Reviewerly/exHarmony huggingface dataset:
PPTA: A location privacy-preserving and flexible task assignment service for spatial crowdsourcing

With the rapid growth of sensor-rich mobile devices, spatial crowdsourcing (SC) has emerged as a new crowdsourcing paradigm harnessing the crowd to perform location-dependent tasks. To appropriately select workers that are near the tasks, SC systems need to perform location-based task assignment, which requires collecting worker locations and task locations. Such practice, however, may easily compromise the location privacy of workers. In light of this, in this paper, we design, implement, and evaluate PPTA, a new system framework for location privacy-preserving task assignment in SC with strong security guarantees. PPTA takes advantage of only lightweight cryptography (such as additive secret sharing, function secret sharing, and secure shuffle), and provides a suite of tailored secure components required by practical location-based task assignment processes. Specifically, aiming for practical usability, PPTA is designed to flexibly support two realistic task assignment settings: (i) the online setting where tasks arrive and get processed at the SC platform one by one, and (ii) the batch-based setting where tasks arrive and get processed in a batch. Extensive experiments over a real-world dataset demonstrate that while providing strong security guarantees, PPTA supports task assignment with efficacy comparable to plaintext baselines and with promising performance.
Blended multi-class text to image synthesis GANs with RoBerTa and Mask R-CNN

Generation of scenes from text description requires employing computer vision for processing image and natural language processing for decoding the text provided. The task here is difficult as it requires generating multiple images of different classes together. Existing methods construct images from captions using a single dataset class, utilising Generative Adversarial Networks (GANs) approaches. Training several classes with GANs is a cumbersome task that needs a large amount of data and huge computational power. With complex datasets the efficiency of the generated image according to text description may be poor. In this paper, we propose an application that generates images based on the Caltech-UCSD bird and Oxford 102 flowers dataset. It leverages the Attentional Generative Adversarial Network (AttnGANs) as the generative model and the RoBERTa neural language model for word embeddings to build an image from multiple classes trained in isolation. The image created by GANs is segmented using Mask R-CNN and blended together using Poisson blending. The application can create scenes based on the description provided by the user and produce them in less time compared to training multiple classes in a single generative network.
Sentiment analysis on images using different transfer learning models

Opinion expression using visual images has grown more common due to the growth of social media platforms and internet technology. Users can communicate their ideas, emotions, and attitudes by posting images to various social media sites and sharing them. In recent years, models based on transfer learning have demonstrated exceptional results for image classification. Successful image-based sentiment analysis is made possible with transfer learning strategies. This study compares and investigates various transfer learning techniques to categorize image sentiment. The findings are analyzed and contrasted using well-known image sentiment datasets, including CK+, FER2013, and JAFFE. Several parameters, including accuracy, precision, and recall, are considered when analyzing the outcomes. The findings indicate that the model's performance depends on the dataset, i.e., different models work well with other datasets.
Sentiment Analysis using Bidirectional LSTM Network

Sentiment analysis is a cognitive tool to extract the emotional tone of a piece of text. It is an area of research that is actively pursued in Natural Language Processing. Social media, forums and blogs, among other places, have seen a massive surge in the number of personalized reviews since the emergence of Internet-based applications. Sentiment analysis is primarily concerned with the classification and prediction of users' thoughts and emotions from these reviews. In recent years, numerous deep learning techniques have emerged to achieve this task. This paper provides a technical summary of Sentiment Analysis using a Bidirectional LSTM network. This model is capable of handling long-term dependencies by introducing memory into the model for making better predictions. The Amazon Product Review dataset served as the source of data for this research. Concretely, the analysis is performed on 104,975 product reviews reflecting users' attitudes toward mobile electronics products. The proposed model attempts to classify the reviews into two categories: positive and negative. Finally, the paper outlines the results of the analysis and suggests potential avenues for future research.
Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks

Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression.Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN's generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests.We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only.We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System

Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contaminate the training set, misleading the HAR to produce erroneous outcomes. This paper presents the design of a label flipping data poisoning attack for a HAR system, where the label of a sensor reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of a K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.
A Novel Sonar Image Denoising Algorithm based on Block Matching

The existing sonar image denoising methods are either based on the spatial domain or the transform domain, which have their own shortcomings. This paper presents an improved BM3D algorithm for sonar image denoising. The parameters are adjusted according to the noise characteristics of sonar image, and a Gaussian filter and gray correction are added before basic estimation in order to remove the speckle noise, and finally its effect is verified on side scan sonar image. The experimental results show that it can effectively eliminate the noise from the side-scan sonar images and retain good structural information.
CNN-LSTM Based Smart Real-time Video Surveillance System

