Reviewerly/exHarmony huggingface dataset:
Re-annotation of training samples for robust maritime object detection

Machine learning and specifically deep learning techniques address many of the issues faced in visual object detection and classification tasks. However, they have the caveat of needing large amounts of annotated training data. In the maritime domain one may encounter objects fairly infrequently, depending on weather and location. This creates an issue of data collection. Areas such as harbors and channels see a lot of traffic, but the ships are of a specific class. Furthermore, the variability of the buoys from region to region and within regions is difficult and expensive to sample. Thus the amount and quality of available data is severely lacking. Furthermore very few publicly available maritime datasets exist In this work, we present a novel approach that detects possible “poor” training samples and automatically re-annotates them, based on the current state of the object detector. We show the applicability of our approach on real-life maritime data and show that the poor annotation quality of the datasets used can be mitigated. We show performance gain with respect to a baseline approach is proportional to the amount of poorly annotated data in the dataset. When 25% of the data is poor we achieve a 5.5%, 13.7%, and 8.0% increase in performance on 3 separate datasets, compared to a baseline model. With 50% noise we reach 58.5%, 18.7% and 94.2% increase respectively. Our approach also allows for the iterative improvement of a given dataset by providing a set of pseudo-annotations to replace the current incorrect ones.
A human evaluation of English-Slovak machine translation

The paper aims to obtain an error profile for machine translation (MT) from English into Slovak. We present an adjusted framework for MT evaluation, which is based on Vanko's categorical framework, but reflects machine translation peculiarities of synthetic and/or inflectional languages. Based on the framework, we analyse the errors generated by Google Translate and identify the most frequent categories of errors occurring in machine translation when translating newspaper articles from English into Slovak. While we have seen research on widely-spoken languages, such as English or other major official EU languages, little is known about Slovak, which is also an official EU language. This paper provides the first human MT evaluation study of English-Slovak machine translation using professional translators for a more detailed depiction of translation quality. Our research has revealed that the highest numbers of errors occurred in the sphere of lexical semantics, as well as in syntactic-semantic correlativeness, both being closely related. Additionally, based on the results of the Cochran Q test, we show how individual MT errors located in the examined categories differ in co-incidence and in how they impact translation quality.
Behavior of local quantum Fisher information and local quantum uncertainty in dipolar spin system with DM and KSEA interactions

In this paper, a two-spin-1/2 particle coupled via dipolar interaction with the interplay of antisymmetric Dzyaloshinskii–Moriya and symmetric Kaplan–Shekhtman–Entin–Wohlman–Aharony interactions is considered at thermal equilibrium. The local quantum uncertainty and local quantum Fisher information are investigated as the pairwise nonclassical correlations quantifiers. The effects of the different system parameters on the quantified quantum correlation are analyzed in detail. Our results showed that the two studied thermal quantum correlation measures have similar qualitative behavior as well as quantitative sometimes. Also, it is shown that the quantumness of correlations is lost as the temperature rises sufficiently. Nevertheless, the antisymmetric and symmetric contributions of spin-orbit coupling considerably improve the amount of quantum correlations. Our analysis emphasized also that the local quantum uncertainty is bounded by local quantum Fisher information.
Facial Expression Recognition Using a Novel Modeling of Combined Gray Local Binary Pattern

Facial Expression Recognition (FER) is an active research field at present. Deep learning is a good method that is widely used in this field but it has extreme hardware requirements and it is hard to apply in normal terminal devices. So, many other methods are being researched to apply FER in such devices and systems. This work proposes fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features in facial expression recognition to enhance the recognition rate that can apply FER in the kind of devices and systems. The work included the main steps such as the technique of cropping an input face image from a camera or dataset, the approach of dividing face images into nonoverlap regions for extracting LBP features, applying the fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features, using uniform feature to reduce the lengths of descriptors, and finally using Support Vector Machine (SVM) for emotion classification. Four popular facial emotion datasets are used in experiments and their results demonstrate that the recognition rate of the proposed method is better in comparison with two types of existent features: Local Binary Pattern (LBP) and Combined Local Binary Pattern (CLBP). The accuracy of experiments performed on four facial expression datasets with different sizes is from about 95% to more than 99%.
A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to these models. We use a Bayesian inference approach to identify posterior distributions of these parameters such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM’s quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in some scenarios.
Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph

