Reviewerly/exHarmony huggingface dataset:
Vaccine Supply Management using Blockchain

In late 2019, we witnessed the apparition of the COVID-19 virus. It first appeared in Wuhan, China and due to people traveling worldwide, it spread globally. The world went into a global lockdown because of its exponential spread and high mortality rates. Social distancing measures, crises in the economy, and healthcare posed many challenges for scientists, researchers, and healthcare workers. To try to combat this situation, multiple vaccines were tried, tested, and created in a very short amount of time. Therefore, a robust yet transparent supply chain system is very crucial in ensuring an efficient vaccination campaign. We propose a blockchain-based vaccination supply chain system to manage the registration, storage and distribution of the vaccines. Blockchain is a decentralized, distributed ledger that records all the transactions happening between the nodes in the network. The data is stored in the ledger in the form of blocks that are linked to each other by hashes, this makes tampering with the data virtually impossible. Blockchain properties will provide a distributed system along with greater privacy, transparency, and authenticity. Smart contracts deployed will help in monitoring and tracking the proper vaccine distribution conditions against the rules and regulations given by the authorities. The tamperproof nature of blockchain would ensure the supply of proper distribution of only authentic vaccines in a timely manner and traceability of the supply chain will help in increasing the people’s trust in the vaccination campaign.
A Comprehensive Survey On Recommendation Systems Based On Collaborative Filtering

In e-trade websites and associated micro-blogs, users deliver online evaluations expressing their opportunities regarding diverse gadgets. Such evaluations are commonly in the textual remarks form, and account for a precious data supply about character interests. On the Internet, where the quantity of alternatives is overwhelming, there may be want to filter out, prioritize and successfully deliver applicable statistics to relieve the hassle of facts overload, which has created a potential trouble to many Internet customers. Recommendation Systems (RS) have established to be of awesome useful resource in coping with the difficulty of Information overload by using improving the customer enjoy via pleasant recommendations. One of the maximum extensively used RS depend upon the collaborative filtering (CF) approach, where suggestions are made based on the person’s ratings of the items. However, their exquisite use has revealed some actual challenges, consisting of information sparsity and facts scalability, with often growing the wide sort of customers and items. This paper affords a complete survey on RSs for addressing a few essential issues of CF algorithms.
A comprehensive study on the performance of different Multi-class Classification Algorithms and Hyperparameter Tuning Techniques using Optuna

Multi-class classification talks about classification tasks that have three or more classes. It takes the assumption that every data sample in the dataset is assigned to one and only one class. It is a key idea in machine learning that is used in a variety of applications. Machine Learning includes different types of multi-class classification algorithms like SVM, Decision Tree, Random Forest, etc. This paper will discuss the workings of these algorithms and also emphasize the performance analysis of these algorithms. For performance analysis, this paper takes four multi-class datasets, namely the fetal-health classification dataset, the LED display dataset, the mobile price classification dataset, and the E-Coli dataset. For analyzing the performance of above mentioned three algorithms, this paper uses a machine-learning automated tool for hyperparameter optimization called OPTUNA. Different hyperparameter tuning techniques like Tree-structured Parzen Estimator (TPE) optimization, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization, Random Search, Grid Search, etc. were used for analysis purposes.
Artificial Intelligence Surveillance System

The surveillance systems typically employed are CCTV-based monitoring systems which require an administrator to scan the footage or manual monitoring of the area to detect if something is amiss. These methods create serious lapses in security along with requiring the manpower to scan large areas. The system designed here will automatically analyze the areas scanned by the cameras, and send real-time alerts to the security personnel. The user can access the system through the web application developed with a user-friendly GUI. The application’s main function would be to collect the CCTV camera footage and pass them to the ML engine, which would be based on Darknet neural network because they are more accurate. The engine would do the processing task and send alerts to all the relevant personnel in the database via email.
Should Alexa be a Police Officer, a Doctor, or a Priest?

