Reviewerly/exHarmony huggingface dataset:
Incorporating a Decision Support Approach within the Agile Mobile Application Development Process

According to the literature, traditional desktop-based application development processes profit from the adoption of Multi-Criteria Decision-Making (MCDM) approaches under specific conditions. However, agile methodology often shuns this method, instead encouraging a cooperative decision support structure. Within the domain of mobile-based applications, an optimized structure is constitutionally introduced, thus, the presentation of a powerful decision-making approach in agile mobile application development should prompt a more significant level of contentment with mobile applications built in this way. The present paper introduces an approach to adopting the MCDM method for the agile mobile application development process. The paper consists of a framework for investigating insertion points for decision-making method - namely, the Best- Worst Method (BWM) - and a practical example of the application of the Best-Worst decision support method in the development process of agile mobile applications.
Weeping and Gnashing of Teeth: Teaching Deep Learning in Image and Video Processing Classes

In this rather informal paper and talk I will discuss my own experiences, feelings and evolution as an Image Processing and Digital Video educator trying to navigate the Deep Learning revolution. I will discuss my own ups and downs of trying to deal with extremely rapid technological changes, and how I reacted to, and dealt with consequent dramatic changes in the relevance of the topics I've taught for three decades. I arranged the discussion in terms of the stages, over time, of my progression dealing with these sea changes.
Driverless Wheelchair for Patient’s On-Demand Transportation in Hospital Environment

The transport of patients from the inpatient service to the operating room is a recurrent task in the hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented the design of a driverless wheelchair under development capable of providing an on-demand mobility service to hospitals. The proposed wheelchair can receive transportation requests directly from the hospital information management system, pick-up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated destination.
A Teaching Language for Building Object Detection Models

Object detection is a key application of machine learning. Currently, these detector models rely on deep networks that offer model builders limited agency over model construction, refinement and maintenance. Human-centered approaches to address these issues explore the exchange of knowledge between a human-in-the-loop and a learning system. This exchange, mediated through a teaching language, is often restricted to the specification of labels and constrains user expressiveness communicating other forms of knowledge to the system. We propose and assess an expressive teaching language for specifying object detectors which includes constructs such as concepts and relationships. From a formative study, we identified language building blocks and articulated design goals for creating interactive experiences in teaching object detection. We applied these goals through a design probe that highlighted further research questions and a set of design takeaways.
FUZZ-EQ: A data equalizer for boosting the discrimination power of fuzzy classifiers

The definition of linguistic terms is a critical part of the construction of any fuzzy classifier. Fuzzy partitioning methods (FPMs) range from simple uniform partitioning to sophisticated optimization algorithms. In this paper we present FUZZ-EQ, a preprocessing algorithm that facilitates the construction of meaningful fuzzy partitions regardless of the FPM used. The proposed approach is radically different from any existing FPM: instead of adjusting the fuzzy sets to the training data, FUZZ-EQ adjusts the training data to a hypothetical uniform partition before applying any FPM. To do so, the original data distribution is transformed into a uniform distribution by applying the probability integral transform. FUZZ-EQ allows FPMs to provide classifiers with more granularity on high density regions, increasing the overall discrimination capability. Additionally, we describe the procedure to reverse this transformation and recover the interpretability of linguistic terms. To assess the effectiveness of our proposal, we conducted an extensive empirical study consisting of 41 classification tasks and 9 fuzzy classifiers with different FPMs, rule induction algorithms, and rule structures. We also tested the scalability of FUZZ-EQ in Big Data classification problems such as HIGGS, with 11 million examples. Experimental results reveal that FUZZ-EQ significantly boosted the classification performance of those classifiers using the same linguistic terms for all rules, including state-of-the-art classifiers such as FARC-HD or IVTURS.
Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects

In recent years, neural networks with an additional convolutional layer, referred to as convolutional neural networks (CNN), have widely been recognized as being effective in the field of image recognition. In the majority of these previous researches, the structures of networks were designed by hand, and were based on experience. However, there is no established theory explaining how to build networks with higher learning abilities. In this chapter, we propose a framework on automatically obtaining network structures with the highest learning ability for image recognition, through the combination of the various core technologies. We employ EC (evolutionary computation) for the automatic extraction and synthesis of network structures. Additionally, we attempt to perform an effective search in a larger parameter space by gradually increasing the number of training epochs during the generation change process. In order to show the effectiveness of our approach, we apply the proposed method to the task of detecting dangerous objects in an X-ray image data set. Compared with the previous results, we have achieved an improvement in the mAP value. We can also find several by-passes in the structures that were actually obtained.
Ability estimation in computerized adaptive test using Mamdani Fuzzy Inference System

Abstract Assessment is an activity to find out the learning outcomes of a course. Conventionally, assessment is done using a pencil and paper test. Along with the development of information and communication technology, evaluation can be done computerized, known as Computer Based Test (CBT). The Computerized Adaptive Test (CAT) is one form of CBT where the items given are chosen based on the students’ abilities. This research aims to design a system that can estimate the ability of students based on the parameters of the questions and answers given. The estimation method uses the Mamdani Fuzzy Inference System (MFIS). The input MFIS is a level of difficulty and discrimination of the questions, the probability of students being able to answer correctly, and the student’s answer, while the output is an ability estimated. Based on this fuzzy system output, the next item questions will be determined according to the ability of the students. 24 IF-THEN rules are used for fuzzy systems. CAT simulations are carried out for Linear Algebra Course with six topics, namely vector, matrices concepts, matrices operation, determinant, inverse, and matrices applications. The type of question given is multiple choice. Giving items will be stopped if the value of the estimated ability of the students has not changed. From the simulations carried out, for each topic, the ability of the student can be acquired with the number of questions as many as six questions. So that the CAT system can minimize the time of the exam, reduce the subjectivity of the assessment and can arrange for each student to get the questions according to his abilities.
WNET: An End-to-End Atlas-Guided and Boundary-Enhanced Network for Medical Image Segmentation

Medical image segmentation is one of the most important pre-processing steps in computer-aided diagnosis, but it is a challenging task because of the complex background and fuzzy boundary. To tackle these issues, we propose a double U-shape-based architecture named WNet, which is capable of capturing exact positions as well as sharpening their boundary. We first build an atlas-guided segmentation network (AGSN) to obtain a position-aware segmentation map by incorporating prior knowledge on human anatomy. We further devise a boundary-enhanced refinement network (BERN) to yield a clear boundary by hybridizing a Multi-scale Structure Similarity (MS-SSIM) loss function and making full use of refinement at training and inference in an end-to-end way. Experimental results show that the proposed WNet can accurately capture an organ with sharpened details and hence improves the performance on two datasets compared to the previous state-of-the-arts.
The effect of local primacy-based comic media on students’ conservation attitudes

Abstract The purpose of this study was to analyze the influence of the implementation of local primacy-based comic media on students’ conservation attitudes. This research was a quasi-experimental that adopted and modified separate sample pretest-postest designs. Two experimental schools, namely SDN 1 Gili Indah and SDN Teratak, West Nusa Tenggara, Indonesia were taken randomly with a total of 52 students. Data on student conservation attitudes was collected through questionnaires. The data were converted into percentages then analyzed using the paired and independent sample t-test. The results of this research showed that the implementation of local primacy-based comic media can increase students’conservation attitudes ( p &lt;0.05) and there was no difference students’conservation attitudes of the two observation schools ( p &gt;0.05).
Implementation of the Poles of IIR Digital Filters with a Declared Structural Precision

