Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Resource Allocation for Energy Efficiency in OFDMA Multicell Networks With Wireless Power Transfer

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.
Accelerated Non-negative Latent Factor Analysis on High-Dimensional and Sparse Matrices via Generalized Momentum Method

Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. On the other hand, a momentum method is frequently adopted to accelerate a learning algorithm explicitly depending on gradients, yet it is incompatible with learning algorithms implicitly depending on gradients, like SLF-NMU. To build a fast NLF model, we firstly propose a generalized momentum method compatible with SLF-NMU. With it, we propose the single latent factor-dependent, non-negative, multiplicative and momentum-integrated update (SLF-NM2U) algorithm for accelerating the building process of an NLF model, thereby achieving a fast non-negative latent factor (FNLF) model. Empirical studies on six HiDS matrices from industrial application indicate that with the incorporated momentum effects, FNLF outperforms NLF in terms of both convergence rate and prediction accuracy for missing data. Hence, compare with an NLF model, an FNLF model is more practical in industrial applications.
A Method to Accelerate the Training of WGAN

Generative adversarial network draw much attention in recent years because of its impressive results. The idea of adversary was cleverly introduced to the training of the network. And the result was used in the value function to evaluate the quality of the network. During the adversarial process, the discriminator was trained to separate the training data from the fake data, and the generator learn to estimate the real data distribution from the feedback of the discriminator. Both sides of the network learn from the back-propagation and finally reach a state of balance. After the training, the generator can produce very visually appealing samples. In this paper, we review the main route of the GANs, and we find the weakness of the value function from the experiments and propose new optimal method to improve the training of the GAN.
Consensus-Based Data Statistics in Distributed Network Systems

Data has become increasingly important in network systems because a lot of data is needed in new technologies such as machine learning. To obtain statistics (e.g. maximum, average, and distribution) in a fully distributed way with low complexity is challenging. Existing research on consensus algorithms can successfully obtain the max/min, average and median in a distributed network, but little work has been done on how to compute other statistics, especially probability density function (PDF). In this paper, consensus-based algorithms are proposed to obtain PDF in a fully distributed way with low complexity. The key idea of our algorithms is to divide the range of nodes' values into several sections and calculate the proportions of values in each section in a fully distributed way. If nodes have their unique identifications (IDs), repeatedly run max/min consensus algorithm in the network to reach the partially max/min values and then erase them in order to reach all values exactly once. We prove that the algorithm converges in finite time. When nodes' IDs are not available, the main challenge is to solve the conflicts when two or more nodes have the same value. We propose an asymptotically converged algorithm to solve the problem in this scenario.
Robust Action Selection in Partially Observable Markov Decision Processes with Model Uncertainty

Partially observable Markov decision processes (POMDPs) are models for sequential decision-making under state transition uncertainty, and sensing uncertainty of the underlying state. Model uncertainty is an important concern when the models, for which an action policy was optimized, change in time, e.g., degrading sensors that result in a drift in the observation function. Replanning a policy whenever a model drifts (if feasible) is both a time consuming and computationally expensive process. At the other extreme, ignoring the drift and following the original policy can lead to high-risk actions with high costs. We present an efficient approach that post-processes a policy computed using initial models to select actions robust to changes in the observation function. The key idea is to maintain a belief region rather than a belief point about the state of the system, and perform online robust action selection w.r. t. the current belief region. Specifically, we formulate a convex optimization problem to select the action that maximizes the worst case reward function for a convexified belief region. Simulation results demonstrate the ability of our approach to avoid high-risk actions when the system is in uncertain states.
APEnergy: Application Profile-Based Energy-Efficient Framework for SaaS Clouds

In the past decade, there has been a steady increase in the focus on green initiatives for data centers. Various energy efficiency measures have been proposed and adopted, however the optimal tradeoff between performance and energy efficiency of data centers is yet to be achieved. Addressing this issue, we present APEnergy, an Application Profile-based energy efficient framework for small to medium scale data centers. The proposed framework leverages information on the completed application with certain workloads in the data center to build profiles for workflows. The framework utilizes a novel scheduler to obtain a near-optimal mapping for placement of workflow tasks in the data center based on three criteria including CPU utilization, power cost and task completion time. We compare the performance of the proposed scheduler to similar RTC and HEFT schedulers. Extensive simulation studies are carried out to verify the scalability and efficiency of APEnergy framework. Results show that the proposed Scheduler is 2% and 14% more energy efficient than RTC and HEFT respectively.
Two Variants of the Full Steiner Tree Construction Problems

