION OF MACHINE LEARNING TO COLLEGE ADMISSIONS: THE SUMMER MELT PROBLEM

We present educational data mining research, specifically an application of machine learning to college admissions. Meeting targets for the number of students admitted and enrolled is crucial for many institutions of higher education since tuition-based income often serves as a major component of the operating revenue budget. Enrollment targets and diversity goals can be hampered by summer melt: the phenomenon in which students who, after being admitted and having committed to attend a college or university in the spring, do not actually enroll in the fall. Using 6 years of data from 2014 through 2019 of students admitted to a small liberal arts college in California, we investigate the application of supervised machine learning models to predict and identify those admitted students who will decline their admission offers, those that will accept their admission offers, and those students who are in danger of "melting away" over the summer. Institutions can use our summer melt model to estimate how many and identify which students will fail to enroll in order to implement activities and provide support to achieve their enrollment goals. The results of our research should encourage other institutions of higher education to apply machine learning algorithms to their admissions data to effectively estimate the size of the incoming student body and achieve other institutional goals.