Reviewerly/exHarmony huggingface dataset:
Performance Analysis of K-means clustering based Hyperbolic Tangent Instituted Classification of Automated Coding Contracts

The conceptual development of software coding specific contracts wherein programmers' liaison being very moderate is very definitive to substantially develop contracts that are optimized adequately alleviating the problems in concurring with software specifications, aiding thorough and extended testing of diverse software from the real world. The relevant framework put forward preliminarily infers dependency specifications indicative of behavior and semantic aspects organized as constraints or conditions in a Decision tree transpired as Automated Programming Contracts. The technicalities to refine the generated contracts are furthermore applied for enhanced verification practices via a Modified form of K-Means Clustering and the contracts are further classified using Activation function instituted TanH2-Entropy Classifier. The classification performance of the proposed algorithm is then compared with prevalent algorithms to show effective Memory and Computation influential performances and substantial soundness of errors identification ability.
Compromising device security via NVM controller vulnerability

This paper introduces a new vulnerability found in a low-cost secure authentication IC that stores its security settings in non-volatile memory (NVM). Such devices are widely used for prevention of counterfeiting in consumable products and accessories (printer cartridges, batteries etc.) and for aftermarket control. The particular device targeted here uses hardwired application logic and lacks a microprocessor, however, more sophisticated security devices used in medical and banking applications could similarly be vulnerable. The newly discovered self-induced fault attacks exploit implications of the use of error-correction codes inside modern embedded NVM blocks and their associated control logic, which can leave the application vulnerable to early termination of NVM write operation. This could potentially be used to change the security settings of a device in a way that bypasses the intended state machine controlling access and allow reverting the stored hardware security level back to the factory test/debug mode. The paper also outlines some measures that could substantially reduce the chances of a successful attack.
Intelligent Online Shopping Store for Specialties of Fujian

The development of Internet technology has enabled online shopping stores to continue to increase in Fujian. People use online shopping stores to reduce transaction costs and products distribution to sell regional specialties. On the other hand, users buy the various specialties of Fujian from all over the country. OpenCart is an open source with PhpStudy software that integrates PHP language, MySQL database and Apache server. This paper uses OpenCart to design an intelligent online shopping store for specialties of Fujian. It first introduces useful information technologies to design the online shopping store. Furthermore, we use the Apriori algorithm to recommend valuable specialties of Fujian for users intelligently.
A Motor Fault Detection Method Based on Optimized Extreme Learning Machine

To solve the problems that the motor fault detection algorithm based on mathematical model is hard to be adapted to those categories with complex non-linear fault. The accuracy of existing fault detection algorithms is not satisfied. In this paper, two methods of feature selection and parameter optimization of simulated annealing-based whale optimization algorithm with (SAWOA) optimized extreme learning machine (ELM) are proposed. SAWOA is utilized to optimize the selection of network input variables and to determine parameters of hidden layer nodes of ELM. In order to verify proposed method, a real motor fault detection case is studied in the paper. Several methods are compared under the same conditions to find out the best method with satisfied accuracy and reliability. The experiment results show that proposed method is capable to improve the classification accuracy and reliability, effectiveness and application value are introduced in the article.
EARLY DETECTION OF FACTORS, INCLUDING PANDEMICS AND DISASTERS, LEADING TO LANGUAGE ENDANGERMENT: THINKING STATISTICALLY

The target of this research work is to use a statistical technique on different languages to identify significant factors of endangered languages with similar characteristics to build a model for language endangerment. Factor analysis is used to identify factors. The factors are used to construct a model with and without interaction terms. First three variables (i.e. speakers, longitude and latitude) are analyzed to identify two factors and then these three variables and three interaction terms are used to construct the model. Different variables were identified and a model with and without interaction terms is built using the identified factors. The result shows that the model has significant predictive power. The predictors were retrieved from the dataset. The outcome encourages future studies towards defining techniques of language endangerment prediction for analyzing factors of language endangerment.
NSF AI institute for research on trustworthy ai in weather, climate, and coastal oceanography

NSF AI Institutes In 2019, the National Science Foundation (NSF) launched a new national investment in Artificial Intelligence (AI) to create a network of national AI institutes. Each institute will serve as a nexus of collaboration to create next-generation theory and applications of AI and to work synergistically with academia and industry. In the fall of 2020, NSF announced 5 new NSF AI institutes and 2 additional institutes funded by the United States Department of Agriculture (USDA) and the National Institute of Food and Agriculture (NIFA). Each institute is funded for approximately $20M over 5 years to make significant advances in foundational and applied AI research.
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors

Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification. However, the quality of the captured depth is sometimes insufficient for 3D reconstruction, tracking and other computer vision tasks. In this paper, we propose a self-supervised depth denoising approach to denoise and refine depth coming from a low quality sensor. We record simultaneous RGB-D sequences with unzynchronized lower- and higher-quality cameras and solve a challenging problem of aligning sequences both temporally and spatially. We then learn a deep neural network to denoise the lower-quality depth using the matched higher-quality data as a source of supervision signal. We experimentally validate our method against state-of-the-art filtering-based and deep denoising techniques and show its application for 3D object reconstruction tasks where our approach leads to more detailed fused surfaces and better tracking.
Multi-target Localization and Pose Estimation Based on Mixed Programming of Halcon and C#

The paper proposes a method of multi-objective recognition and pose estimation based on mixed programming of HALCON and C#. The target we choose is the screws that are often used in industry. Using machine vision to identify, locate and position it in order to achieve a series of applications such as grabbing and installation, thereby reducing labor and achieving high effectiveness. Firstly, the target image is acquired using the calibrated camera. Then, the image is subjected to operations such as gradation, image filtering, threshold segmentation, and morphological processing to extract target information in HALCON. Processing information what are extracted to get the position and pose of multiple screws. Finally, designing the interface by mixed programming of HALCON and C# to improve the program's practicality and convenience.
Infrared Image Enhancement Based on Segmentation and Bin Reallocation of Salient Intensity Levels

Image enhancement is an essential processing for raw infrared images to improve contrast. In this paper, an infrared image enhancement method is proposed to solve the problem of over-enhancement on background in occurrence probability histogram based methods and the problem of uneven enhancement for targets in the saliency weight based method. First, the saliency histogram is constructed, where the bin values of target-related intensity levels are significantly larger than the bin values of background-related intensity levels. According to the value distribution of bins in saliency histogram, the intensity levels with several largest bin values, defined as the salient intensity levels, are segmented. Salient intensity levels are separated further away from non-salient levels to improve the contrast between targets and background. Second, salient intensity levels are divided into groups and each group is related to targets of a certain range of temperatures. Bin values of salient intensity levels are reallocated within each group to keep these intensity levels compact, for the purpose of performing equalized enhancement for targets. Experimental results demonstrate that the proposed method achieves better improvement on target-to-background contrast and better visual quality on targets by performing equalized enhancement for targets.
Customer Profile Generation and Textual Behavior Analysis on Twitter Data

With the rapid development of modern technology, automated customer supporting services became famous among service providers. Customer support service-related tasks such as data processing, analyzing, providing recommendations rapidly changed to automated systems. Collecting customers' data and providing a personalized service for customers to enhance the user experience is a trending topic in recent researches. This paper will discuss a novel approach to the design and the implementation of customer profiles and textual behavior analysis for transportation service. A rule-based system and a supervised learning method have been used to develop the textual behavior analysis module. Extracted data of the customers are stored in a non-relational database for further analysis. The attitude classifier that was based on supervised learning achieved a higher accuracy compared to existing lexicon-based systems that are used to analyze customers' textual attitude using twitter data.
Semi-markov CRF Model Based on Stacked Neural Bi-LSTM for Sequence Labeling

In this paper, we present a model for sequence labeling. Based on word vector representations of neural network model algorithms and artificial features of statistical machine learning, a stack Bi-LSTM and new semi-Markov conditional random field frameworks are proposed. On the one hand, we present stack Bi-LSTM to achieve segmentation and chunking task on both word-level and character-level. On the other hand, we use traditional CRF and NSCRF to make some restrictions on the labeling entities to improve the accuracy of labels and then use them to decode the unknown sequence. We test the trained model in the CoNLL2000 and CoNLL2003 datasets.
Wall Following Control System with PID Control and Ultrasonic Sensor for KRAI 2018 Robot

Indonesian Abu Robot Contest (KRAI) in 2018 with the theme "Throwing a Blessing Ball". The main purpose of this robot is to be able to navigate automatically in an area that is bordered by walls to complete the mission. The main problem with the robot is the navigation system. The application of PID control in the wall following system has been able to make robot movements smooth, responsive, and fast. In this study, PID control aims to smooth the movement of the robot while walking along the wall in the race arena. The PID parameter is obtained from the results of tuning with the trial and error method, the values of KP = 3, KI = 0, and KD = 5. At the PWM 150 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 4.4. cm. At the PWM 200 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.4 cm. At the PWM 250 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.8 cm. This error does not have an effect on the performance of the wall following system, because the system only uses the distance value reading data with a decimal value in front of the comma. So it can be concluded that the wall following system which is designed using ultrasonic sensors with measurement error that occurs is zero.
List decoding of convolutional codes over integer residue rings

