Reviewerly/exHarmony huggingface dataset:
Is a Classification Procedure Good Enough?—A Goodness-of-Fit Assessment Tool for Classification Learning

In recent years, many nontraditional classification methods, such as random forest, boosting, and neural network, have been widely used in applications. Their performance is typically measured in terms of classification accuracy. While the classification error rate and the like are important, they do not address a fundamental question: Is the classification method underfitted? To our best knowledge, there is no existing method that can assess the goodness of fit of a general classification procedure. Indeed, the lack of a parametric assumption makes it challenging to construct proper tests. To overcome this difficulty, we propose a methodology called BAGofT that splits the data into a training set and a validation set. First, the classification procedure to assess is applied to the training set, which is also used to adaptively find a data grouping that reveals the most severe regions of underfitting. Then, based on this grouping, we calculate a test statistic by comparing the estimated success probabilities and the actual observed responses from the validation set. The data splitting guarantees that the size of the test is controlled under the null hypothesis, and the power of the test goes to one as the sample size increases under the alternative hypothesis. For testing parametric classification models, the BAGofT has a broader scope than the existing methods since it is not restricted to specific parametric models (e.g., logistic regression). Extensive simulation studies show the utility of the BAGofT when assessing general classification procedures and its strengths over some existing methods when testing parametric classification models. Supplementary materials for this article are available online.
Research on data acquisition of novel list based on Python

Nowadays, online novels are all the rage. It is very important for online literature to understand the reading interests of modern readers. This will not only help online literature to position itself more accurately in its future development, but will also strike deep into the hearts of readers. It also helps to understand how readers have changed over the years and what they want in their novels. This research mainly uses Python’s request library, Beautiful Soup Library, numpy library, Pandas Library, Matplotlib library and Jieba library to crawl the weekly, monthly and total list of recommended tickets.
Knowledge-based Embodied Question Answering

In this paper, we propose a novel Knowledge-based Embodied Question Answering (K-EQA) task, in which the agent intelligently explores the environment to answer various questions with the knowledge. Different from explicitly specifying the target object in the question as existing EQA work, the agent can resort to external knowledge to understand more complicated question such as "Please tell me what are objects used to cut food in the room?", in which the agent must know the knowledge such as "knife is used for cutting food". To address this K-EQA problem, a novel framework based on neural program synthesis reasoning is proposed, where the joint reasoning of the external knowledge and 3D scene graph is performed to realize navigation and question answering. Especially, the 3D scene graph can provide the memory to store the visual information of visited scenes, which significantly improves the efficiency for the multi-turn question answering. Experimental results have demonstrated that the proposed framework is capable of answering more complicated and realistic questions in the embodied environment. The proposed method is also applicable to multi-agent scenarios.
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation

Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - to feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideations. We conducted formative and controlled user studies to understand the usage and usefulness of explanations to improve ideation diversity and quality. Users appreciated that explanation feedback helped focus their efforts and provided directions for improvement. This resulted in explanations improving diversity compared to no feedback or feedback with scores only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation and creativity support tools.
Machine learning techniques for breast cancer diagnosis and treatment: a narrative review

Objective: This narrative review describes the recent developments and applications of machine learning (ML), a part of artificial intelligence, concerning breast cancer. Background: The advent of new bioinformatic approaches and artificial intelligence-based computational technologies has led to a shift in the decision-making of oncologists regarding breast cancer diagnostics and treatment processes. Various successful applications of ML on image processing, especially the use of deep neural networks and convolutional neural networks, to detect tumor and lymph nodes regions have been reported. Recent high-throughput molecular quantifications, i.e., quantitative omics techniques have enabled simultaneous monitoring of thousands of molecules to understand the molecular-level pathology. These data, including gene expression, protein, metabolite, and methylation profiling, have been analyzed via deep learning, network analysis, clustering, and dimension reductions to explore intrinsic subtypes and new biomarkers. Clinical-pathological features have been conducted by multivariable analysis to predict various outcomes, e.g., the sensitivity of adjuvant therapy and prognosis. The quantitative relationships among their variables have been visualized as nomograms. To analyze complex structures of a larger number of variables, ML combining multiple clinical-pathological features has been developed to predict the prognosis, metastasis, and treatment outcomes of breast cancer. Methods: We provided the narrative review of ML-related topics especially in the quantitative omics data and clinical-pathological prediction models. Conclusions: ML-based prediction methods are powerful tools and contribute to realizing personalized medicine for breast cancer.
Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults

