Reviewerly/exHarmony huggingface dataset:
COVID-19 contact-tracing smartphone application usage—The New Zealand COVID Tracer experience

Contact tracing has been a central pillar of the nonpharmaceutical health system response to the COVID-19 pandemic. Countries around the world have devoted unprecedented levels of resources to build up their testing and tracing capabilities, including the development and deployment of smartphone-based applications. Yet despite these nontrivial investments, the body of academic literature evaluating the effects of the smartphone-based applications remains scant and many apps have not delivered the promised benefits (Bano et al. 2021). We contribute to this body of empirical evidence by analysing data on uptake and usage of New Zealand's QR code-based application New Zealand COVID Tracer (NZCT). Our paper uses descriptive statistics and regression analysis to focus on the likelihood of an individual registering to use the application, the likelihood that a registered user will use the application to scan QR codes, and the extent of that usage, measured as the number of codes scanned daily. In addition, we take advantage of the "natural experiment" offered by the decision in August 2021 following the establishment of endemic community transmission of COVID-19 to make use of the application mandatory from 7 September 2021 to assess the effect of this policy on usage. We find that despite increasing numbers of individuals downloading the application over time, the number of active devices and scanning activity was very low, even when community outbreaks occurred (i.e. actual infection risk was higher). Mandating use (separate and distinct from increased infection risk) led to an increase in the number of scans in total via an increase in the number of active devices only.
Product online review analysis using fuzzy ontology

Abstract This paper analyses the online text comments in the online trading platform and identifies the abnormal and contradictory comments. Firstly, the abnormal contradiction detection and analysis model of online text reviews is constructed based on fuzzy ontology to identify the abnormal and contradictory online text reviews. Secondly, the fuzzy emotional ontology and emotional tendency analysis method identify abnormal and contradictory online text reviews. The online text review anomaly detection and analysis model constructed enriches the process and method of online review anomaly identification.
Privacy Protection of Digital Information using Frequency Domain Watermarking Technique

Now-a-day’s every field focuses on digital content. Information is handled on the internet in digital form. Illegal copyright material must be restricted to prevent digital information from being copied. A better answer to this problem is digital watermarking. Digital watermarking is a copyright-protection technique that involves obscuring a picture. The suggested work is primarily concerned with the frequency domain, and it employs a discrete cosine transform (DCT) technique to transform the original image (cover image) into the frequency domain to add a watermark. DCT algorithm increases the speed of the process of watermarking the image. The algorithm performs picture decoding, image processing, watermarking, and decoding of the watermarked image, as well as applying a frequency transformation on the host data to embed a watermark. The transform coefficients and possible picture transformations are then modified. Embedding is done at the sender side and extraction is done at the receiver side. This algorithm also supports secure and faster transmission of images to the receiver. In the future, watermarking applications will include secure extensions, and digital watermarks must meet certain requirements such as integrity, robustness, and complexity.
Feature Selection for Metaheuristics Optimization Technique with Chaos

Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, the chaos map and dynamic-weight Particle Swarm Optimization (CPSO) are combined with PSO to improve the search process by adjusting the inertia weight of PSO and changing the position update formula in the Chaos dynamic-weight Particle Swarm Optimization (CPSO), resulting in efficient balancing for local and global PSO feature selection processes. Using eight numerical functions, the performance of CPSO was compared to that of two metaheuristic techniques which are the original PSO and Differential Evolution (DE). The results reveal that the CPSO is an efficient feature selection technique that generates good results by balancing the exploration and exploitation search processes.
Secure Shared Data in the Private Cloud With an EA Algorithm

Cloud computing allows users to save personal data on a server while maintaining privacy. Because data owners outsource their information to the cloud, data validation and confidentiality have become a severe issue. To avoid this manipulation, a cloud supplied certificate to all ordinary person who accesses information in the cloud. Once the client revises the report, the CSP must ensure that the certificate is presented on that given chunk. If the user is rescinded from trying to access the cloud, the accessible data center users must provide notice to the data agreed to sign by the invalidated user. This problem is solved using the proxy signature's novel public auditing approach. Furthermore, data protection is improved with the help of a public verifier to evaluate the integrity of data without getting the complete data on or after the cloud. We present an Efficient Auditing (EA) method for security auditing and user revocation in the private cloud. This work also explains the data integrity inspection design for cloud users that is characterized by multi-user modifications, collusion tolerance, and a consistent consistency checking computing cost. Our system is also entirely secure and supports public verification and quick user deactivation.
RGB image Encryption and Decryption with Neural Chaotic Functions

