Reviewerly/exHarmony huggingface dataset:
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression

Bounding box regression is an important component in object detection. Recent work achieves promising performance by optimizing the Intersection over Union (IoU). However, IoU-based loss has the gradient vanish problem in the case of low overlapping bounding boxes, and the model could easily ignore these simple cases. In this paper, we propose Side Overlap (SO) loss by maximizing the side overlap of two bounding boxes, which puts more penalty for low overlapping bounding box cases. Besides, to speed up the convergence, the Corner Distance (CD) is added into the objective function. Combining the Side Overlap and Corner Distance, we get a new regression objective function, Side and Corner Align Loss (SCALoss). The SCALoss is well-correlated with IoU loss, which also benefits the evaluation metric but produces more penalty for low-overlapping cases. It can serve as a comprehensive similarity measure, leading to better localization performance and faster convergence speed. Experiments on COCO, PASCAL VOC, and LVIS benchmarks show that SCALoss can bring consistent improvement and outperform ln loss and IoU based loss with popular object detectors such as YOLOV3, SSD, Faster-RCNN. Code is available at: https://github.com/Turoad/SCALoss.
An intermediate stage of artificial intelligence similar to human

An analysis of the way of further development of artificial intelligence similar to human was carried out, namely its intermediate stage, a state when he can communicate freely with people, can realistically imitate a some personality, freely change it, and thus become convenient for large-scale use in everyday life. An approximate view of the system was described, which it may become in the near future, given the recent technologies. The list of possible settings, which will be actual for similar systems, is resulted. The possible areas of application of this artificial intelligence are listed.
An Improved End-To-End Memory Network for QA Tasks

At present, End-to-End trainable Memory Networks (MemN2N) has proven to be promising in many deep learning fields, especially on simple natural language-based reasoning question and answer (QA) tasks. However, when solving some subtasks such as basic induction, path finding or time reasoning tasks, it remains challenging because of limited ability to learn useful information between memory and query. In this paper, we propose a novel gated linear units (GLU) and local-attention based end-to-end memory networks (MemN2N-GL) motivated by the success of attention mechanism theory in the field of neural machine translation, it shows an improved possibility to develop the ability of capturing complex memory-query relations and works better on some subtasks. It is an improved end-to-end memory network for QA tasks. We demonstrate the effectiveness of these approaches on the 20 bAbI dataset which includes 20 challenging tasks, without the use of any domain knowledge. Our project is open source on github<sup>4</sup>.
Cultivating data visualization literacy in museums

Purpose This paper aims to explore what design aspects can support data visualization literacy within science museums. Design/methodology/approach The qualitative study thematically analyzes video data of 11 visitor groups as they engage with reading and writing of data visualization through a science museum exhibition that features real-time and uncurated data. Findings Findings present how the design aspects of the exhibit led to identifying single data records, data patterns, mismeasurements and distribution rate. Research limitations/implications The findings preface how to study data visualization literacy learning in short museum interactions. Practical implications Practically, the findings point toward design implications for facilitating data visualization literacy in museum exhibits. Originality/value The originality of the study lays in the way the exhibit supports engagement with data visualization literacy with uncurated data records.
Surgical Video Summarization

While surgical videos are valuable support material for activities around surgery, their summarization demands great amounts of time from surgeons, resulting in the production of very few videos. We study the practices involving surgical video to motivate and inform the future design of tools for their summarization. Through interviews and observations in a field study, we find that (1) video summaries provide an important support for surgery, being used for self-improvement, education, discussing cases, scientific research, patient communication and as legal resources; (2) video summarization follows a process hindered by the loss of knowledge that originates during recording; and, (3) surgeons develop ad-hoc coordination strategies which involve using the video itself for articulation work, making it both the field of work and coordination artifact. We discuss ways in which tools can facilitate capturing knowledge during live action using these strategies.
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures

We study the problem of Cross-lingual Event Argument Extraction (CEAE). The task aims to predict argument roles of entity mentions for events in text, whose language is different from the language that a predictive model has been trained on. Previous work on CEAE has shown the cross-lingual benefits of universal dependency trees in capturing shared syntactic structures of sentences across languages. In particular, this work exploits the existence of the syntactic connections between the words in the dependency trees as the anchor knowledge to transfer the representation learning across languages for CEAE models (i.e., via graph convolutional neural networks – GCNs). In this paper, we introduce two novel sources of language-independent information for CEAE models based on the semantic similarity and the universal dependency relations of the word pairs in different languages. We propose to use the two sources of information to produce shared sentence structures to bridge the gap between languages and improve the cross-lingual performance of the CEAE models. Extensive experiments are conducted with Arabic, Chinese, and English to demonstrate the effectiveness of the proposed method for CEAE.
Real-time Obstacle Avoidance for AUV Based on Reinforcement Learning and Dynamic Window Approach

