Reviewerly/exHarmony huggingface dataset:
The Numbers of De Bruijn Sequences in Extremal Weight Classes

In this paper, we analyze the weight class distribution of de Bruijn sequences. The main tool we use is the generating function theory, proposed recently by Coppersmith et al. By analyzing the weights of cycles generated by the pure circulating register, we give explicit formulas for the numbers of de Bruijn sequences in the extremal weight classes. Moreover, we use these formulas to prove some conjectures proposed by Fredricksen and Mayhew, which seems have been opened for a long time. In addition to these theoretical results, some experimental results are also provided.
On Readability Metrics of Goal Statements of Universities and Brand-Promoting Lexicons for Industries

The statements for vision and mission act as a guide for creating objectives and goals in the organization and to provide a path for individuals. As both of these statement documents are meant to be read by external stakeholders and are supposed to present an image of the organization, it is imperative that they are easily comprehended by the reader. Further, mission statements are required to be designed considering in line with the vision statements. The current paper presents the results of the work on the readability metrics of the vision and mission statements of well-known 25 universities of India. This work recommends about vision and mission statements formulation for emerging universities. Additionally, the readability metrics for mission statements for more than 100 well-known corporate houses were also calculated and analyzed. Further, most commonly used lexicons were also found to be used for branding purposes using Term Frequency–Inverse Document Frequency (TF-IDF) through text mining. This paper advocates the correlation between the found lexicons and the revenues generated by the considered companies. Technically, Pearson’s correlation coefficient and Flesch Readability Index (FRI) are deployed for the calculation of various metrics to form the basis of our conclusions.
Post-Non-Classical Pedagogical Research Strategies

The article reveals the essence of the concept “methodological strategy of pedagogical research”, focuses attention on the potential of polyparadigmin pedagogical research, the alternative of scientific ideals, methodological strategies, approaches, methods, criteria for solving a specific scientific and pedagogical problem, the potential polyconceptuality – the variability of pedagogical concepts (values, educational goals), content and technologies, development scenarios and models of educational systems of the future. The hierarchy of methodological concepts (paradigm, strategy, approach) is presented, as well as the author’s typology of potential strategies for post-non-classical pedagogical research, including into monodisciplinary, interdisciplinary and transdisciplinary methodological strategies, which differ in methodological guidelines, type of reflection and the “nature” of the methodological sources of research. The transdisciplinary strategy of pedagogical research implies interpretation of education as a complex multidimensional phenomenon characterized by multifactorial, non-linear development; centering on the “human dimension” of education. The implementation of the transdisciplinary methodological strategy is represented as the result of convergence of pedagogy with culture and science. Designing a methodological research strategy demands to construct a system of methodological approaches, defining a system-forming approach, and ensuring the functional complementarity of approaches. In this case the attention of the scientific community is focused on the specificity and functional complementarity of interdisciplinary, transdisciplinary, humanitarian, technological methodological strategies in the context of specific pedagogical research as an imperative of post-non-classical pedagogical science.
Improved Sentiment Urgency Emotion Detection for Business Intelligence

The impact of social media on people’s lives has significantly grown over the last decade. Individuals use it to promote discussions and a way of acquiring data. Industries use social media to market their goods and facilities, advise and inform clients about future offers, and follow up on their direct market. It also offers vital information concerning the general emotions and sentiments directly connected to welfare and security. In this work, an improved model called Improved Sentiment Urgency Emotion Detection (ISUED) has been created based on previous work for opinion and social media mining implemented with Multinomial Naive Bayes algorithm and based on three classifiers which are sentiment analysis, urgency detection, and emotion classification. The model will be trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is elevated. This model will be applied on the same data set of previous work acquired from a general business Twitter account of one of the largest chains of supermarkets in the United Kingdom to be able to see what sentiments and emotions can be detected and how urgent they are.
A trusted-node-free eight-user metropolitan quantum communication network

Complex quantum networks are vital for quantum technologies. Entanglement based networks are the most versatile in terms of supported applications (including beyond just quantum communication). Here we present an 8 user city wide entanglement network.
DETECTION OF LAND COVER DISPLACEMENTS THROUGH TIME-SERIES ANALYSIS OF MULTISPECTRAL SATELLITE IMAGERY: APPLICATION TO DESERT

