Reviewerly/exHarmony huggingface dataset:
Student Services Center (SSC): Upaya pembinaan prestasi, karir, dan kewirausahaan bagi mahasiswa melalui community based participatory research

The purpose of this service is to develop academic competence, non-academic competence, entrepreneurial competence and prepare the careers of students and alumni of the Islamic Economics and Business Faculty IAIN Salatiga. The service is carried out using a Community Based Participatory Research (CBPR) approach. Laying foundation is done by analyzing the situation, reasons for choosing partners, conditions of assistance. partner engagement plans, and framing research questions for action plans. The tools used are community mapping in the hope of capturing the strengths, potentials, and issues that exist among students. Action plans carried out by establishing the Students Service Center (SSC), Launching Students Service Center (SSC), developing student academic competencies through presentation skill training, debate competition mentoring/coaching, business plan mentoring/coaching, training in writing scientific papers, developing non-technical competencies student academics through futsal day and volleyball day, developing student entrepreneurship potential by mentoring millennial entrepreneurs, entrepreneurship talk shows, halal food festivals, student career development through advanced study seminars, scholarship interview mentoring, leadership talk shows, work simulations. The contribution of this research creates social changes in the research process and meaningful results that can improve campus reputation through student achievement
Report on the fourth workshop on recommendation in complex environments

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
Stratigraphic and structural control over permeability distribution in poorly consolidated siliciclastic rocks

Permeability models are very relevant for the characterization of oil systems. However, limitations related to the resolution of seismic data make it difficult to identify subseismic, sedimentary, and tectonic structures, which can significantly impact the flow pattern. This study analyzed the spatial variability of permeability according to stratigraphic and structural geology control to propose a useful model for poorly consolidated, fractured, and faulted siliciclastic reservoirs. In an outcrop analogue to this type of reservoir, air permeability was measured in 3 orthogonal directions at 24 points, spaced 2 m apart.The models were obtained by sequential Gaussian simulation (SGS) after statistical data treatment. The models were validated to ensure the consistency of the generated scenarios. Permeability values showed a positive asymmetric distribution and reduced medians toward tectonic structures. The fitted semivariogram model was exponential, with higher spatial continuity in the horizontal flow direction and lower in the vertical one. The permeability models emphasized the importance of considering subseismic structures in the flow analysis of reservoirs since they have proven to play a significant role in the permeability distribution in the outcrop assessed.
Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss

Deep neural networks have recently thrived on single image depth estimation. That being said, current developments on this topic highlight an apparent compromise between accuracy and network size. This work proposes an accurate and lightweight framework for monocular depth estimation based on a self-attention mechanism stemming from salient point detection. Specifically, we utilize a sparse set of keypoints to train a FuSaNet model that consists of two major components: Fusion-Net and Saliency-Net. In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy. The proposed method achieves state-of-the-art results on NYU-Depth-v2 and KITTI while using 3.1-38.4 times smaller model in terms of the number of parameters than baseline approaches. Experiments on the SUN-RGBD further demonstrate the generalizability of the proposed method.
Kernel-Based Sampling of Arbitrary Signals

Point sampling is widely used in several Computer Graphics’ applications, such as point-based modelling and rendering, image and geometry processing. Starting from the kernel-based sampling, which approximates an input signal on a regular grid as the sum of Gaussian kernels, we introduce a set of additional variables that control the kernels’ width and height. These additional variables allow us to improve the quality of the distribution of the samples, and to achieve a higher approximation accuracy and a more accurate feature preservation, with a slightly higher computational cost. To further improve the sampling with respect to the input data, we introduce a sampling initialisation for processing high resolution signals, without incurring in limits for memory allocation, and a sampling optimisation, which adaptively selects the number and location of the samples to achieve the target approximation accuracy, without oversampling the input signal. To show the generality of the proposed approach for unstructured data of arbitrary dimension, we apply our kernel-based sampling to different types of data, such as 2D images, solutions to PDEs on arbitrary domains, and vector fields.
Research on the Benefit-Sharing Model of Collaborative Innovation Mechanism in Power Innovation Park

Currently, innovation is no longer limited to a single enterprise. The joint realization of collaborative innovation by multiple parties has become the main driving force for innovation and development. The Power Innovation Park is an emerging innovation and entrepreneurship model. It is guided by power grid companies, and the school, government, and enterprise are combined in the park. The Power Innovation Park urgently needs theoretical guidance in terms of the resource allocation mechanism, support incentive mode, and benefit-sharing mode, so this paper takes the park as an example to study the benefit-sharing mode of collaborative innovation. In this paper, the transformation methods of the Power Innovation Park are divided into two types: cooperation between members within the park and dominated by external enterprises in the park. This paper applies cooperative game theory and HJB equation to study the benefit-sharing model of each member in the Power Innovation Park under two different result transformation modes. Research has shown that an appropriate method of transformation of results can maximize the overall and individual interests of the park and that individual interests are affected by many factors. Finally, this paper puts forward policy recommendations to promote the collaborative innovation development of the innovation park through the research results.
Teaching Reform of Modern Electrical Control Technology

