Reviewerly/exHarmony huggingface dataset:
Smart Sustainable Cities: A Chatbot Based on Question Answering System Passing by a Grammatical Correction for Serving Citizens

The smart city concept has been developed as a strategy for working with cities as they become systematically more complex through interconnected frameworks and increasingly rely on the use of Information and Communication Technology to meet the needs of their citizens. At this end, as users struggle to navigate the wealth of on-line information now available, the need for automated question answering systems becomes more urgent. Most QA systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. QA System that described in this paper, built using a seq2seq model, BiLSTM as encoder, a LSTM as decoder, and Attention mechanism for boosting the model performance whether Grammatical Correction Model or Question Answering Model, we have proposed to build a grammatical model first to pass a corrected question to the second system in order to improve QA system results.
Who Responded to Equifax? Self-Protection Strategies When Guardians Fail

Routine activity theory suggests that crime occurs when a motivated offender meets a suitable target and no capable guardian is present. In identity theft that occurs online, potential victims can sometimes act as their own guardians. Self-protection strategies, such as identity theft monitoring software, can be implemented by potential victims to act as guardians against identity theft. Little is known about the situations when a suitable target becomes their own guardian. This article seeks to understand this situation by looking at self-protection behaviors after the Equifax breach of 2017. Using data from a North Carolina Poll Survey, we examine self-protection measures implemented following this potential victimization. When do individuals enact their own self-guardian behaviors? We find that measures of computer use do not predict whether an individual will enact self-protection measures following the Equifax breach. Rather, we find that negative emotions around identity theft lead individuals to enact self-protection measures.
Equivalence of Non-Perfect Secret Sharing and Symmetric Private Information Retrieval With General Access Structure

We study the equivalence between non-perfect secret sharing (NSS) and symmetric private information retrieval (SPIR) with arbitrary response and collusion patterns. NSS and SPIR are defined with an access structure, which corresponds to the authorized/forbidden sets for NSS and the response/collusion patterns for SPIR. We prove the equivalence between NSS and SPIR in the following two senses. 1) Given any SPIR protocol with an access structure, an NSS protocol is constructed with the same access structure and the same rate. 2) Given any linear NSS protocol with an access structure, a linear SPIR protocol is constructed with the same access structure and the same rate. We prove the first relation even if the SPIR protocol has imperfect correctness and secrecy. From the first relation, we derive an upper bound of the SPIR capacity for arbitrary response and collusion patterns. For the special case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {n}$ </tex-math></inline-formula> -server SPIR with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {r}$ </tex-math></inline-formula> responsive and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {t}$ </tex-math></inline-formula> colluding servers, this upper bound proves that the SPIR capacity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\mathsf {r}-\mathsf {t})/\mathsf {n}$ </tex-math></inline-formula> . From the second relation, we prove that a SPIR protocol exists for any response and collusion patterns.
Semi-Autonomous Robotic Avatar System for Patients with Motor Disabilities

This paper presents a method of semiautonomous navigation in a robotic avatar system for patients with physical disabilities. Gaze tracking system is used as an effective handsfree teleoperation interface. However, in the manual control, the operator needs to continuously control the robot by using eye movement. We therefore propose a method of environmental modeling and path planning to realize the adaptive semi-autonomous navigation in unknown environment. Moreover, we developed an intelligent controller dealing with allocation of the tasks, such as target tracking and collision avoidance. Furthermore, we show an experimental example to discuss the applicability of the system
Interactivity for Artificial Intelligence Systems: NL2SQL

For sustainability purposes, Artificial Intelligence modules that help translating natural language sentences to SQL queries are gaining more and more momentum in the recent decade, since they allow the automatic conversion of questions in English to the SQL without any interference from the user during the process of translation. We show in this paper the utility of the previously proposed models and their added value to the resolution of the complicated problem of inferring complex queries from English sentences. We will also discuss their limits in addition to the necessity of using interactivity between the user and the system in order to enhance the quality of the outputs. In this paper, we show how the majority of models can’t independently predict the appropriate queries of SQL without the feedback, the guidance as well as the help of the user. And finally, we present the conclusion together with the future work.
Sustainability of Artificial Intelligence and Deep Learning Algorithms for Medical Image Classification: Case of Cancer Pathology

