Reviewerly/exHarmony huggingface dataset:
Supply Chain for Safe &amp; Timely Distribution of Medicines using Blockchain &amp; Machine Learning

Because fake drugs are so prevalent, researchers, managers, and policymakers are paying close attention to the issue. When fake medications are used, they can harm or even kill the person who is taking them. The majority of the issues with the safety of counterfeit drugs in the supply chain are related to how the drugs are manufactured in the first place. Because it is difficult to track down the right active pharmaceutical ingredients during manufacturing, finding medicines that do not have the right active ingredients can harm or kill patients. Because Blockchain is so advanced, it can be used to track medicines from the manufacturer to the person who takes them. It can also be used to detect counterfeit medications. Blockchain technology can be promising to transform and improve the healthcare industry because it is secure, dependable, and open, and it can store data in real time. Blockchain technology and Machine Learning(ML) will be used in this study to investigate drug safety and ensure timely distribution of medicines. The use of Blockchain technology, proposes a supply chain management system based on a permissioned Blockchain network and role-based authorization. This would prevent people from tampering with the network’s data. By ensuring that all item units are transparent, traceable, and consistent, the Blockchain-based decentralized solution will make it easier for people to collaborate and trust one another. By using data from the Blockchain network based machine learning algorithms will be used to predict delays in deliveries and hence help the Supply Chain Managers to take preventive action and save further supply chain expenses.
Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique.
SQL Injection Detection using Machine Learning and Convolutional Neural Networks

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server’s validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not.
A Survey on Recommendation Systems using Collaborative Filtering Techniques

The main objective of this paper is to deal with the view of various bigdata as well as filtering approaches involved in the review of Recommendation Systems. Recommendation System is a model which has the capacity to identify the right products based on user preferences or ratings. It has a wide area of applications ranging from ecommerce industries to OTT sites such as Hotstar, Netflix etc. There are many algorithms used in recommendation system such as Content filtering, Collaborative filtering, Hybrid Filtering and Random based popularity method. Among them, Collaborative filtering techniques are the more efficient filtering techniques because of their problem-solving approach. In the era of big data, this study provides a solution to the data sparsity problem in user recommendation systems with the help of Collaborative filtering techniques.
Spam SMS (or) Email Detection and Classification using Machine Learning

Spam is an unwanted message or SMS sent on mobile phones whose content may be malicious. Scammers send fake text messages to trick people into responding to their SMS and they may hack personal information, password, account number, etc. To avoid being tricked by scammers, proposed a model based on Machine learning Algorithms. The proposed model is implemented using the Na誰ve Bayes algorithm and term frequency-inverse document frequency vectorizer. Obtained the dataset from Kaggle and trained the model using it. This model consists of a local host website which is obtained through PyCharm IDE. Obtained results show that the model accuracy of 95% and a precision of 100%.
Blockchain and Cloud-based Technology in Automotive Supply Chain

The study focuses on the utilization of Blockchain and Cloud based technologies in the automotive supply chain in India. It is based on a questionnaire-based survey conducted through a Google Form based questionnaire over 75 company executives of automobile sector having any idea on Blockchain. Among the participants, 73% was Male& 27% was Female. 32% Executives with Job experience 2-5 years, 27% with 5-10 years and 41% with More than 10 years. In terms of working profile, 41% Production Manager, 17% Tech Manager and 32% Supply Manager were there.The study uses a 5-point Likert scale questionnaire to assess the three broad parameters: Blockchain Technology Adoption (BTA), Supply Chain Integration (SC1) with Internal Integration, Customer Integration and Supplier Integration as sub-items and Sustainable Supply Chain Performance (SSCP). It has found that the company executives perceive that the blockchain is well integratedin the supply chain from both supplier and customer's perspective and also perceive that the blockchain is well integrated in the supply chain from both supplier and customer's perspective. organizationsalso has the capability of achieving sustainability in embracing Blockchain Technology. Not only in the mainstream production process, but also in the supply chain encompassing the suppliers and transporters, the usage of blockchain technology is widespread acknowledged. The Customer services also embrace Cloud-based Blockchain services to a greater extent. It has been observed that the adoption of blockchain and cloud technologies improve supply chain sustainability and coordination among different units in supply chain. However, the blockchain-based customer services such as vehicle monitoring, theft prevention, insurance scrutiny need to be focused more as the automotive sector in India is inclined to the Blockchain Technology towards the backward linkages up the supply chain rather than the forward linkages downward. The cloud integration in the blockchain network do enhances the performance and utilities in the automobile sector. Still, it needs to be more sophisticated and gain industry-wide acceptance.
A Top-View Hand Gesture Recognition System for IoT Applications