Despite Meta’s efforts to promote health information in the COVID-19 pandemic, the growing number of ads is making online content control extremely challenging. To effectively categorize the ads, this work investigates the major discourses shared across Meta ads with various categories related to COVID-19. We propose an interpretable classification model that captures common discourses in the form of keywords and phrases in ads. Particularly, we propose to use hypergraph to connect ads and discourses to capture their high-order interactions. Experiments on a curated Meta Ads dataset show that our model can provide subject-specific discourses and improve classification performance significantly.
Revisiting Linus’ Law in OpenStreetMap: An Agent-Based Approach

OpenStreetMap, a wiki-style map of the world, has become a popular source of free and open geospatial data by leveraging the power of an immense community of volunteers. Much like other collaboration-based peer production projects such as Wikipedia, OpenStreetMap is decentralized and participant-driven, so questions of data reliability are frequent. Within these participant-driven projects, the assumption known as Linus’ Law suggests that data quality increases with the number of contributors. In this paper, we evaluate Linus’ Law as applied to the co-production of volunteered geographic information using an agent-based model and examine the effects of knowledge level, variability, and prioritization on emergent production patterns and overall data quality. We demonstrate how diminishing returns limit Linus’ Law and that, in the end, data quality is dependent more on the experience of contributors, rather than the number of contributors.
Pedagogy Modeling for Building SeisTutor Intelligent Tutoring System

This chapter introduces the detailed design of SeisTutor and the adaptive features incorporated in the pedagogy model. This is followed by an in-depth discussion of the adaptive features: custom-tailored curriculum sequencing module, tutoring strategy recommendation module, and learner performance analyzer module (emotion recognition module and the degree of understandability). The emotion recognition module is used to track learner excitement, while the degree of understandability module is used to determine the learner’s degree of understanding after completing the learning session. The overall performance and activity of the learner are visualized in the form of a progress report.
Execution of Developed Intelligent Tutoring System

This chapter introduces execution of SeisTutor, which includes sub-components of the learner model, the pedagogy model, the domain model, the expert model, and the learner interface model. The domain model includes the Course Manager along with the Knowledge Repository sub-components. The pedagogy model includes the Curriculum Identification Module, the Tutoring Strategy Recommendation Module, and the Learner Performance Analyzer Module sub-components. The interconnection, functioning, and data flow are described in the chapter using the DFDs.
What can scatterplots teach us about doing data science better?

A scatterplot is often the graph of choice for displaying the relationship between two variables. Scatterplots are useful for exploratory analysis, but can do much more than just identifying correlations. As data sets get larger and more complex, relying solely on “eye power” alone may cause us to miss interesting associations, or worse, make wrong interpretations. We show that by combining scatterplots with statistical and logical reasoning (the sliding window and two-axis median bisection), we may identify interesting associations in a case study of Graduate Record Examination admission versus graduation outcomes, and whether low detectability of proteins in a biological sample are truly associated with low abundance. Due to subjective visual interpretability, we recommend graphing the data using a multitude of visual variables and graph types before concluding the absence of an association. Finally, even if associations are demonstrable, developing causal models that could explain the observed fuzziness and lack of apparent correlations in the scatterplot are helpful for better decision-making and interpretation.
Extended Privacy for Extended Reality: XR Technology Has 99 Problems and Privacy is Several of Them

Americans are rapidly adopting innovative technologies which are pushing the frontiers of reality. But when they look at how their privacy is protected within the new extended reality (XR), they will find that U.S. privacy laws fall short. The privacy risks inherent in XR are inadequately addressed by current U.S. data privacy laws or court-created frameworks that purport to protect the constitutional right to be free from unreasonable searches. Many scholars, including Ryan Calo, Danielle Citron, Sherry Colb, Margaret Hu, Orin Kerr, Kirsten Martin, Paul Ohm, Daniel Solove, Rebecca Wexler, Shoshana Zuboff, and others, have highlighted the gaps in U.S. privacy protections stemming from big data, artificial intelligence, and increased surveillance technologies. However, the depth and breadth of what XR technology reveals about a person, the risks it poses to bystanders, and the imminent paradigm shift of a public space versus a private space are new problems. This article provides three central contributions to technologists, legislators, and anyone interested in privacy rights: first, a brief guide to understanding XR technology; second, a survey of the current U.S. privacy landscape and the gaps in U.S. privacy protections for XR; and third, an easily digestible list of solutions that legislators and technologists can pursue to better protect privacy in XR.
Deep active learning with Weighting filter for object detection

