Reviewerly/exHarmony huggingface dataset:
Effects of ATutor Platform on Learning Outcomes in Agricultural Science among University Students in North-Central, Nigeria

The study examined the effects of ATutor Platform on learning outcomes in Agricultural Science among university students in North-Central, Nigeria. The researcher used quasi-experimental design (pretest, posttests, non-randomized, non-equivalent experimental and control groups design). The population of the study was 4,562 students. The sample size for the study comprised 237 students from two Universities in North-Central, Nigeria drawn from the population. The research instrument was developed by the researchers and validated by two experts, from the Department of Educational Technology, Federal University of Technology, Minna. To determine the reliability of Agricultural Science Achievement Test (ASAT), Spearman Brown Coefficient was used to analyse the data that was collected from ASAT administered to the students using SPSS version 20, a reliability coefficient of 0.81 was obtained which is above the bench mark of 0.70. This shows that the instrument was reliable. To determine the reliability of Agricultural Science Satisfaction Questionnaire (ASSQ), Cronbach Alpha was used to analyse the data collected and a reliability coefficient of 0.85 was obtained, which shows that the instrument was reliable. To determine the reliability coefficient of Agricultural Science Interest Inventory (ASII), Cronbach Alpha was also used and when analysed, a reliability coefficient of 0.75 was obtained which showed that the instrument is suitable for the categories of students in this study. Three research questions guided the study while the three hypotheses formulated were tested at 0.05 level of significance. Frequency counts, mean (X) and standard deviation (SD) were employed to answer the research questions while t-test was used to test the hypotheses. Findings showed significant difference between the students taught using ATutor Platform and those taught using lecture method. It was concluded that ATutor Platform should be employed to teach Agricultural Science. It was also recommended that ATutor Platform be used in teaching and learning process among others.
Analyzing Games with the AGE and 6-11 Frameworks

Inspired by the original Mechanics, Dynamics, Aesthetics (MDA) model proposed in R. Hunicke et al, the Actions, Gameplay, Experience framework was gradually formalized following the original work in R. Dillon and ultimately finalized in Dillon. Analyzing Actions and Gameplay should be relatively straightforward, but the Experience involves emotions and can be, henceforth, subjective. Like the original MDA, it breaks down a game into three different conceptual layers. At the most basic level we have the Actions, which represent the atomic actions a player can perform in a game. Virtual reality should make the fundamental aspect of games played in a first-person perspective very straightforward and easy to accomplish. The onboarding process and tutorial works well and players have a chance to know more about the world the game takes place in by checking out newspaper articles and TV news, which are easily available and scattered all around.
Influence Of Professional Development Programmes On Basic School Teachers’ Classroom Practices In North-Central Nigeria

This paper evaluated the influence of PDP on Basic School Teachers’ Classroom Practices in North-Central, Nigeria. The study adopted a descriptive survey design. In selecting samples for this study, 746 basic school teachers were purposively selected from 84 sampled Basic Schools across three sampled states using stratified sampling technique out of the six states and Federal Capital Territory, Abuja. The Questionnaire “Influence of Professional Development Programmes ”(IPDP) with reliability index of 0.74 was used. One - Way Analysis of Variance was used to test the hypothesis at 0.05 level of significance. The findings of the study revealed no significance influence of teachers’ participation in PDPs on psychomotor and affective classroom practices based on experience (F (2,724) = .018, p = 982); and F(2,724) =.628, p = .534) were observed .Consequently, teachers’ participation in PDPs had no significant influence in psychomotor and affective classroom practices based on years of experience. There is need to make the programme more effective in term of organization and implementation as antidotes to the hindrances confronting the PDPs in North-Central, Nigeria.
Reducing Risk of Model Inversion Using Privacy-Guided Training

Machine learning models often pose a threat to the privacy of individuals whose data is part of the training set. Several recent attacks have been able to infer sensitive information from trained models, including model inversion or attribute inference attacks. These attacks are able to reveal the values of certain sensitive features of individuals who participated in training the model. It has also been shown that several factors can contribute to an increased risk of model inversion, including feature influence. We observe that not all features necessarily share the same level of privacy or sensitivity. In many cases, certain features used to train a model are considered especially sensitive and therefore propitious candidates for inversion. We present a solution for countering model inversion attacks in tree-based models, by reducing the influence of sensitive features in these models. This is an avenue that has not yet been thoroughly investigated, with only very nascent previous attempts at using this as a countermeasure against attribute inference. Our work shows that, in many cases, it is possible to train a model in different ways, resulting in different influence levels of the various features, without necessarily harming the model's accuracy. We are able to utilize this fact to train models in a manner that reduces the model's reliance on the most sensitive features, while increasing the importance of less sensitive features. Our evaluation confirms that training models in this manner reduces the risk of inference for those features, as demonstrated through several black-box and white-box attacks.
Tagore’s Educational Philosophy in Modern India

