Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Discretization Process applied to Continuous Random Variables in the Density Estimation Context

Lately, with the emergence of large data-generating experiments, there is a growing demand to optimize the algorithms responsible for interpreting this volume of information so that it uses as little data as possible to perform the desired operation. This work permeates this context, proposing alternatives in one of the most elementary choices in estimation/classification algorithms: discretization of a given variable. This paper proposes to evaluate the characteristics of different discretization methods applied to probability density function estimation considering the trade-off between performance and simplicity, as well as susceptibility to outliers. In addition, this work analyzes the advantages and disadvantages of each method and indicates possible ways to extend the knowledge about the addressed subject.
Toward Scalable Neural Dialogue State Tracking Model

The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.
Novel Biodegradable Polymeric Microparticles Facilitate Scarless Wound Healing by Promoting Re-epithelialization and Inhibiting Fibrosis

Despite decades of research, the goal of achieving scarless wound healing remains elusive. One of the approaches, treatment with polymeric microcarriers, was shown to promote tissue regeneration in various in vitro models of wound healing. The in vivo effects of such an approach are attributed to transferred cells with polymeric microparticles functioning merely as inert scaffolds. We aimed to establish a bioactive biopolymer carrier that would promote would healing and inhibit scar formation in the murine model of deep skin wounds. Here we characterize two candidate types of microparticles based on fibroin/gelatin or spidroin and show that both types increase re-epithelialization rate and inhibit scar formation during skin wound healing. Interestingly, the effects of these microparticles on inflammatory gene expression and cytokine production by macrophages, fibroblasts, and keratinocytes are distinct. Both types of microparticles, as well as their soluble derivatives, fibroin and spidroin, significantly reduced the expression of profibrotic factors Fgf2 and Ctgf in mouse embryonic fibroblasts. However, only fibroin/gelatin microparticles induced transient inflammatory gene expression and cytokine production leading to an influx of inflammatory Ly6C+ myeloid cells to the injection site. The ability of microparticle carriers of equal proregenerative potential to induce inflammatory response may allow their subsequent adaptation to treatment of wounds with different bioburden and fibrotic content.
A Practical Application of a Dataset Analysis in an Intrusion Detection System

In this paper a systematic analysis of a public intrusion detection dataset has been developed in order to understand how the traffic behaves in this particular context. This analysis is used for avoiding common pitfalls introduced because of a misunderstanding of data peculiarities and for selecting and tailoring correctly the algorithms. Specifically, we have employed machine learning algorithms to classify the traffic into normal and attack flows. In addition, it is important to decide what features should be evaluated. Typically, standard metrics do not take into account time spent in the classification, what is essential in an intrusion detection system. This is the reason why we introduce a metric that considers both the accuracy and the delay to make the decision and that is employed for evaluating machine learning algorithms in other domains. The conclusions obtained from our dataset analysis can be used to develop new models that could fit the data better than existing approaches.
LHONE: Label Homophily Oriented Network Embedding

Network embedding is to learn effective low-dimensional vector representations for nodes in a network and has attracted considerable attention in recent years. To date, existing methods mainly focus on network structure information and cannot leverage abundant label information, which is potentially valuable in learning better vector representations. Due to the noise and incompleteness of label information, it is intractable to integrate label information into the vector representations in a partially labeled network. To address this issue, we investigate the effects of label information based on the label homophily. Briefly, label homophily can not only drive nodes sharing similar labels to be connected to each other, but also produce a division of a network into densely-connected, homogeneous parts that are weakly connected to each other. Furthermore, we propose a novel Label Homophily Oriented Network Embedding (LHONE) model to make the best of label homophily by converting a partially labeled network to two bipartite networks, and learning vector representations combined with a Gaussian mixture model (GMM). Extensive experiments on two real-world networks demonstrate the effectiveness of LHONE compared to state-of-the-art network embedding approaches.
Joint Knowledge Base Embedding with Neighborhood Context