Abstract. Detection of changes occurring on Earth surface has become an established practice in remote sensing science since the availability of high-resolution, global coverage, and multitemporal satellite imagery that has constantly increased during the last decades. Meanwhile, the open data policies embraced by some of the principal Earth Observation programs have boosted the spread of such analyses. This asset has attracted also the interest of the private sector which is developing strategies to exploit the potential of these data. In view of the above, we present an experimental procedure to investigate land cover displacements through an application on open multispectral imagery from the Landsat 8 and Sentinel-2 missions for desert sand dunes movements analysis. While most of the change detection techniques focus on locating changes and describing them by means of variation in the pixel spectral responses, the proposed technique aims at describing spatial and temporal patterns of displacements (i.e. directions and magnitude) applying cross-correlation analysis on a multitemporal images stack. Results of the proposed analysis are critical to a number of construction engineering operations that require sand mitigation planning and continuous site monitoring to prevent windblown sand interactions with infrastructures in the desert environment. An overview of the preliminary results is presented together with an extensive discussion on further improvements requested by the procedure. Computational steps leverage exclusively open data and free and open source GIS software thus providing large rooms to empower, replicate and improve thereof.
Boolean Role Inclusions in DL-Lite With and Without Time

Traditionally, description logic has focused on representing and reasoning about classes rather than relations (roles), which has been justified by the deterioration of the computational properties if expressive role inclusions are added. The situation is even worse in the temporalised setting, where monodicity is viewed as an almost necessary condition for decidability. We take a fresh look at the description logic DL-Lite with expressive role inclusions, both with and without a temporal dimension. While we confirm that full Boolean expressive power on roles leads to FO^2-like behaviour in the atemporal case and undecidability in the temporal case, we show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to much lower complexity in the atemporal case and to decidability (and ExpSpace-completeness) in the temporal case, even if one admits full Booleans on concepts. The latter result is one of very few instances breaking the monodicity barrier in temporal FO. This is also reflected on the data complexity level, where we obtain new rewritability results into FO with relational primitive recursion and FO with unary divisibility predicates.
Study on the Multi-Task Model for Legal Judgment Prediction

In this study, the multi-task learning(MTL) classification method based on CNN-BiGRU model is proposed, which is used to improve the accuracy and efficiently of legal judgment prediction. The subtasks of legal judgment prediction are law artivles, charges and the terms of penalty. However, the single task learning(STL) models are used to analyze legal documents, which ignoring the correlation among the subtasks. The MTL model of CNN-BiGRU enhance the task learning process, which can extract the shared information among subtasks and learn multiple tasks at the same time. Therefore, in view of the shorcomings of STL, this study explored the affilication of the MTL method to predict the three subtasks of legal judgment. CNN-BiGRU has combined the good extraction ability of CNN for local feature information and RNN for longterm dependencies information of the text classification. Compared with the CAIL2018-Small dataset, the accuracy and F1-score are the highest of all baselines models. The accuracy and F1-score of the law articles, charges and the terms of penalty are 95.1%,95.2%,72.6% and 95.2%, 95.4%, 72.7%, respectively. The proposed model improves the interpretability and the gneralization ability. The effectiveness and suitability of the model are validated on legal judgment prediction tasks.
Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation

Segmentation of cell nuclei in fluorescence microscopy images provides valuable information about the shape and size of the nuclei, its chromatin texture and DNA content. It has many applications such as cell tracking, counting and classification. In this work, we extended our recently proposed approach for nuclei segmentation based on deep learning, by adding to its input handcrafted features. Our handcrafted features introduce additional domain knowledge that nuclei are expected to have an approximately round shape. For round shapes the gradient vector of points at the border point to the center. To convey this information, we compute a map of gradient convergence to be used by the CNN as a new channel, in addition to the fluorescence microscopy image. We applied our method to a dataset of microscopy images of cells stained with DAPI. Our results show that with this approach we are able to decrease the number of missdetections and, therefore, increase the F1-Score when compared to our previously proposed approach. Moreover, the results show that faster convergence is obtained when handcrafted features are combined with deep learning.
Toward Digital Twins Based Marine SCADA System

As a software representation of assets and processes, digital twins is valuable for the design of future marine monitoring and control systems. Such a system usually requires the integration of technologies from cloud and edge, WebAccess/SCADA and SaaS Composer from Advantech Technology were adopted in our proposed framework for ships running in coastal waters and a digital twins based marine monitoring application was given in the paper. The framework is compatible with various network access technologies, including near shore oriented cellular communication and off shore oriented satellite communication. The focus of this paper is on how to realize the virtual modeling and innovated application with marine monitoring data and its universality can derive to more promising applications.
Using Serious Games and Motion Tracking for Physical Rehabilitation

