Reviewerly/exHarmony huggingface dataset:
A Scalable Trustworthy Infrastructure for Collaborative Container Repositories

Within cloud computing containerization has become ubiquitous. As the availability of pre-built containers increases there is a need for methods capable of efficiently securing large repositories of software containers. We present a “Trustworthy Container Repository” (TCR) system which provides security assurances (confidentiality, integrity, and authenticity) regarding such a repository in a scalable manner. Trust within the TCR architecture is rooted in a low-complexity, tamper-resistant trusted module, which leverages index-ordered Merkle trees (IOMTs) to efficiently track a large number of container images and provide assurances of repository integrity to its users. The key contributions of the study are, identification of the required security model, a novel TCR data-structure, and verifiable algorithms to operate on it. Through experiment, we observe closely logarithmic time complexity of the proposed system up to a high container count ( N = 2 25 ≈ 10 7 ).
Segment Based Approach to Travelling Salesman Problem

The paper presents the Segment Based Approach (SBA) - a novel technique to solve the Traveling Salesman Problem. In this technique, the path is constructed from segments, which are sequences of adjacent edges. Although the approach is memory extensive, it has built-in measures for identifying useful segments and controlling its memory requirements. The paper contains a detailed description of the SBA operation and an analysis of the impact that its parameters have on the obtained results. The SBA has some advantages over solutions of the TSP that are inspired by Ant Colony System. It is capable of finding shorter paths than the ACO methods but its main advantage is low computational complexity and the superiority of SBA over the ACO grows with the increase of the distance matrice size. The parallelization of its operation is also possible in a more straightforward manner than in the case of the ACO.
An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation

Recently, researchers have raised several questions related to the explainability of deep learning (DL) model predictions. Indeed, the inherent and undeniable risk remains the abandonment of human control and monitoring in favor of these DL models. Thus, many tools that allow humans to verify the agreement between the DL model predictions and ground truth have been explored. These tools fall into the field of explainable artificial intelligence (XAI) which focuses on proposing methods to explain how the AI-based systems generate their decisions. To contribute to this purpose, we propose in this paper a framework, called DocSegExp, for explaining the decisions of DL models applied for historical document image segmentation (HDIS). The proposed framework is evaluated using three XAI attribution methods on two DL architectures that are performed on a large-scale synthetic dataset having 12K pixel-wise annotated images. Besides, we propose an adaptation of four state-of-the-art metrics (that were previously introduced in the context of object classification) in order to evaluate the generated explanations in the case of a pixel-wise segmentation task.
Multi-module Natural Language Search Engine for Travel Offers

In this work, we present an advanced semantic search engine dedicated to travel offers, allowing the user to create queries in the Natural Language. We started with the Polish language in focus. Search for e-commerce requires a different set of methods and algorithms than search for travel, search for corporate documents, for law documents, for medicine, etc. In travel, the complexity of data is bigger than in other domains, and the search process requires more parameters. In e-commerce, one product has 1 price, while in travel, one product (holiday package) has got tens of thousands of prices depending on time, board type, room type, number of people, children’s age, etc. Providing a search for one middle-size tour operator, we need to search within hundreds of millions documents. We present a set of methods based on natural language processing to improve the search for travel. We also present our new application for annotating travel offers, prepared in a human-in-the-loop paradigm that enables iterative system improvement. We also show a large dataset containing more than 3,000 manually constructed queries and more than 23,000 manually annotated answers, a large fraction by at least two independent experts, and a semi-automatically constructed ontology of tourism terms in OWL format containing nearly 2,000 concept classes.
On-wrist Based Datasets Exploration for an IoT Wearable Fall Detection

Fall presents a vulnerable health burden, especially in the elderly, due to its after-affecting longnsequences. Wrist-based fall detection presents an accessory-like comfortable solution yet very unsteady as to IMU sensors. Hence, this paper focuses on inspecting the impact of the use of IMU sensors and their sampling rates in the efficacy of a wrist worn fall detector, a subject still underexplored in the recent literature. We assessed benchmark datasets using supervised dictionary learning. We also introduced an IoT-based framework for monitoring using a mobile application. Various exploration experiments showed that an optimal accuracy of 99.5% is attained when using only an accelerometer with 20 Hz sampling rate using SRC algorithm. Tests in real situations of the prototype were also carried out, demonstrating adequate performance in facing several ADL and fall-alike activities, with an autonomy of 14 running hours.
Observing and Understanding Agent’s Characteristics With Environmental Changes for Learning Agents

