Miracle-dz/UniFAR-FTUs huggingface dataset:
Online convex optimization: algorithms, learning, and duality

Online Convex Optimization (OCO) is a field in the intersection of game theory, optimization, and machine learning which has been receiving increasing attention due to its recent applications to a wide range of topics such as complexity theory and graph sparsification. Besides the usually simple description and implementation of OCO algorithms, a lot of this recent success is due to a deepening of our understanding of the OCO setting and their algorithms by using cornerstone ideas from convex analysis and optimization such as the powerful results from convex duality theory. In this text we present a mostly self-contained introduction to the field of online convex optimization. We first describe the online learning and online convex optimization settings, proposing an alternative way to formalize both of them so we can make formal claims in a clear and unambiguous fashion while not cluttering the readers understanding. We then present an overview of the main concepts of convex analysis we use, with a focus on building intuition. With respect to algorithms for OCO, we first present and analyze the Adaptive Follow the Regularized Leader (AdaFTRL) together with an analysis which relies mainly on the duality between strongly convex and strongly smooth functions. We then describe the Adaptive Online Mirror Descent (AdaOMD) and the Adaptive Dual Averaging (AdaDA) algorithms and analyze both by writing them as special cases of the AdaFTRL algorithm. Additionally, we show simple sufficient conditions for Eager and Lazy Online Mirror Descent (the non-adaptive counter-parts of AdaOMD and AdaDA) to be equivalent. We also present the well-known AdaGrad and Online Newton Step algorithms as special cases of the AdaReg algorithm, proposed by Gupta, Koren, and Singer, which is itself a special case of the AdaOMD algorithm. We conclude by taking a bird’s-eyes view of the connections shown throughout the text, forming a “genealogy” of OCO algorithms, and discuss some possible path for future research.
Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Unmanned aerial vehicles (UAVs) are envisioned to be widely deployed as an integral component in the next generation cellular networks, where spectrum sharing between the aerial and terrestrial communication systems will play an important role. However, there exist significant security and privacy challenges due to the untrusted broadcast features and wireless transmission of the UAV networks. This article endeavors to resolve the security issues through proposing a novel privacy-preserving secure spectrum trading and sharing scheme based on blockchain technology. Specifically, from the operator’s perspective, a pricing-based incentive mechanism is first introduced, in which a primary mobile network operator (MNO) leases its owned spectrum to a secondary UAV network in exchange for some revenue from the UAV operators. To address the potential security issues, a spectrum blockchain framework is then proposed to illustrate detailed operations of how the blockchain helps to improve the spectrum trading environment. Under this framework, a Stackelberg game is formulated to jointly maximize the profits of the MNO and the UAV operators considering uniform and nonuniform pricing schemes. Security assessment and numerical results confirm the security and efficiency of our schemes for spectrum sharing in UAV-assisted cellular networks.
Comparative Study of NeuroEvolution Algorithms in Reinforcement Learning for Self-Driving Cars

Neuroevolution has been used to train neural networks for challenging deep Reinforcement Learning (RL) problems like Atari, image hard maze, and humanoid locomotion. The performance is comparable to the performance of neural networks trained by algorithms like Q-learning and policy gradients. This work conducts a detailed comparative study of using neuroevolution algorithms in solving the self-driving car problem. Different neuroevolution algorithms are used to train deep neural networks to predict the steering angle of a car in a simulated environment. Neuroevolution algorithms are compared to the Double Deep Q-Learning (DDQN) algorithm. Based on the experimental results, the neuroevolution algorithms show better performance than DDQN algorithm. The Evolutionary Strategies (ES) algorithm outperforms the rest in accuracy in driving in the middle of the lane, with the best average result of 97.13%. Moreover, the Random Search (RS) algorithm outperforms the rest in terms of driving the longest while keeping close to the middle of the lane, with the best average result of 403.54m. These results confirm that the entire family of genetic and evolutionary algorithms with all their performance optimization techniques, are available to train and develop self driving cars.
Digital twin and its application to power grid online analysis

