Reviewerly/exHarmony huggingface dataset:
Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.
Measurement uncertainty of phase measuring deflectometry

Phase measuring deflectometry (PMD) is a proven optical measurement method for measuring the shapes of objects. This method is suitable for measuring the shape of an object with an optically smooth (mirror-like) surface. The measured object is used as a mirror through which the camera observes a defined geometric pattern. We derive the theoretical limit of measurement uncertainty using the Cramér–Rao inequality. It shows that the measurement uncertainty is expressed in the form of an uncertainty product. The factors of the product are the angular uncertainty and lateral resolution. The magnitude of the uncertainty product depends on the mean wavelength of the light used and the number of photons detected. The calculated measurement uncertainty is compared with the measurement uncertainty of other deflectometry methods.
EBMICQL: Improving Efficiency of Blockchain Miner Pools via Incremental and Continuous Q-Learning Framework

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental &amp; Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios.
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis

Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unfavourable decision by a black-box machine learning model (e.g., being denied a loan request). Existing solutions have mainly focused on generating feasible interventions without providing explanations on their rationale. Moreover, they need to solve a separate optimization problem for each user. In this paper, we take a different approach and learn a program that outputs a sequence of explainable counterfactual actions given a user description and a causal graph. We leverage program synthesis techniques, reinforcement learning coupled with Monte Carlo Tree Search for efficient exploration, and rule learning to extract explanations for each recommended action. An experimental evaluation on synthetic and real-world datasets shows how our approach generates effective interventions by making orders of magnitude fewer queries to the black-box classifier with respect to existing solutions, with the additional benefit of complementing them with interpretable explanations.
Flow with an intelligent tutor: A latent variable modeling approach to tracking flow during artificial tutoring

Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development.
Text embedding techniques for efficient clustering of twitter data

World wide web is abundant with various types of information such blogs, social media posts, news articles. With this type of magnitude of online content, there is a need to deeply understand the insights of it in order to make use of the information for practical applications such as event detection, polarity, sentiment analysis and so on. Natural Language Processing (NLP) is the study of such information which is used for text classification, sentiment analysis, clustering of similar text. NLP makes use of linguistic knowledge and build machine learning models to analyse textual information. NLP finds its way in various applications like classification of online review into positive and negative without actually reading the reviews and feedback. For text analysis, there should be a way to quantify the text based on its frequency of occurrence, correlation with neighbouring words, contextual similarity of words, etc. One such way is word embedding. This study applies various word embedding techniques on tweets of popular news channels and clusters the resultant vectors using K-means algorithm. From this study, it is found out that Bidirectional Encoder Representations from Transformers (BERT) has achieved highest accuracy rate when used with K-means clustering.
Special Issue: Symmetric and Asymmetric Encryption in Blockchain

The technology that is most likely to change the corporate world in the next ten years is not social networks, Big Data, cloud computing, robots, not even artificial intelligence, but blockchain [...]
Exploiting Temporal Coherence to Improve Person Re-identification

The uncontrolled characteristics of long-term scenarios, like ultra-running competitions, are challenging for person re-identification approaches based on computer vision methods. State-of-the-art techniques have reported hardly moderate success for whole-body runner re-identification due to the existence of distinct illumination conditions, as well as changes of clothing and/or accessories like backpacks, caps, and sunglasses. This paper explores integrating these biometric cues with the particular spatio-temporal context information present in the competition live track system. Our results confirm the significance of this strategy to limit the gallery size and boost re-identification performance.
Beyond the Boolean: How Programmers Ask About, Use, and Discuss Gender

Categorization via gender is omnipresent throughout society, and thus also computing; gender identity is often requested of users before they use software or web services. Despite this fact, no research has explored how software developers approach requesting gender disclosure from users. To understand how developers think about gender in software, we present an interview study with 15 software developers recruited from the freelancing platform Upwork as well as Twitter. We also collected and categorized 917 threads that contained keywords relevant to gender from programming-related sub-forums on the social media service Reddit. 16 posts that discussed approaches to gender disclosure were further analyzed. We found that while some developers have an understanding of inclusive gender options, programmers rarely consider when gender data is necessary or the way in which they request gender disclosure from users. Our findings have implications for programmers, software engineering educators, and the broader community concerned with inclusivity.
Unrestricted Black-Box Adversarial Attack Using GAN with Limited Queries

