Reviewerly/exHarmony huggingface dataset:
Information Literacy and Digital Competence of Teachers in the Age of Digital Transformation

The study discusses the specifics of digital literacy and digital competence of a big community of teachers in one of the regions of St. Petersburg. It contains the calculations of digital literacy indices and digital competency index of the teachers that were made using the range of diagnostic tools of NAFI research center. It was found that today teachers are most advanced in content and assessment, while digital resource technology and management is the least developed field of pedagogical activity where digital technologies are applied. Two levels of digital transformation are introduced in the teaching and learning process—replacement and improvement, to ensure that the zone of traditional teaching is operative. For teachers to be able to transfer successfully to the zone of pedagogical engineering, programs have to be developed to provide psychological and pedagogical support as they improve their ICT competence.
Learning Transferable and Discriminative Representations for 2D Image-Based 3D Model Retrieval

Existing research on the 2D image-based 3D model retrieval task focuses on learning transferable representations directly to narrow the domain discrepancy. However, it is not easy to achieve in practice due to the significant variations across two domains. In addition, some methods design a domain discriminator to distinguish the feature arising from source or target domains for transferable feature representations learning, which will lead to an unexpected deterioration of the feature discriminability. To settle these problems, we propose jointly learning transferable and discriminative representations for 2D image-based 3D model retrieval. Specifically, we extract features from the 2D images and 3D models (described as multiple views) by CNN. Considering the difficulty of directly narrowing the discrepancy of two domains, we are prone to connect 2D image and 3D model domains to an intermediate domain, where the domain gap aims to be eliminated. However, the feature transferability does not denote well discriminability. Based on the batch spectral penalization (BSP) theory, the feature transferability is dominated by feature vectors with higher singular values, while the feature discriminability depends on more eigenvectors with lower singular values to convey rich discriminative structures. Therefore, we penalize the largest singular values so that the feature vectors with lower singular values are appropriately enhanced, thereby strengthening feature discriminability. A series of experiments on two challenging datasets, MI3DOR and MI3DOR-2, indicate that our method can significantly improve performance.
A Class of Solvable Markov Decision Models with Incomplete Information

This paper investigates natural conditions for the existence of optimal policies for a Markov decision process with incomplete information (MDPII) and with expected total costs. MDPII is the classic model of a controlled stochastic process with incomplete state observations which is more general than Partially Observable Markov Decision Processes (POMDPs). For MDPII we introduce the notion of a semi-uniform Feller transition probability, which is stronger than the notion of a weakly continuous transition probability. We show that an MDPII has a semi-uniform Feller transition probability if and only if the corresponding belief MDP also has a semi-uniform Feller transition probability. This fact has several corollaries. In particular, it provides new and implies all known sufficient conditions for the existence of optimal policies for POMDPs with expected total costs.
A Visual Analytics Framework for Distributed Data Analysis Systems

This paper proposes a visual analytics framework that addresses the complex user interactions required through a command-line interface to run analyses in distributed data analysis systems. The visual analytics framework facilitates the user to manage access to the distributed servers, incorporate data from the source, run data-driven analysis, monitor the progress, and explore the result using interactive visualizations. We provide a user interface embedded with generalized functionalities and access protocols and integrate it with a distributed analysis system. To demonstrate our proof of concept, we present two use cases from the earth science and Sustainable Human Building Ecosystem research domain.
WGAN-Based Image Denoising Algorithm

Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory

Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it.However, previous methods for knowledge selection only concentrate on the relevance between knowledge and dialogue context, ignoring the fact that age, hobby, education and life experience of an interlocutor have a major effect on his or her personal preference over external knowledge.Without taking the personalization issue into account, it is difficult to select the proper knowledge and generate persona-consistent responses.In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue.We propose a variational method to model the underlying relationship between one's personal memory and his or her selection of knowledge, and devise a learning scheme in which the forward mapping from personal memory to knowledge and its inverse mapping is included in a closed loop so that they could teach each other.Experiment results show that our method outperforms existing KGC methods significantly on both automatic evaluation and human evaluation.
Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing

Probabilistic bits (p-bits) have recently been presented as a spin (basic computing element) for the simulated annealing (SA) of Ising models. In this brief, we introduce fast-converging SA based on p-bits designed using integral stochastic computing. The stochastic implementation approximates a p-bit function, which can search for a solution to a combinatorial optimization problem at lower energy than conventional p-bits. Searching around the global minimum energy can increase the probability of finding a solution. The proposed stochastic computing-based SA method is compared with conventional SA and quantum annealing (QA) with a D-Wave Two quantum annealer on the traveling salesman, maximum cut (MAX-CUT), and graph isomorphism (GI) problems. The proposed method achieves a convergence speed a few orders of magnitude faster while dealing with an order of magnitude larger number of spins than the other methods.
Instance-based Label Smoothing For Better Calibrated Classification Networks

