Reviewerly/exHarmony huggingface dataset:
Quantum Code Constructions

Quantum codes are fundamental to the error protection in quantum computers. Several families of good or optimal quantum codes were constructed by several researches over time [4, 17, 25, 27, 28, 30, 39, 45, 46, 54, 55, 62, 72–74, 77–81, 89, 91, 105, 106, 111, 138, 147, 148, 162, 165]. The Calderbank–Shor–Steane (CSS) construction is a remarkable technique to construct quantum codes derived from classical ones. As was said previously, such technique has been applied by a great number of quantum coding researchers in order to derive efficient quantum codes. With the possible advent of efficient quantum computers, it is extremely important to investigate how to obtain families of efficient quantum codes. Based on these facts, we present here some constructions of quantum codes derived from several classes of classical codes by means of the CSS construction. The classical codes utilized here are the well-known Bose–Chaudhuri–Hocquenghem (BCH) (Sects. 5.1, 5.2, 5.3), algebraic geometry codes (Sect. 5.4) and quantum synchronizable codes (Sect. 5.5).
Mobile Phone Forensics

Collection and analysis of digital evidence from mobile phones plays vital role to solve many civil and criminal cases. This evidence is a potential source of information which helps the prosecutors win more conviction. Sometime it also helps to crack organised crime and terrorist activities. Digital forensics expert need specialised tools and techniques to extract the evidence from mobile phones for analysis. Extracting the evidence from mobile phones in forensically sound manner is never been an easy task, as the entire process must ensure the integrity of evidence and its admissibility in the court of law. Rapid advancement in technology and frequent release of latest make and models always poses new challenges for the investigator. There are various tools and techniques available for mobile forensics, which are classified based on its complexity and its physical characteristics. Forensics examiner need to assess the complexity of the case and select the tools accordingly. This paper discusses in-detail about the systematic approach, which can be used for mobile forensics. Each approach has its own advantages, disadvantages, cost and complexities which is highlighted in the paper along with the list of standard tools and their key features.
Building User Interfaces with Piu

This chapter elaborates on Piu, the object-oriented user interface framework that uses the Poco renderer to draw and simplifies the process of creating sophisticated user interfaces. The chapter provides an overview of how Piu works and introduces its key capabilities through a series of examples.
Syntactic Parsing in Humans and Machines

To process the syntactic structures of a language in ways that are compatible with human expectations, we need computational representations of lexical and syntactic properties that form the basis of human knowledge of words and sentences. Recent neural-network-based and distributed semantics techniques have developed systems of considerable practical success and impressive performance. As has been advocated by many, however, such systems still lack human-like properties. In particular, linguistic, psycholinguistic and neuroscientific investigations have shown that human processing of sentences is sensitive to structure and unbounded relations. In the spirit of better understanding the structure building and long-distance properties of neural networks, I will present an overview of recent results on agreement and island effects in syntax in several languages. While certain sets of results in the literature indicate that neural language models exhibit long-distance agreement abilities, other finer-grained investigation of how these effects are calculated indicates that that the similarity spaces they define do not correlate with human experimental results on intervention similarity in long-distance dependencies. This opens the way to reflections on how to better match the syntactic properties of natural languages in the representations of neural models.
Desempenho de modelos de radiação solar para obtenção da evapotranspiração de referência para Santa Maria-RS, Brasil - DOI:10.5039/agraria.v15i1a7661

In the absence of observed data from Solar radiation (Sr), it is possible to estimate it using mathematical models. In general, the models vary in degree of complexity and the adjustment coefficients, and these must be calibrated to the place of interest to obtain the best performance. In this regard, the present work aims to evaluate the performance of simplified models of solar radiation in obtaining reference evapotranspiration. The data were obtained from the automatic meteorological station of the National Meteorological Institute (Inmet), located in Santa Maria – RS, Brazil. In the estimates of global solar radiation and its influence on reference evapotranspiration, ten models were evaluated, five models with the coefficients calibrated and five models, with the coefficients determined by the authors. It was observed that all the models studied presented performance index above 0.82, indicating “optimal” performance. Thus, both calibration and non-calibration models to obtain Sr do not interfere with ETo estimation. Therefore, given the impossibility of obtaining Sr data in meteorological stations, it is possible to use any of the models analyzed.
Performance of Advanced Scientific Computers for the Efficient Solution of an Elastic Wave Code for Seismic Modeling

