Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Experience Reuse with Policy Residual Representation

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may require experience across multiple granularities. In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience. PRR network is trained on a set of tasks with a multi-level architecture, where a module in each level corresponds to a subset of the tasks. Therefore, the PRR network represents the experience in a spectrum-like way. When training on a new task, PRR can provide different levels of experience for accelerating the learning. We experiment with the PRR network on a set of grid world navigation tasks, locomotion tasks, and fighting tasks in a video game. The results show that the PRR network leads to better reuse of experience and thus outperforms some state-of-the-art approaches.
Networked Ontologies with Contextual Alignments

The problem of knowledge heterogeneity in the Semantic Web or in the context of information systems remains a major challenge for the scientific community, in particular when several ontologies developed independently and separately have to be exploited to exchange their knowledge. Several works have addressed the semantic heterogeneity issue in ontologies and proposed to align them with additional knowledge. Recently a formalism taking into account the challenge of applied techniques to represent and reason on aligned ontologies was proposed by the authors. The authors proposed a contribution that can be seen as an extension of existing work on the heterogeneous ontologies integration. This formalism allows dealing with contextual representation and reasoning where ontologies and alignments by pairs of ontologies are developed in different and incompatible context. In this paper, some aspects of multi-level networked knowledge are recalled, detailing its semantics and discussing the comparison of the two semantics, DL-approach and DDL-approach, according to certain criteria, in order to measure their relevance and to give to readers a way to choose one semantics rather than another according to the context or the intended application.
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning

Many potential applications of reinforcement learning in the real world involve interacting with other agents whose numbers vary over time. We propose new neural policy architectures for these multi-agent problems. In contrast to other methods of training an individual, discrete policy for each agent and then enforcing cooperation through some additional inter-policy mechanism, we follow the spirit of recent work on the power of relational inductive biases in deep networks by learning multi-agent relationships at the policy level via an attentional architecture. In our method, all agents share the same policy, but independently apply it in their own context to aggregate the other agents' state information when selecting their next action. The structure of our architectures allow them to be applied on environments with varying numbers of agents. We demonstrate our architecture on a benchmark multi-agent autonomous vehicle coordination problem, obtaining superior results to a full-knowledge, fully-centralized reference solution, and significantly outperforming it when scaling to large numbers of agents.
Chinese Named Entity Recognition with Changed BiLSTM and CRF

This paper is aimed at improving name entity recognition (NER) accuracy. We replace the traditional bidirectional long short-term memory network (BiLSTM) with a changed BiLSTM, and then uses a CRF layer behind the changed BiLSTM layer to add the probabilistic relation of different Chinese characters.
Correcting Codes for Asymmetric Single Magnitude Four Error

An error model with asymmetric single magnitude four error is considered. This paper is about constructions of codes correcting single error over $\mathbb{Z}_{2^{a}3^{b}r}$. Firstly, we reduce the construction of a maximal size $B_{1}[4](2^{a}3^{b}r)$ set for $a\geq4$ and $\gcd(r,6)=1$ to the construction of a maximal size $B_{1}[4](2^{a-3}3^{b}r)$ set. Further, we will show that maximal size $B_{1}[4](8\cdot3^{b}r)$ sets can be reduced to maximal size $B_{1}[4](3^{b}r)$ sets. Finally, we give a lower bounds of maximal size $B_{1}[4](2r)$ and $B_{1}[4](2\cdot3^{b}r)$ sets.
Modal Logics that Bound the Circumference of Transitive Frames

