Reviewerly/exHarmony huggingface dataset:
Development of a 3-Dimensional (3D) Virtual Guided Walkthrough (VGW)

In this research proposal, we would like to develop a 3-Dimensional (3D) Virtual Guided Walkthrough (VGW). In addition to the virtual environment, we propose a non-playable character (NPC) who will act as a guide in the Virtual Reality (VR) walkthrough. The purpose of the NPC is to guide the user/player on where to go next or to prompt the user to do a certain action.
mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Training

Memory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural Networks (DNNs), especially when training on Graphics Processing Units (GPUs). Existing solutions for multi-GPU training setups partition the neural network over the GPUs in a way that favors training throughput over memory usage, and thus maximum trainable network size. We propose mCAP, a partitioning solution for pipeline-parallel DNN training that focuses specifically on memory usage. It evenly distributes Deep Learning models over the available resources with respect to per-device peak memory usage. Our partitioning approach uses a novel incremental profiling strategy to extract per-layer memory usage statistics. A model-based predictor uses the profiling data to recommend a partitioning that balances peak memory usage. Our approach is DL-framework agnostic and orthogonal to existing memory optimizations found in large-scale DNN training systems. Our results show that our approach enables training of neural networks that are 1.55 times larger than existing partitioning solutions in terms of the number of parameters.
Architecture cognitive et comportements

Cognitive Machinery and Behaviors. We present a functional model based on the articulation of three information processing systems that aim to model the sensorimotor cycle realized by the cognitive system of Man. To establish this bioinspired model, we use the formalism of information processing systems introduced in the 1950s by John Von Neumann, Norbert Wiener and Alan Turing, to characterize the cooperation of a set of heterogeneous systems in system of systems. This theoretical work uses the different studies conducted in Artificial Intelligence (connectionist models and symbolic models), in Cognitive Psychology (models describing decision-making in limited rationality) and in Neurobiology (experimental models on free will) to propose a systemic and extended approach of the human cognitive apparatus. Moreover, this modeling is based on the hypothesis according to which the conscious mechanisms emerge automatically from an unconscious activity, and from its computational counterpart, namely the possibility to make a symbolic logic emerge from the activity of a connectionist mechanism. The architecture that we have named IPSEL (Information Processing Systems with Emergent Logic), while it can not be considered as a theory of the mind, has the usefulness of being able to explain some empirical observations that we also present. Finally, the implications and limitations of such a model and the research that is being carried out to present its usefulness and likelihood as a model of the human cognitive system as part of the questions of trust in the decisions provided by automatic system.
Digital Library Analysis and Design Using Iconix Process Method (Case Study: SMA Negeri 1 Ngimbang)

The application of information technology in various companies will improve employees’ performance so that the business results obtained by the company are of higher quality. The school library has a relatively significant influence on the learning process and expands students’ knowledge. However, in reality, many libraries still have not utilized information technology to manage their management, so some parties sometimes encounter obstacles that can hinder performance and efficiency. This study built a web-based library information system using the Iconix process method with the case study SMA Negeri 1 Ngimbang, Indonesia. The findings of this study will be able to be implemented, promoting the efficiency of data and library information management and boosting.&#x0D;
For Technological Literacy Education: Comparing the Asymmetrical View of Heidegger and Symmetrical View of Latour on Technology

Students today are habitual users of digital technology. However, they do not examine the nature of their relationship with technology. Even though we are all enduring severe environmental crises including the COVID-19 pandemic, our students do not appear to see the interrelated connections between the environmental crisis and themselves. A case in point is that they have difficulty drawing a connection between environmental crises and their participation in industrial civilization. This is why it is necessary to consider technological literacy seriously for our environmental education. This study seeks to investigate an alternative way to set up the relation of technology for the current environmental education by comparing the views of Heidegger and Latour on technology. Even though the question of the two thinkers starts from the same critics of the instrumentality of technology, their strategies to overcome it are quite different. While Heidegger suggests a human-centered relationship between humans and technology, Latour criticizes the limitation of this approach and maintains that more symmetrical relations with technology are needed as a form of a network between human and nonhuman actors. In this paper, I argue for the urgency of cultivating a sense of connection for our students’ technological literacy in this environmental crisis. For that purpose, I insist Latour’s advice for teachers and researchers to be considered more seriously to encourage students to take symmetrical relationship with technology in these aggravating environmental crises.
Improvement Of Query Speaking on The Indonesian to Madura Dictionary Using Levenshtein Distance Method

