Reviewerly/exHarmony huggingface dataset:
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning

In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks. The first hypothesis is driven by the question of whether a deep reinforcement learning algorithm, trained on two similar tasks, is able to outperform two single-task, individually trained algorithms, by more efficiently learning a new, similar task, that none of the three algorithms has encountered before. The second hypothesis is driven by the question of whether the same multi-task deep RL algorithm, trained on two similar tasks and augmented with elastic weight consolidation (EWC), is able to retain similar performance on the new task, as a similar algorithm without EWC, whilst being able to overcome catastrophic forgetting in the two previous tasks. We show that a multi-task Asynchronous Advantage Actor-Critic (GA3C) algorithm, trained on Space Invaders and Demon Attack, is in fact able to outperform two single-tasks GA3C versions, trained individually for each single-task, when evaluated on a new, third task—namely, Phoenix. We also show that, when training two trained multi-task GA3C algorithms on the third task, if one is augmented with EWC, it is not only able to achieve similar performance on the new task, but also capable of overcoming a substantial amount of catastrophic forgetting on the two previous tasks.
Research on Construction and Automatic Expansion of Multi-source Lexical Semantic Knowledge Base

With the development of research on improving the performance of deep learning models in combination with the rich knowledge resources in traditional knowledge bases, more and more research on building knowledge bases has become a hot topic. How to use the rich semantic information of existing knowledge bases such as HowNet and Tongyici-Cilin to build a more comprehensive and higher quality knowledge graph has become the focus of scholars' research. In this work, we propose a way to integrate a variety of knowledge base information to build a new knowledge base, combined with deep learning techniques to expand the knowledge base. Successfully build a multi-source lexical semantic knowledge base through the steps of new ontology construction, data cleaning and fusion, and new knowledge expansion. Based on the establishment of the knowledge base, we use the graph database and JavaScript script to store and visualize the data separately. Through experiments, we obtained a lexical semantic knowledge base containing 153754 nodes, 1598356 triples and 137 relationships. It can provide accurate and convenient knowledge services, and can use a large number of semantic knowledge resources to support research on semantic retrieval, intelligent question answering system, semantic relationship extraction, semantic relevance calculation and ontology automatic construction [1].
Usage Analysis of University of Manitoba Licensed Databases via EZproxy’s Logs

Compared to vendor-provided data, Transaction Log Analysis (TLA) can provide unique benefits to a library in analyzing database usage. Meanwhile, it also requires librarians to have a broad knowledge of Information Technology in order to implement a TLA. This article will present an in-house database system developed at the University of Manitoba Libraries to analyze EZProxy’s Starting Point Uniform Resource Locator logs. The database visits that happened in the past two years have been imported into this system and are split into two categories, on-campus and off-campus visits. Unique statistical information from each category is also discussed.
Implicit Coordination Using FOND Planning

Epistemic planning can be used to achieve implicit coordination in cooperative multi-agent settings where knowledge and capabilities are distributed between the agents. In these scenarios, agents plan and act on their own without having to agree on a common plan or protocol beforehand. However, epistemic planning is undecidable in general. In this paper, we show how implicit coordination can be achieved in a simpler, propositional setting by using nondeterminism as a means to allow the agents to take the other agents' perspectives. We identify a decidable fragment of epistemic planning that allows for arbitrary initial state uncertainty and non-determinism, but where actions can never increase the uncertainty of the agents. We show that in this fragment, planning for implicit coordination can be reduced to a version of fully observable nondeterministic (FOND) planning and that it thus has the same computational complexity as FOND planning. We provide a small case study, modeling the problem of multi-agent path finding with destination uncertainty in FOND, to show that our approach can be successfully applied in practice.
TEACHER EFFECTIVENESS FACTORS IN THE USA

