Miracle-dz/UniFAR-FTUs huggingface dataset:
FussyCache: A Caching Mechanism for Emerging Storage Hierarchies

In this paper, we propose a novel caching mechanism, called FussyCache, that differs from the traditional DRAM caching mechanisms that automatically cache a data block when it is requested. Instead, FussyCache evaluates each requested data block for its caching eligibility, and reads ineligible blocks directly from the device each time. We show that the Fussy-Cache performs substantially better than the traditional caching algorithms and its performance increases with the storage device speed. In particular, for the first generation Intel Optane based storage, FussyCache provides 25–30% reduction in the average access latency as compared to the native caching mechanism such as plain LRU. We also observe close to 15-20% improvement in performance even for a mainstream TLC SSD. Furthermore, the FussyCache design includes two mechanisms that allow for its easy deployment in any environment: (a) a self-monitoring stage that reverts it to a normal LRU when partial caching is not beneficial, and (b) a training phase that automatically tunes the configurable parameters for the deployment environment.
Mobility-Aware Latency-Efficient Cache Placement in Mobile Edge Networks

Future mobile services and applications are bounded by user location, data, and network. These services will suffer from poor support from wireless networks due to the huge amount of mobile traffic and user mobility. The demand for contents by these services results in constraints put on latency and quality of service (QoS). Considering these problems, researchers investigated caching contents locally and proactively at the edge of the mobile edge networks (MENs). In this work, we proposed new formulation of mobility-aware latency-efficient cache placement problem for mobile edge networks (MENs) taking into account different storage capacities, users mobility, content popularity, contact probability, and latency to download the contents to user terminals (UTs). Our formulated multi-objective optimization problem aims to maximize the cache hit rate. We apply weighted-sum decision theory approach to model the decision of placing contents at the edge of the network. Simulation results are shown to gain insight of the impact of different factors on our proposed system and and evaluated the results with three other cache placement techniques.
Validation of Wavelia Microwave Breast Imaging System Using Mammography Breast Density

This paper presents a method for transforming the coordinates of regions of interest from 2D mammograms to a 3D spatial reference frame using estimates of breast tissue thickness provided by an available software for mammography data analysis. The method was developed to assist in interpreting and validating the 3D findings of a microwave breast imaging apparatus known as Wavelia against mammography. Results from the First-in-Human clinical investigation with the Wavelia system are used to illustrate the potential and feasibility of the proposed technique.
Development of H-Slotted DGS Based Dual Band Antenna Using ANN for 5G Applications

The proposed work conducts a new approach for modeling a dual band monopole antenna design using DGS assisted by ANN. In the aim of efficient dual band antenna design with gain and optimal impedance matching, the Artificial Neural Networks technique (ANN) is used for the development process. This work presents a modeling for H-slotted Defected Ground Structure (DGS) based dual band antenna using ANN for 5G Sub-6 GHz applications. The designed antenna operates at 3.76 GHz and 6.1 GHz. The antenna gain is 2.18 dB and 2.75 dB at both frequencies, respectively. Firstly, a simulation is performed using CST EM simulator, then the predicted results in term of return losses and frequencies are fed into the ANN model. Secondly using a hybrid algorithm based on both feed-forward back-propagation and Levenberg-Marquart (LM) learning algorithm, the optimal position of the H-Slotted DGS in terms of 5G Sub-6 GHz band is extracted. Finally, the experimental validation is conducted and compared with the simulation results, a good agreement is obtained.
Effective Exploration of Thread Throttling and Thread/Page Mapping on NUMA Systems

NUMA systems have become commonly used in HPC. However, to fully take advantage of these systems, the right thread-to-core allocation and page placement are essential. On top of that, considering that many parallel applications have limited scalability, applying thread throttling (i.e., artificially reducing the number of active threads) most of times will further improve energy and/or performance. Because it involves many variables, most of the previous research has not considered applying the aforementioned approaches altogether. Therefore, not using a smart method to converge to a satisfactory solution precludes an efficient design space exploration, not unlocking NUMA systems’ full potential. Considering that, we propose a new methodology, which involves evaluating the number of threads, thread-to-core allocation, and page placement independently (i.e., we apply a local search to each one). By evaluating and using the right sequence of local searches, we significantly reduce the design space exploration and still maintain the quality of results (w.r.t to an exhaustive global search), making it applicable to real-world scenarios. Our experiments using several parallel applications running on two different NUMA systems show that our methodology speeds up the search by to $9 \times$, on average, reducing the search space in at least 70% and up to 86%, while finding optimal or near-optimal solutions.
Ternary Compound Matching of Biomedical Ontologies with Compact Multi-Objective Evolutionary Algorithm Based on Adaptive Objective Space Decomposition