Gesture recognition integrated into Internet of Things (IoT) systems is a growing field with opportunities for improvement. Identifying hand gestures from vertical angles with a cooperative network of cameras can enable a broader collection of control methods for controlling IoT devices within residential and commercial environments. Integrating this smart home framework with other IoT control methodologies also creates a more user-friendly experience. To generate a proof of concept of the effectiveness with this control framework, a convolutional neural network was created using the YOLOv7 deep learning architecture and Roboflow software. This image detection network was connected to a simulated IoT device via Home Assistant home automation software and communicates using the MQTT protocol. The gesture detection system was tested on different camera setups to evaluate the importance of camera position and data sharing within a smart-home device network.
An Improved Machine Learning Approach to Detect Real Time Face Mask

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly.
Context based Emotion Recognition from Bengali Text using Transformers

Every individual’s everyday life has become a routine of expressing their ideas, opinions, emotions, and experiences through social networking sites and platforms on the internetin this era of rapid technological innovation. These points of view can be utilized to develop strategies for increasing efficiency in a range of fields, including business, politics, research, and analysis. Natural language processing (NLP) uses emotion detection to automatically monitor, evaluate, and categorize people’s ideas and opinions in order to get a sense of how they feel. To date, a substantial amount of study has been done on the Emotion Recognition of the English language, with notable results. Unfortunately, there has been a scarcity of research on the Bangla language in the subject of Emotion Recognition. Despite the fact that social networks have increased the popularity of romanized Bangla among Bangla speakers, there is even less research on romanized Bangla text. As a result, the focus of this study was on emotion recognition for both raw and romanized Bangla texts. A corpus of romanized Bangla texts was created from a raw Bangla feeling corpus in this study. Datasets of military, medical, religious and general context are collected and tested with the Bidirectional Encoder Representations from Transformers. Finally, the outputs are studied and evaluated.
Animal Intrusion Detection using Deep Learning for Agricultural Fields

Crop yield is severely threatened by animal invasion, which has consequences for both food security and farmer income. This suggested model details how this issue might be addressed via the integration of IoT and ML methods. As image processing and Internet of Things (IoT) sensor monitoring network technologies have advanced, sensors have undergone radical changes. Animal-human conflict is a serious issue in the agricultural sector and the forest zone, posing a threat to human life and causing significant material loss. Wireless sensors can analyze video clips from a gathered dataset to create an animal incursion detection system. This study introduces Invariant Feature Extraction (FE) using SlowFast architecture. The videos are annotated first, and then the IFE model is used to extract the spatial data. Classifying animals based on their pictures, can keep better tabs on them. Accidents involving animals and vehicles may be avoided, animals can be traced, and theft can be avoided if detection and categorization methods are used. The efficient deep learning techniques are helpful for this.
An in-depth investigation on the behavior of measures to quantify reproducibility

Science is facing a so-called reproducibility crisis, where researchers struggle to repeat experiments and to get the same or comparable results. This represents a fundamental problem in any scientific discipline because reproducibility lies at the very basis of the scientific method. A central methodological question is how to measure reproducibility and interpret different measures. In Information Retrieval (IR), current practices to measure reproducibility rely mainly on comparing averaged scores. If the reproduced score is close enough to the original one, the reproducibility experiment is deemed successful, although the identical scores can still rely on entirely different result lists. Therefore, this paper focuses on measures to quantify reproducibility in IR and their behavior. We present a critical analysis of IR reproducibility measures by synthetically generating runs in a controlled experimental setting, which allows us to control the amount of reproducibility error. These synthetic runs are generated by a deterioration algorithm based on swaps and replacements of documents in ranked lists. We investigate the behavior of different reproducibility measures with these synthetic runs in three different scenarios. Moreover, we propose a normalized version of Root Mean Square Error (RMSE) to quantify reproducibility better. Experimental results show that a single score is not enough to decide whether an experiment is successfully reproduced because such a score depends on the type of effectiveness measure and the performance of the original run. This study highlights how challenging it can be to reproduce experimental results and quantify the amount of reproducibility.
Universal Quantum Rewinding Protocol with an Arbitrarily High Probability of Success

We present a universal mechanism that, acting on any target qubit, propagates it to the state it had T time units before the experiment started. This protocol works by setting the target on a superposition of flight paths, where it is acted on by uncharacterized, but repeatable, quantum operations. Independently of the effect of each of these individual operations on the target, the successful interference of the paths causes it to leap to its past state. We prove that, for generic interaction effects, the protocol will herald a successful rewinding with probability 1 after some finite number of steps.
Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.
Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution

There exist various attack strategies in continuous-variable quantum key distribution (CVQKD) system in practice. Due to the powerful information processing ability of neural networks, they are applied to the detection and classification of attack strategies in CVQKD systems. However, neural networks are vulnerable to adversarial attacks, resulting in the CVQKD system using neural networks also having security risks. To solve this problem, we propose a defense scheme for the CVQKD system. We first perform low-rank dimensionality reduction on the CVQKD system data through regularized self-representation-locality preserving projects (RSR-LPP) to filter out some adversarial disturbances, and then perform sparse coding reconstruction through dictionary learning to add data details and filter residual adversarial disturbances. We test the proposed defense algorithm in the CVQKD system. The results indicate that our proposed scheme has a good monitoring and alarm effect on CVQKD adversarial disturbances and has a better effect than other compared defense algorithms.
Effect of Weight Adjustment in Virtual Co-embodiment During Collaborative Training

Acquisition of motor skills plays an essential role in various contexts, such as sports, factory jobs, and nursing. For effective motor skill learning, “virtual co-embodiment” has been proposed as a novel virtual reality (VR) based method in which a virtual avatar is controlled based on the weighted average of the learner’s and teacher’s movements. Using virtual co-embodiment, a learner can learn the motor intention because they can feel a strong sense of agency in the avatar’s movements modified by the teacher. However, after the assistance of the virtual co-embodiment vanishes, there is a performance drop problem; the learner cannot move as they learned, even if they understand the correct movement or motor intention, because the difference in body positions between the co-embodied avatar and the learner requires the latter to move differently after termination of the assistance. One way to match their positions is to increase the weight assigned to the learner’s weight. However, simply assigning the learner a high weight does not allow the teacher to correct the avatar’s movements and convey the correct movement and motor intention. By allowing the teacher a greater influence in the early stages of learning, and decreasing the influence as the learning progresses, it is expected to gradually allow the student to learn to operate independently. Therefore, we propose a method to prevent performance drop by adjusting weights according to the learning performance, thereby maintaining a high learning efficiency and helping advanced learners learn to independently demonstrate their abilities. In this study, we experimented with dual task learning to evaluate the automation of movement, which is considered an essential element of motor skills. We compared the performance drop when the virtual co-embodiment assist was terminated with static or adjusted weights based on the performance. Consequently, although the learning efficiency was slightly lower, the use of adjusted weights resulted in a significantly smaller performance drop after the termination of virtual co-embodiment assistance than that after the use of static weight.
The Development of a Data Collection and Browser Fingerprinting System

The urgent need to protect user privacy and security has emerged as the World Wide Web has become an increasingly necessary part of daily life. Browser fingerprinting is a very interesting topic in the industry of technology security. New technology will always raise new security issues and browser fingerprinting will undoubtedly follow the same process. It has become one of the most popular topics in online privacy because, to date, there is still no exact solution as to how to stop it entirely. The majority of solutions just aim to reduce the likelihood of obtaining a browser fingerprint. Research on browser fingerprinting is unquestionably required since it is essential to educate users, developers, policymakers, and law enforcement about it so that they can make strategic choices based on knowledge. Browser fingerprinting must be recognised in order to defend against privacy problems. A browser fingerprint is described as data gathered by the receiving server to identify a distant device, and it is different from cookies. Websites frequently utilize browser fingerprinting to obtain information about the type and version of the browser, as well as the operating system, and other current settings. It has been known that even when cookies are disabled, fingerprints can be used to fully or partially identify users or devices. In this communication paper, a new insight into the challenge of browser fingerprint is encouraged as a new venture. Thus, the initial way to truly understand the browser fingerprint is the need to collect browser fingerprints. In this work, the process of data collection for browser fingerprinting through scripting, to offer a complete all-in-one fingerprinting test suite, has been thoughtfully divided into appropriate sections and grouped with key information to be carried out. The objective is to gather fingerprint data with no personal identification information and make it an open source of raw datasets in the industry for any future research purposes. To our best knowledge, there are no open datasets made available for browser fingerprints in the research field. The dataset will be widely accessible by anyone interested in obtaining those data. The dataset collected will be very raw and will be in the form of a text file. Thus, the main contribution of this work is to share an open dataset of browser fingerprints along with its collection methodology.
Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators under Kinematic Constraints

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.