The purpose of this study is to identify underlying conditions of effective teaching, as well as perceived determiners influencing the quality of teachers’ work in the American educational discourse. To this end, the work applies the following research methods: the interpretative-analytical method aimed at investigating current research trends and pertinent pedagogical conceptualizations of the concept of effective teaching, complemented by synthesis of the available research propositions.Considerable proliferation of theoretical and empirical research in the field of teaching quality both worldwide and in the USA is attributed to teachers’ exceptional role undergirding the final outcomes of any endeavours at improving education, be it students’ learning gains or educational reforms. In conceptualizing and defining the concept of teacher effectiveness three verticals serve as a starting point, termed by the author of this study as individual, activity-related and resultative. The individual vertical embraces teachers’ professional knowledge and skills, personal traits, motivation, professional qualities, educational background and teaching competence. The activity-related vertical pertains to the enactment of the aforementioned elements of the individual vertical in the instructional process and their correlation with student learning. The resultative vertical concerns teachers’ influence on the students’ learning outcomes. It is the primary point for consideration in teacher effectiveness measurements, although, the first two verticals are also included in some evaluation methods.The quality of teaching and its perceived effectiveness is determined by the influence of multifarious factors, the main being teachers’ characteristics, relevant educational background and teaching experience, professional development, socio-cultural contexts in which teachers work, students’ academic results and practices adopted for evaluation purposes. In an effort to improve the quality of teaching, the American educational community has sought and is undertaking empirically supported feasible measures. Among the many important attempts are the implementation of standards of teacher preparation and development, accountability measures (teacher licensing, certification), optimizing models of teacher education and seeking out exemplary programs, working out criteria for selecting teaching candidates, offering opportunities for professional development, teacher induction and support, collaboration, and more importantly, developing reliable tools providing evidence of gains in teachers’ performance.
Simulated evaluation of new switching based median filter for suppressing SPN and RVIN

&lt;p&gt;In the past two decades, the SPN (salt and pepper noise) suppressing method is worldwide interested researches on computer vision and image processing hence many SPN suppressing methods have been proposed. In general, the primary goal of SPN removal method is the suppressing of SPN in digital images thereby one of the recent effective and powerful SPN suppressing methods is a new switching-based median filtering (NSMF), which is innovated for suppressing high density SPN. Consequently, this paper thoroughly examines its efficiency and constrain of a new switching-based median filtering when this filter is used for contaminated image, which is synthesized by SPN and RVIN (random-value impulsive noise). In these simulations, six well-known images (Lena, Mobile, Pepper, Pentagon, Girl, Resolution) with two impulsive noise classes (SPN and RVIN) are used for measuring the its efficiency and constrain. An evaluation of the efficiency is conducted with many previous methods in forms of subjective and objective indicators.&lt;/p&gt;
OVL: One-View Learning for Human Retrieval

This paper considers a novel problem, named One-View Learning (OVL), in human retrieval a.k.a. person re-identification (re-ID). Unlike fully-supervised learning, OVL only requires pretty cheap annotation cost: labeled training images are only provided from one camera view (source view/domain), while the annotations of training images from other camera views (target views/domains) are not available. OVL is a problem of multi-target open set domain adaptation that is difficult for existing domain adaptation methods to handle. This is because 1) unlabeled samples are drawn from multiple target views in different distributions, and 2) the target views may contain samples of “unknown identity” that are not shared by the source view. To address this problem, this work introduces a novel one-view learning framework for person re-ID. This is achieved by adversarial multi-view learning (AMVL) and adversarial unknown rejection learning (AURL). The former learns a multi-view discriminator by adversarial learning to align the feature distributions between all views. The later is designed to reject unknown samples from target views through adversarial learning with two unknown identity classifiers. Extensive experiments on three large-scale datasets demonstrate the advantage of the proposed method over state-of-the-art domain adaptation and semi-supervised methods.
An Improve Image Watermarking using Random Spread Technique and Discrete Cosine Transform

Watermarking is a powerful technique to protect copyright from cyber-crime. This study proposes a random spread technique based on the Discrete Cosine Transform (DCT) to improve image watermarking performance. The use of the random spread technique is proven to be able to improve the output quality of the watermarked image, where the watermark image is increasingly invisible to the eye after being pinned on the cover image. Better PSNR and MSE values prove this, besides watermark security also increases indirectly because embedding techniques spread randomly.
IntroVNMT: An Introspective Model for Variational Neural Machine Translation

