Reviewerly/exHarmony huggingface dataset:
Measuring and Predicting Students’ Effort: A Study on the Feasibility of Cognitive Load Measures to Real-Life Scenarios

Students’ effort is often considered to be a key element in the learning process. As such, it can be a relevant element to integrate in learning analytics tools, such as dashboards, intelligent tutoring systems, adaptive hypermedia systems, and recommendation systems. A prerequisite to do so is to measure and predict it from learning data, which poses some challenges. We propose to rely on the cognitive load theory to infer the students’ perceived effort using subjective, performance, behavioral and physiological data collected from 120 seventh grade students. We also estimate students’ effort in future tasks using the data from previous tasks. Our results show a high relevance of interaction data to measure students’ effort, especially when compared to physiological data. Moreover, we also found that using the data collected on previous tasks allows us to achieve slightly higher accuracy values than the data collected during the task execution. Finally, this approach also allowed us to predict students’ perceived effort in future tasks, which, to the best of our knowledge, is one of the first attempts towards this goal.
Semi-Supervised Exaggeration Detection of Health Science Press Releases

Public trust in science depends on honest and factual communication of scientific papers.However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings.Given this, we present a formalization of and study into the problem of exaggeration detection in science communication.While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging.We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task.Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary clozestyle QA tasks to improve few-shot learning.We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task. 1
Robust Hashing for Character Authentication and Retrieval Using Deep Features and Iterative Quantization

This paper proposes a hashing approach for character authentication and retrieval based on the combination of a convolutional neural network (CNN) and the iterative quantization (ITQ) algorithm. This hashing approach is made up of two steps: feature extraction and hash construction. The feature extraction step involves the reduction of high-dimensional data into low-dimensional discriminative features by applying a CNN model. While, the hash construction step quantizes continuous real valued features into discrete binary codes by applying ITQ. These two steps are combined together in this work to achieve two objectives: (i) a hash should have a good anti-collision (discriminative) capability for distinct characters. (ii) a hash should also be quite robust to the common image content-preserving operations. Experiments were conducted in order to analyze and identify the most proper parameters to achieve higher authentication and retrieval performances. The experimental results are performed on two public character datasets including MNIST and Font-Char74K. The results show that the proposed approach builds hashes quite discriminative for distinct characters, and is also quite robust to the common image content-preserving operations.
Meningkatkan Minat Serta Hasil Belajar PAI Dan BP Melalui Metode Diskusi Kelompok

The purpose of this study was to increase the interest and learning outcomes of fifth grade students at SDN Sidamulya 01 in PAI and BP learning using the group discussion method. The type of research is Classroom Action Research (CAR). This study uses the Kemmis and Mc. Taggart. The research subjects were 14. Data collection techniques were test, observation and documentation techniques. Validation of data by triangulation technique Data analysis method using qualitative data analysis. The results of the study prove that the use of group discussion methods can increase student interest, showing the acquisition in the initial study of 6 students (42.86%) to 9 students or 64.29% and 13 students or 92.86% in the last cycle. Student learning outcomes from an average of 57.14 in the initial study, to 66.43, and in the second cycle to 76.43. The level of mastery learning just reached 4 students or 28.57% in the initial conditions, increased to 6 students or 42.86% and 13 students or 92.86% in the second cycle.
Estimation of Economic Inducement Effect of Urban Mining Upon Applying Laser Induced Breakdown Spectroscopy Technology

This study analyzed the economic inducement effect of urban mining owing to the application of laser induced breakdown spectroscopy (LIBS) technology with input-output analysis by considering energy demand and CO2 emission reduction, as well as production and employment.The estimation result showed that, as a result of the installation of LIBS facilities, the production inducement coefficient is 1.25, the value-added inducement coefficient is 0.46, the job inducement is 4.56 persons per 1 billion KRW, and the employment inducement is 3.74 persons per 1 billion KRW.Owing to the operation of LIBS facilities, the production inducement coefficient is 1.13, the value-added inducement coefficient is 0.50, job inducement is 6.35 persons per 1 billion KRW, and the employment inducement coefficient is 5.34 persons per 1 billion KRW.In addition, the application of LIBS technology can induce positive effects in urban mining with respect to energy demand and CO2 emission reduction, as well as production and employment, by reducing 0.0003% of CO2 eq. and 0.02% of the total energy input.
An overview on advanced multimedia forensic techniques and future direction

