Reviewerly/exHarmony huggingface dataset:
The Elephant in the Dark: A New Framework for Cryptocurrency Taxation and Exchange Platform Regulation in the US

The proliferation of cryptocurrencies and the remarkable expansion of novel economic practices associated with them pose an unprecedented challenge to established norms of taxation and market regulation. Drawing on two years of fieldwork, surveys, as well as big data analysis of the most valuable 100 cryptocurrencies’ white papers and the terms of service agreements of all cryptocurrency exchange platforms, this paper proposes an evidence-based framework to design a novel regulation and taxation approach to cryptocurrencies and their markets by using the US as case study. This new framework calls for approaching cryptocurrencies as data money. Drawing on the material political economy of new digital financial practices, the paper locates the universe of taxable events and invisible/vague regulation areas by approaching exchange platforms as stacked economization processes. We need to make sense of these new economic spaces in order to imagine more effective regulative instruments addressing questions of economic actor protection and efficiency. The paper concludes by proposing a new instrument of taxation (Data Money Tax) and a dynamic regulative approach to cryptocurrency exchange platforms (Stack Regulation).
DigBug—Pre/post-processing operator selection for accurate bug localization

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports as well as workflow and results of state-of-the-art approaches, is that most approaches attempt localization for every bug report without considering the different characteristics of the bug reports. We propose DigBug as a straightforward approach to specialized bug localization. This approach selects pre/post-processing operators based on the attributes of bug reports; and the bug localization model is parameterized in accordance as well. Our experiments confirm that departing from "one-size-fits-all" approaches, DigBug outperforms the state-of-the-art techniques by 6 and 14 percentage points, respectively in terms of MAP and MRR on average.
Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to process big data efficiently, big data is processed using newer distributed storage and processing frameworks. Big data view materialization is a technique to process big data queries efficiently on these distributed frameworks. It generates valuable information, which can be used to take timely decisions, especially in cases of disasters. As there are a very large number of big data views, it is not possible to materialize all of them. Therefore, a subset of big data views needs to be selected for materialization, which optimizes the query response time for a given set of workload queries with minimum overheads. This big data view materialization problem, having objectives minimization of the query evaluation cost of a set of workload queries, while simultaneously minimizing the update processing costs of the materialized views, has been addressed using improved strength pareto evolutionary algorithm (SPEA-2) in this paper. The proposed big data view selection algorithm, which is able to compute a set of diverse non-dominated big data views, is shown to perform better that existing big data view selection algorithms..
An overview of the surge of rape incidents in Bangladesh during COVID-19 pandemic

The COVID-19 pandemic left impact on domestic violence, sexual assault and increases the pandemic related rape cases in Bangladesh. The present study aimed to give an overview COVID-19 related rape cases so that awareness can be created among public in the outbreak. Three English and Bangla online news portals were selected purposively to include in the study and news of „Rape Case‟ were searched. Search was conducted by the word “rape news”. After removal of the repetitions news, we select ten news that reported detailed rape cases during the country wide lockdown. From ten rape cases, age of victim ranges from 8-23, the victim of eight rape cases were below 18 years (In some cases the age of the victim were not mentioned). In addition, there were five incidents of gang rape, one rape attempts and among ten cases seven of the rape were general types. Additionally, the majority of rape cases occurred at rural areas. Women of all ages are at risk of rape during the pandemic and particular attention should be given to rural areas, as most cases of rape are registered there. Public awareness about rape would be effective to report in due time with preserving the evidence of crime.&#x0D; CBMJ 2021 January: vol. 10 no. 01 P: 43-49
UAV path planning based on modified ant colony algorithm and DUBINS curves

In view of the communication loss caused by the heavy rain and other major natural disasters in Henan Province, when the emergency vehicles could not enter the scene, in order to quickly restore the communication, the method of unmanned aerial vehicle(UAV) flying over the affected area to set up a temporary base station is studied. The UAV needs to avoid obstacles, so a path planning method based on the combination of modified ant colony algorithm and DUBINS curves is proposed. By this algorithm, the global path of UAV is shorter, the path is smooth and the kinematics constraint is satisfied. Finally, the feasibility, validity and superiority of the proposed algorithm are showed by comparing with the basic ant colony algorithm.
Overview of the Role of the Internet of Things and Cyber-Physical Systems in Various Applications

