Reviewerly/exHarmony huggingface dataset:
Privacy-preserving verifiable delegation of polynomial and matrix functions

Abstract Outsourcing computation has gained significant popularity in recent years due to the development of cloud computing and mobile services. In a basic outsourcing model, a client delegates computation of a function f on an input x to a server. There are two main security requirements in this setting: guaranteeing the server performs the computation correctly, and protecting the client’s input (and hence the function value) from the server. The verifiable computation model of Gennaro, Gentry and Parno achieves the above requirements, but the resulting schemes lack efficiency. This is due to the use of computationally expensive primitives such as fully homomorphic encryption (FHE) and garbled circuits, and the need to represent f as a Boolean circuit. Also, the security model does not allow verification queries, which implies the server cannot learn if the client accepts the computation result. This is a weak security model that does not match many real life scenarios. In this paper, we construct efficient (i.e., without using FHE, garbled circuits and Boolean circuit representations) verifiable computation schemes that provide privacy for the client’s input, and prove their security in a strong model that allows verification queries. We first propose a transformation that provides input privacy for a number of existing schemes for verifiable delegation of multivariate polynomial f over a finite field. Our transformation is based on noisy encoding of x and keeps x semantically secure under the noisy curve reconstruction (CR) assumption. We then propose a construction for verifiable delegation of matrix-vector multiplication, where the delegated function f is a matrix and the input to the function is a vector. The scheme uses PRFs with amortized closed-form efficiency and achieves high efficiency. We outline applications of our results to outsourced two-party protocols.
BOSH: An Efficient Meta Algorithm for Decision-based Attacks

Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model. In this paper, we consider hard-label black-box attacks (a.k.a. decision-based attacks), which is a challenging setting that generates adversarial examples based on only a series of black-box hard-label queries. This type of attacks can be used to attack discrete and complex models, such as Gradient Boosting Decision Tree (GBDT) and detection-based defense models. Existing decision-based attacks based on iterative local updates often get stuck in a local minimum and fail to generate the optimal adversarial example with the smallest distortion. To remedy this issue, we propose an efficient meta algorithm called BOSH-attack, which tremendously improves existing algorithms through Bayesian Optimization (BO) and Successive Halving (SH). In particular, instead of traversing a single solution path when searching an adversarial example, we maintain a pool of solution paths to explore important regions. We show empirically that the proposed algorithm converges to a better solution than existing approaches, while the query count is smaller than applying multiple random initializations by a factor of 10.
Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities

We present a new task of query auto-completion for estimating instance probabilities. We complete a user query prefix conditioned upon an image. Given the complete query, we fine tune a BERT embedding for estimating probabilities of a broad set of instances. The resulting instance probabilities are used for selection while being agnostic to the segmentation or attention mechanism. Our results demonstrate that auto-completion using both language and vision performs better than using only language, and that fine tuning a BERT embedding allows to efficiently rank instances in the image. In the spirit of reproducible research we make our data, models, and code available.
Investigating the Effectiveness of Word-Embedding Based Active Learning for Labelling Text Datasets

Manually labelling large collections of text data is a time-consuming, expensive, and laborious task, but one that is necessary to support machine learning based on text datasets. Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them. The representation mechanism used to represent text documents when performing active learning, however, has a significant influence on how effective the process will be. While simple vector representations such as bag of words have been shown to be an effective way to represent documents during active learning, the emergence of representation mechanisms based on the word embeddings prevalent in neural network research (e.g. word2vec and transformer-based models like BERT) offer a promising, and as yet not fully explored, alternative. This paper describes a large-scale evaluation of the effectiveness of different text representation mechanisms for active learning across 8 datasets from varied domains. This evaluation shows that using representations based on modern word embeddings---especially BERT---, which have not yet been widely used in active learning, achieves a significant improvement over more commonly used vector-based methods like bag of words.
Evolutionary Design of Fuzzy Systems Based on Multi-objective Optimization and Dempster-Shafer Schemes

