Reviewerly/exHarmony huggingface dataset:
HUBUNGAN TINGGI MUKA AIR DAN DEBIT ALIRAN SUNGAI KARANG MUMUS DI LOKASI DESA PAMPANG KOTA SAMARINDA

Karang Mumus River Pampang Village Samarinda City has a postal system in accordance with what is needed to overcome or overcome problems related to the environment. This research was carried out around the Pampang River in Samarinda City which was at coordinates 117 ° 06 '44 .00 BT, 00 ° 27 '36.00 LS - 117 ° 07 '11 .00 BT, 00 ° 27' 18.00 LS. In this graph measurements are taken to obtain a Rating Curve ( ) and water level (TMA) of 51 data. These values ​​are processed with the help of Microsoft Excel program to get the graphic model. Furthermore, the modeling results are published to get an average value. The results of the study show that the graph in the polynomial shows the best value. Obtained from the results of the modeling of sediment discharge flow that was found one point that was seen to be very increased in data 21.
Categorization of Gaming Attributes in Devops Using Structure Equation Modeling

Organizations are introducing agile and lean software development techniques in operations to increase the pace of their software development process and to improve the quality of their software. They utilize the term Devops, a portmanteau of operations along with the development, as an umbrella term to portray their endeavors. In this paper we portray the manners by which organizations actualize Devops and the results they experience. We initially condense the aftereffects of a Literature review systematically that we performed to find what researchers have expounded on Devops. We at that point depict the aftereffects of explanation based interview study including six organization of variable sizes that are dynamic in different ventures. As a major aspect of our discoveries, we saw that all organizations were certain about the encounters and just minor issues were experienced while receiving Devops. The techniques used in Devops are beneficial and that can be used in mobile gaming development to make these games more reliable and high efficiency based on their performances across various platforms such as Android, iOS and other mobile operating systems. There are a lot of factors that affect the performance of any mobile game.
INTERNET VE E-TİCARET

The quality of life improves with the rate of progress in technology anil comminications. The internet, as a last development of virtual reality, has eventually become an indispensible component of modern living. Services related to education, health, entertainment and even match-making services, are supplied via the internet. Consepuently, a rapidly increasing number of companies conduct their transactions on the web and internet thus become the most preferablef medium of marketing.
End-to-End Motion Planning: A Data Driven Approach for Mobile Robot Navigation

A lot research has been conducted in the field of autonomous navigation of mobile robots with focus on Robot Vision and Robot Motion Planning. However, most of the classical navigation solutions require several steps of data pre-processing and hand tuning of parameters, with separate modules for vision, localization, planning and control. All these modules work independently and make their own parameter assumptions to optimize their own performance without taking into account the effect these assumptions have on the performance of rest of the modules. Hence, even though each module in the whole system tries to achieve an optimal performance for the task it has been assigned, the lack of interdependence exhibited by these modules for decision making means that the overall performance of the whole system is sub-optimal in most of the cases. An alternating approach for addressing these issues is to train certain parts of the vision module to incorporate partial tasks from the planning module. Deep Learning architectures have achieved great success in the field of pattern recognition and object detection and as a result are usually being deployed to design such a module that jointly learns to carry out perception and path planning. This master's thesis, making use of Deep Learning, proposes an End-to-End Learning architecture that learns to directly map raw sensor readings to control commands for a ground based mobile robot. The research makes use of the simulation of Jackal UGV from Clearpath Robotics and the proposed network is able to produce collision free trajectories for the robot to navigate in it's environment.
Sentiment Analysis using Ensemble Classifier on Real Time Data Set

Social media has been growing day by day, monitoring and analysis of social media data play an important role in knowing people's behavior. We are performing analysis on Twitter Tweets using Ensemble Classifier which determines the opinion of the people regarding government schemes that are announced by the Central Government. Article Info This is based on social media twitter datasets of particular schemes and its polarity of sentiments. The popularity of the Internet has been rapidly increasing. Sentiment analysis and opinion mining is the field of study that analyzes people's sentiments, opinions, attitudes, and emotions from written language. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, microblogs, Twitter, and social networks. It is difficult to analyze or summarize the user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. These contents are very important for Organizations, Government and Research and Development department to make decisions. For this Sentiment analysis and opinion mining research is a hot research area which comes underData Science and Machine learning.We plot and calculate numbers of positive, negative and neutral tweets from each event.
Filled pauses and lengthenings detection using machine learning techniques