Rabindranath Tagore accepted that the point of education is self-acknowledgment. He himself was a writer and a holy person, who had, through his creative mind and understanding, understood the all-inclusive soul in himself and in nature. He accepted that this acknowledgment was the objective of education. Since the general soul is the foundation of our own spirit, man s point in life is to arrive at that general soul of which all individuals are parts. The advancement of nature is intentionally or unwittingly driving us towards this all-inclusive soul, a procedure that can be helped by education. Regardless of whether it isn’t helped, the advancement towards the general soul will proceed, yet then people will be denied of self- realization. It is along these lines obvious that Rabindranath educational philosophy is a subordinate of his general philosophy of life. He accepted that each individual is one who has possibilities of advancing towards the Super person, the widespread soul. His origination of the all-inclusive soul bore clear engraving of the Gita and Upanishad ways of thinking. The objective of the paper is to analyses the educational thoughts of Tagore, his basic conception of education and its process. The paper is primarily based on secondary sources like the Books, Journals and Articles etc. The method used is historic-analytic method. Tagore was a great champion of education for international understanding. Keywords: Conception, education, historic, journals, philosophy, Rabindranath Tagore
Towards Differentially Private Text Representations

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many applications. To tackle this problem, we develop a new deep learning framework under an untrusted server setting, which includes three modules: (1) embedding module, (2) randomization module, and (3) classifier module. For the randomization module, we propose a novel local differentially private (LDP) protocol to reduce the impact of privacy parameter $\epsilon$ on accuracy, and provide enhanced flexibility in choosing randomization probabilities for LDP. Analysis and experiments show that our framework delivers comparable or even better performance than the non-private framework and existing LDP protocols, demonstrating the advantages of our LDP protocol.
Access-based intuitionistic knowledge

Abstract We introduce the concept of $\textit{access-based}$ intuitionistic knowledge which relies on the intuition that agent $i$ knows $\varphi$ if $i$ has found $\textit{access to a proof}$ of $\varphi$. Basic principles are distribution and factivity of knowledge as well as $\square\varphi\rightarrow K_i\varphi$ and $K_i(\varphi\vee\psi) \rightarrow (K_i\varphi\vee K_i\psi)$, where $\square\varphi$ reads $`\varphi$ is proved'. The formalization extends a family of classical modal logics (Lewitzka, 2017, Journal of Logic and Computation, 27, 201--212) designed as combinations of $\mathit{IPC}$ and $\mathit{CPC}$ and as systems for the reasoning about proof, i.e. intuitionistic truth. We adopt a formalization of common knowledge from (Lewitzka, 2011, Studia Logica, 97, 233--264) and interpret it here as access-based common knowledge. We compare our proposal with recent approaches to intuitionistic knowledge (Artemov and Protopopescu, 2016, The Review of Symbolic Logic, 9, 266--298; Lewitzka, 2019, Annals of Pure and Applied Logic, 170, 218--250) and bring together these different concepts in a unifying semantic framework based on Heyting algebra expansions.
Efficient Black-Box Adversarial Attack Guided by the Distribution of Adversarial Perturbations.

This work studied the score-based black-box adversarial attack problem, where only a continuous score is returned for each query, while the structure and parameters of the attacked model are unknown. A promising approach to solve this problem is evolution strategies (ES), which introduces a search distribution to sample perturbations that are likely to be adversarial. Gaussian distribution is widely adopted as the search distribution in the standard ES algorithm. However, it may not be flexible enough to capture the diverse distributions of adversarial perturbations around different benign examples. In this work, we propose to transform the Gaussian-distributed variable to another space through a conditional flow-based model, to enhance the capability and flexibility of capturing the intrinsic distribution of adversarial perturbations conditioned on the benign example. Besides, to further enhance the query efficiency, we propose to pre-train the conditional flow model based on some white-box surrogate models, utilizing the transferability of adversarial perturbations across different models, which has been widely observed in the literature of adversarial examples. Consequently, the proposed method could take advantage of both query-based and transfer-based attack methods, to achieve satisfying attack performance on both effectiveness and efficiency. Extensive experiments of attacking four target models on CIFAR-10 and Tiny-ImageNet verify the superior performance of the proposed method to state-of-the-art methods.
Heuristic AND/OR Search for Solving Influence Diagram