The paper considers a novel intelligent approach to the design of fuzzy systems based on Multi-Objective Evolutionary Fuzzy Systems (MOEFSs) theory. The presented approach is based on the principle of Pareto optimality using evidence combination schemes of Dempster-Shafer. The evidence combination scheme is used in evolutionary operators of search algorithms during implementation of fitness assignment and solution selection. The paper proposes new representation forms for integral and vector criteria reflecting not only accuracy and complexity of multi-objective fuzzy systems, but also their interpretability characterizing readability of fuzzy-rule base and semantic consistency. The main advantage of the considered MOEFSs is that they satisfy to many criteria simultaneously, which include interpretability properties of fuzzy systems, such as compact description, readability, semantic consistency and description completeness. The novel technique of solution selection and combination based on fusion of fitness estimations from several individuals using Dempster-Shafer theory is proposed. Here, Dempster-Shafer theory allows to select those solutions from Pareto-optimal ones, which are most satisfactory in multi-objective design terms. Solution selection and combination based on probability theory of evidence combination increase objectivity of the best solution selection in evolutionary algorithms. The novel techniques of fitness ranging in evolutionary algorithms and expert preferences integration into MOEFSs design based on Dempster-Shafer modified network models are proposed. The comparison results of MOEFSs design using several evolutionary algorithms are shown in the example of railway task decision. These results prove that the proposed evolutionary design provides a better compromise between accuracy and interpretability in comparison with conventional algorithms.
Study on Application of Internet Technology in Individualized Teaching of College English

Internet technology and new curriculum reform have prompted the education to explore and innovate constantly, and gradually shaped a new form of "Internet Plus Education Models".Teaching methods and resources of college English have become richer because of the advantages of Internet.Teachers can make full use of Internet technology to realize individualized teaching mode, improving students' comprehensive strength in English learning and promoting development of college English teaching.Therefore, based on the individualized teaching, this paper mainly studies the application of Internet technology in College English teaching.
A Mathematical Model for Universal Semantics

We characterize the meaning of words with language-independent numerical fingerprints, through a mathematical analysis of recurring patterns in texts. Approximating texts by Markov processes on a long-range time scale, we are able to extract topics, discover synonyms, and sketch semantic fields from a particular document of moderate length, without consulting external knowledge-base or thesaurus. Our Markov semantic model allows us to represent each topical concept by a low-dimensional vector, interpretable as algebraic invariants in succinct statistical operations on the document, targeting local environments of individual words. These language-independent semantic representations enable a robot reader to both understand short texts in a given language (automated question-answering) and match medium-length texts across different languages (automated word translation). Our semantic fingerprints quantify local meaning of words in 14 representative languages across five major language families, suggesting a universal and cost-effective mechanism by which human languages are processed at the semantic level. Our protocols and source codes are publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yajun-zhou/linguae-naturalis-principia-mathematica</uri> .
The Development of Orthogonal Teaching in Higher Education

In this paper, we discuss six formulas about orthogonalization.These formulas are simple and interlinked.The first three formulas are used for continuous functions, and the corresponding last three formulas are used for discrete data.Whether the data is high frequency or low frequency, there is always a very effective formula.Therefore, we recommend teaching these orthogonal formulas in the management and social sciences.
Deducibility of Identicals, Reflection Principle and Synthetic Connectives

Sambin et al. (2000) introduced Basic Logic as an uniform framework for various logics. At the same time, they also introduced the principle of reflection as a criterion for being a connective of Basic Logic. We make explicit a relationship between Hacking's deducibility of identicals condition (Hacking, 1979) and the principle of reflection by proving the equivalence between them. Moreover, despite Sambin et al.'s conjecture that only six connectives satisfy the principle of reflection, we show the following; a logical connective satisfies the principle of reflection if and only if the connective is Girard's synthetic connective.
Augmented Reality in E-Learning- Review of Prototype Designs for Usability Evaluation

The concept of e-learning has reached beyond use of PowerPoint presentations and audio-visual contents. After virtual reality (VR), augmented reality (AR) based systems are preferred to support teaching and learning activities along with these elearning systems. As an emerging technology, AR has become a point of interest for researchers in education field. So this paper will review some of the prototypes developed for educational purposes and compare their usability for finding benefits of AR. It also focuses on use of web 2.0 tools along with AR in educational environments.
The Readiness of Universitas Negeri Medan to Deal with the Industrial Revolution 4.0

