Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Parameters of a Non-dissipative Snubber Network Using a Genetic Algorithm

This paper presents a novel topology which reduces losses through soft switching technique and has an energy recovery circuit. The proposed topology takes advantage of inductor and capacitor parallel connection to reduce switching losses to off. The values of the capacitor and the inductor are selected using a genetic algorithm (GA). The circuit and operating principle of the proposed topology are presented. Validation is carried out by means simulation and the results are presented. Simulations using LTspice of the proposed topology and conventional hard switching topologies are compared. The soft switching proposed topology shows a reduction of switching losses of up to 12.5 % with respect to conventional hard switching topology.
Keyphrase Guided Beam Search for Neural Abstractive Text Summarization

As a recently proposed way of text summarization, abstractive text summarization features the use of new phrases to obtain a condensed version of the source text. Most approaches nowadays in this category are under the sequenceto-sequence framework, which is the bedrock of many text generation tasks. However, these approaches usually fail to get a sound representation of the source text, and they are liable to produce summaries with a low semantic relevance. In this work, we devise a novel structure of convolutional recurrent neural network-based encoder to get a better latent representation of the source text. Further, we propose to exploit keyphrases to guide the summary selection in a modified beam search process, thus contributing to a closer semantic relevance between the source text and the generated summary. With this new approach, we outperform the state-of-the-art systems on several datasets at no extra cost of training, particularly on the benchmark CNN/Daily Mail dataset where the source texts are lengthy [1].
Generative Adversarial Networks for Road Crack Image Segmentation

In this paper, we present a road crack segmentation method based on generative adversarial networks (GAN). Our GAN networks consist of two neural network models in terms of a generator and a discriminator, where two improved networks CU-Net and FU-Net are proposed based on U-Net. The U-Net, CU-Net and FU-Net are used as the generator, while two-class networks are used as the discriminator. The purpose of using the generator is to generate fake crack images which are very similar to real crack images. And the recognition is done by the discriminator to distinguish the real crack images from the fake crack images. After iterative training between the generator and the discriminator, the generator can generate fake crack images that are very similar to the real crack image. Finally, the generator can be used to segment the road crack images. Compared with the other state-of-the-art methods on three datasets, the proposed method achieves better performance. Specifically, the precision, recall and F1-score are 91.46%, 73.40%, and 77.33%, respectively on one of the public datasets.
Multi-Agent Deep Reinforcement Learning with Emergent Communication

When compared with their single-agent counterpart, multi-agent systems have an additional set of challenges for reinforcement learning algorithms, including increased complexity, non-stationary environments, credit assignment, partial observability, and achieving coordination. Deep reinforcement learning has been shown to achieve successful policies through implicit coordination, but does not handle partial-observability. This paper describes a deep reinforcement learning algorithm, based on multi-agent actor-critic, that simultaneously learns action policies for each agent, and communication protocols that compensate for partial-observability and help enforce coordination. We also research the effects of noisy communication, where messages can be late, lost, noisy, or jumbled, and how that affects the learned policies. We show how agents are able to learn both high-level policies and complex communication protocols for several different partially-observable environments. We also show how our proposal outperforms other state-of-the-art algorithms that don’t take advantage of communication, even with noisy communication channels.
Advantages and Characteristics of a WPT System with a Resonant Wired-2-Coil Repeater

A near-field wireless power transfer (WPT) with magnetic resonant coupling (MRC) offers a dynamic means for charging various electric apparatuses. This paper presents the modeling and characteristics of a system with a resonant wired-2-coil repeater that can charge devices in any available space using a repeater wire. Moreover, resonant frequencies are discussed, and a method to tune the frequencies is described. Experimental results show that the modeling and tuning methods are valid and that the system can maintain a power transfer efficiency of more than 80% even when the repeater wire is 10-m long.
A Proof of Local Convergence for the Adam Optimizer

Adaptive Moment Estimation (Adam) is a very popular training algorithm for deep neural networks, implemented in many machine learning frameworks. To the best of the authors knowledge no complete convergence analysis exists for Adam. The contribution of this paper is a method for the local convergence analysis in batch mode for a deterministic fixed training set, which gives necessary conditions for the hyperparameters of the Adam algorithm. Due to the local nature of the arguments the objective function can be non-convex but must be at least twice continuously differentiable.
Representation-dimensionality Trade-off in Biological Sequence-based Inference

