Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Maximum Usable Frequency for HF Communications Based on Federated Learning

Spectrum prediction technology can not only improve spectrum utilization rate but also improve communication quality to a certain extent. In order to give full play to the advantages of high-frequency (HF) communication and improve the quality and stability of HF communication, it is urgent to predict the HF spectrum. We propose a prediction model of maximum usable frequency (MUF) of HF based on federated learning. The proposed model has great advantages in protecting user privacy, reducing communication loss, and shortening training time. First, the ionospheric vertical sounding dataset is analyzed and processed to obtain the training dataset and test dataset, and the data are standardized and reconstructed. Second, the pre-processed training data set was used as the input data to enter the long short-term memory (LSTM) network for local model training. The federated learning (FL) server station aggregates all local training models to generate global models, and delivers model parameters to local users for updating local models and carrying out the next round of training. Finally, the experimental results show that the proposed model has obvious advantages in the prediction efficiency and accuracy, compared to traditional centralized way.
Online Knowledge Distillation for Financial Timeseries Forecasting

Recent advances in Deep Neural Networks (DNNs) led to enormous progress in many different fields, covering a wide range of applications, including financial time-series analysis. Many different financial time-series forecasting tasks have been successfully tackled using such approaches, including predicting the next day’s return of FOREX currency pairs. However, using DNNs for such tasks is not always straightforward due to training stability issues that often arise. Indeed, the noisy nature of the data can often cause considerable different behaviors between DL models, despite following the same training process, model architecture, and hyper-parameters. At the same time, the methods proposed for generating the training labels can sometimes further reinforce such issues. All these phenomena can reduce the reliability of training DL models for financial forecasting tasks, while also making the training process especially time-consuming, requiring several validation and back-testing runs. To overcome these limitations, we propose an ensemble-based online distillation method that can significantly reduce this behavior. The proposed method is efficient since, in contrast to offline distillation approaches, it works in a single step, while it also allows for reducing the number of hyper-parameters to be tuned, e.g., the number of epochs for training the teachers. As demonstrated in the conducted experiments, the soft labels extracted through the proposed approach can mitigate the effect of noisy annotation that often exists in FOREX data, leading to significant performance improvements.
Transmission of CRC Bits with Rotated Polar Coded Signals

In this paper, we propose a transmission scheme of cyclic redundancy check (eRe) bits with rotated polar coded signals and present the corresponding list decoding algorithm. An information bit sequence is encoded by a CRC encoder, resulting in CRC bits. The CRC bits are divided into two parts. One is transmitted by a rotated constellation scheme and the other with the information bit sequence is encoded by a polar encoder. We present a list decoding algorithm to detect the part of the CRC bits transmitted by the rotated constellation scheme. To reduce the decoding complexity, an early termination scheme is proposed. Simulation results show that the polar codes with 5G standard sequence using the proposed transmission scheme can obtain a performance gain of 0.75 dB compared with the conventional CRC-polar coded modulation.
Energy Consumption Optimization under Multi-link Target Wake Time scheme in WLANs

Since the stations (STAs) are usually powered by batteries in the wireless local area network (WLAN), the energy consumption of STAs is of great importance. The target wake time (TWT) mechanism is designed to save energy in the WLAN. Besides, the multi-link operation (MLO) technology is proposed recently to achieve the extremely high throughput transmissions in the WLAN. In this paper, we study the TWT with the newly proposed MLO. We analyze the impact of TWT and MLO both on the energy consumption of STAs. We optimize the TWT and MLO jointly with the goal of energy minimization for STAs. Through adjusting the LI parameter in TWT and the traffic allocation in MLO, we balance the tradeoff among energy consumption, transmission delay, queueing stability, as well as multi-link scheduling in WLANs. We formulate this energy optimization problem as a mixed-integer non-convex problem, and design a multi-link optimal algorithm to optimally solve it. Simulation results show that the proposed scheme has a great advantages in terms of energy consumption.
Interaction Behavior and Stability Analysis of Low-Voltage Multi-Terminal DC System

