Reviewerly/exHarmony huggingface dataset:
Karapan Sapi as Android-Based Learning Module Material of Physics

Abstract The research was aimed to produce an android-assisted module feasibly for physics learning in the Newton’s Laws. The developing model used was 4-D (define, design, develop, &amp; disseminate). In this research, the subject was students in the X grade in one of Madrasah Aliyah Negeri in the District of Banyuwangi, Province of East Java. Hence, the implementation of android-based learning module material of physics was performed by installing aplication of modules into smartphone using android operating system. As the result, the result of this research was that the development of android-based learning module material of physics was feasible used in the physics learning in Senior High School. According to the assessment result conducted by materials expert, the mean of each aspect was 4.36 (four point thirty-six), including highly feasible category. Based on the assessment of media expert, thus, concerning on technologi-based learning, the mean of each aspect was 4.47 (four point fourty-seven), and it included into highly feasible category. While, according to the assessment of learning expert, the mean of each aspect was 4.39 (four point thirty-nine), including as highly feasible category. Further, limited trial test conducted to 10 students was chosen by sampling and the percentage result showed 4.55, including in highly feasible category.
Quantifying Bias and Variance of System Rankings

When used to assess the accuracy of system rankings, Kendall's tau and other rank correlation measures conflate bias and variance as sources of error. We derive from tau a distance between rankings in Euclidean space, from which we can determine the magnitude of bias, variance, and error. Using bootstrap estimation, we show that shallow pooling has substantially higher bias and insubstantially lower variance than probability-proportional-to-size sampling, coupled with the recently released dynAP estimator.
Offset-Adjustable Deformable Convolution and Region Proposal Network for Visual Tracking

Spatial information is considered as the key information in many computer vision tasks including visual tracking. We attempt to use spatial information to get essential knowledge of the tracking targets in videos. Adapting to the geometry variations of the objects, the deformable convolutional networks (Deformable ConvNets) show the successful exploitation of the potential of spatial information. Through investigating the work of Deformable ConvNets, we propose the offset-adjustable deformable convolutional network for tracking. Provided with the offset adjustability technique, our network is able to have a good grasp of spatial information for the tracking objects. An appropriate target localization method is crucial to efficient tracking. One novel region proposal network with Siamese structure is designed to propose the candidate target regions and plays a vital role in promoting the effectiveness and efficiency of our model. We test our method on widely-applied OTB2015 and VOT2017 benchmark datasets. The experimental results show our method realizes state-of-the-art effects and achieves great improvement in comparison to other trackers.
Set Semantic Similarity for Image Prosthetic Knowledge Exchange

Concept information can be expressed by text, images or general objects which semantic meaning is clear to a human in a specific cultural context. For a computer, when available, text with its semantics (e.g., metadata, comments, captions) can convey more precise meaning than images or general objects with low-level features (e.g., color distribution, shapes, sound peaks) to extract the concept underlying the object. Among semantic measures, web-based proximity measures e.g., confidence, PMING, NGD, Jaccard, Dice, are particularly useful for concept evaluation, exploiting statistical data provided by search engines on terms and expressions provided in texts associated with the object. Where Artificial Intelligence can be a support for impaired individuals, e.g., having disabilities related to vision and hearing, understanding the concept underlying an object can be critical for an intelligent artificial assistant. In this work we propose to use the set semantic distance, already used in literature for semantic similarity measurement of web objects, as a tool for artificial assistants to support knowledge extraction; in other words, as prosthetic knowledge.
Formal Specification of Robotic Architectures for Experimental Robotics

Robotic systems are becoming more safety critical systems as they are deployed in unstructured human-centered environments. These software intensive systems are composed of distributed, heterogeneous software components interacting in a highly dynamic, uncertain environment. However, no systematic software development process is followed in robotics research. This is a real barrier for system level performance analysis and reasoning, which are in turn required for scalable bench-marking methods and reusing existing software. This chapter provides an end-to-end overview on how robotic software systems can be formally specified from requirement modeling, through solution space exploration, and architecture modeling, and finally to generate executable code. The process is based on SafeRobots framework—a model-driven toolchain for designing software for robotics. Several domain-specific modeling languages that are developed as a part of this integrated approach are also discussed.
Blind Ensemble Classification of Sequential Data