Tele-rehabilitation has evolved significantly in recent years thanks to the latest advances and low cost of the 3D movement acquisition technologies. Autonomous physical rehabilitation from home through the use of serious games requires measuring angles of interest and compensatory angles of the patients when they perform the exercises. this work has been focused in creating a game with an architecture based on the Unity 3D engine to support rehabilitation activities. It encompasses the acquisition of an articulated skeleton, the customization of a character (avatar) in friendly scenes, and the compute of the required angles to evaluate a set of exercises.
Multilingual Dependency Parsing from Universal Dependencies to Sesame Street

Research on dependency parsing has always had a strong multilingual orientation, but the lack of standardized annotations for a long time made it difficult both to meaningfully compare results across languages and to develop truly multilingual systems. The Universal Dependencies project has during the last five years tried to overcome this obstacle by developing cross-linguistically consistent morphosyntactic annotation for many languages. During the same period, dependency parsing (like the rest of NLP) has been transformed by the adoption of continuous vector representations and neural network techniques. In this paper, I will introduce the framework and resources of Universal Dependencies, and discuss advances in dependency parsing enabled by these resources in combination with deep learning techniques, ranging from traditional word and character embeddings to deep contextualized word representations like ELMo and BERT.
SuDocu

Text document summarization refers to the task of producing a brief representation of a document for easy human consumption. Existing text summarization techniques mostly focus on generic summarization, but users often require personalized summarization that targets their specific preferences and needs. However, precisely expressing preferences is challenging, and current methods are often ambiguous, outside the user's control, or require costly training data. We propose a novel and effective way to express summarization intent (preferences) via examples : the user provides a few example summaries for a small number of documents in a collection, and the system summarizes the rest. We demonstrate SuDocu, an example-based personalized Document Summarization system. Through a simple interface, SuDocu allows the users to provide example summaries, learns the summarization intent from the examples, and produces summaries for new documents that reflect the user's summarization intent. SuDocu further explains the captured summarization intent in the form of a package query , an extension of a traditional SQL query that handles complex constraints and preferences over answer sets. SuDocu combines topic modeling, semantic similarity discovery, and in-database optimization in a novel way to achieve example-driven document summarization. We demonstrate how SuDocu can detect complex summarization intents from a few example summaries and produce accurate summaries for new documents effectively and efficiently.
Almost Optimal Distribution-Free Sample-Based Testing of k-Modality

For an integer k ≥ 0, a sequence σ = σ₁,… ,σ_n over a fully ordered set is k-modal, if there exist indices 1 = a₀ < a₁ < … < a_{k+1} = n such that for each i, the subsequence σ_{a_i},… ,σ_{a_{i+1}} is either monotonically non-decreasing or monotonically non-increasing. The property of k-modality is a natural extension of monotonicity, which has been studied extensively in the area of property testing. We study one-sided error property testing of k-modality in the distribution-free sample-based model. We prove an upper bound of O({√{kn}log k}/e) on the sample complexity, and an almost matching lower bound of Ω(√{kn}/e). When the underlying distribution is uniform, we obtain a completely tight bound of Θ(√{kn/e}), which generalizes what is known for sample-based testing of monotonicity under the uniform distribution.
Experiments in Lifelog Organisation and Retrieval at NTCIR

Gurrin, Cathal Joho, Hideo Hopfgartner, Frank Zhou, Liting Albatal, Rami Healy, Graham Nguyen, Duc-Tien DangLifeloggingLifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented.
Interlinking through Lemmas. The Lexical Collection of the LiLa Knowledge Base of Linguistic Resources for Latin

This paper presents the structure of the LiLa Knowledge Base, i.e. a collection of multifarious linguistic resources for Latin described with the same vocabulary of knowledge description and interlinked according to the principles of the so-called Linked Data paradigm. Following its highly lexically based nature, the core of the LiLa Knowledge Base consists of a large collection of Latin lemmas, serving as the backbone to achieve interoperability between the resources, by linking all those entries in lexical resources and tokens in corpora that point to the same lemma. After detailing the architecture supporting LiLa, the paper particularly focusses on how we approach the challenges raised by harmonizing different strategies of lemmatization that can be found in linguistic resources for Latin. As an example of the process to connect a linguistic resource to LiLa, the inclusion in the Knowledge Base of a dependency treebank is described and evaluated.
Interoperating Deep Learning models with ONNX.jl

