Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Yelp Review

Digital data has developed very fast in the current era. Digital data has various forms, one of which is text data. There are a lot of text data source from many ways, such as review text data. Yelp is a local business directory and forum to review products, services, or places. We used Yelp's review data to determine user's sentiment or opinion about products, services, or places. Sentiment or opinion are classified into positive reviews, or negative reviews. The algorithms that used are Gaussian Naïve Bayes, Gaussian Naïve Bayes with AdaBoost, and K-NN. In this research review text data will be through a preprocessing and feature extraction stage. We used n-gram to extract the feature from the data with unigram, bigram and uni+bi-gram for indexing text. The result of this research that algorithms that has the highest accuracy rate was Gaussian Naïve Bayes with combined unigram and bigram with 86.7% from 5 fold cross-validation.
Bayesian Solutions to State-Space Structural Identification

Characterisation of structural dynamic systems remains a key feature of modern engineering analysis. Commonly, methods for performing this identification will be based on deterministic solutions such as a least-squares approach. However, quantifying uncertainty in the parameters of a given model can add significant value. This quantification can be propagated forward into further analyses such as model updating, fatigue damage assessment, and activities related to structural health monitoring. The state-space representation of structural dynamic systems is a flexible and powerful framework for performing identification tasks due to its flexibility, natural dynamic structure, and computational efficiency. The current work is concerned with recovering Bayesian solutions to these systems, in terms of the posterior distribution over the states and the parameters. The Bayesian solution offers a number of advantages; these include the ability to formally incorporate prior knowledge (for example from existing models of the system such as from finite element models) and to recover the posterior distributions over the parameters of the system and the internal states (displacements and velocities). Two algorithms are compared here, the first is a traditional Markov Chain Monte Carlo approach based on the Metropolis-Hasting algorithm, the second, a Sequential Monte Carlo approach called Iterated Batch Importance Sampling.
On the Interplay of Foveated Rendering and Video Encoding

Humans have sharp central vision but low peripheral visual acuity. Prior work has taken advantage of this phenomenon in two ways: foveated rendering (FR) reduces the computational workload of rendering by producing lower visual quality for peripheral regions and foveated video encoding (FVE) reduces the bitrate of streamed video through heavier compression of peripheral regions. Remote rendering systems require both rendering and video encoding and the two techniques can be combined to reduce both computing and bandwidth consumption. We report early results from such a combination with remote VR rendering. The results highlight that FR causes large bitrate overhead when combined with normal video encoding but combining it with FVE can mitigate it.
An Introduction to Answer Set Programming and Some of Its Extensions

Answer Set Programming (ASP) is a rule-based language rooted in traditional Logic Programming, Databases, Knowledge Representation, and Nonmonotonic Reasoning. It offers a flexible language for declarative problem solving, with support of efficient general-purpose solvers and reasoners. The larger part of this article provides an introduction to ASP, with a historical perspective, a definition of the core language, a guideline to knowledge representation, and an overview of existing ASP solvers. One part focuses on one commonly used feature: aggregates and generalized atoms. The inclusion of aggregates in ASP (and Logic Programming at large) has long been motivated, however there are some issues with semantics to be addressed, in particular when aggregates occur in recursive definitions. Very similar considerations are needed when coupling ASP with other formalisms, which we collectively refer to as “generalized atoms”. An overview of these semantic challenges and proposals for addressing them is provided, along with an overview of complexity results and system support.
FIOU Tracker: An Improved Algorithm of IOU Tracker in Video with a Lot of Background Inferences

Multiple object tracking(MOT) is a fundamental problem in video analysis application. Associating unreliable detection in a complex environment is a challenging task. The accuracy of multiple object tracking algorithms is dependent on the accuracy of the first stage object detection algorithm. In this paper, we propose an improved algorithm of IOU Tracker–FIOU Tracker. Our proposal algorithm can overcome the shortcoming of IOU Tracker with a small amount of computing cost that heavily relies on the precision and recall of object detection accuracy. The algorithm we propose is based on the assumption that the motion of background inference is not obvious. We use the average light flux value of the track and the change rate of the light flux value of the center point of the adjacent object as the conditions to determine whether the trajectory is to be retained. The tracking accuracy is higher than the primary IOU Tracker and another well-known variant VIOU Tracker. Our proposal method can also significantly reduce the ID switch value and fragmentation value which are both important metrics in MOT task.
Using computer graphics in design

