Reviewerly/exHarmony huggingface dataset:
Toward Data-Adaptable TinyML using Model Partial Replacement for Resource Frugal Edge Device

Demand to perform machine learning (ML) tasks in microcontroller unit (MCU)-based edge devices instead of the server, that have limited resources, is gradually increasing. TinyML framework makes possible that creating ML firmware in a language that can be ported to the MCU. This paper aims at a technique that flexibly responds to various inputs by partial replacement of the network model part among the ML firmware operating in the MCU. Before implementing the proposed technique, a preliminary experiment was performed. As the number of words trained on the network in the speech command dataset increases, the size of the model increases, but the evaluation accuracy decreases. The experimental results show the possibility of a technique that replaces small learning models corresponded to each domain, instead of using a huge model that trains all input data variations for different domains.
ANALISIS KEBUTUHAN PENGEMBANGAN MEDIA POWERPOINT INTERAKTIF SEBAGAI MEDIA PEMBELAJARAN TEMATIK KELAS V SD

This study aims to 1) obtain information about the use of computer-based learning media as a learning resource in thematic learning; 2) obtaining views and obstacles faced by teachers and students in thematic learning; and 3) designing and making learning media that need to be developed in thematic learning. The subjects of this study were classroom teachers and students at SDN 1 Karanganyar. The technique of collecting information and data is in the form of observation guidelines, interviews and documentation. The analysis technique used is descriptive qualitative analysis. The preliminary findings conclude that 1) The use of computer-based learning media as a learning resource in thematic learning that has been carried out has been less attractive to students. 2) Limited use of learning media to support independent learning. 3) There are obstacles in delivering abstract material such as historical material and the lack of appropriate learning media, so it requires a lot of learning media that can visualize the material so that it is more interesting and more concrete. 4) Learning media that need to be developed in thematic learning, namely the material of the colonial period national events with interactive Powerpoint.
Accelerate High Resolution Image Pedestrian Detection With Non-Pedestrian Area Estimation

Detecting pedestrians at high speed in high resolution (HR) images is important for preventing collisions in autonomous vehicle. The HR images provide more detail information while creating a heavy computational load, resulting in slower detection speed. We find that the pedestrians are usually not present in the upper part of the HR image, due to the camera vertical field of view and the distance between pedestrian and camera. We named this area a non-pedestrian area (NPA). Therefore, we are able to reduce the calculation time while maintaining the detection accuracy by removing NPA from the HR image. According to this concept, we propose a novel pedestrian detection acceleration algorithm called Non-Pedestrian Area Estimation (NPAE). The NPAE algorithm estimates and removes non-pedestrian areas of the image, followed by pedestrian detection of the NPAE output image. Two datasets with three different resolution images are used to test our proposed NPAE algorithm on both GPU and CPU platforms. The RetinaNet is chosen as the reference pedestrian detector. Our algorithm increases the detection speed with 42% on the GPU platform and with 52% speed increase on the CPU platform, for the test images with a resolution of $1920 \times 1080$ pixel. In terms of detection accuracy, NPAE improves by up to 37.94% compared to classical data optimization acceleration methods. The detection accuracy results are well preserved in both platforms.
Probabilistic Model-Based Malaria Disease Recognition System

In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex.
A Genetic Programming Hyper-Heuristic Approach to Design High-Level Heuristics for Dynamic Workflow Scheduling in Cloud

Workflow scheduling in the cloud is the process of allocating tasks to scarce cloud resources, with an optimal goal. This is often achieved by adopting an effective scheduling heuristic. Workflow scheduling in cloud is challenging due to the dynamic nature of the cloud, often existing works focus on static workflows, ignoring this challenge. Existing heuristics, such as MINMIN, focus mainly on one specific aspect of the scheduling problem. High-level heuristics are heuristics constructed from existing man-made heuristics. In this paper, we introduce a new and more realistic workflow scheduling problem that considers different kinds of workflows, cloud resources and high-level heuristics. We propose a High-Level Heuristic Dynamic Workflow Scheduling Genetic Programming (HLH-DSGP) algorithm to automatically design high-level heuristics for workflow scheduling to minimise the response time of dynamically arriving task in a workflow. Our proposed HLH-DSGP can work consistently well regardless of the size and pattern of workflows, or number of available cloud resources. It is evaluated using a popular benchmark dataset using the popular WorkflowSim simulator. Our experiments show that with high-level scheduling heuristics, designed by HLH-DSGP, we can jointly use several well-known heuristics to achieve a desirable balance among multiple aspects of the scheduling problem collectively, hence improving the scheduling performance.
About Viola-Jones image classifier structure in the problem of stamp detection in document images

