Reviewerly/exHarmony huggingface dataset:
An AI-powered Smart Routing Solution for Payment Systems

In the current era of digitization, online payment systems are attracting considerable interest. Improving the efficiency of a payment system is important since it has a substantial impact on revenues for businesses. A gateway is an integral component of a payment system through which every transaction is routed. In an online payment system, payment processors integrate with these gateways by means of various configurations such as pricing, methods, risk checks, etc. These configurations are called terminals. Each gateway can have multiple terminals associated with it. Routing a payment transaction through the best terminal is crucial to increase the probability of a payment transaction being successful. Machine learning (ML) and artificial intelligence (AI) techniques can be used to accurately predict the best terminals based on their previous performance and various payment-related attributes. We have devised a pipeline consisting of static and dynamic modules. The static module does the initial filtering of the terminals using static rules and a logistic regression model that predicts gateway downtimes. Subsequently, the dynamic module computes a lot of novel features based on success rate, payment attributes, time lag, etc. to model the terminal behaviour accurately. These features are updated using an adaptive time decay rate algorithm in real-time using a feedback loop and passed to a random forest classifier to predict the success probabilities for every terminal. This pipeline is currently in production at Razorpay routing millions of transactions through it in real-time and has given a 4-6% improvement in success rate across all payment methods (credit card, debit card, UPI, net banking). This has made our payment system more resilient to performance drops, which has improved the user experience, instilled more trust in the merchants, and boosted the revenue of the business.
The dream of recapture

Abstract As a response to the semantic and logical paradoxes, theorists often reject some principles of classical logic. However, classical logic is entangled with mathematics, and giving up mathematics is too high a price to pay, even for nonclassical theorists. The so-called recapture theorems come to the rescue. When reasoning with concepts such as truth/class membership/property instantiation, (These are examples of concepts that are taken to satisfy naive rules such as the naive truth schema and naive comprehension, and that therefore are compatible with a solution to paradox cast in the logics considered below. Other notions of similar kind can be added to the list.) if one is interested in consequences of the theory that only contain mathematical vocabulary, nothing is lost by reasoning in the nonclassical framework. This article shows that this claim is highly misleading, if not simply false. Under natural assumptions, some well-established approaches to recapture are incorrect.
Room Light Intensity Control with Temperature Monitoring System Using Arduino

The light intensity controller automatically varies the brightness of an LED light depending on the natural light available in the room. In addition to this is room temperature and humidity monitoring. This system uses an Arduino and PWM technology for controlling the intensity of the LED. LDR is used as the LUX meter. The power losses incurred in PWM switching devices is extremely low. A PWM voltage regulator is built using a LM2596 buck converter which is driven by the PWM signal from the Arduino. By mapping the LDR output values to the PWM signal duty cycle the LED light intensity is varied. In addition to the light intensity controller is a temperature monitoring system, using a DHT11 sensor to measure temperature and humidity. This system is more functional than analog dimmers and timer based light controllers. Through the intended system we aim to reduce power consumption of lights through light dimming using PWM technology. It uses the available resources and is suitable for other light dimming applications as well.
Sentiment Analysis on Mixed-Languages Customer Reviews: A Hybrid Deep Learning Approach

Machine Learning (ML), Natural Language processing (NLP) techniques are used for Sentiment Analysis (SA) problems. Many study results shown predicting more accurate sentiment in mixed language customer reviews are remains challenge. Customer sentiments are classified as positive negative and neutral. Many businesses providing products and services rely on reviews from customers through social media to improve customer experience and increase revenue. Based on our SA research studies and experiments, deep learning neural networks and fastText sequential models are shown better performance and more accurate sentiment prediction in text datasets compare to traditional classification algorithms. In this experiment we build a novel hybrid deep learning SA model using fastText word embedding library and authors multi-layer SAB-LSTM model called fastText-SA-BLSTM. Compare the hybrid model result with traditional linear SVM (LSVM), fastText and authors SAB-LSTM models. For this experiment, data has been collected from online customer reviews and social media posts. This experiment result shows the proposed hybrid model outperforms traditional models and shows more accurate context-based sentiment results. We conclude this paper with more details of our findings and experimental results as well as a proposed future work.
Hunting Phishing Websites Using a Hybrid Fuzzy-Semantic-Visual Approach