Label smoothing is widely used in deep neural networks for multi-class classification. While it enhances model generalization and reduces overconfidence by aiming to lower the probability for the predicted class, it distorts the predicted probabilities of other classes resulting in poor class-wise calibration. Another method for enhancing model generalization is self-distillation where the predictions of a teacher network trained with one-hot labels are used as the target for training a student network. We take inspiration from both label smoothing and self-distillation and propose two novel instance-based label smoothing approaches, where a teacher network trained with hard one-hot labels is used to determine the amount of per class smoothness applied to each instance. The assigned smoothing factor is non-uniformly distributed along with the classes according to their similarity with the actual class. Our methods show better generalization and calibration over standard label smoothing on various deep neural architectures and image classification datasets.
A Flexible Wearable Electrooculogram System With Motion Artifacts Sensing and Reduction

Electrooculogram (EOG) is a well-known physiological metric picked up by placing two or more electrodes around the eyeball. EOG signals are known to be extremely susceptible to motion artifacts. This paper presents a single channel, wireless, wearable flexible EOG monitoring system with motion artifacts sensing and reduction feature. The system uses two non-contact electrode pairs for EOG/motion artifacts detection and motion artifacts reduction. It is implemented on a four-layer flexible polyimide substrate. It is light-weight (only 8.75 gram), battery operated, and uses a microcontroller and a BLE 5.0 transceiver for wireless EOG data transmission, while consuming only 56 mW of power. The system metrics such as gain around 37 dB, bandwidth from 1 Hz to 40 Hz, and noise are evaluated. The system is tested for different electrode configurations and it is demonstrated that horizontally parallel electrode pairs achieve an acceptable motion artifact reduction at the output, while preserving perfect EOG features (such as eye-blinking). The average sensitivity for horizontally parallel non-contact electrodes is found out to be more than 50 times with respect to commercial gold electrodes, whereas the average response time of the sensor is around 380 mS. The flexible EOG system is comfortable to wear and the use of non-contact electrode eliminates the need of skin preparation. Therefore, the system can be easily integrated with eye-masks and headbands, thus making it an excellent prototype for many smart applications.
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework

Abstract In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.
LPC Augment: an LPC-based ASR Data Augmentation Algorithm for Low and Zero-Resource Children’s Dialects

This paper proposes a novel linear prediction coding-based data augmentation method for children’s low and zero resource dialect ASR. The data augmentation procedure consists of perturbing the formant peaks of the LPC spectrum during LPC analysis and reconstruction. The method is evaluated on two novel children’s speech datasets with one containing California English from the Southern California Area and the other containing a mix of Southern American English and African American English from the Atlanta, Georgia area. We test the proposed method in training both an HMM-DNN system and an end-to-end system to show model-robustness and demonstrate that the algorithm improves ASR performance, especially for zero resource dialect children’s task, as compared to common data augmentation methods such as VTLP, Speed Perturbation, and SpecAugment.
Security Risk Assessment Methodologies in The Internet of Things: Survey and Taxonomy

Internet of Things (IoT) is the next-generation internet paradigm where billions of smart objects embedded with software and sensing capabilities are connected to the backbone network. IoT has applications in many fields such as healthcare, manufacturing, commerce, agriculture, energy production, and transportation. The advent of Industry 4.0 has enabled the democratization of IoT deployment and the emergence of innovative business models. However, IoT systems carry numerous security and privacy risks. Traditional security risk assessment methodologies cannot be effectively applied in the IoT context, because they do not necessarily take into account the complexity and dynamics of IoT. This paper presents a survey of security risk assessment methodologies for IoT to aid security researchers, policymakers, and stakeholders in developing novel risk assessment frameworks that are more fit for IoT. The paper reviews various security standards, industry best practices, and security controls related to IoT security assessment, then proposes a taxonomy of risk assessment methodologies. This will help identify the gaps and pave the way for future research to improve the security and resilience of IoT systems by putting more emphasis on the high presence of zero-day vulnerabilities and the role that Artificial Intelligence will play in threat assessment.
Fast prediction of distances between synthetic routes with deep learning

Abstract We expand the recent work on clustering of synthetic routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on a long short-term memory representation of a synthetic route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The machine learning approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source.
Online Intention Recognition With Incomplete Information Based on a Weighted Contrastive Predictive Coding Model in Wargame

