Miracle-dz/UniFAR-FTUs huggingface dataset:
A Click-through Rate Prediction Method Based on Interaction Features Extraction for High-dimensional Sparse Data

The dataset for Click-through rate prediction contains a large number of categorical features, and the high-dimensional sparsity presented after encoding these features will take challenges for the prediction task. Existing click-through prediction methods focus on the effective learning of interaction features of different orders, ignoring the influence of the importance of interaction features on the prediction results. This paper proposes a click-through rate prediction method based on ECANet and DeepGBM for high-dimensional sparse data, called ECA-DeepGBM. Specifically, this method improves FNFM, called ECA-FNFM, and establishes it as the base model for CatNN in DeepGBM to extract categorical features. In ECA-FNFM, we introduce ECANet attention mechanism into the feature interaction layer of FNFM, to dynamically capture the importance weight of different interaction features. Ultimately, ECA-DeepGBM model is constructed by CatNN, the improved ECA-FNFM, and GBDT2NN. Experiments were conducted on three public datasets, and the results show that the proposed method achieves better performance.
Application in Student Performance Prediction Using Graph Regularization Nonnegative Matrix Factorization

Student performance prediction is an important educational problem in designing personalized teaching and learning strategies. Although matrix decomposition or neural network based algorithms have achieved great success in prediction applications, there are still many problems. Firstly, they are semantic and interpretive inadequacies. Secondly, these methods only focus on the performance information of students, ignoring the additional information such the learning background and courses. In this paper, a graph regularization nonnegative matrix factorization algorithm (GNMF) is proposed for the grade prediction of students in programming courses. Compared with classical matrix decomposition and neural network methods, non-negativity of GNMF means that students' achievement information can be decomposed into the sum of scores of multiple ability elements. Moreover, GNMF generates adjacency matrix graph based on questionnaire data related to student programming, which is integrated into NMF objective function as regularization term. Experiments on education datasets demonstrate that, compared with other methods, GNMF has smaller prediction error and higher prediction accuracy. The research result could facilitate personalized teaching in higher education.
Tool Development of Daubechies Wavelet Estimation for Stochastic Counting Processes

Daubechies wavelets proposed by Ingrid Daubechies are a family of orthogonal wavelets and are used for frequency analysis, multi-resolution analysis, statistical feature value analysis, etc. Kuhl and Bhairgond (2000) applied the Daubechies wavelet to estimate stochastic counting processes with time-varying intensity functions. In this paper, we develop a Daubechies wavelet estimation tool for the stochastic counting processes; Daube-WET, as a web-based free application. A case study is given to illustrate how to use Daube-WET with real failure time data of a repairable system.
Visible Light Communication Based Positioning Using Color Sensor

Based on light-emitting diode (LED) lighting and recent advances in visible light communication (VLC), we propose a new visible light-based localization scheme that uses a grid color-coding principle to develop a highly accurate localization system. By measuring the light received through the projected area on the color-coded film (for light filtering) and comparing it with a predefined color code, the system can determine the 2-dimensional position of the receiver relative to an LED, i.e., the reference point (RP). It is worth noting that the proposed solution has the following attractive features and differs from existing systems: (i) it uses only one photodiode (PD) on the user device for localization, and (ii) it can provide highly accurate indoor localization with centimeter resolution. Interesting applications include localization for unmanned ground vehicles (UGVs), advanced warehousing and logistics. Since PD or light sensor can have a response time of about 25 ms or even faster, one can also expect low latency in localization, which is especially suitable for supporting mobile users and new location-based services. Simulation results have shown that an average positioning error of less than 10 centimeters is achievable under suitable conditions.
A method to accelerate the design of sheet beam traveling wave tube depressed collector

An efficient collector is one of the key factors in improving the overall efficiency of a sheet beam traveling wave tube(SB-TWT). In this paper, K-means algorithm is used to process the massive data of sheet electron beam (EB) after beam-wave interaction, so that a more representative and smaller amount of data is obtained. These data will be imported into the CST Tracking module for the quickly simulation design of the collector. The designed two-stage depressed collector is applied to a 6 kW continuous wave Ku-band SB-TWT with 26 kV Voltage and 1.3 A current. The collector efficiency is up to 80.79% with a backflow current of 0.13%. The overall efficiency of SB-TWT reaches about 52.9%.
Coefficient Complexity in Low-Access Quantized Linear Computations

