Reviewerly/exHarmony huggingface dataset:
Exploring the Point Feature Relation on Point Cloud for Multi-View Stereo

Learning-based multi-view stereo (MVS) is gaining prominence as a method for 3D reconstruction. However, existing methods in the process of feature learning fail to focus on the structural information implied in the scene. This oversight prevents the network from perceiving the geometric properties of the scene and weakens the generalizability of the network. Therefore, we propose a novel framework named Point Feature Relation Network for Multi-view Stereo (PFR-MVSNet), which is composed of a Dynamic Structure Perception (DSP) module, an Adaptive Feature Exploration (AFE) module, and a Point Transformer Block (PTB) module, to solve the problems caused by the oversight. The DSP module first augments the feature of the 3D point cloud from multi-view 2D features, then establishes spatial structure relations within local regions on the point cloud and guides the feature learning of points through the aggregated structure information. After the network has fully learned the intra-region structure features, the AFE module repartitions perception regions with similar features. The point features within the regions are further learned by the PTB module. We evaluate our method on three benchmark datasets: DTU, Tanks & Temples, and ETH3D. The experimental results show that our method achieves superior accuracy of 0.289 mm on the DTU dataset and exhibits more robust generalization on the Tanks & Temples and ETH3D datasets compared with other learning-based MVS methods.
Deep-Learning based Reputation Model for Indirect Trust Management

In the digital era, human and thing behavioral patterns have been merged, which leads to the need for trust management to secure the relationship among people and things (e.g., driverless cars). Due to the dynamism and complexity of digital environments, trust management depends largely on indirect trust to support its reasoning by building the reputation of trustees based on recommendations reflected in the feedback of sentiment and non-sentiment objects. However, different biases are still affecting the accuracy of indirect trust that reflects a collective trustworthiness belief or societal stereotypes. This work focuses on enabling indirect trust management by leveraging deep learning in combination with synthetic data for bias management. Specifically, this paper proposes a reputation model to support decision-making in trust management by minimizing bias in indirect trust information and fostering fairly the relationship among sentiment and non-sentiment objects. Our experimental results show that the synthetic data can significantly improve the classification accuracy in trust management.
SaaSC: Toward Pay-as-You-Go Mode for Software Service Transactions Based on Blockchain's Smart Legal Contracts

Usage-based pricing or Pay-as-You-Go is a relatively new SaaS business model that may provide customers the option to pay for only what they use. Yet, it is more challenging to implement than traditional Pay-before-Use subscriptions considering that it need not only realize financial payment on consumption-based behaviors, but also regulate the rights and obligations among the providers, consumers, and platforms in a legal form. To address these challenges, in this article Smart Legal Contract (SLC) is integrated into a service computing platform by introducing a new architecture, called Service as a Smart Contract (SaaSC). On the aspect of service legalization, through combining SaaS and SaaSC, we establish three kinds of terms in SLC-based software subscription contract to support service's registration, discovery and customization, so that a complete transaction procedure can be regulated in terms of service states, transaction process, and interactions among contracting parties. On the aspect of service financialization, we propose a new scheme of service interface declaration in the SLC-based SPESC contract. By automatically executing smart contracts and checking the terms, the pay-as-you-go billing form can be made fine-grained payment after using service interface calls. Moreover, we take weather forecast service as a case to implement and analyze the acquisition, delivery, and contractual payment of software service on Blockchain smart contract. The experimental results demonstrate the feasibility and effectiveness of the proposed SaaS+SaaSC architecture so that it provides a practicable approach for contractual software service.
A Strategy-Oriented Bayesian Soft Actor-Critic Model

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks – Hopper-v2, Walker2d-v2, and Humanoid-v2 – in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.
Job Recommendation Based on Recurrent Neural Network Approach

Employers couldn't find relevant candidates without great effort, and job seekers must spend a long time researching the right jobs. To fix this issue, recommender systems are an effective way to make talent and job research more effective and easier to complete. It is possible to analyze job offers and candidate profiles and make pertinent recommendations using techniques like the Recurrent Neural Network (RNN) approach. To accomplish this, we propose a model based on the classification of job profiles using a variant of RNN called LSTM ("Long short-term memory"). It is a long-term memory model based on a deep learning algorithm. To maintain the semantic meaning, first, the input of the LSTM model is transformed into a set of vectors using the doc2Vec method, which transforms a document into a vector of numbers.
Ensure the generation and processing of inventory transactions through a web application for ground freight transportation equipment

