Reviewerly/exHarmony huggingface dataset:
An Analysis on English Teacher Strategies in Teaching Reading Comprehension

The purpose of this study was to analyze what strategies the teacher used in teaching reading comprehension and how the teacher applies the strategies used in teaching reading comprehension. The subjects of this study were teacher who taught English at SMAN 10 Kota Bengkulu . This type of research is designed as descriptive qualitative research. To collect data in the study, researcher used Checklist Observation, Interview as an instrument in collected data and documentation. The procedure of this research is by means of researchers coming and participating in seeing and understanding how the teacher teaches reading comprehension in the classroom. The results of the study found that the two strategies used by the teacher, Scaffolding and QARs (Question Answers Relationship). Scaffolding strategy students can develop ideas that can be read by students and make students confident in reading comprehension. QARs (Question Answer Relationship), the teacher is able to know the extent to which students understand what the teacher has given them and the limits of the students' ability to do assignments after reading the text that has been given. Thus, it can be concluded that the teacher is good enough to implement this strategy in the classroom, but was slightly hindered by its application during the Covid-19 Pandemic.
Being-with Smartphones

In a social situation, why is it sometimes off-putting when a person reaches for his smartphone? In small-group contexts such as a college seminar, a business meeting, a family meal, or a small musical performance, when a person begins texting or interacting with social media on a smartphone he may disengage from the group. When we do find this off-putting, we typically consider it to be just impolite or inappropriate. In this essay, I argue that something more profound is at stake. One significant way in which individuals shape their self-identities is through interactions with others in small groups. Much identity-work is interdependent; it requires generating and preserving social contexts. I argue that the smartphone-use of some individuals can fracture a group’s context and thus negatively affect the identity-work of others. In this essay, I examine identity-work, sociality, and personal technology from a perspective of existential phenomenology.
Concealed Data Poisoning Attacks on NLP Models

Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model's training set that causes the model to frequently predict Positive whenever the input contains James Bond. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (Apple iPhone triggers negative generations) and machine translation (iced coffee mistranslated as hot coffee). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.
Cryptocurrency and Privacy - An Introduction to the Interface

The largest and best-known Cryptocurrency in the global economy is Bitcoin but it is only one of approximately 2,000 cyptocurrencies in circulation today. A Bitcoin was worth 8,790.51 U.S. dollars as of March 4, 2020 and all the Bitcoins in the world were worth roughly $160.4 billion. In Bitcoin you don’t need to explicitly register or reveal your real world identity, but the pattern of your behavior might itself be identifying. This is the fundamental privacy question in a Cryptocurrency like Bitcoin leading to emergence of Privacy coins such a Monero and Zcash that utilize complex cryptography to achieve the greater Privacy levels with more anonymity features. However, in the zest for increased Privacy with focus on anonymity in Cryptocurrency transaction, the law and order issues are compromised wherein it would be near to impossible for the law enforcement agencies to track criminals dealing in money laundering, terrorist financing, tax evasion and other frauds by using Crypto currencies.In this background, it is necessary to understand “Cryptocurrency and Privacy” as an interface to better comprehends the subject of Cyptocurrency which is very dynamic in technology with multiple global implications on the economic and legal front. The present work aims to study Cryptocurrency in the context of Privacy. The foundational concepts and definitions of the two competing subjects: ‘Cryptocurrency and Privacy’ is taken up for better understanding the background of the interface. Tor, an anonymous communication network is referred in brief to state that the dilemma in the Privacy context is not unique to use of Crypto currencies technology in so far its negative effects are concerned . The conclusion suggest for finding an appropriate balance between an individual’s privacy and State’s security in Cryptocurrency technology.
Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption

This work utilizes a novel image encryption technique for block image encryption with the help of nonlinear 2D chaotic map and DNA sequences (NL2DCM-DNA). 2D chaotic map has generated the several key sequences for encryption, and DNA rules are applied to perform fast block encryption of image. Nonlinearity of 2D chaotic map and complexity of DNA sequences are used to perform highly secure image encryption. The experimental outputs illustrate the higher efficiency of NL2DCM-DNA in terms of security, attack resilience, entropy, histogram, running time and diffusion against some previous image encryption algorithms.
Analysis of the Morphology and Mechanical Properties of Polymer Composite Materials (PCM) from Silicon Dioxide (SiO&lt;sub&gt;2&lt;/sub&gt;) and Multiwalled Carbon Nanotubes (MWNTs) Reinforced Volcanic Stone

