Reviewerly/exHarmony huggingface dataset:
The English Reading Habits of English Language Education Undergraduate Students at English Department

Abstract: Good reading habits help comprehension and logic because they can empower to absorb material analytically, critically, and reflectively. However, the technique for encouraging an interest in reading, particularly in schools and universities, has not obtained the best outcomes. In this case, family, community, and educational institutions simultaneously play a significant role in promoting English Foreign Language students' enthusiasm for reading. Therefore, the aim of this study is to determine how ELE undergraduate students reading habits in English Department, Universitas Negeri Malang. This type of research used descriptive qualitative. The data collection technique used was a questionnaire form and distributed via Google form. The questionnaire discussed students' attitudes towards reading, purpose of reading, reading frequency, and reading preferences. The results showed that most of the ELE undergraduate students have high reading habit. However, students’ reading habits still need to be improved. It is shown that the students like reading as an indispensable part of their lives because of the reading treatment they get from an early stage of age. It makes them have a positive attitude towards reading, they also have a purpose for reading, and increase their reading preferences. Furthermore, they also need to increase their frequency of reading practice and the number of books read. Keywords: reading habits, English Language Education (ELE), English Department Abstrak: Kebiasaan membaca yang baik dapat membantu pemahaman dan logika karena dapat memudahkan untuk menerima materi secara analitis, kritis, dan reflektif. Namun, teknik yang digunakan untuk mendorong minat membaca, khususnya di sekolah dan universitas, masih belum mendapatkan hasil yang terbaik. Dalam hal ini, keluarga, masyarakat, dan lembaga pendidikan secara bersamaan memiliki peran penting dalam mendorong minat membaca Bahasa Inggris siswa. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui bagaimana kebiasaan membaca mahasiswa Jurusan Bahasa Inggris Universitas Negeri Malang. Jenis penelitian yang digunakan adalah deskriptif kualitatif. Teknik pengumpulan data yang digunakan adalah kuesioner dan disebarluaskan melalui google form. Kuesioner tersebut membahas sikap siswa terhadap membaca, tujuan membaca, frekuensi membaca, dan preferensi membaca. Hasil penelitian menunjukkan bahwa sebagian besar mahasiswa juusan pendidikan Bahasa Inggris memiliki kebiasaan membaca yang tinggi. Namun, kebiasaan membaca siswa masih perlu ditingkatkan lagi. Terlihat bahwa siswa menyukai membaca, karena sebagai bagian yang tak terpisahkan dari kehidupan karena mereka telah dilatih membaca sejak usia dini. Oleh karena itu, mereka memiliki sikap positif terhadap membaca, mereka juga memiliki tujuan untuk membaca, dan meningkatkan preferensi membaca mereka. Selain itu, mereka juga perlu meningkatkan frekuensi latihan membaca dan jumlah buku yang dibaca. Kata kunci: kebiasaan membaca, Pendidikan Bahasa Inggris, Jurusan Sastra Inggris
PLATAFORMA WEB DE RECURSOS DIDÁCTICOS MATEMÁTICOS (WEB PLATFORM FOR MATHEMATICAL TEACHING RESOURCES)

Resumen Este trabajo de investigacion tuvo como objetivo desarrollar una pagina web amigable resaltando la imagen institucional bajo estandares w3c ofrece areas de trabajo, accesibles, responsivas, semanticas, con motores de busqueda y de inferencia eficientes para crear ambientes de aprendizajes personalizados, que integren un sistema de consulta y fortalezcan la ensenanza de las ciencias basicas de los estudiantes de la UTHH.Se utilizo la metodologia ingenieria web [Pinzon, 2002]: Formulacion, planificacion, analisis, generacion de paginas y evaluacion del cliente.Como resultado se contara con una pagina web en la plataforma Moodle como soporte al aula virtual, tiene un servidor primario y un servidor espejo que garantiza la disponibilidad de la informacion. El docente puede consultar tiempo de actividad, fecha y estatus de tareas; la comunidad estudiantil contara con un espacio de repaso y retroalimentacion de temas vistos en clase para fortalecer el aprendizaje de las ciencias basicas. Palabras Clave : E-learning, ingenieria web, Moodle, recursos didacticos, semanticos. Abstract This investigation work has as a goal, develop a friendly web page to stand out the institutional image under the w3c’s standards, to give semantics, accesibles and responsives work stations with efficient search and inference engines to create custom learning enviroments, that integrates a referral system and strengthen the teaching of basic sciences of UTHH’s students.It was used the web engineering method [Pinzon, 2002]: Formulation, planning, analysis, generation of pages and customer's evaluation.As a result, it’ll have a web page in the Moodle platform as a support to virtual classroom, a primary and mirror server that will guarantee the information availability. The teacher can consults, the time, date and status of the activities; the student community will have with a space of review and feedback of the topics seen in class to strengthen the learning of the basic sciences. Keywords : E-learning, Web engineering, Moddle, Teaching resources. Semantics.
Breaking down and making up - a lens for conversing with compilers