Internet of things (IoT) and cyber-physical system (CPS) have a common intend of integrating network connectivity and computational capability to physical devices and systems. This seamless integration is the foundation of CPS and helps deploy more capable, adaptable, and reliable systems. Depending on the application CPS can be small and closed or an exceptionally large and complex system. In general, a CPS includes a network of devices that interact with a physical system that simultaneously controls or monitor communication and computing resources. In the recent decade with the advent of technology such as IoT/IET and artificial intelligence (AI) the interaction and connectivity b\etween people, places, and things have taken a whole new perspective. The progress of IoT has made it possible to collect, store, and analyze plenty of data and provide feedback from cyberspace to physical space. Initially, the internet was a medium to connect humans, but with the integration of sensors, the internet, IoT/IET forming CPS it is possible to connect the physical world and cyberspace without human intervention.
False-Positive Results of Mammography Screening in the Era of Digital Breast Tomosynthesis

Thao-Quyen H. Ho, MD, PhD; Michael C. S. Bissell, PhD; Karla Kerlikowske, MD; Rebecca A. Hubbard, PhD; Brian L. Sprague, PhD; Christoph I. Lee, MD, MS; Jeffrey A. Tice, MD; Anna N. A. Tosteson, ScD; Diana L. Miglioretti, PhD
Children’s Digital Art Ability Training System Based on AI-Assisted Learning: A Case Study of Drawing Color Perception

This study proposed a children’s digital art ability training system with artificial intelligence-assisted learning (AI-assisted learning), which was designed to achieve the goal of improving children’s drawing ability. AI technology was introduced for outline recognition, hue color matching, and color ratio calculation to machine train students’ cognition of chromatics, and smart glasses were used to view actual augmented reality paintings to enhance the effectiveness of improving elementary school students’ imagination and painting performance through the diversified stimulation of colors. This study adopted the quasi-experimental research method and designs the pre-test and post-test for different groups. The research subjects are the Grade 4 students of an elementary school in Taitung City, Taiwan. The test tools included an imagination test and an evaluation of painting performance ability. The test results of a total of 30 students before and after the experiment included the experimental group that received the children’s digital art ability training system with AI-assisted learning and 30 students in the control group that had not received the teaching were analyzed by covariance. These results were supplemented by the description and interpretation of student feedback, teachers’ reflection notes, and other qualitative data to understand the performance of the students in the experimental group in terms of imagination and painting performance.
Nanolearning: A New Paradigm Shift in Teaching and Learning

Nanolearning is the process of learning in short pieces of information. The content must be within five minutes, in most cases two minutes or less. Nano learning is effective in modern education, especially Generation Z, since students' attention span has reduced drastically. The students are finding it challenging to focus for a longer duration. Nano learning is bite-size learning in shorter condensed learning capsules. This paper discusses methodologies for applying nanolearning in both classroom and blended learning environments. This paper deliberates nanolearning in the remote learning environment. The paper also discusses applications of nanolearning in different fields. The paper concludes with the future enhancements of nanolearning.
A Systematic Review of Mobile-Assisted Language Learning Research Trends and Practices in Malaysia

Mobile technology integration in Malaysia’s English language education has a bright future, owing to the many advantages offered by mobile devices. However, there are inadequate studies that systematically review existing work in Malaysia to uncover gaps in the use of mobile technologies for English teaching and learning. Thus, a systematic review was conducted to offer an up-to-date synthesis of Mobile-Assisted Language Learning (MALL) integration in Malaysia from January 2019 to January 2021. The review followed the PRISMA Statement's guidelines which resulted in the identification of eleven articles that met all the inclusion criteria. Following that, the eleven articles were analysed and synthesised quantitatively and qualitatively using seven codes derived from two earlier systematic reviews of the literature. The codes were: (1) research objectives, (2) research methodologies, (3) educational level, (4) learning context, (5) device types, (6) theories, and (7) research outcomes. The systematic review discovered several significant findings, including the fact that the majority of the studies examined the effect of MALL integration on students' English acquisition and performance, and that MALL was more prevalent in Malaysian higher education institutions than other levels of education, with the lowest prevalence in primary schools. In light of this information, recommendations for future research were proposed. Malaysian academics are encouraged to broaden the scope of their future research and engage students from all levels of education in future MALL practices in order to better understand MALL's usability in English language instruction in Malaysia.
Blockchain localization spoofing detection based on fuzzy AHP in IoT systems

