Reviewerly/exHarmony huggingface dataset:
MediBot: An Ontology-Based Chatbot to Retrieve Drug Information and Compare its Prices

In this article, we present the MediBot. MediBot is a chatbot for querying drugs information. The presented system acted as a single access point for natural and simplified information retrieval of drugs, prices, and its risks. The chatbot has two modes of operation: Quick Response and Interactive modes. The first answers questions asked in natural language, while the second has three interactive tasks, namely Browser, Query, and Price Comparison. We present here the system architecture, the Linked Data Mashup’s construction process, and Chatbot MediBot’s activities modes, focusing on the new Price Comparison’s task. This task presents the best prices for medicines and their best potential substitutes extracted in real-time from the Web with the help of the information obtained from a linked data mashup.
Financial System Development in Emerging Economies: An Array of MINT Economic Bloc

Financial system development in emerging economies precipitates emancipation of her economic throes in global competitive environment, as developing economies strives to foster economic integration beyond regional and continental organizations by forming economic blocs.Aside mutual economic policy, economic transformation of emerging markets hugged on her establishment and expansion of financial institutions, instruments and markets to facilitate multilateral trade, economic growth processes and sustainability.However, financial innovations continually brought colossal dynamism in development of the financial system which poses as a global phenomenon for virile economy.As the matrix of financial system development and economic performance has been intellectually explored, relegating the array of financial system development indicators in the contemporary issues with no attention on the presumable symbiotic dispositions.Consequently, this study investigates the level of financial system development in Mexico, Indonesia, Nigeria and Turkey (MINT) economic bloc. Literature ReviewThe critical role of financial sector spurred economies across the globe and heralds the development of her financial system especially emerging markets.Financial system development thus involves the establishment and expansion of financial institutions, instruments and markets which supports the investment and growth process through improvements in the quantity, quality and efficiency of financial intermediary services Osuji (2015).Ronoh and Omwenga (2017) assert that the developments in the financial sector have not only led to the increase in the number of financial institutions, but also the development in level of sophistication with new payment systems and asset alternatives to holding money.While, Stiglingh, Muzindutsi and Bezuidenhout (2018) succinctly posit that there seems to be a lack of strong financial systems and policies to deliver the required economic results in most developing countries.Iheonu et al. (2020) collaborate that development of the financial sector enhances efficient access to financial services and products.
Construction of Live Broadcast Training Platform Based on “Cloud Computing” and “Big Data” and “Wireless Communication Technology”

With the rapid development of information technology, a scientific theory is brought by the rapid progress of science and technology. The advancement of science and technology of the impact on every field, changing the mode of transmission of information, the advent of big data for promotion and dissemination of resources played their part, let more and more people benefit. In the context of cloud computing, big data ushered in another upsurge of development and growth. Given this, the live broadcast training platform, which focuses on enterprise staff training and network education, arises at the right moment. People favor its convenience, real-time performance, and high efficiency. However, the low-value density of big data and cloud computing’s security problem has difficulties constructing a live broadcast training platform. In this paper, the live broadcast training platform’s structure is improved by constructing three modules: the live training module based on cloud computing, the user recommendation module based on big data, and the security policy guarantee module. In addition, to ensure that the trainees can receive training anytime and anywhere, this paper uses wireless communication technology to ensure the quality and speed of all users’ live video sources.
Automatic Detection of Fractures Based on Optimal Path Search in Well Logging Images

Reservoir fractures are essential locations to gather oil and gas. Recently, imaging logging technology has become a mainstream method for obtaining stratigraphic information. This paper proposed a combined optimal path search strategy to effectively identify and extract the fracture information in well logging images. Specifically, the threshold segmentation of logging images is used to obtain the binary image. In addition, the identification of connected fractures in the logging image is transformed into the optimal path search, and the identification and extraction of reservoir fractures are realized by constructing the optimal path between the two ends of fractures. Finally, an improved ant colony algorithm is applied to filter irrelevant information and extract fractures automatically by recording all the ants’ exploration trajectories in the ant colony. Compared with previous approaches, the proposed method can eliminate irrelevant background features and merely reserve pixels corresponding to fractures. Simultaneously, relative to the conventional strategy, the time consumption is reduced by more than 98%. The findings of this study can help for better extracting fractures automatically and reducing manual workload.
Deep Fast Embedded CapsNet: Going Faster with Deep-Caps

