Reviewerly/exHarmony huggingface dataset:
Selective Multi-Scale Feature Learning by Discriminative Local Representation

In the computer vision community, the general trend has been to capture and select discriminative features in order to yield significantly better performance. Recent advances in attention mechanism proposed several attention blocks to adaptively recalibrate the feature response. However, most of them overlooked the context information at a multi-scale level. In this paper, we propose a simple yet effective building block for ResNeXt-style backbones, namely discriminative local representation (DLR) module, which allows discriminative local representation learning for multi-scale feature information across multi-parallel branches. Our DLR module contains two sub-modules: channel selective module (CSM) and spatial selective module (SSM). Given an intermediate feature map, the CSM first selectively generates the channel-wise attention maps and recalibrates the response from different branches according to the weight vector calculated by softmax layer. And then, the SSM further captures the spatial discriminative information at different scales respectively and emphasizes the interdependent channel maps. Besides, we place a high-order item during the process of multi-branch fusion and residual connection to enhance the intensity of structure nonlinearity. Various DLR modules can be stacked to a deep convolution network named DLRNet. To validate our DLRNet, we conduct comprehensive experiments on classification benchmarks (i.e. CIFAR10, CIFAR100 and ImageNet-1K), as well as two publicly available fine-grained datasets (i.e. CUB-200-2011 and Stanford Dogs). The experiments show consistent improvement gains over previous baseline models with reasonable overhead, and demonstrate the capability of our proposed method for discriminative local representation.
Arduino-based Hardware Platform for the {Realization of an Intelligent Home Through the Focus of Engineering Education

This article presents the Arduino-based hardware platform for the realization of a smart home by students trained in the engineering specialties of the Plovdiv University “Paisii Hilendarski”. Using the Arduino environment to implement a home automation system is an excellent choice for electronics training of students. Arduino environment based on Wiring, turns it into a multi-platform application that manages LCD displays and various types of sensors for the design and implementation of a “Smart home”.
Abnormality Detection and Severity Classification of Cells based on Features Extracted From Papanicolaou Smear Images using Machine Learning

A Papanicolaou Smear (PAP) test is a screening method developed for cervical cancer that involves the microscopic examination of cervical cells carefully extracted and spread out as a smear and stained specially. A Pap test reveals premalignant and malignant changes and the changes that are due to non-carcinogenic conditions like inflammation. The diagnosis of this test are based upon key features of the nucleus and cytoplasm of the affected cell or the cell under observation. This work is aimed at devising a classification algorithm using supervised methods to efficiently classify the affected cells from normal cells and further group the affected cells Logistic Regression. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</sup> All algorithms and models were trained and validated using the Azure Machine Learning Studio.
Video-Based Convolutional Attention for Person Re-Identification

In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras. We propose a Siamese framework in which video frames of the person to re-identify and of the candidate one are processed by two identical networks which produce a similarity score. We introduce an attention mechanisms to capture the relevant information both at frame level (spatial information) and at video level (temporal information given by the importance of a specific frame within the sequence). One of the novelties of our approach is given by a joint concurrent processing of both frame and video levels, providing in such a way a very simple architecture. Despite this fact, out approach achieves better performance than the state-of-the-art on the challenging iLIDS-VID dataset.
Mechanization of G, C and D Chord playing system using servomotors for Acoustic Guitar

Playing an acoustic guitar requires a skilled approach and involves hours of practice. Chord playing on the guitar requires proper placement and movement of human fingers along with the time synchronized plucking and strumming action of the strings. However, the involvement of human-based guitar control does not guarantee precise music generation repeatedly. The designed system offers reliable control to play G, C and D chord precisely. The system comprises of an Arduino based control unit and a servo-motor based actuation system that enables accurate pressing of strings on the fretboard along with time coordinated plucking or strumming operations at neck area and hole area of the acoustic guitar. The scheduler System is responsible for the generation of particular musical tune. The chords played were validated with existing audio processing tools.
The Multicopter Control Algorithms with Unstable Modes

In the paper, the solution of the drive control problem for a multicopter-type moving object in an uncertain environment with obstacles is considered. The moving object is represented by a multicopter. Its mathematical model is based on equations of kinematics and dynamics of 6DOF in relation to steady-state propellers characteristics. The control algorithms for moving to the target point are presented. This work offers the obstacle avoiding control algorithms used for moving an object to target points that are based on the usage of unstable modes. The correction of the multicopter's movement is carried out using dynamic variables. The effectiveness of suggested algorithms is proved by simulation results and experimental studies.
Hybrid Algorithms for Denoising Electrical Waveforms Containing Steady Segments

