Reviewerly/exHarmony huggingface dataset:
Online Travel Agency (OTA) Data Maturity Assessment: Case Study PT Solusi Awan Indonesia -“Flylist”

According to survey that was being held by DailySocial to 2030 smartphone users in Indonesia, more than 91 percent of respondents have booked flight tickets via online with more than 86 percent of them have booked through online travel agency (OTA). Flylist is the OTA that has been created by PT Solusi Awan Indonesia and published on August 2018 with the vision to retrieve 20 percent of the cheapest price that has not been sold by premium and non-premium airlines and reduce the risk of losing the chances of selling, refund, and overbooking processg. Flylist's vision is relatively huge and will be difficult for new system. To achieve it, data maturity and management should be assessed. Paper will discuss OTA theory, framework (DMBOK and other framework by James M. B.), IATA theory and inovation, research data from interviewees, and document study that will be assessed with HESA data maturity assessment.Based on data maturity assessment, we found that overall score is level 3 (proactive) with the result is Flylist can be improved by doing recommended data governance, standards & compliance, data architecture management and NDC theory on customer service contact, communication, and business intelligence system, technology, data security management activity.
Fast k-Fuzzy-Rough Cognitive Networks

Fuzzy-Rough Cognitive Networks (FRCNs) are neural networks that use rough information granules with soft boundaries to perform the classification process. Unlike other neural systems, FRCNs are lazy learners in the sense that we can build the whole model when classifying a new instance. This is possible because the weight matrix connecting the neurons is prescriptively programmed. Similar to other lazy learners, the processing time of FRCNs notably increases with the number of instances in the training set, while their performance deteriorates in noisy environments. Aiming at coping with these issues, this paper presents a new FRCN-based algorithm termed Fast k-Fuzzy-Rough Cognitive Network. This variant employs a multi-thread approach for building the information granules as computed by k-fuzzy-rough sets. Numerical simulations on 35 classification datasets show a notable reduction on FRCNs' processing time, while also delivering competitive results when compared to other lazy learners in noisy environments.
A Service-Oriented Human Capital Management Recommendation Platform

Human Capital Management (HCM) involves various processes in finding the right talent for job openings, or finding satisfying jobs for employees in the service industry. With the arrival of automation and the help of Machine Learning (ML), models of complete or partial automation of HCM processes become more achievable. Such models become a necessity as organizations scale up. In this paper, we present a stateless, scalable, micro-service based architecture to push towards automation through recommendation using Machine Learning and Statistical Methods. A recommendation system for candidates in finding jobs, and vice versa, is presented. A Profile Score is allotted to a candidate/job and used as the metric for ranking. We show solutions to engineering challenges for such systems, like evaluating the location, educational and professional background, and career interests.
Super-resolution Doppler Beam Sharpening based on Sparse Covariance Fitting

Conventional Doppler beam sharpening (DBS) technique forms the narrow-band Doppler filter groups by fast Fourier transform (FFT), resulting in poor cross-range resolution and high sidelobe level. Various super-resolution DBS methods are developed to enhance the cross-range resolution, but suffer from limited resolution improvement. In this paper, we exploit the inherent sparseness of the target distributions to formulate a super-resolution DBS methodology based on covariance fitting criterion. The method is global convergent, user-parameter free, and can yield an improved sparseness and higher cross-range resolution. Simulation results are presented to demonstrate the superior performance of the resulting super-resolution DBS method.
The Role of Blockchain Technology to Make Business Easier and Effective

Blockchain is the new “buzz” word that has attracted the attention of industries and businesses. It is an innovative technology that provides information exchange in an efficient and transparent manner. It has a wide range of application varying from cryptocurrency, healthcare, risk management, education, financial services, internet of things (IoT), border security to public services. However, security issues and threats of this novel technology is also an important topic. In this chapter, the authors provide a comprehensive study of applications, challenges, and issues and how to combat them in the blockchain. Major areas of concern are security, scalability, cryptocurrency's malicious attacks, etc.
A User Profile Awareness Service Collaborative Recommendation Algorithm Under LBSN Environment

Nowadays, location-based social network (LBSN) has become one of the most popular applications with the rapid development of mobile Internet. However, due to the spatial and real-time properties, mobile service recommendation under LBSN environment faces too many challenges especially data sparsity problem. To tackle these challenges, a recommendation framework is proposed in this paper which has four layers defined as data collection layer, user profile modeling layer, information processing layer and recommendation feedback layer, respectively. Furthermore, the ISC-CF algorithm is implemented to integrate users’ interest profile, social influence and current location context to effectively overcome the data sparsity problem. Thus, the social influence is quantified by a modified measure way. Finally, a dynamic and personalized adjustment algorithm is built by using the users’ profile tracking and the current location context. The experiment results show that the algorithm proposed in this paper has significantly superior performance compared with the other baseline recommendation methods in both hometown area and out-of-town area.
Layered task planning for mobile manipulators

