Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Lung Sounds with Convolutional Neural Network

In this work, respiratory sounds are classified as normal and pathological sounds using convolutional neural network (CNN) and effect of the depth (number of layers) of CNN on classification performance is investigated. Respiratory sound records which were obtained from various sources are down sampled to a common sampling frequency and normalized to ensure that all the data lies in the same data space. Training and test data is chosen randomly from the set of normal and pathological sounds. Architecture of the CNN has 1, 2, and 3 convolution layers for 3 different experiments and each model has one fully connected layer after convolution layers. Classification is completed with logistic regression which is the last step of the architecture. When the results of the experiments are evaluated, it is understood that convolutional neural networks for classification of respiratory sounds is an active research area.
Cognitive Natural Language Search Using Calibrated Quantum Mesh

This paper describes the application of a search system for helping users find the most relevant answers to their questions from a set of documents. The system is developed based on a new algorithm for Natural Language Understanding (NLU) called Calibrated Quantum Mesh (CQM). CQM finds the right answers instead of documents. It also has the potential to resolve confusing and ambiguous cases by mimicking the way a human brain functions. The method has been evaluated on a set of queries provided by users. The relevant answers given by the Coseer search system have been judged by three human judges as well as compared to the answers given by a reliable answering system called AskCFPB. Coseer performed better in 57.0% of cases, and worse in 16.5% cases, while the results were comparable to AskCFPB in 26.6% of cases. The usefulness of a cognitive computing system over a Microsoft-powered key-word based search system is discussed. This is a small step toward enabling artificial intelligence to interact with users in a natural manner like in an intelligent chatbot.
Detecting Ransomware using GURLS

Ransomware is a malware, which upon execution scrambles the framework and it denies the client from accessing the data until the point when a payoff sum is not met from the victim. Recently, this kind of malware has shown a massive growth and had affected nearly 100 nations around the globe. In this paper we propose GURLS (Grand Unified Regularized Least Square) based approach to detect ransomware and classify it into different categories. The features used for training and testing are application programming interface (API) invocations and strings. This paper compares the performance of each of these features for classification and the effectiveness of RBF Kernel. The results obtained shows that using RBF kernel gives better accuracy.
The Circulation of Worthless Tokens Aids Cooperation: An Experiment Inspired by the Kula

Many anthropological records exist of seemingly worthless tokens exchanged in traditional societies. The most famous instances of such tokens are probably the Kula necklaces and armbands first described by B. Malinowski. In our experiment, each participant can send a token to another participant before each round of a repeated public good game. We use as examples of tokens a bracelet built by the participants in the lab, a simple piece of cardboard provided by the experimenter, and an object brought from home by the participants. Notwithstanding the cheap-talk nature of the decision to send the token, both sending and receiving the token are associated with a significant increase in contributions to the public good. Regression analysis shows that contributions to the public good in the treatments featuring a bracelet and a cardboard piece are higher than in a control study. The home object appears not to have been equally useful in increasing contributions.
Real-Time Congestion Estimation in Sightseeing Spots with BLE Devices

Recently, there is a growing demand to know con- gestion information on sightseeing spots in real-time to provide a satisfactory tour plan to tourists. Many studies on a congestion estimation have been conducted so far. However, most of them suffer from high deployment/operation costs and/or rely on contributions by users with smartphones/sensors. In this paper, we propose a novel system that estimates congestion of sightseeing spots in real-time without attaching any device to tourists by observing the distribution of per-RSSI intensity occurrences in each time window when beacon signals are periodically sent between BLE (Bluetooth Low Energy) transceivers installed in sightseeing spots. In other words, our system can estimate the congestion degree in sightseeing spots simply by using the property of RSSI intensity which dramatically changes depending on the number of people. Therefore, the proposed system is simple and low cost and can estimate the congestion easily without any special devices attached to tourists. In the demonstration, we show the congestion degree in three levels (low, medium, and high) changing in real-time depending on the number of audience in the demonstration site.
FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network

Tactile sensing is essential to the human perception system, so as to robot. In this paper, we develop a novel optical-based tactile sensor "FingerVision" with effective signal processing algorithms. This sensor is composed of soft skin with embedded marker array bonded to rigid frame, and a web camera with a fisheye lens. While being excited with contact force, the camera tracks the movements of markers and deformation field is obtained. Compared to existing tactile sensors, our sensor features compact footprint, high resolution, and ease of fabrication. Besides, utilizing the deformation field estimation, we propose a slip classification framework based on convolution Long Short Term Memory (convolutional LSTM) networks. The data collection process takes advantage of the human sense of slip, during which human hand holds 12 daily objects, interacts with sensor skin and labels data with a slip or non-slip identity based on human feeling of slip. Our slip classification framework performs high accuracy of 97.62% on the test dataset. It is expected to be capable of enhancing the stability of robot grasping significantly, leading to better contact force control, finer object interaction and more active sensing manipulation.
Event-Triggered Synchronization Control for Chaotic Lur'e Systems with Time Delays