Modern surveillance technologies are intended to be precise, cost-effective, and run without the need for human interaction. In this regard, few topologies were employed in the previous decade which treated human movement as traditional object detection. Because of this approach, suspicious behavior was also tackled as an object, which made the existing human activity recognition (HAR) systems slow and inefficient when it came to classification for real-time application. In these recent years, machine learning algorithms have made significant advancements in the time-dependent classification of events. This research presents an HAR system that employs a Convolution Neural Network (CNN) to extract spatial information along with a Long Short-Term Memory (LSTM) approach for the rapid and precise sequential tracking of an identified object. This CNN-LSTM technique not only lowers the model's complexity but also improves its accuracy which allows it to be executed in real-time. Therefore, the proposed CNN-LSTM approach can detect suspicious activities in real-time at 10–13 FPS and obtain the best tracking performance in any circumstance while implemented on Raspberry Pi which works as a standalone system.
A blockchain-based decentralized collaborative learning model for reliable energy digital twins

This paper proposes a blockchain-based decentralized collaborative learning method for the Industrial Internet environment to solve the trust and security issues in Federated Learning. Deploy a decentralized network for collaborative learning based on the alliance chain, design a block data structure suitable for asynchronous learning, and model three stages of computing event triggering, computing task distribution, and computing result integration for cross-domain device collaborative learning. List the critical steps for network deployment, including inspection, tearing down old networks, creating organizational encryption material, creating channels, and deploying chaincode. It also introduces the development of crucial chaincode such as initialization, creation, query, and modification. Finally, the correlation between the number of data pieces of the network, the number of communications, and the time of communications are analyzed through experiments. This paper also proposes a decentralized asynchronous collaborative learning algorithm, develops chaincode middleware between the blockchain network and Artificial Intelligence training, and conducts experimental analysis on the industrial steam volume prediction data set in thermal power generation. The performance on the data set, and the experimental results prove that the asynchronous collaborative learning algorithm proposed in this paper can achieve a good convergence effect. It is also compared with the single-machine single-card regression prediction algorithm, proving that the proposed model has better generalization.
Video Frame Interpolation: A Comprehensive Survey

Video Frame Interpolation (VFI) is a fascinating and challenging problem in the computer vision (CV) field, aiming to generate non-existing frames between two consecutive video frames. In recent years, many algorithms based on optical flow, kernel, or phase information have been proposed. In this article, we provide a comprehensive review of recent developments in the VFI technique. We first introduce the history of VFI algorithms’ development, the evaluation metrics, and publicly available datasets. We then compare each algorithm in detail, point out their advantages and disadvantages, and compare their interpolation performance and speed on different remarkable datasets. VFI technology has drawn continuous attention in the CV community, some video processing applications based on VFI are also mentioned in this survey, such as slow-motion generation, video compression, video restoration. Finally, we outline the bottleneck faced by the current video frame interpolation technology and discuss future research work.
The heterogeneous effects of human development and good governance on mobile diffusion in the sub-Saharan region: Evidence from panel quantile regression

This methodological-theoretical study aims to tackle the predominant assumption of mobile diffusion studies that relies too much on technological determinism by implementing various composite indices measuring qualitative factors such as human development and good governance through panel quantile regression with fixed effect in the context of the sub-Saharan region. Our statistical results show that the effect of the independent variables on mobile diffusion is heterogeneous across quantiles. Specifically, (a) the effect of human development is positively associated with all quantiles, which supports the legitimacy of inclusion of qualitative factors when understanding mobile diffusion of the sub-Saharan region; and (b) the results also show that government capacity to create a better environment for business on mobile diffusion is clearly heterogeneous, and the effects are mixed. This study is to provide associational evidence between mobile diffusion and various qualitative indicators through social constructivists' perspectives. The results indicate that it is not necessary to see technological determinism and the social construction of technology as a pair of absolute opposites. Rather, heterogeneous variables from both sides can be mixed to discover new understandings of the impacts of technology diffusion.
Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.
Smart contract watermarking based on code obfuscation

Most blockchains are entirely open-source software. This gives auditors ability to review the security of cryptocurrencies such as Bitcoin. Because only after the source code of the cryptocurrency has been publicly audited by the market, users can transfer their tokens in a secure manner. However, the open-source feature has led to frequent plagiarism of smart contracts and introduces the risk of vulnerability proliferation. Copyright protection for smart contracts has never been investigated thoroughly. Due to performance overhead problems, some traditional watermarking techniques are no longer suitable for smart contracts running in a distributed environment. To reduce the negative impact of watermark implantation on processing performance, we use opaque predicate obfuscation to inject watermarks. Further, we propose a novel layout obfuscation via spaces and tabs to completely eliminate the negative performance impact. We conduct comprehensive experiments to evaluate our scheme. The experimental results demonstrate the effectiveness of our scheme.
Effect of blockchain technology initiatives on firms’ market value

