Reviewerly/exHarmony huggingface dataset:
Probabilistic Neural–Symbolic Models With Inductive Posterior Constraints

Neural-symbolic models provide a powerful tool to tackle complex visual reasoning tasks by combining symbolic program execution for reasoning and deep representation learning for visual recognition. A probabilistic formulation of such models with stochastic latent variables can obtain an interpretable and legible reasoning system with less supervision. However, it is still nontrivial to generate reasonable symbolic structures without the guidance of domain knowledge, since it generally involves an optimization problem with both continuous and discrete variables. Despite the challenges, the interpretability of such symbolic structures provides an interface to regularize their generation by domain knowledge. In this article, we propose to incorporate the available domain knowledge into the learning process of probabilistic neural-symbolic (PNS) models via posterior constraints that directly regularize the structure posterior. In this way, our model is able to identify a middle point where the structure generation process mainly learns from data but also selectively borrows information from domain knowledge. We further present inductive reasoning where the posterior constraints can be automatically reweighted to handle noisy annotations. The experimental results show that our method achieves state-of-the-art performance on major abstract reasoning datasets and enjoys good generalization capability and data efficiency.
Teaching Agile the Agile Way — Employing Self-Organizing Teams in a University Software Engineering Course

Kowark is a former member of the HPI research school on "Service-oriented Systems Engineering" and now a researcher at the "Enterprise Platform and Integration Concepts" group.After completing both the Bachelor's and the Master's program at HPI he decided to continue with his Ph.D. studies at this institute, but broadened the focus from a completely technical to a more design research driven level.His main focus is the replicability of
Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External Consistency

The evaluation of knowledge quality (KQ) in multisource knowledge graphs (KGs) is an essential step for many applications, such as fragmented knowledge fusion and knowledge base construction. Many existing quality evaluation methods for multisource knowledge are based on validation from high-quality knowledge bases or statistical analysis of knowledge related to a specific fact from multiple sources, named external consistency (EC)-based methods. However, high-quality KGs are difficult to obtain, and there might exist incorrect knowledge in multisource KGs interfering with KQ evaluation. To address the issue, this article refers to the internal structure of a KG to evaluate the degree to which the contained triples conform to the overall semantic pattern of the KG, such as KG embedding and logic inference-based approaches, defined as internal consistency (IC) evaluation. The IC is integrated with the EC to identify possible incorrect triples and reduce their influences on the KQ evaluation, thus alleviating the interference of incorrect knowledge. The proposed method is verified with multiple datasets, and the results demonstrate that the proposed method could significantly reduce wrong evaluations caused by incorrect knowledge and effectively improve the quality evaluation of triples.
Overview of Learning Cybersecurity Through Game Based Systems

Cybersecurity awareness and skills training are very essential and challenging.Cybersecurity in itself involves the defense of systems, networks and programs from digital attacks.These attacks are capable of gaining unauthorized access to computers and networks.Game based learning allows students have fun whilst learning by actively learning and practicing the right ways things should be done.Often, the game is started on a slow pace gradually advancing gain in skill until the student is able to successfully navigate the difficult levels.There is a constant increase in cyberattacks all over the world, an estimate of $106 Billion was recorded for cyber hacks in the United States in 2016 alone.The cybersecurity skills shortage is also posing a major concern.Hence, it has become imperative to develop a learning platform for the next generation of cybersecurity professionals to learn and be further equipped by introducing cybersecurity with the concept of gaming.Some of the games developed offer some challenges and a higher level of thinking.Learning through this system introduces an immersive, learner-centered experience with effectiveness on cybersecurity awareness training and practical skill acquisition for learners from diverse backgrounds.This paper evaluates the different game based learning system in cybersecurity, identifying the various tools in existence, the benefits and shortcomings of each system and feasible ways to improve on the existing systems.
ACERAC: Efficient Reinforcement Learning in Fine Time Discretization

One of the main goals of reinforcement learning (RL) is to provide a way for physical machines to learn optimal behavior instead of being programmed. However, effective control of the machines usually requires fine time discretization. The most common RL methods apply independent random elements to each action, which is not suitable in that setting. It is not feasible because it causes the controlled system to jerk and does not ensure sufficient exploration since a single action is not long enough to create a significant experience that could be translated into policy improvement. In our view, these are the main obstacles that prevent the application of RL in contemporary control systems. To address these pitfalls, in this article, we introduce an RL framework and adequate analytical tools for actions that may be stochastically dependent in subsequent time instances. We also introduce an RL algorithm that approximately optimizes a policy that produces such actions. It applies experience replay (ER) to adjust the likelihood of sequences of previous actions to optimize expected n -step returns that the policy yields. The efficiency of this algorithm is verified against four other RL methods [continuous deep advantage updating (CDAU), proximal policy optimization (PPO), soft actor-critic (SAC), and actor-critic with ER (ACER)] in four simulated learning control problems (Ant, HalfCheetah, Hopper, and Walker2D) in diverse time discretization. The algorithm introduced here outperforms the competitors in most cases considered.
Neural Network Gaussian Processes by Increasing Depth