A biomedical ontology provides a formal definition on the concepts and their relationships in the biomedical domain, which supports applications such as biomedical data annotation, knowledge integration, search and analysis. Different biomedical ontologies are mostly developed independently, and thus, establishing meaningful links between their entities, so-called ontology matching, is critical to implement their inter-operation. Since biomedical research usually spans multiple domains and topics, which motivates a new type of complex ontology matching, i.e. compound ontology matching, which involves more than two ontologies. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, there exist different aspects of a solution that are partially or wholly in conflict, and the single-objective EA may lead to unwanted bias to one of them and reduce the solution's quality. To improve the ternary compound alignment's quality when matching three biomedical ontologies, in this work, a compact Multi-Objective Evolutionary Algorithm Based On Adaptive Objective Space Decomposition (cMOEA-AOSD) based matching technique is proposed. In particular, a Ternary Compound Concept Similarity Measure (TCCSM) is proposed to calculate the similarity value of three biomedical concepts, a mathematical model for ternary compound matching problem is constructed, and a cMOEA-AOSD is presented to address it, which is able to adaptively decompose the objective space to ensure the diversity of the solutions in Pareto Front (PF) and the quality of the final solution. The experiment uses six testing cases that consists of nine biomedical ontologies to test our proposal's performance, and the experimental results show that cMOEA-AOSD significantly out performs other MOEA-based matching technique and the state-of-the-art ternary compound matching techniques.
Applying Network Reliability in Business Management Activities

A new application of network reliability is proposed to help analyze a business process performance in usual business activities. This research is to construct an easy, formal and mathematical tool to probe the health of daily-run business activities. A business activity consists of a series of value-added tasks, which are carried out by their related attenders, to meet business objectives. Traditional management behaviors include practices and skills to sustain the plan, sanction, management, and analysis of business activities. However, the assessment of activity performance remains a weak gap in analytic requirements. The approach proposed in this paper is one of the expected tools to fill the gaps in traditional management practices. The paper is to provide managers several new visions of modeling and analysis, which are not easily derived by traditional management practices. A distributor process with several scenarios to explain the approach are explored, and discussed.
Optimization of 16-QAM for Mitigating Impairments in 60 GHz Six-port Receivers

In this paper, an optimized circular four rings 16-level quadrature amplitude modulation (C-16QAM) is designed to mitigate the I/Q imabalance and phase noise in 60 GHz millimeter-wave six-port direct down-conversion receiver. This modulation technique is performed through the optimization of both, Euclidean distance between symbols and the gap between the rings. The experimental results show that the proposed constellation can achieve lower bit error rate (BER) compare to the typical square 16-QAM and other C-16QAM modulations reported in the literature.
A Parametric Simulative Study for Lifetime Prediction of Sintered Silver Die Attach Under Different Accelerated Testing Conditions

Sintered silver (SAG) is a widely-used die attach (DA) technology in automotive industry, especially for power modules in electric vehicles (EV) defined by higher power densities. High operation temperatures (> 175°C) are particularly resulting from the application of dies based on wide bandgap semiconductors materials like SiC and GaN. The thermo-mechanical stress resulting from large temperature swings in EV applications fatigues the porous layer of SAG. In order to estimate the reliability of die attach layers physics-of-failure (PoF) based lifetime models are needed. In this study, comparative parametric simulations are used to estimate the influence of geometric factors on the lifetime of the die attach layer. In addition, the state of the art of accelerated fatigue experiments is presented.
M-BCJR algorithm with channel shortening based on ungerboeck observation model for faster-than-Nyquist signaling

The M-BCJR algorithm based on the Ungerboeck observation model is a recent study to reduce the computational complexity for faster-than-Nyquist (FTN) signaling [1]. In this paper, we propose a method that can further reduce the complexity with the approximately same or better bit error rate (BER) performance compared to [1]. The information rate (IR) loss for the proposed method is less than 1% compared to the true achievable IR (AIR). The proposed improvement is mainly by introducing channel shortening (CS) before the M-BCJR equalizer. In our proposal, the Ungerboeck M-BCJR algorithm and CS can work together to defeat severe inter-symbol interference (ISI) introduced by FTN signaling. The ISI length for the M-BCJR algorithm with CS is optimized based on the criterion of the IR maximization. For the two cases τ = 0.5 and τ = 0.35, compared to Ungerboeck M-BCJR without CS benchmark [1], the computational complexities of Ungerboeck M-BCJR with CS are reduced by 75%. Moreover, for the case τ = 0.35, the BER performance of Ungerboeck M-BCJR with CS outperforms that of the conventional M-BCJR in [1] at the low signal to noise ratio region.
Ultra Wide-Band PRGW Based Magneto-Electric Dipole Antenna