Statistical inference from the analysis of biological sequences is widely used in the prediction of structure and biochemical functions of newly found macromolecules. For the application of machine learning methodologies such as kernel methods and artificial neural networks for such inference, variable length sequence data is often embedded in a finite dimensional real-valued space. The corresponding embedding dimensions are often high, leading to technical difficulties centred around the statistical concept of the curse of dimensionality. We demonstrate a trade-off between fidelity of representation of amino acids of proteins and the resulting dimensionality of the embedding space. Clustering chemically similar amino acids, thereby reducing the alphabet size, reduces the accuracy in their variation, but achieves a reduction in the corresponding feature space. We show this trade-off in three different problems of statistical inference, namely, protein-protein interaction, remote homology and secondary structure prediction. We show that in the reduced space performance often improves similar to what is seen in "diminishing returns" type reward-effort curves. We find alphabet reduction schemes taken from the literature, which are based on some biochemical rationale, perform significantly better than arbitrary random clustering of the alphabets. Statistical feature selection from the full 20 amino acid representation is not competitive with any of these. Dimensionality of representation has an important role when mapping sequence data onto fixed dimensions of an Euclidean space. This work shows that dimensionality reduction based on compressing the amino acid alphabet improves inference performance in two widely studied problems and degrades gracefully in the third. Alphabet reduction, which has a principled biochemical basis, is shown to be superior to feature selection which is purely a statistical exercise.
Unsupervised Feature Learning for Visual Place Recognition in Changing Environments

Visual place recognition in changing environments is a challenging and critical task for autonomous robot navigation. Deep convolutional neural networks (ConvNets) have recently been used as efficient feature extractors and obtained excellent performance in place recognition. However the success of Con-vNets’ learning highly relies on the availability of large datasets with millions of labeled images, the collection of which is a tedious and costly burden. Thus we develop an unsupervised learning method (the siamese VisNet) to autonomously learn invariant features in changing environments from unlabeled images. The siamese VisNet has two identical branches of sub-networks. With a Hebbian-type of learning rule incorporating a trace of previous activity patterns, the siamese VisNet learns features with increasing invariance in changing environments from layer to layer. Experiments conducted on multiple datasets demonstrate the robustness of the siamese VisNet against viewpoint changes, appearance changes, and joint viewpoint-appearance changes. In addition, the siamese VisNet, with lower complexity in architecture, outperforms the state-of-the-art place recognition ConvNets such as the CaffeNet and the PlaceNet. The proposed siamese VisNet constitutes a biologically plausible yet efficient method for unsupervised place recognition.
Per-Flow Fairness in the Datacenter Network

Datacenter network (DCN) design has been actively researched for over a decade. Solutions proposed range from end-to-end transport protocol redesign to more intricate, monolithic and cross-layer architectures. Despite this intense activity, to date we remark the absence of DCN proposals based on simple fair scheduling strategies. In this paper, we evaluate the effectiveness of FQ-CoDel in the DCN environment. Our results show, (i) that average throughput is greater than that attained with DCN tailored protocols like DCTCP, and (ii) the completion time of short flows is close to that of state-of-art DCN proposals like pFabric. Good enough performance and striking simplicity make FQ-CoDel a serious contender in the DCN arena.
Flow Cache Cleansing with FPGA Hash Pipe for Highly Stabilized Software Data Plane

Recent expansion of cloud services is accelerating the development of flexible and fast network technologies. Software and hardware co-design of data plane packet processing pipelines is attracting growing attentions to achieve both flexibility and high-speed performance at the same time. In this paper, we propose a technique called flow cache cleansing for the data plane of a software switch co-designed with an FPGA hashing pipeline. If a lot of small flow rushes to the target system, the flow cache of the software switch is contaminated, causing performance degradation and instability. Our contribution is to clarify the factor determining the performance and overcome the stability issue. Experimental results show that the proposed method can mitigate the flow cache pollution and achieve throughput as much as several times faster than some commodity technologies. Moreover, the results demonstrate that the proposed method produced at least several times higher performance stability than existing technologies.
Cloud-Based Online Ageing Monitoring for IoT Devices

