Miracle-dz/UniFAR-FTUs huggingface dataset:
A decentralized algorithm for large scale min-max problems

We consider a distributed saddle point problem, in which a collection of nodes collaboratively optimize a sum of local component functions through local computations and information exchange with neighbouring nodes. To solve this problem, we propose a decentralized algorithm based on the Extragradient method, whose centralized implementation has been shown to achieve good performance on a wide range of min-max problems. We show that our proposed method achieves linear convergence under suitable assumptions and explicitly characterize how the convergence rate depends on the condition number and the spectral gap of the communication graph. We also present numerical simulations that corroborate our theoretical results.
A Deep Learning Approach to Detect Anomaly in Software-Defined Network

Software-Defined Network (SDN), an innovating technique, endows flexibility and programmability to the network in contrast to the conventional one. Its idea of decoupling control and data plane brings convenience to manage networks like load balancing, traffic engineering, virtual firewall etc. Nevertheless, the separated plane structure results in novel threats, composed by the common in conventional network and the specialized in SDN. In this paper, we propose a deep learning approach to detect anomaly in SDN with OpenFlow by analyzing multiple metrics extracted from OpenFlow switch metadata. Evaluated by four trained deep learning models to classify the multivariate time series, we obtained an average accuracy of 83.8%.
LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization

Predicting lncRNA-protein association is essential for insights into fundamental biological processes and disease etiology in plants and animals. There has been an enormous increment in the number of identified long noncoding RNAs (lncRNAs). However, less efforts has been directed towards lncRNA-protein interaction (LPI) prediction to help in characterizing the huge array of plant lncRNAs. This study presents LPI-DL, a deep learning method for predicting potential plant lncRNA-protein interaction based on sequence features and compact LSTM. The optimal combination of k-nucleotide frequencies and codon-based encoding features are used as input to the model. The recurrent neural network learns the discriminative features characterizing the long-term dependencies between sequences. We select optimal features using recursive feature elimination and support vector machine (RFE-SVM) and impose sparse projection onto the hidden states of input sequences through connection pruning. Evaluation of two plant datasets corroborates that LPI-DL is more competitive over other methods. Comparative experiments denote that the proposed method achieves state-of-the-art prediction performance. This study effectively improves the accuracy of interaction prediction and lays a foundation to foster lncRNA functional studies.
An assume-guarantee approach to sampled-data quantized glucose control

Contract-based design is a compositional approach that enables to satisfy specifications for complex systems by partitioning responsibilities among the constituent subsystems. A natural application of assume-guarantee reasoning arises in the context of biological systems, which are composed of different modules, each responsible for particular actions. This work presents an application of the assume-guarantee contracts theory to the glucose regulation system, a topic of major importance in diabetes treatment, in the context of the so-called Artificial Pancreas. The glucose control problem is defined and solved in a decentralized way by defining suitable contracts to be satisfied by the glucose and insulin subsystems, while the framework naturally takes into account sampling and quantization arising from the digital environment. The results show that the approach is feasible and promising for further investigation.
Compact SIW-Based Self-Quadruplexing Antenna for Wearable Transceivers

This letter proposes a low-profile substrate integrated waveguide (SIW) cavity backed self-quadruplexing antenna for wearable transceivers. The full-mode circular SIW cavity is split into four unequal quarter-mode SIW (QMSIW) cavities using the two slots on the upper layer of the cavity. The proposed antenna uses C-shaped and arc-shaped slots of different dimensions in each QMSIW resonator to operate at wireless local area network bands (3.5, 4.9, 5.4, and 5.8 GHz). The measured gains are 4.4, 5.07, 5.40, and 5.7 dBi at 3.5, 4.9, 5.4, and 5.8 GHz, respectively. The antenna is simulated and optimized inside a 48 $\times \text{76}\times$ 21 mm3 rectangular device by keeping the device on the chest of a realistic human model. The proposed antenna has unidirectional radiation patterns in all four operating bands with low backward radiations. Thus, it has low specific absorption rate at all four operating bands.
Multilayer Neural Network-based Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-time Systems