Quantizing linear computations over the reals is a common practice in many distributed systems, with applications in efficient, private, or robust machine learning algorithms. Recent work studied the access parameter of such computations, i.e., the maximum number of data entries required to accommodate any given computation. It was shown that lower access can be achieved with higher redundancy and vice versa, and a corresponding scheme based on covering codes was given. Surprisingly, the resulting scheme is universal to all possible two-valued quantizations, meaning that the same storage protocol can be used to retrieve any linear combination with two distinct coefficients—regardless of what those coefficients are—with the same access parameter. In this paper we extend this universal protocol to all possible quantizations with any number of values; while the storage parameter remains identical, the access parameter increases according to a new additive-combinatorics property we call coefficient complexity. We then turn to study the coefficient complexity—we characterize the complexity of small sets of coefficients, provide bounds, and identify coefficient sets having the highest and lowest complexity. Interestingly, arithmetic progressions have the lowest possible complexity, and some geometric progressions have the highest possible complexity, the former being particularly attractive for its common use in uniform quantization.
A D-Band 5-bit Active Phase Shifter Supporting Bi-Directional Operations in 65nm CMOS

This paper presents a D-band 5-bit bi-directional active vector synthesis phase shifter in the 65nm CMOS technology for phased arrays. The possibility of implementing bi-directional active phase shifter at D-band frequency is verified. The bi-directional variable gain amplifier (Bi-VGA) as the core of the circuit utilizes the Gilbert cell. And the neutralized bi-directional technique is employed to improve the stability and gain of the circuit. The RMS phase error of the post-simulation can reach 0.88° and 0.98°, the RMS gain error can reach 0.83 dB and 0.9 dB respectively for forward and reverse mode from 135 to 145 GHz. The area of the chip is 0.32 mm2 including pads. And the power consumption of the chip is less than 52.44 mW at 1 V supply voltage.
ML-surrogate modeling for the estimation of random system performance parameter progress by the Chained Gaussian Process Regression method

The ability to accurately forecast productivity parameters is of great importance in various fields, such as engineering designs and structures, workforce management, and economic analysis. However, forecasting such parameters is challenging due to their inherent stochastic nature and time dependence, as well as the presence of heteroscedasticity in data. In this study, we aim to address these challenges by developing a surrogate model that can effectively predict the productivity parameter, taking into account its random process over time and heteroscedastic characteristics.
Testing of Computing-In Memories: Faults, Test Algorithms, and Design-for-Testability

Computing-in memories (CIMs) support memory and computing functions. The operations of computing function are much different from the read/write operations of conventional memories, e.g., multiple wordlines are activated, specific sensing methods, and so on. Those cause that the CIM has computing faults. Conventional march tests for memories cannot cover the computing faults and new test algorithms are needed. In this embedded tutorial, therefore, we introduce fault modeling and test development methods for CIMs with logic operations. Furthermore, we will provide perspectives on the design-for-testability techniques for CIMs as well.
Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text Data

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.
A Privacy-Preserving Authenticated Key Exchange Protocol for V2G Communications Using SSI

The popularity of Electric Vehicles (EVs) has been rising globally in recent years. With the demand for EVs, associated cyber threats are also increasing. Users expose their personal information while charging their EVs, leading to privacy threats. This article proposes a user-empowered, privacy-aware, authenticated key exchange protocol for Vehicle-to-Grid (V2G) communications. The proposed protocol leverages the concept of Self-Sovereign Identity (SSI) and is based on the concept of Decentralized Identifiers (DID) and Verifiable Credentials (VCs). The use of DIDs empowers users by helping them to have complete control over their identities. The charging station and the user verify that the other party is legitimate by verifying the VC before proceeding with the charging services. Key recovery is another issue we address in this article. A method to recover lost keys is incorporated into the proposed protocol. The proposed protocol also incorporates an effective user revocation policy. We present formal security proof and informal analysis to show the protocol's robustness against several attacks. We also provide a detailed performance analysis to show that the proposed protocol is efficient.
High-Speed Surface Roughness Recognition by Scattering on Terahertz Waves

Recognizing surface roughness constitutes a crucial aspect of both channel modeling and environmental perception. Despite the availability of numerous scattering analysis methods in terahertz band, most of them are geared towards low-velocity environments and are primarily analytical, rendering them ill-suited for performing effective classification tasks in high-speed scenarios with limited data accessible and greater environmental complexity. This paper proposes using deep learning methods to address the problem of scattering-based surface roughness recognition in fast scanning scenarios by terahertz frequencies. To achieve this, we build a terahertz high-speed sampling platform operating at 140GHz, design seven surfaces with varying degrees of roughness, and apply deep learning data processing method and a Long Sort-Term Memory (LSTM) model. We conduct experimental verification on low-speed and high-speed scenarios, propose and verify a down-sampling method that applies low-speed data to high-speed scenarios. Through experiments, we find that the deep learning method can achieve good results even in scenarios involving high speed and few sampling points. This method provides a reference for surface roughness recognition and further channel modeling in various scenarios such as vehicle scanning, shipborne, and even airborne and spaceborne scanning.
Dynamic Load Balancing of Selfish Drivers between Spatially Distributed Electrical Vehicle Charging Stations

