Reviewerly/exHarmony huggingface dataset:
Hand Gesture Interface for Robot Path Definition in Collaborative Applications: Implementation and Comparative Study

The article explores the possibilities of using hand gestures as a control interface for robotic systems in a collaborative workspace. The development of hand gesture control interfaces has become increasingly important in everyday life as well as professional contexts such as manufacturing processes. We present a system designed to facilitate collaboration between humans and robots in manufacturing processes that require frequent revisions of the robot path and that allows direct definition of the waypoints, which differentiates our system from the existing ones. We introduce a novel and intuitive approach to human-robot cooperation through the use of simple gestures. As part of a robotic workspace, a proposed interface was developed and implemented utilising three RGB-D sensors for monitoring the operator's hand movements within the workspace. The system employs distributed data processing through multiple Jetson Nano units, with each unit processing data from a single camera. MediaPipe solution is utilised to localise the hand landmarks in the RGB image, enabling gesture recognition. We compare the conventional methods of defining robot trajectories with their developed gesture-based system through an experiment with 20 volunteers. The experiment involved verification of the system under realistic conditions in a real workspace closely resembling the intended industrial application. Data collected during the experiment included both objective and subjective parameters. The results indicate that the gesture-based interface enables users to define a given path objectively faster than conventional methods. We critically analyse the features and limitations of the developed system and suggest directions for future research. Overall, the experimental results indicate the usefulness of the developed system as it can speed up the definition of the robot's path.
Shrinkage and Redundant Feature Elimination Network-Based Robust Image Zero-Watermarking

To address the contradiction between watermarking robustness and imperceptibility, a zero-watermarking method based on shrinkage and a redundant feature elimination network (SRFENet) is proposed in this paper. First, in order to have the capability of resisting different image attacks, a dense connection was used to extract shallow and deep features from different convolutional layers. Secondly, to reduce unimportant information for robustness and uniqueness, in SRFENet, a shrinkage module was utilized by automatically learning the threshold of each feature channel. Then, to enhance watermarking uniqueness, a redundant feature elimination module was designed to reduce redundant information for the remaining valid features by learning the weights of inter-feature and intra-feature. In order to increase watermarking robustness further, noised images were generated for training. Finally, an extracted feature map from SRFENet was used to construct a zero-watermark. Furthermore, a zero-watermark from the noised image was generated for copyright verification, which is symmetrical to the process of zero-watermark construction from the original image. The experimental results showed that the proposed zero-watermarking method was robust to different single-image attacks (average BER is 0.0218) and hybrid image attacks (average NC is 0.9551), proving the significant generalization ability to resist different attacks. Compared with existing zero-watermarking methods, the proposed method is more robust since it extracts the main image features via learning a large number of different images for zero-watermark construction.
An Introspective Analysis of Inclusive Growth in Africa, With an Eminence on the Influence of Governance and Financial Development Interaction

The study considered the relationship between the interaction of financial development with all six distinct governance indicators from the world development indicators, as well as a combination of them, and inclusive growth in 48 African economies from 2000 to 2019. The countries were classified into low-income countries and middle-income countries. The models were then estimated with Panel Quantile Regression with Fixed Effects estimations, and Dumitrescu &amp; Hurlin causality tests after going through robust econometric tests. From the outcome, the interaction of financial development with corruption control is essential for inclusive growth in only middle-income countries. The interaction of financial development with political stability and the absence of violence/terrorism, and the interaction of financial development and governance in general are relevant to inclusive growth in Africa as a whole and middle-income countries. However, the interaction of financial development with voice and accountability, and the interaction of financial development with rule of law are relevant to inclusive growth in all country groupings. JEL: G20; F62; R50
Safe semi-supervised clustering based on Dempster–Shafer evidence theory