Reliability of an electronic device, concerning if it can function reliably over its designated lifetime in the field (such as 10 or 15 years), has become more and more important in today’s safety-critical applications such as automotive electronics. Traditionally, the ageing has been performed in an offline setting where stress test has been applied to accelerate the ageing process and then a model is established to make the futuristic prediction. This kind of offline method has a drawback of not being able to take into account the factor of the unique operating condition and environment that a device could have experienced in the field. In this work, we present the first cloud-based ageing monitoring system to the best of our knowledge, for the Internet-of-Things (IoT) devices. It has many advantages. First of all, one can know of the ageing status of an IoT device remotely and continuously. Secondly, through data analysis in a cloud server, more accurate prediction can be achieved. Thirdly, an ageing hazard can be alarmed in advance before it actually strikes, and thereby pre-caution actions (such as online repair, or even call-for-maintenance request) can be taken in advance to avoid unnecessary system fatal failure. A prototype system using test chips with built-in design-for-ageing-monitoring circuitry will be demonstrated with measurement data collected through a cloud server.
Personalizing Session-based Recommendation with Dual Attentive Neural Network

Session-based recommendation which aims to recommend the next item in an anonymous session for users, becomes one of the most popular tasks in recommendation area. Traditional methods such as matrix factorization and item-to-item perform very poorly because they only take into account the last click of the session and ignore the information of the whole click sequence. On the other hand, Recurrent Neural Network (RNN) based methods have performed excellently in session-based recommendation. However, they only consider the user’s sequential behavior in the current session or only use cross-session information to track user’s interests over time, whereas the user preference is not emphasized in cross-session. Therefore, in this work, we design a novel neural network framework for personalized session-based recommendation, named Dual Attentive Neural Network (DANN). DANN considers user’s main purpose of current session and user’s personalized preference of cross-session. Specifically, in DANN we exploit a user-level attention mechanism to model user’s personalized preference and capture user’s main purpose in the current session via a session-level attention mechanism. The experimental results on two real-world datasets show that our DANN model outperforms other baseline models. Furthermore, we find that DANN achieves improvement when modeling user personalized preferences, which shows the advantage of modeling user’s preference and user’s purpose simultaneously.
The Development of Machine Learning In Energy Trading

The energy industry has changed at unforeseeable speed. The level of renewable energy in the power system has reached record high year on year. This has brought certain challenges to the prediction and trading of the energy using conventional methods in the ever weather dependent market. In this paper, we will review some of the machine learning technologies that have been used in financial market and can be extended to energy trading. The paper will also cover the unique situation of energy market, i.e. Not economical for large scale of storage. This paper will also have a brief overview of the use of machine learning in demand forecasting.
Reaching development through visuo-proprioceptive-tactile integration on a humanoid robot - a deep learning approach

The development of reaching in infants has been studied for nearly nine decades. Originally, it was thought that early reaching is visually guided, but more recent evidence is suggestive of “visually elicited” reaching, i.e. infant is gazing at the object rather than its hand during the reaching movement. The importance of haptic feedback has also been emphasized. Inspired by these findings, in this work we use the simulated iCub humanoid robot to construct a model of reaching development. The robot is presented with different objects, gazes at them, and performs motor babbling with one of its arms. Successful contacts with the object are detected through tactile sensors on hand and forearm. Such events serve as the training set, constituted by images from the robot's two eyes, head joints, tactile activation, and arm joints. A deep neural network is trained with images and head joints as inputs and arm configuration and touch as output. After learning, the network can successfully infer arm configurations that would result in a successful reach, together with prediction of tactile activation (i.e. which body part would make contact). Our main contribution is twofold: (i) our pipeline is end-to-end from stereo images and head joints (6 DoF) to armtorso configurations (10 DoF) and tactile activations, without any preprocessing, explicit coordinate transformations etc.; (ii) unique to this approach, reaches with multiple effectors corresponding to different regions of the sensitive skin are possible.
Towards Optimizing Convolutional Neural Networks for Robotic Surgery Skill Evaluation