Industrial Revolution 4.0 has urged the higher education to change the way they work.Changes in the form of work indeed require the readiness of infrastructure and human resources.To address these challenges, Universitas Negeri Medan (Unimed), Indonesia, has developed an integrated learning information system to facilitate learning in the digital era.However, the next challenge is to prepare human resources who will use the information system to optimize learning activities.Therefore, this study aims to map Unimed's human resource readiness to carry out learning in the industrial revolution era 4.0.The subject of this study was an active lecturer at the state public university.With random sampling, this study received 158 samples from 7 Faculties in Unimed.Furthermore, the data were analyzed by descriptive statistics to review the competency of using IT for educational and research activities held by lecturers.The results of this study indicate that human resources in Unimed have high readiness and potential in the implementation of digital learning.Policymakers can synchronize competencies and specific needs of lecturers related to their field of work with the SI that has been developed.
Textual evaluation vs. User testing: a comparative analysis

One of the ways to evaluate a system is from Textual Evaluation. This type of evaluation consider the textual user’s opinions to infer aspects of the interaction with the system. Although this method covers many texts and consi- ders spontaneous narratives of the users, it takes a lot of time and effort. Some authors have reported on the need to compare evaluations techniques in order to investigate their effectiveness in revealing issue or to supplement the results of a systems assessment. This study presents a comparative analysis between the textual evaluation and user testing. A case study was performed evaluating the usability and user experience of a health app. As a result, the techniques were analyzed based on aspects that involved describing the results, resources needed and description of problems and users.
Penguasaan Guru Kelas Atas Sekolah Dasar dalam Pembelajaran Berbasis Multimodal Teks pada Abad 21

Wigung Bayu Saputro/A510150091. Mastery Of Primary Grade Teachers InMultimodal Text-Based Learning In The 21st Century. Thesis. Faculty of teachertraining and education, Muhammadiyah University of Surakarta. June, 2019.The study aims to identify the understanding and skills of top-class teachersin implementing text-based multimodal learning, identifying the benefits ofmultimodal-based learning text, identifying the problems encountered Teachers whenapplying multimodal text-based learning, and identifying solutions teachers do toaddress problems in implementing text-based multimodal learning. The type ofresearch used is qualitative research with a descriptive research design. The datacollection techniques used are observations, interviews, documentation, and polls.Data is analyzed through data reduction, data presentation, and withdrawal ofconclusions. The validity of data inspection techniques uses the triangulationtechnique and triangulation source. The results showed that, 1) a high-end teacher'sunderstanding of text-based multimodal learning was good with a percentage scoreof 76.6%. 2) The teacher's skill in implementing multimodal text-based learning hasbeen good enough with a score percentage of 60.2%. 3) The benefits that teachersgain when applying multimodal text-based learning add to understanding andimprove teacher skills on multimodal text, learning more efficiently and improvingstudent literacy and reading interest. 4) The problem that teachers encounter whenapplying multimodal text-based learning is the skill of the teacher in the field oftechnology is not evenly distributed, teachers in delivering learning materials arestill conventional. 5) The teacher's solution to solve problems when implementingmultimodal text-based learning is the teacher evaluating the learning activities thathave been implemented, looking for the reference model of learning according to thematerial to be taught, teachers Seek help and find information to teacher friends whoare experts in technology.
Robust Incremental State Estimation through Covariance Adaptation

Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.
A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.
Interpretable Few-Shot Learning via Linear Distillation

It is important to develop mathematically tractable models than can interpret knowledge extracted from the data and provide reasonable predictions. In this paper, we present a Linear Distillation Learning, a simple remedy to improve the performance of linear neural networks. Our approach is based on using a linear function for each class in a dataset, which is trained to simulate the output of a teacher linear network for each class separately. We tested our model on MNIST and Omniglot datasets in the Few-Shot learning manner. It showed better results than other interpretable models such as classical Logistic Regression.
Performance Assessment Of Irrigation System Using External Indicators For Decision Support System