In this paper, we propose a safe semi-supervised clustering algorithm based on Dempster–Shafer (D–S) evidence theory. The motivation is that D–S evidence theory can be used to fuse multiple base clustering results and obtain robust confidence estimations of mislabeled samples. Firstly, the proposed algorithm constructs multiple base clusters using fuzzy c-means and kernel fuzzy c-means. Base clusters with good performance are selected according to a clustering validity function. Then, D–S evidence theory is used to fuse the results of the selected base clusters, and the confidence of labeled samples is calculated based on the fused results. Finally, we construct a p-nearest neighbor graph to limit the outputs of labeled samples with low confidence to be those of the p nearest unlabeled samples. It is desired to reduce the negative influence of labeled samples with low confidence and achieve safe exploitation. To verify the effectiveness of the proposed algorithm, we compare it to several unsupervised and semi-supervised clustering algorithms. The results demonstrate that our algorithm yields higher accuracy and is more stable.
SOCDet: A Lightweight and Accurate Oriented Object Detection Network for Satellite On-Orbit Computing

In recent years, the performance of the deep learning based object detection models for remote sensing images improves with the increase of the hyperparameter scale. However, the state of the art of detection models are generally too cumbersome to adapt to the resource-constrained satellite platforms, and even general lightweight detection models cannot satisfy performance requirements. To solve those problems, a lightweight and accurate oriented object detection network for satellite on-orbit computing (SOCDet) is proposed from three levels. At the computing unit level, we propose an efficient computing unit Single-kernel Omni-dimensional Dynamic Convolution to make SOCDet feature extraction more efficient. At the network module level, we design a structural reparameterization block based on composite structure reparameterization to improve inference accuracy. A guided attention module for guiding SOCDet is designed to extract object features. We design a lightweight and concise one-stage detection architecture at the network architecture level to accommodate satellite platforms with extremely constrained computing and storage resources. We conduct ablation experiments on the ground server and compare experiments with the state-of-the-art methods on embedded GPU, using DOTA, HRSC2016 and FAIR1M. The experiment results show that SOCDet can achieve 2.36 times faster than the baseline in inference speed with 10.78 more mAP, 72.6% fewer Params and 92.56% fewer floating point operations. Compared with state-of-the-art methods, SOCDet improves the inference speed and derives competitive mAP results with affordable computing capability.
Determination Human Behavior Prediction Supported by Cognitive Computing-Based Neural Network

By the development of image processing systems, human behavior prediction has been grown for research. Many research outcomes are previously available to recognize human behavior based on different features in form of image sequences. The combination of human and machine learning is very interesting to determine human behavior without much complexity. Nevertheless, there was little have a look at concerning the mixture of presently identified conduct statistics with conduct prediction. The dataset is shaped with the aid of using very own captured pictures accompanied with the aid of using anger, happy, sad, disgust, etc. TensorFlow framework in conjunction with CNN version make this device higher for end result generation. Using numerous length of epoch defines accuracy of prediction and performances of the proposed device. In this work, cognitive neural community is used to make prediction easily and generate higher results.
A Literature Survey on Vision Assistance System Based on Binocular Sensors for Visually Impaired Users

According to research, in 2015, there were an estimated 253 million people with visual impairment worldwide. Of these, 36 million were completely blind and a further 217 million had moderate to severe visual impairment (MSVI). This forms a considerable part of the population if you look at it. But the visually impaired still face a lot of difficulties in just going about their daily life. Most have to rely on the people around, on a basic walking cane or on a guide dog. This takes away a good part of their independence as they have to rely on someone for their basic needs. Many wearable technologies have been proposed to assist the visually impaired in their daily routines in order to solve this problem. This paper is an attempt to comprehend all of the past research done on the subject.
Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs.These applications require intelligent decisionmaking that relies on human sensing.Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy.Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns.Sensor-based techniques refer to a prospective method that employs various combinations of sensors.These solutions are static and necessitate costly equipment installation and maintenance.Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions.Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering).Due to its low cost and non-intrusive nature, wirelessbased human activity detection has received substantial attention and become a key topic of study.This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems.We classify the reviewed research studies based on the technical measures and applications they employ.In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.
A New Approach to Analysis of Public Sentiment on Padma Bridge in Bangla Text