An influence diagram is a graphical representation of sequential decision-making under uncertainty, defining a structured decision problem by conditional probability functions and additive utility functions over discrete state and action variables. The task of finding the maximum expected utility of influence diagrams is closely related to the cost-optimal probabilistic planning, stochastic programmings, or model-based reinforcement learning. In this position paper, we address the heuristic search for solving influence diagram, where we generate admissible heuristic functions from graph decomposition schemes. Then, we demonstrate how such heuristics can guide an AND/OR branch and bound search. Finally, we briefly discuss the future directions for improving the quality of heuristic functions and search strategies.
C. Peningkatan Hasil Belajar IPS Materi Dokumen Pribadi Dan Dokumen Keluarga Menggunakan Model Pembelajaran Jigsaw Pada Siswa Kelas II SD Negeri Sidomukti Bungah Gresik Tahun Pelajaran 2018 / 2019

AbstractThis study aims to improve student learning outcomes in social studies subjects through the use of the Jigsaw cooperative learning model.This research is a classroom action research conducted with 2 cycles. Each cycle consists of four stages, namely: planning, implementation, observation, reflection. As the subject of this study were second grade students of SD Negeri Sidomukti Bungah Gresik with a total of 18 students. Data collection techniques used are test, observation, documentation. Tests for learning outcomes and observation data for teacher activity data.The results of this study that have been carried out in two cycles by applying the Jigsaw cooperative learning model succeeded in improving student learning outcomes in social studies learning. This is evidenced by the increase in learning outcomes of class II Sidomukti Bungah Gresik Elementary School students who initially did not complete 10 students with classical completeness 44% to 4 students with 77% classical completeness.Based on the results of this study, it can be concluded that social studies learning in the Jigsaw type cooperative learning model in class II SD Negeri Sidomukti Bungung Gresik can improve student learning outcomes. Keywords: Improvement, Learning Outcomes, Cooperative Type Jigsaw Model
Deformable classifiers

Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transformations. In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output,
Face-to-Music Translation Using a Distance-Preserving Generative Adversarial Network with an Auxiliary Discriminator

Learning a mapping between two unrelated domains-such as image and audio, without any supervision is a challenging task. In this work, we propose a distance-preserving generative adversarial model to translate images of human faces into an audio domain. The audio domain is defined by a collection of musical note sounds recorded by 10 different instrument families (NSynth \cite{nsynth2017}) and a distance metric where the instrument family class information is incorporated together with a mel-frequency cepstral coefficients (MFCCs) feature. To enforce distance-preservation, a loss term that penalizes difference between pairwise distances of the faces and the translated audio samples is used. Further, we discover that the distance preservation constraint in the generative adversarial model leads to reduced diversity in the translated audio samples, and propose the use of an auxiliary discriminator to enhance the diversity of the translations while using the distance preservation constraint. We also provide a visual demonstration of the results and numerical analysis of the fidelity of the translations. A video demo of our proposed model's learned translation is available in https://www.dropbox.com/s/the176w9obq8465/face_to_musical_note.mov?dl=0.
Aggregation of Support-Relations of Bipolar Argumentation Frameworks

In many real-life situations, individuals may have different opinions on support-relations between arguments. When confronted with such situations, we may wish to aggregate individuals' argumentation views on support-relations into a collective view, which is acceptable to the group. In this paper, we assume that under bipolar argumentation theory, individuals are equipped with a set of arguments and a set of attacks between arguments, but with possibly different support-relations. Using the methodology in social choice theory, we analyze what semantic properties of bipolar argumentation frameworks can be preserved by desirable aggregation rules during aggregation of support-relations.
End-to-end training of deep kernel map networks for image classification