Abstract Location spoof detection is a major component of location proofing mechanisms in internet of things (IoT), and it is significant for the system to assess the trustworthiness of the location data associated with the user. Unlike the work that employs physical layer features, we interest in building the infrastructure for a solution to establish location spoofing detection capabilities in blockchain-based IoT systems. In detail, at the node and the mobile trajectory level, we create an IoT system for evaluating the trustworthiness of location proofs with blockchain location system features. A blockchain-based multilayer fuzzy hierarchical analysis process (AHP) evaluation method is contemplated to detect location spoofing in the IoT system. Simulation results indicate the proposed method has a superior performance and provides a basis for the trustworthiness assessment of location proofs.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System

Graph embedding based retrieval has become one of the most popular techniques in the information retrieval community and search engine industry. The classical paradigm mainly relies on the flat Euclidean geometry. In recent years, hyperbolic (negative curvature) and spherical (positive curvature) representation methods have shown their superiority to capture hierarchical and cyclic data structures respectively. However, in industrial scenarios such as e-commerce sponsored search platforms, the large-scale heterogeneous query-item-advertisement interaction graphs often have multiple structures coexisting. Existing methods either only consider a single geometry space, or combine several spaces manually, which are incapable and inflexible to model the complexity and heterogeneity in the real scenario. To tackle this challenge, we present a web-scale Adaptive Mixed-Curvature ADvertisement retrieval system (AM-CAD) to automatically capture the complex and heterogeneous graph structures in non-Euclidean spaces. Specifically, entities are represented in adaptive mixed-curvature spaces, where the types and curvatures of the subspaces are trained to be optimal combinations. Besides, an attentive edge-wise space projector is designed to model the similarities between heterogeneous nodes according to local graph structures and the relation types. Moreover, to deploy AMCAD in Taobao, one of the largest e-commerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval. Extensive evaluations on real-world datasets and A/B tests on online traffic are conducted to illustrate the effectiveness of the proposed system.
CAISE: Conversational Agent for Image Search and Editing

Demand for image editing has been increasing as users' desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality into the editing tool is a potential remedy for this demand. Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests. To build such a system, we first collect image search and editing conversations between pairs of annotators. The assistant-annotators are equipped with a customized image search and editing tool to address the requests from the user-annotators. The functions that the assistant-annotators conduct with the tool are recorded as executable commands, allowing the trained system to be useful for real-world application execution. We also introduce a generator-extractor baseline model for this task, which can adaptively select the source of the next token (i.e., from the vocabulary or from textual/visual contexts) for the executable command. This serves as a strong starting point while still leaving a large human-machine performance gap for useful future work. Data and code are available: https://github.com/hyounghk/CAISE.
Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey

Advances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distributed machine learning, which, however, still faces many security challenges. Among them, model poisoning attacks have a significant impact on the security and performance of FL. Given that there have been many studies focusing on defending against model poisoning attacks, it is necessary to survey the existing work and provide insights to inspire future research. In this paper, we first classify defense mechanisms for model poisoning attacks into two categories: evaluation methods for local model updates and aggregation methods for the global model. Then, we analyze some of the existing defense strategies in detail. We also discuss some potential challenges and future research directions. To the best of our knowledge, we are the first to survey defense methods for model poisoning attacks in FL.
Signing and Verifying Encrypted Medical Images Using Double Random Phase Encryption

