Miracle-dz/UniFAR-FTUs huggingface dataset:
An Auto Optimized Payment Service Requests Scheduling Algorithm via Data Analytics through Machine Learning

Traditional customer payment service scheduling approaches cannot cope with the modern demand for timely, high-quality service due to the disruption of big data within small and medium-sized payment solution providers (SaMS-PSP). While many customers have access to modern technologies to lodge their service requests easily and fast, SaMS-PSPs do not have equally automated big data-driven capabilities to handle the growing demands of these service requests. To effectively improve SaMS-PSP’s customer payment service requests processing speeds, personnel optimization, throughput, and low latency scheduling, we have developed a new customer payment service request scheduling algorithm via matching request priority with the best personnel to handle the request based on data analytics through machine learning. Our experiments and testing have confirmed the merits of this new algorithm. We are also in the process of applying this new algorithm in real-world payment operations.
Design and Simulation of Micro-ring Resonator based Optical Comparator

Digital comparators are one of the basic combinational logics used in digital electronic circuits. These conventional devices consist of copper interconnects and use CMOS transistors as building blocks. CMOS technology has almost reached its limit with the transistor sizes being reduced to as low as 7 nanometers. Micro-ring resonators (MRR) have found its application in designing optical combinational and sequential circuits. In this paper, an all-optical magnitude comparator has been designed and simulated using two different wavelengths of light with same or different values of amplitude which are supplied to the dual micro-ring resonator.The intensity and position of outputs determine the results of comparison. Ring resonator designs occupy a small area of 1500 $\mu \mathrm{m}^{2}$ and also have a high quality factor of 627 for a single ring resonator design whose effective refractive index is calculated to be 2.285. The FSR and FWHM values are 33.34 nm and 5.07 ns respectively. These devices can operate at high frequencies of 1.6 THz. Unlike conventional devices, the propagation delay is only 640 fs.
Palm-Vein Verification Using Images From the Visible Spectrum

In this paper, we investigate into utilization of images from the visible light (RGB) spectrum for identity verification based on the palm-veins. This is differentiated from the commonly utilized Near-infrared (NIR) images for palm-vein feature extraction. Our goal is to explore into the often omitted palm-vein information from the RGB palm images considering the vast deployment of the RGB cameras. Essentially, the vein line features are extracted at various scales based on an efficient difference image projection. The extracted features from the gallery and the probe images are matched based on a fast Hamming distance implementation. The resultant similarity scores are finally fused at score level for accuracy enhancement. Experiments are conducted on two public multi-spectral palm databases. The results show encouraging matching accuracy and computational efficiency of the proposed method which extracts the palm-vein utilizing only the visible spectrum. The outcome of this study can be deployed as a standalone biometric or as part of a multibiometric system for secure authentication.
Parameter Identification of Distributed Thermal Network for Surface Mount Type Power Semiconductor Packages

Power electronics is becoming more important than before with motor application expansion. For size reduction of inverter integrated motor design, accurate temperature prediction of power devices is becoming critical. For up to several hundred-watt motor system, inverter is designed with discrete power devices with standard package. This paper investigates and constructs thermal impedance distribution of heat transfer path for a DPAK package test board.
Top-k Publish/Subscribe for Ride Hitching

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe the ride orders published by riders and continuously receive the matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top-k subscription queries called Top-k Ride Subscription (TkRS) query, which continuously returns to drivers the best k ride orders that match their trip plans. We propose two efficient algorithms to enable the top-k result maintenance. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.
CHANGE: Delay-Aware Service Function Chain Orchestration at the Edge

In Mobile Edge Computing (MEC), the network’s edge is equipped with computing and storage resources in order to reduce latency by minimizing communication with remote clouds. However, the available computing capacity at the edge is limited compared to that of remote clouds. A promising solution for efficient utilization of the limited capacity at the edge is fine-grained processing of user demands via Virtual Network Functions (VNFs). In this approach, user service demands are expressed as Service Function Chains (SFCs), which are composed of virtual network functions. Such service composition allows constituent VNFs to be flexibly deployed at the edge or in the cloud such that the service latency is minimized. The increasing number of users, however, challenges the scalability of system-managed SFC orchestration. To address this problem, we propose a user-managed online SFC orchestration framework at the edge of the network, called CHANGE, that minimizes service latency by jointly considering the effect of user mobility, edge capacity and service migration. We first present the theoretical foundations of CHANGE and then evaluate its performance via model-driven simulations and realistic Mininet-WiFi emulations. Our results show that CHANGE can improve latency performance by nearly 20% compared to other approaches.
Network Data Resource Scheduling Strategy for Safety-Critical Testing Systems