For each natural number n we study the modal logic determined by the class of transitive Kripke frames in which there are no cycles of length greater than n and no strictly ascending chains. The case \(n=0\) is the Gödel-Löb provability logic. Each logic is axiomatised by adding a single axiom to K4, and is shown to have the finite model property and be decidable. We then consider a number of extensions of these logics, including restricting to reflexive frames to obtain a corresponding sequence of extensions of S4. When \(n=1\), this gives the famous logic of Grzegorczyk, known as S4Grz, which is the strongest modal companion to intuitionistic propositional logic. A topological semantic analysis shows that the n-th member of the sequence of extensions of S4 is the logic of hereditarily \(n+1\)-irresolvable spaces when the modality \(\Diamond \) is interpreted as the topological closure operation. We also study the definability of this class of spaces under the interpretation of \(\Diamond \) as the derived set (of limit points) operation. The variety of modal algebras validating the n-th logic is shown to be generated by the powerset algebras of the finite frames with cycle length bounded by n. Moreover each algebra in the variety is a model of the universal theory of the finite ones, and so is embeddable into an ultraproduct of them.
Single-Net Continual Learning with Progressive Segmented Training (PST)

There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of PST successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning.
Local Characteristics Content: An Analysis of ELT Textbook for Young Learners

The purpose of this study was to analyse the media used in ELT textbook for beginners based on local characteristics of students by showing the data from analysed textbook, which is inappropriate for the the young learners in Indonesia, and this media made the teaching learning process ineffective and inefficient. Descriptive research design was applied in this study. The technique for collecting the data was quantitative approach. There were three (3) textbooks commonly used in primary level grade 4 in Indonesia. Those three were analyzed based on the local characteristics of students using demographic analysis. The result shows that only 5.6% of the media was related to local characteristics of students since the rest were to related general and foreign characteristic which reached 49% and 45.4% respectively. Therefore, the reason why the Englishlearners in Indonesia particularly on primary level grade 4 could not reach the standard of English Learning was because the media was 94.4% not related to the local characteristics of students.
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit

An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selected algorithms. In this paper, we propose a cascaded approach for algorithm selection and hyper-parameter optimization. While a search procedure is employed at the level of hyper-parameter optimization, a bandit strategy runs at the level of algorithm selection to allocate the budget based on the search feedbacks. Since the bandit is required to select the algorithm with the maximum performance, instead of the average performance, we thus propose the extreme-region upper confidence bound (ER-UCB) strategy, which focuses on the extreme region of the underlying feedback distribution. We show theoretically that the ER-UCB has a regret upper bound $O\left(K \ln n\right)$ with independent feedbacks, which is as efficient as the classical UCB bandit. We also conduct experiments on a synthetic problem as well as a set of AutoML tasks. The results verify the effectiveness of the proposed method.
NEED ANALYSIS OF ENGLISH FOR SPECIFIC PURPOSES (ESP) TO INFORMATIC STUDENTS

The aim of the research is to find out 1) the aims of students to learn English; 2) the aspects of English students want to learn, 3) the purpose of learning four English language skills namely reading, writing, speaking, and listening, and 4) the difficulties students face when learning English. This research employs a descriptive analysis method with a qualitative approach. The respondents consist of students taken from six regular classes in an informatics study program. The data are obtained by distributing questionnaires. The result of the research indicates that 1) the main aim of students to learn English is for the needs for finding a job; 2) the students want to learn all aspects of language, especially reading; 3) the students’ main difficulties when learning English are limited vocabulary and insufficient understanding of grammar.
Active Learning for Binary Classification with Abstention

We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: \emph{fixed-cost} and two variants of \emph{bounded-rate} abstention, and for each of them propose an active learning algorithm. All the proposed algorithms can work in the most commonly used active learning models, i.e., \emph{membership-query}, \emph{pool-based}, and \emph{stream-based} sampling. We obtain upper-bounds on the excess risk of our algorithms in a general non-parametric framework and establish their minimax near-optimality by deriving matching lower-bounds. Since our algorithms rely on the knowledge of some smoothness parameters of the regression function, we then describe a new strategy to adapt to these unknown parameters in a data-driven manner. Since the worst case computational complexity of our proposed algorithms increases exponentially with the dimension of the input space, we conclude the paper with a computationally efficient variant of our algorithm whose computational complexity has a polynomial dependence over a smaller but rich class of learning problems.
A Randomized Approach for Data Embedding in Audio Steganography