Recent years have witnessed an increasing interest in the correspondence between infinitely wide networks and Gaussian processes. Despite the effectiveness and elegance of the current neural network Gaussian process theory, to the best of our knowledge, all the neural network Gaussian processes (NNGPs) are essentially induced by increasing width. However, in the era of deep learning, what concerns us more regarding a neural network is its depth as well as how depth impacts the behaviors of a network. Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning. Beyond the proposed Gaussian process by depth, we theoretically characterize its uniform tightness property and the smallest eigenvalue of the Gaussian process kernel. These characterizations can not only enhance our understanding of the proposed depth-induced Gaussian process but also pave the way for future applications. Lastly, we examine the performance of the proposed Gaussian process by regression experiments on two benchmark datasets.
Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets

One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to their logic-based expressions. However, decision trees can grow too deep, and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more flexible. However, the unstructured visual representation of rules makes it hard to make inferences across rules. In this paper, we focus on tabular data and present novel algorithmic and interactive solutions to address these issues. First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and an interactive surrogate rule visualization, the Feature-Aligned Tree, which depicts rules as trees while aligning features for easier comparison. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees and find that it scales reasonably well and overcomes the shortcomings of surrogate decision trees. We evaluate the visualization and the system through a usability study and an observational study with domain experts. Our investigation shows that the participants can use feature-aligned trees to perform non-trivial tasks with very high accuracy. We also discuss many interesting findings, including a rule analysis task characterization, that can be used for visualization design and future research.
Adaptive Semantic-Enhanced Transformer for Image Captioning

In the research on image captioning, rich semantic information is very important for generating critical caption words as guiding information. However, semantic information from offline object detectors involves many semantic objects that do not appear in the caption, thereby bringing noise into the decoding process. To produce more accurate semantic guiding information and further optimize the decoding process, we propose an end-to-end adaptive semantic-enhanced transformer (AS-Transformer) model for image captioning. For semantic enhancement information extraction, we propose a constrained weaklysupervised learning (CWSL) module, which reconstructs the semantic object's probability distribution detected by the multiple instances learning (MIL) through a joint loss function. These strengthened semantic objects from the reconstructed probability distribution can better depict the semantic meaning of images. Also, for semantic enhancement decoding, we propose an adaptive gated mechanism (AGM) module to adjust the attention between visual and semantic information adaptively for the more accurate generation of caption words. Through the joint control of the CWSL module and AGM module, our proposed model constructs a complete adaptive enhancement mechanism from encoding to decoding and obtains visual context that is more suitable for captions. Experiments on the public Microsoft Common Objects in COntext (MSCOCO) and Flickr30K datasets illustrate that our proposed AS-Transformer can adaptively obtain effective semantic information and adjust the attention weights between semantic and visual information automatically, which achieves more accurate captions compared with semantic enhancement methods and outperforms state-of-the-art methods.
Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method

Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network ( [Formula: see text]OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. [Formula: see text]OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.
Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm

In this article, we propose an algorithm that combines actor-critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems. Specifically, convergence analysis of a consensus algorithm for a type of nonlinear system with a Lyapunov method is developed, and we use this result to analyze the convergence properties of the actor training parameters and the critic training parameters in our algorithm. Through the convergence analysis, it can be verified that all agents will converge to the same optimal model as the training time goes to infinity. To validate the implementation of our algorithm, a multiagent training framework is proposed to train each Universal Robot 5 (UR5) robot arm to reach the random target position. Finally, experiments are provided to demonstrate the effectiveness and feasibility of the proposed algorithm.
Penguin coloration affects skin friction drag

Why do penguins have black and white patterns?Some theories claim that it is camouflage against predators.Others believe that this helps with thermal regulation: black absorbs more heat than white does.There are even theories that suggest this coloration protects from abrasion and helps with energy balance.This study suggests that the specific color of penguins results in temperature differences on top and bottom surfaces reducing their skin drag.To prove this hypothesis, we conducted several experimental and computational studies.In experiments, an infrared camera was used to measure the penguin's temperature of the penguin's back and front.These temperatures were used to calculate energy balance and friction drag.The results show that penguin coloration affects the drag.
Using Chatbots as Smart Teaching Assistants for First-Year Engineering Students

As the artificial intelligence (AI) field advances, more research areas within AI are emerging including machine learning, neural networks, fuzzy systems, and much more.In the last decade, chatbot technology has emerged as a new area of AI.Chatbot use-cases are surfacing across various sectors including banking, customer services, medicine, and e-commerce.Recently, chatbots have begun being used in education, and the potential advantages are notable.In this paper, we are reviewing related literature and presenting a mobile application system, named Alpha, that utilizes chatbots as smart teaching assistants.The system consists of an artificially intelligent (smart) chatbot, cloud-based database, speech recognition, and web services.We conducted a student survey and a usability assessment of the implemented system.We have integrated our system into a first-year engineering course, and more courses will be added in the future.The system aims to support students' learning processes and engagement in addition to providing real-time 24/7 assistance to students.
A Writing Tool that Provides Real-Time Feedback to Students on their Grammar Using Deep Learning

