Reviewerly/exHarmony huggingface dataset:
NATIONAL EDUCATIONAL CHARACTERS ON ENGLISH HAND BOOK USED BY ENGLISH AS A FOREIGN LANGUAGE (EFL) TEACHERS

This research aims to analyze the English textbooks used by teachers in the learning and teaching process and to determine the suitability of the character education values in English textbooks with the character education values set in the 2013 curriculum. The English text used is 12 values of character education out of 18 total values of character education in the 2013 curriculum. The research design used is descriptive qualitative research method because the data collected is in the form of words and does not use numbers. The researcher chose this method because the data obtained were descriptive data, then analyzed and described in the form of words and language. descriptive qualitative is a research method that utilizes qualitative data and is described descriptively. The results showed that the value of character education in English textbooks used by teachers in the teaching and learning process did not fully contain the value of character education in the 2013 curriculum. There were only 12 values of character education from 18 values of character education based on the 2013 curriculum in the English textbook. Which means that there are 6 values of character education based on the 2013 curriculum that are not contained in the English textbook. As a conclusion, the suitability between the values of character education in English textbooks and character education in the 2013 curriculum is not fully appropriate.
Newer Parameters on Electronic Cell Counter for Diagnosis of Infections

Blood stream infection (BSI) and sepsis are life threatening conditions and early identification of pathogen with prompt intervention is important for patient management. Need for early diagnosis of sepsis has incredibly boosted the research for innovative biomarkers. The cellular and morphological characterization of cells is an indispensable part of hematology workup. The automated hematology analyzers are being routinely used for total cell counts and differential leukocyte analysis. But technological advancements in automated hematology analyzers have paved the way for many additional parameters which are capable of providing extended information to help clinicians and laboratory professionals. This review emphasizes the application of additional innovative parameters reported by modern hematology analyzers as a tool for early recognition of sepsis with impact on clinical practice.
Penerapan Metode Fuzzy Time Series Chen Orde Tinggi Pada Peramalan Hasil Penjualan (Studi Kasus: KPRI “Serba Guna” Kecamatan Selorejo Kabupaten Blitar)

The most developed forecasting method currently is the time series, which uses a quantitative approach with past data as a reference for future forecasting. Fuzzy time series is a solution that uses time series data by applying fuzzy methods in forecasting. This research using fuzzy time series is applied on data from the sale of the Republic of Indonesia Employee Cooperative (KPRI) Selorejo District, Blitar Regency in 2015-2021. This study describes the problem of forecasting the results of cooperative sales using the Fuzzy Time Series (FTS) which was developed with the High Order. The development of the method is done by improving the FTS method with mathematical rules and is applied to the stages of the process of forecasting the results of cooperative sales. Testing the results of the High Order Fuzzy Time Series forecasting using the best Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) accuracy values . The High Order Fuzzy Time Series consists of second order FTS, third order FTS and fourth order FTS. The results of the calculation of the smallest accuracy values are found in the fourth-order FTS, namely MSE of 19,333,658,980,372, MAPE of 11%, and MAE of 267,749. So it can be concluded that the fourth-order FTS is the best method in this study.
Music as an Organizing System

This article applies elements of information architecture to the analysis of musical works. The intention is to provide an effective and logical way for people with minimal background in music to learn to analyze music. The authors draw on both musical literature and the work of Robert J. Glushko in “The Discipline of Organizing” (2013) to give non-musicians a way to think about music as organized information so that they can add ever-increasing layers of complexity as their skills at applying this approach improve.
An Ecological Framework for Information Architecture

In the field of information architecture, practice keeps pace as the domains and supporting technologies for digital design expand, leading to occasional updates of theory to account for change. Pervasive information architecture expanded tenets of clarity and findability from classical information architecture to account for placemaking as information began shape-shifting across devices and situations. This article suggests that, building on classical and pervasive information architecture, the field is ready to expand to ecological information architecture. This time, in addition to situational changes (new technologies and domains), the discovery of more facets in information as the raw material of design, and information behavior, drawn from adjacent fields, play major roles. Even as ecological information architecture is introduced, glimmers of what the next reframing must address are already surfacing. As information architects, we welcome this ongoing discovery as we look to situate designs coherently within our actors’ shifting surroundings, and engage them with information in ways that feel deeply human.
Solving Uav Path Problem Using Meta-heuristic Optimization Algorithms

