Reviewerly/exHarmony huggingface dataset:
Collaborative Group Learning

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.
Ukrainian MOOC: Quantitative and Thematic Analysis of Online Courses

The article is devoted to the quantitative analysis of the courses hosted on Ukrainian mass open online courses (MOOC) platforms. It has been found that MOOC, being an effective mechanism of lifelong learning, provides the development of general and professional competencies; it supports principles of openness and accessibility. We have analyzed the online courses located on such well-known Ukrainian online platforms as Prometheus, EdEra, and the Open University of Maidan. It has been found that Ukrainian online platforms started to provide their services not so long ago; they have been working since 2014. Most of the online courses (55.3% of their total number) can be found on the Prometheus platform. We have also analyzed the dynamics of online course development over the years on each Ukrainian online platform. The average number of advanced online courses on each platform and on all the platforms, in general, has been revealed. The years 2017 and 2019 turned out to be the most productive in the development of courses. The analysis of thematic areas of the developed online courses has been carried out; the identical sections available on each considered platform have been revealed. The largest number of courses is presented among such sections as Education/EIE (External Independent Evaluation) and Law, proving their relevance among users. The largest number of thematic areas is presented on the Prometheus platform. It has been found that Ukrainian MOOCs have a significant difference in the number of courses compared to a number of well-known foreign online platforms.
Graph Relational Topic Model with Higher-order Graph Attention Auto-encoders

Learning low-dimensional representations of networked documents is a crucial task for documents linked in network structures.Relational Topic Models (RTMs) have shown their strengths in modeling both document contents and relations to discover the latent topic semantic representations.However, higher-order correlation structure information among documents is largely ignored in these methods.Therefore, we propose a novel graph relational topic model (GRTM) for document network, to fully explore and mix neighborhood information of documents on each order, based on the Higher-order Graph Attention Network (HGAT) with the log-normal prior in the graph attention.The proposed method can address the aforementioned issue via the information propagation among document-document based on the HGAT probabilistic encoder, to learn efficient networked document representations in the latent topic space, which can fully reflect document contents, along with document connections.Experiments on several real-world document network datasets show that, through fully exploring information in documents and document networks, our model achieves better performance on unsupervised representation learning and outperforms existing competitive methods in various downstream tasks.
The AI4Media Project: Use of Next-Generation Artificial Intelligence Technologies for Media Sector Applications

Artificial Intelligence brings exciting innovations in all aspects of life and creates new opportunities across industry sectors. At the same time, it raises significant questions in terms of trust, ethics, and accountability. This paper offers an introduction to the AI4Media project, which aims to build on recent advances of AI in order to offer innovative tools to the media sector. AI4Media unifies the fragmented landscape of media-related AI technologies by investigating new learning paradigms and distributed AI, exploring issues of AI explainability, robustness and privacy, examining AI techniques for content analysis, and exploiting AI to address major societal challenges. In this paper, we focus on our vision of how such AI technologies can reshape the media sector, by discussing seven industrial use cases that range from combating disinformation in social media and supporting journalists for news story creation, to high quality video production, game design, and artistic co-creation. For each of these use cases, we highlight the present challenges and needs, and explain how they can be efficiently addressed by using innovative AI-driven solutions.
Effects of Students’ Home Environment, Tools, and Technology Used on Online Learning Experience in a Civil Engineering Program

The COVID-19 pandemic has caused major disruptions in every sector of human activity around the world. The education sector is no exception. Due to physical restrictions in accessing campus infrastructures, students and instructors at academic institutions have forcibly adapted to the new norm of virtual learning with mixed expectations and outcomes. There have been several studies conducted by educational practitioners since the pandemic began, which mostly focused on how academic constituents quickly adapt to virtual learning in a general sense. The main objective of this study is to examine the effects of virtual learning infrastructure and environment on student learning, specific to the civil engineering program at Prince Mohammad Bin Fahd University (PMU). A survey was distributed to civil engineering students at PMU at the end of the fall 2020 term to gather perceptions about virtual learning and check the adequacy of online learning tools and associated environments. Student performance during virtual learning in terms of grade achievement was also investigated and compared with a normal situation. In general, it was found that students are satisfied with the current technology used to facilitate virtual learning. The anticipated outcomes of this study, including online technology readiness, curriculum adjustment, and teaching styles or methods, are to be used for virtual learning improvement should the current pandemic restriction extend to the end of the 2020–21 academic year and possibly far beyond.
An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning Processing

Criminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of 0.808, recall of 0.722, and F1-score of 0.733 were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain.
Knowledge-Base Degrees of Inconsistency: Complexity and Counting

Description logics (DLs) are knowledge representation languages that are used in the field of artificial intelligence (AI). A common technique is to query DL knowledge bases, e.g., by Boolean Datalog queries, and ask for entailment. But real world knowledge-bases are often obtained by combining data from various sources. This, inherently, might result in certain inconsistencies (with respect to a given query) and requires to estimate a degree of inconsistency before using a knowledge-base. In this paper, we provide a complexity analysis of fixed-domain non-entailment (NE) on Datalog programs for well-established families of knowledge bases (KBs). We exhibit a detailed complexity map for the decision cases, counting and projected counting, which may serve as a quantitative measure for inconsistency of a KB with respect to a query. Our results show that NE is natural for the second, third, and fourth level of the polynomial (counting) hierarchy depending on the type of the studied query (stratified, normal, disjunctive) and one level higher for the projected versions. Further, we show fixed-parameter tractability by bounding the treewidth, provide a constructive algorithm, and show its theoretical limitation in terms of conditional lower bounds.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction

Developing link prediction models to automatically complete knowledge graphs has recently been the focus of significant research interest. The current methods for the link prediction task have two natural problems: 1) the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations. These two problems limit the training effectiveness and practical applications of the existing link prediction models. We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. GRL conducts a generalized relation learning, which is aware of semantic correlations between relations that serve as a bridge to connect semantically similar relations. After training with GRL, the closeness of semantically similar relations in vector space and the discrimination of dissimilar relations are improved. We perform comprehensive experiments on six benchmarks to demonstrate the superior capability of GRL in the link prediction task. In particular, GRL is found to enhance the existing link prediction models making them insensitive to unbalanced relation distributions and capable of learning unseen relations.
Reservoir Computing on Atomic Switch Arrays with High Precision and Excellent Memory Characteristics

Reservoir computing (RC) refers to an artificial neural network framework that exhibits temporal dynamic behavior, and its computational structure can be implemented in physical systems. However, the network performance of conventional RC is limited in terms of power scale, readability, spatial and temporal scalabilities, mass producibility, and precision. Therefore, we propose an RC with atomic switches. An atomic switch is a new type of nanodevice that exhibits superior power scale, readability, spatial and temporal scalabilities, and mass producibility. In the proposed RC architecture, we arranged atomic switches sequentially in a ring formation and used time-division multiplexing. The resistance of the atomic switches in the proposed architecture changes nonlinearly with a change in input and memorizes the input; therefore, this RC architecture is expected to have high precision and a large memory capacity (MC). In this study, we simulated this architecture and compared it with the conventional architecture in terms of precision and MC. The results showed that the proposed RC architecture had higher precision and greater MC.
A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.
On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces

We propose an approach to learn an extensional representation of a discrete deterministic planning domain from observations in a continuous space navigated by the agent actions. This is achieved through the use of a perception function providing the likelihood of a real-value observation being in a given state of the planning domain after executing an action. The agent learns an extensional representation of the domain (the set of states, the transitions from states to states caused by actions) and the perception function on-line, while it acts for accomplishing its task. In order to provide a practical approach that can scale up to large state spaces, a “draft” intensional (PDDL-based) model of the planning domain is used to guide the exploration of the environment and learn the states and state transitions. The proposed approach uses a novel algorithm to (i) construct the extensional representation of the domain by interleaving symbolic planning in the PDDL intensional representation and search in the state transition graph of the extensional representation; (ii) incrementally refine the intensional representation taking into account information about the actions that the agent cannot execute. An experimental analysis shows that the novel approach can scale up to large state spaces, thus overcoming the limits in scalability of the previous work.
Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.
TOWARDS EVOLVING ENGINEERING EDUCATION BASED ON SYMBIOTIC COGNITIVE DIGITAL TWINS