Abstract In the modern world, with the development of technology, it becomes possible to create design art objects from the developed sketch to the implementation of the project in material using computer graphics. Thanks to vector graphics programs and the emergence of laser and milling equipment with numerical software (CNC), designers were able to transfer the drawing to the material using material cutting technology. The object of the study is vector graphics programs, laser and milling cutting technology. As a result of the study, the authors conclude that thanks to software products and the development of technologies and equipment, designers were able to simplify the process of making decorative products.
Research on Security Control Mechanism of Power Distribution IoT Based on Trusted Computing

Abstract Performing an in-depth study on the Power Internet of Things (IoT), it is known that the distribution network faces higher security risks due to a large number of terminals, various types of equipment, and the flexible access of each device to the distribution network structure and dynamic boundary. Therefore, it is urgent to make significant research on the security control mechanism for the power distribution Internet of Things, to realize the safe access of IoT devices in the Power IoT. Firstly, this paper analyses the shortage of power system security under the background of the IoT. Secondly, trusted computing technology is represented by the TrustZone and is deeply studied, and the feasibility of its application for the security of power distribution IoT is analyzed. Furthermore, the TrustZone-based security management and control architecture for power distribution novel IoT equipment is proposed, and its integrity measurement and trusted network connection process are analyzed. Finally, the security of this architecture is analyzed briefly.
How Agile Software Development Affect Software Reusability (Study Case: New Student Admissions Portal in East Java Province)

Software quality has a very important role in the software development process. Several attributes can be used to measure the quality of software. In this study, the attribute chosen was reusability. The main objective of this research is to understand how the Agile method affects application's reusability. The object research is New Student Admissions Portal (PPDB) in East Java Province. The modules chosen in this study are system modules that support student ranking and registration. Experiments are conducted by reviewing and analyzing its features manually. The research conducted shows that form 6 modules analyzed, there are 2 modules have medium reusability and 4 modules have high reusability.
Automatic Hyper-Parameter Tuning for Gradient Boosting Machine

Machine learning and predictive modeling have become widely used in many fields. Utilizing the algorithm without tuning the hyper-parameter can lead to the model cannot perform to its best capabilities. Gradient Boosting Machine (GBM) is one of the tree-based models in which the performance can differ greatly depending on its setting. Tuning hyper-parameters of the model requires background knowledge of the algorithm. Moreover, both the performance and cost of the tuning process need to be kept in consideration. In this paper, we proposed an approach based on Genetic algorithm (GA) in process of GBM tuning. The GA is often used in the optimization problem because of the ability to handle more complex problems. We have implemented the GA variation called hyper-genetic to tune the hyperparameter of GBE and explored the best setting possible of the genetic parameters. In the end, we have compared our best setting with the results from Grid-search, Bayesian optimization, and random approach over four sets of data. Our proposed algorithm has competitive performance and outperforms the other algorithm in the dataset with a high dimension while it required smaller computation time on the most dataset at the optimum point.
Noise Robust Illumination Invariant Face Recognition via Contourlet Transform in Logarithm Domain

Face recognition under varying lighting conditions is an important topic in many real-life applications. In this paper, we propose a novel algorithm for illumination invariant face recognition. We first convert the face images to the logarithm domain, which makes the dark regions brighter. We then use contourlet transform to generate face images that are approximately invariant to illumination change and use collaborative representation-based classifier (CRC) to classify the unknown faces to one known class. We set the approximation subband and a few highest frequency contourlet coefficient subbands to zero values, and then perform the inverse contourlet transform to generate illumination invariant face images. Experimental results show that our proposed algorithm outperforms two existing methods for the Extended Yale Face Database B for high noise levels. Nevertheless, our new method is not as good as existing methods for low noise levels. In addition, our new method is comparable to existing methods for the CMU-PIE face database.
Debottlenecking of Supply Chain Management Process with Blockchain Technology and Automation: A Case Study of a Typical Sub-Saharan African Oil and Gas Company