As its general objective, the present investigation had to ensure the generation and processing of inventory transactions, along with the operating procedures that will enable its processing. The software was developed in the form of a responsive web application. The organization didn't have an operational model of systematized inventory transactions, and this is what is proposed in this investigation. The methodology for the development of the software product is the "Boehm's Spiral" model since it fits perfectly with the context of the project. For this project four cycles were identified:1) User administration module. 2) Request generation module: Requests Related to a work order. Requests not related to a work order. Request without consumption voucher. 3) View entered request. 4) Processing Module requests: Requests related to a work order. Requests not related to a work order. Request without consumption voucher. The final purpose was the development of a responsive application to facilitate the processing of inventory transactions.
Categorizing TLS traffic based on JA3 pre-hash values

The JA3 algorithm for fingerprinting TLS client traffic has become a popular additional tool in the tool set of network security professionals. The pre-hash value of the JA3 fingerprint lists parameter values from the TLS handshake supported by the TLS client. In this paper we present two different machine learning methods for identifying the endpoint application from TLS traffic based on the JA3 pre-hash string. Both methods were able to identify applications from Mozilla in our sample set, but had more variation with other applications. The methods can be used for improving network security accuracy.
FPGA Implementation of High Performance Hybrid Encryption Standard

Now a day's data hacking is the main issue for cloud computing, protecting a data there are so many methods in that one most usable method is the data Encryption. Process of Encryption is the converting a data into an un readable form using encryption key, encoded version that can only be read with authorized access to the decryption key. This paper presenting a simple, energy and area efficient method for endurance issue in secure resistive main memories. In this method, by employing the random characteristics of the encrypted data encoded by the Advanced Encryption Standard (AES) as well as a rotational shift operation. Random Shifter is simple hardware implementation and energy efficient method. It is considerably smaller than that of other recently proposed methods. Random Shifter technique used for secure memory with other error correction methods. Due to their reprogram ability, Field Programmable Gate Arrays (FPGA) are a popular choice for the hardware implementation of cryptographic algorithms. The proposed random shifter algorithm for AES and DES (Hybrid) data is implemented in the VIRTEX FPGA and it is efficient and suitable for hardware-critical applications. This Paper is implemented using model sim and Xilinx 14.5 version.
Facial Expression Recognition of Unborn babies by Lip Detection

Classification of unborn baby facial expressions is done to recognized mood of the fetus. The emotions of unborn baby facial actions is a progressive process related to brain growth. From the 4D scans of unborn baby, showed that they develop expression at 24 weeks by making very simple one-dimensional emotion, such as moving their lips to complex emotions such as “pain” expressions. Proposed work created the landmark points of all images and then it is possible to compare the fetal images with landmarks. Fetal images of various expressions created by landmark points of face considering eyes, nose and lips corner points by using matlab 15. Propose work used dark channel prior algorithm for fetal image enhancement, a multi-layered feature extraction system using Local Feature Detectors and Descriptors like Harris key-points, Speed up Robust Features (SURF) and compared result by Gabor features extraction method. AI is used to classify the mood of the fetus. Also the relation between fetus age and expressions are studied, which can be used to give another index of the health indicator of a baby in womb to the doctors.
Detection of Malware in the Network Using Machine Learning Techniques

The increased use of the internet has presented the most significant challenge to the digital world's security. Malicious URLs are the most common way for people to engage in phishing schemes and spread viruses like trojans and worms. Various dangerous URLs attempt to obtain user information by distributing different harmful malware. End-users can be susceptible if a genuine user is unable to recognize and remove fraudulent URLs. Attackers can potentially acquire unauthorized access to user data by using malicious URLs. As a result, identifying countermeasures to halt such operations using new and advanced technology is a critical step. One of its most effective methods has been thought to be based on ML approaches for correctly identifying a URL as malicious or benign. The project's main goal is to deploy the system in a way that is both efficient and accuracy.
Two-Layer Secure Mechanism for Electronic Transactions

