Miracle-dz/UniFAR-FTUs huggingface dataset:
COLLECTIVE PROBLEM-SOLVING IN EVOLVING NETWORKS: AN AGENT-BASED MODEL

Research works in collective problem-solving usually assume fixed communication structures and explore effects thereof. In contrast, in real settings, individuals may modify their set of connections in the search of information and feasible solutions. This paper illustrates how groups collectively search for solutions in a space under the presence of dynamic structures and individual-level learning. For that, we built an agent-based computational model. In our model, individuals (i) simultaneously conduct search of solutions over a complex space (i.e. a NK landscape), (ii) are initially connected to each other according to a given network configuration, (iii) are endowed with learning capabilities (through a reinforcement learning algorithm), and (iv) update (i.e. create or severe) their links to other agents according to such learning features. Results reveal conditions under which performance differences are obtained, considering variations in the number of agents, space complexity, agents’ screening capabilities and reinforcement learning.
A Novel Digital-Analog Integrated Communication System Based on the PARATUCK-2 Model

Tensor algebra is an important mathematical analysis tool in designing the deep learning frameworks. With the increasing of data processing in the communication field, many researchers have applied tensors into the designing of communication systems. So in this paper, we make use of the PARATUCK −2 tensor decomposition model to propose a novel digital-analog integrated communication system. The proposed system can simultaneously transmit digital and analog signals, in which, the analog signal is considered as a sampled signal without any other operation in this system. At the receiver, the original digital and analog signals can be separated and recovered by the proposed semi-blind receiver which is based on the ALS (alternate least square) algorithm. Finally, experimental results demonstrate the performances of the proposed digital-analog integrated communication system.
Low Switching Frequency Model Predictive Control of Three-Level Inverter-Fed IM Drives With Speed-Sensorless and Field-Weakening Operations

This paper proposes a single-vector-based model predictive control (MPC) for an induction motor drive supplied by a three-level neutral-point-clamped inverter operating at a low switching frequency. Unlike the torque and flux control in the conventional MPC, the proposed MPC tries to minimize the error between the applied voltage vector and a reference voltage vector obtained based on the principle of deadbeat control, thereby reducing the number of weighting factors in the cost function. To reduce the computational burden and restrict the high jumps in both phase and line voltages, two switching tables are proposed and compared for the preselection of candidate voltage vectors. The neutral point potential fluctuation and switching frequency are also included in the cost function to achieve the balance between the upper and lower dc voltages and a relatively low switching frequency. Furthermore, a speed adaptive stator flux observer with a novel gain matrix is proposed to achieve speed-sensorless operation, which has a higher speed and flux estimation accuracy than conventional fixed gains. Finally, the proposed MPC is extended to the field-weakening operation by adjusting the torque and stator flux references online, which significantly widens the speed range and improves the practical value of the MPC. The effectiveness of the proposed method is confirmed by the experimental results obtained at an average switching frequency of less than 600 Hz.
Automated Bengali Document Summarization by Collaborating Individual Word & Sentence Scoring

Bengali documents are increasing on the World Wide Web and it is becoming a overwhelming problem for the increasing large number of web users to reviewing and reduce the information. Many researches have been conducted in the field of Natural Language Processing for English documents and in order to serve with satisfactory accuracy. This research work proposed a simple and powerful extraction based method for summarizing of the Bengali text documents. The system could summarize a single document at a time. The ultimate objective of the proposed methodology helps readers to get summary and insight of the Bengali documents without reading revealing the in-depth details. In the proposed Bengali documents summary generation method there are four features: Preprocessing, Sentence Ranking and Summarization, Combining Parameters for Sentence Ranking, Summary Generator. The results of performance evaluation show that the average scores of Precision, Recall and final scores are 0.80, 0.67, and 0.72 respectively.
Double window spectrogram difference method: A blind estimation of frequency-hopping signal for battlefield communication environment

In the area of battlefield communication, blindly hopping period and hopping frequency estimation from a series of noisy frequency-hopping (FH) signals is an important research topic. With the use of short-time Fourier transform, an accurate jointly hopping period and hopping frequency estimator, namely, double window spectrogram difference (DWSD), is developed. Simulation results are included to demonstrate the low complexity and superiority of the developed method over several conventional parameter estimators.
Robust Image Hashing Based on Hybrid Approach of Scale-Invariant Feature Transform and Local Binary Patterns