Deep Capsule Network is a proven concept for understanding complex data in computer vision. Deep Capsule Networks achieved state-of-the-art accuracy Canadian institute for advanced research (CIFAR10), which is not achieved by shallow capsule networks. Despite all these accomplishments, Deep Capsule Networks are very slow due to the ‘Dynamic Routing’ algorithm in addition to their deep architecture. In this paper, the deep fast embedded capsule network (Deep-FECapsNet) is introduced. Deep-FECapsNet is a novel deep capsule network architecture that uses 1D convolution-based dynamic routing with a fast element-wise multiplication transformation process. It competes with state-of-the-art methods in terms of accuracy in the capsule domain and excels in terms of speed and reduced complexity. This is shown by the 58% reduction in trainable parameters and 64% decrease in the average epoch time in the training process. Experimental results show excellent and verified properties.
EthVer: Formal Verification of Randomized Ethereum Smart Contracts

Despite the great potential capabilities and the mature technological solutions, the smart contracts have never been used at a large scale, one of the reasons being the lack of good methods to verify the correctness and security of the contracts—although the technology itself (e.g. the Ethereum platform) is well studied and secure, the actual smart contracts are human-made and thus inherently error-prone. As a consequence, critical vulnerabilities in the contracts are discovered and exploited every few months. The most prominent example of a buggy contract was the infamous DAO attack—a successful attack on the largest Ethereum contract in June 2016 resulting in $70 mln-worth Ether stolen and the hard fork of the Ethereum network (80% of Ethereum users decided to revert the transaction and hence two parallel transaction histories exist from that event).
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations

Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models.We explore this problem from a novel angle of geometric algebra and semantic space.A simple but highly effective method "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data.A post-training and model-agnostic method, LIR only uses simple linear operations, e.g.matrix factorization and orthogonal projection.LIR reveals that for weak-alignment multilingual systems, the principal components of semantic spaces primarily encodes language identity information.We first evaluate the LIR on a cross-lingual question answer retrieval task (LAReQA), which requires the strong alignment for the multilingual embedding space.Experiment shows that LIR is highly effectively on this task, yielding almost 100% relative improvement in MAP for weakalignment models.We then evaluate the LIR on Amazon Reviews and XEVAL dataset, with the observation that removing language information is able to improve the cross-lingual transfer performance.
Penerapan model pembelajaran berbasis proyek pada pembuatan strip komik untuk meningkatkan kemampuan menulis bahasa inggris

This class action research aims to improve students' writing skills through the application of PJBL model in comic strips project.The study was conducted in class X social 2 of SMA Negeri 2 Kuta in the second semester of the academic year 2019/2020 involving 36 students. This class action research consisted of 2 cycles with Expressions material. Student writing assessment data was collected from the writing assessment rubric while students' responses to the application of the PJBL model in the comic strips project were collected by questionnaire. The collected data was analyzed descriptively. This research is said to be successful if the student's average writing score is at least in good category, and the average value of student responses is in the positive category.The results showed (1) the average writing score of students in the first cycle was 77.36 with good category, whereas in the second cycle was 80.56 with good category, (2) the average value of responses Class X IPS 2 students of SMA Negeri 2 Kuta on the application of PJBL model in comic strips project amounted to 43.78 with positive category. These results indicate that the application of the PJBL model in comic strips project can improve students' English writing skills in class X social 2 SMA Negeri 2 Kuta in the academic year 2019/2020.Penelitian tindakan kelas ini bertujuan meningkatkan kemampuan menulis siswa melalui penerapan model PJBL pada pembuatan strip komik. Penelitian dilaksanakan di kelas X IPS 2 SMA Negeri 2 Kuta pada semester genap tahun ajaran 2019/2020 dengan melibatkan 36 orang siswa. Penelitian tindakan kelas ini terdiri dari 2 siklus dengan materi Expressions. Data penilaian menulis siswa dikumpulkan dari rubrik penilaian menulis sedangkan tanggapan siswa terhadap penerapan model PJBL pada strip komik dikumpulkan dengan angket. Data yang telah terkumpul dianalisis secara deskriptif. Penelitian ini dikatakan berhasil jika diperoleh skor rata-rata menulis siswa minimal berada pada kategori baik, dan nilai rata-rata tanggapan siswa terhadap penerapan model PJBL pada proyek strip komik minimal berada pada kategori positif. Hasil penelitian menunjukkan (1) skor rata-rata menulis siswa pada siklus I sebesar 77,36 dengan kategori baik, sedangkan pada siklus II diperoleh skor rata-rata menulis siswa sebesar 80,56 dengan kategori baik, (2) nilai rata-rata tanggapan siswa kelas X IPS 2 SMA Negeri 2 Kuta terhadap penerapan model PJBLproyekstrip komik sebesar 43,78 dengan kategori positif. Hasil tersebut menunjukkan bahwa penerapan model PJBL pada proyek strip komik dapat meningkatkan kemampuan menulis siswa dalam pelajaran bahasa Inggris kelas X IPS 2 SMA Negeri 2 Kuta tahun ajaran 2019/2020.
A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence

The connected and autonomous vehicle (CAV) is the next-generation mobility service—powered by intelligent automation and robust communication—which is aimed at replacing human-maneuvered vehicles with the software agent matching or even exceeding the human-level intelligence, control, and agility and minimizing errors. The next generation of transportation and mobility envisions safe, reliable, agile, automated, trustworthy, and service-based mobility architecture. The architecture should be able to eliminate human errors by using intelligent decision-making software agents based on the situational and behavioral information collected by sensors and transceivers through communication. Apart from that, service-based architecture removes the concept of vehicle ownership and incorporates more diversity in passengers, including the disabled and elderly people. CAV is the evolving technology to achieve the envisioned goals of future mobility and transportation. This chapter sheds light on cyber-physical vulnerabilities and risks that originated in informational technology (IT), operational technology (OT), and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence.
Anaphora Resolution in English Ambiguous Sentences by ESL Learners

The present study investigated the anaphora resolution in English ambiguous sentences.The study focused on the automatic anaphora resolution process in English ambiguous sentences by ESL learners in the natural language through anaphora resolution strategies.The aim of this research study was to know whether the ESL learners used anaphora resolution strategies.The study further explored the most frequently and least frequently used strategies by the ESL learners.A proficiency test was used as a research tool to collect data.The questions were about ambiguous English sentences containing both inter-sentential and intra-sentential anaphora.The learners had no significant difficulty in the use of most of most of the strategies.The strategies of gender and number agreement, syntactic knowledge and real-world knowledge were the most frequently used strategies; while, the least frequently used strategy for anaphora resolution was discourse knowledge.The study also found out that one of the strategies known as discourse was used commonly by the ESL learners.The findings of the current study are more fruitful for the ESL learners.They will aware the ESL learners and the instructors to include anaphora resolution strategies in teaching English ambiguous sentences.
Sigma Identification Protocol Construction Based on MPF

A new sigma identification protocol (SIP) based on matrix power function (MPF) defined over the modified medial platform semigroup and power near-semiring is proposed. It is proved that MPF SIP is resistant against direct and eavesdropping attacks. Our security proof relies on the assumption that MPF defined in the paper is a candidate for one-way function (OWF). Therefore, the corresponding MPF problem is reckoned to be a difficult one. This conjecture is based on the results demonstrated in our previous studies, where a certain kind of MPF problem was proven to be NP-complete.
Generating Formal Software Architecture Descriptions from Semi-Formal SysML-Based Models: A Model-Driven Approach

The critical nature of many complex software-intensive systems requires formal architecture descriptions for supporting automated architectural analysis regarding correctness properties. Due to the challenges of adopting formal approaches, many architects have preferred using notations such as UML, SysML, and their derivatives to describe the structure and behavior of software architectures. However, these semi-formal notations have limitations regarding the sought support for architectural analysis. This paper presents an approach to bridge the rigor of formal architecture descriptions and the ease of use of SysML-based notations widely used elsewhere. The main concern is providing formal semantics to SysADL, a SysML-based language to describe software-intensive system architectures. The formal semantics is provided by \(\uppi \)-ADL, a formal architecture description language. A model-to-model transformation was defined and implemented to concretize the mapping between the elements of these languages and hence automatically generate formal architecture descriptions in \(\uppi \)-ADL from SysADL. This paper describes a proof-of-concept to illustrate the mapping between SysADL and \(\uppi \)-ADL and an exploratory study on the transformation performance.
Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making. To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities’ names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Bi-order-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF. Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.
Oriented object detection in aerial images based on area ratio of parallelogram

Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parameter or eight-parameter representations, which have representation ambiguity for orientated object definition and border loss discontinuity in the regression process. We proposed an innovative representation method based on area ratio of parallelogram, called area ratio of parallelogram (ARP). Specifically, ARP regresses the minimum bounding rectangle of the oriented object and three area ratios. Three area ratios include the area ratio of a directed object to the smallest circumscribed rectangle and two parallelograms to the minimum circumscribed rectangle. It simplifies offset learning and eliminates the issue of angular periodicity or label point sequences for oriented objects. To further remedy the confusion issue of nearly horizontal objects, the area ratio between the object and its minimal circumscribed rectangle is employed to guide the selection of horizontal or oriented detection for each object. Moreover, the rotated efficient intersection over union loss with horizontal bounding box and three area ratios are designed to optimize the bounding box regression for rotating objects. Experimental results on remote sensing datasets, including HRSC2016, DOTA, and UCAS-AOD, show that the proposed method achieves superior detection performance than many state-of-the-art approaches.
Well-Behaved Transformer for Chinese Medical NER

Medical named entity recognition (NER) is an important task of clinical natural language processing (NLP). It is a hot issue in intelligent medicine research. Recently, the proposed Lattice-LSTM model has demonstrated that incorporating information of words in character sequence into character-level Chinese NER has achieved new benchmark results on the Chinese datasets in multiple other fields. However, due to the lattice structure is dynamic and complex. These lattice-based models are difficult to fully use of the GPUs parallel computing, which limits its application. In this work, we propose a Well-Behaved Transformer (WB-Transformer) model for Chinese medical named entity recognition, using a high-performance encoding strategy to separately encode the character of Chinese electronic medical records (EMRs) and words corresponding to the character. This reduces the impact of word segmentation errors while obtaining the word boundary information, and makes full use information of characters and words for Chinese medical NER. Experimental on three Chinese medical entity recognition datasets show that our proposed model outperforms other methods.
An Effective Slant Detection and Correction Method Based on the Tilted Rectangle Method for Telugu Manuscript Terms

This paper proposes an efficient method called tilted rectangle (TR) for detecting and correcting of slant angle of the manuscript Telugu words (MTW). Telugu language is one of India's common languages spoken by over 80 million individuals. The complex characters are attached with some extra marks known as “maatras” and “vatthus,” and it is challenging to detect slant angle. The proposed TR method initially performs preprocessing and identifies a connected component within the given Telugu manuscript word. Then, it estimates the slant angle of each connected component by deriving connected slant lines on the boundary of each connected component. After this process, the proposed TR method estimates the entire word's overall slant angle from the average of estimated slant angle and height of all connected components. The correction of the word's slant angle is done in the reverse direction by applying a simple shear transformation. With 1000 manuscript records of three different kinds, the algorithm is tested. Experimental findings indicate the efficacy of the approach proposed.
The Rise of “Big Data” on Cloud Computing

In the past few years, abundant data have provided opportunities to researchers and organizations to enable/improve more decision support applications. These abundant data are nothing but big data. It has given an unprecedented value to the engineering, business, and sciences. However, there are concerns with big data for storing, analyzing, and serving data at a rapid speed as resources required for it would be more. For ensuring successful data processing and analysis, a large number of resources are required, which is a challenge in big data. With the concept of shared computing, cloud computing can resolve this fundamental problem of big data. Cloud computing provides resources like computing, storage, and networking to big data for not only storing, analyzing, and serving but also for transferring/using it from any geographical location. Social networking using cloud computing is also generating a lot of big data. Thus, cloud computing helps not only to serve but also to generate big data, which has further played a pivotal role in the increase of big data. In this chapter, the relationship of big data with cloud computing is done by discussing the rise of each and the dependency of one over the other.
iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.