The paper deals with an original hybrid denoising algorithm applicable to waveforms containing steady segments, polluted by white noise. The algorithm relies on 2 denoising techniques. Firstly the “average signal method” is used to get an estimation of the noise power. An average signal (AS) is computed across 4 consecutive periods of the polluted signal (SP). A “per period” evaluation of the noise (NP) is then performed by subtracting AS from each period of SP. Each NP is divided into 6 equal subintervals, getting sets of 6 estimated noise signals (NS) for each period. The values of standard deviation (SD) are computed considering the absolute values of local peaks for all NS-s. 3 metrics were used to evaluate the power of estimated noises: NS's power (PNS), SD and the average value of NS (ANS). The minimum value (P) of PNS across all subintervals from a dataset (DS) represents the estimated power of the noise associated to DS. P is used in the 2-nd stage of the algorithm - denoising with a wavelet based thrashing tree (TT). The TT's number of levels is determined considering the best match between the power of noises yielded by TT and P. The algorithm was additionally validated by proving that all the minimum values of PNS appear only for subintervals associated to almost minimum values of SD and almost zero values of ANS. The original hybrid algorithm yielded reliable results for all datasets.
Design of Standard Operating Procedure for Requirement Engineering in Software Development: Case Study Data Processing Integration Subdirectorate Statistics Indonesia

A design of Standard Operating Procedure in software development has been produced. SOP for Requirement Engineering was designed using modified Soft Systems Methodology (SSM). Scrum method is used as an approach. Interview with thematic analysis was also conducted to determine the needs of Requirement Engineering SOP. The design of Requirement Engineering SOP consists of three steps: Initiation, Development, and Iteration Planning. With the design of requirement engineering SOP, frequently changing requirements problems in application development can be solved.
Effect of grain angle on the strain distribution during orthogonal cutting of hinoki (Chamaecyparis obtusa) measured using a digital image correlation method

The strain associated with orthogonal cutting with and against the grain of hinoki (Chamaecyparis obtusa) was measured. Digital image correlation method was used to measure strain distributed in the area within 0.5 mm of the cutting edge. The relationship between strain and the use of different grain angles (− 15° ≤ $$\varphi $$ ≤ + 15°) was investigated. The compressive strain parallel to the cutting direction was detected in the area above the path of the cutting edge regardless of $$\varphi $$ . The tensile strain normal to the cutting direction which generated cleavage ahead of the tool decreased with increasing $$\varphi $$ , when the cutting angle was 30°. While cutting with the grain, shear strain above the path of the cutting edge was positive when the cutting angle was 30°, although it changed to negative with larger cutting angles. The maximum principal strain given by a cutting angle of 50° was less affected by $$\varphi $$ than those given by the other cutting angles. The maximum principal strain ahead of the tool was minimized when the cutting angle was 50° and + 5° ≤ $$\varphi $$ ≤ + 10°.
A Context-Aware Recommender Method Based on Text Mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. In their traditional form, recommender systems do not consider information that might enrich the recommendation process, as contextual information. In this way, we have the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including the contextual one. Thus, in this paper, we propose a context-aware recommender method based on text mining (CARM-TM) that includes two context extraction techniques: (1) CIET.5 $$_{embed}$$ , a technique based on word embeddings; and (2) RulesContext, a technique based on association rules. For this work, CARM-TM makes use of context by running the CAMF algorithm, a context-aware recommender system based on matrix factorization. To evaluate our method, we compare it against the MF algorithm, an uncontextual recommender system based on matrix factorization. The evaluation showed that our method presented better results than the MF algorithm in most cases.
Edge-preserving smoothing filters for improving object classification

Edge-preserving smoothing filters have had many applications in the image processing community, such as image compression, restoration, deblurring and abstraction. However, their potential application in computer vision and machine learning has never been fully studied. The most successful feature descriptors for image classification use gradient images for extracting the overall shapes of objects, thus edge preserving filters could improve their quality. The effects of various edge-preserving filters were evaluated as a pre-processing step inhuman detection. In this work, three smoothing filters were tested, namely the total variation (TV), relative total variation (RTV) and L0 smoothing. Significant performance gains were realised with TV and RTV for both colour and thermal images while the L0 smoothing filter only realised a slight improvement on thermal images and poorer performance on colour images. These results show that smoothing filters have a potential to improve the robustness of common statistical learning classifiers.
Latency Fairness Scheduling for Shared Storage Systems