Men are distinguished from other living beings by their use of language, which becomes one of their most distinctive and humanistic qualities. Many different languages are spoken worldwide, including Indonesian, which has approximately 742 different dialects. Due to the unique language of Madura, which is located on a large island with numerous beach tourism destinations, tourists will have difficulty navigating the island. People outside Madura Island who come to visit or vacation will find it difficult to communicate with the locals during their stay or holiday. An Indonesian to Madurese translation dictionary is therefore required in this case. The Levenshtein Distance method was employed in this investigation. The algorithm in the dictionary is used to process the search for the closest distance (dif) between the words being inputted and the words that are already in the database. To provide a prototype for the use of dictionaries. Indonesian and Madurese data sets were used in the investigation by the researcher. According to the simulation results acquired after multiple trials, the error accuracy was 90 % for the first letter input, 84 % for the middle letter input, and 84 % for the last letter input for the first letter. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %. The first letter received 90 % of the votes, the middle letter received 84 %, and the last letter received 84 %. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %. The first letter received 90 % of the votes, the middle letter received 84 %, and the last letter received 84 %. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %.
SeCAM: Tightly Accelerate the Image Explanation via Region-Based Segmentation

In recent years, there has been an increasing trend of applying artificial intelligence in many different fields, which has a profound and direct impact on human life. Consequently, this raises the need to understand the principles of model making predictions. Since most current high-precision models are black boxes, neither the AI scientist nor the end-user profoundly understands what is happening inside these models. Therefore, many algorithms are studied to explain AI models, especially those in the image classification problem in computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations, such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we will propose a new method called Segmentation - Class Activation Mapping (SeCAM)/ This method combines the advantages of these algorithms above while at simultaneously overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, InceptionV3, and VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results were achieved when the algorithm has met all the requirements for a specific explanation in a remarkably short space of time.
A Hierarchical Memory Model for Task-Oriented Dialogue System

Traditional pipeline methods for task-oriented dialogue systems are designed individually and expensively. Existing memory augmented end-to-end methods directly map the inputs to outputs and achieve promising results. However, the most existing end-to-end solutions store the dialogue history and knowledge base (KB) information in the same memory and represent KB information in the form of KB triples, making the memory reader's reasoning on the memory more difficult, which makes the system difficult to retrieve the correct information from the memory to generate a response. Some methods introduce many manual annotations to strengthen reasoning. To reduce the use of manual annotations, while strengthening reasoning, we propose a hierarchical memory model (HM2Seq) for task-oriented systems. HM2Seq uses a hierarchical memory to separate the dialogue history and KB information into two memories and stores KB in KB rows, then we use memory rows pointer combined with an entity decoder to perform hierarchical reasoning over memory. The experimental results on two publicly available task-oriented dialogue datasets confirm our hypothesis and show the outstanding performance of our HM2Seq by outperforming the baselines.
Development of a Blockchain-Based Online Secret Electronic Voting System

This paper presents the design, implementation, and verification of a blockchain-based online electronic voting system that ensures accuracy and reliability in electronic voting and its application to various types of voting using blockchain technologies, such as distributed ledgers and smart contracts. Specifically, in this study, the connection between the electronic voting system and blockchain nodes is simplified using the REST API design, and the voting opening and counting information is designed to store the latest values in the distributed ledger in JSON format, using a smart contract that cannot be falsified. The developed electronic voting system can provide blockchain authentication, secret voting, forgery prevention, ballot verification, and push notification functions, all of which are currently not supported in existing services. Furthermore, the developed system demonstrates excellence on all evaluation items, including 101 transactions per second (TPS) of blockchain online authentication, 57.6 TPS of secret voting services, 250 TPS of forgery prevention cases, 547 TPS of read transaction processing, and 149 TPS of write transaction processing, along with 100% ballot verification service, secret ballot authentication, and encryption accuracy. Functional and performance verifications were obtained through an external test certification agency in South Korea. Our design allows for blockchain authentication, non-forgery of ballot counting data, and secret voting through blockchain-based distributed ledger technology. In addition, we demonstrate how existing electronic voting systems can be easily converted to blockchain-based electronic voting systems by applying a blockchain-linked REST API. This study greatly contributes to enabling electronic voting using blockchain technology through cost reductions, information restoration, prevention of misrepresentation, and transparency enhancement for a variety of different forms of voting.
Inductive-transductive learning for very sparse fashion graphs