The incomplete and imperfect essence of the battlefield situation results in a challenge to the efficiency, stability, and reliability of traditional intention recognition methods. For this problem, we propose a deep learning architecture that consists of a contrastive predictive coding (CPC) model, a variable-length long short-term memory network (LSTM) model, and an attention weight allocator for online intention recognition with incomplete information in wargame (W-CPCLSTM). First, based on the typical characteristics of intelligence data, a CPC model is designed to capture more global structures from limited battlefield information. Then, a variable-length LSTM model is employed to classify the learned representations into predefined intention categories. Next, a weighted approach to the training attention of CPC and LSTM is introduced to allow for the stability of the model. Finally, performance evaluation and application analysis of the proposed model for the online intention recognition task were carried out based on four different degrees of detection information and a perfect situation of ideal conditions in a wargame. Besides, we explored the effect of different lengths of intelligence data on recognition performance and gave application examples of the proposed model to a wargame platform. The simulation results demonstrate that our method not only contributes to the growth of recognition stability, but it also improves recognition accuracy by 7%-11%, 3%-7%, 3%-13%, and 3%-7%, the recognition speed by 6- 32× , 4- 18× , 13-* × , and 1- 6× compared with the traditional LSTM, classical FCN, OctConv, and OctFCN models, respectively, which characterizes it as a promising reference tool for command decision-making.
Geolocation estimation of target vehicles using image processing and geometric computation

Estimating vehicles' locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system. We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle's latitude and longitude dynamically.
Static Profiling of Alloy Models

Modeling of software-intensive systems using formal declarative modeling languages offers a means of managing software complexity through the use of abstraction and early identification of correctness issues by formal analysis. Alloy is one such language used for modeling systems early in the development process. Little work has been done to study the styles and techniques commonly used in Alloy models. We present the first static analysis study of Alloy models. We investigate research questions that examine a large corpus of 1,652 Alloy models. To evaluate these research questions, we create a methodology that leverages the power of ANTLR pattern matching and the query language XPath. Our research questions are split into two categories depending on their purpose. The Model Characteristics category aims to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what</i> language constructs are used commonly. Modeling Practices questions are considerably more complex and identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how</i> modelers are using Alloy's constructs. We also evaluate our research questions on a subset of models from our corpus written by expert modelers. We compare the results of the expert corpus to the results obtained from the general corpus to gain insight into how expert modelers use the Alloy language. We draw conclusions from the findings of our research questions and present actionable items for educators, language and environment designers, and tool developers. Actionable items for educators are intended to highlight underutilized language constructs and features, and help student modelers avoid discouraged practices. Actionable items aimed at language designers present ways to improve the Alloy language by adding constructs or removing unused ones based on trends identified in our corpus of models. The actionable items aimed at environment designers address features to facilitate model creation. Actionable items for tool developers provide suggestions for back-end optimizations.
The Economics of Content Moderation: Theory and Experimental Evidence from Hate Speech on Twitter

Social media platforms ban users and remove posts to moderate their content. This "speech policing" remains controversial because little is known about its consequences and the costs and benefits for different individuals. I conduct two field experiments on Twitter to examine the effect of moderating hate speech on user behavior and welfare. Randomly reporting posts for violating the rules against hateful conduct increases the likelihood that Twitter removes them. Reporting does not affect the activity on the platform of the posts' authors or their likelihood of reposting hate, but it does increase the activity of those attacked by the posts. These results are consistent with a model in which content moderation is a quality decision for platforms that increases user engagement and hence advertising revenue. The second experiment shows that changing users' perceived content removal does not change their willingness to pause using social media, a measure of consumer surplus. My results imply that content moderation does not necessarily moderate users, but it marginally increases advertising revenue. It can be consistent with both profit- and welfare-maximization if out-of-platform externalities are small.
Application of Distributed Ledger Platforms in Smart Water Systems—A Literature Review

The application of distributed ledger technologies, including blockchain, is rapidly growing in governance, transport, supply chain, and logistics. Today, blockchain technology is promoted as the heart of Smart Cities. This study reviews the potential of blockchain application in water management systems. We surveyed the literature and organized the previous studies based on three main application topics: Smart Water Systems, Water Quality Monitoring, and Storm Water Management. Also, we addressed technical, organizational, social, and institutional challenges that may hinder the adoption of Blockchain technology. Water management systems need to have a long-term commitment plan, update their organizational policies, and acquire relevant knowledge and expertise before successfully adopting any distributed ledger technology.
Semisupervised Feature Learning by Deep Entropy-Sparsity Subspace Clustering

While feature learning by deep neural networks is currently widely used, it is still very challenging to perform this task, given the very limited quantity of labeled data. To solve this problem, we propose to unite subspace clustering with deep semisupervised feature learning to form a unified learning framework to pursue feature learning by subspace clustering. More specifically, we develop a deep entropy-sparsity subspace clustering (deep ESSC) model, which forces a deep neural network to learn features using subspace clustering constrained by our designed entropy-sparsity scheme. The model can inherently harmonize deep semisupervised feature learning and subspace clustering simultaneously by the proposed self-similarity preserving strategy. To optimize the deep ESSC model, we introduce two unconstrained variables to eliminate the two constraints via softmax functions. We provide a general algebraic-treatment scheme for solving the proposed deep ESSC model. Extensive experiments with comprehensive analysis substantiate that our deep ESSC model is more effective than the related methods.