As technology progress and reinforcement learning is implemented in many domains, from gaming to stock trading, it has become more important to understand agent characteristics. How it approaches the changes in the environment to understand its decision-making and make the system more reliable. Behavioral game theory broadens typical game theory by taking care of how players sense the reward, rewards that other players receive, limit strategic reasoning, the impact of situations, the effects of information and, how players use the information for further learning. In this paper, we have presented a card game that would enable us to enhance the analysis of learning agents’ characteristics with environmental changes. Environment change will also bring challenges regarding agent cooperation. Sometimes taking such a strategy not to share agents’ previous experiences could make itself more reliable to other agents. Our approach will provide more space or knowledge about how to improve the agent.
Privacy-preserving in smart grids using Ethereum and Hyperledger blockchain

The smart grid concept was developed as a modern-day economic construct for holding up the traditional power infrastructure to determine the most effective strategy for incorporating renewable and green energy technology to everyday energy use by people. The smart grid has been developing as a novel solution for ensuring energy access whenever required and from any location. The overriding goal of such developments is really to foster the development of a sustainable society. Nevertheless, connecting and managing the rapidly rising number of users and connections seems to be a challenge in the existing centralized grid architecture. As a result, the smart grid is evolving from a centralized to a decentralized topology. On the other hand, the blockchain offers several advantageous features of a packaged software ready to be unpacked and installed which can benefit smart grid paradigm. We intend to present a complete assessment of blockchain applications in smart grids in this study. As such, we highlight the security solutions that blockchain can resolve in the smart grid.
Input Image Pixel Interval method for Classification Using Transfer Learning

Deep learning has been used in many applications where patterns from past-trained data can be extracted to predict future outcomes. Deep learning is characterized by training and testing data with the identical input feature space and same data distribution. However, whereas the data distribution is same between the training and testing data, the results might be different. This study introduces input image preprocessing, an enhanced neural network optimization method, and prediction probability ensemble to minimize the number of trainable parameters but maintain the outcome accuracy. In the suggested methodology, input images are separated into pixel interval and the fully connected layer jointly used with saved weights. Outcome results of separated input images are ensembled to the corresponding class probabilities of the original image. The results of the proposed method were compared with those of other previous methods in the image classification task and achieved successful performance accuracies in several datasets.
Proposal of a Method for Reframing Irrational Beliefs with Forced Generation of Intermediate Concept

There is a need to increase the flexibility of thinking. However, some people stick to conventional ideas and may not be able to be flexible. As one of the supports, it has been proposed to utilize reframing to change our framework or perspective of the idea that is causing the impasse. An existing research method aimed at reframing beliefs. This method has some problems, and the need to reframe irrational beliefs into rational beliefs has been pointed out. Therefore, in this study, we proposed a conception method for reframing a persistent idea called irrational beliefs among beliefs to increase the flexibility of thinking. In this method, ideas that are completely different from the first idea are freely conceived according to the content of either Time, Space, or Positivity. It is a reframing method that obtains a new perspective by forcibly thinking about the content that comes to mind in the center of the two extreme ideas. As a result of evaluating the effects of this method, it was confirmed that this method has a certain effect on increasing the flexibility of thinking and reframing irrational beliefs. The goal of this research was to develop a concrete method that solves the problems of an existing research method and a worksheet that can be put into practice and to confirm its effectiveness.
An Analysis of Educational Cloud Platforms using Multi-agent Learning

With the rapid increment of benefits in on-demand service, large network access, and availability, more and more industries move their focus into cloud platforms, and the field of education is no different. With the covid-19 pandemic situation, educational cloud platforms are getting more popularity and relevance among educational institutions such as open and distance universities and research institutes. This paper presents a multi-agent reinforcement learning-based approach for supporting better use of educational cloud platforms by trying to come up with a mechanism to recognize the best available option of educational cloud platforms for a specific user, identifying the adverse effects of using the selected options of platforms, if there's any and to come up with a mechanism to monitor plagiarism across platforms. In this paper, we explore multi-agent reinforcement learning techniques in finding adaptive solutions for this issue.
Statistical Learning Models for Japanese Essay Scoring Toward One-shot Learning

A lot of studies of automatic essay scoring are conducted using machine learning models. The previous studies show high performance for scoring large scale essays with machine learning models, however, more than hundreds of scored answers are required to train the neural network models. In this paper we discuss the possibility of one-shot learning, that is, using only one model essay as a training sample of a highest score. For this purpose, we apply regression models to estimate essay scores with different embedding models, that are, BERT and bag-of-words based encoding models. In preliminary experiments, feature analyses of one-shot learning with UMAP for the two embedding models reveal that the bag-of-words based model has more potential to score the test essays comparing to the BERT encoding model. Thus, to clarify the performance of the bag-of-words based encoding model, we conduct two experiments: firstly, we evaluate the performance of models to estimate the scores of test essays using 80% of score essays are used as training data; secondly, one-shot learning is applied to the models. The experimental results show that the proposed bag-of-words based encoding model is promising.
Blockchain for energy transactions