The numerical analyst, in order to design and implement an efficient algorithm on modern computers, needs more than a cursory understanding of the architecture of the machine. The mathematical formulation of the physical problem and the subsequent numerical model may be better suited for one architecture than another. Careful consideration must be given not only to CPU speed but to many other factors such as number of processors or memory hierarchy. The physical domain is further complicated by interior interfaces across which the elastic parameters are discontinuous. The motivation for obtaining a numerical scheme with high performance on high-speed computer systems lies in the fact that geophysicists and seismologists typically are interested in problems whose physical domain covers 10,000 feet in the horizontal direction and 20,000 feet in the vertical direction. Linearized elastodynamics theory governs the response of a seismic structure to a surface or buried source.
Constructions of QCCs

This chapter is devoted to the construction of quantum convolutional codes with good or even optimal parameters (in the sense that the code parameters attain the generalized quantum Singleton bound). We begin by presenting the necessary background for the constructions, i.e., the class of classical convolutional codes.
The Role of Social Networks in the Adaptation of People with Disabilities

The relevance of the problem is due to the low activity of patient non-profit organizations in using the Internet space to involve people with disabilities in social and professional activities. The purpose of the study is to identify the main trends in interaction of people’s with disabilities in social networks and to develop methods for evaluating the effectiveness of managing their groups in social networks. Research methods include a quantitative analysis of the activity of groups in social networks engaged in promoting content for people with disabilities. Based on the results of the analysis of social networks content, a link was found between the nature of content posted in social groups of people with disabilities and their willingness to be engaged in communication, use social networks for socialization and professional self-determination. The result of the study was the development of a methodology for evaluating the communication formation process in social networks. This method allows determining the most effective ways to work in social networks that contribute to the formation of an active communication environment for people with disabilities.
Predicting the Concreteness of German Words

Concreteness of words has been measured and used in psycholinguistics already for decades. Recently, it is also used in retrieval and NLP tasks. For English a number of well known datasets has been established with average values for perceived concreteness.We give an overview of available datasets for German, their correlation and evaluate prediction algorithms for concreteness of German words. We show that these algorithms achieve similar results as for English datasets. Moreover, we show for all datasets there are no significant differences between a prediction model based on a regression model using word embeddings as features and a prediction algorithm based on word similarity according to the same embeddings.
Energy-Aware Path Planning for Autonomous Mobile Robot Navigation.

Battery life is yet one of the main limiting factors to a robot's total mission time, and efficient energy management is paramount in a robotic application. In this paper, we integrate energy awareness in the path planning of a mobile robot performing autonomous navigation. Our contributions are: 1) The formalization of a planning domain for mobile robot path planning which accounts for energy consumption and integrates energy actions in the generated plans; 2) A proof of concept of automatic path planning that avoids high energy areas in a known environment. We test our approach in simulation, extending an embedded computer's total battery discharge time by approximately 42.8%, and in a real ground mobile robot, achieving a mean energy draw reduction of 52.02%, both compared to conventional path planning.
MEERKATS BEHAVIOR MODELLING USING NETLOGO

This work studies the foraging behavior of Meerkat and the parameters for simulating Meerkats foraging behavior are designed. The designed parameters include the number of agents, number of groups, range of perception and number of foods. However, there are not many works done on Meerkats therefore, survey form is used in designing these 14 sets of parameters. Only the choices that have a higher percentage is focused on designing the 14 sets of parameters for the simulation. The performance of each 14 sets of the simulation are compared based on the result obtained from the simulations such as the highest mean quality of food can be achieved, and the time required to reach the highest mean quality of food. The higher the mean quality the better the performance. The smaller the number of ticks (simulation second) required to reach the highest mean quality the better the performance
Recognition Hydropower Stations from Remote Sensing Images by Multi-stage CNN Detection and Segmentation

Global energy Internet is China’s national strategy. However, due to the lack of public information of power energy infrastructure, it is difficult to investigate foreign power facilities through public information. Therefore, it is a feasible way to recognize power facilities through remote sensing images. In this paper, we aim to recognize hydropower stations efficiently and accurately from high-resolution remote sensing images. Traditional target detection methods need to extract target features from images manually, so the designed features are not very robust. In this paper, we propose a method to recognize hydropower stations by multi-stage DCNN. Concisely, our task includes two parts: (1) Preliminary detection of hydropower station. DCNN models are used as feature extractor to get a rough detection result of hydropower stations. (2) Extract water surface by segmentation. We utilize the spatial relationship between hydropower station and water surface to obtain the final recognition results. Compared with other detection methods of deep learning, our method uses multi-stage CNN to detect hydropower stations and water surface, respectively, and uses the spatial relationship between hydropower stations and water surface, so as to further improve the recognition accuracy. The results show that our proposed method has high accuracy and confidence.
To compress or not to compress? A Finite-State approach to Nen verbal morphology

