Reviewerly/exHarmony huggingface dataset:
Trademark Law, AI-Driven Behavioural Advertising and the Digital Services Act – Towards Source and Parameter Transparency for Consumers, Brand Owners and Competitors

In its Proposal for a Digital Services Act (“DSA”), the European Commission highlighted the need for new transparency obligations to arrive at accountable digital services, ensure a fair environment for economic operators and empower consumers. However, the proposed new rules seem to focus on transparency measures for consumers. According to the DSA Proposal, platforms, such as online marketplaces, must ensure that platform users receive information enabling them to understand when and on whose behalf an advertisement is displayed, and which parameters are used to direct advertising to them, including explanations of the logic underlying systems for targeted advertising. Statements addressing the interests of trademark owners and trademark policy are sought in vain. Against this background, the analysis sheds light on AI-driven behavioural advertising practices and the policy considerations underlying the proposed new transparency obligations. In the light of the debate on trademark protection in keyword advertising cases, it will show that not only consumers but also trademark owners have a legitimate interest in receiving information on the parameters that are used to target consumers. The discussion will lead to the insight that lessons from the keyword advertising debate can play an important role in the transparency discourse because they broaden the spectrum of policy rationales and guidelines for new transparency rules. In addition to the current focus on consumer empowerment, the enhancement of information on alternative offers in the marketplace and the strengthening of trust in AI-driven, personalized advertising enter the picture. On balance, there are good reasons to broaden the scope of the DSA initiative and ensure access to transparency information for consumers and trademark owners alike.
Toward More Effective Use of Assertions for Mobile App Development

It is a good programming practice to include runtime checks called assertions in the code to check assumptions and invariants. Assertions are said to be often most effective when they encode design decisions and constraints. In this paper, we show our preliminary work on translating design constraints to assertions for mobile apps. Design properties and constraints are specified formally in the Object Constraint Language (OCL) and translated to executable assertions written in Dart, the language of the Flutter cross-platform framework. We consider various language and platform-specific features of OCL, Dart, and Flutter. In our approach, assertions are enabled only in debug mode and removed from the production code. It is important to reduce the memory footprint of a mobile app as the memory on a mobile device is a limited resource.
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single  RGB Image

In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image. We observe that each pixel in an image corresponds to a surface in the underlying 3D geometry, where a canonical frame can be identified as represented by three orthogonal axes, one along its normal direction and two in its tangent plane. We propose an algorithm to predict these axes from RGB. Our first insight is that canonical frames computed automatically with recently introduced direction field synthesis methods can provide training data for the task. Our second insight is that networks designed for surface normal prediction provide better results when trained jointly to predict canonical frames, and even better when trained to also predict 2D projections of canonical frames. We conjecture this is because projections of canonical tangent directions often align with local gradients in images, and because those directions are tightly linked to 3D canonical frames through projective geometry and orthogonality constraints. In our experiments, we find that our method predicts 3D canonical frames that can be used in applications ranging from surface normal estimation, feature matching, and augmented reality.
IlluminatedFocus: Vision Augmentation using Spatial Defocusing

We propose IlluminatedFocus, augmented reality (AR) glasses enabling depth-independent spatial defocusing of a human vision. Our technique spatially manipulates the depth-of-field by synchronizing a periodic focal sweep of head-worn electrically tunable lenses and fast illumination control with a high-speed projector. In this demonstration, we show a system that switches focused and defocused views independently at each area of a 3D real scene. We realize various vision augmentation applications based on our method to show its potential to expand the application field of optical see-through AR.
Long-term Labeling: Reusing Annotations for Changing Detections

Perception and sensor fusion systems yield intermediate 2D and 3D representations of detected objects. In combination with high-definition maps, automated driving systems use these detections to track, predict, and assign properties to all stationary and dynamic objects within a vehicle's sensor field of view. Perception and fusion systems are regularly updated to improve accuracy and computational efficiency. This often changes underlying properties of detected objects. Machine learning models trained on these characteristics will therefore deteriorate over time, as the current state of the system and the state of the system during annotation diverge over time. To solve this problem, we propose a novel solution to annotate groundtruth objects with target attributes in raw sensor data. This enables us to run our updated systems on stored sensor data, associate detections to groundtruth objects, and transfer target attributes. Annotated groundtruth data can be labeled once to train machine learning models for multiple iterations of a system, which decreases development costs and time spent on annotation efforts. As part of this work, we introduce comprehensive lists for spatial and temporospatial detection-to-groundtruth-object relations to highlight the complexity of adequately reusing groundtruth data for machine learning models and tracking systems. Finally, we describe a sample application for reusing annotations for a parked car classifier, achieving an accuracy of 95.0% after transferring parked car attributes to detections within our test set.
Hardeep’s Egg