Knowledge graph embedding significantly promotes the performance of link prediction and knowledge reasoning, which aims to encode both entities and relations into a low-dimensional semantic space. Existing translation-based methods have achieved state-of-art performances. However, the diversity of connectivity patterns observed in knowledge graph, i.e., structural equivalences, may not be effectively utilized to enhance knowledge graph embedding. To address this issue, we propose a concise but effective model, Context-enhanced Knowledge Graph Embedding (CKGE), for joint knowledge base embedding with neighborhood context. Neighborhood context obtained in our approach gain a deep insight into the diversity of connectivity patterns of knowledge graph. We incorporate the rich structural information contained in neighborhood context to expand the semantic structure of the knowledge graph, which is enable to model complex relations more precisely. We conduct extensive experiments on link prediction, triplet classification on bench-mark datasets. The experiment results show CKGE achieve significant improvements against the baseline methods.
High Performance Square Spiral Inductor for 65 nm CMOS V-band Low Noise Amplifier

A high performance parallel stacked, square spiral inductor with high resistivity silicon substrate is proposed in this work to achieve higher gain and lower noise figure (NF) in receiver sub-block, that is, low noise amplifier (LNA) designed at 53.5 GHz using standard UMC 65 nm CMOS technology. The LNA consists of four CS stages with current-sharing technique implemented in the last two stages to minimize power dissipation. Using the proposed inductor design, LNA achieves a gain of 29.1 dB, low NF of 4.1 dB at 53.5 GHz consuming a low power of 13.9 mW using 1.2 V DC supply. The proposed square spiral inductor is designed with 2.5 turns, inner diameter of 5.5 um, width of 1.5 um and with the observed area of $72\times 72\ \mathbf{um}$ achieves a Q-factor of 9.35 and inductance of 202.9 nH over V-band (40–75) GHz.
High-Speed Permanent Magnet Synchronous Machine for Short-Term Operation in an Electrically Powered High-Lift System

The noise footprint of aircraft at airports is one of the major reasons, why many of these facilities are located on the outskirts of cities instead of near the city centre. One approach to reduce this footprint is to increase the lift coefficient by using electrically powered high-lift systems (EPHLS) which are located near each wing's flaps. Each system consists of a turbo compressor which is directly driven by a permanent magnet synchronous machine at a maximum rotational speed of 60,000 rpm and a maximum required power of 80 kW. Current investigations focus on the enhancement of the aircraft's take-off and landing which limit the operating time of the EPHLS to a maximum of 240 s. This paper will highlight the design of the three main components of the EPHLS, namely SiC converter, electrical machine, and turbo compressor. The machine design is significantly influenced by the short operating time, thus moving the critical thermal parameters to the transient regime of the temperature characteristics. The thermal parameters for this machine design will be discussed and iterated for several scaled power levels, as the EPHLS consists of several turbo compressors along the wing span with different ratings. These are thermally characterized and compared to conclude what the scaling of electrical machines for short-term operation means for the thermal characteristic parameters. In the end, a different reference design point for the electrical machine is identified, a fast and simple way for the scaling of electrical machine designs for short-term operation with respect to power is found and conclusions for the design and scaling of combined power electronics, electrical machine, and turbo compressor systems are drawn.
The Acoustic properties of gassy sediments due to gas-content fluctuations

Aiming at the problem that the distribution of gas-bubbles in the actual seabed sediments is not necessarily uniform, which will lead different acoustic properties and affect the acoustic propagation in gas-bearing sediments. In this paper, the perturbation theory is applied to the corrected equivalent fluid density model in gassy sediments, which is a theoretical model for predicting sound speed and attenuation coefficient in gassy sediments. The analytic expression of the equivalent wavenumber of gassy sediments is derived under gas-bubble void fraction fluctuations. Assuming that the statistics of the heterogeneities are described by exponential covariance functions, numerical analysis is carried out for sound speed, attenuation coefficient, and normal reflection coefficient at a interface connecting water and gassy sediment. The numerical results show that the scattering caused by slightly random fluctuation of gas-bubbles in sediment has no obvious effect on the velocity of the medium, but has obvious effect on the attenuation coefficient. The acoustic scattering caused by random distribution of gas-bubbles induces an significant increase in attenuation coefficient for the insonifying frequencies below the gas-bubble resonance frequency and leads to a significant spike for insonifying frequencies above the gas-bubble resonance frequency. By changing the simulation parameters of the gas-bubble, one can find that when the bubble content is constant, the smaller the gas-bubble size, the greater the influence of the acoustic scattering of the gas-bubbles on the acoustic attenuation of the gas-bearing sediments. If the inhomogeneity is enhanced, it will lead to obvious resonance peak splitting, which indicates that the influence of the uneven distribution of gas-bubbles must be taken into account when using the measured acoustic characteristics to invert the gas-bubble size distribution.
Structure Learning Using Forced Pruning