The assortments of global retailers are composed of hundreds of thousands of products linked by several types of relationships such as style compatibility, “bought together”, “watched together”, etc. Graphs are a natural representation for assortments, where products are nodes and relations are edges. Style compatibility relations are produced manually and do not cover the whole graph uniformly. We propose to use inductive learning to enhance a graph encoding style compatibility of a fashion assortment, leveraging rich node information comprising textual descriptions and visual data. Then, we show how the proposed graph enhancement substantially improves the performance on transductive tasks with a minor impact on graph sparsity. Although demonstrated in a challenging and novel industrial application case, the approach we propose is general enough to be applied to any node-level or edge-level prediction task in very sparse, large-scale networks.
Cancer Identification in Walker 256 Tumor Model Exploring Texture Properties Taken from Microphotograph of Rats Liver

Recent studies have been evaluating the presence of patterns associated with the occurrence of cancer in different types of tissue present in the individual affected by the disease. In this article, we describe preliminary results for the automatic detection of cancer (Walker 256 tumor) in laboratory animals using preclinical microphotograph images of the subject’s liver tissue. In the proposed approach, two different types of descriptors were explored to capture texture properties from the images, and we also evaluated the complementarity between them. The first texture descriptor experimented is the widely known Local Phase Quantization (LPQ), which is a descriptor based on spectral information. The second one is built by the application of a granulometry given by a family of morphological filters. For classification, we have evaluated the algorithms Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Logistic Regression. Experiments carried out on a carefully curated dataset developed by the Enteric Neural Plasticity Laboratory of the State University of Maringá showed that both texture descriptors provide good results in this scenario. The accuracy rates obtained using the SVM classifier were 96.67% for the texture operator based on granulometry and 91.16% for the LPQ operator. The dataset was made available also as a contribution of this work. In addition, it is important to remark that the best overall result was obtained by combining classifiers created using both descriptors in a late fusion strategy, achieving an accuracy of 99.16%. The results obtained show that it is possible to automatically perform the identification of cancer in laboratory animals by exploring texture properties found on the tissue taken from the liver. Moreover, we observed a high level of complementarity between the classifiers created using LPQ and granulometry properties in the application addressed here.
Leśniewski’s Ontology – Proof-Theoretic Characterization

Abstract The ontology of Leśniewski is commonly regarded as the most comprehensive calculus of names and the theoretical basis of mereology. However, ontology was not examined by means of proof-theoretic methods so far. In the paper we provide a characterization of elementary ontology as a sequent calculus satisfying desiderata usually formulated for rules in well-behaved systems in modern structural proof theory. In particular, the cut elimination theorem is proved and the version of subformula property holds for the cut-free version.
Hypergraph-Based Inference Rules for Computing $$\mathcal{EL}\mathcal{}^+$$-Ontology Justifications

Abstract To give concise explanations for a conclusion obtained by reasoning over ontologies, justifications have been proposed as minimal subsets of an ontology that entail the given conclusion. Even though computing one justification can be done in polynomial time for tractable Description Logics such as $$\mathcal{EL}\mathcal{}^+$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>EL</mml:mi> <mml:msup> <mml:mrow> <mml:mrow /> </mml:mrow> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> </mml:math> , computing all justifications is complicated and often challenging for real-world ontologies. In this paper, based on a graph representation of $$\mathcal{EL}\mathcal{}^+$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>EL</mml:mi> <mml:msup> <mml:mrow> <mml:mrow /> </mml:mrow> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> </mml:math> -ontologies, we propose a new set of inference rules (called H-rules) and take advantage of them for providing a new method of computing all justifications for a given conclusion. The advantage of our setting is that most of the time, it reduces the number of inferences (generated by H-rules) required to derive a given conclusion. This accelerates the enumeration of justifications relying on these inferences. We validate our approach by running real-world ontology experiments. Our graph-based approach outperforms PULi [14], the state-of-the-art algorithm, in most of cases.
Synthetic Tableaux: Minimal Tableau Search Heuristics

