Reviewerly/exHarmony huggingface dataset:
The R Package CDM for Diagnostic Modeling

In this chapter, the R (R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, 2017) pack-age CDM (Robitzsch A, Kiefer T, George AC, Uenlue A, CDM: cognitive diagnosis modeling. R package version 6.0-101. https://CRAN.R-project.org/package=CDM , 2017; George AC, Robitzsch A, Kiefer T, Groß J, Ünlü A, J Stat Softw 74(2):1–24. 10.18637/jss.v074.i02, 2016) for estimating diagnostic classification models is introduced. First, the model classes that can be estimated with the CDM package are introduced. Second, the CDM package structure and some of its features are discussed. Third, the usage of the CDM package is demonstrated in a data application. Finally, potential future developments of the CDM package are discussed.
Inferring Catchment in Internet Routing

BGP is the de-facto Internet routing protocol for exchanging prefix reachability information between Autonomous Systems (AS). It is a dynamic, distributed, path-vector protocol that enables rich expressions of network policies (typically treated as secrets). In this regime, where complexity is interwoven with information hiding, answering questions such as "what is the expected catchment of the anycast sites of a content provider on the AS-level, if new sites are deployed?", or "how will load-balancing behave if an ISP changes its routing policy for a prefix?", is a hard challenge. In this work, we present a formal model and methodology that takes into account policy-based routing and topological properties of the Internet graph, to predict the routing behavior of networks. We design algorithms that provide new capabilities for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). We analyze the properties of these inference algorithms, and evaluate them using publicly available routing datasets and real-world experiments. The proposed framework can be useful in a number of applications: measurements, traffic engineering, network planning, Internet routing models, etc. As a use case, we study the problem of selecting a set of measurement vantage points to maximize route inference. Our methodology is general and can capture standard valley-free routing, as well as more complex topological and routing setups appearing in practice.
Multi-Robot One-Target 3D Path Planning Based on Improved Bioinspired Neural Network

3D Path planning for multi-robot one-target pursuit is an interesting topic. Bioinspired neural network is frequently implemented for path planning of multi-robot, and the bioinspired neural network neural activity value computational cost and time cost will increase sharply with the increase of the number of neurons. This paper explores an improved 3D path planning method based on multi-scale map method to reduce the time cost. Combining the multi-scale map method with the Dijkstra algorithm, the optimal paths of 3D coarse-scale map of multi-robot one-target can be generated. The weights created from the coarse-scale map are employed to yield the 3D path planning of the fine-scale map for the same terrain. Therefore, this improved bioinspired neural network algorithm has proven the ability to calculate the multi-robot 3D optimal paths. By introducing this multi-scale map method into the multi-robot bioinspired neural network algorithm, the time cost and mathematical complexity of the path planning algorithm can be greatly reduced. MATLAB simulation results further reveal the effectiveness and superiority of this method.
Deep Gradient Prior Regularized Robust Video Super-Resolution

This paper proposes a robust multi-frame video super-resolution (SR) scheme to obtain high SR performance under large upscaling factors. Although the reference low-resolution frames can provide complementary information for the high-resolution frame, an effective regularizer is required to rectify the unreliable information from the reference frames. As the high-frequency information is mostly contained in the image gradient field, we propose to learn the gradient-mapping function between the high-resolution (HR) and the low-resolution (LR) image to regularize the fusion of multiple frames. In contrast to the existing spatial-domain networks, we train a deep gradient-mapping network to learn the horizontal and vertical gradients. We found that adding the low-frequency information (mainly from the LR image) to the gradient-learning network can boost the performance of the network. A forward and backward motion field prior is used to regularize the estimation of the motion flow between frames. For robust SR reconstruction, a weighting scheme is proposed to exclude the outlier data. Visual and quantitative evaluations on benchmark datasets demonstrate that our method is superior to many state-of-the-art methods and can recover better details with less artifacts.
A Neural Network based Mobile Robot Navigation Approach using Reinforcement Learning Parameter Tuning Mechanism

