Reviewerly/exHarmony huggingface dataset:
A comparison of language representation models on small text corpora of scientific and technical documents

Text mining for the identification of emerging technology is becoming increasingly important as the number of scientific and technical documents grows. However, algorithms for developing text mining models require a large amount of training data, which carries heavy costs associated with data annotation and model development. The need for avoiding these associated costs has in part motivated recent work in text mining, which indicate value in leveraging language representation models (LRMs) on domain-specific text corpora for domain-specific tasks. However, these results are demonstrated predominantly on large text corpora, which do not address concerns associated with the ability of LRMs to transfer to domains where training data may be scarce. Due to this, we benchmarked the performance of LRMs on identifying quantities and units of measure from text when the number of training samples is small.
Pengujian Black Box pada Aplikasi Admin PT. World Trans Berbasis Web Menggunakan Metode Equivalence Partitioning

Admin Application PT. World Trans which is being developed, found a problem in the database system that failed to communicate with the application system, namely when the admin enters the application using a user name and password, the database does not respond to data calls that are being made and the admin cannot enter the application. The development team has made improvements, and this time the test was carried out in an effort to see the possibility of the same defects or new defects in the application. So, if not tested, it will be very detrimental for business owners and other users. This test uses the Black Box testing technique. Black Box testing method consists of several ways, including Equivalence Partitioning, Boundary Value Analysis, Comparison Testing, Sample Testing, Robustness Testing, and others. Among the many testing methodologies, this test method was chosen using Equivalence Partitioning. Because Equivalence Partitioning is a Black Box testing method that breaks or divides the input domains of the program into data classes so that a test case can be obtained. The design of the Equivalence Partitioning test case is based on evaluating the equivalence class for input conditions that describe a set of conditions that are true or not. The results of this test are the admin application system PT. World Trans can make data calls to the database and there are no problems as before or new problems so that the system can run properly and according to software requirements that have been determined. With the results of this test, the development team can find out the extent of the problem in this application, as well as see the possibility of similar or new problems that can harm the team and business owners.
Aplikasi Game Flatfrom Art Lewat Construct 2 Dengan Menggunakan Metode Quadtree

The use of media as a learning tool is appropriate and interesting in the introduction of traffic signs to increase knowledge. Traffic game application is a media that is run on an android smartphone as a means to recognize and learn the types of traffic signs. The research method in designing a traffic sign game is Quad-Tree which collision collision / collision of two different objects in the game area by dividing the arena of the same size game if there are two or more objects in the same area so divide the area again into four equal sized parts until no objects are in the same area. Providing interesting knowledge and while playing to the community, a traffic game application is designed to provide knowledge of road equipment containing symbols, letters, numbers, sentences or a combination of them, which are used to provide warnings, prohibitions, commands and instructions in the past educational games cross.
Reversible Data Hiding for Quantum Images using Quantum Controlled-NOT Gate Operation

At present, quantum image data hiding technology is restricted by high fidelity and embedding capacity, which frequently leads to permanent distortion of carrier images.A reversible data hiding method based on quantum controlled-NOT (CNOT) gate operation was proposed in the present study to satisfy the requirements of embedding capacity and visual quality and recover the original carrier image without loss.First, the hidden and carrier images were represented by a novel enhanced quantum representation of digital images.The hidden images were then encrypted and scrambled by employing chaos method.The quantum secret image was embedded into the relevant bit plane of the quantum carrier image considering quantum CNOT gate operation.The quantum key image was also generated.Finally, the current work not only extracted the quantum secret image without error but also recovered the carrier image without loss via inverse quantum CNOT gate operation.Simulation experiments were conducted to analyze embedding concealment and visual quality of the embedded steganographic images.Results demonstrate that the quantum circuit based on quantum CNOT gate operation not only enhances the embedding concealment but also improves the peak signal-to-noise ratio at the embedding capacity of 2 bits per pixel by 10% compared with the traditional least significant bit method.Thus, this operation achieves the lossless recovery of the original quantum carrier image.This study also provides certain theoretical importance and practical value for the security authentication and protection of quantum images.
A regulatory and financial stability perspective on global stablecoins