Exchange of information over the internet or an intranet is inevitable in today’s technological atmosphere. Information hiding using steganographic techniques like LSB Steganography do provide us with a decent defense against hackers, however, recent advancements in computing, internet usage have paved the way for hackers to breach data hiding measures like steganography to a considerable extent. This paper presents a novel approach with a randomized embedding scheme for audio steganography, which absolve the lack of security, and the localized nature of the distortions produced in the stego object. The main advantage of a randomized embedding scheme is that, the data values can be stored at any random position in the audio data set. This protects the stego object against an unauthorized breach by unwanted entities. Although the random number generation increases the overall computational complexity of the entire process, the resultant improvement in security and dataset integrity is certainly worth it. Experimental results indicate that the proposed method has an improved security and dataset integrity check with genuine protection for the hidden information, when compared to a basic embedding scheme like the sequential embedding scheme as referred to in the earlier paper [16]. The scope of earlier work confirms the practical feasibility of implementation of a randomized embedding scheme, which could challenge attackers with impregnable protections for data hiding.
Neural Network-Based Semantic Search of Educational Programmes Fitting Labor Market Requirements

With the growth of open educational content, growing demand for professional education from the labor market, and the development of the concept of lifelong learning, the task of updating the content of educational programs today is extremely important. The article discusses the semantic search method to retrieval and ranking of educational content for the specified requirements of the labor market, determined by professional standards. In contrast to traditional approaches of matching and analyzing the content of educational programs based on ontological models and rules, we propose the usage of word embedding and well-known neural network language models word2vec and fastText. The initial requests are specific requirements for knowledge, skills and descriptions of labor activities and labor functions extracted from professional standards. The search results are the descriptions of academic disciplines and online courses, including goals and objectives, learning outcomes, the structure and content of the main topics. We include the results of the expert evaluation of the ranking quality for the semantic search by metrics NDCG (Normalized Discounted Cumulative Gain) and MAP (Mean Average Precision) on the representative corpus of IT disciplines programmes of universities and massive open online courses (MOOC). The best results for the search are shown by the word2vec and fastText models, which are trained without supervision on large specially prepared corpuses of curriculum programs and descriptions of online courses. To move from word vectors to document vectors various combinations of neural network models with the TF-IDF weighting scheme are investigated.
Super-resolution of Time-series Labels for Bootstrapped Event Detection

Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data. In this paper we develop a novel framework that maximises the utility of time-series datasets that contain only small quantities of expertly-labelled data, larger quantities of weakly (or coarsely) labelled data and a large volume of unlabelled data. This represents scenarios commonly encountered in the real world, such as in crowd-sourcing applications. In our work, we use a nested loop using a Kernel Density Estimator (KDE) to super-resolve the abundant low-quality data labels, thereby enabling effective training of a Convolutional Neural Network (CNN). We demonstrate two key results: a) The KDE is able to super-resolve labels more accurately, and with better calibrated probabilities, than well-established classifiers acting as baselines; b) Our CNN, trained on super-resolved labels from the KDE, achieves an improvement in F1 score of 22.1% over the next best baseline system in our candidate problem domain.
DEVELOPING LEARNING MEDIA WITH ADOBE FLASH IN THE SUBJECT OF COMMUNICATION IN NETWORK FOR X GRADE OF VOCATIONAL HIGH SCHOOL

Learning with computer media that has not been multi-direction and lack of activeness of students makes learning less effective. Therefore, this study aims to develop learning media using Adobe Flash CS6, design and implement the development of instructional media on the subject of Communication in the Network, and test the feasibility of learning media. This research is a development research (R&amp;D) with ADDIE model. The results of alpha testing, the assessment of material experts is in the very good category with an average score of 4.28. The assessment of media experts is in a very good category with an average score of 4.86. The results of the beta testing trial, the trial with the teacher, show a very good category with an average score of 5. Trials with students got a very good category with an average score of 4.25
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning

Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a `delay' token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on ChineseEnglish simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.
Architecture Selection via the Trade-off Between Accuracy and Robustness