In this paper, online optimal adaptive tracking control of nonlinear discrete-time systems in affine form with uncertain internal dynamics is presented. The augmented system and the cost function over infinite horizon for the augmented state are defined. Two-layer neural network (NN) -based actor-critic framework is introduced to estimate the optimal control input and value function. The temporal difference (TD) error is derived as a function of the difference between actual and estimated value function. The NN weights of critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference errors and control policy errors, respectively. The proposed scheme ensures the closed-loop stability in the form of boundedness. Simulation results are provided to illustrate the effectiveness of the proposed approach.
Proposed simulator for testing MPPT techniques for photovoltaic systems

Peak power tracking system Proteus model is proposed to design a maximum power point tracking techniques for photovoltaic systems. The simulator is good substitute to implement and verify the performance of MPPT algorithms in the laboratory. The proposed simulator has the same comportment as physical prototype. Simulated results of tracking voltage, tracking current and tracking power have been analyzed. The theoretical operation of the proposed model confirms the good performance even under solar irradiation variation.
Classification of Mild Cognitive Impairment and Alzheimer’s Disease from Magnetic Resonance Images using Deep Learning

Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD). Since AD is unlikely to modify its related and intrinsic decay, early diagnosis is crucial, which gives patients a chance to rearrange their lives. Identifying the MCI level is essential, as it assists with further treatment and preliminary steps to control the forward progression towards AD. Brain tissue segmentation is an important aspect of clinical diagnostic tools, yielding excellent results compared to conventional segmentation methods or individual modalities. In this work, the Meta-Heuristic Markov Random Field Segmentation method is used to separate brain tissue, followed by the extraction of intensity, texture, and shape-based features of the segmented Cerebral Spinal Fluid (CSF) and Grey Matter (GM). With the proposed technique, the pre-processing of the input image improves quality and reduces noise. The segmentation is based on multilevel thresholding using Particle Swarm Optimization (PSO) and further improving using Markov Random Field model. Segmented brain tissue (GM and CSF) are used to extract features on shape, intensity, and texture. The four-layer deep neural network is used to classify features. The proposed method is tested with the standard dataset from Alzheimer’s disease-neuro imaging (ADNI). The proposed method achieved a high accuracy rate of 97.5%. A comparison with the previous work yielded results that demonstrate this method’s superiority in terms of the classification of AD and MCI.
Contribution of reflection in language emergence with an under-restricted situation

Owing to language emergence, human beings have been able to understand the intentions of others, generate common concepts, and extend new concepts. Artificial intelligence researchers have not only predicted words and sentences statistically in machine learning but also created a language system by communicating with the machine itself. However, strong constraints are exhibited in current studies. Models dependent on task settings, or supervisor signals and rewards exist, thus hindering the emergence of languages like the real-world. In this study, we improved Batali and Choi et al.’s research and attempted language emergence under conditions of low constraints such as human language generation. We developed a new language emergence agent that combines a language module and a visual module and included the bias that exists in humans as a “reflection function” into the new emergence algorithm. We used the MNIST dataset for language emergence. Irrespective of the function, messages corresponding to the label of MNIST could be generated. However, through qualitative and quantitative analysis, we confirmed that the reflection function caused pattern structuring in the message. This result suggested that the reflection function performed effectively in creating a grounding language from raw images with an under-restricted situation like the human language generation.
Self Attack Detection and State Estimation Algorithm in Distributed Observer System under Combination Attack

A problem of attack detection and state estimation in continuous time distributed observer system under combination attack which is defined as in the presence of measurement attack and observer communication attack is considered in this paper. We adopt the notion of virtual state to design an algorithm for attack detection. The algorithm is distributedly used in each local observer and does not need nonlocal information, thus it can be run by only itself. Additionally, we derive the inequality relationship between system graph connectivity and the number of the allowable attacked observer to achieve the detection and estimation with the proposed algorithm as a necessary and sufficient condition. A simple numerical example, finally, illustrates the effectiveness of the proposed algorithm.
Performance comparison of native host and hyper-based KVM virtualization

This paper presents the performance comparison of native host and KVM hypervisor. The measurements are carried out under equivalent conditions and applying specific Filebench test methods. Ubuntu 18.04 is used as the base operating system for test procedures. The performance comparison is proceeded for the native host and KVM with one, two, and three virtual machines. We have analyzed the behaviors, verified the assumption with Filebench test software, and provided the concluding remarks.
The Influence to Customer Engagement Behavior by Different Strategy for Different Objective in Online Marketing

