Reviewerly/exHarmony huggingface dataset:
Ethics for Automated Financial Markets

Financial markets are now ecologies of trading algorithms. In this chapter, we propose market effectiveness as the ethical goal of automated markets and review the professional, organizational, and industry-wide ethics in this domain. We present criteria for what counts as both a prudent and ethical automated trading system. We argue that an ecology of such systems unfettered by excessive regulation will evolve to promote the effectiveness goal. Largely, this is already occurring. Then, we look to the future, investigating new issues that are arising around the use of artificial intelligence and alternative data sources and possible courses of action. However markets evolve, as long as their effectiveness as we define it increases, the outcome ought to benefit society.
Linking Words in Inter- and Intralingual Translation—Combining Corpus Linguistics and Key-Logging Data

The goal of this paper is to analyse inter- and intralingual translation by looking at the frequency of linking words in the source and target texts as well as the key-logging reports from the translation processes. With a corpus-based approach, the study attempts to determine whether linking words are more frequent in inter- and intralingual translations than in their source texts and whether translation experience has an effect on that. In the next step, the corpus linguistics method is augmented by an examination of draft versions of the target texts obtained from a key-logging tool. Such a perspective is taken in order to determine in which of the translation process phases the most linking words are produced. Finally, the last stage involves zooming in only on the translation process data to verify if linking words are produced in a more automated manner, i.e. without prior text elimination/text navigation or whether the translator navigates through the text or eliminates characters while arriving at their decision.
Testing Web Service Compositions: Approaches, Methodology and Automation

Web services give a new view of the web as the biggest, widely accepted and the most straightforward distributed software platform.Their composition into applications and business processes is still a complex, non-trivial task, requiring highly rational efforts not only from the software developers, but from the quality assurance specialists.The provision оf web service compositions' quality brings a lot of challenges due to variability of difficulties at infrastructure, service and choreography levels and the need of different types of testing in unknown context and environment.A consolidated quality assurance methodology that advances the fundamental understanding of testing in terms of concepts, models, techniques, standards and automation is required.This methodology needs to enable effective exploration, comparison, evaluation and selection of testing approaches, platforms and tools.This article proposes such a methodology and reviews the current testing approaches for single and composite web services from an objective, holistic perspective.The methodology is presented as an end-to-end testing procedure, in which activities are facilitated by a set of testing approaches, techniques and best practices.A concrete solution is recommended for actual implementation of each activity either through selection among the most appropriate and effective existing approaches or development of new approaches, mainly in case of critical issues such as dependencies analysis, partner web services' isolation and injection of faults.A common framework that integrates different testing tools automates the methodology.Its applicability, completeness, level of automation, and level of novelty is evaluated through testing of real testing scenarios.
Framework for Trustworthy Software Development

Intelligent software applications are becoming ubiquitous and pervasive affecting various aspects of our lives and livelihoods. At the same time, the risks to which these systems expose the organizations and end users are growing dramatically. Trustworthiness of software applications is becoming a paramount necessity. Trust is to be regarded as a first-class citizen in the total product life cycle and should be addressed across all stages of software development. Trust can be looked at from two facets: one at an algorithmic level (e.g., bias-free, discrimination-aware, explainable and interpretable techniques) and the other at a process level by making development processes more transparent, auditable, and adhering to regulations and best practices. In this paper, we address the latter and propose a blockchain enabled governance framework for building trustworthy software. Our framework supports the recording, monitoring, and analysis of various activities throughout the application development life cycle thereby bringing in transparency and auditability. It facilitates the specification of regulations and best practices and verifies for its adherence raising alerts of non-compliance and prescribes remedial measures.
Analysis of information technology governance in the planning and organization of e-learning at Universitas Negeri Malang

