Miracle-dz/UniFAR-FTUs huggingface dataset:
Homophonic Music Composition Using Pipelined LSTMs for Melody and Harmony Generation

Throughout the years, many attempts have been made at creating music procedurally. However, very few of those attempts were concerned with the actual “meaning” of the music that was generated. The goal of this research is to implement a pipelined model of neural networks capable of generating homophonic music without input. The generated music should be “meaningful”, that is to say, it should sound like it has a purpose. This is similar to how a human composer would write music. This idea of “meaning” makes a composition tell a story or express feelings. This is the reason humans write music, as well as create other forms of arts. As the goal of artificial creativity is to approach human creativity as close as possible, Artificial Intelligence should try to imitate humans as close as possible. Therefore, it is important for a music generating AI to understand “meaning” in music. In order to introduce meaning in AI composition, the process is approached step-by-step. Dedicated neural models trained on melodies that express purpose through the use of motifs are first used to generate a melody. That melody serves as input to another neural model, trained on chord progressions that contextualize the melodies they accompany, to generate harmony. Besides the model being symbolic, there are no musical constraints or guidelines for generation. By using this pipelined but unconstrained approach, it is possible to procedurally generate music that sounds as if composed by a human being.
A Unified Framework and a Case Study for Hyperparameter Selection in Machine Learning via Bilevel Optimization

Support vector classification (SVC) is a classical and widely used method for classification problems. A regularization parameter, which significantly affects the classification performance, has to be chosen. In this paper, we propose a unified framework for hyperparameter selection in machine learning via bilevel optimization. Specially, we study the case for $l_{2}$-loss SVC in which the upper-level objective function is the hinge loss function, and the lower-level problems are $l_{2}$-loss SVC models. Following the same way as in our recent work, we reformulate the bilevel optimization model as a mathematical program with equilibrium constraints (MPEC). Similarly to that in our recent work, the MPEC-tailored version of the Mangasarian-Fromovitz constraint qualification (MPEC-MFCQ) automatically holds at each feasible point of the MPEC. Extensive numerical results verify that the global relaxation cross-validation algorithm for $l_{2}$-loss SVC (GRCV-l2) enjoys superior generalization performance over almost all the datasets used in this paper.
A Coordinated Scheduling Model for Wind Power Integrated Systems with Data Centers Based on Chance Constrained Goal Programming

Data centers (DCs) are energy consumers with high electricity demand. Due to their Spatio-Temporal demand response (DR) capabilities, DCs are crucial DR participants. This paper presents a scheduling model for wind power integrated systems with DCs. To maximize the total social welfare, it is considered that the reserve capacity can be flexibly adjusted based on its reference value. Accordingly, a coordinated optimization model based on the chance-constrained goal programming (CCGP) is proposed. To overcome the solving difficulty of the probabilistic chance constraints, a conversion method is adopted in this paper to convert the probabilistic constraints into deterministic forms. Finally, the proposed model is verified on an IEEE 30-bus power system with wind farms. Simulation results prove that DCs’ participation in DR has a positive effect on reducing wind power curtailment and increasing social welfare.
Apparent Color Dataset: How Apparent Color Differs from the Color Extracted from Photos

Apparent color and pixel color are known to be different due to color constancy and contexts of images. An apparent color dataset for photographs and images will be a basis for developing perception-based color extraction and transfer system. In this study, we collected the apparent color dataset for a given image and extracted position through an online experiment that mimics the situation of using a color extraction system. Additionally, we analyzed 1110 data and verified the difference between apparent color and pixel color. As a result, approximately 80% data has $\Delta E_{\mathrm{NBS}} \gt 6.0$ color difference that is defined “Much” or “Very Much” difference by the national bureau of standards (NBS). Luminance (L*) of the apparent color was on average $\triangle E_{00}=[9.90,11.9]$ (95% confidence interval) greater than that of the pixel color. These results indicate that the color deviations cannot be ignored when apparent colors are extracted in images.
PerKG: A Personality Knowledge Graph for Personality Analysis

With the blossoming of online social networks (OSN), personality analysis based on OSN texts has gained much research attention in recent years. The previous methods mainly focus on human-designed features extracted through psychological dictionaries or semantic features extracted through language models. However, the shallow statistics features can not fully convey the personality information and the language models can not capture enough psychological background knowledge. Besides, the lack of large labeled datasets has been a serious obstacle impending further research. To tackle these problems, we propose a personality analysis model, namely PerKG, which combines personality knowledge graph and heterogeneous graph representation learning to exploit external knowledge from psycholinguistics and learn the group-level information to predict users’ personalities accurately. Specifically, we construct a personality knowledge graph based on existing psycholinguistics knowledge. And then, for each user, we align the user information with the knowledge graph to obtain the personality heterogeneous graph. Finally, the personality vector of each entity node is learned for prediction by designing a walk strategy on the personality heterogeneous graph. Detailed experimentation shows that our proposed PerKG architecture can effectively improve the performance and alleviate the label sparsity problem of personality analysis.
Optical and Near-Infrared Photometry of the Type Ia Supernova 2000E in NGC 6951