Phishing attack is a serious threat to websites, as this type of attack is simple to implement and easy to deploy, but it is fatal in its execution. The amount of phishing attack cases is skyrocketing over years. Therefore, phishing intelligence is vital for web owners to prevent potential phishing campaigns. These data are currently obtained from community-based services, which require manual submission of suspicious sites and manual inspection to determine which one is truly a phishing site. This type of bureaucratic threat detection is ineffective against phishing attacks, as phishing sites usually vanish after a few hours. Therefore, our works focus on automating the phishing website detection process using machine learning approaches. We have developed an automated system to actively search for suspicious websites, then it extracts and analyzes those websites. The system generates suspicious domains from the original domain using fuzzy algorithms. After that, it extracts the screenshots and HTML of the suspicious websites and compares them with the original website, using a specialized foreground extraction algorithm and GloVe embedding respectively. The similarities are used by a machine learning model to conclude whether a suspicious website is a phishing site or not. Good results have been achieved in the experiments, with an accuracy of 99.75%, s precision of 96.77% and a recall of 96%.
Effective attribute selection and classification technique for phishing attacks detection

Wireless communications are fast growing technologies. Many online customers have suffered from phishing attacks while accessing the internet services. If customers access a fraudulent webpage using their user ID and secret code, then their credentials have been threatened by the invaders which may be utilized for suspected activities. Generally, a phishing webpage looks very identical to the respective trusted webpage for attracting the vast number of customers. To identify this kind of attacks, different machine learning-based techniques including Support Vector Machine (SVM), Artificial Neural Network (ANN), etc., have been developed in the past few centuries. However, such techniques cannot realize high detection accuracy while using more data and also its training time was high due to the use of more learning variables. Hence, in this article, an effective attribute selection and classification technique is proposed for detecting the phishing attacks in wireless networks. Initially, the email data is collected which comprises more attributes to be extracted by the attribute extractor. Then, the Elephant Herding Optimization (EHO) algorithm is applied to choose the most relevant attributes among the all extracted attributes related to the email. Moreover, the chosen attributes are fed to the Deep Convolutional Neural Network (DCNN)-based classifier for identifying the phishing and authentic emails. Finally, the outcomes show that EHO-DCNN technique achieves a better detection performance when compared to previous techniques.
Hierarchical Path Planning and Obstacle Avoidance Control for Unmanned Surface Vehicle

It is necessary to plan a feasible path and avoid moving obstacles in real time, while the unmanned surface vehicle (USV) performs tasks in the archipelago environment. This paper focuses on the obstacle avoidance of USV in dynamic environment. A hierarchical framework is proposed for the underactuated dynamic system of USV. Devolution of the dynamic obstacle avoidance from planner to controller is more effective. Rapidly-exploring Random Trees Star is used as a global planner to search for a feasible path, combined with a reactive approach to modulate the control input to avoid moving obstacles in real time. The feasibility of the proposed method is verified by the numerical simulation, which can improve the real-time performance of dynamic obstacle avoidance. Different from the replanning, we adjust the controller to avoid local collision, while adopt the planner to generate a global path.
A Novel Oversampling Technique for Imbalanced Learning Based on SMOTE and Genetic Algorithm

Learning from imbalanced datasets is a challenge in machine learning, oversampling is an effective method to solve the problem of class imbalance, owing to its easy-to-go capability of achieving the balance by synthesizing new samples. However several problems still exist such as noise samples, selection of boundary samples and the diversity of synthetic samples. To solve these problems, this paper proposes a new improved oversampling method based on SMOTE and genetic algorithm (GA-SMOTE). The main steps of GA-SMOTE are as follows. Firstly GA-SMOTE uses genetic algorithm to find an optimal noise processing scheme. Then GA-SMOTE assigns different sampling weight to each sample and the sample closer to the boundary is assigned greater weight. Finally, GA-SMOTE divides raw dataset into multiple sub-clusters by K-means clustering and intra-cluster neighborhood triangular sampling method is used in each sub-cluster to improve the diversity of synthetic samples. A large number of experiments have proved that GA-SMOTE is superior to the other five comparison methods in dealing with imbalanced data classification.
On the Security and Safety of AbU Systems

Attribute-based memory updates (AbU in short) is an interaction mechanism recently introduced for adapting the Event-Condition-Action (ECA) programming paradigm to distributed systems, particularly suited for the IoT. It can be seen as a memory-based counterpart of attribute-based communication, keeping the simplicity of ECA rules.
EFCA: An Extended Formal Concept Analysis Method for Aspect Extraction in Healthcare Informatics

With the popularity of social media platforms, patients tend to share their experiences and opinions on them, and patient feedback is key to improving health services. Sentiment analysis techniques have been applied to automatically analyze the patients’ opinions to understand the quality of healthcare. Aspect extraction, which aims to identify the opinion targets in the text, is an important step towards understanding the patient’s opinion towards particular target or entity. However, due to the complex nature of medical domain data, existing approaches take much execution time. To address this, we presents a new approach for aspect extraction and refinement to smooth the sentiment analysis process. Furthermore, this work also introduces an intelligent weighting scheme for classifying the final aspects. For the experimental evaluation, a dataset from Yelp and RateMDs has been utilized. Experimental results show that the proposed model outperforms existing methods.
Data-Aware Storage Tiering for Deep Learning