Abstract Learning paradigm in the 21st century has changed, so that the utilization of information and communication technology in education is absolutely necessary. E-learning becomes one of the alternatives that bring together technological support with the current learning needs. Universitas Negeri Malang (UM) is one of the universities that have a concentration on the implementation and development of e-learning for the implementation of education. The development of e-learning based learning should be balanced with evaluation or audit of the information system so that threats or losses can be avoided or prevented. This study aims to determine the extent to which e-learning performance of the UM viewed from the governance of information technology in planning and organization on e-learning using COBIT 4.1 framework and provide recommendation governance improvement after knowing the gap between the current governance with the expected governance. Data collection techniques were conducted with interviews and questionnaires with UM e-learning managers. Data analysis method is done through mapping of maturity level of planning and organization of information technology governance by using descriptive analysis. The result of research mentioned that maturity level of planning and organization of information technology governance of UM e-learning, which is at level 1.88 which means that managers have realized the need of IT development and formation of more professional organization. The GAP analysis between expected conditions and current conditions averages 1.12. Recommendations that can be given to the development of e-learning UM is to form organizations and sharpen the vision and mission of system development better.
Text Siamese Network for Video Textual Keyframe Detection

In this paper, we propose a novel approach of video text keyframe detection, to achieve the goal of representing the video with textual keyframes and reducing the waste of resources in video review. Different from the works of video summarization which mainly focus on the variation of the scenes in videos, we pay attention to the variances of the text between sequential frames. For the above purpose, Text Siamese Network (TSN) is developed to automatically detect the keyframes which contain text in videos. Specifically, the TSN is composed of two branches, text similarity measurement and text identification. The first branch is utilized to evaluate the similarity between consecutive frames. Furthermore, an attention block is used to select the informative features to identify whether a frame contains text or not in the second branch. Additionally, a new dataset called VTKD2019 is proposed for video text keyframe detection. VTKD2019 contains 571 videos and is spit into three levels (easy, medium and hard) for evaluation. The experimental results on the VTKD2019 and ICDAR2015 demonstrate the effectiveness of our method.
A Deep Learning Approach for Robust Corridor Following

For an autonomous corridor following task where the environment is continuously changing, several forms of environmental noise prevent an automated feature extraction procedure from performing reliably. Moreover, in cases where pre-defined features are absent from the captured data, a well defined control signal for performing the servoing task fails to get produced. In order to overcome these drawbacks, we present in this work, using a convolutional neural network (CNN) to directly estimate the required control signal from an image, encompassing feature extraction and control law computation into one single end-to-end framework. In particular, we study the task of autonomous corridor following using a CNN and present clear advantages in cases where a traditional method used for performing the same task fails to give a reliable outcome. We evaluate the performance of our method on this task on a Wheelchair Platform developed at our institute for this purpose.
A Preliminary Study of Finger Area and Keystroke Dynamics Using Numeric Keypad With Random Numbers on Android Phones

The paper investigates the effectiveness of finger areas and keystrokes dynamics data as the authentication factors using random numeric keypad of touchscreen smartphones. The users' finger areas and keystrokes dynamics data are collected via android application. Preliminary experiment was conducted, involving 30 subjects typing a predefined 10-digit numbers for ten times continuously. The features used in the analysis are gender of the subjects, dwell times, flight times and finger areas. Random forest classifier is used and the weighted results were true positive rate of 80.7%, false positive rate of 0.7%, precision of 81.3% and recall of 80.7%.
Review of Big Data Analytics (BDA) Architecture: Trends and Analysis

The problem of constructing a big data analytics capabilities system, it is not only ingesting large volume of data, but also simultaneously computes vast volume and variety types of data, which is driven by required analytics use cases. This affects data architects, data engineers and data scientists in an organization to discover insight of data and turn to value. The promise of adopting a big data strategic architecture, it maximizes technologies capabilities in automate decision making and drives these values through innovation. A successful solution adopts a highly flexible and scalable architectural design to best-fit the organization BDA system. This paper surveys and discusses BDA and its architecture for applying the appropriate technologies.
A PMU Data Recovery Method Based on Feature Component Extraction

Phasor Measurement Units (PMUs) have become the basic means of real-time monitoring and closed-loop control of power grids due to their synchronization, rapidity and accuracy. However, PMUs usually have different levels of data loss problems due to many factors, which seriously restricts its applications in the power systems. In this paper, a PMU data recovery method based on feature component extraction is proposed. First three types of data loss are summarized. Then the original signal is decomposed to extract all the components. And data is recovered by reconstructed signal with optimal components. The proposed method has been tested using artificial and field data. And the case studies verify that this method could realize a decent recovery for different types of data loss, which is of great significance and value for ensuring PMU data quality. This paper also reveals the interesting direction for future work.
Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication

Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.
A Semantic Approach to Non-prioritized Belief Revision

