Reviewerly/exHarmony huggingface dataset:
Review of Kinne (2020): Particle Placement in English L1 and L2 Academic Writing: A Triangulated Learner-Corpus and Experimental Study of Weight Effects

Abstract This article reviews Particle Placement in English L1 and L2 Academic Writing: A Triangulated Learner-Corpus and Experimental Study of Weight Effects
A Survey on Supporting the Software Engineering Process Using Artificial Intelligence

Software engineering has been one of the highly researched sub-fields in computer science in the past years. Software engineering utilizes engineering approaches to develop software. Developing software has been facing several challenges like producing high quality software while meeting strict project time schedules with minimum resources hence maximum cost savings and of course to meet the client’s requirements. Such challenges can be approached as a multi objective optimization problem. Previous literature exists that explores the potential of utilizing machine learning approaches to support the process of software engineering where it can benefit from the adaptability and learnability of such techniques. Evolutionary algorithms have been one of the highly researched machine learning approaches where it has been utilized by researchers in previous literature to solve single\multi objective optimization problems . Dealing with the challenges facing the process of software engineering as a multi-objective optimization problem promotes evolutionary algorithms as a possible solution to tackle such problems. Genetic algorithms have been among the highly researched evolutionary algorithms to tackle multi-objective optimization problems. In this paper, previous literature of techniques used to support software engineering will be reviewed as well as the possibility of using hybrid evolutionary algorithms to tackle the same problem will be discussed.
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems

Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples.In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples?We explore this question using program synthesis.We develop a synthesis model to learn phonology rules as programs in a domain-specific language.We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability.In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.
Kantian Computational Linguistics

Language is a defining characteristic of higher order species—particularly of us, homo sapiens. For that reason, it is only appropriate to conclude that linguistic capability—the ability to understand and generate natural language—is essential in any tools that we build for cognitive assistance. Fast and superior automated solutions exist in most other competencies, including numeracy and vision, but parity in linguistic skills remains elusive [1] for many reasons. In this chapter, we seek to identify why we find ourselves here and propose an alternative approach to overcome identified deficiencies. We do not know yet if this approach is useful or effective, but we proceed to share our thoughts because the biggest risk is doing nothing. First, we review seminal work on linguistics and the structure of language. We then examine the most popular contemporary solutions, identify their drawbacks and propose alternatives to address them.
An appraisal mechanism for a social marketplace

Social networks have become indispensable in our daily lives, and have significant implications for the development of electronic commerce. In centralized e-marketplaces, buyers face two main problems in purchasing decisions. Firstly, for some products, sellers do not provide product information. Secondly, buyers can be deceived by fake reviews or ratings manipulated by malicious providers. In this research, after analyzing users’ social activities, we propose an appraisal mechanism for the social marketplace, which customers can use to find products and to obtain credible referrals. The proposed mechanism can effectively discover suitable and trustworthy sellers to expedite the online purchasing process and enable social commerce to flourish.
Framework for Controlling KNX Devices Based on Gestures

The demand for comfort and versatility in the management of different types of devices in a home or office is exponentially increasing. More and more people want a comfortable environment for leisure or work, something practical, sustainable and safe. This includes, obviously, all the range in the target user population, including people with disabilities, and cultural differences. With all the above in mind, it is important to develop adaptive interfaces that can adjust on-the-fly to each user needs and conditions. This paper presents an initial framework that integrates human actions: gestures and/or poses in a way to activate and command devices using KNX standard. The pose detection algorithm is used to detect different gestures/poses, where each gesture or group of gestures integrated with KNX allows easy and universal communication with various types of existing automation devices. The initial results show that the framework is capable of commanding different devices effortlessly, with the ability to develop interfaces that adapt to each user needs.
A Study on the Educational Performance Analysis of Classical Education Based on Academic Interest Level