Research on the full Steiner tree problem in the Euclidean plane R2 has important theoretic significance, and it also has broad applications in material construction, VLSI design, WDM optical networks and wireless communications. In this paper, combining with the material construction and the actual cost, we introduce two new variants of the full Steiner tree problem in the Euclidean plane R2: the minimumlength full Steiner tree problem (MLFST, for short) and the minimum-number of Steiner points in the full Steiner tree problem (MNPFST, for short). These problems are both NP-hard, and we design one approximation algorithm for each of them which have important applications in real life. To enhance the convenience of application, a subdivision procedure method and a minimum disk cover of points method are adopted in some designed algorithms.
Dynamic Computing in Memory (DCIM) in Resistive Crossbar Arrays

With Von-Neumann computing struggling to match the energy-efficiency of biological systems, there is pressing need to explore alternative computing models. Recent experimental studies have revealed that Resistive Random Access Memory (RRAM) is promising alternative for DRAM. Resistive crossbar arrays possess many promising features that can not only enable high-density and low-power storage but also non Von-Neumann compute models. Most recent works focus on dot product operation with RRAM crossbar arrays, and therefore are not flexible to implement various logical functions. We propose a low-power dynamic computing in memory system which can implement various functions in Sum of Product (SOP) form in RRAM crossbar array architecture. We evaluate the proposed technique by performing simulation over wide range of MCNC benchmarks. Simulation results show 1.42X and 20X latency improvement as well as 2.6X and 12.6X power saving compared to static and MAGIC computing in memory methods.
Novel Approaches to Activity Recognition Based on Vector Autoregression and Wavelet Transforms

The recognition of daily activities has been a long-running research domain, which has received increasing attention over the past few years. This is due to the proliferation of personal devices which are capable of reporting the physical signals generated during these activities. Being a classification problem, the primary focus is on suitable modalities for feature extraction and proper choice of classifiers. In this work we investigate the performance of two novel approaches to feature extraction based on Vector Autoregression and Wavelet Transforms together with four different classifiers. The results indicate that the two proposed feature extraction methods are suitable for this domain. In addition, the Canonical Correlation Forests classifier has been found to be a promising candidate for inference in the domain of Activity Recognition.
Mutual Coupling Reduction in an Array of Patch Antennas Using CLL Metamaterial Superstrate for MIMO Applications

The capability of a magnetodielectric superstrate to suppress the surface wave propagation is investigated. It is shown that the capacitively loaded loop (CLL) metamaterial (MTM) superstrate exhibits a high degree of surface wave attenuation. One of the important applications of such structure is to reduce the mutual coupling between the microstrip patch elements in array antennas. The results of numerical simulations show a significant reduction in the mutual coupling between the antenna elements. To verify and confirm the simulation results, a prototype of the CLL-MTM superstrate incorporating an array of rectangular patch antennas are fabricated and tested. The proposed two-element array antenna has a dimension of approximately $1.6\lambda \times 0.9\lambda \times 0.16\lambda $ while achieving a gain and an efficiency of 8.2 dB and 97%, respectively. Measurement shows the mutual coupling reduction of over 55 dB. The CLL-MTM superstrate provides the gain and efficiency almost the same of unloaded array antennas with 0.1 dB and 2% changes, respectively.
Modeling of Multi-domain Switching in Ferroelectric Materials: Application to Negative Capacitance FETs

We present a new multi-domain model for polarization switching in ferroelectric materials. The computationally efficient model captures the time evolution of multi-domain ferroelectrics with good accuracy along with the frequency dependent switching behavior. We have fabricated (PVDF) and measured P-E characteristics of PZT and PVDF capacitors and have validated the model with measurements. The model allows the visualization of time dependent domain switching allowing further physical insights. We have also proposed a method to extract the distribution of domain orientations experimentally.
Best Practices for Using Electrostatic Discharge Protection Techniques for Single-Event Transient Mitigation

