Reviewerly/exHarmony huggingface dataset:
The Effect of Android-Based Learning Media Using Crossword Puzzles On English Vocabulary Mastery

The aim of the study were how is the application of Android-based learning media using Crossword Puzzles in learning English vocabulary for class XI students of SMKN I Sumenep, the effect and the respon of Students about crossword puzzles. This Study is a quasi-experimental with quantitative method using true experimental design for their control class and experiment. The sampel was 74 students that grouped into experimental class 38 student and control class 36 students. It is not taken randomly but chosen by the school authority. Data collection techniques used in this study is observation techniques, testing, and questionnaires. Analysis of the observation data carried out by theEnglish teachersobtained results of 92.55% at the first meeting and 94.31% at the second meeting. This is considered to have met the "very strong" criteria. In the statistical analysis of the data, the results obtained tcount = 4.80 and db = 70, with a significant level of 5%. With t count = 4.80 and db = 70, it is known that the price ts 0.05 = 2.00 indicates that t is greater than the t table (4.80 ≥ 2.00), which means that H0 is rejected and H1 is accepted. The results of the student response questionnaire on the use of android-based media through Crossword Puzzles on the mastery of English vocabulary showed a positive response with an average value of 61% -80% and 81% -100%. Thus, it can be concluded that Android-based learning media through Crossword Puzzleshas a significant effect on the mastery of English vocabulary for class XI students of SMKN I Sumenep.
Summary report APE 2022 The future of the permanent record

This paper summarizes the 17th Academic Publishing in Europe (APE) Conference: The Future of the Permanent Record, held online from 11 until 13 January, 2022, and organized by the Berlin Institute of Scholarly Publishing (BISP), a not-for-profit organization dedicated to bringing publishers, researchers, funders and policymakers together. The conference consisted of keynote speeches, presentations, and panel discussions on a variety of scholarly communication topics. Main themes were the value of the Version of Record and how to preserve quality and trust in the scholarly record. Collaboration across all stakeholders and high-quality metadata are key, especially when looking at the future where a “record of versions” could exist, connecting all research outputs. Other conference topics involved the threat of paper mills, research integrity and the importance of digital preservation. To ensure trustworthy and high-quality publications, knowledge exchange and cooperation are crucial. Effective partnerships and a multi-perspective approach are key in fostering inclusion, diversity and equity in scholarly communication too. Several speakers stressed the important role publishers play in moving towards an inclusive and trusted Open Science ecosystem, in which data sharing is accelerated and quality assurance and control are implemented across the entire research cycle. To achieve this, new ways of working and new business models need to be developed, and entrepreneurship and innovation encouraged. The 17th APE ended with a session in which five startups showcased their innovative products to advance science.
A Comprehensive Exploration of Item Recommendation Text Ranking

&lt;p&gt;In this paper, we study the personalized ranking problem for item's recommendation text. Item's recommendation text is the sentence that describe the item highlights for user decision (such as buy or click). The recommendation texts is shown under the item, and we also call them rec-texts. One item has multiple rec-texts, and different rec-texts have different affect for user decision. So the problem is to capture the user preference for each item's rec-texts and to personalized display. In this paper, we study multiple methods to train a model which learn the user preference scores for each item's rec-texts. The online experimental results demonstrate our methods work.&lt;/p&gt;
Total Rejection Sampling and the Reduction of the Wave Function

&lt;p&gt;We propose an exact new sampling algorithm using classical statistical theory. The proposed method will process an infinite amount of rejected samples in parallel before accepting one exact single sample from a target distribution using a winner-take-all mechanism. The algorithm is applied for sampling from general distributions in addition to sampling from the wave function density distribution which lies at the heart of the measurement problem in quantum physics.&lt;/p&gt;
Multi-User Linearly-Separable Distributed Computing

&lt;p&gt;In this work, we explore the problem of multi-user linearly-separable distributed computation, where N servers help compute the desired functions (jobs) of K users, and where each desired function can be written as a linear combination of up to L (generally non-linear) subtasks (or sub-functions). Each server computes some of the subtasks, communicates a function of its computed outputs to some of the users, and then each user collects its received data to recover its desired function. We explore the computation and communication relationship between how many servers compute each subtask vs. how much data each user receives.&lt;br&gt; For a matrix F representing the linearly-separable form of the set of requested functions, our problem becomes equivalent to the open problem of sparse matrix factorization F=DE over finite fields, where a sparse decoding matrix D and encoding matrix E imply reduced communication and computation costs respectively. This paper establishes a novel relationship between our distributed computing problem, matrix factorization, syndrome decoding and covering codes. To reduce the computation cost, the above D is drawn from covering codes or from a here-introduced class of so-called `partial covering' codes, whose study here yields computation cost results that we present. &lt;/p&gt;
Machine Learning for the Early Detection of Breast Cancer