This study is an integral part of a project focusing on the synthesis of IIR digital filters with finite word length. The implementation of the project involves considering the finite word length already at the stage of computation zeros and poles. The results of this stage are final and are not distorted at the stage of structural synthesis. The computation uses the fact that the zeros and poles of a practically implemented digital filter are elements of the set of algebraic numbers of the corresponding degree. At the stage of structural synthesis, a digital filter block diagram is generated. This block diagram provides specified degrees of algebraic numbers, which are zeros and poles. The exact values of the coefficients of the generated digital filter structure are calculated taking into account previously calculated zeros and poles. Different structures are characterized by structural precision, independent of specific coefficient values. This paper is devoted to the study of the generation of structures that provide a given structural precision of the denominator of the transfer function and a given degree of algebraic numbers that are poles.
Trajectory-Tracking Control of Robotic Systems via Deep Reinforcement Learning

This paper studies the trajectory tracking problems for a robotic manipulator and a mobile robot by using deep reinforcement learning based methods. A medel-free deep reinforcement learning method based on Deep Deter-ministic Policy Gradient (DDPG) is designed for training. The priority replay memory is adopted to sample more significant transitions at each update. A distributed framework with multiple workers is proposed. Synchronous workers generate transitions and compute gradients for the global network, and collecting workers explore the environment with different policies and exploration noises. During the training, we adopt random reference state initialization to solve the exploration problem, which can make the robots learn from the reference trajectory effectively. Numerical simulations are provided to demonstrate the effectiveness and efficiency of the proposed methods. It can be seen from the simulation results that the agent trained by the proposed distributed DDPG could learn faster and achieve smaller tracking errors than DDPG.
Computationally efficient sparse clustering

Abstract We study statistical and computational limits of clustering when the means of the centres are sparse and their dimension is possibly much larger than the sample size. Our theoretical analysis focuses on the model $X_i=z_i \theta +\varepsilon _{i}, \ z_i \in \{-1,1\}, \ \varepsilon _i \thicksim \mathcal{N}(0, I)$, which has two clusters with centres $\theta $ and $-\theta $. We provide a finite sample analysis of a new sparse clustering algorithm based on sparse Principal Component Analysis (PCA) and show that it achieves the minimax optimal misclustering rate in the regime $\|\theta \| \rightarrow \infty $. Our results require the sparsity to grow slower than the square root of the sample size. Using a recent framework for computational lower bounds—the low-degree likelihood ratio—we give evidence that this condition is necessary for any polynomial-time clustering algorithm to succeed below the Baik-Ben Arous-Péché (BBP) threshold. This complements the existing evidence based on reductions and statistical query lower bounds. Compared with these existing results, we cover a wider set of parameter regimes and give a more precise understanding of the runtime required and the misclustering error achievable. Our results imply that a large class of tests based on low-degree polynomials fail to solve even the weak testing task.
Blockchain Eco-System for Thai Insect Industry: A Smart Contract Conceptual Framework

As the world population is increasing day by day, providing food for this increasing population is a difficult task due to climatic changes and related water shortage. To meet the nutrition and food challenges of today, the world is nowadays looking for a new food source other than animals and plants as some of the most common food sources. As a future perspective, the research community is now eyeing the use of insects as a future food source. Despite some existing markets, Thailand has the highest edible insect species in the world. The disruptive technology has been playing the leading role to promote competitiveness for small businesses recently. It is, therefore, in this paper, a blockchain ecosystem for Thai insect industry is proposed to create a credible and traceable insect business in Thailand. Additionally, the smart contract is mainly used for creating a transparent and reliable insect business in the modern world.
Intelligence chatbot for Indonesian law on electronic information and transaction

Abstract In the Law Fiction Theory, it is assumed that once the government enforces legal norms at that time, everyone is considered to know the law. So that someone’s ignorance of the law cannot free him from lawsuits. This condition led to the emergence of legal cases caused by a lack of public understanding of the law. This paper introduces a potential solution by providing chatbots platforms. We propose chatbots designed to provide information, for those who need information about applicable laws. Users can ask about anything about applicable legal documents. Furthermore, the bot performs a search according to requests related to legal documents. Various request commands are given so that bots can behave like humans and provide the information needed by users. The experimental results have shown that chatbot could recognize all of the questions from user and could answer correctly.
JPLink: On Linking Jobs to Vocational Interest Types

