Miracle-dz/UniFAR-FTUs huggingface dataset:
Collecting Preference Rankings Under Local Differential Privacy

With the deep penetration of the Internet and mobile devices, preference rankings are being collected on a massive scale by diverse data collectors for various business demands. However, users’ preference rankings in many applications are highly sensitive. Without proper privacy protection mechanisms, it either puts individual privacy in jeopardy or hampers business opportunities due to users’ unwillingness to share their true rankings. In this paper, we initiate the study of collecting preference rankings under local differential privacy. The key technical challenge comes from the fact that the number of possible rankings could be large in practical settings, leading to excessive injected noise. To solve this problem, we present a novel approach SAFARI, whose main idea is to collect a set of distributions over small domains which are carefully chosen based on the riffle independent (RI) model to approximate the overall distribution of users’ rankings, and then generate a synthetic ranking dataset from the obtained distributions. By working on small domains instead of a large domain, SAFARI can significantly reduce the magnitude of added noise. In SAFARI, we design two transformation rules, namely Rule I and Rule II, to instruct users to transform their data to provide the information about the distributions of the small domains. In particular, we propose a method called LADE to precisely estimate the required distributions used for the structure learning of RI model. We also propose a new LDP method called SAFA for frequency estimation over multiple attributes that have small domains. We formally prove that SAFARI guarantees $\varepsilon$ɛ-local differential privacy. Extensive experiments on real datasets confirm the effectiveness of SAFARI.
Recycling of Anhydride-Cured Epoxy Resin Through Alcoholysis and Its Insulation Property after Re-Preparation

Anhydride-cured epoxy resin (ACER) has excellent performance and is commonly employed as insulation material in power equipment. However, it is difficult to decompose and recycle epoxy resin owing to its threedimensional network structure, and conventional waste disposal processes would damage the environment. Hence, it is necessary to degrade or even recycle epoxy resin by other methods. In this paper, chemical degradation of ACER by alcoholysis reaction is realized under mild conditions, and the degraded epoxy resin (DER) is utilized to replace part bisphenol A (BPA) to prepare new epoxy resins. Additionally, the effect of DER content on the dielectric property of re-prepared epoxy resin (resistivity, relative permittivity, dielectric loss, and AC breakdown strength) is studied. Meanwhile, ab-initio calculations on the electrostatic potential, dipole moment and energy level distribution are calculated on the alcoholysis products. The results indicate that when the content of DER replacing BPA is less than 15 wt%, the re-prepared epoxy resin still maintains excellent insulation property. The ester group in the smallmolecule product has electronegativity and induces trap level in the electron band structure, which can be regarded as a “voltage stabilizer”. The recycling of epoxy resin is accomplished through this technology, which can save carbon emissions and benefit the ambient.
Dielectric Response Characteristics of Fluorinert Liquid-Impregnated Paper Insulation

This paper introduce a new fluorinert liquid-paper composite insulating material with environment-friendly and high insulation. The dielectric properties of the fluorinert liquid-paper material are discussed. The fluorinert liquidimpregnated paper samples were prepared by vacuum impregnating the dried papers for 48h. Then, broadband dielectric spectrum measurements were carried out to study the dielectric response characteristics of the composite samples. The results indicated that the tested dielectric permittivity of the fluorinert liquid is low (1.8). The low frequency dielectric loss was described to the ion conduction. The fluorinert liquid-paper insulation indicated a relative higher permittivity than paper insulating material and a lower permittivity than fluorinert liquid. The dielectric response characteristics is determined by the conduction loss and diople or interface polarization loss, respectively. The fitting results by the H-N model considering the low frequency conduction behaviors is benefical to understand the dielectric response of the fluorinert liquid-paper insulation. This new type of liquid-solid insulation has potential application in power electronic transformer.
Change detection by masking reversible areas

Many issues addressed today in remote sensing based on data from multi-temporal satellite images are related to change detection, for the implementation techniques having aims at the location, characterization and quantification changes in the state of an object in same scene area which have evolved between different instants. Unfortunately, most of these techniques confront the problem of persistent shadows and cloud cover, even after selecting the best images. Knowing, that clouds and shadow are considered as false alarms (no real change). However, in this paper, we propose to detect the changes by eliminating the false alarms (shadow and clouds) from the areas covered by the shadows of the objects and the cloud cover. In order, to improve and obtain almost real change results. For this we used the combination of two methods: the change detection method, clouds and shadows detection method, to eliminate their information detected as change. The obtained results show the efficiency of the proposed method.
Diabetic Retinopathy Classification using Vision Transformer