Abstract Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area. The critical challenge against its detection is classifying tumors into malignant (cancerous) or benign(non-cancerous). In this article, with the help of machine learning algorithms, healthy people and people with tumors were separated from each other with acceptable accuracy.
A blockchain-based platform for trading weather derivatives

Abstract This study investigated the technical and economic viability of hedging electricity consumption using weather derivatives and smart contracts. For this purpose, we priced call options to hedge against excess of temperature for 5 Brazilian cities. We also developed a distributed autonomous application (DApp) using smart contracts, which allows individuals to negotiate these financial instruments. Our findings suggest that blockchain technology can be useful in providing a low-cost infrastructure to develop financial instruments to hedge weather-related losses. The cost of providing this platform has been estimated to be less than300 USD. The price of options has been estimated under 50 USD. This is particularly useful for electricity consumers and small businesses in poor countries. In addition, this study provides a comprehensive guide for the development of financial solutions using smart contracts to mitigate climate change impacts.
Annotated Data Augmentation for Arabic Sentiment Analysis using Semi-Supervised GANs

Abstract With the increasing number of web users, a tremendous amount of data is being generated in an enormous and dynamic way. In this regard, analyzing the sentiment of the user’s generated data is becoming essential to reveal valuable insights. However, analyzing this massive amount of data requires a lot of manual effort. Therefore, sentiment analysis systems automate the analysis of the generated text which helps in extracting people’s opinions and attitudes. The great success of sentiment analysis systems is due to the availability of a copiously annotated corpus. Unfortunately, Arabic Language does not possess such an abundance of annotated data. Obtaining annotated sentiment corpus is an expensive and challenging task due to the huge amount of corpus required to train efficient sentiment analyzers. The purpose of this research is to tackle the problems of Sentiment Analysis for Arabic language and the inadequate annotated data available for this task. Owing to the limited resources for Arabic sentiment analysis, this work is motivated to investigate data augmentation using GAN. GANs showed breakthrough results in data augmentation due to their capabilities of generating realistic data samples. In this work, we propose using Semi-Supervised GANs for Arabic sentiment analysis to minimize the number of annotated data required for this task. As a result, we were able to prove that GAN can be used to augment realistic textual data samples that can improve the performance of the sentiment analysis task. Results also showed that Semi-Supervised GAN can drastically reduce the requirement of manually annotated samples. In this context, our model successfully excels the task of Arabic Sentiment Analysis with only 10–30% of dataset being manually labeled.
A Visual Path Planning Framework Based on Path Search Area

In this paper, a path planning problem of a mobile camera is studied for visual monitoring task. In order to solve this problem, the visible area for each target point is calculated according to the FOV of camera, in which the target point can be viewed by the mobile camera. Then, the path planning problem is solved based on the obtained path search area with monotonic genetic algorithm which has faster convergence rate than standard genetic algorithm. At last, some simulation examples show the effectiveness of the proposed optimization approach.
Intuitionistic Logics of Confirmation and Discouragement

We analyse intuitionistic logics of confirmation and discouragement. This means that our acceptance of some formula φ in a possible world w gets an additional support or disapprovement. We use pre-ordered neighbourhood frames to model this situation. Our systems have certain connection with so-called logics of false belief and unknown truths.
SE LEGGO E COMPRENDO, ALLORA APPRENDO. LA “RISCRITTURA FUNZIONALE” IN CLASSE PER CONTRASTARE LA COMPLESSITÀ DEI MANUALI SCOLASTICI

