Reviewerly/exHarmony huggingface dataset:
Construction of LCD-MRD codes of length n &gt; N

An MRD code [Formula: see text] is a [Formula: see text]-dimensional [Formula: see text]-linear subspace of the [Formula: see text]-dimensional vector space [Formula: see text] over [Formula: see text] for [Formula: see text]. Linear codes [Formula: see text] (of length [Formula: see text], dimension [Formula: see text]) that are obtained from MRD codes [Formula: see text] satisfying the property [Formula: see text] are defined to be LCD MRD codes. An inherent relationship between the generator G and parity-check matrices H of LCD MRD codes is observed. This in fact identifies LCD MRD codes as trivial ([Formula: see text]) and nontrivial ([Formula: see text]) codes. Further, two classes of LCD MRD codes of length [Formula: see text] are constructed from [Formula: see text] (trivial and nontrivial) LCD MRD codes of length [Formula: see text]. Examples are provided for demonstration of results obtained.
Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Abstract This paper presents fault diagnosis and problem solving under uncertainty by a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. In this system, the main goal is to diagnose the causal failures behind the symptoms in complex and uncertain domains. The system’s architecture is described in three aspects: the general, structural, and functional architectures. The domain knowledge is represented by formally defined methods. An integration of semantic networks, Bayesian networks, and CBR is employed to deal with the domain uncertainty. An experiment is conducted from the oil well drilling domain, which is a complex and uncertain area as an application domain. The system is evaluated against the expert estimations to find the most efficient solutions for the problems. The obtained results reveal the capability of the system in diagnosing causal failures.
Security of Multi-Adjustable Join Schemes: Separations and Implications

Database management systems (DBMS) are one of cloud services with major applications in industry and business. In the use of such services, since the cloud service provider cannot be entrusted with the plain data, the databases are typically encrypted prior to outsourcing. One of the most challenging issues in designing these services is supporting SQL join queries on the encrypted database. The multi-adjustable join scheme (M-Adjoin) [Khazaei-Rafiee 2020], an extension of Adjoin [Popa-Zeldovich 2012 and Mironov-Segev-Shahaf 2017], is a symmetric-key primitive that supports the join queries for a list of column labels on an encrypted database. In previous works, the following security notions were introduced for <inline-formula><tex-math notation="LaTeX">$\text{Adjoin}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{M-Adjoin}$</tex-math></inline-formula> schemes: <inline-formula><tex-math notation="LaTeX">$\mathtt {3Partition}$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathtt {M3Partition}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathtt {M3P}_{k}$</tex-math></inline-formula> , for every integer <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> . In this article, we first extend the simulation-based and indistinguishability-based security notions for <inline-formula><tex-math notation="LaTeX">$\text{Adjoin}$</tex-math></inline-formula> , defined by Mironov <i>et al.</i> , to <inline-formula><tex-math notation="LaTeX">$\text{M-Adjoin}$</tex-math></inline-formula> . Then, we study the relations between all these security notions for <inline-formula><tex-math notation="LaTeX">$\text{M-Adjoin}$</tex-math></inline-formula> . In particular, some non-trivial relations are proved which resolve some open problems raised by Mironov <i>et al.</i>
Student Informal Learning at Museum Field Trips using Tablet Computers

Informal learning at museums are an important complement to formal school learning. Children arriving on field trips can expand their knowledge, and gain new understanding and perspectives of real world phenomena. Electronic mobile applications are often used in museums to provide information about the exhibits, as well as support student's engagement with the museum items. However, it is unclear whether they support learning better than conventional non-technological aids. Furthermore, it is unclear what type of electronic guide best supports learning. In this work, we examine young students’ mobile learning in the museum, comparing three types of activity guides: a paper booklet, an information-based mobile application and a constructivist-based mobile application. Results indicate that students using the constructivist guide learned better than students using the informative guide but not better than ones using the paper booklet, and that overall, students preferred the mobile application over the paper booklet.
Estimation of Uncertainty in the Atmospheric Pressure Measurement From the Indian Ocean Moored Buoy Systems

Abstract Precise and accurate monitoring of atmospheric pressure by barometric pressure sensors used in moored buoys in the Indian Ocean is a key factor in cyclone monitoring and climate studies. Underperformance of these sensors has significant impacts on societal protection, weather predictions, and climate modeling. Experimental studies are carried out on the barometric pressure sensors used in moored buoys to quantitatively estimate the uncertainties due to sensor accuracy, drift, static pressure head, and satellite telemetry resolution. The overall measurement uncertainty of the pressure sensors is identified to be ±0.13 hPa, which is within the accuracy limits recommended by the World Meteorological Organization (WMO).
Evaluating the Success of E-Learning Systems and Strategy Creation: The Perspective of Students in Universitas Indonesia