Hardeep’s Egg is a collaborative work by Pandhal and Steans that proposes a definition for a new noun, based on a real-life experience of Pandhal’s. The definition is illustrated in different ways each time it is presented.
Influencia de la COVID-19 sobre el sistema cardiovascular

Introduction: the incidence of coronavirus-2 infection, which causes the infectious illness known as COVID-19, has affected populations worldwide. Objective: to collate available evidence regarding the influence of COVID-19 on the cardiovascular system. Method: at the General Teaching Hospital Dr. Agostinho Neto, between March and June 2020 a narrative review was made on this topic through a search in different bibliographic databases: Pubmed/Medline, ScienceDirect and SciELO. Results: the information was structured in general aspects of this disease, cardiovascular manifestations, implications of interaction between cardiovascular drugs and those used to treat the infection, prognosis and natural history of the patient from the cardiovascular perspective. The following needs were identified: to determine the effectiveness of treatments for patients with comorbidities, to investigate the influence of treatment on the genesis of myocardial damage and the drugs that prevent it, to clarify the natural history of the survivors, to determine the relationship between viral load and cardiac manifestations, among others. Conclusions: the clinical condition of patients with this condition may be impaired by the presence of myocardial damage, but the pathophysiological basis of how SARS-CoV-2 generates this is not clear. Its appearance and the history of cardiovascular disease adversely affect the prognosis of the patient, which legitimizes the need for professional preparation to be able to make an accurate assessment of the cardiovascular system for the prevention and control of this complication.
Evolutionary Autonomous Networks

The communication networks of today can greatly benefit from autonomous operation and adaptation, not only due to the implicit cost savings, but also because autonomy will enable functionalities that are infeasible today. Across industry, academia and standardisation bodies there has been an increased interest in achieving the autonomous goal, but a path on how to attain this goal is still unclear.&#x0D; In this paper we present our vision for the future of autonomous networking. We introduce the concepts and technological means to achieve autonomy and propose an architecture which emerges directly through the application of these concepts, highlighting opportunities and challenges for standardisation. We argue that only a holistic architecture based on hierarchies of hybrid learning, functional composition, and online experimental evaluation is expressive and capable enough to realise true autonomy within communication networks.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond. and segmentation of small, crossing, moving and transparent instrument(s) (parts).
Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal

Herbal plants are plants that can be used as alternatives in natural healing of diseases, parts of plants that can be used such as roots, stems, tubers and leaves, in Southeast Sulawesi there are currently 1000 herbal plants and 10 sub-ethnicities that have been inventoried, according to research conducted by the Ministry of Health (Kemenkes). Indonesia has 6,000 - 7,000 medicinal plants, Southeast Sulawesi Province has a variety of herbal plants that are not found in other areas, such as Komba - Komba or Balakacida (Chromolaena Odorata). However, in the present era, the number of herbal plants is not accompanied by the knowledge of the community about the herbal plants themselves. The purpose of this study is to classify herbal plants and to compare the performance results of the K-Nearest Neighbor Method and Local Binary Pattern Histogram. From the test results of five types of herbal leaves in Southeast Sulawesi with a total of 100 data sets, the accuracy value for the K-Nearest Neighbor (KNN) method is obtained total accuracy value is 97,5%, while for the Local Binary Pattern Histogram (LBPH) method the total value is 94% of total accuracy value.
Critique of 'A Legal Perspective on the Trials and Tribulations of AI: How Artificial Intelligence, the Internet of Things, Smart Contracts, and Other Technologies Will Affect the Law'