Digital twin (DT) framework is introduced in the context of application for power grid online analysis. In the development process of a new power grid real-time online analysis system, an online analysis digital twin (OADT) has been implemented to realize the new online analysis architecture. The OADT approach is presented and its prominent features are discussed. The presentation, discussion, and performance testing are based on a large-scale grid network model (40K+ buses), exported directly from the EMS system of an actual power grid. A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.
A Mutual Technique for Reconfiguration of Feeder Network by Optimal Locating and Sizing of Distribution Generator

Common technique has been discussed in this paper for the reconfiguration of feeder network by optimal location and measuring of Distribution Generator (DG) in electrical power system. The consolidated execution of both Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) strategies are the curiosity of the proposed strategy. The optimization techniques are utilized for optimizing the optimum location and DG capacity for radial distribution network. The BBO algorithm requires radial distribution network voltage, real and reactive power for deciding the optimum location and capacity of the DG. Here, the input parameters of BBO are classified into sub parameters and permitted as the PSO algorithm optimization process. The PSO develops the sub solution with the assistance of sub parameters by issue synthesis. For identifying the optimum location and capacity of DG the BBO movement and mutation process is applied for the sub solution of PSO. At that point the proposed mutual technique is actualized in the MATLAB/simulink platform and by contrasting it with the BBO and PSO systems the effectiveness is scrutinized. The comparison results demonstrate the predominance of the proposed approach and affirm its capability to comprehend the issue.
Geometric transformations embedded into convolutional neural networks

This paper presents a novel extension to convolutional neural networks. While CNNs are known for invariance to object translation, changes to the other parameters could make the image recognition tasks di ﬃ cult – that includes rotations and scaling. Some improvement in this area could be achieved with embedded geometric transformations used inside the CNNs. In order to provide a practical solution, which allows fast propagation and learning of the modiﬁed networks, “fast geometric transformations” are introduced.
A General Framework for Time Series Forecasting Model Using Autoregressive Integrated Moving Average-ARIMA and Transfer Functions

Major current econometric stochastic series forecast research are established on the failure of the scholastic process tests to differentiate between finite and stationary alternative samples of the unit root hypothesis results. The importance of forecast evaluation allows researchers to reasonably monitor and improve forecast performance. While a structured improved forecast framework have often been suggested as one possible alternative, an extended the multivariate model which incorporate distributed-lag period for independent variable gives a unique advantage over the traditional distributed-lag model and the mathematical formulation does essentially guarantee that predicated equation irrespective of the values of the predictor variables. Hence, the primary objective is mainly to determine the likelihood of autoregressive integrated moving average (ARIMA) method for practicable process choice used for predicting key economic variables for a set of market data. Once the process has been known, parameters have been obtained, and the adequacy of the model has been determined, forecasts can be checked for reliability.
CBIM-RSRW: An Community-Based Method for Influence Maximization in Social Network

Influence maximization is an important problem, which seeks a small set of key users who spread the influence widely into the network. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. The current studies treat nodes in social network with equal weights, and the influence possibility mainly decide by node degree. In this paper, we study the influence maximization problem in social networks and we improve the independent cascade model to realize the goal of different weights for different users, and the differentiation of influence probability. Meanwhile, We take advantage of the community structure to speed up the algorithm. Then, we propose a method called the reverse reachable index method based on random walk (RSRW) to select potential high-impact nodes from those communities. The experimental result on four actual data set shows that these improvements can greatly reduce the calculation time while ensuring the accuracy of the results.
A Three-Phase Heuristic for Cyclic Crew Rostering with Fairness Requirements

