Reviewerly/exHarmony huggingface dataset:
Finding hierarchical structure in binary sequences : evidence from Lindenmayer grammar learning

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether the brain learns the higher order regularities of a highly simplified input where only sequential order information marks the hierarchical structure. To this end, we implemented sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants’ pattern of anticipation could not be accounted for by “flat” statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions.
Foreign Direct Investment and Economic Growth: The Role of Corruption Control in North Africa

This paper aims to study the interaction of corruption control in the relationship between foreign direct investment (FDI) and economic growth in North African countries. To achieve this, econometric estimates were made using the Generalized Moments Method (GMM) in dynamic panel of the Five North African countries over the period from 1996 to 2019. The results obtained show that FDI combined with corruption control although having a positive sign has no effect on the economic growth of North Africa. In light of these results, recommendations have been made to make the fight against corruption in the FDI-economic growth relationship more effective, through decentralization, streamlining of administrative procedures and relaxation of tax regulations.
Theil-Sen Regressive Miyaguchi–Preneel based Cryptographic Hash Blockchain for Secure Data Transmission using Remote Sensing Data in IoT

Abstract IoT is the maximum current Internet that integrates various intelligent devices used by management or individuals to maintain a variety of applications, including smart health and environmental monitoring, remote sensing data analysis. IoT, devices interfere and interact with each other, and this poses a number of security threats. But, because of this freedom, there is a strong need for consistent security to exchange data between IoT devices. For solving this issue, in this manuscript Theil-Sen Regressive Miyaguchi–Preneel Cryptographic Hash Blockchain is proposed for secure data transmission using remote sensing data (TSR-MPCHB-SDT-RSD). To improve the security of data transfer, Theil-Sen Regression and Miyaguchi-Preneel Cryptographic based Blockchain technology is introduced. IoT devices are used to collect remote sensing data. At first, Theil-Sen Regression is used to analyze the data collected on the basis of geometric neutrality. This process reduces the processing time of secure data transfer. MPCHB is then used for secure data transfer. The Miyaguchi-Preneel cryptographic function is used to generate a confusing value of each data. The hash data then generated is transferred to an authorized hub, which avoids unauthorized additions. This helps to improve the data confidentiality and integrity ratio. Finally the proposed method is simulated using the java language. Evaluation metrics such as data confidentiality ratio, data reliability ratio, process time average delay, average power consumption and blockchain data transfer efficiency are measured. Then the simulation performance of the proposed TSR-MPCHB-SDT-RSD method provides 91.03%, 92.11%, 85.33% and 93.78% higher data confidentiality rate, 96.12%, 97.66%, 88.26% and 93.11% higher data integrity rate and 65.12%, 44.33%, 71.59% and 69.36% minimum processing rate which are compared with existing method like Blockchain-Envisioned Secure Data Delivery and Collection Scheme for IoT-Enabled Internet of Drones for Secure Data Transmission using Remote Sensing Data (BESDDC-IOD-RSD), blockchain-assisted health record management using IoT for Secure Data Transmission using Remote Sensing Data (BIoTHR-SDT-RSD), Lamport Merkle Digital Signature Generation blockchain for Secure Data Transmission using Remote Sensing Data (LMDSG-SDT-RSD) and Ant Colony optimization with Multi Kernel Support Vector Machine based blockchain for Secure Data Transmission using Remote Sensing Data (ACOMKSVM-SDT-RSD).
Runtime Evolution of Bitcoin's Consensus Rules

&lt;p&gt;The manuscript has been submitted to IEEE TSE.&lt;/p&gt;
Competition Law and Artificial Intelligence – Challenges and Opportunities

The dynamic development and expansion of artificial intelligence (AI), one of the most important technologies currently being developed in the world, which may herald the fourth technological revolution, brings not only numerous benefits, but also threats in many spheres of socio-economic life, including law. This breakthrough technology also creates a number of challenges and opportunities for the science and practice of competition law (or antitrust law). The aim of this study is to show the pro- and anticompetitive effects of AI implementation, as well as to identify and outline the key challenges and opportunities that these effects have on competition law. This paper is intended to serve as an introduction to the subject matter. It does not claim to examine it exhaustively, but rather to inspire its further, more extensive and in-depth analysis.
A framework for cost, profit, and pricing evaluation of cloud native CPU resources

