Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation on Retransmission Scheme for IDMA-Based Random Access

This paper investigates a packet retransmission scheme appropriate for interleave division multiple access (IDMA)-based random access that supports massive machine-type communications (mMTC) in the fifth generation mobile communication system. A conventional major approach to packet retransmission in a random access-based system is to apply a random retransmission time backoff to each terminal by preparing a time-slot window to resolve packet collisions in a probabilistic manner. However, this approach does not achieve a proactive packet-collision avoidance effect between new and retransmitted packets. The proposed scheme introduces two specific features to the conventional packet retransmission scheme in order to reduce the number of packet collisions between new and retransmitted packets. The first feature is that the base station probabilistically prepares a time-frequency slot that is exclusively used for packet retransmission. The second feature is that terminals that have retransmission packets exclusively use the defined time-frequency slot for packet retransmission in a probabilistic manner. The proposed scheme proactively reduces the frequency of the packet collisions between new and retransmitted packets so that the IDMA-receiver can successfully decode the set of simultaneously transmitted packets. We show the effectiveness of the proposed scheme quantitatively based on computer simulation.
Parallel Transfer Learning in Multi-Agent Systems: What, when and how to transfer?

Multi-agent Reinforcement Learning (RL) is frequently used in large-scale autonomous systems to learn the behaviours that best suit the system’s operating environment. Learning can take a significant amount of time during which an RL system’s performance is necessarily suboptimal. Transfer learning (TL), a method of reusing knowledge which has been gained in one task to improve the performance in another, has been used to speed up learning in single RL agent systems. TL requires learning on a source task to complete before transferring it to a target task, i.e., transfer is done offline. Parallel Transfer Learning (PTL), a technique which enables the source and target tasks to run concurrently, has been proposed to enable online transfers. However, the online selection of knowledge to be transferred, as well as online ways of integration of that knowledge on the receiving agents remain open issues. This paper proposes methods for selecting the knowledge to be transferred in PTL, frequency and size of transfers, and methods for knowledge integration into the target task. We evaluate the proposed approaches in two canonical RL examples: Mountain Car and Co-operative Predator Prey Pursuit. We show that PTL, similarly to RL, is highly sensitive to parameter selection and that suitable parameters differ per scenario.
StepEncog: A Convolutional LSTM Autoencoder for Near-Perfect fMRI Encoding

Learning a forward mapping that relates stimuli to the corresponding brain activation measured by functional magnetic resonance imaging (fMRI) is termed as estimating encoding models. Computational tractability usually forces current encoding as well as decoding solutions to typically consider only a small subset of voxels from the actual 3D volume of activation. Further, while reconstructing stimulus information from brain activation (brain decoding) has received wider attention, there have been only a few attempts at constructing encoding solutions in the extant neuro-imaging literature. In this paper, we present StepEncog, a convolutional LSTM autoencoder model trained on fMRI voxels. The model can predict the entire brain volume rather than a small subset of voxels, as presented in earlier research works. We argue that the resulting solution avoids the problem of devising encoding models based on a rule-based selection of informative voxels and the concomitant issue of wide spatial variability of such voxels across participants. The perturbation experiments indicate that the proposed deep encoder indeed learns to predict brain activations with high spatial accuracy. On challenging universal decoder imaging datasets, our model yielded encouraging results.
Energy Efficiency of Cooperative Relay and Spectrum Sharing

Underutilization of spectrum causes wastage. Harmful jamming disrupts the reception of data. How to overcome the two problems with one solution? Based on a physical layer security measure, we propose a spectrum sharing model that defends against jamming. When a channel is unused by its primary user, it can be shared with others. Being capable of sensing the state of channel occupancy, cognitive radio is introduced to share the channel. When the channel is unoccupied, cognitive radio accesses the channel for its own data transmission. When primary user is sensed to be actively transmitting, cognitive radio helps relaying the primary message. Information redundancy certainly benefits the primary user, but to what extent this model benefits the cognitive radio? The main concern is its energy efficiency, i.e. the ratio of throughput to energy consumption. We investigate the relationships between energy efficiency and (1) the percentage of frame duration cognitive radio allocates for sensing, (2) power allocated for relaying the primary data, and (3) detectable level of primary signal. In a typical setting, we find that allocating 5% to 30% of a time frame for sensing (and the remaining for relaying the primary data) yields good energy efficiency.
Self-Attention based Network For Medical Query Expansion