We present optical (UBVRI) and near-infrared (JHK) photometry, along with optical spectra, of the Type Ia supernova SN 2000E in the spiral galaxy NGC 6951. It was discovered by the staff of the Teramo Observatory during the monitoring of the SN 1999el. The observations span a time interval of 234 days in the optical and 134 days in the near-infrared (starting ~16 and ~7 days before maximum B light, respectively). Optical spectra are available from 6 days before maximum B light to 122 days after it. The photometric behavior of SN 2000E is remarkably similar to that of SN 1991T and SN 1992bc: it exhibits a Δm15(B) = 0.94 mag, thus being classifiable as a slow-declining Type Ia supernova (SN) and showing the distinctive features of such a class of objects both in the visible and in the near-infrared. Spectroscopically, SN 2000E appears as a "normal" Type Ia SN, like SN 1990N. We could constrain reddening [E(B-V) ~ 0.5 mag] and distance (μ0 ~ 32.14 mag) using a number of different methods. The bolometric luminosity curve of SN 2000E, which displays a bump at the epoch of the secondary near-infrared peak, allows a determination of the 56Ni mass, amounting to 0.9 M☉.
Use of machine learning and multilevel analysis in hierarchical approaches of public expenditure forecasting

On the one hand, public expenditure can be decomposed into several levels of expenditure regarding each administrative unit and expenditure nature groups that happens thru time. Therefore, it can be considered as a hierarchical time series. On the other hand, accurate forecasting methods are desirable for planning and management due to the need to identify and anticipate future scenarios. Thus, considering the hierarchical nature of the problem, this work aims to develop a a predictive model which takes into consideration the hierarchical structure of public expenditure in order to assure financial coherence and achieve improved results. Experimentally, this work uses time series of public expenditure execution in the State of Pernambuco, Brazil. The experiments were conducted on two axes in order to establish a multilevel analysis considering conciliatory approaches of the hierarchical levels of expenditure and the predictive models. By applying these methods, valuable public expenditure forecasts in the State of Pernambuco considering different hierarchical levels were produced.
Speech Enhancement Generative Adversarial Network Architecture with Gated Linear Units and Dual-Path Transformers

Generative Adversarial Networks (GANs) have been used in the field of speech enhancement due to their huge potentials in reducing the noise mixed in the signals. Most of existing GAN-based speech enhancement approaches either operate on time domain or exploit the magnitude spectra in time-frequency domain, but lack consideration of direct optimization of the phase. In this paper, we propose a GAN architecture for speech enhancement based on gated linear units (GLUs) and Dual-Path Transformers (DPTs), which simultaneously deals with the amplitude and phase information on the time-frequency domain. The generator of the proposed GAN architecture is designed following an autoencoder structure fed by the real and imaginary parts of the time-frequency frames. The encoder of the generator is constructed by multiple cascaded convolutional GLUs (ConvGLUs), while the decoder consists of two groups of cascaded deconvolutional GLUs (DeconvGLUs), one for the real part of the spectrogram and the other for the imaginary part. The GLUs are adopted since they are potential in avoiding the gradient vanishing issue dwelling in deep architectures by providing a linear path for the gradients while retaining non-linear capabilities. Aiming at capturing the long-range dependent features in speech, we place DPTs between the encoder and the decoder of the generator, which contains multi-head attention modules and Bi-directional Gated Recurrent Units (BiGRUs). Moreover, the DPT structure is also merged with multiple one-dimensional convolutional layers in the discriminator of the GAN. Such a design not only improves the speech enhancement performance of GAN by focusing on multiple features of speech, but also reducing the volume of model parameters of GAN. Experimental results suggest that the proposed GAN architecture outperforms the existing benchmark GANs in terms of both objective speech intelligibility and quality with less computational complexity.
HallMonitor: A Framework for Identifying Network Policy Violations in Software

Debloating helps to remove unused and poten-tially vulnerable code from software. While such techniques are becoming more mature and practical, they focus on the features that are unwanted by users, and not on a wealth of functionality that is disallowed by administrative policy. For instance, while an administrator may use a firewall to block certain types of traffic, hosts readily interact with such traffic when the firewall is bypassed (e.g., via an encrypted tunnel). In this paper, we present HALL MONITOR, a tool that helps trim software functionality based on the violations of network policy. HALLMONITOR translates violations expressed in firewall and intrusion detection system rules (specifically, iptables and Snort) into parameters for detecting the implementation of such functions in source code spread amongst clients. We demonstrate the power of this approach first by removing echo functionality from ICMP, showing the ability to remove functions that enable higher-level attacks (e.g., network mapping). We then use network filtering rules to tag 14 out of 16 CVEs in Curl and Nginx (based on ground-truth from patches) to show the efficacy of our approach. In doing so, we demonstrate that network policy can be used to guide the removal not simply of code that users may not want, but instead of features that they are not allowed to use.
Impact of Voltage Unbalance on Application of IEEE Std 112 Test Procedure in North American Electric Motor Service Centers