Adversarial examples are inputs intentionally generated for fooling a deep neural network. Recent studies have proposed unrestricted adversarial attacks that are not norm-constrained. However, the previous unrestricted attack methods still have limitations to fool real-world applications in a black-box setting. In this paper, we present a novel method for generating unrestricted adversarial examples using GAN where an attacker can only access the top-1 final decision of a classification model. Our method, Latent-HSJA, efficiently leverages the advantages of a decision-based attack in the latent space and successfully manipulates the latent vectors for fooling the classification model. With extensive experiments, we demonstrate that our proposed method is efficient in evaluating the robustness of classification models with limited queries in a black-box setting. First, we demonstrate that our targeted attack method is query-efficient to produce unrestricted adversarial examples for a facial identity recognition model that contains 307 identities. Then, we demonstrate that the proposed method can also successfully attack a real-world celebrity recognition service. The code is available at https://github.com/ndb796/LatentHSJA .
Search-engine-augmented dialogue response generation with cheaply supervised query production

Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of previous work assumes that the relevant knowledge is given as input or retrieved from a static pool of knowledge. However, this assumption violates the real-world situation, where knowledge is continually updated and a chatbot has to dynamically retrieve useful knowledge. We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine. We design a training algorithm using cheap noisy supervision for the query producer, where the signals are obtained by comparing retrieved articles with the next dialogue response. As the result, the query producer is adjusted without any human annotation of gold queries, making it easily transferable to other domains and search engines. Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge, and the overall model generates better responses over strong knowledge-aided baselines using BART and other typical systems.
Enhancing Classification with Hierarchical Scalable Query on Fusion Transformer

Real-world vision based applications require fine-grained classification for various applications of interest like e-commerce, mobile applications, warehouse management, etc. where reducing the severity of mistakes and improving the classification accuracy is of utmost importance. This paper proposes a method to boost fine-grained classification through a hierarchical approach via learnable independent query embeddings. This is achieved through a classification network that uses coarse class predictions to improve the fine class accuracy in a stage-wise sequential manner. We exploit the idea of hierarchy to learn query embeddings that are scalable across all levels, thus making this a relevant approach even for extreme classification where we have a large number of classes. The query is initialized with a weighted Eigen image calculated from training samples to best represent and capture the variance of the object. We introduce transformer blocks to fuse intermediate layers at which query attention happens to enhance the spatial representation of feature maps at different scales. This multi-scale fusion helps improve the accuracy of small-size objects. We propose a twofold approach for the unique representation of learnable queries. First, at each hierarchical level, we leverage cluster based loss that ensures maximum separation between inter-class query embeddings and helps learn a better (query) representation in higher dimensional spaces. Second, we fuse coarse level queries with finer level queries weighted by a learned scale factor. We additionally introduce a novel block called Cross Attention on Multi-level queries with Prior (CAMP) Block that helps reduce error propagation from coarse level to finer level, which is a common problem in all hierarchical classifiers. Our method is able to outperform the existing methods with an improvement of about 11% at the fine-grained classification.
Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

Recently, Amazon Web Services (AWS) announced a plan to develop a computer vision artificial intelligence service. The field of computer vision has been receiving constant attention like this, and object detection technology is a very important part in this field of computer vision. Although object detection technology is used in many computer vision fields, object detection technology has many limitations in small object detection and night object detection. Various noise factors degrade the image quality, and it is difficult to expect high accuracy when detecting small objects in this environment. In this paper, we develop a technology that can solve these problems by using multi-modal data. In addition, this object detection technology can be used in various fields by designing and developing a lightweight system that can work well in a low-resource environment.
Switch-Constrained Multiple-Model Algorithm for Maneuvering Target Tracking