In this study, the path planning problem for UAVs has been investigated. Out goal was finding a method to go from the source to the target point in the shortest time and the shortest route. A mathematical programming model that takes into account the environmental safety conditions has been developed and solved with the MBO algorithm. MBO algorithm [2] gives better results than simulated annealing, tabu search, genetic algorithm, scatter search, particle swarm optimization, differential evolution and guided evolutionary simulated annealing approaches, as stated in a previous study [2].
Security Best Practices: A Critical Analysis Using IoT as a Case Study

Academic research has highlighted the failure of many Internet of Things (IoT) product manufacturers to follow accepted practices, while IoT security best practices have recently attracted considerable attention worldwide from industry and governments. Given current examples of security advice, confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. We explore a surprising lack of clarity, and void in the literature, on what (generically) best practice means, independent of identifying specific individual practices or highlighting failure to follow best practices. We consider categories of security advice, and analyze how they apply over the lifecycle of IoT devices. For concreteness in discussion, we use iterative inductive coding to code and systematically analyze a set of 1013 IoT security best practices, recommendations, and guidelines collated from industrial, government, and academic sources. Among our findings, of all analyzed items, 68% fail to meet our definition of an (actionable) practice, and 73% of all actionable advice relates to the software development lifecycle phase, highlighting the critical position of manufacturers and developers. We hope that our work provides a basis for the community to better understand best practices, identify and reach consensus on specific practices, and find ways to motivate relevant stakeholders to follow them.
Are Attribute Inference Attacks Just Imputation?

Models can expose sensitive information about their training data. In an attribute inference attack, an adversary has partial knowledge of some training records and access to a model trained on those records, and infers the unknown values of a sensitive feature of those records. We study a fine-grained variant of attribute inference we call \emph{sensitive value inference}, where the adversary's goal is to identify with high confidence some records from a candidate set where the unknown attribute has a particular sensitive value. We explicitly compare attribute inference with data imputation that captures the training distribution statistics, under various assumptions about the training data available to the adversary. Our main conclusions are: (1) previous attribute inference methods do not reveal more about the training data from the model than can be inferred by an adversary without access to the trained model, but with the same knowledge of the underlying distribution as needed to train the attribute inference attack; (2) black-box attribute inference attacks rarely learn anything that cannot be learned without the model; but (3) white-box attacks, which we introduce and evaluate in the paper, can reliably identify some records with the sensitive value attribute that would not be predicted without having access to the model. Furthermore, we show that proposed defenses such as differentially private training and removing vulnerable records from training do not mitigate this privacy risk. The code for our experiments is available at \url{https://github.com/bargavj/EvaluatingDPML}.
Elaboration-Generating Commonsense Question Answering at Scale

In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models -- an elaboration generator and an answer predictor -- allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap on GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.
Attribute Network Representation Learning with Dual Autoencoders

The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute information cannot be well integrated. In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the node’s high-order neighborhood information and calculate the neighborhood weight; The other channel is for the node attribute information, and a low-pass Laplace filter is designed to iteratively obtain the attribute information in the neighborhood of the node. The dual-channel autoencoder ensures the learning of structure and attribute information respectively. The adaptive fusion module is constructed in this method to increase the acquisition of important information through the consistency and difference constraints of two kinds of information. The method trains encoders by supervising the joint reconstruction of loss functions of two autoencoders. Based on the node clustering task on four authentic open data sets, and compared with eight network representation learning algorithms in clustering accuracy, standardized mutual information and running time of some algorithms, the experimental results show that the proposed method is superior and reasonable.
‘It can facilitate so much!’ Student writers’ practice of self-efficacy to develop their use of formulaic phrases