This paper describes the development of a verbal morphological parser for an underresourced Papuan language, Nen.Nen verbal morphology is particularly complex, with a transitive verb taking up to 1, 740 unique features.The structural properties exhibited by Nen verbs raises interesting choices for analysis.Here we compare two possible methods of analysis: 'Chunking' and decomposition.'Chunking' refers to the concept of collating morphological segments into one, whereas the decomposition model follows a more classical linguistic approach.Both models are built using the Finite-State Transducer toolkit foma.The resultant architecture shows differences in size and structural clarity.While the 'Chunking' model is under half the size of the full decomposed counterpart, the decomposition displays higher structural order.In this paper, we describe the challenges encountered when modelling a language exhibiting distributed exponence and present the first morphological analyser for Nen, with an overall accuracy of 80.3%.
THE CLASSIFICATION OF PROMOTION MEDIA OF A COLLEGE TO IMPROVE THE MANAGEMENT OF PROMOTION FOR A PRIVATE COLLEGE IN THE EAST OGAN KOMERING ULU DISTRICT

Educational institutions are strategic sectors that can produce quality human resources where educational institutions take place quickly and dynamically. The development and progress of an educational institution are very dependent on the human resources that manage the institution. Promotion media is very important for the progress of an educational institution.  Promotion media is means used to introduce and communicate a product or brand to be known to the wider public. There are three categories of promotional media namely print media, electronic media, and online media. Based on the 2018 BPS, those interested in print and electronic media as a source of information were 25.53% while for online media in 2019 there were 64.8%. So that promotions can be carried out appropriately according to regional conditions, it is necessary to classify promotional media by region. This study aims to classify promotional media according to the region using the C45 algorithm method. The C45 algorithm is used as a decision tree classification model to predict promotional media that is suitable for the promotional target area. To form a decision tree, attributes needed are used as a reference for classification, in this study six attributes are used, namely the availability of internet services, the availability of electricity services, community work, community income, education, and access from the region to the district government.
Learning Higher-Order Programs through Predicate Invention

A key feature of inductive logic programming (ILP) is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce ILP techniques to learn higher-order programs. We implement our idea in Metagolho, an ILP system which can learn higher-order programs with higher-order predicate invention. Our experiments show that, compared to first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times.
What relations are reliably embeddable in Euclidean space

We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledge graphs, we obtain characterizations of which relations they are able to capture, as well as bounds on the minimal dimensionality and precision needed.
Analyzing the Linguistic Structure of Questions to Make Unanswered Questions Answered

There are many questions present on Question and Answer websites like quora.com, stackoverflow.com, mindthebook.com, yahoo.com, etc. which are open. There are many factors for which a question remains open; namely, subjectivity, vagueness, openendedness, ambiguity, etc. To find the reason why the question is open, there is a need to design an automatic framework that is designed to deal with the issues of question’s answerability. There are two types of questions: open questions which are unanswered and answered questions. We study the linguistic structure of question text on which we have to perform certain linguistic activities. There are 2 types of activities: first is “User-level” and second is “Question-level”. These two type of activities can give many discriminating factors to define the answerability of a question and also distinguishes the answered question from the unanswered one. The motivation of the current paper is to analyze the linguistic structure of the questions so that minimal changes to an unanswered question makes it answered.
Teaching Technical Journalism with an Engineering Foundation

Clear, impartial and engaging dissemination of engineering, technological and scientific endeavors is an important requirement for democratic societies. A massive amount of news and information is continuously available online - creating new challenges for journalists. We have developed a project-base bachelor course that introduces students to modern, professional technical journalism and advances their soft skills. Each student is required to create three features (i.e. two multimedia articles and one social media piece) that report on technology. Each created feature must contain an original multimedia component using state-of-the-art journalistic tools (e.g. videos, interactive graphics, animations). Those features that meet this requirement and fulfill journalistic standards are subsequently published online using a professional platform (i.e. WordPress). While writing in German is allowed, the use of English language is highly encouraged for crossing cultural boundaries and engaging the world-wide community. Students who are seeking a technical journalistic degree must also pass engineering courses. This concurrent technical journalism education is important for developing critical thinking and reporting. Furthermore the course increases gender-sensitivity in reporting, an important ongoing issue in a traditionally male dominated discipline.
Adaptive Knowledge Transfer based on Transfer Neural Kernel Network