The formation of Polymer Composite Materials (PCM) based on mineral stones reinforced with Silicon Dioxide (SiO 2 ) and Carbon Nanotubes (CNTs) using the Squeze Casting Method was carried out to obtain an increase in surface hardness, while the inner side of the elongation remained high. Based on SEM-EDX testing, it is known that the crystalline form of igneous rock and SiO 2 is tapered, with large and small variations, CNTs with fine crystalline structures, which can bind well to specimen formation. The results of bending and hardness tests showed that dimensional variations, the aggregate ratio with resin and the addition of CNT and SiO 2 reinforcement had the highest hardness 89.55 HD (Hardness of durometer), mixing of silica sand – vulkanic sand had the highest bending strength of 18.7 MPa, the lowest is a mixture of granite-sand of Merapi at 11.63 MPa. The variation of silica sand – volcanic sand has the highest bending strength, the lowest is the mixing of granite-volcanic sand.
Optimal query complexity for private sequential learning against eavesdropping

We study the query complexity of a learner-private sequential learning problem, motivated by the privacy and security concerns due to eavesdropping that arise in practical applications such as pricing and Federated Learning. A learner tries to estimate an unknown scalar value, by sequentially querying an external database and receiving binary responses; meanwhile, a third-party adversary observes the learner's queries but not the responses. The learner's goal is to design a querying strategy with the minimum number of queries (optimal query complexity) so that she can accurately estimate the true value, while the eavesdropping adversary even with the complete knowledge of her querying strategy cannot. We develop new querying strategies and analytical techniques and use them to prove tight upper and lower bounds on the optimal query complexity. The bounds almost match across the entire parameter range, substantially improving upon existing results. We thus obtain a complete picture of the optimal query complexity as a function of the estimation accuracy and the desired levels of privacy. We also extend the results to sequential learning models in higher dimensions, and where the binary responses are noisy. Our analysis leverages a crucial insight into the nature of private learning problem, which suggests that the query trajectory of an optimal learner can be divided into distinct phases that focus on pure learning versus learning and obfuscation, respectively.
UoT-UWF-PartAI at SemEval-2021 Task 5: Self Attention Based Bi-GRU with Multi-Embedding Representation for Toxicity Highlighter.

Toxic Spans Detection(TSD) task is defined as highlighting spans that make a text toxic. Many works have been done to classify a given comment or document as toxic or non-toxic. However, none of those proposed models work at the token level. In this paper, we propose a self-attention-based bidirectional gated recurrent unit(BiGRU) with a multi-embedding representation of the tokens. Our proposed model enriches the representation by a combination of GPT-2, GloVe, and RoBERTa embeddings, which led to promising results. Experimental results show that our proposed approach is very effective in detecting span tokens.
Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs

The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key exchange protocols to maximize the protocol execution efficiency. We observed that the number of key exchanges handled by primitive functions in parallel, and the dimension of the grids in the GPUs have significant impacts on both the latencies and throughputs of MLWE key exchange protocols. By properly adjusting the related parameters, in the experiments, we show that performance of MLWE based key exchange protocols can be improved across GPU platforms.
A 23.8Tbps Random Number Generator on a Single GPU

In order to build a high performance communication link simulation platform, random number streams with high throughput and good randomness properties are required. In this paper, we investigate high throughput pseudo random number generators (PRNGs) on CUDA enabled GPUs for computer based simulation tasks. We propose a new low complexity and hardware friendly PRNG algorithm with a reasonably long period of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> -1. In the algorithm, we generate random numbers on the CPU by using MT19937 PRNG, which are used as seeds for the PRNGs running on the GPU. Low complexity and memory efficient xorshift algorithm is employed on the GPU to generate random numbers in an iterative manner. We analyze the computation complexity and memory requirements of our proposed PRNG and benchmark PRNGs. Experiment results demonstrate that our proposed PRNG achieves a 23.8 Tbps throughput in generating uniformly distributed binary random sequences, which is 49% higher than that achieved by the Warp PRNG on the same GPU. In addition, the generated binary random sequences pass the NIST randomness tests.
Efficient Statistics for Sparse Graphical Models from Truncated Samples