The present work introduces a simple scheme for classifying sequential data using blind ensembles of classifiers. Blind refers to the combiner who has no knowledge of ground-truth labels to learn the optimal classifier combination. The sequence of data along with annotator responses are modeled using a hidden Markov model (HMM). The HMM parameters are learned using a decoupling and moment-matching approach. Preliminary tests on synthetic data showcase the potential of the proposed approach.
Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing

In semi-autonomous robot conferencing, not only the operator controls the robot, but the robot itself also moves autonomously. Thus, it can modify the operator’s movement (e.g., adding social behaviors). However, the sense of agency, that is, the degree of feeling that the movement of the robot is the operator’s own movement, would decrease if the operator is conscious of the discrepancy between the teleoperation and autonomous behavior. In this study, we developed an interface to control the robot head by using an eye tracker. When the robot autonomously moves its eye-gaze position, the interface guides the operator’s eye movement towards this autonomous movement. The experiment showed that our interface can maintain the sense of agency, because it provided the illusion that the autonomous behavior of a robot is directed by the operator’s eye movement. This study reports the conditions of how to provide this illusion in semi-autonomous robot conferencing.
Identification of Malignancy from Cytological Images Based on Superpixel and Convolutional Neural Networks

This chapter explores two methodologies for classification of cytology images into benign and malignant. Heading toward the automated analysis of the images to eradicate human intervention, this chapter draws curtain from the history of automated CAD-based design system for better understanding of the roots of the evolving image processing techniques in the analysis of biomedical images. Our first approach introduces the clustering-based approach to segment the nucleus region from the rest. After segmentation, nuclei features are extracted based on which classification is done using some standard classifiers. The second perspective suggests the usage of deep-learning-based techniques such as ResNet and InceptionNet-v3. In this case, classification is done with and without segmented images but not using any handcrafted features. The analysis provides results in favor of CNN where the average performances are found better than the existing result using feature-based approach.
Comparison of Discrete Sun Tracking Methods for Photovoltaic Panels

Low efficiency of the existing photovoltaic cells is one of the important problems of the alternative energy. To increase efficiency of PV panels up to 40-55% solar trackers are used. Continuous sun tracking is complex and it could require significant energy costs. To reduce energy consumption of sun trackers the methods of discrete solar tracking could be implemented. There are three different methods of discrete solar tracking for photovoltaic panels. The first method with a minimum number of steps is called three-step sun tracking, when PV panel rotates 3 times a day: morning, noon and evening. The second method with a maximum number of steps is the continuous-discrete sun tracking, when the angle of rotation is in the range of 1-3°. The third method with an average number of steps, which is calculated with optimal ratio of energy losses and efficiency increase. In the paper, the authors are analyzing limitations of existing discrete sun tracking methods, making comparison of it. Each of the method has its own advantages and disadvantages. Taking them into account, it could be universal discrete sun tracking method developed.
Decoding the Perceived Difficulty of Communicated Contents by Older People: Toward Conversational Robot-Assistive Elderly Care

In this study, we propose a semi-supervised learning model for decoding of the perceived difficulty of communicated content by older people. Our model is based on mapping of the older people's prefrontal cortex (PFC) activity during their verbal communication onto fine-grained cluster spaces of a working memory (WM) task that induces loads on human's PFC through modulation of its difficulty level. This allows for differential quantification of the observed changes in pattern of PFC activation during verbal communication with respect to the difficulty level of the WM task. We show that such a quantification establishes a reliable basis for categorization and subsequently learning of the PFC responses to more naturalistic contents, such as story comprehension. Our contribution is to present evidence on effectiveness of our method for estimation of the older people's perceived difficulty of the communicated contents during an online storytelling scenario.
The massively parallel <i>mixing genetic algorithm</i> for the traveling salesman problem