Abstract Belief revision is concerned with belief change fired by incoming information. Despite the variety of frameworks representing it, most revision policies share one crucial feature: incoming information outweighs current information and hence, in case of conflict, incoming information will prevail. However, if one is interested in representing the way actual humans revise their beliefs, one might not always want for the agent to blindly believe everything they are told. This manuscript presents a semantic approach to non-prioritized belief revision. It uses plausibility models for depicting an agent’s beliefs, and model operations for displaying the way beliefs change. The first proposal, semantically-based screened revision, compares the current model with the one the revision would yield, accepting or rejecting the incoming information depending on whether the ‘differences’ between these models go beyond a given threshold. The second proposal, semantically-based gradual revision, turns the binary decision of acceptance or rejection into a more general setting in which a revision always occurs, with the threshold used rather to choose ‘the right revision’ for the given input and model.
Application of Batu Belah Batu Bertangkup Folklore In Riau Province With Augmented Reality

Batu Belah Batu Bertangkup is one of the folk stories originating from Riau Province. This folk tale has a fairly good moral value to be conveyed to the next generation, which is to be kind, caring, and maintaining the mandate given by parents. The current generation, commonly referred to as Z generation, is not accustomed to reading books, this generation uses smartphones more often to access technology, while the folklore of Batu Belah Batu Bertangkup is mostly still in book form. This research raises the folklore of Batu Belah Batu Bertangkup using Augmented Reality technology, so the Z generation is interested in seeing and listening to this folk tale. This study uses Blender for making 3D animations and Kudan SDK libraries with markerless techniques for Augmented Reality and user interface uses Unity application and runs on the Android operating system. The Augmented Reality of Batu Belah Batu Bertangkup folklore concluded that the application can display 3D animated in dim light with the intensity of 30 lux light and the distance to display an object was at least 5 cm to 90 cm with an angle of 10-90 degrees. Based on user validation, 90% of respondents strongly agree that Batu Belah Batu Bertangkup folklore is made in the form of an Augmented Reality application.
A Dataflow Pipelining Architecture for Tile Segmentation with a Sparse MobileNet on an FPGA

Implementation of fast semantic segmentation in an embedded system is necessary due to the increasing interest in automatic driving and energy-efficiency is a fundamental metric in such a scenario. Because high-resolution images are required to achieve high segmentation accuracy, its accelerator must prepare large buffers for the corresponding feature maps, and it is not suitable for the limited on-chip memory of an FPGA. To address this, we propose a tile segmentation algorithm and develop an FPGA-based accelerator for a sparse MobileNet-based PSPNet. To reduce the buffer size, the tile segmentation algorithm splits incoming images into the numbers determined by the stride on an FPGA. The PSPNet then performs many split tiles. Moreover, we propose a pipelined sparse convolutional circuit to compute multiple tiles with high-speed. We compared the proposed FPGA-based system with the NVIDIA RTX 2080 Ti using the Cityscapes benchmark. The FPGA achieved 139 FPS with 24 W power consumption for a 1024×512 image, and its accuracy (mIoU) was 64.2%. Compared with the GPU, it was 1.5 times faster, its power consumption was 9.3 times lower, and its performance per power consumption was 13.8 times better.
Things as a Service: Service model for IoT

Leveraging the benefits of service computing technologies for Internet of Things (IoT) can help in rapid system development, composition and deployment.But due to the massive scale, computational and communication constraints, existing software service models cannot be directly applied for IoT based systems.Service discovery and composition mechanism need to be decentralized unlike majority of other service models.Moreover, IoT services' interfaces require to be light weight and able to expose the device profile for seamless discovery onto the IoT based system infrastructure.In addition to this, the "things" data should be associated with its present context.To address these issues, this paper proposes a formal model for IoT services.The service model includes the physical property of "things" and exposes it to the user.It also associates the context with the "things" output, which in turn helps in extracting relevant information from the "things" data.To evaluate our IoT service model, a weather monitoring system and its associated services are implemented using node.js[31].The service data is mapped to SSN ontology for generating contextrich RDF data.This way, the proposed IoT service model can expose the device profile to the user and incorporate relevant context information with the things data.
Test Case Generation from BPMN with DMN