In this paper, we study high-dimensional estimation from truncated samples. We focus on two fundamental and classical problems: (i) inference of sparse Gaussian graphical models and (ii) support recovery of sparse linear models. (i) For Gaussian graphical models, suppose $d$-dimensional samples ${\bf x}$ are generated from a Gaussian $N(μ,Σ)$ and observed only if they belong to a subset $S \subseteq \mathbb{R}^d$. We show that $μ$ and $Σ$ can be estimated with error $ε$ in the Frobenius norm, using $\tilde{O}\left(\frac{\textrm{nz}(Σ^{-1})}{ε^2}\right)$ samples from a truncated $\mathcal{N}(μ,Σ)$ and having access to a membership oracle for $S$. The set $S$ is assumed to have non-trivial measure under the unknown distribution but is otherwise arbitrary. (ii) For sparse linear regression, suppose samples $({\bf x},y)$ are generated where $y = {\bf x}^\top{Ω^*} + \mathcal{N}(0,1)$ and $({\bf x}, y)$ is seen only if $y$ belongs to a truncation set $S \subseteq \mathbb{R}$. We consider the case that $Ω^*$ is sparse with a support set of size $k$. Our main result is to establish precise conditions on the problem dimension $d$, the support size $k$, the number of observations $n$, and properties of the samples and the truncation that are sufficient to recover the support of $Ω^*$. Specifically, we show that under some mild assumptions, only $O(k^2 \log d)$ samples are needed to estimate $Ω^*$ in the $\ell_\infty$-norm up to a bounded error. For both problems, our estimator minimizes the sum of the finite population negative log-likelihood function and an $\ell_1$-regularization term.
Automatic image co-segmentation: a survey

Image co-segmentation is important for its advantage of alleviating the ill-pose nature of image segmentation through exploring the correlation between related images. Many automatic image co-segmentation algorithms have been developed in the last decade, which are investigated comprehensively in this paper. We firstly analyze visual/semantic cues for guiding image co-segmentation, including object cues and correlation cues. Then, we describe the traditional methods in three categories of object elements based, object regions/contours based, common object model based. In the next part, deep learning-based methods are reviewed. Furthermore, widely used test datasets and evaluation criteria are introduced and the reported performances of the surveyed algorithms are compared with each other. Finally, we discuss the current challenges and possible future directions and conclude the paper. Hopefully, this comprehensive investigation will be helpful for the development of image co-segmentation technique.
MENINGKATKAN INTERAKSI DAN HASIL BELAJAR SISWA MELALUI PENGGUNAAN GOOGLE CLASSROOM UNTUK KELAS DARING BIOLOGI DI SMA

This action research was to increase the student interaction in the learning process. The subjects were the students at class X MIPA 4 SMA Negeri 1 Tarakan, consisting of 35 students (18 male and 17 female). It was held in the odd semester of the 2020-2021. The treatment given was by introducing the use of Google Classroom as the platfom of the learning. The result shows that there was an increase of the student interaction in the learning process on the Protista subject matter, by looking at the interactive score, namely the difference between the final score and the previous score &gt; 30 with a score of 25, from 5.7% in cycle I to 0.0%, indicating an increase in student interactive, it even occurs interactively for all students in the learning process in cycle II, and this has a significant effect on student learning outcomes. Student learning outcomes increased from 76.2% in cycle I to 78.1% in cycle II, with attendance of students in cycle I of 99.0% and cycle II of 96.2%. There was an increase in student interaction in the learning and completeness of 80.0% with an average value of 65.9. It can be concluded that learning using Google Classroom
Flood Quantification Index Based on Inundation Indicators and Risk Scale

Flood management requires an assessment of flood levels that occur appropriately. Determining flood levels quantitatively can help control floods more efficiently. This study aims to make flood quantification based on the inundation indicators and scale of risk. The case study is in Citarik, Cilember, Cimahi, Cisangkan, and Cibeureum, located in the Citarum watershed, West Java, Indonesia. The method used is a mathematical model of flow verified by the river's physical dimensions. Four indicators were analyzed based on unsteady one-dimensional river flow and overland flow models. The four flood quantity indicators with different weights are namely percentage of inundation area (Ai), percentage of area activity on the inundation area (Aac), inundation duration (D), and inundation depth (H) which have consecutive indexes 0.3, 0.2, 0.2 and 0.3. The initial discharge was analyzed based on unit hydrograph at the upstream river cross-section of the inundation region. The study's results by verification of field observations showed that the highest peak inundation was 3.0 m in Cilember, inundation duration was a maximum of 18 hours in Citarik and Cilember, the highest inundation area was 329.23 % of the watershed area of Cilember. Results of calculations with the formulations, the highest quantity is 16.18 in Cilember flood that occurs in Melong village with a risk scale of 5. These results conclude that the Cilember flood is high-level damage so that it is a priority to be overcome.
Related-key Impossible Differential Cryptanalysis on Lightweight Block Cipher ESF

Eight-Sided Fortress (ESF) is a lightweight block cipher with a generalized Feistel structure, which can be used in resource-constrained environments such as protecting Radio Frequency IDentification (RFID) tags in the internet of things. At present, the research on the security of ESF mainly adopts the impossible differential cryptanalysis. The ability of ESF to resist the related-key impossible differential cryptanalysis is studied based on the characteristics of its S-boxes and key schedule. By constructing an 11-round related-key impossible differential distinguisher, an attack on 15-round ESF is proposed by adding 2-round at the top and 2-round at the bottom. This attack has a time complexity of 240.5 15-round encryptions and a data complexity of 261.5 chosen plaintexts with 40 recovered key-bit. Compared with published results, the time complexity is decreased and the data complexity is ideal with the number of attack rounds increased.
RVBT: A Remote Voting Scheme Based on Three-Ballot