Nowadays, with the ever-increasing worldwide connectivity, on the whole, the planet helps the people to share a continuously large amount of text, images, audios, and videos regularly. Due to this globalization connectivity, computer crimes are growing through the Internet these days. The media content lifecycle is becoming complex constantly due to an increase in exchanging, adapting, modifying, accessing, and repetitive use of digital information. Further, due to the easy access and coupling with user-friendly multimedia editing software, this constitutes several challenges related to the trustworthiness and reliability of digital content. For this, forensics techniques for multimedia applications play a significant role to minimize their unchecked and rampant usage. Multimedia forensics techniques are used to find out the evidence or proof through different methods or tools which is an extremely tough and complex task. In the previous years, the field of multimedia forensics has undergone nonstop expansion, drawing increasing attention among researchers. The growing requirement of multimedia’s content authentication techniques without deductive information has led researchers to grow various forensic approaches designed for passive work. Again, these designed forensic approaches face issues such as forgery detection, source identification, and content discrimination. The fact is that in multimedia data the inherent traces left during content creation and successive processing. Thus, in this book chapter, various potential and advanced multimedia (image, audio, and videos) forensic techniques have been presented and discussed. Specifically, the comprehensive wide range of multimedia forensic techniques to identify multimedia distortions have been studied with powerful features useful in multimedia forensic investigations. Besides, the significant solutions to different open challenges and issues in multimedia forensic techniques have been described. Also, the potential application and advanced feasible future directions are discussed.
A Crowd-driven Dynamic Neural Architecture Searching Approach to Quality-aware Streaming Disaster Damage Assessment

Streaming disaster damage assessment (DDA) aims to automatically assess the damage severity of affected areas in a disaster event on the fly by leveraging the streaming imagery data about the disaster on social media. In this paper, we focus on a dynamic optimal neural architecture searching (NAS) problem in streaming DDA applications. Our goal is to dynamically determine the optimal neural network architecture that accurately estimates the damage severity for each newly-arrived image in the stream by leveraging human intelligence from the crowdsourcing systems. Our work is motivated by the observations that the neural network architectures in current DDA solutions are mainly designed by AI experts, which often leads to non-negligible costs and errors given the dynamic nature of the streaming DDA applications and the lack of real-time annotations of the massive social media data inputs. In this paper, we develop CD-NAS, a crowd-driven dynamic NAS framework that is inspired by novel techniques from AI, crowdsourcing, and estimation theory to address the dynamic optimal NAS problem. The evaluation results from a real-world streaming DDA application show that CD-NAS consistently outperforms the state-of-the-art AI and NAS baselines by achieving the highest disaster damage assessment accuracy.
Studi tentang Batik Cahaya Utama di Desa Sendangagung Kecamatan Paciran Kabupaten Lamongan

The purpose of this study is to describe Batik Cahaya Utama in Lamongan district. This study focused on pre-production, production, and post-production. The researcher used a descriptive research design with qualitative approach. Data collection was obtained from the interview process and observation. To check the validity of the findings, it used triangulation. Based on the result, the planning that carried out by Cahaya Utama is complete. But they did not have IPR (Intellectual Property Right) for batik that has been produced. In addition, they have not a special place to put their waste disposal site. Cahaya Utama has batik products included: handmade batik, stamp batik and printing batik. However, the stamp batik and printing batik made in outside of Cahaya Utama. They collaborate with other industry to make it. While post-production activities conducted by Cahaya Utama. The consumer not only from local people but also, they from other country. The promotion used online and offline. It helped the Cahaya Utama to promotions even though their place is not strategies for business. Based on the finding, the researcher hopes Lamongan district government will immediately find a solution related with waste disposal site. In addition, the results of this study can be used as a reference for similar research.&#x0D; Penelitian ini bertujuan mendeskripsikan batik Cahaya Utama di Desa Sendang Kecamatan Paciran Kabupaten Lamongan dengan fokus pra produksi, produksi dan pasca produksi. Peneliti menggunakan rancangan penelitian deskriptif dengan pendekatan kualitatif. Pengumpulan data diperoleh melalui proses wawancara, observasi dan dokumentasi. Pengecekan keabsahan data menggunakan triangulasi teknik dan triangulasi sumber. Berdasarkan hasil temuan, tahap perencanaan yang dilakukan oleh Cahaya Utama tergolong bagus mulai dari perencanaan sumber daya modal, manusia, tempat usaha, alat dan bahan, namun belum memiliki HKI (Hak Kekayaan Intelektual) untuk batik yang telah diproduksi. Selain itu dalam hal tempat pembuangan limbah belum mempunyai tempat yang layak sebagai tempat pembuangan limbah pewarnaan. Cahaya Utama memiliki produk batik tulis, batik cap dan batik printing atau sablon. Namun untuk batik cap dan printing pengerjaan dilakukan di luar usaha Cahaya Utama atau bekerja sama dengan usaha lain dalam proses produksi. Kegiatan pasca produksi yaitu tergolong bagus, pemasaran produk tidak hanya di dalam negeri namun juga dilakukan sampai luar negeri.
Signal Extraction for Classification of Noisy Images Compressed using Autoencoders