In this paper, a Circularly Polarized (CP) Magneto-Electric (ME) dipole antenna excited by low loss Printed Ridge Gap Waveguide (PRGW) feed network is proposed. The PRGW design consists of substrate filled periodic mushrooms arranged around the ridge of the transmission line. An air gap layer is added above the ridge structure covered with a top metallic layer. The PRGW excites the ME dipole antenna through a transverse radiating slot above the air gap. A ME dipole antenna is designed in the form of horizontal trapezoidal metallic patches supported with vertical pins. It is operating over the frequency band from 54 GHz to 68 GHz where the reflection coefficient is below -10 dB. The maximum gain is 8 dB and the axial ratio bandwidth with maximum value 2.5 dB is achieved all over the band.
A Compact Design for Dual-band Implantable Antenna Applications

This paper presents the development of a dual-band antenna working in the Industrial, Scientific, and Medical (ISM) band (902 – 928 MHz, 2.4 – 2.5 GHz). The proposed antenna is compact, has a frequency-independent response between the lowest and the highest frequency, has a small size of 6 mm x 6 mm x 2.54 mm, and exhibits a high gain for implantable application of -13.14 dBi at 915 MHz and -28 dBi at 2.45 GHz. This design does not use any via or defected ground plane making the antenna very useful for this kind of application. The miniaturized dual-band antenna has been tested in the presence of salmon fish and chicken meat to replicate the behavior of the antenna in the human body.
Application of the EfficientDet Algorithm in Traffic Flow Statistics

With the continuous development of artificial intelligence technology, deep learning technology is used to process a large number of real-time traffic scene information helping the management of public transportation, and traffic flow statistics can reflect the real-time traffic conditions. The paper uses the EfficientDet target detection algorithm to detect and analyze the traffic video frame information and carry out statistics of vehicle and pedestrian flow at traffic intersections.The system can calculate the vehicle speed and perceive the degree of traffic congestion in real-time. It's convenient for the traffic department to increase the utilization rate of the road.
A Deep Learning Method for Modeling the Magnetic Signature of Spacecraft Equipment Using Multiple Magnetic Dipoles

In this letter, a deep-learning-based neural network for magnetic dipole modeling (MDMnet) is introduced in the framework of dc magnetic cleanliness for space missions. The developed method targets modeling the static magnetic signature of a spacecraft unit that is obtained during the unit-level characterization stage of the extensive prelaunch electromagnetic compatibility test campaign. By employing synthetic magnetic flux density data generated by virtual dipole sources, the MDMnet can be trained to accurately estimate the magnetic parameters of real equipment based on its near magnetic flux density measurements. The target of the deep learning algorithm is, on the one hand, to effectively minimize the prediction errors (loss function) throughout the training process and, on the other hand, to enable the generalization of the model predictions, i.e., exhibit accurate model estimations with unseen magnetic induction data. Extensive simulations toward the stabilization of the MDMnet hyperparameters are outlined, and indicative model inferences employing artificial magnetic flux density data are carried out. Finally, the MDMnet can achieve a predictive accuracy of 0.8 mm with respect to the dipole localization and 1% with respect to the magnetic induction magnitude, verifying the potency of the developed method.
The 5G NOMA networks planning based on the multi-objective evolutionary algorithm

This paper studies the base station (BS) planning problem of 5G non-orthogonal multiple access (NOMA) heterogeneous network, and considers the goal of maximizing network throughput and minimizing the construction cost. Due to the conflict between the two objectives, the BS planning problem of 5G NOMA heterogeneous network is modeled as a multiobjective integer optimization problem. It is very difficult to solve the multi-objective integer optimization problem by using traditional optimization methods. Therefore, we adopt the multiobjective evolutionary algorithm to solve the problem. Simulation results show that the proposed multi-objective optimization scheme for 5G heterogeneous network planning can effectively improve the network transmission performance and reduce the network construction cost.
Simulation Study of the Origin of Ge High Speed Photodetector Degradation

The reliability of Si photonics and optoelectronics devices is emerging as a major new topic. By using TCAD simulations, this work investigates the microscopic origins of the Ge High Speed Photodetector (HSPD) performance losses during stress obtained in [1]. It confirms the key roles of the carrier lifetime degradation on both dark current increase and photonics current decrease, which could be triggered by surface recombination (SR), especially at the Buried Oxide (BOX). Other sources of degradation are studied, as fixed charges in the SiO2passivation layer and interface state (Dit).
Heuristic Search Inspired Beam Selection Algorithms for mmWave MU-MIMO System With Discrete Lens Array