In this paper, we consider the Cyclic Crew Rostering Problem with Fairness Requirements (CCRP-FR). In this problem, attractive cyclic rosters have to be constructed for groups of employees, considering multiple, a priori determined, fairness levels. The attractiveness follows from the structure of the rosters (e.g., sufficient rest times and variation in work), whereas fairness is based on the work allocation among the different roster groups. We propose a three-phase heuristic for the CCRP-FR, which combines the strength of column generation techniques with a large-scale neighborhood search algorithm. The design of the heuristic assures that good solutions for all fairness levels are obtained quickly, and can still be further improved if additional running time is available. We evaluate the performance of the algorithm using real-world data from Netherlands Railways, and show that the heuristic finds close to optimal solutions for many of the considered instances. In particular, we show that the heuristic is able to quickly find major improvements upon the current sequential practice: For most instances, the heuristic is able to increase the attractiveness by at least 20% in just a few minutes.
Task-Dependent Recruitment of Modality-Specific and Multimodal Regions during Conceptual Processing

Abstract Conceptual knowledge is central to cognitive abilities such as word comprehension. Previous neuroimaging evidence indicates that concepts are at least partly composed of perceptual and motor features that are represented in the same modality-specific brain regions involved in actual perception and action. However, it is unclear to what extent the retrieval of perceptual–motor features and the resulting engagement of modality-specific regions depend on the concurrent task. To address this issue, we measured brain activity in 40 young and healthy participants using functional magnetic resonance imaging, while they performed three different tasks—lexical decision, sound judgment, and action judgment—on words that independently varied in their association with sounds and actions. We found neural activation for sound and action features of concepts selectively when they were task-relevant in brain regions also activated during auditory and motor tasks, respectively, as well as in higher-level, multimodal regions which were recruited during both sound and action feature retrieval. For the first time, we show that not only modality-specific perceptual–motor areas but also multimodal regions are engaged in conceptual processing in a flexible, task-dependent fashion, responding selectively to task-relevant conceptual features.
Analysis and design of a terminal sliding mode control applied to a step-down power converter

Generally, step-down converters are controlled by a linear control called average model. This control technique results in a system behavior highly dependent on the working point. Therefore, non-linear techniques have been introduced in order to solve this problem. More specifically, terminal sliding mode control is known for its robustness and simplicity and it improves the transient closed-loop system respon
Comparative Analysis of Transmission Power Level and Packet Size Optimization Strategies for WSNs

Wireless sensor networks (WSNs) are envisioned to be a widely utilized technology in a smart grid (SG). Maximizing the network lifetime (NL) of WSNs in SG deployments is necessary to provide reliability and efficiency of the services offered to the entities involved. Harsh channel conditions in SG environments have a profound impact on the communication channel utilized by WSNs. Therefore, optimizing the transmission power level (TPL) and data packet size (DPS) is of paramount importance for prolonging the WSN NL. In both TPL and DPS optimizations, two general approaches are followed: utilizing a constant TPL and/or DPS or optimizing TPL and/or DPS for each link. However, the joint optimization of the TPL and DPS for each link has not been investigated in the literature. In this study, we present a mixed-integer programming framework, which utilizes a detailed link-layer abstraction to analyze nine TPL and DPS assignment strategies for WSNs employed in SG environments. The results of our analysis reveal that there is a large margin to be exploited in prolonging the WSN NL by an appropriate selection of the strategy to be used in TPL and DPS optimization.
Personalized Tour Recommender through Geotagged Photo Mining and LSTM Neural Networks

In this study, a tour recommendation system based on social media photos is proposed. The proposed recommendation system can generate trip tours considering both the user’s current location and interests. First, we exploited the geotagged photo dataset from social media websites, which includes photo related information such as user ID numbers, timestamps, hashtags, and GPS coordinates. With this information, the second step is to group photos and identify those places that could be considered relevant for travellers using clustering algorithms. The third step characterizes the resulting clusters by grouping them into different categories using latent dirichlet allocation (LDA) topic modelling approach. The last step is the generation of tours using a long-short term memory neural network (LSTM). The experiments show that the proposed system can be efficient to advise future travellers about the places they would be more likely to visit and arrange trips for them.
Algortmika i programowanie: od graficznych schematów blokowych do programu Flowgorithm