As an important tool for exploring the ocean, autonomous underwater vehicle (AUV) plays an irreplaceable role in various marine activities. Due to the complexity and uncertainty of the marine environment, AUV is required to develop in a more intelligent direction. How to ensure that AUV avoids obstacles and reaches the target point smoothly is a key research issue of the AUV. The dynamic window approach (DWA) is adopted to AUV in this paper to achieve AUV's autonomous obstacle avoidance for static obstacles. The DWA is used to search for the optimal velocity command in its admissible velocity space by maximizing the objective function, however, the weights of its objective function are constant, which makes AUV lack flexibility in complex environments, and even unable to avoid obstacles. To address the above problem, reinforcement learning is introduced to optimize DWA. Q-learning, a reinforcement learning algorithm, is used to learn the weights of the DWA's objective function, which enables appropriate weights can be selected in different environments and improves the applicability of DWA in the complex environment. Compared with the original DWA, the DWA combined with Q-learning is effective and suitable for complex obstacle environments.
Multi-output quantum teleportation of different quantum information with an IBM quantum experience

Multicast-based quantum teleportation (QT) is extensively used in quantum information transmission where a sender sends different information to multiple receivers at the large distance through the quantum entangled channel. In this paper, we introduce the multi-output QT scheme, which deals with the situation that the synchronous transfer of the arbitrary m- and (m+1)-qubit GHZ-class states from one sender to two receivers. Notably, the requirement about synchronous diverse information transmission is satisfied in our scheme with high efficiency. Moreover, we demonstrate the implementation of the special case of above quantum multi-output teleportation scheme on a sixteen-qubit quantum computer and a 32-qubit simulator provided by IBM quantum platform, then discuss it in four types of noisy environments, and calculate the fidelities of the output states.
Bleeding Entity Recognition in Electronic Health Records: A Comprehensive Analysis of End-to-End Systems.

A bleeding event is a common adverse drug reaction amongst patients on anticoagulation and factors critically into a clinician's decision to prescribe or continue anticoagulation for atrial fibrillation. However, bleeding events are not uniformly captured in the administrative data of electronic health records (EHR). As manual review is prohibitively expensive, we investigate the effectiveness of various natural language processing (NLP) methods for automatic extraction of bleeding events. Using our expert-annotated 1,079 de-identified EHR notes, we evaluated state-of-the-art NLP models such as biLSTM-CRF with language modeling, and different BERT variants for six entity types. On our dataset, the biLSTM-CRF surpassed other models resulting in a macro F1-score of 0.75 whereas the performance difference is negligible for sentence and document-level predictions with the best macro F1-scores of 0.84 and 0.96, respectively. Our error analyses suggest that the models' incorrect predictions can be attributed to variability in entity spans, memorization, and missing negation signals.
The formation of bachelors’ digital competencies at the university

The development of modern information and communication technologies leads to the emergence of new requirements for the preparation of bachelors of higher educational institutions. These requirements justify the need for digital competencies.
Anonymous Tokens with Public Metadata and Applications to Private Contact Tracing

Anonymous single-use tokens have seen recent applications in private Internet browsing and anonymous statistics collection. We develop new schemes in order to include public metadata such as expiration dates for tokens. This inclusion enables planned mass revocation of tokens without distributing new keys, which for natural instantiations can give 77 % and 90 % amortized traffic savings compared to Privacy Pass (Davidson et al., 2018) and DIT: De-Identified Authenticated Telemetry at Scale (Huang et al., 2021), respectively. By transforming the public key, we are able to append public metadata to several existing protocols essentially without increasing computation or communication. Additional contributions include expanded definitions, a more complete framework for anonymous single-use tokens and a description of how anonymous tokens can improve the privacy in dp $$^3$$ t-like digital contact tracing applications. We also extend the protocol to create efficient and conceptually simple tokens with both public and private metadata, and tokens with public metadata and public verifiability from pairings.
Reinforcement Learning Guided by Double Replay Memory

