Reviewerly/exHarmony huggingface dataset:
Efficient State Management in Distributed Ledgers

Distributed ledgers implement a storage layer, on top of which a shared state is maintained in a decentralized manner. In UTxO-based ledgers, like Bitcoin, the shared state is the set of all unspent outputs (UTxOs), which serve as inputs to future transactions. The continuously increasing size of this shared state will gradually render its maintenance unaffordable. Our work investigates techniques that minimize the shared state of the distributed ledger, i.e., the in-memory UTxO set. To this end, we follow two directions: a) we propose novel transaction optimization techniques to be followed by wallets, so as to create transactions that reduce the shared state cost and b) propose a novel fee scheme that incentivizes the creation of “state-friendly” transactions. We devise an abstract ledger model, expressed via a series of algebraic operators, and define the transaction optimization problem of minimizing the shared state; we also propose a multi-layered algorithm that approximates the optimal solution to this problem. Finally, we define the necessary conditions such that a ledger’s fee scheme incentivizes proper state management and propose a state efficient fee function for Bitcoin.
Mothers as Teachers: The New Role of Mothers in the New Normal

The COVID-19 pandemic had brought havoc to everyone but much more to the child-rearing mothers. This qualitative study explored the new roles of child-rearing mothers in the new normal specifically as being teachers to their children. Six mothers, who had public and private elementary grade children were purposely chosen. Information were gathered through a researcher-made validated interview questionnaire. Collection of information were done online through Facebook messenger and emails. Data analysis produced six themes namely complication in becoming mother-teachers, benefits in teaching own children, supervision of children’s studies, impression of children of the modular schooling, considerations on children’s learning and reflection on mothers’ learning. Each theme was discussed thoroughly. The Department of Education may consider giving attention and support through home-visitation tutorials and tapping the assistance of the Local Government Unit (LGU) in finding alternative solutions to unburden the mothers of their situations.
The Impact of Future Core Competencies of Hair and Beauty Students on Edutech Awareness and Online Learning Transfer: a Two-year College Focus

The purpose of this study is to identify the future core competencies of students majoring in beauty, to investigate the effects of learning competencies on the perception of educational skills and prior learning, and to prepare a plan to provide higher-quality beauty and beauty education services The future core competencies were represented by three sub-factors, the sub-factor of education skills perception was represented by four factors, and learning transfer was represented by a single factor. As a result of examining the effects of future core competencies on education skills perception and learning, statistically significant results were found in sociocultural competencies, cognitive competencies, and emotional competencies. Future core competencies can be said to be important variables for edutech awareness and learning. Therefore, establishing curriculum in a strategic direction to strengthen the future core competency sub-elements of students majoring in beauty is expected to increase the awareness and transfer of learning in edutech. Although this study was conducted with limited subjects and variables, it is expected that a comprehensive and practical study considering various variables will be conducted insubsequent studies.
Secret sharing scheme based on Latin squares

Secret sharing is a method of distributing a secret among a group of participants such that if (i) an authorized set of participants cooperate, then they can reconstruct the secret and (ii) an unauthorized set of participants can’t recover the secret and doesn’t know anything about the secret. In 1994, Joan Cooper proposed a secret sharing scheme based on the critical set of Latin square[3]. This has some drawbacks. In 2015, Rebecca J. Stones published a secret sharing scheme based on autotopism of Latin square[1]. The scheme solves all the issues associated with the Cooper’s scheme. However, we noticed that, her scheme has the following shortcomings: (i) The scheme construction is limited to a particular pattern of the Latin square only, (ii) The scheme works only as k-out-of-k technique and not as a proper threshold scheme, and (iii) the dealer is the central part of the whole process, if he is dishonest then the whole secret is revealed. So, in this paper, we modify Rebecca’s scheme so that it works (i) for a couple of more contours with only one autotopism, and (ii) as a t-groups-out-of-k scheme. So to reconstruct the secret anyone of the available t groups can cooperate and recover it. That is, the modified scheme could reduce the key availability problem to some extent. As a byproduct of our modification the security from the brute force attack increases as well.
Mixing and Matching Emotion Frameworks: Investigating Cross-Framework Transfer Learning for Dutch Emotion Detection