American standards like ANSI/NEMA MG 1 and IEEE Std 112 recommend test procedures for determination of polyphase induction machine losses which may be used for loss segregation and/or efficiency evaluation. All procedures described in those standards require balanced sinusoidal voltages, which can only be achieved in well-equipped laboratories. In real industrial environments, a balanced sinusoidal power supply is impossible. Thus, tests are generally conducted with a certain level of voltage unbalance and harmonic pollutions without any knowledge of how test accuracies are affected. This research work investigates the impact of voltage unbalance on the accuracy of the friction and windage loss, and core loss evaluations based on the IEEE Std 112 no-load test procedure. The impact is demonstrated and quantified by testing a 60 Hz, inverter-duty, premium efficiency, 3 hp induction motor under balanced voltages and with voltage unbalance in the range of 0.5%-5%. The objective of this technical paper is to update the current IEEE Std 112 with the findings of this research work.
A Data-driven Approach for Travel Time Prediction and Analysis

Realtime estimation of travel time is a key traffic parameter for designing and planning for transportation systems, particularly when providing mobility-on-demand (MOD) services. However, the analysis and prediction of travel time can be delayed significantly due to the complexity and huge computational requirements of microsimulation models. Thus, as an alternative solution, we propose a data-driven approach for the efficient and reliable prediction of travel time. Our approach takes advantage of the strengths of SVM and ARIMA for fully capturing the traffic patterns in the traffic data. We introduce a new parameter $\kappa$ into the SVM-ARIMA model to adjust the weight of the ARIMA component, which significantly improves the performance. We validate the performance of the proposed approach using data generated from a microsimulation platform. Our experimental results and comparisons with the existing ML-based methods demonstrates the efficacy of the proposed data-driven approach.
Hybrid Modulation Scheme with Current-Stress Optimization of DAB DC-DC Converter to Enhance Efficiency

The increasing popularity of power electronic (PE) converters in different applications has led the research work to obtain higher efficiency and power density of the system. The dual active bridge (DAB) converter becomes very important in the domain of DC-DC isolated converters, due to its several advantages. Multiple research articles have studied the operation procedure of DAB converter, and proposed various modulation techniques for the same. Several phase shift modulation techniques have improved the operation of DAB for wide voltage and power range. However, at low power and wide voltage variation of converter, some limitations of those phase sift modulation techniques can be identified such as, low efficiency, hard switching operation, high peak and rms current of transformer, high computational effort etc. Targeting those issues, a hybrid modulation technique is proposed combining single phase shift and variable phase shift modulation techniques. The proposed modulation scheme a) optimizes the transformer peak current at its minimum value for full range of power, b) provides zero voltage switching (ZVS) operation for all operating conditions, c) ensures minimum voltage stress on switches, d) does not use any current sensor for the optimization process and e) operates with simplified control strategy which results low computational efforts. The optimization of peak current is achieved using two different methods, which are differential method and Lagrangian multiplier method. The optimization process is carried out for full range of voltage and power to ensure the ZVS operation for all operating conditions. All the claimed characteristics of the proposed modulation technique are verified through simulation and the experimental results derived from a hardware prototype.
Revenue Uncertainty Analysis for Autonomous Mobility-on-Demand System

With the development of mobility on-demand and artificial intelligence technologies, autonomous electric vehicle (AEV)-based ride-hailing fleets are playing an increasingly important role in the urban ground transportation system. Due to the stochastic nature of travel request arrival and charging price, there exists intrinsic uncertainties in decision-making process for AEVs in the autonomous mobility-on-demand (AMoD) system. It is important to investigate the impact of those uncertainties on the power transportation coupling system. In this paper, we propose an operation modeling scheme for AMoD fleet via quantile regression deep Q-learning network (QR-DQN) to analyze the corresponding revenue uncertainties. First, we establish a semi-MDP framework to model the AMoD fleet management and charging scheduling decisions under an uncertain environment. Using quantile regression method, we develop a QR-DQN based decision-making method for AEVs in AMoD fleet. Finally, we conduct multiple experiments based on real-world data to verify the operation characteristic of AMoD system under an uncertain environment.
Systematically Analyzing Vulnerabilities in the Connection Establishment Phase of Wi-Fi Systems