Robotic navigation mechanism is one of the most adhoc research topics on mobile robots, which requires a robot to find a suitable path and move from its current position to a destination position without colliding with any obstacles. This paper employs the intelligent method of reinforcement learning to explore a solution to address the aforementioned problem. It considers the laser beam detected distances and the relative movement angle as the input of neural network model, and the robot’s action posture is denoted as the output. This neural network model is trained by a deep Q-learning network (DQN) algorithm via positive and negative feedback rewards defined by task-specific learning goals. In this sense, the trained model helps the robot determine the appropriate action to take at each state to safely reach the destination point without any manually interference. According to the results on a simulation platform, the trained neural network model makes the robot move from random starting point to random destination successfully, which proves the effectiveness of DQN algorithm in the field of robot navigation.
Multimodal Representation Learning using Deep Multiset Canonical Correlation

We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities. We use deep learning framework to learn non-linear transformations from different modalities to a shared subspace such that the representations maximize the ratio of between- and within-modality covariance of the observations. Unlike linear discriminant analysis, we do not need class information to learn these representations, and we show that this model can be trained for complex data using mini-batches. Using synthetic data experiments, we show that dMCCA can effectively recover the common signal across the different modalities corrupted by multiplicative and additive noise. We also analyze the sensitivity of our model to recover the correlated components with respect to mini-batch size and dimension of the embeddings. Performance evaluation on noisy handwritten datasets shows that our model outperforms other CCA-based approaches and is comparable to deep neural network models trained end-to-end on this dataset.
Pengaruh Bahan Ajar Berbasis Literasi Dengan Pendekatan Kontekstual Terhadap Hasil Belajar Siswa Sekolah Dasar

The purpose of this study was to see the effect of the application of literacy based thematic teaching materials with a contextual approach to student learning outcomes. The method used in this study is a type of quantitative research. The research design used in this study was Quasi Experimental Design with the form of Nonequivalent Control Group Design. In the design of this study researchers used one experimental group with a control group that began with the pretest in each group. The results of this study found that the use of thematic teaching materials had a significant effect on student learning outcomes. Contextual based thematic teaching materials with a literacy approach can be used as supporting teaching materials in addition to the main teaching material.
Ontology-Based Representation of Crisis Response Situations

Crisis situations are highlighted by the involvement of various emergency response organizations aiming at saving lives and reducing crisis impacts. However, this variety makes communication and information sharing challenges rise. The use of semantic Web technologies has been recognized as a suitable solution to address these challenges. This paper proposes an ontology-based representation of crisis situations providing a unified and sharable knowledge base between crisis response stakeholders. The developed ontology, named CROnto (Crisis Response Ontology), merges three pertinent aspects of the domain of crisis response. First, crisis features which represents crisis characteristics. Second, crisis effects which describes the various impacts that could be engendered by a disaster. Third, crisis response which represents strategic response plans. For the purpose of evaluating CROnto ontology, we adopt criteria-based evaluation approach.
A Sentiment Based Non-Factoid Question-Answering Framework

With the rapid advances in Artificial Intelligence, a question of emotional intelligence of a system may become as important as its accuracy. This paper investigates whether emotions should be considered for non-factoid “how” Question-Answering systems with the eventual goal of enabling the system to retrieve answers in a more emotionally intelligent way. This study proposes an architecture that adds extended representation of sentiment information to questions and answers, and reports on to what extent a prediction of the best answer be improved by the proposed architecture.
How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making

We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filtering private information. According to its private be-lief, the agent performs an action, which is observed by an adversarial agent. We have recently shown how the adversarial agent can reconstruct the private belief of the decision-making agent via inverse optimization. The main contribution of this paper is a method to obfuscate the private belief of the agent from the adversary, by performing a suboptimal action. The proposed method optimizes the trade-off between obfuscating the private belief and limiting the increase in cost accrued due to taking a suboptimal action. We propose a probabilistic relaxation to obtain a linear optimization problem for solving the trade-off. In numerical examples, we show that the proposed methods enable the agent to obfuscate its private belief without compromising its cost budget.
Logic program proportions

