Reviewerly/exHarmony huggingface dataset:
Difficult topics in Biology from the view point of students and teachers based on KBSM implementation

Biology subject is a part of the science curriculum ensuring that students can master in biology, aware of themselves and their environment, know the nature of science and biology as well as able to analyse the problems they encounter in daily life. Biology subject consists of many topics and concepts that must be comprehended by the students. However, there are several Biological topics that are considered difficult for many students. Thus, this study was carried out to identify the difficult topics in Form 4 biology from teachers' and students' perspectives. This study involved 352 students and 71 biology teachers from one of the districts in Selangor, Malaysia. The Biological Difficult Topics Questionnaire was used to assess the view on the difficulty of biology topics. Data from the questionnaire given were processed and analysed descriptively using the Statistical Package for the Social Sciences (SPSS) software. The results of this study revealed that the students have difficulty learning the topics of Nutrition, Cell Division and Chemical Composition in Cells. Meanwhile, the most three difficult topics identified by teachers were the Cell Division, Respiration and Nutrition. This shows that the Cell Division and Nutrition topics can be considered difficult by teachers and students. Based on the teachers' comments in the survey, it was found that the difficulties faced among students in learning the subject were due to the various terminologies and concepts that they could not understand. Findings provide valuable information that lead to a possible solution. The difficulties experienced by the students while learning Biology must be dealt seriously.
Issues of Legislation of Cryptocurrency in Pakistan: An Analysis

Cryptocurrencies have been gaining popularity in Pakistan since 2015. However, in 2018, digital currencies were banned in Pakistan by the State Bank of Pakistan and the Federal Board of Revenue. The current paper will explore the issues of the legalization of cryptocurrencies in Pakistan. The study shares a brief description of international and national reviews about the trend of cryptocurrencies and their legality. There are various cryptocurrencies, including Bitcoin, Pakcoin, OKeX, Blockfolio, Crypto and KuCoin. By adopting the qualitative methodology, the researchers have analyzed the social media context, famous news websites and views of famous personalities involved in attempts to legalize and trend cryptocurrencies in Pakistan. It has been found in the study that there are various policy and legislative issues along with the consent of different governments of all provinces of Pakistan that declared cryptocurrency illegal and banned at different times. There is a need for some serious concerns regarding people's awareness of cryptocurrency to enhance the country's economy on practical grounds.
Strategi Diversifikasi Produk Buku Fiksi Sebagai Pengembangan Karya Tugas Akhir Penerbitan

This research departs from the lack of development in the work of the Final Project students of the Publishing Study Program, especially in books with the fiction genre. This limitation causes the work produced in terms of content and medium is still monotonous and has not developed in the desired direction. This research uses the concept of product development strategy which is commonly used in business and industry, especially the publishing industry. This study uses a qualitative descriptive method to analyze, describe, and explain the diversification that may be used for students of Publishing. The object of the research is the student's final thesis product Publishing from 2015 to 2020. This research also aims to find a new formulation for writing a publishing final project product, especially in the fiction category. The results of this study show that in the 2015 to 2020 period, there are only 4 types of fiction that keep repeating itself, and there is only 1 work in the form of a flashlight book as part of manual product development. This research resulted in a further mapping of the stages of diversification that students could choose and their product forms.
Pengaruh penggunaan media berbasis IT dan kompetensi pedagogik guru terhadap hasil belajar siswa di gugus 3 Kecamatan Pamulihan pada masa pandemi covid-19

Learning outcomes obtained by students, especially in class VI mathematics subjects have decreased. Therefore, the role of the teacher when improving pedagogic abilities (planning, implementing and evaluating learning) and the use of IT-based media (Google Classroom) is one way to improve online learning, when learning is done well automatically the learning outcomes obtained by students will good. The method used in this research is an explanatory survey research method. Meanwhile, for the population in this study, Grade 6 students were indicated to have been able to fill out the questionnaire independently. There are 307 grade 6 students from 8 elementary schools in Cluster 3, Pamulihan Subdistrict and are registered at Dapodik as active students. The sample is 76 students through the sampling technique using the proportionate stratified random sampling technique. The results of this study reveal that there is a significant influence between the use of media and the pedagogic competence of IT-based teachers on learning outcomes. This means that the variable of using IT-based media and the variable of teacher pedagogic competence simultaneously has a significant and positive impact on learning outcomes
Reading Difficulty and Development of Fluent Reading Skills: An Action Research