Encryption is one of the best ways to store information. Encryption involves the process of converting information using an algorithm, so that the encrypted information is unreadable to all, except authorized users with special knowledge who actually have the keywords. This research presents a fast and efficient method with the best rate of peak signal-to-noise ratio compared to previous methods, so that it offers a combination of deep neural network based on chaotic functions. The Neocognitron deep neural network is considered for the placement and permutation of image pixels for the purpose of encryption and the Fractal chaotic function used. This research deals with cryptographic operations with decoding by combining Neocognitron deep neural network and fractal model. Due to the fact that chaotic systems have complex and ergodic behavior and also have a quasi-random structure that is very sensitive to the initial values and conditions, so they can be used in image encryption. The results of the proposed approach indicate that the evaluation criteria have improved in compare of the previous similar methods.
AB-FGSM: AdaBelief optimizer and FGSM-based approach to generate adversarial examples

Deep neural networks (DNNs) can be misclassified by adversarial examples, which are legitimate inputs integrated with imperceptible perturbations at the testing stage. Extensive research has made progress for white-box adversarial attacks to craft adversarial examples with a high success rate. However, these crafted examples have a low success rate in misleading black-box models with defensive mechanisms. To tackle this problem, we design an AdaBelief based iterative Fast Gradient Sign Method (AB-FGSM) to generalize adversarial examples. By integrating the AdaBelief optimizer into the iterative-FGSM (I-FGSM), the generalization of adversarial examples is boosted, considering that the AdaBelief method can find the transferable adversarial point in the ε ball around the legitimate input on different optimization surfaces. We carry out white-box and black-box attacks on various adversarially trained models and ensemble models to verify the effectiveness and transferability of the adversarial examples crafted by AB-FGSM. Our experimental results indicate that the proposed AB-FGSM can efficiently and effectively craft adversarial examples in the white-box setting compared with state-of-the-art attacks. In addition, the transfer rate of adversarial examples is 4% to 21% higher than that of state-of-the-art attacks in the black-box manner.
Modern Open Source IC Design tools for Electronics Engineer Education

The article researches the problems of using free and open source tools for integrated circuits (IC) design as viable alternatives for expensive commercial products in electronics engineer education. Main steps of modern IC design flow discussed. OpenLane as an example of a software that combines different free and open source tools capable of performing such flow was used in this work. The practical example of OpenLane tollkit use is shown.
DeepBreast: Building Optimized Framework for Prognosis of Breast Cancer Classification Based on Computational Intelligence

Breast cancer is a significant issue for women worldwide and a leading cause of death. So automated detection of tumors is required. Numerous research studies have attempted to classify breast cancer using machine learning algorithms, and numerous researchers have stated that machine learning algorithms are preferable in the Prognosis process. An optimized framework based on Seven machine-learning algorithms was used in this paper. The machine learning algorithms are optimized using Grid Search. The performance of the framework was compared to determine which classifier performs the best on the Wisconsin dataset. The results indicated a significant increase in predicted accuracy, with the highest value being 78.6% of accuracy in the Wisconsin Prognosis Breast Cancer dataset (WPBC). These results demonstrate significant improvement in the area of breast cancer detection as compared to the existing state of art results of baseline machine learning models.
Quaternion keyed Least Square Approximation for image encryption

Securing image data from prying hackers is crucial in safeguarding the secrecy of data. Over the years, this was done by encrypting the image using an algorithm and a key, where the visible image was converted into a meaningless object. It is a difficult problem to design an image encryption technique based on chaotic systems with predictable cryptographic features. In this paper, the Quaternion, along with the Rossler attractor, was used to generate the key combination. The ciphering was done using the Least Square Approximation Algorithm (LSA). The algorithm was tested on a grayscale image database. The algorithm was initially tested in software using MATLAB R2018b, and was implemented in the Cyclone II EP2C35F672C6 device FPGA. On average, for a cipher image, the Peak Signal to Noise ratio (PSNR) was 9.09303 dB and the entropy was 7.9990 bits. For the cipher image, the Number of Pixels Change Rate (NPCR) and Unified Average Change Intensity (UACI) were 99.6039 and 33.4980, respectively. This proved that the algorithm could effectively mitigate the statistical and differential attacks. The key space was 2 (M ×N ×7 ×8), which was sufficiently high and mitigated the brute force attacks. The obtained results confirm that the cipher images resulting from the proposed ciphering scheme possess good cryptographic properties in terms of entropy, PSNR, UACI, NPCR, and keyspace analysis. Furthermore, the strength of the key is evaluated by the NIST test suite.
Exploring Security Vulnerabilities in Competitive Programming: An Empirical Study