The aim of clinical decision support implementing electronic health records is to satisfy the physicians’ information needs. We are motivated to propose a self-attention based network on query expansion. Considering the difficulty and cost of medical text annotation and inspired by the idea of migration learning, we choose the Semantic Textual Similarity dataset for model training. Different from the previous work, the proposed approach is not only considering the score of a single term as an expansion term, but also taking the score of term combination into account. Our model utilizes Convolutional Neural Networks (CNN) to obtain sentence representation and self-attention mechanism for entity representation. With self-attention, it is able to estimate the weight of each entity to learn better representation for all entities. We conduct the experiments on three standard datasets of Text REtrieval Conference Clinical Decision Support Track, where the approach has a promising overall performance over the strong baselines.
An Automatic Annotation Method on MOOC's Learning Content

The teaching and learning process in current lectures can be done only by attending online classes through the Massive Open Online Course (MOOC). But in practice, learners find it difficult to find an appropriate course since its subject is not complemented with adequate descriptions. When uploading a new course material, the instructors tend to be reluctant to clearly define the course's descriptions, learning outcomes, and course matter. They would be likely only to upload a set of sentences that cover these things. This paper explains the method of extracting learning content using classification then automatically adds annotations to the learning content. The annotation label contains a course name, description, learning outcomes, and course matters. The dataset was obtained from a set of learning contents in Bahasa Indonesia. It was classified using four methods, rule-based implementation without machine learning, Machine Learning (ML) implementation with Random Forest, Support Vector Machine, and Naive Bayes. The non-ML classification method produced the worst result with an accuracy value of 71.7%. However, the best result was obtained from the ML with Random Forest Classifier. We implemented this method to train the over-sampled training data and hit an accuracy value of 93.3%. Besides, the model was able to produce appropriate annotation output from the new testing data.
Regional Spatiotemporal Collaborative Prediction Model for Air Quality

Numerous countries in the world pay close attention to the change of air quality and the control of air pollution. Air quality prediction has become a challenging issue owing to the complex characteristics with time-space nonlinearity and multi-dimensional feature interaction. This paper inventively proposes a three-dimensional presentation of the air quality data and establishes a deep model using spatiotemporal collaborative strategy to predict regional air quality, which can properly deal with multiple characteristics of air quality. Firstly, a theory of regional air quality prediction is given with reasonable analyses and scientific definitions, for systematically analyzing the closely connected areas and simultaneously predicting multiple locations. Secondly, a three-dimensional data structure called Relevance Data Cube is constructed utilizing a clustering algorithm, time sliding windows and correlation analysis of factors. This structure depicts the whole relationships among multiple dimensions of air quality data clearly and provides a basic analysis foundation for subsequent processing. Thirdly, based on the above theory and data structure, a prediction model is proposed to demonstrate the applicability of our work. Spatial relevance and factor influence are processed using dimension reduction technique provided by CNN, which performs automatic mining of spatial correlation and factors reaction of air quality. Besides, LSTM is combined with CNN to deal with the temporal dependency among the data dynamically. Finally, the proposed model is compared with other neural networks by a large number of experiments. The model is proved to better adapt to the characteristics and predict the air quality more accurately, which is more suitable and reliable for the field of air quality.
Influence of Grid-Tied PV Systems and EV Charging Stations on Power Transformers Failure Rate Behaviour

This document presents the analysis of the failure rate of an oil-inmmersed header distribution transformer due to the presence of current harmonics produced by a Grid-Tied PV system and slow charging stations. Monte-Carlo simulations were performed to generate harmonic profiles in order to calculate the accelerated aging factor that along with the transformer health index determine the behavior of the transformer’s failure rate. The results show that harmonic distortion does not have a significant influence on the transformer life reduction, and that degradation of solid and liquid insulation is one of the cause of increase in the failure rate in long-term as they have a high relative weight in the health index.
Anomaly Detection for Visual Quality Control of 3D-Printed Products

We present a method for detection of surface defects in images of 3D-printed products that enables automated visual quality control. The data characterising this problem is typically high-dimensional (high-resolution images), imbalanced (defects are relatively rare), and has few labelled examples. We approach these challenges by formulating the problem as probabilistic anomaly detection, where we use Variational Autoencoders (VAE) to estimate the probability density of non-faulty products. We train the VAE in an unsupervised manner on images of non-faulty products only. A successful model will then assign high likelihood to unseen images of non-faulty products, and lower likelihood to images displaying defects.We test this method on anomaly detection scenarios using the MNIST dataset, as well as on images of 3D-printed products. The demonstrated performance is related to the capability of the model to closely estimate the density distribution of the non-faulty (expected) data. For both datasets we present empirical results that the likelihood estimated with a convolutional VAE can separate the normal and anomalous data. Moreover we show how the reconstruction capabilities of VAEs are highly informative for human observers towards localising potential anomalies, which can aid the quality control process.
Modified Taguchi-Based Approach for Optimal Distributed Generation Mix in Distribution Networks

