Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate and Real Time Assisted Cataloging in E-commerce using Dual Images

Product classification is an essential first step in e-commerce starting from cataloging. It impacts various downstream processes such as: surfacing relevant attribute lists to capture product information, recommending right categories for sellers to list their product in(seller support), deciding on what quality control steps a product has to go through and so on. We identify the challenges involved in fashion product classification and show how dual-image based approach addresses them. We demonstrate this using large scale, real-world Fashion/Lifestyle data from Flipkart - India’s largest e-commerce company. This dataset contains a mix of well known western concepts of fashion such as top, t-shirt, blazer and India specific fashion concepts such as sari, lehenga choli, kurta etc. We show that a dual-image based classifier gives 6% improvement in f1-score compared to a single-image based classifier. We show how this is helping sellers to list their products in Flipkart. Cataloging is the first step before selling the product online. Very first step in cataloging is to select appropriate product category. Flipkart, like all other e-commerce companies has its own categorisation of products which are known as verticals. A vertical construct is defined as a collection of attributes that define a product range. Eg. of verticals are ‘t-shirt’, ‘watch’, ‘bag’, etc. Since, there is no universal standard on how to group products into categories across various business avenues (both online and offline), the sellers get confused when selecting ’verticals’ for a given product. We demonstrate the how dual image model product classification is used to address this problem. This system is in production and serving Flipkart sellers. It improved right first time (RFT) of listing by 2%.
Evaluation of Ability of Chaotic Resonance under Noises in Neural Systems Comprising Excitatory–Inhibitory Neurons

Recent studies on stochastic resonance have been considered in various fields for engineering applications. Chaotic dynamics derive a phenomenon called chaotic resonance, which is similar to stochastic resonance. The engineering applications of chaotic resonance are limited due to its controlling difficulty, although it exhibits a high sensitivity to signal responses. To address this limitation, we previously proposed a "reduced region of orbit" (RRO) feedback method which induces chaotic resonance using external feedback signals. This method was evaluated under noise-free conditions. However, in practical scenarios, background noise and measurement error are observed when estimating the RRO feedback strength. The influence of these factors on chaotic resonance must be evaluated for preliminary practical application of chaotic resonance. Therefore, in this study, we evaluated chaotic resonance induced by the RRO feedback method in chaotic neural systems under stochastic noise. We focus on chaotic resonance induced by RRO feedback signals in a discrete neural system comprising excitatory and inhibitory neurons, which are typical neural systems that entail chaotic resonance under additive noise and feedback signals, including measurement errors (called contaminant noise). Although both types of noise commonly degrade the degree of synchronization of chaotic resonance induced by the RRO feedback strength, their characteristics are considerably different. In actual neural systems, the influence of noise is inevitable; therefore, this study highlighted the importance of noise countermeasures during the application of chaotic resonance.
IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85% and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist’s guidelines to validate the correctness and usability.
Transformer-XL With Graph Neural Network for Source Code Summarization

Source code summarization is the task of generating a readable natural language to describe the functionality of source code. Code summarization is rapidly expanding, especially as the research takes great advantage of advances in neural networks and artificial intelligence technologies. Some mainstream methods input the structural information (abstract syntax tree (AST)) of the source code into the language model to generate relatively satisfactory comments. However, existing methods can not capture code’s long dependencies from AST for effective code summarization. In this paper, we provide a novel way to generate code summaries by combining a graph-based neural network and a Transformer-XL network. We utilize the graph-based neural network to better capture the structure information of AST, and the Transformer-XL network to learn important tokens in the AST and alleviate the problem of long dependency. We evaluate our technique on the standard Java dataset. The experimental results show that the effectiveness of our model is remarkable. It pushes the precision score to 60.73% (5.21% absolute improvement) and the F1 score to 51.06%.
Frequency band selection for a lower-limb MI BCI to control a treadmill

Motor imagery (MI) is defined as the process of imaging the execution of a movement. This brain task has been used as a control paradigm for brain-computer interfaces (BCI). A BCI has the objective to decode brain patterns and translate them into commands to provide a communication with output devices. In this work, three different approaches have been compared for the design of a lower-limb MI BCI that will control the activation/deactivation of a treadmill: features from alpha, beta and lower gamma frequency band. The average accuracy for training trials was 71.43 ±10.62%. and the average accuracy for test trials was 70.40 ±11.09%.
An N-of-1 Investigation into Stress-Related Hemodynamics in the Prefrontal Cortex During the First Sleep Cycle*