This paper addresses the issue of filled pauses and lengthenings detection and classification in Russian using machine learning techniques, such as ELM.We use such parameters as formants and energy variation and MFCC coefficients.The experiments on FPs detection and classification, that are carried out on the joint material of SPIIRAS task-based dialogs corpus, Russian casual conversations from Binghamton Open Source MultiLanguage Audio Database, reports from the appendix No5 to the phonetic journal "Bulletin of the Phonetic Fund" belonging to the Department of Phonetics of Saint Petersburg University and small part of SWITCHBOARD corpus.For evaluation of the experiments results we calculate the F1 score.The best achieved F1 score was 0.42.
Learning Design of Indonesian Phonology Based on Digital Learning Management System

The 4.0 industrial revolution that occurs today, demands the changes of learning process in universities towards learning based on digital. The form of digitalization of the learning process will later penetrate the boundaries of space and time that have been blocking the interaction between the lecturers and the students. This simple writing, then will describe the design of the digital learning process in colleges. The digitalization in this paper means an android-based learning design utilizing software/learning management system (LMS) for activities in the network (online) in Indonesian phonology courses. Some features that can be utilized in managing LMS-based learning are: a) Courses, with courses menu, the users can create new classes, join classes that have already existed or browse through the predefined class list. b) Groups which functions like the wall messages of the group members and can also post on the wall messages. When joining a group, the users can search for parts of the group that the users want. c) Resources are to maintain, to track the documents, files and images that users upload in the class. d) Recent Activity is to display the latest news contained in the account. e) Calendar, to display the calendar page that was posted earlier in the Recent Activity. f) Messages are to send messages or view the messages among the fellow users, and g) People is to be able to see a list of the users in a class.
Extracting and Validating Explanatory Word Archipelagoes using Dual Entropy

The logical connectivity of text is represented by the connectivity of words that form archipelagoes. Here, each archipelago is a sequence of islands of the occurrences of a certain word. An island here means the local sequence of sentences where the word is emphasized, and an archipelago of a length comparable to the target text is extracted using the co-variation of entropy A (the window-based entropy) on the distribution of the word's occurrences with the width of each time window. Then, the logical connectivity of text is evaluated on entropy B (the graph-based entropy) computed on the distribution of sentences to connected word-clusters obtained on the co-occurrence of words. The results show the parts of the target text with words forming archipelagoes extracted on entropy A, without learned or prepared knowledge, form an explanatory part of the text that is of smaller entropy B than the parts extracted by the baseline methods.
Asymmetric public key cryptosystem using digital holographic encryption method

A novel asymmetric public key cryptographic algorithm is proposed by modifying the conventional RSA public key protocol, which can be optically implemented by using the phase-shifting digital holography. The proposed method involves a two-key encryption scheme so that it provides stronger security than the one-key encryption method. Optically, two unknown randomly generated numbers are changed to digital holograms which are used as public keys for asymmetric cryptography. The generated public keys and cipher texts by using them are Fourier transformed digital holograms with 256 gray level quantized intensities which are recorded on CCDs. The original plain text can be decrypted only with correct pairs of public-private keys and the holographic encryption-decryption keys in the proposed algorithm. The proposed optical schematic has an advantage of producing an enhanced asymmetric public key cryptosystem compared to the conventional RSA cryptosystem. The results of computer simulations verify that the proposed method shows the feasibility of application to an asymmetric public key cryptosystem with high security strength.
Nearest Neighbor-Based Differential Evolution for Reconstructing Gene Regulatory Network

Reconstruction of gene regulatory network is a very pertinent problem to computational biologist. GRN is crucial to understand the internal functions of a cell. The reconstruction of GRN is based on DNA-microarray time series gene expression data. This provides an insight that can be applied to drug development for a disease. Here, recurrent neural network (RNN) is used to model the GRN. The aim is to find the optimal parameter set for RNN which belongs to NP-Hard class. To search the optimal parameter set of RNN, a modified version of differential evolution algorithm, namely, nearest neighbor-based differential evolution (NNDE_RNN) has been proposed here and has been implemented on two real-life gene expression datasets. The final optimum parameter set of RNN obtained from the experiment is used to design the gene regulatory network of our interest. The inferred GRN has been analyzed on the basis of three performance metrics. It is observed that in many performance metrics, the proposed algorithm outperformed the state-of-the-art algorithms.
Reports of a Blockchain Revolution in Trade Finance Are Greatly Exaggerated