The aging population has resulted in interest in remote monitoring of elderly individuals’ health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual’s pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual’s typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual’s observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject’s health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.
Owlet

Miniaturization is an inherent trend in ubiquitous computing. Insect-scale robots add new capabilities to disaster management, tiny headphones are emerging as complete computing devices, and small wearable health monitors can track vital signs around the clock. Advancements in sensing technologies play a pivotal role in this development. Spatial sensing at this form factor, however, is a skill yet to be mastered, particularly at low-frequency signals like audible sounds. Traditionally, spatial sensing requires sampling in both time and space using an array of microphones, which comes with a strict size requirement and multiplies its power requirement. In this article, we explore the possibility of an alternative design for spatial sensing for miniaturized and power-constrained devices.
Fear appeal cues to motivate users' security protection behaviors: an empirical test of heuristic cues to enhance risk communication

Purpose Firms continue to struggle with end users who do not follow recommended actions for safeguarding information security. Thus, the authors utilize insights gained from studies on heuristic processing of risk information to design cues in fear appeal messages more effectively so as to more strongly engender fear among users, which can in turn lead them to take protective actions toward information security. Specifically, four types of fear appeal cues are identified: numeric risk communication, social distance and goal framing in verbal risk communication and visual risk communication. Design/methodology/approach Drawing from protection motivation theory, the authors hypothesize that these fear appeal cues can engender fear among users to a greater extent. In addition, the authors hypothesize that users will perceive a higher level of severity and susceptibility when they perceive a large amount of fear. The research hypotheses were tested employing data collected through a laboratory experiment. Analysis of variance (ANOVA) and regression analyses were performed to analyze the data. Findings The study's results suggest that numeric and visual risk communication cues in security notices can significantly increase the amount of fear felt by users. In addition, social distance was found to marginally increase the amount of fear felt by users. However, unlike our expectation, goal framing was not found to increase the amount of fear when the other three types of fear appeal cues were also given in a security notice. It was also found that induced fear can increase the severity and susceptibility of threats as perceived by users. Originality/value The study contributes to the literature on fear appeal cues designed to promote users' security protection behaviors. No prior study has designed security notices featuring the four different types of fear appeal cues and empirically tested the effectiveness of those cues in inducing fear among users. The findings suggest that the design of fear appeal cues can be improved by understanding individuals' heuristic processing of risk information, which can be subject to cognitive biases.
Basic Programming Concepts as Explained for Preschoolers

The development of information technologies has formed a socio-economic request to reduce the age at which children can be introduced to programming. After 6 years’ efforts, the authors managed to develop and on a large scale introduce a one-year programming course for preschoolers, which is built on a metaphor of programmed control. During the course development, a set of basic programming concepts was selected and specified to be mastered by preschoolers aged 6+ in the activity-game form. This set of concepts goes back to Seymour Papert's ideas about teaching programming by writing programs that control moving objects using intuitive sets of commands. The main feature of the proposed system of concepts, allow at the initial stage of training to demonstrate and assimilate all the elements of the concept in the real, not in the virtual world. In the picture of the World, which we explain and demonstrate to children, only one function remains at the computer - the execution of the program it has memorized. Everything else happens in the real world. In the real world, an environment is created in which a real robot will move. In the real world, a program is created from tangible objects, which will then be "shown" to the computer so that it stores it in its memory and can then execute it at the command of a human. In the real world, obeying the signals of the computer, the robot performs the work stipulated by the program. This allows you to begin acquaintance with the programming of children from 4 years old, without working individually or collectively with electronic screens, which in today's Russia is prohibited by federal medical authorities in the educational process of children under 5 years old. The course is built on the text-free pictographic programming system PiktoMir developed by Russian Academy of Sciences. The methodological content of the course allows each preschooler to gain experience in the development and debugging of 120-150 simple programs by the end of the course. The final part of the article discusses the authors' plans for the development of a three-year textless programming course, methodologically and instrumentally connected with the primary school programming course.
Deviation bounds and concentration inequalities for quantum noises