It is well-known that stress affects sleep quality, suggesting abnormal brain activity during sleep when people are stressed. However, no study has examined where brain processes stress during sleep. This study aims to explore the associations between bedtime stress and the hemodynamics in the prefrontal cortex (PFC) during the first sleep cycle under free-living conditions. Stress biomarkers including salivary cortisol and secretory immunoglobulin A (sIgA) were measured using the SOMA Dual Analyte LFD test kits on the experiment nights between 22:00-23:00 to control the circadian oscillation of the stress-related hormones. Perceived stress level was rated on a 1-10 Likert scale right after the collection of the salivary samples. The hemodynamics of the pre-frontal cortex (PFC) was measured using a wearable functional near-infrared spectroscopy (fNIRS) device. Correlation analysis with statistical test was performed to examine the associations between different stress indicators and a set of time-domain and frequency-domain features derived from the hemodynamic responses. Significantly positive linear correlations were observed in the standard deviation, skewness, and kurtosis between the average concentration change of oxyhemoglobin and that of deoxyhemoglobin in the whole region of interest. Stress was found to correlate to the hemodynamics in the mid-DLPFC, the caudal-DLPFC, and the left RLPFC. The relationships between stress and these PFC subregions depends on the stress indicator adopted. Our finding provides supplementary support to the role of the PFC in processing stress. The preliminary results also shed light on the development of stress response markers in brain activity that can be measured with wearable brain-computer interface technologies.
Semi-Active Assistive Exoskeleton System for Elbow Joint

Assistive wearable exoskeleton can help to restore respective muscle functions of people with disabilities and provide support to workers within strenuous activities. The design of a practical exoskeleton should be user-friendly, compact, lightweight without any obtrusive effect on the user, which justifies the execution of optimisation steps for the design of the links and configuration of the system. Most exoskeletons struggle to accommodate a slight variation in functionality from their original design. This work addresses the design and development of a light-weight semi-active elbow exoskeleton. The main objectives consist of the development of a semi-active actuation mechanism with an adaptive exoskeleton frame and harness to accommodate varying user anthropometrics. The effectiveness of the proposed exoskeleton design has been evaluated through simulation. Furthermore, the system has been implemented in order to determine the physical feasibility and practicality through rapid fabrication prototyping. The outcomes of the study show that the exoskeleton can adjust to varying anthropometrics and assist elbow flexion, reducing stress and fatigue on the human elbow joint and muscles induced by gravity during flexion.
Customer Behavior Analysis and Classification Based on Process Mining

This paper proposes a novel approach of in-store route data obtained by RFID for customer segmentation based on process mining (PM). The PM technique can dig out the hidden information of customers' in-store shopping route deeply. Firstly, we use PM to customize and decompose consumers' in-store movement paths. Then, we divide the fish area into patterns, and define these patterns as passing area, staying area and entering area respectively. Finally, we decompose the customer’s shopping path into several sub-processes according to the customer’s moving sequences obtained by RFID technology. In the experiment, we use the K-mediods algorithm based on Levenshtein distance to classify and analyze the characteristics of consumer moving path process logs, and investigate correlation analysis of different types of consumers obtained by analyzing the consumers' in-store movement paths through PM technology will be carried out.
SceneRecog: A Deep Learning Scene Recognition Model for Assisting Blind and Visually Impaired Navigate using Smartphones

Deep learning models have recently gained popularity in the research community due to their high classification success rates. In this paper, we proposed an EfficientNet-Lite based scene recognition model for scene recognition as a part of the smartphone-based navigation support application for the blind and visually impaired. We created a custom dataset with both indoor and outdoor scenes for training and testing of the model. The main objective of this work is to support people with visual impairments navigate by providing information about the scene via a smartphone application. The results from the experiment show encouraging performance from the proposed model. As a proof of concept, a prototype app was developed on the Android platform. However, the model can be implemented and deployed in any modern smartphone with good processing power.
Adaptive impedance control of a 6-DOF cable-driven compliant upper limb rehabilitation robot

