Reviewerly/exHarmony huggingface dataset:
Students’ Problems in Answering Reading Comprehension Question of Descriptive Text in The “Mandiri” Textbook Published by Erlangga at SMPN 1 Kepahiang

This research aimed to find out the students’ problems in answering reading comprehension questions of descriptive text in the “Mandiri” textbook published and the causes of students’ problems in answering reading comprehension questions of descriptive text in the “Mandiri” textbook published by Erlangga at SMPN 1 Kepahiang. The design of this research was a descriptive quantitative research. This research was designed based on the problems identified and arranged to adjust the main purpose of the research, so that the researcher employed descriptive method. The researcher took 50 students as the sample of this research. The instruments of this study were reading test and interview guideline. The researcher used a reading test in multiple choice forms consisted of fourty numbers of questions included in Mandiri textbook published by Erlangga. Another instrument was an interview guideline. The interview guideline was arranged based on the result of the reading test to reconfirm the result of the reading test and to get an indepth data on the causes of students’ difficulties in reading comprehension. The findings of this research were; (1) the students’ problems in answering reading comprehension questions of descriptive text in the “Mandiri” textbook published by Erlangga at SMPN 1 Kepahiang were finding main idea, identifying specific information, inference, and vocabulary; (2) there were four causes of students’ problems in answering reading comprehension questions of descriptive text in the “Mandiri” textbook published by Erlangga at SMPN 1 Kepahiang; (a) finding main idea is rather complicated, the students need to read the text repeatly to find the main idea; (b) the students need to read from the beginning to finish this description so that they can get the information and answer the questions given; (c) to find inferences they must know the text well and clearly; (d) There are some words that they find difficult to understand and they have never met. They answered that there are some words which are not familiar to them.
A Robust Method to Enhance Degraded Color Images Under Diverse Conditions

This paper presents a Wavelet Transform and Multi-Scale Retinex (WT-MSR) based color image enhancement method. The proposed WT-MSR method initially transforms the colored RGB image into the HSI color space. In the HSI color space, the low-frequency coefficients in the wavelet domain are processed by using Contrast Limited Adaptive Histogram Equalization (CLAHE). Later, the high-frequency components are scaled and enhanced by expanding contrast in the wavelet domain. Meanwhile, the saturation component of the HSI converted image is processed through enhanced intensity factor followed by the conversion of the image into the RGB color space. In the RGB color space, the PCA is applied to isolate the luminance channel. The obtained luminance channel is further processed with three steps of the SSRs. Finally, the contrast stretching is used to further enhance the contrast of the image to obtain the pleasing effects. Simulation results on NASA Retinex dataset reveal that the proposed WT-MSR algorithm outperforms some of the existing methods in terms of the image quality and statistical methods compared therein.
Recommendations for a radically secure ISA

The rising number of attacks targeting processors at micro-architecture level encourages more research on hardware level solutions.In this position paper, we specify a new RV32S “secure” instruction set architecture (ISA) derived from the RV32I RISC-V ISA.We propose modifications in the ISA to prevent timing side-channels, strengthen control flow integrity and ensure micro-architectural state isolation.The goal is to provide a new minimal hardware/software approach through which software attacks exploiting hardware vulnerabilities can be circumvented.
Research on New Media Content Production Based on Artificial Intelligence Technology

Abstract At present, the main application of artificial intelligence in the media content production industry is to greatly reduce the application threshold, to a certain extent, to enable media workers to collect data from the low-end and produce visualization works for media terminals free from repetitive labor. Such as visual effects production, special effects design, video editing, subtitles sorting, data collection and comparison, image-text audio-video conversion, host broadcasting, and press release writing, are now increasingly completed through artificial intelligence programs. The working procedures of media content production are becoming more and more automated and intelligent.
Detection of zinc-lead mineralization and associated alteration in the Mehdiabad deposit, Yazd province, using ASTER and Landsat 8-OLI satellite images

Detection of zinc-lead mineralization and associated alteration in the Mehdiabad deposit, Yazd province, using ASTER and Landsat 8-OLI satellite images
Impact of Video Surveillance Systems on ATM PIN Security

