Reviewerly/exHarmony huggingface dataset:
Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis

Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.
Prospecting non-fungible tokens in the digital economy: Stakeholders and ecosystem, risk and opportunity

Non-fungible tokens (NFTs) are a highly nascent and emerging phenomenon revolutionizing how digital assets are traded. NFTs embody immutable rights to unique digital assets such as digital art and collectibles and are represented as digital tokens that can be traded across marketplaces utilizing blockchain technologies. NFTs engender new ways to organize, consume, move, program, and store digital information and have experienced a rapid rise in various adaptations across art, sports, broadcasting, content creation, and tech-crypto businesses. In this article, we define NFTs and look at how they fit with blockchain and cryptocurrencies, how they are used by various industries, and the opportunities and risks they present. Our key contribution is a conceptual map of an initial NFT ecosystem. In doing so, we provide relational mapping between and among key stakeholders: content creators, core and related technical and business intermediaries, consumers, investors, and speculators. We also highlight implications for managers and tie them to conceptual exploration and exploitation frameworks.
Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

An accurate solar energy forecast is of utmost importance to allow a higher level of integration of renewable energy into the controls of the existing electricity grid. With the availability of data in unprecedented granularities, there is an opportunity to use data-driven algorithms for improved prediction of solar generation. In this paper, an improved generally applicable stacked ensemble algorithm (DSE-XGB) is proposed utilizing two deep learning algorithms namely artificial neural network (ANN) and long short-term memory (LSTM) as base models for solar energy forecast. The predictions from the base models are integrated using an extreme gradient boosting algorithm to enhance the accuracy of the solar PV generation forecast. The proposed model was evaluated on four different solar generation datasets to provide a comprehensive assessment. Additionally, the shapely additive explanation framework was utilized in this study to provide a deeper insight into the learning mechanism of the algorithm. The performance of the proposed model was evaluated by comparing the prediction results with individual ANN, LSTM, and Bagging. The proposed DSE-XGB method exhibits the best combination of consistency and stability on different case studies irrespective of the weather variations and demonstrates an improvement in R2 value of 10%–12% to other models.
Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy

Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.
SRDiff: Single image super-resolution with diffusion probabilistic models

Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from given low-resolution (LR) images. It is an ill-posed problem because one LR image corresponds to multiple HR images. Recently, learning-based SISR methods have greatly outperformed traditional methods. However, PSNR-oriented, GAN-driven and flow-based methods suffer from over-smoothing, mode collapse and large model footprint issues, respectively. To solve these problems, we propose a novel SISR diffusion probabilistic model (SRDiff), which is the first diffusion-based model for SISR. SRDiff is optimized with a variant of the variational bound on the data likelihood. Through a Markov chain, it can provide diverse and realistic super-resolution (SR) predictions by gradually transforming Gaussian noise into a super-resolution image conditioned on an LR input. In addition, we introduce residual prediction to the whole framework to speed up model convergence. Our extensive experiments on facial and general benchmarks (CelebA and DIV2K datasets) show that (1) SRDiff can generate diverse SR results with rich details and achieve competitive performance against other state-of-the-art methods, when given only one LR input; (2) SRDiff is easy to train with a small footprint(The word “footprint” in this paper represents “model size” (number of model parameters).); (3) SRDiff can perform flexible image manipulation operations, including latent space interpolation and content fusion.
SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval

Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing (SSDH) approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: (1) We propose a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. (2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the first deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on 5 widely-used datasets show that our proposed approach outperforms the state-of-the-art hashing methods.
Contract Mechanism and Performance Analysis for Data Transaction in Mobile Social Networks

In this paper, a novel mobile data offloading method is proposed based on an external-infrastructure-free approach. Specifically, through the hotspot function of smartphones, data demands from some mobile users, who have used out the data with their monthly data plans, can be offloaded by some other mobile users who still have redundant unused data. In order to model this data transaction among mobile users, which can be realized with assistance of current mobile social platforms, we introduce an auction-based contract mechanism, and then we further analyze the system performance. To improve the efficiency and performance of the system, a socially-aware mobility model is also designed. In this model, betweenness of the data auctioneers is introduced, according to which the number of data requesters in different auctions can be balanced and the performance of the system can be optimized. The proposed data transaction mechanisms and friendship-aware mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced effectively through the proposed mobility model, and the income per unit time of sellers in the networked data transaction can also be increased.
Learning a Local Hamiltonian from Local Measurements

