Reviewerly/exHarmony huggingface dataset:
Fake Product Review Detection and Elimination using Opinion Mining

Identification and elimination of fake reviews and their removal from the dataset provided using the supervised machine learning algorithm and natural language processing techniques based on a vast variety of aspects. In this proposed paper, we trained the counterfeit review dataset by the process of using two independently developed machine learning algorithm models for assessing the extent to which the information being provided is real. The counterfeit product evaluations can be found on numerous online retailers are mostly influencing the customers to buy those products and profit for those products is probably dependent on the reviews of those products. Hence these counterfeit reviews must be noticed so that large E-commerce companies like Meesho, Amazon, Flipkart, Nykaa, etc. can address this issue so that fraudsters and fraudulent critics are taken out, sustaining users’ credibility in shopping sites. This approach may be utilized for websites and apps with relatively few consumers, estimating the authenticity of reviews so that online businesses can respond to them suitably. This model is developed using Naive Bayes, Support Vector Machine,and TF-IDF (term frequency-inverse document frequency)Vectorizer. To detect spam reviews on a website or application instantly, one can make use of these models. However, effectively countering spammers requires a sophisticated model that has to undergo training on a large dataset of millions of reviews. In this work” Reviews of 20 Hotels in Chicago hotel dataset” a limited dataset is utilized to train the models on a small scale, but it can be expanded to achieve greater accuracy and authenticity in the reviews.
A Nonlinear Activation Function for Optical Neural Networks Using a Mach-Zehnder Interferometer with a III-V-on-Si Amplifier

With the benefit of high speed, wide bandwidth, and low latency, optical neural networks (ONN) form a promising neuromorphic computing platform for accelerating artificial intelligence tasks. ONNs contain two kinds of fundamental functional operations: multiply-accumulate (MAC) stages and nonlinear activation function units, as shown in Fig. 1(a). In the past years, different optical MAC units have been developed using wavelength-division multiplexing (WDM) schemes [1]–[2] or coherent multipath interferometers [3]. However, the optical nonlinear activation function units still remain a challenge for the integration of large-scale ONNs. Most nonlinear activation functions are implemented in optical-electrical (OE) conversion schemes [2]–[3], where processing speed is limited by the latency time of OE conversion. Therefore, an integrated all-optical nonlinear activation function is an attractive solution for optical neuromorphic computing.
Gesture Controlled Humanoid Robot with Autonomous Movement in a Mapped Region

In hazardous conditions such as fires, tremors, earthquakes, and so on, where human life is at risk, the humanoid robot will be able to navigate and retrieve precious goods. It also finds applications in various fields such as delivery and service bots. The humanoid robot developed here works in later applications. The robot can be controlled by the user as well as operate autonomously. The user is provided a suit on which multiple Inertial Measurement Units (IMU) are mounted. The processor on the suit reads the IMU values and communicates them to the processor on the robot using a web server. The robot would then mimic the movements of the user. The robot also performs manual-operation and autonomous movement in a mapped region with the help of LiDAR. A camera mounted on the robot will allow the user to have access to the visuals of its surroundings by live streaming the video.
Weak Segmentation-Guided GAN for Realistic Color Edition

Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post processing. The design of an automatic tool is a complex and long addressed challenge. In particular, two properties are difficult to meet altogether: generating realistic results without artifacts and the possibility to precisely choose the future color. Conventional methods using segmentation and histogram matching maximize the controllability but also introduce a lack of realism or are complex to automate. On the contrary, GANs that specialize in realism are difficult to control. To overcome these challenges, we propose a novel GAN architecture leveraging any differentiable segmentation model. We demonstrate the genericness of our framework that presents state of the art results on different use cases. It generates images that look realistic while offering a precise color control.
HERO: A Multi-modal Approach on Mobile Devices for Visual-Aware Conversational Assistance in Industrial Domains

We present HERO, an artificial assistant designed to communicate with users with both natural language and images to aid them carrying out procedures in industrial contexts. Our system is composed of five modules: 1) the input module retrieves user utterances and collects raw data, such as text and images, 2) the Natural Language Processing module processes text from user utterances, 3) the object detector module extracts entities by analyzing images captured by the user, 4) the Question Answering module generates responses to users' specific questions on procedures, and 5) the output module selects the final response to give to the user. We deployed and evaluated the system in an industrial laboratory furnished with different tools and equipment for carrying out repair and test operations on electrical boards. In this setting, the HERO system allows the user to retrieve information on tools, equipment, procedures, and safety rules. Experiments on domain-specific labeled data, as well as a user study suggest that the design of our system is robust and that its use can be beneficial for users over classic methods for retrieving information and guide workers, such as printed manuals.
Exploiting Exif Data to Improve Image Classification Using Convolutional Neural Networks

