Reviewerly/exHarmony huggingface dataset:
IoT Based Website for Identification of Acute Lymphoblastic Leukemia using DL

A form of cancer known as leukemia, attacks the body's blood cells and bone marrow. This happens when cancer cells multiply rapidly in the bone marrow. The uploaded image is analyzed by the website, and if leukemia is present, the user is notified-a collection of pictures depicting leukemia as well as healthy bones and blood. Once collected from Kaggle, the data is preprocessed using methods like image scaling and enhancement. To create a Deep Learning (DL) model, we use the VGG-16 model. The processed data is used to “train” the model until optimal results are achieved. A Hypertext Markup Language (HTML) based website is built to showcase the model. Using a DL model, this website returns a response indicating whether or not the user's uploaded photograph shows signs of leukemia. The primary aim of this site is to lessen the likelihood that cancer cells may multiply while the patient waits for test results or is otherwise unaware of their condition. Waiting for results after a leukemia test can cause further stress and even other health problems, even if the person is found to be leukemia-free. This problem can be fixed if this website is used as a screening tool for leukemia.&amp;nbsp;&lt;br&gt;
Image Blending with Osmosis

Image blending is an integral part of many multi-image applications such as panorama stitching or remote image acquisition processes. In such scenarios, multiple images are connected at predefined boundaries to form a larger image. A convincing transition between these boundaries may be challenging, since each image might have been acquired under different conditions or even by different devices. We propose the first blending approach based on osmosis filters. These drift-diffusion processes define an image evolution with a non-trivial steady state. For our blending purposes, we explore several ways to compose drift vector fields based on the derivatives of our input images. These vector fields guide the evolution such that the steady state yields a convincing blended result. Our method benefits from the well-founded theoretical results for osmosis, which include useful invariances under multiplicative changes of the colour values. Experiments on real-world data show that this yields better quality than traditional gradient domain blending, especially under challenging illumination conditions.
Assessing the effects of hyperparameters on knowledge graph embedding quality

Abstract Embedding knowledge graphs into low-dimensional spaces is a popular method for applying approaches, such as link prediction or node classification, to these databases. This embedding process is very costly in terms of both computational time and space. Part of the reason for this is the optimisation of hyperparameters, which involves repeatedly sampling, by random, guided, or brute-force selection, from a large hyperparameter space and testing the resulting embeddings for their quality. However, not all hyperparameters in this search space will be equally important. In fact, with prior knowledge of the relative importance of the hyperparameters, some could be eliminated from the search altogether without significantly impacting the overall quality of the outputted embeddings. To this end, we ran a Sobol sensitivity analysis to evaluate the effects of tuning different hyperparameters on the variance of embedding quality. This was achieved by performing thousands of embedding trials, each time measuring the quality of embeddings produced by different hyperparameter configurations. We regressed the embedding quality on those hyperparameter configurations, using this model to generate Sobol sensitivity indices for each of the hyperparameters. By evaluating the correlation between Sobol indices, we find substantial variability in the hyperparameter sensitivities between knowledge graphs with differing dataset characteristics as the probable cause of these inconsistencies. As an additional contribution of this work we identify several relations in the UMLS knowledge graph that may cause data leakage via inverse relations, and derive and present UMLS-43, a leakage-robust variant of that graph.
The business case for ransomware exercises for business and technology teams.

Ransomware attacks are increasing in complexity, scope and frequency. These attacks have had mounting impacts on critical global infrastructures and populations, with significant financial ramifications. This paper underscores the business case for the investment and execution of preparedness efforts to increase the readiness and resilience of business and technology teams. It begins by exploring the present characteristics of ransomware and the landscape of organisational preparedness, as described in various reports cataloguing current and forecasted trends in the field. This is supported by a commentary on recent noteworthy ransomware attacks. Emergency management, crisis management and general business development strategies are then merged to inform and offer actionable solutions for immediate implementation. The paper describes a systematic approach that provides opportunities to embed ransomware preparedness efforts into broader organisational strategies and goals. The business case concludes that investment in ransomware exercises offers additional benefits to organisations and companies, including employee retention and goal attainment.
Few-Shot Partial Multi-View Learning