In the long past, professional education lasted for a lifetime. Since then, the industrial revolutions haveaccelerated the pace of knowledge doubling from a lifetime to months, and shortened the half-life of pertinent knowledge. Those changes have altered the working environment of professionals who have to move between many jobs in their life. Are we capable of adjusting to that pace? How can we learn all that is needed in the old Prussian model of education based on one-program-fits-all? We should revamp theeducational system at the core. The new system must be personalized to match the diversity of individual abilities and styles of learning. The new system must also be based not only on the body of knowledge (BoK), but body of experience (BoX) and body of humanity (BoH). The new personalized system oflearning must be sufficiently agile and interactive so that it would become evolving in its symbiosis with humans. For that to happen, we must coexist with symbiotic autonomous cognitive systems, specifically involving digital twins. This paper addresses some aspects of this view.
A Unifying Splitting Framework

Abstract AVATAR is an elegant and effective way to split clauses in a saturation prover using a SAT solver. But is it refutationally complete? And how does it relate to other splitting architectures? To answer these questions, we present a unifying framework that extends a saturation calculus (e.g., superposition) with splitting and embeds the result in a prover guided by a SAT solver. The framework also allows us to study locking, a subsumption-like mechanism based on the current propositional model. Various architectures are instances of the framework, including AVATAR, labeled splitting, and SMT with quantifiers.
Provably Good Batch Reinforcement Learning Without Great Exploration

Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Doing batch RL in a way that yields a reliable new policy in large domains is challenging: a new decision policy may visit states and actions outside the support of the batch data, and function approximation and optimization with limited samples can further increase the potential of learning policies with overly optimistic estimates of their future performance. Recent algorithms have shown promise but can still be overly optimistic in their expected outcomes. Theoretical work that provides strong guarantees on the performance of the output policy relies on a strong concentrability assumption, that makes it unsuitable for cases where the ratio between state-action distributions of behavior policy and some candidate policies is large. This is because in the traditional analysis, the error bound scales up with this ratio. We show that a small modification to Bellman optimality and evaluation back-up to take a more conservative update can have much stronger guarantees. In certain settings, they can find the approximately best policy within the state-action space explored by the batch data, without requiring a priori assumptions of concentrability. We highlight the necessity of our conservative update and the limitations of previous algorithms and analyses by illustrative MDP examples, and demonstrate an empirical comparison of our algorithm and other state-of-the-art batch RL baselines in standard benchmarks.
PhD Forum: Data traffic classification using deep learning models

The growth in data traffic is exponential with the usage of network applications in mundane activities. The network operators are posed with the challenge of providing Quality of Experience (QoE) to the deluge of internet users. Network traffic classification plays a significant role in network resource management with prominent security, billing, and accounting applications. In this paper, the network data traffic classification is performed using the Deep Learning (DL) models. The previous data traffic mechanism fails when the scale in data generated is in Exabytes per day from the perspective of the internet user. The network resource management is automated by classification of the traffic without intervening by the operator. The dataset is collected from the campus network for different applications.The network traffic classification is performed using the AlexNet, ResNet, and GoogLeNet DL models. The accuracy obtained for ResNet is 95%, AlexNet is 75%, and GoogLeNet is 91%. The challenge in network traffic classification is converting the packet capture files to the data type requirements of the Convolution Neural Networks (CNN) as an image. The four different network applications are considered for traffic classification. In the next-generation network architecture, artificial intelligence will be an integral part. It reduces the human intervention in traffic characterization and analysis, which aids in meeting the QoE requirements of the users.