In this paper we explore a set of modifications of the cascade structure of the Viola-Jones detector on the example of solving stamp detection problem. The experiments on the public “SPODS” dataset for various document attributes extraction problems with extremely limited training set are presented. The positive training set is augmented by applying various image processing algorithms relevant to the stamp model to an available in a single instance image for each stamp type. We describe and analyze such structures of the Viola and Jones classifiers as the original cascade structure, tree, soft cascade, and perform the training experiments. Experimental results show that each modification of the cascade structure of the classifier has its own advantages and disadvantages, and the choice of the Viola-Jones classifier design significantly affects the quality of solving object detection problem.
User Attribution Through Keystroke Dynamics-Based Author Age Estimation

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%.
Taxonomy and Survey of Interpretable Machine Learning Method

Since traditional machine learning (ML) techniques use black-box model, the internal operation of the classifier is unknown to human. Due to this black-box nature of the ML classifier, the trustworthiness of their predictions is sometimes questionable. Interpretable machine learning (IML) is a way of dissecting the ML classifiers to overcome this shortcoming and provide a more reasoned explanation of model predictions. In this paper, we explore several IML methods and their applications in various domains. Moreover, a detailed survey of IML methods along with identifying the essential building blocks of a black-box model is presented here. Herein, we have identified and described the requirements of IML methods and for completeness, a taxonomy of IML methods which classifies each into distinct groupings or sub-categories, is proposed. The goal, therefore, is to describe the state-of-the-art for IML methods and explain those in more concrete and understandable ways by providing better basis of knowledge for those building blocks and our associated requirements analysis.
Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches

Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.
A Systematic Review of Semantic Clone Detection Techniques in Software Systems

Code clones are repeated program structures of significant similarities occurring in a software system. In various studies, it has been found that there is at least 20–30 percent of the code which is duplicated in a software system. These code clones induce software maintenance related difficulties that might lead to bug fixing, bug propagation, etc. that seed accretion of maintenance time, effort, and cost. To achieve better efficiency, it is required to detect all types of clones, i.e., syntactic and semantic clones that exist in a software system. Detection of semantic clones is considered to be a more difficult task in comparison to the syntactic clones because it requires semantic information of the given program rather than a similar structure. To detect semantic clones, techniques based on program dependency graphs, abstract syntax trees, machine learning, deep learning, support vector machines, etc. are very popular. This paper presents a systematic and extensive analysis of clone detection tools and techniques with a special focus laid on semantic clone detection. It also highlights the pros and cons of existing clone detection techniques along with the results viz. precision, recall, etc.
Searching relevant papers for software engineering secondary studies: Semantic Scholar coverage and identification role