This paper aims to find the correlation between each strategy for different objectives of tweets with customer engagement behaviors, and different effects on three behaviors. The prior research proved that diverse strategy and objective have a unique influence on customer engagement. However, the interaction effect within these strategies and objectives are not discussed. Thus, in this paper, we collect data from Adidas' official Twitter account, and categorize by three objectives, brand build, product sale, and marketing campaign. Then experiment the influence of every strategy for each objective. This paper finds the strategy have different performance for a specific objective, for instance, celebrity and mega-event have a positive influence on customer engagement for brand build, which is opposite to the general situation. Meanwhile, in one objective, the same strategy has different effects on different customer behaviors. Marketers can use these findings to achieve the marketing objective efficiently.
Automatic Correction Assisted Microwave Photonic Sensor System for Resolution Enhancement in Humidity Measurement

In the paper, we propose the automatic correction assisted microwave photonic sensor system in humidity measurement with greatly enhanced resolution throughout the measurement range. The humidity sensing probe is based on the microresonator coated with hydrophilic material. By realizing the automatic correction on the modulation sidebands being filtered by the microresonator, the impact of the optical resonance line-shape variation caused by the refractive index change of the hydrophilic cladding in response to the humidity change on creating the perfect cancellation of RF beating currents is well mitigated. The resulting RF transmission notch which synchronously shifts with the optical resonance is thus maintained with ultra-high rejection and ultra-narrow tip width, greatly enhancing the resolution performance. The proposed system was experimentally demonstrated using the humidity sensor probe made from the microdisk resonator coated with PMMA, the refractive index of which will increase or decrease correspondingly with the rise or drop of the environmental humidity at room temperature. The measured RF notch tip widths are kept 19800-fold narrower than the optical notch of the microresonator and only occupy 0.015 ‰ of the total RF scanning range. Thus, a high humidity resolution of 0.0015 %RH can be achieved in the whole humidity measurement range.
A Novel Hierarchical Deep Matrix Completion Method

The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications. We proposed a new matrix completion model called hierarchical deep matrix completion (HDMC), where we assume that the variables lie in hierarchically organized groups. HDMC explicitly expresses either shallow or high-level hierarchical structures, such as taxonomy trees, by embedding a series of so-called structured sparsity penalties in a framework to encourage hierarchical relations between compact representations and reconstructed data. Moreover, HDMC considers the group-level sparsity of neurons in a neural network to obtain a pruning effect and compact architecture by enhancing the relevance of within-group neurons while neglecting the between-group neurons. Since the optimization of HDMC is a nonconvex problem, to avoid converting the framework of the HDMC models into separate optimized formulations, we unify a generic optimization by applying a smoothing proximal gradient strategy in dual space. HDMC is compared with state-of-the-art matrix completion methods on applications with simulated data, MRI image datasets, and gene expression datasets. The experimental results verify that HDMC achieves higher matrix completion accuracy.
New and Unique CMP Material Solution for the Enablement of High Removal Rate Polymer CMP and other Advanced Packaging Applications

Polymer materials like epoxy mold compounds (EMC), polyimides (PI) are used in the advanced packaging applications. Chemical mechanical planarization (CMP) is a process useful in removing undesired surface topography and surface defects and in forming features on a substrate by removing excess deposited material. However, removal rates (RR) of polymers are too low by conventional CMP to be useful in advanced packaging technology. A novel slurry system has been developed. A two-step sequence combining both high-speed mechanical abrasion with novel slurries for coarse polishing and conventional polymer CMP technology for final fine polishing has been evaluated. A removal rate of more than $10\ \mu \mathrm{m}/\min$ has been observed for EMC which is roughly one order of magnitude higher than that obtained with conventional CMP process. A final fine polish step with a regular CMP slurry has almost restored the initial roughness in case of PI. The novel slurry has shown high RR for multiple substrates and has shown about 7:1 selectivity for PI removal against copper. This solution can bring grinding and CMP under one platform with more process control compared to grinding alone.
Evolution of defect signatures at ferroelectric domain walls in Mg‐doped LiNbO3

