Reviewerly/exHarmony huggingface dataset:
Domain-General Tutor Authoring with Apprentice Learner Models

Abstract Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8 , 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1 , 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10 , 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25 (1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.
Improved Collaborative Filtering Recommendation Through Similarity Prediction

Collaborative Filtering (CF) approaches have been widely used in various applications of recommender systems. These methods are based on estimating the similarity between users/items by analyzing the ratings provided by users. The existing methods are often domain-specific and have not considered the time of the ratings being made in the calculation of the similarity. However, users' preferences vary over time, and so their similarity. In this paper, a novel method is proposed by re-ranking the users/items neighborhood set considering their future similarity trend. The trend of similarity is predicted, and depending on increased/decreased trend, we update the final nearest neighbor sets that are used in CF formulation. This method can be applied on a broad range of CF methods that are based on similarities between users and/or items. We apply the proposed approach on a set of CF algorithms over two benchmark datasets and show that the proposed approach significantly improves the performance of the original CF recommenders. As the proposed method only re-ranks the neighborhood set, it can be applied to any existing non-temporal similarity-based CF recommenders to improve their performance.
Text-Guided Image Inpainting

Given a partially masked image, image inpainting aims to complete the missing region and output a plausible image. Most existing image inpainting methods complete the missing region by expanding or borrowing information from the surrounding source region, which work well when the original content in the missing region is similar to the surrounding source region. Unsatisfactory results will be generated if there is no sufficient contextual information can be referenced from source region. Besides, the inpainting results should be diverse and this kind of diversity should be controllable. Based on these observations, we propose a new inpainting problem that introduces text as a kind of guidance to direct and control the inpainting process. The main difference between this problem and previous works is that we need ensure the result to be consistent with not only the source region but also the textual guidance during inpainting. By this way, we want to avoid the unreasonable completion and meanwhile make it controllable. We propose a progressively coarse-to-fine cross-modal generative network and adopt the text-image-text training schema to generate visually consistent and semantically coherent images. Extensive quantitative and qualitative experiments on two public datasets with captions demonstrate the effectiveness of our method.
AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling

Most state-of-the-art object detection systems follow an anchor-based diagram. Anchor boxes are densely proposed over the images and the network is trained to predict the boxes position offset as well as the classification confidence. Existing systems pre-define anchor box shapes and sizes and ad-hoc heuristic adjustments are used to define the anchor configurations. However, this might be sub-optimal or even wrong when a new dataset or a new model is adopted. In this paper, we study the problem of automatically optimizing anchor boxes for object detection. We first demonstrate that the number of anchors, anchor scales and ratios are crucial factors for a reliable object detection system. By carefully analyzing the existing bounding box patterns on the feature hierarchy, we design a flexible and tight hyper-parameter space for anchor configurations. Then we propose a novel hyper-parameter optimization method named AABO to determine more appropriate anchor boxes for a certain dataset, in which Bayesian Optimization and sub-sampling method are combined to achieve precise and efficient anchor configuration optimization. Experiments demonstrate the effectiveness of our proposed method on different detectors and datasets, e.g. achieving around 2.4% mAP improvement on COCO, 1.6% on ADE and 1.5% on VG, and the optimal anchors can bring 1.4%–2.4% mAP improvement on SOTA detectors by only optimizing anchor configurations, e.g. boosting Mask RCNN from 40.3% to 42.3%, and HTC detector from 46.8% to 48.2%.
A Proof-of-Concept of Farmer-to-Consumer Food Traceability on Blockchain for Local Communities