The stability problem of low-voltage multi-terminal DC (MTDC) systems has drawn much interest. This paper focuses on the oscillation instability phenomenon of low-voltage MTDC systems and analyzes the interaction behavior between each voltage-source converter and the DC network oscillation mode. The typical structure and control strategy of a low-voltage MTDC system under master-slave control is first described, and its equivalent circuit is established. Then, an interaction behavior analytical model is proposed, and the source-network and network-load interaction characteristics are studied. Furthermore, a system stability analysis method is built based on the modularized modeling, which can solve the problem of how to address the complexity of high-order low-voltage MTDC systems. The transfer function of the whole system is conveniently established, revealing the system instability mechanism. The proposed method can provide effective stability assessment for low-voltage MTDC systems. Finally, the results are verified using electromagnetic transient simulation and experimentally validated using a low-voltage three-terminal DC system test platform with AC 0.4 kV/DC 800 V. This research can be flexibly extended to high-order systems for controller design and performance optimization.
Interference-aware Resource Allocation in Satellite Integrated Terrestrial Networks

In this paper, we consider a downlink SITN, where the densely deployed low earth orbit (LEO) satellites provide backhaul to terrestrial base stations (TBSs) and TBSs serve the ground users (GU) over different bands. Note that the user sum rate is dependent on the user association and backhaul capacity. Moreover, the multi-connectivity feature between TBSs and densely deployed LEO satellites aggravate the inter-satellite interference, which degrades the backhaul capacity. To this end, we propose an interference-aware resource allocation (IRA) scheme to maximize the user sum rate. Specifically, the TBSs can remove the satellites that will fly out of line-of-sight in the matching process by introducing an adjustment coefficient. Accordingly, the searching space of satellites can be reduced and the backhaul capacity can be improved with high time efficiency. Moreover, the user association is achieved by dividing GUs into different groups, while the required user demands can be satisfied. Considering the dense deployment of LEO satellites, with the well tuned adjustment coefficient, the backhaul capacity can be improved over 12%. Moreover, with the increasing user density, the gap between the user sum rate of IRA and the optimal solution can be reduced to less than 4%.
Content Distribution with Resource Optimization for Multi-beam Satellite-Terrestrial Cache-enabled Networks

After entering the 5G era, satellite-terrestrial in-tegrated network has become a hot issue of common concern, which combines the advantages of terrestrial communication and satellite communication to provide high-quality services for dense users. In this paper, we design a content distribution system model for multi-beam satellite-terrestrial cache-enabled network-s. Based on beam-hopping, we propose a greedy algorithm to achieve the goal of flexible and reasonable allocation of on-board resources including caching, power and bandwidth, according to the changing user demand preferences, thereby maximizing the system throughput by the multidimensional resource optimization. The simulation results show that the proposed resource allocation algorithm is superior to the traditional fixed power and bandwidth methods in terms of system throughput, and it also shows the outperformance of the most popular caching methods.
Reduced-Order Generalized Integrator-Based Phase-Locked Loop: Performance Improvement for Grid Synchronization of Single-Phase Inverters

Generalized integrator-based orthogonal signal generator phase-locked loops (GI-OSG PLLs) are very popular in single-phase grid synchronization. Generally speaking, the OSG of this PLL is realized from a second-order integrator perspective. As a result, the OSG implementation impacts the PLL dynamics. Improving the PLL performance by employing a reduced-order OSG implementation is the main motivation for this paper. Therefore, a reduced order-OSG PLL is proposed. In the proposed PLL, a single state and two output equations are employed to obtain a pair of orthogonal signals. This results in the loss of orthogonality between the OSG outputs and the single-phase supply voltage. Also, harmonic filtering capability provided by SOGI-OSG becomes non-existent. These issues are resolved by incorporating steady-state phase angle-offset error compensation at the PLL output and a moving average filter (MAF) within the PLL loop. To counteract the influence of MAF on the PLL’s stability and dynamics performance, a phase-lead compensator is designed. Through experimental studies, it is shown that the proposed reduced order-OSG PLL offers excellent dynamic performance. Also, the proposed PLL achieves minimal fluctuation of PV power when employed for grid synchronization of a PV system.
A General Purpose Turkish CLIP Model (TrCLIP) for Image&Text Retrieval and its Application to E-Commerce

In this paper, we introduce a Turkish adaption of CLIP (Contrastive Language-Image Pre-Training). Our approach is to train a model with the same output space as the Text encoder of the CLIP model while processing Turkish input. For this, we collected 2.5M unique English-Turkish data. The model we named TrCLIP performed 71% in CIFAR100, 86% in VOC2007, and 47% in FER2013 as zero-shot accuracy. We have examined its performance on e-commerce data and a vast domain-independent dataset in image and text retrieval tasks. The model can work in Turkish without any extra fine-tuning. Models and dataset can be reachable from https://github.com/yusufani/TrCLIP.
Performance Analysis of Different Sentiment Polarity Dictionaries on Turkish Sentiment Detection