The domain structure of uniaxial ferroelectric lithium niobate single crystals is investigated using Raman spectroscopy mapping. The influence of doping with magnesium and poling at room temperature is studied by analysing frequency shifts at domain walls and their variations with dopant concentration and annealing conditions. It is shown that defects are stabilized at domain walls and that changes in the defect structures with Mg concentration can be probed by the shift of Raman modes. We show that the signatures of polar defects in the bulk and at the domain walls differ. (© 2016 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)
Prediction Model of Wind Speed and Direction using Convolutional Neural Network - Long Short Term Memory

This paper proposes the prediction model of wind speed and direction using convolutional neural network - long short-term memory (CNN-LSTM). The proposed prediction model combines CNN, LSTM, and fully connected neural networks (FCNN) which are useful for getting high prediction accuracy of wind speed and direction for wind power. Performances of the prediction models are evaluated by using root mean square error (RMSE) between actual measurement data and predicted data. To verify the effectiveness of the proposed prediction model in comparison with that using FCNN, CNN, or LSTM model. The usefulness of the proposed prediction model is evaluated from the improvement of prediction accuracy for each season. The proposed prediction model using CNN-LSTM can improve 27.95 – 42.16% for wind speed and 28.71 – 35.15% for wind direction depending on the season in comparison with using the FCNN that is a higher accuracy than CNN and LSTM models, and also it indicates the strongest prediction model.
Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim’s machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird’s eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.
Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signals obtained from individual channel. Each channel of EEG trial is decomposed into a set of subband signals using multivariate discrete wavelet transform. The selected subbands are organized in two different ways namely vertical arrangement of subbands (VaS) and horizontal arrangement of subbands (HaS) to regenerate the trials. The features are extracted from each of the arrangements using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. The effectiveness of two classifiers– linear discriminant analysis (LDA) and support vector machine (SVM) are studied. The performances of the proposed methods are evaluated using publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.
High-Gain Nonlinear Observer Using System State Augmentation

This paper deals with the problem of state estimation of dynamic systems with Lipschitz nonlinearities using a new high gain observer design. The aim of this new design procedure is to reduce the value of the tuning parameter and the observer gain compared to the standard high gain observer on the one hand without solving a set of LMIs as in the LMIbased observer on the other hand. Towards this end, a novel approach based on system state augmentation that transforms the original system of dimension n into a new system whose dimension is (n + js), where the new nonlinear function does not depend on js last components of the new state. A numerical example is reported to evaluate the effectiveness of the proposed observer for different values of the Lipschitz constant.
Distribution Grid Three-Phase Power Flow Algorithm Based On Flow Model

Distribution grid analysis is based on distribution grid power flow calculation. New power flow algorithm for distribution network is developed. Three-phase flow model is presented. The presented model is a development of single-line flow model. Three-phase flow model give the opportunity use advantages of flow model and take into account unbalanced condition of distribution grids. In addition, in three-phase power flow there is a convergence problem in system with wye delta transformer. Presented three-phase flow model solve this problem. Comparative numerical analysis with another three-phase power flow methods performed. Experiments show that presented flow model has better convergence characteristics.
Applying clinical guidelines to conformance checking for diagnosis and treatment: a case study of ischemic stroke

It is necessary to check the actual implementation of clinical guidelines, which provide the best practices for therapy of stroke patients. Based on the rules extracted from clinical guidelines for diagnosis and treatment of ischemic stroke, we abstracted the relationship of medical events using declarative process mining framework, and constructed the de jure model manually. To verify the proposed method, a real-life data set from hospital is checked and analyzed with the model of medical process. The results show that the manually generated model can discover abnormal activities with flexibility, and our approach could be applied to actual business environments.
Towards detecting morning surge from sleep self-evaluations

This paper aims to analyze the blood pressure transition during sleep. Morning surge is a sudden increase in blood pressure from awakening or before awakening. Morning surge is implicated in cardiovascular diseases, such as Stroke, Angina Pectoris, and Myocardial Infarction. Morning surge has been detected mainly by the ABPM (Ambulatory Blood Pressure Monitoring) method, which measures blood pressure for 24-hours. Since the ABPM method cannot distinguish awakening and sleep automatically, their alternative method is forcibly delimiting time or manually processing based on behaviour records. Therefore, it is necessary to capture the blood pressure change under clear sleep separation. This paper employs two sleep criteria for accurate blood pressure during sleep.