A new evolutionary algorithm called the Mixing Genetic Algorithm is introduced for the Traveling Salesman Problem. The Mixing Genetic Algorithm does not use selection or mutation, and the children replace the parents every generation. The recombination operator is partition crossover. Partition crossover is respectful and transmits alleles (edges); this makes it possible to generate two offspring, the best possible offspring and the worst possible offspring, such that no edges are lost during recombination. The Mixing Genetic Algorithm organizes the population so that better solutions are usually recombined with other good solutions. Because no edges are lost or created during recombination, there is no need to access the evaluation function after the first generation. This dramatically reduces communication costs; this makes it possible to implement the Mixing Genetic Algorithm on massively parallel SIMD machines with limited memory. The Mixing Genetic Algorithm never loses diversity and cannot prematurely converge. We compare the Mixing Genetic Algorithm to EAX, one of the best inexact solvers for the Traveling Salesman Problems. For many problems the Mixing Genetic Algorithm finds optimal solutions using fewer recombinations than EAX.
Detection and extraction of shockwave signal in noisy environments

The shockwave signal is affected by the weapon launch and the external environment, and it is often mixed with many kinds of noise, some even submerged. To detect and extract the shockwave signal under low signal-to-noise ratio, the transient signal SNR, the power-law detector of the higher-order c umulant spectrum (HOCS) and the Dual-tree complex wavelet transform (DTCWT) extraction model are proposed in the study. The average power of noise under different SNR was calculated by comparing the average power of the background noise with the instantaneous power of the shockwave. Based on the power-law detection of HOCS, the power-law of the two spectra was analyzed. After the DTCWT, the optimal threshold of the maximum posterior estimation was denoted by layer by layer, then the shockwave signal was extracted by the inverse transform, and the validity of the model was verified by the measured data. Results demonstrate that the signal to noise ratio of the transient signal can reflect the true magnitude of the average power of the noise, and the conventional SNR reduces the average power of the noise, and the error ratio is up to 70%. The power-law detector of bispectrum diagonals has the good effect on Gaussian white noise suppression, and can detect the signal to noise ratio of -15dB. The DTCWT can realize multiple peak shockwave extraction with the smaller amplitude, and the mean square error (MSE) of measured signal extraction can reach 0.0189. The proposed method provides a good reference for the detection of shockwave signal and the extraction of the multi-peak waveform in low signal-to-noise ratio.
Improving vocational training in the Philippines using AR

Currently, Augmented Reality Training is already being used by different industries such as medicine, military, manufacturing, automotive and aerospace. However, many Academic and vocational training institutions, particularly in the Philippines, are not yet utilizing this technology because of expensive AR HMD and smart-glass devices. To shorten the gap in realizing the benefits of this wearable technology between industry and academic and training institution, the paper introduced AR4Juan Technical Vocational Training Solution. The main contributions of this paper are as follows: 1) The researchers introduced cost-effective hands-free AR4Juan based training solution for k-12 technical vocational training, and 2) Shows how A4Juan Mobile application could address the trainee difficulties identified in the current training methods utilized by Academic and Technical Vocational institutions in the Philippines. AR4Juan training solution could revolutionize the training approach and realize AR based hands-free training benefits accessible to everyone. The next phase of the study is to evaluate AR4Juan training solution on its degree of usefulness, ease of use, user enjoyment and engagement and ease of access. In addition, the ARBox1 and ARBox2 headsets will be evaluated to identify which headset is more suitable for hands-free AR-based training. Other researchers could use the AR4Juan training solution and evaluate its usefulness. AR4Juan utilization and evaluation are essential to improve AR-based training delivery in academic and technical vocational institutions continuously.
Youth, Politics &amp; Civic Participation

We present the Manifesto Machine, a collaborative writing environment for drafting and designing manifestos. In this instance, we report on a workshop with a group of local youths. We used the environment as a thought probe for provoking reflections on politics and civic participation. Our insights indicate that while there is a tendency to view youths as apolitical, there is scope for using such a tool to encourage active discussion and engage communities around the topics that affect them.
Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems

Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the "beyond-accuracy" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graph-theory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.
srcPtr: A Framework for Implementing Static Pointer Analysis Approaches

A lightweight pointer-analysis framework, srcPtr, is presented to support the implementation and comparison of points-to analysis algorithms. It differentiates itself from existing tools by performing the analysis directly on the abstract syntax tree, as opposed to an intermediate representation (e.g., LLVM IR), by using srcML, an XML representation of source code. Working with srcML and the abstract syntax allows easy access to the actual source code as the programmer views it, thus better supporting comprehension. Currently the framework provides example implementations for both Andersen's and Steensgaard's pointer-analysis algorithms. It also allows for easy integration of other points-to algorithms for comparison of accuracy/speed. The approach is very scalable and can generate pointer dependencies for a 750 KLOC program in less than a minute.
How Much Do You Perceive This?