In this study, it was aimed to remediate the reading difficulties of a fifth-grade student having no physical or mental problem but experiencing reading difficulties and to develop his skills of reading fluency. For this purpose, the repeated reading, paired reading, and word repetition techniques were used in the research process. In addition, the meaning analysis, word map and vocabulary notebook techniques were also used due to the positive effect of rich vocabulary knowledge in remediating reading difficulties and improving skills of reading fluency. This study was designed as action research, one of the qualitative research designs, and it was carried out with a fifth-grade primary school student in Sivas in the spring term of 2018-2019. The Error Analysis Inventory was used to determine reading errors. In the case study to determine the levels of reading and comprehension, it was found that the participant was at the level of frustration in the fifth, fourth and third grade texts, and at the instructional level in the second-grade text. As a result of the reading, comprehension and vocabulary development activities, the student's reading motivation, desire and self-confidence increased, and he started to detect and correct reading aloud mistakes by himself. It was observed that the participant, who was at the second-grade instructional level at the beginning, reached the independent level in the text of the third grade and the instructional level in the text of the fourth grade at the end of the study lasted for 31 class hours.
A Well-Founded Semantics for Basic Logic Programs with Arbitrary Abstract Constraint Atoms

Logic programs with abstract constraint atoms proposed by Marek and Truszczynski are very general logic programs.They are general enough to captureaggregate logic programs as well asrecently proposed description logic programs.In this paper, we propose a well-founded semantics for basic logic programs with arbitrary abstract constraint atoms, which are sets of rules whose heads have exactly one atom. Weshow that similar to the well-founded semanticsof normal logic programs, it has many desirable properties such as that it can becomputed in polynomial time, and is always correct with respect to theanswer set semantics. This paves the way for using our well-founded semanticsto simplify these logic programs. We also show how our semantics can be applied toaggregate logic programs and description logic programs, and compare itto the well-founded semantics already proposed for these logic programs.
Man the Interpreter - From Natural Science to Hermeneutics in Swedish Archaeology

The epistemological principles of natural science dominate the archaeological discourse. Methods and theories developed exclusively for natural science are used in archaeology without further ado. Archaeological institutions employ experts on scientific methods. University departments, scholarship foundations and other institutions spend large amounts of money on projects and education with an explicit connection to natural science. The significance and outcome of such projects are hardly ever questioned. In this article the background of the present situation is analysed. It is also argued that archaeologists should pay more attention to life. It is in the ontology of life that we as archaeologists seek a significant meaning in history, not in explanations of present conditions constructed with methods developed for natural science. It is stated that archaeologists should tum to the first science —philosophy —if our mission, which is to explore the ontological aspects of life, shall become explicit in the discourse of archaeology.
Inputs and Outputs: Engagement in digital media from the maker's perspective

&#x0D; In the process of developing a technology assembly that can objectively measure engagement on a moment-by-moment basis, subjective responses to stimuli must be shown to correlate with the component technologies, such as motion capture or psychophysiology. Subjective scales for engagement are not all consistent in segregating the measurement of causes (inputs to the audience) and effects (outputs from the audience); this lack of separation can obscure appropriate inferences in the relationship between cause and effect. Inputs to the audience are scripted, and are controllable by the maker. An output is what the designed experience engenders in the end-user, and outputs can include both mental states (satisfaction) and physical activities (heart rate) during the stimulus and subsequently. Inputs can be maximised by design, whereas to optimise outputs from the end-user, one needs an empirical process because outputs are dependent upon an interpretive process or entry into a biological system. Outputs will be highly dependent on audience and context, and they will often be quite variable, even in individuals from a similar audience profile. It is critical that, in instruments assessing the relationship between inputs and outputs, controllable inputs to the end-user must not be conflated with outputs engendered in the end-user.&#x0D;
Mr. Clue — A Virtual Agent that Can Play Word-Guessing Games

This demonstration showcases a virtual agent, Mr. Clue, capable of acting in the role of clue-giver in a word-guessing game. The agent has the ability to automatically generate clues and update its dialogue policy dynamically based on user input.
A Study of Diagrammatic Reasoning from Verbal and Gestural Data

This paper reports on an exploratory study of diagrammatic reasoning. Concurrent think-aloud protocols and gestures of subjects solving a set of device behavior hypothesis problems presented as labeled diagrams were collected. In addition to analyzing verbal protocols, the gestures and marks made by the subjects were examined and used to annotate encoded verbal data. A model of diagrammatic reasoning in this task is proposed and compared with results of analyzing the protocols. Besides lending support to results of previous experimental studies, this study also revealed some interesting aspects of diagrammatic reasoning that merit further investigation.
Experiences With Alloy In Undergraduate Formal Methods