Although many studies have certified the advantages of human-robot cooperation under impedance control, its robustness and adaptive ability need to be further improved especially for compliant rehabilitation robots. This paper proposes an adaptive sliding mode impedance control (ASMIC) algorithm for a 6-DOF compliant upper limb rehabilitation robot (ULRR) driven by series elastic actuators (SEAs). A friction model is built to compensate for the friction between the cable and the shell. Considering the variation of human states, this adaptive impedance controller can adapt to the changing parameters of the robot’s dynamic model. Experiments have been conducted to validate the effectiveness of the controller on the elbow joint of the robotic system. The results indicate that the controller can reduce impedance error compared to PD impedance control (PDIC) controller.
GCFI++: Embedding and Frequent Itemset based Incremental Hierarchical Clustering with Labels and Outliers

Customer support is an essential part of any company to ensure customer satisfaction and business growth. The responsibility and scale get even higher for enterprise companies like Freshworks that build customer support platforms for its enterprise customers from multiple and previously unseen domains. A ticket may carry issues never seen before, therefore static, and predefined problem labels will not always work. These predefined problem labels are often created manually by admins. For a large customer account, roughly 10-35% of the problem labels could get discovered by this manual process and only 5-9% of the available open tickets could get linked to a problem label. So, there is a need for a system for a new incoming ticket. The system should either automatically detect an existing problem topic with its possible suggestions or systematically generate new problem topic(s) for its assignment. Our system is based on an incremental approach to mine ticket data streams and assigns them to hierarchical soft clusters with auto-generated problem/issue topics. Our system achieves an improvement in ticket assignments up to 97%. To compare with recent clustering advancements, we’ve also evaluated our approach on a public dataset [9] and achieved comparable results. Source code and results of the evaluation are available in detail at: https://github.com/vinidixit/hierarchical-labelled-clustering-evaluation.
SciPred: An end to end approach to classify predatory journals

In today’s ruthless academic world, researchers are constantly under pressure to publish work. This has led to significant growth in the number of predatory publishers. With a questionable peer-review process, these publishing houses charge high processing fees in exchange for publishing articles. Given the heterogeneity in academia, identifying such articles is a herculean task. While conventional approaches use metadata of articles, recently an attempt has also been made to use the text itself to filter out such articles. We propose an end-to-end neural network-based pipeline to classify predatory journals. We use a higher dimensional SciBERT model and fine-tune it to the dataset. We also augment the dataset by data from other similar journals.
Application of Monte-Carlo method in the reliability calculation of bus travel time

Making use of MonteCarlo stochastic analogy method,the reliability of bus travel time for one line is calculated,the reliability model of bus travel time is founded and the service time level for bus line is evaluated.The result is valuable for analyzing time reliability of bus lines,which have fixed schedule under the condition of providing advanced public transportation system.
Adopting Some Arresters to Suppress Switching Overvoltage Deeply in 1000kV UHV Transmission Line

The idea of adopting some arresters installed along line to suppress switching overvoltage deeply in UHV is realized in this paper. Based on the UHV transmission line project, effects of arrestor’s number and position on switching overvoltages, including three phases energizing line with no load, single phase auto-reclosing and overvoltage caused by fault are calculated by using the electromagnetic transient program PSCAD/EMTDC. The calculation shows that in the case without any arresters installed along line, the maximal overvoltage of three phases energizing line with no load, single phase auto-reclosing and overvoltage caused by fault are 1.83, 1.83 and 1.53 p.u. respectively. When arresters are applied along line, the three overvoltage can be suppressed to 1.50, even lower than 1.50 pu, which means arresters along line is a good measure to suppress switching overvoltage in UHV. Besides amplitude of overvoltage, another main factor considered in this paper is the energy passing arresters. Calculation results show in three ovevoltages above, maximal energy passing single arrester is 2107.94 kJ, 866.43 kJ and 17.32 kJ respectively. And maximal total energy passing arrestors of one phase is 3534.67 kJ, 1215.61 kJ and 86.06 kJ respectively. By comparing the results, it is found that more arresters used along line, lower the overvoltage is, different position where arresters are placed effects the result of suppressing, and the maximal difference of overvoltage caused by position is 0.07 p.u. Calculation of the energy shows that total energy passing arrestors of one phase generally increases with the arrestor's number increasing, but for any single arrestor, the energy decreases. In the case of single phase auto-reclosing, the energy decreases from 866.43 kJ to 337.68 kJ when the number of arrester along line increases from 1 to 4. In the case of three phases energizing line with no load, energy decreases from 2107.94 to 958.67 while arrester’s number increases from 1 to 4.
An intelligent monitoring system for high-speed milling process