Abstract The purpose of this paper is to present a fresh idea on how symbolic learning might be realized via analogical reasoning. For this, we introduce directed analogical proportions between logic programs of the form “ P transforms into Q as R transforms into S ” as a mechanism for deriving similar programs by analogy-making. The idea is to instantiate a fragment of a recently introduced abstract algebraic framework of analogical proportions in the domain of logic programming. Technically, we define proportions in terms of modularity where we derive abstract forms of concrete programs from a “known” source domain which can then be instantiated in an “unknown” target domain to obtain analogous programs. To this end, we introduce algebraic operations for syntactic logic program composition and concatenation. Interestingly, our work suggests a close relationship between modularity, generalization, and analogy which we believe should be explored further in the future. In a broader sense, this paper is a further step towards a mathematical theory of logic-based analogical reasoning and learning with potential applications to open AI-problems like commonsense reasoning and computational learning and creativity.
Image quality improvement for optical imaging interferometric microscopy

Imaging interferometric microscopy (IIM) is an optical microscopy resolution enhancement technique involving combining multiple sub-images to increase resolution. Several image reconstruction challenges can degrade the image quality including the frequency, phase deviations between sub-images, and maintenance of a uniform frequency response across the entire space. This work proposes methods to address these issues. The methods are first compared in simulation using a Manhattan structure of 260-nm critical dimension with 2-µm-pitch calibration grating on the sides. The proposed correction methods are then applied to the experimental results and found to be effective in improving the image quality of IIM.
Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.
Supervised Learning Methods in Classifying Organized Behavior in Tweet Collections

The successful use of social media to manipulate public opinion via bots and hired individuals to spread (mis)information to unsuspecting users reached alarming levels due to the manipulations during the 2016 US elections and the Brexit deliberations in the UK. Fake interaction such as “liking” and “retweeting” are staged to foster trust in the posts of bots and individuals, which makes it difficult for individuals to detect the posts that are part of greater schemes. We propose an approach based on supervised learning to classify collections of tweets as “organized” when they inhabit premeditated intent and as “organic” otherwise. Features related to users and posting behavior are used to train the classifiers using 851 data sets totaling above 270 million tweets. Further classifiers are trained to assess the effectiveness of the selected features. The random forest algorithm persistently yielded the best results with scores greater than 95% for both accuracy and f-measure. For comparison purposes, unsupervised learning methods were used to cluster the same data sets. The Gaussian Mixture Model clustered [organized vs organic] data set with 99% agreement with the labels. The success of using only behavioral features to detect organized behavior is encouraging.
Learning to Communicate with Multi-agent Reinforcement Learning Using Value-Decomposition Networks

Recent research focuses on how agents can learn to communicate with each other. This communication between the agents allows them to share information and coordinate their behaviour. Recent efforts have proven successful in these cooperative problems. A major problem we face in multi-agent reinforcement learning is the lazy agent problem, where some agents take advantage of the successful actions of other agents. This results in agents not being able to learn a functional policy. In this paper we will combine state-of-the-art methods to design an architecture to address cooperative problems using communication while also eliminating the lazy agent problem. We propose two approaches for learning to communicate that use value decomposition to address the lazy agent problem. We find that the additive version of value decomposition gives us results which exceeds the results of the state of the art.
Optimizing job completion time with fairness in large-scale data centers