Markov networks are widely used in many Machine Learning applications including natural language processing, computer vision, and bioinformatics . Learning Markov networks have many complications ranging from intractable computations involved to the possibility of learning a model with a huge number of parameters. In this report, we provide a computationally tractable greedy heuristic for learning Markov networks structure. The proposed heuristic results in a model with a limited predefined number of parameters. We ran our method on 3 fully-observed real datasets, and we observed that our method is doing comparably good to the state of the art methods.
Reduced Capacitance Battery Storage DC-Link Voltage Regulation and Dynamic Improvement Using a Feedforward Control Strategy

This paper proposes a feedback/feedforward control strategy to attenuate the dc-link voltage variations in a reduced-capacitor battery energy storage system. It also identifies the impact of dc-link voltage variations on a battery energy storage system's response dynamics and power capabilities. It shows that addition of a feedforward controller to the existing feedback controller improves the dc-link voltage regulation and consequently the response dynamics of the reduced-capacitor battery energy storage system. It formulates a power setpoint change as a disturbance to the battery energy storage control system. The battery energy storage system is analyzed using an averaged-value modeling technique. Small-signal analysis is then used to tune feedforward controller parameters. The performance of the proposed controller is evaluated via an electromagnetic transient software (PSCAD/EMTDC), and the findings are experimentally validated using a laboratory-sized test bench.
Artificial Intelligence to Manage Network Traffic of 5G Wireless Networks

The deployment of 5G wireless communication systems is projected to begin in 2020. With new scenarios, new technologies, and new network architectures, the traffic management for 5G networks will present significant technical challenges. In recent years, AI technologies, especially ML technologies, have demonstrated significant success in many application domains, suggesting their potential to help solve the problem of 5G traffic management. In this article, we investigate the new characteristics of 5G wireless network traffic and discuss challenges they present for 5G traffic management. Potential solutions and research directions for the management of 5G traffic, including distributed and lightweight ML algorithms and a novel AI assistant content retrieval algorithm framework, are discussed.
Massive Access in the Presence of Imperfect Successive Interference Cancellation

In this paper, we address a practical but adverse problem that successive interference cancellation (SIC) is imperfect in cellular internet of things (IoT) with massive connections. The benefits of spatial degrees of freedom offered by a multiple-antenna base station (BS) are exploited to support massive access through user clustering in spatial domain, and alleviate the impact of imperfect SIC. In particular, transmit beams and powers are jointly optimized to mitigate the intra-cluster and inter-cluster interference, so as to improve the overall performance in the presence of imperfect SIC. Specifically, we design a joint optimization algorithm for maximizing the weighted sum rate. Moreover, in order to reduce the computational complexity, we provide a massive access algorithm with zero-forcing beamforming (ZFBF) fixedly. Finally, simulations results show the performance gain of the proposed algorithms over the baseline ones.
Usability Evaluation of Main Function on Three Mobile Banking Application