Providing latency support is an important problem for clustered storage systems. In this paper, we present Fair-EDF, a framework for latency guarantees in shared storage servers. It provides fairness control while supporting latency guarantees. Fair-EDF extends the pure earliest deadline first (EDF) scheduler by adding a controller to shape the workloads. Under overload it selects a minimal number of requests to drop and to choose the dropped requests in a fair manner. The evaluation results show Fair-EDF provides steady fairness control among a set of clients with different runtime behaviors.
A Segmented Attractor Network for Neuromorphic Associative Learning

This work describes a segmented attractor network that records memories across different sets of information. Unlike typical attractor networks that can associate any given inputs with one another, the attractor network presented here tracks information across multiple sets where each set possesses one or more features. Hyperparameter analysis is performed on the network's recall capability by altering factors such as the network's size, the number of memories within the network, the amount of information required to perform recall, and the amount of feedback a synapse can provide. The analysis done shows an improved hit rate when increasing the features per set and allows the network to obtain a memory capacity higher than other standard attractor networks.
Designing New Hospitals - Who Cares About the Patients?

The design of hospital environments and processes impacts patients' experiences and feelings. This paper explores how patients' experiences are considered in the design process of new hospitals, specifically focusing on the use of co-design methods including patients. Prior work, largely focused on improvements to ongoing care processes, has highlighted a variety of challenges. Our investigation reflects on observations from weekly planning meetings and the use of virtual reality tools in the design phase of two new hospitals. Our findings suggest that, although opportunities exist for patient experience-centric innovation, the tight schedules and intangibility of new-build limit what is achievable.
Decision Support Systems to Determining Programme for Students Using DBSCAN And Naive Bayes: Case Study: Engineering Faculty Of Universitas Nusantara PGRI Kediri

Determination of programme interest has not been supported by adequate information. This causes students who have chosen programme to be less concentrated in college. This is because the chosen programme turns out to be not as expected to determine the programme. There is a system that is used to help make decisions. The system is a decision support system. The decision support system has several methods. The method is used to obtain modeling. There is a DBSCAN method that is used for grouping or clustering. This clustering is needed in order to decision maker is not input manually about preferences. Clustering contributes to add preferences automatically. So, decision maker can only focus on selecting alternatives decision. Furthermore, this alternative decision can be found using Naive Bayes Method. In addition, the Naive Bayes also has a fairly high accuracy. This is indicated by the results of testing in this research. The accuracy of Naïve Bayes has a high value compared to other classifiers.
Structural controls of gold mineralization in western Mali: Insights from Electromagnetic data analysis

NE-SW and N-S trending structures have been identified by previous studies as controls for gold mineralisation in Kedougou-Kenieba Inlier (KKI), at heart of West Africa Craton (WAC). Whereas some of these structures are not carriers of gold deposits. Thus, they need discrimination. Airborne electromagnetic method is used for this goal. Here, we are interested to electrical part. The magnetic results aren't part of this study. Three available orders of conductance in the electromagnetic data have been used to characterize resistive and conductive structures. Field observations show that resistive trends (NE-SW and N-S) coincide with dolerite and diorite rocks. In the same way, conductive trends (NE-SW and N-S) corresponding to Artisanal Mining Sites (AMS), where we also note quartz veins in ore of gold. Thus, this comparison show that only conductive directions hosts mining sites in Kenieba area. Therefore, we are able to discriminate these directions toward gold mineralisations. This is made for a true asset in mining exploration in western Mali.
Improving the prediction of files changed by programming tasks

Integration conflicts often damage software quality and developers' productivity in a collaborative development environment. For reducing merge conflicts, we could avoid asking developers to execute potentially conflicting tasks in parallel, as long as we can predict the files to be changed by each task. As manually predicting that is hard, the TAITI tool tries to do that in the context of BDD (Behaviour-Driven Development) projects, by statically analysing the automated acceptance tests that validate each task. TAITI computes the set of files that might be executed by the tests of a task (a so called test-based task interface), approximating the files that developers will change when performing the task. Although TAITI performs better than a random task interface, there is space for accuracy improvements. In this paper, we extend the interfaces computed by TAITI by including the dependences of the application files reached by the task tests. To understand the potential benefits of our extension, we evaluate precision and recall of 60 task interfaces from 8 Rails GitHub projects. The results bring evidence that the extended interface improves recall by slightly compromising precision.
Representing complementary user perspectives in a language atlas