Alcune questioni come la comprensione a scuola e la complessità dei manuali scolastici, che sono già state oggetto di studio in passato, si rivelano ancora oggi attuali e irrisolte. La scuola, che diventa di anno in anno sempre più linguisticamente e culturalmente variegata, deve poter garantire a tutti gli studenti il diritto di apprendere. Affinché possa esserci apprendimento, però, è necessario che l’alunno sia in grado (e sia messo nelle condizioni) di comprendere: come afferma Emanuela Piemontese (1996), infatti, senza comprensione non può esserci apprendimento. Per contrastare la complessità dei manuali scolastici sono state attivate diverse strategie di semplificazione del testo; una di queste, la “riscrittura funzionale”, è stata utilizzata per semplificare il manuale di antologia durante un’esperienza didattica vissuta a gennaio 2021, a distanza, in una scuola secondaria di secondo grado di Padova. In questo contributo si presentano le lezioni svolte in classe e i materiali utilizzati.&#x0D; &#x0D; If i read and understand, then i learn. “Functional rewriting” in the classroom to counteract the complexity of school textbooks&#x0D; Some issues such as comprehension at school and the complexity of school textbooks, which have already been studied in the past, still turn out to be topical and unresolved today. Schools, which are becoming more and more linguistically and culturally diverse each year, must be able to guarantee the right to learn to all students. In order for there to be learning, however, it is necessary that the student be able (and be enabled) to understand: as Emanuela Piemontese (1996) states, in fact, without understanding there can be no learning. In order to combat the complexity of school textbooks, various strategies have been activated to simplify the text; one of these, "functional rewriting", was used to simplify an anthology textbook during a didactic experience in January 2021, online, in a secondary school in Padua. This paper presents the lessons carried out in class and the materials used.
Why Does Propositional Quantification Make Modal and Temporal Logics on Trees Robustly Hard?

Adding propositional quantification to the modal logics K, T or S4 is known to lead to undecidability but CTL with propositional quantification under the tree semantics (tQCTL) admits a non-elementary Tower-complete satisfiability problem. We investigate the complexity of strict fragments of tQCTL as well as of the modal logic K with propositional quantification under the tree semantics. More specifically, we show that tQCTL restricted to the temporal operator EX is already Tower-hard, which is unexpected as EX can only enforce local properties. When tQCTL restricted to EX is interpreted on N-bounded trees for some N >= 2, we prove that the satisfiability problem is AExpPol-complete; AExpPol-hardness is established by reduction from a recently introduced tiling problem, instrumental for studying the model-checking problem for interval temporal logics. As consequences of our proof method, we prove Tower-hardness of tQCTL restricted to EF or to EXEF and of the well-known modal logics such as K, KD, GL, K4 and S4 with propositional quantification under a semantics based on classes of trees.
Support Protocol 1: Installing MIDAS2 v1

MIDAS2 is written in Python 3 and can be executed on a 64-bit Linux system. MIDAS2 and its dependencies need to be pre-installed in order to run the commands described in the basic protocols. We recommend the conda package manager for installing these. Alternatively, users who want to ensure reproducibility by using a container with MIDAS2 and its computational environment may use Docker.
Towards Clear Expectations for Uncertainty Estimation

If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim this inconsistency results from the unclear requirements the community expects from UQ. This opinion paper offers a new perspective by specifying those requirements through five downstream tasks where we expect uncertainty scores to have substantial predictive power. We design these downstream tasks carefully to reflect real-life usage of ML models. On an example benchmark of 7 classification datasets, we did not observe statistical superiority of state-of-the-art intrinsic UQ methods against simple baselines. We believe that our findings question the very rationale of why we quantify uncertainty and call for a standardized protocol for UQ evaluation based on metrics proven to be relevant for the ML practitioner.
A Direct Construction of Complete Complementary Code with Zero Correlation Zone property for Prime-Power Length

In this paper, we propose a direct construction of a novel type of code set, which has combined properties of complete complementary code (CCC) and zero-correlation zone (ZCZ) sequences and called it complete complementary-ZCZ (CC-ZCZ) code set. The code set is constructed by using multivariable functions. The proposed construction also provides Golay-ZCZ codes with new lengths, i.e., prime-power lengths. The proposed Golay-ZCZ codes are optimal and asymptotically optimal for binary and non-binary cases, respectively, by \emph{Tang-Fan-Matsufuzi} bound. Furthermore, the proposed direct construction provides novel ZCZ sequences of length $p^k$, where $k$ is an integer $\geq 2$. We establish a relationship between the proposed CC-ZCZ code set and the first-order generalized Reed-Muller (GRM) code, and proved that both have the same Hamming distance. We also counted the number of CC-ZCZ code set in first-order GRM codes. The column sequence peak-to-mean envelope power ratio (PMEPR) of the proposed CC-ZCZ construction is derived and compared with existing works. The proposed construction is also deduced to Golay-ZCZ and ZCZ sequences which are compared to the existing work. The proposed construction generalizes many of the existing work.
Unbiased Active Inference for Classical Control

