Reviewerly/exHarmony huggingface dataset:
Two Types of Verb Reduplications in Mandarin Chinese

Abstract This paper analyzes verb reduplication in Mandarin Chinese under a lexicalist framework. By adopting the Lexicalist Hypothesis proposed by Chomsky (1970), a distinction has been made between syntactic and morphological verb reduplications by means of five tests: productivity, le insertion, categorial stability, transitivity, and input/output constraints. It is found that the AA and ABAB patterns of verb reduplication have relatively high productivity and regular syntactic behaviors, whereas the AABB pattern of verb reduplication shows extremely low productivity and syntactic idiosyncrasy. Given these observations, this paper proposes that the AA and ABAB patterns should be syntactic verb reduplications derived at the syntactic level, whereas the AABB pattern should be morphological verb reduplication formed in the lexicon. The two types of verb reduplications have different generative mechanisms.
State Estimation with Event-Based Inputs Using Stochastic Triggers

Event-based communication and state estimation offer the potential to improve resource utilization in networked sensor and control systems significantly. Sensor nodes can trigger transmissions when data are deemed useful for the remote estimation units. To improve the estimation performance, the remote estimator can exploit the implicit information conveyed by the event trigger even if no transmission is triggered. The implicit information is typically incorporated into the measurement update of a remote Kalman filter. In this paper, event-triggered transmissions of input data are investigated that enter the prediction step of the remote estimator. By employing a stochastic trigger, the implicit input information remains Gaussian and can easily be incorporated into the remote Kalman filter. The proposed event-based scheme is evaluated in remote tracking scenarios, where system inputs are transmitted aperiodically.
A web platform to support mentoring programs in higher education

Mentoring Academy is a program which aims to integrate students of the Instituto Politécnico de Braganca in the academy through peer mentoring and peer tutoring. This program needed an application that permits its execution. Motivated by this demand, we aimed at proposing a web application that complies with the Mentoring Academy requirements. The system requirements were defined together with the program stakeholders. Based on the literature review, we opted for the technologies ASP.NET Core and MySQL, for the backend, and Angular 6, for the frontend. Subsequently, the system architecture was planned. SCRUM methodology was used in the application development, with biweekly meetings with the stakeholders to evaluate what was done and to define the efforts for the following fortnight. Eighteen features were developed and four were presented in this paper. Finally, the system was deployed on a Linux server provided by the Instituto Politecnico de Braganca, allowing it to be tested and later used by the Mentoring Academy users. Approximately 61% of the functional requirements defined in the system modeling were developed (23 out of 38) and some functionalities not previously defined were also implemented. The units tests developed for the web application proved promising in terms of components generation and HTTP request services, both exception-free. That said, the system provides students' integration, permitting them to have socio-academic inclusion and support in curricular units.
Identifying Artificial Intelligence Pathfinding Algorithms for Platformer Games

Pathfinding has been a major research area in video games for decades. It is usually utilised as the core of any Artificial Intelligence movement system in computer games. This paper reviews the pathfinding algorithms employed in the platformer genre of video games and focuses on determining better-suited algorithms to be used. In order to determine the algorithms used in platformer games, first, we need to determine the existing pathfinding algorithms that are currently in use today. Then, the algorithms are narrowed down to ones that are most widely used due to their advantages over other algorithms. The findings indicate that A* and Djiktra are the most suitable algorithms to be used. However, past researches did not include vertical movements in 2D platformers, thus, it raises further questions concerning how much of a difference in terms of efficiency will be seen if these algorithms are tested in that particular environment.
On the use of positive definite symmetric kernels for summary extraction

The task of creating a short, accurate and fluent summary starting from a larger text document or group of documents is called text summarization. When the summary is generated by extracting parts from the original text, the process is known as extractive summarization. This work is focused on the use of convex optimization and positive defined symmetric kernels for the extractive summarization of a text. The paper includes two new contributions. First, we show how the kernel method can be used for unsupervised extractive summarization. Second, we investigate empirically the performance of different kernel functions with respect to quality and execution time.
Tractable Lexical-Functional Grammar