Blockchain is a decentralized network with peer-to-peer communication. Peers are the active participants of the network. Peers are also called nodes performing transactions inside the network in the presence of other peers. Other non-participating peers in the network act as examiners and verifiers of every transaction happening in the network. Blockchain obtained its name from the creation of a chain with blocks. The energy sector is ever-blooming with the constant demand for energy in every walk of life. To make the energy grid sturdy against forgeries, energy utilities are introducing the blockchain into the energy grid. This chapter will explore the techniques involved in securing the energy transactions happening in the energy grid with the blockchain framework.
Design of Mobile Learning Content with a Mobile-Based Live Multimedia System

With the rise of world progress, information technology and telecommunications to the Internet have become a top priority. These advancements mark a watershed moment in mobile learning, or "mobile learning," as it is known (m-learning). M Learning has some extra learning capacity that can be accessed by anybody, at any time, from anywhere. The issues still exist in m-learning, including a shortage of hardware and platforms required by the system architecture and accessibility. In order to become comfortable for the user, a thorough examination of each component dealing with existing m-learning is also required. This research aims to address the problem by employing multimedia as a material that can provide more precise and detailed information. Multimedia m-learning services necessitate an internet connection, excellent access, and adequate hardware to benefit from the overall bias with good services provided by phones that offer mobile multimedia facilities. The study's primary goal is to create m-learning with multimedia services, and the expected results are a software-based content provider.
Fully homomorphic encryption: A case study

Fully Homomorphic Encryption (FHE) is the holy grail of encrypted communications. It opens the door to several advanced functionalities to overcome the security and trust issues of the IT world. After 2009, once Craig Gentry had shown that FHE could be achieved, a study in this field boomed, and significant improvement was made in identifying more efficient and realistic programs. FHE is primitive cryptography that enables arbitrary functions to be calculated via encrypted data. These systems are applicable in different ways since they permit users to encrypt their private information securely while still outsourcing the processing of protected data without fearing disclosing the real data. In 2012, LTV12 presented the first multi-key FHE system and demonstrated the possibility of using multi-key systems in somewhat homomorphic encryption (SHE). Like in the one key context, there have been many advances in the field, but no effort has been made to develop the multi-key methods. This paper presents a discussion of FHE and MKFHE with a specific focus on the current techniques and three implementations, comprising the first in the multi-key setups, to the extent of our understanding.
The integration of Acehnese words in Kamus Besar Bahasa Indonesia

This study aimed to investigate the integration of Acehnese words in Kamus Besar Bahasa Indonesia (KBBI, or Indonesia Dictionary). The contribution of Acehnese words enriches Indonesian vocabulary as the national language. This study followed the descriptive-analytical research method using observational and note-taking techniques. The data source was the online version of KBBI, available on the following web page: https://kbbi.kemdikbud.go.id/. The data comprised the Acehnese vocabulary registered on KBBI and became part of the country's national language. The data analysis used Miles et al.’s (2014) models, consisting of condensation data, display data, and conclusion drawing/verification. The results showed that 134 Acehnese words are integrated into Indonesian. The contribution of the Acehnese language to Indonesian was relatively small compared to the contribution of other local languages. Of 134 Acehnese words, they comprise nouns (70.90%), verbs (21.64%), adjectives (6.72%), and adverbs (0.75%). It was also revealed that the Acehnese words that had been integrated successfully into Indonesian came from 24 areas of use. Based on these findings, it can be concluded that the Acehnese language has enriched the Indonesian vocabulary, and this integration is integral to interlanguage word borrowing. The present study’s findings contribute to the national language digitalization policy, particularly the digitalization of the Indonesian language dictionary, especially to achieve the quality level of the national language dictionary.
Artificial Neuron-Based Model for a Hybrid Real-Time System: Induction Motor Case Study

Automatic Machine Learning (AML) methods are currently considered of great interest for use in the development of cyber-physical systems. However, in practice, they present serious application problems with respect to fitness computation, overfitting, lack of scalability, and the need for an enormous amount of time for the computation of neural network hyperparameters. In this work, we have experimentally investigated the impact of continuous updating and validation of the hyperparameters, on the performance of a cyber-physical model, with four estimators based on feedforward and narx ANNs, all with the gradient descent-based optimization technique. The main objective is to demonstrate that the optimized values of the hyperparameters can be validated by simulation with MATLAB/Simulink following a mixed approach based on interleaving the updates of their values with a classical training of the ANNs without affecting their efficiency and automaticity of the proposed method. For the two relevant variables of an Induction Motor (IM), two sets of estimators have been trained from the input current and voltage data. In contrast, the training data for the speed and output electromagnetic torque of the IM have been established with the help of a new Simulink model developed entirely. The results have demonstrated the effectiveness of ANN estimators obtained with the Deep Learning Toolbox (DLT) that we used to transform the trained ANNs into blocks that can be directly used in cyber-physical models designed with Simulink.
A structure-protecting kernelized semi-supervised space adjustment for classification