This paper proposes a ‘tool for thinking with’: that we can describe the interaction between people and computers, and especially people and developer tools, as a form of conversation. We outline this perspective, construct a work in progress analytical frame, and use it to talk about a couple of different examples and draw implications for future work.
Regression Algorithms in Machine Learning

Recently, artificial intelligence, machine learning (ML), and data analysis are gaining traction for real-time scenarios. With the learning of Big Data Analytics, the industries are gaining automated solutions to real-time problems. Supervised ML algorithms have been considered in distinct domains of ML, such as image processing, data mining, and DevOps. Similarly, regression models are basically used to identify and predict a continuous value. Regression algorithm generally falls in the supervised ML-based algorithmic approaches. Supervised learning algorithms works on the basis of the past modeling experiences, that they model dependencies and relationships based on inference rules between the desired output and input features. Today, regression models are used in many applications that are developed using the concepts of ML and data analytics. In this work, various regression algorithms are discussed in detail with respect to different application domains.
Is initial training with blurry images beneficial for the development of object recognition systems?

A recent study claimed that the initially poor visual acuity of infants might be critical for the visual system to learn to integrate information over larger spatial scales. Specifically, when convolutional neural networks (CNNs) were initially trained on blurry face images, followed by progressively clearer images, the CNN acquired robustness to variations in spatial resolution (Vogelsang et al., 2018). Here, we asked whether initial training with blurry images would confer a similar benefit to general object recognition, possibly offering a clue to the robust nature of human vision. To evaluate this question, we trained AlexNet with 1,000 ImageNet object categories in which the resolution of inputs was gradually increased over training epochs. Although training with blurry objects initially led to good performance on blurry test images, such robustness to blur soon disappeared after training with clear images. These findings deviated from the robustness to blur that occurred for a CNN trained with face images. Curiously however, we found that a CNN trained concurrently on faces and objects, progressing from blurry to clear, lost the ability to recognize both blurry faces or objects. This problem of catastrophic forgetting can be attributed to the fact that proportionally greater discriminating information resides in the higher spatial frequencies of objects as compared to faces. As the CNN learns to leverage this finer scale information, it tends to lose the ability to leverage information at coarser spatial scales for both objects and faces. While our findings do not rule out the possibility that poor initial acuity might lead to important developmental benefits in face recognition ability, they do bring into question whether people’s ability to recognize blurry faces and objects in adulthood can be explained by these early experiences alone.
Workshop Pengenalan Openmediavault Sebagai Sistem Operasi Jaringan Pada Sekolah Menengah Kejuruan (SMK) Mandiri

The development of technology in the field of computer networks in the era of the 21st century is very fast along with its rival, namely information system technology. Both of these technologies play an important role in the world of informatics because they complement each other and contribute to each other. For example, the information system already exists but the storage is still constrained, which means that the information system only runs on standalone, meaning it runs on its own device. as an operating system network attached storage is one of the data storage in a computer network where everything related to data can be stored in it including information systems that can be run through the operating system. At the level of Vocational High School (SMK) the Department of Computer Network Technology still rarely practices in making data storage using a network-based operating system. Therefore, it is necessary to make efforts to upper secondary level students, especially vocational schools majoring in computer network technology in knowing or increasing their knowledge of data storage in computer networks by utilizing open source services. Workshop is the last solution to provide direct practice to these students because in essence they are very happy with hands-on practice. In order for this workshop to run as expected and coincide with the COVID-19 pandemic, the implementing team or community service participants obey the health protocol. The practicum software media used are virtualbox, openmediavault, while the hardware is in the form of a laptop that has been provided by the Budi Darma University which has been given prior permission by the campus. The final result of this activity is that the students are able to install app templates from portainer.io
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling

Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions. Here we introduce BiToD, the first bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic bilingual knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and cross-lingual transfer learning approaches. We provide state-of-the-art baselines under three evaluation settings (monolingual, bilingual, and cross-lingual). The analysis of our baselines in different settings highlights 1) the effectiveness of training a bilingual ToD system compared to two independent monolingual ToD systems, and 2) the potential of leveraging a bilingual knowledge base and cross-lingual transfer learning to improve the system performance under low resource condition.
Knowledge-Driven Joint Posterior Revision of Named Entity Classification and Linking

In this paper we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content. More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called JPARK, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination. We present a comprehensive evaluation of JPARK along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.
A Novel Blood Group Detection Using Deep Learning

Division and tallying of platelets are considered as a significant advance that assists with separating highlights to analyze some particular ailments. The manual tallying of RBCsin minuscule pictures is an amazingly dreary, tedious, and off base procedure. Programmed investigation will permit hematologist specialists to perform quicker and more precisely. Examination of blood classification plays an imperative gathering in the restorative field for any treatment. False transfusion of blood will prompt numerous issues. This framework gives simple and quick methods for distinguishing proof of blood classifications and Rhesus factor none obtrusively. Our structure is tried on a few genuine informational collections of numerous individual images of human finger-tip images. Blood classification is grouped dependent on the nearness and nonappearance of certain organic substances called antibodies and furthermore dependent on the nearness or nonattendance of acquired antigenic protein substances on the surfaces of the erythrocytes in the body. Along these lines by utilizing the optical properties of the antigens and the rhesus calculate present the blood, the blood gatherings can be ordered.
Importance of ICT for a successful collaborative negotiation process