In this paper, a new two-stage optimization framework is proposed to determine the optimal-mix integration of dispatchable Distributed Generation (DG), in power distribution networks, in order to maximize various techno-economic and social benefits simultaneously. The proposed framework incorporates some of the newly introduced regulatory policies to facilitate low carbon networks. A modified Taguchi Method (TM), in combination with a node priority list, is proposed to solve the problem in a minimum number of experiments. Nevertheless, the standard TM is computationally fast but has some inherent tendencies of local trapping and usually converges to suboptimal solutions. Therefore, two modifications are suggested. A roulette wheel selection criterion is applied on priority list to select the most promising DG nodes and then modified TM determines the optimal DG sizes at these nodes. The proposed approach is implemented on two standard test distribution systems of 33 and 118 buses. To validate the suggested improvements, various algorithm performance parameters such as convergence characteristic, best and worst fitness values, and standard deviation are compared with existing variants of TM, and improved genetic algorithm. The comparison shows that the suggested corrections significantly improve the robustness and global searching ability of TM, even compared to meta-heuristic methods.
Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction

Forecasting stock market behavior is an interesting and challenging problem. Regression of prices and classification of daily returns have been widely studied with the main goal of supplying forecasts useful in real trading scenarios. Unfortunately, the outcomes are not directly related with the maximization of the financial gain. Firstly, the optimal strategy requires to invest on the most performing asset every period and trading accordingly is not trivial given the predictions. Secondly, price fluctuations of different magnitude are often treated as equals even if during market trading losses or gains of different intensities are derived. In this paper, the problem of stock market forecasting is formulated as regression of market returns. This approach is able to estimate the amount of price change and thus the most performing assets. Price fluctuations of different magnitude are treated differently through the application of different weights on samples and the scarcity of data is addressed using transfer learning. Results on a real simulation of trading show how, given a finite amount of capital, the predictions can be used to invest in high performing stocks and, hence, achieve higher profits with less trades.
Current PI Control for PV Systems in DC Microgrids: A PBC Design

This paper proposes a passive PI control for applications of photovoltaic (PV) systems integrated with boost DC–DC converters. The proposed controller guarantees asymptotically stability in closed–loop for the boost DC–DC converter using Lyapunov theory. In addition, the proposed controller is robust to parametric uncertainties and unmodeled dynamics since it does not depend on the system parameters. The current control mode is selected for the PV system since it is modeled as a current source, where its current is computed as a function of solar irradiance and the cells temperature. The current reference is calculated to a perturbing and observe MPPT algorithm with a current-mode controlled to extract the maximum power available in this solar source. The PI-PBC applied to the boost DC–DC converter is compared with a classical PI approach for validating its effectiveness and the robustness. Simulation results are performed in MATLAB/Simulink with a switching frequency of 5 kHz.
Multi-frequency channel characterization for massive MIMO communications in lobby environment

In this paper, a massive multiple input multiple output (MIMO) channel measurement campaign with two setups is conducted in an indoor lobby environment. In the first setup, two types of 256-element virtual uniform rectangular arrays (URAs), i.e., the 4×64 virtual URA and the 64×4 virtual URA are used. The carrier frequency is 11 GHz; in the second setup, measurements are performed at 4, 6, 11, 13, 15, 18 GHz at two different user locations. The channel characterization is presented by investigating the typical channel parameters, including average power delay profile (APDP), K factor, root mean square (RMS) delay spread, and coherence bandwidth. Moreover, the channel characteristics in angular domain are investigated by applying the space-alternating generalized expectation-maximization (SAGE) algorithm. The extracted multipath components (MPCs) are preliminarily clustered by visual inspection, and related to the interacting objects (IOs) in physical environment. Multipath structures at multiple frequency bands are examined. Direction spread of departure is estimated to evaluate the directional dispersion at the base station (BS) side. The results in this paper can help to reveal the propagation mechanisms in massive MIMO channels, and provide a foundation for the design and application of the practical massive MIMO system.
Modeling and Dynamic Simulation of an Integrated Storage System in a Hybrid Solar-Wind Power Generation System