Abstract In this paper, we aim to design new algorithms to schedule jobs efficiently and fairly. In particular, we consider that different jobs in a shared cluster have different degrees of sensitivity to their completion times, and parallel frameworks should provide differential treatment to jobs with different degrees of sensitivity. The above problem called CORA (Completion-time Optimal Resource Allocation) can be formulated as a lexicographical maximization problem. But it is challenging to solve in practice due to its inherent multi-objective, integer, large-scale, and non-convex nature. To address these challenges, we propose efficient algorithms under different problem scales. In particular, when the problem is of small-to-median scale, we propose a method called SILM (Single Iteration Lexicographical Maximization) so that the optimal schedule can be found efficiently. When the problem is of large-scale, we propose a method called MILM (Multiple Iterations Lexicographical Maximization), which can be further accelerated by the UCDM (Uniform Coordinate Descent Method), to find the optimal schedule iteratively. Last but not least, we also propose a heuristic algorithm, called Multiple Resource Water Filling (MRWF) that can reach close-to-optimal solution with fast run-time. The simulation using CVXPY (Python version of CVX) shows that, compared with the state-of-art method called Linearized-CORA, our SILM method can reduce run time by at most 87.7%. Furthermore, the performance gap of the heuristic method is within 15% compared with the optimal solutions.
Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.
Sequential Fuzzy Description Logic: Reasoning for Fuzzy Knowledge Bases with Sequential Information

Description logics are known to be a family of logic-based knowledge representation formalisms, and fuzzy description logics are expressive description logics for representing and handling fuzzy (vague or imprecise) knowledge bases. A sequential fuzzy description logic, which is introduced in this paper, is an extended fuzzy description logic where a sequence modal operator is introduced. In this paper, a translation from the proposed sequential fuzzy description logic to a standard fuzzy description logic is defined. Further, a theorem for embedding the sequential fuzzy description logic into the standard fuzzy description logic is proved using this translation. A theorem for relative decidability of the sequential fuzzy description logic with respect to the standard fuzzy description logic is established using the embedding theorem. The proposed logic and translation are intended for effective handling of fuzzy knowledge bases with sequential information (i.e., information expressed as sequences). Moreover, using the translation, existing methods and algorithms for the standard fuzzy description logic can be reused to effectively handle fuzzy knowledge bases with sequential information described by the sequential fuzzy description logic.
On asymptotic stability of quantum trajectories and their Cesaro mean

We address the question of asymptotic stability of quantum trajectories, also referred as quantum filters. We determine the limit of the quantum fidelity between the true quantum trajectory and the estimated one. Under a purification and absolute continuity of initial conditions assumptions we show that this limit equals to one meaning that quantum filters are asymptotically stable. In the general case, under an identifiability, a spectral assumption and absolute continuity of initial conditions assumption we show that the limit of the Cesaro mean of the estimated trajectory is the same as the true one.
Equivalence Proof for Intuitionistic Existential Alpha Graphs

We give a formal proof of the mathematical equivalence between a proposed system of existential graphs and intuitionistic propositional calculus. Along the way, we obtain a new set of algebraic rules axiomatizing intuitionistic propositional logic.
Generative Adversarial Training for Supervised and Semi-supervised Learning

Neural networks have played critical roles in many research fields. The recently proposed adversarial training (AT) can improve the generalization ability of neural networks by adding intentional perturbations in the training process, but sometimes still fail to generate worst-case perturbations, thus resulting in limited improvement. Instead of designing a specific smoothness function and seeking an approximate solution used in existing AT methods, we propose a new training methodology, named Generative AT (GAT) in this article, for supervised and semi-supervised learning. The key idea of GAT is to formulate the learning task as a minimax game, in which the perturbation generator aims to yield the worst-case perturbations that maximize the deviation of output distribution, while the target classifier is to minimize the impact of this perturbation and prediction error. To solve this minimax optimization problem, a new adversarial loss function is constructed based on the cross-entropy measure. As a result, the smoothness and confidence of the model are both greatly improved. Moreover, we develop a trajectory-preserving-based alternating update strategy to enable the stable training of GAT. Numerous experiments conducted on benchmark datasets clearly demonstrate that the proposed GAT significantly outperforms the state-of-the-art AT methods in terms of supervised and semi-supervised learning tasks, especially when the number of labeled examples is rather small in semi-supervised learning.