Digital Signature using Self-Image signing is introduced in this paper. This technique is used to verify the integrity and originality of images transmitted over insecure channels. In order to protect the user's medical images from changing or modifying, the images must be signed. The proposed approach uses the Discrete Wavelet Transform to subdivide a picture into four bands and the Discrete Cosine Transform DCT is used to embed a mark from each sub-band to another sub-band of DWT according to a determined algorithm. To increase the security, the marked image is then encrypted using Double Random Phase Encryption before transmission over the communication channel. By verifying the presence of the mark, the authority of the sender is verified at the receiver. Authorized users' scores should, in theory, always be higher than illegal users' scores. If this is the case, a single threshold might be used to distinguish between authorized and unauthorized users by separating the two sets of scores. The results are compared to those obtained using an approach that does not employ DWT.
Image recognition of interference fringes in polishing by convolutional neural network with data augmentation by deep convolutional generative adversarial network

With the growing need for high specification requirements for the latest manufacturing processes and optical designs of glass lenses, the technical requirements for lens polishing have increased. The manufacturing parameters must be adjusted in a timely manner to meet the required specifications. We use a Fizeau interferometer to classify and analyze interference fringes measured in the actual polishing process of glass lenses. Given the low incidence of interference fringes in practice, the training dataset contained a disproportionate ratio of data for each data type. To reduce the manufacturing cost and data collection time, this study focused on three common types of interference fringes in the manufacturing processes and integrated a deep convolutional generative adversarial network with convolutional neural networks (CNNs) for fringe type classification. The deep convolutional generative adversarial network was used to establish a data augmentation generator, and Jensen–Shannon divergence was employed to identify the epoch number that yielded distributions of interference fringe numbers closest to the real distributions; this approach could achieve the diversity of interference fringes in the generated images. Finally, the generated data were used to train the CNN models, and the accuracy of image recognition reached above 86%.
Multivariate Lipschitz Analysis of the Stability of Neural Networks

The stability of neural networks with respect to adversarial perturbations has been extensively studied. One of the main strategies consist of quantifying the Lipschitz regularity of neural networks. In this paper, we introduce a multivariate Lipschitz constant-based stability analysis of fully connected neural networks allowing us to capture the influence of each input or group of inputs on the neural network stability. Our approach relies on a suitable re-normalization of the input space, with the objective to perform a more precise analysis than the one provided by a global Lipschitz constant. We investigate the mathematical properties of the proposed multivariate Lipschitz analysis and show its usefulness in better understanding the sensitivity of the neural network with regard to groups of inputs. We display the results of this analysis by a new representation designed for machine learning practitioners and safety engineers termed as a Lipschitz star. The Lipschitz star is a graphical and practical tool to analyze the sensitivity of a neural network model during its development, with regard to different combinations of inputs. By leveraging this tool, we show that it is possible to build robust-by-design models using spectral normalization techniques for controlling the stability of a neural network, given a safety Lipschitz target. Thanks to our multivariate Lipschitz analysis, we can also measure the efficiency of adversarial training in inference tasks. We perform experiments on various open access tabular datasets, and also on a real Thales Air Mobility industrial application subject to certification requirements.
‘How should my family assistant be?’: initial perceptions about prospective and anticipated use of in-home virtual assistants in an emerging context

Virtual Assistants (VAs) are at the forefront of state-of-the-art interactions for smart technologies at home. The prospective users' prior to use perception of such assistants is crucial to discover new possibilities for design and to be able to elicit positive user experiences. However, this has not been investigated in detail within family life in emerging contexts though demand for their ownership increases. This study scrutinises potential users' initial perceptions about the prospective and anticipated uses of VAs within family life in Turkey, as an emerging context, through in-depth interviews with 15 families. During the interviews, the participant families watched publicly available videos of four different VAs to elicit their perceptions. Results reveal that prospective users' perceptions and expectations focus heavily on VAs' possible effects on the family well-being, besides the concerns about usability and issues related to privacy, safety, and security. While proposing VAs for home use in such an emerging market, their potential positive effects on the family well-being should be promoted whereas perceived negative effects should be resolved by considering smartness, personality and trust dimensions. The study reveals relationships between these dimensions and perceptions of and expectations about VA use in home context in an emerging market.