In the context of Artificial Intelligence’s Sustainability (AI), deep learning has sparked tremendous global interest in recent years. Deep Learning has been widely adopted in image recognition, speech recognition, and natural language processing, but is only beginning to impact on health care. In pathology, artificial intelligence and especially deep learning algorithms has been applied to pathology image analysis tasks such as tumor region identification, prognosis prediction, and detection of cancer areas and classification. This chapter presents different deep learning methods such as convolutional networks (ConvNets) for automatic classification of medical images—cancer pathology. We have proposed different models and different architectures. The first proposed model consists of feature extraction and classification with simple CNN; the second one is composed of two parts: we have used CNN as feature extractor by removing the last classification layers and we have passed the features to Random Forest; and the last one is by using transfer learning–Fine-Tuning–pre-trained CNN “DenseNet201” as classifier. Finally, we have evaluated our models using three metrics: accuracy, precision, and F1 score.
Study of State of Arts Methods for Event Extraction

The amount of online published news articles increases day by day so readers need to find news articles related to his or her interest is very difficult. Lots of important events happed in every day but they are reported in different manner in different online published news articles. In the event extraction technique, detection of main event from the news articles is the most important task including some other tasks like identifying the argument, trigger and named entities. Event extraction and categorization have a wide range of applications in various domains. This article discusses the various states of arts and current methods used in the area of event extraction from text as well as news articles. We also discuss the comparative study about the various methods used in event extraction field. After that, we will discuss the current challenges and research opportunities in the methods of event extraction and categorization.
Adaptive Directional Mutation for an Adaptive Differential Evolution Algorithm

Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters, mutation strategies and so on. One of the most successful studies on controlling the parameters is JADE. In this study, we focus on mutation strategies and propose an adaptive mutation strategy to improve JADE. Some directional mutation strategies, which utilize directional difference vectors from bad individuals to good individuals, have been proposed to improve search speed. However, in order to avoid local solutions, the vectors in opposite direction may be necessary. The proper frequency of such vectors depends on the problem and the search process. We propose an adaptive mutation strategy that controls the frequency of directional and opposite directional vectors adaptively to realizes efficient and stable search. The advantage of JADE with the proposed method is shown by solving thirteen benchmark problems.
The Elias Type Upper Bound of Codes over Symbol-pair Read Channels

Motivated by the application of high-density data storage technologies, symbol-pair read channels are proposed in which every two consecutive symbols are read together. A lot of work has been done on this coding model. Bounds of codes play an important role in coding theory, which act as criterions to measure the optimality of a code. Several bounds have been developed for symbol-pair codes, among of which the Singleton type bound is most popular one. In this paper, we continue the research of bounds and establish the Elias type upper bound for binary symbol-pair codes.
COVID-19, Lockdowns and Herding Towards Cryptocurrency Market Specific Implied Volatility Index

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Formal verification of authenticated, append-only skip lists in Agda

Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying of an authenticated log (such as a blockchain) without requiring any single party to store or verify the entire log, or to trust another party regarding its contents. AAOSLs can help to enable efficient dynamic participation (e.g., in consensus) and reduce storage overhead.
A Hybrid Sequential Model for Text Simplification