For complex safety-critical systems such as spacecraft, a large amount of various types of data will be generated during the integrated testing process. In the case of limited network bandwidth and many users querying these test data at the same time, it will inevitably cause network communication delays and congestion. This will reduce the user experience and even cause delays in some important matters. In order to avoid the delay of important transactions and the phenomenon of network delay and congestion as much as possible, and to improve the user experience, this paper proposes a bandwidth allocation strategy based on the classification of access transaction importance, according to the characteristics of the complex safety-critical systems that emphasizes the importance of transactions. This method sets different levels for the importance of access transactions, and assigns different access authority priorities and network bandwidths according to these different levels. For access transactions with the same importance level, the first-come, first-served principle is adopted for resource allocation. Experimental results show that the network bandwidth allocation strategy proposed in this paper effectively avoids the delay of important transactions, reduces network congestion, and improves user satisfaction.
A Hybrid Data Cleaning Framework Using Markov Logic Networks (Extended Abstract)

With the growth of dirty data, data cleaning turns into a crux of data analysis. In this paper, we propose a novel hybrid data cleaning framework, termed as MLNClean, which is capable of learning instantiated rules to supplement the insufficient integrity constraints. MLNClean consists of two steps, i.e., pre-processing and two-stage data cleaning. In the pre-processing step, MLNClean first infers a set of probable instantiated rules according to Markov logic network (MLN) and then builds a two-layer MLN index to generate multiple data versions and facilitate the cleaning process. In the two-stage data cleaning step, MLNClean first presents a concept of reliability score to clean errors within each data version separately, and then, it eliminates the conflict values among different data versions using a novel concept of fusion score. Considerable experimental results on both real and synthetic scenarios demonstrate the effectiveness of MLNClean.
Behavior-based Outlier Detection for Indoor Environment

In this paper, we introduce a system that can detect the space outlier utilization of residents in indoor environment at low cost. Our system facilitates autonomous data collection from mobile app logs and the Google app server and generates a high-dimensional dataset required to detect outlier behaviors. For this, we used density-based clustering algorithm with t-distributed stochastic neighbor embedding (t-SNE). Our system enables easy acquisition of large volumes of data required for outlier detection. Our methodology can assist spatial analysis for indoor environments housing residents and help reduce the cost of space utilization feedback.
Sliding-Window Gelfand–Pinsker Coding: General K-User Broadcast Channels

A low-complexity coding scheme, termed as sliding-window Gelfand–Pinsker coding, is proposed. It is shown that in a general K-user broadcast channel, every rate point in the Marton’s inner bound can be achieved using single-user encoders and decoders. The scheme provides us with a low-complexity alternative to implement the conceptual K dimensional multi-coding, which is an irreplaceable component in many important network communication schemes, such as Marton coding in Gaussian MIMO broadcast channels and distributed decode–forward in cloud radio access networks, but has not been adopted in practical systems due to high computational complexity. Key features in the proposed scheme include staggered message scheduling, successive Gelfand–Pinsker coding, and sliding-window decoding.
Magnesium Silicide Source Double Palladium Metal Gate TFET for Highly Sensitive Hydrogen Gas Sensor

This work reports the application of novel magnesium silicide (Mg2Si) source heterojunction double gate tunnel field effect transistor with palladium (Pd) gate metal for highly sensitive hydrogen gas sensor. The impact of hydrogen gas pressure on sensor is considered by change in the workfunction of palladium gate metal as a function of hydrogen gas pressure. Heterojunction formed by Mg2Si (source) and silicon (channel) at the source-channel interface in the gas sensor under consideration compensates for the low ON currents of conventional TFET devices. All the simulation results are obtained from Silvaco atlas TCAD simulator. Investigations reveal that the behavior of proposed sensor is reasonably sensitive to the hydrogen gas as it displayed a wide range of sensitivity from the order of 103 to 107.
Dose-Conditioned Synthesis of Radiotherapy Dose With Auxiliary Classifier Generative Adversarial Network