Recovering an unknown Hamiltonian from measurements is an increasingly important task for certification of noisy quantum devices and simulators. Recent works have succeeded in recovering the Hamiltonian of an isolated quantum system with local interactions from long-ranged correlators of a single eigenstate. Here, we show that such Hamiltonians can be recovered from local observables alone, using computational and measurement resources scaling linearly with the system size. In fact, to recover the Hamiltonian acting on each finite spatial domain, only observables within that domain are required. The observables can be measured in a Gibbs state as well as a single eigenstate; furthermore, they can be measured in a state evolved by the Hamiltonian for a long time, allowing us to recover a large family of time-dependent Hamiltonians. We derive an estimate for the statistical recovery error due to approximation of expectation values using a finite number of samples, which agrees well with numerical simulations.
Voices in and of the machine: Source orientation toward mobile virtual assistants

Research regarding source orientation has demonstrated that when interacting with computers, people direct their communication toward and react toward the technology itself. Users perceive technology to be a source in human-machine communication (HMC). This study provides a new dimension to those findings with regard to source orientation with voice-based, mobile virtual assistants enabled by artificial intelligence (AI). In qualitative interviews regarding their conceptualizations of mobile conversational agents (Apple's Siri, Google Voice, Samsung S-Voice) and their perceptions of interactions with these specific technologies, some participants describe the agent they can hear but not see as a voice in the mobile phone (assistant as distinct entity) while others perceive the technology that they command to be the voice of the phone (assistant as the device). Therefore, congruent with existing research, users of mobile assistants orient toward a technology, instead of thinking they are interacting with a human, but, in contrast to existing research, attend to different technologies. When technologies possess a disembodied voice and are designed with various social cues and degrees of intelligence, the locus and nature of the digital interlocutor is not uniform in people's minds.
DEFT: A Distributed IoT Fingerprinting Technique

Identifying IoT devices connected to a network has multiple security benefits, such as deployment of behavior-based anomaly detectors, automated vulnerability patching of specific device types, dynamic attack mitigation, etc. In this paper, we look into the problem of IoT device identification at network level, in particular from an ISP's perspective. The simple solution of deploying a supervised machine learning algorithm at a centralized location in the network neither scales well nor can identify new devices. To tackle these challenges, we propose and develop a distributed device fingerprinting technique (DEFT), a distributed fingerprinting solution that addresses and exploits the presence of common devices, including new devices, across smart homes and enterprises in a network. A DEFT controller develops and maintains classifiers for fingerprinting, while gateways located closer to the IoT devices at homes perform device classification. Importantly, the controller and gateways coordinate to identify new devices in the network. DEFT is designed to be scalable and dynamic-it can be deployed, orchestrated, and controlled using software-defined networking and network function virtualization. DEFT is able to identify new device types automatically, while achieving high accuracy and low false positive rate. We demonstrate the effectiveness of DEFT by experimenting on data obtained from real-world IoT devices.
Using protection motivation theory in the design of nudges to improve online security behavior

We conducted an online experiment (n = 2024) on a representative sample of internet users in Germany, Sweden, Poland, Spain and the UK to explore the effect of notifications on security behaviour. Inspired by protection motivation theory (PMT), a coping message advised participants on how to minimize their exposure to risk and a threat appeal highlighted the potential negative consequences of not doing so. Both increased secure behavior – but the coping message significantly more so. The coping message was also as effective as both messages combined, but not so the threat appeal. Risk attitudes, age and country had a significant effect on behavior. Initiatives seeking to promote secure behavior should focus more on coping messages, either alone or in combination with fear appeals.
An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations

Green cloud computing attracts significant attention from both academia and industry. One of the major challenges involved, is to provide a high level of Quality of Service (QoS) in a cost-effective way for the end users and in an energy-efficient manner for the cloud providers. Towards this direction, this paper presents an energy-efficient, QoS-aware and cost-effective scheduling strategy for real-time workflow applications in cloud computing systems. The proposed approach utilizes per-core Dynamic Voltage and Frequency Scaling (DVFS) on the underlying heterogeneous multi-core processors, as well as approximate computations, in order to fill in schedule gaps. At the same time, it takes into account the effects of input error on the processing time of the component tasks. Our goal is to provide timeliness and energy efficiency by trading off result precision, while keeping the result quality of the completed jobs at an acceptable standard and the monetary cost required for the execution of the jobs at a reasonable level. The proposed scheduling heuristic is compared to two other baseline policies, under the impact of various QoS requirements. The simulation experiments reveal that our approach outperforms the other examined policies, providing promising results.
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks

Real-time recognition of dynamic hand gestures from video streams is a challenging task since (i) there is no indication when a gesture starts and ends in the video, (ii) performed gestures should only be recognized once, and (iii) the entire architecture should be designed considering the memory and power budget. In this work, we address these challenges by proposing a hierarchical structure enabling offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach. The proposed architecture consists of two models: (1) A detector which is a lightweight CNN architecture to detect gestures and (2) a classifier which is a deep CNN to classify the detected gestures. In order to evaluate the single-time activations of the detected gestures, we propose to use Levenshtein distance as an evaluation metric since it can measure misclassifications, multiple detections, and missing detections at the same time. We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures. ResNeXt-101 model, which is used as a classifier, achieves the state-of-the-art offline classification accuracy of 94.04% and 83.82% for depth modality on EgoGesture and NVIDIA benchmarks, respectively. In real-time detection and classification, we obtain considerable early detections while achieving performances close to offline operation. The codes and pretrained models used in this work are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
The Evolution of Blockchain: A Bibliometric Study

Blockchain as emerging technology is revolutionizing several industries, and its abundant privileges have opened up a bunch of research directions in various industries; thereby, it has acquired many interests from the research community.The rapid evolution of blockchain research papers in recent years has resulted in a need to conduct research studies that investigate a detailed analysis of the current body of knowledge in this field.To address this need, a few review papers have been published to report the latest accomplishments and challenges of blockchain technology from different perspectives.Nonetheless, there has not been any bibliometric analysis of the state of the art in blockchain where Web of Science (WoS) has been taken into consideration as a literature database.Hence, a thorough analysis of the current body of knowledge in blockchain research through a bibliometric study would be needed.In this paper, we performed a bibliometric analysis of all Blockchain's conference papers, articles, and review papers that have been indexed by WoS from 2013 to 2018.We have analyzed those collected papers against five research questions.The results revealed some valuable insights, including yearly publications and citations trends, hottest research areas, top-ten influential papers, favorite publication venues, and most supportive funding bodies.The findings of this paper offer several implications that can be used as a guideline by both fresh and experienced researchers to establish a baseline before initiating a blockchain research project in the future.
A robust image encryption algorithm based on Chua's circuit and compressive sensing

In terms of Chua's circuit system, compressive sensing (CS) and Haar wavelet, a novel image compression-encryption scheme (CES) is proposed in this paper. Firstly, the plaintext image is decomposed into approximate component and detail components through Haar wavelet. Then the approximate component is diffused by the threshold processing of local binary patterns (LBP) operator-based chaotic sequence which is produced by the combination of Chua's circuit and Logistic map. Next, the Lissajous map is applied to generate the chaos-combined asymptotic deterministic random measurement matrices (CADRMM) which are employed to measure the detail components in different compression ratios. In addition, the combination of mapped approximate and detail components is shuffled by the Logistic map. The experimental results and simulation analysis prove that the proposed cryptosystem is capable of reducing data for transmission and has good security performance under various attacks, especially for the shear and noise attacks.
Who's In Control?

Adoption of commercial smart home devices is rapidly increasing, allowing in-situ research in people's homes. As these technologies are deployed in shared spaces, we seek to understand interactions among multiple people and devices in a smart home. We conducted a mixed-methods study with 18 participants (primarily people who drive smart device adoption in their homes) living in multi-user smart homes, combining semi-structured interviews and experience sampling. Our findings surface tensions and cooperation among users in several phases of smart device use: device selection and installation, ordinary use, when the smart home does not work as expected, and over longer term use. We observe an outsized role of the person who installs devices in terms of selecting, controlling, and fixing them; negotiations between parents and children; and minimally voiced privacy concerns among co-occupants, possibly due to participant sampling. We make design recommendations for supporting long-term smart homes and non-expert household members.