Reviewerly/exHarmony huggingface dataset:
Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk assessment for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating the failure probability density functions over the intervals of time within which a fault may occur. The response of the spacecraft to different faults can result in different schedules of activities, changing both the failure probability density functions and the time intervals of integration. This paper describes an approach to generating the schedules for the different fault cases and determining the associated time intervals. In order to represent the behavior of the spacecraft using a simple, declarative syntax, we employ a language based on ontologies of behavior and scenarios. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. These bounds include the time windows within which certain faults can occur, which are also the bounds for integration in the calculation of failure probability. Determining these time windows is a constraint optimization problem. For an individual fault in Clipper's propulsion subsystem, the engine is able to solve for values of the time window, but it cannot directly solve for the windows when they are functions of the times of previous faults. We used Mathematica to solve for these functions, and for more complicated scenarios, the functions are nearly too large to manage. We discuss this challenge of maintaining a closed-form calculation for more detailed fault models, and present the model of propulsion fault response behavior that illustrates the challenge. We also discuss some advantages of the behavior modeling language, comparing to a mixed integer-linear program model. Time windows are given for some of the many scenarios of interest.
THE ROLE OF INSTRUMENTAL MOTIVATION AMONG EFL STUDENTS IN LANGUAGE LEARNING PROCESS

The aim of this study was to find out the role of instrumental motivation in the English learning process of English as a Foreign Language (EFL) students. Motivation can be described as a process to influence someone or many people to do the desired work, in accordance with certain goals set in advance. For EFL student, motivation was required to achieve success in the process of learning English. Based on the previous study, kind of motivation, called Instrumental motivation, has an important role in English learning process. The participants of the study were 10 EFL students; 5 students were from post-graduate program of English Department of Syiah Kuala University and 5 students were from undergraduate program of English Department of UIN Ar-Raniry. This qualitative study applied interview to gather data. The result of the study revealed that the participants have a strong instrumental motivation involved in their English learning process. Considering the results of this study, everyone, whether parents and teachers need to motivate their children and their EFL students by motivating them instrumentally in the process of learning and teaching English.
A New Anchor Word Selection Method for the Separable Topic Discovery

Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high-dimensional word co-occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word-pair is very low, then the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word-pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word-pair similarity. Following this way, we design a greedy method to find a minimum edge-weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real-world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull-based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF based method.
The Roots of Trust

The present article addresses the question of the ‘roots’ of trust: a debate between cognitive and non-cognitive trust theories, ongoing since the dawn of modern theorising on trust. On the one side, there is the assumption of conceiving trust as a learnt capacity, based on Erikson’s concept of basic trust. On the other side is the hypothesis of innate, built-in trust. After a critical overview of the cognitive and non-cognitive approaches, given that neither side was able to build up a decisive argument, the paper proceeds to some relevant discoveries of the life sciences that serve as proofs of the concept. Michael Polanyi’s principles of marginal control and boundary conditions help avoiding the pitfall of any reductionist determinism. The analysis results in a rejection of the early learning concept of the cognitive approaches. Trusting is proven to be an a priori given human faculty inscribed in our neurobiological system, but neither biologically, nor in any other way, entirely determined. The possibility to trust is always present in the human: the trusting being.
THE EFFECTIVENESS OF PICTURE SERIES IN TEACHING WRITING NARRATIVE TEXT OF THE FIRST GRADE AT SMAN 1 PADEMAWU

The use of picture in the teaching and learning process is very important. The students can understand easily what is happening in the pictures as they provide material that offer guidance on vocabulary, sentence structure, and organization yet let the students write about new subject matter. Meanwhile, there are four skills in learning English and writing skill may be considered as the most difficult of the language skills. The objective of this research is to investigate the effectiveness of picture series in writing skill, particularly writing narrative text at SMAN 1 Pademawu. The population of the research was the first-gradestudents. The research methodology was an experimental research, which was conducted in two groups. Grade X-IPS 1 as the experimental group and grade X-IPS 2 as the control group. The researcher gave writing test (pre-test and post-test) to gather the data. Meanwhile, the data was analyzed using SPSS V.21. The result showed that there was a significant difference between the English writing skill of students taught by using picture series and those who werenot taught by using picture series. It can be proved by the result of t-observation was 0,01<0,05 with the significant level was 0,05. It means that Ha which states that there is significant effect of picture series in teaching writing narrative text is accepted. Meanwhile, Ho which states that there is no significant effect of picture series in teaching writing narrative text is rejected. In other word, it can be concluded that picture series is effective to teach English especially forwriting narrative text of the first grade at SMAN 1 Pademawu.
An Action Recognition network for specific target based on rMC and RPN

The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.
LibGuides: Open Educational Resources (OER) | Speech & Communication Discipline: Finding OER