Recent pandemic presses importance on the trade of quality produce from local farmers to consumers. Traditionally, farmers rely on middlemen to sell their produce, resulting in occasional price injustice, and the lack of direct communication with their customers who could be interested in learning more about the produce they consume. To provide trusts using technology instead of an intermediary, this paper proposes a design of a blockchain-based food traceability system for local farmers to sell produce to health-aware consumers who could trace the farming activities of the produce they purchase. The system is based on a private blockchain platform, Hyperledger Fabric, that supports farmers, carriers, and consumers. Farm activities can be recorded by farmers. Consumers can view produce history before making a purchase, and track transportation by carriers up until delivery. IoT is integrated to record farming environments and quality of farm during plantation is evaluated. This provides a proof-of-concept of how a blockchain-based system can be implemented for local produce marketplace without the need of intermediary.
bpfbox

Process confinement is a key requirement for workloads in the cloud and in other contexts. Existing process confinement mechanisms on Linux, however, are complex and inflexible because they are implemented using a combination of primitive abstractions (e.g., namespaces, cgroups) and complex security mechanisms (e.g., SELinux, AppArmor) that were designed for purposes beyond basic process confinement. We argue that simple, efficient, and flexible confinement can be better implemented today using eBPF, an emerging technology for safely extending the Linux kernel. We present a proof-of-concept confinement application, bpfbox, that uses less than 2000 lines of kernelspace code and allows for confinement at the userspace function, system call, LSM hook, and kernelspace function boundaries---something that no existing process confinement mechanism can do. Further, it does so using a policy language simple enough to use for ad-hoc confinement purposes. This paper presents the motivation, design, implementation, and benchmarks of bpfbox, including a sample web server confinement policy.
How are issue reports discussed in Gitter chat rooms?

Informal communication channels like mailing lists, IRC and instant messaging play a vital role in open source software development by facilitating communication within geographically diverse project teams e.g., to discuss issue reports to facilitate the bug-fixing process. More recently, chat systems like Slack and Gitter have gained a lot of popularity and developers are rapidly adopting them. Gitter is a chat system that is specifically designed to address the needs of GitHub users. Gitter hosts project-based asynchronous chats which foster frequent project discussions among participants. Developer discussions contain a wealth of information such as the rationale behind decisions made during the evolution of a project. In this study, we explore 24 open source project chat rooms that are hosted on Gitter, containing a total of 3,133,106 messages and 14,096 issue references. We manually analyze the contents of chat room discussions around 457 issue reports. The results of our study show the prevalence of issue discussions on Gitter, and that the discussed issue reports have a longer resolution time than the issue reports that are never brought on Gitter.
A Novel Robust Gaussian Approximate Smoother Based on EM for Cooperative Localization With Sensor Fault and Outliers

In this article, a novel robust Gaussian approximation smoother based on expectation-maximization (EM) algorithm is proposed for cooperative localization (CL) with faulty Doppler velocity log (DVL) and heavy-tailed measurement noise. In our model, the autonomous underwater vehicle (AUV) velocity information that is not available due to DVL failure and the bias in the underwater acoustic modem are considered as unknown inputs. Then, the Student's t distribution is used to model the heavy-tailed measurement noise. An EM algorithm is also developed for the state-space model with heavy-tailed measurement noise. The state, noise covariance matrices, and auxiliary random variables are regarded as hidden variables to obtain the maximum likelihood estimation of unknown inputs. Gaussian smoother where modified process and measurement noise covariance are inferred by variational Bayesian (VB) approach is applied to estimate the state. The experimental results illustrate that given the heavy-tailed noise, the proposed method estimates the unknown input, and the state with a high level of accuracy. The proposed algorithm can be used as a backup algorithm for CL in cases where DVL is not available due to failure.
Two-Timescale Multilayer Recurrent Neural Networks for Nonlinear Programming

This article presents a neurodynamic approach to nonlinear programming. Motivated by the idea of sequential quadratic programming, a class of two-timescale multilayer recurrent neural networks is presented with neuronal dynamics in their output layer operating at a bigger timescale than in their hidden layers. In the two-timescale multilayer recurrent neural networks, the transient states in the hidden layer(s) undergo faster dynamics than those in the output layer. Sufficient conditions are derived on the convergence of the two-timescale multilayer recurrent neural networks to local optima of nonlinear programming problems. Simulation results of collaborative neurodynamic optimization based on the two-timescale neurodynamic approach on global optimization problems with nonconvex objective functions or constraints are discussed to substantiate the efficacy of the two-timescale neurodynamic approach.
The Role of Imaginative Thinking in Scientific Thinking

