Reviewerly/exHarmony huggingface dataset:
Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning

Background: Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to inaccuracies and inefficiencies, causing alarm fatigue and compromised patient safety. Objectives: This study shows how machine learning (ML) models can provide auxiliary information for more accurate identification of critical thrombocytopenic patients when compared with the traditional notification system. Research Design: A total of 50,505 patients’ platelet count and other 26 additional laboratory datasets of each thrombocytopenic event were used to build prediction models. Conventional logistic regression and ML methods, including random forest (RF), artificial neural network, stochastic gradient descent (SGD), naive Bayes, support vector machine, and decision tree, were applied to build different models and evaluated. Results: Models using logistic regression [area under the curve (AUC)=0.842], RF (AUC=0.859), artificial neural network (AUC=0.867), or SGD (AUC=0.826) achieved the desired average AUC&gt;0.80. The highest positive predictive value was obtained by the SGD model in the testing data (72.2%), whereas overall, the RF model showed higher sensitivity and total positive predictions in both the training and testing data and outperformed other models. The positive 2-day mortality predictive rate of RF methods is as high as 46.1%—significantly higher than using the traditional notification system at only 14.8% [χ 2 (1) =81.66, P &lt;0.001]. Conclusions: This study demonstrates a data-driven ML approach showing a significantly more accurate 2-day mortality prediction after a critical thrombocytopenic event, which can reinforce the accuracy of the traditional notification system.
Warp and Learn: Novel Views Generation for Vehicles and Other Objects

In this article we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e., entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric. In particular, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance. This careful blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input, and iv) perform shape transfer to completely different 3D models. Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes (e.g., chairs). A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view. Supplementary material, animated results, code, and data are available at: https://github.com/ndrplz/semiparametric.
A Formal Model for the Automatic Configuration of Access Protection Units in MPSoC-Based Embedded Systems

Heterogeneous system-on-chip platforms with multiple processing cores are becoming increasingly common in safety-and security-critical embedded systems. To facilitate a logical isolation of physically connected on-chip components, internal communication links of such platforms are often equipped with dedicated access protection units. When performed manually, however, the configuration of these units can be both time-consuming and error-prone. To resolve this issue, we present a formal model and a corresponding design methodology that allows developers to specify access permissions and information flow requirements for embedded systems in a mostly platform-independent manner. As part of the methodology, the consistency between the permissions and the requirements is automatically verified and an extensible generation framework is used to transform the abstract permission declarations into configuration code for individual access protection units. We present a prototypical implementation of this approach and validate it by generating configuration code for the access protection unit of a commercially available multiprocessor system-on-chip.
Real-time monitoring of water level and storage dynamics of irrigation tank using IoT

Abstract The internet of things (IoT), an emerging technological marvel, consists of a group of physical objects such as vehicles, machines and sensors to monitor and transfer data over the internet with much less human to machine interaction. It relies on a host of technologies like application programming interfaces (API), which in turn, help the devices to get connected with the internet. Efficient irrigation tank management requires a strong database on continuous water level dynamics for irrigation decision-making. Real-time tank water level monitoring is possible through an IoT device by integrating sensors and microcontroller that can send the water level data to the cloud. Google sheet is used to store the water level data that can be viewed using a web application as well as a mobile application. The contour map of the study tank is used to develop the stage (water level) vs volume curve. The volume of water present in the tank at any time can be arrived at for any tank water level using the above curve. The developed device can provide real-time continuous water level data with low cost and simple infrastructure, thus aiding tank water management.
Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation

There is an increasing interest in image-to-image translation withapplications ranging from generating maps from satellite images tocreating entire clothes’ images from only contours. In the presentwork, we investigate image-to-image translation using GenerativeAdversarial Networks (GANs) for generating new data, taking as acase study the morphing of giraffes images into bird images. Morphinga giraffe into a bird is a challenging task, as they have differentscales, textures, and morphology. An unsupervised cross-domaintranslator entitled InstaGAN was trained on giraffes and birds,along with their respective masks, to learn translation betweenboth domains. A dataset of synthetic bird images was generatedusing translation from originally giraffe images while preservingthe original spatial arrangement and background. It is important tostress that the generated birds do not exist, being only the result of alatent representation learned by InstaGAN. Two subsets of commonliterature datasets were used for training the GAN and generatingthe translated images: COCO and Caltech-UCSD Birds 200-2011.To evaluate the realness and quality of the generated images andmasks, qualitative and quantitative analyses were made. For thequantitative analysis, a pre-trained Mask R-CNN was used for thedetection and segmentation of birds on Pascal VOC, Caltech-UCSDBirds 200-2011, and our new dataset entitled FakeSet. The generateddataset achieved detection and segmentation results close tothe real datasets, suggesting that the generated images are realisticenough to be detected and segmented by a state-of-the-art deepneural network.
Methods, methodologies, and tools for threat modeling with case study

The security of each system is essential for its use. In order to make this process as successful as possible, it is advisable to develop a threat model for the system under consideration at the design stage. The purpose of the threat model is to enable the identification of security threats, by whose further analysis we can conclude which are the greatest vulnerabilities of the system and which pose the greatest risk. There exist many different approaches to threat modeling in terms of methods, methodologies, and tools. In this paper, we give an overview of those approaches and apply one of them, i.e., the most represented and mature to a specific system. A STRIDE-based methodology, software-centric method, and Microsoft Threat Modeling Tool (MTMT) mixture has been used to threat model the Web of Things (WoT)-based temperature management system which is in the design phase.
Is Pair Programing in Higher Education a Good Strategy?

Pair programming is by definition two-person programming on the same computer. The technique has been used in many higher education institutions and has been reported in some scientific articles, usually for introductory to programming courses. The aim of this article is to make a situation report analyzing the scientific production on pair programming for curricular units of introduction to programming in higher education, measuring the advantages and disadvantages of the strategy. The sample was composed by 153 articles indexed in Elsevier’s Scopus. The results obtained by bibliometric analysis showed the publication rates, authors, in which journals they are published, which are the organizations and countries that publish the most, which are the most cited articles and what their purpose. The benefits reported are generally better code, improved programming and group skills, advantages for women and reducing the work of instructors. The problems are group compatibility: there are studies that randomly distribute pairs, while other use personality tests or knowledge self-assessment.
Countermeasure Against Quantum Hacking Using Detection Statistics

Detector blinding attacks have been proposed in the last few years, and they could potentially threaten the security of quantum key distribution systems. Even though such attacks are technically challenging to implement, it is important to consider countermeasures to avoid information leakage. In this paper, we present a countermeasure against these kinds of attacks based on the use of multipixel detectors. We show that with this method, we are able to estimate an upper bound on the information an eavesdropper could have on the key exchanged. Finally, we test a multipixel detector based on superconducting nanowire single-photon detectors to show it can fulfill all the requirements for our countermeasure to be effective.
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages

In cloud and edge networks, federated learning involves training statistical models over decentralized data, where servers aggregate models through intermediate updates trained from clients. By utilizing private and local data it improves quality of personalized services and reduces user's concern for privacy. However, federated learning still leaks multimedia features through trained intermediate updates and thereby is not privacy-preserving for multimedia. Existing techniques applied from secure community attempt to avoid multimedia features leakages for federated learning but yet cannot address issues of privacy. In this paper, we propose a privacy-preserving solution that avoids multimedia privacy leakages in federated learning. Firstly, we devise a novel encryption scheme called Non-Informative Transformation (NIT) for federated aggregation to eliminates residual multimedia features in intermediate updates. Based on the scheme, we then propose Just-Learn-over-Ciphertext (JLoC) mechanism for federated learning, which includes three stages in each model iteration. The Encrypt stage encrypts intermediate updates and makes it non-informative distribution at clients. The Aggregate stage performs model aggregation without decryption at servers. Specifically, this stage just computes over ciphertext, and its output of aggregation also keeps non-informative. The Decrypt stage converts non-informative outputs of aggregation to available parameters for the next iteration at clients. Moreover, we implement a prototype and conduct experiments to evaluate its privacy and performance on real devices. The experimental results demonstrate that our methods can defend against potential attacks for multimedia privacy leakages without accuracy loss in commercial off-the-shelf products.
Average-reward model-free reinforcement learning: a systematic review and literature mapping