The formalism for Lexical-Functional Grammar (LFG) was introduced in the 1980s as one of the first constraint-based grammatical formalisms for natural language. It has led to substantial contributions to the linguistic literature and to the construction of large-scale descriptions of particular languages. Investigations of its mathematical properties have shown that, without further restrictions, the recognition, emptiness, and generation problems are undecidable, and that they are intractable in the worst case even with commonly applied restrictions. However, grammars of real languages appear not to invoke the full expressive power of the formalism, as indicated by the fact that algorithms and implementations for recognition and generation have been developed that run—even for broad-coverage grammars—in typically polynomial time. This article formalizes some restrictions on the notation and its interpretation that are compatible with conventions and principles that have been implicit or informally stated in linguistic theory. We show that LFG grammars that respect these restrictions, while still suitable for the description of natural languages, are equivalent to linear context-free rewriting systems and allow for tractable computation.
Adaptive Complex Word Identification through False Friend Detection

Automated complex word identification (CWI) is a crucial task in several applications, from readability assessment to lexical simplification. So far, several works have modeled CWI with the goal of targeting the needs of non-native speakers. However, studies in language acquisition show that different native languages can create positive or negative interferences w.r.t. reading comprehension, favouring or hindering the understanding of a document in a foreign language. Therefore, we propose to modify CWI to address the specific difficulties connected to different native languages. In particular, we present a pipeline that, based on the user native language, identifies complex terms by automatically detecting cognates and false friends on the fly. The selection presented by the CWI module is adaptive in that it changes depending on the native language of the user. We implement and evaluate our approach for four different native languages (French, English, German and Spanish), in a setting where documents are written in Italian and should be read by language learners with low proficiency. We show that a personalised strategy based on false friend detection identifies complex terms that are different from those usually selected with standard approaches based on word frequency.
Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation

Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search and recommendation. Traditional methods for modeling sequential user behaviors usually depend on the premise of Markov processes, while recently recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors. Specifically, we design a network featuring A ttention with L ong-term I nterval-based G ated R ecurrent U nits (ALI-GRU) to model temporal sequences of user actions. Compared to previous works, our network can exploit the information of temporal dimension extracted by time interval-based GRU in addition to normal GRU to encoding user actions and has a specially designed matrix-form attention function to characterize both long-term preferences and short-term intents of users, while the attention-weighted features are finally decoded to predict the next user action. We have performed experiments on two well-known public datasets as well as a huge dataset built from real-world data of one of the largest online shopping websites. Experimental results show that the proposed ALI-GRU achieves significant improvement compared to state-of-the-art RNN-based methods. ALI-GRU is also adopted in a real-world application and results of the online A/B test further demonstrate its practical value.
Weak Supervision Learning for Object Co-Segmentation

The booming of multimedia technologies has promoted the diversity of visual big data. To learn common features across heterogeneous image data, the image co-processing has exhibited its advantages over the separate one. Recently, an active topic of image co-processing is the object co-segmentation, which aims at simultaneously extracting and segmenting shared objects from relevant images. In this paper, we address this problem with a weak-supervision-based probabilistic model. We introduce the weakly supervised priors to alleviate the confusion between common foreground and background, thereby facilitating performance improvement. To ensure the validity of potential background prior knowledge, the nodes on four sides of image are respectively leveraged as the labelled queries. After that, we develop quantitative probabilistic metrics for precisely measuring internal consistencies within single image and correlations between multiple images. Combining the intra-image consistencies with the inter-image correlations, we propose an optimized energy function coupled with binary labeling and graph connectivity to carry out the object co-segmentation. Extensively experimental results on real-world datasets demonstrate that the proposed method achieves superior co-segmentation performance to the state-of-the-arts, with a significantly reduced time consumption.
Access to Empirical Formulations of Combustion Efficiency for Gas Turbine Combustors With Improved Generalization Ability