An article titled A Legal Perspective on the Trials and Tribulations of AI: How Artificial Intelligence, the Internet of Things, Smart Contracts, and Other Technologies Will Affect the by Iria Giuffrida, Fredric Lederer, and Nicolas Vermerys was published in the Case Western Reserve Law Review in 2018. The aim of the article was contribute further to a basic and useful understanding of the legal problems to be generated by technological ecosystem (p.750) but it only covered the difficulties which will occur in establishing tortious liability due to the AI-based unlawful acts. The article has touched each and every aspect of legal issues that can occur due to Artificial Intelligence, Internet of Things, and Smart Contracts only in the field of tort law, and somewhat in criminal law, and contract law. It had failed to cover the legal issues that can occur in Intellectual Property Law, International Law, and Labor Law.In this paper, I summarize the article and offer comments about selected aspects, identify some relevant changes that have occurred since the article was published, and suggest areas where additional legal findings would assist in understanding the current state of artificial intelligence in regard to the legal issues raised.
A review of methods in the field of detecting illegal open-pit mining activities

Abstract Illegal open pit mining might be a very dangerous activity both for the environment and also for the people living in its neighbourhood. This kind of activity is connected with environmental degradation, disruption of sustainable development and lack of the most critical last stage of the mine’s “life”, i.e. land reclamation. An additional element connected with illegal exploitation is the fact of breaking the law and stealing mineral resources. Monitoring of illegal exploitation is therefore an important aspect. The presented here review was intended to investigate which methods can be used directly to detect open pit mining sites and to evaluate their effectiveness. In the reviewed works a wide variety of methods have been used, ranging from manual methods, such as photo-interpretation, to a combination of automatic methods and photo-interpretation, to fully automatic methods. Based on the analysis, it was indicated that different types of classification (supervised, unsupervised, hybrid) are the most commonly u sed. Besides, radar interferometry, image fusion techniques, or images spectral similarity are also used.
PLAM: A plug-in module for flexible graph attention learning

Graph Convolutional Networks (GCNs) are general deep representation learning models for graph-structured data. In this paper, we propose a simple Plug-in Attention Module (PLAM) to improve the representation power of GCNs, inspired by the recent success of the query-key mechanism in computer vision and natural language processing. With this module, our network is able to adaptively learn the weights from a node towards its neighbors. Different from existing attention-based GCNs, the proposed PLAM has several important properties. First, the parameter space for the attention module is isolated from that for feature learning. This ensures that the proposed approach can be conveniently applied to existing GCNs as a plug-in module. Second, the anchor node and neighbor nodes are treated separately when learning the attention weights, which further enhances the flexibility of our structure. Third, our attention module extracts higher-level information by computing the inner product of the features between the anchor node and neighbor nodes, leading to significantly increased representation power. Last, we take a step forward and propose a novel structural encoding technique for the graph attention module to inject local and global structure information. Although being simple, our PLAM models have achieved state-of-the-art performances on graph-structured datasets under both the transductive and inductive settings. Additionally, experiments on image and point cloud datasets show potential applications of PLAM on several computer vision tasks.
Blockchain Based Health Care Management System

Electronic health records (EHRs) are only handled by hospitals and not patients, rendering medical advice in several hospitals more difficult. Patients ought to rely on their own healthcare information and restore their own patient records control. The exponential advancement of blockchain technologies facilitates wellness for the General population, medical records and patient knowledge included. This technology provides treatment professionals and patients with detailed and unchanging details and access to EHRs free of charge. In this article, we introduce an attribute-based signature scheme with several authorities to ensure that the blockchain-encapsulated EHRs are legitimate, where a patient endorses an attribute message and does not share details other than the proof he has attested to them. By swapping the hidden pseudorandom feature seeds between the authorities, the protocol resists N-1 deceptive authorities to attack conspiracies. We also formally show that this multiple authority attribute-based signature approach(MAABS) is protected in a random oracle paradigm by imperfection and absolute privacy in the presumptions of bilinear Diffie-Hellman. Using SHA-256 as Hashing algorithm to provide perfect privacy.
On the Use of Narrative Structure and Encapsulation for Health-related Stories Contained Within Digital News Packages