Stablecoins provide an alternative to volatile crypto-assets. Depending on their asset management function, they may fall under different regulatory regimes or – with certain design features – under none at all. Given their potential size, global stablecoins could pose risks to financial stability. Such arrangements need a robust regulatory framework. JEL Classification: E42, G15, G28
The Recognition of Brazilian Baiano and Gaucho Regional Dialects on Twitter Using Text Mining

The internet has broken geographical barriers and brought people and cultures closer independent to their physical location. However, the language, idiom, dialects and accents continue to characterize individuals in their origins. The Brazilian regional dialect is the object of study of this research, which deals with linguistic corpora analyzed from a volume of data extracted on Twitter. This paper presents the results of the mining phase that makes up a first stage of the project to create a technique for recognizing the Brazilian Portuguese regional dialects. Analysis and conclusions were be made only for the baiano and gaucho dialects, considering the significant size of the samples and the need to reach a diagnosis of the collect data set.
ACTIVE TEACHING METHODS IN HIGHER EDUCATION - MODERN VIEW ON THE PROBLEM OF EDUCATION

This article presents a new look at the problem of updating the use of active teaching methods in the educational process of higher education; the socio-psychological foundations of the actualization of this problem in the system of higher education are highlighted; the prospects and possibilities of using active teaching methods in the educational practice of a modern university, etc. are shown. It is shown that at the present stage of the development of society, in the process of developing new «amendments» to the Constitution of the Russian Federation, on the way to a new look at the nature of man, at his rapidly changing world, at the significance of the level of development of his literacy and professionalism, the awareness of the requirements for the education system in whole - inexorably increase. Education in modern Russia is becoming strong, responsible, creative, socially active, patriotic-minded, legally-economic, politically-socially literate, with a high position, ready and able to live and work in a democratic society-oriented state, in a secular society, with highly moral and spirituallyoriented ideals. To cope with this practice is possible only if there is a generally accessible strategy of a humanistic strategy in education, reliable and active approaches, as well as an individually-oriented training paradigm in solving professional problems using practical teaching methods.
From Feature Selection to Continuous Optimization

Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an alternative tool to do so. The proposed method, called MaNet, is motivated by the fact that most of the DL models often need to solve massive nasty optimization problems consisting of millions of parameters. Feature selection is the main adopted concepts in MaNet that helps the algorithm to skip irrelevant or partially relevant parameters and use those design variables which contribute most to the overall performance. The introduced model is applied on several unimodal and multimodal continuous problems. The experiments indicate that MaNet is able to yield competitive results compared to one of the best hand-designed algorithms for the aforementioned problems, in terms of the solution accuracy and scalability.
A Classification of non-Cryptographic Anonymization Techniques Ensuring Privacy in Big Data

Recently, Big Data processing becomes crucial to most enterprise and government applications due to the fast growth of the collected data. However, this data often includes private personal information that arise new security and privacy concerns. Moreover, it is widely agreed that the sheer scale of big data makes many privacy preserving techniques unavailing. Therefore, in order to ensure privacy in big data, anonymization is suggested as one of the most efficient approaches. In this paper, we will provide a new detailed classification of the most used non-cryptographic anonymization techniques related to big data including generalization and randomization approaches. Besides, the paper evaluates the presented techniques through integrity, confidentiality and credibility criteria. In addition, three relevant anonymization techniques including k-anonymity, l-diversity and t-closeness are tested on an extract of a huge real data set.
Dynamic Neural Network Channel Execution for Efficient Training.

Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference. On the other hand, in this work, we propose a novel method which reduces the memory footprint and number of computing operations required for training and inference. Our framework efficiently integrates pruning as part of the training procedure by exploring and tracking the relative importance of convolutional channels. At each training step, we select only a subset of highly salient channels to execute according to the combinatorial upper confidence bound algorithm, and run a forward and backward pass only on these activated channels, hence learning their parameters. Consequently, we enable the efficient discovery of compact models. We validate our approach empirically on state-of-the-art CNNs - VGGNet, ResNet and DenseNet, and on several image classification datasets. Results demonstrate our framework for dynamic channel execution reduces computational cost up to 4x and parameter count up to 9x, thus reducing the memory and computational demands for discovering and training compact neural network models.
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks)applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output.
Navigating the Landscape of Games.