This study is about student writers’ development of their own approaches to using formulaic phrases from a compendium (Academic Phrasebank). While the essential role of formulaic phrases in academic texts has been well-established in research, teaching about the effective use of these phrases is not widely available, and little attention has been paid to how students learn to employ formulaic phrases in their own writing. Therefore, this research aims to explore this gap in understanding how student writers develop individual approaches to using formulaic phrases through the lens of self-efficacy.&#x0D; Twelve self-selected student writer participants at undergraduate, Master’s and PhD levels were interviewed and asked about how they used formulaic phrases from the resource. Three key findings emerged from the data: firstly, that the resource may support inclusion as an empowering tool to enable student writers to participate confidently in academia; secondly, that students could employ the resource flexibly at different stages of the writing process depending on their individual approach to text construction; thirdly, that it could offer particular support with writing to students who have a specific learning difficulty (SpLD).&#x0D; This paper contributes to understanding these individual student learning processes in the use of formulaic phrases for writing through self-efficacy. The implication for learning development is that making more guidance about formulaic phrases widely available and accessible would be beneficial to students’ writing processes.
Impact of Injecting Ground Truth Explanations on Relational Graph Convolutional Networks and their Explanation Methods for Link Prediction on Knowledge Graphs

Relational Graph Convolutional Networks (RGCNs) are commonly applied to Knowledge Graphs (KGs) for black box link prediction. Several algorithms, or explanations methods, have been proposed to explain the predictions of this model. Recently, researchers have constructed datasets with ground truth explanations for quantitative and qualitative evaluation of predicted explanations. Benchmark results showed state-of-the-art explanation methods had difficulties predicting explanations. In this work, we leverage prior knowledge to further constrain the loss function of RGCNs, by penalizing node embeddings far away from the node embeddings in their associated ground truth explanation. Empirical results show improved explanation prediction performance of state-of-the-art post hoc explanations methods for RGCNs, at the cost of predictive performance. Additionally, we quantify the different types of errors made both in terms of data and semantics.
Detecting a Photon-Number Splitting Attack in Decoy-State Measurement-Device-Independent Quantum Key Distribution via Statistical Hypothesis Testing

Measurement-device-independent quantum key distribution (MDI-QKD) is innately immune to all detection-side attacks. Due to the limitations of technology, most MDI-QKD protocols use weak coherent photon sources (WCPs), which may suffer from a photon-number splitting (PNS) attack from eavesdroppers. Therefore, the existing MDI-QKD protocols also need the decoy-state method, which can resist PNS attacks very well. However, the existing decoy-state methods do not attend to the existence of PNS attacks, and the secure keys are only generated by single-photon components. In fact, multiphoton pulses can also form secure keys if we can confirm that there is no PNS attack. For simplicity, we only analyze the weaker version of a PNS attack in which a legitimate user's pulse count rate changes significantly after the attack. In this paper, under the null hypothesis of no PNS attack, we first determine whether there is an attack or not by retrieving the missing information of the existing decoy-state MDI-QKD protocols via statistical hypothesis testing, extract a normal distribution statistic, and provide a detection method and the corresponding Type I error probability. If the result is judged to be an attack, we use the existing decoy-state method to estimate the secure key rate. Otherwise, all pulses with the same basis leading to successful Bell state measurement (BSM) events including both single-photon pulses and multiphoton pulses can be used to generate secure keys, and we give the formula of the secure key rate in this case. Finally, based on actual experimental data from other literature, the associated experimental results (e.g., the significance level is 5%) show the correctness of our method.
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding

Understanding the intention of the users and recognizing the semantic entities from their sentences, aka natural language understanding (NLU), is the upstream task of many natural language processing tasks. One of the main challenges is to collect a sufficient amount of annotated data to train a model. Existing research about text augmentation does not abundantly consider entity and thus performs badly for NLU tasks. To solve this problem, we propose a novel NLP data augmentation technique, Entity Aware Data Augmentation (EADA), which applies a tree structure, Entity Aware Syntax Tree (EAST), to represent sentences combined with attention on the entity. Our EADA technique automatically constructs an EAST from a small amount of annotated data, and then generates a large number of training instances for intent detection and slot filling. Experimental results on four datasets showed that the proposed technique significantly outperforms the existing data augmentation methods in terms of both accuracy and generalization ability.
A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy

Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing works related to node differential privacy assume a trusted data curator who holds the whole graph. However, in many applications, a trusted curator is usually not available due to privacy and security issues. In this paper, for the first time, we investigate the problem of publishing the graph degree distribution under Node Local Differential privacy (Node-LDP), which does not rely on a trusted server. We propose an algorithm to publish the degree distribution with Node-LDP by exploring how to select the optimal graph projection parameter and how to execute the local graph projection. Specifically, we propose a Crypto-assisted local projection method that combines LDP and cryptographic primitives, achieving higher accuracy than our baseline PureLDP local projection method. On the other hand, we improve our baseline Node-level parameter selection by proposing an Edge-level parameter selection that preserves more neighboring information and provides better utility. Finally, extensive experiments on real-world graphs show that Edge-level local projection provides higher accuracy than Node-level local projection, and Crypto-assisted parameter selection owns the better utility than PureLDP parameter selection, improving by up to 79.8% and 57.2% respectively.
Spatio-Temporal Action Detection Under Large Motion

Current methods for spatiotemporal action tube detection often extend a bounding box proposal at a given keyframe into a 3D temporal cuboid and pool features from nearby frames. However, such pooling fails to accumulate meaningful spatiotemporal features if the position or shape of the actor shows large 2D motion and variability through the frames, due to large camera motion, large actor shape deformation, fast actor action and so on. In this work, we aim to study the performance of cuboid-aware feature aggregation in action detection under large action. Further, we propose to enhance actor feature representation under large motion by tracking actors and performing temporal feature aggregation along the respective tracks. We define the actor motion with intersection-over-union (IoU) between the boxes of action tubes/tracks at various fixed time scales. The action having a large motion would result in lower IoU over time, and slower actions would maintain higher IoU. We find that track-aware feature aggregation consistently achieves a large improvement in action detection performance, especially for actions under large motion compared to the cuboid-aware baseline. As a result, we also report state-of-the-art on the large-scale MultiSports dataset. The Code is available at https://github.com/gurkirt/ActionTrackDetectron.
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents

Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by training an adversarial agent to minimize a target agent's rewards. Prior work has studied black-box versions of these attacks where the adversary only observes the world state and treats the target agent as any other part of the environment. However, this does not take into account additional structure in the problem. In this work, we study white-box adversarial policies and show that having access to a target agent's internal state can be useful for identifying its vulnerabilities. We make two contributions. (1) We introduce white-box adversarial policies where an attacker observes both a target's internal state and the world state at each timestep. We formulate ways of using these policies to attack agents in 2-player games and text-generating language models. (2) We demonstrate that these policies can achieve higher initial and asymptotic performance against a target agent than black-box controls. Code is available at https://github.com/thestephencasper/lm_white_box_attacks
Impact of Scaled Image on Robustness of Deep Neural Networks

Deep neural networks (DNNs) have been widely used in computer vision tasks like image classification, object detection and segmentation. Whereas recent studies have shown their vulnerability to manual digital perturbations or distortion in the input images. The accuracy of the networks is remarkably influenced by the data distribution of their training dataset. Scaling the raw images creates out-of-distribution data, which makes it a possible adversarial attack to fool the networks. In this work, we propose a Scaling-distortion dataset ImageNet-CS by Scaling a subset of the ImageNet Challenge dataset by different multiples. The aim of our work is to study the impact of scaled images on the performance of advanced DNNs. We perform experiments on several state-of-the-art deep neural network architectures on the proposed ImageNet-CS, and the results show a significant positive correlation between scaling size and accuracy decline. Moreover, based on ResNet50 architecture, we demonstrate some tests on the performance of recent proposed robust training techniques and strategies like Augmix, Revisiting and Normalizer Free on our proposed ImageNet-CS. Experiment results have shown that these robust training techniques can improve networks' robustness to scaling transformation.