Reviewerly/exHarmony huggingface dataset:
Learning Subgoal Representations with Slow Dynamics

In goal-conditioned Hierarchical Reinforcement Learning (HRL), a high-level policy periodically sets subgoals for a low-level policy, and the low-level policy is trained to reach those subgoals. A proper subgoal representation function, which abstracts a state space to a latent subgoal space, is crucial for effective goal-conditioned HRL, since different low-level behaviors are induced by reaching subgoals in the compressed representation space. Observing that the high-level agent operates at an abstract temporal scale, we propose a slowness objective to effectively learn the subgoal representation (i.e., the high-level action space). We provide a theoretical grounding for the slowness objective. That is, selecting slow features as the subgoal space can achieve efficient hierarchical exploration. As a result of better exploration ability, our approach significantly outperforms state-of-the-art HRL and exploration methods on a number of benchmark continuous-control tasks. Thanks to the generality of the proposed subgoal representation learning method, empirical results also demonstrate that the learned representation and corresponding low-level policies can be transferred between distinct tasks.
Continuous Time Analysis of Momentum Methods

Gradient descent-based optimization methods underpin the parameter training of neural networks, and hence comprise a significant component in the impressive test results found in a number of applications. Introducing stochasticity is key to their success in practical problems, and there is some understanding of the role of stochastic gradient descent in this context. Momentum modifications of gradient descent such as Polyak's Heavy Ball method (HB) and Nesterov's method of accelerated gradients (NAG), are also widely adopted. In this work our focus is on understanding the role of momentum in the training of neural networks, concentrating on the common situation in which the momentum contribution is fixed at each step of the algorithm. To expose the ideas simply we work in the deterministic setting. Our approach is to derive continuous time approximations of the discrete algorithms; these continuous time approximations provide insights into the mechanisms at play within the discrete algorithms. We prove three such approximations. Firstly we show that standard implementations of fixed momentum methods approximate a time-rescaled gradient descent flow, asymptotically as the learning rate shrinks to zero; this result does not distinguish momentum methods from pure gradient descent, in the limit of vanishing learning rate. We then proceed to prove two results aimed at understanding the observed practical advantages of fixed momentum methods over gradient descent. We achieve this by proving approximations to continuous time limits in which the small but fixed learning rate appears as a parameter. Furthermore in a third result we show that the momentum methods admit an exponentially attractive invariant manifold on which the dynamics reduces, approximately, to a gradient flow with respect to a modified loss function.
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning

Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the synchronous setting (such that independent samples for all state-action pairs are drawn from a generative model in each iteration), substantial progress has been made recently towards understanding the sample efficiency of Q-learning. To yield an entrywise $\varepsilon$-accurate estimate of the optimal Q-function, state-of-the-art theory requires at least an order of $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^5\varepsilon^{2}}$ samples for a $\gamma$-discounted infinite-horizon MDP with state space $\mathcal{S}$ and action space $\mathcal{A}$. In this work, we sharpen the sample complexity of synchronous Q-learning to an order of $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^4\varepsilon^2}$ (up to some logarithmic factor) for any $0<\varepsilon <1$, leading to an order-wise improvement in terms of the effective horizon $\frac{1}{1-\gamma}$. Analogous results are derived for finite-horizon MDPs as well. Our finding unveils the effectiveness of vanilla Q-learning, which matches that of speedy Q-learning without requiring extra computation and storage. A key ingredient of our analysis lies in the establishment of novel error decompositions and recursions, which might shed light on how to analyze finite-sample performance of other Q-learning variants.
Metadata management and application

In the parlance of Library and Information Sciences, metadata refers to information dimensions which are created, stored and shared in order to understand the value and contextual relevance of an information resource. It is inspired from the very idea of cataloguing library resources that has been the bread and butter of librarians for centuries. With the evolution of the World Wide Web and more recently the social semantic web, the importance of metadata has acquired a new dimension specific to web information management systems. The paper attempts to be a primer of various dimensions of metadata management for students, researchers and practitioners in the domain of Library and Information Sciences. It addresses wide ranging theoretical, technical and research issues in metadata management, and concludes with a fervent appeal to take up metadata practice and research.
Unified Computational Modelling for Healthcare Device Security Assessment

This article evaluates the security techniques that are used to maintain the healthcare devices, and proposes a mathematical model to list these in the order of priority and preference. To accomplish the stated objective, the article uses the Fuzzy Analytic Network Process (ANP) integrated with Technical for Order Preference by Similarities to Ideal Solution (TOPSIS) to find the suitable alternatives of the security techniques for securing the healthcare devices from trespassing. The methodology is enlisted to rank the alternatives/ techniques based on their weights’ satisfaction degree. Thereafter, the ranks of the alternatives determine the order of priority for the techniques used in healthcare security. The findings of our analysis cite that Machine Learning (ML) based healthcare devices obtained the highest priority among all the other security techniques. Hence the developers, manufacturers and researchers should focus on the ML techniques for securing the healthcare devices. The results drawn through the aid of the suggested mathematical model would be a corroborative reference for the developers and the manufacturers in assessing the security techniques of the healthcare devices.
Image Segmentation Based on Improved Unet