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhouyuan888888/UGDA</uri> .
The war against AI web scraping

Elon Musk and Reddit are leading a new wave of objections to the long-accepted practice of scraping content from websites, discovers Matthew Sparkes
Continuity of robustness measures in quantum resource theories

Abstract Robustness measures are increasingly prominent resource quantifiers that have been introduced for quantum resource theories such as entanglement and coherence. Despite the generality of these measures, their usefulness is hindered by the fact that some of their mathematical properties remain unclear, especially when the set of resource-free states is non-convex. In this paper, we investigate continuity properties of different robustness functions. We show that their continuity depends on the shape of the set of free states. In particular, we demonstrate that in many cases, star-convexity is sufficient for Lipschitz-continuity of the robustness, and we provide specific examples of sets leading to non-continuous measures. Finally, we illustrate the applicability of our results by defining a robustness of teleportability and of quantum discord.
Automatic Text Summarization

Abstract: Text Summarization is a Natural Language Processing (NLP) method that extracts and collects data from the source and summarizes it. Text summarization has become a requirement for many applications since manually summarizing vast amounts of information is difficult, especially with the expanding magnitude of data. Financial research, search engine optimization, media monitoring, question-answering bots, and document analysis all benefit from text summarization. This paper extensively addresses several summarizing strategies depending on intent, volume of data, and outcome. Our aim is to evaluate and convey an abstract viewpoint of the present scenario research work for text summarization.
Secure computing: logistic regression analysis with differential privacy

Logistic regression has the threat of privacy disclosure during data analysis. Differential privacy can prevent this security threat. However, differential privacy mechanism needs to add noise to the protected algorithm, which will affect the quality of data analysis. To improve the quality of data analysis, we propose dpLogic algorithm based on differential privacy and functional mechanism. This algorithm constructs an auxiliary function, which can help us get the noisy model as close as possible to the original model. Besides, dpLogic optimizes privacy budget allocation, reducing the disturbance of noise on the model. Experimental results illustrate that this algorithm can effectively improve the accuracy of data analysis at a high level of privacy protection.
A semi-homomorphic privacy computing solution based on SM2 and blockchain

In recent years, China's digital industrialisation and digitisation of industries have continued to develop in depth, and online transactions have become the shopping choice of many internet users. The strong endogenous dynamics have put forward requirements for high efficiency, low cost, high security and high privacy for online mobile payments. Blockchain is a technology that has emerged with the popularity of digital cryptocurrencies such as Bitcoin, which is characterised by "decentralisation" and "immutability", but blockchain transactions have privacy protection flaws and problems that seriously threaten the privacy and security of transaction users. This paper proposes a clear solution to this problem by combining homomorphic encryption and ethereum's smart contract technology to design a blockchain online transaction privacy protection system that is compatible with heterogeneous trading platforms. This effectively makes up for the shortcomings of blockchain technology in terms of privacy protection. The security of this scheme has been proven to be unforgeable and the privacy data is safe. Through performance simulation experiments and theoretical derivation, it is proved that the privacy data is distributed, shared and computed in a ciphertext state in a more efficient way, and the proposed scheme can protect customer privacy more effectively than the traditional model.
RC5-CBC: best matching combination of block cipher and model of operation for implementing ORAM schemes

The encryption and decryption time of semantically secure encryption techniques have a great influence on the total end-to-end delay of an ORAM scheme. However, to the best of our knowledge, there is no work to study the impacts of combinations of block cipher and common model of operation on implementing semantically secure encryption techniques by comparing and analysing their execution time. This paper examines the running time of commonly used combinations of block cipher and model of operation to find the most matchable one for an ORAM scheme. We first selected the candidates of symmetric block encryption algorithms based on the existing block ciphers included in the LibTomCrypt, a popular cryptography toolkit. We second designed a comprehensive experiment in terms of prominent application scenarios of ORAM. We finally analysed the experimental results and found that the encryption algorithm of RC5-CBC outperforms others. In the end, we conclude that the RC5-CBC is the most matchable combination of block cipher and model of operation for implementing an ORAM scheme.
Constant Boolean search with arbitrary expressions in the Internet of Vehicles environment