ATM transactions are verified using two-factor authentication. The PIN is one of the factors (something you know) and the ATM Card is the other factor (something you have). Therefore, banks make significant investments on PIN Mailers and HSMs to preserve the security and confidentiality in the generation, validation, management and the delivery of the PIN to their customers. Moreover, banks install surveillance cameras inside ATM cubicles as a physical security measure to prevent fraud and theft. However, in some cases, ATM PIN-Pad and the PIN entering process get revealed through the surveillance camera footage itself. We demonstrate that visibility of forearm movements is sufficient to infer PINs with a significant level of accuracy. Video footage of the PIN entry process simulated in an experimental setup was analyzed using two approaches. The human observer-based approach shows that a PIN can be guessed with a 30% of accuracy within 3 attempts whilst the computer-assisted analysis of footage gave an accuracy of 50%. The results confirm that ad-hoc installation of surveillance cameras can weaken ATM PIN security significantly by potentially exposing one factor of a two-factor authentication system. Our investigation also revealed that there are no guidelines, standards or regulations governing the placement of surveillance cameras inside ATM cubicles in Sri Lanka.
Some unresolved issues in perceptual learning.

Perceptual learning has been studied extensively using a procedure in which subjects (involving both humans and nonhuman animals) are exposed to two similar stimuli (to be referred to as AX and BX, where A and B represent distinctive features and X the features they hold in common), prior to a test of their ability to discriminate between AX and BX. Performance on the discrimination is found to be enhanced by this procedure, particularly if the preexposure arrangement involves intermixed presentations of AX and BX (a regime that might be expected to facilitate comparison of the stimuli). This perceptual learning effect has generated a range of theoretical interpretations that have focused, for the most part, on how exposure to a stimulus (or feature of a stimulus) can change its effectiveness, by which is meant its ability to command processing and to control responding. But a consensus is difficult to achieve, given that some aspects of what must be explained remain uncertain. Three issues are discussed here: Does appropriate exposure to the stimuli reduce the effectiveness of the common, X, elements? Does exposure enhance the effectiveness of the unique (A and B) elements? Are any such effects enhanced by the opportunity to compare the stimuli? It will require further experimental work to answer these questions, but raising them may promote this and thus facilitate achieving a satisfactory theoretical analysis. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
An analysis of teachersâ€™ classroom interaction by using self-evaluation of teacher talk

This study focuses on classroom interaction in English teaching and learning at SMA Muhammadiyah 1 Bengkulu. The objective of this study is to 1. Describe student-teacher interaction pattern, 2. Find out the type of interactional features in the classroom. The data were first-grade students. This study shows how teacher and student talk and give a response to each other. This study is conducted in form of discourse analysis. The writer used observation to get the data. The data were in a form video recording of classroom interactional both teachers and students. The writer made transcription from recorded data and analyzed it through IRF exchange structure and applied Walshâ€™s framework in interactional features. The result of the research showed teacher A and B interaction pattern is Initiation (I)- Response (R)- Feedback (F). Teacher Aâ€™s pattern was I (initiation), R (response) and F (feedback). In her classrooms, teacher A provides lots of initiation to her students.Â Teacher Bâ€™s pattern was IRF in this class with the highest amount of initiation in form question. Teacher A used 12 (twelve) interactional feature based on Walsh framework (2006). There were extended learner turn (ELT) 171 frequencies, extended teacher turn (ETT) 90 frequencies,Â Display Question (DQ) 66 frequencies, confirmation check (CC) 36 frequencies, seeking clarification (SC) 35 frequencies, Teacher echo (TE) 27 frequencies, referential question (RQ) 20 frequencies, Scaffolding (SCF) 16 frequencies, extended wait time (EWT) 13 frequencies, turn completion (TC) 12 frequencies, direct repair (DR) 5 frequencies, content feedback (CF) 1 frequency. Teacher B used 12 (twelve) interactional feature based on Walsh framework (2006). There were extended learner turn (ELT) 155 frequencies, Display Question (DQ) 53 frequencies, extended teacher turn (ETT) 42 frequencies, referential question (RQ) 23 frequencies, confirmation check (CC) 17 frequencies, seeking clarification (SC) 13 frequencies, Form focus feedback (FFF) 12, Teacher echo (TE) 8 frequencies, Scaffolding (SCF) 8 frequencies, extended wait time (EWT) 7 frequencies, turn completion (TC) 4 frequencies, direct repair (DR) 3 frequencies. Both of teacherÂ A and B provide a lot of extended learner turn by giving a direct question and referential question. Studentsâ€™ response frequently in English, in Indonesian and in another hand in their mother tongue.Â
Average fidelity and fidelity deviation in noisy quantum teleportation