Searching relevant papers is a fundamental task for the elaboration of secondary studies. This task is known to be tedious and time-consuming when it is made manually, especially with the presence of several academic repositories. Recently, Semantic Scholar has emerged as a new artificial intelligence-based search engine enabling a set of valuable features. The present study investigates the role of Semantic Scholar in retrieving relevant papers for performing secondary studies in software engineering. For this sake, an examination is performed to check the ability of Semantic Scholar to locate included papers in recent and well-established secondary studies. Afterwards, a hybrid and automatic search strategy is introduced making use of Semantic Scholar as a sole search engine and it incorporates: automatic search, snowballing, and use of Computer Science Ontology (CSO) and Software Engineering Body of Knowledge (SWEBOK) for refining queries. The proposed strategy is validated by replicating the search of high-quality secondary studies in the software engineering field. To guarantee objectivity, a systematic search is conducted of recent secondary studies published in the field since 2015. For the coverage test, Semantic Scholar is examined to locate primary papers of selected secondary studies and identify missing venues. The proposed search strategy is used to check the ability to retrieve primary papers of each secondary study. The systematic search yielded 20 high-quality secondary studies with 1337 distinct primary papers. The coverage test revealed that Semantic Scholar covers 98.88% of the papers. The proposed search strategy enabled the full replication of 13 studies and more than 90% for the 7 remaining studies.
Constrained Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a powerful subclass of generative models.Yet, how to effectively train them to reach Nash equilibrium is a challenge.A number of experiments have indicated that one possible solution is to bound the function space of the discriminator.In practice, when optimizing the standard loss function without limiting the discriminator's output, the discriminator may suffer from lack of convergence.To be able to reach the Nash equilibrium in a faster way during training and obtain better generative data, we propose constrained generative adversarial networks, GAN-C, where a constraint on the discriminator's output is introduced.We theoretically prove that our proposed loss function shares the same Nash equilibrium as the standard one, and our experiments on mixture of Gaussians, MNIST, CIFAR-10, STL-10, FFHQ, and CAT datasets show that our loss function can better stabilize training and yield even better high-quality images.
Information Retrieval from Semantic Memory: BRDL-Based Knowledge Representation and Maude-Based Computer Emulation

This paper presents a formal model for the representation of relational information in semantic memory and for its retrieval as a reaction to triggering questions which are normally used in experimental psychology. Information is represented using the Behaviour and Reasoning Description Language (BRDL), while the engine for its retrieval is given by the real-time extension of the Maude rewrite language. Maude’s capability of specifying complex data structures as many sorted algebras and the time features of Real-Time Maude are essential in providing a means for formalising alternative human memory models. Furthermore, using Maude’s object-oriented modelling style, aspects of such alternative memory models may be implemented in separate, interchangeable modules, thus providing a way for their comparison through in silico experiments. Finally, the results of in silico experiments may be contrasted with the data produced through lab experiments and natural observations to yield, on the one hand, a calibration of the emulation engine underlying BRDL and, on the other hand, important insights into alternative theories of cognition.
DenseNet-CTC: An end-to-end RNN-free architecture for context-free string recognition

String recognition is one of the challenging tasks in document analysis and recognition areas. Recently, with the surge of interest in end-to-end segmentation-free methods, CRNN (Convolution Recurrent Neural Network), which is a combination of CNN (Convolutional Neural Network) and RNN-CTC (Recurrent Neural Network-Connectionist Temporal Classification), has been widely applied to string recognition. However, in some context-free cases, where a character is followed by arbitrary characters like the digit string, there may be no or very few context links in these strings. In this paper, we propose a new end-to-end RNN-free architecture especially for context-free string recognition and apply it to Handwritten Digit String Recognition (HDSR) task. The proposed architecture is based on CNN and CTC, but without the usage of RNN, and we apply column-wise fully connected layers to connect the convolutional layers and CTC directly. Moreover, to compensate for the possible reduction in modeling capabilities caused by the absence of RNN, we apply densely connected convolutional layers to extract efficient features. We test this new architecture on three public HDSR benchmarks (ORAND-CAR-A, ORAND-CAR-B and CVL HDS) and three other datasets that include a handwritten telephone/postcode dataset PhPAIS and two non-Arabic digit datasets (C-Bangla and C-Hindi). Furthermore, we generate three handwritten digit string datasets to further analyze the influence of RNN. The recognition results on all datasets demonstrate the superiority of the proposed model.
An Agile, Coaxial, Omnidirectional Rotor Module: On the Development of Hybrid, All Terrain Robotic Rotorcrafts