Recently, in higher education, both the necessity and the importance of a liberal education that contains an element of classical reading have been emphasized. However, it is difficult to find educational performance related with those educational programs. In order to investigate the educational performance of classical reading, surveys were given to students who were taking a ‘Reading Famous Writings’ course established at the Liberal Arts College of A university.&lt;br/&gt;Moreover, to investigate the differences in the educational effects of classical education, we conducted additional analysis according to the level of individual interest. To this end, pre and post surveys were conducted, which included a classical education performance scale and an academic interest scale.&lt;br/&gt;As a result of this study, we found that students who were taking this course improved certain competencies, namely, reading comprehension, communication, and creativity. In contrast, there was no improvement in academic interest, whether it be on an individual or situational level. However, we did find that the individual interest of students who were categorized as having a lower level of individual interest did indeed improve after taking this course. Through this study, we proved that some positive effects of a liberal education containing a classical reading element, especially as seen in the course called ‘Reading Famous Writings’, were experienced by the students.
Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.
Elemental Software Architecture

This paper discusses a software architecture methodology, called Elemental Software Architecture, which is derived from two other existing methodologies: Juval Lowy’s Method and Robert Martin’s Clean Architecture.Juval Lowy’s Method decomposes requirements into core use case and further decomposes these use cases using volatility decomposition into layers that contain Clients, Managers, Engines, Resource Accessors and Resources.Robert Martin’s Clean Architecture decomposes requirements into use cases that are further decomposed into different parts using the SOLID principles to make system resilient against changes in requirements and scaling.Similarities exist between two methodologies, and both can be mapped to common elements. The methodology proposed in this paper uses these common elements and a decomposition method to decompose the requirements of a system into these common elements.
Analisa Keruntuhan Bendungan Batujai dan Pengga dengan Aplikasi HEC-RAS 5.0.7

ABSTRACT: Batujai and Pengga Dam are cascade dams with homogeneous types, are authorized in 1982 and 1994. These dams are located at the same mainstream that ends at Alas Strait. Batujai and Pengga Dam break may occur due to overtopping and piping. According to HEC-RAS 5.0.7 simulation suggested that the collapsed of each or both dams has the biggest impact when conditioned with design flood of QPMF are due to overtopping, when the peak of discharge are at 2036.151 m3/second and 3204.089 m3/second, which caused 34.127 km2 and 107.025 km2 area to be flooded, with maximum inundation of 19.656 meters and 19.837 meters. According to InaSAFE, if the collapse of these dams were merged, reported that there are 34 villages and 29,100 people are exposed to the risk, classified with high disaster risk. The cost that has to be paid by the damâ€™s owner due to the collapse is estimated to be Â Rp 201,584,808.36/Ha or Rp. 2,845,409,886,907 in total.
Synthesis of Images from SMRI through Extreme Integrand Unstable Learning

Abstract In conventional deep learning medical imagery research, appropriate healthcare images are often invaluable. Still, it can limit acquisitions of such texture features because of many concerns, such as high cost, patient problems, etc. However, due to recent developments in profound education techniques that can greatly ease the challenge mentioned above, synthesizing medical photographs has already synthesized different modalities such as MRI images, PEI images, heart infrared detector, retinal images, etc. Unfortunately, a synthesis picture of the Arterial Spin Marking, now an important fMRI predictor in diagnosing dementia disorders, has not yet been thoroughly studied. For the first time in this research, ASL images from magnetic resonance structural images have been prepared successfully. Theoretically, ASL objects’ production from functional magnetic resonance imaging will be indicated by a new, highly unstable, discrimination-based paradigm fitted with new resNet post carried out a broad variety of tests. Useful statistical evaluation of this newly released model to synthesize ASL pictures close to the actual ones acquired during the actual scanning of ASL photographs from the current model shows excellent performance while undergoing extreme regional and voxel-based partial volume correction checks which are necessary for ASL pictures.
The Online Platform „eSchool“

The internet is a global network that is used in any industry. Large corporations have long used the electronic way of doing business. Due to the COVID-19 pandemic, more and more government and commercial structures are switching to online and, therefore, using the electronic circulation of documents and other important things for them. Educational institutions were the most unprepared for such changes, and therefore, they faced an acute question of how to transfer everything online. The purpose of the development is to design and implement a convenient, simple, and most importantly, fast accounting system for educational institutions, which can reflect the educational process in a convenient way, both for students/students and teachers/teachers.
Virtual Reality-Based Sensory Triggers and Gaze-Based Estimation for Mental Health Care

