Reviewerly/exHarmony huggingface dataset:
Performance of Undershot Water Wheel with Bowl-shaped Blades Model

Water power is a type of power obtained from the force created by flowing water.Energy created from flowing water can be harnessed as a form of mechanical energy that can be utilized to generate electricity.Undershot water wheels have been extensively used to take advantage of the water flowing from rivers or waterfalls.This research was conducted by using water turbines with bowl-shaped blades made of iron and acrylic.The diameter of the turbines was 30 cm, and the diameter of the blades was 9 cm.Four, six, and eight blades were used in the three water turbines for this research.The blades were discharged and loaded to turn the turbine to generate force.The results of the study showed that the highest efficiency (η = 74.22%)was found in the six-blade turbine with a discharge of 0.01228 m 3 /s.It can be concluded that water turbines with bowl-shaped blades can be used as an alternative energy in small-scale electric generators.
Multi-interactive Encoder-decoder Network for RGBT Salient Object Detection.

RGBT salient object detection (SOD) aims to segment the common prominent regions by exploring and exploiting the complementary information of visible and thermal infrared images. However, existing methods simply integrate features of these two modalities, and thus could not explore the potentials of their complementarity. In this paper, we propose a novel multi-interactive encoder-decoder network to achieve an elaborative fusion for RGBT SOD. Our network relies on an encoder-decoder for the feature extraction and fusion, and we design a multi-interaction block (MIB) to model the interactions of different modalities, different layers and local-global information. In particular, we interact and integrate the multi-level features of different modalities in a two-stream decoder, which could fuse modal information sufficiently while maintaining their own specific feature representations for more robust detection performance. Moreover, each MIB block accepts both information from previous MIB and global context to restore more spatial details and object semantics respectively. Extensive experiments on the existing RGBT SOD datasets show that the proposed method achieves outstanding performance against the state-of-the-art algorithms.
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions

In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study one-shot methods that construct weighted combinations of ridge regression estimators computed on each machine. By analyzing the mean squared error in a high dimensional random-effects model where each predictor has a small effect, we discover several new phenomena. 1. Infinite-worker limit: The distributed estimator works well for very large numbers of machines, a phenomenon we call infinite-worker limit. 2. Optimal weights: The optimal weights for combining local estimators sum to more than unity, due to the downward bias of ridge. Thus, all averaging methods are suboptimal. We also propose a new Weighted ONe-shot DistributEd Ridge regression (WONDER) algorithm. We test WONDER in simulation studies and using the Million Song Dataset as an example. There it can save at least 100x in computation time, while nearly preserving test accuracy.
Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning’s ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML’s growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.
Trustable Environmental Monitoring by Means of Sensors Networks on Swarming Autonomous Marine Vessels and Distributed Ledger Technology

The article describes a highly trustable environmental monitoring system employing a small scalable swarm of small-sized marine vessels equipped with compact sensors and intended for the monitoring of water resources and infrastructures. The technological foundation of the process which guarantees that any third party can not alter the samples taken by the robot swarm is based on the Robonomics platform. This platform provides encrypted decentralized technologies based on distributed ledger tools, and market mechanisms for organizing the work of heterogeneous multi-vendor cyber-physical systems when automated economical transactions are needed. A small swarm of robots follows the autonomous ship, which is in charge of maintaining the secure transactions. The swarm implements a version of Reynolds' Boids model based on the Belief Space Planning approach. The main contributions of our work consist of: 1) the deployment of a secure sample certification and logging platform based on the blockchain with a small-sized swarm of autonomous vessels performing maneuvers to measure chemical parameters of water in automatic mode; 2) the coordination of a leader-follower framework for the small platoon of robots by means of a Reynolds' Boids model based on a Belief Space Planning approach. In addition, the article describes the process of measuring the chemical parameters of water by using sensors located on the vessels. Both technology testing on experimental vessel and environmental measurements are detailed. The results have been obtained through real world experiments of an autonomous vessel, which was integrated as the 'leader' into a mixed reality simulation of a swarm of simulated smaller vessels. The design of the experimental vessel physically deployed in the Volga river to demonstrate the practical viability of the proposed methods is shortly described.
Hiding data in cover images using a pseudo-random sequences

In this article are discussed techniques of hiding information messages in cover image using direct spectrum spreading technology.This technology is based on the use of poorly correlated pseudorandom (noise) sequences.Modulating the information data with such signals, the message is presented as a noise-like form, which makes it very difficult to detect.Hiding means adding a modulated message to the cover image.If this image is interpreted as noise on the communication channel, then the task of hiding user's data is equivalent to transmitting a noise-like modulated message on the noise communication channel.At the same it is supposed that noise-like signals are poorly correlated both with each other and with the cover image (or its fragment).However, the latter assumption may not be fulfilled because a realistic image is not an implementation of a random process; its pixels have a strong correlation.Obviously, the selection of pseudo-random spreading signals must take this feature into account.We are investigating various ways of formation spreading sequences while assessing Bit Error Rate (BER) of information data as well as cover image distortion by mean squared error (MSE) and by Peak signal-to-noise ratio (PSNR).The purpose of our work is to justify the choice of extending sequences to reduce BER and MSE (increase PSNR).
Variational Inference MPC for Bayesian Model-based Reinforcement Learning