Task planning for mobile manipulators is a challenging problem in robotics. Mobile manipulators can perform a wide range of tasks in complex environments. They must process the symbolic knowledge in order to find a sequence of actions that achieves the specified goals. This paper proposes a hybrid approach named Planning with Shortcuts. It uses a data structure called Layered Task Graph (LTG) which groups primitive actions, robot skills and task patterns in a strictly hierarchical manner. It then uses the LTG to create shortcuts in the state space in order to accelerate the search. A task planner is called to find a set of actions that achieves the goal task. The result of the search is a sequence of high level actions and primitive actions. The high level actions are then decomposed using the task decomposition approach.
How Open is the SBES PC Community?

The Brazilian Symposium on Software Engineering (SBES) is one of the most important Latin American software engineering coferences. In 2019 it completes its 33rd edition. Over these years, many researchers participated in the SBES attending the conference, submitting papers, or reviewing papers. In particular, the researchers that participated in the program committee, playing the reviewers' role, are fundamentally important to the whole event since their evaluations (e.g., deciding whether a paper is accepted or not) have the potential of drawing what SBES is now. Knowing that diversity is an important aspect of any group work, in this paper we wanted to understand how diverse (in terms of gender and geographic location) is the SBES program committee community. Among the findings, we observed that although the number of participants in the SBES PC increased over the years, most of them are men (~80%) and from the Southeast and Northeast of Brazil, with very few members from the North region.
Case Study of Science Communication in Ergonomics: Introduction of ErgonomicThon Workshop

To progress a science communication (SciCom) in the field of ergonomics, this study organized the project of “ErgonomicThon.” It was an ergonomics version of a kind of the Ideathon or Hackathon famous for brainstorming events. This case study introduced the first workshop of ErgonomicThon that was the special program to share useful research findings about disaster and evacuation behavior. Theme discussed in the program was the human behavior under emergency evacuation. The final goal of this program was sharing the ergonomic perspective of “4M (Man–Machine–Media-Management)” to utilize disaster risk management. The main program was group discussion and presentation to create new ideas about how stimulate appropriate human behavior under serious evacuation with sharing each experience. According to the results of questionnaire survey, many participants satisfied the ErgonomicThon workshop. Simultaneously, this study lead three suggestion to increase satisfaction levels among participants in the future challenge; (1) Localization of SciCom guideline for ergonomics, (2) Selection of academic topics for SciCom, and (3) Development of the specific guideline for short time event.
A Conceptual Metamodel to Bridging Requirement Patterns to Test Patterns

Requirement patterns represent an abstraction of an application's behaviors and services that, in turn, may be replicated in similar applications. However, there has been a lack of efforts exploiting the benefits of requirement patterns in other phases of the software development life cycle, besides the requirements engineering itself. To address this gap, we propose the Software Pattern MetaModel (SoPaMM) that bridges requirement patterns to groups of scenarios with similar behaviors in the form of test patterns. SoPaMM allows the description of the behavior of a requirement pattern through a time executable and easy-to-use language aiming at the automatic generation of test patterns. Using SoPaMM, we model and implement a behavior-driven functional requirement pattern for a web-based user authentication application. Our preliminary results point out that a requirement pattern can be an executable specification capable of generating automated tests.
Risk and social influence in sustainable smart home technologies

Because of the emerging spread of smart home technologies, reaching and influencing people's behaviour is easier than ever before. Introduction of innovative information systems (IS), present in everyday life, should not ignore the users' security concerns for privacy. We believe that this notion also applies to Green IS that trigger sustainable behaviour change. To understand better the users' perceptions of sustainable persuasive smart home technologies, we inspect a case of a persuasive smart metering system. Specifically, we look at how persuasive systems design influences intention to continue using a smart metering system as well as how risk and self-disclosure affect the impact of the persuasive systems design on a smart metering system. We developed a research model and formed hypotheses by drawing on Persuasive Systems Design model and Adaption Level Theory. We used a smart metering system enhanced with the persuasive features as an illustration of a sustainable persuasive smart home technology. Results of our study provide insights relevant for the further research of security issues in sustainable persuasive smart home technologies as well as for practitioners who introduce similar technologies to the users.
Symbolic Analysis of Maude Theories with Narval