Insecure code leading to software vulnerabilities can result in damages of the order of millions of dollars, and in critical systems, the loss of life. Hence, developing secure systems free of exploitable vulnerabilities has been a thrust area of research in recent years. Understanding developers’ approach towards vulnerabilities in their code can pave the way for improvements in insecure coding practices. Recent studies have explored online Q&A forums, open-source code repositories, and other code information sources to gain important insights into the pervasiveness of security vulnerabilities. However, to the best of our knowledge, competitive programming (CP) data, a rich source of information about coding practices, has not been explored from the perspective of insecure coding practices. The evaluation and assessment of coding practices used in CP is particularly intriguing because it has become a key player in developer recruitment in recent times. In this paper, we make one of the first attempts to draw the attention of the community to the emergent concern of insecure coding practices in CP. We use static analysis tools to identify the prevalence and nature of vulnerabilities in a large amount of CP data (6.1 million submissions) obtained from a top-rated CP platform, CodeChef, and find that 34.2% of submissions contain vulnerabilities. We observe that many programmers consistently follow insecure coding practices and most of the detected vulnerabilities are characterized by security standards (CWE, CVSS) based on real-world software.
A Research on College Students' English Learning Methods through Mobile Phone-Based software

In contemporary society, information technology has been highly developed. The learning of English vocabulary is no longer limited to the traditional classroom or schooling. The research adopts quantitative research method, using questionnaire survey. Based on the basic survey of college students using mobile devices to learn English vocabulary, it is found that the download of English learning software in college students' mobile devices has been very popular, reaching about 92%. However, there are some problems, such as low utilization rate or lack of obvious learning effect. Therefore, to solve these problems in reality, the author puts forward the following suggestions: for learners, first of all, they should stimulate their learning motivation. and cultivate their interest of English; Secondly, they should formulate reasonable learning goals according to their own actual ability; finally, they should plan time reasonably and make efficient use of learning time. For mobile learning resource developers, they should improve the quality of resources, and understand learners' vocabulary learning needs, which aims to create a good language vocabulary learning environment for learners.
A framework for flexibly guiding learning agents

Reinforcement Learning (RL) enables artificial agents to learn through direct interaction with the environment. However, it usually does not scale up well to large problems due to its sampling inefficiency. Reward Shaping is a well-established approach that allows for more efficient learning by incorporating domain knowledge in RL agents via supplementary rewards. In this work, we propose a novel methodology that automatically generates reward shaping functions from user-provided Linear Temporal Logic on finite traces ( $${\text {LTL}}_{\rm f}$$ ) formulas. $${\text {LTL}}_{\rm f}$$ in our work serves as a rich language that allows the user to communicate domain knowledge to the learning agent. In both single and multi-agent settings, we demonstrate that our approach performs at least as well as the baseline approach while providing essential advantages in terms of flexibility and ease of use. We elaborate on some of these advantages empirically by demonstrating that our approach can handle domain knowledge with different levels of accuracy, and provides the user with the flexibility to express aspects of uncertainty in the provided advice.
Data augmentation based on spatial deformations for histopathology: An evaluation in the context of glomeruli segmentation

The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations.The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations.We show that augmenting training data with spatially deformed images yields an improvement of up to 0.23 in average Dice score, with respect to training with no augmentation. We demonstrate that deformations with relatively strong distortions yield the best performance increase, while previous work only report the use of deformations with low distortions. The selected deformation models yield similar performance increase, provided that their parameters are properly adjusted. We provide bounds on the optimal parameter values, obtained through parameter sampling, which is achieved in a lower computational complexity with our single-parameter method. The paper is accompanied by a framework for evaluating the impact of random spatial deformations on the performance of any U-Net segmentation model.To our knowledge, this study is the first to evaluate the impact of random spatial deformations on the segmentation of histopathological images. Our study and framework provide tools to help practitioners and researchers to make a better usage of random spatial deformations when training deep models for segmentation.
Creating Smart House via IoT and Intelligent Computation