Active learning has been demonstrated to be effective in reducing labeling costs by selecting the most valuable data from the unlabeled pool. However, the training data of the first epoch in almost all active learning methods is randomly selected, which will cause an instability learning process. Additionally, current active learning, especially uncertainty-based active learning methods, is prone to the problem of data bias because model learning inevitably prefers partial data. For the above issues, we propose Weighting filter (W-filter) tailored for object detection in this paper, which is an image filtering algorithm that can calculate the contribution of a single image to the neural network training as well as remove similar ones in the entire selected data to optimize the sampling results. We first use W-filter to select the training data of the first epoch, which can guarantee better performance and a more stable learning process. Then, we propose to resample the uncertain data from the perspective of the frequency domain to alleviate the problem of data bias. Finally, we redesign several classical uncertainty methods specifically for classification to make them more suitable for the task of object detection. We do rigorous experiments on standard benchmark datasets to validate our work. Several classical detectors such as Faster R-CNN, SSD, R-FCN, CenterNet, EfficientDet, and effective networks including ResNet, DarkNet, MobileNet are used in experiments, which shows our framework is detector-agnostic and network-agnostic and thus can meet any detection scenario.
An Improved Biometric Fuzzy Signature with Timestamp of Blockchain Technology for Electrical Equipment Maintenance

The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore, how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions, the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally, the effectiveness of the proposed method is verified by experiments.
Grid Adaptive Bucketing Algorithm Based on Differential Privacy

With the popularity of location-based services, the restricted relationship between the availability of big location data and user’s privacy security has become a challenging issue. The spatial division is an effective measure for statistical location data characteristics. This paper proposes a grid adaptive bucketing algorithm based on differential privacy (GAB) to solve the problem that the existing differential privacy location data division method does not fully consider the distribution characteristics of spatial data points and noise superimposition. The algorithm first divides the mobile app location spatial data set into two layers of grids. It then performs adaptive binning judgment according to the square sum error value in the divided area. It puts areas with similar distributions into the same bucket, reducing the uniform assumption error and the noise error caused by many blank areas. Finally, a noise allocation strategy based on Hopkins statistic is adopted to achieve a reasonable noise allocation. The experimental results on two real large-scale location data sets, Checkin and Beijing, show that, compared with other classical algorithms based on differential privacy space partitioning, the GAB algorithm performs better in common evaluation indicators such as relative error and absolute error, which means GAB can obtain better range query effects and privacy protection effects.
Application of Lightweight Thermal Insulation Building Materials for Green Building Design

In order to focus on the application of green environmental protection lightweight thermal insulation materials, an in-depth study is carried out on the basis of joint architectural design. The application of green environmental protection materials in the construction industry was successfully analyzed through methods such as layout design method, optimization design method, and scientific optimization of material selection. In the process of urbanization development in our country, the number of urban population has increased sharply, and the demand for buildings has also increased. Therefore, it is necessary to rapidly promote green building design while the construction industry is developing rapidly and complete the upgrading and transformation of the building in terms of form design. We pay attention to the needs of buildings in terms of shading and lighting, and improve the conditions of building shading and lighting through green building technology. As a result, on the basis of maintaining the residual heat of the building, the diffuse radiation of the ultraviolet rays of the sun is realized, which effectively improves the utilization efficiency of natural energy and reduces the consumption of internal energy. Architectural design under the concept of green building design not only can it meet the current functional requirements of people for architecture but also imperceptibly promote the stable development of our country’s construction industry. After the research on the application of green environmental protection materials, the integration and application of green building design concepts in architectural design is of great significance not only to meet people's health and ecological needs of buildings but also to promote social stability and sustainable development.
Self-Attention Based Time-Rating-Aware Context Recommender System