The term “blockchain” describes technologies that selectively incorporate some design elements first popularized by the Bitcoin cryptocurrency. Belief in the potential of blockchain (or “distributed ledger”) technologies to revolutionize financial markets appears in some quarters to border on religious fanaticism. Trade finance (i.e., financing cross-border transactions in goods and services) is one major global financial market burdened with anachronistic business models and obsolete technologies, and thus ripe for disruption. But that fact alone does not demonstrate that that blockchain will be the engine of digital transformation when it finally occurs. Magical thinking about technology that cannot live up to the hype about it can usually be distinguished from the early stages of the success of a disruptive new technology merely by asking 1) whether what is being discussed is a pilot project (in which case it can be ignored for the time being); and if not, then 2) what share of the relevant market the commercialized version of the technology has achieved (anything less than 1% may be considered still a pilot no matter what its promoters claim). By 2019, the only blockbuster success for blockchain remained the speculative cryptocurrency markets that originally spawned it a decade earlier, but the hype surrounding blockchain has been intense enough to overshadow any serious discussion of its lack of success. A pragmatic approach to the challenge of transforming trade finance starts by acknowledging the bewildering variety of business contexts and immense diversity of local interests that any new global trade finance system would have to accommodate. Furthermore, blockchain enthusiasts appear oblivious to the progress that parties engaged in cross-border trade have already made in updating trade finance systems. The eventual triumph of countless local instances of incremental innovation over the hubris of ill-informed evangelists of disruption may be obscured by “blockchain washing,” however, if blockchain enthusiasts manage to affix the label blockchain to whatever solution ultimately emerges.
Reducing Geographic Performance Differential for Face Recognition

As face recognition algorithms become more accurate and get deployed more widely, it becomes increasingly important to ensure that the algorithms work equally well for everyone. We study the geographic performance differentials-differences in false acceptance and false rejection rates across different countries-when comparing selfies against photos from ID documents. We show how to mitigate geographic performance differentials using sampling strategies despite large imbalances in the dataset. Using vanilla domain adaptation strategies to fine-tune a face recognition CNN on domain-specific doc-selfie data improves the performance of the model on such data, but, in the presence of imbalanced training data, also significantly increases the demographic bias. We then show how to mitigate this effect by employing sampling strategies to balance the training procedure.
Collusion Attacks on Decentralized Attributed-Based Encryption: Analyses and a Solution

Attribute-based Encryption (ABE) is an information centric security solution that moves beyond traditional restrictions of point-to-point encryption by allowing for flexible, fine-grain policy-based and content-based access control that is cryptographically enforced. As the original ABE systems are managed by a single authority, several efforts have decentralized different ABE schemes to address the key escrow problem, where the authority can issue secret keys to itself to decrypt all the ciphertext. However, decentralized ABE (DABE) schemes raise the issue of collusion attacks. In this paper, we review two existing types of collusion attacks on DABE systems, and introduce a new type of collusion among authorities and data users. We show that six existing DABE systems are vulnerable to the newly introduced collusion and propose a model to secure one of the DABE schemes.
Digital awareness among youth

This paper reviews the awareness of digital technology among today’s youth. It also gives an insight into the young minds regarding their attitudes toward use of digital world .India being a developing country has an upper hand in terms of manpower but due to lack of literacy rates India still has to work its way in order to make population much aware of current changes in online world.India having a cultural diversity among different states the digital awareness varies too. Some states like southern part of India have higher digital literacy while its counter part of north India lacks in terms of digital literacy as those states being predominantly rising in terms of agriculture.
Debiasing Stochastic Gradient Descent to handle missing values

Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.However, a major caveat of large data is their incompleteness.We propose an averaged stochastic gradient algorithm handling missing values in linear models. This approach has the merit to be free from the need of any data distribution modeling and to account for heterogeneous missing proportion.In both streaming and finite-sample settings, we prove that this algorithm achieves convergence rate of $\mathcal{O}(\frac{1}{n})$ at the iteration $n$, the same as without missing values. We show the convergence behavior and the relevance of the algorithm not only on synthetic data but also on real data sets, including those collected from medical register.
Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.
Legal protection for electronic work in Algerian law

The digital workbook is a product of modern technology, whose appearance coincided with the advent of the computer. The latter includes many works that respond to an electronic support. These works are devoted to protection under the copyright and related rights law as a type of literary and artistic works enjoying legal protection, but it differs from it in terms of its definition, and it shares it in terms of characteristics. Copyright on his work published on the computer is most closely related to his personality and branches into literary and material rights, they are the same as those contained in literary and artistic works because they are among their types, and they also enjoy legal protection of any kind.
LCC Research Guides: Library Makerspace: Sewing and Textiles

The Library Makerspace is temporarily closed to walk-in use while the campus is closed due to Covid-19. We look forward to seeing you again when it is safe to do so.
Kesiapan Politeknik Negeri Lhokseumawe Menuju Smart Campus Berbasis Revolusi Industri 4.0

The rapid development of innovative science and technology can change the way people live and work processes that interconnected with one another. Changes that occur in people's lives with the emergence of new technologies packaged in the form of the revolution industry 4.0. Utilising information and communication technology (ICT) to improve services to the campus community and stakeholders, it must go to the smart campus. Problems with the ICT system that are found, especially the information system that runs on the Politeknik Negeri Lhokseumawe (PNL), according to the author's observation, were not able to provide accurate information and have not offered much up-to-date information as decision making. The happens because they do not yet have a clear and directed roadmap, blueprint and planning towards a smart campus based on the revolution industry 4.0 so that the system developed is not in line with current needs. If left unchecked, the implications for the success of the institution to be achieved so that when implementing smart campus will fail. This failure caused by poorly planned planning, users and experts who are not ready to use technology, infrastructure that does not support, the unavailability of IT development funding and there is no clear policy. This research activity aims to see the availability and readiness of the PNL system towards a reliable, smart campus so that it can use as a reference in system development. This research uses the Research and Development (R&D) method with the development of the ICT4D Model which consists of 4 components (ICT Use, ICT Readiness, ICT Capability and ICT impact). Analysis of the readiness of ICT development towards smart campus is carried out by quantitative descriptive research where researchers conduct stages ranging from literature studies, surveys, questionnaires, data analysis and empirical findings. In this study the test subjects are leaders, lecturers, education staff and students. The results of the research that have been carried out in PNL based on testing of the overall smart campus development model obtained an index of 52.97%, meaning that this component is quite ready to welcome the smart campus, but this aspect requires many improvements to achieve success.
Multi-scale fusion and non-local attention mechanism based salient object detection

With the development of deep learning, researches in the field of computer vision are attracting more attention. As the pre-processing operation of visual tasks, a salient model may focus on pure architectures. The paper proposes a new multi-scale fusion network to enrich high-level redundant information with the enlarged receptive field. With the guidance of attention mechanism, the framework can capture more effective correlation spatial and channels information. Building a short-connection between high-level and each level features to transmit the contextual features. The model can be used in a variety of complex scenes for end-to- end image detection, with simple structure and strong versatility. Experimental results obtained on multiple common datasets have shown that the proposed model achieved better performance both in the visual effect and the accuracy for small object and multi-target detection.
Dependently Typed Knowledge Graphs

Reasoning over knowledge graphs is traditionally built upon a hierarchy of languages in the Semantic Web Stack. Starting from the Resource Description Framework (RDF) for knowledge graphs, more advanced constructs have been introduced through various syntax extensions to add reasoning capabilities to knowledge graphs. In this paper, we show how standardized semantic web technologies (RDF and its query language SPARQL) can be reproduced in a unified manner with dependent type theory. In addition to providing the basic functionalities of knowledge graphs, dependent types add expressiveness in encoding both entities and queries, explainability in answers to queries through witnesses, and compositionality and automation in the construction of witnesses. Using the Coq proof assistant, we demonstrate how to build and query dependently typed knowledge graphs as a proof of concept for future works in this direction.