Abstract Concurrent functional languages that are endowed with symbolic reasoning capabilities such as Maude offer a high-level, elegant, and efficient approach to programming and analyzing complex, highly nondeterministic software systems. Maude’s symbolic capabilities are based on equational unification and narrowing in rewrite theories, and provide Maude with advanced logic programming capabilities such as unification modulo user-definable equational theories and symbolic reachability analysis in rewrite theories. Intricate computing problems may be effectively and naturally solved in Maude thanks to the synergy of these recently developed symbolic capabilities and classical Maude features, such as: (i) rich type structures with sorts (types), subsorts, and overloading; (ii) equational rewriting modulo various combinations of axioms such as associativity, commutativity, and identity; and (iii) classical reachability analysis in rewrite theories. However, the combination of all of these features may hinder the understanding of Maude symbolic computations for non-experienced developers. The purpose of this article is to describe how programming and analysis of Maude rewrite theories can be made easier by providing a sophisticated graphical tool called Narval that supports the fine-grained inspection of Maude symbolic computations.
Reading Speed Decreases for Fast Readers Under Gaze-Contingent Rendering

Gaze-contingent rendering and display could help meet the increasing resolution and frame rate demands of modern displays while reducing the required latency, bandwidth, and power. However, it is still unclear how degradation of the peripheral image impacts behavior, particularly for the important task of reading. We examined changes in reading speed with different levels of peripheral degradation, varying the size of the text, foveal region, and sub-sampling kernel. We found a wide spread of responses across subjects, with the average change in reading speed ranging from -123 words per minute (WPM) to +67 WPM. We did not find significant effects across types of peripheral degradation, but the change in reading speed was significantly inversely correlated with baseline reading speed (r=-0.513, n=17, p=0.0352), indicating that faster readers were more negatively impacted.
Modeling Pattern Set Mining Using Boolean Circuits

Researchers in machine learning and data mining are increasingly getting used to modeling machine learning and data mining problems as parameter learning problems over network structures. However, this is not yet the case for several pattern set mining problems, such as concept learning, rule list learning, conceptual clustering, and Boolean matrix factorization. In this paper, we propose a new modeling language that allows modeling these problems. The key idea in this modeling language is that pattern set mining problems are modeled as discrete parameter learning problems over Boolean circuits. To solve the resulting optimisation problems, we show that standard optimization techniques from the constraint programming literature can be used, including mixed integer programming solvers and a local search algorithm. Our experiments on various standard machine learning datasets demonstrate that this approach, despite its genericity, permits learning high quality models.
DPA Protected Implementation of OCB and COLM Authenticated Ciphers

Authenticated ciphers are designed to provide two security requirements simultaneously, i.e. confidentiality and integrity. The CAESAR competition was ended with introducing six authenticated ciphers for several applications as the winners. The OCB and COLM authenticated ciphers are two AES-based winners, respectively for high-speed and defense in-depth applications. Similar to the implementation of any other cryptographic algorithm, unprotected implementations of these ciphers could also be vulnerable to side-channel attacks, especially differential power analysis (DPA). In this work, first, the OCB and COLM ciphers are implemented on FPGA of SAKURA-G board. Then their vulnerability is shown with power leakage detection using t-test over the power traces. Also, the first-order protected version of these ciphers is presented using two masking scheme, i.e. threshold implementation (TI) and domain-oriented masking (DOM). To verify these countermeasures, the first and second-order t-test is conducted, to indicate the resistance of protected schemes to the first-order DPA attacks. Finally, the hardware implementation of two protected and unprotected versions of ciphers on FPGA are benchmarked based on the criteria of area, maximum frequency, and throughput. Additionally, the ratio of the increased area and decreased throughput to the unprotected ciphers have been compared with previous works.
Joint Manifold Diffusion for Combining Predictions on Decoupled Observations

We present a new predictor combination algorithm that improves a given task predictor based on potentially relevant reference predictors. Existing approaches are limited in that, to discover the underlying task dependence, they either require known parametric forms of all predictors or access to a single fixed dataset on which all predictors are jointly evaluated. To overcome these limitations, we design a new non-parametric task dependence estimation procedure that automatically aligns evaluations of heterogeneous predictors across disjoint feature sets. Our algorithm is instantiated as a robust manifold diffusion process that jointly refines the estimated predictor alignments and the corresponding task dependence. We apply this algorithm to the relative attributes ranking problem and demonstrate that it not only broadens the application range of predictor combination approaches but also outperforms existing methods even when applied to classical predictor combination settings.
Swift distance transformed belief propagation using a novel dynamic label pruning method