Would you trust your gossiping nosey neighbour to look after your house while you are away? Despite being sold the idea of a conversational user interface (CUI) as a trustworthy, helpful, and non-judgmental mate, the reality that many CUI users face is a relationship characterised by uncertainty and volatility, owing to the CUI's ambiguous social role. To escape the Weisserian nightmare of the ubiquitous, ambiguous, and manipulative CUI, we must first consider the social roles and relationships we encounter every day and use those as models for how future CUIs can be built.
Ethical Self-Disclosing Voice User Interfaces for Delivery of News

Voice User Interfaces (VUIs) such as Alexa and Google Home that use human-like design cues are an increasingly popular means for accessing news. Self-disclosure in particular may be used to build relationships of trust with users who may reveal intimate details about themselves. This information can be (mis)used by algorithms to tailor and deliver partisan, critical news at the cost of journalistic ethics. In this position paper, we argue that self-disclosing VUIs may be beneficial to build trust with users and deliver news. We explain how a self-disclosing relationship may not only be a step towards human-like voice assistants but also aid in acceptance of, and exposure to different news events. We caution against obvious pitfalls from both a system and journalistic perspective and provide measures to address such concerns.
IBAttack: Being Cautious About Data Labels

Traditional backdoor attacks insert a trigger patch in the training images and associate the trigger with the targeted class label. Backdoor attacks are one of the rapidly evolving types of attack which can have a significant impact. On the other hand, adversarial perturbations have a significantly different attack mechanism from the traditional backdoor corruptions, where an imperceptible noise is learned to fool the deep learning models. In this research, we amalgamate these two concepts and propose a novel imperceptible backdoor attack, termed as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IBAttack</i> , where the adversarial images are associated with the desired target classes. A significant advantage of the adversarial-based proposed backdoor attack is the imperceptibility as compared to the traditional trigger-based mechanism. The proposed adversarial dynamic attack, in contrast to existing attacks, is agnostic to classifiers and trigger patterns. The extensive evaluation using multiple databases and networks illustrates the effectiveness of the proposed attack.
A human behavior analysis model to track object behavior in surveillance videos

In potential disasters, real world scenarios and public events, understanding of crowd psychology is a challenging process and detection of crowd behaviors in those events is quite complicated. Therefore, in this article, an effective crowd analysis mechanism is introduced using human behavior analysis model based on motion heat flow enabled optical flow method. Here, OCP descriptor evaluates required object points from motion heat map to detect objects and generate feature weights. Here, three type of histogram gradients are evaluated using proposed HBA model such as inconsistencies between neighboring points and OCP descriptor, gradients obtained considering spatial angle and gradients obtained considering temporal angle. Features obtained considering spatial as well as temporal domain are encoded using feature encoding scheme. The performance of proposed Human behavior Analysis (HBA) model is tested on Web and Violent-Flow Video Dataset. The performance of the proposed HBA model is compared against several traditional behavior analysis methods considering performance matrices like Accuracy, Precision and Recall to detect behaviors in public events. Performance of proposed HBA model is superior to multiple state-of-art-crowd behavior analysis techniques.
Symmetric Cryptography on RISC-V: Performance Evaluation of Standardized Algorithms

The ever-increasing need for securing computing systems using cryptographic algorithms is spurring interest in the efficient implementation of common algorithms. While the algorithms can be implemented in software using base instruction sets, there is considerable potential to reduce memory cost and improve speed using specialized instructions and associated hardware. However, there is a need to assess the benefits and costs of software implementations and new instructions that implement key cryptographic algorithms in fewer cycles. The primary aim of this paper is to improve the understanding of the performance and cost of implementing cryptographic algorithms for the RISC-V instruction set architecture (ISA) in two cases: software implementations of the algorithms using the rv32i instruction set and using cryptographic instructions supported by dedicated hardware in additional functional units. For both cases, we describe a RISC-V processor with cryptography hardware extensions and hand-optimized RISC-V assembly language implementations of eleven cryptographic algorithms. Compared to implementations with only the rv32i instruction set, implementations with the cryptography set extension provide a 1.5× to 8.6× faster execution speed and 1.2× to 5.8× less program memory for five of the eleven algorithms. Based on our performance analyses, a new instruction is proposed to increase the implementation efficiency of the algorithms.
Uncertainty-Aware Personal Assistant for Making Personalized Privacy Decisions

Many software systems, such as online social networks, enable users to share information about themselves. Although the action of sharing is simple, it requires an elaborate thought process on privacy: what to share, with whom to share, and for what purposes. Thinking about these for each piece of content to be shared is tedious. Recent approaches to tackle this problem build personal assistants that can help users by learning what is private over time and recommending privacy labels such as private or public to individual content that a user considers sharing. However, privacy is inherently ambiguous and highly personal . Existing approaches to recommend privacy decisions do not address these aspects of privacy sufficiently. Ideally, a personal assistant should be able to adjust its recommendation based on a given user, considering that user’s privacy understanding. Moreover, the personal assistant should be able to assess when its recommendation would be uncertain and let the user make the decision on her own. Accordingly, this article proposes a personal assistant that uses evidential deep learning to classify content based on its privacy label. An important characteristic of the personal assistant is that it can model its uncertainty in its decisions explicitly, determine that it does not know the answer, and delegate from making a recommendation when its uncertainty is high. By factoring in the user’s own understanding of privacy, such as risk factors or own labels, the personal assistant can personalize its recommendations per user. We evaluate our proposed personal assistant using a well-known dataset. Our results show that our personal assistant can accurately identify uncertain cases, personalize them to its user’s needs, and thus helps users preserve their privacy well.
Blockchain-enabled efficient distributed attribute-based access control framework with privacy-preserving in IoV