Active inference is a mathematical framework that originated in computational neuroscience. Recently, it has been demonstrated as a promising approach for constructing goal-driven behavior in robotics. Specifically, the active inference controller (AIC) has been successful on several continuous control and state-estimation tasks. Despite its relative success, some established design choices lead to a number of practical limitations for robot control. These include having a biased estimate of the state, and only an implicit model of control actions. In this paper, we highlight these limitations and propose an extended version of the unbiased active inference controller (u-AIC). The u-AIC maintains all the compelling benefits of the AIC and removes its limitations. Simulation results on a 2-DOF arm and experiments on a real 7-DOF manipulator show the improved performance of the u-AIC with respect to the standard AIC. The code can be found at https://github.com/cpezzato/unbiased_aic.
ALBench: A Framework for Evaluating Active Learning in Object Detection

Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architecture design, active learning aims at automating training data selection. It is especially critical for training a long-tailed task, in which positive samples are sparsely distributed. Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection. Instead of annotating all unlabeled samples, it iteratively selects and annotates the most valuable samples. Active learning has been popular in image classification, but has not been fully explored in object detection. Most of current approaches on object detection are evaluated with different settings, making it difficult to fairly compare their performance. To facilitate the research in this field, this paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection. Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols. We hope this automated benchmark system help researchers to easily reproduce literature's performance and have objective comparisons with prior arts. The code will be release through Github.
AutoTransition: Learning to Recommend Video Transition Effects

Video transition effects are widely used in video editing to connect shots for creating cohesive and visually appealing videos. However, it is challenging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on performing automatic video transitions recommendation (VTR): given a sequence of raw video shots and companion audio, recommend video transitions for each pair of neighboring shots. To solve this task, we collect a large-scale video transition dataset using publicly available video templates on editing softwares. Then we formulate VTR as a multi-modal retrieval problem from vision/audio to video transitions and propose a novel multi-modal matching framework which consists of two parts. First we learn the embedding of video transitions through a video transition classification task. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both quantitative and qualitative experiments, we clearly demonstrate the effectiveness of our method. Notably, in the comprehensive user study, our method receives comparable scores compared with professional editors while improving the video editing efficiency by \textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire other researchers to work on this new task. The dataset and codes are public at \url{https://github.com/acherstyx/AutoTransition}.
Iterative Scene Graph Generation

The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large solution space, existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation feasible (e.g., assuming that objects are conditionally independent of predicate predictions). However, this fixed factorization is not ideal under all scenarios (e.g., for images where an object entailed in interaction is small and not discernible on its own). In this work, we propose a novel framework for scene graph generation that addresses this limitation, as well as introduces dynamic conditioning on the image, using message passing in a Markov Random Field. This is implemented as an iterative refinement procedure wherein each modification is conditioned on the graph generated in the previous iteration. This conditioning across refinement steps allows joint reasoning over entities and relations. This framework is realized via a novel and end-to-end trainable transformer-based architecture. In addition, the proposed framework can improve existing approach performance. Through extensive experiments on Visual Genome and Action Genome benchmark datasets we show improved performance on the scene graph generation.
Rethinking Efficacy of Softmax for Lightweight Non-Local Neural Networks

Non-local (NL) block is a popular module that demonstrates the capability to model global contexts. However, NL block generally has heavy computation and memory costs, so it is impractical to apply the block to high-resolution feature maps. In this paper, to investigate the efficacy of NL block, we empirically analyze if the magnitude and direction of input feature vectors properly affect the attention between vectors. The results show the inefficacy of softmax operation which is generally used to normalize the attention map of the NL block. Attention maps normalized with softmax operation highly rely upon magnitude of key vectors, and performance is degenerated if the magnitude information is removed. By replacing softmax operation with the scaling factor, we demonstrate improved performance on CIFAR-10, CIFAR-100, and Tiny-ImageNet. In Addition, our method shows robustness to embedding channel reduction and embedding weight initialization. Notably, our method makes multi-head attention employable without additional computational cost.