This study mainly uses the concept of the Internet of Things (IoT) to establish a smart house with an indoor, comfortable, environmental, and real-time monitoring system.In the smart house, this investigation employed the temperature-and humidity-sensing module and the lightness module to monitor any condition for an intelligent-living house.The data of temperature, humidity, and lightness are transmitted wirelessly to the human-machine interface.The correlation of the weight of the extension theory is used to analyze the ideal and comfortable environment so that people in the indoor environment can feel better thermal comfort and lightness.In this study, improved particle swarm optimization (IPSO) is employed-an effective evolutionary method used to search the function extreme.It is simple and has a fast convergence.The convergence accuracy of this algorithm is not high at the beginning, and it can easily fall into the local extreme points.The effect of the inertia weight in mix extension theory and PSO becomes IPSO-Extension Neural Network (ENN), which was analyzed and found reliable.Motivated by the idea of power function, a new non-linear strategy for decreasing inertia weight (DIW) was proposed based on the existing linear DIW.Then, a novel hierarchical multi-sensor data fusion algorithm adopting this strategy was presented, and the weight factor of the data fusion was estimated.The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there is no available information about the reliability of the information sources, the degree of redundancy/complementarities of the information sources, and the structure of the hierarchy.It obtained effective information from the fusion data, successfully removed the noise disturbance, and achieved favorable results.
Framework for Effective Utilization of Distributed Scrum in Software Projects

There is an emerging interest in using agile methodologies in Global Software Development (GSD) to get the mutual benefits of both methods.Scrum is currently admired by many development teams as an agile most known methodology and considered adequate for collocated teams.At the same time, stakeholders in GSD are dispersed by geographical, temporal, and socio-cultural distances.Due to the controversial nature of Scrum and GSD, many significant challenges arise that might restrict the use of Scrum in GSD.We conducted a Systematic Literature Review (SLR) by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the challenges.To validate our review findings, we conducted an industrial survey of 305 practitioners.The results of our study are consolidated into a research framework.The framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated by five experts of distributed Scrum.Results of the expert review were found supportive, reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.
Can Gaze Inform Egocentric Action Recognition?

We investigate the hypothesis that gaze-signal can improve egocentric action recognition on the standard benchmark, EGTEA Gaze++ dataset. In contrast to prior work where gaze-signal was only used during training, we formulate a novel neural fusion approach, Cross-modality Attention Blocks (CMA), to leverage gaze-signal for action recognition during inference as well. CMA combines information from different modalities at different levels of abstraction to achieve state-of-the-art performance for egocentric action recognition. Specifically, fusing the video-stream with optical-flow with CMA outperforms the current state-of-the-art by 3%. However, when CMA is employed to fuse gaze-signal with video-stream data, no improvements are observed. Further investigation of this counter-intuitive finding indicates that small spatial overlap between the network’s attention-map and gaze ground-truth renders the gaze-signal uninformative for this benchmark. Based on our empirical findings, we recommend improvements to the current benchmark to develop practical systems for egocentric video understanding with gaze-signal.
Bayesian Nonparametric Modelling of Multiple Graphs with an Application to Ethnic Metabolic Differences

Abstract We propose a novel approach to the estimation of multiple Gaussian graphical models (GGMs) to analyse patterns of association among a set of metabolites, under different conditions. Our motivating application is the SABRE (Southall And Brent REvisited) study, a triethnic cohort study conducted in the United Kingdom. Through joint modelling of pattern of association corresponding to different ethnic groups, we are able to identify potential ethnic differences in metabolite levels and associations, with the aim of gaining a better understanding of different risk of cardiometabolic disorders across ethnicities. We model the relationship between a set of metabolites and a set of covariates through a sparse seemingly unrelated regressions model and we use GGMs to represent the conditional dependence structure among metabolites. We specify a dependent generalised Dirichlet process prior on the edge inclusion probabilities to borrow strength across groups and we adopt the horseshoe prior to identify important biomarkers. Inference is performed via Markov chain Monte Carlo.