Abstract This research addresses the bottlenecks experienced in supply chain management (SCM) in an African hydrocarbon company, which slows down processes and makes the procedures tedious, this leads to operational inefficiencies. This paper discusses the security of the supply chain management data and the overall automation of procurement value chain, providing transparency to the stakeholders involved in the entire supply chain management process. It also provides a system that is not prone to malware or data alteration. The existing system lacks trust and greater transparency. A new technology will be developed using Blockchain Hyperledger which is a distributed ledger technology (DLT). The SCM application/portal serving the request and procurement process will utilize the Hyperledger Fabric SDK or REST web service to interface with the Hyperledger Fabric network. The organizations that participate in developing the Hyperledger Fabric network are known as the "members". Every member organization in the blockchain network is capable to set up their peers for taking part in the network. All these peers’ needs are configured with suitable cryptographic materials like Certificate Authority and other data. Chaincode will be actualized in peers to initiate transaction invocation request. This process will result in the chaincode providing an executable smart contract. The system executes the terms of the contract (agreement) by all necessary stakeholder's peer; for example: when goods or services are completely delivered, work Completion Certificate will be issued. This will be on the blockchain network and will be transparent to all stakeholders so once invoice is presented, the verification will be automatically ascertained and payment will be made. Procurement data like invitation to tender (ITT), request for proposal (RFP), request for quote (RFQ), contract award, work completion certificate (WCC), and invoice will be distributed among the nodes uniformly at the same time, making it impossible for cyber-invasion on all the nodes at the same time. Blockchain has been identified industry-wide as the next level in SCM operations because of its greater transparency, increased efficiency, enhanced data security, reduced costs, improved traceability, smart contract and unbroken chain of transaction history. It was observed that the blockchain implementation will further enable easy audit trail, automated and transparent Issuance of ITT/RFP/RFQ to Vendors, end-to-end SCM automation with secured data, and quality reporting system. In conclusion Blockchain Technology will enhance Supply chain manpower efficiency and hydrocarbon production. Blockchain Technology in Supply chain management will present a new knowledge in the area of integrated supply chain management for procurement process transparency and end- to-end data security
Evaluation of diatoms biodiversity models by applying different discretization on the class attribute

One of the main goals of knowledge discovery from environmental data is through data analysis to find the relationship between the living organisms, represented with the diversity of the diatoms community members, and the characteristics of the environment. This is very important information for both ecologists and decision makers. Therefore, in this paper we apply various machine learning algorithms for revealing this relationship by using different number of discretization levels for the target attribute. The target attribute represents the biodiversity index of the community and it is calculated based on the abundances of the diatoms. For building models, different types of machine learning algorithms are considered including decision trees, rule induction algorithms, neural networks and Naive Bayes. The obtained models are also examined regarding resistance to over-fitting, as well as statistical significance.
How do artificial neural networks lead to developing an optimization method?

Simply looking at artificial neural networks (ANNs), we can see there exists many potentials from this mathematical model.
Analysis Model of Customer Reviews Based on Neural Network

Reviews of customers affect the sales of e-commerce and positive customer reviews will boost store sales. Thereby, the emotional tendency of customer reviews is important to the evaluation of the business status of online store. In this paper, we use the Neuro-Linguistic Programmin algorithm to quantify the customer review, and then use the neural network to predict the emotional tendency of customer reviews by effectively analyzing the nonlinear relationship among affecting parameters. With the emotional tendency predication, customer reviews can help manage online store well. Simulation results show that our method can achieve high prediction accuracy. The accuracy rates of satisfaction prediction of the three commodities are 91.95%, 89.93%, 90.96%, respectively.
Subject Tracking with Camera Movement Using Single Board Computer

Majority of the events happening around us actively involve camera operators to record the event. One may observe that if the camera operator is recording a video for the event, he/she has to continuously track the subject without losing a sight. No wonder such events (like classroom lectures, guest lectures, etc.) continue for long hours leaving the camera operator to feel exhausted in the tiresome tracking process. In this paper, we explore and discuss different experimental setups that could be potentially used in order to design a subject tracking camera system. The work presents a system that is able to track subjects and accordingly pan in real time. The system replicates a camera operator by detecting humans or objects and pan accordingly to track and record them. This subject tracking camera system can be implemented in classroom lectures to help teacher's record video without the aid of any camera operator, or to ease the work of a camera operator in particular. Therefore, the idea here is not to replace camera operators, but to assist them during the video recording process and also to make people self-dependent to automate the entire process and carry out the task on their own. The application of such subject tracking cameras can be extended to a variety of events like guest lectures, keynotes and dance performances.
Improving Face Recognition Accuracy for Brazilian Faces in a Criminal Investigation Department