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Experiences with Alloy in Undergraduate Formal Methods Introduction At the core of all engineering endeavors is the modeling of proposed system designs and the use of these models to determine system properties. While some models are physical, the vast majority use mathematics to both describe and analyze the consequences of design decisions. In the case of traditional engineering disciplines, most models are based on continuous mathematics, e.g., calculus and differential equations. The situation is quite different in software engineering, however, where the applicable models are more likely to be drawn from discrete mathematics, logic, and set theory. The term of art for such modeling approaches is formal methods. One complaint about formal methods, voiced by both practitioners and students alike, is the lack of applicability to “real” problems. While some of these objections are undoubtedly based on an unwillingness to learn the relevant mathematics, this does not mean they can be dismissed out- of-hand. To be useful in practice, a modeling method must provide engineers with information that more than compensates for the cost of learning the technique and creating its models. Model checking is one formal method that has proven its value as a tool for describing and analyzing concurrency effects1,2. Alloy3,4,5, a modeling tool created by Daniel Jackson’s research group at MIT, provides similar value when modeling and analyzing the structural and behavioral consequences of software design decisions. This paper reports on the value Alloy has brought to the undergraduate formal methods course within the software engineering program at RIT. The next section introduces Alloy by way of a well-known example problem, the birthday book6. This is followed by a section discussing the advantages Alloy for teaching undergraduates, especially as compared to traditional methods such as VDM7 and Z8. The final section discusses some areas where Alloy’s support for instruction needs improvement. Birthday Book Example Consider a system for maintaining a birthday book (that is, a book that lists birthdays for some set of persons); this example was originally presented in The Z Notation6. In Alloy, we would start by defining the necessary signatures: Named sets of indivisible, immutable, atomic objects and the relations that hold among these sets. sig Person{} sig Date{} sig BirthdayBook { known : set Person, // those persons known in this book dates : Person -> Date // the birth day for each known person } Here we have defined three signatures, Person, Date, and BirthdayBook, along with two relations, known and dates. The signature declarations implicitly state that the three underlying sets of atoms partition the universe of all atoms (that is, the three sets are pair-wise disjoint and
Counting-MLNs: Learning Relational Structure for Decision Making

Many first-order probabilistic models can be represented much more compactly using aggregation operations such as counting. While traditional statistical relational representations share factors across sets of interchangeable random variables, representations that explicitly model aggregations also exploit interchangeability of random variables within factors. This is especially useful in decision making settings, where an agent might need to reason about counts of the different types of objects it interacts with. Previous work on counting formulas in statistical relational representations has mostly focused on the problem of exact inference on an existing model. The problem of learning such models is largely unexplored. In this paper, we introduce Counting Markov Logic Networks (C-MLNs), an extension of Markov logic networks that can compactly represent complex counting formulas. We present a structure learning algorithm for C-MLNs; we apply this algorithm to the novel problem of generalizing natural language instructions, and to relational reinforcement learning in the Crossblock domain, in which standard MLN learning algorithms fail to find any useful structure. The C-MLN policies learned from natural language instructions are compact and intuitive, and, despite requiring no instructions on test games, win 20% more Crossblock games than a state-of-the-art algorithm for following natural language instructions.
Tracking Visual Memes in Rich-Media Social Communities

We propose tools and methods to track visual memes on community-centric rich-media repositories, such as YouTube. Visual memes refer to frequently reposted short video segments. Our method can be used to monitor the reflections of real-world events in rich media, including images and videos. We first design a large-scale event-based social video collection system to continuously monitor events that unfold in real-time. We design a scalable detection algorithms that can detect visual memes with over 96\% precision and 80\% recall. Visual memes are used for various analysis such as tracking the fraction of original content, extracting the iconic picture of an event, inferring influential users in the community, and so on. We present example observations on several real-world video collections from YouTube, containing up to 1.2 million video shots.
IL ROSSO AMMONITICO INFERIORE (BAIOCIANO - CALLOVIANO) NEI MONTI LESSINI SETTENTRIONALI (VERONA)

The aim of this paper is the description, from both the biostratigraphical and sedimen• tological point of view, Of the lower member of the Rosso Ammonitico Veronese, one of the most known red, nodular, pelagic limestones of the mediterranean Jurassic.&#x0D; The R.A.V. is a condensed sequence Of red, mostly nodular, limestones, Often rich in internal molds Of ammonites, which is areally restricted to the "Trento Ridge", a structural high isolated in the early Jurassic by the rifting Of a Triassic, large epicontinental carbonate—platform. It spans in age from late Bajocian to Tithonian and is separated from the underlying shallow—water carbonates Of Liassic and Aalenian age by a sharp break in sedimentation, underlined by a mineralized hard ground, which lasted for all early and middle Bajocian.&#x0D; Two members may bc recognized in the R.A.V.: a lower (Rosso Ammonitico Inferiore) and an upper one (Rosso Ammonitico Superiore) separated by a stratigraphic gap. The lower member, ranging in age from late Bajocian to middle Callovian, is well developed only in the southern part of the 'Trento Ridge" (i. e. in the Lessini Veronesi area) and has a variable thickness which reaches a maximum of 8 meters.
One Pixel Attack for Fooling Deep Neural Networks

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a black-box attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate low-cost adversarial attacks against neural networks for evaluating robustness.
SciBERT: A Pretrained Language Model for Scientific Text

Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.We release SCIBERT, a pretrained language model based on BERT (Devlin et al., 2019) to address the lack of highquality, large-scale labeled scientific data.SCIBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks.We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains.We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks.The code and pretrained models are available at https://github.com/allenai/scibert/.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.
Energy and Policy Considerations for Deep Learning in NLP

Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data.These models have obtained notable gains in accuracy across many NLP tasks.However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption.As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP.Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.