This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.
instruMentor: An Interactive Robot for Musical Instrument Tutoring

Musical instrument education has typically faced challenges in providing students with a cost-efficient and long-term solution for personalised tutoring. To address these challenges, we propose a musical instrument tutor robot for students learning the recorder, called instruMentor. Equipped with robotic hands and a multimodal interface, the robot interacts with users by playing the recorder and demonstrating in real-time the proper handling of the instrument. A pilot study was conducted to investigate the effectiveness of a robot tutor for instrument learning. Experimental results suggest that instruMentor is successful at teaching the recorder and is positively appreciated by users, showing promise for the future coupling of music tutoring and social robots.
Event Stream Processing on Heterogeneous System Architecture

Due to the widespread availability of general purpose GPUs, an integration of their processing capabilities into an event stream pipeline presents an exciting opportunity riddled with challenging requirements: Even though the single instruction multiple data (SIMD) model is a natural fit to answer long running event queries on high volume streams, those queries are usually associated with latency requirements that make transferring data to GPUs unfeasible. Traditionally, this challenge is solved through software by scheduling some tasks to the GPU and some to the CPU. However, the assumptions about transfer do not hold for widely adopted integrated GPUs (iGPUs), which directly share memory with the CPU. We develop a prototypical event processing framework based on the Heterogeneous System Architecture (HSA) and show that a variety of new HSA features enable iGPUs to be an affordable accelerator for a wide variety of event processing queries.
Metaheuristics and Swarm Methods: A Discussion on Their Performance and Applications

Nature-inspired metaheuristics are easily the largest family of optimization techniques currently on existence and they have become widely-known among researchers from virtually every single area of scientific application. While the rapid development of this area of science has originated a vast amount of novel problem-solving schemes, it has also brought many interesting questions. Nowadays, researchers are centering their attention on studying the properties on nature-inspired methods that have a direct impact on their performance, and how these properties contribute on better solving particular optimization problems. In this chapter, we present a discussion centered on several observable characteristics in nature-inspired methods and their influence on its overall performance. Furthermore, we also present a survey on some of the most important areas science and technology where nature-inspired algorithms have found applications. Finally, we expose some of the current research gaps regarding to the development and application of nature-inspired metaheuristics, as well as some of the potential directions that this area of science may take in the future.
A Fault-Driven Combinatorial Process for Model Evolution in XSS Vulnerability Detection

We consider the case where a knowledge base consists of interactions among parameter values in an input parameter model for web application security testing. The input model gives rise to attack strings to be used for exploiting XSS vulnerabilities, a critical threat towards the security of web applications. Testing results are then annotated with a vulnerability triggering or non-triggering classification, and such security knowledge findings are added back to the knowledge base, making the resulting attack capabilities superior for newly requested input models. We present our approach as an iterative process that evolves an input model for security testing. Empirical evaluation on six real-world web application shows that the process effectively evolves a knowledge base for XSS vulnerability detection, achieving on average 78.8% accuracy.
User Interface Distribution Method Based on Pi-Calculus

The rapid growth of computing devices has led to the emergence of distributed user interfaces. A user interface is called distributed if a user can interact with it using several devices at the same time. Formal methods for designing such interfaces, in particular methods for the distribution of interface elements across multiple devices, are yet to be developed. This is the reason why every time a new application requires a distributed user interface, the latter has to be designed from scratch, rendering the entire venture economically inefficient. In order to minimize costs, unify and automate the development of distributed interfaces, we need to formulate general formal methods for designing distributed interfaces that will be independent from a particular application or device. This article paper proposes a formal distribution method based on the pi-calculus.