&lt;p&gt;Online service provisioning involves two main entities, i.e., cloud providers renting cloud resources to service providers. In this process, the service provider would like to minimize its costs, while the cloud providers seek ways to increase their profit. Novel container orchestration platforms like Kubernetes allow deploying services on the same physical or virtual (e.g., virtual machines) infrastructure while delivering both hard and soft resource isolation. When the soft resource isolation is allowed, guaranteed (or request) resources of one service can be used (if idle) by another one as limit resources, for short time intervals in a best-effort manner. The use of limit resources represents an extra font of revenue for the cloud provider to charge different service providers for the same resources. At the same time, soft isolation allows service providers to decrease the number of request resources and rely on more limit resources, paid only when accessed, reducing the overall resources needed. Therefore, soft resource isolation has potential benefits for both cloud and service providers. To enable these benefits, the price of limit resources should be carefully set by the cloud provider to generate, on one hand, extra profits and, on the other, be appealing to service providers. This paper proposes a framework for evaluating the pricing window for the limit resources under which it is possible to reduce the cost for service providers and increase the profits of cloud providers. Results in a sample simulated scenario show that by pricing limit resources within six to twelve times the request resources, cloud and service providers can achieve financial gains in the order of 10%-20%.&lt;/p&gt;
Teaching Indonesian L2 Learners Academic Writing: A Proposed Instructional Model

This article reports a proposed instructional model to teach the second language (L2) academic writing in education with citations and references to learners in English Education departments. The model is suggested to be implemented in two semesters in a row. In the first semester of the implementation, learners are to take essay writing and academic reading courses simultaneously. In the former, learners learn to write coherent and cohesive sandwich paragraphs, do peer reviews, and write five-paragraph essays in education without citations and references. In the latter, learners familiarise themselves with various academic sources, write in-text citations and reference lists using Microsoft Word Reference Manager, paraphrase and summarise information from journal articles, and write a short paragraph using citations and references. In the second semester of the implementation of the model, learners take an academic essay writing course which is the progressing combination of the two aforementioned courses in the previous semesters. In this writing course, learners write two 1,200-word essays in education with citations and references from at least five sources. During the implementation of the model, a no-plagiarism policy is upheld through teachers' emphasising on paraphrasing and checking learners' works with plagiarism checkers before submission. Gradual progress is continuously monitored through teachers' feedback on progress. All in all, the model emphasises step-by-step progress in L2 academic writing supported by ample writing preparation time and continuous feedback on progress.
Archeological research on virtual deaths

Archeologists are trying to learn about the worlds of the past. And it is indeed a very challenging task. Well, how about practicing in the virtual realm?
The impact of environmental risk and platform trust on satisfaction with health qr code use

In response to the economic and social hazards posed by the COVID-19 global pandemic, many countries have adopted various information technologies to rapidly track and control the spread of the epidemic. Health Quick Response (QR) codes are emergency measures implemented by the Chinese government in the epidemic environment to balance epidemic prevention and control with recovery of economic and social development, and facilitate public mobility across regions and access to various public spaces. The use of health codes by the public is a practical necessity, but the satisfaction of their use is influenced by multiple factors such as environment, technology, and organization. In this paper, we collected data through a questionnaire to analyze the basic situation of public satisfaction with the use of health QR codes in China and its influencing factors. The results show that perceived quality and platform trust directly affect the satisfaction of health code usage, while environmental risk and platform trust indirectly affect the satisfaction of health code usage through the mediating effect of perceived quality.
A Novel Effective Edge-Based Image Denoising Algorithm

This article proposes an edge-based denoising algorithm to restore the original image, which is highly degraded by the salt and pepper noise. Most of the existing image denoising algorithms consider edge as a noise. Here, the proposed algorithm can set out to resolve this ambiguity. The concept of directional filters is being used to delineate the edges from noise. The proposed algorithm performance is tested for different noise densities ranging from 5% to 90% on both the greyscale and colour images. It is compared with the current state of art techniques using several performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) values, and image enhancement factor (IEF). The results showed that the proposed algorithm has achieved an improvement of 60% over the state of art techniques.
PRODUCTION MASK SCUBA TYPE ON UMKM CAHAYA PRINTING