DNN models trained with very large datasets can perform rich deep learning tasks with high accuracy. However, feeding huge volumes of training data exerts significant pressure on IO subsystems as the entire data is re-loaded in random order on every iteration to enable convergence, with very little scope for reuse. To address this challenge, we co-optimize data tiering and iteration in DNN training for any given dataset and model with bandwidth and convergence conscious mini-epoch training (MET). This approach can substantially reduce the IO bandwidth required to provide sustained read throughput matching the processing speed of accelerators. Further, we introduce two different feedback mechanisms to adjust the repeating factor over each mini-epoch during the training. We have evaluated three different applications with MET. Most of them work outof-box with modest MET parameters. The adaptive repeating factor design was able to gain back most of the accuracy drop due lo large MET parameters.
InterHG: an Interpretable and Accurate Model for Hypothesis Generation

Hypothesis generation, which tries to identify implicit associations between two concepts, has attracted much attention due to its ability of linking key concepts scattered in different articles and enriching plausible new hypotheses. Among existing approaches for hypothesis generation, matrix factorization based methods have achieved start-of-the-art performance. However, matrix factorization based methods suffer from the following limitations: 1) Bridge concepts are determined only as a post-hoc analysis of matrix factorization results; 2) The embeddings of concepts by matrix factorization cannot be explained, and thus it is hard to understand whether the concepts are linked in a semantically meaningful way. To overcome these limitations, we propose an interpretable and accurate hypothesis generation model (InterHG), which improves both accuracy and interpretability compared with existing methods. First, we propose to explicitly model the relationship between bridge concepts and given concept pairs, and conduct tensor factorization to identify link concepts. This reduces information loss and improves accuracy compared with post-hoc approaches. Second, we leverage the description of categories in the tensor factorization, which can output concept embedding as a weighted combination of known categories. With this meaningful embedding representation, medical researchers are able to check the correctness of the suggested link concepts for a given concept pair. We conduct experiments based on MeSH terms (a controlled vocabulary of biomedical concepts) extracted from MEDLINE corpus and category information obtained from UMLS (a comprehensive biomedical concept database). Results demonstrate that the proposed InterHG is highly accurate and produces meaningful embeddings for explanations.
Intelligent CCTV Footage Analysis with Sound Source Separation, Object Detection and Super Resolution

CCTV cameras are found everywhere nowadays and are used to monitor, secure, and protect your property, or at the very least serves as intelligent CCTV footage analysis with sound source separation, object detection and super resolution. However, according to recent statistics, 80% of CCTV footage is discarded in the case of an investigation and is deemed uninformative. The reason being the grainy and low-quality video feed from CCTV cameras. Nonetheless, people thought about it and created video processing software or forensic tools that can improve the quality of the footage. Despite this, the latter are usually expensive or are only available to the authorities. Here developed is an open-source solution that is cross-platform and offers a seamless user interface for your average consumer. The application uses super-resolution to enhance image quality, object detection using YOLO v3, and sound extraction. Using actual CCTV footage as an example, the overall quality and output satisfying results for every functionality.
Toward an Automated Pipeline for a Browser-Based, City-Scale Mobile 4D VR Application Based on Historical Images

The process for automatically creating 3D city models from contemporary photographs and visualizing them on mobile devices is well established. 4D city models that can display a temporal dimension are far more complex to generate automatically. In this article, we focus on major challenges in the process of developing an automated pipeline, starting from content-based image retrieval applied to historical images, via automatic historical image orientation, up to visualization of the 4D data in Virtual Reality (VR). The result is an interactive browser-based device-rendered 4D visualization and information system for mobile devices. This pipeline has been in development since 2015. In this article, we present initial results and early-stage findings in the process of overcoming three major challenges on the way to 4D city models: (1) to identify photographs with corresponding views, (2) to reconstruct the position and orientation of photographs and (3) to design a user-centered, browser-based 4D mobile application.
Open Challenges of Communication Security in an IoT Environment—A Survey

Internet of Things (IoT) is a continuously growing concept in ICT concerning the progression of ubiquitous computing, machine-to-machine (M2M) communication, and wireless sensor networks. IoT is the network of physical objects such as watches and bicycles integrated with electronic equipment, sensors, and software. These devices gather and exchange data using network connectivity. However, with such an enormous number of IoT device connections on the network, the security of the data and communication become a major concern. This paper gives an overview of the IoT and the associated limitations and challenges of applying security in IoT devices and the classification of IoT attacks. Furthermore, the paper also discussed the security issues at different layers. Moreover, it demonstrates some identified solutions to the existing challenges and provides future direction to tackle the challenges present in IoT security.
A 40nm 33.6Tops/W 8T-SRAM Computing-in-Memory Macro with DAC-less Spike-Pulse-Truncation Input and ADC-less Charge-Reservoir-Integrate-Counter Output