We provide a stochastic interpretation of non-commutative Dirichlet forms in the context of quantum filtering. For stochastic processes motivated by quantum optics experiments, we derive an optimal finite time deviation bound expressed in terms of the non-commutative Dirichlet form. Introducing and developing new non-commutative functional inequalities, we deduce concentration inequalities for these processes. Examples satisfying our bounds include tensor products of quantum Markov semigroups as well as Gibbs samplers above a threshold temperature.
Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology

Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine.This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired.We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images.The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging.The open-source application is publicly available at , with a short video demonstration at .
Flexibility of the factorized form of the unitary coupled cluster <i>Ansatz</i>

The factorized form of the unitary coupled cluster Ansatz is a popular state preparation Ansatz for electronic structure calculations of molecules on quantum computers. It is often viewed as an approximation (based on the Trotter product formula) for the conventional unitary coupled cluster operator. In this work, we show that the factorized form is quite flexible, allowing one to range from a conventional configuration interaction, to conventional unitary coupled cluster, to efficient approximations that lie in between these two. The variational minimization of the energy often allows simpler factorized unitary coupled cluster approximations to achieve high accuracy, even if they do not accurately approximate the Trotter product formula. This is similar to how quantum approximate optimization algorithms can achieve high accuracy with a small number of levels.
MLFC: From 10 to 50 Planners in the Multi-Agent Programming Contest

In this paper, we describe the strategies used by our team, MLFC, that led us to achieve the 2nd place in the 15th edition of the Multi-Agent Programming Contest. The scenario used in the contest is an extension of the previous edition (14th) "Agents Assemble" wherein two teams of agents move around a 2D grid and compete to assemble complex block structures. We discuss the languages and tools used during the development of our team. Then, we summarise the main strategies that were carried over from our previous participation in the 14th edition and list the limitations (if any) of using these strategies in the latest contest edition. We also developed new strategies that were made specifically for the extended scenario: cartography (determining the size of the map); formal verification of the map merging protocol (to provide assurances that it works when increasing the number of agents); plan cache (efficiently scaling the number of planners); task achievement (forming groups of agents to achieve tasks); and bullies (agents that focus on stopping agents from the opposing team). Finally, we give a brief overview of our performance in the contest and discuss what we believe were our shortcomings.
Bounding the Benefit of Adaptivity in Quantum Metrology Using the Relative Fidelity

Protocols for discriminating between a pair of channels or for estimating a channel parameter can often be aided by adaptivity or by entanglement between the probe states. This can make it difficult to bound the best possible performance for such protocols. In this Letter, we introduce a quantity that we call the relative fidelity of a given pair of channels and a pair of input states to those channels. Constraining the allowed input states to all pairs of states whose fidelity is greater than some minimum "input fidelity" and minimizing this quantity over the valid pairs of states, we get the minimum relative fidelity for that input fidelity constraint. We are then able to lower bound the fidelity between the possible output states of any protocol acting on one of two possible channels in terms of the minimum relative fidelity. This allows us to bound the performance of the most general, adaptive discrimination and parameter estimation protocols. By finding a continuity bound for the relative fidelity, we also provide a simple confirmation that the quantum Fisher information (QFI) of the output of an N-use protocol is no more than N^{2} times the one-shot QFI.
A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving

Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.
Dive in Deep Learning

Deep learning, a subfield of machine learning, has driven the fast advancement in artificial intelligence research, prompting astounding forward leaps on long-standing issues in a plenty of fields, for example, computer vision, natural language processing, and signal processing. Deep learning isn’t just a neural network with at least two hidden layers. The genuine substance of deep learning lies in the techniques that empower the expanded extraction of data obtained from a neural network with more than one hidden layer. Adding more hidden layers to a neural network would give little profit without the techniques that support the effective extraction of data. This chapter proposes an exhaustive and in-depth analysis of the literature; how, where, and why deep learning models are applied in image processing, natural language processing, and signal processing. With recent progressions in deep learning-based computer vision models, applications are simpler to create than at any other time. This chapter investigates computer vision algorithms and deep convolutional neural networks (CNNs) to identify, track, and perceive continuously objects added with image segmentation. Natural language processing considers associations among computer and people utilizing common language. By and by, it is extremely regular to utilize common language preparing strategies to measure and dissect text. The portrayal of every token (word or subword) can be pretrained on an enormous corpus, utilizing word2vec, GloVe, or subword implanting models and text classification. Sound information investigation is tied in with breaking down and understanding sound signs caught by advanced gadgets, with various applications in different endeavors.