Recent studies underline the importance of the cognitive reserve for mental health, especially in dementia care, which is supported by stress reduction, joyful experience and meditation. Mindfulness training has previously been successfully applied to dementia and indicates a lasting positive effect on cognitive reserve, well-being and motivation [1.Clin. Psychol. Rev. 31:449–464]. We investigated the potential of unobtrusive technology for the measurement of eye movements in Virtual Reality (VR)-based mindfulness training. The objective of this research is to develop software estimators for cognitive assessment and mindfulness trait in order to apply VR technology in the future as a screening instrument, monitoring tool and thereby serving for decision support in mental health care. Eye movement analysis within a pilot study demonstrated significantly different results for persons with Alzheimer’s dementia and healthy controls. These results indicate that significant conclusions are drawn on relevant mental health parameters even within a very short eye movement measurement period applying few minutes of observation of carefully selected video-based stimuli.
European copyright system as a suitable incentive for AI-based journalism?

This Chapter analyses the aims of the current European copyright system and how these aims fit with AI-based journalism. It assesses the need to revise the current legal status and outlines some paths for the future of AI and news. Special attention is given to problems of too extensive or mistargeted protection.
The Role of Information Technology and Communication Technology as Online Learning Media

The use of the right learning model greatly affects the success of the learning process between lecturers and students. Learning media can be a vehicle for channeling messages and learning information. Learning media that are well designed will greatly help students digest and understand the course material. IT-based learning media can be in the form of internet, intranet, mobile phone, and CD Room. Its main components include the learning management system (LMS) and learning content (LC). Learning in higher education will always change according to the characteristics of students, learning materials and the ever-evolving environment. This study aims to examine the role of information communication technology in learning. The research method in this research is a quantitative method with a survey method, distributing online questionnaires to information systems lecturers at Bina Nusantara University in Indonesia. This study uses SPSS Statistics 24 software to test the hypotheses built in the study. The data obtained were analyzed by means of a comparative test. The results of the analysis of this study indicate that (1) there is no significant difference regarding the role of information communication technology in learning; (2) Inadequate internet connection; (3) low/inadequate power source; (4) Lack of manpower for teaching and administrative development; (5). Ignorance and low level of computer literacy, (6) The high cost of computer information technology equipment; (7) less time and less interest from lecturers in the use of Information Communication Technology facilities.
Partition Fuzzy Median Filter for Image Restoration

In this paper, a novel adaptive median filter, called the partition fuzzy is proposed. The proposed filter achieves its effect through a summation of the weighted output of the median filter and the related weighted input signal. The weights are set in accordance with the fuzzy rules. In order to design this weight function, a method to partition of observation vector space and a learning approach are proposed so that the mean square error of the filter output can be minimum. Based on constrained least mean square algorithm, an iterative learning procedure is derived and its convergence property is investigated. As details, extensive experimental results demonstrate that the proposed filter outperforms the other median-based filters in literature.
Nudge for Note Taking Assist System: A Learning Strategy Feedback System Among Learners Through Their Tablet

In this paper, we propose a learning strategy feedback system, Nudge for Note Taking Assist System (NoTAS), developed for students to facilitate learning strategies among others in class using nudges. The system has three functions: note-taking function, learning log function, and learning visualization function. The purpose of this study is to develop software to apply nudge theory and to provide feedback on note-taking among students in class. We evaluated the effectiveness of NoTAS for high school students from two perspectives: whether the use of NoTAS interfered with the class and whether the nudges were encouraged in class. From the results of the questionnaires, we found that the students were able to concentrate on learning in the class using NoTAS, and the interface was well-received. Moreover, students can use the learning visualization function of NoTAS to see if other students are note-taking in class. This information promotes learning among students, indicating that the nudge makes achievements regarding learning awareness.
Diagnosis of an agile project in the academic field