Despite blockchain's potential to transform corporations by providing new ways of organizing business processes and handling information, extant research pays inadequate attention to how and under what conditions blockchain technology provides additional financial value for shareholders. Drawing on the efficient market hypothesis and signaling theory, we examined the relationship between firms' blockchain use, development announcements, and stock market reactions. We used the event study methodology to analyze a sample of blockchain projects initiated by US firms between 2016 and 2019. The sample contains 114 firm-event observations. The findings show that the average abnormal return over a 2 days event period (including the day of the announcement and the day after the announcement) was positive. This positive stock market reaction is even more substantial when firms announce blockchain projects that focus on saving cost or time. Our findings also indicate that blockchain announcements tend to elicit more positive market reactions from smaller firms. We analyzed 249 firm-event observations containing firms from around the world and conclude that blockchain technology has a non-significant long-term impact on operating performance. The contingency approach adopted in our research provides advice for selecting the right mix of blockchain investment initiatives that is most suitable for a given organizational context.
A Flexible ASIC-Oriented Design for a Full NTRU Accelerator

Post-quantum cryptosystems are the subject of a significant research effort, witnessed by various international standardization competitions. Among them, the NTRU Key Encapsulation Mechanism has been recognized as a secure, patent-free, and efficient public key encryption scheme. In this work, we perform a design space exploration on an FPGA target, with the final goal of an efficient ASIC realization. Specifically, we focus on the possible choices for the design of polynomial multipliers with different memory bus widths to trade-off lower clock cycle counts with larger interconnections. Our design outperforms the best FPGA synthesis results at the state of the art, and we report the results of ASIC syntheses minimizing latency and area with a 40nm industrial grade technology library. Our speed-oriented design computes an encapsulation in 4.1 to 10.2μs and a decapsulation in 7.1 to 11.7μs, depending on the NTRU security level, while our most compact design only takes 20% more area than the underlying SHA-3 hash module.
The Information Security Management Systems in E-Business

Enterprises trading on the electronic markets are exposed to security risks due to the active use of ICT in several transformation process activities. Realized risks cause particular damage to the enterprises that lack ISMS (information security management systems) or a basic process approach to IS management. In this article, the authors identify similarities and differences in information security management models from various aspects. The scientific article compares the presented models, their essence, goal, focus, and starting points. Based on advantages and disadvantages, the authors evaluate the possibilities of applying models in electronic business, which determines which models can be applied to all processes or only to specific processes of e-business. The representative data set was obtained from a sample of e-commerce enterprises using the Slovak electronic market. Research hypotheses based on scientific assumptions and statistical analysis are verified. The research conclusions provide an insight into practice of ISMS and an information security management system in e-commerce.
Optimization of CNOT circuits on limited-connectivity architecture

A CNOT circuit is the key gadget for entangling qubits in quantum computing systems. However, the qubit connectivity of noisy intermediate-scale quantum (NISQ) devices is constrained by their {limited connectivity architecture}. To improve the performance of CNOT circuits on NISQ devices, we investigate the optimization of the size/depth of CNOT circuits under the limited connectivity architecture. We present a method that can optimize the size of any $n$-qubit CNOT circuit $O\left(\frac{n^2}{\log \delta}\right)$ on any connected graph with minimum degree $\delta$, and prove this bound is optimal for the regular graph. For the near-term sparsely connected structure, we additionally present a method that can optimize the size of any $n$-qubit CNOT circuit to below $2n^2$. The numerical experiment shows that our method performs better than state-of-the-art results. Specifically, we present an example to illustrate the applicability of our algorithm. For the grid structure, which is commonly used in current quantum devices, we demonstrate that the depth of any $n$-qubit CNOT circuit can be optimized to be linear in $n$ with certain ancillary qubits (ancillas). Experimental results indicate that this method has significant improvements compared with all of the existing methods. We additionally test our algorithms on the five-qubit IBMQ devices, and the experiments show that the measurement results of the optimized circuit with our algorithm are more robust to noise compared with the IBM mapping method.
A posture detection method for augmented reality–aided assembly based on YOLO-6D

The assembly of small batch electrical and mechanical products still relies on manual operation, with the problems of high rechecking time and low assembly efficiency and quality. The augmented reality technology can be used to assist assembly to improve efficiency. In view of the high professionalism, labor, and time cost for the traditional posture detection method, a posture detection method is proposed to match the assembly posture by pre-calibrating the assembly posture template. The YOLO-6D model is brought in to increase the robustness of tracking registration. And the detection from both translation and rotation perspectives is designed to enhance the adaptability to different assembly tasks. For the generation of training datasets, a weighted sampling method based on part features is proposed to optimize the accuracy of position estimation with the limited training samples. Taking the assembly process of a typical electronic product as an example, the developed augmented reality aided assembly system is used to boost the assembly efficiency of the assembly operator effectively as compared to the traditional posture detection method.