The sequential recommendation can predict the user's next behavior according to the user's historical interaction sequence. To better capture users' preferences, some sequential recommendation models propose time-aware attention networks to capture users' long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user's displayed feedback (rating) on an item reflects the user's preference for the item, which directly affects the user's choice of the next item to a certain extent. In different periods of sequential recommendation, the user's rating of the item reflects the change in the user's preference. In this paper, we separately model the time interval of items in the user's interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users' preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.
Copy-Move Image Forgery Detection Using SURF, SIFT, and KAZE

In this digital world, image forgery is the most common cause for any digital forgery. Nowadays, people accept digital images blindly, even without checking their integrity and authenticity. One of the critical facts in digital image forensics is that we need to keep high confidence in forgery detection algorithms. In this work, multiple image forger detection algorithms are evaluated to check their efficacy using their detection accuracy and execution time. Discrete wavelet transform (DWT) is used for image decomposition at the preprocessing stage. The objective of this work is to suggest a best copy-move forgery detection algorithm among SIFT, SURF, and KAZE. For modeling the code, MATLAB is used as a programming language. Using the SURF algorithm, the accuracy rate is 56.50% with average execution time 1.28 s, while it is 66.75% and 2.78 s using SIFT. The detection rate by using the KAZE algorithm is reported as 61.24% with execution time is 1.50 s.
A Survey of the Workload Forecasting Methods in Cloud Computing

A few years ago, cloud computing had widely altered the way of computation and storage. It is quite challenging for cloud service providers to maintain the required quality of service (QoS) standard without violating a service level (SLA) agreement. To improve the performance of the cloud, accurate workload prediction plays a vital role. Cloud computing promises scalability, on-demand service, and virtualization to improve the quality of service (QoS) offered to end-users. In this paper, we present a comprehensive survey of workload prediction approaches in cloud environments. It also highlights the emerging challenges like resource wastage, excess power consumption, quality of service violations, etc.
Liver Cancer Detection Using Hybrid Approach-Based Convolutional Neural Network (HABCNN)

Liver cancer is one of the serious disease these days. Every year almost 800,000 people get detect of liver cancer and 700,000 died out of that. But it is very difficult to detect it at early stage. To detect this manually is a tedious task and cannot be perform easily as there was a serious of tests and all through which patient has to go through and still did not have any surety of detection. So CT images after applying watershed segmentation can be used to get this liver cancer detected by assessing tumor load, clinical treatment response by using the concept of deep learning. In this paper, a Hybrid Approach-Based Convolutional Neural Network (HABCNN) is proposed for liver tumor segmentation which has been mathematically modeled so that issue of cancer detection get resolved. The kidney and spleen is segmented initially. After cancer tissue segmentation, GLCM was used to extract features from tumor parts. Eventually, Hybrid Approach-Based Convolutional Neural Network (HABCNN) was used to identify the hepatocellular and metastatic carcinoma for hepatic cancer. A better 99.44% identification precision came from the suggested classifier. The modern segmentation approach should insure that the precise boundary structure of the cancer area is known so that important information can be better diagnosed. A comparison based on specificity, sensitivity, accuracy and DSC has been done with some existing techniques like Multilayer Perception [14] and C4.5 [15]. Without human intervention, the classification method allows successful cancer damage defection. So this can be used by medical practitioner for early detection of liver cancer.
Artificial Intelligence

The definition of the smart city has changed over time and is expected to continue to change. For example, Japan stepped into the Society 5.0 vision, which encompasses robotics, artificial intelligence, the Internet of Things, and big data. Society 5.0 is Japan’s national vision within the fifth Science and Technology Fundamental Plan, which aims to realize a data-driven, human-centered, next-generation society (Kobashi et al., 2020; Zhang et al., 2020). This vision means that everyone, regardless of location, will benefit from innovation and advances in technology, including the elderly population in rural areas. Community 5.0 emphasizes solving all kinds of social problems while harmonizing sustainability and economic growth. Therefore, Community 5.0 guides cities of all sizes on how to build smart infrastructure. It is no longer just about new technologies, but also about understanding a community’s problems and providing appropriate solutions through these technologies. In the context of the smart city, this approach is called “problem-solution-oriented smart city” (Neirotti et al., 2014), and it is the basis for achieving the greater goals of Community 5.0.