Emotion detection has become a growing field of study, especially seeing its broad application potential. Research usually focuses on emotion classification, but performance tends to be rather low, especially when dealing with more advanced emotion categories that are tailored to specific tasks and domains. Therefore, we propose the use of the dimensional emotion representations valence, arousal and dominance (VAD), in an emotion regression task. Firstly, we hypothesize that they can improve performance of the classification task, and secondly, they might be used as a pivot mechanism to map towards any given emotion framework, which allows tailoring emotion frameworks to specific applications. In this paper, we examine three cross-framework transfer methodologies: multi-task learning, in which VAD regression and classification are learned simultaneously; meta-learning, where VAD regression and emotion classification are learned separately and predictions are jointly used as input for a meta-learner; and a pivot mechanism, which converts the predictions of the VAD model to emotion classes. We show that dimensional representations can indeed boost performance for emotion classification, especially in the meta-learning setting (up to 7% macro F1-score compared to regular emotion classification). The pivot method was not able to compete with the base model, but further inspection suggests that it could be efficient, provided that the VAD regression model is further improved.
An Extreme Learning Machine based Approach to Detect the Alzheimer's Disease

Alzheimer's Disease (AD) is a gradually escalating brain disorder that affects the brain cells and neurotransmitters. It affects certain brain activities such as memory and behavior and causes dementia in the elderly at an end stage. There is currently no cure for the disease but the availability of treatment at an initial stage can help in delaying the progression of the disease. There are major issues in diagnosing the disease by the use of conventional algorithms. One of them is the availability of a huge number of plausible feature representations. Conventional machine learning algorithms are known to have low training speed. In order to overcome this, we have applied Extreme Learning Machine algorithms on the Open Access Series of Imaging Studies (OASIS) dataset. We have also discussed the preprocessing filter, segmentation method applied along with the features extracted on these MRI scans.
Contemporary Chinese Marxism: Social visions and philosophy of education – An EPAT collective project

Michael A. PetersSchool of Philosophy,Beijing Normal University, Beijing, ChinaChengbing Wang,Shanxi University, Taiyuan, China,When I spoke to my colleague and very good friend of many years Wang ...
Software And Hardware System For Diagnosing Deposits Inside The Vortex Chamber

The work is devoted to the creation of software for the system of visualization of deposits inside vortex chambers and the development of image processing algorithms to improve the quality of visualization of the geometry of objects. In this paper, software was created for the microcontroller, which controls the entire hardware part. An application for a personal computer has been developed to visualize the image from the camera to the experimenter. Algorithms for correcting distortion and subtracting the background for analyzing the geometry of deposits are implemented.
Blockchain-Based Multi-party Smart Contract for Service Digitalization and Automation

Blockchain as a distributed-decentralized computing platform enables users to share, manage and monitor digitally signed assets through the smart contract. It finds application in numerous domains includes operations in the supply chain and transportation, by improving transparency and efficiency. The miss transparency and efficiency may induce enormous problems in the supply chain and causing human life loss and economic damages. Similarly, Beirut was the case where many lives were lost, and enormous economic losses resulting from the not transparent and efficient treatment of Dangerous Goods (DG). To avoid a situation where DG remains static in a seaport or any warehouse, and while the risk exposition is very high, we propose the concept of a multi-party smart contract. Multi-party SC will help in selecting the appropriate transporter to carry DG from port to any other destination according to the criteria specified in the blockchain. The responsibilities of all stakeholders involved in the SC are clearly defined.
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines

The aim of this paper is to formalise a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL@IJCAI) ( https://sites.google.com/view/sscl-workshop-ijcai-2021/ ), with the aim of raising the field’s awareness about this problem and mobilising its effort in this direction. After a formal definition of continual semi-supervised learning and the appropriate training and testing protocols, the paper introduces two new benchmarks specifically designed to assess CSSL on two important computer vision tasks: activity recognition and crowd counting. We describe the Continual Activity Recognition (CAR) and Continual Crowd Counting (CCC) challenges built upon those benchmarks, the baseline models proposed for the challenges, and describe a simple CSSL baseline which consists in applying batch self-training in temporal sessions, for a limited number of rounds. The results show that learning from unlabelled data streams is extremely challenging, and stimulate the search for methods that can encode the dynamics of the data stream.
The syntax of argument structure