The growth of internet usage has led to changes in digitalization and also boost the increased development in Information and Communication Technology. This development brings various innovation and development in various sectors, one of which is in education. The result of information technology innovation in the education field leads to a stimulus of the adoption of e-learning as the answer for distance-based learning methods. Based on a previous survey conducted, it is found that as many as 65% of well-known faculties in the world have used open educational resources in their teaching methods. It becomes a powerful medium of learning and catalyst that enhance knowledge, skills, and other positive outcomes of learners. The existence of e-learning in higher education is considered more important in the present, especially due to the global pandemic all over the world. This study using the Evaluating E-learning System Success model introduced by Al-Fraihat et al. and has been empirically validated by fitting the model to data collected from 401 Science, Technology, Engineering and Mathematics (STEM) students, 394 social and humanities students and 296 health science students of Universitas Indonesia engaged with LMS. This study was analyzed using PLS-SEM method and shows that perceived satisfaction, perceived usefulness, and use in the e-learning system positively affect the benefits felt by students. As a practical implication, this study created an interactive design in e-learning system for student which was prioritized by expert using relationship diagram.
A novel recommendation scheme with multifactorial weighted matrix decomposition strategies via forgetting rule

Recommendation systems play more and more important roles in many fields, such as movie recommendation, book recommendation, music recommendation. Sparse data and large-scale data result in low recommendation efficiency and a cold start problem. Recently, matrix decomposition plays an active role in recommendation with its high recommendation efficiency and fast recommendation speed. But in most matrix completion mechanisms, time influence and users features are not fully utilized. In this paper, a continuous function is constructed to grasp the time-varying rule of users’ interests, and the scores of different scoring time are processed by this function. Thus, the processed scores contain not only the user interests but also their memory rule. Moreover, user interests may change slowly over time, so we keep the original rating matrix in the recommendation process. Finally, a new matrix decomposition optimization model is constructed by considering the score matrix that combines time information and the original score matrix. Compared with the recent matrix decomposition recommendation algorithms, the effectiveness of our proposed algorithm is verified based on the recommended evaluation metrics and several datasets.
Multiword Expressions in the Medical Domain: Who Carries the Domain-Specific Meaning

This paper is a continuation of work in natural language processing in the medical domain for Croatian. After we have annotated single nouns from our corpus consisting of pharmaceutical instructions for medicaments, we are shifting the focus to multiword expressions (MWEs). The project still relies on the nouns from the previous step to detect MWEs where the noun is the main carrier of the medical meaning. However, in cases where the main noun is more general and not directly associated with the medical domain (e.g., bubrežna funkcija 'kidney function'), we use the power of NooJ morphology grammar to check if the preceding adjective root is associated with the noun found in the main dictionary and annotated as a medical domain noun. Thus, we are checking if the adjective (endoskopski 'endoscopic') has a corresponding noun (endoskopija 'endoscopy') that is already marked in the NooJ dictionary as a noun belonging to the medical domain. In such cases, we assume that the adjective belongs to the same domain as the noun and that the attribute for the medical domain can be inherited, not only for the adjective, but for the entire MWE as well. The project hopes to help with the automatic extraction and annotation of single adjectives from the medical domain, but also to help identify medical MWEs. Additionally, we wanted to learn more about who carries the domain-specific meaning in Croatian MWEs.
Semantic Annotations in Clinical Guidelines

Clinical guidelines are evidence-based recommendations developed to assist practitioners in their decisions on appropriate care for patients with specific clinical circumstances. They provide succinct instructions such as what drugs should be given or taken for a particular condition, how long such treatment should be given, what tests should be conducted, or other situational clinical circumstances for certain diseases. However, as they are described in natural language, they are prone to problems such as variability and ambiguity. In this paper, we propose an approach to automatically infer the main components in clinical guideline sentences. Knowing the key concepts in the sentences, we can then feed them to model checkers to validate their correctness. We adapt semantic role labelling approach to mark the key entities in our problem domain. We also implement the technique used for Named-Entity Recognition (NER) task and compare the results. The aim of our work is to build a reasoning framework that combines the information gained from real patient data and clinical practice, with clinical guidelines to give more suitable personalised recommendations for treating patients.
Named Entity Recognition of Power Substation Knowledge Based on Transformer-BiLSTM-CRF Network