This paper presents a DAC-less and ADC-less 8T-SRAM CIM macro, which converts digital input-activation into spike pulse sequence based on a novel DAC-less pulse truncation unit and employs an ADC-less charge-reservoir-integrate counter for result output. Our design can significantly reduce area and energy overhead of the DAC and ADC circuits. Further, a system-level variation-tolerant quantization/training method by exploiting the hardware features was proposed to guarantee computing accuracy. A 4-Kb 8T-SRAM CIM macro with 4-bit input, 4-bit weight and 4-bit output was fabricated using a foundry’s 40-nm CMOS process. Our test results show that the chip achieves energy efficiency of 33.6 Tops/W under 4-bit input and 4-bit weight precision, supporting the connection of intelligent things with high computing efficiency and low energy.
Augmented reality-based border management

Abstract A proficient border management has typically been tied to its capability to support information structuring and to make exchanges from the distributed sources. The lack of a proper access interface to information at the right time and the right place to conduct various activities. Augmented reality (AR) has been proposed as an efficient interface in order to improve the efficiency and the effectiveness of activities in the real world. There has only been a limited amount of research that has evaluated the effectiveness and usability of AR in the border management domain. This research aims to evaluate the effectiveness of border management and AR system integration to enhance the activities’ efficiency through improving the information retrieval process. The system development steps were adopted to design, develop, and evaluate the border management AR (BM AR) system. The system contains three AR services that include pointing to border objects, showing the borderline, and locating border objects. The system also integrates the information from different resources in an interoperable way using GIS web services. The results revealed the effectiveness of using AR for border activities, which can reduce the operating costs and effectively, access the required information for doing different activities in the border field.
A Data Lightweight Scheme for Parallel Proof of Vote Consensus

Each blockchain’s node needs to store a backup of all blocks, resulting in the whole network needs O(n) storage space, which greatly affects the nodes’ scalability. Parallel proof of vote(PPoV) is a permissioned blockchain algorithm that uses block groups as the basic data structures. This paper proposes a data lightweight scheme for PPoV. In the bock group generation stage, the BLS algorithm is used to realize signature aggregation, and the storage space of block signature and vote signature is reduced from O(n) to O(1) without affecting the performance. In the storage stage, we use erasure code to implement storage partition for block groups and ensure the real-time recoverability of complete data. To speed up reading, the timeline-based model applies different storage strategies for hot and cold data. The experimental results show that the empty block group generated by BLS aggregation signature is smaller and less sensitive to the number of nodes. And the reduction ratio of storage space under a large number of transactions can be similar to the number of normal nodes.
Evaluation of Deep Learning Approaches for Anomaly Detection

Deep learning is a machine learning technique which is inspired by basic human instincts and functionality of the brain. It can be leveraged to tackle anomaly detection problems due to their ability in performing complex learning and prediction. However, this has been challenging due to the diversity of anomalies, class imbalance and curse of dimensionality. This research study focused on analyzing the performance of deep learning models for anomaly detection in various domains. Multi-Layer Perceptron, Deep Neural Network, Recurrent Neural Network and Auto Encoder algorithms were tested on 7 numerical datasets ranging from small scale to large scale in terms of both data size and features. The experimental design used one class classification to train the models from non-anomalous data to identify new instances as either anomalous or non-anomalous. The experimental results indicate that deep learning algorithms improve performance with the increase of data size. This study also identified certain limitations of deep learning models on anomaly detection.
Trajectory planning in the presence of dynamic obstacles for Anguilliform-inspired robots

This paper presents an optimal trajectory replanning approach for anguilliform-inspired robots in a dynamic environment based on the model predictive planning framework. The dynamic constraints were captured via simulations and were expressed as motion primitives. The developed approach utilizes the motion primitives for generating search trees and a replanning algorithm based on D-star lite. Along with the position, the velocity of obstacles was also used to predict collision. After the way-point generation, line-of-sight guidance was used for trajectory tracking. The trajectory generated by the above approach was found to be dynamically feasible, collision-free and optimal. This approach can help not only anguilliform-inspired robots but other bio-inspired robots to plan in a dynamic environment where they can interact with other creatures and complete tasks without colliding with them. It is envisaged that the developed approach can help in imparting autonomy to fish-inspired robots which can find applications in the applications like underwater rig maintenance, surveillance, and stealth operations for the military.