Aiming at solving the challenges of the Internet of Vehicles (IoV), such as privacy sensitive data exposing, data being vulnerable to unauthorized access and tampered with and the single point of failure of the cloud server, this paper introduces a blockchain-enabled data access method based on attribute-based encryption. To enhance the privacy protection, the attributes are hidden and all generated transactions are recorded on the blockchain for auditing. Even if it is untrusted to a third party, it can guarantee the user’s control over his own data. To reduce the computation cost, decentralized edge nodes jointly maintain the blockchain and assist users with limited resources to complete most of the calculations. Meanwhile, the use of smart contracts not only reduces the verification workload, but also evaluates multiple factors such as the communication range, communication time, and calculation overhead of the roadside unit (RSU) to ensure that the vehicle can receive messages in a timely manner. In addition, timely updating the attributes can reduce the user’s computational burden and increase the flexibility of the system. Numeral experiments to show the proposed scheme has many advantages of over others such as the storage cost, the whole time consume of the proposed algorithms and the proxy computation cost.
Extracting Funder Information from Scientific Papers - Experiences with Question Answering

Abstract This paper is about automatic recognition of entities that funded a research work in economics as being expressed in a publication. While many works apply rules and/or regular expressions to candidate sections within the text, we follow a question answering (QA) based approach to identify those passages that are most likely to inform us about funding. With regard to a digital library scenario, we are dealing with three more challenges: confirming that our approach at least outperforms manual indexing, disambiguation of funding organizations by linking their names to authority data, and integrating the generated metadata into a digital library application. Our computational results by means of machine learning techniques show that our QA performs similar to a previous work (AckNER), although we operated on rather small sets of training and test data. While manual indexing is still needed for a gold standard of reliable metadata, the identification of funding entities only worked for a subset of funder names.
Developing the EOSC-Pillar RDM Training and Support Catalogue

Today's many research infrastructures and European projects offer training catalogues to store and list multiple forms of learning materials. In EOSC-Pillar project we propose a web application catalogue, which consists of training materials as well as day-to-day operational resources with the aim to support data stewards and other RDM (research data management), FAIR data (findable, accessible, interoperable, reusable) and open science actors. In this paper we briefly describe the scope and technical implementation of the EOSC-Pillar RDM Training and Support Catalogue and how we are addressing current challenges such as metadata standards, controlled vocabularies, curation, quality checking and sustainability.
Knowledge-Infused Text Classification for the Biomedical Domain

Extracting knowledge from unstructured text and then classifying it is gaining importance after the data explosion on the web. The traditional text classification approaches are becoming ubiquitous, but the hybrid of semantic knowledge representation with statistical techniques can be more promising. The developed method attempts to fabricate neural networks to expedite and improve the simulation of ontology-based classification. This paper weighs upon the accurate results between the ontology-based text classification and traditional classification based on the artificial neural network (ANN) using distinguished parameters such as accuracy, precision, etc. The experimental analysis shows that the proposed findings are substantially better than the conventional text classification, taking the course of action into account. The authors also ran tests to compare the results of the proposed research model with one of the latest researches, resulting in a cut above accuracy and F1 score of the proposed model for various experiments performed at the different number of hidden layers and neurons.
Assessing the impacts of petrochemical industrial facilities on groundwater in Zubair district