Power substations have accumulated a large number of knowledge texts in various forms. Named Entity Recognition (NER) of power substation knowledge can identify entities from these texts and lays the foundation for the subsequent knowledge management. To realize the entity recognition of power substation knowledge more efficiently, an improved Transformer- BiLSTM-CRF model is proposed. The model consists of embedding layers, improved Transformer module and BiLSTM-CRF module. The proposed model is compared with LSTM, BiLSTM, CRF, Transformer and their combined models in the dataset provided by power enterprises. The result on the test dataset shows that the precision is 84.8%, the recall is 85.8%, and the F1-score is 0.853, which are all better than the comnarative models.
Prediction and Analysis of Corona Virus Disease (COVID-19) using Cubist and OneR

Abstract Inflammatory syndrome usually occurs with the adults who have already affected with corona virus. Multi system syndrome in children may affect different parts of the body like lungs, liver, kidneys and brain. The level of current infection can be known by proceeding with two kinds of tests, namely viral tests and anti body tests. Viral tests determine the level of current infection and antibody tests are about past infection. Though decision on test will be provided by health care units, there will be an uncertainty in evaluation of results. The variation in potential of antibodies will influence the resulting parameters. The effect of inaccurate test results is to be traced, because people may suffer from reinfection in their bodies over a period of time. In this paper the uncertainty analysis is carried out using Cubist and OneR algorithms. In the cubist algorithm leaf nodes can be analyzed using regression models. Accuracy of the predictions are analyzed using contingency table and false positives and true negatives are tracked using confusion matrix. This analysis assists in determining the trans conditional state of the disease with dynamism. With the analysis carried out cubist proves to be the best algorithm.
A Direct Anonymous Attestation Scheme Based on Mimic Defense Mechanism

Machine-to-Machine (M2M) communication is a essential subset of the Internet of Things (IoT). Secure access to communication network systems by M2M devices requires the support of a secure and efficient anonymous authentication protocol. The Direct Anonymous Attestation (DAA) scheme in Trustworthy Computing is a verified security protocol. However, the existing defense system uses a static architecture. The “mimic defense” strategy is characterized by active defense, which is not effective against continuous detection and attack by the attacker. Therefore, in this paper, we propose a Mimic-DAA scheme that incorporates mimic defense to establish an active defense scheme. Multiple heterogeneous and redundant actuators are used to form a DAA verifier and optimization is scheduled so that the behavior of the DAA verifier unpredictable by analysis. The Mimic-DAA proposed in this paper is capable of forming a security mechanism for active defense. The Mimic-DAA scheme effectively safeguard the unpredictability, anonymity, security and system-wide security of M2M communication networks. In comparison with existing DAA schemes, the scheme proposed in this paper improves the safety while maintaining the computational complexity.
An Investigation of Positional Encoding in Transformer-based End-to-end Speech Recognition

In the Transformer architecture, the model does not intrinsically learn the ordering information of the input frames and tokens due to its self-attention mechanism. In sequence-to-sequence learning tasks, the missing of ordering information is explicitly filled up by the use of positional representation. Currently, there are two major ways of using positional representation: the absolute way and relative way. In both ways, the positional in-formation is represented by positional vector. In this paper, we propose the use of positional matrix in the context of relative positional vector. Instead of adding the vectors to the key vectors in the self-attention layer, our method transforms the key vectors according to its position. Experiments on LibriSpeech dataset show that our approach outperforms the positional vector approach.
Product-harm crisis intelligent warning system design based on fine-grained sentiment analysis of automobile complaints

Abstract Due to the huge losses caused by product-harm crises and subsequent recalls in the automobile industry, companies must urgently design a product-harm crisis warning system. However, the designs of existing warning systems use the recurrent neural network algorithm, which suffers from gradient disappearance and gradient explosion issues. To compensate for these defects, this study uses a long and short-term memory algorithm to achieve a final prediction accuracy of 90%. This study contributes to the research and design of automatic crisis warning systems by considering sentiment and improving the accuracy of automobile product-harm crisis prediction.
Exponential stabilization of stochastic quantum systems via combined feedback

We propose a novel control strategy by combining state feedback with noise-assisted feedback to exponentially stabilize the target eigenstate for two-level stochastic quantum systems in this paper. The state space is divided into two subspaces, and the state feedback and noise-assisted feedback work in the corresponding subspace, respectively, to achieve the faster state convergence than that of using the two feedback controls individually. We prove the exponential stabilization of target eigenstate under the combined feedback, whose the effectiveness and superiority are verified in numerical simulations.
Efficient Channel Attention Feature Fusion for Lightweight Single Image Super Resolution