This paper considers an electrical vehicle recharging infrastructure made up of physically separate stations serving spatially distributed requests for charge. Arriving EVs receive feedback on transport times to each station, and waiting times at congested stations, based on which they make a selfish selection. We present a fluid model of the resulting dynamics, in particular modeling queueing delays as a function of fluid queues, and two different models of client departures: given sojourn times, or given service times. In each case, the joint load balancing dynamics is related to a convex program, suitable variant of a centralized optimal transport problem. In particular, we use Lagrange duality to show the correspondence between equilibrium points and optima, and to analyze the convergence properties of the dynamics. The results have similarities and differences with classical work on selfish routing for transportation networks. We present illustrative simulations, which also explore the validity of the model beyond the fluid assumption.
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0

Decoding spoken speech from neural activity in the brain is a fast-emerging research topic, as it could enable communication for people who have difficulties with producing audible speech. For this task, electrocorticography (ECoG) is a common method for recording brain activity with high temporal resolution and high spatial precision. However, due to the risky surgical procedure required for obtaining ECoG recordings, relatively little of this data has been collected, and the amount is insufficient to train a neural network-based Brain-to-Speech (BTS) system. To address this problem, we propose BrainTalker—a novel BTS framework that generates intelligible spoken speech from ECoG signals under extremely low-resource scenarios. We apply a transfer learning approach utilizing a pre-trained self-supervised model, Wav2Vec 2.0. Specifically, we train an encoder module to map ECoG signals to latent embeddings that match Wav2Vec 2.0 representations of the corresponding spoken speech. These embeddings are then transformed into mel-spectrograms using stacked convolutional and transformer-based layers, which are fed into a neural vocoder to synthesize speech waveform. Experimental results demonstrate our proposed framework achieves outstanding performance in terms of subjective and objective metrics, including a Pearson correlation coefficient of 0.9 between generated and ground truth mel-spectrograms. We share publicly available Demos and Code 1.
A 12~14.6GHz Fractional-N Sub-Sampling PLL for Wavelength Modulation Spectroscopy of CSMCs

This paper presents a fractional-N sub-sampling PLL (SSPLL) for wavelength modulation spectroscopy of chip-scale molecular clocks (CSMCs). The proposed SSPLL adopts a 5-bit vector modulator (VM) based phase-interpolator (PI) and a 5-bit digital-to-time converter (DTC) for in-band phase noise suppression. The proposed 5-bit VM based PI achieves high phase linearity with INL/DNL < 0.25 LSB. It reduces the required dynamic range and intrinsic jitter of the DTC. According to the simulation, the SSPLL spans from 12GHz to 14.6GHz with a rms jitter of 99.4fs, The DC power consumption is 26.7mW. The SSPLL’s figure-of-the-merit (FoM) achieves -245.8 dB.
The Japanese 55-year Reanalysis "JRA-55": An Interim Report

The Japan Meteorological Agency (JMA) started the second Japanese global atmospheric reanalysis project named the Japanese 55-year Reanalysis (JRA-55). It covers 55 years, extending back to 1958, when the global radiosonde observing system was established. Many of the deficiencies found in the first Japanese reanalysis, the Japanese 25-year Reanalysis (JRA-25), have been improved. It aims at providing a comprehensive atmospheric dataset that is suitable for studies of climate change or multi-decadal variability, by producing a more time-consistent dataset for a longer period than JRA-25. Production of JRA-55 started in 2010, and computations for more than 16 years have been completed as of August 2011. The entire JRA-55 production will be completed in early 2013 and thereafter JRA-55 will be continued as a new JCDAS on real time basis. This paper is a brief report to introduce the JRA-55 reanalysis project. The data assimilation and prediction (DA) system used in JRA-55 is introduced and compared to that used in JRA-25. Early results of JRA-55 are presented and discussed, showing general improvements.
Study of the Influence of Rolling Modes on the Operation of Interconnected Electric Drives of a Rolling Mill