E-commerce applications allow merchants to sell the good and consumer to purchase the variety of products. Electronic transactions (e-transaction) used in e-commerce sites currently send the encrypted bank account information in text format using Secure Electronic Transaction (SET). The existing payment transaction contains encrypted information of the bank card information, the security code information, and the order information in the form of text. Quick Response code (QR code) is an image which hides the information in a 2-dimensional (2D) barcode like in steganography technique. This information can be about the product, website, payment, tracker id that links to an application. The static QR code contains only one fixed data which can be hacked in cyber-attacks. Dynamic QR code contains the unique code for the transaction created on the order creation. This paper proposes a secured two-layer mechanism for e-transactions. It proposes to use dynamic QR code as a payload for the payment transaction. The first layer is to enclose the payment information in dynamic QR code. The dynamic QR code is created with the bank information, user information, secure code and order information which is unique for an order. Second layer of security is encryption of this QR code using Secure Electronic Transaction (SET) transactions to secure the payment transaction. The receiver decrypts using the unique public key the payment information which is a 2-D QR code. This image data is decrypted to retrieve the bank information, code, user information and order information. Dynamic unique code per order which would enhance the security to reduce the vulnerability to the cyber-attacks. The two-layer secure payment transactions enhance the security two times by using dynamic QR code image as payment transaction.
LaFea: Learning Latent Representation Beyond Feature for Universal Domain Adaptation

Universal Domain Adaptation (UniDA) is a recent advent problem that aims to transfer the knowledge from the source domain to the target domain without any prior knowledge on label sets. The main challenge is to separate common samples from private samples in the target domain. In general, existing methods achieve this goal by performing domain adaptation only on the features extracted by the backbone networks. However, solely relying on the learning of the backbone network may not fully exploit the effectiveness of features, due to that 1) the discrepancy between two domains can naturally distract the learning of backbone network and 2) the irrelevant content of samples (e. g., backgrounds) likely goes through the backbone network, and accordingly may hinder the learning of domain-informative features. To this end, we describe a new method to provide extra guidance to the learning of the backbone network based on the latent representation beyond features (LaFea). We are motivated by the fact that the latent representation can be learned to contain the domain-relevant information scattered in features, and the learning of this latent representation can naturally promote the effectiveness of corresponding features in return. To achieve this goal, we develop a simple GAN-style architecture to transform features into the latent representation and propose new objectives to adversarially learn this representation. It should be noted that the latent representation only serves as an auxiliary in training, but it is not needed in inference. Extensive experiments on four datasets corroborate the superiority of our method compared to the state-of-the-arts.
Security for IoT using Federated Learning

In recent years, the government, universities, and industry have paid attention to the distributed approach in machine learning (ML) and cybersecurity for the developing Internet of Things (IoT). Federated cybersecurity (FC) is a strategy for making the Internet of Things (IoT); more secured and effective in the future. This new approach has an ability to detect problems regarding security, implement solutions, and across the IoT systems it manages effectively to limit the propagation of vulnerabilities. Forming a federation of shared information is one way to attain a cybersecurity. Federated learning (FL) is a machine learning paradigm which is especially effective for securing the sensitive IoT environment. The origin of FL, and also FL for cyber security are presented in this article. The various security assaults, and responses are also discussed as an outcome of this article. Experiments are carried out in Google Colaboratory, using well-known python libraries. The results shows that FL provides highest level of security in comparison with centralised learning.
Sentiment analysis on E-commerce reviews and ratings using ML &amp; NLP models to understand consumer behavior

This research paper covers the sentiment analysis of fashion e-commerce products by comparing their reviews (electronic word-of-mouth), and ratings using the ML model and NLP concepts. The scope of the paper dives into the aspect of understanding consumer behavior in a virtual environment. It explores the impact of eWOM and ratings of a product, on customer attitude, and the likelihood of that product being purchased. We have further established a relationship between ratings, reviews, and product recommendations. The paper contains an exploratory analysis backed with proper reasoning. We have made use of ML classification models like logistic regression, ADA boost, SVM, naïve Bayes, and random forest on customer reviews. We have further used Vader and text blob technologies to perform sentiment analysis.
SAR Ship Target Detection Based on Lightweight YOLOv5 in Complex Environment

In recent years, target detection methods based on depth learning have been widely used in the field of synthetic aperture radar (SAR) images, which is an indispensable part of the marine military field. Due to the particularity of SAR imaging system hardware platform, there are strict requirements for processor power consumption, which cannot meet the requirements of miniaturization. However, after the model is lightweight, the accuracy loss is often large. Therefore, to improve this common problem this paper compares the results of several YOLOv5 based on lightweight structure for ship target detection in complex scenes, and proposes an improved lightweight structure. The network performance is verified on our simulation dataset and SAR ship detection dataset(SSDD). The experimental results show that the lightweight structure proposed in this paper is superior to all lightweight structures at present in accuracy.
Energy Efficient Resource Allocation in Cloud Data Center: A Comparative Analysis