Faced with various security threats from the Internet, remote voting system usually uses cryptography to identify voters, create ballots, cast ballots and count ballots. It not only makes the voting system more complex, but also increases the computational cost. This paper proposes a remote voting scheme based on Three-Ballot which provides voters to cast their ballots to the voting machine and the Storage and Auditor. The scheme reduces the complexity of voters, while keeping the security of voters when casting as they intend. Furthermore, the scheme ensures individual verifiability for voters to check whether the ballots are counted as they cast, and universal verifiability for the public to check whether the voting system has correctly counted all the ballots.
Continual Learning on the Edge with TensorFlow Lite

Deploying sophisticated deep learning models on embedded devices with the purpose of solving real-world problems is a struggle using today's technology. Privacy and data limitations, network connection issues, and the need for fast model adaptation are some of the challenges that constitute today's approaches unfit for many applications on the edge and make real-time on-device training a necessity. Google is currently working on tackling these challenges by embedding an experimental transfer learning API to their TensorFlow Lite, machine learning library. In this paper, we show that although transfer learning is a good first step for on-device model training, it suffers from catastrophic forgetting when faced with more realistic scenarios. We present this issue by testing a simple transfer learning model on the CORe50 benchmark as well as by demonstrating its limitations directly on an Android application we developed. In addition, we expand the TensorFlow Lite library to include continual learning capabilities, by integrating a simple replay approach into the head of the current transfer learning model. We test our continual learning model on the CORe50 benchmark to show that it tackles catastrophic forgetting, and we demonstrate its ability to continually learn, even under non-ideal conditions, using the application we developed. Finally, we open-source the code of our Android application to enable developers to integrate continual learning to their own smartphone applications, as well as to facilitate further development of continual learning functionality into the TensorFlow Lite environment.
Measurement of Hotel Service Quality Based on Online Comment Sentiment Analysis

To solve the problems of difficult quantification of service quality evaluation and low reliability of data sources, this paper proposes a hotel service quality measurement model based on online comment sentiment analysis and multi-attribute method. The sentiment semantic analysis of online comments is used as the data source of service quality measurement, and TOPSIS multi-attribute method as the measurement method. The effectiveness and scientific rationality of the research method were proved by experimental analysis. The research results not only serve as an exploration for the application of sentiment analysis research, but also provide a feasible technical route for the research of service quality evaluation.
Deep learning for sentence clustering in essay grading support

Essays as a form of assessment test student knowledge on a deeper level than short answer and multiple-choice questions. However, the manual evaluation of essays is time- and labor-consuming. Automatic clustering of essays, or their fragments, prior to manual evaluation presents a possible solution to reducing the effort required in the evaluation process. Such clustering presents numerous challenges due to the variability and ambiguity of natural language. In this paper, we introduce two datasets of undergraduate student essays in Finnish, manually annotated for salient arguments on the sentence level. Using these datasets, we evaluate several deep-learning embedding methods for their suitability to sentence clustering in support of essay grading. We find that the choice of the most suitable method depends on the nature of the exam question and the answers, with deep-learning methods being capable of, but not guaranteeing better performance over simpler methods based on lexical overlap.
Data Compression and Protection Through Representation With Combinations and Spiral Path

We are living in Industry 4.0 era where enormous data need to be stored and processed. Though hardware is also becoming more abundant, its limitation in medium transmission speed and memory storage still beats this need. Therefore, data compression seems always an emerging necessity. Another big threat to data is also the unauthorized access especially while dealing with sensitive data. In this paper, we introduce an enhancement of a 2D signals compression with security tools through cryptography. Spiral path compression technique uses data representation through combinations to achieve satisfactory lossless compression rate but it also offers intrinsic encryption of data. Instead of representing an image as a matrix of pixels, we represent it through a group of index numbers, each belonging to a part of the image called mini-images. Every index is performed through a spiral path inside the mini-image starting from the most repeated pixel value. The histogram not only helps on defining these starting points of spirals but also decreases the number of bits needed to represent the index. Since there are many starting points possible for each mini-image, we use a random distribution to decide which of them to be selected. We also use a matrix of private keys to make possible the protection of the image from unauthorized use. We conclude that using this technique, we can achieve satisfactory compression rates compared to actual compression rates used nowadays and many other cryptographic possibilities are available for future studies.