Opinion mining or emotion analysis of people is generally called sentiment analysis. Sentiment analysis is necessary for getting customer or user notions about any products or services. This research paper investigated and recorded the public sentiment on Padma Bridge. As a result, the Government of Bangladesh can make decisions easily on future mega-construction projects based on the recorded results. Padma bridge is a mega construction event for Bangladesh due to the big budget and banned World Bank loans. Bangladeshi people express their feelings, suggestions, opinions, and thoughts about the Padma Bridge project on Facebook, YouTube and other social media. The main focus of this paper is sentiment analysis of people’s reactions to Padma bridge is based on the Bangla comment dataset. We have collected more than 15K data which has two types of sentiment: Positive and Negative. Then we used three machine learning models (SVM, RF, LSVC) and one deep learning model (LSTM) for sentiment analysis. In our proposed system, we used an innovative voting method that can count and compare the sentiments produced by the mentioned ML and DL models. Finally, our model makes decision-based on the maximum voting results. This paper concludes that the voting method technique improves the accuracy by around 7.5% compared to every single ML and DL model.
Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Our daily lives are significantly impacted by intelligent Internet of Things (IoT) application, services, IoT gadgets, and more intelligent industries.Artificial Intelligence (AI) is anticipated to have a substantial impact on training machine learning algorithms on IoT devices without sharing data.As data privacy has become a serious societal concern, Federated Learning (FL) has emerged as a hot research area for enabling the collaborative training of machine learning models across many smart IoT devices while adhering to privacy constraints.Although, FL is utilized for preserving privacy in IoT networks, but it is also facing some challenges such as privacy, effectiveness, and efficiency.In this article, a taxonomy of FL-based IoT systems is proposed and an analysis of related works on FL-based IoT systems is presented.Further, a comprehensive study of various types of threats, attacks, and frameworks is done.In addition to this, a taxonomy on privacy-preserving FL techniques for IoT networks is also devised.Finally, the study is concluded by highlighting the various open research challenges in FL-based IoT networks.
Artificial Intelligence, Blockchain, Big Data Analytics, Machine Learning and Data Mining in Traditional CRM and Social CRM: A Critical Review

In the ever-increasing competitive and customer-centered global market, the role of Customer Relationship Management (CRM) is essential to the performance, longevity, competitiveness and profitability of enterprises. The study aims to provide an overview of the impact of emerging technologies on traditional CRM and Social CRM. Therefore, through a critical review, this study explores the use of Artificial Intelligence (AI), Blockchain, Big Data Analytics (BDA), Data Mining and Machine Learning (ML) technologies in CRM. Based on the results, the above-mentioned technologies can significantly improve CRM. More specifically, their successful implementation enables enterprises to stay ahead of competition, to get to know their customers, improve their overall performance, Quality of Experience (QoE) and Quality of Service (QoS), increase profits, and reduce time consumption, risks and costs. Finally, by integrating these technologies, enterprises will offer more personalized and customized products and services and greatly improve customers' satisfaction, acquisition, relationships, loyalty, experience and retention.
Tongue Gestures for Hands-Free Interaction in Head Worn Displays

Head worn displays are often used in situations where users' hands may be occupied or otherwise unusable due to permanent or situational movement impairments. Hands-free interaction methods like voice recognition and gaze tracking allow accessible interaction with reduced limitations for user ability and environment. Tongue gestures offer an alternative method of private, hands-free and accessible interaction. However, past tongue gesture interfaces come in intrusive or otherwise inconvenient form factors preventing their implementation in head worn displays. We present a multimodal tongue gesture interface using existing commercial headsets and sensors only located in the upper face. We consider design factors for choosing robust and usable tongue gestures, introduce eight gestures based on the criteria and discuss early work towards tongue gesture recognition with the system.
The Role of Indigenous Communication Systems for Rural Development in the Tolon District of Ghana

This study explores the role of Indigenous Communication Systems (ICSs) in the Tolon District of Ghana, and how they are used as sites for cultural constructions, and platforms for propagating information for rural development.The study adopts social constructivism epistemological worldview and qualitative content analysis to interpret participants’ perspectives on the role of ICSs as sites for cultural constructions and propagating information for rural development in the Tolon District of Ghana. The study revealed that ICSs such as storytelling, drumming, the town crier, provide an avenue for the indigenous people to disseminate important information to rural communities in a language the local people are familiar with, understand, and as well inculcate good ethical and moral values to the up-coming generation. Considering this, the study recommends that ICSs should be refined and maintained to promote indigenous knowledge communication. Finally, the study uncovered that indigenous people hybridized or synchronized indigenous communication systems with new media technologies to enhance information dissemination in the rural communities. Therefore, the study suggests that rural communities could integrate both western communication technologies and traditional media forms to promote participatory media content creation and effective information dissemination in the rural communities for effective development.
Digital Currency Security with the Intervention of Blockchain