We analyze the average fidelity (say, F) and the fidelity deviation (say, D) in noisy-channel quantum teleportation. Here, F represents how well teleportation is performed on average and D quantifies whether the teleportation is performed impartially on the given inputs, that is, the condition of universality. Our analysis results prove that the achievable maximum average fidelity ensures zero fidelity deviation, that is, perfect universality. This structural trait of teleportation is distinct from those of other limited-fidelity probabilistic quantum operations, for instance, universal-NOT or quantum cloning. This feature is confirmed again based on a tighter relationship between F and D in the qubit case. We then consider another realistic noise model where F decreases and D increases due to imperfect control. To alleviate such deterioration, we propose a machine-learning-based algorithm. We demonstrate by means of numerical simulations that the proposed algorithm can stabilize the system. Notably, the recovery process consists solely of the maximization of F, which reduces the control time, thus leading to a faster cure cycle.
Libraries in Colombia and Covid-19: Delivery library services to the public amid limits and uncertainties

Covid-19 – or coronavirus disease forced libraries in Colombia to close the doors as well as forced them to strengthen or to create virtual and remote services. Biblioteca Nacional de Colombia (BNC) leads in Colombia the implementation of measures for the library sector during the current health crisis, providing guidelines and recommendations on how to deal with the emergency, as well as giving information resources, and alternatives on how to provide library services, to doing the paperwork, to implement the biosecurity protocols, and the definition of phases for reopening in the future. Also, BNC itself focused on the support BNC researchers throughout virtual services. This article was written during the first months of the health emergency, so that on the date of publication the situation may have changed.
NTRU Over Galois Rings

Abstract As a cryptosystem, Nth Truncated Polynomial Ring (NTRU) is established on the fast and easy calculation. Improving the security is aimed by enlarging a ring where the processes execute and enhancing the number of a private key and a public key. In this study, NTRU takes over the Galois rings and is analysed by adding a new private key.
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries

Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the "black-box" setting where one only has query access to the classifier. However, all prior work in the black-box setting attacks the classifier by repeatedly querying the same image with minor modifications, usually thousands of times or more, making it easy for defenders to detect an ensuing attack. In this work, we instead show that it is possible to craft (universal) adversarial perturbations in the black-box setting by querying a sequence of different images only once. This attack prevents detection from high number of similar queries and produces a perturbation that causes misclassification when applied to any input to the classifier. In experiments, we show that attacks that adhere to this restriction can produce untargeted adversarial perturbations that fool the vast majority of MNIST and CIFAR-10 classifier inputs, as well as in excess of $60-70\%$ of inputs on ImageNet classifiers. In the targeted setting, we exhibit targeted black-box universal attacks on ImageNet classifiers with success rates above $20\%$ when only allowed one query per image, and $66\%$ when allowed two queries per image.
From Digital to Computational Pathology and Integrated Diagnostics: The Future of Histopathology

Among the various branches of clinical pathology, histopathology has been the most conservative and slowest in adapting to automation and digitisation in this era of information technology (IT).It is still largely a manual and qualitative science; and heavily dependent on the opinion of medical experts. 1 The last attribute of it imparts subjectivity to the field.One of the main reasons for its slow adaptation to automation is the fact that several, manual and largely independent steps, are involved in the preparation of material for visualisation under the microscopes by the pathologists and beyond, which, on a cursory look, seem unfit for automation and digitisation.However, there is one other example of a medical diagnostic field with similar objections and reservations in the beginning; but which, during recent past, has undergone successful digitisation, i.e. radiology.There are many similarities between radiology and histopathology: both are used for making diagnosis, both are subjective, and both involve images.Nowadays, radiology has been successfully transformed into filmless field in most parts of the world.It is now the turn of histopathology to convert into slideless arena and the change is on the horizon.Digital pathology (DP) is defined as the practice of pathology using digital images of whole microscopic sections on computers rather than under the microscopes as is done in traditional histopathology, such that computers serve, as workstations for pathologists. [2]3][4] It has become possible in recent years by the development of whole-slide imaging (WSI) scanners, which capture high--resolution images of microscopic sections at a very high speed and at high magnification.They transform the glass slides into digital slides, which are immediately archived in computer systems and made accessible to all concerned.The digital slides can be efficiently stored, accessed, analysed, retrieved and shared with pathologists or scientists from across the world via internet, using slide viewing and management softwares.In DP, the glass slides are prepared in much the same way as in conventional microscopy; but instead of examining the slides under a microscope, the slides are visualised on a computer screen.
Approach for Cross-Domain Study Curricula in Cyber-Physical Systems for Belarusian and Ukrainian Universities