In addition to photo data, many digital cameras and smartphones capture Exif metadata which contain information about the camera parameters used when a photo was captured. While most semantic image recognition approaches only use pixel data for classification decisions, this work aims to examine whether Exif data can improve image classification performed by Convolutional Neural Networks (CNNs). We compare the classification performance and training time of fusion models that use both, image data and Exif metadata, for image classification in contrast to models that use only image data. The most promising result was obtained with a fusion model which was able to increase the classification accuracy for the selected target concepts by $$7.5\%$$ compared to the baseline, while the average total training time of all fusion models was reduced by $$7.9\%$$ .
SID: Service Identification and Discovery Framework for Decentralised Supply Chains

The traditional decentralised supply chains are facing challenges to meet the increasing demands for openness, transparency, trust and efficiency. As a result, blockchain-based decentralised supply chains are emerging. However, how to identify and discover various services in a decentralised supply chain remains an unsolved issue. This paper presents a new framework for modelling and discovering services in decentralised supply chain systems. In the framework, W3C Decentralised Identifier (DID) is adopted to describe service attributes to meet different business and technical requirements for a supply chain service. While blockchain is used for publishing and storing DID, a graphic-database-based service repository is proposed to organize these DID strings in a natural way as a graph for better service discovery. An Ethereum-based prototype is implemented as a proof of concept to demonstrate its feasibility and usefulness.
Machine Learning-Based Sentiment Analysis of Movie Review

An important method for evaluating a film’s performance is through movie reviews. A collection of movie reviews is what provides us with a deeper qualitative insight on various aspects of the movie, whereas providing a movie with a numerical rating in the form of stars tells us about the success or failure of the movie quantitatively. We can learn about the movie’s strengths and weaknesses from a textual review, and a morein-depth analysis of a movie review can tell us if the movie overall meets the reviewer’s expectations. One of the most important areas of machine learning is sentiment analysis, which seeks to extract subjective information from written reviews. Natural language processing and text mining are closely related to sentiment analysis. It can be used to determine the reviewer’s perspective on a variety of subjects or the review’s overall polarity. Using sentiment analysis, we can determine whether the reviewer was ”positive,” ”negative,” and so on while providing their feedback. In this project, we want to use Sentiment Analysis on a set of movie reviews written by reviewers to figure out how they felt about the movie overall, such as whether they liked it or hated it. We want to use the relationships between the words in the review to predict the review’s overall polarity.
A First Look at User-Controlled Moderation on Web3 Social Media: The Case of Memo.cash

Web3 social media strives to eliminate the need for centralized management by building upon on technologies such public ledgers and smart contracts. This has generated significant hype, but creates many notable challenges, many of which remain unaddressed. One such challenge is content moderation. Specifically, the immutable nature of blockchain means that it becomes impossible to retrospectively delete posts. This means that illegal content posts cannot be moderated or removed. In an attempt to overcome this, Web3 platforms, such as memo.cash, allow users to filter out posts from their personal timelines. Taking memo.cash as a use case, the goal of this work is to study the efficacy of the approach, and identify associated challenges. A particularly unique feature of memo.cash is that users must pay money (satoshi) for each social action (e.g., posting and blocking other users). We conjecture that this may impact the nature of moderation, particularly among poorer users. To explore this, we gather data from memo.cash covering 24K users, 317K posts, and 2.57M user actions. We investigate how the need to pay may impact the moderation system and propose potential solutions to address the challenges that arise.
Bipol: A novel multi-axes bias evaluation metric with explainability for NLP

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to classify bias using SotA architectures, we evaluate two popular NLP datasets (COPA and SQuADv2) and the WinoBias dataset. As additional contribution, we created a large English dataset (with almost 2 million labeled samples) for training models in bias classification and make it publicly available. We also make public our codes.
ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes

The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively.
Design of reverse converters for the general RNS 3-moduli set {2k, 2n − 1, 2n + 1}