Searchable encryption is the best choice for secure retrieval solutions in Internet of Vehicles scenarios. However, the search time is positively correlated with the number of keywords among most of public-key searchable encryption schemes. Also, the traditional keyword search strategy cannot meet the specific search needs of users, that is, the needs of supporting any Boolean expression search strategy. In order to reduce the search time and improve the search flexibility, this paper proposes a Boolean search scheme that supports constant search overhead for Internet of Vehicles users. Boolean search expressions in the proposed scheme can support search strategy for arbitrary Boolean expressions. It uses vectorized search expressions and keyword values in the form of predicate vectors, and the cloud server only needs to pair once to complete the Boolean search operation. This scheme can effectively resist the chosen keyword attack, and experimental simulation shows that the proposed scheme can efficiently realize ciphertext retrieval in the Internet of Vehicles environment.
Fake information mechanism based privacy-preserving average consensus

In order to reach an agreement, each agent in distributed systems needs to exchange its state information with neighbours. In this case, however, it is difficult to guarantee sensitive information will not be leaked. To tackle the problem, this paper proposes an encryption method, which does not need to compromise accuracy to privacy, nor does it need to encrypt and decrypt state information every time. Meanwhile, the method in this paper not only keeps the initial state secret, but also considers the real-time state privacy. Then, we further elaborate two aspects of the proposed method by simple proofs. Finally, simulations are presented to verify the effectiveness of our algorithm.
DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect

DarNERcorp is a manually annotated named entity recognition (NER) dataset in the Moroccan dialect, also called Darija. The dataset consists of 65,905 tokens and their corresponding tags according to BIO scheme. 13.8% of the tokens are named entities spanning four categories: person, location, organization, and miscellaneous. The data were scraped from the Moroccan Dialect section of Wikipedia and processed and annotated using open-source libraries and tools. The data are useful for the Arabic natural language processing (NLP) community as they address the lack in dialectal Arabic annotated corpora. This dataset can be used to train and evaluate named entity recognition systems in dialectal and mixed Arabic.
A REAL-TIME GPS-BASED ANDROID SYSTEM FOR ENHANCED FLEET MANAGEMENT

The proposed solution of an android application that provides real-time updates on the location and the estimated time of arrival of vehicles can certainly be beneficial for people living in busy metropolitan cities like Mumbai and Delhi.By providing users with real-time information about the location and ETA of public transport, the application can help them save time and plan their commute more efficiently, which can lead to increased productivity on the whole.It is based on a real-world problem.It is an app that tracks available public vehicles live (e.g.buses, E-Rikshaws, and Auto Rikshaws, etc.) and provides mobile tracking solutions for these vehicles as well as passengers looking for options to commute.If someone is new to a city then they have to book Ola and Uber even for a shorter distance which costs much whereas autos and E-rickshaws are available at very lesser prices, but people don't know about the available options in the beginning and end up paying high fares.The app will show all the people which auto and buses go from where to where.And if he wants to check which bus has reached where then he can easily track it.The user can compare the vehicle fare also.The use of GPS as the basis for the application is a good choice, as it allows for accurate ETA of vehicles.The application can also be designed with user-friendly features and an intuitive interface, which will enhance the user experience and make it easy for users to navigate and access the information they need.Overall, the proposed android-based application has the potential to provide a valuable solution to the problem of long waiting times for public transport in busy metropolitan cities.By providing users with real-time updates on the location and ETA of vehicles, the application can help users save time and increase productivity, which can ultimately improve their quality of life.
TRUSTWORTHY ELECTRONIC VOTING USING ADJUSTED BLOCKCHAIN TECHNOLOGY

The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies.The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time.This paper suggests a framework by using effective hashing techniques to ensure the security of the data.The concept of block creation and block sealing is introduced in this paper.The introduction of a block sealing concept helps in making the blockchain adjustable to meet the need of the polling process.The use of consortium blockchain is suggested, which ensures that the blockchain is owned by a governing body (e.g., election commission), and no unauthorized access can be made from outside.The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms' utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method.This paper claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.
LEUKEMIA CANCER DETECTION USING DEEP LEARNING