The increasing importance of ever more complex CPS (Cyber-Physical Systems) challenges encourages the universities worldwide to organize up-to-date CPS oriented educational programs. These programs should include a decent scientific and technical formation in combination with Problem/Project Based Learning. Relying on a CDIO-based philosophy (Conceive - Design - Implement - Operate), the multidisciplinary and even transdisciplinary needs of a CPS oriented curriculum must be taken into account. The Erasmus+ project “Development of practically oriented student centred education in the field of modelling of Cyber-Physical Systems” aims to support such CPS oriented educational programs in Belarus and Ukraine. In order to apply these pedagogical approaches to the most appropriate CPS applications, surveys have been performed in Belarus and Ukraine to reveal these major applications. A discussion of the findings of these surveys is included in the present paper and will be taken into account when a number of e-books are developed by the European, Belarusian and Ukrainian partners of the Erasmus+ project. Additionally, it is important to transfer knowledge and experience, including the use of ICT based e-learning tools, between European universities and the Belarusian and Ukrainian partners. Cooperation between academia and industry will be stimulated.
MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks

Deep learning compiler frameworks are gaining ground as a more portable back-end for deep learning applications on increasingly diverse hardware. However, they face the daunting challenge of matching performance offered by hand-tuned target-specific libraries. While auto-tuning frameworks with statistical cost models can provide dynamic and efficient code optimization, they suffer from large space exploration and cost model training overheads. This paper proposes MetaTune, a meta-learning based cost model that more quickly and accurately predicts the performance of optimized codes with pre-trained model parameters. MetaTune encodes convolution kernel codes as structurally similar graphs to facilitate meta-learning, meta-trains a GNN model with a very small input data set, and then predicts optimization parameters for unseen convolution operations with varying sizes and structures during compilation. The resulting framework with MetaTune provides 8 to 13% better inference time on average for four CNN models with comparable or lower optimization time while outperforming transfer learning by 10% in cross-platform cases.
Image Highlight Removal based on Double Edge-preserving Filter

In order to effectively remove the highlights of the image while retaining the texture information and color information of the image. Based on the two-color reflection model, this paper proposes an image highlight removal method based on a double edge-preserving filter. First, the maximum diffuse reflection chromaticity is estimated by the MSF image principle and the specular detection algorithm proposed in this paper. Then based on the characteristic of local smoothness of the maximum diffuse reflection chromaticity, the maximum diffuse reflection chromaticity estimation value is used as the guide value of the joint bilateral filter and the guided filtering to separately processes the maximum chromaticity map of the image to make it quickly diffuse and spread in the neighborhood to remove the highlights in the image. Compared with the traditional method, the method in this paper can not only remove the image highlights more efficiently, but also retain the color and texture information of the image to a greater extent.
Penerapan Metode K-Means Clustering Untuk Daging Ayam Buras

Free-range chicken is a local Indonesian poultry whose population is widely found in every region of Indonesia. However, the level of consumption of free-range chicken meat in North Sumatra Province is still relatively low. This research data contains data on the production of free-range chicken in North Sumatra Province. One data mining technique is clustering. Grouping itself is a method by grouping data. The data of this study contains data on the production of free-range chicken in North Sumatra Province which began in 2010, 2011, 2012, 2013, 2014, 2017. (7 years), and data obtained from the Directorate General of Animal Husbandry and Animal Health as well as on animal access to BPS Sumatra site North (Central Statistics Agency). The purpose of this study is to group Regencies / Cities in North Sumatra Province into 2 parts. These are the Regency / City group with high free-range chicken meat and Regency / City with low-free chicken meat. This can be a contribution of the provincial government of North Sumatra, Regency / City which is of more concern than free-range chicken based on the cluster that has been done. The results of this research are taken from 33 data of free-range chicken meat in regencies / cities in North Sumatra province, 3 high-level districts, namely: Mandailing Natal, Langkat, Serdang Bedagai and 30 other regencies / cities including
An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

Document classification is the task to split the document set into distinct highly relative classes or groups based on nature of the document contents.Here, an improved approach of document classification called keywordbased document classification (KBDC) is introduced.It focuses on splitting the unstructured text document set into K number of dissimilar classes based on K predetermined keywords text models by improved probability technique.This new system comprises of the following stages.Namely, pre-processing, classification and classifier stage respectively.Initial, the proposed system (KBDC) recognizes all the immaterial existing contents in the input text document through constructed Predetermined Irrelevant Text Pattern Model (PITPM).Next, it divides the pre-processed document set into 'K' different groups or classes by K number of Pre-determined Keyword Text Pattern Models (PKTPM) through probability technique, where K denotes the number of groups or classes or models.Finally, the KBDC system classifies the trial test text document without any class label that belongs to either of the existing group based on the K different class models (PKTPs).Experimentation results show that the KBDC is appropriate to split and identifies the unstructured text document set into K distinct extremely comparative classes.