Learning different subjects to enhance knowledge of students and children, reading habit plays an important role. Students often face problems or reading difficulties are aroused when the students are non-native English learners or suffering from dyslexia. Thus, in the present work, we have built a hybrid sequential model for text simplification (i.e. to translate complex English sentences into simple English sentences). Generally, text simplification is treated as monolingual translation task, in which translation occurs between the same languages. In recent trends, encoder-decoder model plays an important role in text simplification. However, a difference exists between normal machine translation and text simplification. Text simplification can be achieved using other different operations like splitting, merging and removing complex word, etc. It is very difficult to deal with the type of operation (i.e. split, delete or replace) that is required for text simplification if we use encoder decoder model. Our encoder-decoder model is mainly based on two different approaches, one is character-based and another is word-based. We also employed a Named Entity Recognizer (NER) to improve the accuracy of our model because named entities do not change while converting a complex sentence into a simple sentence. For learning process, we also use bi-directional encoder-decoder model to improve the result. In the present attempt, we used a well-known text simplification dataset (i.e. PWKP). Our proposed model achieved 0.82 BLUE score using NER module and word-based bi-directional encoder-decoder model.
Household smart cat litter box

Abstract With the change and development of the times, more and more young people live alone in the cities, and with the pressure of work, they are often lack of company. Cats are docile in nature, like keeping neat and tidy, and don’t need too much time with their owners, so keeping pet cats is more convenient for most of people. Therefore, pet cats are very popular among young people, more and more young people start to keep pet cats as companions. In addition, with the improvement of people’s living standards, the average family can also afford to raise a cat. Whether as a companion to a child or a member of the family, pet cats are also very popular in ordinary families. According to the surveys, most families now have some pets. But obviously it’s a problem for these families to deal with the excrement of the cats. Because of the heavy smell, every time shoveling the feces will cause a lot of trouble to people. What’s worse, the smell of feces will evaporate in the air, causing air pollution. In order to solve the above problems, we designed a smart litter box that can separate and completely seal the cat feces. It solves the problem of the difficulty in handling cat feces and the heavy smell of cat feces, and can maximize the utilization of household space. Through the fully automated function and effective human-computer interaction function of this litter box, people can greatly save the time and energy spent in processing cat feces.
Harbor Ship Detection Based on Channel Weighting and Spatial Information Fusion

Abstract Aiming at the problems of false alarms and missed detection caused by large differences among ship types and complex background in harbor remote sensing images, a robust single-stage ship detection method is proposed. First, a Channel Weighting Mechanism is devised, which self-learns the weights of different channel to enhance valid features in channel dimension. Second, a Spatial Information Fusion Module is designed to enhance features in spatial dimension, which extracts more information of ship appearance from shallow feature maps, then excavates potential contextual information in deep semantic features. Finally, to promote the detection ability of ships of various scales, a Multi-stage Weighted Fusion Pyramid is applied to optimize the fusion of high-stage features and low-stage features. Extensive experiments conducted on self-established dataset for harbor ships show that the proposed method provides the performance by 3.08%mAP compared to RetinaNet.
Land Records System Using Hybrid Blockchain

Maintaining land records is a difficult task for countries with large area and population. Having a central database server is not a 100% safe alternative to paper-based traditional records as it is susceptible to cyber-attacks and frauds. This paper proposes an idea to solve this issue using blockchain. The proposed land records system is based on a hybrid blockchain network. The existing land records are put on the blockchain by the government. All land sales transactions are recorded on this hybrid blockchain. These transactions are mined (verified) by a miner which is run on government authorized peers or nodes. These records or transactions cannot be tampered with. The citizens can view their owned lands and corresponding transactions but cannot make changes to any data on the blockchain. Being based on hybrid blockchain, this system is benefitted by advantages of both the types of blockchain-public and private and is more secure and reliable than databases and paper records.
Reading Opportunities in a Provincial Russian City during the First World War (Materials from the Volga Region)

The article deals with the problem of resources for reading for the townspeople in the Volga city during the First World War. It is noted that the dealing with the problems, allows the researcher to identify the mechanisms that allowed society to adapt to the conditions of extreme reality. The provision of the Volga cities with libraries has been studied. The possibilities of reading in the provincial and district cities of the studied region are compared. The novelty of the research is seen in the fact that the development of a complex of public and private resources (libraries), available for leisure reading in the Volga city during the First World War, was revealed. The relevance of the study is due to the importance of analyzing the sociocultural practices of society as a compensatory mechanism, as well as a resource reflecting mass sentiment in extreme periods of state development. Comparison of provincial and county city libraries in terms of ownership, attendance, material security is carried out. The conclusion is made about the diversity of reading opportunities as a form of leisure for the Volga region residents during the First World War. The cities of the Volga region were identified, in which the resource of libraries was more diverse. It is shown that there were more libraries in the provincial towns than in the county ones; they had a different organizational basis and more complete funds.
Asynchronous Parallel Multiple Markov Chains Simulated Annealing Algorithm to Transient Electromagnetic Inversion