Games are traditionally recognized as one of the key testbeds underlying progress in artificial intelligence (AI), aptly referred to as the Drosophila of AI. Traditionally, researchers have focused on using games to build strong AI agents that, e.g., achieve human-level performance. This progress, however, also requires a classification of how 'interesting' a game is for an artificial agent. Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but also helps to determine what game an AI should address next as part of its training. Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes, characteristics, and complexities. We illustrate our findings in various domains, ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another. Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.
AN ERROR ANALYSIS OF COMMUNITY COLLEGE OF QATAR (CCQ) STUDENTS’ ARABIC AND ENGLISH EMAILS TO FACULTY

This study analyzes Arabic and English emails written by CCQ students to focus on both the formal as well as grammatical issues. The emails in general have missing parts, such as Introduction, Conclusion and Closing parts. As for the mechanical and grammatical side, both emails in Arabic and English have various and numerous errors in spelling, grammar and punctuation. In Arabic emails, spelling is a major factor, while punctuation errors are highest in English emails. Some English emails have sentences that lack linking verbs and sentences with problems in the article system. L1 interference is a major factor here. As for Arabic, some students wrote sentences where English structures supersede Arabic sentence structure. Students wrote sentences (S+V), when the normal structure should be (V+S). More importantly, some of the errors come from the email genre or students’ sacrificing formalities and protocols for the sake for speed and convenience. Chat language and online communication are factors, too. Article visualizations:
Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering

Purpose:This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service.&#x0D; Design/methodology/approach: By using Octoparse we mine opinion or comment data in the form of customer online reviews, after getting the data we group the data using the k-emans clustering methode to obtain cluster&#x0D; Findings: Cluster Analysys can can help potential customers to make a decision before deciding to buy a product or service&#x0D; Research limitations/implications: WWW.Lazada.Com&#x0D; Practical implications: State your implication here.&#x0D; Originality/value:&#x0D; Paper type: This paper can be categorized as case study paper&#x0D; &#x0D; &#x0D;
Pengaruh Komik Isi Piringku terhadap Asupan Karbohidrat dan Protein pada Anak Gizi Lebih

Background: Communication, Information and Education (IEC) of nutrition for school children can shape children's eating habits from an early age in order to achieve a better individual situation in the future. Comics are able to display simple stories and writing in colloquial so that they are easy to understand. Research Methods. This is a quasi experimental study with control group pretest-posttest design. Subjects were taken with a simple random sample of 60 students who were divided into two groups, the first group was given education with comic Isi Piringku and the second group was given education without comic Isi Piringku. The carbohydrate and protein intake data in two groups were analyzed using the paired t-test. Research Result. The analysis showed that the highest protein intake enhancement occurred in the treatment group with a delta of 16.7 g, while the control group was 6.2 points. The carbohydrate intake before treatment was 191,9 g and increased after given comic Isi Piringku by 193,4 g. The results of different test showed that there were significant differences in the enhancement of protein intake before and after the provision of nutrition education between the control group and the treatment group with p value &lt;0.05, carbohydrate intake was not significantly different before and after treatment.
A minimalistic decision tree for blockchain business cases in healthcare

Distributed Ledger Technology (DLT) is an emerging technology to remove middlemen from an eco-system. However, many DLT/BC projects are very technologically oriented and fail to address the business case and the re-design of its eco-system. Therefore, many DLT/BC projects do fail. We propose a minimalistic decision tree to check whether a business case is suitable for implementation by DLT/BC technology and evaluate the tree in the healhtcare domain.
A Discussion of a Luxury Apparel Brand Strategy in an Emerging Market: Conceptual Model with Network Perspectives

This study explores the Burberry apparel brand’s position and its strategic behaviour seeking new customers in the new market. This exploratory research pays particular attention to business potential and opportunities in an emerging market, Vietnam. To evaluate Burberry’s strategies and the potential, the collaborative network relationships are discussed in details, that is followed by the discussion of the key themes for Burberry to consider in the Vietnamese market, where Burberry has recently begun expanding its businesses. The outcome of the study suggests that the Western luxury brand apparel needs to understand the targeted markets’ systems and consumer behaviour, from there it is inevitable to design strategic plan how to engage the stakeholders in the ubiquitous supply chain relationships. In the end, a conceptual model with network perspectives is proposed: The model will be an analytical framework with key agenda items for the luxury brand which aims to enter the new market, and also the research limitations and further research opportunities are discussed.
Extended Kalman Filtering and Maximum-Likelihood Estimation for Point Target Localisation