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes

Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignores low probability catastrophic events with highly negative impact on the system. On the other hand, risk-averse policies require the probability of undesirable events to be below a given threshold, but they do not account for optimization of the expected payoff. We consider MDPs with discounted-sum payoff with failure states which represent catastrophic outcomes. The objective of risk-constrained planning is to maximize the expected discounted-sum payoff among risk-averse policies that ensure the probability to encounter a failure state is below a desired threshold. Our main contribution is an efficient risk-constrained planning algorithm that combines UCT-like search with a predictor learned through interaction with the MDP (in the style of AlphaZero) and with a risk-constrained action selection via linear programming. We demonstrate the effectiveness of our approach with experiments on classical MDPs from the literature, including benchmarks with an order of 106 states.
Copyright Embedding Analysis in Color Image Channel based on Non-Blind DCT Method

Image watermarking is the most popular copyright protection technique. Discrete Cosine Transform (DCT) is a transformation method that is still widely used today in image watermarking research. In the grayscale cover image there is only one channel so that the watermark is directly inserted on the channel, while the color cover image generally uses red, green and blue (RGB) color channels. Some previous research did direct copyright insertion on one color channel based on hypothesis and color theory related to the sensitivity of changes in pixel values to the human visual system. So, this research aims to carry out watermarking technique analysis based on the non-blind DCT method on color cover images. The watermarked image was tested by measuring image quality and watermark resistance to attacks. Based on the results of the tests that have been conducted, it can be concluded that the red channel has the best quality based on the value of MSE and PSNR, while the green channel has the best SSIM value. While for the durability of the blue channel watermark has the best average resistance based on the correlation calculation of the results of watermark image extraction.
Mobile Application for Training of Foreign Students with Gamification Techniques and Speech Recognition Technology

The purpose of this research is to produce a mobile learning product that uses gamification techniques and valid speech recognition technology as a source of learning and is effective for training the pronunciation of foreign students. This type of research is a development research that uses the Lee &amp; Owens model and is validated by 1 media expert, 1 material expert and 1 gamification learning design expert. The results of this study indicate that this application is valid and can be used for learning, students successfully complete the material, practice questions and reach the specified learning standards.
Survey on Abstractive Text Summarization using various approaches

Text summarization is the core aspects of Natural Language processing.Summarized text should consist of unique sentences.It is used in many situations in today's Information technological word, one of the best examples is in understanding customer feedbacks in companies.This job can be done by humans, but if the text or data that has to be summarized then it will consume lot of time and work force.This situation lead to birth of different approaches in summarization.This paper addresses and concentrates on various methods and approaches and their results in abstractive text summarization.This survey gives an insight about different types of text summarization and various methods used in abstractive text summarization in recent developments.
Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Dimensionality reduction of 3D-handwritten characters can be problematic because of random mirror rotations and angle rotations which appear after using the traditional algorithms. In order to overcome the above drawbacks of the traditional methods, this study proposes a new algorithm for dimensionality reduction of 3D-handwritten characters based on oriented bounding boxes. First, we get a 3D discrete point set T and generate a 3D trajectory. Then, we apply an oriented bounding box model and determine the projection surface. Next, we perform three coordinate transformations, including (1) pre-transforming the 3D discrete point set T into the projection point set T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , (2) converting T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> to a two-dimensional point set T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> which has solved the problem of mirror rotation, and (3) converting T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to a dimensionally reduced point set T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> which has solved the problem of angle rotation. Finally, we obtain a dimensionally reduced image without mirror and angle rotations. The experimental results confirm that the proposed method can not only obtain a better visual dimensionally reduced image, but also has a higher recognition rate than the conventional ones.
Guiding CDCL SAT Search via Random Exploration amid Conflict Depression

The efficiency of Conflict Driven Clause Learning (CDCL) SAT solving depends crucially on finding conflicts at a fast rate. State-of-the-art CDCL branching heuristics such as VSIDS, CHB and LRB conform to this goal. We take a closer look at the way in which conflicts are generated over the course of a CDCL SAT search. Our study of the VSIDS branching heuristic shows that conflicts are typically generated in short bursts, followed by what we call a conflict depression phase in which the search fails to generate any conflicts in a span of decisions. The lack of conflict indicates that the variables that are currently ranked highest by the branching heuristic fail to generate conflicts. Based on this analysis, we propose an exploration strategy, called expSAT, which randomly samples variable selection sequences in order to learn an updated heuristic from the generated conflicts. The goal is to escape from conflict depressions expeditiously. The branching heuristic deployed in expSAT combines these updates with the standard VSIDS activity scores. An extensive empirical evaluation with four state-of-the-art CDCL SAT solvers demonstrates good-to-strong performance gains with the expSAT approach.
Noise removal and edge detection in ultrasound images by the topological gradient method