course devoted to algorithms programming is presented. The third part shows results of two surveys based on surveys conducted by Konecki in Croatia and by Malik and Coldwell-Neilson in Oman. Final remarks are accompanied by repeating an open question raised four years ago – “how to motivate digital natives to learn”. Students are generally against programming. There are absolutely satisfied even by their poor knowledge of IT limited to some basic editing skills. Flowgorithm proved to be very effective lecture tool allowing to present algorithms and their results. During laboratories Flowgorithm was used mainly only when students were obliged to do this, which is the result of negative attitude to programming. Flowgorithm enabled to distinguish between programming (creating an algorithm) and coding (representing an algorithm in a particular programming language) and concentrate on algorithms and programming.
Handling Data Gaps in Reported Field Measurements of Short Rotation Forestry

Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under a reduced sample size. From a complete dataset containing yearly first-rotation tree growth measurements over a period of eight years, we gradually retrieved two and then four years of measurements, hence operating on 72% and 43% of the original data. Secondly, 15 statistical models, five forest growth functions, and one biophysical, process-oriented, tree growth model were employed for filling these data gap representations accounting for 72% and 43% of the available data. Several models belonging to (i) regression analysis, (ii) statistical imputation, (iii) forest growth functions, and (iv) tree growth models were applied in order to retrieve information about the trees from existing yearly measurements. Subsequently, the findings of this study could lead to finding a handy tool for both researchers and practitioners dealing with incomplete datasets. Moreover, we underline the paramount demand for far-sighted, long-term research projects for the expansion and maintenance of a short rotation forestry (SRF) repository.
A comparison between virtual constraint-based and model predictive-based limit cycle walking control in successful trip recovery

Falling is one of the main causes of the injuries among healthy adults. The foremost causes of the falls are: slipping and tripping. Understanding the phenomenon of human balance recovery against these disturbances is a very important issue in the field of biomechanics as well as in the robotics. Previous studies have shown that human movements can be reproduced using engineering techniques and computational facilities. The prediction of movements can be related to an optimization problem. In the present study, control and prediction of human movements in successful trip recovery are addressed. To formulate the optimization problem, a hybrid dynamic model of the human body with seven degrees of freedom is considered. The tripping perturbation is modeled as an instantaneous contact of the swing leg with an obstacle and the dynamics of impact are derived. Two optimization based methods are used to control and predict the gait: (i) virtual constraint-based limit cycle optimization (ii) model predictive based limit cycle optimization. The simulated results are compared with the human-observed experimental data from the literature. The results show that the second method provides more humanlike predictions than the first method in the kinematic level. The second method can predict proper actions to keep away violating constraints in the future. The theoretical results are in agreement with the results of experimental studies on movement adjustments during trip recovery. Review History:
An Adaptive Multi-Projection Metric Learning for Person Re-Identification Across Non-Overlapping Cameras

Person re-identification is one of the most important and challenging problems in video analytics systems; it aims to match people across non-overlapping camera views. For person re-identification, metric learning is introduced to improve the performance by providing a metric adapted for cross-view matching. The essence of metric learning is to search for an optimal projection matrix to project the original features into a new feature space. However, most existing metric learning methods overlook the inconsistency of feature distributions in multiple cameras. In this paper, we propose a multi-projection metric learning (MPML) method to overcome the inconsistency among multiple cameras in person re-identification. Our solution is to jointly learn multiple projection matrices using paired samples from different cameras to project features from different cameras into a common feature space. To make our method adaptive to newly added cameras without affecting the learned projection matrices, we further propose an adaptive MPML method, which can learn new camera projection matrices without having to update any of the obtained projection matrices. The proposed methods are evaluated on four major person re-identification data sets, with comprehensive experiments showing the effectiveness of the proposed methods and notable improvements over the state-of-the–art approaches.
The Implementation of Deep Neural Networks Algorithm for Malware Classification

Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep Neural Network has succeeded in resolving the malware problem by producing an accuracy rate of 99.42%, precision level 99% and recall 99.4%.
Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.