People of all ages, including children and adults, are susceptible to the deadly condition known as leukemia, which is a cancer-related illness.It is a leading cause of death in the world.It is especially related to White Blood Cells (WBC), which affect the bone marrow and/or blood and are followed by a rise in the number of immature lymphocytes.As a result, a timely and accurate cancer diagnosis is a crucial prerequisite for effective treatment that increases survival rates.Currently, this condition is diagnosed manually via a study of blood samples collected from microscopic pictures, which is frequently highly sluggish, time-consuming, and less precise.Additionally, leukemic cells appear and shape remarkably similar to normal cells under a microscope, making detection more challenging.RCNN -based deep learning has produced state-of-the-art algorithms for image classification challenges during recent years, however there is still room for improvement in terms of effectiveness, learning process, and performance.In order to diagnose Leukemia by examining the microscopic images of blood samples, we proposed the deep learning algorithms yolov5, yolov8, faster RCNN, and SSD in this research study.Incorporating squeeze and excitation learning, which iteratively executes calibration on channel-wise feature outputs by explicitly modelling channel interdependencies, the proposed deep learning architecture emphasizes the channel associations on all levels of feature representation.Incorporating the squeeze-and-excitation process also improves the ability to distinguish between leukemic and normal cells based on their features, helps to strategically reveal informative features of Leukemia cells while suppressing fewer valuable ones, As0a0result, deep learning algorithms are better able to represent features.We show how combining the squeeze and excite learning procedures in a deep learning0model can improve the computer's capability to recognize leukaemia from microscopic pictures based on patient blood samples.To solve the issue of insufficient data and improve their performance even further, a comprehensive set of experiments are carried out on both cropped cells and full-size microscopic images.
Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection

Quantum circuit simulation provides the foundation for the development of quantum algorithms and the verification of quantum supremacy. Among the various methods for quantum circuit simulation, tensor network contraction has been increasing in popularity due to its ability to simulate a larger number of qubits. During tensor contraction, the input tensors are reshaped to matrices and computed by a GEMM operation, where these GEMM operations could reach up to 90% of the total calculation time. GEMM throughput can be improved by utilizing mixed-precision hardware such as Tensor Cores, but straightforward implementation results in insufficient fidelity for deep and large quantum circuits. Prior work has demonstrated that compensated summation with special care of the rounding mode can fully recover the FP32 precision of SGEMM even when using TF32 or FP16 Tensor Cores. The exponent range is a critical issue when applying such techniques to quantum circuit simulation. While TF32 supports almost the same exponent range as FP32, FP16 supports a much smaller exponent range. In this work, we use the exponent range statistics of input tensor elements to select which Tensor Cores we use for the GEMM. We evaluate our method on Random Circuit Sampling (RCS), including Sycamore’s quantum circuit, and show that the throughput is 1.86 times higher at maximum while maintaining accuracy.
Blockchain for General Digital Ledgers

The general idea of blockchain has implications in almost every aspect where computers and computation have laid their foot on. However, there are domains where its practical feasibility is challenged by conventional alternatives. One such domain is general-purpose digital ledgers. This includes all ledgers ranging from basic lending to high-scale businesses. The prime impedance to the use of blockchain technologies in the general digital ledger can be attributed to high transactional fees and a lack of available access to the general public. This paper lays insight into a solution to the above problem which will enable the use of a system that offers the robustness of a blockchain system. This system can be used for general ledgers as well as bill settlements.
A Blockchain Technology Application for Managing Blood Supply Chain

The current study proposes a blockchain-based technology application for managing blood supply chain. Every peer-to-peer transaction in a blockchain that is recorded and stored is suggested to be shared with the entities participating in the transactions. The characteristics of any blockchain used for blood supply chain are that it is immutable, decentralised, consensus-driven and transparent. Being process centric, it would be helpful in managing an efficient blood supply chain while protecting the privacy of the blood donors. Further, the use of Radio frequency identification (RFID) technologies is recommended to prevent errors in blood transfusion as well as improve the quality and productivity of the blood supply chain.