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.
Privacy-Preserving Cloud Storage in Medical IoT using Hybrid Cryptography Algorithm

Medical electronics is additionally visiting advance with the applying of IoT, it is the fastest-growing technology. IoT is about finding an application everywhere and in everything. during this paper, we proposed a straightforward patient heartbeat display as an IoT application that could read the heart beat rate that continuously monitors the heartbeat rate and updates to an IoT platform which is the Thingspeak IoT Platform.Cloud is utilized in various fields like industry,healthcare,military, etc. Data stored during this cloud are often accessed or retrieved at the user's request without direct access to the server computer. But the foremost important concern regarding the storage of data online that's on the cloud is that of Security. These security concerns are often solved using various ways, the foremost commonly used techniques are cryptography and steganography. But sometimes one technique or algorithm alone cannot provide high-level security. So we've to introduce a replacement security mechanism that uses a combination of multiple cryptographic algorithms of symmetric key and steganography. During this proposed system 3DES (Triple encoding Standard), BlowFish, and AES (Advanced Encryption Standard) algorithms are used to provide security to data. Key information will contain the knowledge regarding the encrypted part of the file, the algorithm, and thus the key for the algorithm. File during encryption is split into 7 fragments.
Molecular pathology: the new era of clinical pathology

Histopathology and molecular pathology are two important branches of human pathology, which provide a bridge between clinical and basic medicine.As a basic discipline of clinical pathology, histopathology is the basic morphology for the study of pathogenesis.As a developing discipline, molecular pathology has gradually penetrated the entire process of clinical management.Digital pathology based on artificial intelligence has prompted the deep integration of histopathology, molecular pathology and clinical information to enrich the mission of pathology.Clinical pathologists should integrate histopathology and molecular pathology to accurately practise personalized medicine by new technologies and methods.
High Confidence Attribute Recognition For Vehicle Re-Identification

Vehicle re-identification aims to associate images or videos of the same vehicle collected from different cameras. Many existing methods address the vehicle re-identification problem by explicitly learning distinguishable global features. However, vehicle attributes, i.e., logo category and orientation, play an indispensable role in identifying vehicles. In this paper, we first propose deep models to recognize vehicle attributes. Then, based on these attributes, we adopt a High Confidence Attribute Network (HCANet) to extract weighted global features. A comprehensive evaluation on the VehicleID dataset shows that our approach achieves competitive results.
Reservoir sedimentation regime analysis: case study of Kedungombo reservoir and Sermo reservoir

The Kedungombo and the Sermo Reservoirs have problems in fulfilling basic services because of sedimentation. Sedimentation that occurs in each of the reservoirs would form a specific reservoir sedimentation pattern that is supposed to be similar because the hydrology and physiography conditions of the reservoir's catchment area are similar. This study aims to determine the dynamics of sedimentation patterns that occur in the dead storage for reviewing the characteristics/sedimentation regime of the two reservoirs. The analysis was carried out by processing bathymetrical data which were processed into a digital terrain model (DTM) using ArcGIS. Furthermore, the storage volume, sedimentation volume, storage percentage, and specific reservoir sedimentation rate are calculated. The results showed that the two reservoirs showed an increase in sedimentation volume each year so that the reservoir characteristic curve shifted from the plan graph. The dead storage capacity of Kedungombo Reservoir is 100% in 1989 to 43% in 2016 and 100% of Sermo Reservoir in 1997 to 58% in 2011. The specific reservoir sedimentation rate, i.e. 0.0031 and 0.0042 million m3/year/km2 for the Kedungombo Reservoir (between 1989 and 2016) and the Sermo Reservoir (between 1997 and 2011) respectively, indicating that the two reservoirs are in the same regime
Comparative Analysis of Hash Authentication Algorithms and ECC Based Security Algorithms in Cloud Data

Cloud computing is ensuring the security of stored data in cloud computing servers is one of the mainly demanding issues. In Cloud numerous security issues arises such as authentication, integrity and confidentiality. Different encryption techniques attempt to overcome these data security issues to an enormous extent. Hashing algorithm plays an important role in data integrity, message authentication, and digital signature in modern information security. For security purpose using encryption algorithm like ECC (Elliptic Curve Cryptography) and Authentication of data integrity using hashing algorithms like MD5, and SHA-512. This combination method provides data security, authentication and verification for secure cloud computing.
A New Multi-Stage Feature Selection and Classification Approach: Bank Customer Credit Risk Scoring