Abstract Correlating combustion performance parameters to the main operating variables of combustors with mathematical expressions contributes to reducing the number of experiments and simplifying the design procedure of gas turbines. The application of empirical formulations meets the requirement with finite precision. The present study aims at adopting symbolic regression method to establish empirical formulations to correlate combustion efficiency with the main operating variables of gas turbine combustors. Differing from ordinary data modeling methods that search model parameters only with model structures fixed, symbolic regression method can search the structures and parameters of mathematical models simultaneously. In this article, attempts to correlate the experimental data of Combustor I using the mechanism model of burning velocity model, neural network, polynomial regression, and symbolic regression are shown sequentially. Burning velocity model has not satisfactory accuracy by comparing the predictions with the experimental data which means its lower generalization ability. Comparatively, the predictions of the empirical formulation obtained by the present symbolic regression method are in good agreement with the experimental data and also excel those of neural network and polynomial regression in generalization ability. Another two formulations are obtained by symbolic regression using the experimental data of Combustor II and III, and the different model structures of the two formulations indicate that there is still room for improvement in the present method.
Envisioning the Digital Transformation of Financial Documents: A Blockchain-Based Bill of Exchange

A Bill of Exchange (BoE) is a paper-written contract involving three parties A, B and C where A is economically in debt with B and in credit with C. Once the parties approve a BoE, C is legally bound to pay B on behalf of A within a set deadline, so that the debt of A towards B is extinguished. Although regarded as an elegant and powerful variant of a promissory note, over time the BoE has become unpractical to use in a global market where suppliers and customers aren’t next-door companies anymore. On the other hand, the blockchain distributed ledger, AES authentication, and digital archiving with suitable long-period standards (e.g., PDF/A) may encourage the revival of such an instrument, while ensuring legal validity, strength and a non-tampering warranty. This paper exploits said state-of-the-art technologies to bring the paper-based BoE into the digital era as the DigiBoE. Its envisioned applications are B2B, C2C and B2C secure and legally acknowledged transactions for debt resolution no longer requiring financial intermediaries.
Securing Text Messages using the Beaufort-Vigenere Hybrid Method

Abstract Beaufort and Vigenere are fast and strong substitution encryption methods against brute force attacks if they have good key quality. But both of these methods by default can not produce a good avalanche effect. Avalanche effect is a measuring tool that can determine the strength of encryption from differential attacks. This research proposes hybrid techniques of Beaufort and Vigenere algorithms to optimize the security of text message encryption by modifying key generators. With this method, the encryption process can be done by input text messages and keys. The key is processed with a key generator to produce two new keys that are used for encryption using the Beaufort and Vigenere hybrid methods. Based on the test results, the Avalanche effect value rose significantly compared to the standard method, and the value of the avalanche effect was more stable and close to ideal compared to the previous method. The time required is also relatively very fast for the encryption or decryption process, experimental result shows it takes less than 0.1 seconds for more than 43 thousand of characters in the text message.
The Statistical Independence for Words in Printed Romanian Language

The paper revisits the notion of statistical independence for printed Romanian when the language is considered as a chain of words. The analysis is carried out on a literary corpus of approx. 6 million words. We aim to improve the perception of the concept of statistical independence for natural texts and to use this concept to evaluate the numerical properties of the printed language. One main objective is to estimate the minimum distance in words that ensures statistical independence.Here, we followed up on an idea previously researched by the authors - the investigation of statistical independence for m-grams (m successive letters). The previous results showed that 100 characters are enough to ensure statistical independence for letter m-grams (m = 1, 2, 3) either for the 32-symbol corpus or when the 47-symbol corpus was analyzed. In the present research, we could notice that 100 words can be considered practically enough for the minimum statistical independence sampling distance. As there is a huge number of distinct words to be considered, detailed investigations have been conducted regarding the creation of one or more Artificial Words consisting of groups of the low probability words (based on previous findings on the type II statistical error in word probability investigation) and the results support the above-mentioned minimum statistical independence distance.
Robotic Process Automation as a Precursor to e-Government in the Fourth Industrial Revolution