In most of the existing state estimation algorithms for maneuvering target tracking, the formulation of the mode switching process fails to accurately capture the deterministic law that the target mode will persist for a period of time once it turns to be in effect. In this paper, two strategies are presented to describe this characteristic. First, a constraint that model switching occurs no more than once over three consecutive time steps is proposed to provide a deterministic description that mode does not switch continuously. Second, considering target mode always lasts for some time while mode switching is completed instantly, the transition probabilities from one model to itself are fixed at extremely large values to enhance the certainty in mode sojourn segments. Based on these strategies, two recursive multiple model (MM) filters are derived in the framework of generalized pseudo-Bayesian (GPB) estimation and interacting multiple model (IMM) estimation, respectively. In both algorithms, although the large probability of model staying unchanged causes high peak errors at mode switching instants, the introduction of the switching constraint prevents the input to the mode-matched filter from being affected by the model transition probability, thereby obtaining a near-optimal performance in mode sojourn segments. Additionally, in order to reduce the peak estimation errors, a mode switching detector based on likelihood function is presented to work in parallel with the recursive MM filters. Once the detector declares that a mode switch occurs, the final estimate is adjusted from the output of the recursive MM filters to the estimate of the corresponding elemental filter, performance of which is guaranteed by the switching constraint. Simulation results demonstrate the capability of the proposed recursive MM filters to obtain performance close to that of a filter without model uncertainty in mode sojourn segments and the effectiveness of the mode switching detector to reduce peak errors at the same time.
Image Comparison Research of Smart Electricity Meter

The standardization of the installation and operation and maintenance of the smart meter box is particularly important to ensure the reliable and safe operation and maintenance of the smart power system. The smart meter box is generally maintained by manual inspection, which requires experienced power professionals to judge the meter box. Status anomalies often lead to false negatives. With the development of fifth-generation mobile communication, machine learning and other technologies, operation and maintenance personnel can take on-site photos of the meter box through mobile devices and upload them to the cloud. The photos of regular inspections can be compared through the image analysis method based on machine learning to automatically determine whether the meter box is damaged. In this paper, target detection, image perspective transformation and adaptive local affine matching algorithm are combined. First, target detection is used to identify and locate the areas in the smart meter box that need to be compared, and the detected objects are cut into opposite subsections. Image, then transform the target to be determined through image perspective transformation, remove the area that does not need to be matched, and then use the adaptive local affine matching algorithm to calculate the similarity between each feature point according to the local features of the image. The accuracy of the image comparison process.
FogCrypto: Towards Cryptographic protocols for securing Fog assisted cloud System

In fog computing, issues with authentication and permission are brought on by a shaky link between the fog and the cloud. By utilising the idea of Stand-Alone Authentication (SAA) and outfitting it with Attribute-Based Encryption (ABE) for its security in fog assisted cloud system. In such a system, a user’s access right is a set of private key-linked qualities. In this article, it observes that if a user can generate a new private key for a portion of his/her access right, this could violate the access control policy. It offers a new security scheme to strengthen CP- ABE schemes for message encryption and decryption. Formalize the security requirements for such a property, then build a CP- ABE scheme based on the elliptic curve. In this paper, It utilises the the different attack model for secure the information. It also proposes the secure fog-cloud security framework for optimise the various security services.
A Personalized Recommender System Based-on Knowledge Graph Embeddings

Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better capture the implicit connections between them and provide more accurate recommendations. In this paper, we investigate and propose the construction of a personalized recommender system via knowledge graphs embedding applied to the vehicle purchase/sale domain. The results of our experimentation demonstrate the efficacy of the proposed method in providing relevant recommendations that are consistent with individual users.
Inter-institutional Resource Sharing in Undergraduate HPC Education

High performance computing (HPC), typically used only in research until recently, is being integrated more into undergraduate curricula across disciplines. HPC resources are expensive but lend themselves easily to sharing among institutions. However, in the context of undergraduate education, inter-institutional sharing of such resources is still relatively rare. The authors hold the belief that generating administrator support early in system-wide initiatives for sharing resources is crucial for long term success. Taking the case of a specific HPC cluster at the University of Wisconsin-Eau Claire, part of the University of Wisconsin System (UW-System) of higher educational institutions, the authors asked 15 administrators from the system about the barriers as well as the path forward for better cluster sharing in undergraduate education. The responses to the interview questions were analyzed qualitatively for themes and sentiments. Five themes arose from this analysis, related to awareness of and access to HPC, cost-sharing structures, industry needs, institutional factors, and variations in benefits depending on the field. The sentiments were overwhelmingly positive, but with variations depending on the questions asked and approaches taken by the interviewees. Overall, the responses indicated a positive attitude and willingness to incorporate HPC in undergraduate education as well as the importance of awareness of such resources. They also indicate that cost-sharing structures and institutional factors are both the biggest barriers and the keys to better sharing practices and industry partnerships. At the end, the authors make recommendations for anyone looking to establish a framework to share HPC resources across institutions.
Perfect Strangers

This EIC message brings attention to a cybercrime worse than having your identity stolen—catfishing attempts toward children while they are playing video games. Our children (and parents) need to be warned.