Abstract In order to help doctors diagnose and treat liver lesions and accurately segment liver images, this paper proposes an improved Unet network, which adds compression extraction modules and full-scale connection blocks, extracts input image features, and achieves accurate segmentation of liver images. The compression extraction module distributes weights to convolutional layers of different sizes, which is conducive to the extraction of image spatial information and context information. Full-scale blocks are connected by skipping,combining the higher semantic information from the decoder and corresponding the lowwer semantic information from the encoder to strengthen the ability to extract tumor edge information. This article includes 25 cases from the Lits liver dataset. The dataset is classified as the training dataset and the test dataset, and the image blocks are extracted after gray-scale normalization and input to the network to acquire the final segmentation results. The segmentation result is evaluated by F1 score. Comparing multiple sets of experiments, compared with general network structures such as Unet and AttenUnet, it shows that the network architecture proposed in the Dissertation improves the accuracy and efficiency of liver image segmentations.
PENGARUH MODEL DISCOVERY LEARNING TERHADAP KETERAMPILAN MENULIS TEKS EKSPOSISI SISWA KELAS VIII SMP NEGERI 31 PADANG

ABSTRACT The purpose of this research was to describe things as follow. First, describes the writing skills of the exposition text of grade VIII Junior High School 31 Padang before using the discovery learning model. Secondly, describes the writing skills of the exposition text of grade VIII Junior High School 31 Padang after using the discovery learning model. Third, analyze the effect of discovery learning models on the exposition writing skills of grade VIII studensts of Junior High School 31 Padang. The type of this research is quantitative research using experimental methods Based on the results of the study, can be concluded three things as follows. First, the writing skills of the exposition text of grade VIII students of Junior High School 31 Padang before using discovery learning model are in fair qualifications (61,59). Secondly, the writing skills of exposition text of grade VIII students of Junior High School 31 Padang after using discovery learning model are in good qualifications (78,26). Third, there is a significant influence in the use of discovery learning models on the exposition writing skills of grade VIII students of Junior High School 31 Padang, so it can be concluded that H1 is accepted in H0 is rejected because the test results prove that tcount is greater than ttable (2,04&gt;1,70). Kata Kunci: Pengaruh, Model Discovery Learning, Menulis Teks Eksposisi
Environmental Factors and Metal Mobilisation in Alluvial Sediments—Minas Gerais, Brazil

In areas contaminated by potentially toxic elements (PTEs), knowledge of processes of metal mobilisation is the basis for the choice of appropriate remediation methodologies. The mobilisation of metals is a function of several factors, and the response to these factors must be well known during the planning of remediation strategies. The activity of an ore metallurgical plant in South-East Brazil resulted in major contamination by several heavy metals. Reversing the contamination’s negative impact required geochemical assessment of the area, including the physicochemical characterisation, quantification, and delimitation of PTEs, and the rating of the solubilisation/mobilisation capacity of these elements. The definition of spatial patterns for PTEs’ distribution allowed the construction of contamination risk maps which work as a tool for the mitigation and control of the contamination plume. The chemical analysis of interstitial water and selective and sequential extraction methodologies showed that elements that occur in the environment in critical concentrations (Zn, Cd, Pb, As) are mostly associated with easily mobilised forms (soluble, exchangeable cations, associated with Mn oxides). Given the great mobility of the contamination plume, any process of removal of contaminated material becomes unfeasible, thus the strategy of remediation for the stream and associated alluvial deposits must be based on methods of in situ decontamination.
Multiobjective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.
Research on Image Steganography Based on Sudoku Matrix

At present, the Sudoku matrix, turtle shell matrix, and octagonal matrix have been put forward according to the magic matrix-based data hiding methods. Moreover, the magic matrices to be designed depend on the size of the embedding capacity. In addition, by determining the classification of points of pixel pairs after applying a magic matrix and by determining the traversal area, the average peak signal-to-noise ratio (PSNR) can be improved. Therefore, this topic intends to propose a data hiding method based on a 16 × 16 Sudoku matrix by using the 16 × 16 Sudoku matrix and extending it to a double-layer magic matrix. Low-cost data embedding methods are also studied, in order to improve the PSNR and maintain good image quality with the same embedding capacity.
Generating Biomedical Question Answering Corpora From Q&amp;A Forums

Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q\&A web forums, which can be used for document retrieval, a crucial step of most QA systems. The proposed framework extracts from selected forums the questions and the respective answers that contain citations. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the BiQA corpus by applying our framework to three forums, obtaining 7,453 questions and 14,239 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we demonstrated that the articles given as answers are significantly similar to the questions and trained a state-of-the-art deep learning model that obtained similar performance to using a dataset manually annotated by experts. The proposed framework can be used to update the BiQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents. The BiQA corpus and the framework used to generate it are available at \url{https://github.com/lasigeBioTM/BiQA}.
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning

In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories.
Human Activity Prediction-aware Sensor Cycling in Smart Home Networks

Activity recognition in smart homes required extensive machine learning methods operating over data obtained from the sensors connected to various things at homes. However, human behavior is always dynamic and there is a possibility of missing important activities. Similarly, the sensors operate over battery power, and detecting frequent activities can drain the battery eventually. In this paper, we propose Human Activity Predication-aware Sensor Cycling (HAPSC) approach that efficiently detects the future activities and designates the sensors to each predicated activity using a hybrid Long-Short Term Memory (LSTM) model with the Quality Learning (QL) support. The QL is added to detect those activities which are missed during the training process. The simulation is performed in comparison with the leading machine learning algorithms for the detection accuracy and energy consumption of the sensors. The performance evaluation shows that the proposed scheme detects the activities with an accuracy of 85.7% to 99.4%. Similarly, the energy consumption of the sensors and the network lifetime is significantly improved compared to existing duty cycling schemes.
Digital citizenship under lockdown: promoting the healthy use of technology for adolescents growing-up in Perú during COVID-19

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author.Additional informationNotes on contributorsLucía Magis-WeinbergLucía Magis-Weinberg, MD, PhD, is a cross-cultural developmental scientist at the Institute of Human Development at UC Berkeley. She leads Transitions, a researcher-practitioner partnership which includes a large-scale culturally and developmentally informed school intervention to foster digital citizenship for youth in Perú and México. She received her MD from UNAM and her PhD in Developmental Cognitive Neuroscience from UCL. She is Executive Editor of Neuromexico.org, a science communication platform for Latin America.
Peer Instruction in Software Testing and Continuous Integration

Peer Instruction (PI) is an active learning pedagogy in which students actively participate in the classroom. There have been several research studies regarding the value of PI in computer science. The present work adds to this body of knowledge by examining outcomes from an undergraduate software engineering course with specific focus on the effects of PI on student learning in the topics of unit testing, integration testing and continuous integration. We find encouraging increases in levels of success as measured through a cognitive pre- and post-course survey for those topics. This work also documents and hypothesizes reasons for the cognitive gains from PI, as well as student attitudes towards PI.
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

In recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems’ vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid QuantumClassical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models’ effectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models’ performance in terms of accuracy and consumption of computational resources.
Envisioning Arduino Action

Micro-controllers like Arduino are increasingly used in classroom settings to teach skills, attitudes and knowledge around technology. Education around these tools is often set in group contexts and collaboration is typically considered an important part of the learning process. However, much of the software tools currently available are still designed around a laptop programming paradigm that tends to restrict collaboration rather than encourage shifting of roles and sharing of experiences. This paper explores how to design tools that invite collaborative interactions, in particular how mobile software tools, with heavy usage of video as an interactive resource, could allow for collaborative sketching and understanding. Based on contextual inquiries with educators, observations and engagements with students in classroom settings, and a workshop with Arduino Education, this paper sketches out a vision for how the software tools used for learning physical computing can be re-designed to better work within the context of group work from the observations, for groups of students aged 14–16-years old.
Blockwise Recursive Moore–Penrose Inverse for Network Learning

Training neural networks with the Moore–Penrose (MP) inverse has recently gained attention in view of its noniterative training nature. However, a significant drawback of learning based on the MP inverse is that the computational memory consumption grows along with the size of a dataset. In this article, based on the partitioning of the MP inverse, we propose a blockwise recursive MP inverse formulation (BRMP) for network learning with low-memory property while preserving its training effectiveness. The BRMP is an equivalent formulation to its batchwise counterpart since neither approximation nor assumption is made in the derivation process. Our further exploration of this recursive method leads to a switching structure among three different scenarios. This structure also reveals that the well-known recursive least squares method is a special case of our proposed technique. Subsequently, we apply BRMP to the training of radial basis function networks as well as multilayer perceptrons. The experimental validation covers both regression and classification tasks.
Design and Smartphone Implementation of Chaotic Duplex H.264-Codec Video Communications

In this paper, a chaotic duplex H.264-codec-based secure video communication scheme is designed and its smartphone implementation is also carried out. First, an improved self-synchronous chaotic stream cipher algorithm equipped with a sinusoidal modulation, a multiplication, a modulo operation and a round down operation (SCSCA-SMMR) is developed. Using the sinusoidal modulation and multiplication, the improved algorithm can resist the divide-and-conquer attack by traversing multiple nonzero component initial conditions (DCA-TMNCIC). Meanwhile, also by means of the round down operation and modulo operation, on the premise that the DCA-TMNCIC does not work, the original keys cannot be further deciphered only by the known-plaintext attack, the chosen-plaintext attack and the chosen-ciphertext attack, respectively. Then, the Android low-level multimedia support infrastructure MediaCodec class is used to access low-level media encoder/decoder components and the H.264 hardware encoding/decoding is performed on real-time videos, so the chaotic video encryption and decryption can be realized in real-time by smartphones. Security analysis and smartphone experimental results verify the effectiveness of the proposed method.