A convolutional code C over Zpr[D] is a Zpr[D]-submodule of Zprn[D] where Zpr[D] stands for the ring of polynomials with coefficients in Zpr. In this paper, we study the list decoding problem of these codes when the transmission is performed over an erasure channel, that is, we study how much information one can recover from a codeword w∈C when some of its coefficients have been erased. We do that using the p-adic expansion of w and particular representations of the parity-check polynomial matrix of the code. From these matrix polynomial representations we recursively select certain equations that w must satisfy and have only coefficients in the field pr−1Zpr. We exploit the natural block Toeplitz structure of the sliding parity-check matrix to derive a step by step methodology to obtain a list of possible codewords for a given corrupted codeword w, that is, a list with the closest codewords to w.
Extended Target Measurement Set Partition Algorithm Based on KFCM Clustering

Aiming to the problem of measurement set partition of multiple extended target, a measurement set partition algorithm based on kernel fuzzy C-means (KFCM) clustering is proposed. Firstly, according to the FCM algorithm, the initial cluster centers are generated. Then, the distance function of FCM algorithm are changed by kernel function. Finally, this paper constructs new interation cycle to obtain tracking results. Experimental results shows that the proposed algorithm improves the performance of multiple extended target tracking compared with FCM partition and K-means++ partition effectively.
Dynamics of inflation and its impact on economic growth in selected East African Countries: Ethiopia, Sudan and Kenya

Despite Ethiopia's, Sudan's, and Kenya's fastest-growing economies' performance over the last two decades, the overall average inflation rate remains double digits.It subsequently incorporates a detrimental impact to support economic growth in an extended period.This study aims to assess the dynamics of inflation and its impacts on economic growth Ethiopia, Kenya, and Sudan using time-series macroeconomic data collected from the African Development Bank.The research used the Autoregressive Distributed Lag (ARDL) econometrics model and investigated the presence of cointegration and long-term relationships between macroeconomic factors.The result indicates that the exchange rate and the supply of the long-run economic growth rate influence Ethiopia's money supply.Inflation rates and foreign direct investments have impacted economic growth rates in Kenya and Sudan.The economic growth rate in all counties is generally influenced by both the availability of money and the exchange rate-also, real per capita GDP affecting the economic growth rate of Sudan and Ethiopia.Policies on introducing new technologies, building capacities in public and private sectors, youth and gender parity, mobilizing domestic resources, and public participation are recommended for Ethiopia, Kenya, and Sudan.
An Intrinsic Review on Trade Finance Using Blockchain

This paper presents how blockchain can be leveraged in the domain of trade finance, to provide an adept model, which simplifies the end-to-end process. The paper elaborates upon the integration of the components of trade finance with blockchain. We discuss the traditional trade finance model and how the integrated blockchain model helps mitigate its pain points. Using this integrated model, a trade transaction that would normally take close to a week can be successfully executed in, approximately, a quarter of a day. The process flow of the events of a transaction powered by blockchain are elucidated upon. This paper highlights how distributed ledgers, smart contracts, events, and system integration will power trade finance. The key features which are focused on include - authentication, decentralization, immutability, and consensus mechanisms. Furthermore, we explore the benefits and functionalities of adopting blockchain, which includes - efficiency, transparency, collaboration, and auditability and the way they can be achieved without compromising security, confidentiality, and interoperability. The objective of the paper is to illustrate how blockchain technology can reshape the landscape of Trade Finance and improve financial mechanisms.
Method for Protection of Copyright on Vector Data

The paper proposes a method of copyright protection for vector maps, based on the embedding of raster digital watermarks. The method combines original ideas, namely, a vector data watermarking approach that does not distort the coordinate information of vector objects and embedding of noise-like images that encode a binary sequence. The use of noise-like carrier allows to simplify the verification procedure, and also to ensure the resistance of the hidden bit sequence to distortions, including interpolation errors. In comparison with previous investigations made by the authors when solving problems of vector data protection, the robustness of the embedded noise-like digital watermarks has been significantly increased by developing a new algorithm for detection of the binary sequence hidden in the spectrum of a noise-like image. The purpose of the experimental studies was to assess the attainable probability of correct sequence extraction for various parameters and embedding conditions. The study of information capacity showed that the potential amount of information embedded in each polygon without distorting the watermark sequence depends on the type of a map, namely on the number of vertices that make up each polygon. An experimental study on robustness against potentially possible transformations aimed at maliciously deleting the digital watermark from the vector map is performed. The developed method demonstrates resistance to affine transformations, cropping, addition, removal and reordering of vertices / objects, and, thus, seems to be the optimal solution to the problem of copyright protection for vector data. The analysis of the method quality according to the criteria of accuracy, durability, information capacity, and computational complexity is performed. The results of all conducted studies confirm the efficiency and the effectiveness of the proposed method for vector map protection, as well as its distinct advantage over the prior art.
F-MIFGSM: adversarial attack algorithm for the feature region