To avoid the pre-designed " teaching" or " training" phase for the condition monitoring system at present,an intelligent monitoring system for high-speed milling process is proposed based on the analysis of the tool wear rule and different tool wear stages.Self-learning was introduced to the system to automatically identify different tool wear states and estimate the wear value,which is independent of the pre-designed " teaching" or " training" phase.Three-direction components of the cutting force signals generated in high-speed milling process were processed using discrete wavelet decomposition technology.Features in different time and frequency domains were extracted and selected through correlation analysis method.The real-time intelligent monitoring system was built on the cycle process of linear fitting and Mahalanobis distance(MD) calculation.A series of experiments on a CNC vertical milling machine tool shows that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools.
Development of Adaptive Soft-Sensors Based on Kalman-Elman Neural Network with Applications in Wastewater Treatment

Soft-sensors are usually used for difficult-to-measure variables prediction in wastewater treatment. Nowadays, neural networks, especially the dynamic neural networks, are widely used to predict and monitor these variables. However, traditional training methods of dynamic neural network cannot fully consider the uncertainties of measurements and model, resulting in inaccurate estimations of parameters and deviation of prediction performance. Therefore, this paper proposes a novel adaptive multi-output soft-sensor. In the proposed method, the square root unscented Kalman filter (SR-UKF) is adopted to timely update the weights of neural network in each layer. Through this strategy, we are able to effectively improve the prediction accuracy of the standard neural networks. What's more, providing a new way to update the parameters online as the sequential property of Kalman filter. The proposed method is verified by a data set from University of California database (UCI database). The results illustrated that the proposed model can achieve better prediction accuracy compared with the traditional models.
Application of neural network to back analysis of mechanical parameters and initial stress field of rock masses

At present,BP neural network has been widely used in back analysis of material parameters and initial stress field of rock masses in geomechanics.However,BP neural network is prone to over-being-trained,slow in convergence,not global minimum but local ones obtained and number of neurons in hidden layer hard to be determined.Authors using RBF neural network and BP neural network respectively identified mechanical parameters and initial stresses according to measured normal stresses of some specific points.Direct computations based on fast Lagrangian analysis of continuum(FLAC) were performed to get enough training samples for RBF neural network and BP neural network.An example shows that combination of RBF neural network with FLAC is more effective and rapid than application of BP neural network.
Coupling effects of LEMP on aerial multiconductor power lines

Based on the transmission line model,this paper proposes a method that integrates the lightning return stroke channel,the earth and the overhead lines together for calculating the lightninginduced voltages on overhead lines.The finite-difference time-domain(FDTD) method is used to obtain the electromagnetic field values near a cloud-to-ground lightning return stroke channel,and then these field values are put into the discretized transmission line equations to calculate the induced voltage at the end of overhead lines.Analyses show that the induced voltage on one conductor of the multi-conductor loaded lines is affected by the presence of other lines,and the voltages induced on each of the line conductors are generally 10%～20% lower than those corresponding to a single conductor located at the same height.The minimum induced voltage is generated on the line conductor nearest to the ground for vertical configuration of the three lines,while on the middle line conductor for horizontal configration of the three lines.
AN EFFICIENT MODEL TO DETECT SOCIAL NETWORK MENTAL DISORDERS USING MACHINE LEARNING TECHNIQUES

Today, social network users around the world are rising significantly. This forum is useful for exchanging information, debating different topics, and even much of the time they spend on social media such as Twitter, Facebook, etc.Because of these physical human relationships, website users are reliant on regular Facebook updates, Twitter, etc.The latest studies indicate a relationship between mental health and the actions of the social network and how mental illness and social networks respond to each other is still unclear.This article attempts to use the data analysis of social network research to find a pattern for mental disorders without consulting the patient by using a Naïve Bayes classifier algorithm.