Unmanned Aerial Vehicles (UAV) are typically based on rotorcraft designs and have been extensively used to perform mechanical interaction tasks that could involve inspection as well as search-and-rescue operations in critical unstructured and dynamic environments. UAV based aerial manipulation has also received an increased interest and it has attracted considerable research effort in the robotics community. However, the state-of-the-art control approaches that are being applied to traditional underactuated UAVs show critical stability issues during the execution of manipulation tasks. This is due to the fact that these tasks require the exertion of significant forces with respect to the vehicle-weight ratio. Designs of fully-actuated and overactuated systems show better suitability for such tasks but are still specific to certain functions and tasks, lacking the needed agility and flexibility. In this paper, we propose a novel design of an agile, coaxial, omnidirectional rotor module that can move and exert forces in all directions, without limitations on orientation and wiring. This independent rotor unit presents high mobility and maneuverability, high fight autonomy, and offers high potential for all terrain robotic manipulation applications. In this work, the proposed rotor module has been employed to develop an unconventional, hybrid, all terrain robotic rotorcraft that can act both as a drone and as a mobile robot.
Stochastic Modeling of Explosive Eruptive Events at Galeras Volcano, Colombia

A statistical analysis of explosive eruptive events can give important clues on the behavior of a volcano for both the time- and size-domains, producing crucial information for hazards assessment. In this paper, we analyze in these domains an up-to-date catalog of eruptive events at Galeras volcano, collating data from the Colombian Geological Survey and from the Smithsonian Institution. The dataset appears to be complete, stationary and consisting of independent events since 1820, for events of magnitude ≥2.6. In the time-domain, Inter-Event Times are fitted by various renewal models to describe the observed repose times. On the basis of the Akaike Information Criterion, the preferred model is the Lognormal, with a characteristic time scale of ∼1.6 years. However, a tendency for the events to cluster in time into “eruptive cycles” is observed. Therefore, we perform a cluster analysis, to objectively identify clusters of events: we find three plausible partitions into 6, 8 and 11 clusters of events with magnitude <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml-math1"><mml:mrow><mml:mo>≥</mml:mo><mml:mn>2.6</mml:mn></mml:mrow></mml:math> the 6-cluster partition being the preferred. The Inter-Event Times between cluster onsets (inter-cluster) and between events belonging to the same cluster (intra-cluster) are also modeled by renewal models. For inter-cluster data, the preferred model is the Brownian Passage Time, describing a periodical occurrence (mean return time ∼36 years) perturbed by a Gaussian noise. For the intra-cluster explosions, the preferred model is the Lognormal, with a characteristic time scale of ∼0.9 years. In the size-domain, we analyze only single events, due to the low number of clusters. Considering two independent parts of the catalog, we cannot reject the null hypothesis of the erupted mass being described by a power law, implying no characteristic eruption size. Finally, looking for time- and size-predictability, we find a significant inverse linear relationship between the logarithm of the erupted mass during a cycle and the time to the subsequent one. These results suggest that, presently, Galeras is still in the eruption cycle started in 2007; a new eruptive cycle may be expected in a few decades, unless the present cluster resumes to activity with magnitude ≥2.6.
Knowledge distillation via softmax regression representation learning

This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning. Previous distillation methods which typically impose direct feature matching between the student and the teacher do not take into account the classification problem at hand. On the contrary, our distillation method decouples representation learning and classification and utilizes the teacher's pre-trained classifier to train the student's penultimate layer feature. In particular, for the same input image, we wish the teacher's and student's feature to produce the same output when passed through the teacher's classifier which is achieved with a simple L2 loss. Our method is extremely simple to implement and straightforward to train and is shown to consistently outperform previous state-of-the-art methods over a large set of experimental settings including different (a) network architectures, (b) teacher-student capacities, (c) datasets, and (d) domains.
Pedagogical and user interface usability evaluation of an educational mobile app that promotes visual literacy

Visual is a mobile app designed and developed to use as a resource to support a blended learning course of visual literacy for in-service teachers. The main goal of the Visual app is to help participants to achieve better comprehension about the theoretical content by doing the app activities. Hence, as a personal learning environment, the quality of this resource should be evaluated to identify flaws and to improve the educational content and the interface if necessary. In consequence, users will learn with the app and do not waste time learning how to use it or doing activities without an educational purpose.