Traditional language atlases present a single view of language that of their makers (Girnth, 2010). This bias is especially evident in the production of language distribution maps. Appealing to structural and functional criteria in their assessments of language identification and relation, which are in turn precursors to describing linguistic geography, linguists bring one approach to the discussion. However, this position is not only measured arbitrarily, but is in practice also dependent on social factors. Cybercartography (Taylor, 1997, Taylor, 2005, Taylor, 2019; Taylor and Lauriault, 2014, Taylor and Pyne, 2010), as expressed through the Nunaliit Atlas Framework (GCRC (Geomatics and Cartographic Research Centre), 2006–2019, Hayes et al., 2014, Hayes and Taylor, 2019), facilitates the simultaneous representation of diverse user perspectives within a single atlas. This study looks at the Nunaliit-driven Atlas of the Languages of Iran (ALI) (Anonby et al., 2015–2019) as a case study. In this atlas, user perspectives are foregrounded through development and application of a three-dimensional language relation web; production and comparison of user-defined language distribution maps with each other, and with linguistic structure maps; and contributions from individual users. This chapter demonstrates that incorporation of perspectives and content from all audiences is not only essential in achieving a grounded and balanced understanding of language distribution in any language atlas, but also possible.
Artificial Intelligence in Human-Robot Interaction

Human-Robot Interaction challenges the field of research on Artificial Intelligence in many ways, especially regarding the complexity of the physical world. While physical interactions require Artificial Intelligence techniques to handle dynamic, nondeterministic, and partially unknown environments, the communication with humans requires socially acceptable responses and common-sense knowledge to handle a broad variety of situations with complex semantics to interpret and understand. In the context of emotional design, different Artificial Intelligence techniques are necessary to allow robots to express, understand, and induce emotions as part of the interaction process. This chapter explores Human-Robot Interaction from the Artificial Intelligence point of view, presenting the main challenges, techniques, and our particular vision for future developments in this research area.
Classification of Javanese Language Level on Articles Using Multinomial Naive Bayes and N-Gram Methods

Abstract Javanese language articles can now be found in electronic media. But for people who are just learning the Javanese language, they have difficulty in knowing the language level that is contained in the article because there is no explanation regarding the type of language level. For this reason, it is necessary to classify the language level in Javanese language based on the article, where the Javanese language is divided into 4 levels, namely ngoko, ngoko alus, krama madya, and krama alus. Before beginning the classification process, the data must be preprocessed. One of the steps in preprocessing method is stemming, which is used to convert the affixes in words into basic words. The Javanese stemming process in this research refers to the Indonesian stemming rules based on the adjusted Nazief-Adriani algorithm. The extraction feature process was carried out using N-Gram with n = 2, 3, 4 (bigram, trigram, and quadgram). After finishing the preprocessing method, the classification process then was executed using Multinomial Naïve Bayes method. In the classification process, there often exist problems related to imbalance data between categories. To overcome this problem, SMOTE resampling method is utilized to balance the data. The classification with N-Gram variations accompanied by SMOTE and using stemming results to maximum accuracy of 67.01% at n = 2, then for the highest precision was at N-Gram n = 4 with condition not using stemming but using SMOTE at value 72.67%. The highest recall value was obtained in two conditions, namely at N-Gram n = 2 either using or not using SMOTE and use stemming, which is 67.00%. From the results of this study, it can be concluded that stemming rules which are adapted from Nazief-Adriani algorithm with the addition of stemming steps and affix list from Javanese language expert can be implemented properly for Javanese language stemming process. And the classification of the Javanese Language level using Multinomial Naïve Bayes and N-Gram methods can result in good enough accuracy, precision, and recall.
Copy Detection Pattern-Based Authentication for Printed Documents with Multi-Dimensional Features

A copy detection pattern (CDP) is an effective method to distinguish counterfeits of printed documents. CDP is a digital image filled with pixels of random grey levels, which is embedded into digital documents and printed as the legitimate ones. A general counterfeit method is to scan the legitimate documents and reprint them. The printed documents will be scanned to proceed the authentication. CDP will undergo distortion during the print-scan operation and we will detect counterfeits by measuring the extent of the distortion. In this paper, we propose a novel CDP-based authentication method for printed documents. Firstly, we analyze some attack methods which make the existing CDP-based detection methods ineffective. Secondly, we propose new metrics for measuring the distortion of CDP and construct a classifier based on support vector domain description (SVDD). Experimental results show that the proposed authentication method significantly outperforms previous methods.