The article discusses the role of creative thinking in scientific thinking and in particular in the teaching of mathematics in higher educational institutions. From this point of view, the relationship between abstract analytical and visual geometric methods in mathematics is highlighted, and it is concluded that the latter form the basis for the application of creative thinking in mathematics.
Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis

Research in conceptual modeling is today undertaken in a large number of fields. We describe the results from a bibliometric analysis of major outlets for conceptual modeling research for investigating the evolution of research topics. As a basis for the study we used the openly accessible DBLP dataset which we enriched with data from related publisher’s websites and databases. Besides a descriptive analysis of the publication data, we conducted a content-based analysis of more than 3.200 papers using Latent Dirichlet Allocation. This permits to gain insights into the past trends in conceptual modeling research and derive future prospects for the community.
Score-Based Automatic Detection and Resolution of Syntactic Ambiguity in Natural Language Requirements

The quality of a delivered product relies heavily upon the quality of its requirements. Across many disciplines and domains, system and software requirements are mostly specified in natural language (NL). However, natural language is inherently ambiguous and inconsistent. Such intrinsic challenges can lead to misinterpretations and errors that propagate to the subsequent phases of the system development. Pattern-based natural language processing (NLP) techniques have been proposed to detect the ambiguity in requirements specifications. However, such approaches typically address specific cases or patterns and lack the versatility essential to detecting different cases and forms of ambiguity. In this paper, we propose an efficient and versatile automatic syntactic ambiguity detection technique for NL requirements. The proposed technique relies on filtering the possible scored interpretations of a given sentence obtained via Stanford CoreNLP library. In addition, it provides feedback to the user with the possible correct interpretations to resolve the ambiguity. Our approach incorporates four filtering pipelines on the input NL-requirements working in conjunction with the CoreNLP library to provide the most likely possible correct interpretations of a requirement. We evaluated our approach on a suite of datasets of 126 requirements and achieved 65% precision and 99% recall on average.
InteractGAN: Learning to Generate Human-Object Interaction

Compared with the widely studied Human-Object Interaction DE-Tection (HOI-DET), no effort has been devoted to its inverse problem, i.e. to generate an HOI scene image according to the given relationship triplet <human, predicate, object>, to our best knowledge. We term this new task "Human-Object Interaction Image Generation" (HOI-IG). HOI-IG is a research-worthy task with great application prospects, such as online shopping, film production and interactive entertainment. In this work, we introduce an Interact-GAN to solve this challenging task. Our method is composed of two stages: (1) manipulating the posture of a given human image conditioned on a predicate. (2) merging the transformed human image and object image to one realistic scene image while satisfying the ir expected relative position and ratio. Besides, to address the large spatial misalignment issue caused by fusing two images content with reasonable spatial layout, we propose a Relation-based Spatial Transformer Network (RSTN) to adaptively process the images conditioned on their interaction. Extensive experiments on two challenging datasets demonstrate the effectiveness and superiority of our approach. We advocate for the image generation community to draw more attention to the new Human-Object Interaction Image Generation problem. To facilitate future research, our project will be released at: http://colalab.org/projects/InteractGAN.
Eyelid Gestures on Mobile Devices for People with Motor Impairments