An adult's death from diabetes ranks among the top 10 global causes of death. Eye conditions like diabetic retinopathy (DR) are more common in people with diabetes. Loss of eyesight may arise from DR's damage to the retina's blood vessels. Grading the severity of DR is a crucial step to aid in early identification and treatment and to halt the disease's progression to vision impairment. Most currently available solutions are built using conventional image processing and machine learning methods. This paper applies the emerging vision transformer (ViT) model to the DR dataset with different optimizers. The dataset is available publicly and is highly imbalanced. The optimizers such as Adam (Adaptive moment estimation), Nadam (Nesterov Adam), and Follow the Regularized Leader (FTRL) are used for minimizing the loss function. A convolutional neural network (CNN) model is also implemented with different optimizers, and the results are compared with the ViT model. The Adam optimizer with the ViT model shows a better F1-score (0.732) than the CNN model.
Deep Learning for the Detection of Emotion in Human Speech: The Impact of Audio Sample Duration and English versus Italian Languages

Identification of emotion types is important in the diagnosis and treatment of certain mental illnesses. This study uses audio data and deep learning methods such as convolutional neural networks (CNN) and long short-term memory (LSTM) to classify the emotion of human speech. We use the IEMOCAP and DEMoS datasets, consisting of English and Italian audio speech data in our experiments to classify speech into one of up to four emotions: angry, happy, neutral, and sad. The classification performance results demonstrate the effectiveness of the deep learning methods and our experiments yield between 62 and 92 percent classification accuracies. We specifically investigate the impact of the audio sample duration on the classification accuracy. In addition, we examine and compare the classification accuracy for English versus Italian languages.
High performance TM-pass polarizer via subwavelength grating bandgap engineering

Silicon photonics systems exhibit a strong birefringence which makes polarization management critical. Here we demonstrate, with full 3D-FDTD simulations, a low-loss and high-extinction ratio TM-pass polarizer based on tilted subwavelength gratings that reflects the TE0 mode into the TE1 mode, with a 150 nm bandwidth,
Ensemble Method for Reinforcement Learning Algorithms Based on Hierarchy

The article proposes an ensemble method for reinforcement learning algorithms. The proposed approach is on average more efficient than each of the algorithms in the ensemble separately. The article discusses the implementation of the method, which includes an ensemble of REDQ and SAC algorithms. The output from the ensemble is the output of the algorithm selected following the output of the DQN acting as the control algorithm. It is possible to ensemble other algorithms in a different quantity. Reinforcement learning is a promising area in machine learning. An important unsolved problem of reinforcement learning is the generalization of complex problems, and their solution using meta-algorithms. The proposed method can be used in complex problems consisting of many subtasks, effective solutions for which can be offered by various algorithms from the ensemble.
Study on the Adsorption of SF6 and its Major Superheated Decomposition Products on the Surface of Al(1 1 1)

Sulfur hexafluoride (SF 6) is widely used in gasinsulated equipment. When partial discharge, spark discharge, or local overheating occurs in gas-insulated equipment, the insulation medium SF6 will decompose and generate $\mathrm{SOF}_{2},\ \mathrm{SO}_{2}\mathrm{~F}_{2},\mathrm{SOF}_{4},\mathrm{SO}_{2},\mathrm{HF},\mathrm{H}_{2}\mathrm{~S}$, and other products. The role of the metal interface in the generation of these products remains unclear. In this paper, the calculations are conducted in the framework of Dmol3 modules in Materials Studio. It is found that SF6 is adsorbed on the Al surface by chemisorption. When chemisorption occurs, SF6 and SO2 are bonded to the Al surface by the 2p orbital, while $\mathrm{SOF}_{2},\mathrm{SO}_{2}\mathrm{~F}_{2},\mathrm{HF}$, and $\mathrm{H}_{2}\mathrm{~S}$ are only physisorbed. The adsorption energy, bond length change, charge transfer, and density of states analysis revealed that SF6 and SO2 interacted strongly with the Al surface and were less compatible, and the atomic 2p orbital was the primary cause of bonding. The findings establish the groundwork for ultimately understanding how SF6 and Al respond at their interfaces.
Modeling and Stability Analysis for Inverter-based Stand-alone Grids: Examining Grid-forming Methods and the Interactions