Estimation of the location of point targets from radar observations will be investigated. The location can be determined from the echo's delay and the aspect angles to the target, which can be inferred by e.g. an maximum-likelihood (ML) estimator. For rapidly moving targets delay and aspect angles undergo fast variations. Therefore, short time observations are only considerable for the estimator application. Hence, the estimation effort becomes high for a long time observation of the targets. Target tracking by extended Kalman filter (EKF) will be considered in order to reduce the computational effort. A simulation based comparison of ML and EKF based target localisation accuracy will be drawn.
Smart Mind-based Approach to Control a Wheelchair Wirelessly

The number of elderly and disabled people worldwide has increased, and their day-to-day activities depend on others' help.Improving the quality of life of these people has become the most important responsibility of society, and it is the role of technology specialists to make their life as normal and easy as possible so that they can perform their day-to-day activities at the relevant time without others' help.Many researchers have proposed several solutions, but these have limitations such as poor performance and usability.Hence, there is a need to design a wheelchair that is smart and easy to use for the elderly and people with partial or full quadriplegia.In this paper, we propose a smart mind-based wireless controlled wheelchair (SMWCW) approach that completely controls the motion of a wheelchair wirelessly through brain signals and eye blinks to help partially quadriplegic individuals to perform their daily activities easily.In SMWCW, NeuroSky's electroencephalography (EEG) device is used by users sitting on the wheelchair to capture brain activity and the frequency of eye blinks, and it translates these data into movement commands using an Arduino microcontroller unit that is directly connected to the wheelchair to move the wheelchair.The proposed approach has been simulated, and results show its effectiveness and applicability for use by most physically disabled persons.
Distributed Frequent Itemset Mining with Bitwise Method and Using the Gossip-Based Protocol

Nowadays, distributed systems are prevalent and practical in network environments. In distributed systems, pattern recognition help to extract information from network nodes. Meanwhile, data mining in such systems needs resource consideration in terms of storage and computational time. The primary requirement of these systems is a scalable mechanism to distribute the tasks on several databases. Moreover, to do a centralized process, relocating data from all nodes or partial nodes to a central node has confidential risks and traffic overhead. Therefore, distributed data mining in distributed environments needs systematic and structural techniques. In this paper, we propose a new algorithm to extract frequent itemsets in Wireless Sensor Networks. Through this algorithm, nodes frequent local itemsets are obtained with a Bitwise approach, and nodes are classified into clusters by using the Low Energy-Adaptive Clustering Hierarchy (LEACH) algorithm. Connecting the head cluster is performed by a Gossip-based protocol to achieve the values of global support, and it finally resulted in the extraction of frequent itemsets. The proposed algorithm has been simulated in various scenarios using Java software, and algorithm efficiency is evaluated in terms of execution time and average accuracy. Our algorithm is compared with a Gossip-based algorithm, and then some improvements in execution time have been presented.
Real-Time Gesture Recognition with Shallow Convolutional Neural Networks Employing an Ultra Low Cost Radar System

Ultra-low-cost radar hardware (HW) in combination with low-cost processing units is investigated in order to create and evaluate a holistic ultra-low-cost gesture recognition system. We study the real-time performance of novel machine learning (nML) methods: neural networks (NNs), in particular shallow architectures of convolutional neural networks (CNNs). The real-time performance of each approach is judged by computational complexity, prediction time, accuracy, and false-positive rate (FPR). As HW, a two-channel radar system with continuous wave (CW) modulation at a carrier frequency of 10 GHz has been employed throughout the investigations. The algorithms are designed, trained, evaluated, and juxtaposed. The results show that the classification process on low-cost HW is feasible and allows to achieve accuracies of 97.9% and FPRs of 1.72%, all of which with a response time of less than 180 ms.