Summary The simulated annealing algorithm (SA) is a global optimization algorithm. It has the advantages of not relying on the initial model and being easy to jump out of the local optimum. It has many applications. However, excessively long runtimes have prevented it from being used in more complex and wide areas. Scholars have tried many acceleration methods to improve the practicality of sa algorithm. Based on research of Aarts and Korst (1986) , LEE (1996) propsed the asynchronous Multiple Markov chains Parallel SA (asynchronous MMC PSA) algorithm. Due to the high parallelism of this method, SA can be significantly speeded up. Asynchronous MMC PSA has been used to solve many optimization problems. However, no related studies on the inversion of asynchronous MMC PSA in electromagnetic data have been found. In this paper, the asynchronous MMC PSA algorithm is applied to invert a synthetic model, and we analyzes the detailed performance of the algorithm and the solution quality.
Volker Müller &amp; Co.: Electronic Music and Sound Engineering at the WDR

This article is about Volker Müller (b. 1942), a German sound engineer who worked at the WDR Studio for Electronic Music in Cologne for approximately 30 years. I look into Müller’s training as a sound engineer at the Düsseldorf Sound Engineering College and his activities at the WDR studio as a way of expanding on West German post-war state’s building and cybernetics pedagogy. By complicating merely technical, operational and mechanistic views of the sound engineer’s role in the studio, I argue that Volker Müller embodied a particular culture of sound engineering and certain political and artistic utopias that resonate with broader political, economic, and pedagogical structures developed in the context of Cold War.
Automatic Correction of Syntax Errors in SuperSQL Queries

SuperSQL is an extended language of SQL. By structuring the output of relational databases, SuperSQL enables the user to generate various types of structured documents with various layouts which are not represented in SQL. There is a problem that the larger and more complicated the SuperSQL query is, the more difficult it is to detect errors and the more time is spent on debugging. In this study, we propose a system that automatically detects and corrects syntax errors in user queries. When a query parsing fails, the system reanalyzes the query and predicts a correction by using deep learning. To modify the query, we use recurrent neural network and attention mechanism. By presenting the predicted modifications to users, the burden of debugging can be reduced and the efficiency of user's work can be improved.
Ecuadorian Higher Education in COVID-19: A Sentiment Analysis

COVID-19 is a major medical problem worldwide, but it also leads to educational problems. The aim of this work is to contribute with information about the feelings generated in university students and to know the main characteristics of Higher Education in Ecuador in times of pandemic. Specifically, the question What are the feelings of university students in times of the COVID-19? was answered. For this purpose, a quantitative, transversal and non-experimental research was carried out. Fifty-five unstructured anonymous interviews were conducted. It was applied to students from 16 Higher Education Institutions. Feelings were analyzed using techniques such as Latent Dirichlet Allocation (LDA), and through the computer programs MATLAB and NVIVO applied to the 500 phrases, obtained from 55 interviews with an average of 47 words per phrase. When carrying out the sentiment analysis, it was obtained that approximately 64% was negative, 11% neutral and 25% positive. LDA found that the 55 interviews were explained by 2 unobserved groups represented by the word clouds of topics 7 and 14. The unobserved groups show feelings such as stress, tiredness, problems and effort that may be related to the words people, evaluation, type, education and strength. This research can be complemented with a study that allows to deepen the type of negative, neutral and positive feelings and to determine their possible causes.
Efficient similarity search on multidimensional space of biometric databases

The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality . This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy).
Quantum Stochastic Neural Maps and Quantum Neural Networks

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