In recent years, there are more and more researches on automatic radiotherapy planning based on artificial intelligence technology. Most of the work focuses on the dose prediction of radiotherapy planning, that is, the generation of radiation dose distribution image. Because of the small sample nature of radiotherapy planning data, it is difficult to obtain large-scale training data sets. In this paper, we propose a model of Dose-Conditioned Synthesis of Radiotherapy dose by using Auxiliary Classifier Generative Adversarial Network(ACGAN), and a method of customize and synthesis dose distribution images of specific tumor types and beam types is considered. This method can customize and generate dose distribution images of tumor types and beam types. The dose distribution images generated by our model are evaluated by MS-SSIM and PSNR, the results show that the image quality of dose distribution generated by ACGAN model was excellent, which was very close to the real data and shows high diversity, it can be used for data enhancement work of training data sets of dose prediction methods.
A 0.6 V 1.6 nA Constant Current Reference Circuit with Improved Power Supply Sensitivity

This paper presents a low power current reference circuit with improved power supply sensitivity. The proposed circuit is designed in SCL 180 nm CMOS technology and simulated using Cadence Virtuoso. It generates a reference current (Iref) of 1.6 nA at 0.6 V supply voltage (Vdd) at room temperature (27° C) with a total power consumption of 10 nW (0.6 V). A new circuit configuration based on composite transistor operating in weak inversion has been proposed in this work. The proposed circuit is an improvisation on beta-multiplier circuits proposed earlier. The circuit improves the power supply sensitivity of the current source and provides a better performance in terms of process variability.
Data Disclosure Mechanism Design with Non-zero Leakage

We study an information-theoretic privacy problem, where an agent observes useful data Y and wants to reveal the information to a user. Since the useful data is correlated with sensitive data X, the agent employs a privacy mechanism to produce data U that can be disclosed. Thus, we study the privacy mechanism design that maximizes the revealed information about Y while satisfying an ℓ1-privacy criterion under the Markov chain X−Y −U. When a sufficiently small leakage is allowed, we show that the optimizer of the design problem has a specific structure which allows us to use a local approximation of mutual information. More specifically, we show that the optimizer vectors are perturbations of fixed distributions. By using this approximation the original optimization problem can be reduced to a linear programming problem and an approximate solution for privacy mechanism design can be obtained.
Blind Separation for Multiple Moving Sources With Labeled Random Finite Sets

This paper proposes a novel solution for separating an unknown and time-varying number of moving acoustic sources in a blind setting using multiple microphone arrays. A standard steered-response power phase transform method is applied to extract source position measurements, which inevitably contain noise, false detections, missed detections, and are not labeled with the source identities. The imperfect measurements lead to the space-time permutation problem, as there is no information on how the measurements are associated to the sources in space, nor how the measurements are connected across time, if at all. To solve this problem, a labeled random finite set tracking framework is adopted to jointly estimate the source positions and their labels or identities. Based on these trajectory estimates, a corresponding set of time-varying generalized side-lobe cancellers is constructed to perform source separation. The overall algorithm operates in a block-wise or an online fashion and is scalable with the number of microphone arrays. The quality of the measurements, tracking, and separation, are evaluated respectively, with the OSPA metric, OSPA(2) metric, and ITU-T P.835 based listening tests, on both real-world and simulated data.
Adaptive Secure Transmission Scheme in Multiple Buffer-aided Relay Network

In this paper, we consider secure transmission design issue in multiple buffer-aided relay network, where one source is supposed to deliver confidential message to one destination via multiple buffer-aided relays in the presence of one eavesdropper node. We proposed an adaptive secure transmission scheme to leverage the potential of buffer-aided relaying strategy by jointly considering relay selection, time allocation, and power allocation in order to improve the achievable secrecy throughput. To this end, we formulated the average secrecy throughput maximization problem. By using the Lyapunov optimization framework, the original optimization problem can be decomposed into a set of subproblems to derive the online adaptive secure transmission scheme via multiple buffer-aided relay network. Our analysis discloses that, the adaptive secure transmission (AST) scheme outperforms the relaying scheme that only considers the relay selection strategy. In addition, the relay placement issue in terms of the maximized end-to-end secrecy throughput was addressed for the two buffer-aided relay networks for the given eavesdropper location. Our analysis unveils that, there exists optimal relay placement of two intermediate relays for the sake of maximizing the achievable secrecy throughput performance.
Emotion Recognition Based Emoji Retrieval Using Deep Learning