Image hash functions find extensive applications in content authentication, database search, and digital forensic. Robust image hash has been widely investigated to authenticate the reliability of images transmitted by a trustless channel. In this paper, we propose a novel image hashing algorithm which is robust to content-reserved and multiple manipulations. To achieve the perceptual robustness and sensitivity, the proposed scheme combines scale-invariant feature transform (SIFT) with local binary pattern (LBP). SIFT extracts plenty of descriptors which are robust to geometric distortion and luminance transformation. LBP generates hash values that contain local information and are sensitive to content manipulation. We further investigate the performance of proposed scheme and other existing algorithms via statistical analysis of recognition rate, and the results show that our method outperforms conventional methods.
Efficient Robust Fuzzy Model Predictive Control of Discrete Nonlinear Time-Delay Systems via Razumikhin Approach

In this paper, two efficient robust fuzzy model predictive control algorithms are investigated for discrete nonlinear systems with multiple time delays and bounded disturbances. The famous Takagi–Sugeno (T–S) fuzzy systems are utilized to represent nonlinear systems. Instead of the Lyapunov–Krasovskii functional, the Lyapunov–Razumikhin function is adopted to deal with time delays because it involves invariant sets in the original state space of the system. A sequence of explicit control laws corresponding to a sequence of constraint sets are computed offline so that the online computational burden associated with the classical model predictive control algorithms is significantly reduced. In particular, the set invariance theory behind the Razumikhin approach, which is more complicated than the one for nondelayed systems, is directly observed. Additionally, it is proved that all (delayed) states can enter the terminal set in finite time. Moreover, robust positive invariance and input-to-state stability for time-delay systems concerning disturbances are realized. Additionally, an online optimization algorithm is also provided based on the offline computed ellipsoidal sets. Therefore, the conservatism induced by the Razumikhin approach is relaxed, while the computational cost is not significantly increased.
Recognition of ErrP in P300 Speller Based on Time Series Pattern

Error-related potentials (ErrP) are changes in EEG signals that can be elicited when users perceive errors. Recognising ErrP can help improve the performance of P300 Speller. However, it is a hard task due to the low signal-to-nosie ratio and high variability from trial to trial. In this study, a novel method is proposed to reveal the difference in time series pattern between single trial EEG epochs that contain ErrP and ones that do not contain ErrP. For testing our approach, four healthy subjects were recruited to take part in P300 Speller experiments with feedback. The performance was evaluated with sensitivity and specificity metric. The attained results were 54.7%, 89.6% respectively. The comparison to other methods showed its effectivity.
Minimisation of Networks Based on Computational Geometry Data Structures

In this paper, we deal with a problem of finding the shortest interconnection of points given in a Euclidean plane. The traditional strategy starts from the complete graph and finds its minimum spanning tree. However, this approach is proportional to the second power of the number of vertices, and, therefore, not very efficient. Additionally, if, instead of minimum spanning trees, minimum Steiner trees are considered, then the total length of the final network is decreased. Since the Steiner tree problem is NP-hard, in the case of large instances, heuristics must be used. Here, we propose a Delaunay triangulation-based deterministic heuristic and show that it gives very good results in short times.
ESTIMATION OF MOMENT INDEPENDENT IMPORTANCE MEASURES USING A COPULA AND MAXIMUM ENTROPY FRAMEWORK

The moment-independent sensitivity analysis technique introduced by E. Borgonovo has gained increasing attention to characterize the uncertainty of complex systems and optimize their reliability. The estimation of corresponding indices is a challenging task. This paper aims at presenting a new estimation scheme valid for dependent model inputs. This scheme is build on the copula representation of indices and uses maximum entropy methods to estimate this copula. Accuracy of the proposed method is evaluated through numerical simulations and is compared to two competitive methods, namely an importance sampling based approach and a second one which uses the Nataf transformation.
High-Resolution All-Optical Imaging of Multimode Profiles in a Saturated Absorption Medium