This research is aimed to study the production cost for determining feasibility and return of investment (ROI) of farming and farmer income based on the crop yield in the Pante Lhong technical irrigation system.These system performances were evaluated using measured/rewarded/collected internal and external indicators that covered different aspect of evaluation.The Rapid Appraisal Process (RAP) is a visual assessment tool was used to evaluate the external indicators performance.The external indicators were crop yield and production cost.The data of crop yield and production cost were collected from the farmers in the three regions which were upstream, middle stream and downstream.Data were collected through field observation and face to face interviews with the irrigation staff and the farmers.The production cost data were calculated based on the land preparation cost, growth stage cost and harvesting cost.In the crop yield indicator, the average maximum productivity was 3.91 ton/ha and the income generated was Rp.11.730 million rupiahs/ha (US$ 1289/ha).The total production cost of the Pante Lhong technical irrigation system was Rp. 4.126 million rupiahs/ha (US$ 453.50/ha) and hence in term of return of investment (ROI), it is still profitable and feasible to be developed for the farmers.
IMPROVING STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXT THROUGH DIRECTED READING THINKING ACTIVITY (DRTA) TECHNIQUE AT THE SECOND GRADE OF SMPN 22 BANDAR LAMPUNG

Reading is considered difficult for all learners especially junior high schoolstudents. This is obvious since this activity involves the students not only to readthe text but also skills used to comprehend it. Therefore, Directed ReadingThinking Activity (DRTA) technique in teaching reading comprehension wasapplied. The objectives of this study were: to find out whether there was animprovement of the students’ reading comprehension, and to find out whichaspect of macro reading skills improved the most after being taught by DRTAtechnique. This study employed a quantitative design. The population of thisresearch was the second grade of SMPN 22 Bandar Lampung. The sample of thisresearch was VIII J which consisted of 28 students. The data were analyzed usingPaired Samples T-test. Evidently, the result showed that there was animprovement of the students’ reading comprehension. It was proved by theincrease of the students’ mean score in the post-test which was higher than in thepre-test. In addition, main idea was the aspect of reading skills which improvedthe most after being taught by DRTA technique. This evidence proved thatteaching reading using DRTA technique facilitates more in determining mainidea. In conclusion, it can be said that DRTA technique can be used to improvestudents’ reading comprehension.Keywords: directed reading thinking activity, reading comprehension, readingskills, teaching reading, students’ improvement
Learning Objectness Transfer Networks for Visual Tracking

Existing deep trackers mainly use deep neural networks pre-trained on the object recognition training sets to generate deep features as target representation. However, pre-trained deep features are not effective in representing arbitrary forms of target objects which are likely to be unseen for the pre-trained deep networks. To narrow the gap of representation capability, we propose to transfer the objectness information within pre-trained deep networks. The transferred objectness information is utilized to generate deep features aware of any arbitrary form of target objects for robust visual tracking. Specifically, we design a novel network branch on top of pre-trained deep models to perform incremental transfer learning. The learned network with the transferred objectness information helps to locate target objects undergoing large appearance changes precisely. Experimental results on standard benchmark datasets demonstrate that the proposed algorithm performs favorably against the start-of-the-art trackers.
CHINESE GRAMMAR AUTO-CORRECT WEBSITE CHINESEGRAMMARCHECKER.COM DEVELOPMENT MODEL

In the process of learning and using Chinese, Indonesia students may have grammatical errors due to negative migration of their native language. At present, there is no Mandarin grammar correction website or application that can answer the needs of Mandarin learners. Therefore, researchers want to try to provide a website chinesegrammarchecker.com that can answer the needs of Chinese language learners in Indonesia so as to facilitate Chinese language learners in mastering Chinese grammar. Hope researchers are learners can learn independently in making sentences in Chinese using proper and correct Chinese grammar. The method used in this study is a qualitative approach. The researcher will collect basic level Chinese grammar patterns in the textbooks used in the Chinese Language I courses, namely Er Ya Basic Chinese: Comprehensive Course (I) and Basic Chinese: Comprehensive Course (II). Then from these patterns user can check their sentences on this website. After the website chinesegrammarchecker.com was completed, then it was tested for several users to see the advantages and disadvantages of this website chinesegrammarchecker.com.