Blockchain provides an innovative method for storing information, carrying out functions, executing transactions, and establishing trust. In light of the increasing popularity of digital payments, it is critical to use blockchain technology to carry out transactions safely and to build trust among people. This paper presents my point of view on the role that blockchain technology plays in ensuring the safety of digital currencies. The significance and application of integrating blockchain and digital currency security as part of Industry 4.0 technologies will primarily be utilized in cybersecurity. Despite the Blockchain ledger’s open and distributed nature, the data remains safe and verifiable. As a result, we can state that blockchain technology is essential and have vast scope in coming future. Therefore, it is safe to say that the blockchain technology is revolutionary. Every transaction can be recorded in a way that is both permanent and indestructible. Hacking, data theft, and information loss are all rendered impossible by this digital ledger’s impenetrability.
Imperative Role of Artificial Intelligence and Big Data in Finance and Banking Sector

In contrast to human intelligence, which comes from innate knowledge, artificial intelligence (AI) and big data refers to the mental capacity demonstrated by robots. AI has been revolutionized banking and the financial sector and affected on human labor as well as other stakeholders. The concept of “industry 4.0” dramatically altered how organizations operate today. The five maturity levels of the model are initial, managed, defined, established, and digitally oriented. Requirement to secure, improve the quality, and meet the interests of both clients and financial institutions is increasing day by day. Technology become base of the financial institute. This paper examines various technology models used in the financial and banking industries. The study focusses on digital technology in the banking and finance industries for gaining understanding of the topic and identifying new areas. Study examines futuristic challenges of technology in banking sectors.
Instinctual Synced Ventilation for Mushroom Growing using IoT

The adoption of IoT systems in the agricultural sector is widely employed for effective farming solutions. Agriculture is a key area of economic development. A dynamic website is used to integrated with data obtained from wireless network system for monitoring real-time data for a user interface, and while minimizing human intervention is developed. The STM32F446RE serves as the system’s central processing unit (CPU), with which various Esp8266 nodes interact to exchange real-time data. The Blynk app serves as both a user interface and a tool for automating the procedure. All of the issues can be solved utilizing the method employed in this study. In the way to get beyond the limitations of the conventional process and thus paved the way for intentionally synchronized ventilation for mushroom cultivation utilizing IoT, which will keep track of the mushroom growth. On the Blynk application, the outcome of the mushroom condition will be virtually displayed.
Implementation of Novel Machine Learning Methods for Analysis and Detection of Fake Reviews in Social Media

Now-a-days social media plays a key role in our daily life. For many things we rely on this social media. This social media plays a very crucial role in several fields. People started believing the things which are written in internet before taking their decisions, such as having a look on reviews which are written in the social media for various purposes like buying a product in online or booking a hotel room for vacation or for visiting a place, for all these things people rely on these reviews which are written by several people. By these reviews there will be advantages as well as disadvantages. Some people to improve their company standards or for high lightening their products, they generate few fake reviews which attracts the users towards them and by that people starts choosing them. A target object’s positive ratings may draw more consumers and boost sales, whereas a target object’s negative reviews may result in less demand and lower sales. Our research aims to determine if a review is genuine or fraudulent. To avoid these fake reviews and to go with genuine reviews we need a fake review detection system. In this system some machine learning techniques are used. The proposed method consists of machine learning algorithms. They performed a benchmark analysis with different types of (1) traditional ML algorithms like logistic regression (LR), support vector machines (SVM), decision trees (DT), Naive bayes (NB), random forests (RF), & XG Boost (XGB), as well as an ensemble learning approach of such algorithms, and (2) cutting-edge ML algorithms like bidirectional long short-term memory (BIIS TM), etc. These algorithms assist in identifying bogus reviews. These algorithms are all contrasted with one another to provide precise results.