In places such as social media and news websites, the number of web-based textual materials those deal with today’s events and contain people’s emotions continues to increase inevitably day by day. All of these texts, with their own importance, affect our society in some way. For this reason, it is very important to automatically detect the sentiment polarities of these texts. In this study we developed several statistical-based semantic algorithms for Turkish sentiment analysis task. Furthermore, we conducted experiments on sentiment analysis in Turkish texts using different sentiment polarity dictionaries. We perform a number of experiments on some datasets which we collected from Twitter platform. In our experimental environment we also use two dictionaries to get the sentiment polarity score of Turkish terms and phrases: We built the first sentiment polarity dictionary by using a translator and this dictionary includes about 159,876 Turkish words. We built the second sentiment polarity dictionary by using GDELT (Global Data on Events, Languages and Tone) and this dictionary includes about 84,744 Turkish words. We also implement the state of the art baseline algorithms in order to compare the performance results. There are three important outcomes of this study: 1. ) We built two publicly available sentiment polarity dictionaries for Turkish, 2. ) We developed statistical-based novel semantic algorithms for Turkish sentiment analysis task, 3. ) We report the observations of the effect of using different semantic-polarity dictionaries on Turkish sentiment polarity detection problem. Experiment results show that the algorithms we have developed are valuable because they give higher classification performance than the baseline algorithms on Turkish sentiment polarity detection task.
A 30.2-µ Vrms Horizontal Streak Noise 8.3-Mpixel 60-Frames/s CMOS Image Sensor With Skew-Relaxation ADC and On-Chip Testable Ramp Generator for Surveillance Camera

This article presents an 8.3-Mpixel 60-frames/s CMOS image sensor with skew-relaxation analog-to-digital converters (ADCs) and an on- chip testable ramp generator for low-noise and high-sensitive surveillance cameras. Three novel techniques are proposed to improve the circuit noise and linearity characteristics under low illuminance. A local multiply circuit (LMC) reduces a differential non-linearity (DNL) of the column-wise ADC by halving the delay difference of long-distance wiring signals. A noise suppression ramp generator (NSRAMP) can reduce the horizontal streak noise to 1/4 by compressing only the noise component in the low-light signal. A flexible test circuit (FTC) enables high-speed on- chip test of the ramp generator separately from the ADC characteristics. Test chip fabricated in 55-nm backside illumination (BSI) process achieves DNL of +0.42 LSB/−0.32 LSB and the horizontal streak noise of 30.2 $\mu $ Vrms. DNL is reduced by 27% (circuit skew) and 50% (propagation skew), respectively, and the horizontal streak noise is also reduced by 72% compared to the conventional scheme.
Enhanced Computation of the EEG-IC Polarities using a Genetic Algorithm

Electroencephalogram (EEG) is nowadays an essential tool for clinical and cognitive brain research. ICA algorithms are applied to EEG recordings to find maximally temporally independent statistical source signals, which are further referred to as independent components (ICs) and which represent brain and non-brain processes. Clustering of IC topographies is a popular way to assess the results of EEG-based experiments and usually implies the computation of average images of such IC topographies. This computation, however, requires a previous process to determine the polarity of the different ICs because component scalp maps have not absolute polarity. This sign ambiguity problem is solved in the bibliography by using an image as a reference and setting the polarity of each IC according to the sign of the correlation of this IC with such reference. This method, however, has issues with low values of correlation. Thus, this paper introduces a different approach that addresses the polarity computation from a more general view, trying to find the polarity vector that minimizes the global error, and proposes the use of a genetic algorithm to find it. The results obtained with this approach outperform the two main methods currently used.
Visual Metaphor Construction for Image and Description of Fashion Goods

In this study, we propose a method for constructing visual metaphors for fashion goods. The input of the proposed method consists of a product image and its product description. For example, for a red jacket with good stretch, the visual metaphor constructed by the proposed method will be "leaping fire of youth". For a pair of black high heels that can create an elegant temperament, the constructed visual metaphor will be "beautiful slender swallows". A visual metaphor is something that is similar in appearance to a fashion goods. Construction of visual metaphor will bring inspiration to poster design or any other form of propaganda. The process of the proposed method consists of two parts. First, we build a deep learning model to predict the similarity between fashion goods and metaphor on color and pattern, and obtain metaphor words based on the predicted similarity. After that, we use the obtained metaphor word and product description to generate rhetoric.
1 Throughput Fairness-Aware Optimization of Cognitive Backscatter Networks with Finite Alphabet Inputs