In medicine courses, improve the skills of surgery students is an essential part of the program. For training the surgeon residents the institutions normally using a standard checklist to evaluate the student evolution. However, the checklist evaluation is susceptible to evaluator bias, inter-evaluator variability, besides being time-consuming. The automation of this process is an important evolution in medical training. An alternative to the instructor checklist is capturing and evaluation of kinematic data regarding the surgical motion. We propose a novel CNN architecture for automated robot-assisted skill assessment. We explore the use of the SELU activation function and a global mixed pooling approach based on the average and max-pooling layers. Finally, we examine two types of convolutional layers: real-value and quaternion-valued. The results suggest that our model presents a higher average accuracy across the three surgical subtasks of the JIGSAWS dataset.
Monitoring System for Prediction and Detection of Epilepsy Seizure

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.
Deep Neural Networks for Network Routing

In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g. with mixed integer linear programming. However, determining these solutions requires solving complex optimization problems and, thus, cannot be typically done at runtime. Instead, heuristics for these problems are often created but designing them is non-trivial in many cases. The routing framework proposed here presents an alternative to the design of heuristics, whilst still achieving good performance. This is done by building a DL model trained on the optimal decisions over flows from known traffic demands. To evaluate our solution, we focused on the problem of network congestion, even though a wide range of alternative objectives could be fitted into this framework. We ran experiments using two publicly available datasets of networks with real traffic demands and showed that our solution achieves close-to-optimal network congestion values.
Evaluation of Heart Disease Diagnosis Approach using ECG Images

Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient’s chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way to diagnosis two types of heart arrhythmia, by using the ECG record as an image. To access the performance of our system, five feature extraction methods well-known in literature are used along with five different classifiers are tested. We were able to identify heart disorders with over 96.00% of accuracy, using a vanilla neural-network, Multilayer Perceptron (MLP), and Local Binary Patterns (LBP) from ECG images. This investigation has shown promising results from a medical point-of-view.
Age and Gender Estimation via Deep Dictionary Learning Regression

The paper addresses the problem of estimating age and gender from frontal photos. Most prior studies on deep learning based estimation formulate it as a convolutional neural network based classification problem. For gender it is a two class problem; for age, several age brackets are created to form classes. In this work we formulate it as a regression problem. This is a natural way to handle both gender and age. Gender can be represented as a single variable possible of taking binary values (male or female) whereas age can be represented by a single variable taking non-negative real values. We formulate regression on the newly proposed deep dictionary learning framework. Prior work on this topic, is on unsupervised representation learning; in this work we in-built regression into the deep dictionary learning framework making the formulation supervised. Testing has been done on several state-of-the-art datasets – Adience, MORPH, ChaLearn LAP, LFWA and CelebA. Our method yields age and gender estimation results better than the state-of-the-art.
Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration

Successful deep neural network models tend to possess millions of parameters. Reducing the size of such models by pruning parameters has recently earned significant interest from the research community, allowing more compact models with similar performance level. While pruning parameters usually result in large sparse weight tensors which cannot easily lead to proportional improvement in computational efficiency, pruning filters or entire units allow readily available off-the-shelf libraries to harness the benefit of smaller architecture. One of the most well-known aspects of network pruning is that the final retained performance can be improved by making the process of pruning more gradual. Most existing techniques smooth the process by repeating the technique (multi-pass) at increasing pruning ratios, or by applying the method in a layer-wise fashion. In this paper, we introduce Dynamic Unit Surgery (DUS) that smooths the process in a novel way by using decaying mask values, instead of multi-pass or layer-wise treatment. While multi-pass schemes entirely discard network components pruned at the early stage, DUS allows recovery of such components. We empirically show that DUS achieves competitive performance against existing state-of-the-art pruning techniques in multiple image classification tasks. In CIFAR10, we prune VGG16 network to use 5% of the parameters and 23% of FLOPs while achieving 6.65% error rate with no degradation from the original network. We also explore the method’s application to transfer learning environment for fine-grained image classification and report its competitiveness against state-of-the-art baseline.
Hierarchical Classification Feature Extraction for Moving Target Detection Using Radar Echo

Firstly, this paper presents a novel hierarchical classification feature extraction technology for moving target detection using radar echo. Secondly, a progress is made to acquire the envelope and Doppler feature in the MATLAB simulation analysis. Finally, an efficient combination of the empirical mode decomposition (EMD) with the all-phase FFT (apFFT) to implement the hierarchical classification feature extraction of the moving target. The computation used by this technology is very simple and efficient and needs much less memory resource and should be perfectly adequate for computation in real time.