Business Process Model and Notation (BPMN) is applied to manage business processes. Currently, business processes are becoming more complex with increasing conditions. As a result, it is highly difficult to manage activities and decision-making in business processes. However, BPMN still cannot handle the decision-making in business processes. Object management group (OMG) created Decision Model and Notation (DMN) to complement BPMN and to manage the decision-making of business processes. In software development process, creating a business process model can occur earlier during requirement gathering. However, a tester can generate test cases to verify accuracy of information in the business process. Previously, many researchers have focused on BPMN test cases generation. None of their studies has been conducted on the creation of BPMN test cases with DMN. Consequently, this paper proposes an approach to generate test cases from BPMN with DMN. BPMN along with DMN, paths traverse from the entry to the exit of the BPMN, and existing test cases are inputs of our approach. BPMN, DMN, and existing test cases are analyzed to check whether they cover all rules in the DMN. In case that all rules are not covered, new test cases will be generated. This paper proposes an approach to generate test cases cover all rules in the DMN to effective monitoring BPMN with DMN. This approach is used to check the rules within the DMN decision table without affecting BPMN process. It ensures the business processes are implemented correctly.
The research and development of the hybrid algorithm based on the collective behavior of Fish schools and the classical optimization methods

Abstract In this paper, we consider the hybridization problem of the evolutionary-inspired Fish School Search algorithm. The algorithm shows accurate results on solving multi-extremal optimization problems, outperforms most other swarm intelligence algorithms and is computationally inexpensive. But, the accuracy of the algorithm highly depends on the defined iteration count to compute. The accuracy increases with the increase of iteration count, but this also affects computation time required to find the suboptimal solution. In this paper, we propose two hybrid approaches which intend to accelerate the Fish School Search algorithm using either gradient descent or Newton’s algorithm. We provide the comparative analysis of the original algorithm and the two hybrid algorithms applied to the Styblinsky-Tang, Eggholder and other test functions for optimization, including multi-extremal and multi-dimensional ones. The obtained results confirm that the proposed hybrid algorithms are more effective, compared to the original population-based algorithm. The results demonstrate the strong advantage of the hybrid algorithm, where the gradient descent is used as its part. Additionally, we provide the solution of the XOR problem using a perceptron with one hidden layer, where the most effective hybrid approach is used to train the network. We visualize the loss function surface near the global extreme point to show the effectiveness of the proposed hybrid algorithm.
Investigation into the development and historical roots of collection development by referenced publication year spectroscopy

Purpose The purpose of this study is the application of a recently developed quantitative method named Referenced Publication Year Spectroscopy (RPYS) in the spectrum of Collection Development. RPYS portrays peak years to be recognized in citations in a research field that guarantees to assist in the identification of significant contributions and groundbreaking revelations in a research field. Design/methodology/approach Preliminary data of the study has been extracted from Web of Science (WoS) by using two phrases “collection development” and “collection building” to search in terms of the topic (comprising four parts: title, abstract, author keywords and KeyWords Plus). The search was restricted to the time period 1974-2017, which formulated a data set of 1,682 documents covering 29,017 cited references. The program CRExplorer ( www.crexplorer.net ) was used for the extraction of cited references from the data sets downloaded from WoS. Further analysis was performed manually using MS-Excel 2016. Findings The present study identified seminal works, which contributed to a high extent to the evolution and development of collection development. The analysis of all cited references using the RPYS method showed nine peaks, which present historical roots of collection development and revealed that the basic idea of this very subfield of library science dates centuries back. Moreover, the results of the investigation on most effective documents (in the form of peaks) revealed that the field of collection development significantly influenced by the works of authors such as Gabriel Naudé, Gabriel Peignot, Giulio Petzholdt, P L Gross, E M Gross, Richard Trueswell, Allen Kent, Ross Atkinson, etc. Practical implications The analysis of works cited in publications helps to ascertain important intellectual contributions related to a particular domain of knowledge. It not only helps in extracting the most important works but also it helps to reconstruct the history of a specific research field by examining the specific role of the cited references. Therefore, the results of the study could be useful for researchers, practitioners, scholars and more specifically bibliophiles, bibliographers and librarians to gain a better understanding of seminal works in the spectrum of collection development. Originality/value To the best of authors’ knowledge, the present research work is unique and novel in the spectrum of collection development, which explored and examined the pivotal works in the field by using the RPYS method.