During software development, it is normal that the process is subject to constant, regular changes. However, these changes should not compromise the result of the project, the stipulated budget, or even the delivery dates. When agile methodologies emerged, they came to help overcome the problems mentioned above. The adoption of agile methods has had a very strong growth in recent years, having a positive impact on software development, since it can handle and adjust to constant changes, without compromising the result of the product. Despite this, measuring agility is important for organizations to ensure that these methods are being leveraged and providing the best results for organizations. In this paper, a proposal is presented for a tool that meets these needs in an academic setting to make projects and the teams themselves more agile, mentioning the points of possible improvement. A questionnaire was created that covers the main goals of agile methodologies and is based on the agile manifesto.
New results on Nyldon words and Nyldon-like sets

Grinberg defined Nyldon words as those words which cannot be factorized into a sequence of lexicographically nondecreasing smaller Nyldon words. He was inspired by Lyndon words, defined the same way except with "nondecreasing" replaced by "nonincreasing." Charlier, Philibert, and Stipulanti proved that, like Lyndon words, any word has a unique nondecreasing factorization into Nyldon words. They also show that the Nyldon words form a right Lazard set, and equivalently, a right Hall set. In this paper, we provide a new proof of unique factorization into Nyldon words related to Hall set theory and resolve several questions of Charlier et al. In particular, we prove that Nyldon words of a fixed length form a circular code, we prove a result on factorizing powers of words into Nyldon words, and we investigate the Lazard procedure for generating Nyldon words. We show that these results generalize to a new class of Hall sets, of which Nyldon words are an example, that we name "Nyldon-like sets."
Research on the Robotic Aiming System with Image Recognition and Deep Learning

This paper introduces a robotic aiming system with the function of image classification and target detection, which can be deployed to lightweight and mobile devices. The construction and training process of this model is described. The construction of the entire model is based on TensorFlow2 and structure of it is mainly based on SSD-MobileNetV2. Two methodologies for constructing and evaluating the system are presented. The transfer learning approach used other trained model’s weight and gained superior results than the base approach. This model is evaluated through TensorBoard and is able to achieve the accuracy of 95% in real world when deployed to Raspberry Pi.
Dynamic Mechanism for Video Streaming on Peer-to-Peer Networks using OPNET

Quality for live video streaming technology is based on quality Experiences parameters (QoE). Approaching the peer-to-peer (P2P) or peer-assisted networks like sympathetic solution is highly required, especially in light of its authentic scalability and its extremely low initial cost requirements. However, the design of robust, efficient, and performing P2P streaming systems remains a big challenge when real-time necessity are partnof the quality for service. One of the P2P main issues is the neighbor selection methodology. The proposed work is an operative mesh-based neighbor selection algorithm for live video streaming based on QoS and QoE Parameters. We simulates different scenarios up to 4000 online peers under failure / recovery tests. The simulation results demonstrates that the proposed algorithm achieves good performance in end-to-end dela variation..A new algorithm “UPDA” used to improve streaming quality though a new neighbor selection methodology. This categories peers into super peer and slow peer."APDA" algorithm that defines the role of each peer existing in the streaming session. With the coming of services and applications such as media and videos streaming over the internet. some server service model a client must made request to the video server,CDN (Content Delivery Network ) approximation a set of CDN server are set on at the network instead by downloading the video from the multimedia server.
Shift Learning by Integrating Image and Subsystem Training for Edge Detection in Medical Images

Abstract Several approaches for segmenting clinical data are focused on controlled vertex shade labelling. In particular, such strategies work well if the training set reflects the test pictures per chapter. However, issues can occur in the preparation and testing process, for instance, due to variations in scanners, procedures, otherwise patient classes, lead to distinct concentrations. In these situations, weighing images based on distribution similarities has shown a substantial improvement in inefficiency. To suggest that most of the training examples reflect the quiz information; it should not be similar to the deceiving information. Therefore, we examine the importance of kernel learning to weigh images to minimize discrepancies between training and test results. A local feature measurement scheme has been proposed to minimize the average distance between training and testing data that allows image weights and Kernel to be jointly optimized. Experiments on brain tissues, lesion of white material, and hippocampus division demonstrate because both kernel processing and image calculation boost the efficiency of heterogeneous data dramatically if used separately. MMD weighting here works similarly to the image weighting approaches previously proposed. The combination of image measurement and kernel processing, independently or jointly optimized, could result in a slight additional performance increase.