E-banking or electronic banking is used to facilitate the bank customers when conducting financial transaction in easy and convinient way. As one of the e-banking form, Mobile banking (m-banking) is a banking service that allows its customers to conduct financial transactions in real time using mobile devices such as smartphones or tablets. Although there is an increasing number of mobile banking service users every year in Indonesia, the utilization of the actual mobile banking apps is still very low compared to other country in Asia. One of the problems that is often encountered by mobile banking's customers is the aspect of trust that can be influenced by interface and usability of mobile banking applications. Therefore, in order to achieve effective interaction between human-computers with minimal effort, various aspects of usability need to be the main focus in developing an application to support the effectiveness of human-computer interaction so that the initial goal of creating mobile banking to facilitate customer banking transactions can be achieved. Using four criteria of usability mesurement set by ISO, this research aim to evaluate three m-banking application from the largest bank in Indonesia which are Mandiri Mobile, BNI Mobile, and BCA Mobile. Evaluation and analysis from this research shows that BNI Mobile has the best usability among the other two application. Overall, the three mobile banking have quite good usability although there are still several aspects that need to be improved.
On-Demand Band-Rejected Wideband Antenna Based on Peelable Resonator Membrane

In this letter, we present a wideband planar antenna with on-demand band-rejected characteristics based on the peelable double electric-LC (DELC) resonator membranes. By applying microfabrication techniques, the subwavelength DELC structure is completely encapsulated in a transparent and flexible thin film with a total thickness of 10 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m. Resonators with four different widths (4.0, 3.6, 3.2, 2.8 mm) are packaged as a solution kit, providing the rejection bands of 4.24–5.11, 4.61–5.38, 4.85–5.48, and 5.20–5.70 GHz, respectively. Owing to the passive and detachable features of this functional sticker, no biasing network is needed for the rejection band's <sc>on</sc>/<sc>off </sc> transition and the membrane could tolerate 1 mm horizontal mismatch and 10<inline-formula> <tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> rotational mismatch during assembly. This work provides a comprehensive solution for the design, manufacture, and implementation of a bias-free wideband antenna with on-demand rejection band and motivates the development of an effective and compact anti-interference system.
Explore-Exploit: A Framework for Interactive and Online Learning

Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit framework.
Iterative SDR-based Hybrid Precoding Algorithm for mmWave massive MIMO Systems

Practical hybrid precoding algorithms for millimeter wave massive multiple-input multiple-output (mmWave massive MIMO) systems with huge number of radio frequency (RF) chains must achieve high spectral efficiency at low complexity. In this paper, we consider the hybrid precoder with sub-connected structure which presents reduced power consumption and low-complexity hardware implementation. Then, we propose a new hybrid precoder based on iterative semidefinite relaxation (ISDR) approach. The proposed ISDR algorithm iteratively realizes the SDR one using the Gauss-Seidel (GS) method. We analyze the spectral efficiency and computational complexity of our ISDR-based hybrid precoder, we compare them to those of previously proposed hybrid precoders in the literature, and we prove that our proposal significantly reduces the computational complexity while providing spectral efficiency slightly comparable to that of the SDR-based one.
Towards Accurate Task Accomplishment with Low-Cost Robotic Arms

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.
Video Super Resolution with Generative Adversarial Network

This paper exhibits a novel implementation of video super resolution through Generative adversarial network. Generative adversarial networks (GANs) are a class of artificial intelligence algorithm implemented by a system of two neural networks provide a unique way to learn deep representations. The results of GAN shows slightly lower PSNR compared to traditional methods, since the frames produced by GAN is more appealing when viewed by human. GAN can learn from large datasets and automatically add high-frequency details and features to frames while traditional methods can't. The generator discriminator architecture in GAN pushes it to generate more realistic and appealing frames.
Page Object Detection from PDF Document Images by Deep Structured Prediction and Supervised Clustering

Page object detection in document images remains a challenge because the page objects are diverse in scale and aspect ratio, and an object may contain largely apart components. In this paper, we propose a hybrid method combining deep structured prediction and supervised clustering to detect formulas, tables and figures in PDF document images within a unified framework. The primitive region proposals extracted from each column region are classified and clustered with conditional random field (CRF) based graphical models which can integrate both local and contextual information. Both the unary and pairwise potentials of CRFs are formulated as convolutional neural networks (CNNs) to better exploit spatial contextual information. The CRF for clustering predicts the linked/cut label of between-region links. After CRF inference, the line regions of same class within a cluster are grouped into a page object. The state-of-the-art performance obtained on the public available ICDAR2017 POD competition dataset demonstrates the effectiveness and superiority of the nronosed method.