Deep kernel map networks have shown excellent performances in various classification problems including image annotation. Their general recipe consists in aggregating several layers of singular value decompositions (SVDs) -- that map data from input spaces into high dimensional spaces -- while preserving the similarity of the underlying kernels. However, the potential of these deep map networks has not been fully explored as the original setting of these networks focuses mainly on the approximation quality of their kernels and ignores their discrimination power. In this paper, we introduce a novel "end-to-end" design for deep kernel map learning that balances the approximation quality of kernels and their discrimination power. Our method proceeds in two steps; first, layerwise SVD is applied in order to build initial deep kernel map approximations and then an "end-to-end" supervised learning is employed to further enhance their discrimination power while maintaining their efficiency. Extensive experiments, conducted on the challenging ImageCLEF annotation benchmark, show the high efficiency and the out-performance of this two-step process with respect to different related methods.
A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning

Multi-instance (MI) learning is a branch of machine learning, where each object (bag) consists of multiple feature vectors (instances)—for example, an image consisting of multiple patches and their corresponding feature vectors. In MI classification, each bag in the training set has a class label, but the instances are unlabeled. The instances are most commonly regarded as a set of points in a multi-dimensional space. Alternatively, instances are viewed as realizations of random vectors with corresponding probability distribution, where the bag is the distribution, not the realizations. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback–Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasizing the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets, and propose a dissimilarity measure that fulfils them. Its performance is demonstrated on synthetic and real data. The probability distribution space is valid for MI learning, both for the theoretical analysis and applications.
A Fullly Automatic Framework for Food Donation

Around 67 million tons of food have been squandering in India consistently.Then again, more than 7000 Indians kick the bucket of craving every day.The tremendous increment in the measure of food wastage makes the need for donations for poor people.This online application will be useful to donate the food that has been squandered in everyday schedule.Here the benefactor can donate the food and the recipient can arrange the food.The food will be post and request utilizing individual login.On the off chance that the request needs to be dropped or needs to see the history is likewise conceivable utilizing this application.I.
Distributed multi-view multi-target tracking based on CPHD filtering

This paper addresses distributed multi-target tracking (DMTT) over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinality probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has a limited FoV, the commonly adopted fusion methods become unreliable. In fact, the monitored area of multiple sensor nodes consists of several parts that are either exclusive of a single node, i.e. exclusive FoVs (eFoVs) or common to multiple (at least two) nodes, i.e. common FoVs (cFoVs). In this setting, the crucial issue is how to account for this different information sets in the fusion rule. The problem is particularly challenging when the knowledge of the FoVs is unreliable, for example because of the presence of obstacles and target misdetection, or when the FoVs are time-varying. Considering these issues, we propose an effective fusion algorithm for the case of unknown FoVs, where: i) the intensity function is decomposed into multiple sub-intensities/groups by means of a clustering algorithm; ii) the corresponding cardinality distribution is reconstructed by approximating the target random finite set (RFS) as multi-Bernoulli; and iii) fusion is performed in parallel according to either generalized covariance intersection (GCI) or arithmetic average (AA) rule. Simulation experiments are provided to demonstrate the effectiveness of the proposed approach.
Automated Scoring of Junior and Senior High Essays Using Coh-Metrix Features: Implications for Large-Scale Language Testing.

An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describ...
Dialogical communication as a means of formation of art-speech skills of future music art teachers in the process of hormeisters preparations

REFERENCES1. Krupsky, T. (2005).Poshuk resursiv dlya diyalʹnosti orhanizatsiyi (Fund Rasing).[Search for resources for the activities of the organization (Fund Rasing) ].Кyiv.2. Bashun, O. V. (1999).Vplyv marketynhu i fandreyzynhu na
Phase estimation algorithm for quantum states classification with NISQ devices

Methods of processing quantum data become more important as quantum computing devices improve their quality towards fault tolerant universal quantum computers. These methods include discrimination and filtering of quantum states given as an input to the device that may find numerous applications in quantum information technologies. In the present paper, we address a scheme of a classification of input states, which is nondestructive and deterministic for certain inputs, while probabilistic, in general case. This can be achieved by incorporating phase estimation algorithm into the hybrid quantum-classical computation scheme, where quantum block is trained classically. We perform proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience. Another aspect we are interested in is a mitigation of errors occurring due to the quantum device imperfections. We apply a series of heuristic tricks at the stage of classical postprocessing in order to improve raw experimental data and to recognize patterns in them. These ideas may find applications in other realization of hybrid quantum-classical computations with noisy quantum machines