In this paper, an event-triggered synchronization control scheme is developed for a class of chaotic Lur’ e systems with time delays. A novel Lyapunov-Krasovskii functional (LKF) is designed to extend the sampling period of the systems. Moreover, an improved event-triggered mechanism is constructed to decrease the amounts of communication between controller and sensor, and the numbers of controller updates. Based on the Lyapunov stability theory, it is proved strictly that the chaotic Lur'e systems are asymptotically stable with the proposed controller. Finally, a numerical simulation is presented to illustrate the effectiveness of the event-triggered synchronization control strategy.
A Low Complexity Adaptive Control for a Class of Nonlinear Systems Subject to State Constraints

In this paper, a new control framework is proposed for a class of nonlinear parametric strict feedback systems in the presence of state constraints. Different from the traditional techniques, an error transformation is implemented to guarantee that the full state constraints are not violated. It is proved that the stabilization of the transformed system is sufficient for the original system. To simplify the structure of the controller, a filter commander is designed to approximate the virtual input and its derivative. Besides, an auxiliary system is introduced to compensate for the effect of the approximate error. The designed controller can ensure the state within the limited range. Moreover, all of the signals in the closed-loop system are globally bounded. Simulation is provided to verify the effectiveness of the designed controller.
Projective Inference in High-dimensional Problems: Prediction and Feature Selection

This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We argue that in many cases one can benefit from a decision theoretically justified two-stage approach: first, construct a possibly non-sparse model that predicts well, and then find a minimal subset of features that characterize the predictions. The model built in the first step is referred to as the \emph{reference model} and the operation during the latter step as predictive \emph{projection}. The key characteristic of this approach is that it finds an excellent tradeoff between sparsity and predictive accuracy, and the gain comes from utilizing all available information including prior and that coming from the left out features. We review several methods that follow this principle and provide novel methodological contributions. We present a new projection technique that unifies two existing techniques and is both accurate and fast to compute. We also propose a way of evaluating the feature selection process using fast leave-one-out cross-validation that allows for easy and intuitive model size selection. Furthermore, we prove a theorem that helps to understand the conditions under which the projective approach could be beneficial. The benefits are illustrated via several simulated and real world examples.
Hybrid Genetic Algorithm and Support Vector Machine Performance in Public Fiscal Revenue Prediction

Local public fiscal revenue (PFR) is an important indicator to measure the level of local economic development. Accurate prediction of local public fiscal revenue can provide theoretical support for governments and relevant departments to make scientific decisions. Firstly, we use the principal component analysis to reduce the dimensions of variables. Secondly, we predict Harbin public fiscal revenue by using hybrid genetic algorithm (GA) and support vector machine (SVM) model. Then we compare the results with a back propagation neural network model and a gray neural network model optimized with the genetic algorithm. The results show that the hybrid genetic algorithm and support vector machine model is more effective for forecasting Harbin public fiscal revenue.
Model and Training Methods of Autonomous Navigation System for Compact Drones

The paper presents a novel model of convolutional neural network for visual feature extraction, support vector machine for position prediction and information-extreme classifier for obstacle prediction with new training methods to build decision rules of autonomous navigation system for compact drones are presented in the paper. Sparse-coding neural gas algorithm for unsupervised training of the convolution filters, supervised incremental learning method for training the regression model and particle swarm optimization algorithm for training the classifier model are proposed. The complex criterion for choosing parameter of feature extractor model is considered. Simulation results with optimal model on test open datasets confirm the suitability of proposed algorithms for practical usage.
Provenance Identification in Diffusion Networks with Incomplete Cascades

We consider the problem of provenance identification for diffusion processes on social networks based on cascade information observed from a small set of nodes during the observation windows. The diffusion dynamics are described by the susceptible-infected (SI) model and a constrained maximum likelihood (ML) estimator is formulated to maximize the probability of the diffusion provenance. The identification approach consists of two steps: first, a pruning rule is defined to obtain a set of suspected provenance nodes from susceptible node observers; and then a correlation coefficient is maximized to find the provenance node from the candidate ones. Experiments on synthetic networks and real-world networks verify the effectiveness of our approach.
Analysis on Channel Parameters and Signal Processing methods at mm-wave for 5G networks