The electric power provided by a hybrid Solar-Wind power generation system is strongly affected by weather conditions and load profile. Unpredictable fluctuations of the load and the variation of the wind speed, temperature, and irradiation for different seasons require the use of a supplement storage system to absorb the excess of generated power and to compensate for the shortages in case of high demand. This paper focuses on the dynamic modeling, and performance analysis of a Solar-Wind-Lithium battery hybrid energy system connected to an alternating AC load. The MPPT (Maximum Power Point Tracking) control is used to extract the maximum power from the photovoltaic panels and wind turbine. Simulations are performed, and the dynamic performance of the storage system (SS) to respond to the reduced production or a sudden load demand is analyzed.
ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks

Despite the improvements in perception accuracies brought about via deep learning, developing systems combining accurate visual perception with the ability to reason over the visual percepts remains extremely challenging. A particular application area of interest from an accessibility perspective is that of reasoning over statistical charts such as bar and pie charts. To this end, we formulate the problem of reasoning over statistical charts as a classification task using MAC-Networks to give answers from a predefined vocabulary of generic answers. Additionally, we enhance the capabilities of MAC-Networks to give chart-specific answers to open-ended questions by replacing the classification layer by a regression layer to localize the textual answers present over the images. We call our network ChartNet, and demonstrate its efficacy on predicting both in vocabulary and out of vocabulary answers. To test our methods, we generated our own dataset of statistical chart images and corresponding question answer pairs. Results show that ChartNet consistently outperform other state-of-the-art methods on reasoning over these questions and may be a viable candidate for applications containing images of statistical charts.
Learning Distributed Coordinated Policy in Catching Game with Multi-Agent Reinforcement Learning

Although learning-based methods such as reinforcement learning have been applied to multi-agent systems design successfully, it is still difficult to learn efficient coordinated policies for agents in partially observed environment settings. Centralized learners contain much more information, but add more complexity, while independent learners suffer from partial observation. To address these problems, we propose a directed multi-agent actor-critic algorithm to directly learn the coordinated policy from experience. The directed critic model can obtain all information including global information and actions, which provides effective learning signals for distributed learning actors. We take Multi-Agent Catching Game as the test scenario, where the task is to coordinate multiple moving paddles to catch balls dropping from the top of the screen. We perform several experimental evaluations and show that our method leads to superior results in learning performance, coordination effect and scalability, compared with both centralized and independent learning approach.
Patent Strategy in the Digital Transformation Era

The digital transformation and big data paradigms have expanded across many research fields, including both strategy and innovation. Although existing research attempts to keep up with the pace of these phenomena, more in-depth knowledge of how patent big data can help firms and managers in their decision-making process is still needed. Based on patent co-classification analysis, this paper aims to provide two different but complementary patent tools; the first exploits ex-ante patent information whereas the latter integrates it with ex-post details extracted by patent documents. We further investigate the technology positioning and links as well as examine the industry’s «excellence» technology structure conceived as the combination of the technology elements that has yielded high-impactful inventions.
A novel network intrusion detection algorithm based on Fast Fourier Transformation

Deep learning techniques have been widely used in intrusion detection, but their application on convolutional neural networks (CNN) is still immature. The main challenge is how to represent the network traffic to improve performance of the CNN model. In this paper, we propose a network intrusion detection algorithm based on representation learning using Fast Fourier Transformation (FFT), which is first exploration that converts traffic to image by FFT to the best of our knowledge. Each traffic is converted to an image and then the intrusion detection problem is turned to image classification. The experiment results on NSL-KDD dataset show that the classification performence of the algorithm in the CNN model has obvious advantages compared with other algorithms.
Design and Control of a Dual-Stage Actuation Active Vibration Isolation System

Active vibration isolation system is used in various fields to reduce the vibrations transmitted. In this paper, a novel dual-stage actuator is proposed, which combines a voice coil motor and a piezoelectric actuator. The proposed dual-stage actuator maintains the advantages, in terms of the long stroke, the high precision, and the wide band. The dual-stage actuation active vibration isolation system (DSA-AVIS) with the proposed dual-stage actuator is initially modeled. The characteristic of the DSA-AVIS is subsequently analyzed. $H_{\infty }$ controller is finally designed to consider the vibration isolation performance and the output range of the actuators. The experimental results indicate that the closed-loop transmissibility of the DSA-AVIS is less than −18 dB from 0 to 100 Hertz.