As emerging technologies for power grids dominated by renewable energy, grid-forming (GFM) inverters, as well as the motor-generator pair (MGP), were proposed to enhance the inertia, damping, and system strength. Although some researches have been conducted on the transient stability of GFM inverters and MGPs, the dynamics of grid-connected generators can be dictated by the bulk power system. As a result, the stability of a stand-alone grid with mixed GFM generators should be thoroughly investigated. To fulfill this gap, first, this paper adopts a component connection method (CCM) for modeling a standalone grid integrated with multiple generators; second, the models of synchronous generators (SGs), grid-following (GFL) inverters, GFM inverters, and MGPs are studied in detail; then, the full-order state-space models of four systems with interconnected generators are built in this paper, and the simulation models are established to verify the effectiveness of the parameters; finally, the small-signal dynamics of different grids and interactions between GFM and traditional generators are fully examined.
Research on Chinese Patent IPC Classification Based on ERNIE Model and Deep Pyramid Convolutional Neural Networks

For the purpose of automatic classification of patent documents, a Chinese patent IPC classification method based on Enhanced Representation through Knowledge Integration(ERNIE) model and Deep Pyramid Convolutional Neural Networks (DPCNN) proposed. The ERNIE model is used to obtain a more fully semantic distributed text representation; Then the deep convolution neural network is introduced to further extract the coding features of the context to obtain a deeper level of text feature expression; Finally, we use the classifier to achieve chinese text classification. The experiment on the public data set of the State Intellectual Property Office shows that the accuracy of this model is improved by 5.53% compared with the traditional classification model based on Bdirectional Encoder Representation from Transformers (BERT). It shows that the text classification method based on ERNIE model can effectively improve the performance of Chinese text classification, and can provide reference for automatic classification of patent documents.
Novel resonance manipulation method in coupled resonators using “coupling structure technique” for Quantum coherence effect and optical communication applications (Invited)

Recently, we reported a general technique that uses a “cross-coupling structure” as a new vehicle to manipulate the resonance features of the coupled resonators. The technique is important for the (i) search for new optical analogues of Quantum Coherence Effects (QCEs) and (ii) generation of fresh and richer optical behaviors of the coupled resonators that could open future applications in optical fiber communication. Here, we review two new configurations based on this technique. The first configuration uses a modified standard double resonator (SDR) which we refer to as Cross-coupled SDR (CC-SDR). It is a new analogue circuit of QCE that generates a new QC phenomenon we call Cross-coupled Resonator Induced Shifted Absorption (CRISA). It is the first-time a cross-coupled structure has been suggested in studying QCE. Its core mechanism is due to its “cross-coupling structure”. Second, we present a modified coupled ring reflector (CRR) configuration where its original directional couplers (DCs) are replaced with cross-coupler(s) to connect the two ring resonators. We refer to it as crisscrossed-assisted CRR (or X-CCR). We review the unique characteristics of these two configurations and discuss their applications as optical devices.
Current Trends in the Search for Similarities in Source Codes with an Application in the Field of Plagiarism and Clone Detection

There are many methods and approaches for determining the similarity between two source codes. Many of them were inspired by developments in the field of NLP (Natural Language Processing) since the source text can be considered a special type of text. These methods have been implemented in many software tools and surprisingly, many of them are still in use (MOSS, JPlag). Artificial intelligence brought new procedures in the area of NLP, and they were also applied to the area of source code analysis. The article provides an overview of the methods for similarity detection in the source code, which we have not yet found in the literature to such an extent. Although it is certainly not exhaustive, it provides an overview of approaches from the oldest to those that are only beginning to gain attention at the present time.
Phase Change Cooled Encapsulation Material FC-72 Pyrolysis Process Simulation

In view of the phenomenon that the internal medium of power electronic devices applying immersion phase change cooling technology has been boiling during operation, it is necessary to carry out research on whether long-term boiling of the medium will lead to the pyrolysis of the medium and the pyrolysis process and products when the medium is pyrolyzed. In this paper, 25 FC-72 simulated molecular models are set up in the ReaxFF force field by molecular dynamics simulation method to study the dynamic pyrolysis process of FC-72 at different temperatures, and the following conclusions are drawn by analyzing the pyrolysis products: the pyrolysis process mainly generates some small molecule gases such as CF4 and F2, and the reaction process is significantly accelerated with the increase of temperature, the higher the temperature, the more C6F14 participates in pyrolysis, and the faster the reaction system reaches stability.
A posteriori error estimates of fully discrete nite element method for Burgers equation in 2-D I

Here, a posteriori error estimates of finite element method are derived for Burgers equation in two-dimension. By use of constructing an appropriate Burgers reconstruction, some posteriori estimates in L ∞ (L 2 ), L ∞ (H 1 ) and L 2 (L 2 ) norms with optimal order of convergence are established for the semidiscrete scheme. Furthermore, a fully discrete scheme with corresponding posteriori estimates are studied based on backward Euler scheme. Finally, some numerical results are presented to verify the performance of the established posteriori error estimators.
Efficiency Analysis Based on Electrical Network Theory for a Multi-load WPT System