The world is experiencing an increasing boom in computer vision.This is more and more used in many domains such as robotics, medicine, industry, security systems, etc.In this context, Deep Neural Networks (DNNs) have great capabilities and are widely used.Convolutional Neural Networks (CNNs) present a particular class of DNNs that is most commonly leveraged to analyzing visual imagery.However, CNN performances completely depend on two main issues.The first issue is related to the quality of the images generated by capture cameras.All images captured by remote sensors and modern imaging systems are practically noisy, which can prevent the image from being correctly classified and identified by a CNN.The second issue is the throughput available for the transmission of the large amount of data between capture sensors and units processing CNNs.A seamless transmission can be ensured by compression techniques that help reducing the size of data, while affording the required quality for computer vision algorithms.Since lossy compression of noise-free and noisy images differ from each other, this work firstly raises the question of CNNs resilience to noisy images compression using the particular autoencoders.We secondly propose a method that aims to improve this resilience so that CNNs can achieve better classification performances.The compressed noisy images are passed, as a test set, along a model that is learnt from a noise dataset.The subtraction of the so captured noise from the noisy images is then performed to extract the useful signal to classify.This will be first work, where we learn the autoencoder from the noise sample, and not the noisy sample, while denoising.Obtained results prove the efficiency of the proposed method.
Neoteric Algorithm Using Cell Population Data (VCS Parameters) as a Rapid Screening Tool for Haematological Disorders

Hitherto, there has been no comprehensive study on the usefulness of cell population data (CPD) parameters as a screening tool in the discrimination of non-neoplastic and neoplastic haematological disorders. Hence, we aimed to develop an algorithm derived from CPD parameters to enable robust screening of neoplastic from non-neoplastic samples and subsequently to aid in differentiating various neoplastic haematological disorders. In this study, the CPD parameters from 245 subtypes of leukaemia and lymphoma were compared against 1103 non-neoplastic cases, and those CPD parameters that were vigorous discriminants were selected for algorithm development. We devised a novel algorithm: [(SD-V-NE*MN-UMALS-LY*SD-AL2-MO)/MN-C-NE] to distinguish neoplastic from non-neoplastic cases. Following that, the single parameter MN-AL2-NE was used as a discriminant to rule out reactive cases from neoplastic cases. We then assessed CPD parameters that were useful in delineating leukaemia subtypes as follows: AML (SD-MALS-NE and SD-UMALS-NE), APL (MN-V-NE and SD-V-MO), ALL (MN-MALS-NE and MN-LMALS-NE) and CLL (SD-C-MO). Prospective studies were carried out to validate the algorithm and single parameter, MN-AL2-NE. We propose these CPD parameter-based discriminant strategies to be adopted as an initial screening and flagging system in the preliminary evaluation of leukocyte morphology.
Student Acceptance of E-Learning System During Covid-19 Pandemic

This study was conducted at Bina Nusantara University and had the purpose of determining the student acceptance of the e-learning system during the COVID-19 pandemic.The data used are secondary data from articles and journals and primary data collected by distributing an online questionnaire to 100 students majoring in management as a respondent with simple random sampling as sampling technique.The data collected was analyzed by using linear regression.The result showed that both perceived usefulness and perceived ease to use have a positive and relatively strong correlation with the intention to use.Furthermore, the study also showed that perceived usefulness and perceived ease to use significantly affect intention to use partially and simultaneously.Since the pandemic will last for a long time, so this study can be an input to the university to improve its e-learning system in the future.
Achieving Angular Superresolution of Control and Measurement Systems in Signal Processing

Algebraic methods of processing data obtained by control and measurement systems to achieve angular superresolution are presented. The efficiency of using the methods in the formation of approximate images of objects at low signal-to-noise ratios is shown. The results of numerical experiments demonstrate the possibility of obtaining images with a resolution exceeding the Rayleigh criterion by 3-10 times. The robustness of the solutions obtained by the methods of algebraic exceeds many well-known approaches. The relative simplicity of the presented methods allows the use of inexpensive computing devices and perform real-time measurement processing.
A Tool for Detecting Ambiguity in Software Requirements Specification