Technology has gained much prominence in recent decades, For this reason, the main objective of the research is to provide information on the importance of information and communication technologies, within the different negotiation processes, it should be noted that by mentioning these technologies, the analytical conclusion can be reached that they are an infinite set of knowledge and tools, expressly linked to the consumption and transmission of information and based on the abrupt and technological change that society has experienced in recent years to achieve effective negotiation, without the need for the process to deal with face of it, managing to incorporate the technological world into the business world. In another vein, it is important to note that the methodology used in the research is descriptive, documentary, and field, having a non-experimental nature since it only seeks the storage and collection of data relevant to the objective of the research, In addition to this, the selected population is people belonging to Ecuador, with a sample of 80 people, and the technique and tool used for data collection is a closed type survey, which in turn is classified in simple selection.
PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt &amp; Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt &amp; Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt &amp; Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt &amp; Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.
Review Pelatihan Daring Pembelajaran Berbasis Riset Menggunakan Mobile Learning (M-learning)

The innovative learning model used in Research Based learning requires students to learn actively, independently and not be limited to the space and time that has been scheduled on campus. Searching for data and learning information to support Research Based learning uses the internet network a lot. Besides being able to be more effective and efficient in all respects, the internet network is also very much needed anytime and anywhere by students. In the future, online learning platforms have great potential to be developed not only in times of pandemics like today. And the easiest learning platform for students to use is through mobile learning (m-learning) because it can be ascertained that all young people, in this case students, have phones cell (phones cell). Training activities aim to increase the knowledge and motivation of lecturers in developing learning strategies using Research Based learning in the application of m-learning. So that it can present inspiring and scientific learning
Image encryption algorithm based on the fractional Hermite transform

A new image encryption algorithm is proposed by combining fractional Hermite transform (FHT) with hyper-chaos map. In the first round of pixel scrambling, the original image is performed by the Zigzag operation and the Arnold transform. Then the scrambled image matrix is XORed with an encryption sequence composed of an 8-bit linear feedback shift register. Compared with the traditional Hermite transform, the FHT has better noise tolerance, and it could process the non-stationary input signals better. Subsequently, the encrypted data is re-encrypted by the cyclic shift operation based on the hyper-chaotic system. The four initial values of the hyper-chaotic system are obtained by combining plaintext image with SHA-512. Finally, the processed image matrix is XORed with a chaotic matrix generated by the three-dimensional Logistic-sine-coupling map. The presented image encryption algorithm has a large key space and strong anti-noise ability. Simulation results show that the proposed algorithm is feasible, secure and effective.
A qualitative analysis of the Russian cryptomarket Hydra

Hydra is regarded as the largest Russian-language online marketplace for illicit goods with no less than four million Euro of annual revenue. In this paper, we provide a qualitative analysis of the unique organization of the Hydra cryptomarket. The theoretical framework for this study draws on information asymmetry and assumptions of signaling theory. Data collected include longitudinal parsing and non-participant observation from 2019 to 2021. Hydra is a monopolistic marketplace with no alternatives of the same scale in Russia, and exhibits considerable differences with regard to international cryptomarkets. In the analysis, we present the three themes: physical stashes, formalized rules, and the architecture of the Hydra market. Contrary to the reliance on the postal system for delivering drugs internationally, Hydra introduces a novel delivery method called stashes, which puts buyers at higher risks to get caught by law enforcement agents and make them more “active” in obtaining drugs. Buyers not only have to choose and pay for drugs; they also need to go out in the streets and personally look for their purchased stuff. While elaborated formalized rules enabled by strong sanctioning mechanisms (e.g., fines) cover every possible aspect of interaction connected to the marketplace usage, a multilayered platform architecture allows for the emergence of complex hierarchies and segregation between users. Hydra also provides automatic services related to drug selling (instant purchases, non-mediated disputes, etc.). In addition, personal communications between actors are confined by the architecture, so that visible public communications give buyers the only substantial grounds for decisionmaking. These findings raise questions considering the role of credibility signals, asymmetry of information, and formalized rules in resolving issues of product quality, stable exchange processes and communication processes.
Community Connection Needs Some Actions for Health

Connecting with community in development programs, especially in health is a decisive component. It is required at planning level as well as during implementation and monitoring, developing the understanding of needs and ownership on resources. Planning process needs to be in the hands of community itself, so that they can ensure their participation in execution and monitoring. Though people are not experts of managerial issues, and lack technical knowledge but with a support system a lot can be achieved. In the Health Sector, establishing the connection with community emerged in the form of Community Monitoring under NRHM and now it is present as Community Action for Health. Present paper is an effort towards analyzing issues, understanding context and summarizing the process of connecting community for health with the background of communicating health in Rajasthan.