Abstract We discuss the results of our work on heuristics for generating minimal synthetic tableaux. We present this proof method for classical propositional logic and its implementation in Haskell. Based on mathematical insights and exploratory data analysis we define heuristics that allows building a tableau of optimal or nearly optimal size. The proposed heuristics has been first tested on a data set with over 200,000 short formulas (length 12), then on 900 formulas of length 23. We describe the results of data analysis and examine some tendencies. We also confront our approach with the pigeonhole principle.
Formula Simplification via Invariance Detection by Algebraically Indexed Types

Abstract We describe a system that detects an invariance in a logical formula expressing a math problem and simplifies it by eliminating variables utilizing the invariance. Pre-defined function and predicate symbols in the problem representation language are associated with algebraically indexed types, which signify their invariance property. A Hindley-Milner style type reconstruction algorithm is derived for detecting the invariance of a problem. In the experiment, the invariance-based formula simplification significantly enhanced the performance of a problem solver based on quantifier-elimination for real-closed fields, especially on the problems taken from the International Mathematical Olympiads.
Bayesian Ranking for Strategy Scheduling in Automated Theorem Provers

Abstract A strategy schedule allocates time to proof strategies that are used in sequence in a theorem prover. We employ Bayesian statistics to propose alternative sequences for the strategy schedule in each proof attempt. Tested on the TPTP problem library, our method yields a time saving of more than 50%. By extending this method to optimize the fixed time allocations to each strategy, we obtain a notable increase in the number of theorems proved.
Actions over Core-Closed Knowledge Bases

Abstract We present new results on the application of semantic- and knowledge-based reasoning techniques to the analysis of cloud deployments. In particular, to the security of Infrastructure as Code configuration files, encoded as description logic knowledge bases. We introduce an action language to model mutating actions ; that is, actions that change the structural configuration of a given deployment by adding, modifying, or deleting resources. We mainly focus on two problems: the problem of determining whether the execution of an action, no matter the parameters passed to it, will not cause the violation of some security requirement ( static verification ), and the problem of finding sequences of actions that would lead the deployment to a state where (un)desirable properties are (not) satisfied ( plan existence and plan synthesis ). For all these problems, we provide definitions, complexity results, and decision procedures.
Toward Understanding Most of the Context in Document-Level Neural Machine Translation

Considerable research has been conducted to obtain translations that reflect contextual information in documents and simultaneous interpretations. Most of the existing studies use concatenation data which merge previous and current sentences for training translation models. Although this corpus improves the performance of the model, ignoring the contextual correlation between the sentences can disturb translation performance. In this study, we introduce a simple and effective method to capture the contextual correlation of the sentence at the document level of the current sentence, thereby learning an effective contextual representation. In addition, the proposed model structure is applied to a separate residual connection network to minimize the loss of the beneficial influence of incorporating the context. The experimental results show that our methods improve the translation performance in comparison with the state-of-the-art baseline of the Transformer in various translation tasks and two benchmark machine translation tasks.
Effective Denoising Architecture for Handling Multiple Noises

Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images.In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality.Thus, an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously.In this paper, we propose a convolutional denoising autoencoder (CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark (ExDark) Image dataset, experimental results show that the Sequential filtering architecture showed superior mean average precision(mAP) compared to other architectures.
An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

Most remote systems require user authentication to access resources.Text-based passwords are still widely used as a standard method of user authentication.Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security.One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users.Graphical user authentication methods have recently been proposed to verify the user identity.However, the fundamental limitation of a graphical password is that it must have a colorful and rich image to provide an adequate password space to maintain security, and when the user clicks and inputs a password between two possible grids, the fault tolerance is adjusted to avoid this situation.This paper proposes an enhanced graphical authentication scheme, which comprises benefits over both recognition and recall-based graphical techniques besides image steganography.The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts.To evaluate the effectiveness of the proposed scheme, peak signal-to-noise ratio and mean squared error parameters have been used.