The current mainstream attack algorithms IFGSM and MI-FGSM will carry out global perturbation to the original example when they carry out adversarial attack. F-MIFGSM algorithm is proposed to solve the problem of poor attack concealment. This algorithm extracts the output information of the convolutional layer of the neural network, marks out the feature region of the original example through the deconvolution network and mapping algorithm, and uses the MI-FGSM algorithm to carry out the specific region attack. In the experiment, 60,000 training sets in MNIST data set are used to train the self-constructed convolutional neural network, and 10,000 test sets in the data set are attacked, and the generated adversarial examples are input into the neural network for classification prediction. The experimental results show that the F-MIFGSM algorithm can significantly reduce the disturbance range to the original example and reduce the defects between the adversarial example and the original example under the condition of increasing the attack success rate.
Petrogenesis of gold-hosting Neoproterozoic syenite from the Tulu Kapi area, western Ethiopia

The Tulu Kapi syenite-hosted gold mineralization in the Western Ethiopian Shield has been under exploration during the last few decades. The geochemical and petrological characterization of the intrusive rocks and their alterations including the gold hosting syenites are crucial in appraising the economic potential of the mineralized belt on a larger scale. The present study concerns with the geochemical and petrogenetic behaviour of the gold-hosting syenites and other associated intrusions using an integrated field, geochemical and petrological investigation. The Tulu Kapi area is situated within a strongly sheared and NE-SW foliated, low-grade (lower greenschist to lower amphibolite facies) volcano-sedimentary belt (the “Birbir belt”) that is intruded by mafic-ultramafic and granitic intrusives (chlorite-serpintine-talc schist, birbirite, metagabbro, metadiorite, metagranite and metasyenite). Major and trace element geochemistry revealed that the gold mineralization hosting syenites, associated diorites and granites are ferroan, calc-alkaline, metaluminous to slightly peraluminous bodies formed in a volcanic arc subduction to syn-collisional tectonic setting by the fractionation of underplated, LREE-enriched basaltic magma with substantial crustal input to possible crustal melting. The underplated basaltic magma was sourced from a LREE-enriched mantle by reworking of sediment laden crustal slabs at depth in a subduction zone. Fractional crystallization resulted in the formation of Ca-rich plagioclase, which continually reacted with hydrothermal fluids to more fractionated Na-rich plagioclase, forming the gold mineralization hosting altered syenites viz. albitization.
K-Mean Clustering Algorithm in Grouping Prospective Scholarship Recipients

Abstract The accumulation files of scholarship recipient candidates currently become a problem in the selection process. Essentially, the accumulated data can be process to produced useful knowledge by utilizing data mining techniques. One of the data mining techniques is clustering that used as a method in this research. The purpose of this research is to generate new knowledge from accumulated data that can be used, one of example is in selecting prospective scholarship recipients. The research method used is the ADDIE version of Research and Development (RnD) (Analyze, Design, Develop, Implement, Evaluate). In generating new knowledge from a data warehouse, the K-Means Clustering Algorithm is used as a method of partitioning data into one or more clusters. The results indicates that the products produced were valid, practical, and effective to be used as a system in grouping prospective scholarship recipient data with the respective values of validity, practicality, and effectiveness were 0.94, 0.91, and 0.93.
Exploring the Use of Electronics to Customize Pervasive Health Technologies with Older Adult Crafters

As the worldwide population ages, HCI researchers are designing technologies to better support older adults. We investigated how older adult crafters would customize technologies using electronics by building on their crafting skills. This supported them to explore customizing devices for themselves and advance the design of pervasive health technologies for older adults. We first conducted a survey of 42 older adult crafters to learn more about their crafting habits and gauge interest in technology and health tracking. We then conducted a participatory design workshop with 10 older adult crafters, focused on mutual learning to support them in prototyping how they would customize technology with maker electronics. They brainstormed customized devices around health, games, and safety, as well as aesthetically enhanced artifacts integrating electronics. We discuss how promoting older adult crafters to design and build customized pervasive health technologies impacts future research, and we provide guidelines on how to do so.