Cognitive backscatter network (CBN) is a promising paradigm for energy-constrained IoT networks, in which backscatter devices (BDs) harvest energy from the primary signals and backscatter information to a cooperative receiver. Traditional resource allocation schemes maximizing the throughput are based on the impractical Gaussian inputs and energy harvesting (EH) model, which leads to performance degradation. Taking the above factors into account, this paper focuses on the fairness-aware resource allocation scheme for multiuser CBN with finite-alphabet inputs and nonlinear EH model. We formulate a max-min throughput maximization problem to ensure the fairness among BDs, subject to the quality-of-service (QoS) of primary user and energy-causality constraints of BDs. As the formulated joint optimization problem is non-convex, we use the approximation, slack and auxiliary variables methods to transform it into a convex problem and propose an iterative algorithm to solve it. Simulation results are provided to verify the effectiveness of the proposed algorithm.
On Scheduling Policy for Multiprocess Cyber–Physical System With Edge Computing

In this article, we consider a cyber–physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers, and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different servers and processes. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average mean square error (MSE) of the entire system. To address this issue, we formulate a Markov decision process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. Furthermore, the processing preemption mechanism is considered to schedule the status updates more flexibly, and its consistency property is proved. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.
Stock Price Prediction using Bidirectional LSTM with Attention

The stock market is one of the most important topics of today’s economy due to its fluctuating nature and far-reaching impact. Despite the difficulty of mathematical modeling of time-series of stock prices, deep learning and black-box modeling have been reported to perform well in literature. In this work, we have experimented with different recurrent neural network architectures to predict stock prices for elite Nasdaq companies. We have improved the baseline models by reducing the number of parameters and using bidirectional processing with attention. We show that using a Gaussian noise vector can regularize the model to improve the robustness without using any dropout. Our proposed model achieves the lowest mean square error for AMZN (0.12), AAPL (0.15), NFLX (0.03), GOOG (0.08), GOOGL (0.06), CSCO (0.01), COST (0.06), FB (0.03), 8 out of 10 companies. Our work aims to show that with better architecture design, RNN variants can outperform baseline models despite having a fraction of the parameters.
Enhanced Quantile Portfolio for Multifactor Model with Deep Learning

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Although machine learning methods are increasingly popular and effective in stock return prediction in the cross-section, still most of the previous studies rely on a simple quantile portfolio. In this paper, we apply deep learning for stock return prediction in the cross-section and propose a more sophisticated portfolio construction framework called Enhanced Quantile Portfolios. These portfolios are inspired by Pure Quantile Portfolio that overcome the main drawbacks of simple quantile portfolios based on a single sort. The formulation of Enhanced Quantile Portfolio is a quadratic programming problem that considers the trade-off between portfolio alpha and stock diversification, while maintaining the characteristics of a simple quantile portfolio. The experimental comparison shows that the proposed approach outperforms a simple quantile portfolio.
A Machine Learning Model for Predicting Performance of Gamified Software Test Specialist

Gamification is one of the new trend in software development and it has already gained a well-deserved popularity in finance, healthcare, education and even manufacturing. Software testing is a continuous cycle layered with several stages and spanning across multiple types of testing. Teams need to design test suites and implement test execution methodologies in each stage of development. For this reason, software testing teams comprise many individuals skilled in different aspects of software testing. The inclusion of gamification in this course can lead to positive benefits based on the idea that it is used to influence behavior. This paper presents an preliminary study of Machine Learning (ML) approach for predicting performance gamification of software tester specialists under a gamified testing environment. ImonaGame is a software company that delivers gamification as a service for software testing team of 30 members with different static and dynamic data. User behavior collected in dynamic data sets was classified into categories by deconstructing complex activities into behavior chains using supervision of domain experts. The classification approach was centered on the system’s testing processes’ performance objectives and potential for encouragement or dissuasion. Motivators and obstacles for the target activity and its behaviors will be found when the model has been developed. After conducting preliminary research, it is possible to determine whether gameful design is an effective and efficient tactic for achieving the desired result by analyzing needs, motives, and obstacles. The source data was classified target data in four categories such as I: Static feature (personal information; 4), II: Daily feature (gamification elements; 14), III: Mission feature (points; 7 sources) and IV: Cumulative futures (Sum of daily and mission features; 13).