People rely on the media to serve as purveyors of health information and keep them apprised of current knowledge and advancements in medicine, pharmaceuticals, healthcare, and lifestyle management. A digital news package is an immersive feature story housed on the internet and this study examined how the combination of media elements (e.g., photographs, graphics, video, etc.) contained within digital news packages are capable of supporting the presentation of engaging and insightful coverage of health-related topics. Specifically, we investigated what aspects of immersive feature stories should be captured or encapsulated by static and animated graphics, and how these items should be arranged relative to the stories that they accompany. Our findings revealed that effective visual storytelling is facilitated by parity between the written components and visual components of digital news packages.
A Deep Learning based Human Activity Recognition Model using Long Short Term Memory Networks

The recent development of machines which shows different intellectual characteristics are being created by making changes in system hardware and software. In this modern period different hardware tools, smart wearables, machine learning models, and deep learning models are being applied in Human Activity Recognition (HAR) applications. The major significance of the problem is to develop a HAR model with high accuracy rate using lower cost sensors. To reach this goal, the sensor-based data are collected from two low cost sensors namely accelerometer and gyroscope sensors, which are used along with the construction of an Artificial Neural Networks (ANN) based deep learning model. Long Short-Term Memory (LSTMs) Networks are used to make the model learn and recognize the type of activities that the user is performing. The model is trained using UCI-HAR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</sup> dataset. In the HAR model, a person's activity is identified based on sensor readings. Here, six distinct activities such as sitting, walking, laying, standing, up-stairs and down-stairs are identified and recognized. Knowing the activities performed by users it can help in having instant interaction with them.
A Proposal of Systematic SMS Spam Detection Model Using Supervised Machine Learning Classifiers

Short message service (SMS) is a conventional and facile medium for communication around the world. Nowadays, each individual in each generation has their handheld mobile device, and they are also using SMS services for means of quick communication. On this account, SMS services become a desirable medium for spammers. To diminish this problem, there are many content and non-content-based approaches are being introduced for detecting spam SMS on mobile devices. But they are not as comprehensive as compared with email spam detection. Hence, in this paper, we tried to develop a systematic model using machine learning classifiers to detect SMS spam. We used five supervised machine learning classifiers and a large dataset that contains 11,132 data labeled as spam or ham. Then, we applied different data preprocess methods and feature extraction on the dataset, to train and test the ML classifiers and evaluate the model in terms of accuracy, precision, recall, and F1-score. Final results indicated that logistic regression (LR) showed the lowest evaluation results for the model with an accuracy score of 99.28%, precision score of 99.27%, recall score of 95.16%, and F1-score of 97.17%. On the contrary, random forest (RF) showed the best classification for the model with an accuracy score of 99.73%, precision score of 1.0%, recall score of 97.92%, and F1-score of 98.95%. The overall procedure is implemented on Jupyter notebook using the Python programming language.
3D Object Recognition Method Using CNNs and Slicing

Three-Dimensional (3D) object recognition has been receiving more and more attention in the field of Computer Vision, as the development of AI algorithms and advancements of hardware components allow for better results nowadays more than ever before. Neural Networks are used to better classify different types of objects, these are being fed with pre-processed 3D objects represented in a 2D domain for better highlighting the important features and for a better performance. In this paper, we propose a strategy of feeding 2D representations of 3D meshes made by using different types of slicing to a CNN. In this way, we want to minimize the information loss and the redundancy the data suffers going from a 3D representation to a 2D one. To assess the results, we used Princeton’s state-of-the-art ModelNet10 dataset, consisting of 4899 3D objects divided into 10 categories.
BEST: Blockchain-Enabled Secure Technology in a healthcare management system

Blockchain technology provides a way to store information that makes it difficult to alter and hack. In today’s health scenario, tracking of medical devices, drugs, claims, payments, data sharing, and exchanging and using information is a cumbersome job. Blockchain technology overcomes this and provides safe and secure healthcare data management for various activities. The Ethereum framework is used for generating blockchain-based healthcare smart contracts that help us to maintain patient records while managing claim payment, security, and privacy. Further, this gives secure and proficient admittance to clinical information while protecting the patient’s personal data. In this work, we discuss how blockchain technology can achieve the security of electronic medical records. We present a blockchain-based intelligent and smart contract designed and developed in the Solidity language on REMIX IDE.