This paper presents a new approach for the restoration and edge detection in ultrasound images corrupted with Rayleigh-distributed multiplicative noise (speckle).We consider a semilinear PDE from which we derive the asymptotic expansion of the associated Dirichlet energy functional.We give a theoretical expansion of the topological sensitivity approach to detect and preserve the edge set in the ultrasound image during the restoration process.For the numerical solution, we design a fully-discrete forward Euler-Galerkin stepping algorithm and we present some results which illustrate the efficiency of the proposed approach.
A Parallelization of Non-Serial Polyadic Dynamic Programming on GPU

Parallelization of Non-Serial Polyadic Dynamic Programming (NPDP) on high-throughput manycore architectures, such as NVIDIA GPUs, suffers from load imbalance, i.e. non-optimal mapping between the sub-problems of NPDP and the processing elements of the GPU. NPDP exhibits non-uniformity in the number of subproblems as well as computational complexity across the phases. In NPDP parallelization, phases are computed sequentially whereas subproblems of each phase are computed concurrently. Therefore, it is essential to effectively map the subproblems of each phase to the processing elements while implementing thread level parallelism. We propose an adaptive Generalized Mapping Method (GMM) for NPDP parallelization that utilizes the GPU for efficient mapping of subproblems onto processing threads in each phase. Input-size and targeted GPU decide the computing power and the best mapping for each phase in NPDP parallelization. The performance of GMM is compared with different conventional parallelization approaches. For sufficiently large inputs, our technique outperforms the state-of-the-art conventional parallelization approach and achieves a significant speedup of a factor 30. We also summarize the general heuristics for achieving better gain in the NPDP parallelization.
Artificial Intelligence In The Context Of Noosphere Studies

The article analyzes consequences of the predicted convergence of artificial intelligence systems and telecommunications networks. The predicted convergence represents a qualitatively new level of convergence and interaction of high order. Artificial intelligence systems can influence behavior of transpersonal informational structures which can transform public consciousness. The analogy of global systems with neural networks is used. The best evidence is a neural network model of the noosphere. Its features are determined by fundamental properties of arbitrary neural networks. In the theory of neural networks, it has been proved that their memory is distributed, it is impossible to identify separate “memory cells”. The neural network can be likened to a hologram: each individual fragment restores the same wave front as a source of a worse quality. Based on this analogy, one can say that there is a certain amount of information that is inherent in all members of the community; it is on this basis that a completely objectified interpretation of the collective unconscious phenomenon can be given. The behavior of transpersonal informational structures is illustrated on a number of prototypes which are topologically equivalent to the neural Hopfield processor; for mathematical description of the global communication network, a simulation model is used. The conclusion about the existence of a global communication network, whose ultimate case is the noosphere, is important. Spontaneous” development of artificial intelligence systems (integrated with telecommunication networks), is associated with certain hazards. Thus, it is necessary to understand consequences of the development of artificial intelligence systems.
Inferring Emphasis for Real Voice Data: An Attentive Multimodal Neural Network Approach

To understand speakers’ attitudes and intentions in real Voice Dialogue Applications (VDAs), effective emphasis inference from users’ queries may play an important role. However, in VDAs, there are tremendous amount of uncertain speakers with a great diversity of users’ dialects, expression preferences, which challenge the traditional emphasis detection methods. In this paper, to better infer emphasis for real voice data, we propose an attentive multimodal neural network. Specifically, first, beside the acoustic features, extensive textual features are applied in modelling. Then, considering the feature in-dependency, we model the multi-modal features utilizing a Multi-path convolutional neural network (MCNN). Furthermore, combining high-level multi-modal features, we train an emphasis classifier by attending on the textual features with an attention-based bidirectional long short-term memory network (ABLSTM), to comprehensively learn discriminative features from diverse users. Our experimental study based on a real-world dataset collected from Sogou Voice Assistant ( https://yy.sogou.com/ ) show that our method outperforms (over 1.0–15.5% in terms of F1 measure) alternative baselines.