Flux [17] is a machine learning framework, written using the numerical computing language Julia [4].The framework makes writing layers as simple as writing mathematical formulae, and it's advanced AD, Zygote [11] , applies automatic differentiation (AD) to calculate derivatives and train the model.It makes heavy use of Julia's language and compiler features to carry out code analysis and make optimisations.For example, Julia's GPU compilation support [3] can be used to JIT-compile custom GPU kernels for model layers [19].Flux also supports a number of a hardware options, from CPUs, GPUs and even TPUs via XLA.jl, that compiles Julia code to XLA: an advanced compiler for linear algebra that is capable of greatly optimizing speed and memory usage in large deep learning models.ONNX.jl is an Open Neural Network Exchange backend for the Flux.jldeep learning framework.ONNX.jl supports directly importing high quality ONNX standard models into Flux, thus saving time and reducing the need for additional computation resources.This paper aims at introducing ONNX.jl and explaining how it fits into the bigger picture: How we can use the Julia Language, specifically Flux.jl and ONNX.jl as a starting for high quality transfer learning of large deep learning models.
Experimenting with Different Machine Translation Models in Medium-Resource Settings

State-of-the-art machine translation (MT) systems rely on the availability of large parallel corpora, containing millions of sentence pairs. For the Icelandic language, the parallel corpus ParIce exists, consisting of about 3.6 million English-Icelandic sentence pairs. Given that parallel corpora for low-resource languages typically contain sentence pairs in the tens or hundreds of thousands, we classify Icelandic as a medium-resource language for MT purposes. In this paper, we present on-going experiments with different MT models, both statistical and neural, for translating English to Icelandic based on ParIce. We describe the corpus and the filtering process used for removing noisy segments, the different models used for training, and the preliminary automatic and human evaluation. We find that, while using an aggressive filtering approach, the most recent neural MT system (Transformer) performs best, obtaining the highest BLEU score and the highest fluency and adequacy scores from human evaluation for in-domain translation. Our work could be beneficial to other languages for which a similar amount of parallel data is available.
Omissions in Constraint Acquisition

Interactive constraint acquisition is a special case of query-directed learning, also known as “exact” learning. It is used to assist non-expert users in modeling a constraint problem automatically by posting examples to the user that have to be classified as solutions or non-solutions. One significant issue that has not been addressed in the literature of constraint acquisition is the possible presence of uncertainty in the answers of the users. We address this by introducing Limited Membership Queries, where the user has the option of replying “I don’t know”, corresponding to “omissions” in exact learning. We present two algorithms for handling omissions. The first one deals with omissions that are independent events, while the second assumes that omissions are related to gaps in the user’s knowledge. We present theoretical results about both methods and we evaluate them on benchmark problems. Importantly, our second algorithm can not only learn (a part of) the target network, but also the constraints that cause the user’s uncertainty.
Differential Evolution Algorithm for Multiple Inter-dependent Components Traveling Thief Problem

Differential evolution was mainly proposed for solving optimization problems with continuous decision variables because of its Euclidean distance-based learning concept. This made it unsuitable for many binary and discrete problems. However, several studies approved the applicability of differential evolution algorithm for effectively solving such problems. In this paper, a new design of differential evolution, which incorporates mapping and repairing methods, modified mutation operator and local searches, is proposed to solve the complex multicomponents traveling thief problems that are characterized by both binary and discrete parameters. Also, a novel initialization and repairing method, which enables differential evolution's operators to only evolve solutions of one component and optimally distribute/update the solutions of the other one with considering the inter-dependency between both components, is introduced. To judge the performance of the proposed algorithm, 13 strongly correlated instances of traveling thief problems have been solved and the results have been compared with those from 24 selfdesigned and state-of-the-art algorithms. Results demonstrated the competitive performance of the proposed algorithm in terms of the quality of obtained solutions and computational time.
Cryptocurrency Transaction Processing

In a cryptocurrency network such as Bitcoin or Helium, the fundamental unit of interaction is a transaction where value is transferred from one entity to another. In this type of network, the blockchain is an immutable, distributed ledger of all of the transactions that have transpired on the network. Value is transferred from one public address to another public address. We can examine and enumerate all of the transactions associated with a public address by scanning the entire blockchain for transactions that involve this address. In fact, if we scan the blockchain forward from the genesis block, then the last transaction involving this address also provides the cryptocurrency balance which is associated with this address.