We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. We propose a method and define quantities to characterize the trade-off between accuracy and robustness for a given architecture, and provide theoretical insight into the trade-off. Specifically we introduce a simple trade-off curve, define and study an influence function that captures the sensitivity, under adversarial attack, of the optima of a given loss function. We further show how adversarial training regularizes the parameters in an over-parameterized linear model, recovering the LASSO and ridge regression as special cases, which also allows us to theoretically analyze the behavior of the trade-off curve. In experiments, we demonstrate the corresponding trade-off curves of neural networks and how they vary with respect to factors such as number of layers, neurons, and across different network structures. Such information provides a useful guideline to architecture selection.
Pengaruh Image Engagement pada Aplikasi Parasit Malaria

The mosquito's parasite is a group of single-celled microorganisms in the plasmodium type that can cause malaria by attacking human blood cells. In this study, a parasitic detection application of Plasmodium Falciparum type at thropozoite, scizont and gametocyte stage was designed using Android Studio 2.2.2 and OpenCV 2.4.9 library. Image detection process begins with preprocessing, then feature extraction and classification phase using Haar Cascade Classifier, then the last stage 3 types of boost compared, namely Gentle Adaboost, Discrete Adaboost, and Real Adaboost. Image Enhancement is one of the preliminary processes in preprocessing that aims to clarify certain features or features of the image to be more easily analyzed carefully in the feature selection process. The results of this study prove that image enhancement can be used to improve image quality so that the information available on the image can be seen more clearly.
REFLEXIVE FUNCTIONAL COMPONENTS OF THE MODEL THE FORMATION OF PROFESSIONAL SUSTAINABILITY BACHELORS OF PHYSICAL AND TECHNOLOGICAL PROFILES OF TRAINING IN THE UNIVERSITY

The article is an attempt to present the results of theoretical and experimental research into the process of formation of professional sustainability of undergraduate students of higher education institutions on the example of teaching bachelors of two pedagogical education profiles – technology and physical culture. The article deals with generalized features and structural components of professional sustainability, aspects of its manifestation in professional activity. The research of the problem was carried out within the framework of the author's reflexiveactivity concept based on the requirements of system, activity and optimization approaches. The main methods of research were methods of analysis, experiment, survey and expert evaluation. According to the concept, professional sustainability is considered as a quality of a person, which includes the interrelated components: professional orientation, professional reflection (self-consciousness) and readiness for creative solution of professional tasks. The article presents the refined features of the concept of “professional sustainability”. It discloses the main result of the authors ’ study – the reflexive-functional model of the formation of the professional sustainability of bachelors, discloses the provisions of the methodology for the formation of professional sustainability, taking into account the peculiarities of university students and the requirements of the education standards. It has been proved that the formation of professional sustainability of a Bachelor student at a higher educational institution has a general structure and is determined by the requirements of professional competencies and general professionally significant personal qualities. The obtained results confirm that the structure, characteristics, methodological apparatus of the process of formation of the professional sustainability of bachelors have common characteristics regardless of the direction and profile of the preparation of students. It was experimentally confirmed that the organization of the process of preparing bachelors in the framework of the reflexiveactivity model ensures the fulfillment of the requirements of the Federal State Educational Standards of Higher Education and contributes to the formation and development of professional competencies. The developed reflexive-functional model of formation of professional sustainability is a significant contribution to the solution of problems of higher education of students in the aspect of their preparation for competitive professional activity.
Experimental Demonstration of Force Driven Quantum Harmonic Oscillator in IBM Quantum Computer

Though algorithms for quantum simulation of Quantum Harmonic Oscillator (QHO) have been proposed, still they have not yet been experimentally verified. Here, for the first time, we demonstrate a quantum simulation of QHO in the presence of both time-varying and constant force field for both one and two dimensional case. New quantum circuits are developed to simulate both the one and two-dimensional QHO and are implemented on the real quantum chip "ibmqx4". Experimental data, clearly illustrating the dynamics of QHO in the presence of time-dependent force field, are presented in graphs for different frequency parameters in the Hamiltonian picture.