Loopy belief propagation (LBP) suffers from high computational time, specifically when each node in the Markov random field (MRF) model has lots of labels. In this study, a swift distance transformed belief propagation (SDT-BP) method is proposed. SDT-BP employs an efficient dynamic label pruning approach together with distance transformation to boost the running time of the LBP. The proposed dynamic label pruning approach is independent of any specific message scheduling. The resultant solution's energy is less than Priority-BP. Furthermore, SDT-BP guarantees convergence in fewer numbers of iterations. The direct combination of distance transformed belief propagation (DT-BP) with the dynamic label pruning in Priority-BP has O(KTNlog N) computational complexity. However, the proposed method results in O(KTN) complexity. Where N is the number of nodes, K is the number of labels for each node, and T is the number of iterations. The authors conduct several experiments on image inpainting case studies, to evaluate this method. According to this analysis, DT-BP faces nearly 90% speedup by preserving the energy of the solution at almost the same level. Furthermore, this method can be utilised in any MRF model where its distance function is transformable, i.e. in various image processing and computer vision problems.
Survivability Analysis of K-variant Architecture for Different Memory Attacks and Defense Strategies

Many systems require high security during a limited operational timeframe. These systems can be exposed to many attacks that prevent tasks from being completed. To reduce the chances of a successful attack and increase the security of these systems, many fault tolerance architectures and defense strategies have been developed. N-version architecture is one technique to improve reliability and security. In this architecture, functionally equivalent variants of a program are developed and executed concurrently. However, due to the high cost of developing and maintaining N versions, it is used for large budget projects. In this paper, an alternative approach to improve security is K-variant architecture. By applying safe and automated program transformations, functionally equivalent variants of the original program are generated. The goal is to shift vulnerabilities in variants into different locations. These variants are executed simultaneously to complete a task so that the chance of a successful exploitation attack is decreased. In this paper, several types of memory exploitation attacks are analyzed along with the proposed four defense strategies. The experimental study investigates the effectiveness of defense strategies against these attacks. The results suggest that K-variant architecture with the proposed defense strategies may significantly improve the security of systems.
An Algorithm of Merging Small Files in HDFS

Small files are files that are much smaller than configured HDFS block size (by default 64MB or 128 MB on most up-to-date Hadoop installation). HDFS is meant for storing large files, but due to input source and ingestion methods large number of small files need to be stored in HDFS, and HDFS cannot handle small files in an effective and efficient way as it is designed to read large chunks of data in a sequential way. This paper describes one algorithm of merging small files into one container file than can resolve the issue.
Container Based Analysis Tool for Vulnerability Prioritization in Cyber Security Systems

A container-based analysis tool is developed for application in vulnerability prioritization for cyber security systems. The specific way in which the software is developed, i.e. containerization, allows easy scaling and thus helps improving the data processing speed thus reducing time to vulnerability remediation. The results obtain confirm that significant computational time saving can be easily obtained by increasing the number of containers. This makes the developed software particularly suitable for application within cloud computing environment.
An Evaluation of Human Detection Methods on Camera Images in Heavy Industry Environments

In this paper we evaluate different machine learning models for human body detection in heavy industry environments. Contributing a framework to asses the reliability of a detection system in industrial environments, we compare techniques of feature extraction for support vector machines to artificial neural networks. To accommodate for common environmental challenges in heavy industry, such as dust, difficult light conditions and partially covered persons, we apply programmatic changes to our test image set and evaluate the accuracy of person detection, foot point estimation and the tendency of erroneous detections.
Obstacle Detection for Assisting Navigation of Visually Impaired People Based on Segmentation Process

Blindness is a condition when a person's sense of sight experiences a disturbance. Hence generally, it requires a tool for help him. One of the assistive devices for people with blind disabilities in carrying out their mobility is a stick or known as "The White Cane. In general, the blind stick has a permanent shape or cannot be folded. This stick works by tapping or sliding in all directions around the blind person standing, so it is very possible for the surrounding environment to feel disturbed. Based on these problems, we propose a device that can help the blind in walking or doing mobility based on computer vision analysis without having to disturb the surrounding environment. Connected Component Labeling is used to get a blob from an image. The blob that has been detected, then analyzed using segmentation based on the threshold process. The experimental results show that our proposed method using Min-max threshold is able to detect obstacles with an accuracy rate of 82.89 %.