Abstract Recent advances in deep learning and convolution neural network have greatly improved the reconstruction performance of SISR compared with the traditional methods. However, complicated models and huge amount of parameters limit the application of those methods in real-world scenes. In our paper, we propose an efficient channel attention feature fusion method on the lightweight super-resolution network (ELSRN) for SISR. We reduce our network parameters through several modules, including binary cascading feature fusion. Besides, we propose to build efficient inverted residual block (EIRB) and stack several EIRBs to capture effective feature information of different scales. Last, we fuse multi-scale features in pairs step by step and finally refine final feature information with different scale features. Several experiments have proved that our EIRBs module and binary cascading method are effective and our network can achieve a great trade-off between reconstruction performance and model size.
Comparison of Paul Viola – Michael Jones algorithm and HOG algorithm for Face Detection

Abstract The computer technology which helps to find the face of a person in video and image is face detection. It is like a biometric application that finds a person by analyzing and comparing the person’s face patterns. The face detection technique helps for security purposes, and also it has an rising interest in many fields. This technology has received good attention. It has some good applications based on law enforcement. For detecting face in an digital image, we have lot of algorithms. In this, Viola Jones algorithm and histogram of oriented gradients is used to detect the face of a person. The paper is proposed for a comparison between viola jones and Hog. By analyzing this we can find the gender, age and also emotions of the person. Here different type of images were used for detection. The Matlab tool and Jupyter is used for implementing this face detection process.
Multi-user Fuzzy Keyword Searchable Encryption Scheme Based on Certificateless Cryptosystem

Abstract Nowadays, the application prospect of cloud storage is more and more extensive, and the security problem of cloud storage has become one of the hotspots of research. Searchable encryption can make full use of the huge computing resources of cloud servers and solve the problem of secure search after data encryption and upload. However, the existing searchable encryption schemes have the problems of high computational cost, low security, and insufficient flexibility in the way of searching keywords. In order to solve these problems, we propose a new certificateless multi-user fuzzy keyword searchable encryption scheme, which introduces the key distribution center, effectively reduces the computational cost, solves the problem of certificate management and key escrow, and uses the optimized wildcard technology to realize the fuzzy search of multiple users, which is not only brings convenience to users, but also realizes flexible search methods. Finally, the analysis results show that our scheme has indistinguishable security under keyword guessing attacks. Simulation experiments and performance analysis results show that our scheme has higher computational efficiency.
Marketing-Mix Metamorphosis and New Trusted Business Practices

This chapter discusses the impact on the marketing-mix due to the confluence of the internet of things and the internet of value which seems to be made possible by the blockchain technology. This “perfect storm” induces a vortex of reliability and business trust between people (“peer-to-peer”) and machines (“bot-to-bot”), without the traditional need of third parties to ensure confidence in a negotiation. This implies innovative business practices and self-executing contracts that will take place in a more decentralized and trustworthy environment, speeding up the metamorphosis of the four marketing-mix elements in such a way that marketers will have to deal with a “product” that is always in a “beta-version”; a dynamic “price” that initially has to be free; an atomized “promotion” of reliable messages found by costumers (not the opposite); and a new virtual secure “place,” which is made possible due to augmented reality and blockchain.
Extracting information from textual descriptions for actuarial applications

Abstract Initial insurance losses are often reported with a textual description of the claim. The claims manager must determine the adequate case reserve for each known claim. In this paper, we present a framework for predicting the amount of loss given a textual description of the claim using a large number of words found in the descriptions. Prior work has focused on classifying insurance claims based on keywords selected by a human expert, whereas in this paper the focus is on loss amount prediction with automatic word selection. In order to transform words into numeric vectors, we use word cosine similarities and word embedding matrices. When we consider all unique words found in the training dataset and impose a generalised additive model to the resulting explanatory variables, the resulting design matrix is high dimensional. For this reason, we use a group lasso penalty to reduce the number of coefficients in the model. The scalable, analytical framework proposed provides for a parsimonious and interpretable model. Finally, we discuss the implications of the analysis, including how the framework may be used by an insurance company and how the interpretation of the covariates can lead to significant policy change. The code can be found in the TAGAM R package ( github.com/scottmanski/TAGAM ).
Intelligent Blacklist Security System for Protecting Spammer in Corporate Email Solution: A Case of Corporate Email Service Provider in Thailand

In the present, there're billions of information transactions around the world each day including email. Technology Land Co., Ltd. (TL) is the email service provider organization that provides the email domain name to the client's organization. However, there have been problems that occurred in the TL email service provider (TLESP) system which must be solved immediately to solve the existed problems and prevent further damage. Additionally, the improved TLESP system has used artificial intelligence (AI) as the core for email spamming, viruses, hackers, and unusual behaviour detection. Also, the advanced TL's email system incoming process and outgoing process security have been explained together with system results and user benefits after the improvement of the system in this study.