In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge model free methods, especially in simulated robotics tasks. Probabilistic ensembles with trajectory sampling (PETS) is a leading type of MBRL, which employs Bayesian inference to dynamics modeling and model predictive control (MPC) with stochastic optimization via the cross entropy method (CEM). In this paper, we propose a novel extension to the uncertainty-aware MBRL. Our main contributions are twofold: Firstly, we introduce a variational inference MPC, which reformulates various stochastic methods, including CEM, in a Bayesian fashion. Secondly, we propose a novel instance of the framework, called probabilistic action ensembles with trajectory sampling (PaETS). As a result, our Bayesian MBRL can involve multimodal uncertainties both in dynamics and optimal trajectories. In comparison to PETS, our method consistently improves asymptotic performance on several challenging locomotion tasks.
LSTM-Based Anomaly Detection for Non-Linear Dynamical System

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. Specifically, we first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection. According to the prediction requirement, two types of training modes are explored in multi-step prediction, where samples in a wall shear stress dataset are collected by an adaptive sliding window. On the basis of the multi-step prediction result, a Local Average with Adaptive Parameters (LAAP) algorithm is proposed to extract local numerical features of the time sequence and estimate the upcoming anomaly. The experimental results show that our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset, and the LAAP algorithm performs better than the absolute value-based method in anomaly detection task.
Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs

Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.We show that characters' written form, Glyphs, in ideographic languages could carry rich semantics.We present a multi-modal model, Glyph2Vec, to tackle Chinese out-of-vocabulary word embedding problem.Glyph2Vec extracts visual features from word glyphs to expand current word embedding space for out-of-vocabulary word embedding, without the need of accessing any corpus, which is useful for improving Chinese NLP systems, especially for lowresource scenarios.Experiments across different applications show the significant effectiveness of our model.
THE ONLINE LABORATORY SIMULATION WITH CONCEPT MAPPING AND PROBLEM BASED LEARNING (OLS-CMPBL): IS IT EFFECTIVE IN IMPROVING STUDENTS’ DIGITAL LITERACY SKILLS?

Digital literacy skills of students in Indonesia are still very low. This study aims to examining the effectiveness of Online Laboratory Simulation with Concept Mapping and Problem-Based Learning model in improving students’ digital literacy skills. The research objectives have been describing the significances of the difference between learned students using the PBL model, Online Laboratory Simulation with Problem-Based Learning (OLS-PBL) model, Online Laboratory Simulation with Concept Mapping and Problem-Based Learning (OLS-CMPBL) model. It is a quasi-experimental with pre and post-test design. The population of this research is students in Islamic State School Yogyakarta (MAN), Indonesia. The random sampling technique was used from the three schools of Islamic State Schools. There were 97 students of class XI science at Islamic State School 1 (MAN 1) as participant. The technique of data analysis was used the paired-sample t-test, comparative-descriptive analysis, and ANOVA mixed design using SPSS 24. The study reveals: (1) The OLS-CMPBL group has a significant difference in the level of digital literacy skills (sig .0000); (2) the OLS-CMPBL was most effective to improve the skills with gain score 0.207 (low). Learning with the OLS-CMPBL model can be an alternative for policymakers and teachers to solve the problems of digital literacy.
Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement Learning

Complex global behavior patterns can emerge from very simple local interactions between many agents. However, no local interaction rules have been identified that generate some patterns observed in nature, for example the rotating balls, rotating tornadoes and the full-core rotating mills observed in fish collectives. Here we show that locally interacting agents modelled with a minimal cognitive system can produce these collective patterns. We obtained this result by using recent advances in reinforcement learning to systematically solve the inverse modelling problem: given an observed collective behavior, we automatically find a policy generating it. Our agents are modelled as processing the information from neighbour agents to choose actions with a neural network and move in an environment of simulated physics. Even though every agent is equipped with its own neural network, all agents have the same network architecture and parameter values, ensuring in this way that a single policy is responsible for the emergence of a given pattern. We find the final policies by tuning the neural network weights until the produced collective behaviour approaches the desired one. By using modular neural networks with modules using a small number of inputs and outputs, we built an interpretable model of collective motion. This enabled us to analyse the policies obtained. We found a similar general structure for the four different collective patterns, not dissimilar to the one we have previously inferred from experimental zebrafish trajectories; but we also found consistent differences between policies generating the different collective pattern, for example, repulsion in the vertical direction for the more three-dimensional structures of the sphere and tornado. Our results illustrate how new advances in artificial intelligence, and specifically in reinforcement learning, allow new approaches to analysis and modelling of collective behavior.
Towards Visual Dialog for Radiology

Current research in machine learning for radiology is focused mostly on images.There exists limited work in investigating intelligent interactive systems for radiology.To address this limitation, we introduce a realistic and information-rich task of Visual Dialog in radiology, specific to chest X-ray images.Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-theart models.We show that incorporating medical history of the patient leads to better performance in answering questions as opposed to conventional visual question answering model which looks only at the image.While our experiments show promising results, they indicate that the task is extremely challenging with significant scope for improvement.We make both the datasets (synthetic and gold standard) and the associated code publicly available to the research community.
Experiencing and programming the ENIAC in VR