We introduce a semi-supervised space adjustment framework in this paper. In the introduced framework, the dataset contains two subsets: (a) training data subset (space-one data (SOD)) and (b) testing data subset (space-two data (STD)). Our semi-supervised space adjustment framework learns under three assumptions: (I) it is assumed that all data points in the SOD are labeled, and only a minority of the data points in the STD are labeled (we call the labeled space-two data as LSTD), (II) the size of LSTD is very small comparing to the size of SOD, and (III) it is also assumed that the data of SOD and the data of STD have different distributions. We denote the unlabeled space-two data by ULSTD, which is equal to STD - LSTD. The aim is to map the training data, i.e., the data from the training labeled data subset and those from LSTD (note that all labeled data are considered to be training data, i.e., SOD ∪ LSTD) into a shared space (ShS). The mapped SOD, ULSTD, and LSTD into ShS are named MSOD, MULSTD, and MLSTD, respectively. The proposed method does the mentioned mapping in such a way that structures of the data points in SOD and MSOD, in STD and MSTD, in ULSTD and MULSTD, and in LSTD and MLSTD are the same. In the proposed method, the mapping is proposed to be done by a principal component analysis transformation on kernelized data. In the proposed method, it is tried to find a mapping that (a) can maintain the neighbors of data points after the mapping and (b) can take advantage of the class labels that are known in STD during transformation. After that, we represent and formulate the problem of finding the optimal mapping into a non-linear objective function. To solve it, we transform it into a semidefinite programming (SDP) problem. We solve the optimization problem with an SDP solver. The examinations indicate the superiority of the learners trained in the data mapped by the proposed approach to the learners trained in the data mapped by the state of the art methods.
Double-ANN-Aided Resource Partitioning Scheme for Data Transfers in MEC System over EON

In this paper, we use the wavelet transform and the ANN model to predict the dynamic traffic demands. Moreover, we use ANN models to estimate the task performance in an MEC system. By leveraging the ANN predictor and the ANN estimators, we present a resource partitioning scheme for data transfers in the MEC system to dynamically adjust the resource partitioning based on the predicted demands and the performance requirements. Results show that our proposed scheme offers near-optimal results compared with the existing scheme.
Time-Varying Graph Learning for Node-Wise Anomaly Detection

With the explosive growth of data, many problems in data analysis can be framed as learning an underlying graph best revealing the dependencies between graph signals. However, long-time observations rely on dynamic graphs that capture their evolving relationship over time. In this paper, we propose a general framework of learning time-varying graphs for node-wise anomaly detection. Specifically, we consider a graph-based signal model where the hidden graph smoothly varies most of the time but there exists a sudden change of graph disrupted by node perturbations. To learn dynamic graphs from a sequence of observations and capture the anomaly, we formulate a convex optimization problem by introducing a row-column overlap norm penalty. A perturbed-node time-varying graph learning (PNTVGL) method is then proposed to solve such problem based on the alternating direction method of multipliers (ADMM). The performance of our method is assessed on synthetic and real-world networks and shows superior results over the state-of-the-art graph learning methods.
External Knowledge Document Retrieval Strategy Based on Intention-guided and Meta-learning for Task-oriented Dialogues

The external knowledge-enhanced task-oriented dialogues system is designed to cover the user requests beyond the predefined data base (DB) and application-specific interface (API). Existing methods mainly focus to improve the retrieval accuracy of the external knowledge but ignore the modeling of the entity-aware dialog intention, which cannot fast locate the document-level external API in the practical scenarios. Thus, this paper has introduced the entity-aware dialogue intention information and proposed a two-stage training method for the intention-guided knowledge document retrieval model, which can effectively enhance the retrieval accuracy of the Bi-encoder retrieval model in the task-based dialogue system. Besides, to further improve the generalization ability of the retrieval model based on the intention-guided, a meta-task based on the domain offset is constructed and make the Bi-encoder retrieval model get the more robust semantic representation through the meta-learning, which can improve the generalization capability of the model in invisible domains. Experimental results are shown to prove the correctness and effectiveness of the proposed strategy.