Facial expression is a verbal act of speech that is expressed on the face in terms of our emotions. Emotional recognition to identify facial expression plays crucial role in various applications like psychology, linguistics etc. Playing an important role in the fields of artificial intelligence and robotics, the automatic recognition of facial expression is therefore a need for generation. Emotion recognition plays an important role in the area of human machine communication. Emotional recognition is usually done in four stages which include pre-processing, facial recognition, feature extraction, and classification. In this paper, we have used deep learning to identify the seven main human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality.
Dynamic Converter Capacity Allocation for Multifunctional Energy Storage Systems in Oil and Gas Applications

In this paper, a power management strategy is proposed for enhancing the benefits of energy storage systems to wind powered offshore Oil and Gas platforms. Different functionalities of the converter, such as following a charging schedule from an upper layer control and active power filtering are dynamically assessed, and the converter capacity is optimally allocated among the different services, while preserving the stability of the converter-controllers interactions. To achieve that, a non-linear controller is developed, allowing for full range regulation of the energy storage. To demonstrate the proposed power management strategy, a dynamic model of a platform section is developed and time domain simulations are carried out. The results revealed that load generated reactive, harmonic and unbalanced currents could be successfully compensated, improving the local power quality indices while following the storage charging schedule and respecting the converter limitations under both static and transient conditions, so that the converter cost-benefit relation is improved.
Modeling and Optimization of Semantic Segmentation for Track Bed Foreign Object Based on Attention Mechanism

The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways. To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation. Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image. The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects. To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized. Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category. The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces. The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%. The anomaly detection results on actual lines also verify the effectiveness of the method.
Linear Transformations and Signal Estimation in the Joint Spatial-Slepian Domain

We develop a framework for generalized linear transformations of the joint spatial-Slepian domain representation of signals on the sphere. Such a representation is enabled by the spatial-Slepian transform on the sphere. We formulate a least-square signal estimation framework for reconstruction of the spherical signal from the modified (transformed) spatial-Slepian representation specified by the spatial-Slepian transformation kernel. We specialize the form of the kernel to present analytical expressions for the multiplicative and convolutive transformations, and use the latter to present illustrations on a Mars topography map.
Automated Detection of skin and nail disorders using Convolutional Neural Networks

Dermatological diseases are highly complex to diagnose due to their shared characteristics and subjectivity of human interpretation. Early diagnosis of these diseases helps in getting cured at the right time. Hence, a system has been developed to predict skin and nail disorders in the early stages, when the rate of recovery is high. This, in turn, aids in saving lives and protects a lot of patients from various other health issues. This paper proposes an automated system for the detection of skin and nail disorders by using Convolutional Neural Networks (CNN). The developed model is deployed on a web application named “DermaDoc” to predict these disorders and to aid the users by providing details about the lesion and remedies for temporary relief if available. The diseases considered are Psoriasis, Eczema, Guttate psoriasis, Sebaceous cysts, Paronychia and Yellow nail syndrome. Using various data augmentation techniques and Transfer learning approach on CNN, a higher accuracy of about 92.5% has been successfully achieved.
Performance Improvement and Comparative Analysis of Memristive Emulator Networks

This paper reviews and analyzes a memristive emulator network based on mosfets and accordingly its design and operating parameters are optimized for enhanced performance. The Double Hysteresis Loop phenomenon, a unique feature of memristors/memristive systems is seen in the improved circuit. Parametric analysis for various operating parameters like input voltage, frequency, temperature is performed along with the calculation of average dynamic power and static power requirements of the circuit. A comparative study with another transistor-based memristor circuit is also carried out. The possible applications of this memristive circuit are also proposed in the paper. All the proposed designs and simulation results are performed using Cadence Virtuoso ADE $180\mathrm{n}\mathrm{m}$ technology.