Abstract Lots of information about customers are stored in the databases of banks. These databases can be used to assess the credit risk. Feature selection is a well-known concept to reduce the dimension of such databases. In this paper, a multi-stage feature selection approach is proposed to reduce the dimension of database of an Iranian bank including 50 features. The first stage of this paper is devoted to removal of correlated features. The second stage of it is allocated to select the important features with genetic algorithm. The third stage is proposed to weight the variables using different filtering methods. The fourth stage selects feature through clustering algorithm. Finally, selected features are entered into the K-nearest neighbor (K-NN) and Decision Tree (DT) classification algorithms. The aim of the paper is to predict the likelihood of risk for each customer based on effective and optimum subset of features available from the customers.
Accelerating GAN training using highly parallel hardware on public cloud

With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&amp;D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall efficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.
Aplikasi Chatbot Berbasis Teks Menggunakan Algoritma Naive Bayes Classifier FAQ GrabAds

Almost all aspects of human life has been touched by the development of information and communication technology. Especially in the current pandemic where face-to-face interactions are very limited, so sometimes information is not conveyed clearly and precisely. For example, in the case of work when there are new employees who have difficulty getting information or just asking colleagues because they cannot convey information clearly. The lack of access and the amount of access needed to obtain certain information makes it difficult for new employees to work even though the questions asked are the same questions asked by one employee or another. Chatbot is the result of technological innovation that aims to facilitate the process of exchanging information by using interactive programs. With this text-based Chatbot application, you can minimize the time needed to answer questions because it is automated. The Naive Bayes Classifier algorithm was used in making Chatbot in this study by previously creating a collection of frequently arising questions (FAQ) containing 10 questions and their answers which will be used as a dataset. By using a split ratio of 0.8 and a total of 60 questions, the resulting accuracy value is 93.33% and the error value is 6.66%.
Website-Based Sales Information System at One Coffe Point Cafe

. Currently the development of technology in the marketing of a product is growing and easy, The amount of competition in the business world requires business actors to develop an innovation and strategy in marketing their products so that they can increase sales results. One of them is by using website media in their marketing process and data processing. With good data processing and marketing, it can be an evaluation for entrepreneurs in their product development and marketing. So that it can reduce unwanted things in the future. The purpose of this research is to facilitate entrepreneurs in marketing and processing data from their business by creating a website-based sales information system. While the method used is the waterfall method where this method has several stages of manufacture from start to finish and in making it is made as attractive as possible. The results of this study are the website can provide all information about the point one coffee cafe to customers and can be accessed anywhere from the device available by the customer.
Accelerated Cuckoo Search With Extended Diversification and Intensification

Metaheuristics have been great to solve NP-hard class problems in the deterministic time, but due to so many parameter settings, they lack in generality (i.e., not easy to implement on all types of problems) and also lack in global search. But the cuckoo search (CS) algorithm has only one parameter as input and also has a good reachable probability to global solution due to Levy flight. But this algorithm lacks self-adaptive parameters and extended strategies. In this paper, a deep study and improvement of cuckoo search performance has been done by introducing self-adaptive step size, extended alien egg discovery replacement (on each dimension with the use of good neighbor study), and adaptive discovery probability, and it has been named accelerated cuckoo search (ACS). Then this ACS has been utilized as an example in the load balancing problem in cloud with minimum makespan time as an objective parameter to evaluate the performance of ACS over CS. Furthermore, to validate ACS superiority over CS in all problems, these have been successfully compared on a few benchmark functions.
Sentiment Analysis on Myocardial Infarction Using Tweets Data

In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.
Quantum Network Aggregation

We introduce the concept of quantum network aggregation where the multiplexing of independent quantum channels allows us to protect the transmission of information between users on that network – even when that network has limited resources.
Web Development Using Angular: A Case Study

Web development is same as building a house. Just like we require a plan, a building permit and license from city, web development also requires documentation, appropriate server, designing and a programming language. Since the standards of web de- signing are always increasing and so does the complexity of the technology required, frameworks have now become a crucial part of developing websites or web applications. It is absolutely unreasonable to reinvent the wheel, thus for designing rich and attractive websites, it is very much sensible to use frameworks endorsed by developers all over the world. Django, Angular, Spring, React, Vue, Express are some of the well-known web development frameworks. In my project I have used Angular.