The paper is devoted to study of the influence of rolling modes on the performance of powerful interconnected electric drives of a hot rolling mill under the action of an electromagnetic coupling circuit between the electric drives of the roughing and finishing groups. The action of the electromagnetic coupling circuit manifests itself in the form of the influence of impact loads of synchronous electric drives on the angular velocity of direct current electric drives of the finishing group of the mill. The parameters of the electromagnetic coupling circuit are determined by the power supply scheme and equipment parameters. Values of impact loads applied to a synchronous motor depend on the rolling process parameters, in particular, the chemical composition of the rolled steel and the temperature of the ingot. The paper presents the results of calculating the rolling power for various rolling mode parameters. Using mathematical modeling in Simulink, numerical estimates of the influence of rolling modes on the magnitude of the voltage drop in the power supply unit and the drop in the angular velocity of the electric drive of the finishing group were obtained on the example of a wide-strip hot rolling mill 1700 of ArcelorMittal Temirtau JSC.
Computational Study of the Thermal Transport Properties of Hollow-Core Si Nanowires

In this paper, we investigate the thermal transport in Si hollow-core (tubular) nanowires by using the quasi-harmonic Green-Kubo lattice dynamics formalism. We focus on the effect of a cylindrical cavity on the thermal conductivity of the Si nanowires. We compare the heat transport of the hollow-core structure with that of the solid Si nanowire. Furthermore, the dependence of the thermal conductivity on temperature and cavity radius is evaluated in the nanowire direction. We find that by increasing the cavity radius, the thermal conductivity of the hollow-core nanowires decreases. We explore the effects of hollow-core geometry on the thermal conductivity of silicon nanowires. Our simulation results exhibit that increasing the mean temperature of the nanowire leads to a reduction in the thermal conductivity. Our findings provide insights into the thermal control of silicon-based nanodevices for different applications such as thermoelectric ones.
An Enhanced Approach for RFID Reader Deployment in Industrial IoT Systems with Collision Avoidance and Optimized Coverage

This paper introduces an innovative approach to tackle the issue of radio frequency identification (RFID) reader-coverage collision avoidance deployment in industrial Internet of Things (IoT) systems. The primary objective of our proposed methodology is to enhance the dependability and reliability of reader deployment. To accomplish this, we present a deterministic algorithm that divides the target field into grids and identifies interlaced positions as potential deployment locations for readers. Additionally, we evaluate the coverage weight of tags for each position and strategically choose the position with the highest weight to deploy a reader. This iterative process continues until all tags are adequately covered by readers. Furthermore, we optimize the reading range of each reader by considering its adjustable range capability, thus ensuring efficient and reliable tag coverage while minimizing potential interference among readers.
Profile Dynamic Memory Allocation in Autonomous Driving Software

The software-defined vehicle has driven the autonomy and electrification of the automotive industry. A technical challenge for software designers is how to leverage existing software from AI research and autonomous driving (AD) development and make it useful, reliable, and efficient for customer requirements and functional safety standards. However, the software is critical in autonomous driving (AD) systems, where it should ensure reliability and real-time guarantee simultaneously. Further, the AD industry may re-utilize existing mature software implementation (e.g., C++ STL libraries) in order to accelerate development. However, the jeopardy of reliability and real-time guarantee caused by dynamic memory management inside remains a major concern for practitioners in the field. This paper presents a software tool (called MemTrace) to conveniently analyze the dynamic memory management behavior of AD software and provide important analytical results for software designers to make judgments on software quality, run-time efficiency, and safety with high confidence. MemTrace relies on interception and instrumentation for profiling the explicit allocation behavior of general software, as well as the implicit memory allocations of using C++ STL containers and smart pointers. The profiling data will be analyzed for the behavior that could jeopardize software safety, for example, memory leak and memory external fragmentation. Our experiment results show that MemTrace can effectively provide detailed periteration results for AD software modules, and identify potential memory-related hazards. Through the profiling of prototype AD software with MemTrace, we have gained 6 insightful observations, including the potential risks associated with prolonged usage, and suggestions for effective utilization of STL containers and smart pointers in AD software, which can assist AD software developers during the development process.
Towards the Digital Twin Based Tool Condition Monitoring System for Micro Milling Application: A Review

This paper reviews the developments of tool condition monitoring (TCM) systems in the domain of micro-milling applications. The elaboration of TCM taxonomy is focused on the three essential components, i.e., approach, sensor type, and wear modelling. Further, the digital twin-based TCM developments are also presented. The review shows that the digital twin-based TCM adaptation still needs to be improved despite its potential benefits. The physics-based approach of TCM can be explored more in the digital twin development as the digital model.