Transfer agents are widely used in the challenging problems where knowledge is cross-used among different tasks. One popular research approach is to design a transfer kernel that controls the strength of knowledge transfer based on the similarity of tasks. In this paper, we propose a Transfer Neural Kernel Network (TNKN), which enables flexible modeling of the task similarity. The proposed TNKN is constructed by compositions of primitive kernels and represented by a neural network. Two coupled compositional kernel structures are used to characterize data covariance, one for the intra-task data covariance and another for the inter-task one. A sufficient condition that validates the transfer agent using TNKN for any data is given. This condition also discloses the relationship of the two compositional kernel structures, and can be used as a constraint in the agent learning. Since the overall architecture of TNKN is differentiable, the learning of the transfer agent using TNKN is end-to-end trainable with gradient-based optimization. Extensive experiments on various real-world datasets demonstrate the transfer effectiveness of TNKN.
A Study on the Multivariate Combined Control Chart

In this research, we propose multivariate combined control charts for the mean vector and covariance matrix to control simultaneously in the process under the normality assumption. For doing this, first of all, we review the multivariate simultaneous tests proposed by Park (2018) in some detailed fashion, which makes use of the likelihood ratio principle. Also we consider the simultaneous tests with various combining functions to combine individual tests. Then we propose to construct the multivariate combined charts by investigating the relation between the hypothesis test and control chart to utilize the multivariate simultaneous tests. We note that the proposed control charts will be consisted with a series of hypothesis tests instead of plotting the values of sample mean vector and covariance matrix in the control chart sheets. Then we investigate the efficiency among the proposed control charts by obtaining empirical average run length which represents the average runs until detecting out of control state when the process is out of control. Finally, we observe some interesting features related with the multivariate combined control charts.
A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains

In this work, we aim to build a unifying framework for relation extraction (RE), applying this on 3 highly used datasets with the ability to be extendable to new datasets. At the moment, the domain suffers from lack of reproducibility as well as a lack of consensus on generalizable techniques. Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.
Xiaomi’s Submissions for IWSLT 2020 Open Domain Translation Task

This paper describes the Xiaomi’s submissions to the IWSLT20 shared open domain translation task for Chinese<->Japanese language pair. We explore different model ensembling strategies based on recent Transformer variants. We also further strengthen our systems via some effective techniques, such as data filtering, data selection, tagged back translation, domain adaptation, knowledge distillation, and re-ranking. Our resulting Chinese->Japanese primary system ranked second in terms of character-level BLEU score among all submissions. Our resulting Japanese->Chinese primary system also achieved a competitive performance.
Access Control Mechanism for Cloud Data Using Block Chain and Proxy Re-Encryption

This paper studies block chain and proxy re-encryption methods in our system. Block chain provides transparent property and serves privacy and confidentiality to the system. The storage of block chain can be a barrier for implementing the records in the system. Moreover, it is difficult to store the large amount of data exceeding into the practical storage capabilities of the current block chain usages. The most important issues of block chain are the performance, limited storage, privacy, energy consumption and scalability. Using normal public-key encryption, User A (data owner) needs to download and decrypt the requested data and then re-encrypts data using the target user’s public key. This process requires extra computation cost and communication to the data. Another solution for this is to allow data owners to define access policies and encrypt the data with encryption under the access policies; only authenticated users whose keys are matching their policies can decrypt the ciphertext. But the thing is here data owner needs to download, decrypt, and re-encrypt the requested data in case data access policies change dynamically and frequently. To overcome these drawbacks, in this paper our system proposed a proxy re-encryption scheme that gives solutions for securing data and sharing in cloud computing environment. This system will reduce communication cost and extra computational cost.
Programming in MicroPython

Now that we have a basic understanding of the XBee modules, we can learn more about programming in MicroPython—a very robust and powerful language that you can use to write very powerful applications. Mastering MicroPython is very easy, and some may suggest it doesn’t require any formal training to use. This is largely true, and thus you should be able to write MicroPython scripts with only a little bit of knowledge about the language.