Scuba mask is a type mask which made from a tight and elastic type of fabric that provides comfort when used. PT. Cahaya Digital Printing is one of companies engaged in digital printing that produces masks which are currently used by the public. The purpose of this study was to analyze production process of scuba masks from raw materials to mask products. The scuba mask production process uses scuba cloth as raw material which though a printing process on a digital printing machine which has a print area of up to 1.5 m with a print speed of up to 15 m2 per hour to print according design to customer requirement, manual press process to cut the mask to size and scuba printing and sewing and overlay processes using a sewing machine that has a stitch length of 0-5 mm and a sewing speed of up to 4500 s.p.m to sew the edges of the mask to make look neat and not stringy.
Kesalahan Berbahasa Indonesia pada Penulisan Media Luar Ruang di Kecamatan Mesuji Makmur

Facts on the ground show that there are still many mistakes in the practice of speaking Indonesian in the community. The Indonesian language errors in question include errors in the oral and written realms. In the realm of writing, Indonesian language errors are often found in outdoor media, such as posters and bulletin boards. This study aims to describe errors in Indonesian in writing outdoor media in Mesuji Makmur District. The research was conducted using a qualitative descriptive method. The data in this study are in the form of words, sentences, or discourses that are obtained directly in the field through various outdoor media, such as posters, bulletin boards, stretch cloth, and similar media in the Mesuji Makmur sub-district. As for analyzing the data, the following steps were used; (1) data reduction, (2) data presentation, (3) temporary conclusion drawing, and (4) verification. The results showed various forms of language errors were found in the writing of outdoor media in the Mesuji Makmur sub-district. The errors in question include; (1) spelling errors, (2) word choice errors, (3) morphological errors, and (4) errors in the use of prepositions
Activity discovery using Dirichlet multinomial mixture models from discrete sensor data in smart homes

Abstract To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, an activity model tailored to individuals must be constructed from less privacy-invasive sensors to avoid interference in daily life. Unsupervised machine learning techniques are desirable to automatically construct such models without costly data annotation, but their application has not yet been sufficiently successful. In this study, we show that an activity model can be effectively estimated without activity labels via the Dirichlet multinomial mixture (DMM) model. The DMM model assumes that sensor signals are generated according to a Dirichlet multinomial distribution conditioned on a single unobservable activity and can capture the burstiness of sensors, in which even sensors that rarely fire may fire repeatedly after being triggered. We demonstrate the burstiness phenomenon in real data using passive infrared ray motion sensors. For such data, the assumptions of the DMM model are more suitable than the assumptions employed in models used in previous studies. Moreover, we extend the DMM model so that each activity depends on the preceding activity to capture the Markov dependency of activities, and a Gibbs sampler used in the model estimation algorithm is also presented. An empirical study using publicly available data collected in real-life settings shows that the DMM models can discover activities more correctly than the other models and expected to be used as a primitive activity extraction tool in activity analysis.
A Prototype For Quantum Database in Hybrid Quantum

&lt;p&gt; This report proposes a prototype And a possibility to converge both this quantum database and classical database. This report mostly identifies the gap between this classical database and quantum database but it shows no result rather it proposes only the hypothesis and prototype which can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. So we will propose a logical design for memory management for the quantum database security enhancement model. &lt;/p&gt;
DEVELOPMENT OF A SMART DOLL PROTOTYPE FOR EARLY AGE CHILDREN COLOURS LEARNING IN THREE LANGUAGES