Wireless data traffic is expected to increase 10000 fold in next 20 years. To meet this ever increasing demand of increased wireless traffic, the fifth-generation (5G) cellular systems are getting prepared to be deployed by 2020. 5G cellular systems are most likely to operate in millimeter wave (mm-wave)frequency bands. Communication at mm-wave is setting a new era of wireless communication. The mm-wave frequencies offer higher bandwidth channels up to 2 GHz. Signal processing techniques are critical for implementing in the next generation mm-wave communication systems. Millimeter wave technology enables the use of large antenna arrays at the transmitter (Tx) and receiver (Rx). Along with high operating frequency and mixed signal power constraints, incipient multiple-input multiple-output (MIMO) communication signal processing methods are essential. Due to large bandwidths, designing low complexity transceiver algorithms becomes critical. Millimeter wave technique provides enough opportunities to utilize the signal processing techniques such as compressed sensing technique in channel estimation and beamforming (BF). This article presents an overview of efficacious signal processing methods and challenges in using mm-wave technique, with an incremented fixate on MIMO technology in achieving larger data rates and issues with limited availability of frequency spectrum. There is an immense interest in mm-wave BF predicated for 5G networks. An important aspect in mm-wave communications is to exploit the increased number of deployable antennas at both Tx and Rx to combat high path loss, to tackle increased interference due to higher user density and to tackle multipath effects in frequency selective channels.
Effective Road Lane Detection and Tracking Method Using Line Segment Detector

In this paper, we present an effective lane detection and tracking system using a fusion of Line Segment Detector (LSD) and Kalman filter. We employ line segment as low-level features to detect lane markings on structured road. Firstly, we segment the road surface region through adaptive method, and then a different grayscale method is used to achieve strong contrast than the previous studies. Secondly, we apply LSD algorithm on them and remove incorrect line segments in an adaptive way. Finally, two Kalman filters are used to track the virtual boundary points, which also be applied to predict the next processed region of interests (ROIs). Meanwhile, real-time performance can be better achieved. Experiments are conducted on several open datasets which are collected in real-world scenarios, such as Caltech dataset. The results demonstrate the efficiency and the robustness of our proposed method.
Low-Loss Highly Linear Integrated Passive Phase Shifters for 5G Front Ends on Bulk CMOS

Two passive phase shifters (PSs) are proposed to minimize insertion loss (IL) in bulk CMOS technology for 28-GHz fifth generation integrated phased-array transceivers. The first PS is based on a true delay phase shift structure, where a modification is proposed to facilitate the use of larger switch size minimizing the IL in the bypass mode. The second PS is based on matched high-pass and low-pass sections to maximize the bandwidth of the phase shift. Two 3-bit passive PS prototypes, implemented in 40-nm bulk CMOS technology, are designed to be shared between the transmitter and receiver units. The first and second prototypes achieve low IL of 6 dB across 24–34- and 22–36-GHz frequency ranges with root-mean-square phase error less than 13° and 12.8°, respectively. The two prototypes have input <inline-formula> <tex-math notation="LaTeX">$P_{1\,\text {dB}}$ </tex-math></inline-formula> and IIP3 higher than 8.5 and 21 dBm, respectively.
Predict Disease Progression With Reaction Rate Equation Modeling of Multimodal MRI and PET

Neurodegenerative dementia often has multiple types of underlying pathology, for example, beta-amyloid, misfolded tau, chronic neuroinflammation and neurodegeneration may coexist in Alzheimer’s disease. However, the relationship between them is often unclear, in other words, whether one pathology is upstream or downstream of others can be very difficult to investigate directly. This is partly because the underlying pathology in dementia may precede detectable symptoms by several years if not decades. The time scale associated with disease progression in dementia generally exceeds that in conventional longitudinal imaging studies in humans, so it is difficult to directly observe the temporal ordering of different pathologies. Also, animal studies are not always transferable to patients due to obvious differences between the two systems. To investigate the disease progression and relationships among underlying pathological changes, we propose a novel computational modeling approach for multimodal MRI and PET inspired by reaction rate equation in chemical kinetics. We also discuss the possibility and prerequisites to use cross-sectional data to generate preliminary hypothesis for future longitudinal studies. It has been shown that the rate of change in some biomarkers can be approximated by the average trajectory across patients at different stages of disease severity in cross-sectional studies. The relationship modeled in our approach is akin to that in the control theory, and can be assessed by demonstrating that the presence of one disease related biomarker predicts dynamics in another. We argue that the proposed framework has important implications for trials targeting different pathologies in dementia.
Backstepping Sliding Mode Control Design for Magnus Rotating Roll Stabilizer at Low Speed