Abstract This paper presents a new design method of the reverse (residue-to-binary) converter for the flexible 3-moduli residue number system (RNS) set $$\{ 2^k, 2^n-1, 2^n+1 \}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>k</mml:mi> </mml:msup> <mml:mo>,</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> , where k and n are a pair of arbitrary integers $$\ge 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≥</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> . The basic equation of the reverse converter is formulated in two alternative forms, each of which consists of two separate parts: one depending on input variables of the converter, and the other being a single constant. The constant can be either added inside the reverse converter or shifted out to the residue datapath channels, in most cases at no hardware cost or extra delay. From the set of basic functions, which are essentially different than those of the only two known general converters proposed for this moduli set, four versions of a converter can be designed for any pair of k and n . Experimental results obtained using the commercial 65-nm low-power design kit and industrial synthesis tools for all dynamic ranges from 8 to 40 bits suggest that, compared to the state-of-the-art designs, at least one version of the newly proposed converters is superior w.r.t. delay, power consumption, and area, for all dynamic ranges considered. The savings for the best versions (these with constants moved to the datapath channels) are up to 12.7% for the area and from 2.5% to 14% (5.8 % on average) for the delay, while the power consumption is reduced up to 23.2% (5.6% on average).
Towards machines that understand people

Abstract The ability to estimate the state of a human partner is an insufficient basis on which to build cooperative agents. Also needed is an ability to predict how people adapt their behavior in response to an agent's actions. We propose a new approach based on computational rationality, which models humans based on the idea that predictions can be derived by calculating policies that are approximately optimal given human‐like bounds. Computational rationality brings together reinforcement learning and cognitive modeling in pursuit of this goal, facilitating machine understanding of humans.
Visual crowd analysis: Open research problems

Abstract Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep‐learning approaches have made it possible to develop fully automated vision‐based crowd‐monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state‐of‐the‐art.
Heterogeneous Ensemble for Medical Data Classification

For robust classification, selecting a proper classifier is of primary importance. However, selecting the best classifiers depends on the problem, as some classifiers work better at some tasks than on others. Despite the many results collected in the literature, the support vector machine (SVM) remains the leading adopted solution in many domains, thanks to its ease of use. In this paper, we propose a new method based on convolutional neural networks (CNNs) as an alternative to SVM. CNNs are specialized in processing data in a grid-like topology that usually represents images. To enable CNNs to work on different data types, we investigate reshaping one-dimensional vector representations into two-dimensional matrices and compared different approaches for feeding standard CNNs using two-dimensional feature vector representations. We evaluate the different techniques proposing a heterogeneous ensemble based on three classifiers: an SVM, a model based on random subspace of rotation boosting (RB), and a CNN. The robustness of our approach is tested across a set of benchmark datasets that represent a wide range of medical classification tasks. The proposed ensembles provide promising performance on all datasets.
Stabilized Temporal 3D Face Alignment Using Landmark Displacement Learning

One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome the problem, we introduce neural networks to reconstruct stable and precise faces in time. The reconstruction network extracts the 3DMM parameters from video sequences to represent 3D faces in time. Meanwhile, our displacement networks learn the changes in facial landmarks. In particular, the networks learn changes caused by facial identity, facial expression, and temporal cues, respectively. The proposed facial alignment network exhibits reliable and precise performance in reconstructing static and dynamic faces by leveraging these displacement networks. The 300 Videos in the Wild (300VW) dataset is utilized for qualitative and quantitative evaluations to confirm the effectiveness of our method. The results demonstrate the considerable advantages of our method in reconstructing 3D faces from video sequences.
Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies

This study aims to achieve rapid and stable control of quadrotor unmanned aerial vehicles’ (UAVs) attitude by using an Active Disturbance Rejection Control (ADRC) controller. Addressing the challenge of numerous and complex ADRC parameters, optimization algorithms are employed for parameter tuning. This paper draws on the group mechanism of the Ant Colony Optimization (ACO) algorithm and innovatively introduces population search into the Beetle Antennae Search (BAS) algorithm. The refined algorithm is then applied to tune the ADRC parameters, reducing complexity and human intervention while enhancing intelligence and efficiency. The advanced optimization algorithm exhibits an exceptional global optimization capacity, convergence speed, and stability. Ultimately, flight simulation and experimental results suggest that the optimized ADRC controller demonstrates superior control and antidisturbance capabilities.
Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning.
Generalized Quantification Function of Monogenic Phase Congruency

Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.