To establish a secure Wi-Fi connection, several un-protected management frames are exchanged between an access point and a station before they mutually authenticate each other and start a protected session. In this paper, we are the first to formally model and analyze this connection establishment phase, based on the latest IEEE 802.11 standard, and accordingly, expose a new denial of service (DoS) vulnerability and three new variants of a known man-in-the-middle (MitM) attack. We also formally show that the optional operating channel validation technique introduced in the latest standard is capable of protecting the system only against multi-channel MitM. To validate our identified DoS vulnerability, we test it against the latest wpa_supplicant daemon, showing that an adversary can stealthily prevent a station from connecting to a preferred AP for up to 90 minutes, likely more. We also propose a mitigation approach to counter it.
Error Reduction in Sensorless Speed Control Technique for PMSM Drive using New Sliding Mode Reaching Law

This paper presents an error reduction in sensorless speed control technique for Permanent Magnet Synchronous Motor (PMSM) drive using New Sliding Mode Reaching Law (NSMRL). The control concept of the sliding surface function is included in the NSMRL, as well as the system state variable. The value of the switching function, in particular, binds the power term, allowing the reaching law to be expressed in different ways during the trying to reach methodology. Not only does this process actually suppress implicit chattering, but it also increases the speed of the system state trying to reach the sliding mode surface. A sliding mode control technique of PMSM is developed with reaching law. Thereafter an enhanced sliding mode controller using an anti-disturbance property, method called Sliding Mode Control Pulse Extended State Observer (SMC+ESO) method is developed, which considers the large chattering phenomenon due to high switching gain. The proposed algorithm not only suppress the chattering problem, but it also enhances the accuracy of estimation. The proposed sensorless control technique is validated over a small-scale experimental setup.
Xiezhi: An Intelligent Judical Trial System

Nowadays, the growing integration of artificial intelligence with various fields has promoted the rapid development of each field. However, in the judicial field, the application of artificial intelligence is limited, and there is still a great demand and potential for development. In this paper, we devise an intelligent judicial trial system-XieZhi, which realizes the following functions: crime prediction, sentence prediction, relevant law articles recommendation and similar cases recommendation. Specifically, we use the BERT model to design a text classification network for the crime and sentence prediction. For the relevant law articles recommendation we create a statistical-based method, and for the similar cases recommendation, we select the approach that using TF-IDF to calculate text similarity. Through experiments, it shows that all functions of our Xiezhi system have a high accuracy and good performance.
An Opinion Dynamics Model with Cross-Link Interaction

At present, online social networks have become the main platform for people to express their opinions and interact with them, which has a great impact on the evolution of opinions. Therefore, the research on the evolution of opinions in online social networks has become a current hotspot. Opinion dynamics is an important tool to study the evolution law of opinions in the network. In the opinion dynamics model for online social networks, agents often can only interact with others who have links on the network. However, in reality, the interaction of agents’ opinions is not limited to agents with links on the network. Therefore, due to the lack of sufficient interaction, the traditional model cannot reflect the true final state of public opinion evolution. In view of the above situation, we propose a novel cross-link interaction mechanism which enables agents interact opinion with others without the limit of network inks and use machine learning methodology to get the cross-link interaction distance. After that, we introduce the mechanism to the bounded confidence opinion dynamics model. With this mechanism, the interaction of the agent will be more reasonable and more like social behavior. The simulation results show that the proposed model fits the real data better than traditional models and even under a very small bounded confidence value, agents opinions will still be around high-influence agents’ opinions.
Radar Target Detection in Strong Clutter Using Spatial-Temporal U-NET

In radar target detection problems, the detection performance relies heavily on the signal-to-clutter-ratio (SCR). In this paper, we investigate the problem of radar target detection in strong clutter. When the clutter is extremely strong, it is almost impossible to detect the targets with only one range-Doppler-matrix (RDM). To address this issue, we resort to another time dimension to improve the detection performance, and the Spatial-Temporal U-Net (ST U-Net) is employed. Compared with ordinary deep learning based detectors which only take the RDM under test as the input, the ST U-Net simultaneously takes in the RDM of the current coherent processing interval (CPI) and the neighboring CPIs to jointly learn the spatial and temporal patterns, which leads to better detection performance. Extensive simulation results show that the proposed ST U-Net outperforms other detectors in strong clutter environment.
PM-based Persistent Key Value Stores: a Survey

The advent of persistent memory(PM) technologies provides an opportunity for building persistent key-value stores by making the best of its data persistence and access performance close to DRAM. In recent years, researchers have proposed many key-value store designs, and these improvements come from the database and operating system communities. This paper particularly introduces the optimization schemes and improvements of various index structures, such as hash tables, B+ trees and LSM-Trees on PM. It discusses their highlights and trade-offs in write efficiency, data persistence, crash consistency and concurrency control. Finally, this paper provides researchers with optimization directions to cultivate persistent key-value stores deeply.