The system efficiency is analyzed by electrical network theory from a load perspective for a multi-load wireless power transfer (WPT) system, and the optimal load value that can maximize the system efficiency is also given in this paper. Meanwhile, the genetic algorithm is used to verify the correctness of the analysis results by electrical network theory. Finally, an experimental platform of an inductive wireless power transfer system based on series-series(SS) topology is built, and the three groups of experiments are carried out to verify the correctness of the theory analysis of the system efficiency for the multi-load WPT system.
A Novel and High-precise Approximate Multiplier with evolvable Truncation

In many applications, multipliers are one of the most important arithmetic functional units, and those applications frequently call for numerous multiplications, which consume a significant amount of power. An emerging technique for applications with error tolerance is using an approximation multiplier. to reduce power consumption and key path delay. With an approximation multiplier, performance and efficiency can be sacrificed for precision. In this study, we present a rough multiplier that can be adjusted and dynamically truncated partial products to fulfil various accuracy criteria. We also suggest a highly accurate rough 4-2 compressor. To decrease error distance, we also advise using a straightforward error compensation circuit. According to the needs of the user, the suggested approximation multiplier can alter the precision and processing speed needed for multiplications at run-time. When A proposed adjustable approximate multiplier shows superior performance to Wallace tree multipliers can reduce delay and average power consumption by 27% and 40.33% (up to 72%), respectively. Furthermore, we show that our proposed multiplier is suitable for and reconfigurable To meet different requirements at different layers of convolutional neural networks(CNNs).
Task Offloading for Deep Learning Empowered Automatic Speech Analysis in Mobile Edge-Cloud Computing Networks

With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delay-sensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DL-empowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers’ storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.
Influence of the Output Circuits in Piezoelectric Vibrational Harvesters

Determining the energy that can be obtained from energy harvesting devices is one of the important tasks in the process of their design, since the main difficulty is the relatively minute amount of energy that must be precisely estimated for a better evaluation of their capabilities. Therefore, the output circuit of these devices is an important factor in determining the output power.
Application of Neural Networks to Predict Power Consumption of a Megapolis

A model for daily schedules short-term forecasting of active power in Moscow is proposed in the article. It is formed on the basis of neural network algorithms, taking into account actual and forecast data on air temperature, cloudiness, precipitation and natural light. To predict daily active power curves, a hybrid model based on multivariate singular spectral analysis and fuzzy neural networks is proposed. Time series of power consumption and meteorological factors are decomposed into independent components in this model, with the help of which additive trend, harmonic and random components, used in the fuzzy neural network module, are formed. The initial data were archives of power consumption time series, air temperature, cloudiness, precipitation and natural light for the territory of Moscow in the period from 2017 to 2019. Predictive data on natural light were obtained using a neural network with long-term memory LSTM. Short-term forecasts of daily active power schedules for August 2019 were made. The results of short-term forecasting of daily active power schedules show that in most test cases the outcomes were obtained within acceptable errors.
Traffic Sign Recognition and Voice-Activated Driving Assistance Using Raspberry Pi

Traffic sign recognition and voice-activated driving assistance systems are crucial technologies for enhancing road safety and improving the driving experience. This paper presents a design and implementation of a low-cost, real-time traffic sign recognition system and voice-activated driving assistance using Raspberry Pi. The system consists of a camera module, a Raspberry Pi, and an interface board that connects the camera and Raspberry Pi. The Raspberry Pi runs an image processing algorithm to identify traffic signs and provides audio feedback to the driver. The voice-activated driving assistance system allows drivers to control the system with voice commands, such as adjusting the volume and changing the mode of operation. The image processing algorithm uses a deep learning-based on traffic sign recognition and includes a convolutional neural network trained on a dataset of traffic signs. The system was tested on a variety of road scenarios and achieved an accuracy rate of over 95% for traffic sign recognition. The voice-activated driving assistance system was integrated with the Amazon Alexa Voice Service, allowing drivers to interact with the system through voice commands. The proposed system is highly cost-effective, as it utilizes a Raspberry Pi, a low-cost single-board computer, as the main processing unit. This makes the system suitable for widespread deployment and accessibility to a larger audience. Additionally, the system has low power consumption and is portable, making it suitable for use in various road scenarios and vehicles. In conclusion, the traffic sign recognition and voice-activated driving assistance system using Raspberry Pi presents a practical solution for improving road safety and enhancing the driving experience. The system's low cost, portability, and real-time performance make it a promising technology for widespread deployment in vehicles.