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits domain knowledge available in an occupation-specific knowledge base known as O*NET to improve feature representation of job posts. To incorporate relative ranking of RIASEC labels of each job, JPLink proposes a listwise loss function inspired by learning to rank. Both our quantitative and qualitative evaluations show that JPLink outperforms conventional baselines. We conduct an error analysis on JPLink’s predictions to show that it can uncover label errors in existing job posts.
Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing

In this work, we introduce an intelligent video sensor for the problem of Action Proposals (AP). AP consists of localizing temporal segments in untrimmed videos that are likely to contain actions. Solving this problem can accelerate several video action understanding tasks, such as detection, retrieval, or indexing. All previous AP approaches are supervised and offline, i.e. they need both the temporal annotations of the datasets during training and access to the whole video to effectively cast the proposals. We propose here a new approach which, unlike the rest of the state-of-the-art models, is unsupervised. This implies that we do not allow it to see any labeled data during learning nor to work with any pre-trained feature on the used dataset. Moreover, our approach also operates in an online manner, which can be beneficial for many real-world applications where the video has to be processed as soon as it arrives at the sensor, e.g., robotics or video monitoring. The core of our method is based on a Support Vector Classifier (SVC) module which produces candidate segments for AP by distinguishing between sets of contiguous video frames. We further propose a mechanism to refine and filter those candidate segments. This filter optimizes a learning-to-rank formulation over the dynamics of the segments. An extensive experimental evaluation is conducted on Thumos'14 and ActivityNet datasets, and, to the best of our knowledge, this work supposes the first unsupervised approach on these main AP benchmarks. Finally, we also provide a thorough comparison to the current state-of-the-art supervised AP approaches. We achieve 41% and 59% of the performance of the best-supervised model on ActivityNet and Thumos'14, respectively, confirming our unsupervised solution as a correct option to tackle the AP problem. The code to reproduce all our results will be publicly released upon acceptance of the paper.
Data Access &amp; Sharing Approach for Trade Documentations Based on Blockchain Technology

Nowadays, there are many documents involved in cross-border trade transactions. A multitude of documents have to be inter-exchanged between two main categories of actors: businesses(B) and government authorities(G). However, one of the important problems is how to effectively control access to documents and securely share documents in the scenario of national G2G and certain B2G border procedures. In order to achieve fine-grained access control and secure sharing in crossborder trade transaction electronic platform, we present an attribute-based encryption(ABE) approach for trade document access and sharing using blockchain technology in this paper. In this approach, a user’s private-key is associated with a set of attributes, a document ciphertext with an access policy over a defined universe of attributes stores to the Interplanetary File System (IPFS), and then the simple hash of IPFS files is kept on the blockchain. Through using smart contract in blockchain technology, the proposed mechanism of data access control and sharing can ensure the access control information is tamper-resistant, auditability and verifiability.
Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees

Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management of water resources. Hence, it is necessary to extract those “anomalous” knowledge that can provide valuable and useful information for future hydrological analysis and forecasting from hydrological data. This paper focuses on the problem of detecting anomalous patterns from hydrological time series data, and proposes an effective and accurate anomalous pattern detection approach, TFSAX_wPST, which combines the advantages of the Trend Feature Symbolic Aggregate approximation (TFSAX) and weighted Probabilistic Suffix Tree (wPST). Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data.
Self-Dual Group Codes in Finite Dihedral Group Algebra

In this letter, we always assume that n is an odd integer, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is a finite field of characteristic 2, and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sub> is a dihedral group of 2n elements, i.e., D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sub> = 〈x, y|x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> = 1, y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 1, yxy = x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> 〉. We give self-dual group codes with parameters in the finite dihedral group algebra F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sub> ]. Some numerical examples are also presented to illustrate our main results.