This work addresses a critical problem in the use of the Face Recognition (FR) task by a police department state of Brazil. FR is a valuable crime-fighting tool that can help the police service prevent and detect crime, preserve public safety, and bring offenders to justice. Although significant advances have been shown in the last years, the works are based on large labeled datasets and supervised training. But with this approach, the lack of representative data distribution is an issue, known as data bias, mainly according to some aspects that makes FR harders: gender and race. Recent works have suggested that these two aspects may cause a significant accuracy drop. Thus, the paper is concerned over the FR data bias problem for Brazilian faces. Using pre-trained models learned from public datasets, we demonstrate that even in the small training dataset, it is possible to improve the accuracy of Brazilian faces with simple yet effective implementation tricks in fine-tuning. Two important conclusions wast obtained from this study using a non-public police dataset. First, there is a strong suggestion of data bias concerning ethnicity when evaluating models trained with public datasets on Brazilian faces, and second, the fine-tuning task implemented over non-public police dataset showed a relevant improvement to minimize the dataset bias problem.
On the number of ones in outcome sequence of extended Pohl generator

Abstract Formulas for distributions of number of ones (non-zeroes) in the cycle of the output sequence of generalized binary Pohl generator are obtained. Limit theorems for these distributions are derived in the case when the lengths of registers are coprime and tend to infinity, the contents of different registers are independent, but cell contents within each register may be dependent. The consequences of these theorems are given for the case when the contents of cells are independent random variables having equiprobable distribution on {0, 1}.
Analysis of water surface profile and flood discharge of Cijangkelok river

Abstract The downstream of Cisanggarung River Basin is often affected by floods with an increasing degree of intensity every year. For instance, the 2017 severe floods caused economic losses and fatalities, especially along the border of Cirebon and Brebes Regency. The flood was attributed to high rainfall intensity and the meeting point between Cisanggarung River and the Cijangkelok River. Therefore, a study on the flood in this area is highly vital to reduce the affected area in the future. This study aims to analyze the maximum flood discharge capacity in the Cijangkelok River and analyze cross-sectional river capacity using numerical model simulations. Based on the hydrological calculations and analysis, it is known that the planned flood discharge in the Cijangkelok River in the 5-year return period is 256,25 m 3 /s, the 10-year return period is 310,35 m 3 /s, the 25-year return period is 392,91 m 3 /s, and the 50-year return period is 465,93 m 3 /s. The analysis of the water surface profile of each flood discharge return period shows that overflow occurs in the cross-section 4090, 8254, 9166, 9753, and 11970. It is also revealed that the existing cross-section of the Cijangkelok River is unable to accommodate hydrological discharge. Field visit results show that the river has siltation or sedimentation, the river embankments are irregular, and land-use changes around the Cijangkelok River that affects the river.
Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented &amp; system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.
A Research on the Priority of Power Distribution Based on Graph Computing

Abstract With the construction and gradual development of microgrids, multiple interest themes have emerged in traditional distribution networks, such as distributed power sources, flexible loads, etc. Microgrids can be integrated into large power grids or operated as independent power grids. Effectively improve the power grid’s ability to resist disasters and ensure the safety of power supply within the grid. It can also effectively achieve high-quality matching of power supply and consumption and optimize the distribution of power resources within the network. The ability to fully and effectively solve the problems of grid connection of distributed energy sources such as wind and solar is of great significance to the reasonable planning of the power system and the safe operation of the power system. This paper proposes a planning evaluation system that takes into account the microgrid connection, constructs a graph calculation-based distribution priority division and a node model under the microgrid central control system, and uses the Mean-shift clustering algorithm to obtain node attributes. Use the optimal sequence graph method to calculate the index weight of the distribution network planning scheme, and finally calculate the distribution plan between nodes through the graph path finding algorithm.