Reinforcement learning is important part of artificial intelligence. In this paper, we review model-free reinforcement learning that utilizes the average reward optimality criterion in the infinite horizon setting. Motivated by the solo survey by Mahadevan (1996a), we provide an updated review of work in this area and extend it to cover policy-iteration and function approximation methods (in addition to the value-iteration and tabular counterparts). We present a comprehensive literature mapping. We also identify and discuss opportunities for future work.
Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision

Humans learn language by listening, speaking, writing, reading, and also, via interaction with the multimodal real world. Existing language pre-training frameworks show the effectiveness of text-only self-supervision while we explore the idea of a visually-supervised language model in this paper. We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora. Therefore, we develop a technique named "vokenization" that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images (which we call "vokens"). The "vokenizer" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks such as GLUE, SQuAD, and SWAG. Code and pre-trained models publicly available at https://github.com/airsplay/vokenization
Coherent coupling of a trapped electron to a distant superconducting microwave cavity

We theoretically investigate the coupling of a single electron in a planar Penning trap with a remote superconducting microwave (MW) cavity. Coupling frequencies around Ω=2π·1 MHz can be reached with resonators with a loaded quality factor of Q=105, allowing for the strong coupling regime. The electron and the cavity form a system of two coupled quantum harmonic oscillators. This is a hybrid and linear microwave quantum network. We show that the coherent interaction can be sustained over distances of a few mm up to several cm. Similar to classical linear MW circuits, the coherent quantum exchange of photons is ruled by the impedances of the electron and the cavity. As one concrete application, we discuss the entanglement of the cyclotron motions of two electrons located in two separate traps.
Free Speech Under Pressure: The Effect of Online Harassment on Literary Writers

In the perception of writers in Germany, free speech is under strong pressure. A survey study, in which 526 literature writers took part, reveals innumerable aspects of hate speech, online harassment, and even physical assaults. Every second person has already experienced assaults on his or her person and is also aware of attacks on colleagues. Three quarters are concerned about freedom of expression in Germany and complain of an increase in threats, intimidation, and hateful reactions. The research project was developed in collaboration between the Institute for Media Research, University of Rostock, and the PEN Center Germany.
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks. We first generalize existing algebraic characterizations of acyclicity to a class of matrix polynomials. Next, focusing on a one-parameter-per-edge setting, it is shown that the Karush-Kuhn-Tucker (KKT) optimality conditions for the NOTEARS formulation cannot be satisfied except in a trivial case, which explains a behavior of the associated algorithm. We then derive the KKT conditions for an equivalent reformulation, show that they are indeed necessary, and relate them to explicit constraints that certain edges be absent from the graph. If the score function is convex, these KKT conditions are also sufficient for local minimality despite the non-convexity of the constraint. Informed by the KKT conditions, a local search post-processing algorithm is proposed and shown to substantially and universally improve the structural Hamming distance of all tested algorithms, typically by a factor of 2 or more. Some combinations with local search are both more accurate and more efficient than the original NOTEARS.
Multi-Agent Collaboration via Reward Attribution Decomposition

Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-of-magnitude more training rounds than humans and don't generalize to new agent configurations even on the same game. In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play. We first formulate multi-agent collaboration as a joint optimization on reward assignment and show that each agent has an approximately optimal policy that decomposes into two parts: one part that only relies on the agent's own state, and the other part that is related to states of nearby agents. Following this novel finding, CollaQ decomposes the Q-function of each agent into a self term and an interactive term, with a Multi-Agent Reward Attribution (MARA) loss that regularizes the training. CollaQ is evaluated on various StarCraft maps and shows that it outperforms existing state-of-the-art techniques (i.e., QMIX, QTRAN, and VDN) by improving the win rate by 40% with the same number of samples. In the more challenging ad hoc team play setting (i.e., reweight/add/remove units without re-training or finetuning), CollaQ outperforms previous SoTA by over 30%.
VisualNews : Benchmark and Challenges in Entity-aware Image Captioning.

In this paper we propose VisualNews-Captioner, an entity-aware model for the task of news image captioning. We also introduce VisualNews, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata. Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance. Our proposed method is able to effectively combine visual and textual features to generate captions with richer information such as events and entities. More specifically, we propose an Entity-Aware module along with an Entity-Guide attention layer to encourage more accurate predictions for named entities. Our method achieves state-of-the-art results on both the GoodNews and VisualNews datasets while having significantly fewer parameters than competing methods. Our larger and more diverse VisualNews dataset further highlights the remaining challenges in captioning news images.
A first look into the carbon footprint of federated learning

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and social groups advocating for privacy protection. \textit{However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL.} First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that, depending on the configuration, FL can emit up to two order of magnitude more carbon than centralized machine learning. However, in certain settings, it can be comparable to centralized learning due to the reduced energy consumption of embedded devices. We performed extensive experiments across different types of datasets, settings and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
Combinatorial Q-Learning for Dou Di Zhu

Deep reinforcement learning (DRL) has gained a lot of attention in recent years, and has been proven to be able to play Atari games and Go at or above human levels. However, those games are assumed to have a small fixed number of actions and could be trained with a simple CNN network. In this paper, we study a special class of Asian popular card games called Dou Di Zhu, in which two adversarial groups of agents must consider numerous card combinations at each time step, leading to huge number of actions. We propose a novel method to handle combinatorial actions, which we call combinatorial Q-learning (CQL). We employ a two-stage network to reduce action space and also leverage order-invariant max-pooling operations to extract relationships between primitive actions. Results show that our method prevails over other baseline learning algorithms like naive Q-learning and A3C. We develop an easy-to-use card game environments and train all agents adversarially from sractch, with only knowledge of game rules and verify that our agents are comparative to humans. Our code to reproduce all reported results is available on github.com/qq456cvb/doudizhu-C.
Designing Context-Sensitive Norm Inverse Reinforcement Learning Framework for Norm-Compliant Autonomous Agents

Human behaviors are often prohibited, or permitted by social norms. Therefore, if autonomous agents interact with humans, they also need to reason about various legal rules, social and ethical social norms, so they would be trusted and accepted by humans. Inverse Reinforcement Learning (IRL) can be used for the autonomous agents to learn social norm-compliant behavior via expert demonstrations. However, norms are context-sensitive, i.e. different norms get activated in different contexts. For example, the privacy norm is activated for a domestic robot entering a bathroom where a person may be present, whereas it is not activated for the robot entering the kitchen. Representing various contexts in the state space of the robot, as well as getting expert demonstrations under all possible tasks and contexts is extremely challenging. Inspired by recent work on Modularized Normative MDP (MNMDP) and early work on context-sensitive RL, we propose a new IRL framework, Context-Sensitive Norm IRL (CNIRL). CNIRL treats states and contexts separately, and assumes that the expert determines the priority of every possible norm in the environment, where each norm is associated with a distinct reward function. The agent chooses the action to maximize its cumulative rewards. We present the CNIRL model and show that its computational complexity is scalable in the number of norms. We also show via two experimental scenarios that CNIRL can handle problems with changing context spaces.