By employing beam selection in beamspace, the costs of hardware and power consumption in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system can be significantly reduced without obvious performance loss. In most existing schemes, there is a limitation that the number of beams (radio frequency (RF) chains) and users must be equal, which leads to degradation of system performance and flexibility. To overcome this limitation, we formulate beam selection as a discrete combinational optimization problem of binary selection vector to maximize the spectral efficiency of multi-user MIMO (MU-MIMO) system. In order to solve this problem, we propose enhanced CE (ECE)-based and GA-based algorithms inspired by cross entropy (CE) method and genetic algorithm (GA) in heuristic search. Moreover, we analyze the complexity of proposed algorithms and provide a graphical representation of convergence property for ECE-based algorithm. Simulation results demonstrate that the proposed algorithms are able to achieve good performance with variable number of beams, and verify the validity of graphical representation of convergence property for ECE-based algorithm.
Polystyrene latex with partially hydrolyzed pnai grafts at the surface as Pt(IV) sorbent

The ability of the polystyrene latex with partially hydrolyzed poly[(N-acetylimino) ethylene] grafts at the surface (PSt-PNAI, h) as a sorbent for Pt(IV) was evaluated. The process characteristics were established by analysis according to the sorption isotherm models (Freundlich and Langmuir) and by kinetic study. It was found that the sorbed amount and percentage of Pt(IV) recovery from the aqueous solutions increase with increasing pH and initial concentration, and reach a maximum at pH = 10, with a sorption capacity of 1111 mg/g and a sorption half-time of about 90 min. The equilibrium distribution of Pt(IV) between the two phases (sorbent and solution) is described by means of Langmuir model of monomolecular layer adsorption. The sorption data indicate the applicability of a pseudo-second order kinetics.
A local core number based algorithm for the maximum clique problem

The maximum clique problem (MCP) is to determine a complete subgraph of maximum cardinality in a graph. MCP is a fundamental problem in combinatorial optimization and is noticeable for its wide range of applications. In this paper, we present two branch-and-bound exact algorithms for finding a maximum clique in an undirected graph. Many efficient exact branch and bound maximum clique algorithms use approximate coloring to compute an upper bound on the clique number but, as a new pruning strategy, we show that local core numbers are more efficient. Moreover, instead of neighbors set of a vertex, our search area is restricted to a subset of the set in each subproblem which speeds up clique finding process. This subset is based on the core of the vertices of a given graph. We improved the MCQ and MaxCliqueDyn algorithms with respect to the new pruning strategy and search area restriction. Experimental results demonstrate that the improved algorithms outperform the previous well-known algorithms for many instances when applied to DIMACS benchmark and random graphs.
DDoS ATTACKS DETECTION USING AUTOENCODERS

: Computer networks have several issues such as insertion attacks, denial of service attacks, traffic jamming, and unauthorized access. Due to these issues network security is most important. In a network, Distributed Denial of Service (DDoS) attacks may cause significant degradation of the performance of any application. It is very challenging to detect such attacks and undetected attacks are considered as a threat. This paper describes comparative analysis of Denial of Service attacks detection using Feed Forward Neural Networks and Autoencoders which are machine learning based approaches and are usually used for feature learning.
Acute leptin response after high intensity interval and moderate intensity continuous runs

The possible direct role of exercise intensity and duration on leptin concentrations is conflicting. The aim of this study was to evaluate the acute effects of high intensity interval (HIIE) and moderate intensity continuous (MICE) exercise on plasma leptin response. Seven young volunteers underwent three tests: 1) a treadmill graded exercise test to identify running peak velocity (PV); 2) HIIE: 5 × 2 min work bouts at 90% of PV, interspersed by 2 min of passive recovery and; 3) MICE: 30 min at 70 % of PV. Blood samples were drawn for the assays of leptin before and 30 minutes after HIIE and MICE. A 2-way repeated measures ANOVA showed a significant main effect of time [F(1,6) =17,52; p=0,006], no significant effect of condition (type of exercise) (F(1,6) = 0,16; p = 0,68) and no significant interaction (condition × time) (F(1,6)= 0,48, p=0,51). Leptin decreased 30 min after HIIE (t= 2,95, p=0,025) and MICE (t=4,18; p=0,005). There was no difference between the HIIE and MICE conditions immediately after exercise (t=0,90; p=0,40). After HIIE and MICE, leptin decreased in the same magnitude. It appears that both exercise modalities result in physical stress which is sufficient to improve short-term leptin sensibility.
Endek Classification Based On GLCM Using Artificial Neural Networks with Adam Optimization

Not many people know that endek cloth itself has 4 known variances. Nowadays. Computing and classification algorithm can be implemented to solve classification problem with respect to the features data as input. We can use this computing power to digitalize these endek pattern. The features extraction algorithm used in this research is GLCM. Where these data will act as input for the neural network model later. There is a lot of optimizer algorithm to use in back propagation phase. In this research we prefer to use adam which is one of the newest and most popular optimizer algorithm. To compare its performace we also use SGD which is older and popular optimizer algorithm. Later we find that adam algorithm generate 33% accuracy which is better than what SGD algorithm give, it is 23% accuracy. Longer epoch also give affect for overall model accuracy.