In recent years, enormous progress has been made in the development and availability of empirical techniques in language research. They have been fruitfully integrated into the discussion of theoretical models at all linguistic layers. The present article sets out this background and identifies relevant questions and challenges that arise from these novel empirical data for the study of the syntax of argument structure. It presents the articles collected in this volume in their contribution to progress in the field
Study motivation and students' participation in distance learning during the Covid-19 pandemic

This research aims to discover the relationship between study motivation and participation of Elementary School Teacher Education students during the course of distance learning as a consequence of the Covid-19 pandemic. This was a quantitative research that used the ex-post facto type technique. The total number of samples in this research were 250 students of 2nd semester of total population of Elementary School Teacher Education students in Yogyakarta. This research implemented the technique of data collection in the form of an online survey. Then, the data was analyzed by using the moment product correlation test. The result of the research shows that there is a relationship marked by positive and significant results. It can be seen through the significant number of study motivation and participation is 0.000 &lt; 0.05 and r-calculator of study motivation and participation is 0.62 &gt; r-table, 0.124 as well as has a relationship in the category of “moderate.”
Error Refactor loss based on error analysis in image classification

The loss function is a criterion to evaluate the learning quality of a deep convolutional neural network, which represents the gap between prediction and ground truth. However, as the most commonly used loss function in image classification tasks, Cross-Entropy loss does not encourage the model to distinguish the similarity between features. In this work, the authors investigate inter-class separability of similar features learnt by convolutional networks and propose a loss function called Error Refactor Loss (ER-Loss). ER-Loss is based on the error caused by convolutional networks; it can improve the inter-class separability and is simple to implement and can easily replace the Cross-Entropy loss. Compared with softmax loss, ER-Loss adds a dynamic penalty item which can help ER-Loss monitor the actual situation of model training and adjust the value of the penalty item according to model training. The ER-Loss on CIFAR100 and part of ImageNet ILSVRC 2012 is evaluated and the experimental result showed that the ER-Loss can improve the accuracy of the model.
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity.

Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes. Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by chamfer distance or voxel IOU. We find that when applied to realistic, cluttered robotics environments, these systems produce reconstructions with low physical realism, resulting in poor task performance when used for model-based control. We propose ARM, an amodal 3D reconstruction system that introduces (1) a stability prior over object shapes, (2) a connectivity prior, and (3) a multi-channel input representation that allows for reasoning over relationships between groups of objects. By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard visual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments. Code is available at this http URL.
Improving Automated Scoring of Student Open Responses in Mathematics

Open-ended questions in mathematics are commonly used by teachers to monitor and assess students' deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students' progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previously-published benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method.
A Survey on Sentiment Analysis Algorithms and Techniques For Arabic Textual Data

The concept Sentiment means the feeling, behavior, belief, or attitude towards something that almost being embedded. sentiment analysis is the process of analyzing, extracting, studying, and classifying the various reviews, opinions are given by people, and human’s emictions into positive, negative, neutral. It is considered one of the most significant scientific branches that aim to determine the behavior of the speaker, the attitude of the writer according to some topic, or the overall emotional reaction to website, document, event, interaction, products, or services. many users can share every day various opinions on different topics that may be detected or embedded by using micro-blogging which considered a rich resource for sentiment analysis and belief mining such as Facebook, Twitter, forums, and Blogs. recently a huge number of posted comments, tweets, and reviews of different social media websites include rich information in addition to most of the on-line shopping sites provide the opportunity to customers to write reviews about products in order to enhance the sales of those products and to improve both of product quality and customer satisfaction. manual analysis of these large reviews is practically impossible thus it is needed to discover an automated approach to solving such a hard process. In the Middle East and particularly in the Arab world, social media websites continue to be the top-visited websites especially with the current social and political changes in this part of the world. the main objective of that research is to differentiate between various algorithms and techniques of sentiment analysis and classification dependent on the Arabic language as a little number of researchers discusses that point relevant to the Arabic language. Different algorithms and techniques of data mining such as Support Vector Machine (SVM), Naïve Bayes (NB), Bayesian Network (BN), Decision tree (DT), k-nearest neighbor (KNN), Maximum Entropy (ME), and Neural Network (NN) in addition to many other alternative techniques which are used for analyzing and classifying textual data. For the reasons of difficulties in analyzing and mining a large number of linguistic words for their Those techniques are estimated based on the Arabic language due to its richness and diversity. The comparison between data mining techniques showed that the most accurate technique is the support vector machine (SVM) algorithm. every successful sentiment depends on two essential analysis tools are language and culture.
Iterative Teaching by Label Synthesis