Experience replay memory in reinforcement learning enables agents to remember and reuse past experiences. Most of the reinforcement models are subject to single experience replay memory to operate agents. In this article, we propose a framework that accommodates doubly used experience replay memory, exploiting both important transitions and new transitions simultaneously. In numerical studies, the deep <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Q</mi> </math> -networks (DQN) equipped with double experience replay memory are examined under various scenarios. A self-driving car requires an automated agent to figure out when to adequately change lanes on the real-time basis. To this end, we apply our proposed agent to the simulation of urban mobility (SUMO) experiments. Besides, we also verify its applicability to reinforcement learning whose action space is discrete (e.g., computer game environments). Taken all together, we conclude that the proposed framework outperforms priorly known reinforcement learning models in the virtue of double experience replay memory.
Anyone else have this experience

Self-tracking technologies, ranging from digital thermometers to wearable fitness trackers, allow users to use personal data accumulated from their everyday activities. But, to use these data, people have to make sense of how these numbers and figures are relevant to their lives in some way in order to make decisions and gain new insight. This process is impacted by people's emotional reactions to their data. While seeking support from others can be an effective strategy for overcoming these emotional challenges, self-trackers face unique barriers in sharing their personal data. Our study investigates 1) how users seek out support online for emotional barriers elicited by their self-tracking data and 2) what self-described impact this sharing has on their self-tracking practices. To investigate these topics, we analyzed discussions in two online communities on Reddit.com centered around infertility and trying to conceive that consistently describe self-tracking experiences. We found that community members described three distinct driving emotional tensions with their self-tracking data. In seeking community input, users were focused on support for understanding and acting upon their feelings and emotions. Even when data was uncertain, frustrating, or viewed as inaccurate, comparing and learning with others benefited users through feelings of connection, control, and humor this collective sense-making provided. Additionally, we found that users taking breaks from self-tracking in whole or part appeared to support their emotional well-being and long-term motivation to track. Based on these findings, we conclude that self-tracking data has social and emotional value beyond perceived accuracy and individual treatment goals.
Learning Strategies in English Skills used by Good Language Learners in Millennial Era: A Positive Case Study in Universitas Negeri Makassar

This article is another part of the research results conducted with the aim of developing a theory of learning English as a foreign language. The other part has been reported in the article entitled Information Technology Used by Good Language Learners in Millennial Era in Solid State Technology Volume 3 Issue 5. Theory development is carried out by utilizing best practice as a data source. The research question is "What English learning strategies are used by high achievers in the millennial era?". The main questions are translated into 4 (four) questions based on 4 types of language skills: Listening, Speaking, Reading, and Writing. Research subjects were selected based on academic achievement. Subjects were students of the 4th semester of English Department with 20% of the best academic achievement. The selected subjects were 40 students from the English Education Study Program and the English Literature Study Program. They were called as good language learners in Universitas Negeri Makassar. The instrument used to collect data was a list of open questions consisting of 12 questions. The list of questions was distributed from March to May 2020 via google form to the WhatsApp account number of the research subjects. The collected verbal data were analyzed by using coding. The results of the study contain descriptions of the types of strategies used by research subjects in improving English listening, speaking, reading, and writing skills. This description also includes the length of time used to improve the four types of English skills.The result shows that, (1) the millennial good language learners’ use a number of language learning strategies to improve their four types of language skills: listening, speaking, reading, and writing. This information is beneficial for ELT teachers, learners, and further researchers for formulating post pandemic language pedagogy.
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent.

Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the lines of converges at least as fast as gradient by assuming the conditions for the convergence of gradient descent apply to EM. This approach is not only loose, in that it does not capture that EM can make more progress than a gradient step, but the assumptions fail to hold for textbook examples of EM like Gaussian mixtures. In this work we first show that for the common setting of exponential family distributions, viewing EM as a mirror descent algorithm leads to convergence rates in Kullback-Leibler (KL) divergence. Then, we show how the KL divergence is related to first-order stationarity via Bregman divergences. In contrast to previous works, the analysis is invariant to the choice of parametrization and holds with minimal assumptions. We also show applications of these ideas to local linear (and superlinear) convergence rates, generalized EM, and non-exponential family distributions.
Timely and Efficient AI Insights on EHR: System Design.