Abstract Teaching English writing skills to students from non-English backgrounds is a challenge in undergraduate colleges. Grammar teaching is centered on accuracy of form and rule learning. Grammar pedagogy has received a lot of attention in recent years by researchers. One of the main challenges for first-language English teacher’s approach to teaching writing is introducing formulaic approaches to grammar. Another challenge is the amount of time it takes for a teacher to provide feedback to students on their written work. Exceptional rules in the language, cultural and social differences cause a lot of language issues. Applying correct grammar, syntax, spelling, and vocabulary into written work might be challenging especially for international students. In our work, we address both the challenges by proposing a writing tool for students. In our work we use a state- of-the-art deep learning model which helps students identify grammatically incorrect sentences in real time. Our tool not only identifies grammatically incorrect sentences but also gives students an opportunity to correct the sentence themselves. The tool does not correct the sentence for the student. Using this active approach with feedback, students learn by reflecting on their writing and correcting sentences on their own. Our tool identifies incorrect sentences with an accuracy of 80%. We have tried our tool on student essays and have shown that it helps them better understanding grammatical rules of English.
An Interactive Learning System for Cyber Security Education

This paper describes a learning system that uses virtualization technology to build a multiplayer cyber-attack and defense learning system infrastructure.The infrastructure emulated a realistic network that included numerous student network environments.Virtual machines were implemented in each student's network environment and they served as both attack and defense.In addition, the system infrastructure allowed interaction among students' network environments, therefore making it more similar to a realistic world network.The learning system functioned as a competition environment to encourage students to interact with each other.Each student acted as both attacker and defender.From the perspective of the attacker, students were able to perform hacking activities to other class members.Students were required to identify other students' system vulnerabilities and model the actions of the attacks.From the defender's point of view, students needed to apply a certain technique to prevent the corresponding attacks.The system allowed students access to the infrastructure anytime and anywhere.The system provided students with opportunities to learn cyberattack and prevention techniques in a simulation environment.
Applied Classroom Use of a Mobile Multifunctional Weather Station

Abstract This project is rooted in the need for a mobile device to use outside of my future science classroom to measure, collect, and compile weather and water data. Incorporating this device into a secondary school classroom will not only allow students to use the weather station, they will also integrate engineering practices into their science education by building the weather station in small teams. Intelligent weather stations have been around for many years, often functioning on a large scale to collect weather, wind, and solar data to be used for forecasts, energy management, agriculture, and in the planning of clean energy projects. These tools have also been named automatic weather stations, they have been developed to function wirelessly and to remotely send their collected data to be stored or displayed for research and development purposes. Weather stations typically consist of an array of sensors to measure temperature, humidity, wind speed and direction, solar radiation, and atmospheric pressure. They also include a battery to power the station and a base that processes collected data and has the capabilities to send and/or store the data. Currently, the Python 3 code to implement individual sensors is complete. The device is able to upload data to the cloud using ThingSpeak. In addition, I have begun prototyping and testing possible circuit designs for the final model. Due to limitations of the Raspberry Pi's ability to supply power to multiple sensors in parallel, device calls must be done sequentially and with regard to time. The design, development, and use of a wireless weather station device can be used in science, technology, engineering, and math (STEM) experiences to introduce secondary students to the science and engineering practices laid out in the Next Generation Science Standards (NGSS). After the completion of this project in my future science class, students will have experience using engineering practices and problem-solving skills to assemble a simple IoT device. Thus far, I have encountered obstacles that students will likely face, such as limitations in the required power of all sensors, and I am now better equipped to assist them when these problems arise.
Blockchain Database for a Cyber Security Learning System

Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts.We intend to use a blockchain database to secure data from this learning system.The data being secured are students' scores accumulated by successful attacks or defends from the other students' implementations.As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain.With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system.In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted.In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data.Moreover, we explored using a blockchain database for our data.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution

As a highly ill-posed issue, single-image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According to the Nyquist sampling theory, the degradation leads to the aliasing effect and makes it hard to restore the correct textures from low-resolution (LR) images. In practice, there are correlations and self-similarities among the adjacent patches in the natural images. This article considers the self-similarity and proposes a hierarchical image super-resolution network (HSRNet) to suppress the influence of aliasing. We consider the SISR issue in the optimization perspective and propose an iterative solution pattern based on the half-quadratic splitting (HQS) method. To explore the texture with local image prior, we design a hierarchical exploration block (HEB) and progressive increase the receptive field. Furthermore, multilevel spatial attention (MSA) is devised to obtain the relations of adjacent feature and enhance the high-frequency information, which acts as a crucial role for visual experience. The experimental result shows that HSRNet achieves better quantitative and visual performance than other works and remits the aliasing more effectively.