Early childhood is a golden age to learn a language, not only the mother tongue but also a second or third language. However, learning media that can be used to attract children's interest in learning multi-language is still limited. Therefore, this study aims to develop a smart doll that attracts children to learn Javanese, English, and Indonesian. In particular, this media is intended to help children recognise colours in three languages. This research is developmental research with the following steps: First, collecting information about the product being developed, conducting a literature review and interviewing teachers and parents. Second, planning product design in accordance with the results of the needs analysis. Third, developing the initial product form. Fourth, conducting a preliminary field test on 3 children. Fifth, revising the main product. Sixth, field testing on eight children. Seventh, evaluate the testing result for the next development plan. The result of this research is a prototype of a smart doll that is able to recognize colours in 3 languages. Based on the field trials results, the prototype of this smart doll can attract children’s enthusiasm for learning colours and improve their ability to recognise colours in three languages. Some things that need to be improved will be discussed further.
Towards an African cybersecurity community of practice

In recent years cybersecurity challenges and concerns have become a common theme for discussion by both the government and private sector. These challenges are partly brought on by the continued use of and dependence on information technology, such as the internet, wireless networks and the development and use of smart devices. Additionally, the Covid-19 pandemic has also led to the increase in internet use as it altered the way in which people live and work through forcing businesses and even schools to move to remote working. All these events have made cybersecurity challenges and concerns spiral and more so in Africa where cybercrime continues to rise and be a constant threat. This study proposes a cybersecurity community of practice as a strategy to address African contextual cybersecurity challenges. This qualitative enquiry, based on organizations on the African continent, identifies key characteristics and objectives of an African cybersecurity CoP. These findings provide practical implications for CoP African members and a steppingstone on what to consider prior to implementing an African CoP for addressing cybersecurity challenges and concerns.
Deep and Lightweight Neural Network for Histopathological Image Classification

Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy. However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues. We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.
Starting a New Midcareer Librarian Position During COVID-19

This article chronicles the experience of two midcareer librarians who were new hires at the University of South Florida one month before the entire campus was sent to work from home due to the COVID-19 pandemic. They recount the obstacles encountered and how they overcame them. They include lessons learned and ways to improve a transition to remote working. Specific areas that are discussed: technology, training, communication, self-advocacy, relationship building, and mentorship.
STUDENTS’ DIFFICULTIES IN ANSWERING READING COMPREHENSION QUESTIONS OF REPORT TEXT AT THIRD GRADE OF SMPN 1 BENGKULU SELATAN

This research aimed to find out the students difficulties in answering reading comprehension questions of report text and the causes of the students difficulties in answering reading comprehension questions of report text. The participants of this research were 63 third grade students at SMPN 1 Bengkulu Selatan. This research used descriptive quantitative as research design. The data were obtained through the reading comprehension test and interview section. The reading comprehension test was adapted from Mandiri textbook published by Tiga Serangkai consisted 25 questions of multiple choice. The reading comprehension test was validated by using the interpretation guidelines according to SPSS. In reliability obtains by using Cronbach’s Alpha formula in SPSS software. The result of this research showed that students faced some difficulties in answering reading comprehension questions of report text and there were some causes of their difficulties in answering reading comprehension questions of report text. The students difficulties in answering reading comprehension questions were locating pronouns, understanding vocabulary and making inference. Making inference became the most difficult aspect with percentage of students who answered questions about making inference incorrectly was 73%. The causes of students difficulties in answering reading comprehension questions of report text were lack of vocabulary, lack of strategies and lack of interest or motivation.
The Development Of Two Room Board Media With Ilustrated Story Boards Based On Realistic Mathematics On Integers Through Blended Learning Strategies

Blended learning as a combination of synchronous and asynchronous learning is a limited face-to-face learning solution in the current pandemic era. For this reason, the authors develop two-space boards interactive media to facilitate students learning integers material with realistic math-based illustrated story books. The development uses a 4D model from Tiagarajan (1974) which consists of four steps: define, design, develop, and disseminate. Through a blended learning strategy that provides opportunities for students to interact with a two-room board media combined with realistic math-based illustrated story books about the application of integers, it is hoped that students will be able to understand the concept of integers better. This teaching aid was tested on sixth grade students of SDN Jenang 02 Academic on 2021/2022. The result is that the use of a two-room board combined with a picture story book about the application of integer arithmetic operations in everyday life can improve understanding of the concept of counting integers, and the integration of its use with question cards can improve students' skills in arithmetic operations of addition and subtraction of integers.