Abstract This study aims to explore the environmental impacts of the solid and liquid wastes of the Petrochemical Industries Complex in the Al-Zubair district (southern Iraq) on the groundwater in the areas surrounding the complex. The waste emerging from the industrial complex is buried in the soil near the factories which has become a major threat to the groundwater of the area. The objectives of the study were to determine the environmental impact of the petrochemical complex and the level of damage caused to neighboring areas as well as to identify the wells most affected by pollution. Field surveys of wells and pollutant disposal sites were conducted in the study area and spatial analysis using the ArcGIS program was used. The results revealed the wells affected by pollution, the type of released pollutants, the extent of their spread and concentration, as well as the factories that produce the most significant percentage of contaminants. Interpretation of spatial information by GIS revealed that liquid chlorine, industrial salts, and ethylene, respectively, had the highest concentrations as pollutants for soil and groundwater, and don't comply with environmental standards. The study also revealed that the chlorine factory, soda, low-density polyethylene granules, and PVC factory are the most polluting factories in the surrounding environment.
Evaluation of deepfake detection using YOLO with local binary pattern histogram

Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of the video is a critical process due to the negative pattern of the image. In the detection of forged images or videos, various image processing techniques were implemented. Existing methods are ineffective in detecting new threats or false images. This article has proposed You Only Look Once-Local Binary Pattern Histogram (YOLO-LBPH) to detect fake videos. YOLO is used to detect the face in an image or a frame of a video. The spatial features are extracted from the face image using a EfficientNet-B5 method. Spatial feature extractions are fed as input in the Local Binary Pattern Histogram to extract temporal features. The proposed YOLO-LBPH is implemented using the large scale deepfake forensics (DF) dataset known as CelebDF-FaceForensics++(c23), which is a combination of FaceForensics++(c23) and Celeb-DF. As a result, the precision score is 86.88% in the CelebDF-FaceForensics++(c23) dataset, 88.9% in the DFFD dataset, 91.35% in the CASIA-WebFace data. Similarly, the recall is 92.45% in the Celeb-DF-Face Forensics ++(c23) dataset, 93.76% in the DFFD dataset, and 94.35% in the CASIA-Web Face dataset.
A boundary-preserving conditional convolution network for instance segmentation

Single-stage instance segmentation approaches have been attracting increasing attention in research communities because of faster speed, more straightforward design, and competitive accuracy than two-stage methods. However, the currently available methods only generate pixel-level classification, which ignores the information of shapes and object boundaries and thus results in wild and fuzzy mask predictions and inaccurate localization. To address these issues, we propose BCondInst, an effective boundary-preserving conditional convolution network aiming at adopting object boundary information to improve the accuracy of mask localization. Specifically, BCondInst replaces the original mask branch with a boundary-mask branch containing two sub-networks designed to jointly learn object boundaries and masks. Different from the existing segmentation approaches, in which the parameters of filters in the boundary head remain unchanged for all instances, the filters in the mask's head and boundary's head in BCondInst are created according to specific instances. Furthermore, we design a post-processing mechanism that adopts the predicted boundaries to further improve the quality of predicted masks in the testing phase. Consequently, masks predicted by BCondInst generally preserve more details and more accurate boundaries. Extensive experimental results on Microsoft COCO 2017 benchmark indicate that the performance of the proposed BCondInst exceeds the current popular one-stage instance segmentation approaches by a large margin.
JSON document clustering based on schema embeddings

The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections. Existing research uses various structural similarity measures to perform clustering. However, differently annotated JSON structures may also encode semantic relatedness, necessitating the use of both syntactic and semantic properties of heterogeneous JSON schemas. Using the SchemaEmbed model, this paper proposes an embedding-based clustering approach for grouping contextually similar JSON documents. The SchemaEmbed model is designed using the pre-trained Word2Vec model and a deep autoencoder that considers both syntactic and semantic information of JSON schemas for clustering the documents. The Word2Vec model learns the attribute embeddings, and a deep autoencoder is designed to generate context-aware schema embeddings. Finally, the context-based similar JSON documents are grouped using a clustering algorithm. The effectiveness of the proposed work is evaluated using both real and synthetic datasets. The results and findings show that the proposed approach improves clustering quality significantly, with a high NMI score of 75%. In addition, we demonstrate that clustering results obtained by contextual similarity are superior to those obtained by traditional semantic similarity models.
Dynamic DNA coding multi-image encryption based on compound chaos

We present a dynamic DNA coding multi-image encryption algorithm based on integer lifting wavelet transform and compound chaos. In the encryption process, the low-frequency sub-bands of four plaintexts are extracted to get a combined image containing the multiple plaintext image information. With the help of a chaotic system, the pixel positions and pixel values of the combined image are modified by the permutation and the diffusion operations. Then we divide the image into equal-sized blocks, and the dynamic DNA coding method is used to complete the final encryption. To enhance security, we employ the RSA algorithm to protect the keys used in our encryption algorithm. Simulation results and security analysis demonstrate the robustness and the security.