The main goal of requirements engineering is to establish software requirements specification (SRS). The requirements in SRS are mostly specified in natural languages (NL), therefore, one of the common problems of SRS is requirements ambiguity. The requirement is said as ambiguous when it has more than one interpretation, subsequently, can lead to requirements inconsistency and conflict. Besides, to detect ambiguous requirements manually is time-consuming and tedious process. Thus, this paper presents a tool called SRS Ambiguity Detector, that able to detect automatically the major types of ambiguity; lexical, syntactic and syntax ambiguity. This tool uses ambiguity words from the ambiguity handbook to detect lexical ambiguity, while, parts of speech (POS) tagging technique has been applied to detect syntactic and syntax ambiguities. Evaluation was conducted to assess the effectiveness, and the result has shown that the proposed tool able to identify more ambiguous requirements as compared to manual detection.
Investigation of mineral chemistry and causes of tourmaline formation in biotite amphibolits of Gol-e-Gohar mining and industrial area (Sirjan), Kerman province

Investigation of mineral chemistry and causes of tourmaline formation in biotite amphibolits of Gol-e-Gohar mining and industrial area (Sirjan), Kerman province
Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter

Currently, the spread of information Covid-19 is spreading rapidly. Not only through electronic media, but this information is also disseminated by user posts on social media. Due to the user text posted is varies greatly, it’s needs a special approach to classify these types of posts. This research aims to classify the public sentiment towards the handling of COVID-19. The data from this study were obtained from the social media application i.e., Twitter. This study uses a derivative of the Naïve Bayes algorithm, namely Multinomial Nave Bayes to optimize the classification results. Three class labels are used to classify public sentiment namely positive, negative, and neutral sentiments. The stage starts with text preprocessing; cleaning, case folding, tokenization, filtering and stemming. Then proceed with weighting using the TF-IDF approach. To evaluate the classification results, data is tested using confusion matrix by testing accuracy, precision, and recall. From the test results, it is found that the weighted average for precision, recall and accuracy is 74%. Research shows that the accuracy of the proposed method has fair classification levels.
Ten Assumptions About Artificial Intelligence That Can Mislead Patent Law Analysis

The paper examines a set of assumptions about artificial intelligence, particularly machine learning, often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where AI systems autonomously generate inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of specific uncertainties under patent law. Overall, it is argued that none of the posited assumptions on closer examination appears to raise fundamental uncertainty about the appropriateness of the patent system.
Pattern-based and Time-Synchronised Passwords

World has been changed; every person is using a number of software, websites, and other systems that are using text-based passwords as a method of authentication.These passwords need to be strong, hard to guess, and need to be stored in a secure environment.Major problems with passwords are caused by human limitations to remember passwords for different accounts.A trade-off between password security and human-memorability made it difficult to create passwords that are strong enough and easy to remember.No satisfactory solutions have been offered to problems associated with a password such as shoulder surfing, eavesdropping, keylogging programs, Trojan horse, brute force attacks, etc.This study suggests a new easy to use approach for creating a password that is easy to remember even for a large number of accounts.Here in this paper, we proposed two methods one is pattern-based passwords, a simple method that is solving the problem of memorability, another is the idea of Time-Synchronized Passwords (TSP), a novel method for creating passwords that are dynamic in nature and change with the passage of time.The novality of TSP is that instead of storing the passwords in database the patterns are stored, and these patterns are related linked with time.The significance of storing pattern instead of actual password is that at a specific time, the password will have only one instance known to the creator of the password, and this particular instance will be different from instances at other times and thus avoids shoulder surfing, eavesdropping, keylogging, and other problems associated with passwords.These methods are easy to implement and can be used in any system.
An Analysis of the Assessment of English Grammar Courses in Five Tertiary Institutions in Viet Nam

English language assessment should be aligned with teaching objectives; therefore, if the teaching goal is that English learners are able to use their target language in their real life, the learners’ language performance should be assessed. By analysing the course syllabi, grammmar tests and teacher interview, this study aims to investigate the assessment of grammar courses of four tertiary institutions in Vietnam in connection to Purpura’ s (2014 &amp; 2005) grammatical assessment theories regarding assessed grammatical ability aspects, test tasks and grammar assessment approaches. The results should that (1) only learners’ grammatical forms and semantic meanings are assessed; (2) Selected-response task types and The Discrete-Point Approach are mainly used to assess learners.
Hospital Emergency Color Codes: A Way to Disseminate Unexpected

Hospital emergency codes are medical emergency codes used in a hospital. It is a way to disseminate essential life-saving information quickly to the hospital staff. The message is in coded language so that panic among visitors or other patients in hospital can be avoided. For example, code blue means cardiac emergency. The goal of these codes is to provide precise emergency notification to staff, which is then immediately followed by a co-ordinated response. Training and mock drill are conducted to avoid misunderstanding among staff. Emergency codes are not standardized, and it varies from one hospital to another.