Cloud computing is a computational paradigm that provides multiple services over the internet on a pay-per-use basis. Its enticing qualities, such as adaptability, uptime, flexibility, cost reductions, and on-demand service, have encouraged service providers to adopt this technology. To meet customer demand for services, providers were invigorated by the rising popularity of cloud computing to expand the underlying computer infrastructure of the data centers. These large-scale data centers have resulted in a significant rise in energy usage and carbon emissions. The ever-increasing energy consumption of data centers has become a significant concern for both researchers and Cloud service providers. Another concern in the cloud data center is the inefficient allocation of resources that results in excessive energy utilization. Hence this paper highlights the challenges of cloud data centers, resource allocation and its techniques. This paper showcases the relation between the resource allocation and energy utilization of data center. The aim is to study various approaches to overcome these challenges.
Age Based News Classification using LSTM and BERT

News has been playing a major role in our day-to-day life in educating, informing, alerting, and even entertaining people. In our research, we aim on classifying news articles as adult news and kid news with a view to providing age-appropriate news. News classification as adult news or kid news is done by news article analysis. For fulfilling our target, we developed our news dataset and constructed two different models: one using Long short-term memory(LSTM) and the other using Bidirectional Encoder Representations from Transformers(BERT). The LSTM model achieved an accuracy of 94.84% and the BERT model managed to reach an accuracy of 95.68%, which is a bit higher than the LSTM model for news classification.
Characterization of Cubature Kalman Filter for GPS Delayed Environments in INS-GPS Integrated Navigation

Position estimation of an Unmanned Aerial Vehicle (UAV) can be done using inertial navigation system (INS). Even though there exists a number of sensors for inertial navigation, MEMS based sensors are preferred over others in applications related to UAVs because of their compact size and low cost. Inertial sensors alone can be used for navigation but as MEMS sensors causes error to accumulate over time, INS can be aided with GPS signal to improve its accuracy over time. Since GPS signals may not be available always, a better fusion algorithm which fuses the INS measurements with GPS signal is needed. In this paper, a fusion algorithm called Cubature Kalman filter (CKF) is used for the position estimation of the vehicle which is more reliable than the conventionally used Extended Kalman filter (EKF) algorithm. Both the algorithms are used to estimate the position of the vehicle and they are compared. It is evident from the analysis that CKF effectively improves the error in estimation compared to EKF.
Plan commitment: Replanning versus plan repair

While executing its plan in a dynamic environment where multiple agents are operating, an autonomous agent may suffer a failure due to discrepancies between the expected and actual context and thus must replace its obsolete plan. In its endeavour to fix the failure and reach its original goals, the agent may unknowingly disrupt other agents executing their plans in the same environment. We present a property for plan repair called plan commitment to ensure a responsible repair policy among agents that aims to minimise the negative impact on others. We present arguments to support the claim that plan commitment is a valuable property when an agent may have made bookings and commitments to others. We then propose C-TFLAP, an implementation of a plan repair heuristic that allows adapting a failed plan to the new context while committing as much as possible to the original plan. We demonstrate empirically that: (1) our plan repair achieves more committed plans than plan-stability repair when an agent has made bookings and commitments to others, and (2) compared to typical replanning and plan-stability repair, it can reduce the revisions among agents when failures are avoidable and can decrease the time-loss otherwise. In addition, to demonstrate extensibility, we integrate context-aware knowledge extension with committed repairing to increase the agent's chances of repairing.
The Role of A Priori Belief in the Design and Analysis of Fault-Tolerant Distributed Systems

The debate around the notions of a priori knowledge and a posteriori knowledge has proven crucial for the development of many fields in philosophy, such as metaphysics, epistemology, metametaphysics etc. We advocate that the recent debate on the two notions is also fruitful for man-made distributed computing systems and for the epistemic analysis thereof. Following a recently proposed modal and fallibilistic account of a priori knowledge, we elaborate the corresponding concept of a priori belief: We propose a rich taxonomy of types of a priori beliefs and their role for the different agents that participate in the system engineering process, which match the existing view exceedingly well and are particularly promising for explaining and dealing with unexpected behaviors in fault-tolerant distributed systems. Developing such a philosophical foundation will provide a sound basis for eventually implementing our ideas in a suitable epistemic reasoning and analysis framework and, hence, constitutes a mandatory first step for developing methods and tools to cope with the various challenges that emerge in such systems.