Virtual Reality (VR) applications are applied in various domains (e.g. education, training, engineering or entertainment) and offer new possibilities especially when it comes to training of complex tasks which are not suitable or available in a real physical environment. In this paper, we present a VR application which supports experiencing and learning basic steps in programming the ENIAC in a virtual environment. The ENIAC is one of the world's first general-purpose programmable electronic computers which occupied a 40 x 60 foot room, weighed 30 tons, and was programmed using knobs, switches, and cables. Our implemented VR application has twofold goals. On the one hand it enables to re-experience an early computer and understand its historical context and architecture. On the other hand it demos how to program a computer in a completely different "language" (i.e., knobs and switches) by guiding the user step-by-step through a predefined configuration of the ENIAC.
A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.
Theory of Cooperative-Competitive Intelligence: Principles, Research Directions, and Applications

We present a theory of cooperative-competitive intelligence (CCI), its measures, research program, and applications that stem from it. Within the framework of this theory, satisficing sub-optimal behavior is any behavior that does not promote a decrease in the prospective control of the functional action diversity/unpredictability (D/U) potential of the agent or team. This potential is defined as the entropy measure in multiple, context-dependent dimensions. We define the satisficing interval of behaviors as CCI. In order to manifest itself at individual or team level, this capacity harnesses properties such as degeneracy, pleiotropy (pluri-potentiality), synergies, and metastability. Intelligence is embodied because intelligent behavior is deeply dependent on body functionalities, defined as entropy measures. We base our theory on three principles: (a) relativity of functional entropy/information in agent (team)-environment systems, (b) tendency toward the satisficing level of D/U potential, and (c) tendency toward the non-decreasing D/U potential. The conjunction of these three principles provides existence of sub-optimal behaviors associated with CCI. First, we deal with the problem of how to reduce multidimensional behavior to a concept that accounts for the vast set of scenarios in which CCI is manifested. Secondly, we define and discuss the three interacting principles that underpin CCI behavior as well as providing an outline for a future CCI research program supported by agent-based modeling and empirical research. Finally, we provide some preliminary practical issues that stem from the theory.
Learning Spatial-Temporal Representations Over Walking Tracklet for Long-Term Person Re-Identification in the Wild

Long-term person re-identification (re-ID) aims to build identity correspondence of the Target Subject of Interest (TSI) exposed under surveillance cameras over a long time interval. Compared to the conventional short-term re-ID studied by most existing works, it suffers an additional problem: significant dressing change observed with time lapsing. Unfortunately, this variation in long-term person re-ID case contradicts the assumption of prior short-term re-ID approaches, and thus causes significant difficulties if conventional short-term re-ID methods are applied. To address the problem, this paper proposes to learn hybrid feature representation via a two-stream network named SpTSkM, including a spatial-temporal stream and a skeleton motion stream. The former performs directly on image sequences, which tends to learn identity-related spatial-temporal patterns such as body geometric structure and body movement. The latter operates on normalized 3D skeletons by adapting graph convolutional network, which tends to learn pure motion patterns from skeleton sequences. Both streams extract fine-grained level time-gap stable information that is robust to appearance changes in long-term re-ID and meanwhile maintains sufficient discriminability to differentiate different people. The final matching metric is obtained by mixing information of the two streams in a score-level fusion strategy. In addition, we collect a Cloth-Varying vIDeo re-ID (CVID-reID) dataset particularly for long-term re-ID. It contains video tracklets of celebrities posted on the Internet. These videos are snapshots under extremely different scenarios that include highly dynamic background, diverse camera views and abundant cloth variations on each TSI. These factors cause CVID-reID more complicated and closer to practice. Our experiments demonstrate the difficulty of long-term person re-ID and also validate the effectiveness of the proposed SpTSkM, showing the best performance.
Deep Local Binary Coding for Person Re-Identification by Delving into the Details

Person re-identification (ReID) has recently received extensive research interests due to its diverse applications in multimedia analysis and computer vision. However, the majority of existing works focus on improving matching accuracy, while ignoring matching efficiency. In this work, we present a novel binary representation learning framework for efficient person ReID, namely Deep Local Binary Coding (DLBC). Different from existing deep binary ReID approaches, DLBC attempts to learn discriminative binary codes by explicitly interacting with local visual details. Specifically, DLBC first extracts a set of local features from spatially salient regions of pedestrian images. Subsequently, DLBC formulates a new binary-local semantic mutual information (BSMI) maximization term, based on which a self-lifting (SL) block is built to further exploit the semantic importance of local features. The BSMI term together with the SL block simultaneously enhances the dependency of binary codes on selected local features as well as their robustness to cross-view visual inconsistency. In addition, an efficient optimizing method is developed to train the proposed deep models with orthogonal and binary constraints. Extensive experiments reveal that DLBC significantly minimizes the accuracy gap between binary ReID methods and the state-of-the-art real-valued ones, whilst remarkably reducing query time and memory cost.