We provide “best practices” for single-event transient (SET) mitigation using electrostatic discharge (ESD) protection techniques. We investigate the correlation between SET suppression and RF performance when ESD protection circuits for SET mitigation are combined with various RF switches. The three different single-pole single-throw (SPST) switch configurations (conventional design, floating body, and floating body/n-well) were implemented to provide insight on how to best apply dc ESD protection techniques for also achieving robust SET mitigation. Based on two-photon absorption laser experiments and RF measurements, a conventional SPST switch configuration shows relatively poor RF performance but is effective for SET mitigation due to the SET discharge current path provided by ESD protection circuit. On the other hand, the combination of the SPST switch and the ESD circuit, which exhibits the best RF performance among the three configurations, is relatively vulnerable to SETs. Based on these results, strategies and methodologies for ESD protection techniques to effectively mitigate SET are proposed.
Trends in Linked Data-Based Educational Studies: A Review of Contributions in SSCI Journals

This paper reviews the research articles published in the years between 2016 and 2017 with a focus on using Linked Data technologies for enhancing learning. For this purpose, a survey was conducted and articles were selected according to two main criteria: (1) the paper must be in the technology-enhanced learning domain and published in one of the well-recognized journals indexed by the Institute for Scientific Information (ISI) and listed in the Social Science Citation Index (SSCI), and (2) the paper must clearly show how to consume, produce, or both consume and produce Linked Data in the learning context. The survey results show the importance of using Linked Data as a powerful educational technology.
Multiband Reconfigurable Patch Antenna for Millimeter Wave Applications

The main objective of this research work is to design a multiband reconfigurable patch antenna for millimeter wave applications. In this paper, a slotted circular patch antenna is designed on Polymide substrate material with dielectric constant 3.5. To reconfigure patch antenna, two MEMS switches are placed within slots inside the patch and are investigated under four different conditions by alternatively turning ON/OFF MEMS switches. The different performance parameters such as return loss, gain and Voltage Standing Wave Ratio (VSWR) are measured and it is found that the proposed antenna is efficiently resonating on three frequency bands by tuning the switches with effective gain and VSWR. The proposed reconfigurable antenna is resonating on important frequencies such as 30GHz, 35GHz, 45GHz and 60GHz with effective gain and VSWR without changing the dimension of patch antenna. This antenna can be used for millimeter waves based high speed short range wireless communications, sensing and security applications.
Partially Observable Multi-Agent RL with Enhanced Deep Distributed Recurrent Q-Network

Many real-world problems are naturally modeled as multi-agent problems, and most of them are partially observable. But multi-agent problems will make the environment became nonstationary from the point of view each agent, which causes the combination of experience replay with IQL to be problematic. DDRQN as a method to solve partially observable RL problems, it didn't solve that. In this article we propose a method based on importance sampling and address DDRQN's disadvantages which can't enable memory replay. Results on the SC2LE environment confirm that this method significantly improve performance compared to original DDRQN
SystemC-AMS Simulation of Energy Management of Electric Vehicles

Electric vehicles (EV) are rapidly invading the market, since they are clean, quiet and energy efficient. However, there are many factors that discourage EVs for current and potential customers. Among them, driving range is one of the most critical issues: running out of battery charge while driving results in serious inconvenience even comparable to vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities.As a result, the dimensioning of the energy subsystem of an EV is a crucial activity. The choice of the power components and of the adopted policies should thus be validated at design time through simulations, that estimate the vehicle driving range under reference driving profiles. It is thus necessary to build a simulation framework that takes into account an EV power consumption model, dependent on the characteristics of the vehicle and of the driving route, plus accurate models for all power components, including batteries and green power sources.The goal of this paper is to achieve early EV simulation, so that the designer can estimate at design time the driving range of the vehicle, validate the adopted components and policies and evaluate alternative configurations.
A Study on Extending $f_{\mathrm{T}}$ in TIIMCA LNA Topology

This paper presents a study on the cut-off frequency of the low noise cascode amplifier with transformer-based input integrated matching. It shows that two poles of the short-circuit current gain are affected by the inductance of the secondary spiral inductor of the integrated transformer, and that it is possible to extend the cut-off frequency by means of a proper sizing. The results of the theoretical analysis are confirmed by circuit simulations in a 180nm CMOS technology commercially available.
Enhancing Digital Forensic Analysis Using Memetic Algorithm Feature Selection Method for Document Clustering