We proposed an all-optical imaging technique based on saturated absorption spectroscopy in an atomic vapor. The atoms are driven by a counter-propagating strong pump laser on a transition which is probed by a comparatively weak probe laser. We show that the different orders of Hermite–Gaussian beam carried by pump laser can be efficiently imaged onto a Gaussian-shaped probe laser within a Rayleigh length. The feature size of transmitted probe beam is reduced by a factor 4 when compared to the original pump beam. To generalize the scheme, an arbitrary image imprinted initially on pump beam is successfully transferred onto the output probe beam. Such a method of high resolution multimode imaging may find potential applications in all-optical imaging and lithography technologies.
Deep Ensemble Learning for Human Activity Recognition Using Smartphone

The ubiquity of smartphones and their rich set of onboard sensors have created many exciting new opportunities. One important application is activity recognition based on smartphone inertial sensors, which is a fundamental building block for a variety of scenarios, such as indoor pedestrian tracking, mobile health care and smart cities. Though many approaches have been proposed to address the human activity recognition problem, a number of challenges still present: (i) people’s motion modes are very different; (ii) there is very limited amount of training data; (iii) human activities can be arbitrary and complex, and thus handcrafted feature engineering often fail to work; and finally (iv) the recognition accuracy tends to be limited due to confusing activities. To tackle those challenges, in this paper we propose a human activity recognition framework based on Convolutional Neural Network (CNN) using smartphone-based accelerometer, gyroscope, and magnetometer, which achieves 95.62% accuracy, and also presents a novel ensembles of CNN solving the confusion between certain activities like going upstairs and walking. Extensive experiments have been conducted using 153088 sensory samples from 100 subjects. The results show that the classification accuracy of the generalized model can reach 96.29%.
Towards Model Checking of the Internet of Things Solutions Interoperability

The Internet of Things paradigm implementation is infeasible without the interoperability aspect fulfillment. Both the diversity of “smart” devices involved and the difference of Internet protocols used make it a challenging task to achieve the true interoperability. Moreover, there is still an open question concerning the consistency of certain potentially interoperable Internet of Things solution. With this statement in mind, the approach to the Internet of Things solutions consistency checking has been proposed. These solutions are assumed to be implemented on the basis of Web Services. The proposed approach has been grounded on the concept of “action” providing the scalable and reconfigurable way for formal model description. To describe the framework, a Kripke structure has been used. The applicability of proposed approach has been discussed on the basis of smart home scenario as a case study. To formulate the recommendations regarding the automation of model checking procedure, two opposite approaches to state space search have been contemplated – the Breadth-first Search and the Depth-first Search.
Electro-Thermal Erasing at 104-Fold Faster Speeds in Charge-Trap Flash Memory

An erasing method capable of speeds 104-fold faster compared with those by the conventional Fowler–Nordheim (FN) erasing technique is experimentally demonstrated in a gate-all-around junctionless (JL) charge-trap flash memory device using thermal excitation with the aid of electric field. A gate electrode serving as a built-in heater generates Joule heat for thermal excitation of trapped electrons in the charge-trap layer. The electrons excited by thermal excitation are further accelerated for injection into the silicon body by an in-situ applied E-field between the gate and the JL body. Hence, the trapped electrons are removed within 70 ns by electro-thermal erasing.
Applying Convolution Method to Solve Facility Location Problem on Generalized Meshwork

The facility location problem is very important which can be mathematically modeled as a fixed point problem. the feasible area of the decision is defined mostly on a non-convex meshwork region. By the weaker existence conditions of the fixed point problem, the constraint set can be extended to the nonconvex bounded set, nevertheless the smoothness and continuity of the constraints function must be holed for solving the problem. Usually, the feasible set of problem are expressed with the inequalities of constraints function. The contribution of this paper is to use convolution method to reconstruct a smooth and continuous constraints function for a meshwork region, by means of which the numerical combined homotopy pathway tracing method is capable of to solve the facility location problem on a generalized meshwork region. The numerical example illustrates that the method can solve the location problem, and much of effective.
Review on One-Bit Compressive Sensing and its Biomedical Applications

As a specific class of compressive sensing, one-bit compressive sensing can use sign measurements (one-bit information) to recover sparse signals. One-bit information can be sampled in a very high rate with a relatively low cost. The progress of 1bit-CS, including models, algorithms, and applications, is reviewed in this paper. Additionally, mixed one-bit compressive sensing, which can deal with analog and sign measurements simultaneously, is introduced together with an experiment on computed tomography for a phantom.
Multiresolution Discrete Finite Difference Masks for Rapid Solution Approximation of the Poisson’s Equation