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.
MCMC Variational Inference via Uncorrected Hamiltonian Annealing

Given an unnormalized target distribution we want to obtain approximate samples from it and a tight lower bound on its (log) normalization constant log Z. Annealed Importance Sampling (AIS) with Hamiltonian MCMC is a powerful method that can be used to do this. Its main drawback is that it uses non-differentiable transition kernels, which makes tuning its many parameters hard. We propose a framework to use an AIS-like procedure with Uncorrected Hamiltonian MCMC, called Uncorrected Hamiltonian Annealing. Our method leads to tight and differentiable lower bounds on log Z. We show empirically that our method yields better performances than other competing approaches, and that the ability to tune its parameters using reparameterization gradients may lead to large performance improvements.
MyML: User-Driven Machine Learning

Machine learning (ML) on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the privacy of user data. In contrast, the type of data processed at edge devices is user specific and limited to few inference classes. In this work, we explore the opportunity of building smaller, user-specific machine learning models, rather than utilizing a generic, compute-intensive machine learning model that caters to a diverse range of users. We first present a hardware-friendly, light-weight pruning technique to create user-specific models directly on mobile platforms, while simultaneously executing inferences. The proposed technique leverages compute sharing between pruning and inference, customizes the retraining backward-pass and chooses a pruning granularity for efficient processing on edge. We then propose architectural support to prune user-specific models on a systolic edge ML inference accelerator. We demonstrate that user-specific models provide a speedup of $2.3\times$ over the generic model on mobile CPUs.
Scalable Thompson Sampling using Sparse Gaussian Process Models

Thompson Sampling (TS) from Gaussian Process (GP) models is a powerful tool for the optimization of black-box functions. Although TS enjoys strong theoretical guarantees and convincing empirical performance, it incurs a large computational overhead that scales polynomially with the optimization budget. Recently, scalable TS methods based on sparse GP models have been proposed to increase the scope of TS, enabling its application to problems that are sufficiently multi-modal, noisy or combinatorial to require more than a few hundred evaluations to be solved. However, the approximation error introduced by sparse GPs invalidates all existing regret bounds. In this work, we perform a theoretical and empirical analysis of scalable TS. We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS. These conceptual claims are validated for practical implementations of scalable TS on synthetic benchmarks and as part of a real-world high-throughput molecular design task.
Influence of MIS components on efficiency of e-marketing strategies: Evidence from telecommu-nication organizations in Jordan

The study aimed to highlight the role of management information system (MIS) and its components in improving the effectiveness and efficiency of e-marketing strategies in telecommunications companies in Jordan. By relying on the quantitative methodology and by dealing with the questionnaire as a research tool, 131 individuals from the marketing departments in the organizations under study responded, and after the analysis, the study demonstrated an impact of MIS and its components on e-marketing strategies by influencing how and the mechanism of data processing and presentation as information that contributes to making the most appropriate marketing decision. The study also proved that all components of MIS have an impact on e-marketing strategies, most of which were “human resources” or people, which proved that the efficiency of individuals and their ability to deal with technology carries significant effect on the effectiveness of MIS in managing and organizing e-marketing strategies. The study recommends the necessity to focus on human resources with STEM skills, namely science, technology, engineering, and mathematics in order to ensure the best outcomes of MIS.