Text clustering is an effective way that helps crime investigation through grouping of crime-related documents. This paper proposes a Memetic Algorithm Feature Selection (MAFS) approach to enhance the performance of document clustering algorithms used to partition crime reports and criminal news as well as some benchmark text datasets. Two clustering algorithms have been selected to demonstrate the effectiveness of the proposed MAFS method; they are the k-means and Spherical k-means (Spk). The reason behind using these clustering methods is to observe the performance of these algorithms before and after applying a hybrid FS that uses a Memetic scheme. The proposed MAFS method combines a Genetic Algorithm-based wrapper FS with the Relief-F filter. The performance evaluation was based on the clustering outcomes before and after applying the proposed MAFS method. The test results showed that the performance of both k-means and spk improved after the MAFS.
The Effect of Motivational Goals on Information Search for Tasks of Varying Complexity Levels

Not all search engine users perform equally well in terms of search result quality and search time. This variety depends on many different external and personal factors. In this work, we investigate how different motivational goals affects users' search behavior while conducting search tasks with different complexity levels. Specifically, we consider four different settings: 1: information search without a numeric goal specification, 2: information search with a required number of correct answers (criteria oriented reference norm), 3: information search with a required number of correct answers chosen by the user himself (individual reference norm) and 4: information search with the instruction to be better than another participant (social reference norm). Data of 115 participants is used for the analysis. To evaluate the relationship between search activities, motivational goals, task complexity and search outcome, we conducted several descriptive and statistical tests. Our results suggest amongst others that motivational goals have a significant impact on specific aspects of the search behavior while other aspects are unaffected. Furthermore, the recorded data shows that a motivational goal setting increases the task processing speed but also the error rates.
On the Analysis of EEG Features for Mental Workload Assessment During Physical Activity

Assessment of mental workload is crucial for applications which require constant attention and where conditions such as mental fatigue and drowsiness must be avoided. As such, electroencephalography (EEG) based mental workload models have been developed in the past. The majority of these models, however, have assumed individuals are not ambulant, thus bypassing the issue of movement-related EEG artefacts. While such models may be useful for a number of applications (e.g., operators are sitting), they may not apply in situations in which operators are performing their task under different physical activity levels. Representative examples can include first responders, such as paramedics, firefighters, or police officers. In this work, we take the first steps towards overcoming this limitation and present results of an experiment simultaneously eliciting increasing mental workload states at varying physical activity levels. EEG data from forty-seven participants was collected while they performed the NASA Revised Multi-Attribute Task Battery II (MATB-II) under three different activity level conditions (no, medium, high). In this study, we report the effects of activity on the noise-robustness and distribution of several spectral, amplitude/phase coherence, and amplitude modulation features, with the ultimate goal of deriving a feature set tailored towards automated workload assessment during physical activity. Preliminary results show spectral features acquired from the frontal area of the cortex as the most promising and that activity aware mental workload models should be developed.
Intention-Based Anticipatory Interactive Systems

Intention-based, anticipatory, interactive systems (IAIS) represent a new class of user-centered assistance systems. IAIS uses actions and system intentions derived from signal data, and the affective state of the user. By anticipating the further action of the user, solutions are interactively negotiated. The active roles of humans and systems change strategically, which requires behavioral models, which in turn can be specified by life sciences' results. Deployed human-machine-systems and lab tests have the goal of understanding of the situated interaction. This supports integration of assistance systems for Industry 4.0 and in the context of demographic change. We provide a definition of IAIS, discuss the underlying requirements, goals and challenges and provide a brief review of the state-of-the-art in the involved research areas.
Blockchain Energy Market Place Evaluation: An Agent-Based Approach

The growing interest for decentralized production of renewable energies calls for new market approaches. In this context, the blockchain is considered as a key technology for enabling decentralized local energy markets. Following this idea, several market solutions were proposed by the academic community. However, their technical feasibility or economical viability are yet to be assessed due to the lack of simulation frameworks. In this paper, we propose an agent-based simulation framework to experiment blockchain-backed energy market places. Moreover, based on realistic data of households located in northern France, we perform a sensitivity analysis to assess the impact of parameters on economics and blockchain system performances. Finally, we have implemented our solution on Raspberry Pies IoT devices to measure the actual power consumption of such systems.