The Poisson’s equation is an essential entity of applied mathematics for modelling many phenomena of importance. They include the theory of gravitation, electromagnetism, fluid flows and geometric design. In this regard, finding efficient solution methods for the Poisson’s equation is a significant problem that requires addressing. In this paper, we show how it is possible to generate approximate solutions of the Poisson’s equation subject to various boundary conditions. We make use of the discrete finite difference operator, which, in many ways, is similar to the standard finite difference method for numerically solving partial differential equations. Our approach is based upon the Laplacian averaging operator which, as we show, can be elegantly applied over many folds in a computationally efficient manner to obtain a close approximation to the solution of the equation at hand. We compare our method by way of examples with the solutions arising from the analytic variants as well as the numerical variants of the Poisson’s equation subject to a given set of boundary conditions. Thus, we show that our method, though simple to implement yet computationally very efficient, is powerful enough to generate approximate solutions of the Poisson’s equation.
An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.
Modeling of the Data Center Resource Management Using Reinforcement Learning

Cloud data centers are most dynamic systems in a modern digital world. To deliver the high-performance and fault-tolerant IT services to end users effectively it is necessary to develop new methods for data center resource management while adapting to the emergence of new requirements. In this paper, the authors refine and evaluate the previously proposed method for cloud data center resource management based on the reinforcement learning approach. The proposed method takes into account the power consumption and the number of SLA violations in the management policy. The power consumption is managed by switching physical servers to active or sleep state depending on current utilization of three resources: CPU, memory, and network bandwidth. The proposed reinforcement learning agent allows to determine the optimal policy for managing the physical servers without creating an environment model and preliminary information about the workload. The evaluation results show that the proposed method allows to decrease the SLA violation time, to serve more VM schedule requests when the number of VMs is changing frequently, and to decrease the utilization of data center network due to decreased number of migrations.
Coherent ePIC Receiver for 64 GBaud QPSK in 0.25 μm Photonic BiCMOS Technology

In this paper, we present a monolithically integrated coherent receiver with on-chip grating couplers, 90° hybrid, photodiodes and transimpedance amplifiers. A transimpedance gain of 7.7 kΩ was achieved by the amplifiers. An opto-electrical 3 dB bandwidth of 34 GHz for in-phase and quadrature channel was measured. A real-time data transmission of 64 GBd-QPSK (128 Gb/s) for a single polarization was performed.
Incentive Propagation Mechanism of Computation Offloading in Fog-enabled D2D Networks

Fog computing is a promising technology to provide economical and low latency data services, where devices can offload their intensive computation tasks to other devices which have idle computation resources. However, devices can only execute task offloading within their neighbors in conventional computation offloading schemes, which are not efficient for the whole network. We propose an incentive propagation mechanism (IPM) in fog-enabled device-to-device (D2D) networks, which incentivizes devices to not only truthfully report their charge price on the computation task, but also further propagate the task information to other devices. The proposed algorithm achieves more efficient computation offloading of the entire fog network. Simulation results validate its effectiveness.
Latent Dirichlet Allocation in Discovering Goals in Patients Undergoing Bladder Cancer Surgery

As we begin to leverage Big Data in health care settings and particularly in assessing patient-reported outcomes, there is a need for novel analytics to address unique challenges. One such challenge is in coding transcribed interview data, typically free-text entries of statements made by interviewees during face-to-face interviews. Conventional coding of such qualitative data into themes is labor-intensive and prone to inconsistencies. Latent Dirichlet Allocation (LDA) may offer statistical rigor in summarizing patients' concerns and coping strategies in a life-threatening illness. We aim to apply LDA to interview data collected as part of a prospective, longitudinal study of QOL in patients undergoing radical cystectomy and urinary diversion for bladder cancer. LDA showed that, prior to surgery, patients' priorities were primarily in cancer surgery and recovery. Six months after the surgery, however, their goals shifted to a desire to spend more time with family, resume work, and enjoy life to its fullest extent. Novel analytics such as LDA offer the possibility of summarizing personal goals in real time without the need for conventional fixed-length measures and qualitative data coding.