A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review and find what they are looking for under the time constraints of the clinical setting. Although recent advances in artificial intelligence (AI) in healthcare have shown promise in enhancing clinical diagnosis and decision-making in clinicians' day-to-day tasks, the problem of how to implement and scale such computationally expensive analytics remains an open issue. In this work, we present a system architecture that generates AI-based insights from analysis of the entire patient medical record for a multispecialty outpatient facility of over 700,000 patients. Our resulting system is able to generate insights efficiently while handling complexities of scheduling to deliver the results in a timely manner, and handle more than 30,000 updates per day while achieving desirable operating cost-performance goals.
A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions

Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures and external disturbances are among the most common causes of degraded mode of operation. To deal with this problem, in this work, we present a meta-learning-based approach to improve the trajectory tracking performance of an unmanned aerial vehicle (UAV) under actuator faults and disturbances which have not been previously experienced. Our approach leverages meta-learning to train a model that is easily adaptable at runtime to make accurate predictions about the system’s future state. A runtime monitoring and validation technique is proposed to decide when the system needs to adapt its model by considering a data pruning procedure for efficient learning. Finally, the reference trajectory is adapted based on future predictions by borrowing feedback control logic to make the system track the original and desired path without needing to access the system’s controller. The proposed framework is applied and validated in both simulations and experiments on a faulty UAV navigation case study demonstrating a drastic increase in tracking performance.
Practical Verification of Quantum Properties in Quantum-Approximate-Optimization Runs

In order to assess whether quantum resources can provide an advantage over classical computation, it is necessary to characterize and benchmark the nonclassical properties of quantum algorithms in a practical manner. In this paper, we show that using measurements in no more than three out of the possible ${3}^{N}$ bases, one can not only reconstruct the single-qubit reduced density matrices and measure the ability to create coherent superpositions, but also possibly verify entanglement across all $N$ qubits participating in the algorithm. We introduce a family of generalized Bell-type observables for which we establish an upper bound to the expectation values in fully separable states by proving a generalization of the Cauchy-Schwarz inequality, which may serve of independent interest. We demonstrate that a subset of such observables can serve as entanglement witnesses for states of the quantum-approximate-optimization algorithm for the MaxCut problem (QAOA MaxCut), and further argue that they are especially well tailored for this purpose by defining and computing an entanglement potency metric on witnesses. A subset of these observables also certifies, in a weaker sense, the entanglement in GHZ states, which share the ${\mathbb{Z}}_{2}$ symmetry of QAOA MaxCut. The construction of such witnesses follows directly from the cost Hamiltonian to be optimized, and not through the standard technique of using the projector of the state being certified. It may thus provide insights to construct similar witnesses for other variational algorithms prevalent in the noisy intermediate-scale quantum era. We demonstrate our ideas with proof-of-concept experiments on the Rigetti Aspen-9 chip for ansatz containing up to 24 qubits.
WALD-EM: Wald accumulation for locations and durations of eye movements.

Describing, analyzing, and explaining patterns in eye movement behavior is crucial for understanding visual perception. Further, eye movements are increasingly used in informing cognitive process models. In this article, we start by reviewing basic characteristics and desiderata for models of eye movements. Specifically, we argue that there is a need for models combining spatial and temporal aspects of eye-tracking data (i.e., fixation durations and fixation locations), that formal models derived from concrete theoretical assumptions are needed to inform our empirical research, and custom statistical models are useful for detecting specific empirical phenomena that are to be explained by said theory. In this article, we develop a conceptual model of eye movements, or specifically, fixation durations and fixation locations, and from it derive a formal statistical model-meeting our goal of crafting a model useful in both the theoretical and empirical research cycle. We demonstrate the use of the model on an example of infant natural scene viewing, to show that the model is able to explain different features of the eye movement data, and to showcase how to identify that the model needs to be adapted if it does not agree with the data. We conclude with discussion of potential future avenues for formal eye movement models. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Implementation of Tesseract Algorithm to Extract Text from Different Images

Image processing is one of the most growing fields in research and technology in today’s world. There is a high demand of a computer system that can store the information available in newspapers and other hard copy paper documents. One of the most simplest ways to store the information of text into computer systems in by scanning the paper. It can then be stored in the computer and changes can be made on it if required. But, detection of text from the captured image is a very challenging task. Thus, an attempt has been made using the Tesseract algorithm that makes it easier to extract text from images.