Collection of Open Educational Resources pertaining to and supporting the Speech Discipline at Miami Dade College
Representation Learning-Assisted Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction. In particular, DeepMCP contains three parts: a matching subnet, a correlation subnet and a prediction subnet. These subnets model the user-ad, ad-ad and feature-CTR relationship respectively. When these subnets are jointly optimized under the supervision of the target labels, the learned feature representations have both good prediction powers and good representation abilities. Experiments on two large-scale datasets demonstrate that DeepMCP outperforms several state-of-the-art models for CTR prediction.
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them. To address this, we introduce the knowledge graph language model (KGLM), a neural language model with mechanisms for selecting and copying facts from a knowledge graph that are relevant to the context. These mechanisms enable the model to render information it has never seen before, as well as generate out-of-vocabulary tokens. We also introduce the Linked WikiText-2 dataset, a corpus of annotated text aligned to the Wikidata knowledge graph whose contents (roughly) match the popular WikiText-2 benchmark. In experiments, we demonstrate that the KGLM achieves significantly better performance than a strong baseline language model. We additionally compare different language model's ability to complete sentences requiring factual knowledge, showing that the KGLM outperforms even very large language models in generating facts.
Multi-views Embedding for Cattle Re-identification

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets. However, little research has been conducted on animal identification and re-identification, even if this knowledge may be useful in a rich variety of different scenarios. Here, we tackle cattle re-identification exploiting deep CNN and show how this task is poorly related with the human one, presenting unique challenges that makes it far from being solved. We present various baselines, both based on deep architectures or on standard machine learning algorithms, and compared them with our solution. Finally, a rich ablation study has been conducted to further investigate the unique peculiarities of this task.
Quaternion Convolutional Neural Networks

Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years. However, the extensions of the neural network models in other number fields and their potential applications are not fully-investigated yet. Focusing on color images, which can be naturally represented as quaternion matrices, we propose a quaternion convolutional neural network (QCNN) model to obtain more representative features. In particular, we redesign the basic modules like convolution layer and fully-connected layer in the quaternion domain, which can be used to establish fully-quaternion convolutional neural networks. Moreover, these modules are compatible with almost all deep learning techniques and can be plugged into traditional CNNs easily. We test our QCNN models in both color image classification and denoising tasks. Experimental results show that they outperform the real-valued CNNs with same structures.
PENGGUNAAN MEDIA KONKRET UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA POKOK BAHASAN GARIS BILANGAN PADA PESERTA DIDIK KELAS III MI DATOK SULAIMAN KOTA PALOPO

This research is a Classroom Action Research (CAR) which is the main problem of the low level of mathematics learning outcomes of class III students of MI Datok Sulaiman Palopo City. The research subjects were MI Datok Sulaiman third grade students in the odd semester of 2018/2019 school year with 28 students. This study was carried out in two cycles with number line material. Data collection techniques used consist of tests, observations, and documentation. Data processing and analysis techniques used are quantitative data analysis and qualitative analysis. The results showed that the learning outcomes with the number line material obtained by the average learning outcomes of students in cycle I was 64.85 including the category of not achieving the KKM score (Minimum Completeness Criteria) with the number of students completing learning were 11 people with a percentage of 39%, and learning outcomes in the second cycle on average obtained 82.89 including very good category with the number of students completing 26 with a percentage of 93%. This research was declared successful with the use of concrete media to improve mathematics learning outcomes.
LibGuides: University of Vaasa writing guidelines: General

The guidelines contain instructions about the layout and contents of theses and other written assignments as well as instructions on how to refer to source literature and other material used in the work.
Uncovering Algebraic Structures in the MPC Landscape.

A fundamental problem in the theory of secure multi-party computation (MPC) is to characterize functions with more than 2 parties which admit MPC protocols with information-theoretic security against passive corruption. This question has seen little progress since the work of Chor and Ishai (1996), which demonstrated difficulties in resolving it. In this work, we make significant progress towards resolving this question in the important case of aggregating functionalities, in which m parties \(P_1,\dots ,P_m\) hold inputs \(x_1,\dots ,x_m\) and an aggregating party \(P_0\) must learn \(f(x_1,\dots ,x_m)\).
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary launches an exploratory (inference) attack by querying the API of an online machine learning system (in particular, a classifier) with input data samples, collecting returned labels to build up the training data, and training an adversarial classifier that is functionally equivalent and statistically close to the target classifier. The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public. In return, a generative adversarial network (GAN) based on deep learning is built to generate synthetic training data from a limited number of real training data samples, thereby extending the training data and improving the performance of the inferred classifier. The exploratory attack provides the basis to launch the causative attack (that aims to poison the training process) and evasion attack (that aims to fool the classifier into making wrong decisions) by selecting training and test data samples, respectively, based on the confidence scores obtained from the inferred classifier. These stealth attacks with small footprint (using a small number of API calls) make adversarial machine learning practical under the realistic case with limited training data available to the adversary.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches.

Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.
Peningkatan Kemampuan Menulis Puisi Melalui Teknik Clustering dengan Media Foto Jurnalistik Pada Siswa Kelas VIII.6 di SMP Negeri 13 Pekanbaru

Based on preliminary observations made, it was found that the ability to write poetry of class VIII students of SMP Negeri 13 Pekanbaru was still lacking. The lack of students' ability to write poetry is caused by three factors, namely factors from the teacher, students, and the environment. The factor of the teacher is the lack of use of techniques and media that vary in learning to write. Factors of students, namely students find it difficult to express ideas that are in their minds, difficulty finding the right words to be arranged into poetry, and the difficulty to imagine. The factors of the environment are the lack of facilities and infrastructure owned by the school. Based on the explanation above, this study examines two problems, namely (1) how is the improvement in poetry writing ability of eighth grade students of SMP Negeri 13 Pekanbaru after learning through clustering techniques with photojournalism media, and (2) how is the behavior of class VIII students of SMP Negeri 13 Pekanbaru in learning poetry writing uses clustering techniques with journalistic photo media. This study uses a classroom action research design conducted in two stages, namely cycle I, and cycle II with research subjects of class VIII Pekanbaru State Junior High School 13. Data collection is done by using test and non-test techniques. Non-data was obtained from observations, interviews, student diaries, teacher journals, and photo documentation. Data analysis includes quantitative and qualitative data. From the results of the first cycle, and the second cycle that students have experienced an increase in each aspect of writing poetry, this is evident from the acquisition of each aspect of writing poetry such as the suitability of the title with the content in the first cycle. increased by 6% to 82.7. For aspects of the choice of words or diction, the increase in the first cycle obtained an average value of 76.0 and the second cycle became 80.5 with an increase of 4.5%. Figurative language aspects of the average value of students in the first cycle 63.2, then in the second cycle increased by 17.3% with an average value of 80.5. For the versification aspect the acquisition of the average value in the first cycle was 60.2 and in the second cycle it was 80.5 with an increase of 20.3%. The latter for typographic aspects of the acquisition of the average value of the first cycle of 72.7 and in the second cycle became 76.7 so as to obtain an increase of 04%.
A note on extensions of multilinear maps defined on multilinear varieties

Let $G_1, \dots, G_k$ be finite-dimensional vector spaces over a finite field $\mathbb{F}$. A multilinear variety of codimension $d$ is a subset of $G_1 \times \dots \times G_k$ defined as the zero set of $d$ forms, each of which is multilinear on some subset of the coordinates. A map $\phi$ defined on a multilinear variety $B$ is multilinear if for each coordinate $d$ and all choices of $x_i \in G_i$, $i\not=d$, the restriction map $y \mapsto \phi(x_1, \dots, x_{d-1}, y, x_{d+1}, \dots, x_k)$ is linear where defined. In this note, we show that a multilinear map defined on a multilinear variety of codimension $d$ coincides on a multilinear variety of codimension $d^{O(1)}$ with a multilinear map defined on the whole of $G_1\times\dots\times G_k$.
An Approach to Block Negative Posts on Social Media at Server Side

These days' social media is the new friend of human beings.Whatever a person wants to say or someone feels, he posts everything on social media.These sentiments can be analysed to detect the behavior of a person.Sometime a person posts some negative thoughts that hurt the sentiments of other persons or may cause several riots.These types of negative posts must be filtered out before being public.One way to block these types of comment is to keep the surveillance on each and every post by humans themselves which is almost an impossible task.Instead of this technique server can be trained to keep track of each and every comment and use its own intelligence to rectify positive comments and block the negative comments.This paper introduces a technique to block these negative comments even before they can be public on any social media site.In this technique sever uses its artificial intelligence to separate positive and negative words or sentences from a post.The server finds the average of the negative statements and if it exceeds a particular threshold, the server discards that post, else server will allow the post to public.This technique can be efficient and very successful if the server properly depicts the positive and negative words.
Widely Linear Kernels for Complex-Valued Kernel Activation Functions

Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain. One of the major challenges in scaling up CVNNs in practice is the design of complex activation functions. Recently, we proposed a novel framework for learning these activation functions neuron-wise in a data-dependent fashion, based on a cheap one-dimensional kernel expansion and the idea of kernel activation functions (KAFs). In this paper we argue that, despite its flexibility, this framework is still limited in the class of functions that can be modeled in the complex domain. We leverage the idea of widely linear complex kernels to extend the formulation, allowing for a richer expressiveness without an increase in the number of adaptable parameters. We test the resulting model on a set of complex-valued image classification benchmarks. Experimental results show that the resulting CVNNs can achieve higher accuracy while at the same time converging faster.