The best way of reducing roll motion is by increasing roll damping. Magnus rotating roll stabilizers are the most efficient devices to provide roll damping at low speed through the rotating rotor wing at the bilge of ship hull based on Magnus effect. In this paper, the work aims to design a suitable controller for Magnus rotating roll stabilizer system. Roll motion control system contains two subsystems, hydraulic servo system and roll motion system. The uncertainties in the two subsystems both are mismatched uncertainties. The backstepping algorithm makes the controller immune to mismatched uncertainties and the sliding mode control (SMC)provides robustness. Therefore, this paper combines the backstepping method with sliding mode control method to guarantee fast convergence rapidity of rolling angle and improve the robustness of the control system to the mismatched uncertainties. Finally, the roll reduction system of Magnus rotating roll stabilizer with a BSMC is developed to demonstrate its effectiveness for roll reduction at low speed.
Clust-LDA: Joint Model for Text Mining and Author Group Inference

Social media corpora pose unique challenges and opportunities, including typically short document lengths and rich meta-data such as author characteristics and relationships. This creates great potential for systematic analysis of the enormous body of the users and thus provides implications for industrial strategies such as targeted marketing. Here we propose a novel and statistically principled method, clust-LDA, which incorporates authorship structure into the topical modeling, thus accomplishing the task of the topical inferences across documents on the basis of authorship and, simultaneously, the identification of groupings between authors. We develop an inference procedure for clust-LDA and demonstrate its performance on simulated data, showing that clust-LDA out-performs the "vanilla" LDA on the topic identification task where authors exhibit distinctive topical preference. We also showcase the empirical performance of clust-LDA based on a real-world social media dataset from Reddit.
Influence of volume variety of waste cooking palm oil as carbon source on graphene growth through double thermal chemical vapor deposition

In this paper, the growth of graphene on a nickel (Ni) substrate using waste cooking palm oil (WCPO) as a carbon precursor by double thermal chemical vapor deposition (DTCVD) has been reported. Various amounts of WCPO which comprised of 5µl, 10 µl, 50 µl and 100 µl are employed for the growth of graphene. At 10 µl, graphene effectively formed due to segregation and precipitation more appreciable amount of carbon on the Ni surface during cooling process. Using an ample amount of carbon source resulted in the formation of graphene oxide and thick amorphous carbon. The prepared samples were characterized using FESEM, XRD and Raman spectroscopy. FESEM analysis reveals prominent structural changes upon applying various amount of waste cooking palm oil. From Raman spectroscopy, 10 III samples suggesting multilayer of the graphene derived from the ratio peak intensity (I2D/ IG equal to ~0.30).
ADMM-Based Decentralized Charging Control of Plug-In Electric Vehicles with Coupling Constraints in Distribution Networks

The charging behaviors of large-scale plug-in electric vehicles (PEVs) may negatively impact the power distribution networks by causing the significant drop of the voltages. In this paper, we design a decentralized PEV charging coordination method based on the so-called alternating direction method of multipliers (ADMM) to solve the underlying optimization problems with non-separable objective function and coupled constraints. By applying the proposed method, the generic high-dimension complicated problem is solved in a decentralized manner such that each individual PEV can independently update its own best strategy. We demonstrate the optimality and convergence of the proposed method with certain numerical simulations.
Design and optimization of multielement antennas and RF circuits for beamforming with a reduced number of RF Front-ends

This work describes an overview of antennas and RF circuits concepts developed in XLIM laboratory and LabeX Sigma-Lim to offer an interesting trade-off between performances, energy consumption and overall complexity to address the problematic of beamforming in civil and mass-market applications. 5G and its future evolution is a great driver to develop such solutions. A reconfigurable antenna with a reduced number of RF excitations is presented, as well as RF frontend architecture used for a Ka-band demonstration. A main target of the work is to develop a system level simulation chain to optimize the interfaces between the elements of the antenna and front-ends. To this end, a powerful simulation platform, PREMISS, has been used to study and optimize a full-system.
A Diversity Promotion Study in Constrained Optimizations

An empirical comparison is conducted to analyze three different elements involved in population diversity management in constrained numerical optimization problems: (1) optimization algorithms, (2) constraint-handling techniques and (3) diversity promotion techniques. Combinations of basic algorithm versions, differential evolution, and real-coded genetic algorithm, applying feasibility rules, $\epsilon$-Constrained method and stochastic ranking as constraint-handling techniques, and also implementing a diversity promotion technique, were generated. Each combination (48 in total) was tested by solving 36 well-known benchmark problems. The final results highlight the combination of differential evolution, $\epsilon$-Constrained method and a couple of non-disruptive diversity promotion techniques, as the most promising combinations.