Eye-based interactions for people with motor impairments have often used clunky or specialized equipment (e.g., eye-trackers with non-mobile computers) and primarily focused on gaze and blinks. However, two eyelids can open and close for different duration in different orders to form various eyelid gestures. We take a first step to design, detect, and evaluate a set of eyelid gestures for people with motor impairments on mobile devices. We present an algorithm to detect nine eyelid gestures on smartphones in real-time and evaluate it with twelve able-bodied people and four people with severe motor impairments in two studies. The results of the study with people with motor-impairments show that the algorithm can detect the gestures with .76 and .69 overall accuracy in user-dependent and user-independent evaluations. Moreover, we design and evaluate a gesture mapping scheme allowing for navigating mobile applications only using eyelid gestures. Finally, we present recommendations for designing and using eyelid gestures for people with motor impairments.
Cooperative Relative Localization Using Range Measurements Without a Priori Information

The ability for autonomous vehicles to cooperatively navigate, especially in GPS denied environments, is becoming increasingly important. It also requires the ability to initialize, or reinitialize estimation algorithms for cooperative systems on-the-fly in cases where precise a priori state information is unavailable. In this paper, we provide a framework that allows estimation of the relative pose and orientation between vehicles in the presence of high initial uncertainty. Effects of cooperation among multiple vehicles exchanging estimates of heading rate and velocity and external sensor measurements are analyzed. A Multi-Hypothesis Extended Kalman Filter (MHEKF) technique is used to initialize pairwise vehicles using range-only measurements. Using solutions identified by the MHEKF algorithm, a joint filter comprising of multiple vehicles is initialized. Sufficient conditions to maintain bounded errors are derived through nonlinear observability analysis using Lie derivatives for the pairwise and the multi-vehicle cases. Using these conditions as passive constraints in the system, simulation and hardware experiments are performed to demonstrate the advantages of using MHEKF when the initial conditions are unreliable. A multi-vehicle testbed for heterogeneous platforms with different sensing modalities is developed to facilitate hardware testing. Improvements in the system performance when cooperation is introduced among vehicles is also highlighted through experiments.
Intelligent English translation system based on evolutionary multi-objective optimization algorithm

The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs.
Self-Adaptive Gathering for Energy-Efficient Data Stream in Heterogeneous Wireless Sensor Networks Based on Deep Learning

Big data streams are available across the growing heterogeneous wireless sensor networks, with characteristics of vast volume and dynamic transmission. Energy efficiency improvement in big data stream gathering is becoming a challenge. In this article, a self-adaptive gathering algorithm for multisource heterogeneous big data streams with sliding windows is proposed, which can improve the energy efficiency for data stream processing due to adaptively adjusting the window size based on the difference of data probability distribution between adjacent windows. In order to save the spatial correlation of a heterogeneous data stream, the Gaussian Bernoulli Matrix Variable Restricted Boltzmann Machine (GBMVRBM) is proposed to deal with the multi-source data separately, and then a joint layer is used to fuse the data features of different modalities. The probability distribution of sliding window data is obtained by the energy function of the GBMVRBM, and the Hoeffding boundary is adopted to ensure that the probability distribution variation between the windows can be detected in time. The algorithm is tested on the Clemson University Audio Visual Experiments database, and it can be concluded that the algorithm proposed in this article can not only detect the data change in time, but also expand the window size to process the data efficiently.
A Survey on Contact Tracing: The Latest Advancements and Challenges

Infectious diseases are caused by pathogenic microorganisms, such as bacteria, viruses, parasites or fungi, which can be spread, directly or indirectly, from one person to another. Infectious diseases pose a serious threat to human health, especially COVID-19 that has became a serious worldwide health concern since the end of 2019. Contact tracing is the process of identifying, assessing, and managing people who have been exposed to a disease to prevent its onward transmission. Contact tracing can help us better understand the transmission link of the virus, whereby better interrupting its transmission. Given the worldwide pandemic of COVID-19, contact tracing has become one of the most critical measures to effectively curb the spread of the virus. This paper presents a comprehensive survey on contact tracing, with a detailed coverage of the recent advancements the models, digital technologies, protocols and issues involved in contact tracing. The current challenges as well as future directions of contact tracing technologies are also presented.