A lot of research has been done focusing on adoption of e-Government by individuals and businesses in the different parts of the world. Despite that being the case, there is a clear gap in understanding the different nuances of ubiquitous e-Government especially in the realm of the Fourth Industrial Revolution (4IR) where automation and intelligence is emphasized. In Africa and other resource-constrained environments, there is a clear knowledge void with regards to unique challenges faced when attempting to design and implement contemporary e-Government. The chapter is anchored on descriptive informetrics as a philosophical orientation. This orientation enables it to employ a detailed bibliographic analysis of work published in scholarly and policy outlets. This enables it to ground the discussion using both formulaic and conceptual definitions of key concepts explored. This chapter is as a result of continuous research in e-Government that has been going on for the past decade. This research intended to explore the emerging dimensions of e-Government in the realm of 4IR. The chapter discusses the emerging model of public service delivery given the growing enshrinement of 4IR in public service delivery platforms. Finally, a conceptual framework that can be used as a blueprint in the design of automated public services especially considering Robotic Process Automation (RPA) is proposed. Fundamental concepts leading to the understanding of 4IR, AI and RPA are adequately explored in the chapter.
TauJud: test augmentation of machine learning in judicial documents

The booming of big data makes the adoption of machine learning ubiquitous in the legal field. As we all know, a large amount of test data can better reflect the performance of the model, so the test data must be naturally expanded. In order to solve the high cost problem of labeling data in natural language processing, people in the industry have improved the performance of text classification tasks through simple data amplification techniques. However, the data amplification requirements in the judgment documents are interpretable and logical, as observed from CAIL2018 test data with over 200,000 judicial documents. Therefore, we have designed a test augmentation tool called TauJud specifically for generating more effective test data with uniform distribution over time and location for model evaluation and save time in marking data. The demo can be found at https://github.com/governormars/TauJud.
A Secure Screen and App Lock System for Android Smart Phones Using Face Recognition

Mobile operating systems pervade our modern lives as we interact with these almost every day. Security and privacy are of particular concern on these systems, as they have access to a wide range of sensitive resources and can access private data. While just using the Settings app on iOS smart phones you can both secure the access to the screen and installed apps, in Android smart phones you can only lock the screen. For locking the apps, you must download a special application from a third-party store like Google Play. The objective of this paper is to emphasize the importance of having only one application which secures access to both the screen and the apps on Android operating systems using facial recognition. We created an application that can restrict the access to the installed apps in the system, control them remotely through a web platform and lock the screen. All of these ensure complete security, beginning from the screen, the main gateway that offers access to the phone, to the installed applications that may contain important information. Thus, using a single application, users can secure the entire phone without needing to use the phone settings.
Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .
Smart Grid Data Security using Practical CP-ABE with Obfuscated Policy and Outsourcing Decryption

Smart grid consists of multiple different entities related to various energy management systems which share fine-grained energy measurements among themselves in an optimal and reliable manner. Such delivery is achieved through intelligent transmission and distribution networks composed of various stakeholders like Phasor Measurement Units (PMUs), Master and Remote Terminal Units (MTU and RTU), Storage Centers and users in power utility departments subject to volatile changes in requirements. Hence, secure accessibility of data becomes vital in the context of efficient functioning of the smart grid. In this paper, we propose a practical attribute-based encryption scheme for securing data sharing and data access in Smart Grid architectures with the added advantage of obfuscating the access policy. This is aimed at preserving data privacy in the context of competing smart grid operators. We build our scheme on Linear Secret Sharing (LSS) Schemes for supporting any monotone access structures and thus enhancing the expressiveness of access policies. Lastly, we analyze the security, access policy privacy and collusion resistance properties of our cryptosystem and provide an efficiency comparison as well as experimental analysis using the Charm-Crypto framework to validate the proficiency of our proposed solution.
Risk Analysis Based On Design Version Control Data

Early identification of areas that are at-risk in a design under development can help design and verification teams take early preventive and corrective measures. We propose a novel system that predicts which files are at risk of being buggy. Our simple, machine learning-based, regression system receives data from various sources, including the version control and defect tracking tools. After automatically labeling historic data and training sets, the system predicts the risk level of each file. Experimental results show that the proposed system has fairly high positive precision and recall and that its predictions are better than trivial predictions, such as files that recently changed or had bugs.